Development Of System Research Articles

Bringing gut microbiota into the spotlight of clinical research and medical practice

Despite the increasing scientific interest and expanding role of gut microbiota (GM) in human health, it is rarely reported in case reports and deployed in clinical practice. Proteins and metabolites produced by microbiota contribute to immune system development, energy homeostasis and digestion. Exo- and endogenous factors can alter its composition. Disturbance of microbiota, also known as dysbiosis, is associated with various pathological conditions. Specific bacterial taxa and related metabolites are involved in disease pathogenesis and therefore can serve as a diagnostic tool. GM could also be a useful prognostic factor by predicting future disease onset and preventing hospital-associated infections. Additionally, it can influence response to treatments, including those for cancers, by altering drug bioavailability. A thorough understanding of its function has permitted significant development in therapeutics, such as probiotics and fecal transplantation. Hence, GM should be considered as a ground-breaking biological parameter, and it is advisable to be investigated and reported in literature in a more consistent and systematic way.

Angpt1 binding to Tie1 regulates the signaling required for lymphatic vessel development in zebrafish.

Development of the vascular system is regulated by multiple signaling pathways mediated by receptor tyrosine kinases (RTKs). Among them, angiopoietin (Ang)/Tie signaling regulates lymphatic and blood vessel development in mammals. Of the two Tie receptors, Tie2 is well known as a key mediator of Ang/Tie signaling, but unexpectedly, recent studies reveal that the Tie2 locus has been lost in many vertebrate species, while the Tie1 gene is more commonly present. However, Tie1-driven signaling pathways, including ligands and cellular functions, are not well understood. Here, we performed comprehensive mutant analyses of angiopoietins and Tie receptors in zebrafish and found that only angpt1 and tie1 mutants show defects in trunk lymphatic vessel development. Among zebrafish angiopoietins, only Angpt1 binds to Tie1 as a ligand. We indirectly monitored Ang1/Tie1 signaling and detected Tie1 activation in sprouting endothelial cells (ECs), where Tie1 inhibits nuclear import of EGFP-Foxo1a. Angpt1/Tie1 signaling functions in EC migration, proliferation, and lymphatic specification during early lymphangiogenesis, at least in part by modulating Vegfc/Vegfr3 signaling. Thus, we show Angpt1/Tie1 signaling to constitute an essential signaling pathway for lymphatic development in zebrafish.

Fabric perceptions in digital contexts: exploring the correlation between certainty and accuracy

As the fashion industry increasingly adopts digital platforms, fashion designers face challenges in digitally exploring and selecting fabrics. This transition underscores the complexities in digitally capturing fabrics. Extending our previous research, this study specifically examined the relationship between designers' certainty and their perceptual accuracy in digital fabric evaluations because uncertainty can diminish confidence in digital assessments, leading to an over-reliance on physical samples and slowing digital progress. The research employed a comparative analysis, engaging designers in both digital and physical fabric evaluations. The objectives of this study were: to evaluate the precision of digital fabric representations against physical assessments; to assess designers' psychological certainty and its correlation with assessment accuracy; and to identify fabric attributes that are most prone to misrepresentation digitally. The findings indicated that there is no direct correlation between evaluation accuracy and psychological certainty. Notably, for drapability, digital evaluations exhibited both high levels of certainty and accuracy, implying potential enhancements in digital representations of other textile attributes. The results of this study will contribute to the development of advanced digital fabric evaluation systems that can accurately replicate the physical fabric experience, thus ensuring a high level of certainty. This, in turn, will enhance the reliability and effectiveness of fabric selection on digital platforms.

From the devil’s square to the sustainable angels diamond and holistic bow-tie model: Development of a method for the holistic analysis, design and development of sustainable work systems

From the devil’s square to the sustainable angels diamond and holistic bow-tie model: Development of a method for the holistic analysis, design and development of sustainable work systems

Biophysics of frequency-dependent variation in paresthesia and pain relief during spinal cord stimulation.

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting network output and typically producing anti-nociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.Significance Statement The effects of spinal cord stimulation (SCS) on the nervous system are poorly understood, resulting in inadequate clinical success rates. Here, we use a biophysical modeling approach to investigate the neural response to SCS. We demonstrate that low- and high-frequency stimulation produce contrasting responses in the dorsal columns, brainstem, and dorsal horn. Importantly, our modeling approach was able to accurately predict clinical paresthesia thresholds as a function of frequency, as well as provide plausible biophysical explanations for frequency-dependent effects on paresthesia quality and pain relief. Overall, our results greatly enhance our understanding of the neural response to SCS, thereby offering context for interpreting clinical observations and crucial insight for development of future SCS systems.

Association between social capital and utilization of essential public health services among elderly migrants: a multilevel logistic study based on the 2017 China migrant dynamic survey (CMDS)

BackgroundAs the number of elderly migrants in China continues to grow, it is necessary to pay closer attention to their health and health services. Some studies have confirmed that social capital plays a significant role in the utilization of health services. Therefore, an in-depth exploration of the relationship between social capital and the utilization of essential public health services (EPHS) by elderly migrants will not only contribute to improving their overall health but also facilitate a more balanced development of public health service system in China.MethodsBased on the cross-sectional data from the 2017 China Migrants Dynamic Survey (CMDS), this study examined the impact of social capital on the utilization of EPHS among elderly migrants. We evaluated social capital at two distinct levels: the individual and the community, and considered two dimensions of social capital: structural social capital (SSC) and cognitive social capital (CSC). The study aimed to delve into the impact of these forms of social capital on the utilization of EPHS among elderly migrants, and whether the migration range moderates this impact by multilevel logistic regression analysis.ResultsA total of 5,728 migrant elderly individuals were selected. The health records establishment rate and health education acceptance rate were approximately 33.0% and 58.6%, respectively. Social capital influenceed the utilization of EPHS among elderly migrants. Specifically, individual-level SSC and CSC have impacts on both the establishment of health records (OR = 1.598, 95%CI 1.366–1.869; OR = 1.705, 95%CI 1.433–2.028) and the acceptance of health education (OR = 1.345, 95%CI 1.154–1.567; OR = 2.297, 95%CI 1.906–2.768) among elderly migrants, while community-level SSC only affected the acceptance of health education (OR = 3.838, 95%CI 1.328–11.097). There were significant differences in individual-level SSC, health records, and health education among different migration range subgroups among elderly migrants. Migration range moderated the effect of social capital on the utilization of EPHS, crossing provinces could weaken the relationship between SSC and health education.ConclusionsSocial capital is associated with a higher utilization rate of EPHS among elderly migrants. It is necessary to encourage them to actively participate in social activities, strengthen public services and infrastructure construction in the area, and improve their sense of belonging and identity.

Design of a handheld and portable fluorescence imaging system for quantitative detection of pregnancy-specific biomarkers in cattle.

Reproductive management significantly impacts dairy farm productivity, necessitating accurate timely pregnancy detection in cattle. This paper presents a novel handheld and portable fluorescence imaging system designed for quantitative assessment of pregnancy-specific biomarkers, addressing the limitations of current detection methods. The objective was to develop a cost-effective, at-farm solution for detecting pregnancy-specific protein B (PSPB) in bovine plasma samples. The system integrates an imaging module and a custom software application, enabling image capture, data processing, and PSPB concentration determination. Calibration utilizing known PSPB concentrations achieved a 0.6ng/mL limit of detection. Validation encompassed a comparison with a standard ELISA method using 100 bovine plasma samples; minimal bias and good agreement were observed within the linear range of the calibration curve for both methods. The system offers portability, user-friendliness, and potential for multiplex detection, promising real-time, at-farm reproductive management. This study demonstrates the successful development and validation of a portable fluorescence imaging system, offering an efficient and accurate approach to detecting pregnancy-specific biomarkers in cattle. Its implications extend to improving dairy farm productivity by enabling timely and reliable reproductive management practices.

A novel proposed classification system for rock slope stability assessment

The present study introduces “rock slope instability score (RSIS)” a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable.

Effectiveness of vestibular rehabilitation on postural balance in Parkinson’s disease: a systematic review and meta-analysis of randomized controlled trials

IntroductionPostural balance impairment can affect the quality of life of patients with Parkinson’s disease. Previous studies have described connections of the vestibular system with postural functions, suggesting a potential participation of the basal ganglia in receiving vestibular stimuli. This systematic review aims to summarize the evidence on the effectiveness of vestibular rehabilitation on postural balance in patients with Parkinson’s disease.MethodsA systematic review was conducted using the electronic databases: PubMed, Embase, Scopus and PEDro. The study selection was independently conducted by two reviewers, and disagreements were evaluated by a third reviewer. The included studies had no restrictions on publication dates or languages and the last update occurred in July 2023.ResultsFrom the 485 studies found in the searches, only 3 studies were deemed eligible for the systematic review involving a total of 130 participants. The Berg Balance Scale was described as the tool for evaluation of postural balance in all studies. The meta-analysis showed statistically significant results in favor of vestibular rehabilitation (MD = 5.35; 95% CI = 2.39, 8.31; P < 0.001), regardless of the stage of Parkinson’s disease. Although the effect size was suggested as a useful functional gain, the analysis was done with caution, as it only included 3 randomized controlled trials. The risk of bias using the RoB-2 was considered as being of “some concern” in all studies. Furthermore, the quality of the evidence based on the Grading of Recommendations Assessment Development and Evaluation system, produced by pooling the included studies was considered very low.ConclusionCompared to other interventions, vestibular rehabilitation has potential to assist the postural balance of patients with Parkinson’s disease. However, the very low quality of the evidence demonstrates uncertainty about the impact of this clinical practice. More robust studies are needed to confirm the benefits of this therapy in patients with Parkinson’s disease. This study was prospectively registered in PROSPERO: CRD42020210185.

Flip bifurcation analysis and mathematical modeling of cholera disease by taking control measures

To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases which are spread in the world wide. The objective of the research study is to assess the early diagnosis and treatment of cholera virus by implementing remedial methods with and without the use of drugs. A mathematical model is built with the hypothesis of strengthening the immune system, and a ABC operator is employed to turn the model into a fractional-order model. A newly developed system SEIBR, which is examined both qualitatively and quantitatively to determine its stable position as well as the verification of flip bifurcation has been made for developed system. The local stability of this model has been explored concerning limited observations, a fundamental aspect of epidemic models. We have derived the reproductive number using next generation method, denoted as “R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}”, to analyze its impact rate across various sub-compartments, which serves as a critical determinant of its community-wide transmission rate. The sensitivity analysis has been verified according to its each parameters to identify that how much rate of change of parameters are sensitive. Atangana–Toufik scheme is employed to find the solution for the developed system using different fractional values which is advanced tool for reliable bounded solution. Also the error analysis has been made for developed scheme. Simulations have been made to see the real behavior and effects of cholera disease with early detection and treatment by implementing remedial methods without the use of drugs in the community. Also identify the real situation the spread of cholera disease after implementing remedial methods with and without the use of drugs. Such type of investigation will be useful to investigate the spread of virus as well as helpful in developing control strategies from our justified outcomes.

The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning

AbstractThis paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

Research on the income and expenditure forecast of long-term care insurance fund in Shihezi City based on community care

ObjectiveThis study forecasts the income and expenditures of the long-term care insurance fund, provides a basis for formulating the raising standard of the long-term care insurance fund, and explores the measures to improve the pilot work of long-term care insurance.MethodsBy using the exponential smoothing and ARIMA models to forecast the income and expenditure of the old-age care insurance fund in 2022, the problems existing in the operation of the long-term care insurance fund are discussed.ResultsIn 2022, the income of the old-age insurance fund was 28.8934 million yuan, and the fund compensation expenditure was 28.4070 million yuan, with a slight balance of the fund. The highest relative errors of income and expenditure forecast models are −2.03% and − 2.76%, respectively. According to the results of fund expenditure, the annual financing standard should be 132.93 yuan/person, and the individual financing standard should be 66.47 yuan/person.ConclusionThrough the integration of personal payment, welfare, sports lottery public welfare income, social donations, and other ways, we can gradually establish a multi-channel risk-sharing financing. We will appropriately raise the standard for individual financing and the annual contribution standard for individuals from 50 yuan to 66.47 yuan. This will promote sustainable development of long-term insurance system.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Research on the Progressiveness and Deficiency of Laws of the Twelve Tables

The wheel of history rolls forward, leaving a profound mark on the land of ancient Rome. In this long history, Roman law, as the birthplace of Western civilization, has a far-reaching impact. Among them, the bloodshed between the aristocrats and the commoners is undoubtedly the most profound mark in Roman history. However, behind this mark is hidden a great law - the Laws of the Twelve Tables. Laws of the Twelve Tables is the earliest written law of ancient Rome, which had a profound influence on the development of the legal system of later generations. The birth of this law marked the transformation of Roman society from a tribal union to a centralized system, and laid the foundation for the later Roman Empire. This paper will assess the historical significance and impact of the Twelve Bronze Tables Law in light of contemporary legal theories. This will offer a fresh viewpoint and source of inspiration for future research on Roman legal history, help people around the world gain a better understanding of the principles and practices of the rule of law in ancient Rome, and serve as a helpful guide for the development of the rule of law in contemporary society.

Stiffness-tunable biomaterials provide a good extracellular matrix environment for axon growth and regeneration

Abstract Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix–a complex network composed of proteins and carbohydrates secreted by cells. In addition to providing physical support for cells, the extracellular matrix also conveys critical mechanical stiffness cues. During the development of the nervous system, extracellular matrix stiffness plays a central role in guiding neuronal growth, particularly in the context of axonal extension, which is crucial for the formation of neural networks. In neural tissue engineering, manipulation of biomaterial stiffness is a promising strategy to provide a permissive environment for the repair and regeneration of injured nervous tissue. Recent research has fine-tuned synthetic biomaterials to fabricate scaffolds that closely replicate the stiffness profiles observed in the nervous system. In this review, we highlight the molecular mechanisms by which extracellular matrix stiffness regulates axonal growth and regeneration. We highlight the progress made in the development of stiffness-tunable biomaterials to emulate in vivo extracellular matrix environments, with an emphasis on their application in neural repair and regeneration, along with a discussion of the current limitations and future prospects. The exploration and optimization of the stiffness-tunable biomaterials has the potential to markedly advance the development of neural tissue engineering.

Development and optimisation of a Near-Infrared spectroscopic system for glucose quantification in aqueous and intralipid-based samples

Abstract A non-invasive glucose sensing device could revolutionise diabetes treatment. Near Infrared (NIR) spectroscopy is a promising technology for glucose sensing; however, the design and choice of components for NIR spectroscopy can greatly affect the sensing accuracy. We aimed to develop a NIR absorption spectroscopy system to determine liquid glucose concentrations in the physiological range, by evaluating a range of NIR photodetector components and light sources. Three detection assemblies were tested: (i) a dispersive spectrometer with photodiode array, (ii) a Czerny-Turner monochromator with InGaAs photodiode and (iii) a miniature Fourier Transform Infrared (FTIR) spectrometer. A halogen lamp and NIR globar were trialled as potential light sources. The components were systematically tested by comparing the coefficient of determination and standard error of prediction (SEP) for the same set of aqueous glucose samples through 10 mmol/L concentration steps. The Czerny-Turner monochromator with InGaAs photodiode, along with the globar, were identified as the optimal components for the system. A range of concentration steps (1-10 mmol/L) were scanned to identify the physiologically relevant limit of detection, which was identified as 5mmol/L for glucose in solution. Spectra were then collected from glucose samples in 10% intralipid suspension in the 10-20 mmol/L range and the equivalent concentrations in solution. The SEP was greater for the intralipid samples due to strong scattering. Scattering was dominant above 1300nm, whilst absorption was dominant below 1300nm. Although alternative approaches achieve better resolution, our system uses simple and readily-available components and presents a platform for a non-invasive NIR glucose sensing device.&amp;#xD;

Pattern Of Breast Feeding, Weaning And Nutritional Status Of Under Five Children Of Working Mothers Of Bangladesh Armed Forces

Background: Adequate nutrition is essential in early childhood to ensure healthy growth, proper organ formation and function, a strong immune system, and neurological and cognitive development. The objective of the study is to assess the pattern of breast feeding, weaning and dietary behavior of under-five children of working mothers attended Child Welfare Centre (CWC), Malobika and Combined Military Hospital (CMH), Dhaka. Methods: This cross sectional study was conducted at CWC, Malobika and Combined Military Hospital, Dhaka over a period of six months from July 2017 to December 2017. All under-five years old sick children of employed mothers of Bangladesh Armed Forces attended CWC Malobika and CMH, Dhaka were included in the study and mothers were the respondents. Total 130 children were selected purposively for the study. Data were analyzed by using SPSS (statistical package for social sciences), version 25.0. The level of significance was set at 5% and p &lt; 0.05 was considered statistically significant. Results: Majority (92.3%) of the children was nutritionally normal in terms Mid Upper Arm Circumference (MUAC). Only 10(7.7%) children was malnourished; of them 6(4.6%) were severely malnourished and 4(3.1%) were moderately malnourished. In terms of Weight for Age Z score (WAZ) 3.1% were severe underweight and another 3.1% were moderate underweight. In terms of Height for Age Z score (HAZ), 5.4% were severely stunted and 6.2% were moderately stunted, while with respect to Weight for Height Z score (WHZ), 2.3% were severely wasted and 7.7% were moderately wasted. Conclusion: Nutritional status in children of working mothers of Armed Forces at Dhaka Garrison was high compared to the national figure. Bangladesh Armed Forces Med J Vol 56 No (1) June 2023, pp 35-41

Visual Perception and Multimodal Control: A Novel Approach to Designing an Intelligent Badminton Serving Device

Addressing the current issue of limited control methods for badminton serving devices, this paper proposes a vision-based multimodal control system and method for badminton serving. The system integrates computer vision recognition technology with traditional control methods for badminton serving devices. By installing vision capture devices on the serving device, the system identifies various human body postures. Based on the content of posture information, corresponding control signals are sent to adjust parameters such as launch angle and speed, enabling multiple modes of serving. Firstly, the hardware design for the badminton serving device is presented, including the design of the actuator module through 3D modeling. Simultaneously, an embedded development board circuit is designed to meet the requirements of multimodal control. Secondly, in the aspect of visual perception for human body recognition, an improved BlazePose candidate region posture recognition algorithm is proposed based on existing posture recognition algorithms. Furthermore, mappings between posture information and hand information are established to facilitate parameter conversion for the serving device under different postures. Finally, extensive experiments validate the feasibility and stability of the developed system and method.

РОЗРОБКА КІБЕРФІЗИЧНОЇ СИСТЕМИ ДЛЯ АВТОМАТИЗАЦІЇ ТА КЕРУВАННЯ ІНТЕРНЕТОМ РЕЧЕЙ З ВИКОРИСТАННЯМ ПЛАТФОРМИ HOME ASSISTANT

The paper presents the development of a cyber-physical system based on the HomeAssistant platform for efficient automation and control of Internet of Things (IoT) devices. The architectural features, technical implementation and prospects for the development of the system are considered, with an emphasis on the ability to integrate a wide range of sensors and IoT devices into a single network to create adaptive and intelligent solutions. The main focus is on the implementation of automation scenarios for indoor climate control, which optimise living conditions depending on the internal needs of users and external weather conditions, significantly increasing energy efficiency and overall comfort. Data storage and analysis strategies are described in detail, including the use of NAS servers for backup, the use of MariaDB for storage and InfluxDB and Grafana for analytics and visualisation, ensuring a high level of reliability and availability of information. Testing was conducted to assess the delay of notifications transmitted via Telegram and the internal network of the cyber-physical system. The obtained results confirm the high efficiency of the implemented cyber-physical system in ensuring instant delivery of notifications, which is a key aspect for rapid response to critical situations in a dynamic environment. The Home Assistant platform has been found to have extensive capabilities to support artificial intelligence services. It integrates with a variety of services such as Google Assistant, TensorFlow, DeepStack, Amazon Alexa, and allows you to easily expand its functionality with additional components and plug-ins. Using intelligent algorithms and data analysis, the system can independently make decisions on the optimal use of resources, monitor devices in accordance with changing conditions and user needs, and respond to dangerous or unpredictable situations.