ABSTRACT Urban traffic congestion remains a pressing challenge, requiring integrated signal control and lane management. This study proposes a strategy that combines a rule-based dynamic shared bus lane (DSBL) controller with modified NEMA signal control featuring transit signal priority. DSBL permits general vehicles to use the bus lane based on demand, while the signal controller adjusts phase timings to serve buses and maintain intersection efficiency. An enhanced cell transmission model captures intersection flow dynamics and supports coordinated operation. Simulations at a single intersection under eight saturation levels show that the proposed strategy reduces delay and congestion, with the largest gains under oversaturated conditions, compared with intermittent bus-priority lanes, DSBL with fixed-time signals, and dedicated bus lanes with fixed-time signals. Dynamic lane sharing consistently outperforms non-sharing schemes. Sensitivity tests further indicate that larger bus headways and longer approach links can reduce total passenger delay.

The rapid urbanization of cities has exacerbated traffic congestion, resulting in significant environmental impacts, including elevated greenhouse gas emissions and deteriorating air quality. Traffic management systems, while effective in many contexts, often fail to consider the ecological and dynamic complexities of modern urban environments. This paper introduces MM-STMAP, a framework for urban traffic management that integrates multi modal perception with deep reinforcement learning. The approach utilizes a spatio temporal graph convolutional network to model intricate traffic patterns across diverse urban environments, while incorporating real-time environmental data, including meteorological factors, to address the ecological limitations of traditional traffic systems. A linear attention mechanism is employed to optimize computational efficiency in processing large-scale, dynamic traffic data, thereby enhancing both operational performance and energy consumption. The multi agent reinforcement learning structure governs the coordination of traffic signals across intersections, achieving a dual optimization of reduced vehicular delays and minimized emissions. Empirical evaluations on major metropolitan datasets demonstrate that MM-STMAP outperforms existing traffic management methods and significantly enhances traffic flow efficiency. The model's ability to integrate heterogeneous data streams spanning traffic sensors and environmental reports enables a comprehensive and adaptive approach to urban mobility, supporting the development of sustainable smart city infrastructure.

Flexible prioritisation in working memory (WM) is supported by neural oscillations in frontal and sensory brain areas, but the roles of different oscillations remain poorly understood. Recordings in humans suggest an interplay between prefrontal slow frequency (2-8Hz) and posterior alpha-band (10Hz) oscillations regulating top-down control and retrieval of WM representations, respectively. Complementary work, primarily in non-human primates, suggests an additional role for beta (15-30Hz) oscillations in clearing or inhibiting stimuli from entering WM. Here we investigated the role of neural oscillations in prioritising WM content using electroencephalography (EEG) as participants (humans of any sex) performed a task requiring frequent priority switches between two memorized oriented bars. Behavioural performance revealed switch costs, which scaled with the angular distance between the two items, suggesting that priority shifts are modulated by shift magnitude. Time-frequency analyses revealed increased frontal theta (4-8Hz) and decreased central-parietal beta (15-25Hz) power during switches. Crucially, only beta power scaled with the magnitude of the priority shift and predicted the fidelity of neural decoding of the newly prioritized item during subsequent recall. Theta power, by contrast, was elevated on switch trials but did not vary with update magnitude or decoding strength, suggesting a more general role in signaling control demands. Our findings highlight a particular and previously overlooked role for beta-band oscillations in the flexible prioritisation of WM content.Significance Statement Working memory permits flexible switching between mental representations, so we can focus on what is most relevant at the moment. Different brain rhythms in frontal control and sensory memory storage areas orchestrate switches but their respective roles remain unclear. Here, using EEG, we show that power reductions of ∼20Hz oscillations over central-parietal regions, usually associated with the motor system, closely track the magnitude of the required switch and the fidelity of the prioritized memory. In contrast, slower 4-8Hz (theta-band) activity over frontal regions increases during priority switches but tracks neither magnitude nor fidelity. Our findings suggest a unique function for central-parietal beta oscillations in the flexible control of working memory.

With the rapid increase of automation and intelligent assistance in modern traffic operations, human–machine interaction (HMI) has become central to system effectiveness and operational safety. In such high-stakes environments, operators often need to manage complex information flows and dynamic decision-making processes, making situation awareness (SA) a critical determinant of performance. However, the growing reliance on automated systems raises the risk of SA degradation, potentially undermining safety and efficiency. Eye-tracking has emerged as a promising tool to monitor operator SA, but in real-world traffic control environments, data incompleteness and signal loss remain major obstacles. To address this challenge, we introduce a robust eye-tracking-enabled SA recognition framework, the Masked AutoEncoder for EYE-tracking data (MAEYE). MAEYE integrated CNN modules and Transformer layers to effectively capture both structural patterns and temporal dynamics of eye movements. Leveraging a self-supervised learning paradigm, it demonstrates strong resilience against incomplete data, outperforming state-of-the-art methods under varying levels of data loss. An SA-probe experiment with 26 participants validated its effectiveness, confirming reliable and accurate SA decoding from imperfect gaze input. By enabling dependable SA monitoring in traffic operations HMI, this work advances the development of safer, more resilient, and human-centred automation systems for future mobility and transportation management. Highlights A robust eye-tracking–based approach is proposed for SA recognition in traffic operations. Self-supervised learning enhances representation robustness under noisy or incomplete eye-tracking inputs. Adaptive masking strategies in autoencoder training are systematically evaluated for robust feature learning. The method consistently outperforms state-of-the-art baselines across multiple evaluation metrics. Reliable SA recognition supports adaptive HMI for safer traffic management.

Network-based integration of hsa-miR-150-5p reveals key regulatory roles in HIV-HCV coinfection and HCV-associated hepatocellular carcinoma.

ERLIN1: A central regulator of protein quality control, lipid homeostasis, and cellular signaling at the endoplasmic reticulum.

Three-phase induction motor drive systems, especially in elevator applications and other precision motion systems, require optimized acceleration profiles to minimize vibrations and extend mechanical lifespan. Previous studies have primarily focused on fast speed response control but often overlooked the impact of jerk, which affects smoothness and operational safety. This paper proposes a combination of field-oriented control (FOC) and S-curve acceleration profiles to reduce jerk and improve motion quality. A dynamic model of the drive system is developed to simulate the acceleration process, demonstrating that the S-curve significantly reduces torque and current oscillations, thus enhancing stability. The S-curve trajectory generation algorithm is implemented and deployed on a field programmable gate array (FPGA) hardware platform. Experimental hardware results confirm that the generated speed control signals possess high resolution and fast response, making the method suitable for embedded control systems in elevator drives and other sensitive motion-control applications. This integrated approach not only addresses the limitations of previous methods but also provides a practical solution to improve comfort, safety, and durability in various electromechanical drive systems.

Objective.Upper limb amputation severely limits daily activities and independence. Current prosthetic control methods often rely on surface electromyography (sEMG), which suffers from low signal quality and limited functionality. This study investigates whether implanted electrodes in regenerative peripheral nerve interfaces (RPNIs) and residual innervated muscles can provide stable and high-quality control signals to improve dexterous prosthetic hand and wrist function.Approach.Two individuals with upper-limb amputation had RPNIs created by suturing free skeletal muscle grafts to peripheral nerves or nerve fascicles in the residual limb. Intramuscular EMG (iEMG) electrodes were implanted into the RPNIs and muscles in the residual limb. EMG signals were recorded from both sEMG and iEMG electrodes and used to control a virtual prosthetic hand + wrist in real time. Performance was assessed through multiple degrees-of-freedom (DoF) control tasks, comparing RPNIs and iEMG against conventional sEMG.Main Results.Implanted electrodes demonstrated high signal-to-noise ratios and long-term stability, enabling independent and simultaneous control of multiple hand + wrist DoFs. Participants achieved faster, more accurate, and more reliable control using RPNIs and iEMG-based control compared with sEMG-based systems, based on classification accuracy and trial success rate. Importantly, we find that the ability to control wrist rotation reduces total body compensations when performing a functional assessment (Coffee Task), and implanted electrodes greatly reduced task completion times compared to surface electrodes when wrist rotation was added as an additional control movement.Significance.In this study, we demonstrate that RPNIs and iEMG electrodes in combination enable significantly more accurate and stable prosthetic control of hand and wrist movements compared to surface electrodes, especially during dynamic arm movements. These findings suggest that RPNIs and iEMG electrodes offer meaningful advantages over sEMG for achieving more intuitive and reliable control of upper-limb prostheses in real-world conditions.

Light-triggered electrochemical biosensor using singlet oxygen for self-powered operation and glucose detection.

Reducing Time-of-Day Traffic Signal Controller Transitions through Collective Offset Adjustments

Real-time traffic signal control using radio frequency identification and IQRF in distributed urban measurement systems

NCT02810171.

Renal ischemia–reperfusion injury (IRI) is a leading trigger of acute kidney injury (AKI), a syndrome with high incidence and mortality worldwide. The kidney is among the most energy-demanding organs; its mitochondrial content is second only to the heart, rendering renal function highly contingent on mitochondrial integrity. Accumulating evidence places mitochondria at the center of IRI pathogenesis. During ischemia, ATP depletion, ionic disequilibrium, and Ca2+ overload set the stage for injury; upon reperfusion, a burst of mitochondrial reactive oxygen species (mtROS), collapse of the mitochondrial membrane potential (ΔΨm), aberrant opening of the mitochondrial permeability transition pore (mPTP), mitochondrial DNA (mtDNA) damage, and release of mitochondrial damage-associated molecular patterns (mtDAMPs) further amplify inflammation and drive regulated cell-death programs. In recent years, the centrality of mitochondrial bioenergetics, quality control, and immune signaling in IRI-AKI has been increasingly recognized. Building on advances from the past five years, this review synthesizes mechanistic insights into mitochondrial dysfunction in renal IRI and surveys mitochondria-targeted therapeutic strategies—including antioxidant defenses, reinforcement of mitochondrial quality control (biogenesis, dynamics, mitophagy), and modulation of mtDAMP sensing—with the aim of informing future translational efforts in AKI.

This article investigates the mode cluster-based event-triggered control (MCETC) of stochastic Markovian jump systems (SMJSs) under denial-of-service (DoS) attack. First, a novel MCETC framework is designed by considering the interplay among subsystems, DoS attacks, and the event-triggered mechanism (ETM). In this framework, the controller mode is reconstructed, and the number of controller modes is reduced by reclustering the system modes. It significantly reduces the conservatism of the system compared to existing mode-dependent/-independent controllers. Second, a switching ETM is designed for scenarios with and without DoS attack activation, which can effectively save network bandwidth resources and reduce computational load. Third, a multi-Lyapunov function based on DoS attacks is proposed to ensure the stability of the closed-loop SMJSs. Then, the controller gains and event-triggered parameters are jointly solved via the linear matrix inequality (LMI) technique. Moreover, the maximum allowable sampling interval (MASI) is given such that the controller can restore the control signals as soon as a DoS attack ends, which enables faster stabilization of the closed-loop system. Finally, a numerical example is used to verify the effectiveness and superiority of the proposed method.

This paper presents a highly compact linearly polarized planar dual annular ring antenna designed for wireless capsule endoscopy. The focus of this work on the small intestine is motivated by clinical practice in capsule endoscopy. Most capsule endoscopy systems are specifically designed to examine the small intestines, which are difficult to access with conventional wired endoscopes and are the primary region of interest for many gastrointestinal pathologists. The antenna is derived from a conventional annular ring structure and supports dual-band operation, covering both Wi-Fi frequencies at 2.45GHz and 5.8GHz. With a radius of only 4.7mm, the planar geometry occupies minimal space inside the capsule, leaving more room for essential electronics and the battery. The antenna achieves bandwidths of 20.8% at the lower band and 6.7% at the upper band. A key feature of the design is the capability for electronic switching of the higher band, which enables efficient power management. This allows continuous transmission of critical data, such as control signals, over the lower band, while high-volume data, such as images and video, can be transmitted on demand over the upper band through microcontroller-controlled switching. This mechanism ensures battery conservation as well as reduced time average SAR levels for higher safety. In-vitro testing of the prototypes was conducted, and the measured gains of -17.3 dBi and -18 dBi at the lower and upper bands have been achieved. Furthermore, the antenna exhibits specific absorption rate (SAR) values of 21.5W/kg and 24.7W/kg for the two operating bands. To ensure safe operation in compliance with IEEE and ECC standards, maximum transmit powers of 93 mW and 81 mW can be utilized, respectively, while maintaining reliable link quality and extended communication coverage. The link margin remains at 21.1 dB and 12.3 dB at 2.45GHz and 5.8GHz, respectively, ensuring an excellent link reliability at a distance of 4m.

Urban intersections are critical nodes for roadway safety, congestion management, and autonomous vehicle coordination. Traditional traffic control systems based on fixed-time signals and static sensors lack adaptability to real-time risks such as red-light violations, near-miss incidents, and multimodal conflicts. This study presents a grid-enabled framework integrating computer vision and machine learning to enhance real-time intersection intelligence and road safety. The system overlays a computational grid on the roadway, processes live video feeds, and extracts dynamic parameters including vehicle trajectories, deceleration patterns, and queue evolution. A novel active learning module improves detection accuracy under low visibility and occlusion, reducing false alarms in collision and violation detection. Designed for edge-computing environments, the framework interfaces with signal controllers to enable adaptive signal timing, proactive collision avoidance, and emergency vehicle prioritization. Case studies from multiple intersections typical of US cities show improved phase utilization, reduced intersection conflicts, and enhanced throughput. A grid-based heatmap visualization highlights spatial risk zones, supporting data-driven decision-making. The proposed framework bridges static infrastructure and intelligent mobility systems, advancing safer, smarter, and more connected roadway operations.

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO2 savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting.

Integrated optimization of spatiotemporal resources at the intersection (IOSTRI) is crucial for traffic signal control, where both the lane allocation and signal control plans are optimized in a unified framework. This paper addresses the IOSTRI problem with delay minimization, formulating it as a binary mixed-integer nonlinear program (BMINLP) model that fully incorporates all possible uses of shared lanes and lane utilization adjustments. A genetic algorithm tailored to the model’s characteristics is designed, where four modules named lane converter, signal plan converter, flow calculation function and delay calculation function are used to calculate the fitness of each solution. Numerical results show the proposed model and algorithm’s ability to adapt to diverse traffic flow distribution patterns. High-quality solutions are obtained within 40–55 seconds, representing a significant improvement over previous studies and satisfying the requirements for real-time adaptive control of a single intersection.

Protein phosphorylation is a crucial post-translational modification that regulates protein activity, cellular signaling, transcriptional regulation, and cell cycle control. However, the analysis of phosphoproteins in biological samples is often compromised by complex sample matrices and interference from high-abundance proteins. While the top-down phosphoproteomics strategy enables comprehensive analysis of post-translational modifications based on intact proteins, its requirement for higher protein purity due to low protein ionization efficiency poses stern challenges. Consequently, developing appropriate enrichment methods for phosphoproteins in practical samples becomes essential. Immobilized metal ion affinity chromatography (IMAC) represents a common strategy for phosphorylated protein separation and enrichment. Among metal ions, Ti4+ has gained widespread application as IMAC chelating ligands due to its capacity to form multiple coordination networks and its high selectivity for phosphorylated protein enrichment, leveraging the strong chelating ability of phosphate groups toward metal ions. This paper presents the design and preparation of a novel magnetic Ti-IMAC nanocomposite, MNP@MPTMS–VPA–Ti(IV). The material is modified with phosphate groups via facile thiol-ene click chemistry and then immobilizes Ti4+, enabling selective enrichment of intact phosphoproteins through IMAC affinity. The efficiency of enrichment was evaluated using subsequent matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for detection and analysis. This Ti-IMAC material-based magnetic solid-phase extraction (MSPE)-MALDI-TOF MS protocol has been successfully applied to enrich intact phosphoproteins in milk and eel mucus with high selectivity, sensitivity, and suitability.

This paper presents a repetitive learning control scheme to handle systems subject to both time-delay and dead-zone nonlinearities and the state-dependent input gain simultaneously. The adaptive bounding techniques are utilized to deal with the nonparametric uncertainties originated from the time-delay and the state-dependent input gain, in which the indirect learning manner is employed to avoid the appearance of the sign function, alleviating the requirement for the system information. The only prior knowledge of the proposed scheme is the lower bound of the input gain and the dead-zone slope. The desired control signal is recognized as the parametric uncertainties with a constant regressor. The derivation of the convergence analysis is provided in detail, and the boundedness of variables in the closed-loop system is guaranteed. The numerical simulation is conducted to testify the effectiveness of the presented control approach.