A Comparative Machine Learning Approach for Usability Evaluation of Localized Software in Afghanistan

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Usability is one of the most important software quality attributes in determining user satisfaction, productivity and system acceptance. Afghanistan is a multilingual country, and usability problems with localized software applications may occur due to linguistic diversity, cultural differences, and scarce resources for evaluating the usability of software applications. These traditional usability assessment techniques, such as heuristic evaluation and laboratory testing, are difficult to implement in a capacity-constrained environment, as they require significant time, expertise and funds. The study suggests an automatic usability evaluation framework by machine learning techniques for localized software systems in Afghanistan. Data were gathered from ISO 9241-11 usability framework-based structured questionnaires from users of localized software applications. After collecting the responses, they were subjected to a pre-processing procedure involving cleaning, encoding and normalizing. Three supervised machine learning algorithms Decision Tree (DT), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) were trained and tested with 80:20 ratio of training and test data. Model performance was evaluated using the accuracy, precision, recall, F1-score and confusion matrix analysis. Experimental results indicate that the highest accuracy of 91.4% is obtained by DT followed by SVM with 90.0% accuracy and KNN with 88.7%. The results show that the Decision Tree is a good model for balancing the prediction accuracy and interpretation of local software environments. The proposed framework has been designed to be both scalable and cost-effective, and can be used to complement existing usability evaluation methods, and can also be used to aid in software quality improvement efforts in multilingual and developing country settings.

Comparative Analysis of Electron Emission Mechanisms and Evaluation of a Suitable Method for Practical Electron Sources

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Electron emission is a fundamental process in electron sources used in scientific, medical, and industrial applications. This study aimed to comparatively evaluate major electron emission mechanisms and identify their suitability for practical electron sources based on emission performance, beam quality, vacuum requirements, thermal stability, and operational lifetime. A comparative analytical review was conducted using scientific literature obtained through structured searches of major academic databases and scholarly platforms. Five emission mechanisms were evaluated: thermionic emission, field emission, photoelectric emission, secondary electron emission, and plasma cathode emission. Relevant parameters, including current density, operating pressure, beam stability, thermal effects, emission efficiency, and cathode lifetime, were extracted and comparatively analyzed. The findings show that plasma cathodes provide a major advantage for high-current electron sources, achieving current densities of approximately 100 A/cm² and stable operation under fore-vacuum conditions. The analysis also demonstrated that plasma-cathode beam current is strongly influenced by gas pressure and accelerating voltage, with excessive pressure increasing electron scattering and ion neutralization. Thermionic cathodes provide stable operation but are affected by high-temperature requirements and associated thermal deformation. Gallium arsenide photocathodes provide high beam quality and low emittance but require stringent vacuum conditions for stable operation. Discharge-control techniques, including cathode-spot localization and negative current feedback, were also found to improve plasma-source stability. Overall, plasma cathodes are particularly suitable for high-current electron sources operating under non-ideal vacuum conditions, whereas photocathodes are advantageous for applications requiring high beam quality and low emittance. The selection of an emission mechanism should therefore be based on the specific operating and performance requirements of the intended application.

Public Sentiment Toward Electric Vehicle Adoption: A Case Study of Semarang, Indonesia

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The growth of Indonesia’s population has led to a significant increase in vehicle volume, contributing substantially to CO2 emissions and air pollution. Central Java Province ranks third nationally in vehicle numbers, with Semarang City recorded as having the highest air pollution level in Indonesia, reaching an air quality index (AQI) score of 107 in March 2024. One of the strategies to mitigate this pollution is the transition from conventional vehicles to electric vehicles (EVs). However, this transformation requires an understanding of public perception and readiness. This study aims to analyze public sentiment toward the transformation of electric vehicles in Semarang City using sentiment analysis based on the Indonesian RoBERTa model. Data was collected using an online questionnaire distributed to 1,025 respondents aged 17 years or above. Respondents answered questions covering three aspects: (Q1) perception of EV quality as a personal vehicle, (Q2) perception of the government’s role in policy and infrastructure, and (Q3) perception of the EV transformation process in Semarang City. The results indicate that positive sentiment dominates only in Q1 (49.95%), reflecting public confidence in EV quality for personal use. Besides, Q2 and Q3 are dominated by neutral sentiment (41.5% and 52%, respectively), with Q2 even showing a higher percentage of negative sentiment (33.8%) than positive sentiment (24.7%), indicating dissatisfaction with current government policy and infrastructure support. These findings suggest that although Semarang City has strong potential for EV transformation, government policies and supporting infrastructure, such as public EV charging stations (SPKLU), need substantial improvement to accelerate public adoption and strengthen positive sentiment toward electric vehicles.

Performance Evaluation of Laser Spectroscopy Techniques for Isotope Identification and Separation Based on Isotope Shifts

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Laser spectroscopy has become one of the most important technologies for isotope identification and separation because of its high spectral resolution and its ability to accurately measure isotope shifts. Despite significant advances in this field, a comprehensive comparative evaluation of different laser spectroscopy methods in terms of their physical principles, measurement accuracy, and isotope separation capabilities is still required. The aim of this study was to systematically evaluate the performance of laser spectroscopy methods for isotope identification and separation based on their underlying physical principles, spectral resolution, sensitivity, and applications. This study was conducted as a systematic review, in which publications retrieved from major scientific databases were identified, screened, and analyzed. The findings demonstrated that the mass shift and the field shift constitute the fundamental physical basis for isotope discrimination. Collinear Laser Spectroscopy (CLS), owing to its very high spectral resolution, is the most suitable technique for the precise measurement of isotope shifts and the investigation of nuclear structure, whereas Atomic Vapor Laser Isotope Separation (AVLIS) and Molecular Laser Isotope Separation (MLIS) exhibit high efficiency in the selective separation of isotopes. Furthermore, the development of narrow-linewidth lasers, frequency stabilization systems, and advanced data-processing techniques has significantly improved the accuracy and sensitivity of these technologies. Overall, the selection of an appropriate method depends on the intended application, the type of isotope, and the required measurement accuracy. This study provides a comparative framework for understanding the capabilities and limitations of laser spectroscopy methods and can contribute to the future development of isotope identification and separation technologies

Physicochemical Quality of Lamb Se’i Treated with Kusambi (Schleichera oleosa) Liquid Smoke

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This study aimed was to determine the effect kusambi (Schleichera oleosa) wood liquid smoke on the physicochemical quality of Lamb se’i (smoked meat). A Completely Randomized Design (CRD) with a 4 x 4 layout was employed, consisting of four treatments: P0: control/no liquid smoke, P1: 0.5% liquid smoke, P2: 1.0% liquid smoke, and P3 : 1.5% liquid smoke with four replications. The parameters measured were: moisture, protein, fat content, pH and yield. Data were analyzed using Analysis of Variance (ANOVA). The results showed that the use of  kusambi wood liquid smoke at levels of 0.5%–1.5% did not affect yield (P>0.05), however, it increased moisture and protein content while pH and fat content (P<0.01). Yield ranged from 54.30% to 59.41%. The highest moisture and protein contents were observed with the 1.5% liquid smoke treatment (58.59% and 25.65%, respectively), while the lowest pH value and fat content was 5.37 and 4,60% respectively.  In conclusion, the use of kusambi liquid smoke at levels of 0.5%–1.5% increased the moisture and protein content of the se’i and lowered the pH value and fat content, whereas the yield remained unchanged.

Role of Fast Neutrons in Minor Actinide Transmutation and Long-Lived Radioactive Waste Reduction in Lead Cooled Fast Reactor: A Systematic Review

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The long-term sustainability of nuclear energy relies on the safe management of minor actinides (MAs) and long-lived fission products (LLFPs). The present work aims at systematically evaluating the role of the fast neutron spectrum in the nuclear transmutation of these isotopes in the Lead-cooled Fast Reactors (LFRs) to support the realization of a closed fuel cycle. The present work was performed as a systematic review following PRISMA 2020 guidelines and summarized data from 24 peer-reviewed sources to compare transmutation efficiency and neutronic features. The results indicate that the fast neutrons spectrum in LFRs is a very efficient environment for the actinide destruction by increasing the probability of the fission with respect to the neutron capture. In contrast to thermal reactors, which have positive D-factors and a tendency to accumulate heavier actinides, LFRs have negative D-factors that lead to waste destruction. In particular, an optimal loading of 1.5 wt% MAs provides annual transmutation rates of 15.18% for Am-241 and 13.07% for Np-237 with support ratios larger than one. This configuration is stable for core criticality and neutron flux distribution, but technical challenges remain, including accumulation of Curium isotopes (Cm-244 and Cm-245) and nearly 50% reduction in the effective delayed neutron fraction, affecting safety margins. In conclusion, LFR technology provides clear neutronic benefits for waste reduction over thermal systems but operational strategies should be optimized for curium management and reactor safety to allow evidence-based development of Generation IV reactors for sustainable waste management.

A Deep Feature Reliability-Based Framework for Offline Handwritten Signature Verification

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Offline handwritten signature verification is widely employed as a biometric authentication technique in financial, legal, and administrative applications. However, accurately distinguishing genuine signatures from skilled forgeries remains a challenging task because of natural intra-writer variations and the inconsistent discriminative capability of deep feature representations. This paper presents a Deep Feature Reliability-Based Framework for Offline Handwritten Signature Verification that enhances verification performance by identifying and utilizing stable writer-specific deep features. Initially, signature images are preprocessed and represented using 2048-dimensional deep features extracted from a pre-trained ResNet50 network. A feature reliability estimation scheme is then introduced to evaluate the statistical consistency of individual feature dimensions across genuine signatures of each writer. Based on the estimated reliability scores, the most reliable features are selected and incorporated into a weighted cosine similarity measure for signature verification. The proposed framework is evaluated on the CEDAR offline handwritten signature dataset using three training–testing splits of 25–75, 50–50, and 75–25. Experimental results demonstrate that the proposed method achieves its best performance with the 75–25 split, obtaining an accuracy of 77.61%, precision of 83.77%, recall of 68.48%, and an F1-score of 75.33%. The findings indicate that incorporating feature reliability into the verification process improves the robustness of deep feature representations and provides an effective framework for offline handwritten signature verification.

Linking Empirical Nickel Laterite Leaching Responses to Preliminary Economic Performance: Trade-Offs among Ni-Co Extraction, Acid Dosage, Fe Dissolution, and Residence Time

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This revised manuscript develops a transparent metallurgical-economic screening framework for atmospheric sulfuric acid leaching of a ferruginous nickel laterite ore. The objective is not to prove a universal kinetic mechanism or a final project-level feasibility estimate, but to evaluate how experimentally observed extraction responses translate into preliminary economic ranking under clearly stated assumptions. Duplicate leaching tests were performed at 95-100 °C using sulfuric acid dosages of 700-1000 kg H2SO4/t ore and residence times of 30-420 min. The tables report average values used for mass-balance and screening calculations; individual duplicate results are not shown in the main text, and formal significance claims are therefore avoided. Nickel and iron responses were modeled empirically as functions of acid dosage and residence time, while cobalt was included as a revenue component in selected base-case scenarios. The results show that the highest observed Ni extraction, 74.1% at 360 min and 1000 kg H2SO4/t ore, also produced the highest Fe dissolution, 65.8%, and a low Ni/Fe selectivity indicator. Under the base-case economic assumptions, the most attractive evaluated scenario was 420 min and 700 kg H2SO4/t ore, because the lower acid-addition cost and Fe penalty offset the lower Ni and Co extraction. Sensitivity analysis confirmed that this ranking is assumption-dependent and should be interpreted as a feasibility-screening result rather than a global economic optimum. Non-monotonic extraction behavior between 360 and 420 min is discussed cautiously as a possible combination of experimental dispersion, sampling effects, and secondary Ni retention associated with Fe(III) hydrolysis and sulfate-bearing phases. The revised framework provides a practical route for converting duplicated laboratory leaching tests into auditable, assumption-sensitive screening inputs for early-stage nickel laterite project evaluation.

Development and Validation of a Rubric-Based Mobile Scoring Application for Folk Dance Performance Assessment

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Folk dance performance assessment in Philippine Physical Education is still largely conducted through paper-based rubric scoring, a process that is time-consuming, prone to computation error, and difficult to aggregate across multiple judges. This study developed and validated a rubric-based mobile scoring application for Android intended for Physical Education teachers and competition judges scoring Philippine folk dance performances. The application was built using the ADDIE framework across all five phases: Analysis, Design, Development, Implementation, and Evaluation. It digitizes a five-criterion rubric covering rhythm and timing, technique and execution, expression and artistry, costume and presentation, and synchronization, with automatic weighted score computation, live ranking, and PDF or CSV export. The design was validated by a panel of nine experts drawn from Physical Education master teachers, mobile application developers, and members of the PSHS-CRC Engineering and Research Academic Unit, using a five-domain instrument covering functionality, usability, content and rubric accuracy, visual design, and overall acceptability. The panel rated the application 4.61 out of 5.00 (Highly Acceptable), with a content validity index (S-CVI/Ave) of 0.96. A usability pilot with 20 Physical Education teachers produced a mean System Usability Scale score of 84.6, corresponding to an adjective rating of Excellent and grade A. A reliability comparison scored 30 folk dance performances using both the paper rubric and the application with the same five judges; app-computed and manual totals showed a Pearson correlation of r = 0.994 (p < .001) and a two-way mixed, absolute-agreement intraclass correlation coefficient of ICC = 0.991 (95% CI 0.982 to 0.996), indicating near-perfect agreement. A paired-samples t-test found no significant difference between the two methods (t(29) = 1.42, p = .166), while the application reduced mean scoring-and-tally time per performance from 4.7 minutes to 1.3 minutes. The validated application is a complete, reusable, and empirically supported tool that resolves the multi-judge, low-connectivity constraints of live folk dance scoring, and it is now suitable for institutional adoption at PSHS-CRC with continued monitoring.

Development of A Guided Inquiry Model Mathematics Learning Tool to Improve Students’ Analytical Skills in Social Arithmetic

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This study aims to develop a guided inquiry model of mathematics learning tools to improve students’ analytic abilities in social arithmetic material. The study used the Research and Development (R&D) method with a 4D model that includes the define, design, develop, and disseminate stages. The developed tools consist of a user manual, teaching modules, LKPD, and test packages. The trial subjects were 28 seventh-grade students of SMP Negeri 14 Jember. Data were obtained through validation sheets, observations of learning implementation, observations of analyticity behavior, student response questionnaires, and pretest and posttest tests. The results showed that the learning tools met the valid criteria with an average validity score of 3.72–3.85, practical with an average implementation score of 3.33, and effective based on classical completeness of 82.14%, a high category of N-Gain increase, good category of analyticity behavior, and positive student responses of 89.7%.​