Search Results (16,140)

Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article

The effect of corporate ESG performance on firm competitiveness has attracted growing attention from both regulators and market participants. Most studies explore and interpret this effect from the perspective of supply-side factors such as technological innovation; however, the role of customer-side factors remains underexplored. This exploratory study aims to theoretically and empirically analyze the mediation role of the customer-side factors in the impact of corporate ESG on market share. Based on a review of the literature, we develop a theoretical model linking corporate ESG performance to customer purchase behavior. The derived hypotheses are empirically checked using panel data of Chinese listed companies from 2009 to 2023 using two-way fixed-effect regression, three-step mediation analysis, and Sobel test. The results show that the effect of ESG performance on market share is significantly positive, and this relationship is mediated by three variables: corporate reputation, firm visibility, and market coverage. Therefore, we suggest that (i) the Chinese government should strengthen mandatory ESG disclosure requirements and enhance supervision of ESG rating agencies; (ii) corporations should substantially improve their ESG performance and enhance ESG communication capabilities; (iii) customers should pay more attention to public interest, allowing individual benefits to align with social welfare, thereby achieving a win-win outcome for both customers and corporations. Full article

The objective of this study is to evaluate the efficiency of surgical instruments’ manual cleaning versus automated cleaning in an ultrasonic cleaner for the removal of biofilms on surgical forceps contaminated with Staphylococcus epidermidis and Pseudomonas aeruginosa. Subsequently, the residual microbial load was quantified through microbiological culture, aiming to evaluate the effectiveness of biofilm removal under different reprocessing conditions. Cleaning is an essential step in the processing of surgical instruments to ensure the effective removal of dirt and microorganisms. Through adhesion, microorganisms can attach to surfaces and form biofilms, organized structures surrounded by an extracellular matrix consisting of various components, which favor metabolic exchanges, adaptation, resistance, and bacterial dispersion. These biofilms increase the pathogenic potential of microorganisms, contributing to the occurrence of Healthcare-Associated Infections, and to avoid these, it is essential that preventive measures aimed at microbial reduction are adopted. Automated cleaning proved more effective than manual cleaning, and the combined approach achieved the greatest microbial reduction, though persistent contamination was still observed. The ability of adhesion and biofilm formation on the surfaces of surgical instruments is regarded as a challenge for complete microbial removal. These findings enhance the need for more rigorous reprocessing protocols and complementary strategies to ensure greater safety in the use of reusable instruments in clinical practice. Full article

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various strategies, the covalent immobilization of biorecognition elements onto gold surfaces via thiol chemistry is one of the most widely used approaches. In this study, we report the optimization of a mixed self-assembled monolayer (SAM) composed of 11-mercaptoundecanoic acid (11-MUA) and 3-mercaptopropionic acid (3-MPA) for label-free detection of human IgG using EGOFETs. The quality of the SAM was systematically modulated by varying the total concentration from 10 to 400 mM and characterized using X-ray Photoelectron Spectroscopy (XPS), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Atomic Force Microscopy (AFM). The results revealed that a concentration of 50 mM yielded a densely packed and well-ordered monolayer. After covalent immobilization of anti-IgG antibodies via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry and subsequent blocking with ethanolamine and bovine serum albumin (BSA), the functionalized gate electrodes were integrated into poly(3-hexylthiophene) (P3HT)-based EGOFETs. Electrical measurements demonstrated that EGOFET biosensors functionalized with the 50 mM SAM achieved optimal sensing performance. The devices exhibited a highly linear response (R² = 0.998) over a wide concentration range from 1 fM to 10 nM, with a LOD of 2.82 fM, and showed excellent selectivity against non-target immunoglobulins A and M (IgA and IgM). This SAM concentration optimization strategy provides a versatile approach for engineering high-performance EGOFET biosensors, with potential applicability to a broad range of disease biomarkers. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

This paper introduces a new hybrid forecasting architecture that combines Nonlinear Autoregressive (NAR) models, the proxy Standardized Precipitation-Evapotranspiration Index (SPEI), and a Nonlinear Autoregressive with Exogenous Inputs (NARX) framework for Soil Moisture Index (SMI) prediction. The suggested methodology solves the crucial difficulty of combining future climatic knowledge into soil moisture forecasting by using a cascaded approach. Stage 1 uses univariate NAR models to create multi-step-ahead predictions of precipitation and temperature. Stage 2 converts these forecasts into proxy SPEI values using a physically based water balance computation, and Stage 3 employs a NARX model that uses observed historical SMI and forecast-derived proxy SPEI as exogenous inputs. The framework is assessed using high-frequency observations from 2014 to 2020, with training data through 2019 and validation covering the whole 2020 horizon. The study combining forecast-driven climatic indicators with autoregressive soil moisture dynamics resulted in prediction accuracy (R² = 0.9888, RMSE = 0.0827, MAE = 0.0567). This study presents a new NAR–SPEI–NARX model for SMI prediction forecasting, based on three-stage modeling, where NAR models forecast precipitation and temperature and then turn them into SPEI-proxy as an exogenous input for NARX. Full article

Biosurfactant-producing microorganisms play an important ecological role in soils impacted by hydrophobic contaminants by enhancing substrate bioavailability and influencing microbial interactions. In this study, we critically evaluated the reliability of commonly used screening methods for biosurfactant detection. A total of 71 microbial isolates (16 bacteria and 55 fungi) were obtained from vegetable oil-contaminated soil and screened using a multi-step approach combining enzymatic assays (lipolytic and hemolytic activity) and physicochemical methods, including drop-collapse, oil spreading, emulsification index (E₂₄), and surface tension reduction. Although 21 isolates exhibited lipolytic activity and 9 showed hemolysis, inconsistent responses among assays revealed significant limitations of individual screening methods. Only two bacterial isolates consistently tested positive across all criteria. When cultivated in mineral salt medium supplemented with hydrophobic substrates, both isolates produced stable emulsions and significantly reduced surface tension (from 54.26 mN/m to 31.46 mN/m). Substrate-dependent variation was observed for isolate C3, which showed reduced surface tension (39.63 mN/m) when grown with biodiesel. These findings highlight the risk of relying on single assays and emphasize the need for integrated screening strategies to ensure reliable detection of biosurfactant-producing microorganisms. Full article

Mesenchymal stem cells (MSCs) are multipotent cells that have the ability to mediate cellular repair through a combination of soluble paracrine factors, as well as bioactive cargo packaged within extracellular vesicles (EVs). Although MSC-derived EVs have been widely investigated for their regenerative potential, progress toward translational evaluation has been limited in part by challenges in scalable and reproducible manufacturing. We recently reported that human telomerase reverse transcriptase (hTERT)-immortalized MSCs reproducibly produce EVs that retain key characteristics of EVs derived from primary MSCs. Building on this work, three-dimensional (3D) culture systems have emerged as promising platforms for large-scale manufacturing. In this study, we compared the yield, molecular composition, and functional activity of EVs produced from hTERT-immortalized MSCs cultured in either a fixed-bed bioreactor or conventional two-dimensional (2D) flasks. Our data demonstrate that bioreactor culture results in increased EV yield as compared to an equivalent production from 2D cultures. Molecular analyses indicated that bioreactor-derived EVs were associated with a broader spectrum of cargo and were enriched with molecules that may contribute to enhanced reparative function. Importantly, bioreactor-derived EVs also exerted a more pronounced effect in cellular repair assays in vitro. Collectively, these results highlight the potential of fixed-bed bioreactors as scalable platforms for EV production, offering higher yields while preserving molecular composition and functional activity. This approach represents an important step toward achieving the reproducible, high-quality EV production required for research and future translational applications. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1–8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks. Full article

This study presents the application of an in-house developed safety assessment method on the scaled flight demonstrator e-Genius-Mod, which is equipped with distributed electric propulsion. Thereby, simplified aerodynamic and propulsive models are derived from existing flight test data. The safety assessment method is extended by modeling approaches for spanwise lift distribution and propeller slipstream effects on lift generation to incorporate an approximation of aero-propulsive effects. Selected failure case scenarios, namely single propulsor failures, are used to define suitable flight test scenarios as preparation for future validation of model predictions against flight test data. The application of the safety assessment method is shown to yield valuable predictions of failure effects on top-level aircraft performance and indicates that yaw moment-related failure effects are still dominant. Therefore, the effect of reducing vertical tail size on aircraft controllability and performance is examined. Model predictions indicate that propulsor failures at high thrust and low speed may exceed the yaw control authority of the aircraft, especially for the configurations with reduced vertical tail size. Furthermore, a simplified non-dimensionalised failure case depiction is presented to ease the transfer of insights to larger-scale aircraft designs and different powertrain architectures. Full article

Multi-robot systems (MRS) have attracted a lot of attention from researchers due to their widespread application in various environments. However, in multi-robot collaborative search tasks, two problems often arise: sparse rewards for capturing targets and control oscillations. To address these issues, this paper proposes the hierarchical active perception multi-agent deep deterministic policy gradient (HAP-MADDPG) framework. This framework guides robots to efficiently explore maps and discover targets through global utility planning based on global exploration rate and local information aggregation based on local exploration rate. A stability control mechanism, which includes hysteresis logic and reward decay, is introduced to suppress control oscillations. Experimental results show that the HAP-MADDPG framework achieves a success rate of 96.25% and an average search time of 216.3 steps. The path trajectories are smooth, demonstrating the effectiveness of the proposed approach. Full article

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and estimation, particularly in the presence of uncertainty in structural systems. Flexible structures in aerospace and robotics require advanced control to mitigate vibrations under model uncertainty. This paper proposes a data-driven strategy leveraging a Gaussian Process (GP) integrated within a Nonlinear Model Predictive Control (NMPC) framework. The core innovation lies in using a Gaussian Process Nonlinear AutoRegressive model with eXogenous input (GP-NARX) as a probabilistic predictor to capture structural dynamics while quantifying uncertainty. The operational mechanism involves a tight coupling where the GP provides multi-step-ahead forecasts that the NMPC optimizer uses to minimize a cost function subject to constraints. Validated through simulations on Duffing oscillators, linear oscillators, and cantilever beams, the GP-NMPC achieved an 88.2% reduction in displacement amplitude compared to uncontrolled systems. Quantitative analysis shows high predictive accuracy, with a Root Mean Square Error (RMSE) of 0.0031 and a Standardized Mean-Squared Error (SMSE) below 0.05. Furthermore, Mean Standardized Log Loss (MSLL) evaluations confirm the reliability of the predictive uncertainty within the control loop. These results demonstrate strong performance in both regulation and tracking tasks, justifying this Bayesian-predictive coupling as a powerful approach for high-performance structural vibration control and a potential foundation for bio-inspired mechanical design. Full article

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy. Full article