Search Results (18,931)

Early prediction of lymph node metastasis (LNM) following neoadjuvant therapy (NAT) is crucial for timely treatment optimization in esophageal squamous cell carcinoma (ESCC). This study developed and validated a computed tomography-based radiomic model for predicting pathologically confirmed LNM status at the time of surgery in ESCC patients after NAT. A total of 469 ESCC patients from Sun Yat-sen University Cancer Center were retrospectively enrolled and randomized into a training cohort (n = 328) and a test cohort (n = 141). Three signatures were constructed: the tumor-habitat-based signature (Habitat_Rad), derived from radiomic features of three tumor subregions identified via K-means clustering; the multiple instance learning-based signature (MIL_Rad), combining features from 2.5D deep learning models; and the clinicoradiological signature (Clinic), developed through multivariate logistic regression. A combined radiomic nomogram integrating these signatures outperformed the individual models, achieving areas under the curve (AUCs) of 0.929 (95% CI, 0.901–0.957) and 0.852 (95% CI, 0.778–0.925) in the training and test cohorts, respectively. The decision curve analysis confirmed a high net clinical benefit, highlighting the nomogram’s potential for accurate LNM prediction after NAT and guiding individualized therapy. Full article

With the increasing penetration of distributed generation (DG), integrated main-distribution networks (IMDNs) face challenges in rapidly and effectively performing comprehensive operational risk assessments under multiple uncertainties. Thereby, using the traditional hierarchical economic scheduling method makes it difficult to accurately find the optimal scheduling operating point. To address this problem, this paper proposes a multi-objective dispatch decision-making optimization model for the IMDN with static security region (SSR) constraints. Firstly, the non-sequential Monte Carlo sampling is employed to generate diverse operational scenarios, and then the key risk characteristics are extracted to construct the risk assessment index system for the transmission and distribution grid, respectively. Secondly, a hyperplane model of the SSR is developed for the IMDN based on alternating current power flow equations and line current constraints. Thirdly, a risk assessment matrix is constructed through optimal power flow calculations across multiple load levels, with the index weights determined via principal component analysis (PCA). Subsequently, a scheduling optimization model is formulated to minimize both the system generation costs and the comprehensive risk, where the adaptive grid density-improved multi-objective particle swarm optimization (AG-MOPSO) algorithm is employed to efficiently generate Pareto-optimal operating point solutions. A membership matrix of the solution set is then established using fuzzy comprehensive evaluation to identify the optimal compromised operating point for dispatch decision support. Finally, the effectiveness and superiority of the proposed method are validated using an integrated IEEE 9-bus and IEEE 33-bus test system. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

Mechanical components are more difficult to detect at the initial state of failure. To solve this problem, this paper models and simulates the characteristics of an electrostatic oil-line sensor (OLS) wear particles carried in the lubricating oil path are detected. In this study, an OLS that monitors the charge in an oil line using the principle of electrostatic induction is modeled and simulated. The sensor characteristics are simulated and tested using finite element simulation. The sensor efficiency, spatial sensitivity, and length-to-diameter ratio are simulated based on the point charges at different locations. The simulation results show that the sensitivity exhibits different trends when the point charge is inside and outside the probe. The length-to-diameter ratio is proportional to the sensor efficiency, the spatial sensitivity distribution law of multiple charges is consistent with that of a point charge, and the relative deviation rate between the mathematically calculated values and the simulated values is less than 3% under the same conditions. In conclusion, the finite element simulation results of the electrostatic oil line sensor constructed in this study are consistent with the theoretical model calculations and can be used in future mechanical fault diagnosis. Full article

This study developed and validated an integrated theoretical framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to investigate how driving assistance technologies (DATs) influence risky driving behaviors among young urban Chinese drivers. Based on this framework, we proposed and tested several hypotheses regarding the effects of psychological and technological factors on risky driving intentions and behaviors. A survey was conducted with 495 young drivers in Shaoguan, Guangdong Province, examining psychological factors, technology acceptance, and their influence on risky driving behaviors. Structural equation modeling revealed that the integrated TPB-TAM explained 58.3% of the variance in behavioral intentions and 42.6% of the variance in actual risky driving behaviors, significantly outperforming single-theory models. Attitudes toward risky driving (β = 0.287) emerged as the strongest TPB predictor of behavioral intentions, while perceived usefulness (β = −0.172) and perceived ease of use (β = −0.113) of driving assistance technologies negatively influenced risky driving intentions. Multi-group analysis identified significant gender and driving experience differences. Logistic regression analyses demonstrated that model constructs significantly predicted actual traffic violations and accidents. These findings provide theoretical insights into risky driving determinants and practical guidance for developing targeted interventions and effective traffic safety policies for young drivers in urban China. Full article

Background/Objectives: Sunk cost bias—continuing failing endeavours due to prior investments—is among the most studied decision-making biases. Despite decades of vignette-based research, these measures lack systematic psychometric validation. We examined whether widely-used sunk cost scenarios reliably measure the same psychological construct. Methods: Across two experiments (N = 395), we tested established sunk cost vignettes, including classic scenarios from Arkes and Blumer (1985). English-speaking participants from Prolific Academic completed vignettes alongside cognitive reflection and social desirability measures. We assessed internal consistency and intercorrelations between scenarios. Results: Internal consistency was consistently poor (ω = 0.14–0.57) with weak intercorrelations between scenarios. Even highly similar vignettes correlated only moderately. External validity was problematic, showing inconsistent relationships with cognitive reflection and social desirability across vignettes. Conclusions: These measurement failures have critical implications for neuroimaging research, where unreliable behavioural measures may be mistaken for genuine neural differences. The field needs systematic categorization of scenarios to identify which vignettes engage specific psychological processes and neural circuits, enabling more targeted theoretical development. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

This study proposes a novel approach to input data configuration for the fault diagnosis of three-phase induction motors. Conventional neural network (CNN)-based diagnostic methods often employ three-phase current signals and apply various image transformation techniques, such as RGB mapping, wavelet transforms, and short-time Fourier transform (STFT), to construct multi-channel input data. While such approaches outperform 1D-CNNs or grayscale-based 2D-CNNs due to their rich informational content, they require multi-channel data and involve an increased computational complexity. Accordingly, this study transforms the three-phase currents into the D-Q synchronous reference frame and utilizes the D-axis current (Id) for image transformation. The Id is used to generate input data using the same image processing techniques, allowing for a direct performance comparison under identical CNN architectures. Experiments were conducted under consistent conditions using both three-phase-based and Id-based methods, each applied to RGB mapping, DWT, and STFT. The classification accuracy was evaluated using a ResNet50-based CNN. Results showed that the Id-STFT achieved the highest performance, with a validation accuracy of 99.6% and a test accuracy of 99.0%. While the RGB representation of three-phase signals has traditionally been favored for its information richness and diagnostic performance, this study demonstrates that a high-performance CNN-based fault diagnosis is achievable even with grayscale representations of a single current. Full article

Detection of surface defects can significantly elongate mechanical service time and mitigate potential risks during safety management. Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. To this end, we construct a detection model by combining Mask R-CNN, selected for its strong baseline performance in pixel-level segmentation, with Conformal Risk Control. The former evaluates the distribution that discriminates defects from all samples based on probability. The detection model is improved by retraining with calibration data that is assumed to be independent and identically distributed (i.i.d) with the test data. The latter constructs a prediction set on which a given guarantee for detection will be obtained. First, we define a loss function for each calibration sample to quantify detection error rates. Subsequently, we derive a statistically rigorous threshold by optimization of error rates and a given guarantee significance as the risk level. With the threshold, defective pixels with high probability in test images are extracted to construct prediction sets. This methodology ensures that the expected error rate on the test set remains strictly bounded by the predefined risk level. Furthermore, our model shows robust and efficient control over the expected test set error rate when calibration-to-test partitioning ratios vary. Full article

The pursuit of sustainable construction practices has become imperative in the modern era. This paper delves into the research of the properties and application of a specific material called “red clay” from the locality “Crvena Mogila” in Macedonia. A series of laboratory tests were conducted to evaluate the physical, mechanical, and chemical properties of the material. The tested samples show that it is a porous material with low density, high water absorption, and compressive strength in range of 29.85–38.32 MPa. Samples of composite wall blocks were made with partial replacement of natural aggregate with red clay aggregate. Two types of blocks were produced with dimensions of 390 × 190 × 190 mm, with five and six holes. The average compressive strength of the blocks ranges from 3.1 to 4.1 MPa, which depends on net density and the number of holes. Testing showed that these blocks have nearly seven-times-lower thermal conductivity than conventional concrete blocks and nearly twice-lower conductivity than full-fired clay bricks. The general conclusion is that the tested red clay is an economically viable and sustainable material with favourable physical, mechanical, and thermal parameters and can be used as a granular aggregate in the production of composite ceramic blocks. Full article

To meet the European Green Deal targets, the construction sector must improve building thermal performance via advanced insulation systems. Eco-friendly sandwich panels offer a promising solution. Therefore, this work aims to develop and validate a new eco-friendly composite sandwich panel (basalt fibres and recycled extruded polystyrene) with enhanced multifunctionality for lightweight and energy-efficient building façades. Two panels were produced via vacuum infusion—a reference panel and a multifunctional panel incorporating phase change materials (PCMs) and silica aerogels (AGs). Their performance was evaluated through lab-based thermal and acoustic tests, numerical simulations, and on-site monitoring in a living laboratory. The test results from all methods were consistent. The PCM-AG panel showed 16% lower periodic thermal transmittance (0.16 W/(m²K) vs. 0.19 W/(m²K)) and a 92% longer time shift (4.26 h vs. 2.22 h), indicating improved thermal inertia. It also achieved a single-number sound insulation rating of 38 dB. These findings confirm the panel’s potential to reduce operational energy demand and support long-term climate goals. Full article

A steel modular building is a highly prefabricated form of steel construction. It offers rapid assembly, a high degree of industrialization, and an environmentally friendly construction site. To promote the application of multi-story steel modular buildings in earthquake fortification zones, it is imperative to conduct in-depth research on their seismic behavior. In this study, a seven-story modular steel building is investigated using shaking table tests. Three seismic waves (artificial ground motion, Tohoku wave, and Tianjin wave) are selected and scaled to four intensity levels (PGA = 0.035 g, 0.1 g, 0.22 g, 0.31 g). It is found that no residual deformation of the structure is observed after tests, and its stiffness degradation ratio is 7.65%. The largest strains observed during the tests are 540 × 10⁻⁶ in beams, 1538 × 10⁻⁶ in columns, and 669 × 10⁻⁶ in joint regions, all remaining below a threshold value of 1690 × 10⁻⁶. Amplitudes and frequency characteristics of the acceleration responses are significantly affected by the characteristics of the seismic waves. However, the acceleration responses at higher floors are predominantly governed by the structure’s low-order modes (first-mode and second-mode), with the corresponding spectra containing only a single peak. When the predominant frequency of the input ground motion is close to the fundamental natural frequency of the modular steel structure, the acceleration responses will be significantly amplified. Overall, the structure demonstrates favorable seismic resistance. Full article

Background/Objectives: The objective of this study was to validate a new parent-reported scale for tracking Pediatric Acute-onset Neuropsychiatric Syndrome (PANS). PANS is a condition characterized by a sudden and severe onset of neuropsychiatric symptoms. To meet diagnostic criteria, an individual must present with either obsessive–compulsive disorder (OCD) or severely restricted food intake, accompanied by at least two additional cognitive, behavioral, or emotional symptoms. These may include anxiety, emotional instability, depression, irritability, aggression, oppositional behaviors, developmental or behavioral regression, a decline in academic skills such as handwriting or math, sensory abnormalities, frequent urination, and enuresis. The onset of symptoms is usually triggered by an infection or an abnormal immune/inflammatory response. Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is a subtype of PANS specifically linked to strep infections. Methods: We developed a 101-item PANS/PANDAS and Related Inflammatory Brain Disorders Treatment Evaluation Checklist (PTEC) designed to assess changes to a patient’s symptoms over time along 10 subscales: Behavior/Mood, OCD, Anxiety, Food intake, Tics, Cognitive/Developmental, Sensory, Other, Sleep, and Health. The psychometric quality of PTEC was tested with 225 participants. Results: The internal reliability of the PTEC was excellent (Cronbach’s alpha = 0.96). PTEC exhibited adequate test–retest reliability (r = 0.6) and excellent construct validity, supported by a strong correlation with the Health subscale of the Autism Treatment Evaluation Checklist (r = 0.8). Conclusions: We hope that PTEC will assist parents and clinicians in the monitoring and treatment of PANS. The PTEC questionnaire is freely available at neuroimmune.org/PTEC. Full article

The structural units with different characteristic scales in gradient nanostructured (GS) 316L stainless steel act synergistically to achieve the matching of strength and plasticity, and the intrinsic plasticity of nanoscale and ultrafine grains is fully demonstrated. The macroscopic stress–strain responses of each material unit in the GS surface layer can be measured directly by tension or compression tests on microspecimens. However, the experimental results based on microspecimens do not reflect either the extraordinary strengthening effect caused by non-uniform deformation or the intrinsic plasticity of nanoscale and ultrafine grains. In this paper, a method for constructing depth-dependent constitutive relationships of GS materials was proposed, which combines strain hardening parameter (hardness) with physics-informed neural networks (PINNs). First, the microhardness distribution on the specimen cross-sections was measured after stretching to different strains, and the hardness–strain–force test data were used to construct the depth-dependent PINNs model for the true strain–hardness relationship (PINNs_εH). Hardness–strain–force test data from specimens with uniform coarse grains were used to pre-train the PINNs model for hardness and true stress (PINNs_Hσ), on the basis of which the depth-dependent PINNs_Hσ model for GS materials was constructed by transfer learning. The PINNs_εσ model, which characterizes the depth-dependent constitutive relationships of GS materials, was then constructed using hardness as an intermediate variable. Finally, the accuracy and validation of the PINNs_εσ model were verified by a three-point flexure test and finite element simulation. The modeling method proposed in this study can be used to determine the position-dependent constitutive relationships of heterogeneous materials. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. Full article