Browse Articles

In situ swimming behavior of the Mariana snailfish Pseudoliparis swirei

Optimization of a wet-cell electrolyzer for efficient oxyhydrogen (HHO) gas production: a step towards sustainable green energy solutions

Abstract The urgent need for sustainable energy solutions drives innovation in clean fuel technologies. Oxyhydrogen (HHO) gas, produced through water electrolysis, presents a promising green energy vector. While often studied in dry-cell configurations, wet-cell electrolyzers offer advantages for efficient, scalable production but require further optimization. This study systematically investigates the design and operational parameters of a wet-cell HHO generator to maximize efficiency. Four distinct configurations (Alpha, Beta, Gamma, Delta) were constructed, varying in electrode cross-sectional area (75 × 75 mm² vs. 150 × 150 mm²), plate configuration (18 vs. 20 plates), and tested at different potassium hydroxide (KOH) concentrations (10 and 20 g/L). Performance was evaluated based on HHO flow rate, specific energy consumption, and overall system efficiency. Experimental results demonstrate that the Delta generator significantly outperformed all other designs. It achieved a peak HHO flow rate of 3.4 L/min with a specific energy consumption of 3.1 kWh·m⁻³ and a notable overall system efficiency of 59.74%. In contrast, the Alpha, Beta, and Gamma generators attained lower efficiencies of 12.7%, 23.86%, and 41.9%, respectively. The superior performance of the Delta design is attributed to its optimized combination of a larger electrode area, which reduces current density and associated overpotentials, and an effective electrode configuration that maximizes active surface area and thermal management. This study conclusively identifies optimal electrode geometry and electrolyte management as critical drivers for efficient HHO production. The optimized wet-cell electrolyzer presents a sustainable and practical technology for on-demand green hydrogen production, with direct potential applications as a combustion enhancer or a storage solution for intermittent renewable energy, contributing to the advancement of sustainable energy systems.

Habitability at the edge of the redox boundary during the Permian–Triassic mass extinction

Abstract Global superanoxia is widely accepted as one of the main drivers of the end–Permian Mass Extinction (EPME) alongside, oceanic acidification, productivity collapse, and toxification. However, modeling and paleontological studies suggest spatial heterogeneity, with parts of the Tethys Ocean remaining oxygenated. To assess water–column oxygenation in the central Tethys, we studied two shallow–marine Permian–Triassic sections in equatorial paleolatitudes of central Iran; one with terrestrial input, the other fully marine. Continuous sedimentation across the EPME enables reconstruction of the latest Permian environment. U, Th, Mo, and Mn concentration data indicate well–oxygenated conditions until the EPME horizon, followed by Mn concentration peaks in microbialite/black shale intervals that reflect fluctuating oxic–anoxic conditions across the EPME. Micronutrient decline preceding the extinction suggests reduced local productivity. Thus, oxic conditions in microbialite–bearing shallow–marine settings were likely sustained by photosynthetic O₂ production and/or wave agitation. Low productivity also implies limited oxygen demand for organic matter remineralization, minimizing redox stress in these environments. We highlight shallow–marine Tethyan settings as potential oxygenated habitat during deep–sea anoxia, although a fluctuating chemocline repeatedly introduced Mn into marine environments, restricting oxidized habitat to the surface layer in contact with the atmosphere and/or oxygen–producing microbial mats.

Simulation-driven experiments resolve throttle pedal vibration issues

Hydrocarbon Frameworks with Long-Range Order Synthesized via Olefin Metathesis

Effects of a structured fundamental motor skills intervention on executive function in second grade students: a randomized controlled trial

Effects of a single dose of L-histidine on mental fatigue and vigor in participants with high fatigue levels: a randomized controlled trial

Abstract Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity that affects many aspects of daily life. Two weeks of L-histidine daily ingestion in humans significantly improves mood states compared with a placebo; however, the effects of a single dose are unknown. We aimed to assess the effects of a single dose of L-histidine on mood states, including fatigue, in healthy men and women who regularly experienced fatigue and sleep deprivation. Participants were randomly assigned to the L-histidine or placebo group. They were provided a workload using the Uchida–Kraepelin Performance test, their mood states before and after the workload were assessed, and performance on mental tasks was measured. In the all participants analysis set, the results did not show superiority of the L-histidine group over the placebo group for all endpoints. However, in the subgroup with high fatigue levels, the L-histidine group showed significant decreases compared with the placebo group in the changes in fatigue-inertia (FI), confusion-bewilderment (CB), depression-dejection (DD), and total mood disturbance (TMD) T-scores and a significant increase in the change in vigor-activity (VA) T-score ( p  = 0.022, 0.049, 0.039, 0.036, and 0.023, respectively). Although a single ingestion of L-histidine showed no significant improvements in any of the endpoints compared with the placebo in the prespecified all-participants analysis set, the exploratory findings suggest that it may alleviate negative mood states, including fatigue, confusion, and depression, and may also improve vigor after a workload in humans with high fatigue levels. Clinical Trial Registry number: UMIN000055719.

Landmark ancient-genome study shows surprise acceleration of human evolution

Invasive ductal and lobular carcinoma and receptor status in the genetic context of breast cancer

Brain–machine interface reveals the origin of a widely used neural signal

LPCAT1 depletion inhibits colorectal cancer tumorigenesis and is associated with the ECM-receptor-interaction signaling pathway

Active disturbance rejection-based decentralised sensor fault-tolerant control in DC microgrids

Abstract DC microgrids have become a viable solution for modern power distribution systems because they offer better control, improved efficiency, and simpler integration with renewable energy sources and energy storage systems. However, the performance of low-voltage DC microgrids can suffer from stability issues related to unpredictable sensor faults, parameter uncertainty, and equipment failure. In recent years, disturbance-rejection methods and robust control methods have been effective in improving microgrid resilience during these situations. This paper proposes a decentralized sensor fault-tolerant control approach for an islanded low-voltage DC (LVDC) microgrid using the active disturbance rejection control (ADRC). The ADRC control preserves the DC grid stability in the presence of unknown and time variant sensor faults by estimating and compensating for lumped disturbances through an extended state observer without the need for fault detection or reconfiguration of the system. A thorough mathematical model and an analytical control formulation are provided and thoroughly examined through single, consecutive, and simultaneous sensor-fault scenarios. Time-domain nonlinear simulation studies on a multi-DG DC microgrid show that the proposed controller provides better voltage regulation, faster transient recovery, and better robustness compared to other proposed methods in the literature, such as the conventional autotune PI controllers and the attractive ellipsoidal–based methods. The simulation studies’ results verified that the proposed ADRC scheme noticeably increases the reliability and resilience of the DC microgrid under realistic simulation conditions of sensor faults.

B-cell repertoire sequencing reveals frequent rearrangements of IGHD5-5 in patients with systemic sclerosis

Performance improvement of modified light dynamic probing in dense sandy soils with various silt contents

Real-time IIoT-driven machine failure forecasting for industry 4.0

Ozymandias undead

Genetic diversity and breeding efficiency of a salt-tolerant strain of largemouth bass (Micropterus salmoides) revealed by microsatellite analysis and phenotypic evaluations

Improving ofloxacin removal and antifouling performances of polysulfone-based mixed matrix membranes using chitosan-grafted UiO-66-NH2 MOFs

An integrated assessment of climate change on landscape adaptive capacity, vulnerability, and divergence in Avicennia species

Intelligent classification with marine predators algorithm and probabilistic neural networks

Abstract The probabilistic neural network technique is a popular data mining process that is used for addressing a variety of classification, prediction, and pattern recognition challenges. One strategy to increase the accuracy of classification is to modify the probabilistic neural network classifier’s weights using optimization techniques. Metaheuristic algorithms have demonstrated their robustness in addressing a variety of engineering challenges. As a result, multiple researchers have utilized metaheuristic algorithms to improve the search process in order to train artificial neural networks in recent years. In this work, the marine predator metaheuristic algorithm is applied. Eleven benchmark classification datasets are used to assess how well the marine predators algorithm performs in adjusting the probabilistic neural network parameters (weights and biases). The proposed method (MPA-PNN) was compared with probabilistic neural network along with three additional strategies from the literature: african buffalo optimizer, hill climbing, and coronavirus herd immunity algorithms. The findings demonstrate that integrating MPA significantly enhances the classification accuracy of the standard PNN. When benchmarked against three prominent metaheuristic-based PNN hybrids from recent literature–namely CHIO-PNN, ABO-PNN, and B-HC-PNN–the proposed MPA-PNN model achieved superior or competitive accuracy on the majority of the 11 UCI datasets evaluated, attaining the highest average accuracy of 91.047%. Furthermore, the results indicate that MPA-PNN exhibits faster and more stable convergence compared to the baseline PNN. While these findings are promising within the specific niche of metaheuristic optimization for PNNs, we acknowledge that further validation against a broader set of contemporary classifiers, such as gradient boosting machines, is a necessary direction for future work to fully establish its generalizability.