Search Results (206)

Patient education is a crucial component of a physician’s responsibility, and effective patient education can improve patient health outcomes and satisfaction. However, there is currently no formalized training for residents to develop and practice these skills at our large, academic internal medicine residency program. We created a virtual, case-based, interactive session for all residents to practice patient education skills and receive real-time feedback. Residents were given three scenarios: heart failure (HF), breast cancer (BC), and chronic kidney disease (CKD), and role-played as the physician, patient, and observer. The intervention was studied with single-group, pre-post intervention surveys. The session was virtual due to restrictions related to the COVID-19 pandemic. Mean Likert scale scores were compared by paired Wilcoxon rank-sign tests. The sessions were attended by 177 residents; 95 completed both pre- and post-session surveys (53.6%). Participants felt significantly more comfortable teaching patients about HF, BC, and CKD pathophysiology and treatments after the session (HF: pre-median = 4, post-median = 4, p = 0.0032; BC: pre-median = 2, post-median = 4, p < 0.0001; CKD: pre-median = 3, post-median = 4, p = 0.0016). There was a trend towards a significant increase in belief that teaching patients about common diseases should be integrated into the residency curriculum, but this did not reach statistical significance (pre-median = 4, post-median: 5, p = 0.0548). A targeted session for residents to practice patient education improved resident comfort with explaining three common diseases. These data suggest that the session was effective in a virtual format, demonstrating feasibility to be adapted in the increasingly online realm of patient encounters. Full article

Transformerless inverters have received significant attention in solar photovoltaic (PV) applications. The absence of low-frequency transformers contributes to improved efficiency and reduced size compared to other topologies; however, there are concerns about leakage currents. The common ground (CG) connection in PV inverters is an attractive solution to this issue, as it generates a constant common-mode voltage and theoretically eliminates the leakage current. In this context, multilevel CG inverters can eliminate the leakage current while achieving high-quality output voltages. Nonetheless, achieving simultaneous control of the grid current and inner capacitor voltages can be challenging. Furthermore, controlling the capacitor voltages in multilevel inverters requires feedback from measurement sensors, which can increase the cost and may affect the overall reliability. To address these issues, this paper proposes a model predictive controller (MPC) for a CG multilevel inverter with a reduced number of sensors. While conventional MPC uses a classical multi-objective technique with a single cost function, the proposed method avoids the use of weighting factors in the cost function. Additionally, a sliding-mode observer is developed to estimate the capacitor voltages, and an incremental conductance-based maximum power point tracking (MPPT) algorithm is used to generate the current reference. Simulation and experimental results confirm the effectiveness of the proposed observer and MPC strategy. Full article

Control valves are widely applied in nuclear power, offshore oil/gas extraction, and chemical engineering, but suffer from issues like pressure oscillation, flow control accuracy degradation, and motor overload due to unstable fluid loads (e.g., nuclear reactions in power plants and complex marine climates). This paper proposes a dual-motor redundant compensation method to address these challenges. The core lies in a control strategy where a single main motor drives the valve under normal conditions, while a redundant motor intervenes when load torque exceeds a preset threshold—calculated via the valve core’s fluid load model. By introducing excess load torque as positive feedback to the current loop, the method coordinates torque output between the two motors. AMESim and Matlab/Simulink joint simulations compare single-motor non-compensation, single-motor compensation, and dual-motor schemes. Results show that under inlet pressure step changes, the dual-motor compensation scheme shortens the stabilization time of the valve’s controlled variable by 40%, reduces overshoot by 65%, and decreases motor torque fluctuation by 50%. This redundant design enhances fault tolerance, providing a novel approach for reliability enhancement of deep-sea oil/gas control valves. Full article

This paper proposes a second-order low-pass Butterworth multiple-feedback (MFB) filter with a reconfigurable bandwidth and gain, implemented in a 28 nm CMOS. The filter supports independent tuning of the bandwidth from 10 MHz to 100 MHz and the gain from 0 dB to 19 dB, effectively addressing the challenge of a tightly coupled gain and quality factor in traditional MFB designs. Notably, compared to the widely adopted Tow–Thomas structure, the proposed filter achieves second-order filtering and the same degree of flexibility using only a single operational amplifier (OPA), significantly reducing both the power consumption and area. Additionally, an RC tuning circuit is employed to reduce fluctuations in the RC time constant under process, voltage, and temperature (PVT) variations. To meet the requirements for high linearity and low power consumption in broadband applications, a three-stage push–pull OPA with current re-use feedforward and an RC Miller compensation technique is proposed. With the current re-use feedforward, the OPA’s loop gain at 100 MHz is significantly enhanced from 22.34 dB to 28.75 dB, achieving a 2.14 GHz unity-gain bandwidth. Using this OPA, the filter achieves a 48.2 dBm in-band (IB) OIP3, a 53.4 dBm out-of-band (OOB) OIP3, and a figure of merit (FoM) of 185.5 dBJ⁻¹ at a100 MHz bandwidth while consuming only 3.6 mW from a 1.8 V supply. Full article

Generating realistic and contextually appropriate gestures is crucial for creating engaging embodied conversational agents. Although speech is the primary input for gesture generation, adding controls like gesture velocity, hand height, and emotion is essential for generating more natural, human-like gestures. However, current approaches to controllable gesture generation often utilize a limited number of control parameters and lack the ability to activate/deactivate them selectively. Therefore, in this work, we propose the Cont-Gest model, a Transformer-based gesture generation model that enables selective control activation through masked training and a control fusion strategy. Furthermore, to better support the development of such models, we propose a novel evaluation-driven development (EDD) workflow, which combines several iterative tasks: automatic control signal extraction, control specification, visual (subjective) feedback, and objective evaluation. This workflow enables continuous monitoring of model performance and facilitates iterative refinement through feedback-driven development cycles. For objective evaluation, we are using the validated Kinetic–Hellinger distance, an objective metric that correlates strongly with the human perception of gesture quality. We evaluated multiple model configurations and control dynamics strategies within the proposed workflow. Experimental results show that Feature-wise Linear Modulation (FiLM) conditioning, combined with single-mask training and voice activity scaling, achieves the best balance between gesture quality and adherence to control inputs. Full article

The kynurenine (KYN) metabolic pathway sits at the crossroads of immunity, metabolism, and neurobiology, yet its clinical translation remains fragmented. Emerging spatial omics, wearable chronobiology, and synthetic microbiota studies reveal that tryptophan (Trp) metabolism is regulated by distinct cellular “checkpoints” along the gut–brain axis, finely modulated by sex differences, circadian rhythms, and microbiome composition. However, current interventions tackle single levers in isolation, leaving a key gap in the precision control of Trp’s fate. To address this, we drew upon an extensive body of the primary literature and databases, mapping enzyme expression across tissues at single-cell resolution and linking these profiles to clinical trials investigating dual indoleamine 2,3-dioxygenase 1 (IDO1)/tryptophan 2,3-dioxygenase (TDO) inhibitors, engineered probiotics, and chrono-modulated dosing strategies. We then developed decision-tree algorithms that rank therapeutic combinations against biomarker feedback loops derived from real-time saliva, plasma, and stool metabolomics. This synthesis pinpoints microglial and endothelial KYN hotspots, quantifies sex-specific chronotherapeutic windows, and identifies engineered Bifidobacterium consortia and dual inhibitors as synergistic nodes capable of reducing immunosuppressive KYN while preserving neuroprotective kynurenic acid. Here, we highlight a framework that couples lifestyle levers, bio-engineered microbes, and adaptive pharmaco-regimens into closed-loop “smart protocols.” By charting these intersections, this study offers a roadmap for biomarker-guided, multidisciplinary interventions that could recalibrate KYN metabolic activity across cancer, mood, neurodegeneration, and metabolic disorders, appealing to clinicians, bioengineers, and systems biologists alike. Full article

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China. Full article

Carbon nanotube field-effect transistors (CNTFETs) are becoming a strong competitor for the next generation of high-performance, energy-efficient integrated circuits due to their near-ballistic carrier transport characteristics and excellent suppression of short-channel effects. However, CNT FETs with large diameters and small band gaps exhibit obvious bipolarity, and gate-induced drain leakage (GIDL) contributes significantly to the off-state leakage current. Although the asymmetric gate strategy and feedback gate (FBG) structures proposed so far have shown the potential to suppress CNT FET leakage currents, the devices still lack scalability. Based on the analysis of the conduction mechanism of existing self-aligned gate structures, this study innovatively proposed a design strategy to extend the length of the source–drain epitaxial region (L_ext) under a vertically stacked architecture. While maintaining a high drive current, this structure effectively suppresses the quantum tunneling effect on the drain side, thereby reducing the off-state leakage current (I_off = 10⁻¹⁰ A), and has good scaling characteristics and leakage current suppression characteristics between gate lengths of 200 nm and 25 nm. For the sidewall gate architecture, this work also uses single-walled carbon nanotubes (SWCNTs) as the channel material and uses metal source and drain electrodes with good work function matching to achieve low-resistance ohmic contact. This solution has significant advantages in structural adjustability and contact quality and can significantly reduce the off-state current (I_off = 10⁻¹⁴ A). At the same time, it can solve the problem of off-state current suppression failure when the gate length of the vertical stacking structure is 10 nm (the total channel length is 30 nm) and has good scalability. Full article

The impacts of renovation actions on pre- and post-retrofitting building performances are complex to analyse, particularly small and potentially self-actuated actions, such as adding insulation layers to a cold roof slab or changing doors. These interventions are widespread in small residential houses and cases where the owners are the residents. However, a large research gap currently remains regarding the impact of sustainable solutions on building performance. This study aims to address this issue by proposing a methodology based on commercial cloud monitoring solutions and middleware development that analyses and reports on the impact of such solutions to end users, allowing for an analysis of real variations in air temperature levels. The methodology is applied to two single/double-family residential houses, acting as demo cases for verification, across a multi-year time horizon. In both cases, measurements were conducted before and after typical limited renovation actions. Alongside the proposed methodology, descriptions of the smart solutions’ requirements are provided. The results mainly focus on temperature variations. Finally, the impact of the solutions on energy consumption was analysed for one of the buildings, and feedback was briefly provided by the users. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

Background: Percutaneous electrolysis (PE) is an emerging therapeutic approach for tendinopathies, applying a galvanic current through a dry-needling needle to induce regenerative tissue responses. However, current dosing strategies are often empirical and lack objective physiological feedback. Objective: This study aimed to evaluate the echogenic effects of different galvanic current intensities on cadaveric tendon tissue using quantitative ultrasound. Methods: An ex vivo study was conducted on 29 cadaveric patellar tendon samples, each exposed to a single intensity (0–10 mA for 1 s). Quantitative ultrasound analysis was performed post-intervention, and echogenic variables were extracted using UZ eDosifier software. A composite variable, Electrolysis_UZ_Dose, was created via multiple regression to capture the overall ultrasound-visible changes. Data were analyzed using correlation, regression models, and dose–range comparisons. Results: An intensity-dependent response was observed in key echogenic parameters. Minimal changes occurred at low intensities (0–2 mA), whereas a progressive response emerged between 2 and 6 mA. Beyond 6 mA, a plateau effect suggested either tissue saturation or imaging limitations due to gas-induced acoustic shadowing. The Electrolysis_UZ_Dose variable strongly correlated with applied intensity (R² = 0.732). Conclusions: This study suggests an intensity-dependent echogenic effect of PE on tendon tissue in key ultrasound-derived parameters (A_Number, A_Area, A_Perimeter, A_Homogeneity, and A_ASM). However, as this study was conducted under experimental conditions with a single 1 s application per sample, the results should not be extrapolated to clinical practice without further validation. Full article

This paper investigates the system architecture and circuit topology of grid-connected inverters with embedded energy storage (EES), encompassing their modulation strategies and control methodologies. A mathematical model for an EES grid-connected inverter is derived based on capacitor current feedback control, from which the expression for the inverter’s output impedance is obtained. Building on this foundation, this study analyzes the influence of control parameters—such as the proportional coefficient, resonant coefficient, and switching frequency—on the inverter’s output impedance. Subsequently, the stability of single and multiple inverter grid-connected systems under various operating conditions is assessed using impedance analysis and the Nyquist criterion. Finally, the validity of the stability analysis based on the established mathematical model is verified through simulations conducted on the Matlab/Simulink platform, where models for both a single inverter and a two-inverter grid-connected system are constructed. Full article

Malware remains a central tool in cyberattacks, and systematic research into adversarial attack techniques targeting malware is crucial in advancing detection and defense systems that can evolve over time. Although numerous review articles already exist in this area, there is still a lack of comprehensive exploration into emerging artificial intelligence technologies such as reinforcement learning from the attacker’s perspective. To address this gap, we propose a foundational reinforcement learning (RL)-based framework for adversarial malware generation and develop a systematic evaluation methodology to dissect the internal mechanisms of generative models across multiple key dimensions, including action space design, state space representation, and reward function construction. Drawing from a comprehensive review and synthesis of the existing literature, we identify several core findings. (1) The scale of the action space directly affects the model training efficiency. Meanwhile, factors such as the action diversity, operation determinism, execution order, and modification ratio indirectly influence the quality of the generated adversarial samples. (2) Comprehensive and sensitive state feature representations can compensate for the information loss caused by binary feedback from real-world detection engines, thereby enhancing both the effectiveness and stability of attacks. (3) A multi-dimensional reward signal effectively mitigates the policy fragility associated with single-metric rewards, improving the agent’s adaptability in complex environments. (4) While the current RL frameworks applied to malware generation exhibit diverse architectures, they share a common core: the modeling of discrete action spaces and continuous state spaces. In addition, this work explores future research directions in the area of adversarial malware generation and outlines the open challenges and critical issues faced by defenders in responding to such threats. Our goal is to provide both a theoretical foundation and practical guidance for building more robust and adaptive security detection mechanisms. Full article