Search Results (973)

Achieving consensus in group decision-making is both essential and challenging, especially in which non-cooperative behaviors can significantly hinder the process under uncertainty. These behaviors may distort consensus outcomes, leading to increased costs and reduced efficiency. To address this issue, this study proposes a data-driven robust minimum-cost consensus model (MCCM) that accounts for non-cooperative behaviors by leveraging individual adjustment willingness. The model introduces an adjustment willingness function to identify non-cooperative participants during the consensus-reached process (CRP). To handle uncertainty in unit consensus costs, Principal Component Analysis (PCA) and Kernel Density Estimation (KDE) are employed to construct data-driven uncertainty sets. A robust optimization framework is then used to minimize the worst-case consensus cost within these sets, improving the model’s adaptability and reducing the risk of suboptimal decisions. To enhance computational tractability, the model is reformulated into a linear equivalent using the duality theory. Experimental results from a case study on house demolition compensation negotiations in Guiyang demonstrate the model’s effectiveness in identifying and mitigating non-cooperative behaviors. The proposed approach significantly improves consensus efficiency and consistency, while the data-driven robust strategy offers greater flexibility than traditional robust optimization methods. These findings suggest that the model is well-suited for complex real-world group decision-making scenarios under uncertainty. Full article

This paper presents an analysis of the convergence of a numerical procedure used to evaluate the dynamic accuracy of measurement transducers, with particular emphasis on their application in energy and electromechanical systems. The main objective of the study is to assess the effectiveness of a fixed-point algorithm designed to determine test signals that satisfy time and amplitude constraints while maximizing an integral quality criterion of the “energy-optimal” type. The analysis employs numerical modeling of two types of temperature transducers: an NTC-type resistance temperature transducer and a K-type thermocouple. These models are based on a polynomial approximation method, enabling the estimation of the upper bound of the dynamic error—a key parameter in applications involving rapid changes in physical conditions, typical of energy and electromechanical systems operating under variable loads, such as industrial drives, clutches, bearings, and cooling systems, as well as in automation systems, control loops, and diagnostic frameworks. From the perspective of theoretical mechanics, temperature transducers can be modeled as a dynamic system characterized by thermal inertia, whose behavior is governed by first-order differential equations analogous to the equations of motion of a mass in a mechanically damped system. The results are presented graphically, illustrating the algorithm’s convergence behavior and computational stability. The practical application of the proposed approach can contribute to improving the accuracy of temperature transducers, enhancing error compensation algorithms, and optimizing the design of measurement systems in the energy sector and electromechanical industry, as well as in mechanical and electrical systems, especially where fast and reliable measurements under variable thermal loads on machine components are crucial. Full article

This paper addresses the challenge of trajectory tracking in non-minimum-phase systems, which are known for their limitations in performance and stability within process control. The primary objective is to evaluate the feasibility of using linear-algebra-based control strategies to achieve precise tracking in such systems. The primary hypothesis is that internal model-based compensators can transform non-minimum-phase behavior into equivalent minimum-phase dynamics, thereby enabling the application of linear algebra techniques for controller design. To validate this approach, both simulation and experimental tests are conducted, first with a Continuous Stirred Tank Reactor (CSTR) model and then with the TCLab educational platform. The results show that the proposed method effectively achieves robust trajectory tracking, even in the presence of external disturbances and sensor noise. The primary contribution of this work is to demonstrate that internal model-based compensation enables the application of linear control methods to a class of systems that are typically considered challenging to control. This not only simplifies the design process but also enhances control performance, highlighting the practical relevance and applicability of the approach for real-world non-minimum-phase systems processes. Full article

The hot deformation discipline of typical 45CrNi steel under a strain rate ranging from 0.01 s⁻¹ to 1 s⁻¹ and deformation temperature between 850 °C and 1200 °C was investigated through isothermal hot compression tests. The activation energy involved in the high-temperature deformation process was determined to be 361.20 kJ·mol⁻¹, and a strain-compensated constitutive model, together with dynamic recrystallization (DRX) kinetic models, was successfully established based on the Arrhenius theory. An improved second-phase (SP) cellular automaton (CA) model considering the influence of the pinning effect induced by SP particles on the DRX process was developed, and the established SP-CA model was further utilized to predict the evolution behavior of parent austenite grain in regard to the studied 45CrNi steel. Results show that the average absolute relative error (AARE) associated with the austenite grain size and the DRX volume fraction achieved through the simulation and experiment was overall below 5%, indicating good agreement between the simulation and experiment. The pinning force intensity could be controlled by regulating the size and volume fraction of SP particles involved in the established SP-CA model, and the DRX behavior and the average grain size of the studied 45CrNi steel treated by high-temperature compression could also be predicted. The established SP-CA model exhibits significant potential for universality and is expected to provide a powerful simulation tool and theoretical foundation for gaining deeper insights into the microstructural evolution of metals or alloys during high-temperature deformation. Full article

Background: Accurate and stable instrument positioning is critical in dental implant procedures, particularly in anatomically constrained regions. Conventional navigation systems assume a static patient head, limiting adaptability in dynamic surgical conditions. This study proposes and validates a real-time motion compensation framework that integrates optical motion tracking with a collaborative robot to maintain tool alignment despite patient head movement. Methods: A six-camera OptiTrack Prime 13 system tracked rigid markers affixed to a 3D-printed human head model. Real-time head pose data were streamed to a Kuka LBR iiwa robot, which guided the implant handpiece to maintain alignment with a predefined target. Motion compensation was achieved through inverse trajectory computation and second-order Butterworth filtering to approximate realistic robotic response. Controlled experiments were performed using the MAiRA Pro M robot to impose precise motion patterns, including pure rotations (±30° at 10–40°/s), pure translations (±50 mm at 5–30 mm/s), and combined sinusoidal motions. Each motion profile was repeated ten times to evaluate intra-trial repeatability and dynamic response. Results: The system achieved consistent pose tracking errors below 0.2 mm, tool center point (TCP) deviations under 1.5 mm across all motion domains, and an average latency of ~25 ms. Overshoot remained minimal, with effective damping during motion reversal phases. The robot demonstrated stable and repeatable compensation behavior across all experimental conditions. Conclusions: The proposed framework provides reliable real-time motion compensation for dental implant procedures, maintaining high positional accuracy and stability in the presence of head movement. These results support its potential for enhancing surgical safety and precision in dynamic clinical environments. Full article

The invertebrate neuropeptide F (NPF) signaling plays versatile roles in diverse biological activities and processes. Still, whether and how it mediates feeding and digestion in Pomacea canaliculate remain gaps in our knowledge. Herein, we first identified and characterized PcNPFR via bioinformatics analysis in P. canaliculate, which is a polyphagous herbivore with a voracious appetite that causes devastating damages to ecosystem functioning and services in colonized ranges. Double stranded RNA (dsRNA)-based RNA interference (RNAi) and exogenous rescue were utilized to decipher and substantiate underlying mechanisms whereby NPFR executed its modulatory functions. Multiple sequence alignment and phylogeny indicated that PcNPFR harbored typical seven transmembrane domains (7 TMD) and belonged to rhodopsin-like GPCRs, with amino acid sequence sharing 27.61–63.75% homology to orthologues. Spatio-temporal expression profiles revealed the lowest abundance of PcNPFR occurred in pleopod tissues and the egg stage, while it peaked in male snails and testes. Quantitative real-time PCR (qRT-PCR) analysis showed that 4 µg dsNPFR and 10⁻⁶ M trNPF (NPFR agonist) were optimal doses to exert silencing and rescue effects, accordingly with sampling time at 3 days post treatments. Moreover, the dsNPFR injection (4 µg) at 1/3/5/7 day/s delivered silencing efficiency of 32.20–74.01%. After 3 days upon dsNPFR knockdown (4 µg), mRNA levels of ILP7/InR/Akt/PI3Kc/PI3K_R were significantly downregulated compared to dsGFP controls, except FOXO substantially upregulated at both transcript and translation levels. In addition, the activities of alpha-amylase, protease and lipase were significantly suppressed, accompanied by decreased leaf area consumption, attenuated feeding behavior and diminished feeding rate. Moreover, expression trends were opposite and proxies were partially or fully restored to baseline levels post exogenous compensation of trNPF, suggesting phenotypes specifically attributable to PcNPFR RNAi but not off-target effects. PcNPFR is implicated in both feeding and digestion by modulating the ISP pathway and digestive enzyme activities. It may serve as a promising molecular target for RNAi-based antifeedants to manage P. canaliculate invasion. Full article

Aeolian sand transport on beaches is strongly affected by surface moisture, but its influence on transport evolution remains poorly understood. We present a novel discrete particle modeling framework to systematically investigate how moisture from liquid bridges affects the development of transport toward steady state after initiation. Moist sediment particles are modeled using a particle-based approach with evolving liquid bridges coupled to a one-dimensional airflow solver. The model captures realistic grain-scale collision dynamics under moist conditions and reproduces key features of aeolian transport in the dry limit. Simulations reveal two distinct behaviors: In steady state, the transport rate remains insensitive to moisture as lower saltation concentrations are compensated by higher saltation velocities; in the transient phase, however, increasing moisture prolongs the growth phase and delays the peak in transport rate. This delay arises because sand concentration peaks increasingly later than mean saltation velocity as moisture content increases. By projecting the temporal model results into a spatial representation, the position of peak transport is found to scale linearly with wind shear velocity, showing greater sensitivity at higher moisture levels. A preliminary test suggests that evaporation is essential for the initial grain dislodgement by wind alone but is unlikely to affect steady-state transport. This study explains the longer fetch distances observed on moist beaches before transport equilibrates and provides a physics-based tool for predicting sediment transport under varying moisture conditions. Full article

Stimulated Brillouin scattering (SBS) in microresonators offers a unique way to develop narrow-linewidth chip-scale lasers. Yet their coherence performance is hindered by the cascaded SBS process, which clamps the output power and broadens the fundamental linewidth of the first-order Stokes wave. Resonance splitting proves to be an effective approach to suppress intracavity SBS cascading. However, precisely aligning and controlling the resonance splitting behavior remains challenging. We address these issues by proposing a piezoelectrically actuated grating-embedded tantalum pentoxide (Ta₂O₅) microring resonator. This microresonator comprises a Bragg grating segment that induces a counter-propagating wave and a ring segment that is integrated with a lead zirconate titanate (PZT) actuator. The half-circumference Bragg grating has a peak reflectivity of 31% at 1549.8 nm and a bandwidth of 88.89 pm, which is narrow enough to ignite resonance splitting in only one azimuthal mode. The PZT actuator empowers the resonator with a frequency tuning rate of 0.1726 GHz/V, particularly useful for post-fabrication compensation and splitting control. The proposed architecture offers a promising solution to breaking the intracavity cascaded SBS chain with frequency tuning capability, paving the way towards highly coherent chip-scale laser sources. Full article

Background/Objectives: Neural compensation, in which healthy brain regions take over functions lost due to lesions, is a potential biomarker for functional recovery after stroke. However, previous neuroimaging studies often speculated on neural compensation simply based on greater measures in patients (compared to healthy controls) without demonstrating a more direct link between these measures and the functional recovery. Because taking over the function of a lesion region means taking on a similar role as that lesion region in its functional network, the present study attempted to explore neural compensation based on the similarity of functional connectivity (FC) patterns between a healthy regions and lesion regions. Methods: Seventeen stroke patients (13M4F, 63.2 ± 9.1 y.o.) underwent three resting-state functional MRI (rs-fMRI) sessions during rehabilitation. FC patterns of their lesion regions were derived by lesion network analysis; and these patterns were correlated with healthy FC patterns derived from each brain voxel of 51 healthy subjects (32M19F, 61.0 ± 14.3 y.o.) for the assessment of pattern similarity. Results: We identified five healthy regions showing decreasing FC similarity (29–54%, all corrected p < 0.05, effect size η²: 0.10–0.20) to the lesion network over time. These decreasing similarities were associated with increasing behavioral scores on activities of daily living (ADL, p < 0.001, η² = 0.90), suggesting greater neural compensation at early-stage post-stroke and reduced compensation toward the end of effective rehabilitation. Conclusions: Besides direct FC measures, the present results propose an alternative biomarker of neural compensation in functional recovery from stroke. For sensorimotor recoveries like ADL, this biomarker could be more sensitive than direct measures of lesion connectivity in the motor network. Full article

Loop heat pipes are efficiently two-phase heat transfer devices in the field of aircraft thermal management. To investigate the startup behavior and thermal instability of loop heat pipes under acceleration force, this study designed a novel loop heat pipe featuring two visual compensation chambers and a visual condenser. Elevated acceleration experiments were conducted across four different heat loads, acceleration magnitudes, and directions. The heat load ranged from 30 W to 150 W, while the acceleration magnitude varied from 1 g to 15 g, with four acceleration directions (A, B, C, and D). The startup behavior, thermal instability, internal flow pattern, and phase distribution were analyzed systematically. The experimental results reveal the following: (i) The startup behaviors vary across the four acceleration directions. In direction A, startup is more difficult due to additional resistance induced by the acceleration force. In direction C, startup time generally decreases with increasing heat load and acceleration up to 7 g. The longest startup time observed is 372 s at 30 W and 11 g. (ii) At high heat load, periodic temperature fluctuations are observed, particularly in directions B and C. Simultaneously, the vapor–liquid phase interface in the condenser exhibits periodic back-and-forth movement. (iii) The visual DCCLHP exhibits a loss of temperature control under the combined influence of high heat loads and acceleration force, often accompanied by working fluid reverse flow, periodic temperature fluctuations, or wick dry-out. Full article

The low water solubility of sulfamethazine (SMT) limits its clinical efficacy, making it crucial to study techniques such as cosolvency to optimize pharmaceutical formulations. This study aimed to thermodynamically evaluate the solubility of SMT in {acetonitrile (MeCN) + ethanol (EtOH)} cosolvent mixtures over a temperature range of 278.15 to 318.15 K in order to understand the molecular interactions that govern this process. SMT solubility in the mixtures was measured using a flask-shaking method. The solid phases were analyzed using differential scanning calorimetry (DSC) to rule out polymorphisms. Using the Gibbs–van’t Hoff–Krug model, we calculated the apparent thermodynamic functions of the solution and mixture from the obtained data. The results showed that solubility increased almost linearly with MeCN fraction and temperature, indicating that MeCN is a more efficient solvent and that the process is endothermic. Thermodynamic analysis revealed that dissolution is an endothermic process with favorable entropy for all compositions. The higher solubility in MeCN is attributed to the lower energetic cost required to form the solute cavity compared to the high energy needed to disrupt the hydrogen bond network of ethanol. This behavior can be explained by an enthalpy–entropy compensation phenomenon. This phenomenon provides an essential physicochemical basis for designing pharmaceutical processes. Full article

This document focuses on the mitigation of subsynchronous resonance (SSR) in doubly fed induction generators (DFIGs) through the application of an effective solution based on the use of a Static Synchronous Compensator (STATCOM) with fuzzy logic. The STATCOM, a static parallel compensator, improved the stability, quality, and reliability of the power supply in distribution systems by optimizing the response to voltage fluctuations. Combined with fuzzy logic, it provided flexible and efficient control, reducing oscillations arising in the system. Two case studies were carried out in which the DFIG and the STATCOM module with fuzzy logic were implemented in IEEE 13- and IEEE 33-bus systems. Comparative analyses with and without compensation were performed to assess the system’s behavior in response to oscillations generated by the generator, taking voltages as the main variable. The results showed that the fuzzy–PI controlled STATCOM effectively stabilized voltage profiles, mitigating SSR and improving system reliability. In the IEEE 13-bus case, voltage oscillations were reduced by approximately 72% and the bus voltages converged to 0.99–1.01 p.u. within 1.5 s. In the IEEE 33-bus system, the controller achieved a suppression rate of 68%, with voltages restored to 0.98–1.02 p.u. in less than 2 s. These findings demonstrate the efficiency of the proposed fuzzy–PI STATCOM in suppressing subsynchronous oscillations and enhancing stability in DFIG-based networks. Full article

Deep Reinforcement Learning (DRL) has achieved remarkable success in robotic control, autonomous driving, and game-playing agents. However, its decision-making process often remains a black box, lacking both interpretability and verifiability. In robotic control tasks, developers cannot pinpoint decision errors or precisely adjust control strategies based solely on observed robot behaviors. To address this challenge, this work proposes an interpretable DRL framework based on a Causal Correction and Compensation Network (C²-Net), which systematically captures the causal relationships underlying decision-making and enhances policy robustness. C²-Net integrates a Graph Neural Network-based Neural Causal Model (GNN-NCM) to compute causal influence weights for each action. These weights are then dynamically applied to correct and compensate the raw policy outputs, thereby balancing performance optimization and transparency. This work validates the approach on OpenAI Gym’s Hopper, Walker2d, and Humanoid environments, as well as the multi-agent AzureLoong platform built on Isaac Gym. In terms of convergence speed, final return, and policy robustness, experimental results show that C²-Net achieves higher performance over both non-causal baselines and conventional attention-based models. Moreover, it provides rich causal explanations for its decisions. The framework represents a principled shift from correlation to causation and offers a practical solution for the safe and reliable deployment of multi-robot systems. Full article

Clear aligners (CAs) have emerged as a widely accepted alternative to conventional fixed orthodontic appliances due to their aesthetic appeal, comfort, and removability. Despite their increasing use, the precise biomechanical behavior of CAs—particularly in relation to maxillary arch expansion and torque control—remains incompletely understood. This scoping review aims to synthesize and critically examine the recent body of evidence derived from finite element analysis (FEA) studies investigating the performance of clear aligners in managing transverse discrepancies and controlling tooth movement. It considered studies published up to April 2025. All included FEA studies assumed dental and bone tissues as linearly elastic, homogeneous, and isotropic, unless otherwise specified. Five in silico studies were included, all employing three-dimensional FEA models to assess the influence of various clinical and design parameters, such as aligner thickness, movement sequence, attachment configuration, and torque compensation. The findings consistently show that movement protocols involving alternating activation patterns and specific attachment designs can significantly improve the efficiency of maxillary expansion, while reducing undesired tipping or anchorage loss. Additionally, greater aligner thicknesses were generally associated with increased force delivery and more pronounced tooth displacement. Although FEA provides a powerful tool for visualizing stress distribution and predicting mechanical responses under controlled conditions, the lack of standardized force application and limited clinical validation remain important limitations. These findings underscore the potential of optimized aligner protocols to enhance treatment outcomes, but they also highlight the need for complementary in vivo studies to confirm their clinical relevance and guide evidence-based practice. Full article

Ru₃Sn₇ crystallizes in the cubic Ir₃Ge₇-type structure (space group Im3m), a class of intermetallic compounds. Previous studies focused primarily on its crystal structure, band calculations, and basic transport properties. Here, we report a systematic investigation of high-quality single crystals via electrical resistivity, Hall effect, specific heat, and thermal transport measurements. The T₃X₇ intermetallic family—with its diverse electronic ground states—provides an ideal platform for exploring such topology–property relationships. Ru₃Sn₇ exhibits metallic behavior, with consistent Hall effect and Seebeck coefficient data indicating a compensated electron-hole two-band system. Temperature-dependent modulation of electronic states near the Fermi surface alters charge carrier transport, which may imply the presence of a Lifshitz transition in Ru₃Sn₇. More importantly, magnetic quantum oscillations are observed for the first time, confirming the presence of two Dirac points in its band structure. Full article