Search Results (61,614)

Reclaimed asphalt pavement (RAP) from polymer-modified asphalt pavements often contains a recovered binder that is stiff and brittle, which reduces workability and increases durability risk. Waste cooking oil (WCO) is a promising circular rejuvenator, but its effectiveness remains inconsistent because oil source and degradation state are often not well controlled, particularly in polymer-modified RAP systems. This study introduced a controlled simulated WCO approach and compared four oil sources (Palm, Soy, Olive, and Rice) as rejuvenators for recovered RAP binder and RAP mixtures. Simulated oils were added at 4% and 8% by mass of recovered RAP binder. The simulated WCOs produced clear dosage-dependent softening of the recovered binder. Penetration increased, while softening point and rotational viscosity decreased, indicating partial restoration of binder mobility and improved workability. At the mixture level, the 4% dosage provided the most balanced performance, improving moisture resistance and reducing Cantabro loss compared with the control mixture. Specifically, tensile strength ratio (TSR) increased from 75% to 80.9–83.7%, while Cantabro loss decreased from 19.8% to 13.2–14.6%, showing better cohesion and resistance to particle loss. However, Hamburg wheel tracking (HWT) results revealed strong oil-source dependence, with Soy showing the lowest rut depth and Olive the highest, indicating that excessive softening can reduce deformation resistance. The results demonstrate that controlled simulated WCO can support practical oil-source selection for polymer-modified RAP mixtures. A moderate dosage is more effective because it improves binder restoration and mixture durability without causing excessive softening, while rutting verification remains essential before field application. Full article

Gait analysis study comparing unicompartmental vs. total knee arthroplasty, differences in knee kinematics: a retrospective cohort study. Background and Objectives: Total knee arthroplasty (TKA) is an effective treatment for advanced knee osteoarthritis, although functional outcomes may remain suboptimal in many patients. Unicompartmental knee arthroplasty (UKA) often provides better functional recovery but shows lower long-term implant survival. Recently, personalized TKA approaches have been developed to improve kinematic restoration and patient satisfaction. This study aimed to compare knee kinematics among patients who underwent personalized TKA, medial UKA, and healthy controls. Materials and Methods: This retrospective cohort study included 9 patients treated with robotic-assisted personalized TKA, 9 patients treated with medial UKA, and 9 healthy controls matched for age, sex, and BMI. Inclusion criteria were age 60–80 years, Kellgren–Lawrence grade III–IV, a minimum follow-up of 12 months, deviation from neutral HKA < 15°, healthy contralateral knee, and high postoperative functional scores. Exclusion criteria included valgus knees (HKA > 180°), postoperative complications, and neuromotor disorders. In the TKA group, a Medial Congruent implant was implanted with ROSA robotic assistance using a restricted kinematic alignment (±5° HKA) and asymmetric intercompartmental balancing. In the UKA group, a fixed-bearing medial implant (Physica ZUK) was used. Gait analysis was performed on a markerless instrumented treadmill (WalkerView™; Dalmine, Italy). Differences between groups were analyzed using one-way ANOVA and Tukey’s post-hoc test (p < 0.05). Results: UKA patients walked with a stiffer knee during stance. Knee range of motion during stance increased from UKA (6.3° ± 7.2°) to TKA (13.6° ± 8.8°, p = 0.045) and to controls (16.6° ± 4.5°, p = 0.02). During loading response, UKA patients showed lower flexion (10.2° ± 6.1°) than TKA (19.4° ± 7.9°, p = 0.049) and controls (19.6° ± 2.8°, p = 0.004). Knee flexion during swing was comparable between UKA and TKA. Conclusions: UKA patients demonstrated reduced knee flexion during early stance compared with robotic-assisted TKA and healthy controls. The observed differences may reflect multiple factors, including surgical technique, implant design, and patient-related characteristics. Because preoperative functional data were not available, potential selection bias cannot be excluded. These findings should be interpreted cautiously and warrant confirmation in larger prospective studies. Full article

Hot-air drying is widely adopted for herbs because it is robust and easy to control, yet it is often energy-intensive and may operate far from optimal conditions when industrial dryers rely on fixed airflow paths and large air recirculation rates. This work investigates a conventional basket-type, adiabatic hot-air dryer through an instrumented 30 h drying campaign and a psychrometric energy analysis. The hot-air drier is designed to reduce the relative humidity of herbs from the environmental value (highly variable as a function of the species, the weather conditions, and, mostly, the seasonality) to 20%. Temperature and relative humidity were measured at four positions to characterize the shelf-by-shelf drying sequence and to identify process phases. A mass balance indicated that approximately 3.8 t of water was removed during the trial. Based on the measured thermodynamic states of the moist air and estimated airflow rates (35,000–53,000 m³/h), the baseline configuration was analyzed and an upgrade strategy was proposed to improve dehumidification and overall efficiency while preserving the conventional hot-air-drying concept. The alternative solution integrates a refrigeration-based dehumidification loop (heat pump) to decouple moisture removal from sensible heating; three plant layouts and seasonal boundary conditions (summer/winter) were simulated. For the most favorable configurations, the specific final–primary energy demand and the associated CO₂-equivalent emissions were reduced by about 70–85% compared with the baseline, depending on the airflow rate and recirculation strategy. The results highlight practical retrofit options for existing herb dryers and provide a transparent framework for translating measured psychrometric states into energy and emission indicators. The results, achieved and discussed in this study, were used to optimize the utilization of an already existing and operative hot-air dryer. Based on the proposed working configuration, the dryer now allows achieving the fixed target for herb mixtures of the previous configuration and, at the same time, reducing the energy consumption and associated equivalent CO₂ emitted, as well as achieving process completion in less time. Full article

The objective of this study was to evaluate the impact of seasonal changes in the chemical and structural composition of giant miscanthus (Miscanthus × giganteus) biomass on the performance, kinetics, and efficiency of anaerobic digestion (AD), as well as on the overall energy and techno-economic balance of the conversion chain. The AD performance was assessed using batch biochemical methane potential (BMP) assays conducted for eight harvest dates (June–January). Comprehensive characterization included fundamental physicochemical properties of the biomass, lignocellulosic fraction composition, AD kinetics, and methane production yield. A statistically significant (p < 0.05) increase in structural fiber fractions was observed with advancing plant maturity, accompanied by a progressive decline in specific methane yield from 281 ± 32 mL CH₄/g VS in June to 170 ± 11–172 ± 13 mL CH₄/g VS in winter harvests. Despite a relatively stable theoretical biochemical methane potential (TBMP) ranging from 425 to 443 mL CH₄/g VS, the conversion efficiency (BMP/TBMP) decreased from approximately 66% to below 40%, indicating increasing structural and kinetic limitations to substrate biodegradability. Kinetic parameters deteriorated systematically in late harvests, as reflected by a reduction in the first-order rate constant k_CH₄ from 0.115 to approximately 0.072 1/d and an extension of the lag phase λ from 2.19 to over 4 days. Regression analysis revealed strong negative correlations between lignocellulosic complex content and both BMP and k_CH₄, whereas the C/N ratio exhibited a positive association with process performance under the experimental conditions applied. The highest methane production per hectare (3904 ± 720 m³CH₄/ha) and the most favorable economic outcome (1979 ± 465 EUR/ha) were achieved for the September harvest. The results demonstrate that harvest timing constitutes a critical optimization parameter in lignocellulosic biogas systems, governing not only methane yield and process kinetics but also the overall energy output and economic viability of the bioenergy production chain. Full article

Three-phase LLC resonant converters are attractive for high-power applications due to their reduced output current ripple, improved thermal distribution, and inherent scalability. However, component tolerances in resonant tanks often cause phase current imbalance, leading to asymmetric conduction losses among the converter phases, output ripple, and uneven thermal stress. This paper proposes a simple online balancing algorithm that dynamically adjusts input voltage phase shifts to equalize RMS phase currents in real-time. The presented approach is capable of achieving a low unbalance factor even under severe mismatch conditions. The algorithm was validated on an 11 kW LLC prototype, achieving operation with an unbalance factor less than 2% over the full operating frequency range. Results demonstrate improved electrical symmetry and reduced thermal stress, confirming the practicality of the proposed strategy for high-power three-phase LLC converters. Full article

Background and Aim: Lymphovascular invasion (LVI) is a negative prognostic factor for gastric cancer, but detection limitations hinder its clinical utility and subtype analysis. This study aimed to explore the predictive value of LVI and its subtypes in the prognosis and recurrence patterns of gastric cancer using our enhanced detection method. Methods: We reviewed 2057 patients who underwent gastrectomy in 2018, of whom 1073 met the inclusion criteria. Propensity score matching (PSM) was performed to balance baseline clinicopathological characteristics. Results: After PSM, 311 patients were assigned to the LVI+ group and 311 to the LVI- group. The LVI+ group demonstrated a poorer prognosis. Subtype analysis revealed that lymphatic invasion (LI), but not venous invasion (VI), was associated with poor prognosis in the matched cohort. Stratified by pathological tumor-node-metastasis (TNM) stage, LVI+ and LI+ patients had worse prognosis in Stages I and III, while VI+ patients had worse prognosis in Stage III. Stratified by lymph node status, LVI+ predicted poorer prognosis in both node-negative (N0) and node-positive (N+) patients, and LI+ was also associated with worse prognosis among N+ patients, whereas VI+ was not significantly associated with prognosis in either subgroup. Recurrence analysis indicated that LVI+ was associated with distant and peritoneal metastases, whereas LI+ was associated with local recurrence, distant and peritoneal metastases. Conclusions: Lymphovascular invasion was associated with adverse prognosis in resectable gastric cancer, with lymphatic invasion showing a stronger prognostic impact than venous invasion. These findings indicate that refined assessment of lymphovascular invasion may complement conventional TNM staging in postoperative risk stratification. Full article

Mitogen-activated protein kinase kinase kinase 1 (MAP3K1) possesses dual enzymatic functions, i.e., kinase and E3 ubiquitin ligase activities, orchestrating proliferation, survival, apoptosis, DNA damage response, and immune modulation. Recent genomic and mechanistic studies have revealed MAP3K1’s paradoxical, context-dependent roles as both an oncogene and a tumor suppressor. We discuss MAP3K1’s multidomain architecture, featuring an N-terminal RING and PHD domain (E3 ligase activity), a TOG domain (microtubule dynamics), and a C-terminal kinase domain, enabling the integration of c-jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (p38 MAPK), extracellular signal-regulated kinase (ERK), and nuclear factor kappa B (NF-κB) signaling pathways. MAP3K1 functions as a molecular switch balancing survival and apoptosis, with caspase-3 cleavage at Asp878 activating pro-apoptotic JNK/p38 signaling. Genomic analyses across >35 cancer types reveal MAP3K1 alterations at frequencies of <1–14%, highest in breast and endometrial cancers. These alterations show tissue specificity: loss-of-function mutations predominate in hormone receptor-positive breast cancer with a favorable prognosis, whereas gain-of-function mutations in melanoma activate oncogenic ERK signaling. MAP3K1 mutations predict response to mitogen-activated protein kinase kinase (MEK) and phosphoinositide 3-kinase (PI3K) inhibitors, with mutant cancers showing higher MEK inhibitor response than wild-type tumors. Despite substantial progress, critical gaps remain regarding MAP3K1’s E3 ligase substrates, context-dependent activity determinants, and therapeutic strategies. Addressing these through inhibitor development, biomarker validation, and mechanistic studies will accelerate potential clinical translation of MAP3K1 biology. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Accurate energy forecasting is essential for maintaining power system reliability, integrating renewable generation, and ensuring market stability. Although machine learning has improved forecasting accuracy, its operational deployment depends on Machine Learning Operations (MLOps) platforms that automate and scale the entire lifecycle of energy data pipelines. However, the capabilities of existing MLOps platforms for energy forecasting have not been systematically compared. This study adopts a PRISMA-informed review process to identify relevant end-to-end MLOps platforms for energy forecasting and then maps their documented capabilities using an established energy forecasting pipeline lifecycle as the reference structure. A total of 256 records were screened across vendor documentation, open-source repositories, and academic literature, of which 13 MLOps platforms were selected for comparative capability analysis. Platform capabilities are organised and presented across an end-to-end lifecycle covering project setup and governance, data ingestion and management, model development and experimentation, deployment and serving, and monitoring and feedback. Commercial platforms such as Amazon SageMaker and Google Vertex AI generally provide stronger end-to-end integration and production readiness, while open-source platforms such as Kubeflow and ClearML offer modular flexibility that typically requires additional integration effort to achieve end-to-end operation. The mapping identifies four priority areas where platform support remains limited, namely (i) governance workflow automation, (ii) automated data quality validation, (iii) feature management, and (iv) deployment and monitoring support under nonstationary conditions. These findings indicate that platform selection for energy forecasting should be treated as a lifecycle capability decision, balancing end-to-end integration, operational assurance, and long-term flexibility. Full article

Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufacturable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajectories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair ascent, and inclined-plane walking). The extracted joint loads were transferred to a parametric finite element model in ANSYS Workbench 2019, where response surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 50 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured X_M(t) and Y_M(t) trajectories and joint-angle histories for quantitative comparison with simulations via RMSE metrics. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

The active constituents of essential plant oils (EOAIs), monoterpenoid carvacrol and monoterpene p-cymene, are widely distributed in many aromatic plants and their products. They differ in that carvacrol has a phenolic functional group. The numerous pharmacological effects of these two EOAIs are well known. In different doses/concentrations, they exhibit analgesic, neuroprotective, vasorelaxant, anti-inflammatory, antiviral, antibacterial and antiparasitic effects. The acute toxicity of carvacrol and p-cymene in rats and the free-living nematode Caenorhabditis elegans was investigated. Furthermore, the impact of subacute administration of these two terpenes on general health, CNS integration, i.e., motor coordination and balance of rats, as well as their effects on the movement of adult C. elegans, was also examined. The aim was to compare the effects and describe in more detail the selective toxicity of carvacrol and p-cymene. The calculated LD₅₀ value of carvacrol was 790.15 ± 1.15 mg/kg, while the LD₅₀ value of p-cymene is above 3000 mg/kg. Tested doses of carvacrol and p-cymene administered for 28 days (50, 100, and 200 mg/kg) did not exert any effect on the CNS of rats or cause any clinical disorders. LC₅₀ value of carvacrol for adult C. elegans was 184.13 ± 1.51 μM and for p-cymene 1268 ± 1.65 μM. In subacute testing, carvacrol showed negative effects on C. elegans reproduction, distance traveled, movement speed and rotational index at lower concentrations than p-cymene, indicating higher toxicity, which may be due to its phenolic structure. On the other hand, although less toxic to C. elegans, p-cymene exhibited a specific effect on worm motility, with more rolling which should be further investigated, and can be a consequence of cuticle damage or loss of orientation. Full article

In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article