Current Journal of Applied Science and Technology

Integration of Battery Aging into the Energy Management of Hybrid Vehicles

Thu, 03 Sep 2026 00:00:00 +0000

Hybrid electric vehicles use coordinated thermal and electrical power sources to reduce fuel consumption, but battery ageing progressively limits the electrical contribution to propulsion. This study integrates battery ageing into the energy management of a parallel hybrid electric vehicle controlled by an equivalent consumption minimisation strategy (ECMS). The strategy prioritises the battery as the principal power source and updates the equivalence factor using battery state-of-charge feedback. Battery ageing is represented through changes in capacity, internal resistance, and state of health, and its effect on energy management is evaluated through MATLAB/Simulink simulations. Two driving profiles are considered: the FTP-75 cycle and a class cycle derived from a set of driving cycles. For the FTP-75 cycle, the reported test duration and fuel economy are 2,500 s and 60%, respectively, with a new battery, compared with 400 s and 37% with an aged battery. For the class cycle, the corresponding values are 1,100 s and 43% for a new battery and 400 s and 19% for an aged battery. The simulations indicate that battery ageing reduces the duration of effective electric operation and increases reliance on the internal combustion engine as available electrical energy declines. Accordingly, incorporating battery ageing into ECMS-based energy management is necessary when evaluating fuel economy and electric-driving capability over the battery life cycle.

Hybrid AHP and Artificial Neural Networks for Multi-Criteria Modeling of Senegal’s Energy Transition Pathways toward 2040

Sat, 05 Sep 2026 00:00:00 +0000

Senegal’s energy transition requires balancing cost, system resilience, environmental performance, employment, energy independence, and resource availability. This study develops a hybrid Analytic Hierarchy Process (AHP)–artificial neural network (ANN) framework to assess four transition pathways towards 2040: Ambitious Renewable Transition (TAR), Gas–Renewables Mix (MGRE), Domestic Gas Dominance (DGDM), and Slow Transition (TL). AHP is first used to structure the six decision criteria and establish the 2035 baseline ranking. Because the available AHP dataset is limited, perturbation-based oversampling is applied to generate 1,000 synthetic samples for ANN training. The ANN uses six input neurons, two hidden layers with 12 and 6 neurons, respectively, and one output neuron. The dataset is divided into 80% training and 20% testing subsets, and sensitivity is examined using perturbation ranges of ±5%, ±10%, and ±15%. For 2035, MGRE records the highest score (0.78), followed by DGDM (0.72), TAR (0.65), and TL (0.50). Under the specified 2040 weighting assumptions, TAR rises to 0.82, while MGRE, DGDM, and TL decline to 0.70, 0.60, and 0.45, respectively. The results indicate that stronger emphasis on environmental impact and energy independence can shift the preferred pathway towards renewables. These projections should be interpreted as conditional scenario outcomes rather than deterministic forecasts.

Multi-Criteria Decision Analysis of Energy Transition Pathways in Senegal toward 2035 Using the Analytic Hierarchy Process

Mon, 07 Sep 2026 00:00:00 +0000

Senegal’s energy system is undergoing a transition shaped by rising electricity demand, energy-security concerns, environmental objectives, and the growing role of domestic natural gas and renewable resources. This study applies the Analytic Hierarchy Process (AHP) to compare four energy-transition pathways towards 2035: an ambitious renewable transition, a balanced gas-and-renewables mix, domestic gas dominance, and a slow-transition pathway. Six criteria were considered: total discounted cost, power-system resilience and stability, environmental impact, job creation and local content, energy independence, and resource availability. Pairwise comparisons were undertaken by 11 students specialising in energy together with energy-sector experts, and consistency ratios were reported for the comparison matrices. The analysis demonstrates that the four pathways involve different trade-offs across economic, technical, environmental, social, and resource-related criteria. The criteria weights indicated that cost and resilience were the most influential factors (0.284 each), followed by environmental impact (0.158), employment (0.122), energy independence (0.089), and resource availability (0.063). Based on the final weighted scores, the balanced gas and renewables mix obtained the highest score (0.296), followed by domestic gas dominance (0.250), the ambitious renewable transition (0.242), and the slow transition pathway (0.212). These results indicate that no pathway performs uniformly best across all criteria. Within the study’s stated decision framework, the balanced pathway is presented as a compromise among affordability, system stability, environmental performance, employment considerations, energy independence, and resource availability. The findings illustrate the value of a structured multi-criteria framework for comparing alternative national energy-transition pathways while making the underlying decision criteria explicit.

Sparsity-Aware Movie Recommendation through Dynamic User Profiling and Ensemble Machine Learning

Mon, 07 Sep 2026 00:00:00 +0000

Aim: This study aimed to improve movie recommendation accuracy under sparse rating conditions by developing an ensemble system that uses dynamically weighted user profiles to generate personalised recommendations.

Design: An experimental quantitative design was adopted. The ensemble recommendation system integrated Case Evaluation using K-means (CE-KM), Case Evaluation using Weighted Association Rule Mining (CE-WARM), and an Adaptive Naive Bayes Probabilistic technique (ADNB).

Methodology: The framework comprised three stages: dataset preprocessing and user-profile construction, pattern recognition from users’ movie histories, and the generation and ranking of candidate recommendations. Nine weighted profile attributes represented user interactions and content preferences. The component methods were examined in five experiments using different K values. Accuracy and F-measure were used for evaluation, and the predictions from the three methods were combined to produce the top-ranked movies for an active user.

Results: Within the reported experiments, CE-KM achieved its highest accuracy of 0.9323 at K = 70, followed by ADNB which achieved 0.8197 at K = 80 then, CE-WARM which achieved 0.7980 at K = 60. The methods generated different candidate lists, indicating that the profiling, association, and probabilistic components provided complementary outputs. The final ensemble ranking selected movies from the combined predictions.

Conclusion: The framework demonstrates a structured approach to combining dynamic user profiling with multiple machine-learning techniques. However, its comparative performance under controlled sparsity conditions requires further evaluation.

Impact of Skin Presence and Slice Thickness of Ripe Tomato (Solanum lycopersicum l.) on Microwave Drying Kinetics

Wed, 09 Sep 2026 00:00:00 +0000

Background: Tomato is highly perishable because of its high moisture content, resulting in substantial post-harvest losses and a short shelf life. Microwave drying offers rapid volumetric heating, but its efficiency may be strongly influenced by fruit skin and slice thickness, which affect moisture transfer during drying.

Aims: To quantitatively assess the influence of epicarp presence and slice thickness on the microwave drying kinetics of ripe tomato, with a view to reducing the substantial post-harvest losses affecting the tomato value chain in Côte d’Ivoire.

Study Design: A comparative experimental study was conducted on tomato half-portions with and without skin and on slices of three different thicknesses (5, 10 and 20 mm).

Place and Duration of Study: Samples were procured in Yamoussoukro, Côte d’Ivoire, and the investigation was conducted between July and August 2026.

Methodology: Fresh, ripe tomatoes (SODEFEL variety) were cut into half-portions (with and without skin) and into calibrated slices (5, 10 and 20 mm) and then dried in a microwave oven (400 W) until mass stabilisation. Water loss kinetics were monitored by periodic weighing, and effective moisture diffusivity was determined using Fick's second law.

Results: Skin presence slowed initial water loss but promoted rapid internal pressure build-up followed by cracking and marked exudation; unpeeled samples dried more slowly (61 min) than peeled ones (17 min). Drying time increased with thickness (28, 33 and 43 min for 5, 10 and 20 mm, respectively). The apparent effective moisture diffusivity paradoxically increased with thickness, from 3.24 × 10⁻⁸ m². s⁻¹ (5 mm) to 1.19 × 10⁻⁷ m². s⁻¹ (10 mm) and 3.96 × 10⁻⁷ m². s⁻¹ (20 mm), reflecting a shift from a diffusional regime to a convective one.

Conclusion: Skin condition and slice thickness affected microwave drying behaviour. Thinner slicing reduced drying time, while skin micro-perforation is proposed for further evaluation as a means of facilitating moisture removal while limiting exudation.

Kinetic Study and Mathematical Modelling of Microwave Drying of Whole Peeled Tomato

Thu, 10 Sep 2026 00:00:00 +0000

Background: Tomato is highly perishable because of its high moisture content, making efficient drying essential for reducing post-harvest deterioration. Although microwave drying can accelerate moisture removal, limited information is available on the drying kinetics and mathematical modelling of whole peeled tomato under microwave heating.

Aims: To evaluate the combined effect of a constant microwave power and prior peeling on the acceleration of mass transfer during the drying of whole tomato and to identify, among seventeen semi-empirical models, the one that best describes its drying kinetics.

Study Design: An experimental drying-kinetics study combined with comparative mathematical modelling.

Place and Duration of Study: Analyses were carried out at the Laboratoire des Procédés Industriels de Synthèses de l'Environnement et des Energies Nouvelles (LAPISEN) of the Institut National Polytechnique Félix Houphouët-Boigny in Yamoussoukro (Côte d'Ivoire) during July and August 2026.

Methodology: Whole tomatoes, peeled beforehand, were dried by microwave at a constant power of 400 W in a semi-industrial drying tunnel. Drying kinetics were monitored through effective moisture diffusivity, water-loss rate and mean drying flux; seventeen mathematical models were then fitted to the experimental data.

Results: The moisture content fell from 95.76% to 2.61% within 66 minutes, with an effective moisture diffusivity of 6.082 × 10-7 m·s^-1. Peeling combined with volumetric heating eliminated both internal and surface resistances to mass transfer. The Midilli model provided the best description of drying kinetics (r = 0.99472; RMSE = 0.02475; χ² = 0.00062).

Conclusion: Combining peeling with a microwave power of 400 W effectively overcomes the three-dimensional geometric limitations of whole tomato, and the Midilli model offers a reliable tool for industrial scale-up.

Allergen Management Capacity and Training Gaps among School Nutrition Professionals: A Critical Narrative Review of Alignment with United States Department of Agriculture Professional Standards

Ivy Nyantakyiwa Tsibuah — Thu, 03 Sep 2026 00:00:00 +0000

Food-allergic children in the United States are fed each school day by a workforce whose allergen competence is governed less by allergy science than by an administrative training rule. The Professional Standards established under the Healthy, Hunger-Free Kids Act of 2010 impose annual continuing-education hour requirements on school nutrition directors, managers and staff, yet they specify hours and broad key areas rather than allergen-specific competencies, and compliance is documented in hours completed rather than in demonstrated capability. This critical narrative review examines what is known, contested and unknown about allergen management capacity among school nutrition professionals, and evaluates how far the available evidence supports the assumption that the current training architecture produces reliable allergen control in school kitchens. Literature was identified through structured searching of open scholarly databases and indexes, supplemented by citation tracking and by examination of federal regulatory and public health guidance. The synthesis identifies four persistent problems. First, the strongest quantitative evidence on foodservice allergen knowledge derives from restaurant and university operations whose task structures, procurement arrangements and clientele differ materially from school food authorities, so transfer of those estimates to school kitchens is uncertain. Second, allergen knowledge is measured with unvalidated, locally constructed instruments, which prevents meaningful pooling and obscures whether apparent improvement reflects learning or item familiarity. Third, quantitative cross-contact studies demonstrate that shared-equipment hazards are governed by physical transfer dynamics that generic hygiene instruction does not address. Fourth, evaluation of allergen training in this workforce rarely proceeds beyond knowledge and self-reported confidence to observed practice or reaction outcomes. Evidence that site-wide allergen bans reduce epinephrine administration is weak, and reliance on such policies may substitute for staff capability. Priorities include developing a validated allergen competency instrument for school nutrition personnel, linking training records to reaction surveillance, and testing whether specifying allergen content within the existing hour requirement changes operational outcomes.