In today’s digital era, the media landscape has seamlessly transitioned from print to online platforms, leading to an unprecedented increase in information accessibility and exchange. However, this transformation has also intensified a major challenge, the rapid proliferation of fake news, which refers to fabricated or misleading information that can be easily produced and disseminated. This paper addresses the growing global concern of misinformation and explores potential solutions through machine learning techniques. The proposed study develops a model designed to assess the authenticity of news articles by evaluating multiple text vectorization methods, specifically the Bag-of-Words approach using both Count Vectorizer and TF-IDF Vectorizer. Two classification algorithms, namely the Multinomial Naive Bayes Classifier and the Passive Aggressive Classifier, are employed to detect fake news. The study further investigates how text pre-processing influences overall model performance. The dataset chosen for training is comprised of 67.7% curated information, while the remaining 33.3% remains untrained raw data. Notably, the model demonstrates a noteworthy efficiency rate of 93.78% under optimum conditions. This strong result demonstrates how well the suggested methodology works to differentiate between real and fake news.
Indian roads present one of the world's most heterogeneous and unstructured traffic environments, simultaneously accommodating motorized vehicles, e-rickshaws, bicycles, pedestrians, and free-roaming animals without strict lane discipline. Autonomous vehicle (AV) perception systems trained primarily on Western datasets fail catastrophically in such mixed-traffic scenarios. This paper proposes HOPNet (Heterogeneous Object Priority Network), a multi-modal real-time object detection framework integrating YOLOv8-based convolutional neural networks with Swin Transformer attention, camera-LiDAR fusion, and domain-adaptive transfer learning. HOPNet is trained and evaluated on the Indian Traffic Dataset (ITD), a novel dataset of 180,000+ annotated images collected from twelve Indian cities. Experimental results demonstrate a mean Average Precision (mAP@0.5) of 91.4%, surpassing the best baseline (YOLOv7) by 11.1%, with a detection latency of 28 ms on NVIDIA Jetson AGX Xavier edge hardware. Per-class analysis shows particularly significant improvements for underrepresented categories: monkey (+27.6%), goat/sheep (+26.2%), street dog (+21.9%), and loaded bicycle (+18.5%). The proposed Contextual Risk Scoring Module (CRSM) enables priority-based AV decision-making for safety-critical unusual objects. DOI: DOI : https://doi.org/10.52783/pst.3468
In this paper, an Artificial Intelligence based methodology for solar radiation forecast in areas where data is limited is presented, based on satellite images and deep learning approaches. Reliable ground-based solar measurements are still limited, especially in developing countries, which is still a significant challenge in the deployment of solar energy. The proposed solution is to use widely available satellite data that can be derived from data on cloud cover images and land surface indicators as proxies for solar resource assessment. To enhance forecasting accuracy over different geographical regions, the use of various deep-learning architectures such as convolutional neural networks, transformers, and spatiotemporal models is explored. Transfer learning and domain-adaptation techniques are also incorporated into the framework allowing transfer of models to areas with lower historical data. In addition, pre-processing methods like normalization, interpolation, augmentation and feature engineering are used to improve the robustness of the model. Methods to aid reliable decision-making and investment planning, such as probabilistic forecasting, uncertainty quantification and calibration methods are included. The proposed approach has promising prospects for enhancing solar-resource assessment, planning for renewable-energy expansion, and for sustainable energy development in areas with a significant data deficiency.
Solar energy is critical in the mitigation of climate change and this helps in reduction of carbon emissions into the atmosphere caused by fossil fuels. Nonetheless, the fluctuation in the generation of solar energy presents difficulties in the effectiveness of combining and functioning of solar technologies in the market. Correct solar resource forecasts are therefore essential in the proper management and strategic planning in power systems. Numerous methodologies of forecasting have appeared, primarily they are classified into physical models, statistical methods and machine learning methods. The first and second categories use familiar physical phenomena, and need less data to train the model. It is worth noting that simply classifying methods by machine learning methods are not comprehensive enough; even the duration of the forecast horizon is a decisive factor. Machine learning has largely influenced solar photovoltaic (PV) forecasting by making the use of solutions that take into account time-series data by now possible, especially since the availability of regional data is increasing. The choice of a model depends on the application and system characteristics, i.e. there is no single application-system characteristics that will be universally applicable. With the energy transition on the verge of happening, it is necessary to consider hybrid renewable and distributed generation systems since individual technology-related limitations are more evident with an increasing market integration. A solution is offered through task-oriented coordinated optimization, which will exclude the necessity of a strictly delineated hybridization framework. DOI : https://doi.org/10.52783/pst.3434
The changes in climate and environmental degradation have exacerbated the demand for more sustainable patterns of household consumption. In the current study, we investigate the role that conscious consumption behaviours can play in lowering carbon footprints by reducing the energy consumption of households and their food waste. It discusses the link between environmental awareness, choices of action and everyday consumption habits that affect GHG emissions. The methodology used was a mixed methods study, which included qualitative interviews, surveys and a behavioral analysis in various cultural and national settings. The results show that actions at the household level, including planning meals, tracking food stored, using less unnecessary appliances and taking the recommended actions in response to energy consumption feedback, can have a substantial impact on carbon emissions. Additionally, the research underscores the significance of social norms, educational initiatives, behavioral nudges, and institutional frameworks in fostering long-term sustainable practices. Besides, cultural differences were revealed to affect the effectiveness of conscious-consumption practices and environmental attitude. Overall, the paper suggests that by raising awareness about the importance of conscious consumption at the household level, it can contribute to climate-change mitigation measures, resource efficiency, waste reduction and a more sustainable way of living for future generations. DOI : https://doi.org/10.52783/pst.3441
The Techno-Economic Levelized Cost of Energy (TELCOE) is developed in this study as a comprehensive model for effectively costing off-grid renewable energy technologies by factoring variables addressing local peculiarities compared to existing energy models thus giving more detailed, accurate, and excellent guide to energy planners, analysts, regulators, investors, and developers, especially for Sub Saharan Africa and the developing worlds where existing models have so far failed to deliver commercially sustainable mini-grid renewable energy solutions thus hampering the achievement of clean energy in the region. South–West Nigeria was used as a case study, analysing the data acquired for the 8 most available renewable energy sources/technologies, namely solar photovoltaic, solar thermal, hydroelectric, biomass, onshore wind, offshore wind, wave, and tidal power, concerning off-grid renewable and hybrid power generation, especially for rural areas and isolated or exclusive urban communities. After thorough evaluation and tests, the TELCOE values were considerably but slightly and affordably higher than the Levelized Cost of Energy (LCOE) for various sources/technologies with average ratio of 1.07 and differences ranging from 4% for solar PV, small hydro, and biomass to 14% for wave and tidal as a result of accounting for critical local constraints and variables that were not captured by existing models including technical, economic, social, and governance (ESG) factors thus making it a superior sustainable energy pricing, design, and development model for short, medium, and long term planning without the pitfalls that cripples most renewable mini-grid energy projects in Sub Sharan Africa. DOI: https://doi.org/10.52783/pst.3492
In the last few decades, Environmental Impact Assessment (EIA) has become an essential instrument for including environmental, social and economic issues into development planning processes in countries. EIA practices vary considerably, however, because of the differences in legal frameworks, institutional capacities, and methodologies. This study compares and analyzes EIA systems at country level and their effectiveness to support sustainable development. It reviews important aspects such as regulation, evaluation, involvement of stakeholders, and oversight. The study identifies differences between high income, emerging and least developed countries, with advanced economies having more holistic and integrated EIA frameworks and the developing countries having governance, data availability and implementation challenges. Moreover, the research also points out to some common problems faced by the countries of the world, including low enforcement, low public participation and low evaluation of impact. This paper draws on the experience gained in this cross-country study to make strategic recommendations for making EIA more effective, such as further developments in institutional mechanisms, methodological consistency and knowledge sharing. Overall, the results help to improve the understanding of the potential of EIA in supporting informed decision making and in contributing to the sustainable development in the national and international level. DOI : https://doi.org/10.52783/pst.3435
Physical inactivity remains a significant public health concern globally, particularly among working adults with sedentary occupations. This study examined adherence to physical activity and its role in health promotion among lecturers at the University of Delta, Agbor, Nigeria. A descriptive survey design was adopted, involving 150 lecturers selected through stratified random sampling. Data were collected using a validated Physical Activity Adherence and Health Promotion Questionnaire (PAAHPQ). Descriptive statistics, Pearson Product Moment Correlation, and multiple regression analyses were employed using SPSS (version 26). Findings revealed a moderate level of physical activity adherence among lecturers. A significant positive relationship was found between physical activity adherence and health promotion outcomes (r = 0.62, p < .05). Workload significantly reduced adherence, while institutional support significantly enhanced participation in physical activity. The study concludes that adherence to physical activity plays a crucial role in promoting physical, mental, and occupational health among university lecturers. Institutional policies aligned with World Health Organization (WHO) guidelines are recommended to improve physical activity participation in academic settings. DOI: https://doi.org/10.52783/pst.3457
Sustainable development requires organizations to respond effectively to economic, social, and environmental challenges through informed, adaptive, and knowledge-based practices. This study examines Knowledge Management (KM) as the principal theoretical and analytical framework for achieving the Sustainable Development Goals (SDGs), while treating Business Intelligence (BI) as a complementary analytical mechanism that strengthens knowledge-based decision-making. KM contributes to sustainability through the processes of knowledge creation, knowledge sharing, knowledge storage, and knowledge application, which support innovation, organizational learning, and strategic responsiveness. To examine these relationships, the study adopts a quantitative cross-sectional design based on survey data collected from organizational professionals involved in sustainability, knowledge management, and business analytics functions. The unit of analysis is the individual respondent. Data were analyzed using descriptive statistics, reliability analysis, factor analysis, mediation analysis, and moderation analysis. The findings indicate that KM plays a central role in advancing sustainability outcomes, while BI enhances this role by improving evidence-based insight, monitoring, and decision support. The study further shows that organizational culture, stakeholder engagement, technological infrastructure, and regulatory frameworks shape the strength of these relationships. Overall, the study argues that sustainable development is more effectively achieved when KM is institutionalized as a core strategic capability and BI is integrated as an enabling analytical support system. DOI: https://doi.org/10.52783/pst.3437
The increasing penetration of renewable energy sources into modern power systems has intensified the need for efficient and reliable grid-connected photovoltaic (PV) solutions. This paper presents the design, modeling, and performance evaluation of a 1.5 MW grid-tied solar PV system integrated with a Maximum Power Point Tracking (MPPT) controller and a 25 kV three-phase distribution network. The proposed system employs the Perturb and Observe (P&O) MPPT algorithm to maximize solar energy extraction under varying irradiance and temperature conditions. A comprehensive MATLAB/Simulink model is developed to analyze power generation, voltage stability, inverter performance, and grid interaction under dynamic operating scenarios. Simulation results demonstrate that the PV system delivers approximately 2 MW of total output power while maintaining stable grid voltage and DC-link performance. The MPPT controller achieves a tracking efficiency of nearly 95%, ensuring optimal utilization of available solar energy. Furthermore, the three-level Neutral Point Clamped (NPC) inverter effectively reduces voltage fluctuations and enhances power quality during grid integration. The findings confirm that the proposed system provides reliable operation, improved energy harvesting capability, and enhanced grid support, making it a practical and sustainable solution for large-scale renewable energy deployment in modern electrical networks. DOI : https://doi.org/10.52783/pst.3561
Cadmium Telluride (CdTe) quantum dots have garnered significant attention in the fields of solar cells and laser applications due to their unique optical and electronic properties. These nanomaterials, with their tunable band gaps and high absorption efficiency, present a promising avenue for enhancing the performance of solar cells and lasers. In the context of solar energy, CdTe quantum dots offer the potential for improved efficiency in photovoltaic devices by enabling better light absorption and carrier transport. In laser technologies, CdTe quantum dots are explored for their ability to operate at lower thresholds and generate more coherent light compared to traditional materials. However, challenges such as material instability, cost, scalability, and the toxicity of cadmium must be addressed to realize their full potential. This paper provides a comprehensive overview of the advancements in CdTe quantum dot-based solar cells and lasers, highlighting both the significant breakthroughs and the ongoing challenges. Future prospects for overcoming these hurdles are also discussed, emphasizing the role of CdTe quantum dots in next-generation energy and optoelectronic applications. DOI : https://doi.org/10.52783/pst.3119
The study is analysing the role of Machine Learning in predicting the fastest and safest path in the context of post-breaches. The study is applying explanatory design and using qualitative and quantitative data to derive the results. The results reveal how ML is effective in identifying the patterns within the system and user behaviours. The ML is ensuring the quick discerning of the vulnerable areas of the network requiring isolation. The prediction of the fastest and most secure path is possible on account of ML. The companies have been recommended to use ML with a Decision-Tree algorithm and multi-training method for gaining accurate outcomes. DOI : https://doi.org/10.52783/pst.3108
According to the World Health Organization (WHO), arthritis is the biggest cause of disability worldwide. It is also one of the most common inflammatory conditions affecting the musculoskeletal system. Synovial fluid is often present in the joint area and serves as a lubricant, which is an essential function. The fluctuation in density of synovial fluid may be used to detect arthritis. This might be done to assess the severity of the disease. This work provides a non-invasive approach for measuring changes in synovial fluid density based on the electrical bio-impedance idea. The purpose of this study approach is to measure change. The current research studied the parameters estimated by bioelectric impedance spectroscopy (BIS) in persons with healthy knees and knees with osteoarthritis (OA). A comparison was done between the two groups of people. Because tissue may be represented as both resistance and capacitive reactance, bio-impedance values are calculated using the voltage drop in relation to the joint tissue area. When the frequency and voltage are held constant, the resistance and reactance values change from a healthy individual to a patient with arthritis. This happens even when everything else stays the same. DOI : https://doi.org/10.52783/pst.3128
The study analyses smart data quality management models of the AI-based financial decision-making frameworks, overcoming the essential drawbacks of the traditional, manual ones. By considering secondary mixed process, the investigation also summarises the current literature, empirical evidence, and actual case studies to assess the use of AI-based data quality control, data governance, and scalability structures to improve the quality of decisions. The key trends that have been discovered include time-consuming, unstable, and imprecise traditional practice, but AI-moderated automation increases the detection of anomalies by up to 90%, compliance, and real-time financial decision-making. JP Morgan and Capital One Case Evidence Case studies in both financial institutions affirm that the current deep data quality paradigms, managed lifecycle, and embedded in data, are necessary to support trustworthy and sustainable AI-based financial decision-making.
Identification of individuals by their dental features or bite mark impressions constitutes the discipline of forensic dentistry. This article describes the development of an automatic system for comparing bite mark images and dental models of suspects. It determines the best match by comparing bite mark textures to a database of dental casts. These results are compared with expert opinions of forensic odontologists. This preliminary study assesses whether YOLOv8(You Only Look Once) is a theoretically feasible method for identifying and interpreting patterns of bite mark texture evidence imaged on pink wax impressions. The agreement with the experimental data is fairly good. The effectiveness of YOLOv8 for detection and recognition of complex textural patterns in bite marks has great potential to enhance the rapidity and reliability of human identification, providing a hopeful path for the development of forensic odontology. DOI : https://doi.org/10.52783/pst.3226
The purpose of the study was to verify the relevance of the UNDAR Open Edx plugin module for competence-based assessment in music and arts education; for this purpose, an applied research type and pre-experimental design for paired samples with a single group was formulated, pre and posttest for the dependent variable, and a correlation between the independent and dependent variables to establish causality; the study worked with a population of 30 teachers from UNDAR and ISMPDAR, and a sample of 9 teachers who voluntarily participated in the research; The data collection instrument consisted of a data recording form, a questionnaire based on the Likert scale, and an observation guide. The independent variable, "Competency-Based Assessment Instruments," was structured around the following dimensions: Competencies and Assessment Criteria, and Pedagogically Appropriate Holistic and Analytical Rubrics. The corresponding dependent variable was "Level of Relevance of the UNDAR Open EdX Plugin Module for Competency-Based Education in Music and Arts Education," with the following dimensions: Technical Functioning and Usability, Comprehensiveness for Music and Arts Assessment, and Utility and Adoption in the Educational Context. This was achieved through eight closed-ended questions validated by expert judgment. The study concludes that the UNDAR Open EdX plugin module is relevant for competence-based assessment in music and arts education. DOI : https://doi.org/10.52783/pst.3100
The purpose of the study was to evaluate the impact of the 2025 Catholic Religious Music Training Program, implemented by the Directorate of University Social Responsibility and Cultural Extension of UNDAR, on the integral training of musicians and amateurs who make up the parish choirs of the diocese of Huánuco, considering the development of musical skills, pedagogical relevance, participant satisfaction, their contribution to university, social rojection and to the cultural and spiritual strengthening of the Catholic community; for this purpose, an applied research type and pre-experimental design for paired samples with a single group of 33 samples of beneficiaries, 27 samples of volunteers, 8 instructors of the micro workshops, 8 musical performers, and 3 Project managers. The data collection instrument consisted of a questionnaire for each key actor with its corresponding survey technique, which has been structured through an independent variable: Catholic religious music training program 2025, with its dimensions (Contents of the micro workshops, Pedagogical strategies, Organization and methodology of the program), Participation of internal actors (managers, instructors, Band), Involvement of external actors (volunteers, sponsors, backers); with its respective dependent variable Impact on university social responsibility through religious musical training, with its dimensions: Development of musical skills, Pedagogical relevance, Satisfaction of the participants, University social projection, Cultural and spiritual strengthening; through Likert scale questions that were validated by expert judgment. The study concludes that the intervention of the Catholic religious music training program 2025 significantly influences the development of musical skills, pedagogical relevance, participant satisfaction, its contribution to university social outreach, and the cultural and spiritual strengthening of the Catholic community. DOI : https://doi.org/10.52783/pst.3072
Background:The rapid adoption of Artificial Intelligence (AI) within customer relationship management (CRM) ecosystems has significantly transformed enterprise decision-making processes. Among enterprise platforms, Salesforce has emerged as a dominant AI-enabled ecosystem through tools such as Einstein Analytics, predictive lead scoring, recommendation engines, customer segmentation, and automated workflow optimization. However, the increasing complexity of machine learning algorithms has introduced challenges associated with algorithmic opacity, decision bias, regulatory compliance, and user trust. Explainable Artificial Intelligence (XAI) has therefore become essential for enhancing transparency, interpretability, accountability, and ethical governance in enterprise AI systems. This study investigates the integration of XAI frameworks within Salesforce-driven intelligent decision-making systems and evaluates their impact on predictive performance, organizational trust, interpretability, and operational efficiency. Methods:The research employed a hybrid quantitative–experimental methodology using a simulated enterprise CRM dataset comprising 52,486 customer interaction records collected from sales, marketing, and customer support domains. The study implemented three machine learning models within a Salesforce-compatible analytical architecture: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). To enhance interpretability, XAI techniques including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), and feature importance mapping were integrated into the predictive framework. The models were evaluated across key performance indicators including prediction accuracy, precision, recall, F1-score, Area Under Curve (AUC), interpretability index, user trust score, and decision transparency metrics. Statistical validation was performed using ANOVA, Pearson correlation analysis, and paired t-tests with significance measured at p < 0.05. Results:The experimental findings demonstrated that the XGBoost-XAI integrated model achieved the highest predictive performance with an accuracy of 93.8%, precision of 91.6%, recall of 92.4%, F1-score of 92.0%, and AUC value of 0.957. Incorporation of SHAP-based explainability improved model interpretability by 41.3% compared with conventional black-box AI systems. User trust and managerial acceptance increased significantly from a baseline mean score of 3.1 to 4.5 on a 5-point Likert scale (p < 0.001). Feature attribution analysis identified customer engagement frequency (24.7%), response time (18.9%), historical purchase behavior (16.4%), and sentiment polarity (12.8%) as the most influential decision-making variables. Furthermore, explainability-enabled models reduced perceived algorithmic bias by 28.6% and improved regulatory compliance readiness by 35.2%. Correlation analysis revealed a strong positive relationship between interpretability and user trust (r = 0.84), indicating that transparent AI explanations substantially influence organizational confidence in automated decisions. Conclusion:This study establishes that Explainable AI significantly strengthens the effectiveness, transparency, and reliability of Salesforce-enabled intelligent decision-making systems. The integration of interpretable machine learning models not only enhances predictive accuracy but also improves user trust, fairness perception, and organizational acceptance of AI-driven decisions. The proposed framework provides a scalable and practically implementable model for enterprises seeking ethical and transparent AI adoption within CRM ecosystems. The research contributes to the growing body of knowledge on enterprise XAI by presenting statistically validated evidence supporting the role of explainability in sustainable and accountable AI governance for next-generation digital enterprises. DOI : https://doi.org/10.52783/pst.3453
Optimization of slicing parameters in fused deposition modeling (FDM) usually takes place manually and needs a lot of expertise and trial and error. This paper examines the use of a large language model (LLM) to help optimize the contextual FDM printing parameters. A Workflow was developed where a radically deployed LLM analyzed the slicing profiles and calculated input values for the parameters, which were then applied to G-code. The method was tested with a standard print from 3DBenchy, so the original slicing setup was compared to the setup optimized by the LLM. Experimental results reveal 27.7 min printing time reduction in experimental results and a consumption of 7.75 meters in the usage of filament. Some of the adjustments made were increased travel and infill speeds and decreased extrusion flow. Surface roughness and dimensional accuracy exhibited low differences (0.25 and 0.52 um and 0.0% and 0.033%, respectively), which were within acceptable ranges. Though background scaled on one geometry and material configuration, the findings suggest that the tuning of the parameters that the LLM can enhance can also increase the level of efficiency in FDM processes without undermining the quality of the prints. DOI: https://doi.org/10.52783/pst.3414
Introduction: To provide prompt, accurate and quality patient care in the Emergency Department, close cooperation and coordination should exist between emergency physicians and clinical laboratory services. Rapid laboratory investigations and effective communication of diagnostic results aid clinical decision making, prompt early treatment and promote patient safety. In Saudi Arabia, where healthcare transformation as part of Vision 2030 is focusing on integrated and patient-centred care, consideration of the available evidence regarding physician–laboratory coordination is essential to ensure the optimization of emergency healthcare services. Objective: To conduct a systematic literature review of the literature on cooperation and coordination of emergency physicians and clinical laboratory services in emergency department cases, especially in Saudi Arabia health care environment. Methods: A systematic review will take place using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Literature searches conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, ScienceDirect, Cochrane Library and Google Scholar will be carried out with pre-designed Boolean search strategies. Qualitative, cross-sectional, cohort, case-control, quality improvement, and interventional studies, as well as mixed methodologies, will be included. Conclusion: Current evidence indicates that the role of effective collaboration between emergency physicians and clinical laboratory services in improving laboratory turnaround, diagnostic accuracy, clinical decision making, patient safety and emergency department efficiency is crucial. However, there is limited and inconclusive evidence from Saudi Arabia DOI : https://doi.org/10.52783/pst.3664
