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Environmental fee-to-tax reform policy driving corporate green governance performance: the sustainable path to reducing carbon emission intensity
EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba
Palm sEMG-based user identification during doorknob rotation using a convolutional neural network
Abstract Convenient and secure user identification is increasingly important in everyday environments, particularly with the proliferation of contactless interactions and Internet-of-Things (IoT) devices. However, conventional authentication methods often require explicit user input or additional hardware, limiting their usability in natural daily scenarios. To address this issue, we propose a doorknob-rotation-based user identification method using palm surface electromyography (sEMG). sEMG signals were acquired from the abductor pollicis brevis and abductor digiti minimi at 1,000 Hz, denoised using a 60 Hz notch and 20–500 Hz band-pass filters, and transformed into time–frequency spectrograms via continuous wavelet transform. A DenseNet161 model was employed for classification. Using data from five participants, the proposed method achieved 94.00% test accuracy and 93.99% F1-score, with five-fold cross-validation accuracy of 91.66 $$\:\pm\:$$ 2.78%. The approach enables on-device, contact-based identification without wireless pairing, transforming everyday actions into seamless authentication. These results demonstrate the feasibility and practical potential of sEMG-based everyday-action user identification.
The four voltage-sensing domains of T-type calcium channels activate near the resting membrane potential
Abstract Low-voltage-activated (LVA, T-type, or Ca V 3), calcium-selective channels open in response to modest depolarizations, just above the resting membrane potential, supporting neuronal burst-firing patterns and spontaneous firing in cardiac pacemaker cells. How LVA-channels open at low voltages is unclear: traditional gating-current experiments suggest that LVA-channel voltage-sensing domains (VSDs) paradoxically require stronger depolarization to activate than pore opening. Using voltage-clamp fluorometry, we find that the activation of all four VSDs in human Ca V 3.1-channels precedes opening in voltage, solving the longstanding conundrum. We also uncover confounding effects of La 3+ (used for gating-current measurements) on VSD function and clarify the role of distinct LVA-channel structure S6 Cyto . Ca V 3.1-VSDs operate within a narrow voltage-range, resembling the VSDs of related Na V -channels more than those of other Ca V -channels. Likely, Na V -like VSDs emerge before sodium selectivity.
Forecasting US land use through 2067 using multi-method projections applied to seven decades of USDA data
Three-dimensional imaging of sulfur chemical states in polymers with micrometer thickness using sulfur K-edge ptychographic tomography
Abstract Three-dimensional visualization of chemical bonding states in light-element materials remains challenging, particularly for micrometer-thick specimens, due to strong X-ray absorption and limited photon flux in the tender X-ray regime. Here three-dimensional chemical-state imaging of sulfur in sulfurized poly(n-butyl methacrylate) (SPBMA) samples up to ~ 3.3 µm thick is demonstrated using ptychographic X-ray computed tomography at the sulfur K -edge. A high-resolution ptychographic CT system at the NanoTerasu BL10U beamline enables quantitative mapping of electron density, sulfur concentration, and spectral metrics derived from multi-energy imaging, that serve as proxies for sulfur–sulfur and sulfur–carbon bonding states, with ~ 80 nm spatial resolution. Multi-energy volumetric analysis using four carefully chosen photon energies reveals pronounced spatial heterogeneity in sulfur bonding, distinguishing sulfur–sulfur and sulfur–carbon bonds that are inaccessible from spatially averaged spectra. This study establishes sulfur K -edge ptychographic tomography as a potential platform for three-dimensional chemical-state imaging of light-element materials, enabling new nanoscale investigations of functional polymers and energy-related materials.
Association between sleep duration and thirst in a nationally representative cross-sectional survey
Beta-caryophyllene mitigates high fat diet-induced testicular dysfunctions by targeting JNK/ERK-1 and JAK-1/STAT-3 pathways in rats
Identification of chemical and physical key water quality drivers in the urban Grunewald Chain of Lakes, Berlin
Abstract Aquatic ecosystems are threatened by high nutrient loads. Particularly urban lakes that are used as storm water reservoirs are polluted by phosphorus, nitrogen and other pollutants. Improving the water quality of urban lakes is both a benefit for the ecosystem, and for the socio-ecological value of the waterbody. This study investigates the Grunewald chain of lakes in Berlin, Germany which is threatened by high nutrient loads from surrounding urban areas. To date, measures to improve the water quality failed to achieve a resilient, long-term balanced and stable aquatic ecosystem. Connected lakes pose major challenges for water management due to their interactions. To better understand the exchange of nutrients in the Grunewald chain of lakes, a monitoring campaign and data analysis were conducted, with monthly water samples over a period of 13 months at 17 sampling stations, focusing on the inlets, outlets and connections of the lakes. This study reveals the relevance of temperature, volume ratio, depth and phosphorus concentrations affecting the nutrient limitation of the lakes and how water quality of the lakes are affected by each other. The study gives insights to cascading effects on nutrient accumulation along a chain of lakes, providing guidance for further management practices.
An enhanced Deep Q-Network approach for load frequency control in hybrid renewable energy system
Effect of fungal chitosan on the morphology, biochemical, and genetic changes in selected genotypes of Scutellaria barbata D. Don under in vitro conditions
Abstract Scutellaria barbata D. Don is an important plant for the phytopharmaceutical industry, and scutellarin is one of the key compounds produced in its shoots with confirmed anticancer properties. However, there is a lack of studies focused on obtaining phytochemically and genetically homogeneous, sterile plant material that is free from bacterial and fungal contamination and has a high scutellarin content. This study aimed to assess the effect of the biotic elicitor fungal chitosan, derived from Aspergillus niger , on the morphology of microcuttings, scutellarin content, concentrations of plant pigments (anthocyanins, carotenoids, chlorophyll a, and chlorophyll b), and selected oxidative stress biomarkers (free proline and catalase activity). Additionally, the study assessed genetic stability using Start Codon Targeted Polymorphism (SCoT) molecular markers in three genotypes of S. barbata (L5, L6, and L7). The addition of chitosan from A. niger to the medium influenced the morphological features, concentrations of anthocyanins, carotenoids, chlorophyll a, chlorophyll b, scutellarin, and free proline content, as well as the genetic stability of S. barbata microcuttings, with the effects depending on its concentration and genotype. However, catalase activity was not affected.
Decoding peri-urban transformation through multi-scale mapping of bidirectional urbanisation
One pot synthesis of N-7 alkylated xanthine derivatives and their exploration as antioxidant, antimicrobial and nematicidal agents
Betulinic acid is associated with miR-21 modulation, apoptosis and redox changes in breast cancer cells: an in vitro and in silico study
Abstract Betulinic acid is defined as a hydrophobic pentacyclic triterpenoid primarily found in the bark of Betula alba, known for its anticancer activity through mechanisms such as upregulating proapoptotic proteins, modulating NF-kB, and inhibiting topoisomerase I. The current study aimed to explore the anticancer potential effect, molecular targets, and association with miR-21 modulation after BA treatment and its combination with doxorubicin against human triple-negative breast cancer (MDA-MB-231) cells. We determined drug cytotoxicity by MTT assay, and death mechanism by flow cytometry. Besides, the potential effect of BA and DOX treatment on downstream effect of miR-21 on HIF1A, PDCD4, PTEN and SMAD7 expression levels. Finally, a molecular docking study was performed to determine molecular targets, binding affinity, and mode of interactions of BA and DOX with HIF1A, PDCD4, PTEN and SMAD7. Collectively, our data showed that treatment with BA and/or DOX have significantly describing observed differences under the tested conditions associated with increasing apoptosis, downregulation of miR-21, HIF1A and SMAD7 expression and upregulation of PDCD4 and PTEN. Finally, the molecular docking study suggested potential interactions between BA and DOX with PTEN and PDCD4, modulating their activities leading to growth arrest and cell death. In conclusion, this study reported BA as potential antiproliferative compound with modulation of miR-21 expression and identified molecular targets involved in its action.
Neuro-evolutionary computing approach for an epidemic model of ransomware detection using morlet wavelet neural network with meta-heuristic optimization
A two-stage robust non-intrusive load monitoring method against unknown waveform distortions
Realistic benchmark RBD360 dataset for quality assessment of random user generated 360° videos
Peristaltic flow of sutterby nanofluid in a stenosed artery with ciliated endothelium and wall roughness under hall and ion slip effects
Abstract In this dissertation, we examine the nonlinear peristaltic locomotion of a Sutterby nanofluid in the presence of a magnetic field from the exterior through a stenosed capillary with a ciliated endothelium lining and rough sidewalls. This study’s impetus stems from the need for more accurate hemodynamic models that can explain how magnetohydrodynamic forces and geometric irregularities alter blood flow in narrow arterial segments. Nonlinear radiant heat, viscous dissipation, and heating by Joules, Brownian diffusion, thermophoretic transport, activation energy effects, and spreading microbes are all incorporated into a comprehensive mathematical framework. Arterial roughness is modeled using a function that varies with both axial position and time, enabling representation of dynamic wall deformations. The Homotopy Perturbation Procedure is used to estimate analytical solutions for the generated equations, and qualitative compliance with experiments is provided for validation. The findings demonstrate that when the roughness amplitude rises, the critical pumping velocity falls. Furthermore, longer cilia increase hydraulic resistance and decrease axial velocity, whereas more eccentric cilia result in larger forward transport and faster flow. The originality of this integrated strategy is the simultaneous presence of time-dependent roughness and ciliated-wall mechanics for the MHD-driven artery structure. It promotes optimised performance of healthcare diagnosis and therapy, as well as constitutes a useful and accurate predictor for the evaluation of haemodynamics in stenosed arteries.
Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition
Abstract Motor imagery (MI)-based brain–computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG’s low signal-to-noise ratio (SNR), inter-subject variability, and complex spatiotemporal patterns. Existing approaches often suffer from limited accuracy, high computational cost, and poor interpretability. In response to these challenges, we present the first comprehensive benchmarking of the publicly available EEG-hand movement (EEG-HM) dataset. Our study aims to establish a standardized performance baseline, guide the selection of optimal models by jointly considering accuracy, prediction time, and explainability, and ultimately accelerate progress in MI-BCI development. We have proposed a two-stage optimization of machine learning models that employs both feature selection and hyperparameter tuning. We exploit five feature selection algorithms for selecting the best set of EEG electrodes and frequency bands, while Bayesian optimization is exploited for machine learning model optimization through hyperparameter tuning. Furthermore, to validate the neurophysiological basis of our model’s decisions, we leverage explainable AI (XAI) algorithms—LIME and SHAP—quantifying the contributions of specific EEG electrodes and frequency bands to interpret its decision-making process. Through extensive simulations, the proposed two-stage optimization of the machine learning model demonstrates a superior performance in terms of accuracy, precision, and recall. This method outperforms the existing methods by 21.47% in accuracy with competitive prediction time. Its performance is further evaluated on the PhysioNet MI dataset, achieving a 4.67% accuracy improvement over state-of-the-art methods. Through LIME and SHAP, we provide the local and global explanations for no activity, left-hand, and right-hand imagery movements. Additionally, we analyze how various EEG frequency bands and electrode locations interact during the performance of different motor imagery hand movements.