PubMed

The increased depletion of fossil fuels needs alternatives like solar energy to address daily demands. However, solar energy is intermittent, hence it is integrated with thermal energy storage (TES) systems. Phase change materials (PCM) are used in TES systems, but they suffer from meagre thermal conductivity, leading to less fusion. This study enhances PCM fusion using four different cases, namely, case 1 (without fins), case 2 (with two fins placed near the top), case 3 (with two fins placed near the bottom), and case 4 (with two fins placed near the top and bottom), respectively. The study is done with heat transfer fluid (HTF) temperature at 70 °C and 75 °C with a 30 LPH flow rate. Case 4 displayed a melting time reduction of 34%, and 47.7% when compared with case 1. It is also found with high thermal efficiency and effectiveness. The results indicate that case 4 is effective in expediting fusion and enhancing storage performance due to optimal fin placement, hence suggested for solar applications.

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders.

Nigeria's growing digital health startup ecosystem has a lot of potential to fill in gaps in healthcare delivery, but many of these businesses fail to get off the ground after getting initial funding. This narrative review examines funding patterns from 2019 to 2025 and investigates why seed-stage investment often fails to translate into scaled impact. Investment reports, case studies of prominent Nigerian health-tech startups, and comparative insights from peer markets were synthesized. The analysis finds that despite a surge of seed funding and a record venture capital peak in 2021, few Nigerian digital health startups convert initial success into sustainable growth. Constraining factors include regulatory hurdles, infrastructural deficiencies, market trust barriers, talent gaps, and systemic health-sector limitations, challenges that mere capital infusion cannot overcome. Case narratives (e.g., Helium Health's regional expansion and 54gene's post-pandemic collapse) illustrate these dynamics. Key mechanisms to bridge the "post-seed" gap are discussed, ranging from strategic public-private partnerships to specialized follow-on funds, with lessons drawn from ecosystems like Kenya and India. Strengthening support beyond seed stages and addressing non-financial frictions will be crucial for Nigerian health-tech innovators to realize their full scale-up potential.

Electrochemical Discharge Micro-Machining (ECDµM) of borosilicate glass commonly suffers from unstable discharges, surface cracking, low material removal (MR), and excessive tool wear (TW) when conventional liquid or air-based dielectrics are used. These issues limit machining efficiency and sustainable process development, especially when MR and TW are optimized independently. To overcome these limitations, this study investigates nitrogen gas as a regulated gaseous dielectric in combination with an aqueous sodium hydroxide (NaOH) electrolyte to enhance discharge stability and achieve balanced machining performance. Experiments were conducted by varying applied voltage, NaOH concentration and nitrogen gas flow rate, and the responses were analyzed using Response Surface Methodology (RSM), Grey Relational Analysis (GRA), and a Random Forest Algorithm (RFA). Stable machining was achieved within a nitrogen gas flow range of 3–5 L/min. At 120 V and 30 wt% NaOH, increasing the nitrogen gas flow rate from 3 to 5 L/min maintained MR at 4 mg while reducing TW from 3 to 2 mg. The optimal condition (134 V, 20 wt% NaOH, 4 L/min nitrogen gas flow rate) yielded 5 mg MR with negative TW (− 1 mg), while reducing electrolyte concentration by 33%. The Random Forest model predicted near-optimal parameters (119.89 V, 20.107 wt% NaOH, 4.31 L/min) with an average deviation of approximately 8%. These results establish nitrogen gas assisted ECDµM as a stable, energy-efficient and environmentally responsible approach for precision micromachining of brittle materials.

In double-slope solar stills (DSS), a significant amount of solar energy is wasted as heat and reducing the system freshwater generation efficiency. To overcome this, a reconstructed double-slope solar still (RDSS) with a photovoltaic panel (PV) mounted on top is proposed which facilitates for simultaneous generation of freshwater, heat, and electricity. The PV panel partial shading is reduced by transferring the generated power to a heating element that preheats the inlet saline water, while paraffin wax phase change material (PCM) improves thermal storage. The experimental results indicated that the RDSS substantially enhanced productivity by 92.2%, with energy and exergy efficiencies improvement of 44.2% and 46.9%, respectively. The PV panel peak power output was 47.1 W, and the system had an efficiency of 14.1% (electrical), 37.1% (thermal), 88.5% (heating element), 52.1% (overall energy), and 5.9% (overall exergy). Economically, the RDSS reduced the cost per litre (CPL) of distillate by 17.3% and the payback period (PBP) by 18.2% than DSS which indicating higher financial feasibility. Additionally, the environmental study revealed that the RDSS achieved 98.2% more CO₂ reduction than DSS. In sensitivity analysis, the extending system lifespan from 5 to 25 years lowered production costs by 73.1% at a 2.5% interest rate, but CO₂ mitigation and carbon credit earned (CCE) elevated by 536%. Moreover, the RDSS had a 1.6% higher sustainability rating than DSS, though both systems generated potable water that met world health organization (WHO) requirements. Finally, these findings support RDSS as a low-cost, ecologically friendly and scalable alternative for decentralized freshwater and energy generation.

The principal objective of this research is to employ modern machine learning techniques to optimize high-pressure biofuel injection strategies for sustainable energy applications. An engine powered with biofuel and hydrogen (H₂) under dual-fuel (DF) mode was tested under a varied fuel injection pressure range from 180 to 240 bar for optimization and modeling. The results demonstrate that an injection pressure of 220 bar produces enhanced engine performance. At this pressure, enhancements were noted in combustion characteristics, efficiency, and emission levels. The ignition delay (ID) at 220 bar injection pressure was 9.4% longer than at 240 bar injection pressure. The 220 bar IP mix demonstrated reduced peak cylinder pressure (PCP) and heat release rate (HRR) compared to the 240 bar. A 12.4% rise in brake-specific fuel consumption (BSFC) was observed at 220 bar inlet pressure. Nevertheless, although brake thermal efficiency (BTE) increased with increasing injection pressure (IP), the increase at 220 bar was somewhat less than that at 240 bar. Despite elevated nitrogen oxide (NOx) emissions with the 220 bars compared to pure diesel, carbon monoxide (CO) and hydrocarbon (HC) emissions were markedly decreased. Smoke emissions were reduced with the 220 bars in comparison to diesel and other fuel combinations. Three machine learning models were employed to establish a predictive control framework. The decision tree (DT) model had the greatest accuracy, with R² values of 0.9792 for PCP and 0.9710 for HC, alongside near-zero MAPE for BTE and HC This study underscores the potential of AI-driven biofuel optimization for fostering sustainable transportation and renewable fuel strategies, paving the way for large-scale adoption of low-carbon, high-efficiency energy solutions.

This study proposes a novel Dynamic Decision Support System (DDSS) framework aimed at enhancing countries’ pandemic preparedness through robust forecasting and adaptive clustering analysis of Coronavirus Disease 2019 (COVID-19) data. The forecasting module integrates multiple Machine Learning (ML) models with a recursive error-adjustment mechanism that significantly improves long-term prediction accuracy of cumulative COVID-19 cases compared to conventional time series models such as Autoregressive Integrated Moving Average (ARIMA), Error Trend Seasonality (ETS), and Long Short-Term Memory (LSTM). The clustering component performs a dynamic, year-over-year classification of countries based on health, policy, and socio-economic indicators. By employing K-means clustering and Multi-Attribute Decision-Making (MADM) techniques, the study evaluates changes in countries’ pandemic performance over time and identifies the stringency index as a critical determinant influencing cluster shifts. The proposed framework not only reveals performance trajectories and degradation risks for individual countries but also provides a structured data-driven basis for strategic policymaking. Notably, the analysis demonstrates how relaxing governmental measures, despite high vaccination rates, can adversely affect country classification, highlighting the indispensable role of strict interventions. This integrated approach, combining adaptive ML forecasting with temporal clustering and interpretability via Shapley Additive explanations (SHAP) analysis, provides a dynamic and practical decision-support framework for pandemic response.

Classification of time series data is of significant importance across various domains. In particular, an important issue is to ensure the proper operation of vehicles such as drones. In this study, we propose a novel approach that utilizes the paradigm of information granules for time series classification. The proposed methodology transforms consecutive time windows of the original vibration signal into information granules, which are compact statistical summaries describing the signal dynamics within each segment. These granules serve as the input for machine learning classifiers and provide a more interpretable and noise-robust representation of the data. The proposed method is tested on data collected during the operation of unmanned aerial vehicles (UAVs), demonstrating its effectiveness. Experimental results indicate that the classification accuracy significantly improves when using information granules compared to raw data. In this study, the classification task involves distinguishing five classes corresponding to different rotor fault levels (0%, 25%, 50%, 75%, and 100% faulty rotors). This allows for precise identification of UAV operational health. In the case of the Tree Ensemble method, classification accuracy increased from 57.5% (raw data) to 100% (using information granules from four combined time windows), highlighting the substantial enhancement achieved through the proposed approach. Additionally, the study explores the impact of different time window lengths and training set sizes on classification performance, providing insights into optimizing the proposed method for practical applications.

Background: Allergic asthma (AA) is a common type 2 eosinophilic asthma phenotype. Inflammatory (iEOS) and lung-resident (rEOS) eosinophil subtypes may contribute to its underlying inflammatory processes. The biological functions of these eosinophils are tightly regulated by gene expression, with micro ribonucleic acids (miRNAs) emerging as key modulators and potential biomarkers. Purpose: To investigate the expression of miR-185-5p, miR-146b-5p, miR-320b, and miR-21-5p in eosinophil subtypes and relate with their plasma levels in AA patients. Patients and Methods: The study included 13 AA patients and 16 healthy subjects (HS). All AA patients underwent bronchial allergen challenge (BAC) with Dermatophagoides pteronyssinus and blood samples were re-taken after 24 hours. Total blood eosinophils were isolated by high-density centrifugation and magnetic separation, phenotyped using CD62L-conjugated magnetic beads. miRNAs expression quantified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: Before BAC, miR-185-5p and miR-146b-5p expression were elevated in both eosinophil subtypes relative to HS, with miR-185-5p showing higher expression in iEOS-like cells than in rEOS-like cells within the AA group. miR-320b was elevated only in iEOS-like cells relative to HS and remained higher than in rEOS-like cells within the AA group, whereas miR-21-5p was decreased only in iEOS-like cells and remained lower than in rEOS-like cells. After BAC, miR-146b-5p expression decreased in iEOS-like cells but increased in rEOS-like cells. Plasma analysis revealed elevated levels of all investigated miRNAs in AA patients relative to HS before BAC. However, after BAC, miR-185-5p and miR-146b-5p remained elevated, whereas miR-320b and miR-21-5p decreased to levels not significantly different from HS. Conclusion: Elevated miR-185-5p and miR-320b expression in iEOS-like cells compared to rEOS-like cells from AA patients highlights differences in their biological properties. Following the BAC, miR-146b-5p expression decreased in iEOS-like cells but increased in rEOS-like cells. Moreover, miR-185-5p and miR-146b-5p remained elevated in plasma, suggesting their potential to reflect chronic inflammation rather than acute response to allergen.

Migraine is a complex neurological disorder characterized by recurrent, often debilitating headaches. Current evidence suggests that autonomic nervous system (ANS) alterations play a significant role in migraine pathophysiology, affecting sensory, limbic, and homeostatic processing. Heart rate variability (HRV), a well-established, noninvasive marker of ANS function, is associated with migraine severity and treatment efficacy.