It is with great academic pride and editorial responsibility that we introduce this issue of Clareus Scientific Science and Engineering (CSSE), an international, peer-reviewed journal committed to advancing scholarly communication across the broad spectrum of science and engineering. With an inclusive vision and a strong interdisciplinary ethos, CSSE serves as a vital platform where fundamental scientific research and applied engineering innovation converge.

As articulated in our mission, CSSE aims to bridge the gap between scientific discovery and technological development, ensuring that novel insights in the laboratory can effectively inform, influence, and accelerate real-world solutions. In doing so, the journal fosters a holistic approach to knowledge creation—one that is responsive to the pressing challenges and opportunities of our time.

In light of the growing environmental, economic, and social challenges facing the contemporary world, artificial intelligence (AI) emerges as one of the most important technological drivers capable of transforming the path to sustainable development. AI applications have transcended traditional uses into strategic fields that touch the core of sustainability, such as resource management, renewable energy, smart agriculture, low-emission transportation, and smart urban planning. With its ability to process vast amounts of data, discover patterns, and make decisions based on predictive analysis, AI today represents a powerful tool for formulating scientific solutions that meet current needs and anticipate future requirements. Hence, the importance of the increased research focus on this field within the broader framework of engineering and applied sciences, particularly in terms of harnessing it to achieve the 17 Sustainable Development Goals adopted by the United Nations.

The present investigation focuses on a sustainable and innovative strategy for the removal of heavy metals from contaminated water by the application of a low-cost and highly efficient adsorbent. The increasing volume of environmental degradation due to rapid urbanization, industrialization and unsustainable anthropogenic activities has severely impacted the water quality that leads to the depletion of freshwater resources and the accumulation of hazardous wastes. One of the most critical issues arises from the massive discharge of effluents generated by chemical based industries such as electroplating, mining, metal finishing, tanning, textile, fertilizer and paint manufacturing units. These effluents contain significant concentrations of toxic heavy metals that results the environmental pollution. Given these concerns, the systematic analysis and monitoring of surface water and groundwater for heavy metal contamination has become imperative to safeguard environmental sustainability and public health. These include the requirement of extensive land area for operation, complex and costly sludge management processes, high operational and maintenance costs, and dependence on trained technical personnel. Furthermore, many of these conventional techniques are inefficient to treat low-concentration pollutants and the production of secondary pollutants. Batch experiments are carried out for kinetic studies on the removal of heavy metals from aqueous solution. AAS is used for the determination of dissolved and total recoverable heavy metals in industrial effluent. To overcome these limitations, the present study emphasizes the development and utilization of a feasible adsorbent as an alternative remediation strategy.

The rapid advancement of digital ecosystems through the growing use of cloud, remote work, and intricate supply chains has posed a significant challenge to traditional, perimeter-based cybersecurity architectures. To address this challenge, the Zero Trust (ZT) model has emerged as a fundamental cybersecurity model based on the principles of "never trust, always verify," least privilege access, and continuous monitoring. This article provides a comprehensive examination of the status of Zero Trust implementation across industries, including the key strengths and entrenched weaknesses related to identity management, micro segmentation, telemetry, and automation. The results show that while inherent Zero Trust concepts have been widely embraced, their level of maturity remains variable due to disparate policy enforcement, legacy integration issues, and operational complexity. The convergence of DevSecOps initiatives, cloud-native applications, and benefits of artificial intelligence (AI) necessitates that Zero Trust be considered beyond the realm of static policies into adaptive, intelligence-driven architectures. Future work on developing Zero Trust principles focuses on continuous adaptive trust assessment, decentralized identity architectures, quantum-resilient methods for encryption, and privacy-preserving telemetry pipelines. The research concludes that the next generation of Zero Trust Security models must be cognitive, self-operational, and interoperable, to enable the integration of ethical design and human factors that can preserve usability in competitive marketplaces. This should be achieved in a way that provides a scalable and resilient connective infrastructure for securing digital enterprises in an increasingly dynamic threat landscape.

Keywords: Zero Trust; Identity Governance; Telemetry; Policy Orchestration; Micro-Segmentation; Phishing-Resistant Authentication; SBOM (Software Bill of Materials); Automation; Service Mesh; Credential Management; CARTA (Continuous Adaptable Risk and Trust Assessment); AI/ML (Artificial Intelligence / Machine Learning); Attestation Frameworks; Supply Chain Security; Trust Score Computation

Polydopamine (PDA) is a promising synthetic surface modifier that can be deposited on a wide range of substrates providing a chemically reactive surface with catechol and amine groups for functional coatings. In this study, bivalent metal ions were used to dope PDA for applications in functional conductive coatings. X-ray photoelectron spectroscopy (XPS) was used to verify doping in the PDA. To confirm doping in the matrix, the binding energy of characteristic peaks, shift in peaks and new signals indicating interactions between the dopant and PDA matrix were analyzed. Focused ion beam (FIB) cross-sectioning was used to measure the thickness and presence of the FeCo film layer demonstrating the doped PDA can be used for electrodeposition.

In recent years, the field of advanced manufacturing has witnessed a paradigm shift with the emergence of 4D printing—a revolutionary technology that extends the capabilities of 3D printing by incorporating time as the fourth dimension. Unlike static 3D-printed objects, 4D-printed structures possess the ability to autonomously change their shape, properties, or function in response to external stimuli such as temperature, humidity, light, or pH. This transformative capability opens new frontiers in smart materials, adaptive systems, and sustainable engineering solutions.

Engineering managers in industries now rely on AI, a move that is shifting how things have been done before. As organizations work to keep up with the needs for agility, sustainability and efficiency, AI has become very valuable for making decisions, managing resources and improving the way things are done. Engineering management which mostly depended on human experience and traditional data tools has now added efficient smart systems that rapidly handle a lot of information. With this progress, companies experience better productivity and also find new opportunities for innovation in manufacturing, construction, energy and logistics. Integrating AI in both overall planning and daily project work helps engineering managers meet the rapid changes in the world economy.

In a rapidly evolving global economy driven by technological disruption, innovation, and skill-based employment, a critical disconnect between academia and industry continues to hinder progress. While academic institutions remain the bedrock of knowledge creation, industries are the engines of economic application. The synergy between these two pillars is essential for sustainable development. Yet, misaligned goals, outdated curricula, and limited collaboration have created a gap that threatens both graduate employability and industrial competitiveness. Bridging this gap is no longer an academic concern; it is a strategic imperative. Nations that succeed in integrating education with enterprise are better poised to foster innovation, generate employment, and maintain a competitive edge in the global knowledge economy.

The increasing sophistication in cyber threats, it requires advanced AI and ML based solutions that go beyond the historical security measures. AI and ML have become an essential part of cybersecurity as they can analyze Real-time attack risks and respond accordingly. AI plays a Critical role in detecting and preventing attacks, keeping businesses on the cutting edge of cybersecurity barriers.

This paper discusses the role of ML algorithms in anomaly detection, intrusion detection, malware classification, and phishing attack prevention. AI amplifies cybersecurity by detecting patterns and anomalies in network traffic and user behaviour that may indicate a potential cyberattack. Through Cutting - edge data analysis and predictive modelling, AI can proactively prevent attacks by recognizing potential risks before they happen. By analyzing past patterns of attacks and determining similarities, AI systems take proactive measures against breaches before they happen.

One of the other vital responsibilities of Artificial Intelligence in cybersecurity is the development of automatic incident response systems. This kind of system will examine data, identify potential threats, and take instantaneous actions to either contain or mitigate cyberattacks, thus minimizing damage and interferences. Due to the large volume of data processing it can handle in real-time, AI is one of the most important tools in ensuring efficient cybersecurity in the modern digital age.

This paper illustrates the role of AI-driven vulnerability detection in cybersecurity frameworks, in light of such challenges as adversarial AI, data privacy issues, and explainable AI in cybersecurity.

Keywords: AI in Cyber Security; Machine Learning; Intrusion Detection; Anomaly Detection; Malware Classification; Threat Detection

Artificial intelligence (AI) has emerged as a critical set of computational methods and engineered systems that support discovery across science, technology, engineering, and mathematics (STEM) disciplines. While initially developed within computer science, AI applications are now widely utilized to achieve monumental advances across multiple domains, including:

1. Biology and Medicine: AI-based models have enabled real-time protein folding predictions that enhance drug discovery and precision healthcare.
2. Climate Science: Machine learning techniques have improved environmental modeling and long-term forecasting.
3. Materials Engineering: Computational algorithms have accelerated the design of alloys, polymers, and nanomaterials; and
4. Astrophysics: Large-scale data analysis methods support the interpretation of complex astronomical observations.

This paper examines the engineering foundations enabling AI-driven advancements. Relative emphasis was placed on the integral role of infrastructure, algorithm design, and governance frameworks in ensuring that AI applications remain scalable, ethical, and resilient. Current research indicates that AI-enabled engineering not only accelerates innovation but also redefines the competencies required for the future STEM workforce.

By addressing opportunities and challenges, including issues of data quality, bias, and regulatory oversight, this work positions AI’s influence across STEM as a paradigm shift with far-reaching implications. The analysis highlights the importance of interdisciplinary collaboration among engineers, scientists, and educators in navigating the ethical and societal complexities of AI integration, ensuring that innovation is both responsible and sustainable.

The onset of natural convection in a vertical Hele-Shaw configuration under the action of mechanical vibration is studied. Vibration is considered to be a harmonic one and its direction is considered parallel to the temperature gradient. Linear stability analysis for small Pr number leads us to the Mathieu equation. The so-called time averaged method is applied to this equation which gives us a closed form relation for the critical Ra_T number for the harmonic (synchronous) solution. The results are in a perfect agreement with published numerical results.

The engineering challenges of the 21st century demand solutions that are not only innovative but also environmentally responsible. Two fields-Internet of Things (IoT) technologies and green and sustainable chemistry- are now converging to transform how industries operate, how societies respond to crises, and how we safeguard public health and the environment.

The application of IoT in fire safety and disaster response illustrates this promise. My work on an IoT-enabled firefighting robot demonstrated how robotics can be deployed to protect human life in high-risk environments. However, the broader significance lies in coupling such devices with sustainable fire suppression agents and eco-friendly materials, thereby reducing the environmental impact of emergency interventions. This dual approach underscores a new paradigm: engineering solutions must protect lives while minimizing harm to ecosystems.

In recent years, Organ failure and severe tissue damage have been critical medical challenges due to traumatic injuries, the lack of organs for transplantation, and the inherent limitations of conventional artificial implants. To overcome these obstacles, tissue engineering and regenerative medicine have highlighted the urgent need for a transformative approach to the living body that maintains, restores, and improves tissue function. Stem cells serve as the cornerstone of Regenerative medicine due to their unique capability to self-renew and differentiate into various cell types (multilineage) contributing a potential resolution to tissue damage and organ shortage of transplantable organs [1]. However, some significant challenges such as low efficiency of differentiation protocols, poor immune compatibility, and high risk of teratoma formation limit clinical translation when the stem cells are used alone. Therefore, the emergence of advanced biomaterials as a transformative outcome offers precise control over the stem cell microenvironment. Engineered biomaterials can control stem cell behavior by creating optimized niches that copy native tissue environments, these bio-material systems increase differentiation efficiency and improve immune toleration. Therefore, the potential of biomaterials for stem cell engineering is playing a gradually important role in regenerative medicine.

This article provides a comprehensive walkthrough of a series of operations performed in Apache Hive on a Hadoop cluster running on the Ubuntu operating system. It demonstrates fundamental HiveQL commands, including table creation, data loading, data manipulation, schema modification, and indexing, while highlighting the underlying MapReduce job execution. This guide is intended for data engineers and analysts seeking a practical understanding of Hive's functionalities for data warehousing on a Hadoop platform.

This study aimed to assess the ergonomic risks associated with manual material handling (MMH) tasks in rice stores located in the 4th District of Leyte, Philippines specifically in Merida, Isabel, and Palompon. The objective was to identify work-related musculoskeletal symptoms and evaluate risk-prone tasks involving lifting, carrying, pushing, pulling, throwing, and lowering heavy rice sacks. A total of 20 MMH workers from 18 rice stores were purposively selected to participate. Data were collected using the Modified Nordic Questionnaire (MNQ), NIOSH MMH Checklist, Hazard Evaluation Checklist, and the Ovako Working Posture Assessment System (OWAS). MNQ results indicated high prevalence of pain in the shoulders (95%, 80%), upper back (95%, 100%), lower back (80%, 95%), and hips (75%, 100%) in the past 12 months and 7 days, respectively. The NIOSH checklist showed 80.33% of the responses fell within the risk range (60%-100%), while 57.88% of responses to the Hazard Evaluation Checklist indicated conditions that pose a risk of developing low back pain. OWAS analysis revealed that lifting tasks were the most hazardous, falling under Category 4. The findings underscore the need for engineering and administrative interventions and provision of personal protective equipment to reduce musculoskeletal risks and improve the working conditions of rice store laborers.

Keywords: Ergonomic risks; MNQ; NIOSH MMH; OWAS; Musculoskeletal Disorders

The rapid growth of smart cities requires the implementation of advanced technologies to effectively manage and secure distributed energy systems. As energy forms become increasingly interconnected due to urbanization, the systems in place become more complex, raising concerns about security and functionality. This research aims to address these challenges by proposing adaptive AI architectures specifically designed to protect and enhance distributed energy systems in smart urban environments. The main concern is the vulnerability of energy networks to cyberthreats and challenges, which consequently poses a threat to the reliability of energy delivery as well as the stability of urban infrastructures. This study focuses on developing innovative artificial intelligence (AI)-based techniques to mitigate these risks and enhance energy networks. Our approach involves analyzing current energy network management processes and designing new architectural models that incorporate adaptive AI features. The proposed framework, which leverages real-time data, anomaly detection, and predictive maintenance, aims to strengthen both security and operational efficiency. To ensure the framework's stability and alignment with best practices in addressing cyberthreats, it is modeled after the Cyber Assessment Framework provided by the United States National Cyber Security Centre. Future research will focus on refining these AI frameworks through simulations and pilot programs, tailoring them to different cities and ensuring compliance with evolving security protocols. Additionally, ongoing efforts aim to create a viable and flexible strategy for managing and safeguarding distributed energy systems (DERs) in smart cities.

Keywords: Smart Grid; Distributed Energy Network (DENs); Cyber Security and Privacy; Consequence-driven Cyber-Informed Engineering (CCE); Adaptive AI frameworks

In this paper, we present the results of the optical evaluation of the vibrational behavior of a wall (brick). The procedure used is Laser Photo Deflection (LPD), where a laser beam reflected off the sample is redirected to a deflection detector, which detects the sample's vibrations. Using various excitation alternatives, it is possible to establish definitely the first 10 Eigen vibration modes, from which a simple formation pattern based on 2 digits (n,m) can be clearly recognized, for example mode (1,1) (the fundamental mode) corresponds to 29.4 Hz and mode (2,1) to 37.3 Hz, and so on.

Keywords: Physics; Engineering (Structural vibrations; Resonance; Eigen frequencies; Sound excitation; Optical measurements; Laser photo deflection)

In the relentless pursuit of understanding the molecular interactions governing fluid behavior, few theoretical frameworks have had as transformative an impact as the Statistical Associating Fluid Theory (SAFT). Since its introduction in the early 1990s, SAFT has evolved from a theoretical construct into one of the most rigorous and versatile equations of state (EoSs) available, offering accurate predictions of thermophysical properties for a wide range of complex fluids.

Unlike classical equations like van der Waals or Peng–Robinson, which often rely on heuristic corrections to describe fluid behavior, SAFT EoS is firmly rooted in statistical mechanics. It represents molecules as chains of interacting segments and explicitly accounts for intermolecular forces such as dispersion, repulsion, and crucially, association (particularly hydrogen bonding). This molecular-level detail introduces a new paradigm in phase behavior modeling, one that bridges the microscopic and macroscopic worlds with remarkable fidelity.

The Intersection of AI and Environmental Sustainability

Since climate change is growing in impact worldwide, the world needs novel, flexible and smart answers right now. In this situation, more industries are using machine learning (ML) as a key resource to fight the decline of the environment too. Using huge datasets, finding detailed patterns and predicting trends, ML is providing useful support for climate projects and eco-friendly developments in science and engineering [3]. Increasing efficiency in energy, overseeing deforestation and estimating unfriendly weather events are all being improved with machine learning.

Hard, brittle materials such as silicon, germanium are commonly used in infrared optics and semiconductor based products however, these materials cannot be traditional machined like turning and grinding, machining of ceramics is more of a crushing process rather than a metal cutting process. An advance in the precision engineering has led to the possibility and discovery of “Ductile Regime Machining” of brittle materials.

Introduction: Globally, approximately 700,000 people die by suicide each year, and these people work and live in a wide range of social organizations, whose members' suicides can have an impact on family, friends, witnesses, other susceptible people and the organizations themselves. These effects include physical-psychological stress reactions, social influences and suicide contagion. Therefore, the suicide of an organization member should be regarded as a sudden crisis event of the organization, and the main body of the psychological emergency management for the suicide of an organization member should be the organization's decision-making and management personnel, and the target should be the other members of the organization.

Aims: To achieve the effect of curbing suicide contagion, reducing family disputes, restoring the group's mental health level, and reducing negative affect.

Method: Organizational members affected by different suicides were divided into four regions A, F, W, and O through the two dimensions of psychological distance, spatial distance and social distance, and psychological emergency management was carried out according to the characteristics of people in different regions.

Results: The methodology establishes a four-phase emergency management program - psychological prevention and preparedness phase that involves building psychological expectations, a non-punitive organizational culture, and a socially supportive organization; an emergency psychological response phase that involves determining the distribution of suicide-affected populations and blocking the spread of the effects of suicide in the media; an emergency psychological response phase that involves providing first aid through individual counseling, deconstructing social influences through Balint groups, and using the Satir model to enhance the mental resilience of the group; and a final phase that involves tracking the effects of the emergency psychological response and summarizing and improving the process.

Discussion: The following three questions were discussed: Do family members of suicides who are not part of an organization need intervention? How do the principles of emergency management and psychological intervention relate to each other? How do the subjects of emergency management relate to the subjects of psychological intervention?

Implications for Practice: This paper serves as an emergency management plan that can be directly applied to all types of organizations in the event of an employee suicide.

Keywords: organization member suicide; suicide influence; psychological emergency management; psychological distance

This study analyzed players' decision-making process in Werewolf and the success factors of persuasion in a 5-player Werewolf. In Experiment 1, we focused on the villager player and analyzed the process of deciding who to vote for based on what other players say. We proposed a decision-making model in which logical and economic rationality are prioritized, and when they still cannot decide, “empathy” becomes the deciding factor. Experiment 2 focused on persuasion situations and examined how successful persuasion is. The results confirmed that persuasion using logical persuasion materials is successful. This supported the model's validity in Experiment 1 that logical rationality is the overriding criterion for decision-making. However, we could not observe the success factors when both persuaders had logical material. Future research will investigate the aspects of successful persuasion more deeply through complex persuasion situations and games with more proficient players.

Keywords: Werewolf Game; Decision-making; Persuasion; Logical Rationality

As an experimental physicist, I have had the opportunity to work closely with engineers, especially in the fields of optics and semiconductors. However, in the field of Civil Engineering, I have not been fortunate enough to achieve mutual interaction. I must add that, if I currently work now on Civil Engineering topics, it was more by chance than by intention. In this regard, I would like to briefly describe how that foray came about.

It turns out that back in 2006, during a physics presentation, a group of students consulted me about the feasibility of reproducing Tesla's famous "artificial tremor" experiment. My suggestion was to run some tests using sound as an external source of vibration. To do this, we attached a speaker to a wall in the laboratory, which was powered by a wave generator. By sequentially varying the sound frequency, we managed to tune into a resonance of the wall, which effectively produced a sensation of "tremor." Not only did the wall shake, but the floor did as well, producing the characteristic noise of the windows. The workers on the upper floors felt it even more, as they ran through the corridors. Aside from receiving a warning, we won the competition.

In the realm of advanced energy engineering Climatology, Energy Economics and Environment, and Solar Photovoltaic Energy and Systems, gaining expertise in photovoltaic technology, solar cell design, and renewable energy systems have carved out a significant niche due to their remarkable effectiveness. Assessing photovoltaic power plants for remote energy solutions in Bangladesh, implementing low-cost MPPT solar charge controllers for cost-effectiveness, and exploring AI-driven advancements in photovoltaic technology becomes crucial for optimizing their application and enhancing their longevity.

Design and implement MPPT Solar Charge Controllers, integrating AI to optimize solar energy harvest and stabilize the grid. However, challenges such as high initial investment costs and data security concerns need to be addressed. To mitigate these, I suggest conducting cost-benefit analyses, starting small-scale demonstration projects, and implementing robust encryption and access controls for data security. By adopting these strategies, we can overcome these barriers, making renewable energy solutions more accessible and supporting the transition to a sustainable, cleaner energy future.

Cyber-Physical Systems (CPS) represent a transformative technological paradigm that tightly integrates computation, networking, and physical processes. By bridging the cyber (digital/computational) and physical (real-world) domains, CPS are revolutionizing how intelligent systems interact with their environments, offering enhanced autonomy, efficiency, and responsiveness across various sectors.

Definition and Conceptual Framework

Cyber-Physical Systems are engineered systems in which embedded computers and networks monitor and control physical processes, usually with feedback loops where physical processes affect computations and vice versa. These systems consist of sensors, actuators, controllers, and communication modules working in concert to collect data, process it in real-time, and respond with intelligent control actions.

CPS extend beyond traditional embedded systems by incorporating higher degrees of connectivity, interoperability, and autonomous decision-making. They form the backbone of next-generation infrastructures in domains such as smart manufacturing, autonomous vehicles, precision agriculture, smart healthcare, smart grids, and intelligent transportation systems.

Predictive maintenance PdM has helped, in recent decades, manufacturing and industry to save costs and keep their operations safe. This study outlines how advanced machine learning systems, including LSTMs and Transformers, could enhance data-driven maintenance planning. Mainly using C-MAPSS datasets to test Deep Learning (DL) methods and estimate Remaining Useful Life (RUL) values, this research aims to compare LSTM networks and Transformer performance in prognostic via different evaluation criteria. The analysis manifests the peculiarities of both the proposed learning approaches with a marked difference in the performances in favor of the recurrent architecture (e.g., 40% in term R² and 43% in terms of MSE), thus not generally suggesting the usage of transformer-based architecture, especially in a data-scarcity situation common condition when working of critical and costly units, but opening to a new perspective in otherwise operational conditions where data is prosperous.

This study presents a technical evaluation of an integrated automation framework combining ServiceNow, Splunk, and Robotic Process Automation (RPA) to support real-time compliance tracking, incident response, and governance in enterprise IT environments. As regulatory complexity and operational demands increase, automation is applied to reduce manual workload, accelerate incident resolution, and enhance audit traceability.

The framework enables a bidirectional integration: alerts detected in Splunk initiate incident tickets in ServiceNow, while ticket status updates and resolution data from ServiceNow are transmitted back to Splunk for continuous compliance monitoring. RPA bots, developed using UiPath and Automation Anywhere, execute rule-based tasks such as ticket routing, evidence collection, and audit documentation.

Methodologically, the study incorporates architectural analysis, API-level workflow mapping, and scenario-based testing. Performance metrics—including resolution time, documentation completeness, and system responsiveness—were compared across manual and automated workflows. Results indicate measurable improvements in operational efficiency and consistency in compliance reporting.

The framework addresses challenges in interoperability and orchestration, offering a scalable model for automation-driven IT service management. It contributes to the governance, risk, and compliance (GRC) literature by demonstrating how platform integration and automation can advance regulatory alignment and operational performance. Future research may investigate machine learning for intelligent ticket triage, integration with threat intelligence systems, and alignment with zero-trust security models.

Keywords: ServiceNow; Splunk; Robotic Process Automation (RPA); Compliance Automation; IT Operations; Governance Risk and Compliance (GRC); DevSecOps; Artificial Intelligence (AI); Emerging Technologies; Machine Learning (ML)

This work presents the optical evaluation of the vibrational behavior of a concrete beam using an unconventional optoacoustic procedure, the LPD method. In this case, the beam is an integral part of our laboratory construction in Lima; it supports part of the laboratory roof. Using our procedure, we experimentally determined the first 10 Eigen frequencies (14.4 Hz, 40 Hz, 78 Hz, etc.) of the beam, which we can explain using simple cantilever theory.

Keywords: Physics; Engineering (Structural vibrations; Resonance; Eigen frequencies; Sound excitation; Optical measurements; Laser photo deflection)

Gender inequality is one of the biggest challenges leading to several other problems for girls and women, thus hampering women empowerment somewhere both in developed as well as developing countries. One of the key areas to be given importance in the developed countries like India is uplifting the issues related to the gender-wise educational system. It is required to understand the gender-wise differences with the aim of reducing gender-based violence or harmful practices in society. This understanding can be helpful in building and promoting fairness, completeness, and the quality of educational system in the country. According to current reports, states in India like Gujrat and Madhya Pradesh have started working on boosting sex ratio and applying strategies for women empowerment, which is real indication of the concern in this regard. Dealing with the gender- wise differences with reference to the educational system can ensure in the construction of not only a healthy and robust educational system but also in building an equitable society too. The current study shows that, though lot of provisions have been made in this respect, still we need to go a long way for improving the overall growth of the nation in terms of an elevated educational system.

Keywords: Gender-wise education; Educational systems; Gender bias; Educational programs; Education duality; Education inequality

As cyberattacks become increasingly sophisticated and challenging to counter, cybersecurity requires robust, broadly applicable, and easily understandable machine learning models. In the context of Intrusion Detection Systems (IDS), common learning theories often fail because captured data is frequently irregular due to hidden variables, overlearned models, or imbalanced data. Singular Learning Theory provides clear insights into these challenges through mathematical analysis of models exhibiting singularities where traditional statistical assumptions break down. SLT doesn’t depend only on Fisher information and regularity conditions; instead, it uses RLCT and the zeta function to assess the accuracy and complexity of models. By using SLT, the effectiveness of a model trained on known attack patterns or a model trained on cyberattack data is vital in generalizing to unfamiliar or emerging cyber threats, which plays a key role in stopping zero-day exploits and advanced persistent threats. As a result, using SLT can point to ways to strengthen machine learning systems used for detecting malware, catching intrusions, and safeguarding against phishing attacks, since these systems are designed to resist errors caused by network traffic logs. The structured approach of SLT in examining model uncertainty and its abilities enables cybersecurity researchers and users to develop systems that are both precise and backed by solid theory for the future.

First and foremost, we describe a new technique that uses eye movement to control the cursor. Certain solutions impose stringent constraints on individuals with motor impairments and depend on tangible objects such as mice or touchpads. Using eye-tracking technology, our method establishes a connection between human visual attention and computer interaction. We study the subtle gaze patterns that emerge, enabling both cursor control and the formation of an interaction context sense. Our approach converts gaze data into a cursor's movements. It is very adaptable and accurate. Because cursor motions are produced by the pattern of gaze movement, the interface is more dynamically accurate and behaviourally specific. The context- aware interface learns and adapts, maximizing efficiency through its adaptability. Furthermore, our study presents a novel strategy to provide an eye-controlled mouse system using software tools. This system accurately recognizes facial features and interprets eye movements through image processing techniques. The system enables intuitive cursor control by interpreting observable eye movements. Transforming eye motions into a cursor's actions enhances user accessibility and hands-free interaction; in other words, its performance is captured in the experimental analysis.

Keywords: Eye-controlled cursor interface; Visual tracking technology; Pattern analysis; Software tools; User accessibility; User-centric design; Adaptive systems

This research delves into the transformative role of Artificial Intelligence (AI) in fostering sustainable technological advancements. As the urgency to address global environmental challenges intensifies, AI emerges as a pivotal catalyst for enhancing energy efficiency, optimizing resource management, and driving innovative solutions across critical sectors such as energy, healthcare, agriculture, and manufacturing. By integrating machine learning algorithms, data analytics, and predictive models, AI contributes to achieving global sustainability goals. This study explores AI’s contribution toward the United Nations Sustainable Development Goals (UN SDGs), highlighting key applications, the technological challenges faced, and offering policy recommendations. Through a combination of case studies, quantitative analysis, and literature review, the paper underscores AI's potential in shaping a greener, more sustainable future.

Keywords: Artificial Intelligence; Sustainable Development; Technological Innovation; Climate Risk Mitigation; SDGs; Smart Technologies; Predictive Analytics

This work analyzes nuclear physics using the Princonser method, which is based on universal essence, universal principles, and universal laws. Through the Princonser analysis matrix, ten foundations, rules, and universal premises are explored to understand how energy and matter interact in nuclear processes. The development of the analysis is structured in specific arguments that link these concepts to practical applications in nuclear physics, from nuclear fusion and fission to radioactive decay. The conclusion highlights the unity between energy and matter and its relevance in the study of the universe.

This paper analyzes Rome Technopole¹ as a case study of a territorial innovation ecosystem, with particular attention to internal governance mechanisms and stakeholder engagement strategies promoted within the framework of the Italian National Recovery and Resilience Plan (NRRP). Following a review of the regulatory and theoretical background, the article examines the project's organizational architecture and the network-based logic underlying the hub-and-spoke model, highlighting the role of thematic spokes and project flagships. A specific focus is devoted to Spoke 5—responsible for coordinating public engagement, lifelong learning, and social impact activities—where the authors of this paper are actively involved. The paper explores the mechanisms for activating internal collaborations, the operational tools employed, and a selection of high social-impact case studies launched through independently designed calls. Finally, it discusses the structural challenges posed by cooperation among heterogeneous actors—universities, research centers, and industries—highlighting the resulting tensions and opportunities. The experience of Spoke 5 underscores the need to develop systematic practices and adaptive tools for internal stakeholder engagement as a key condition for ensuring the long-term sustainability of complex partnerships beyond the emergency-driven framework of the NRRP.

Keywords: Public Engagement; Innovation Ecosystems; NRRP; Stakeholder Engagement; Networked Governance; Internal Stakeholders; Participatory Models; Rome Technopole; Spoke 5; Multi-level Coordination

Throughout our lives, we all tell stories; whether they are to educate, convince, amuse, inspire, or motivate. They ultimately extend into our livelihood and the spaces we inhabit, transforming the space into a tale through the art of architecture, interior, and spatial design. Even so, not all stories successfully capture the interest of their audience or get across the points they intended to.

Countless live performances have demonstrated the value of spatial design, as they provide background imagery and help structure the show's narrative. Be it any kind of live performance, theatrical show, brand story, fashion show, or concert, the environment that captures and encloses the audience into the new world helps bind the event together.

This paper aims to analyze all the possibilities that set design methodologies and spatial architecture can help enhance and improve the audience's experience.

The research will explore how spatial design components; like lighting, interactive features, and set arrangements, work in tandem with visual storytelling methods to achieve memorable and captivating experiences.

This study examines the intricate details of set design strategies that support immersive storytelling and their effect on viewer engagement. Using a qualitative research methodology combining theoretical frameworks, case studies, and real-world advice from professionals in the field, the research aims to thoroughly grasp how set designers can be crucial in creating experiences for people who enjoy live performances and will remember and find them emotionally stirring.

Keywords: Set design; aesthetics; visualize; narrative; spatial; design; storytelling; performances

In the realm of advanced engineering materials, Aluminium Metal Matrix Composites (AMMCs) have carved out a significant niche due to their remarkable blend of light weight, high strength, and superior wear resistance. As industries such as aerospace, automotive, and defence continually push the boundaries of performance and efficiency, understanding the tribological characteristics of AMMCs becomes crucial for optimizing their application and enhancing their longevity.

According to Bertrand Russell- 'Science'

• "The facts of science, as they appeared to him [Heraclitus], fed the flame in his soul, and in its light, he saw into the depths of the world."

According to Francis Bacon -'The depths of the world'

• "The sciences that sway the imagination rather than the reason are principally three viz. These are: astrology, natural magic, and alchemy (…). Alchemy, may be compared to the man who told his sons/daughters that he had left them gold, buried somewhere in his vineyard/house; while they by digging the vineyard found no gold,
• But by turning up the mould about the roots of the vines procured a plenty vintage. For this reason the search and endeavours to make gold, have brought many useful inventions to light."

This article contains the design of a technical aid for stand-up support in patients with lower limb immobilization syndrome or with reduced mobility. The methodology applied in this article consisted initially in the definition of the design that complied with the necessary requirements for a correct stand up, the height and weight of the user were taken into account, then the pieces of the structure were designed, these pieces were designed independently in order to facilitate the final assembly of the technical aid, then the electronic circuit was designed using the proteus software, finally the final design and assembly was performed using the Autodesk Inventor Professional version 2021 software. We achieved the simulation and articulation of each of the parts that compose the structure of the technical aid to know how it works. The final result of the design will allow the ascent and descent of the patient in a controlled, safe and supervised way by the personnel in charge of the user.

Keywords: Technical assistance; stand-up; design; linear actuated

In today’s fast-paced academic environment, effective knowledge-sharing tools play a crucial role in enhancing collaboration and productivity among students. This study investigates the impact of knowledge management (KM) tool design on knowledge recall and interpretation. Specifically, it evaluates the effectiveness of three communication modalities—Kanban boards, direct messaging, and face-to-face meetings—in facilitating knowledge transfer among university students. A study involving 20 participants was conducted, where web development tasks were assigned and completed using these three modalities. Participants’ recall and interpretation abilities were measured and analyzed using a Likert scale. The results indicate that Kanban boards and direct messaging significantly enhance knowledge retention, whereas face-to-face communication improves comprehension. These findings suggest that optimizing KM tool design according to task complexity can improve collaborative learning outcomes. Future research should explore advanced rating methods, broader participant demographics, and cross-disciplinary applications to refine KM tool effectiveness.

Keywords: Delegation Strategies; Knowledge Transfer; Productivity Optimization

For the first time in the world, a universal generalized method for mono- and multifractals synthesis is proposed, which allows combining and reproducing almost all known artificial and semi-artificial methods of fractal synthesis on a single natural platform - Brownian point dynamics (BPD) in the field of N gravitational forces.

On the basis of the BPD method, a set of algorithms for the synthesis of fractal sets of a wide range of characteristics and properties, such as monofractal, multifractal, isotropic, anisotropic, rectangular, hexagonal, ring, single- and multi-loop, etc., has been developed.

The fractals synthesized by the BPD method enable the production of solid samples of these fractals with predetermined characteristics using modern 3D printing technologies and experimentally study their porosity, fractal dimensions, residual strength, aerodynamic and hydrodynamic drag, etc.

It is shown that the Brownian dynamics of a point in a field of N gravitational forces clearly demonstrates the generation of a fractal order by Brownian chaos with a cascade of discrete levels ordered by scaling, i.e., an important philosophical result of the demonstration of order from chaos. The influence of minimal values of N on the synthesis of linear, planar or volumetric multifractal sets is demonstrated.

Keywords: chaos; multifractal; synthesis of multifractals; tensor structure of multifractals; inverse problem of fractal analysis

The whole country has witnessed a massive change in the functioning at all spheres in the last two years due to Covid-19 pandemic. Education sector and, particularly, higher education in India had never gone through such a transformation which was totally unprepared for. Shifting to online teaching from face-to-face was a natural compulsion at all levels of education. Technical teachers also had to accept this challenge of engaging their students online. Even for the students, this unforeseen change to online learning was filled with several challenges; though some prospects could also be explored. The present study has been undertaken with some crucial objectives; viz. to identify the preference of the students for learning online over conventional learning; to explore the challenges faced in online learning; and to categorise the levels of motivation and confidence of students while studying on virtual platform. Teachers’ perception related to quality parameters in online learning was also observed. Though students were excited about using gadgets in learning, they found the traditional/conventional classes more interesting than those conducted online. Blended learning seemed to be the preferred mode of learning by the students. The teachers realised that apart from the cognitive aspect, the affective domain also matters even in higher education.

Keywords: Covid-19; Pandemic; Online learning; Blended learning; Virtual platform

In the constantly changing world of aerospace technology, innovations that enhance efficiency, safety, and performance are continually highlighted. A particularly revolutionary advancement is the creation and application of laser ignition systems in rocket engines. This technology, which substitutes traditional chemical ignition methods, marks the beginning of a new era in rocket propulsion, offering substantial benefits and future implications for space exploration. Numerous studies and experiments have been conducted globally in this field. The Laser Ignition System (LIS) is a promising technology that facilitates engine restarts and improves the efficiency of the ignition process.

Today, we stand on the brink of unprecedented technological advancements in the fields of Science and Engineering. These advancements are driven by the need to innovate in ways that are not only groundbreaking but also sustainable. One of the most promising avenues in this endeavor is the development and application of polymer composites. Polymer composites, which combine a polymer matrix with reinforcing materials, offer remarkable benefits including enhanced strength, reduced weight, and improved resistance to environmental degradation. These properties make them indispensable across various industries such as aerospace, automotive, construction, and consumer electronics.

In recent years many of the applications have been migrated to the cloud mainly due to its flexibility and capability of cutting down on user Costs. But as we notice that many data are now being stored on the cloud there has been a rising concern on security. However, in a cloud environment, information, identity, database, application servers, and web servers are vulnerable to different types of cyber threats such as data breaches, identity theft, database manipulation, and more. Responses to these threats need sound investigation systems and methods hence giving rise to the concept of cloud forensics. Cloud forensics targets deal with cybercriminals in cloud infrastructures by presenting techniques for the detection, analysis, and prevention of illicit actions.

At Evolwe Inc., we are advancing the frontier of artificial consciousness (AC) by developing emotionally intelligent, conscious AI agents designed to enhance human-AI collaboration. Emotional AI serves as the foundational layer for AC, enabling machines to recognize and respond to human emotions, intuitions, and contextual nuances. This article explores how our research integrates emotional AI with artificial consciousness to create agents that can intuitively interact with humans across diverse industries, including healthcare, manufacturing, and the creative sectors. We highlight the practical applications of these conscious AI agents and discuss the ethical implications of bringing machines closer to human-like consciousness. As emotional AI becomes obligatory in developing AC, Evolwe Inc. is aiming to lead the charge toward a future where emotionally intelligent, conscious agents transform how humans and machines collaborate, creating more natural, intuitive partnerships for solving complex real-world problems.

Our invention/paper relates to the use of a kaolin source material that is mixed with a pozzolanic material in the absence of water, to form a geopolymer cement binder material. It relates to the industrial scale processes that these two major materials go through as they are prepared by special manufacturing processes to obtain optimised raw material constituent materials first and then when they mixed by large scale/commercial processes to obtain products of uniform quality. A geopolymer cement product capable of achieving a compressive strength of 104 Mpa with a compressive strength after a chemical attack of 100.74 Mpa and a thermal expansion of 0,2mm can be achieved after producing the input raw materials into this GPC cement at a Pyro processing temperature of 681 Degrees Celsius resulting in an aluminium material phase of 4.01 and silica to alumina ratio of 2.26. This product should be ground to a particle surface area of 6863 parts/cm² with an alkali activator of 18,78%.

Keywords: metakaolin; pozzolanic materials; geopolymer cement; material phase; optimisation

This study addresses the challenges of compressing and decompressing digital-ink data captured from handwritten inputs on a device- such as pen-tablet, etc. We propose a novel method leveraging sparse Gaussian process regression (GPR) to compress digital-ink data into a compact kernel matrix using pseudo-inputs. To enhance compression accuracy and reconstruction fidelity, we integrate a dynamic programming (DP) strategy for optimal pseudo-input selection. Experimental results validate the proposed method by demonstrating its effectiveness and significant compression ratios.

Keywords: digital-ink; handwriting; compression; decompression; sparse Gaussian process regression; pseudo-inputs; optimal selection; dynamic programming

In today’s changing realm of science and engineering blending creativity with technical skills has become essential. Design Thinking, an approach cantered around needs and innovation serves as a bridge between these two realms providing a solid framework for engineers and scientists to address complex challenges with resourcefulness and empathy.

The Fourth Industrial Revolution (Industry 4.0) represents a paradigm shift, one that transcends mere technological evolution. As researchers and practitioners, we find ourselves at the confluence of digital innovation, cyber-physical systems, and transformative societal implications. In this essay, we dissect the core tenets of Industry 4.0 and its profound significance for science and society.

Artificial intelligence (AI) is becoming a game-changer in the field of dentistry, transforming everything from diagnostics to treatment planning and patient management. With its data-driven capabilities, AI is helping dental professionals provide more accurate, efficient, and personalized care. One of the most significant applications of AI in dentistry is in diagnostic imaging. Machine learning algorithms can analyze dental X-rays, CT scans, and other imaging data with remarkable accuracy. AI systems can detect cavities, root infections, gum diseases, and even early signs of oral cancer by identifying patterns that might be difficult for the human eye to see. For example, in radiographic interpretation, AI can measure bone density and monitor changes over time, helping dentists spot issues before they become severe. Early diagnosis can lead to more effective, less invasive treatments, improving patient outcomes and reducing long-term costs. Another challenging aspect is in the field of treatment planning and predictive analytics as AI also aids in creating more precise and personalized treatment plans. By analyzing a patient’s dental history, genetics, and lifestyle factors, AI-powered systems can predict the progression of certain conditions and recommend optimal treatment plans. For instance, in orthodontics, AI-driven software is used to design custom aligners for treatments like Invisalign, predicting how teeth will move and adjusting the aligners accordingly. This process enhances the accuracy of alignments, shortens treatment times, and improves patient satisfaction. In prosthodontics and implantology, AI can assist in designing and placing dental prosthetics and implants with unparalleled accuracy. Advanced software considers various patient-specific factors, such as bone structure and tooth position, to create the most suitable fit. This helps reduce risks associated with implant failure, ensuring a higher success rate. Moreover, AI shows a promising progress in the field of robotic assistance in dental surgeries as it is paving the way for robotics in dental surgeries, bringing precision and consistency to procedures like implant placement and root canal therapy. Robots powered by AI can assist dentists by stabilizing instruments and guiding them through complex movements with minimal error.

Quantum computing is poised to transform the field of medicine, offering unprecedented possibilities in diagnostics and treatment. With its immense computational power, quantum computing can tackle medical challenges that are currently insurmountable with classical computers. This editorial explores how quantum computing can be harnessed in modern medicine, discussing its potential benefits and the obstacles that must be addressed.

The idea for the Submerge Trimaran hull concept (STR) presented on this paper it’s originated from the Nautilus System (NTLS) stabilizers for motions and currents, which is based on a 2 sides linear approximation using Keulegan-Carpenter number (KC) to obtain the roll motion damping coefficients from the roll decay test of high block coefficient vessels with bilge keels and from which it´s possible to understand that, the damping that’s rules the roll motion of high block coefficient vessels with appendages is more significant at lower oscillations amplitudes (roll decay test latest cycles), it´s mean at lower KC numbers (KC <) were the steady state flow regime is predominant and the GM_T has a linear variation in the stability curve, which however didn’t occur for larger oscillation amplitudes (roll decay test initial cycles) were highest KC numbers (KC >) are relevant (drag forces) meaning that, the action of appendages such as bilge keels is less effective in reducing roll amplitudes as results of the turbulence derived from the back and forth oscillating flow, being the main source for the energy dissipation the vessel hull form capability to generate waves. Therefore, the Submerge Trimaran hull (STR) is a NTLS variation to be applied in conventional vessels with advance velocity to obtain a reduction to the roll amplitudes by increasing the potential damping while restricting the transversal oscillation flow by doing a hull modification from a perspective that considers as a base a hull immerse in a steady state flow regime (lower KC numbers) that´s has the effect of varying the natural roll period, extending the GM_T (STR) linear variation in the transverse stability envelope for a broader range of sea state, as more external excitation energy it´s require to obtain the same conventional hull roll amplitude, as part of the main motivation for the STR hull concept development idea, which is to achieve an overall improvement to a vessel sea keeping performance to cross ocean routes efficiently with energy savings and decabornization.

Keywords: Nautilus System (NTLS); Submerge Trimaran Hull (STR); Roll Motion Control

This paper reports an experimental procedure based on the Constructal Law to reduce the noise level. The study investigates both the component level and the system level noise reduction. It is shown that at 100% load and at the second construct, a noise of 3dB will be reduced.

Keywords: Constructal; Noise Reduction; Correlations; Second Construct

Additive manufacturing (AM) is a valuable addition to engineering design by enabling complex geometries, reduced waste, and rapid prototyping. Despite these advantages, the structural integrity of 3D-printed components can be limited by several factors, such as anisotropy, porosity, and layer adhesion [1]. Adhesive bonding has gained relevance as an effective method to join 3D-printed components, offering benefits such as stress distribution, energy absorption, and compatibility with complex geometries. Adhesive bonding, unlike mechanical fastening or welding, distributes stresses more uniformly and eliminates the need to drill holes, which are required for bolted joints. In 3D-printed components, adhesives offer a versatile solution to join dissimilar materials or repair parts without inducing heat-affected zones, oppositely to welded joints. However, the effectiveness of adhesive bonding relies on parameters such as surface energy, adhesive type, and the quality of surface preparation [2]. Adhesive bonding of 3D-printed components entail difficulties such as high surface roughness, internal defects, and anisotropic properties due to layer-by-layer deposition. These factors complicate adhesion and require an additional design effort in the adhesive selection, surface treatment, and joint design.

This paper discusses a case history of a highway tunnel located in lower Himalayas. The site information was collected primarily from geological mapping and limited site investigations carried out from tunnel portal due to inaccessible terrain conditions. The tunnel alignment, cross section, and elevations were assessed, evaluated, and designed based on the geological and geotechnical parameters ascertained from field and laboratory investigations. Numerical analysis was carried out to study stress-deformation behavior under varying ground conditions and multiple stages of excavation along with wedge stability for given joint conditions. Displacements obtained numerically were compared along with field monitoring values for crown and wall. Finally, a sensitivity analysis is carried out to ascertain impact of strength parameters on deformation and yielding of excavated tunnel face.

keywords: Numerical analysis; Geotechnical investigations; Geological mapping; Road tunnel; Field monitoring

Polyurethane's versatility is limited by its weak flexural strength. This study explores methods to address this limitation. Recycled carbon fiber powder (RCFP) was investigated as a reinforcing agent. The research found that incorporating recycled carbon fiber particles (RCFP), particularly finer particles (100 mesh), significantly enhanced flexural strength and modulus. This improvement is attributed to RCFP effectively filling voids in the polyurethane, increasing its density and resistance to bending loads. While a higher content strengthens the material, it can also lead to excessive stiffness and a decrease in flexural strength. Polyester fabric reinforcement (FR) emerged as another effective method, with five layers increasing flexural strength to 2.1 MPa. FR also introduces flexibility, potentially reducing the risk of catastrophic failure under impact or bending. Additionally, FR improves the composite's compressive, shear, and tensile strength. The study highlights the importance of optimizing both RCFP size and content to achieve the desired mechanical properties. Future research could explore the combined effects of RCFP and FR for even greater improvements. This research paves the way for the development of stronger and more sustainable polyurethane composites using recycled materials.

Keywords: Recycled Carbon Fiber Powder; Fabric Reinforcement; Flexural Properties; Polyurethane

Since its inception on December 10, 1993, when it was decreed by the Prime Minister, Vietnam National University, Hanoi (VNU) has had a substantial impact on higher education in Vietnam for the past three decades. At present, VNU has developed into a thriving center for education and research, with 37 members and connected units. These include 16 service units, 9 universities, 4 affiliated schools, 6 research institutes, 2 training and research centers, and 2 institutes of higher education. This paper presents an exhaustive examination of the research outputs of VNU spanning the years 1993 to 2023. It emphasizes the university's significant contributions to the Scopus Database, which contains more than 10,000 document records, and the Web of Science Database, which contains more than 9,100 records. The research study thoroughly examines the statistical data and analyses pertaining to these outputs, considering many factors including number, quality, subjects, authors, and collaborations. This report presents an exhaustive assessment of the research outputs of VNU spanning a period of thirty years. It emphasizes the efficacy of institutional measures in bolstering scientific production and fostering innovation.

Keywords: Bibliometric analysis; Scientific research; Creative Innovation; Research outputs; Policy impact

There are different ways to train personnel in a manufacturing plant, ranging from in-person to virtual training. However, there is currently no standard model or methodology to measure the effectiveness of this training on the position, work cell, and indicators. This work proposes a method to measure the impact of training strategies through an indicator called a "Real Training Indicator." This method is based on requirement 7.2 of the ISO 9001:2015 Standard, which refers to the "Competence of Personnel." The Kirkpatrick, Wade, and Phillips models are used to measure the impact of training in a transnational organization in Mexico. This method estimates the level of transfer and effectiveness of the training to cover the requirement of Good Manufacturing Practices.

Keywords: Good Manufacturing Practices; Knowledge Transfer; Personal Hygiene; Real Training Indicator; Food Industry; Blue Collars; Mexico

In the ever-evolving cybersecurity landscape, network forensics has emerged as a crucial field, dedicated to investigating and mitigating cyber threats. The rapid growth of digital communication and the increasing sophistication of cyber-attacks necessitate advanced methods for detecting, analyzing, and responding to security breaches. Machine learning (ML), a subset of artificial intelligence (AI), offers transformative potential in network forensics, promising to revolutionize how we protect and investigate our digital environments. Network forensics involves monitoring, capturing, and analyzing network traffic to identify security incidents and gather digital evidence. Traditionally, this process has relied heavily on manual analysis and signature-based detection methods, which can be time-consuming and inadequate against novel or complex attacks. The sheer volume of network data and the subtlety of modern cyber threats demand more sophisticated, scalable solutions. This is where machine learning comes into play.