Constant Supervision Research Articles

A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method

One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions.

Numerical simulation of the first layer printing in electric field-assisted fused filament fabrication for robust unconventionally oriented additive manufacturing

Fused filament fabrication (FFF) stands as a prominent thermoplastic additive manufacturing technique that has a wide range of potential applications. However, the conventional FFF process encounters limitations in unconventional environments, particularly in environments devoid of gravity, such as in extraterrestrial environments or in limited manufacturing conditions where maintaining consistent nozzle standoff distance becomes a challenging task. Fluctuations in nozzle standoff distance lead to inadequate material adhesion and dimensional errors, which results in a high failure rate and prolonged completion time. Thus, the conventional FFF process requires continuous monitoring or human intervention to ensure successful operation. To overcome the shortcomings of the traditional FFF process, an electric field-assisted FFF (E-FFF) process can be utilized to reduce the need for constant supervision and enable reliable production of parts. The E-FFF process integrates a high-voltage power supply unit with a standard FFF printer, generating an electrostatic force between the nozzle and the build plate. The generated electrostatic force can effectively attract the extruded molten polymer toward the build plate, even at a larger z-height of up to 1200 μm in unconventional build platform orientations such as vertical or reverse printing orientation. In this paper, finite element analysis is used to simulate the FFF and E-FFF process. The simulation focuses on the adhesion between extruded material and build plate with extended nozzle standoff distances and predicts the minimum voltage required to achieve a good material adhesion in unconventional orientations for the E-FFF process. The simulation model demonstrated its efficacy by estimating the minimum required voltage levels necessary for successful printing within the E-FFF process across a range of nozzle standoff distances in unconventional orientations.

Inpatient Suicides in Swedish Psychiatric Settings – A Retrospective Exploratory Study from a Nursing Perspective

In Sweden, approximately 1,200 individuals die by suicide annually. Inpatient suicide is considered rare, but death by suicide still occurs when admitted to a psychiatric hospital. This study was part of a national retrospective project covering data from all patients’ medical records for the 2 years before death by suicide in 2015. In this study, 41 patients who died by suicide while being admitted to psychiatric care were identified. The aim was to retrospectively identify documentation of suicide risk, safety measures, and comparisons between those with and without suicide attempts for patients who died by suicide during psychiatric inpatient care. There was documentation of suicidal variables in 80% of the patients; 59% had a previous known suicide attempt, 63% were diagnosed with mood disorders, and 41% were assessed for elevated suicide risk. The most common suicide method was hanging, suffocation (68%), and 22% had died by suicide within 24 h after admission. Almost three-quarters were on voluntary care. No patients had constant professional supervision on a one-to-one basis, and 17% had 15-minute checks. One-third were on agreed leave at the time of the suicide. These results emphasise the lifesaving role of high-level supervision in the early stages of inpatient care.

Unlocking the role of managers in self-managing teams

PurposeRecent reports show that knowledge-based sectors contribute significantly to the global economy and underscore the importance of innovation. Innovation in both products and processes is vital to maintaining competitiveness. Self-managing teams, emphasizing autonomy and limiting the role of management, have been viewed as a solution to encourage innovation. However, management's efforts to coordinate and align employees with the organization's interests can inadvertently stifle innovation. Achieving the appropriate balance between granting employees autonomy and retaining managerial involvement is paramount, creating a paradox known as the control-autonomy dilemma. This article highlights the importance of perceived managerial support to strike the appropriate balance.Design/methodology/approachPractitioner paperFindingsTo address the control-autonomy dilemma, organizational practices that balance management control and employee autonomy are crucial. These include input practices that emphasize skill development and/or output practices that set clear goals. With self-rule, maintaining perceived managerial support becomes harder. Without it, employees may hesitate to engage in innovative behaviors. While not needing constant supervision, employees do need to have the perception that their managers provide essential resources and motivation for their work.Originality/valueThis paper is the first to highlight how the level of perceived managerial support (PMS) will differ due to the nature of the organizational practices and the type of innovation.

Gasifier use for cooking and biochar production by smallholder farmers in Rwanda

Rwanda’s rural households primarily use biomass fuel, leading to fuel waste and air quality related issues. The study compared two-top-up lift draft (TLUD) gasifier types (Gastov and Karundura) with a three-stone fire (TSF) cookstove in Bugesera and Huye Districts, Rwanda, using standardized cooking tests (SCT) and a qualitative survey. Key indicators included cooking time, fuel usage and biochar yield while the survey collected information about fuel availability, frequency of use, gasifier design, benefits and challenges. The study utilised descriptive statistics and ANOVA for analysis. The study revealed that the TSF cooked a standardised meal faster than the gasifier did, but the gasifiers reduced fuelwood consumption by 25–50% and produced biochar. The survey responses revealed low fuelwood availability, with fuel savings, temperature control, reduced need for constant supervision, biochar production, and reduced time needed to gather fuelwood, more hygienic and attractive appearance cooking have been revealed. Most household cooks reported that the gasifier stoves produced less smoke and were safer for women and children’s health. The biochar produced by the TLUD gasifiers was perceived as positive as it combines biochar production and bioenergy, reduces off-farm biomass gathering, improves energy security and produces sufficient biochar to improve soil productivity.

CAUSES OF TEACHER ABSENTEEISM IN SELECTED SECONDARY SCHOOLS OF KAYUNGA DISTRICT IN UGANDA. A DESCRIPTIVE STUDY.

Background: Teacher absenteeism is when the teacher is out of school either by delaying, leaving before official closing time, not attending school at all, or when the teacher is at school but missing classes. Absenteeism is caused by many factors like personal illness, a relative’s sickness, family conflicts, lack of job satisfaction leading to low morale, lack of personal competence, lack of friendly work group norms, poor leadership at the workplace, in effective supervision and inspection, assignment of other duties outside the workplace, bad weather conditions, union influence, and poor working conditions. The study aims to examine the causes of absenteeism in selected secondary schools of Kayunga District in Uganda. Methodology: This is a descriptive survey design in which quantitative research methods were used on 210 who included the DEO, 10 head teachers, 100 teachers, and 100 Students. Results: 54.0% of the respondents were male while 46.0% were females. According to teacher responses, 67% agreed that female teachers are usually absent due to family responsibilities, and 58% agreed that male teachers are usually absent because of attending their business to substitute for their income. 52% agreed that old teachers are mostly absent because they lost their morale.64% of the teachers agreed that teachers are absent due to illness. 46% of teachers agreed that teachers of low level of education attend further education programs. (46%) agreed that teachers absent themselves because they are not satisfied with their job. Conclusion: Teachers absent themselves because they are unsatisfied with their jobs, family responsibilities, substitute income, undertaking further studies, lost morale, and are tired and some miss due to illness. Recommendation: School employers should ensure that there is constant supervision and remuneration to motivate teachers as a way of curbing teachers’ absenteeism.

Enhancing Arabic Language Learning in Class VIII MTsN 5 Jombang: An Evaluation Using the CIPP Model

This qualitative study used CIPP model to assess Arabic language learning for Class VIII at MTsN 5 Jombang. Data collection was carried out by interviews, observation and documentation. The research respondents consisted of 3 Arabic teachers and 29 students of Class VIII-F, including 15 boys and 14 girls. Analysis of the findings including contextual, input, process and product dimensions. The results show that Arabic learning is enhanced by a supportive environment, good infrastructure, and cultural elements with rich Islamic cultural heritage. The program is supported by a balanced curriculum and well-equipped facilities. However, the curriculum must be updated following the latest national curriculum, Emancipated Curriculum, and teachers must be trained to remain relevant. Constant supervision is required to improve the quality of teaching and student engagement. The results of the evaluation showed that 80% of students met the minimum competency standards. However, more interactive teaching for speaking skills need improvement.

Bankruptcy Analysis of Polish Waste Management Companies

The waste management system should be constantly monitored to ensure rational waste management, limit the possibility of contamination of surface and groundwater, reduce the risk of waste fires and illegal storage. Unfortunately, in many cases this system remains without constant supervision, and subsequent waste management enterprises remain bankrupt, are closed or are subject to bankruptcy proceedings. Main aim of this paper is the construction of the author's model of financial bankruptcy prediction dedicated to the specificity of waste management companies activity. That discriminatory model will allow entities dealing with waste management to be given early warning of the risk of bankruptcy and for them to take corrective actions that will allow them to continue their very important activities aimed at protecting the natural environment. Based on the backward stepwise regression procedure on a group of 136 bankrupt and 136 still operating enterprises from the analyzed sector of economy, a discriminatory model was proposed. It was able to accurately predict the bankruptcy/continuation of business activity of over 76% of the analyzed waste management enterprises. The effectiveness of the predictions of the original model is much higher than the predictions calculated using other, popular discriminatory models. Obtained research results indicate that the proposed models should be constantly developed for waste management companies, not only for economic reasons, but above all for environmental reasons.

AUTOMATED REHABILITATION TOOL FOR THE LOWER LIMB

An urgent problem today is the need to develop rehabilitation tools that can be used by the patient in self-care mode. Modern methods of rehabilitation of the lower limb are often limited by the availability of specialists and require constant supervision by medical personnel. The problem of incorrect foot posture can lead to numerous health complications, such as flat feet, which can be associated with diabetes and coronary heart disease. Therefore, the development of a tool for self-diagnosis of pressure on different areas of the foot and control of correction is an urgent task. The availability of this tool will contribute to effective rehabilitation in case of disorders of the lower limbs, improve posture, and slow down the progression of a number of diseases. Previous studies confirm the importance of active participation of patients in the rehabilitation process. The trend toward self-correction requires integrated technologies to provide real-time feedback. The main goal is to create the ability to adjust the position of the foot in shoes using data processing and visualiza-tion on a mobile application. The work presents the development of an automated rehabilitation tool for the lower limb, namely the electrical schematic diagram for the tool. The device uses advanced data processing methods to accurately determine the position of the foot. The analysis of the dynamics of movements and the interaction of the tool with the leg area are aimed at achieving maximum accuracy and efficiency of rehabilitation. The developed tool allows users to receive information about the position of the foot in the shoe in real time. The mobile application in the smartphone becomes a visual interface that displays important information that provides an opportunity for self-correction of posture, running technique, and various physical exercises. The automated rehabilitation tool defines a new stage in the development of technologies for self-correction of the lower limb in case of flat feet. By providing patients with a tool to actively control the rehabilitation process, this tool improves quality of life and reduces the need for ongoing medical supervision. In the future, there will be pro-gramming of the electrical circuit and development of the application for the device according to algorithms.

Real-Time Visual Feedback Based on MIMUs Technology Reduces Bowing Errors in Beginner Violin Students.

Violin is one of the most complex musical instruments to learn. The learning process requires constant training and many hours of exercise and is primarily based on a student-teacher interaction where the latter guides the beginner through verbal instructions, visual demonstrations, and physical guidance. The teacher's instruction and practice allow the student to learn gradually how to perform the correct gesture autonomously. Unfortunately, these traditional teaching methods require the constant supervision of a teacher and the interpretation of non-real-time feedback provided after the performance. To address these limitations, this work presents a novel interface (Visual Interface for Bowing Evaluation-VIBE) to facilitate student's progression throughout the learning process, even in the absence of direct teacher intervention. The proposed interface allows two key parameters of bowing movements to be monitored, namely, the angle between the bow and the string (i.e., α angle) and the bow tilt (i.e., β angle), providing real-time visual feedback on how to correctly move the bow. Results collected on 24 beginners (12 exposed to visual feedback, 12 in a control group) showed a positive effect of the real-time visual feedback on the improvement of bow control. Moreover, the subjects exposed to visual feedback judged the latter as useful to correct their movement and clear in terms of the presentation of data. Although the task was rated as harder when performed with the additional feedback, the subjects did not perceive the presence of a violin teacher as essential to interpret the feedback.

Studying the Quality of Source Code Generated by Different AI Generative Engines: An Empirical Evaluation

The advent of Generative Artificial Intelligence is opening essential questions about whether and when AI will replace human abilities in accomplishing everyday tasks. This issue is particularly true in the domain of software development, where generative AI seems to have strong skills in solving coding problems and generating software source code. In this paper, an empirical evaluation of AI-generated source code is performed: three complex coding problems (selected from the exams for the Java Programming course at the University of Insubria) are prompted to three different Large Language Model (LLM) Engines, and the generated code is evaluated in its correctness and quality by means of human-implemented test suites and quality metrics. The experimentation shows that the three evaluated LLM engines are able to solve the three exams but with the constant supervision of software experts in performing these tasks. Currently, LLM engines need human-expert support to produce running code that is of good quality.

The Influence of Modernity, Lifestyle and Economic Literacy on the Consumptive Behavior of Economics Education Students at Universitas Negeri Gorontalo

People's consumption choices no longer reflect their ability to distinguish between urgent needs and less important wants. Consumption patterns can be recognized by cues evident in an individual's purchasing habits, like acquiring items or services solely for gift-giving purposes without considering their utility, or purchasing based solely on appealing packaging that distinguishes them from similar products. Parents entrust students with the responsibility of managing their finances, granting them the freedom to spend without constant supervision. Consequently, students may exhibit less rational spending habits, indicating a departure from prioritized consumption choices. Students, as members of society, also engage in consumption behaviors which correlates with modernity, lifestyle and economic literacy. The purpose of this study was to determine and analyze the influence of modernity, lifestyle, economic literacy on consumptive behavior in undergraduate students of Economics Education at Universitas Negeri Gorontalo. This research uses quantitative methods with data collection techniques using questionnaires with a sample of 144 students. Data analysis using Structural Equation Models (SEM) based on Partial Least Square (PLS) using the SmartPLS version 3.0 application. The results showed that modernity has an influence on lifestyle, modernity has no influence on economic literacy, lifestyle has an influence on economic literacy, modernity has an influence on consumptive behavior, lifestyle has an influence on consumptive behavior, economic literacy has an influence on consumptive behavior, modernity, lifestyle and economic literacy have an influence on the consumptive behavior of UNG Economics Education undergraduate students.

ACADEMIC PERFORMANCE IN THE LAST 5 YEARS IN SELECTED SECONDARY SCHOOLS OF KAYUNGA DISTRICT IN UGANDA. A DESCRIPTIVE STUDY.

Background: Academic achievement can be defined as excellence in all academic disciplines, in class as well as extracurricular activities. It includes excellence in sports, behavior, confidence, communication skills, punctuality, assertiveness, Arts, Culture, and the like. The study aims to analyze the student’s academic performance in the last 5 years in selected secondary schools of Kayunga district in Uganda. Methodology: In this study, the researcher used a descriptive survey design, used both qualitative and quantitative research methods. study population of 211 respondents included the DEO, head teachers, teachers, and Students. Results: 60% of the headteachers strongly disagreed that Schools in our district have been ranked among the top schools in the country in terms of academic performance.100% of the headteachers disagreed that there have been few students scoring Division 9 in most secondary schools for the past 10 years. 78% of the teachers disagreed that schools have had very many numbers of students scoring Division 1 for the past 5 years. 88% of the students disagreed that most students who complete the Advanced level have had a chance of obtaining government sponsorships in different public universities. Conclusion: Schools in Kayunga district have had very few numbers of students scoring Division 1 for the past 5 years; many students were scoring Division 9 in most secondary schools for the past 10 years; schools in the district have been ranked among the least performing schools in the country in terms of academic performance. Recommendation: School managers should adverse means to improve academic performance, especially poor grades. For example, staff motivation, student motivation, remedial teaching, and constant supervision.

Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50Pa to close to 1kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Alcohol Consumptions and its Impact on Academics in Medical Students of Hyderabad Telangana

Cross sectional and prospective study on alcohol consumption and its impact on academics in medical students of Hyderabad Telangana were conducted in a medical college Hyderabad. 100 medical students, both male and female who consumed alcohol had participated in the study. Socio demographic and academic details were collected from each participant after obtaining an informed consent. Study results revealed that majority of the students consuming alcohol were from urban area, high socio-economic background and whose family members also consume alcohol. Majority of the students were from educated and nuclear families. Social, fun and curiosity were being the top reasons for consuming alcohol. Students suffering from various psychiatric illnesses were also reported in our study. Alcohol consumption had a great impact on their academics (39%) was noticed in our study. Similar results were found in studies conducted in other states of India. Constant supervision by parents, awareness programs by their peer group and regular psychiatric counseling will help to minimize this habitamong medical students.

An IoT-based low-cost architecture for smart libraries using SDN

In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and economical solutions in library management, particularly in the realm of RFID-based processes such as authentication, property circulation, and book loans, underscores the significance of this study. Leveraging the collaborative potential of IoT and SDN technologies, our proposed system introduces a fresh perspective to tackle these challenges and advance intelligent library management. In response to the evolving landscape of smart libraries, our research presents an Internet of Things (IoT)-based low-cost architecture utilizing SDN. The exploration of this architectural paradigm arises from a recognized gap in the existing literature, pointing towards the necessity for more efficient and cost-effective solutions in managing library processes. Our proposed algorithm integrates IoT and SDN technologies to intelligently oversee various library activities, specifically targeting RFID-based processes such as authentication, property circulation management, and book loan management. The system's architecture, encompasses components like the data center, SDN controllers, RFID tags, tag readers, and other network sensors. By leveraging the synergy between RFID and SDN, our innovative approach reduces the need for constant operator supervision in libraries. The scalability and software-oriented nature of the architecture cater to extensive library environments. Our study includes a two-phase investigation, combining practical implementation in a small-scale library with a simulation environment using MATLAB 2021. This research not only fills a crucial gap in current knowledge but also lays the foundation for future advancements in the integration of IoT and SDN technologies for intelligent library management.

The effect of the clinical supervision model on nursing internship students’ nursing process-based performance: an experimental study

BackgroundThe nursing process is a systematic method for identifying the patient’s problems and planning to resolve them. It is also a crucial pillar of high-quality nursing care. Nursing internship students may lack the necessary skills to implement the nursing process due to the increased independence, the absence of constant professorial supervision, and limited experience. The clinical supervision model is a method of clinical education that bridges the gap between theory and practice.ObjectiveThis study was conducted to investigate the impact of the clinical supervision model on the performance of nursing internship students in each of the five stages of the nursing process, as well as overall.MethodThis experimental study was conducted in 2022. The 70 eligible internship students were conveniently selected and randomly assigned to either an intervention or a control group. In the present study, the clinical supervision model was implemented for the intervention group, while the control group received routine supervision. This was carried out over six sessions in three months. The data collection was conducted using a researcher-developed checklist of nursing process-based performance in both groups. Moreover, the Manchester questionnaire was used to evaluate the model in the intervention group. The variables considered as confounding factors included age, gender, marital status, number of monthly shifts, and grades of the nursing process credit completed in the third semester. SPSS version 16 software, descriptive statistics (frequency distribution, percentage, mean, and standard deviation), and analytical statistics (independent t-test, chi square, repeated measures Anova and LSD) were used to analyze the data.ResultsIntergroup analysis revealed that there was no significant difference between the scores of nursing process steps and the total score before the intervention in the control and intervention groups, as well as in baseline characteristics (P > 0.05). According to the intragroup analysis, the intervention group showed a significant increase in both the total scores and scores of nursing process steps over time (P < 0.001), whereas the control group exhibited contradictory results (P > 0.05). Finally, the “P-Value Intervention” demonstrated the effectiveness of this training model in improving the performance of the intervention group based on the nursing process compared to the control group. The mean score of the Manchester questionnaire in the intervention group was 136.74, indicating the high impact of implementing the clinical supervision model in the intervention group.ConclusionThe results indicated that the implementation of the clinical supervision model led to improved utilization of the nursing process by nursing internship students at all stages. Therefore, it is recommended that nurse educators utilize the clinical supervision model by providing feedback on errors in action during supervision sessions to enhance the quality of nursing care provided by nursing internship students and improve patient safety in clinical environment.

Microneedle Assays for Continuous Health Monitoring: Challenges and Solutions.

Continuous health monitoring aims to reduce hospitalization and the need for constant supervision of the patients. For an outpatient monitoring device to be effective, it must meet certain criteria: it should demand minimal patient involvement, be reliable, be connected, remain stable with infrequent replacements, be cost-efficient, be compatible with humans, and ultimately be self-powered. Microneedle (MN) technology, designed for transdermal biosensing, offers a promising solution for meeting a wide range of these demands in the field of continuous health monitoring. A variety of MN platforms have been developed to facilitate this crucial function. Our focus in this Perspective is on the significant challenges linked to MN-based biosensors. These challenges include ensuring skin compatibility, the effective integration of biorecognition elements into the MN systems, and the durability concerns of these sensors in enabling extended periods of continuous monitoring. Tackling these hurdles could pave the way for more effective and reliable MN-based health monitoring solutions in the future.

Development of power qualities for teenagers with delay of psychical development and the role of the school nurse in their health care

They studied the specifics of the development of strength abilities in boys and girls aged 12–14 with mental retardation compared to students of secondary schools. The lag of adolescents aged 12-14 years with mental retardation was found according to the absolute indicators of wrist (hand dynamometer) and static dynamometry (static dynamometer), dynamic and static strength endurance and speed-strength qualities ("holding on the crossbar", "standing long jumps ", "sitting up from a position lying on the back"), both in girls and boys in almost all age groups, compared to students of secondary schools. The analysis of the research results established: 1. The development of strength abilities of children with mental retardation is heterochronic and multidirectional compared to high school students. At the same time, the relative indicators of the strength of the back muscles of teenagers with mental retardation with a clear lag in the absolute indicators of hand and standing dynamometry did not statistically significantly differ from their peers in senior classes. Small differences in the relative indicators are due to the low physical development of adolescents of the mental retardation, namely, the lack of body weight. 2. Dynamic endurance based on the number of sit-ups of the trunk from the lying position is statistically significantly different between adolescent boys and girls of the experimental and control groups (p&lt;0.05-0.001), with the exception of 14-year-old boys. The studied school-aged children with ALS lag behind the control and in the static power endurance of the muscles of the arms according to the results of standing on the crossbar with bent arms. Moreover, both in boys and in girls this difference is highly probable in all studied age groups (p&lt;0.001). 3. It turned out that the speed and strength qualities of the standing long jump in adolescents with mental retardation are statistically significantly lower, and both in girls and boys in all age groups. 4. The obtained data indicate that this category of schoolchildren should be under the constant supervision of the school nurse.

Intelligent Control System for Wood Drying: Scalable Architecture, Predictive Analytics, and Future Enhancements

This article explores the research and development undertaken as part of a Master’s degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project’s objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks.