This volume, Mental Health Challenges in Academia: Stressors Faced by Students and Faculty, bravely confronts the issues that many in higher education endure but few openly discuss. From the strains of balancing teaching, research, and administrative duties to the financial pressures, cultural challenges, and emotional burdens faced by students, it brings together a diverse range of perspectives to paint a holistic picture of academic life. It highlights both the systemic issues and the deep personal stories that reveal how intertwined our professional achievements are with our personal well- being. In doing so, it not only informs but also reassures its readers: you are not alone, and there are ways forward.

Since teaching is the foundation of education, program accreditation and institutional performance are directly correlated with its effectiveness. By creating a competitive and supportive learning environment, faculty performance has a direct impact on an academic institution’s ability to fulfill its vision and goal. To provide a thorough and impartial assessment of teaching performance, this study uses data mining algorithms to extract insightful information about the elements that go into good instruction, including both structured and unstructured data. This is done in response to the urgent need for faculty performance evaluation. To help institutions identify their strengths, rectify their flaws, and encourage ongoing growth in their teaching and learning processes, the system was created. Looking for trends in teacher data. Furthermore, sentiment analysis methods are employed to assess qualitative input, and Laravel 8.0 provides the framework for putting these algorithms into practice. A grand mean score of 4.38, which is considered “Very Acceptable,” was obtained from expert evaluations of the system, demonstrating its dependability and efficacy in assisting with faculty performance reviews.

Floods are among the most devastating natural hazards, causing widespread damage to lives, livelihoods, and infrastructure, particularly in vulnerable river basins. The Cagayan River Basin (CRB), the largest and most flood-prone basin in the Philippines, remains a significant challenge for disaster risk management. This study developed an event-based hydrologic–hydraulic modeling framework by coupling HEC-HMS rainfall–runoff simulations with HEC-RAS 2D unsteady flow routing to produce validated flood hazard maps. Inputs included rainfall from 41 gauge stations and observed inflows from the Magat Dam, processed in HEC-DSS. Validation utilized 137 surveyed flood marks collected from post-flood surveys, community reports, government archives, and household RTK measurements, with a concentration in Tuguegarao City. The coupled model reproduced key hydrograph peaks with moderate accuracy (R2 = 0.56, Bias = +0.32 m, RMSE = 1.61 m, MAE = 1.43 m), although NSE (−2.30) reflected the limits of daily rainfall inputs. Simulated hazard maps identified 767.97 km2 of inundated area (approximately 2.77% of CRB), concentrated along the floodplain and at the Magat confluence. Unlike previous scenario-based or localized efforts, this study delivers the first basin-wide, event-validated flood hazard maps for the CRB using integrated depth and depth–velocity criteria. The resulting hazard layers provide a scientific basis for strengthening evacuation planning, guiding land-use and infrastructure decisions, and supporting long-term resilience strategies in one of the Philippines’ most flood-prone rivers.

This chapter establishes the core foundations of upskilling and reskilling in an AI-driven workplace and discusses their importance in workforce innovation. Ongoing education and continuous learning are highlighted as key factors in making workers responsive to changing industry needs. Through the integration of industry and educational institution case study insights, such as Siemens lifelong learning initiatives, the Center for Integrated STEM Education–Massachusetts Institute of Technology Responsible AI for Social Empowerment and Education (CISTEM-MIT RAISE) AI literacy initiatives, and the Miriam College Technology Business Incubator (MC-TBI), the discussion emphasizes how workforce strategies powered by AI improve skills development, increase productivity, and establish sustainable career paths. Predictive workforce analytics, adaptive learning pathways, and industry-specific training methodologies are examined as high-impact interventions for mitigating skill deficiencies. The transforming character of learning institutions and training schemes is also considered, with special emphasis placed on the necessity for academia–industry–government partnerships for the creation of scalable and accessible learning ecosystems. The findings reveal that AI-supported reskilling initiatives, backed by established assessment methodologies and policy frameworks, significantly improve workforce adaptability and long-term employment prospects, promoting ongoing learning environments, accessibility-focused training solutions, and collaborative partnerships to future-proof workforce in the digital economy.

This book offers a comprehensive guide to navigating the transformation of the workforce due to the influence of artificial intelligence (AI) across industries and discusses detailed strategies for executing reskilling and upskilling programs for professionals and managers in charge of workforce development, training, and employee retention in an AI-driven landscape. As AI continues to reshape sectors and redefine job roles, the need for a strategy for an adaptable and well-equipped workforce has never been more critical. By analyzing AI’s integration with other emerging technologies, such as blockchain and IoT, and their specific impact across sectors, this book prepares readers to meet the unique demands of an AI-driven workforce transformation.

This study aimed to develop models for predicting trip frequency in San Jose City, Province of Nueva Ecija, Philippines incorporating socio-demographic factors (SDF) and attitudinal factors (AF) through the use of artificial neural network (ANN). Socio-demographic factors in the model include age, sex, civil status (CS), number of children (NOC), barangay, number of household members (NHM), educational attainment (EA), employment status (ES), household income (HI), number of driver license holder (DLH), number of personal vehicles owned (PVO), and number of vehicles owned by the household (VOH) while the attitudinal factors in the model include car dependency (CD), convenience, speed, privacy and safety (PS), health and environment (HE), cost, and comfort. The collected data were processed to develop ANN model in different pandemic stages with 19-19-1 (input-hidden-output) network structure used for these models. The sensitivity analysis (SA) results indicate that in the pre-pandemic period, employment status is the most influential parameter (MIP) to the trip frequency in the study area, while the educational attainment is the MIP during the pandemic period and in the post-pandemic period. The findings of the study signify the effectiveness of ANN in forecasting trip frequency as evident to the low mean absolute percentage error (MAPE) values obtained for the three models. The results can be used by policymakers in making informed strategies in further improving the travel experience of the population in the study area.

Eggshells, which are mainly composed of calcium carbonate, have been considered as alternative filler in polymer composites to reduce cost and improve some of the material properties. One of the biofillers that has not yet widely studied for its potential use in rubber is quail eggshell. For this research study, the effects of quail eggshell filler on the abrasion resistance and thermal degradation of room temperature vulcanizing (RTV) silicone rubber were investigated. Samples with 0, 5, 10 and 15 wt. % quail eggshell filler were prepared through manual mixing and open molding process. Abrasion resistance test was performed based on ASTM D4060 while thermogravimetric-differential thermal analysis was utilized to examine thermal degradation. Results revealed that the abrasion resistance of silicone rubber increases as the eggshell filler content rises from 0 to 15 wt. %. The silicone rubber with 15 wt. % filler achieved the best abrasion resistance with a mass loss of about 44.7 % less than the sample without filler. For the thermal analysis, the sample with 15 wt. % eggshell filler achieved a higher peak degradation temperature of about 605.61 oC as compared to the plain sample (511.25 oC) but the latter appears to resist thermal degradation better at high temperatures.

We owe this amazing collection to the vision and dedication of Aras Bozkurt of the Anadolu University (Türkiye), who, with characteristic energy and imagination, has brought together a remarkable community of voices. Having collaborated with Aras on several AI in education research, I can say with certainty that he not only advances the field with rigor but also infuses it with creativity and warmth. This volume reflects that spirit: diverse in scope, rich in insight, and committed to the future of education as a human endeavor. To Aras and to all the book chapter con tributors, not only my thanks but the thanks of every reader who will learn, reflect, and dream alongside these pages. Your work is a gift to our field and a reminder that scholarship can be both courageous in its critique and generous in its vision. Rarely does a book succeed in being both a mirror to our present and a lantern lighting the path ahead.

Mathematics is a subject that often feels distant from everyday life, yet its logic quietly shapes the world around us. As learners continue to question its practical significance, there is a growing need to rethink mathematics education. Recently, the emergence of artificial intelligence (AI) has opened new possibilities for transforming how mathematics is taught and learned. This chapter aims to examine the emerging role of AI in mathematics education by synthesizing current tools, identifying prevailing trends, and exploring transformative applications. Using a narrative review supported by expert-informed synthesis, several themes are identified that reflect how AI are reshaping instructional practices, learner engagement, and pedagogical design. The discussion integrates illustrative examples of AI tools to highlight their instructional relevance and underlying mechanisms. The chapter concludes by reflecting on a redefined landscape for mathematics education, where technology transforms instructional practices, learner experiences, and the development of mathematical thinking.

The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) into cybersecurity education offers a significant opportunity to customize learning, improve engagement, and connect theoretical concepts with practical applications. This study presents the Dynamic Integration and Optimization of NetCyber Activities (DIONA) system which is an AI-enhanced educational tool developed from the NetFusion Learning Academy (NLA) to tackle ongoing issues in conventional cybersecurity education, including restricted adaptability, absence of real-time feedback, and inadequate practical skill application. The study employs a systematic methodology based on four distinct objectives: (1) to assess the efficacy of NLA in improving student learning across five critical domains— knowledge retention, practical skills, engagement, conceptual understanding, and problem-solving; (2) to examine student and faculty perceptions of its educational value; (3) to develop the ACTIVE AI Framework for AI-driven pedagogy; and (4) to create and validate DIONA as an AI/ML-based experiential learning platform. Statistical and thematic analyses indicated that although NLA effectively enhances knowledge and engagement, deficiencies persist in practical skills and problem-solving, necessitating the incorporation of AI-powered tools. The ACTIVE AI Framework and DIONA system offer customized learning trajectories, AI-generated feedback, and immersive simulations that correspond with authentic cybersecurity challenges. Results endorse the significance of intelligent learning analytics and specialized AI systems in transforming technical education and equipping students for changing digital environments.