Volume 20 Issue 11 2025

A web-based program called the Registration and Evaluation Management System (REMS) was developed for CIT Educational Development System Inc. Based on the gathered data, the management handled 200 to 400 students in weekly training sessions, which led to challenges in managing students and affected their manual strategy for recording student information, performance, and payments. This often resulted in misplaced records and a lack of transparency in evaluating student performance and payments. The study focused on automating a variety of tasks carried out by the CIT Director, teachers, staff members, and students. The components of REMS include Teacher Evaluation, Admin Reports, and Online or On-Site Student Registration. The portal allows students to select courses, determine their schedules, and upload payment receipts through several payment centers, such as MLhuillier and Palawan Express. Teachers, on the other hand, may use the system to assess students' outputs for various course sessions by providing grades and comments for evaluation. The system can also generate various reports, including the list of students by session, payments made, and TESDA voucher qualification. Through ViaNet, an Application Programming Intelligence (API), the system is able to produce and deliver SMS notifications regarding a student’s registration status. The project aligns with Sustainable Development Goals with the following SDG’s The selected Sustainable Development Goals include SDG 5: Gender Equality, SDG 8: Decent Work and Economic Growth, SDG 9: Industry, Innovation, and Infrastructure, SDG 11: Sustainable Cities and Communities, SDG 12: Responsible Consumption and Production, and SDG 17: Partnerships for the Goals. Based on the SUS results from alpha and beta testing, the system achieved good performance with an average score of over 80%. The system that was implemented in the company ensured that every field was checked and validated to avoid invalid entries and errors. It also contributed to the training centers by assisting in student registration, session evaluation, student performance monitoring, and file management procedures.

Open System for Earthquake Engineering Simulation (OpenSees) is an object-oriented software framework created to simulate complex earthquake engineering problems. It allows users to analyze and model the effects of seismic activity on structures, such as buildings, bridges, and dams including dynamic and static loading, nonlinear and multiaxial loading, and spatio-temporal loading. The framework is open-source and is continuously updated to meet the latest engineering standards. Unlike other software packages, OpenSees provides users with the flexibility to customize their simulations to fit their specific needs. For example, it can be used to analyze different design alternatives or to simulate different materials and their properties. It also supports a variety of output formats, allowing users to visualize their data and generate reports. Additionally, OpenSees has powerful solvers that can run on distributed hardware systems, allowing users to analyze large-scale problems. All in all, OpenSees is a powerful and efficient tool for simulating earthquake engineering related problems. It is an invaluable resource for researchers and engineers looking to understand and mitigate the effects of seismic events. Moreover, OpenSees is interoperable, meaning it can be used in conjunction with other software packages to benefit from their functions. Overall, OpenSees is a powerful tool that enables users to conduct sophisticated earthquake engineering simulations with ease.

Rapid advances in materials science, biotechnology, digital technologies and process engineering are driving a paradigm shift in food processing and preservation, moving the sector away from energy-intensive, ‘‘one-size-fits-all’’ approaches toward precision, sustainable and consumer-centric solutions. This review synthesizes current and emerging technologies that together define the future landscape of food processing and preservation. Non-thermal and minimal-thermal technologies — including high-pressure processing (HPP), pulsed electric fields (PEF), cold plasma, ultraviolet and pulsed light, ohmic and microwave heating, and ultrasound — enable microbial inactivation and enzyme control while preserving sensory and nutritional quality. Concurrently, novel preservation strategies such as edible coatings, active and intelligent packaging, antimicrobial biopolymers, controlled and modified atmosphere systems, and natural antimicrobials (plant extracts, bacteriocins) offer targeted shelf-life extension with reduced reliance on synthetic additives. At the molecular and product-design levels, advances in synthetic biology, precision fermentation, enzyme engineering and microencapsulation are enabling tailored functional ingredients, targeted release systems and enhanced nutrient stability. Nanotechnology and smart packaging integrate nanoscale sensors and controlled-release carriers to actively monitor and respond to product quality changes. Digitalization — encompassing Internet of Things (IoT) sensors, blockchain for traceability, machine learning for process optimization and predictive shelf-life modelling — permits real-time quality control, waste reduction and supply-chain transparency. Concurrently, additive manufacturing (3D food printing) and modular micro-processing units support mass customization, on-demand manufacturing and localized production that can reduce distribution emissions. Sustainability, circular economy principles and regulatory considerations are central to technology adoption. Life-cycle impacts, energy efficiency, food safety validation, consumer acceptance and equitable access are examined as critical enablers and constraints. The review highlights integrated, hybrid approaches (combining physical, biological and digital methods) as the most promising pathway to reconcile quality, safety and sustainability goals. Keyresearch gaps include standardized metrics for techno-economic and environmental assessment, long-term safety evaluation of novel materials and nanoparticles, and regulatory frameworks for biologically derived preservatives and precision-manufactured foods. The review concludes with a roadmap for translational research, interdisciplinary collaboration and policy actions required to accelerate responsible deployment of next-generation food processing and preservation technologies.

We test various Ensemble of Regression Trees (ERT) models for fish shape alignment. Fish shape is represented as a number of landmarks at various body parts. From the position of these landmarks malformations can be detected, species variation can be discriminated, fish dimension can be estimated, etc. The popular facial shape alignment method ERT has been adapted for fish shape with hardware acceleration. ERT models with 4 or 26 landmarks have been trained with photographs from 2 datasets with underwater fish photographs. The models differ in their ERT parameters (cascades and trees). The normalized errors from each model and their training errors are presented. The average normalized error for 26 landmarks, ranges between 0.36% (1500 trees/stage) and 0.76% (250 trees/stage and tree depth=6). This error ranges between 1.39% (40 splits) and 1.59% (default ERT model) when 4 landmarks are used. Worst error is achieved when 4 landmarks are used although training was performed in a controlled environment with minimal variations in the background. This is due to the fact that the landmarks at the caudal fin and the upper and lower parts of the body cannot be accurately annotated. Thus, 3 of the 4 landmarks could not be placed accurately during the annotation of the training photographs

The behaviour of Ionosphere over desert region in long-term scale, however, has not been well recognized by the Ionosphere research community. This study demonstrates the significant climatological variation of the ionosphere over the great Thar desert for the first time, using five years of ionospheric data for the period 2020-2024, retrieved from the COSMIC-RO satellite. In addition to demonstrating insightful information on long-term electron concentration (Ne) in the Ionospheric E layer, this study primarily focuses on longterm variability of important ionospheric parameters such as NmF2, HmF2, and IECs. Both, NmF2 and IECs exhibit semi-annual variation with twin-peaks during the summer and spring season, whereas HmF2 shows annual variation with an annual peak during post-summer and an annual dip during the winter. The findings decisively demonstrate the significance of integrating multiple ionospheric parameters in studying a desert region where not a single long term ionospheric study conducted before using LEO satellite information. This research sets a strong foundation as background knowledge for future investigations in quantifying disturbance levels and the potential for communication interruptions in satellite broadcasting, improving navigational services during various geophysical phenomena.