Systemic Effects Research Articles

Unveiling the role of DHIS2 in enhancing data quality and accessibility in primary healthcare facilities: Evidence from Ethiopia.

The implementation of DHIS2 in healthcare systems has transformed data management practices worldwide. However, its specific impact on data quality, availability, and performance in Primary Health Unit (PHU) facilities in Ethiopia remains underexplored. Therefore, we investigated the contribution of DHIS2 to enhancing data quality, availability, and performance within PHU facilities in Ethiopia. We employed qualitative methods, specifically Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs), to gather insights from stakeholders, including healthcare providers and administrators at PHCUs across Ethiopia. Convenience sampling was used for FGDs, while purposive sampling targeted key informants with relevant expertise. Data were systematically analysed thematically, identifying patterns and themes related to DHIS2's impact on data management within PHUs. This approach offered a comprehensive understanding of the system's effectiveness and the factors influencing its implementation, highlighting both successes and challenges in integrating DHIS2 into healthcare practices. Participants from various regions reported significant enhancements in the timeliness, completeness, accuracy, and accessibility of health data following the implementation of DHIS2. While some concerns were raised regarding variations in reporting intervals, the consensus indicated marked improvements in data management processes. DHIS2 standardized data collection methods, enabling healthcare providers to input and access data in real-time. This advancement fostered greater accountability and transparency within the healthcare system. Additionally, unexpected benefits arose, including increased digital literacy among staff, equipping them with necessary skills for effective data management, and the creation of job opportunities, particularly for youth. Ultimately, DHIS2 emerged as a pivotal tool for enhancing data quality and promoting health service equity across Ethiopia. DHIS2 has significantly improved data quality and accessibility in Ethiopia, enhancing healthcare management and accountability across facilities. Healthcare providers should continue to leverage its robust features and prioritize ongoing staff training to improve digital literacy and data management skills. Establishing consistent reporting practices and regular audits will further maintain data integrity and foster a culture of accountability within the healthcare system.

The Impact of Monetary Policy on Healthcare Affordability: A Case Study of China's Urban and Rural Basic Medical Insurance

Longer life expectancy and improved health are key indicators of economic growth. As such, strategies to bolster the medical security system are priorities in government budgeting. The government is dedicating all available resources to better healthcare in rural and underdeveloped areas, narrowing the urban-rural healthcare gap, and achieving equitable healthcare resource distribution. This study examines the effect of monetary policy on healthcare affordability, focusing on China's Urban and Rural Basic Medical Insurance (URRBMI). It investigates how macroeconomic policies, such as interest rates and inflation control, affect healthcare costs and shape URRBMI's financial protection. Using an integrated methodology, including a literature review, theoretical framework, and econometric analysis, the research uncovers how monetary policy impacts healthcare costs and URRBMI's effectiveness. The findings highlight the importance of monetary policy in healthcare affordability. The study recommends collaboration between monetary authorities and healthcare policymakers to improve China's healthcare system's sustainability and effectiveness. Furthermore, these insights are crucial for refining URRBMI and provide new understandings of optimizing macroeconomic policy interactions with social welfare programs.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

Polysaccharide-potato protein coacervates for enhanced anthocyanin bioavailability and stability

Anthocyanins (ACNs) possess strong antioxidants, anti-cancer, anti-obesity, anti-diabetic, and anti-inflammatory properties but are limited use by their susceptibility to environmental factors. This study aims to overcome these limitations by developing and assessing a novel coacervate system, consisting of potato protein isolate (PPI) combined with various polysaccharides, to stabilize and encapsulate anthocyanins from black carrot concentrate The polysaccharides included in this system include inulin, gum Arabic, guar gum, pectin, and soluble fiber. The coacervate system's effectiveness in maintaining stability and increasing the bioavailability of anthocyanins was evaluated compared to conventional soybean protein-based systems. The results show that pH considerably influences potato protein solubility, with maximum solubility at strongly acidic (pH 2) conditions. Hygroscopicity and moisture content analysis of the coacervates showed significant variations, with potato protein-guar gum (PPIGG) microcapsules having the lowest moisture content and potato protein gum Arabic (PPIGA) microcapsules having the highest moisture content. SEM imaging illustrated distinct microcapsule morphologies, while FT-IR measurement verified the successful integration of proteins and polysaccharides. The significance of the research reflects its proof that potato protein isolate (PPI) based coacervate systems consists of potato protein with polysaccharides, particularly those containing gum Arabic and pectin, have significant potential for improving anthocyanin stability and bioavailability. These findings guide future studies to investigate other polysaccharides, improve coacervation processes, and explore applications in the food and nutraceutical sectors. It also offers valuable insights for creating efficient encapsulation techniques for bioactive substances.

AI-Enhanced Secure Mobile Banking System Utilizing Multi-Factor Authentication

The increasing reliance on mobile banking has significantly heightened the need for robust security mechanisms to protect users from unauthorized access and fraudulent activities. As mobile banking continues to grow in popularity, safeguarding financial transactions and personal data becomes a top priority. This paper introduces an AI-enhanced secure mobile banking system that leverages Multi-Phase Authentication (MPA) to strengthen the authentication process. In this system, artificial intelligence is integrated with traditional authentication methods, creating a dynamic framework that assesses the risk level associated with each user interaction. Based on this real-time risk assessment, the system adjusts the authentication requirements, making them more stringent when higher risks are detected and more lenient when the risk is lower. This adaptive mechanism not only enhances the security of mobile banking by providing multiple layers of protection but also improves the user experience by reducing unnecessary authentication steps that can cause frustration and delay. The proposed system's effectiveness is validated through a series of simulations and case studies, which demonstrate significant improvements in key security metrics. These include a marked reduction in instances of fraud and lower false positive rates, which indicate that the system can accurately distinguish between legitimate and suspicious activities without imposing undue burden on users. Overall, the results of this study highlight the potential of AI-enhanced multi-phase authentication to provide a scalable and user-friendly solution for secure mobile banking. This approach represents a promising direction for the future of digital financial services, offering a balance between rigorous security and seamless user experience.

Herbal Cigarette Sales Information System Case Study at Stockis Sin Sriwijaya

There is still space for improvement in the application and usage of technology and information in the sales business, as indicated by the significant percentage of clients who continue to make manual purchases. An example would be the present Sriwijaya Herbal Sin Cigarettes retailer. The Sin Cigarette Herbal Sriwijaya stockist shop employs a traditional sales system where consumers physically visit the shop to make purchases. This research aims to enhance consumer convenience by developing a sales information system for herbal cigarettes, enabling online access to sales. This study employs a descriptive research methodology and a waterfall system development approach. This approach involves sequential steps, including needs analysis, design, code production, testing, and support. Data collection from the users was followed by the design and development stage of the program, employing the PHP programming language for implementation. Subsequently, an evaluation was conducted on the functioning system. The outcome is a sales information system that is anticipated to possess the capability to process data into high-quality information efficiently, seamlessly integrate various components, and serve as a repository for data storage. With the existence of this sales information system for herbal cigarettes, the processing of sales data has been computerized automatically, enabling automated report generation. This study introduces an internet-based sales information system for the Sriwijaya Herbal Cigarette Stockist Shop. It replaces traditional sales procedures, expands market reach, streamlines report generation, provides comprehensive buyer information, and maximizes profitability. The innovation employs the waterfall system development methodology and the PHP programming language to enhance retail operations' effectiveness and efficiency. An inherent constraint of this study is the need for the online-based sales information system to address all challenges the Sriwijaya Herbal Cigarette Stockist Shop faces, including technological infrastructure and user proficiency issues. Reliable internet connectivity is crucial for the system's effectiveness, which may require improvements in certain regions. Additionally, employees and customers may require additional training to use the new systems proficiently, which should be considered in terms of time and resources for this research.

Speed Breaker and Potholes Detection Using DL and IoT

This project introduces a novel approach to enhance road safety by implementing a real-time pothole detection system. The system utilizes a camera module to capture video input, which is then processed using a Python file for Image detection. Upon detecting a pothole or a speed breaker, the system triggers a buzzer to alert nearby vehicles and pedestrians. This alert mechanism aims to reduce the risk of accidents and vehicle damage caused by potholes, thereby improving overall road safety. Additionally, the project can contribute to the efficient maintenance of roads by enabling timely repair of identified potholes. The system's effectiveness in alerting road users and facilitating prompt repairs underscores its potential to enhance the quality and safety of transportation infrastructure. Also a concept of RQI (Road Quality Index) will improve the transport and travelling over the road. And to make a database which contain coordinates where speed breakers and potholes are present and this database will be real time

Interactive Haptic System with Multimodal Tactile Sensing and Hydraulic Feedback for Realistic Human–Machine Interaction

With the increasing demand for immersive haptic experiences, multimodal tactile sensing and feedback have become crucial for enhancing user interaction. In this work, an interactive haptic system comprising a robotic hand and wearable tactile feedback device is introduced to facilitate tactile information exchange between humans and robots. The robotic hand, equipped with force and temperature sensors, mimics human movements through vision recognition and captures detailed tactile data. The data are then transmitted to the user through a hydraulic feedback device, providing realistic touch sensations. The dual capabilities of the hydraulic mechanism to simultaneously simulate both pressure and temperature sensations simplify the device structure and enhance reliability compared to systems that require multiple tactile actuators. The force feedback system delivers pressures ranging from 0.5 to 10.5 N across five levels while the temperature feedback mechanism provides thermal sensations ranging from 3 to 50 °C across three levels. Extensive experimental validations demonstrate the system's effectiveness in applications such as remote robotic surgery and telepresence, enabling users to perceive real‐time contact pressure and temperature during remote manipulation tasks. The proposed system, capable of delivering multimodal tactile feedback through a single actuator, represents a significant step toward more immersive and realistic haptic experiences, highlighting its broad applicability.

Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN)

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

Accounting Information Systems’ Effectiveness at Village Credit Institutions (LPDs) in Denpasar City and the Influencing Factors

Companies have been forced to adopt computer-based information systems due to the quick development of information technology. These systems expedite and simplify the management's access to data. The purpose of this study is to investigate how the efficiency of Accounting Information Systems (AIS) at Village Credit Institutions (LPDs) in Denpasar City is impacted by factors such as job experience, educational attainment, training, incentives, and task complexity. The population of the study includes 34 LPDs, with a sample of 136 respondents selected through purposive sampling. Multiple linear regression analysis was involved in the data analysis. The findings indicate that while task complexity has a negative influence on AIS' efficacy, job experience and training have a beneficial impact. However, education level and incentives do not significantly influence the system's effectiveness. These findings suggest that LPD managers should prioritize enhancing work experience and training to improve the effectiveness of their AIS

Research on machine vision online monitoring system for egg production and quality in cage environment

In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0 % (+2.4 %), Recall rate of 92.8 % (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the management system. Overall, this research introduces a comprehensive system for monitoring egg production and quality in cage environments, addressing manual recording and quality assessment challenges in caged poultry farming. This study is crucial for optimizing modern livestock management, enhancing production efficiency, and ensuring animal welfare.

Developing a Knowledge-Based Online Recommendation System to Guide STD Patients Through Their Journey

Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.

Integrated Health Monitoring System using Fingerprint and Disease Risk Prediction

Recent developments in machine learning and biometric systems have had a big influence on healthcare applications. In order to provide a holistic approach to health monitoring, this research introduces a unique method that combines disease risk prediction with fingerprint-based blood group recognition. Based on user-provided health data, the system uses machine learning algorithms to determine the risk of common illnesses including diabetes, cardiovascular disease, and chronic kidney disease (CKD) and convolutional neural networks (CNNs) to identify blood type from fingerprint photos. Through the integration of fingerprint biometric data with health risk modelling, the suggested approach seeks to increase the precision and usability of health forecasts. The system's effectiveness is demonstrated by the evaluation's excellent classification accuracy for blood groups and trustworthy estimates of illness risk.This work demonstrates the possibility of integrating biometric data with predictive health models and offers a promising step toward customized healthcare solutions

Application of PID algorithm in temperature control of pig houses farmers' mental health from the perspective of social ecological system and its implications for agricultural production efficiency

This study investigates the application of PID algorithm-based temperature control systems in pig house environmental management and its impact on agricultural production efficiency. Through field experiments conducted at Dongshun Aquaculture Ecological Development Co., Ltd. in Nanping City, Fujian Province, we evaluated the system's effectiveness in energy efficiency, production performance, and animal health. Results show that the PID system achieved 18.5% energy savings, a 9% increase in daily weight gain, and a 7.1% improvement in feed conversion ratio. Additionally, meat quality scores improved by 8.3%, and disease incidence rates decreased by 21.9%. Economic analysis indicates that the system begins to generate a positive return on investment in the second year, with cumulative benefits reaching 192% within five years. These findings highlight the potential of PID temperature control systems in improving pork production efficiency and sustainability, offering a valuable solution for modern pig farming.

A case study on COVID-19 infected patient’s experience - challenging journey at Colombo East Base Hospital- Mulleriyawa

This study investigates the patient experience within the COVID-19 treatment centre at Colombo East Base Hospital, Mulleriyawa, amidst the global pandemic. A cross-sectional descriptive study was conducted over one month, involving 30 participants recruited through convenience sampling. Participants completed self-administered questionnaires assessing various aspects of care, including staff interaction, cleanliness, health education, counselling, and attention to health-related matters. Results indicate high levels of satisfaction among participants across multiple care domains, with the majority expressing satisfaction with staff interaction, cleanliness, health education, counselling, and attention to health-related matters. These findings underscore the healthcare delivery system's effectiveness in meeting patients' diverse needs and expectations during the pandemic. The study highlights the importance of patient-centred care in promoting positive patient experiences and outcomes, particularly in the context of public health emergencies. Recommendations include sustaining and enhancing best practices in healthcare delivery, prioritizing infection prevention and control measures, and promoting effective communication and patient education. By prioritizing patient-centred care and continuous quality improvement initiatives, healthcare providers can ensure the safety, satisfaction, and well-being of patients amidst crises such as the COVID-19 pandemic.

AGROTECH: A Smart Agricultural System Using IOT and ML

Agriculture remains one of the most important sectors that support human survival which supplies basic needs, a source of income for many dependants' community. Despite these advantages, resource scarcity, adverse environmental conditions and pest infestations remain serious threats to crop products. To mitigate these concerns, we present a smart agriculture system which uses the most modern technologies like IoT (Internet of Things) machine learning (ML) and sensors with automation. The system consists of an intelligent irrigation mechanism for efficient utilization of water, an animal detection system used to trace cattle, and a Light Dependent Resistor (LDR) Sensor joined with buzzer for early detection of any global environmental changes. Additionally, the integration of plant disease detection capabilities further enhances the system's effectiveness. By employing various sensors to collect data on environmental factors, including moisture and temperature, the proposed framework enables timely decision-making for crop management. The aim is to provide farmers with actionable insights, ensuring food security while minimizing resource consumption and economic losses. This review paper discusses the potential benefits and implementation strategies of such an intelligent agricultural system, emphasizing the need for innovation in agricultural practices to meet the growing demands of an increasing population

Design of hydraulic lift system of technical equipment working in amusement industry

This paper examines the design and application of a telescopic hydraulic system for amusement industry equipment that experiences cyclic passenger loads. The system comprises three hydraulic cylinders, each with different diameters, which activate at various points as the device moves. To achieve static balance and lift the main beam from a horizontal to a vertical position, the forces exerted by the cylinders were calculated using static equilibrium equations. The system's design, including the length and angle of each cylinder, was based on the technical specifications of the equipment. Analytical and numerical methods were used to ensure the correct sizing of the cylinders and the system's overall effectiveness, resulting in a structure that operates safely and efficiently.

An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis

Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong students using ensemble learning and multiparametric analysis. It combines several machine learning algorithms, including Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, and Logistic Regression using an ensemble learning approach to enhance the model’s performance. Additionally, a custom 1D Convolutional Neural Network (CNN) is designed for classification. It utilizes multiparametric analysis to identify weak and strong students considering various parameters such as age, academic performance, location, and online learning behavior. The evaluation results indicate the performance of the proposed model has been improved in comparison to MLA FIS, SHMM, and DRL by 16.5%, 5.5%, and 2.4%, in terms of precision, 16.4%, 6.5%, and 3.5 % in terms of accuracy and 10.4%, 2.5% and 6.5% in terms of recall. These improvisations described that the model is efficient for multidomain feature extraction, ensemble classification, and high-variance feature selection, which result in a deeper understanding of student performance.

SIGN-LANGUAGE TRANSLATOR USING MACHINE LEARNING

This research introduces a cutting-edge system designed to facilitate communication between individuals who are deaf and those who are hearing. By employing sophisticated machine learning algorithms and specific convolutional neural networks (CNNs), the proposed system can effectively sign language gestures into text or speech. A meticulously curated collection of sign language gestures serves as the foundation for training the model, ensuring its proficiency in accurately classifying several hand shapes and positions. To optimize classification performance, the system incorporates data preprocessing techniques that highlight the most distinctive features of the hands, thereby streamlining the computational process. This paper provides a comprehensive overview of the system's architecture, training methodology, and evaluation results, emphasizing the critical role of machine learning in developing inclusive communication tools that empower the deaf community. Future research initiatives will focus's on expanding the gesture dataset and refining real-time processing capabilities to further enhance the system's effectiveness and accessibility. Key Words: CNN, TensorFlow, Keras, NLTK

Implementing an intelligent diagnosis and treatment system for in-hospital cardiac arrest in the Utstein style: a multi-center case study

BackgroundCardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.MethodsTo compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.ResultsThe system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3–9 and 9.9–23, respectively.ConclusionsThe intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.