Low-level Control Research Articles

Risk Factors for Developing Occupational Back Pain in Electronics Industry Workers: A Cross-Sectional Study

Back pain is an occupation-related problem among workforces. This cross-sectional study aimed to identify the prevalence of back pain and the risk factors of occupational back pain among workers in the electronics industry. In total, 354 electronics workers in Thailand participated in the study. Data were collected using the Musculoskeletal Disorders Severity and Frequency Questionnaire, the Job Content Questionnaire, and ergonomics risk assessment via the Rapid Upper Limb Assessment (RULA). Risk factors of back pain were identified by multiple logistic regression analysis, providing adjusted odds ratios (ORadj) and 95% confidence intervals (95% CI). The study found that most workers were operators (92.09%) and had repetitive work (83.62%). A high ergonomics risk was observed in workers who stood during work (68.49%) and operated machines (71.70%). The 1-month prevalence of developing back pain was 20.62% and the significant factors correlated with back pain were low levels of job control and decision-making (ORadj = 2.26; 95% CI [1.26 ,4.05]), lack of exercise (ORadj = 8.30; 95% CI [1.35, 24.28]), repetitive work (ORadj = 2.94; 95% CI [1.19, 7.29]), and high ergonomic risk level (ORadj = 2.81; 95% CI [1.16, 5.07]). These findings suggest that measures should be implemented by empowering electronics workers to make decisions and control their jobs, as well as promoting health through muscle-stretching exercise, to support back pain prevention.

Adaptive multimodal control of trans-media vehicle based on deep reinforcement learning

To solve the problem that the control system is prone to instability due to the sudden change of physical characteristics, strong interference, and nonlinear in the process of multimodal movement of trans-media vehicle, an adaptive control method combining the Deep Deterministic Policy Gradient (DDPG) and traditional Proportional-Integral-Derivative (PID) controller is proposed in this paper. In this approach, the upper-level DDPG controller continuously monitors the vehicle's state and environmental conditions, dynamically adjusting the PID parameters in real-time. The lower-level PID controller then utilizes these updated parameters to modulate the output thrust of the vehicle's motors, thereby achieving excellent control over the vehicle's entire movement. Firstly, according to the hydrodynamic analysis, the kinematics and dynamics mathematical model of the self-designed trans-media vehicle is constructed. This model includes the multi-stage motion modal process of aerial flight, underwater navigation, and cross-media motion, which is suitable for the simulation and verification of the control method. Then, an adaptive controller called RL-PID combining DDPG and PID is built, so that PID can adjust parameters in real-time according to the changes in the external environment. Finally, after theoretical stability proof, a comparison study is performed across three approaches, namely the novel RL-PID, Fuzzy PID, and PID. The experimental results illustrate the superiority of the proposed approach over the competing ones and the generalization of the proposed approach under different interference.

An Online Data-Driven Method for Accurate Detection of Thermal Updrafts Using SINDy

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations.

Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss.

Lateral-Stability-Oriented Path-Tracking Control Design for Four-Wheel Independent Drive Autonomous Vehicles with Tire Dynamic Characteristics under Extreme Conditions

This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%.

Lexical Effect Handlers, Directly

Lexically scoping effect handlers is a language-design idea that equips algebraic effects with a modular semantics: it enables local-reasoning principles without giving up on the control-flow expressiveness that makes effect handlers powerful. However, we observe that existing implementations risk incurring costs akin to the run-time search for dynamically scoped handlers. This paper presents a compilation strategy for lexical effect handlers, adhering to the lexical scoping principle and targeting a language with low-level control over stack layout. Key aspects of this approach are formalized and proven correct. We embody the ideas in a language called Lexa: the Lexa compiler translates high-level effect handling to low-level stack switching. We evaluate the Lexa compiler on a set of benchmarks; the results suggest that it generates efficient code, reducing running-time complexity from quadratic to linear in some cases.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Post-Occupancy Evaluation of Adaptively Reused Historical Ecological Buildings in Egypt: The Case of Wekalet Al-Ghoury

Ecological historical buildings were originally designed to maintain acceptable Indoor Environmental Quality (IEQ) for their users and their intended activity. The adaptive reuse of these buildings is considered a great potential opportunity. As a result, these structures and their passive applied strategies might be no longer as effective as they once were. The purpose of this study is to evaluate the user satisfaction with IEQ in an adaptively reused historical ecological building; Wekalet Al-Ghoury. This post-occupancy evaluation was conducted using a mixed method approach that involved the use of a questionnaire and a semi-structured interview. Quantitative results of the questionnaire were statistically evaluated using SPSS software and interviews were thematically analyzed. The questionnaire was first use to capture responses’ diversity among staff and tenants, the semi-structured interview has then helped to discuss and justify quantitative findings. Occupants were generally satisfied with thermal and acoustic conditions and dissatisfied the most with the low level of IEQ control, poor indoor air quality and inconsistent light distribution. On the other hand, the interview has reflected responsive occupant behaviour to perform their work where occupants are free to change their place, change their working hours and introduce some movable lighting and ventilation equipment.

Hierarchical Control in Mechatronic Technological Systems

The topic of hierarchical control of technological machines is one of the most relevant in mechanical engineering technology. The most difficult issue in this area is the organization of interactions between different control levels, on the one hand, and the choice of automatic control methods for each of these control levels (control by deviation, control by disturbance, mixed control, etc.), on the other. In this article, in relation to machining technology, a method and corresponding device are proposed that make it possible to implement the control of cutting force parameters (axial cutting force and cutting torque) in an automatic control system for the deviation of cutting torque by changing the axial cutting force (lower level of control). The lower-level control ensures the required quality of the surface layer (surface integrity) of the machined parts. At the same time, the required dimensional accuracy of parts is ensured at the upper level of control, which is implemented by the CNC system of the machine. At the upper level, automatic control is carried out based on the deviation of the kinematic parameters of the movement of the working parts of the CNC machine (acceleration, speed, displacement). Control switching from upper to lower level and back is carried out without using a spindle linear axial movement sensor. Instead of this expensive sensor, a limit switch (a closed and opened pair of contacts) is used, which fixes the lowest axial position of the spindle (and cutting tool). Based on the signal of closing the specified contacts of the limit switch, a transition from the lower control level to the upper one is carried out. Thus, the upper-level system operates only when these contacts are closed, and the lower-level system operates only when they are open. In relation to the upper-level system, the lower-level control system implements the control “by disturbance” principle, also known in control theory as the “disturbance compensation principle”.

A Survey of Robot Intelligence with Large Language Models

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language. Several studies have explored the enhancement of robot intelligence using multimodal data, including object recognition and description by VLMs, along with the execution of language-driven commands integrated with visual information. This review paper thoroughly investigates how foundation models such as LLMs and VLMs have been employed to boost robot intelligence. For clarity, the research areas are categorized into five topics: reward design in reinforcement learning, low-level control, high-level planning, manipulation, and scene understanding. This review also summarizes studies that show how foundation models, such as the Eureka model for automating reward function design in reinforcement learning, RT-2 for integrating visual data, language, and robot actions in vision-language-action models, and AutoRT for generating feasible tasks and executing robot behavior policies via LLMs, have improved robot intelligence.

B-015 Analytical Verification of MR-proADM Biomarker in Human EDTA Plasma on Immunoassay Analyzer LIAISON® XL of Diasorin

Abstract Background Adrenomedullin (ADM) is a potent vasodilator peptide of 52-aminoacid belonging to the calcitonin family of peptides. Midregional pro-ADM (MR-ProADM) is a precursor for ADM that circulates is in a 1:1 ratio with it, and proportionally represents its levels and activity. Increased ADM circulating concentrations have been described for several disease states, including dysfunction of the cardiovascular system and sepsis. However, reliable ADM quantification has been hampered by the short half-life, the existence of a binding protein, and its physical properties. Hence, quantificacion of MR-proADM represents a more suitable option. The biomarker MR-proADM has been shown to be an accurate prognostic marker for outcome prediction in patients with lower respiratory infections, sepsis, urinary tract infections, and kidney disease. The objective of this study was the verification of the metrological characteristics and the verification of the linearity of quantitative measurement range, as declared by the manufacturer, of the LIAISON® Brahms MR-proADMTM on Liaison® XL (DiaSorin). Methods The verification was performed with two levels of quality controls (QC) from the manufacturer (x̄L=1,19 nmol/L and x̄H=5,48 nmol/L), and residual EDTA plasma samples from patients with an active sepsis code. The evaluation included the assessment of: • Detection capability (including limit of quantification (LoQ) and limit of detection (LoD), measuring three samples at the manufacturer's stated concentration (0.21 nmol/L and 0.09 nmol/L, respectively) in duplicate within 4 runs). • Precision (CV, %) and accuracy (bias, %) within, measuring 10 replicates of each QC level; and between run, measuring 5 replicates for each QC level within 5 runs. • Linearity of the quantitative range of measurement (six intermediate samples prepared by mixing a high concentration sample- XH=9.64 nmol/L- and a low concentration sample, XL=0.59 nmol/L; measured in triplicate). All studies were performed in accordance with the recommendations of the Clinical Laboratory Standards Institute guidelines. Statistical analysis was performed using Microsoft Excel®, 2016. Results All results with diluted samples presented a signal (RLU) higher than that detected in samples without analyte, so the LoD was verified. The precision at the LoQ concentration was 26.7% (acceptance criteria was set as <20%). CV for within-run precision were 4.3% and 2.0% and for between-run precision 6.8% and 7.9% for low (L1) and high (L2) control level, respectively. The average bias was 3.60% for L1 and 4.23% for L2, meeting acceptance criteria (<8%). Linearity was confirmed between 0.59 and 9.64 nmol/L (R2 = 0.995). The biases between the expected and the obtained value were ≤7% through the quantitative range of measurement, except for the lowest concentration sample (close to the quantitation limit). Conclusions Our study showed good compliance with the metrological characteristics declared by the manufacturer for LoD, precision, bias and linearity range. Although the LoQ was not verified, it is outside the clinical decision value in septic patients (< 2.25 nmol/L) and it is not deemed a limitation for clinical practice. Additionally, the Liaison XL® platform permits us to configure an emergency protocol circuit (24/7) with a TAT of 35 min.

B-004 Method Validation of Lactate in the Plasma of Rats

Abstract Background The objective of this study is to validate Lactate (LAC) measurement in rat plasma using the ADVIA 1800 analyzer. Methods LAC was validated in rat plasma using the ADVIA 1800 analyzer. Validation testing included intra-assay and inter-assay precision, accuracy, linearity of dilution, limit of quantitation (LOQ), limit of detection (LOD), reference interval, carry-over, correlation, recovery, and stability. Samples were collected into tubes containing sodium fluoride. Intra-assay precision was determined by analyzing two biological samples and the two quality control levels of Bio-Rad Lyphochek Assayed Chemistry Control 10 consecutive times. Inter-assay precision was determined by analyzing the two quality control levels in duplicate for six days over a 10-day period. Accuracy was calculated using the data obtained from the inter-assay precision testing. For linearity of dilution, Audit MicroControl General Chemistry Linearity Kit and biological samples were diluted and analyzed in duplicate. The LOQ was determined by diluting the calibrator material to produce a value at the low end of the reportable range. Diluted material was tested six times. The LOD was determined by assaying the appropriate blank 10 times. The reference interval was determined by analyzing 21 plasma samples. Correlation between the two ADVIA 1800 analyzers was determined by assaying 10 samples on both analyzers once. The carry-over of the assay was determined by analyzing the high-level quality control material followed by the low-level quality control material. Recovery was determined by analyzing a pair of test samples in duplicate that have been spiked and diluted. The proportional error between the two test samples was calculated. Stability of plasma samples was tested on wet ice, refrigerated, and frozen (at -70°C) for various durations of storage. Results Measurement of LAC in rat plasma met the acceptance criteria for precision, accuracy, linearity of dilution, limit of quantitation, carry-over, recovery, and stability. For intra-assay precision in specimen and control material, the %CV was within ±20%. The inter-assay precision %CV was within ±20%. The total error observed (TEobs) was within ±20% for accuracy and the obtained mean of the control material was within the manufacturer’s acceptable range. For linearity of dilution, the coefficient of determination was ≥ 0.9000, the slope was within 1.00 ± 0.25, and TEobs for each plasma and linearity kit dilution was ≤20%. The LOQ was set at the lowest concentration for which the TEobs was within ±20%. The LOD was calculated by adding the mean concentration obtained and three times the standard deviation. The reference interval was set to the 2.5th to 97.5th percentile interval using Excel software. The carry-over was acceptable at ≤2%. Correlation between the two ADVIA 1800 analyzers was determined to describe any bias in result interpretation. Recovery was acceptable as the proportional error was within ±20%. Stability at all storage conditions was found to be acceptable with a mean %difference within ±20%. Conclusions LAC in rat plasma met the acceptance criteria for all required validation parameters. The ADVIA 1800 analyzer can be used for preclinical studies to test LAC in rat plasma.

Fuel-Efficient and Fault-Tolerant CubeSat Orbit Correction via Machine Learning-Based Adaptive Control

The increasing deployment of CubeSats in space missions necessitates the development of efficient and reliable orbital maneuvering techniques, particularly given the constraints on fuel capacity and computational resources. This paper presents a novel two-level control architecture designed to enhance the accuracy and robustness of CubeSat orbital maneuvers. The proposed method integrates a J2-optimized sequence at the high level to leverage natural perturbative effects for fuel-efficient orbit corrections, with a gated recurrent unit (GRU)-based low-level controller that dynamically adjusts the maneuver sequence in real-time to account for unmodeled dynamics and external disturbances. A Kalman filter is employed to estimate the pointing accuracy, which represents the uncertainties in the thrust direction, enabling the GRU to compensate for these uncertainties and ensure precise maneuver execution. This integrated approach significantly enhances both the positional accuracy and fuel efficiency of CubeSat maneuvers. Unlike traditional methods, which either rely on extensive pre-mission planning or computationally expensive control algorithms, our architecture efficiently balances fuel consumption with real-time adaptability, making it well-suited for the resource constraints of CubeSat platforms. The effectiveness of the proposed approach is evaluated through a series of simulations, including an orbit correction scenario and a Monte Carlo analysis. The results demonstrate that the integrated J2-GRU system significantly improves positional accuracy and reduces fuel consumption compared to traditional methods. Even under conditions of high uncertainty, the GRU-based control layer effectively compensates for errors in thrust direction, maintaining a low miss distance throughout the maneuvering period. Additionally, the GRU’s simpler architecture provides computational advantages over more complex models such as long short-term memory (LSTM) networks, making it more suitable for onboard CubeSat implementations.

Applying the 2023 ESH Hypertension Guidelines to the Irish Population- findings from the Irish Longitudinal Study on Ageing (TILDA)

Abstract Background Hypertension (HTN) is an important risk factor for cardiovascular disease (CVD), the leading cause of mortality worldwide. The aim of this study was to apply the latest European Society of Hypertension (ESH) Guidelines 2023 to the Irish general population. Methods This study used data from Wave 1 (2009-2010) and Wave 3 (2014-2015) of the Irish Longitudinal Study on Ageing (TILDA). TILDA is a prospective cohort study based on stratified random sampling, which is representative of the community living older population in Ireland (Aged 50 years and over). Using this data, the prevalence of HTN in the general population by grade and stage, as set out in the ESH 2023 guidelines, was calculated. Population awareness of diagnosis, treatment status, control on treatment and adherence to recommended anti-hypertensive medication classes were assessed. Subgroup analyses were explored in alignment with specified groups highlighted in the Guidelines including those over 80 years old with and without frailty, those with chronic kidney disease and those with home blood pressure (BP) measurements. Inverse probability weighting was applied to account for the complex survey design used in TILDA and data analysis was conducted using Stata 15.1. Results Results from Wave 3 (N=5,329): The prevalence of hypertension was 64.0% (95% confidence interval (CI) 62.4-65.6%). Of those with HTN, 55.5% (95% CI 53.3-57.7) were aware of their HTN diagnosis and 70.4% (95% CI 68.3-72.2%) were on antihypertensive treatment. Of those on treatment, 53.2% were controlled (BP < 140/90), and only 20.0% (95% CI 18.3-21.8) were on a recommended dual therapy. 55.2% were on at least one agent recommended by the ESH guidelines. Conclusion This study indicates a high prevalence of HTN in the general population in Ireland aged 50+ years with low levels of awareness, control, and poor adherence to the most recent ESH HTN guidelines.

Curriculum Design and Sim2Real Transfer for Reinforcement Learning in Robotic Dual-Arm Assembly

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work, we set up two Franka Panda robots performing a peg-in-hole insertion task of 1 mm clearance. We structure the control system hierarchically, planning the robots’ feedback-based trajectories with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning, novel for such a two-armed control task, iteratively structured with a minimum requirements and fine-tuning phase. We incorporate domain randomization, varying initial joint configurations of the task for generalization of the applicability. After training, we test the system in a simulation, discovering the impact of curriculum parameters on the emerging process time and its variance. Finally, we transfer the trained model to the real-world, resulting in a small decrease in task duration. Comparing our approach to classical path planning and control shows a decrease in process time, but higher robustness towards calibration errors.

Mechatronic technological system based on CNC and intelligent mechatronic mechanism

A mechatronic technological system (MTS) has been developed. It consisting of a CNC metal-cutting machine tool and a mechatronic spindle, made on the basis of an intelligent mechatronic mechanism (IMM). The main elements of the technological system – the CNC and IMM devices – are parts of a single hierarchical, two-level control system, representing, respectively, the upper and lower control levels in this system. The upper control level – the CNC device – ensures stabilization of the kinematic parameters of machining (displacement, speed, acceleration) and their software change in accordance with the CNC control program. The lower control level – the IMM device – ensures stabilization of the power parameters of mechanical machining (cutting axial force and torque) and their software change in accordance with the technological requirements for the surface quality and the physical-and-mechanical state of the surface layer of the machined parts. At both control levels (the upper and lower ones), closed automatic control systems “by deviation” of the adjustable kinematic (upper control level) and power (lower control level) parameters of mechanical machining are functioning. Moreover, the automatic control system of the lower control level of the hierarchy in relation to a similar automatic control system of the upper control level, implements the method of automatic control “by disturbance”. This, in addition to directly ensuring surface finish and surface integrity, allows improving the quality of stabilization of kinematic cutting parameters at the upper control level. Experimental studies of MTS were carried out when machining parts from various materials (steel, polymer composite materials, superhard crystals and stones, other difficult-to-machine materials).

A longitudinal investigation of parenting, depression, and counseling use among Asian American adolescents.

Asian American adolescents are equally or more likely to experience depression but less likely to seek treatment for depression than adolescents from other racial and ethnic groups in the US. The current study examined the long-term effects of parental care, parental control, and parental closeness on depression and counseling use among Asian American adolescents. Using data from the National Longitudinal Study of Adolescent Health (Add Health), we conducted a cross-lagged path analysis with 270 Asian American adolescents (48.1% female; 51.9% male). The study used data from Waves I, II, and III (1994-2002) with participants' mean ages ranging from 14 to 23. Findings indicated that the cross-sectional relationships between parenting characteristics and depression were stronger than the longitudinal relationships suggesting that parenting practices may be a stronger proximal, rather than distal, predictor of depression. Specifically, parental closeness was associated with less depression in early and mid-adolescence (age 12-18), but the relationship changed direction in young adulthood (age 18-26). Additionally, a significant interaction suggested that parental care was related to fewer depressive symptoms for those who reported high, compared to low, parental control in mid-adolescence (age 14-18). Furthermore, high parental care was associated with more counseling use at high levels of control. However, high parental care was associated with less counseling use at low levels of parental control in early adolescence. These results highlight the importance of understanding parenting characteristics using longitudinal designs when examining the development of depression and help-seeking behaviors among Asian American adolescents.

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

The design of LLRF system for STCF storage ring

The Super Tau Charm Facility (STCF) is a new generation of high brightness electron–positron collider planned by China, designed to achieve a center of mass collision energy is 2–7[Formula: see text]GeV and a peak brightness is 0.5–[Formula: see text][Formula: see text]cm[Formula: see text]s[Formula: see text]. The digital low-level RF control system (LLRF), as the essential equipment of the storage ring RF system, is usually responsible for maintaining the stability of the cavity field. This paper presents the design scheme of LLRF for cavity in STCF storage ring, which mainly includes the hardware structure and software design of the system. We perform simulation analysis on the STCF RF system to evaluate the influence of delay and control parameters on the stability of the system and the influence of control parameters on the suppression of two kinds of noise. Desktop testing on the existing prototype demonstrates that the adopted direct-sampling architecture introduces less phase drift compared to the down-conversion architecture. Under the direct-sampling architecture, non-IQ demodulation offers better SNR and stability in both amplitude and phase. Specifically, direct-sampling in non-IQ mode achieves open-loop RMS short-term stability of 0.019% for amplitude and 0.021∘ for phase. Long-term stability in the closed-loop setting measures 0.027% for amplitude and 0.024∘ for phase.

Reducing Viral Load Impact: Blood Transfusions as a Complementary Approach in HIV Treatment

Blood transfusions have traditionally been associated with the management of anemia in individuals living with HIV; however, their potential role as a complementary approach in reducing viral load impact is gaining attention. This review explores how blood transfusions can enhance immune function, improve oxygen delivery, and support the efficacy of antiretroviral therapy (ART), thereby contributing to better health outcomes for patients. By addressing complications associated with HIV, such as anemia and immune dysfunction, blood transfusions may play a crucial role in optimizing treatment strategies and enhancing the overall quality of life for individuals living with the virus. The relationship between viral load and immune function is critical in HIV management, as high viral loads are associated with increased morbidity and mortality. Blood transfusions can mitigate the negative effects of viral load by improving the physiological conditions necessary for an effective immune response. Enhanced oxygenation from transfusions supports immune cell proliferation and activity, potentially leading to better control of viral replication and lower viral load levels. Moreover, improving hemoglobin levels through transfusions can reduce fatigue, increase treatment adherence, and promote active participation in ART. Keywords: anemia, blood transfusions, HIV, immune response, viral load