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.

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)

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

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

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.

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.