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High Entropy Alloys (HEAs) are a versatile material with unique properties, tailored for various applications. They enable pH‐sensitive electrocatalytic transformations like hydrogen evolution reaction (HER) and hydrogen oxidation reactions (HOR) in alkaline media. Mesoporous nanostructures with high surface area are preferred for these electrochemical reactions, but designing mesoporous HEA sis challenging. To overcome this challenge, a low‐temperature triblock copolymer‐assisted wet‐chemical approach is developed to produce mesoporous HEA nanospheres composed of PtPdRuMoNi systems with sufficient entropic mixing. Owing to active sites with inherent entropic effect, mesoporous features, and increased accessibility, optimized HEA nanospheres promote strong HER/HOR performance in alkaline medium. At 30 mV nominal overpotential, it exhibits a mass activity of ≈167 (HER) and 151 A gPt−1 (HOR), far exceeding commercial Pt‐C electrocatalysts (34 and 48 A gPt−1) and many recently reported various alloys. The Mott‐Schottky analysis reveals HEA nanospheres inherit high charge carrier density, positive flat band potential, and smaller charge transfer barrier, resulting in better activity and faster kinetics. This micelle‐assisted synthetic enable the exploration of the compositional and configurational spaces of HEAs at relatively low temperature, while simultaneously facilitating the introduction of mesoporous nanostructures for a wide range of catalytic applications. A micelle‐assisted wet‐chemical method is used to design and synthesize mesoporous high‐entropy alloy nanospheres at low temperatures. By adjusting reaction conditions, the structure and morphology of these nanospheres can be tailored, resulting in a crystalline state akin to that of a single element. The optimized nanospheres offer new insights into electrochemical catalytic reactions, exhibiting remarkably fast kinetics.

Work function (WF) influences electron transport and intermediates adsorption, enabling charge balance and catalytic optimization for the hydrogen evolution reaction (HER). However, the understanding of the role of mesopores and the relationship between composition and WF in pristine Pt‐based alloys remains lacking. Herein, various mesoporous binary Pt‐M alloy films (m‐Pt‐M, M = Pd, Rh, and Ru) with uniform pores and elemental distributions are synthesized, providing an experimental platform to investigate this relationship. It has been demonstrated that the WFs of m‐Pt‐M catalysts are strongly influenced by their compositions and mesoporous structures, thereby impacting HER activities. Among them, m‐Pt‐Ru with tailored WF lowers the thermodynamic energy barrier and accelerates the kinetic processes of HER. The mass activity of m‐Pt‐Ru in alkaline media is 17.8× and 5.1× higher, compared to Pt black and m‐Pt, respectively. This work not only provides a simple method for the fabrication of well‐defined binary metallic alloy films but also offers experimental insights into the rational design of highly efficient electrocatalysts with tunable WFs. This study presents mesoporous binary Pt‐M alloy films (m‐Pt‐M, M = Pd, Rh, and Ru) with uniform porosity and tailored work functions (WFs) as an experimental study platform. The WFs of the m‐Pt‐M films exhibit a volcano‐type relationship with their HER performance. Among them, the m‐Pt‐Ru demonstrates superior activity, requiring only 16 mV overpotential to reach 10 mA cm−2 in alkaline media.

Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. This approach has a fatal weakness when it is used for disadvantaged groups, such as those with academic warnings or who have dropped out, because these groups are often much smaller than other common groups in number. The imbalanced data that have class distribution skew create a big challenge to training good classification models. The significant approach to tackle this challenge is applying oversampling methods to increase the number of minor classes; however, generating good new samples from the existing instances of a minor class is still a hard issue and requires new investigation. This study presents two new methods of handling data imbalance based on the original algorithms SMOTE and adaptive synthetic sampling (ADASYN), called Improved SMOTE (I_SMOTE) and Improved ADASYN (I_ADASYN). These modifications involve a new selecting fit candidate method based on a new similarity measurement and a roulette wheel selection to generate synthetic data samples. The aim is to rebalance data and therefore improve the prediction accuracy of minor groups. The proposal methods were designed and applied to education datasets, and they were tested on public datasets and a dataset collected from a Vietnamese university for evaluation. The experimental results on learning datasets showed the high potential of novel algorithms, I_SMOTE and I_ADASYN, for student academic performance problems in general and at‐risk student groups especially. Empirical results proved that the recall, precision, and F1‐score of the minority class of I_SMOTE and I_ADASYN are strongly better than the original balancing algorithms. Besides, the I_SMOTE and I_ADASYN also improve relatively by 6.6% and 8.0% of the ROC area compared to the original SMOTE and ADASYN, respectively.

Parabens ( p -hydroxybenzoic acid esters) commonly used preservatives (in cosmetics, pharmaceuticals, and foods) can pose potential effects on environmental health. In this study, seven parabens were quantified in marine fish samples using an ultra-high performance liquid chromatography triple quadrupole mass spectrometer (UHPLC-MS/MS) system. Parabens in the fish samples were extracted and purified by a rapid, simple, and effective procedure comprising sample homogenization with solvent, solid-phase extraction clean-up, and solvent evaporation. Results demonstrated that the recoveries of seven compounds (with relative standard deviation < 15%) were 88–103% in matrix-spike samples and 86–105% in surrogate standards. The method detection limits and method quantification limits of seven parabens were 0.015–0.030 and 0.045–0.090 ng/g-ww (wet weight), respectively. The optimized method was applied to measure the concentration of parabens in the 37 marine fish samples collected from Vietnam coastal waters. The concentration ranges of seven parabens found in round scad and greater lizardfish samples were 6.82–25.3 ng/g ww and 6.21–17.2 ng/g-ww, respectively. Among parabens, methylparaben accounted for the highest contribution in both fish species (43.2 and 44.9%, respectively). Based on the measured concentrations of parabens in marine fish samples, the estimated daily intake was calculated for children and adults with the corresponding values of 0.0477 µg/kg/day and 0.0119 µg/kg/day, respectively. However, the presence of parabens in Vietnamese marine fish may not pose a significant risk to human health.

Recognizing human interactions is essential for social robots as it enables them to navigate safely and naturally in shared environments. Conventional robotic systems however often focus on obstacle avoidance, neglecting social cues necessary for seamless human-robot interaction. To address this gap, we propose a framework to recognize human group interactions for socially aware navigation. Our method utilizes color and depth frames from a monocular RGB-D camera to estimate 3D human keypoints and positions. Principal component analysis (PCA) is then used to determine dominant interaction directions. The shoelace formula is finally applied to compute interest points and engagement areas. Extensive experiments have been conducted to evaluate the validity of the proposed method. The results show that our method is capable of recognizing group interactions across different scenarios with varying numbers of individuals. It also achieves high-speed performance, processing each frame in approximately 4 ms on a single-board computer used in robotic systems. The method is implemented as a ROS 2 package making it simple to integrate into existing navigation systems. Source code is available at https://github.com/thanhlong103/social-interaction-detector

Water quality monitoring is essential for protecting aquatic ecosystems and detecting environmental pollution. This paper presents the design and experimental validation of a bio-inspired miniature submarine for low-cost water quality monitoring. Inspired by the jet propulsion mechanism of squids, the proposed system employs pump-driven water jets for propulsion and steering, combined with a pump-based buoyancy control mechanism that enables both depth regulation and water sampling. The vehicle integrates low-cost, commercially available components including an ESP32 microcontroller, IMU, pressure sensor, GPS receiver, and LoRa communication module. The complete system can be constructed at a hardware cost of approximately $122.5, making it suitable for educational and environmental monitoring applications. Experimental validation was conducted through pool tests and field trials in a lake. During a 360 degrees rotation test, roll and pitch deviations remained within +/-2 degrees and +/-1.5 degrees, respectively, demonstrating stable attitude control. Steering experiments showed a heading step response with approximately 2 s rise time and 5 s settling time. Depth control experiments achieved a target depth of 2.5 m with steady-state error within +/-0.1 m. Field experiments further demonstrated reliable navigation and successful water sampling operations. The results confirm that the proposed platform provides a compact, stable, and cost-effective solution for small-scale aquatic environmental monitoring.