Search Results (68)

Background: Large language models (LLMs) are increasingly considered in dentistry, yet their accuracy in orthodontic radiology remains uncertain. This study evaluated the performance of ChatGPT-4 on questions aligned with current radiology guidelines. Methods: Fifty short, guideline-anchored questions were authored; thirty were pre-selected a priori for their diagnostic relevance. Using the ChatGPT-4 web interface in March 2025, we obtained 30 answers per item (900 in total) across two user accounts and three times of day, each in a new chat with a standardised prompt. Two blinded experts graded all responses on a 3-point scale (0 = incorrect, 1 = partially correct, 2 = correct); disagreements were adjudicated. The primary outcome was strict accuracy (proportion of answers graded 2). Secondary outcomes were partial-credit performance (mean 0–2 score) and inter-rater agreement using multiple coefficients. Results: Strict accuracy was 34.1% (95% CI 31.0–37.2), with wide item-level variability (0–100%). The mean partial-credit score was 1.09/2.00 (median 1.02; IQR 0.53–1.83). Inter-rater agreement was high (percent agreement: 0.938, with coefficients indicating substantial to almost-perfect reliability). Conclusions: In the conditions of this study, ChatGPT-4 demonstrated limited strict accuracy yet substantial reliability in expert grading when applied to orthodontic radiology questions. These findings underline its potential as a complementary educational and decision-support resource while also highlight its present limitations. Its role should remain supportive and informative, never replacing the critical appraisal and professional judgement of the clinician. Full article

Produced water treatment (PWT) is a critical process in offshore oil and gas production, ensuring compliance with stringent environmental discharge regulations and minimizing environmental impact. This process is characterized by inherent nonlinearities, coupled system dynamics, and the presence of significant disturbances that can impede operational efficiency and separation performance. Effective control strategies are essential to maintain stable operation and high separation efficiency under dynamic and uncertain conditions. This paper presents a comprehensive evaluation of advanced control methods applied to a pilot-scaled PWT facility designed to replicate offshore conditions. Four control solutions are assessed, i.e., (i) baseline approach using PID controllers; (ii) Multi-Input–Multi-Output (MIMO) $H_{\infty }$ control; (iii) MIMO Model Predictive Control (MPC); and (iv) MIMO Model Reference Adaptive Control (MRAC). The motivation lies in their differing capabilities for disturbance rejection, tracking accuracy, robustness, and computational feasibility. Real-world operational data were used to assess each strategy in regulating critical process variables, the interface water level in the three-phase gravity separator, and the pressure drop ratio (PDR) in the hydrocyclone, both closely linked to de-oiling efficiency. The results highlight the distinct advantages and limitations of each method. In general, the baseline PID solution offers simplicity but limited adaptability, while advanced strategies such as MIMO $H_{\infty }$, MPC, and MRAC solutions demonstrate enhanced reference-tracking and de-oiling performances subject to diverse operating conditions and disturbances, though different control solutions still exhibit different dynamic characteristics. The findings provide systematic insights into selecting optimal control architectures for offshore PWT systems, supporting improved operational performance and reduced environmental footprint. Full article

Background: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. Methods: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions. Results: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system. Conclusions: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system. Full article

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model. Full article

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although various approaches have been proposed to address this issue, their implementation is often challenging due to complexity or uncertain performance. In this study, we introduce the BSN application, whose name is taken from the BSN project. The application is designed for analyzing contact binary system light curves, supporting photometric data, and employing an MCMC algorithm for efficient parameter estimation. The BSN application generates synthetic light curves more than 40 times faster than PHOEBE during the MCMC fitting process. The BSN application enhances light curve analysis with an expanded feature set and a more intuitive interface while maintaining compliance with established scientific standards. In addition, we present the first light curve analyses of four contact binary systems based on the TESS data, utilizing the BSN application version 1.0. We also conducted a light curve analysis using the PHOEBE Python code and compared the resulting outputs. Two of the target systems exhibited asymmetries in the maxima of their light curves, which were appropriately modeled by introducing a cold starspot on one of the components. The estimated mass ratios of these total-eclipse systems place them within the category of low mass ratio contact binary stars. The estimation of the absolute parameters for the selected systems was carried out using the $P-a$ empirical relationship. Based on the effective temperatures and masses of the components, three of the target systems were classified as A-subtype, while TIC 434222993 was identified as a W-subtype system. Full article

In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. The model conducts quantitative analyses of workshop layout, assembly line design, worker status, operating status of robotic arms and AGV vehicles, and production system failure rates. A hybrid NEH-DDQN algorithm is integrated into the simulation model via a COM interface and DLL, where the NEH algorithm ensures the model maintains optimal performance during the early training phase. Four scheduling strategies are designed for workpiece allocation across different workshops. A deep neural network replaces the traditional Q-table for greedy selection among these four scheduling strategies, using each workshop’s completion time as a simplified state variable. This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. Experimental comparisons show that for the data of 500 workpieces, the NEH algorithm in 3 s demonstrates equivalent quality to that produced by the GA algorithm in 300 s. After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue. Full article

Plant fibers (PFs) have been increasingly employed in cement-based gel composites (CCs) on account of their excellent mechanical properties, toughness and sustainability. Researchers have engaged in a lot of studies on plant fiber-reinforced cement-based gel composites (PFRCCs). Based on these studies, the chemical components and mechanical characteristics of PFs are summed up in this review. In addition, the modification methods for matrices and PFs are also discussed. The mechanical properties of PFRCCs, including static and dynamic properties, are reviewed. Predictive equations for the mechanical properties of PFRCCs are summarized in this paper. In the end, the characteristics of the interface transition zones between PFs and CCs are analyzed. According to the results of previous studies, the addition of PFs can enhance the flexural strength and tensile strength of CCs, but it can have an uncertain effect on compressive strength. The elastic modulus and fracture behavior of PFRCCs increases with the addition of PFs. At the same time, modification methods have been proved to be useful in reducing the degradation of PFs in CCs. Generally speaking, PFRCCs are new building materials which have extensive application prospects. The aim of this review is to help researchers understand the mechanical properties of PFRCCs and to promote the application of PFRCCs in future projects. Full article

Background: Textural Interface Opacities (TIOs) following Descemet’s Stripping Automated Endothelial Keratoplasty (DSAEK) have become a notable postoperative concern. Several studies have attempted to identify associations between TIO development and intraoperative factors, including fluid dynamics, irregular stromal surfaces, viscoelastic usage, and recipient immunological responses. Despite these efforts, the precise etiology of TIO remains uncertain. TIO has not been considered predictable in the preoperative setting. Its detection has relied exclusively on slit-lamp biomicroscopy, a subjective approach lacking standardized diagnostic criteria, which limits diagnostic reliability and is highly susceptible to interobserver variability. Methods: Optical Coherence Tomography (OCT) images of DSAEK-processed corneal grafts, prepared using the same microkeratome and technique for transplantation at the Bascom Palmer Eye Institute, underwent blinded analysis using a newly developed grading scale termed “M-TIO”. This analysis focused on DSAEK-processed grafts OCT images to evaluate and categorize the occurrence of TIO and assess the final visual acuity of the patients at the 1-year postoperative evaluation. Results: Based on the results, the M-TIO grading scale demonstrated strong predictive value, with higher grades on OCT of DSAEK lenticules consistently associated with worse postoperative visual acuity. The study included 221 donor corneas transplanted from 2019 to 2023. Greater TIO based on the “M-TIO” grading scale was associated with worse recipient logMAR VA (Mean 0.151 [99% CI: 0.077 to 0.225] for corneas with no TIO, increased to 0.680 [99% CI: 0.532 to 0.828] for corneas with the greatest TIO grade). These findings highlight the clinical utility of the M-TIO scale as an objective and reliable preoperative tool for assessing graft quality and predicting postoperative visual outcomes. Conclusions: This study introduces the “M-TIO” grading scale, which provides a standardized and objective method for evaluating Textural Interface Opacities in DSAEK grafts prior to transplantation. Our results demonstrate a clear association between the severity of TIO as graded by the M-TIO scale, and postoperative visual outcomes, with higher TIO grades correlating with worse visual acuity, emphasizing its value in improving graft selection, and clinical decision-making in DSAEK. Full article

The rapid expansion of the Internet of Things (IoT) has made software security and reliability a critical concern. With multi-language programs running on edge computing, embedded systems, and sensors, each connected device represents a potential attack vector, threatening data integrity and privacy. Symbolic execution is a key technique for automated vulnerability detection. However, unknown function interfaces, such as sensor interactions, limit traditional concrete or concolic execution due to uncertain function returns and missing symbolic expressions. Compared with system simulation, the traditional method is to construct an interface abstraction layer for the symbolic execution engine to reduce the cost of simulation. Nevertheless, the disadvantage of this solution is that the manual modeling of these functions is very inefficient and requires professional developers to spend hundreds of hours. In order to improve efficiency, we propose an LLM-based automated approach for modeling unknown functions. By fine-tuning a 20-billion-parameter language model, it automatically generates function models based on annotations and function names. Our method improves symbolic execution efficiency, reducing reliance on manual modeling, which is a limitation of existing frameworks like KLEE. Experimental results primarily focus on comparing the usability, accuracy, and efficiency of LLM-generated models with human-written ones. Our approach was integrated into one verification platform project and applied to the verification of smart contracts with distributed edge computing characteristics. The application of this method directly reduces the manual modeling effort from a month to just a few minutes. This provides a foundational validation of our method’s feasibility, particularly in reducing modeling time while maintaining quality. This work is the first to integrate LLMs into formal verification, offering a scalable and automated verification solution for sensor-driven software, blockchain smart contracts, and WebAssembly systems, expanding the scope of secure IoT development. Full article

Nitrate is the most prevalent inorganic pollutant in aquatic environments, posing a significant threat to human health and the ecological environment, especially in lakes and groundwater, which are located in the high agricultural activity intensity areas. In order to reveal the sources of nitrogen pollution in lakes and groundwater, this study of the transformation mechanism of nitrogen in the interaction zone between lakes and groundwater has become an important foundation for pollution prevention and control. The coupling effect between the biogeochemical processes of nitrate and iron has been pointed out to be widely present in various water environments in recent years. However, the impact of iron minerals on nitrate reduction in the lake–groundwater interaction zone of a high-salinity environment still remains uncertain. Based on the sediment and water chemistry characteristics of the Chagan Lake–groundwater interaction zone in northeastern China (groundwater TDS: 420~530 mg/L, Na⁺: 180~200 mg/L, and Cl⁻: 15~20 mg/L and lake water TDS: 470~500 mg/L, Na⁺: 210~240 mg/L, and Cl⁻: 71.40~87.09 mg/L), this study simulated relative oxidizing open system conditions and relative reducing closed conditions to investigate hematite and siderite effects on nitrate reduction and microbial behavior. The results indicated that both hematite and siderite promoted nitrate reduction in the closed system, whereas only siderite promoted nitrate reduction in the open system. Microbial community analysis indicated that iron minerals significantly promoted functional bacterial proliferation and restructured community composition by serving as electron donors/acceptors. In closed systems, hematite addition preferentially enriched Geobacter (denitrification, +15% abundance) and Burkholderiales (DNRA, +12% abundance), while in open systems, siderite addition fostered a distinct iron-carbon coupled metabolic network through Sphingomonas enrichment (+48% abundance), which secretes organic acids to enhance iron dissolution. These microbial shifts accelerated Fe(II)/Fe(III) cycling rates by 37% and achieved efficient nitrogen removal via combined denitrification and DNRA pathways. Notably, the open system with siderite amendment demonstrated the highest nitrate removal efficiency (80.6%). This study reveals that iron minerals play a critical role in regulating microbial metabolic pathways within salinized lake–groundwater interfaces, thereby influencing nitrogen biogeochemical cycling through microbially mediated iron redox processes. Full article

Background: The sense of agency (SoA)—the subjective experience of controlling one’s own actions and their consequences—is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human–machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments. Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inference, and optimal control theories—to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain’s own inference and control mechanisms. Methods: I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear–Quadratic–Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts. Results: The SoA emerges from hierarchical inference processes that combine top–down motor intentions with bottom–up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency. Conclusions: Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation. Full article

Jason Allen-Paisant in Self-Portrait as Othello moves unflinchingly through complex histories and genealogies that widen to include Jamaica, Venice, Italy, France, and elsewhere and to locate the duppy manifestations of an unburied past in the pervasive precariousness of Black life. Across his poems, he tracks the chaotic reverberations of intergenerational traumas that persist across time, space and collective memory. This paper contends that the poet, through his use of allusion evident in his grafting and borrowings of other stories, literary syncretism, the symbolism of foreignness and its mysterious power, back and forth journeys through Europe and into homelands (Jamaica), procures an integrated circuit of Black meaning and kindred relations. This interconnectedness lays bare the sociohistorical conditions that have and continue to circumscribe and assault Black lives and deconstructs the perpetuity of anti-Black systems in the modern Western world. For all his worldly travels, the poet-narrator situates himself in an interstitial zone where each crossroad leads to new possibilities and affirmative energy. Allen-Paisant thus offers a way to reconcile a vicious history of Black xenophobia while procuring moments and processes to make peace with rupturous spaces, which necessitates a return to his homeland. However, homecoming complicates the search for self and the idea of return draws him into a dialogue with the fragmented inheritances of his past. He ultimately achieves coherence and fresh understandings through images of sterility and barrenness which he re-purposes as a foundation to make bold leaps of faith across uncertain chasms. This paper thus argues that for the poet of the African diaspora, who aspires to recover a long and complex spiritual history, the interface between domestic and international dramas highlights the luminous transcendence embodied in the journey along complicated routes and the steadfast pursuit of ideas that illuminate the deepest insights about identity, culture and the Black experience. Full article

Canine adenovirus type 1 (CAdV-1) and type 2 (CAdV-2) are well known pathogens of domestic dogs but are little investigated in wild animals. The few available studies about CAdV-1 in wild animals show that it circulates in various species and that transmission of the virus in the interface between wildlife and domestic animals is a frequent event. Furthermore, wild animals are usually subject to asymptomatic infections, but cases of serious and fatal diseases have been documented, with possible effects on the conservation of the species. In contrast, CAdV-2 infection was reported only recently and sporadically in some wild animals, with few data regarding its pathogenic role in these species. However, the real prevalence of these viruses in wildlife is still uncertain due to the use of serological tests that are largely unable to distinguish antibodies against CAdV-1 and CAdV-2. This review, reporting all the data currently available on CAdV-1 and CAdV-2 infection in wild animals, highlights the importance of these pathogens for wildlife conservation and their role in the potential transmission of the infection to domestic dogs. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article