Search Results (1,719)

We introduce an ultra-compact, single-neuron-per-class end-to-end readout for binary classification of noisy, image-encoded sensor time series. The approach compares a linear single-unit perceptron (E2E-MLP-1) with a resonate-and-fire (RAF) neuron (E2E-RAF-1), which merges feature selection and decision-making in a single block. Beyond empirical evaluation, we provide a mathematical analysis of the RAF readout: starting from its subthreshold ordinary differential equation, we derive the transfer function $H\left(\mathrm{j}\omega \right)$, characterize the frequency response, and relate the output signal-to-noise ratio (SNR) to ${\left|H\left(\mathrm{j}\omega \right)\right|}^2$ and the noise power spectral density $S_n\left(\omega \right)\propto {\omega }^{\alpha }$ (brown, pink, and blue noise). We present a stable discrete-time implementation compatible with surrogate gradient training and discuss the associated stability constraints. As a case study, we classify walk-in-place (WIP) in a virtual reality (VR) environment, a vision-based motion encoding (72 × 56 grayscale) derived from 3D trajectories, comprising 44,084 samples from 15 participants. On clean data, both single-neuron-per-class models approach ceiling accuracy. At the same time, under colored noise, the RAF readout yields consistent gains (typically +5–8% absolute accuracy at medium/high perturbations), indicative of intrinsic band-selective filtering induced by resonance. With ∼8 k parameters and sub-2 ms inference on commodity graphical processing units (GPUs), the RAF readout provides a mathematically grounded, robust, and efficient alternative for stochastic signal processing across domains, with virtual reality locomotion used here as an illustrative validation. Full article

Background: Realistic simulation of ECG signals is essential for validating signal-processing algorithms and training artificial intelligence models in cardiology. Many existing approaches model either waveform morphology or heart rate variability (HRV), but few achieve both with high accuracy. This study proposes a hybrid method that combines morphological accuracy with physiological variability. Methods: We developed a mathematical model that integrates Gaussian mesa functions (GMF) for waveform generation and a chaotic Rössler attractor to simulate RR-interval variability. The GMF approach allows fine control over the amplitude, width, and slope of each ECG component (P, Q, R, S, T), while the Rössler system introduces dynamic modulation through the use of seven parameters. Spectral and statistical analyses were applied, including power spectral density (PSD) computed via the Lomb–Scargle, STFT, CWT, and histogram analyses. Results: The synthesized signals demonstrated physiological realism in both the time and frequency domains. The LF/HF ratio was 1.5–2.0 when simulating a normal rhythm and outside these limits in a simulated stress rhythm, consistent with typical HRV patterns. PSD analysis captured clear VLF (0.003–0.04 Hz), LF (0.04–0.15 Hz), and HF (0.15–0.4 Hz) bands. Histogram distributions showed amplitude ranges consistent with real ECGs. Conclusions: The hybrid GMF–Rössler approach enables large-scale ECG synthesis with controllable morphology and realistic HRV. It is computationally efficient and suitable for artificial intelligence training, diagnostic testing, and digital twin modeling in cardiovascular applications. Full article

Traditionally, the development and optimisation of photocatalytic materials have relied on experimental approaches and density functional theory (DFT) calculations. Although these methods have driven significant scientific progress, they are increasingly constrained by high computational costs, lengthy development cycles, and limited scalability. In recent years, machine learning (ML) has emerged as a powerful and sustainable alternative, offering a data-driven framework that accelerates materials discovery through rapid and accurate property prediction. This review highlights the essential components of the ML workflow data collection, feature engineering, model selection, and validation while exploring its application in predicting photocatalytic properties. It further discusses recent advances in forecasting key characteristics such as band edge positions, charge carrier mobility, and surface reactivity using both supervised and unsupervised ML techniques. Persistent challenges, including data scarcity, model interpretability, and generalisability, are also addressed, alongside potential strategies to improve the robustness and reliability of ML-driven materials design. By combining high prediction accuracy with superior computational efficiency, ML holds the potential to revolutionise high-throughput screening and guide the systematic development of next-generation photocatalysts. Full article

Background: Rapidly growing mycobacteria (RGM) are microorganisms with variable pathogenicity, which can cause different clinical forms of mycobacterioses. They can form structured communities at the liquid-air interface and adhere to animate and inanimate solid surfaces, characterizing one of their most powerful mechanisms of resistance and survival, named biofilms. Objectives: Here, a novel series of sulfamethoxazole (SMTZ) Schiff bases were obtained by the condensation of the primary amine from SMTZ core with six different aldehydes to evaluate their antimicrobial and antibiofilm activities, as well as physicochemical and in silico characteristics. Methods: The compounds L1–L6 included: pyridoxal hydrochloride (L1), salicylaldehyde (L2), 3-methoxysalicylaldehyde (L3), 2-hydroxy-1-naphthaldehyde (L4), 3-allylsalicylaldehyde (L5), and 4-(diethylamino)salicylaldehyde (L6). MIC determination was performed against standard strains and seven clinical isolates. Time-kill assays, biofilm inhibition assays, atomic force microscopy, and peripheral blood mononuclear cell cytotoxicity assays were carried out. Density functional theory (DFT) calculations using quantum descriptors, Mulliken charges, Fukui functions, non-covalent interactions (NCI), and reduced density gradient (RDG), along with molecular docking calculations to DHS, LasR, and PqsR, supported the experimental trend. Results: The compounds L1–L6 showed a significant capacity to inhibit the growth of RGM, with MIC values in the range of 0.61 to 1.22 μg mL⁻¹, which are significantly lower than those observed for the parent compound SMTZ, demonstrating superior antimicrobial potency. To deepen antimicrobial activity assays, L1 was chosen for further evaluations and showed a significant ability to inhibit the growth of RGM in both planktonic and biofilm forms. In addition, atomic force microscopy views great changes in topography, electrical force, and nanomechanical properties of microorganisms. The cytotoxic assays with the peripheral blood mononuclear cell model suggest that the new compound may be considered as an antimicrobial alternative, as well as a safe substance showing selectivity indexes in the range of efficacy. Conclusions: Density functional theory (DFT) calculations were performed to obtain quantum descriptors, Mulliken charges, Fukui functions, non-covalent interactions (NCI), and reduced density gradient (RDG), which, with molecular docking calculations to DHS, LasR, and PqsR, supported the experimental trend. Full article

Background/Objectives: Calcific aortic valve disease (CAVD) is a progressive condition marked by thickening and calcification of the valve leaflets, leading to impaired cardiac function and increased cardiovascular risk. As disease progression is strongly influenced by hemodynamics and lipid accumulation, computational modeling provides a powerful tool for understanding the biomechanical drivers of calcification. Methods: This study investigates the effects of coronary artery flow and varying left ventricular outflow tract (LVOT) velocity profiles on low density lipoprotein (LDL) accumulation and associated aortic valve calcification using a partitioned fluid–structure interaction framework coupled with scalar transport modeling, with a focus on understanding the differential behaviors of the three valve leaflets: the non-coronary cusp (NCC), right coronary cusp (RCC), and left coronary cusp (LCC). Four distinct LVOT flow velocity profiles (anterior, lateral, posterior, and medial) and coronary flow are simulated to determine their effects on the distribution of LDL accumulation and associated calcification across the valve leaflets. Results/Conclusions: Our results indicate that the RCC experiences greatest excursion and lowest calcification. The LCC shows lowest excursion and slightly higher susceptibility for calcification. Finally, the NCC experiences intermediate excursion, but is most prone to calcification. Full article

Lead-free chalcogenide perovskites are emerging as promising alternatives to hybrid halide perovskites due to their superior thermal stability, non-toxicity, and strong optical absorption. In this study, the photovoltaic performance of single-junction BaHfSe₃-based perovskite solar cells (PSCs) with the TCO/TiO₂/BaHfSe₃/Cu₂O/Au configuration is systematically investigated using SCAPS-1D simulations. Device optimization identifies TiO₂ and Cu₂O as suitable ETL and HTL materials, respectively. The optimized structure—TCO/TiO₂ (50 nm)/BaHfSe₃ (500 nm)/Cu₂O (100 nm)/Au—achieves a power conversion efficiency (PCE) of 24.47% under standard conditions. Simulation results reveal that device efficiency is influenced by absorber thickness and trap density. A detailed temperature-dependent study highlights that photovoltaic parameter efficiency is governed by the barrier alignment at the TCO/ETL interface. For lower TCO (Transparent Conducting Oxide) work functions (3.97–4.07 eV), PCE decreases monotonically with temperature, attributed to the increase in reverse saturation current resulting from a higher intrinsic carrier concentration. By contrast, higher TCO work functions (4.47–4.8 eV) yield an initial increase in efficiency with temperature, driven by reduced barrier height and favorable Fermi level shifts before efficiency declines at further elevated temperatures. These insights underscore the promise of BaHfSe₃ as a lead-free, environmentally robust perovskite absorber for next-generation PSCs, and highlight the critical importance of interface engineering for achieving optimal thermal and operational performance. Full article

Hybrid AC/DC microgrids (HMGs) are pivotal for integrating renewable resources, yet their stability and resilience are fundamentally constrained by the power electronic converters that interface them. This paper provides a critical review and synthesis of the co-dependent advancements in HMG converter topologies, control strategies, and fault management. Through a systematic analysis of the state of the art, this review examines the evolution from classical control to intelligent, software-defined converter functions. The analysis reveals a fundamental bifurcation in design philosophy between low-voltage (LV) and medium-voltage (MV) systems, driven by a trade-off between power density Gallium Nitride (GaN) and systemic reliability silicon carbide (SiC). Furthermore, it highlights the rise of virtualization, namely virtual Inertia control (VIC) and adaptive virtual impedance control (AVIDC), as a dominant paradigm to compensate for the physical limitations of low-inertia, resistive grids. Finally, this review identifies a critical, synergistic dependency in fault management, where ultra-fast solid-state circuit breakers (SSCBs) guarantee the survivability of vulnerable voltage source converters (VSCs), which in turn enables software-based resilience via fault ride-through (FRT). This synthesis concludes that the converter has become the intelligent nexus of the HMG and identifies the primary barriers to widespread adoption as the computational, economic, and standardization gaps in this new cyber–physical domain. Full article

Passive and active targets, both implanted with gold nanoprisms, were designed to achieve enhanced, uniform power absorption during two-sided illumination with short laser pulses. The capabilities of uniform, single-peaked Gaussian and adjusted nanoresonator number density distributions were compared. The average local E-field inside the gain medium and at the nanoprism surface was mapped as a function of the pump E-field strength and dye concentration, and the optimal parameters were selected based on the achievable local E-field. A comparative study was performed on passive and active targets to determine the most favorable distribution type and to consider the advantages of dye doping. The adjusted distribution is proposed for both passive and active targets. Dye doping is advantageous in all distributions as it results in decreasing the minimal standard deviation of the near-field enhancement (NFE), the delay of the minimal standard deviation in the power loss and deposited energy, and the standard deviation of the NFE, while increasing the FOM of the NFE in the uniform and adjusted distributions. Dye doping allows for decreasing the delay of the minimal standard deviation in the NFE, increasing the mean NFE, and decreasing the standard deviation of the power loss and deposited energy in the uniform, Gaussian, and adjusted distribution, respectively. Full article

This study presents an integrated framework that combines Geographic Information System (GIS)-based spatial analysis with the Analytic Hierarchy Process (AHP) to quantitatively evaluate the tourism and educational reuse potential of abandoned mines. Five spatial indicators—population density, slope, urban ratio, road accessibility, and proximity to tourist attractions—were normalized using logarithmic transformation and cumulative distribution functions and weighted through expert-based AHP evaluation. The framework was applied to large-scale mine datasets in the United States and South Korea, followed by in-depth case validation in the Gangwon region. The results indicate that successfully redeveloped mines consistently exhibited higher composite scores than the national averages in both countries, confirming the framework’s explanatory power. The Gangwon analysis further demonstrated that mine reuse potential extends beyond tourism to include smart mining education and environmental testbed applications. Overall, this study provides a transferable GIS–AHP model for assessing post-mining utilization and supports evidence-based, region-specific policy design for sustainable mine redevelopment. Full article

Background and Objectives: This study investigated the antioxidant, lipid-lowering, and hepatoprotective effects of two fenugreek seed varieties, Trigonella foenum-graecum (TFG) and Trigonella corniculata (TC), and analyzed their bioactive potential using various solvents, doses, and biochemical parameters. Materials and Methods: Antioxidant analyses, including ferric-reducing antioxidant power (FRAP), total phenolic content (TPC), and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assays, were conducted, and interventional studies were performed on rats divided into groups receiving disease + standard basal diet (G₀), standard basal diet only (G₁), and disease + standard basal diet supplemented with TC or TFG at 400 mg/kg/day (G₂, G₃) and 800 mg/kg/day (G₄, G₅). Biochemical blood tests assessing lipid profiles and liver function parameters, coupled with histopathological examination of the liver and heart tissues, were also performed. Results: Antioxidant assessments indicated that TFG exhibited greater free radical scavenging ability, higher total phenolic content, and stronger ferric-reducing power than TC did. In the in vivo experiments, both TFG and TC significantly enhanced lipid profiles by reducing total cholesterol, low-density lipoprotein cholesterol (LDL-c), very-low-density lipoprotein cholesterol VLDL-c, and triglycerides while boosting high-density lipoprotein cholesterol (HDL-c) levels (p < 0.001). Liver function tests indicated significant decreases in bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) levels with dose and plant effects, particularly at 800 mg/kg (G₅). Histopathological examination revealed that TFG at a dose of 800 mg/kg led to an almost normal liver structure and intact myocardial fibers with minimal inflammation, whereas TC groups displayed slight vacuolation of hepatocytes and some inflammatory responses. Conclusions: In conclusion, TFG shows the superior functional food properties of TFG in managing oxidative stress and hyperlipidemia in comparison to TC. Future studies should aim to elucidate the molecular mechanisms, optimize dosing regimens, and evaluate long-term safety and efficacy to support clinical applications. Full article

Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This becomes particularly pertinent when all models portray similar characteristics within specific subsets of the support of the random variable under scrutiny. Since the actual model is unknown in practical scenarios, the challenge of model selection becomes daunting, complicating the study of associated characteristics of the actual data generation process. To address these challenges, the concept of model averaging is embraced. Often, averaging over multiple models helps alleviate the risk of model misspecification, as different models may capture distinct aspects of the data or modeling assumptions. This enhances the robustness of the estimation process, yielding a more accurate and reasonable estimate compared to relying solely on a single model. This paper introduces two novel data-based model selection methods—one using the likelihood function and the other using the density power divergence measure. The study focuses on estimating the Value-at-Risk (VaR) for non-life insurance claim size data, providing comprehensive insights into potential losses for insurers. The performance of the proposed procedures is evaluated through Monte Carlo simulations under both uncontaminated conditions and in the presence of data contamination. Additionally, the applicability of the methods is illustrated using two real non-life insurance datasets, with the VaR values estimated at different confidence levels. Full article

The exponential growth of artificial intelligence (AI), electrified transport, and renewable generation is accelerating a structural shift in how societies produce, deliver, and consume electricity. We argue that the next frontier is not incremental efficiency but Energy Intelligence (EI): the embedding of predictive analytics, adaptive control, and material-aware design directly into power-conversion hardware. In this view, power electronics functions as the cognitive layer that links digital intelligence to the physical flow of energy. Wide-bandgap (WBG) semiconductors—gallium nitride (GaN) and silicon carbide (SiC)—provide the material foundation for higher switching frequencies, superior power density, and real-time controllability, enabling compact and efficient converters for data-centers, EV charging, and grid-interactive resources. We formalize an EI reference architecture (predictive, adaptive, material-efficient, data-driven), review the convergence of AI methods with converter design and operation, and outline a GaN/SiC-enabled data-center power path as an illustrative case. Finally, we examine sustainability and sovereignty, highlighting exposure to critical materials (Ga, Si, In, rare earths) and proposing a roadmap that integrates technology, policy, and education. By reframing power electronics as an intelligent, learning infrastructure, this work sets an agenda for systems that are not only efficient but also self-optimizing, explainable, and resilient. Full article

The development of efficient, highly stable photocatalysts is essential to address the two challenges of environmental remediation and renewable energy. Structurally strong Zeolitic Imidazolate Framework-8 (ZIF-8) has intrinsic drawbacks, including a large bandgap and fast charge-carrier recombination. This paper presents a highly efficient approach to designing the optoelectronic behaviour of ZIF-8 via controlled nickel doping. Ni(x)-ZIF-8 (0, 2.5, 5, 7.5, and 10 mol, x), and bimetallic metal–organic frameworks were prepared via a simple room-temperature process. Through adequate characterization, the incorporation of Ni²⁺ into the ZIF-8 lattice has been demonstrated to be successful, resulting in substantial structural and electronic changes. Framework integrity was confirmed using XRD and FTIR analysis, which revealed increased microstrain and the formation of Ni-N bonds. Most importantly, UV-Vis spectrophotometry and electrochemical studies indicated that the bandgap was systematically narrowed: a pristine ZIF-8 had a high bandgap of 3.65 eV, and a Ni(10)-ZIF-8 had a low bandgap of 3.23 eV, while charge-transfer resistance was reduced significantly. All these synergies led to high photocatalytic performance. The best Ni(2.5)-ZIF-8 catalyst achieved a desirable result, degrading methylene blue to more than 98.5%, which was far superior to that of the pure framework. Moreover, the hydrogen evolution reaction (HER) showed higher electrocatalytic activity, with a significantly lower overpotential and higher current density. This article defines Ni doping as an effective route to convert ZIF-8 into a high-performance, dual-functional photocatalyst. It opens the door to implementing solar-powered environmental remediation and hydrogen generation using ZIF-8. Full article

Background/Objectives: Vector-borne diseases like malaria remain a major global health concern, worsened by insecticide resistance in mosquito populations. Quinoline-based compounds have been extensively studied for their pharmacological effects, including antimalarial and larvicidal properties. Modifying quinoline structures with hydrazone groups may enhance their biological activity and physicochemical properties. This study reports the synthesis, structural characterization, and larvicidal testing of a new series of aryl hydrazones (6a–i) derived from 8-trifluoromethyl quinoline. Methods: Compounds 6a–i were prepared via condensation reactions and characterized using ¹H NMR, ¹⁹F-NMR, ¹³C NMR, and HRMS techniques. Their larvicidal activity was tested against Anopheles arabiensis. Single-crystal X-ray diffraction (XRD) was performed on compound 6d to determine its three-dimensional structure. Hirshfeld surface analysis, fingerprint plots, and interaction energy calculations (HF/3-21G) were used to examine intermolecular interactions. Quantum chemical parameters were computed using density functional theory (DFT). Molecular docking studies were performed for the synthesized compounds 6a–i against the target acetylcholinesterase from the malaria vector (6ARY). In silico ADMET properties were also calculated to evaluate the drug-likeness of all the tested compounds. Results: Compound 6a showed the highest larvicidal activity, causing significant mortality in Anopheles arabiensis larvae. Single-crystal XRD analysis of 6d revealed a monoclinic crystal system with space group P2₁/c, stabilized by N–H···N intermolecular hydrogen bonds. Hirshfeld analysis identified H···H (22.0%) and C···H (12.1%) interactions as key contributors to molecular packing. Density functional theory results indicated a favorable HOMO–LUMO energy gap, supporting molecular stability and good electronic distribution. The most active compounds, 6a and 6d, also showed strong binding interactions with the target protein 6ARY and satisfactory ADMET properties. The BOILED-Egg model is a powerful tool for predicting both blood–brain barrier (BBB) and gastrointestinal permeation by calculating the lipophilicity and polarity of the reported compounds 6a–i. Conclusions: The synthesized arylhydrazone derivatives demonstrated promising larvicidal activity. Combined crystallographic and computational studies support their structural stability and suitability for further development as eco-friendly bioactive agents in malaria vector control. Full article

Memristors, as novel one-port electronic elements, have very good memory and commutating properties, insignificant power consumption, and a good compatibility to present CMOS integrated chips. They are applicable in neural networks, memory arrays, and various electronic devices. This paper proposes a simple LTSPICE model of an adapted activation function and a neuron built on memristors. In the neuron, synaptic bonds are implemented by single memristors, allowing a decreased circuit complexity. The summing and scaling schemes are based on op-amps and memristors. The applied modified tangent-sigmoidal activation function is implemented with MOS transistors and memristors. Analyses and simulations are conducted using a simple and high-rate operating memristor model with parasitic parameters—resistance, inductance, capacitance, and small-signal DC components. Their influence on the normal operation of the memristors in the neuron is analyzed, paying attention to their usage and adjustment. The proposed memristor-based artificial neuron is analyzed in MATLAB–Simulink and LTSPICE simulators. A comparison between the derived results confirms the correct operation of the proposed memristor neuron. The generation and analyses of the suggested memristor-based neuron is a significant and promising step for the design and engineering of high-complexity neural networks and their realization in ultra-high-density integrated neural circuits and chips. Full article