Kualitas pelayanan Kesehatan di Rumah Sakit Umum Daerah (RSUD) Batara Guru Belopa Kabupaten Luwu

This study purposed  to find out  how the quality of health services in Batara Guru Belopa Hospital in Luwu Regency. This study used quantitative descriptive with data sources consisting of primary data and secondary data. The population and research samples were 55 people (purposive sampling). Data collection techniques used observation, interview guidelines and questionnaires. Data analysis techniques used statistical frequency distribution and percentage. The results of this study showed that in general the quality of services in the Batara Guru Belopa Regional General Hospital (RUSD) had run well, in all indicators that included physical evidence, empathy, reliability, responsiveness and assurance that had been implemented by realizing the quality of health services as a matter of The main goal to get  national achievement and regional goals in improving public health to the community in Luwu Regency

Technological Antecedents of Organizational Agility: PLS SEM Based Analysis Using IT Infrastructure, ERP Assimilation, and Business Intelligence

Organizations often ignore the use information technology infrastructure, Business Intelligence and ERP software to improvise their decision making process due to which organizational agility is suffered. Such organizations fail to make decisions according to the needs of the market that leads to the loss of market share. Based on the contingency theory, conceptual model of this study was developed using the constructs of IT Infrastructure Flexibility, Business Intelligence Use, ERP Assimilation, and Organizational Agility. Survey method was used to collect the data from the managers and executives, who are involved in the key decision making process in any organization. Total 253 out of 265 responses were considered valid and PLS SEM approach was used to test the direct and indirect effects. Results indicate that mediating effect of Business Intelligence Use and ERP Assimilation between IT Infrastructure Flexibility and Organizational Agility has been substantiated. Findings of this study conclude that IT Infrastructure should be improvised, specifically when organization is going to adopt the ERP systems and Business Intelligence to make the timely decisions according to the requirements of the market that ultimately affects the Organizational Agility

PLC & SCADA based substation automation

lectrical power systems are a technical wonder. Electricity and its accessibility are the\ud greatest engineering achievements of the 20th century. A modern society cannot exist without electricity.\ud Generating stations, transmission lines and distribution systems are the main components of\ud power system. Smaller power systems (called regional grids) are interconnected to form a larger network\ud called national grid, in which power is exchanged between different areas depending upon surplus and\ud deficiency. This requires a knowledge of load flows, which is impossible without meticulous planning and\ud monitoring .Also, the system needs to operate in such a way that the losses and in turn the cost of\ud production are minimum.\ud The major factors that influence the operation of a power system are the changes in load and\ud stability. As is easily understood from the different load curves and load duration curve, the connected\ud load, load varies widely throughout the day. These changes have an impact on the stability of power\ud system. As a severe change in a short span can even lead to loss of synchronism. Stability is also affected\ud by the occurrence of faults, Faults need to be intercepted at an easily stage and corrective measures like\ud isolating the faulty line must be taken.\ud As the power consumption increases globally, unprecedented challenges are being faced,\ud which require modern, sophisticated methods to counter them. This calls for the use of automation in the\ud power system. The Supervisory Control and Data Acquisition (SCADA) and Programmable Logic\ud Controllers (PLC) are an answer to this.\ud SCADA refers to a system that enables on electricity utility to remotely monitor, co-ordinate,\ud control and operate transmission and distribution components, equipment and real-time mode from a\ud remote location with acquisition at date for analysis and planning from one control location.\ud PLC on the other hand is like the brain of the system with the joint operation of the SCADA\ud and the PLC, it is possible to control and operate the power system remotely. Task like\ud Opening of circuit breakers, changing transformer taps and managing the load demand can be carried out\ud efficiently.\ud This type of an automatic network can manage load, maintain quality, detect theft of\ud electricity and tempering of meters. It gives the operator an overall view of the entire network. Also, flow\ud of power can be closely scrutinized and Pilferage points can be located. Human errors leading to tripping\ud can be eliminated. This directly increases the reliability and lowers the operating cost.\ud In short our project is an integration of network monitoring functions with geographical\ud mapping, fault location, load management and intelligent metering

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design

Research Significance of Clinical Linguistics for Children on Language Speech Therapy in Pakistan: A Paediatric Survey Research

Background: Studying paediatrics’ clinical linguistics can accurately assess the needs and demands of this field and can facilitate impactful and practical advancements in this domain. Objectives: This research aims to identify the research goals of clinical linguistics in Pakistan, as limited studies have been conducted on this subject.Materials and Methods: A comprehensive analysis was conducted on linguistic and speech therapy studies published till 2023 to ascertain the quantity and developmental trajectory of research conducted on clinical linguistics. Findings: The field of linguistics has seen the highest number of research studies undertaken by speech therapists in phonetics/phonology (39%). In comparison, the lowest number of studies have been focused on pragmatics (24%). Linguistics has conducted limited research on diseases, accounting for just 0.4% of the studies. Some of these studies concentrate on aphasia, making up 19% of the research. Consequently, it is necessary to explore other illnesses as well

Terahertz-based joint communication and sensing for precision agriculture: a 6G use-case

By 2050, experts estimate that the agricultural produce must increase by 60%–70% to meet the needs of the ever increasing population of the world. To this aim, the concept of precision agriculture or smart farming has recently been coined. The idea of precision agriculture is well represented as a smart management system, having the ability to monitor, observe, sense, measure and control the health and water contents in plants at nano-scale and crops at macro-scale. The goal is to maximise the production while preserving the vital resources. The combination of terahertz (THz) based sensing technology to estimate plant health at a cellular level, and wireless sensor networks deployed within crops to monitor different variables while making intelligent decisions is far reaching. The integration and operation of such a macro-nano-sensor system requires a sustainable communication infrastructure that considers the demands of remote and agile agricultural environments. In this paper, an integrated sensing and communication system for plant health monitoring that utilises THz signals, is presented as a 6G use case. The joint architecture is outlined and various challenges including energy harvesting, practical implementation among others, followed by recommendations for future research are presented

IMU sensing–based Hopfield neuromorphic computing for human activity recognition

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy

The professional and personal impact of the coronavirus pandemic on US neurointerventional practices: a nationwide survey

Background Little is currently known about the effects of the coronavirus (COVID-19) pandemic on neurointerventional (NI) procedural volumes or its toll on physician wellness. Methods A 37-question online survey was designed and distributed to physician members of three NI physician organizations. Results A total of 151 individual survey responses were obtained. Reduced mechanical thrombectomy procedures compared with pre-pandemic were observed with 32% reporting a greater than 50% reduction in thrombectomy volumes. In concert with most (76%) reporting at least a 25% reduction in non-mechanical thrombectomy urgent NI procedures and a nearly unanimous (96%) cessation of non-urgent elective cases, 68% of physicians reported dramatic reductions (\u3e50%) in overall NI procedural volume compared with pre-pandemic. Increased door-to- puncture times were reported by 79%. COVID-19-positive infections occurred in 1% of physician respondents: an additional 8% quarantined for suspected infection. Sixty-six percent of respondents reported increased career stress, 56% increased personal life/family stress, and 35% increased career burnout. Stress was significantly increased in physicians with COVID-positive family members (P\u3c0.05). Conclusions This is the first study designed to understand the effects of the COVID-19 pandemic on NI physician practices, case volumes, compensation, personal/family stresses, and work-related burnout. Future studies examining these factors following the resumption of elective cases and relaxing of social distancing measures will be necessary to better understand these phenomena

Potential Therapeutic Implications of Caffeic Acid in Cancer Signaling: Past, Present, and Future

Caffeic acid (CA) has been present in many herbs, vegetables, and fruits. CA is a bioactive compound and exhibits various health advantages that are linked with its anti-oxidant functions and implicated in the therapy and prevention of disease progression of inflammatory diseases and cancer. The anti-tumor action of CA is attributed to its prooxidant and anti-oxidant properties. CA’s mechanism of action involves preventing reactive oxygen species formation, diminishing the angiogenesis of cancer cells, enhancing the tumor cells’ DNA oxidation, and repressing MMP-2 and MMP-9. CA and its derivatives have been reported to exhibit anti-carcinogenic properties against many cancer types. CA has indicated low intestinal absorption, low oral bioavailability in rats, and pitiable permeability across Caco-2 cells. In the present review, we have illustrated CA’s therapeutic potential, pharmacokinetics, and characteristics. The pharmacological effects of CA, the emphasis on in vitro and in vivo studies, and the existing challenges and prospects of CA for cancer treatment and prevention are discussed in this review