Time Control Research Articles

The shift from inpatient to outpatient settings of a surgical DRG for pediatric patients in Portugal

Abstract Background The shift of selected surgical procedures from inpatient to outpatient settings has been seen as a solution to improve health care provision. These shifts often occur under pay-for-performance schemes, pay-for-efficiency schemes, or other forms of financial incentives. For this study, we aimed to assess the effect of Decree 132/2009, implemented on February 1, 2009, which increased the relative weights of DRG 60 (“tonsillectomy and/or adenoidectomy, age <18”) performed in outpatient settings, thus acting as a financial incentive. Methods We performed a quasi-experimental study and employed interrupted time series (ITS) and controlled interrupted time series (CITS) designs, using monthly pediatric data provided by The Central Administration of the Health System (ACSS), with discharges between January 2006 and February 2012 (having 37 months pre- and post-decree implementation) of inpatient and outpatient episodes for DRG 60 (affected group) and DRG 163 (“hernia procedures age <18”, the selected control group). We used as study outcomes proportions (number of outpatient episodes divided by the sum of outpatient and inpatient episodes) to quantify increases in outpatient episodes and ratios (number of outpatient episodes divided by the number of inpatient episodes) to quantify decreases in inpatient episodes. Fourier terms were included to adjust for seasonality. Results We obtained statistically significant results only at the trend level for ratios when using the ITS model (0.01574) and for proportions only when using the CITS model (0.00310), although small non-statistically significant increases were obtained for the employed ITS as well as for the CITS design. Conclusions Decree 132/2009, which created financial incentives for the shift from inpatient to outpatient for DRG 60, showed low and non-statistically significant increases using both ITS and CITS models. Key messages • We analyzed the impact of Decree 132/2009 on the pediatric surgeries under DRG 60 performed in outpatient settings in Portugal using interrupted time series and control interrupted time series. • The decree had a small effect at the national level for DRG 60.

Research on Phase Stabilization Algorithm of Femtosecond Timing System

This paper presents the design, implementation, and validation of a femtosecond timing system aimed at achieving precise time control and phase synchronization for large particle accelerators. A prototype system utilizing a continuous wave laser was developed, focusing on minimizing timing jitter and long-term phase drift. Key components include an optical delay line for coarse adjustments and a fiber stretcher for fine-tuning, achieving an adjustment precision of 1 femtosecond. The system incorporates a phase detection module with a non-In-phase/Quadrature downconversion approach, enabling high-accuracy phase measurements. A collaborative algorithm was designed to optimize the interplay between the optical delay line and the fiber stretcher, utilizing a proportional-integral-derivative (PID) control algorithm to enhance adjustment precision. A Field Programmable Gate Array (FPGA) served as the core interface converter, facilitating data communication and real-time phase information acquisition. Experimental results demonstrated significant improvements in phase stability, with average phase deviation reduced from 1374.104 fs to 15.782 fs, showcasing the effectiveness of the proposed system in achieving high precision and stability in phase control. This research provides a solid foundation for future advancements in timing systems for high-frequency reference signals.

Effect of Hyperketonemia on Myocardial Function in Patients with Heart Failure and Type 2 Diabetes.

We examined the effect of increased plasma ketones on left ventricular (LV) function, myocardial glucose uptake (MGU), and myocardial blood flow (MBF) in type 2 diabetes (T2DM) patients with heart failure (HF). Three groups (I,II,III) of T2DM (12 per group) with LV ejection fraction ≤50% received incremental infusions of β-OH-B for 3-6 hours to raise plasma β-OH-B concentration throughout the physiologic (Groups I and II) and pharmacologic (Group III) range. Cardiac MRI was performed at baseline and after each β-OH-B infusion to provide measures of cardiac function. On a separate day, Group II also received NaHCO3 infusion, thus serving as their own control for time, volume, and pH. Additionally, Group II underwent positron emission tomography study with 18F-fluoro-2-deoxyglucose to examine effect of hyperketonemia on MGU. Groups I, II, III achieved plasma β-OH-B levels of 0.7±0.3, 1.6±0.2, 3.2±0.2 mmol/L, respectively. Cardiac output, LVEF, and stroke volume increased significantly during β-OH-B infusion in Groups II (CO, 4.54 to 5.30; EF, 39.9 to 43.8; SV, 70.3 to 80.0) and III (CO, 5.93 to 7.16; EF, 41.1 to 47.5; SV, 89.0 to 108.4) and did not change with NaHCO3 infusion in Group II. The increase in LVEF was greatest in Group III (p<0.001 vs Group II). MGU and MBF were not altered by β-OH-B. In T2DM patients with LVEF≤50%, increased plasma β-OH-B significantly increased LV function dose-dependently. Since MGU did not change, the myocardial benefit of β-OH-B resulted from providing an additional fuel for the heart without inhibiting MGU.

Control of the parameters of thermosetting binders directly during the molding of products made of polymer composite materials

For aviation binders at the moment there is no necessary methodological base on the basis of which it is possible to control the conditions of their curing by viscosity, reaction rate and glass transition temperature in real time. The paper presents a method of control of thermosetting binder parameters directly during the molding process. The epoxy binder VSE-59 was studied. A mathematical model of the relationship between the frequency maximum parameter of the imaginary component of the electrical impedance and the degree of curing is given, the results of dielectric analysis are compared with laboratory methods (differential scanning calorimetry and rheometry). It is shown that the convergence of the values of the degree of conversion is high. Rheological and dielectric analyses revealed a direct correlation between electrical and rheological properties in the range of injection temperatures. Equations were determined and coefficients were experimentally selected to describe the correlation between viscosity and glass transition temperature and dielectric parameters, which provides the possibility of their control in real time. The obtained results can be used to create a hardware and software base, which makes it possible to control the state of the binder at each moment of the process of forming products from polymer composite materials, as well as to carry out optimization to eliminate the emergence of conditions of uneven polymerization. Realization of the detailed control of the binder state in different parts of the product will allow to carry out verification of calculations, to predict and avoid the occurrence of warping and, accordingly, to increase the quality of composite products.

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Association of fluoroquinolones with the risk of spontaneous pneumothorax: nationwide case–time–control study

IntroductionFluoroquinolones can cause severe collagen-associated adverse effects, potentially impacting the pulmonary connective tissue. We investigated the association between fluoroquinolones and spontaneous pneumothorax.MethodsA case–time–control study was performed using the nationwide French...

Contrasting Deficits in the Function of the Phonological Loop Between Children With Attention Deficit Hyperactivity Disorder and Reading Disability.

Attention Deficit Hyperactivity Disorder (ADHD) and reading disability (RD) often co-occur. Impairments in the phonological loop of working memory are central to RD, but it is unclear whether this is linked to ADHD, especially in Chinese reading. Two experiments were conducted with Chinese children aged 7 to 12 (Experiment 1, n = 65; Experiment 2, n = 60). Immediate and delayed recognition paradigms were used to assess phonological encoding and rehearsal. Both the RD-only and comorbid (ADHD + RD) groups performed worse than the control and ADHD-only groups in response time and accuracy. Notably, the comorbid group performed similarly to the RD-only group, indicating that phonological loop deficits in comorbid children are likely due to RD, not ADHD. This highlights phonological loop function as the key to distinguishing between ADHD and RD.

Applicability of Mean-Field Theory for Time-Dependent Open Quantum Systems with Infinite-Range Interactions.

Understanding quantum many-body systems with long-range or infinite-range interactions is of relevance across a broad set of physical disciplines, including quantum optics, nuclear magnetic resonance, and nuclear physics. From a theoretical viewpoint, these systems are appealing since they can be efficiently studied with numerics, and in the thermodynamic limit are expected to be governed by mean-field equations of motion. Over the past years the capabilities to experimentally create long-range interacting systems have dramatically improved permitting their control in space and time. This allows us to induce and explore a plethora of nonequilibrium dynamical phases, including time crystals and even chaotic regimes. However, establishing the emergence of these phases from numerical simulations turns out to be surprisingly challenging. This difficulty led to the assertion that mean-field theory may not be applicable to time-dependent infinite-range interacting systems. Here, we rigorously prove that mean-field theory in fact exactly captures their dynamics, in the thermodynamic limit. We further provide bounds for finite-size effects and their dependence on the evolution time.

ОСОБЛИВОСТІ ВПРОВАДЖЕННЯ МОНІТОРИНГУ ПЕРСОНАЛУ НА СУЧАСНИХ ПІДПРИЄМСТВАХ

Businesses related to various niches and industries use employee monitoring to protect themselves from potential risks of data leakage and other external threats. Employee monitoring is a new strategy in today's business that is central to improving productivity and operational efficiency. The application of scientific methods of monitoring and analyzing the activities of employees in real time allows organizations to achieve a high level of accountability, optimal allocation of resources and making informed management decisions. Such a monitoring system contributes to the rise and development of the enterprise, creating a more productive and motivated work environment. The article examines the peculiarities of the implementation of employee monitoring as an important tool for ensuring the efficient operation of the enterprise, maintaining labor discipline and rational use of resources. Separate forms of monitoring that are used in practice are considered. Attention is focused on the legal aspects of the monitoring process, compliance with the needs of both employers and employees, in particular, taking into account the rights and privacy of the latter. In the modern conditions of business operation and development, employers have an interest in the most efficient use of their staff's working time. Time control is one way to achieve this. Controlling working hours can be efficient and beneficial for a company, but it can also raise certain ethical and legal issues. It is important that this control is carried out in compliance with all relevant legal requirements and taking into account the rights and interests of employees. Therefore, the employer must develop and implement internal regulatory documents that regulate monitoring procedures, as well as ensure that employees are informed about their rights and obligations in this process. This will avoid conflicts and ensure the legal purity of monitoring, maintaining a balance between the needs of the enterprise and the rights of employees. The choice of personnel monitoring and tracking tools is not limited to technical aspects, but includes the creation of a culture of trust and openness in the relationship between the employer and employees.

Assessing the Role of AtGRP7 Arginine 141, a Target of Dimethylation by PRMT5, in Flowering Time Control and Stress Response.

PROTEIN ARGININE METHYLTRANSFERASES (PRMTs) catalyze arginine (R) methylation that is critical for transcriptional and post-transcriptional gene regulation. In Arabidopsis, PRMT5 that catalyzes symmetric R dimethylation is best characterized. PRMT5 mutants are late-flowering and show altered responses to environmental stress. Among PRMT5 targets are Arabidopsis thaliana GLYCINE RICH RNA BINDING PROTEIN 7 (AtGRP7) and AtGRP8 that promote the transition to flowering. AtGRP7 R141 has been shown to be modified by PRMT5. Here, we tested whether this symmetric dimethylation of R141 is important for AtGRP7's physiological role in flowering time control. We constructed AtGRP7 mutant variants with non-methylable R141 (R141A, R141K). Genomic clones containing these variants complemented the late-flowering phenotype of the grp7-1 mutant to the same extent as wild-type AtGRP7. Furthermore, overexpression of AtGRP7 R141A or R141K promoted flowering similar to overexpression of the wild-type protein. Thus, flowering time does not depend on R141 and its modification. However, germination experiments showed that R141 contributes to the activity of AtGRP7 in response to abiotic stress reactions mediated by abscisic acid during early development. Immunoprecipitation of AtGRP7-GFP in the prmt5 background revealed that antibodies against dimethylated arginine still recognized AtGRP7, suggesting that additional methyltransferases may be responsible for modification of AtGRP7.

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Game-Based Assessment of Peripheral Neuropathy Combining Sensor-Equipped Insoles, Video Games, and AI: Proof-of-Concept Study.

Detecting peripheral neuropathy (PNP) is crucial in preventing complications such as foot ulceration. Clinical examinations for PNP are infrequently provided to patients at high risk due to restrictions on facilities, care providers, or time. A gamified health assessment approach combining wearable sensors holds the potential to address these challenges and provide individuals with instantaneous feedback on their health status. We aimed to develop and evaluate an application that assesses PNP through video games controlled by pressure sensor-equipped insoles. In the proof-of-concept exploratory cohort study, a complete game-based framework that allowed the study participant to play 4 video games solely by modulating plantar pressure values was established in an outpatient clinic setting. Foot plantar pressures were measured by the sensor-equipped insole and transferred via Bluetooth to an Android tablet for game control in real time. Game results and sensor data were delivered to the study server for visualization and analysis. Each session lasted about 15 minutes. In total, 299 patients with diabetes mellitus and 30 with metabolic syndrome were tested using the game application. Patients' game performance was initially assessed by hypothesis-driven key capabilities that consisted of reaction time, sensation, skillfulness, balance, endurance, and muscle strength. Subsequently, specific game features were extracted from gaming data sets and compared with nerve conduction study findings, neuropathy symptoms, or disability scores. Multiple machine learning algorithms were applied to 70% (n=122) of acquired data to train predictive models for PNP, while the remaining data were held out for final model evaluation. Overall, clinically evident PNP was present in 247 of 329 (75.1%) participants, with 88 (26.7%) individuals showing asymmetric nerve deficits. In a subcohort (n=37) undergoing nerve conduction study as the gold standard, sensory and motor nerve conduction velocities and nerve amplitudes in lower extremities significantly correlated with 79 game features (|R|>0.4, highest R value +0.65; P<.001; adjusted R2=0.36). Within another subcohort (n=173) with normal cognition and matched covariates (age, sex, BMI, etc), hypothesis-driven key capabilities and specific game features were significantly correlated with the presence of PNP. Predictive models using selected game features achieved 76.1% (left) and 81.7% (right foot) accuracy for PNP detection. Multiclass models yielded an area under the receiver operating characteristic curve of 0.76 (left foot) and 0.72 (right foot) for assessing nerve damage patterns (small, large, or mixed nerve fiber damage). The game-based application presents a promising avenue for PNP screening and classification. Evaluation in expanded cohorts may iteratively optimize artificial intelligence model efficacy. The integration of engaging motivational elements and automated data interpretation will support acceptance as a telemedical application.

Solids retention time (SRT) control in the co-treatment of leachate with domestic sewage in aerobic granular sludge systems: Impacts on system performance, operational stability, and bioresource production

This study investigates the co-treatment of leachate and domestic sewage in municipal wastewater treatment plants using aerobic granular sludge (AGS) systems, focusing on granule formation, system stability, and resource production in two units (R1 and R2). In R2, solids retention time (SRT) was controlled between 10 and 25 days, while R1 maintained approximately 9 days. The results show that low leachate proportions (5 %) did not affect system performance or stability. However, increasing the leachate to 10 % reduced the structural stability of extracellular polymeric substances (EPS), leading to a significant decrease in alginate-like exopolysaccharides (ALE) production in R1 (216 mgALE/gVSS) and R2 (125 mgALE/gVSS). Principal component analysis revealed that SRT was crucial for optimizing biopolymer synthesis. Furthermore, SRT control in R2 improved filamentous control, biomass retention, and total nitrogen removal. Thus, selective biomass discharge is essential for maintaining granule stability, enhancing treatment efficiency, and supporting resource production.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.

Research on Optimal Control Strategy for Hydroelectric Units Based on Generalized Smith Predictor and Shortest Time Control

Abstract At present, most hydroelectric unit controllers use PID speed controllers. The principle of PID control is simple and the technology is mature, but there is less consideration for the need for deep participation of the unit in power grid regulation, resulting in a decrease in regulation quality and even instability of the regulation process when the unit deviates from the design operating conditions, which is difficult to meet the requirements of fast response and high-precision operation of the unit in the new power grid. This article proposes a new control strategy based on the generalized Smith predictor and the shortest time control. Through simulation comparison of the dynamic characteristics of the new control strategy and the conventional PID controller, the results show that the proposed new control strategy has better dynamic characteristics and control accuracy.

Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

Digital Twin and IoT-Enabled Online Learning Model for Real- Time Monitoring and Control of Fraudulent Transaction in the Banking industry

Fraudulent transactions have long been a challenge in the banking industry, causing significant financial losses and operational burdens. Traditional fraud detection systems often struggle to adapt and respond in real-time to the evolving tactics of fraud. This study aims to develop a digital twin and IoT-enabled online learning model for real-time monitoring and control of fraudulent transactions in banking. The proposed system integrates digital twin technology and IoT sensors to create a virtual replica of transaction environments, enabling continuous learning and adaptation using an online learning model. Performance metrics such as detection speed, prediction accuracy, precision, recall, and F1 score were used to evaluate the system. The system was tested using a dataset of banking transactions, including legitimate and fraudulent activities. Key results include detecting fraudulent transactions 40% faster than traditional methods, reducing detection time from 5 minutes to 3 minutes. The model achieved an accuracy of 92%, an 8.24% improvement over traditional fraud detection systems. The false positive rate was reduced by 28%, meaning fewer legitimate transactions were wrongly flagged as fraudulent. Precision increased by 5.95%, reaching 89%, and recall improved by 8.75%, reaching 87%. This led to a 7.32% increase in the F1 score, reaching 88%. The system offers enhanced real-time fraud detection capabilities, reducing false positives and improving overall security and operational efficiency in the banking industry.

MIMO Super-Twisting Controller using a passivity-based design

A novel MIMO Super-Twisting Algorithm is proposed in this paper for nonlinear systems with relative degree one, having a time and state-varying uncertain control matrix. The uncertainty is represented by a time and state-varying but unknown left matrix factor. Sufficient conditions for stability with full-matrix control gains are established, in contrast to the usual scalar gains. For this a smooth Lyapunov function, based on a passivity interpretation, is used. Moreover, continuous and homogeneous approximations of the classical discontinuous Super-Twisting algorithm are obtained, using a unified analysis method. Moreover, the proposed Super-Twisting algorithm is of the discontinuous type, in contrast to the usual unitary type.

Self-learning chebyshev fuzzy neural finite time control with application to active power filter

Self-learning chebyshev fuzzy neural finite time control with application to active power filter

Predefined time fuzzy adaptive control for stochastic nonlinear systems with limited time interval output constraints

This article investigates the problem of fuzzy adaptive predefined time tracking control for stochastic nonlinear systems with limited time interval output constraints. Firstly, the considered output constraints occur within a finite time interval after the system starts running. By constructing shift functions and barrier Lyapunov functions (BLFs), the constrained system is converted into an unconstrained system, which solves the problem of function discontinuity resulting from output constraints within limited time intervals. Then, with the help of adaptive backstepping technique and predefined time control method, which reduces the parameters to be designed and removes the limitation of relatively large initial control input in existing approaches. The designed control technique ensures the boundedness of all variables of the stochastic system, the tracking error converges within a predetermined time, and the outputs do not exceed their constraint boundaries in a finite time interval. And this algorithm is applicable to both infinite time constraints and unconstrained outputs cases. Finally, the validity of this approach is demonstrated through simulation examples.