Search Results (533)

Introduction: Cognitive dysfunction (“brain fog”) is a common manifestation of post-acute COVID-19 syndrome (PACS) and may persist long after the acute infection. While cross-sectional studies have described cognitive deficits, longitudinal evidence on recovery trajectories remains limited. Methods: We conducted a longitudinal observational study of neurocognitive performance and neuropsychiatric symptoms in patients with PACS. Participants underwent assessment with 20 standardized tests covering five cognitive domains (memory, attention, language, executive functions, psychomotor processing speed); anxiety, depression, and sleep quality were assessed at three time points. Changes were analysed using the Friedman test. Results: Forty-two patients were included (median age 57 years; 35.7% female) from a predominantly hospitalized cohort (81% hospitalised; 66.7% requiring respiratory support). Patients who completed all three assessments (completers, n = 42) were compared with those who attended the first evaluation but did not complete follow-up (non-completers, n = 544); completers were more severely ill during the acute phase rather than healthier or more motivated. At the group level, statistically significant improvements over time were observed across the whole sample in verbal short-term learning, visuospatial memory, working memory, constructional praxis, phonological verbal fluency, and psychomotor processing speed (all p ≤ 0.05); after Benjamini–Hochberg adjustment across the twenty cognitive outcomes, visuospatial span forward and backward and psychomotor processing speed remained significant (all FDR-adjusted p ≤ 0.013), with the change confined to the first six months. Sleep quality also improved (p < 0.0001). Conclusion: In this cohort, group-level performance improved in six of the twenty tests administered, of which three remained significant after correction for multiple comparisons, while 28 of 42 patients (66.7%) still scored in the impaired range on at least one test at 12 months, and 17 (40.5%) on two or more. These findings highlight the importance of long-term neuropsychological monitoring and integrated cognitive-psychiatric evaluation in post-COVID care. Given the small, predominantly hospitalized sample, improvements should be interpreted cautiously and confirmed in larger controlled studies, although the use of alternate forms for part of the battery makes task-specific learning an incomplete explanation. Full article

We set out to fix a “contamination” problem in reconstruction-based Temporal Convolutional Network VAEs (TCN-VAEs) for cross-dataset IoT flow anomaly detection: when attack flows share an encoder window with benign flows, the shared latent code is allegedly distorted, inflating benign reconstruction error and producing false positive rates (FPRs) of 22–65% despite an ROC-AUC above 0.93. Our proposed fix, TCN-Pred, excludes the target flow from the encoder and scores it by next-flow prediction error, reducing FPR to 0.65–13%. We subjected this causal explanation to a battery of controlled ablations, holding architecture, decoder, loss, and thresholding fixed while varying one factor at a time. Each one falsified the original hypothesis: target inclusion/masking changes FPR by at most 0.001; context shuffling/reversing/zeroing changes it by at most 0.003; a context-blind constant-output predictor matches TCN-Pred’s FPR and F1 to three decimal places on all three datasets. The actual cause, confirmed on the original trained models with no retraining, is a scoring-granularity mismatch: the TCN-VAE threshold is calibrated from per-window errors averaged over 20 flows but applied to per-flow errors at evaluation (standard deviation $\approx \sqrt{20}\times$ higher, measured ratio 4.46 against a predicted 4.47). Recalibrating the identical model at matching granularity drops FPR from 22.7/47.6/64.6% to 0.65/5.0/12.5% on BoT-IoT, IoT-23 and ToN-IoT, closing 89–97% of the reported FPR gap without changing a single model weight. We report this diagnostic chain, together with an attack-prevalence sensitivity analysis, sample-disjoint calibration, normality diagnostics, and label-free and redundancy-aware (mRMR) feature-selection benchmarks, as a methodology other work should apply before attributing fixed-threshold performance to architecture. The pipeline is supervised source-domain feature selection followed by benign-only detector training, not fully unsupervised, a distinction we quantify later in the paper. Investigating dataset representativeness, we found that all three provided files reduce to only ≈6000 genuinely distinct flows via an undocumented row-duplication procedure, causing 97.8% BoT-IoT train/test near-duplicate overlap; a leakage-free re-evaluation changes FPR by only 0.23 percentage points. We also found that the TLS-metadata columns are already transformed upstream of every available artefact, so the proportion of genuinely TLS-encrypted flows cannot be recovered, and we soften the paper’s encrypted-traffic framing accordingly. Full article

The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions. Full article

Battery packs for new energy vehicles have evolved from simple load-bearing and protective assemblies into multifunctional safety structures integrating structural support, crash protection, thermal-runaway mitigation, flame retardancy, electrical insulation, electromagnetic interference shielding, waterproof sealing, and long-term reliability. Fiber-reinforced polymer composites are promising for upper covers, underbody shields, trays, cross beams, side frames, and local protective structures because of their low density, corrosion resistance, design flexibility, and functional-integration potential. However, composite-part performance is strongly governed by forming. Resin flow, impregnation, curing or cooling shrinkage, fiber orientation, filler dispersion, and interfacial bonding may induce voids, dry spots, resin-rich regions, delamination, warpage, and fiber waviness, thereby affecting load bearing, sealing, thermal protection, and durability. This review focuses on composite-forming technologies for new energy-vehicle battery packs. It summarizes component-level service requirements and material systems and compares representative forming routes, including sheet molding compound (SMC), prepreg compression molding/wet compression molding (PCM/WCM), resin transfer molding/high-pressure resin transfer molding (RTM/HP-RTM), vacuum-assisted resin transfer molding (VARTM), long-fiber thermoplastic direct processing (LFT-D), glass-mat thermoplastic (GMT), thermoplastic sheet forming, pultrusion, and multi-material joining. These routes are evaluated from six dimensions: material form, forming cycle, typical defects, representative mechanical performance, applicable components, and engineering maturity. The review further discusses defect mechanisms, performance effects, detection and control methods, and the roles of in-line monitoring, non-destructive testing, process simulation, machine learning, and digital twins in closed-loop quality manufacturing. Finally, engineering challenges are examined in multi-material joining, thermal-safety integration, low-carbon recycling, and standard certification. Composite-material battery-pack structures should therefore be developed as coordinated design and closed-loop manufacturing systems linking materials, processes, defects, performance, and validation. Full article

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman $\rho =-0.25$ with prediction error, $p=0.006$) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at $F_1=0.70$ with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to $F_1=0.78$ with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator $\kappa =0.74$ on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

Background: Neurocognitive testing in neuro-oncological patients often relies on time-intensive batteries with limited feasibility in this patient cohort. We evaluated the feasibility of the Bonn Leistungs Tracking Test (BLTT), a time-efficient screening tool assessing episodic memory, semantic memory, and executive functions in patients with high-grade temporal lobe gliomas. Methods: Twenty patients undergoing resection of temporal high-grade glioma between 2019 and 2022 underwent preoperative cognitive assessment using either the BLTT or a comprehensive neuropsychological battery routinely applied in temporal lobe epilepsy surgery. Results: Twelve patients (60%) underwent the BLTT, and eight (40%) underwent the standard neuropsychological test battery. The BLTT was feasible in all 12 individuals (0% drop-out), whereas only four of eight patients (50% drop-out) were able to complete the standard battery. The mean BLTT total score was 69.3 (SD 4.5), indicating impaired neurocognitive performance relative to normative data. The BLTT provided evaluable domain-specific measures across episodic memory, semantic memory, and executive functioning despite the high discontinuation rate observed with standard testing. Conclusions: The BLTT showed high feasibility for preoperative neurocognitive testing in patients with temporal high-grade gliomas. Its brief administration time and consistent applicability across relevant cognitive domains support its utility for routine neurocognitive assessment. Full article

This paper proposes a stochastic dispatch framework for multi-terminal high-voltage direct current (MT-HVDC) systems that explicitly accounts for uncertainty in photovoltaic (PV) generation and electrical demand while preserving computational tractability. The economic–environmental dispatch problem is formulated as a mixed-integer second-order cone programming (MI-SOCP) model, where the SOCP relaxation provides a convex representation of the network constraints, and the mixed-integer component captures the discrete charging/discharging states of battery energy storage systems (BESS). This formulation ensures that, for any fixed set of binary decisions, the remaining problem reduces to a standard convex SOCP, enabling efficient solution via branch-and-bound methods with tight continuous relaxations. Uncertainty is incorporated through a chance-constrained optimization (CCP) approach, where forecast errors are modeled using bounded truncated distributions and reformulated into deterministic convex constraints via quantile-based approximations, yielding a risk-aware dispatch strategy that avoids optimistic bias. Numerical studies on an 11-bus MT-HVDC test system demonstrate that accounting for uncertainty increases total operating costs by up to 31.87% and CO₂ emissions by up to 37.41% when both PV and demand uncertainties are considered simultaneously at a confidence level of 0.9, compared to the deterministic solution. Power demand uncertainty has a substantially greater impact than PV generation uncertainty, leading to cost increases of 28.09% versus 3.85% at the highest confidence level. Validation on a modified IEEE 24-bus MT-HVDC system confirms the scalability and computational efficiency of the proposed approach, achieving global optimality in a pure solver time of 1.34 s with a maximum relative relaxation gap of $5.81\times {10}^{-9}$, demonstrating numerical exactness and suitability for day-ahead scheduling. The results also highlight the critical role of BESSs in providing operational flexibility, with storage strategies differing significantly under uncertainty during early hours while converging to deterministic behavior later. The findings reveal a clear trade-off between economic performance and operational reliability as the confidence level increases, confirming the effectiveness of the proposed approach for integrating renewables and storage in modern HVDC grids, while also identifying important limitations regarding independence assumptions and scalability to larger systems. Full article

Physical ability testing is an essential part of police education, as fitness assessments indicate students’ preparedness for police work. Clear documentation and reporting of test protocols and measurement properties are essential for interpreting, evaluating, and comparing the usefulness of different test batteries. Therefore, this scoping review aimed to map the fitness tests used in police education programs internationally, including reporting standards of test protocols, reliability, validity, and sex-based test specificity, thereby providing insight into the physical abilities considered important for police work. The main searches were conducted in the databases CINAHL, PubMed, SPORTDiscus, and Web of Science. The database search yielded 1323 articles, of which 92 met the inclusion criteria. An additional nine articles were included through citation and hand searching. Of the 101 studies, 95 used an average of 4–5 general fitness tests, and 29 included work-related police tests. Test protocols were generally well documented across studies. However, only a few studies included in this review assessed test–retest reliability, and only a limited number of studies, whose primary aim was to validate the tests, evaluated test validity. The most commonly used assessments were upper-body and core muscular endurance (60 s push-up, maximum pull-up, and 60 s sit-up tests), upper-body muscular strength (hand-grip test), lower-body power (standing long jump, and vertical jump tests), and aerobic capacity (20 m multistage fitness, 2400 m Cooper, and 12 min Cooper tests. Only 19 studies reported sex-specific performance standards, including differences in test execution, scoring systems, VO_2max estimation, or time limits. The findings indicate that tests of upper-body muscular endurance and strength, lower-body power, and aerobic capacity are among the most frequently measured physical abilities in police education programs internationally, while also highlighting the need for more standardized reporting of test protocols, reliability, and validity. Full article

This paper presents a generic stochastic simulation framework for evaluating the operational feasibility of solar-powered unmanned aerial vehicles (UAVs) executing an invariant trajectory in high-variability climates. Unlike conventional approaches relying on idealised irradiance conditions, the proposed framework combines a modified ASHRAE radiation model corrected for local bias and variability with a semi-Markov process modelling stochastic transitions between cloud and sunlight states using parametrised state duration times. The environmental model is further extended with diurnal temperature variation and standard atmosphere effects. UAV motion is represented using a rigid body flight dynamics model combined with a cascaded trajectory tracking controller and an energy subsystem incorporating a lithium-ion battery model. Warsaw (Dfb climate) is used as a representative Central European test case characterised by frequent radiation deficits and highly variable atmospheric conditions. The simulations quantify the influence of environmental uncertainty and selected battery capacities on mission success probability across different solar-to-wing area ratios, with the mission entry at 70% initial battery state of charge and no additional manoeuvre losses or external atmospheric perturbations. The evaluations were conducted for a fixed mission start at solar noon on 15 July and were supplemented by an optimised mission scheduling analysis to establish upper flight-time limits. The results demonstrate the strong sensitivity of solar-assisted UAV operations to stochastic cloud conditions and support the design and mission planning for low-altitude long-endurance aircraft. Full article

Vehicle-to-everything (V2X) information can support energy-efficient vehicle operation in signalized traffic. This paper presents a sequential hierarchical eco-driving strategy for fuel-cell hybrid electric vehicles (FCHEVs) traveling through multiple signalized intersections under prescribed signal-phase and road-speed settings. The upper layer employs an ${\mathrm{A}}^{\ast }$-inspired adaptive heuristic graph search in a discretized time–distance graph. A dimensionless motor-power-demand-based numerical ranking score, evaluated at the average speed of each candidate edge under a zero-acceleration edge approximation, introduces approximate powertrain-load information into node ranking. This score follows the supplied numerical implementation and is neither edge-integrated energy nor equivalent hydrogen cost; therefore, the upper-layer procedure is not claimed to inherit the admissibility, optimality, or bounded-suboptimality guarantees of standard ${\mathrm{A}}^{\ast }$ or weighted ${\mathrm{A}}^{\ast }$. The lower layer uses a twin delayed deep deterministic policy gradient (TD3)-based energy management strategy that penalizes raw equivalent hydrogen cost, SOC deviation, fuel-cell degradation increments, and battery degradation increments. The reported simulations show a lower raw equivalent hydrogen cost than the baseline strategy in the tested scenarios, while terminal-SOC correction reveals scenario-dependent trade-offs among corrected equivalent hydrogen cost, SOC regulation, and model-based component degradation indicators. The results support a practical multi-objective balance under the reported deterministic simulation settings rather than uniform superiority in every individual indicator. Full article

The article addresses the problem of estimating the specific electric energy consumption of urban electric transport under real operating conditions. It is substantiated that standardized driving cycles and rated energy consumption values do not always accurately reflect the actual operating modes of vehicles on urban routes, since energy consumption is affected by speed conditions, road conditions, passenger load, ambient temperature, auxiliary systems operation, and the number of stops, accelerations, and braking events. A simplified engineering model is proposed for adjusting the baseline specific electric energy consumption by means of a system of correction factors, which makes it possible to adapt the calculation to specific operating conditions under limited availability of telematics data. A distinctive feature of the proposed approach is the possibility of using a baseline energy consumption value determined from a driving cycle or vehicle specification data, followed by its adjustment according to the characteristics of an actual route. The proposed methodology was experimentally verified using certified trolleybus test data representing a vehicle with characteristics similar to a 12 m urban battery electric bus; however, further validation using dedicated battery electric bus datasets is required. For the reference vehicle operating on route No. 15 in Lutsk, with a route length of 10.2 km, the calculated electric energy consumption was 17.853 kWh at full mass and 12.498 kWh at curb mass, corresponding to approximately 1.75 and 1.23 kWh/km, respectively. The results were compared with experimental data and recent literature sources. The proposed methodology is intended for preliminary engineering assessment of electric energy consumption when detailed operational data are unavailable. Full article

Second-life electric vehicle (EV) batteries are increasingly recognized as valuable resources for stationary energy storage. However, the heterogeneous degradation of retired batteries makes reliable and application-oriented reuse decisions challenging. Existing studies primarily focus on battery health estimation or degradation characterization, while limited attention has been given to systematically translating experimentally measured health indicators into practical second-life deployment decisions. To address this gap, this study proposes an experimental multi-metric battery health assessment and decision-support framework for application-oriented screening and reuse pathway allocation of retired EV batteries. A total of 91 s life lithium-ion battery cells were experimentally characterized through standardized laboratory charge–discharge testing. Multiple complementary health indicators, including State of Health (SoH), discharge capacity, round-trip energy efficiency, and voltage–current time-series characteristics, were extracted and statistically analyzed to evaluate residual battery performance and degradation behavior. The experimental results reveal substantial variability among retired batteries, with SoH values ranging from approximately 22% to 96%, while more than half of the tested cells exhibit SoH below 60%. Furthermore, batteries with comparable SoH frequently demonstrate different energy efficiencies, indicating that capacity retention alone is insufficient for reliable second-life battery assessment. Building upon these findings, a transparent rule-based decision-support framework is developed to map experimentally measured battery health indicators to application-oriented reuse pathways, including grid-support systems, residential energy storage, backup applications, and recycling. The proposed framework establishes a practical bridge between laboratory battery characterization and deployment-oriented second-life decision-making, providing an interpretable and experimentally grounded methodology for scalable battery screening and sustainable reuse planning. Full article

Natural disasters can split distribution networks into isolated microgrids, and the remaining local storage is often not enough to keep critical loads supplied until repairs start. Mobile emergency power vehicles (MEPVs) are useful in bridging this gap, but their dispatch is easily distorted when travel time is treated as static and battery charging is treated as constant-rate. This paper studies MEPV routing and load restoration under scenario-based road degradation and non-linear constant-current/constant-voltage (CC-CV) charging. A limited-communication setting is also stated explicitly, since post-disaster information is usually intermittent rather than fully real-time. The resulting scheduling problem is solved by an Improved Adaptive Harmony Search (IAHS) algorithm. IAHS keeps the harmony–memory structure but moves binary load decisions through a continuous latent space before threshold decoding, so that the differential update can use population differences without repeatedly disturbing load variables that have already become stable. Tests on a six-area system show higher average recovery and lower dispersion than HS, AHS, GHS, and AHS with standard BDE. Additional road-degradation sensitivity tests and single-MEPV scale-up tests show that the method remains feasible under stronger road deterioration and that computation time grows in a manageable way as the number of load variables increases. Full article

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation. Full article

Battery engineers often work with repeated cycle-level monitoring signals that are related to ageing but are not direct capacity or resistance measurements. This paper studies how such a signal can be used to detect faster change and to decide whether the measured record is long enough to locate the transition reliably. We analyse 14 lithium-ion cell records from a processed Hawaii Natural Energy Institute (HNEI) cycle-level table, using the charging-to-discharge duration ratio (C/D) as a practical charge/discharge-duration indicator. The corresponding original HNEI measurement files were checked to improve the cell description and to examine whether a capacity-based comparison was possible. They confirm substantial capacity fade, but they also contain non-physical capacity entries near cycle 370; these entries are not used as validation of C/D transition cycles. We compare a linear reference, broken-line regression, a smoothing-spline curvature check, an aggregate Bayesian smooth-transition model, and a battery-level hierarchical Bayesian model. The hierarchical model estimates a mean battery-level transition cycle of 553.9 cycles (95% credible interval: 547.1–560.9), with substantial battery-to-battery variation (standard deviation about 142 cycles). All 14 batteries show a positive increase in the rate of change of C/D. Randomly removing about half of the measurements while retaining the full test span widens uncertainty but preserves the acceleration conclusion and nearly preserves battery ordering. In contrast, cutting off the late part of the test record strongly destabilizes transition timing and extrapolation. The approach is therefore useful as a retrospective screening and test-interpretation tool for a chosen ageing-related signal. It is not a direct capacity-knee detector, a mechanism diagnosis, or a remaining-useful-life predictor. Full article