Search Results (1,832)

This study examines the effect of temperature during pyrolysis on the capacity of cedar wood-derived biochar to be employed as a sustainable electrode material for supercapacitors. Cedar wood-derived biochars were produced at different temperatures of 800 °C, 900 °C, 1000 °C and 1100 °C and fully characterized in terms of their structural, physicochemical and electrochemical properties, including specific surface area, hydrophobicity, electrical conductivity, and surface functional groups. The results indicated that the cedar wood biochar obtained through pyrolysis at 900 °C (BC900) provided optimal electrical conductivity, hydrophobicity, and porosity characteristics relative to the other cedar wood biochars produced by pyrolysis at 800 °C to 1100 °C. Specifically, when compared to commercial activated carbon (AC), BC900 provided half the specific capacitance at a current density of 1 A g⁻¹ and indicated that there is more potential for improvement with further activation and doping. The influence of the binder (either polyvinylidene fluoride (PVDF) or chitosan) in combination with conductive carbon black (CB) was also examined. Electrodes fabricated with PVDF binder showed higher specific capacitance, while biochar electrodes made from CB and chitosan (BC900/CB/chitosan) showed better electrical conductivity, wettability, and good electrochemical stability with >95% capacity retention even after 10,000 cycles. Full article

Introduction: Actinic cheilitis (AC) is a common precancerous condition affecting the lips, primarily caused by prolonged ultraviolet radiation exposure. Various treatment options are available. However, the optimal treatment approach remains a subject of debate. Objective: To summarize and compare practice-relevant interventions for AC. Materials and Methods: A pre-defined protocol was registered in PROSPERO (CRD42021225182). Systematic searches in Medline, Embase, and Central, along with manual trial register searches, identified studies reporting participant clearance rates (PCR) or recurrence rates (PRR). Quality assessment for randomized controlled trials (RCTs) was conducted using the Cochrane Risk of Bias tool 2. Uncontrolled studies were evaluated using the tool developed by the National Heart, Lung, and Blood Institute. The generalized linear mixed model was used to pool proportions for uncontrolled studies. A pairwise meta-analysis for RCTs was applied, using the odds ratio (OR) as the effect estimate and the GRADE approach to evaluate the quality of the evidence. Adverse events were analyzed qualitatively. Results: A comprehensive inclusion of 36 studies facilitated an evaluation of 614 participants for PCR, and 430 patients for PRR. Diclofenac showed the lowest PCR (0.53, 95% confidence interval (CI) [0.41; 0.66]), while CO₂ laser showed the highest PCR (0.97, 95% CI [0.90; 0.99]). For PRR, Er:YAG laser showed the highest rates (0.14, 95% CI [0.08; 0.21]), and imiquimod the lowest (0.00, 95% CI [0.00; 0.06]). In a pairwise meta-analysis, the OR indicated a lower recurrence rate for Er:YAG ablative fractional laser (AFL)-primed methyl-aminolevulinate photodynamic therapy (MAL-PDT) (Er:YAG AFL-PDT) compared to methyl-aminolevulinate photodynamic therapy (MAL-PDT) alone (OR = 0.22, 95% CI [0.06; 0.82]). The CO₂ laser showed fewer local side effects than the Er:YAG laser, while PDTs caused more skin reactions. Due to qualitative data, comparability was limited, highlighting the need for individualized treatment. Conclusions: This study provides a complete and up-to-date evidence synthesis of practice-relevant interventions for AC, identifying the CO₂ laser as the most effective treatment and regarding PCR and imiquimod as most effective concerning PRR. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

In recent years, DC grids have gained traction, and several proposals regarding measuring strategies and several Power Quality (PQ) parameters have been defined to be used in such networks that differ from traditional AC power grids. As a complement to all this preliminary work, this study on the effect of modifying the sampling frequency on some of those parameters has been conducted. For time series evaluation of mean and RMS voltage values, the Dynamic Time Warping (DTW) algorithm has been used. Additionally, the consequence of varying the sampling rate in voltage event detection has also been analysed. As a result, relevant advice regarding sampling frequency is presented in this paper for an effective and optimum evaluation of RMS or mean voltage values and its implementation in detecting voltage events (dips or swells). At least for the parameters in the monitored DC microgrid, a clue for the minimum sampling rate that guarantees accurate measurements is found. Full article

Background: Youth with chronic rheumatologic diseases undergo medical experiences that can lead to post-traumatic stress disorder (PTSD). Understudied in pediatric rheumatology, medical PTSD can be significantly distressing and impairing. Objective: This study explored the prevalence of medical PTSD symptoms in youth with chronic inflammatory arthritis and associated factors, including pain, disease activity, mental health history, and anxiety sensitivity. Methods: A cross-sectional study of 50 youth (ages 8–18) with juvenile idiopathic arthritis (JIA) and childhood-onset systemic lupus erythematous (cSLE) was conducted at a pediatric rheumatology clinic. Participants completed self-report measures assessing post-traumatic stress symptoms (CPSS-V), pain, anxiety sensitivity (CASI), pain-related self-efficacy (CSES), adverse childhood experiences (ACEs), and fibromyalgia symptoms (PSAT). Clinical data included diagnoses, disease activity, treatment history, and demographics. Results: Forty percent had trauma symptoms in the moderate or more severe range. The 14% likely meeting criteria for probable medical PTSD were older (median 17 vs. 15 years, p = 0.005), had higher pain scores (median 4 vs. 3, p = 0.008), more ACEs (median 3 vs. 1, p = 0.005), higher anxiety sensitivity scores (median 39 vs. 29, p = 0.008), and higher JIA disease activity scores (median cJADAS-10 11.5 vs. 7.5, p = 0.032). They were also more likely to report a history of depression (71 vs. 23%, p = 0.020). No associations were found with hospitalization or injected/IV medication use. Conclusions: Medical trauma symptoms are prevalent in youth with chronic inflammatory arthritis. Probable PTSD was associated with pain and psychological distress. These findings support the need for trauma-informed care in pediatric rheumatology. Full article

We conducted systematic glycomic and glycoproteomic profiling to characterize the dynamic N-glycosylation landscape of rat serum, with particular focus on sex- and time-dependent variations. MALDI-TOF-MS analysis revealed that rat serum N-glycans are predominantly biantennary, disialylated complex-type structures with extensive O-acetylation of Neu5Ac residues, especially in females. LC-MS/MS-based glycoproteomic analysis of albumin/IgG-depleted serum identified 87 glycoproteins enriched in protease inhibitors (e.g., serine protease inhibitor A3K) and immune-related proteins such as complement C3. Temporal analyses revealed stable sialylation in males but pronounced daily fluctuations in females, suggesting hormonal influence. Neu5Gc-containing glycans were rare and mainly derived from residual IgG, as confirmed by glycomic analysis. In contrast to liver-derived glycoproteins, purified IgG exhibited Neu5Gc-only sialylation without O-acetylation, underscoring distinct sialylation profiles characteristic of B cell-derived glycoproteins. Region-specific glycosylation patterns were observed in IgG, with the Fab region carrying more disialylated structures than Fc. These findings highlight cell-type and sex-specific differences in sialylation patterns between hepatic and immune tissues, with implications for hormonal regulation and biomarker research. This study provides a valuable dataset on rat serum glycoproteins and underscores the distinctive glycosylation features of rats, reinforcing their utility as model organisms in glycobiology and disease research. Full article

Island microgrids are essential for the exploitation and utilization of offshore renewable energy resources. However, voltage regulation and accurate reactive power sharing remain significant technical challenges that need to be addressed. To tackle these issues, this paper proposes an algorithm that integrates a dynamic leader election (DLE) mechanism and model-free reinforcement learning (RL). The algorithm aims to address the issue of fixed leaders restricting reactive power flow between buses during heavy load variations in island microgrids, while also overcoming the challenge of obtaining model parameters such as resistance and inductance in practical microgrids. First, we establish a voltage containment control and reactive power error model for island alternating current (AC) microgrids and construct a corresponding value function based on this error model. Second, a dynamic leader election algorithm is designed to address the issue of fixed leaders restricting reactive power flow between buses due to preset voltage limits under unknown or heavy load conditions. The algorithm adaptively selects leaders based on bus load, allowing the voltage limits to adjust accordingly and regulating reactive power flow. Then, to address the difficulty of accurately acquiring parameters such as resistance and inductance in microgrid lines, a model-free reinforcement learning method is introduced. This method relies on real-time measurements of voltage and reactive power data, without requiring specific model parameters. Ultimately, simulation experiments on offshore island microgrids are conducted to validate the effectiveness of the proposed algorithm. Full article

Background: Adhesive capsulitis (AC) causes a global limitation of both active and passive range of motion (ROM) in the shoulder, with or without pain, and no specific radiographic findings. Its course is self-limiting and progresses through three or four stages. The diagnosis is primarily clinical, since imaging tests are nonspecific. Treatment options include physical therapy (PT), intra-articular corticosteroid injections, suprascapular nerve block (SSNB), and hydrodilatation (HD). The latter is useful for expanding and reducing inflammation of the joint capsule through the insufflation of saline solution, anesthetics, and corticosteroids. Objectives: To compare whether patients with AC, stratified by phase 1 and 2, who receive high-volume HD as treatment achieve better outcomes in terms of shoulder pain and function compared to patients who receive low-volume HD. To compare whether there are differences in PT times and to determine mean axillary recess (AR) values. Methods: A randomized, parallel-block, triple-blind clinical trial will be conducted in 64 patients with AC in phases 1 and 2, aged 30 to 70 years, with limited active and passive ROM in two planes, and shoulder pain lasting more than 3 months. HD will be administered with volumes of 20 mL or 40 mL, followed by a conventional rehabilitation program. Outcomes will be reviewed at the 1st, 3rd, and 6th months of HD. Variables collected will include Shoulder Pain and Disability Index (SPADI), Visual Analog Scale (VAS), Range of motion (ROM), Lattinen index (LI), AR size, and time to completion of PT. Results: HD has been gaining clinical relevance in interventional rehabilitation as a treatment for AC, although its medium- and long-term efficacy remains a matter of debate. The variability in the volumes used for capsular expansion, with studies ranging from 18 mL to 47 mL, is compounded by the fact that most of these studies do not differentiate between AC stages. This could influence treatment effectiveness. Furthermore, diagnosis remains a challenge since valid and specific diagnostic parameters are lacking. Conclusions: Understanding the differences between HD techniques, considering the influence of certain factors such as the volume used or the stages of AC, as well as improving diagnosis and the coordination of scientific work. This could facilitate the development of protocols for the use of HD in AC. Full article

Water scarcity poses a formidable challenge around the world, especially in arid regions where limited availability of freshwater resources threatens both human well-being and ecosystem sustainability. Membrane-based desalination technologies offer a viable solution to address this issue by providing access to clean water. This work ultimately aims to develop a novel permselective polymeric membrane material to be employed in an electrochemical desalination system. This part of the study addresses the optimization, preparation, and characterization of a polyvinylidene difluoride (PVDF) polymeric membrane using the electrospinning technique. The membranes produced in this work were fabricated under specific operational, environmental, and material parameters. Five different additives and nano-additives, i.e., graphene oxide (GO), carbon nanotubes (CNTs), zinc oxide (ZnO), activated carbon (AC), and a zeolitic imidazolate metal–organic framework (ZIF-8), were used to modify the functionality and selectivity of the prepared PVDF membranes. Each membrane was synthesized at two different levels of additive composition, i.e., 0.18 wt.% and 0.45 wt.% of the entire PVDF polymeric solution. The physiochemical properties of the prepared membranes were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, contact angle, conductivity, porosity, and pore size distribution. Based on findings of this study, PVDF/GO membrane exhibited superior results, with an electrical conductivity of 5.611 mS/cm, an average pore size of 2.086 µm, and a surface charge of −38.33 mV. Full article

Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model’s ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data. Full article

Background/Objectives: Heart failure with reduced ejection fraction (HFrEF) is associated with significant renal complications, affecting disease progression and patient outcomes. Sodium-glucose co-transporter-2 (SGLT2) inhibitors have emerged as a key therapeutic strategy, offering cardiovascular and renal benefits in these patients. However, interindividual variability in response to dapagliflozin underscores the role of pharmacogenetics in optimizing treatment efficacy. This study investigates the influence of genetic polymorphisms on renal outcomes in HFrEF patients treated with dapagliflozin, focusing on variations in genes such as SLC5A2, UMOD, KCNJ11, and ACE. Methods: This prospective, observational cohort study was conducted at the National Heart Institute, Cairo, Egypt, enrolling 200 patients with HFrEF. Genotyping of selected single nucleotide polymorphisms (SNPs) was performed using TaqMan™ assays. Renal function, including estimated glomerular filtration rate (eGFR), Kidney Injury Molecule-1 (KIM-1), and Neutrophil Gelatinase-Associated Lipocalin (NGAL) levels, was assessed at baseline and after six months of dapagliflozin therapy. Results: Significant associations were found between genetic variants and renal outcomes. Patients with AA genotype of rs3813008 (SLC5A2) exhibited the greatest improvement in eGFR (+7.2 mL ± 6.5, p = 0.004) and reductions in KIM-1 (−0.13 pg/mL ± 0.49, p < 0.0001) and NGAL (−6.1 pg/mL ± 15.4, p < 0.0001). Similarly, rs12917707 (UMOD) TT genotypes showed improved renal function. However, rs5219 (KCNJ11) showed no significant impact on renal outcomes. Conclusions: Pharmacogenetic variations influenced renal response to dapagliflozin in HFrEF patients, particularly in SLC5A2 and UMOD genes. These findings highlighted the potential of personalized medicine in optimizing therapy for HFrEF patients with renal complications. Full article

Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article