Search Results (1,676)

Skin aging is characterized by decreased collagen synthesis and increased extracellular matrix degradation, leading to wrinkle formation and reduced skin elasticity. This study evaluated the anti-aging potential of hydrolyzed fish skin (HFS) extract through complementary in vitro and clinical investigations. In human dermal fibroblasts, treatment with HFS extract enhanced type I procollagen production and suppressed UVB-induced matrix-degrading enzymes, including matrix metalloproteinase-1 (MMP-1) and elastase, suggesting a mechanism that supports dermal matrix homeostasis. A randomized, double-blind, split-face clinical trial was conducted in 20 female participants over 12 weeks. A formulation containing 0.5% HFS extract was applied to one side of the face, while an identical vehicle control formulation without HFS extract was applied to the contralateral side. Wrinkle parameters were assessed using a three-dimensional imaging system. After 12 weeks, the test group showed significant improvements compared to baseline, with reductions of 12.75% in arithmetic mean roughness (Ra), 12.46% in root mean square roughness (Rq), and 11.32% in maximum wrinkle height (Rmax) (p < 0.05). No adverse events were observed. These findings demonstrate that HFS extract improves wrinkle-related skin parameters, potentially through promoting collagen synthesis while inhibiting matrix degradation. The combined molecular and clinical evidence supports its application as a functional cosmetic ingredient in anti-aging formulations. Full article

Background/Objectives: The purpose of this study was to analyze the demographics and injury patterns of children with transportation-related non-fatal injuries occurring on public roads, streets and highways using a nationwide emergency department (ED) database. Methods: Data from the National Electronic Injury Surveillance System (NEISS) All Injury Program (AIP) 2005–2021 was used. Five transportation methods (motor vehicle occupant, bicyclist, pedestrian, motorcyclist, other) occurring on a public highway, street, or road were analyzed. Statistical analyses were performed with SUDAAN 11.0.01™ software to obtain national estimates. Results: There were an estimated 8,188,810 ED visits for traffic-related injuries in children; the median age is 14.3 years. Sex distribution was equal; 93.4% were discharged from the ED, and the head/neck was the most injured area (51.9%). The most common diagnoses were contusion (35.7%), strain/sprain (28.0%), internal organ injuries (13.3%), fracture (8.4%), lacerations (7.4%) and concussions (4.1%). Predictor variable of not being discharged from the ED was the presence of a fracture (OR = 119.7 [71.3, 200.7], p < 0.0001), injury to the trunk (OR = 3.2 [2.7, 3.8], p < 0.0001), a pedestrian (OR = 3.9 [2.8, 5.3], p < 0.0001), those < 1.5 years old (OR = 4.3 [2.8, 6.6], p < 0.001), and males (OR 1.5 [1.4, 1.6], p <0.0001). The greatest prevalence of head/neck fractures was in motor vehicle occupants (23.3%), upper extremity fractures in bicyclists (73.1%) and motorcyclists (49.2%), and lower extremity fractures in pedestrians (56.6%). Conclusions: This detailed study can be used to compare/contrast these injuries to other countries regarding road traffic injuries in children. This data can be used to assess the outcomes of prevention strategies introduced in the future. Full article

The accurate estimation of State of Health (SoH) for lithium-ion batteries in real-world electric vehicles (EVs) is critical for ensuring safety, reliability, optimal energy management, and lifecycle sustainability. Unlike laboratory-controlled conditions, real-world EV batteries operate under highly dynamic loads, irregular charging behaviors, diverse environmental conditions, and user-dependent driving patterns. This review provides a comprehensive and structured overview of recent progress in SoH estimation for real-world EV applications. The fundamentals of battery aging mechanisms are summarized, with a clarification of key SoH definitions, metrics, and influencing factors under practical operating conditions. Subsequently, existing methodologies are systematically categorized into physics-based models, data-driven approaches, hybrid/model-assisted frameworks, and uncertainty-aware probabilistic methods, with a focus on their strengths and limitations in real-world deployment. Key challenges, including domain shift, computational constraints, explainability, thermal variability, and data heterogeneity, are critically and systematically analyzed. Finally, future research directions are outlined, emphasizing transfer learning, foundation models, physics-informed AI, self-supervised learning, digital twins, and the need for standardized benchmarks. This review aims to provide researchers and practitioners with a clear roadmap toward reliable, scalable, and trustworthy SoH estimation for next-generation intelligent battery management systems in electric vehicles. Full article

Background: Recreational motorized vehicles, including all-terrain vehicles (ATVs), dirt and motor bikes, snowmobiles, and e-scooters, are an increasingly recognized source of severe trauma among children. Adult provincial data from Alberta demonstrate high morbidity, mortality, and more than $6 million in acute care costs from ATV-related injuries over a decade; however, pediatric injury patterns remain under-characterized despite rising exposure. Methods: We conducted a retrospective cohort study of pediatric patients presenting with major trauma (Injury Severity Score > 12) to the Stollery Children’s Hospital between December 2019 and June 2023. Recreational motorized vehicle-related cases were analyzed for demographics, injury mechanisms, injury severity, hospital resource utilization, and clinical outcomes. Available Abbreviated Injury Scale data were reviewed descriptively for a subset of ATV-related injuries. Results: Of 345 pediatric major trauma cases, 55 (16%) involved recreational motorized vehicles, accounting for 17% of major blunt trauma presentations. ATVs were the most common mechanism (58%), followed by dirt/motor bikes (23.6%), snowmobiles (14.5%), and e-scooters (3.6%). Patients were predominantly male (72.7%) with a mean age of 13.1 years. Operative intervention was required in 58.2% of cases, 30.9% required pediatric intensive care unit admission, and mortality was 5.5%. Helmet status was incompletely documented; only 36.4% of patients were recorded as wearing helmets. Children from rural regions accounted for 43.6% of injuries. In the ATV subset with available AIS data, head, facial, and extremity injuries were most common, and all patients sustained at least one serious injury (AIS ≥ 3). Conclusions: Recreational motorized vehicles represent a substantial and preventable cause of severe pediatric trauma in Alberta. When contextualized with adult provincial data demonstrating significant mortality and healthcare costs, these findings support strengthened injury-prevention strategies, improved safety enforcement, and evidence-informed policy approaches. Full article

Infrared (IR) object detection on unmanned aerial vehicle (UAV) platforms is fundamentally challenged by low signal-to-noise ratios and extremely tight onboard computational budgets. Conventional CNNs lack sufficient global context, while Transformers suffer from quadratic complexity, hindering real-time deployment. To address these bottlenecks, we propose EfficientIR-Det, a lightweight end-to-end detector featuring a holistic optimization of the backbone, encoder, and sampling mechanisms. Specifically, we design a Partial Star Network (PSN) backbone that achieves implicit high-dimensional feature expansion via element-wise multiplication to amplify weak IR signals with minimal redundancy. Furthermore, a Hierarchical Mamba (HiMamba) encoder leverages selective state-space modeling to provide linear-complexity global enhancement with superior hardware efficiency. To refine cross-scale representations, we introduce an Adaptive Gated Sampling (AGS) module and a Hierarchical Sampling Strategy (HSS) to optimize feature fusion and sampling budget allocation toward dim-small targets. On HIT-UAV, EfficientIR-Det achieves 88.4% mAP@0.5, outperforming the RT-DETR-R18 baseline by 3.3 points while reducing FLOPs and parameters by 48.9% and 44.2%, respectively. On the larger-scale DroneVehicle dataset, it consistently leads with a 74.1% mAP@0.5 and a high inference speed of 140.8 FPS. Our results offer a promising research scheme for robust, real-time infrared perception on edge-constrained UAV platforms. Full article

With the large-scale integration of new energy, electric vehicles, and other new loads, disorderly electricity consumption has led to surging peak loads and heightened overload risks for distribution transformers. Particularly in aging, high-density urban areas constrained by the cost and space limitations of upgrading distribution facilities, there is an urgent need to tap into the flexible load control potential of existing power distribution systems to ensure system safety. This paper proposes a multi-timescale cooperative scheduling framework for flexible loads in distribution systems, deeply integrating the dynamic load capacity of transformers with the dispatchable characteristics of a flexible load. First, a day-ahead scheduling layer based on multi-agent reinforcement learning is constructed to optimize electricity plans and smooth peak–valley loads in the distribution system. Second, a dynamic transformer-rating model for distribution transformers is established to uncover their dynamic load capabilities under varying environmental conditions. Finally, an intraday scheduling layer for flexible loads is developed. It dynamically matches the regulation demands of distribution transformers and flexible loads via real-time optimization of consumption strategies to address electricity price fluctuations and user behavior randomness. Case study results demonstrate that the methods described in this paper effectively reduce power load fluctuations, ensuring the safe and stable operation of distribution and power supply systems. Full article

Accurate battery state-of-health (SOH) estimation is essential for safe and reliable electric-vehicle operation. This paper presents a Tempered Fractional Deterministic Learning (TF-DL) framework that combines deterministic learning with a tempered fractional adaptation law to introduce tunable memory and graceful forgetting into SOH estimation. Two realizations are considered: an exact truncated variant (TF-DL-T) and an embedded low-memory variant (TF-DL-E). The framework is evaluated on a NASA-derived cycle-level battery aging benchmark with capacity-based SOH labels and a battery-level train/test split. After filtering, 14 batteries were retained, of which 9 were used for training and 5 unseen batteries were used for testing. Random Forest achieved the best overall performance (MAE = 0.0436, RMSE = 0.0496), while LSTM was the strongest sequence baseline (MAE = 0.0757, RMSE = 0.0920). Among the online RBF-based methods, TF-DL-E achieved the best performance (MAE = 0.0966, RMSE = 0.1077), outperforming GD-DL and TF-DL-T. Unlike offline methods, TF-DL-E operates online with constant memory, which makes it suitable for embedded battery management systems. The results indicate that TF-DL-E is the more robust and practically relevant tempered variant, whereas TF-DL-T remains more fragile and parameter-sensitive. Full article

This work presents the results of an on-road test campaign on an aged mid-size battery electric vehicle. After a full charge, the vehicle was completely discharged by driving on the road, with different routes (combining speeds and road slopes) and payloads. The resulting driving range and discharged battery energy were measured. The results are compared with those obtained from previous laboratory test campaigns on a chassis dynamometer driving at constant speed or with the standardised testing protocols according to the WLTP. Considerations of the influence of environmental and route conditions on the usable battery energy during the on-road test are made. The new concept of virtual distance related to V2X applications is presented based on the UN GTR No. 22 dealing with in-vehicle battery durability. This is a new concept introduced to account for the additional ageing caused by battery cycling due to applications other than driving or charging. Full article

The real-time performance bottleneck of energy management strategies (EMS) based on model predictive control (MPC) severely restricts their vehicle-grade deployment in series-parallel plug-in hybrid electric vehicles (SPPHEVs). This research develops a real-time adaptive-mode MPC (RTAM-MPC) designed to jointly minimize fuel consumption, electricity usage, and battery aging under strict vehicle-grade execution constraints. An adaptive framework is established by integrating driving pattern recognition (DPR) with MPC, which dynamically adjusts the prediction time grid, solver initialization, and speed prediction configurations. To ensure computational efficiency suitable for embedded systems, a fast numerical optimization method is proposed, alongside a DPR-guided speed prediction model based on a coyote optimization algorithm-optimized kernel extreme learning machine. The results show that RTAM-MPC achieved 98.54% dynamic programming (DP) performance. Compared to the equivalent consumption minimization strategy (ECMS), it demonstrated a 5.37% improvement in economic efficiency and a 24.67% reduction in battery aging. Compared to standard MPC, the average computation time is 11.07 ms, a decrease of 94.48%. Full article

Lithium-ion battery (LiB) ageing is a critical challenge that requires in-depth investigation to extend the useful life of electric vehicles (EVs). This phenomenon drastically impacts cell performance and is primarily influenced by environmental factors and operating conditions, such as charge/discharge rates and the State of Charge (SoC) during rest periods. This study investigates the impact of vehicle operational duty cycles on battery pack (BP) longevity through combined experimental and numerical evaluations. To this end, a lumped electro-thermal BP model was developed and validated at the single-cell level. Furthermore, a capacity fade model, customized for the specific cell chemistry and capacity, was implemented based on the literature benchmarks. The analysis considers user-related parameters, including driving style, charging strategies, and ambient temperatures. The results suggest that aggressive driving significantly accelerates BP ageing when combined with conservative charging strategies in warm climates. Additionally, adopting high DoD values can reduce useful life by up to 30%, while high temperatures can double the rate of capacity fade. Regarding C-rates, fast-charging operations predominantly impact degradation when non-conservative strategies are employed, particularly in cold environments. Full article

Lithium-ion batteries are widely used in applications ranging from portable electronics to electric vehicles and grid energy storage, owing to their high energy density, efficiency, and long lifetime. However, their performance degrades over time due to aging mechanisms such as solid electrolyte interface growth, lithium plating, and electrolyte decomposition, leading to capacity fade and reduced power capability. Accurate state of charge (SOC) estimation is therefore essential for ensuring safe and efficient battery operation, particularly within battery management systems. While many existing methods rely on the direct relationship between open circuit voltage (OCV) and SOC, practical applications require the inverse mapping, i.e., the estimation of SOC from measured OCV values. This inversion is not always straightforward: analytical solutions are only available for simple models, whereas more accurate formulations often require computationally intensive numerical methods. Direct analytical SOC–OCV relationships (inverse OCV–SOC models) provide an effective alternative, enabling simplified SOC estimation without numerical inversion. Previous work proposed a direct generalized Gaussian analytical relationship expressing the absolute state of charge as a function of OCV, thereby simplifying SOC estimation and avoiding numerical inversion, developed and validated on a lithium cobalt oxide battery cycled in the linear region of the OCV curve at constant battery temperature. Building upon this study, the proposed approach was extended to investigate the effects of cycle aging across a wider operating range, considering low, medium, and high voltage/SOC conditions. The model was experimentally validated, at constant battery temperature, on the same type of lithium cobalt oxide batteries through an extensive testing campaign, demonstrating its effectiveness in capturing battery behavior under different operating conditions. Full article

Chronic kidney disease (CKD) is an established risk factor for Parkinson’s disease (PD), but the molecular mechanisms linking these two conditions remain elusive. We performed a systems biology analysis by retrieving high-confidence gene–disease associations from DisGeNET v7.0 (PD: score ≥ 0.8, EI ≥ 0.4; CKD: score ≥ 0.6, EI ≥ 0.4) and constructing a protein–protein interaction (PPI) network via STRING v11.5 (confidence ≥ 0.700). Direct “molecular bridges” between CKD and PD proteins were identified and validated using independent databases. To corroborate biological feasibility, candidate proteins were cross-referenced with ExoCarta and Vesiclepedia databases for exosomal localization. Functional enrichment, tissue expression, and pathway analyses were conducted. Despite zero gene overlap (64 PD genes, 17 CKD genes), the PPI network showed significant convergence (81 nodes, 280 edges, PPI enrichment p < 1.0 × 10⁻¹⁶). Fifteen high-confidence molecular bridges were identified, including the Apolipoprotein A1 (APOA1)–α-synuclein (SNCA) interaction (combined score 0.883), which was independently validated by IntAct. Functional enrichment revealed specific association of APOA1–SNCA with “amyloid fiber formation” (false discovery rate (FDR) = 0.038). Both APOA1 and SNCA are annotated as exosome components (Kyoto Encyclopedia of Genes and Genomes (KEGG) ko04147) and were confirmed as consistent cargo in plasma, urine, and platelet-derived extracellular vesicles within proteomic databases (ExoCarta IDs: 335, 6622). Global pathway analysis highlighted inflammation, oxidative stress, and the advanced glycation end product (AGE)–receptor for AGE (RAGE) pathway. We propose an integrative model wherein CKD-induced dysregulation of APOA1 promotes α-synuclein misfolding and aggregation, and the co-packaging of these proteins into exosomes provides a plausible vehicle for kidney-to-brain propagation. This framework offers testable hypotheses and potential therapeutic targets for PD-CKD comorbidity. Full article

Despite substantial reductions in precursor emissions, persistent summer ozone (O₃) pollution remains a critical environmental challenge in the North China Plain. This study integrated O₃ and volatile organic compound (VOC) data from the summers of 2014–2020 with an observation-based box model (OBM) to analyze O₃ pollution trends, VOC composition, sources, and sensitivity in Zhengzhou. The results indicated a continuous intensification of summer O₃ pollution, a progressive annual increase in polluted days, and an average annual concentration increase of 6.72 μg m⁻³ yr⁻¹. Further, the average VOC concentration on polluted days was 11.7% higher than that on non-polluted days, with alkanes dominating the component distribution, followed by aromatic hydrocarbons, alkenes, and alkynes. Subsequently, a source-apportionment model (positive matrix factorization) was used to identify six VOC sources: motor vehicle emissions (28.4%), industrial emissions (23.2%), solvent use (16.0%), liquefied petroleum gas/natural gas use (15.8%), fuel combustion (11.4%), and biological sources (5.4%). The photochemical age method corrected VOC loss during atmospheric transport, revealing that the traditional O₃-formation potential (OFP) method underestimated the contributions of alkenes and aromatic hydrocarbons, with isoprene, m/p-xylene, and ethylene as key species. Furthermore, multi-scenario simulations showed that solely reducing nitrogen oxides (NO_x) emissions caused an O₃ concentration rebound, while a 4:1 VOC to NO_x reduction ratio provided optimal control. By identifying the causal drivers of O₃ pollution in Zhengzhou, this study provides a scientific basis for designing precise emission-reduction strategies applicable to the North China Plain and analogous urban regions. Full article

The performance attributes of a heterogeneous multi-battery pack system significantly impact the electric vehicle’s performance. This study aims to investigate the power reduction and energy utilization phenomena in heterogeneous battery pack configurations that arise due to an uneven current split, focusing on defining the power ability curves and usable energy for the mixed system. A Multiphysics-based system model has been developed to investigate the factors contributing to power loss and usable energy when the aged packs are mixed with fresh packs. Different methods, viz., scaled, aged, and interpolation, are proposed to estimate the power retention curves for one and two fresh packs mixing into the homogeneous system. Also, energy evaluation helps in identifying the impact on vehicle range, which is an important attribute of vehicle performance. Altogether, having power ability curves and usable battery energy (UBE) for a heterogeneous multi-pack system helps in defining the decision-making strategies for the refurbishment of ESS during replacement and maintenance activities in EVs. Some strategies are introduced at the end using aged and scaled methods to conduct the most conservative power estimations while pack mixing. Energy evaluation is performed at the ESS level, highlighting the impact of fresh pack on the aged system usable energy. Full article

According to the National Cancer Institute, approximately 3.9 billion people worldwide suffer from non-communicable oral diseases, with head and neck cancer patients experiencing exacerbated oral mucositis primarily from radiotherapy. This condition manifests as painful, debilitating mucosal lesions, necessitating effective antimicrobial interventions. This study developed and characterized stable mouthwash formulations containing green-synthesized silver nanoparticles (AgNPs) derived from Baccharis trimera (carqueja) extract for the management of oral mucositis, evaluating their physicochemical stability, antimicrobial efficacy, and biosafety. AgNPs formation was confirmed by color change to brown and a surface plasmon resonance band at 407 nm (UV-Vis), with dynamic light scattering revealing a monomodal hydrodynamic diameter of ~25 nm and stable dispersion; scanning electron microscopy showed spherical particles of 25–35 nm. Four formulations (22–85 ppm AgNPs) in a commercial vehicle exhibited excellent stability over 60 days at 5 °C and 25 °C, maintaining near-neutral pH (~7), low surface tension (<5 mN/m), and unchanged spectral profiles, with no phase separation under centrifugation or thermal stress (up to 70 °C). Antimicrobial assays via broth microdilution demonstrated broad-spectrum activity for the 85 ppm formulation: MICs of 125 µg/mL (S. epidermidis, E. faecalis), 62.5 µg/mL (E. coli, P. aeruginosa), and 250 µg/mL (S. aureus), with MBC of 125 µg/mL (bactericidal) against P. aeruginosa; no activity against C. albicans (MIC > 500 µg/mL). Against human oral microbiota (n = 4 volunteers), it reduced bacterial growth by 14–156% relative to controls (e.g., −5% to 156% inhibition). Cytogenotoxicity tests (A. cepa) confirmed non-toxicity (mitotic index 79–93% of control, low cellular alteration index). These findings establish the carqueja-mediated instant green AgNPs mouthwash as a stable, potent antimicrobial agent, poised to mitigate mucositis-related infections and enhance the quality of life of cancer patients. Full article