Browse Articles

YOLO based stubble burning detection system for Northern regions of India

Influence of activation mode and aeration rate on ferritization efficiency, phase formation, and sediment stability in spent etching solution treatment

Abstract The growing volume of spent etching solutions generated by the metallurgical industry represents a significant environmental challenge due to their high acidity and high concentrations of dissolved iron. Developing efficient treatment methods that ensure both decontamination and resource recovery is a priority for sustainable industrial wastewater management. Ferritization is a process that converts dissolved iron into stable ferrite phases and thus offers an effective pathway for transforming hazardous waste into environmentally safe and technologically valuable materials. This study evaluates the ferritization of diluted sulfuric acid under three activation conditions: thermal, ultrasonic, and alternating magnetic field. Experiments were conducted with air oxygen bubbling rate of 0.02–0.06 dm³/s and reaction durations of 30–75 min. The resulting precipitates were characterized using X-ray diffraction to determine their phase composition, while leaching tests assessed the stability of the ferrite products in aqueous environments. The results show that thermal activation at 75 °C combined with the highest aeration rate significantly enhances the conversion of iron oxyhydroxides into magnetite (Fe₃O₄). Under optimal conditions, magnetite content approached 100%, iron removal reached 99.99%, and leaching of iron ions did not exceed 0.2 mg/dm³. Overall, the study demonstrates that ferritization is an efficient and environmentally viable method for treating spent pickling solutions while obtaining valuable iron-containing products.

Optimizing conductive thermoset composites for bipolar plates using statistical mixture design

Network-nodal tACS induces right-lateralization of thalamocortical connectivity

Effects of hydrodynamic nonlocality on electromagnetic wave propagation and topological properties of magnetoplasmas

Bayesian causal inference reveals declined proprioception, increased integration bias underlie older adults’ stronger visual bias in hand position perception

Abstract Self-localization is fundamental to bodily self-consciousness across the lifespan. Humans estimate body-part position by integrating afferent signals such as vision and proprioception. Rubber and mirror hand illusions highlight the dominant role of vision in hand position perception. Although older adults rely more heavily on visual information, the computational mechanisms underlying age-related increases in visual bias remain unclear. Here, we examined age-related changes in visuo-proprioceptive integration using a Bayesian causal inference (BCI) model. Two experiments introduced spatial discrepancies between visual and proprioceptive hand positions to manipulate the likelihood of integration. Participants reached toward a target after the visual hand disappeared, allowing the BCI model to estimate sensory reliabilities and the prior probability of a common cause ( $$\:{p}_{common}$$ ). Decision-making strategies were also compared within the BCI framework. Older adults exhibited reduced proprioceptive reliability and a higher $$\:{p}_{common}$$ , indicating a stronger tendency to infer a shared source for visual and proprioceptive signals. No age-related differences were observed in decision-making strategy. These findings suggest that age-related visual bias reflects changes not only in sensory reliability but also in causal inference during multisensory integration.

Computational text analysis of emotional expressions related to non-suicidal self-injury in Chinese social media

Long-range structural and magnetic coherence in embedded mesospin metamaterials

Abstract Engineered assemblies of interacting magnetic elements—magnetic metamaterials—provide a powerful route to tailor collective magnetic order and dynamics. By structuring matter at the mesoscale, they bridge atomic magnetism and macroscopic functionality, enabling emergent behaviour inaccessible in conventional materials. However, realizing large-area metamaterials that combine high morphological uniformity with intrinsic long-range order has remained challenging, largely due to the structural disorder inherent to lithographic fabrication. Here we demonstrate a scalable route to structurally and magnetically coherent metamaterials by embedding iron-ions to form mesospins within a non-magnetic thin film palladium host matrix. Using controlled implantation, we realize morphologically uniform arrays that spontaneously develop extended antiferromagnetic order in the as-fabricated state—without the need of external annealing or field cycling. Resonant X-ray scattering and microscopy reveal sharp magnetic Bragg peaks modulated by the mesospin form factor, evidencing long-range antiferromagnetic order coupled to structural coherence. This embedded architecture establishes a platform for exploring coherent spin–photon interactions and functional X-ray scattering in magnetic metamaterials free from lithographic topography and disorder.

Influence of the cutting speed in turning and force in diamond smoothing on the surface properties of pure nickel

Abstract Nickel and its alloys offer excellent chemical resistance and mechanical properties under elevated temperatures. This makes them suitable for high- and low-temperature applications. Microreactors, due to their microscale dimensions, pose challenges for conventional joining techniques. Diffusion bonding offers a promising approach to overcome these challenges, with bond strength influenced by temperature, time, contact pressure, and surface state. This study investigates how face-turning and diamond smoothing affect the surface state of pure nickel. Specimens are face-turned at cutting speeds from 50 m/min to 400 m/min, followed by diamond smoothing with forces of 100 N to 200 N. Surface properties are characterized, and the results indicate that increasing the cutting speed decreases surface roughness values, coarsens crystallites, and increases tensile residual stresses. Smoothing at all forces induces compressive residual stresses and minimizes the surface roughness values at 100 N and 150 N. The findings highlight the influences of turning and diamond smoothing on the surface state of pure nickel, providing a basis for selecting surface preparation parameters that are expected to enhance diffusion bonding performance. With this knowledge, the field of application of diffusion bonding can be improved by achieving the suitable surface state.

Probiotic Bifidobacterium animalis subsp. lactis DS109-B11 ameliorates age-related muscle weakness via AMPK activation

Abstract Sarcopenia, the age-related loss of skeletal muscle mass and function, represents a growing health burden with limited therapeutic options. Given the emerging roles of the gut–muscle axis and AMP-activated protein kinase (AMPK) in muscle homeostasis, we sought to identify gut-derived microbial strains that enhance muscle function via AMPK activation. We identified Bifidobacterium animalis subsp. lactis DS109-B11 as a potent AMPK activator. DS109-B11 microbial culture supernatant (MCS) increased AMPK phosphorylation during C2C12 myoblast differentiation, enhanced myogenic differentiation, and mitigated dexamethasone-induced myotube atrophy in vitro. In aged mice, oral administration of live DS109-B11 improved grip strength and motor performance and increased myofiber cross-sectional area, accompanied by elevated AMPK phosphorylation, upregulated mitochondrial and oxidative phosphorylation genes, and downregulated atrophy- and inflammation-related genes in skeletal muscle. In a botulinum toxin–induced neurogenic atrophy model, DS109-B11 treatment partially preserved tibialis anterior muscle mass, improved myofiber cross-sectional area, and suppressed atrophy-related gene expression. These findings identify DS109-B11 as an AMPK-activating probiotic strain that beneficially modulates skeletal muscle differentiation, enhances resilience to catabolic stress, and improves muscle function in vivo.

Interactive AI assisted pediatric burn assessment based on smartphone images

Performance of eco-friendly geopolymer loadbearing bricks for thermally comfortable structures

Integrated electrical modeling of circulating tumor cells for enhanced dielectrophoretic trapping and electroporation

Abstract This study presents a comprehensive computational and experimental investigation of the integrated dielectrophoresis (DEP) and electroporation framework for the selective manipulation and controlled treatment of circulating tumor cells (CTCs), white blood cells (WBCs), and platelets (PLTs) within a unified microfluidic platform. A multiphysics mathematical model, implemented in COMSOL Multiphysics, is developed to simulate the complete sequence of DEP-driven cell trapping followed by pulsed electric field electroporation, capturing the dynamic processes of membrane charging, pore nucleation, growth, and resealing under short-duration 2 µs electric pulses. The key electroporation parameters, including transmembrane potential, pore radius, pore density, and membrane conductivity, are systematically characterized for each cell type to define cell-specific optimal pulse protocols that maximize treatment efficacy while preserving cell viability and minimizing thermal effects. The DEP mechanism provides stable spatial confinement of target cells between electrode pairs, enabling precise and reproducible exposure to calibrated electric fields with reduced off-target perturbations. Comparative computational analysis across the three cell types reveals that the requisite electric field strength must be tailored to cell dimensions, with 1–4 kV/cm identified as appropriate for CTCs and WBCs and 10–40 kV/cm required for platelets owing to their substantially smaller diameter and correspondingly higher membrane charging threshold. The simulation results demonstrate spatially heterogeneous pore dynamics and electric displacement field distributions across the cell membrane, with the most pronounced effects concentrated at the hyperpolarized pole, underscoring the critical influence of cell geometry and electric field distribution on electroporation outcomes. To experimentally validate the computational predictions, a dedicated microfluidic platform integrating microfabricated electrode arrays with real-time impedance sensing and optical monitoring was developed and applied to THP-1 monocytic cells as a representative model system. The device comprises a central impedance sensor defining the active sensing zone and surrounding focusing electrodes with lateral dimensions of $$L = w = 600$$  µm and an inter-electrode spacing of 200 µm, electrically routed through via connections for independent excitation and measurement. Impedance spectroscopy was performed over a frequency range of $$10^{3}$$ – $$10^{6}$$  Hz under applied voltages from 1 to 25 V across the 200 µm sensing gap. The impedance magnitude (| Z |) exhibited a clear monotonic decrease with increasing applied voltage, with the most pronounced reductions observed in the mid-frequency range (10–100 kHz), confirming enhanced membrane permeability and increased effective conductivity of the cell suspension under stronger electric fields. The reactive impedance component ( $$X_s$$ ) demonstrated progressive suppression of negative reactance with increasing voltage, indicating systematic loss of membrane capacitive behavior, while the series resistance ( $$R_s$$ ) decreased and stabilized at higher voltages, reflecting a transition from capacitance-dominated to conductivity-dominated electrical transport.

Clinicopathological significance of Gli1 expression in hepatocellular carcinoma: a meta-analysis

Enhanced cybersecurity threat detection using novel tri-metaheuristic loss functions in generative adversarial networks with adaptive attention preservation for network traffic augmentation

Abstract This paper proposes a tri-component loss function framework integrated within Generative Adversarial Networks for network traffic augmentation in cybersecurity threat detection. The framework combines nine differentiable loss components: feature importance preservation via attention-based weighting, distribution alignment via Wasserstein distance, gradient regularization via gradient penalty, adversarial discrimination via hinge loss, embedding clustering via triplet constraints, curriculum scheduling via progressive difficulty adjustment, perturbation-aware training via projected gradient descent, multi-scale consistency via wavelet transform, and diversity promotion via cosine similarity regularization. We clarify that these components employ established techniques, with our contribution lying in their systematic integration and domain-specific adaptation rather than fundamentally new algorithms. Energy-aware adaptive attention dynamically allocates computational resources based on threat likelihood, reducing training energy consumption by 40% (76.8 kWh versus 128.4 kWh baseline). Experimental evaluation across seven cybersecurity datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017, CIC-IDS2018, Bot-IoT, CICDDOS2019, CSE-CIC-IDS2018) yielded 98.73% accuracy and 0.987 F1-score. Ablation analysis revealed that 49.4% of improvement stems from addressing class imbalance through augmentation, while 50.6% derives from the proposed loss combination, with 2.0% additional synergistic benefit. Cross-dataset transfer achieved 87.45–94.23% accuracy without retraining. Adversarial robustness evaluation of 95.67% accuracy under perturbation budget ε = 0.3. Limitations include poor infiltration attack detection (16.44–28.13% recall) and ground truth verification covering only 1.8% of deployment samples. Statistical significance was confirmed with p-values below 0.0001 and Cohen’s d exceeding 3.4. The framework provides evidence that systematic integration of established techniques with domain-specific adaptation can yield measurable improvements in cybersecurity applications under the evaluated conditions. Generalization to broader deployment contexts warrants further investigation.

4-Hydroxycinnamic acid and Itaconic acid exhibit broad spectrum synergy with conventional antibiotics in inhibiting multidrug-resistant Salmonella Typhimurium

Visceral fat rating, but not glycemic variability, is strongly associated with arterial stiffness in non-obese adults with type 1 diabetes

Sulfonated cyclodextrin polymer as a bio-based catalyst for the synthesis of 5-HMF from fructose

Precise identification of tomato leaf diseases based on the real background of DPEN

TMPA-HC: a two-stage heterogeneous multi-population algorithm with cooperative search for high-dimensional feature selection

Abstract Feature selection is a fundamental yet challenging task in machine learning, particularly in high-dimensional settings. Although swarm intelligence and evolutionary computation methods, including ant colony optimization and grey wolf optimizer, have shown promising performance in feature selection, they still face two major limitations in high-dimensional spaces. First, the selected feature subsets often contain considerable redundancy, which negatively impacts the performance of classifiers. Second, the computational cost increases rapidly with dimensionality, leading to unsatisfactory efficiency in practical applications. In response to the above challenges, this study introduces TMPA-HC, a two-stage heterogeneous multi-population framework that employs cooperative search for high-dimensional feature selection. The proposed approach adopts a two-stage framework that integrates an initial Fisher-score-based filtering stage with a subsequent wrapper-based heterogeneous multi-population optimization stage. In the second stage, the population is divided into multiple subpopulations with distinct search roles, enabling structured exploration-exploitation behaviors. To facilitate effective collaboration, TMPA-HC incorporates several cooperative mechanisms, including elite cross-population hybridization, cyclic information transfer, and subpopulation reorganization. In addition, a success-rate–driven adaptive control strategy dynamically adjusts the search intensity of each subpopulation, while lightweight elite-guided local search and stagnation-aware restart mechanisms enhance convergence stability and robustness. Comprehensive experiments on multiple high-dimensional benchmark datasets show that TMPA-HC achieves competitive feature selection performance with consistent convergence, demonstrating its effectiveness and stability in handling high-dimensional data.