Search Results (3,630)

Lending to small and medium-sized enterprises (SMEs) on the basis of their environmental, social, and governance (ESG) performance is difficult for commercial banks. The data needed for a proper assessment are rarely at hand, and existing tools cannot capture experts’ fuzziness, complex hesitation, and preference characteristics at the same time. This paper introduces the probabilistic picture hesitant fuzzy set (PPHFS) into SME ESG evaluation and develops a multi-attribute group decision-making (MAGDM) method that rests on the correlation coefficient of PPHFSs. The method has three components. It first defines the information energy of PPHFSs, builds a correlation measure on that foundation, and then derives several correlation coefficients together with their weighted forms. A separate procedure converts the multi-dimensional voting information that evaluators provide through questionnaires and balloting into probabilistic picture hesitant fuzzy information, which keeps the approach close to practice. These components are assembled into a complete MAGDM framework for banks and illustrated on a numerical case of five SME loan applicants. Sensitivity analysis and comparative experiments corroborate the feasibility and effectiveness of the method. By aligning bank credit decisions with ESG screening, the framework supports the extension of green credit to SMEs and contributes to sustainable finance and the Sustainable Development Goals (SDGs). Full article

Non-Saccharomyces yeasts shape the aroma, freshness, and reduced-alcohol profiles of wines, yet their oenological contribution is curtailed by a poor tolerance to ethanol. Because ethanol acts on the plasma membrane, the cell’s selective barrier, this limitation is rooted in the membrane lipidome, whose remodeling has been documented but never integrated into a wine-specific, predictive framework. This review provides that synthesis, keeping the non-Saccharomyces wine yeasts as the subject and Saccharomyces cerevisiae as a reference frame. It first describes how ethanol perturbs the bilayer and how the cell compensates through unsaturated fatty acids, ergosterol, phospholipid-class balance, and membrane fluidity. It then traces the concentration- and time-dependent remodeling of the lipidome, together with the medium-chain fatty-acid, temperature, and oxygen co-stressors accompanying it, and the comparative membrane biology distinguishing these yeasts from S. cerevisiae, including the markedly reduced uptake of exogenous sterols under oxygen limitation. Finally, it weighs the nutritional, process, and non-genetically modified strain-improvement strategies that reinforce them. The same lipid descriptors recur throughout, yet are seldom measured in improved wine strains. We argue that the membrane lipidome can be read as a candidate multivariate predictive signature of ethanol tolerance and of fermentation performance, and outline the validation it still requires. Full article

Background: People living with HIV (PLWH) are at increased risk of cardiovascular disease (CVD). Aortic stiffness measured with carotid-femoral pulse wave velocity (PWV) is a validated predictor of CVD and has been shown to be elevated in PLWH at HIV diagnosis. Objectives: To prospectively evaluate the effect of 9 months of contemporary antiretroviral therapy (ART) on aortic stiffness in newly diagnosed PLWH. Methods: A group of ART-naïve PLWH with modest cardiovascular risk and without known CVD were recruited at the time of HIV diagnosis. Aortic stiffness was measured using carotid-femoral PWV (ViCorder, Skidmore Medical, UK) before ART initiation and after 9 months on treatment. Results: Seventy-three participants were analysed (mean age 32 ± 7 years; 45% female). PWV decreased significantly after 9 months of ART (5.93 ± 1.0 vs. 5.69 ± 1.0 m/s; p = 0.01) with increased aortic distensibility (0.36 [IQR 0.30 to 0.46] vs. 0.40 [IQR 0.34 to 0.50] mmHg⁻¹; p = 0.02). The reduction in PWV remained significant after adjustment for covariates (mean change −0.26 m/s; 95% CI −0.07 to −0.45; p = 0.01). Conclusions: Aortic stiffness improved in a group of newly diagnosed PLWH after ART initiation at 9 months, in keeping with a modifiable component of HIV-associated vascular dysfunction. The findings are consistent with reversible functional vascular abnormality, potentially mediated by changes in vasomotor tone, although it is unknown if this effect will be sustained long-term. The trajectory of aortic stiffness in PLWH and the mechanisms that underpin vasomotor tone require further study. Full article

Deboarding sits on the aircraft turnaround critical path, yet it is usually treated as boarding run in reverse, with schedules validated for boarding assumed to keep their merit when the flow direction changes. This study compares five conventional schedules in both directions under one cabin geometry, in a three-dimensional Airbus A320 economy-cabin simulation at three passenger loads with ten replications per scenario. The ordering is reversed, as the mirror intuition suggests: Back-to-Front is fastest to board at full load and slowest to deboard. Two results are less expected. First, schedule choice loses most of its leverage when the process reverses: the full-load spread is 26 s against 121 s on the boarding side, and a decisive boarding advantage collapses into a statistical tie in the deboarding direction. Second, what survives is not duration but its distribution. At full load, mean seat-to-door time is 40% longer under Back-to-Front than under Front-to-Back, and the last-served passenger waits 11.9 min against 9.0, differences that persist in proportion when compliance is only partial. Deboarding schedule choice is therefore a service-quality instrument rather than a turnaround-duration one, and schedules must be evaluated in the direction the cabin process actually runs. Full article

To address sustained deviation from coverage lanes when an unmanned surface vehicle (USV) executes a complete boustrophedon coverage path under current disturbances, this study proposes an adaptive current-aware line-of-sight (ACA-LOS) guidance method based on online current estimation. The local current is estimated online from the kinematic velocity relationship between the USV and the surrounding water, and its path-normal component is extracted. The estimated path-normal current is used both to construct a current-compensating heading correction and, together with the cross-track error, to adjust the LOS lookahead distance and generate the desired ACA-LOS heading. Complete-path simulations are conducted under constant, time-varying, and spatially varying currents with different current speeds and directions. Relative to conventional LOS, ACA-LOS reduces the mean cross-track root mean square error (RMSE) by 40.80%, increases the tolerance-band compliance rate ($R_b$) by 27.48 percentage points, and reduces the cumulative cross-track deviation area ($A_{\mathrm{d}}$) by 68.68%. ACA-LOS suppresses sustained lane deviation caused by current disturbances and improves lane keeping throughout complete boustrophedon path execution. The method supports the extension of USV operation from single-trajectory tracking to long-duration, continuous coverage execution and provides a reference for unmanned and intelligent waterborne operational equipment. Full article

Blockchain consensus still forces a hard choice among scalability, security, and energy use. Redbelly, a leaderless Byzantine Fault Tolerance protocol, largely settles the scalability-versus-energy side of that tension, yet it accepts any cryptographically valid transaction without judging whether it is economically fraudulent or whether the node behind it is misbehaving. That blind spot is what we target. We present ML-Redbelly, a formally specified extension that attaches four learning components to the Redbelly pipeline: a LightGBM gradient-boosted fraud classifier, an Isolation Forest behavioural anomaly detector, a tabular Q-Learning agent for adaptive committee selection, and a Paillier-based federated learning aggregator that keeps model updates private. We prove that this layer leaves Redbelly’s safety and liveness intact, give pseudocode and complexity bounds for every component, and measure the system on the IEEE-CIS Fraud Detection benchmark (400,000 transactions) paired with a faithful discrete-event Redbelly simulator parameterised from measured inputs and validated against the published Redbelly deployment. LightGBM reaches an F1 of 0.783 (precision 0.858, recall 0.719, AUROC 0.963), a 34 percent relative F1 gain over the conference-baseline Random Forest at five times the inference speed. The Isolation Forest detector attains recall 0.885 at a false-positive rate of 0.047, and the Q-Learning agent settles into a stable policy within about 200 rounds across normal, bursty, and Byzantine-attack conditions. End to end, the framework sustains 48,844 TPS on 32 validators (mean over 30 seeds), and because the leaderless superblock commits every proposer’s block in parallel, this throughput advantage over leader-based BFT grows with the validator count (5.0 times PBFT and 2.9 times HotStuff at 32 validators). The learning layer costs only about 4 percent in throughput, since the measured ML inference is small next to the geo-distributed consensus round. Per-transaction energy is comparable across BFT protocols, being dominated by signature verification, and is orders of magnitude below proof-of-work chains, which expend energy on mining. One federated update epoch takes 36 s across 10 nodes with 2048-bit Paillier keys and reconstructs gradients with negligible error. All performance figures are emergent outputs of the discrete-event simulation, which reproduces the published Redbelly benchmark to within a conservative factor of about 1.7. Taken together, these results outline a simulation-validated design for making consensus intelligent as well as fast and identify the steps needed toward real-cluster deployment. Full article

The direct stator connection of a doubly fed induction generator (DFIG) makes it highly sensitive to grid-voltage disturbances. Although a supercapacitor-supported dynamic voltage restorer (SC-DVR) can restore the machine-terminal voltage, assigning the entire fault-induced power imbalance to the supercapacitor increases the required storage capacity. To address this problem, an adaptive coordinated fault ride-through control strategy is proposed for a DFIG–SC-DVR wind–storage system. A unified dynamic model is first established to describe the electromagnetic response, dc-link energy imbalance, rotor kinetic-energy exchange, and series voltage compensation under symmetrical and asymmetrical voltage sags. The remaining adjustable kinetic-energy margin of the rotor and the remaining adjustable energy margin of the supercapacitor are then used to construct an online coordination coefficient. Accordingly, the active-power references of the DFIG and SC-DVR are dynamically adjusted to match their instantaneous regulation capabilities. Time-domain simulations show that the proposed strategy maintains the machine-terminal voltage and keeps the rotor current, dc-link voltage, and rotor speed within prescribed limits. Compared with non-coordinated control, it reduces the SC-DVR absorbed energy by 51.43% and 57.72% under symmetrical and asymmetrical faults, respectively; compared with fixed-ratio coordination, the corresponding reductions are 34.69% and 29.81%. Supplementary sweeps over wind speeds of 10–12 m/s and short-circuit ratios of 2–10 further verify the bounded applicability of the method without retuning the controller. Full article

The increasing number of Internet of Things (IoT) devices in smart cities creates several challenges of trust management, resource allocation, and secure offloading. This paper introduces BTM-IoT, a blockchain-based trust management solution to improve the multi-access IoT offloading networks in urban areas. The proposed framework employs a hybrid consensus system that combines Delegated Proof-of-Stake (DPoS) with Practical Byzantine Fault Tolerance (PBFT) to make offloading decisions efficiently and securely while keeping the latency and throughput low. A trust-aware offloading optimization model is proposed, which combines the direct and indirect trust evaluation to increase the accuracy of the decisions and attack resistance. Our extensive experiments show that the average reduction in energy consumption of BTM-IoT is 31% over the baseline models, whereas the average reduction in task completion time comes to 27%, and the average improvement in attack detection accuracy is 43%. Further, scalability tests show that it takes only 26% longer to complete when scaling up from 50 to 500 devices, a significant improvement over traditional method. It also provides optimal use of resources, such as CPU, memory, and bandwidth, while delivering an average of 20% improvement in efficiency. Even when analyzed from the overhead of the blockchain, it is light on the IOT device, requiring 0.5 MB of storage and 2% CPU usage. The study results confirm the energy efficiency, scalability and security of BTM-IoT as a solution for multi-access IoT offloading in smart city infrastructures. Full article

This contribution presents a methodology for optimal placement (OP) of meters in power systems, using the state-space reference frame. The goal is to minimize the state estimation error, specifically, the mean squared error (MSE), through OP of a limited number of measurement devices and keep the total observability of the system. The measurement set is applied to the time-domain state estimation based on the Kalman filter (KF) to obtain voltage and current waveforms in real time, and the harmonic content is evaluated through the application of the Fast Fourier Transform (FFT). The effectiveness of harmonic state estimation (HSE) is demonstrated in case studies considering different operating points in a test electrical network, particularly in estimating the dynamic behavior of nonlinear electrical loads. The HSE method is implemented in physical tests using Lab-Volt^® equipment to monitor the system in real time using MATLAB/Simulink^® software through the RL-LAB^® platform; the real-time experimental tests (RTE) allow validation of the real-time digital simulation (RTS). Full article

A smart tensioning system with a controllable elastomer was implemented in TLP-TAD hawser lines to mitigate an excessive increase in hawser tension. The applied elastomer material can be softened by applying an electrical current. Simulations with traditional hawsers showed that snap loadings existed, and they were caused by out-of-sync motions between TLP and TAD. Frequency-domain two-body hydrodynamic analysis was first carried out to obtain hydrodynamic data. Then, fully coupled time-domain simulations were carried out using the in-house program CHARM3D. A rigidity-tuning conductive elastomer (RCE) tensioner was sized considering available data and manufacturability. A control scheme that imitates an ABS (anti-lock braking system) using repeated grab and release was developed. The control scheme repeatedly alternates high and low elastomer stiffnesses with variable electric currents to keep the hawser-snap loading below the target. The control scheme was incorporated into time-domain simulations. The base case with fairlead hawser connections showed a strong correlation in hawser tension and TLP-TAD relative surge. The implementation of the RCE tensioner almost halved the hawser maximum tension while maintaining the allowable maximum tensioner stroke. The present research shows that the use of a smart material with proper control can significantly lower maximum hawser tensions between adjacent floating platforms. Two major innovations include the use of a smart elastomer and an ABS-like control method to reduce the maximum tension of the hawser/cable. Full article

Xylella fastidiosa (Xf) is among the most devastating phytosanitary threats to Mediterranean agriculture, causing Olive Quick Decline Syndrome (OQDS). Since containment efficacy depends on timely intervention, scalable in-field screening tools are needed. This study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling. Three replicate acquisitions were performed on 130 olive trees (390 measurements) across four cultivars (Cellina di Nardò, Ogliarola Salentina, Ogliarola Barese, and Leccino) in four Italian regions (Apulia, Calabria, Lazio, and Tuscany). Plant status was assigned from the official status of the sampling area (demarcated OQDS focus versus Xf-free area) and supported by real-time quantitative PCR (qPCR) on every plant; within demarcated sites, plants with undetectable DNA in sampled twigs were retained as Xf+ following phytosanitary criteria, giving 216 infected and 174 healthy samples. The multidimensional sensor signals were processed with an optimized Shallow Neural Network. Under plant-grouped 80/20 validation, keeping each plant’s replicates in the same subset, the model achieved (93.3 ± 4.0)% accuracy, (97.7 ± 3.5)% sensitivity, and (88.2 ± 8.3)% specificity (mean ± SD). A feature-importance analysis revealed a reproducible, though not chemically resolved, VOC-related response pattern. This low-cost, portable Internet of Things (IoT) device offers a proof-of-concept screening approach for Xf surveillance, pending plant-level and external validation. Full article

A collision-free path is not sufficient for visibility-dependent mobile robot tasks: a moving obstacle can block the camera–target line of sight and cause inspection or visual-servoing failure even when the robot remains physically safe. Maintaining visual contact with targets is therefore important in Industry 4.0 environments, yet visibility-preserving maneuvers can conflict with navigation progress and collision avoidance. This work presents the Visibility-Informed Safety and Target Awareness framework with control barrier function filtering and occlusion-evasive local replanning (VISTA-CBF+ELR). The architecture combines visibility-risk planning, target-bearing control, an ELR supervisor, and a CBF quadratic program that keeps collision constraints hard while relaxing field-of-view and occlusion requirements through slack. Counterproductive interventions are limited through persistence, benefit–cost and feasibility gates, progress protection, bounded dwell, recovery, and cooldown. In locked factory simulations, redesigned VISTA achieved 67% and 73% strict-goal success under clean and nominal sensing, whereas Visibility-CEM-2D achieved 87% and 86% but with lower clearance. In matched Gazebo trials, strict success was 19/30 for redesigned VISTA, 26/30 without ELR, and 16/30 for Nav2 Smac+MPPI; zero-clearance collisions were 8/30, 3/30, and 14/30, with no difference surviving multiplicity correction. A separate CEM stress test sustained 6.875 Hz optimization, missed 26.31% of 100 ms deadlines, and held commands on 31.35% of ticks. The results demonstrate repair of the ELR pathology and conditional visibility-risk reduction while exposing safety–visibility trade-offs, transfer limitations, and real-time constraints. Full article

Bluetooth Low Energy (BLE) resolvable private addresses (RPAs) reduce passive tracking, but RPA-resolving creates receiver-side work: a scanner whose bonded identity set exceeds controller resolving-list capacity which can be forced into repeated host-side Identity Resolving Key (IRK) searches by floods of syntactically valid unknown private-address candidates. We formulate this as capacity-constrained resolving scheduling and propose P3, our persistence-aware admission-control scheduler for BLE RPA resolution, evaluated in its main form P3-Persist. After cache and resolving-list fast paths miss and the unresolved-work budget is exhausted, P3 grants a small reserve only to over-the-air RPA values that reappear. This uses visible repetition rather than an identity-correlated pre-resolution label. In paired simulations, identical visible traces produced identical initial pre-resolution decisions, but later modeled delay/defer features were distinguishable in some tested conditions; accordingly, no end-to-end side-channel-free claim is made. In the evaluated 20-seed, 1800 s simulation matrix (N = 512, RL = 8), P3-Persist keeps modeled unique-flood work in the same bounded approximately 45k AES-equivalent/min regime as budget-only limiting while increasing modeled legitimate resolution to 0.9569 under medium flood and 0.9557 under heavy flood, compared to 0.57–0.62 for budget-only baselines. A 20-seed epoch–loss–density experiment shows that service recovery is conditional rather than universal, and a denser adaptive-address search identifies an overlapping near-cap region around m = 320 and r = 1, with a highest confirmed mean of 146,196.29 AES-equivalent/min (96.69% of the modeled cap). These results establish bounded-work and conditional service-recovery behavior within the simulator; they do not establish production-firmware effectiveness, end-to-end side-channel freedom, on-device timing, energy, current, latency, or queueing behavior. Full article

The relevance of this study stems from the need for a rational allocation of limited financial resources under conditions of intense economic confrontation. The central research question is as follows: how can resources be allocated optimally and zones of guaranteed survival be mathematically justified in a continuous conflict characterized by bilinear dynamics and nonmeasurable shock controls? To address this question, we propose a new computational approach based on a differential game of quality. Unlike existing models focused primarily on abstract equilibrium analysis, the present study applies a limit-transition method for multistep positional strategies to a macroeconomic survival problem. The computational experiments explicitly construct and visualize the preference regions, i.e., the guaranteed-survival regions, of the opposing parties. Sensitivity analysis reveals an important pattern: a linear increase in the parameter $\mathsf{\alpha }$ by 10% expands the system’s survival region by 14%. In addition, the proposed semi-implicit method reduces the computational time by 35–40% while preserving numerical stability. The practical significance of this study lies in establishing an algorithmic foundation for decision support systems (DSS) that can model the boundaries of economic resilience and synthesize optimal strategies for keeping the system within a safe region. Full article

Frequent heat-source drift and environmental disturbances keep hydrogen compressors in a state of thermal non-equilibrium, where transient interactions between thermal transpiration and Poiseuille flows significantly influence system stability and safety. The mechanisms by which time-dependent temperature patterns modulate this coupling remain insufficiently understood. This research addresses this knowledge gap by numerically solving slip-boundary Navier–Stokes equations for six periodic temperature waveforms: rectangular, segmented, square, Gaussian, triangular, and sinusoidal. The results indicate that the heating rate predominantly determines the intensity of forward Poiseuille flow, whereas cooling rate and plateau duration exert comparatively minor effects. Extending the high-temperature plateau from 0.1 to 0.5 s increases both thermal transpiration and backward Poiseuille peaks by more than 100 percent, while slower heating reduces forward Poiseuille peaks by up to 11 percent. Among the six waveforms, the square wave yields the highest peaks for all three flow components. These findings elucidate how waveform structures modulate transient flow responses and offer quantitative guidance for stability assessment and thermal management in hydrogen Knudsen compressors. Full article