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IntroductionChildhood mortality in the emergency setting is disproportionately high in low-income and middle-income countries (LMIC), with limited research dedicated to improving timely interventions, especially for critically ill children during transport. To perform essential prehospital paediatric research, there is a need for a tailored consent process, which reflects the specific needs and concerns of participants in this challenging research context.ObjectiveThe objective is to prospectively investigate stakeholder perceptions and preferences regarding consent processes for a specific paediatric ambulance-based telemedicine trial.MethodsExploratory qualitative study design using face-to-face semistructured interviews and focus group discussions. Data were analysed using thematic analysis. Participants included healthcare providers (paediatric telemedicine physicians and emergency medical technicians) and parents of children who required emergency transportation in Karachi, Pakistan.Results47 participants, ranging from 19 to 47 years old, were involved in in-depth interviews or focus group discussions. The participants comprised 29 healthcare workers and 18 parents. Among them, 9 were women and 38 were men. Expressing diverse attitudes towards different consent methods, the majority recommended a prospective written informed consent approach to build trust and provide legal protection. Participants understood the situational incapacity that occurs in emergency settings, emphasised the importance of keeping the consent brief and recommended a subsequent contact in 2–3 days after the emergency transport to reconfirm consent and answer any questions.ConclusionOur interpretation of the findings revealed that participants preferred a staged consent process for telemedicine trials in LMIC paediatric emergency settings.

Background Healthcare improvement at all levels involves systematic and continuous assessment of the system’s operations, efficiency, and effectiveness to ensure quality care. Likewise, in Emergency Medical Services; performance measurement and root cause analysis may aid in identifying the system inadequacies and address potential shortcomings by developing Key Performance Indicators. In this paper, we propose a tailored framework to supplement the performance measurement and healthcare improvement, primarily to monitor the quality of EMS operations and personnel for ambulance transfers, which results in patient collapses in ambulances. Methods We developed a Performance Measurement Framework (PMF) based on three essential domains– Structure/System, Process, and Outcome. Each domain was further assigned with different KPIs to assess the performance of EMS operations and personnel during patient transfers. The framework was pilot-tested for one year from January to December 2023, where its use was limited to the cases of patient collapse in ambulances, also referred to as out-of-hospital deaths. To assess progress, we compared the incidence of CIA between the pre-implementation and post-implementation phases, with service operational metrics including coverage, fleet size, workforce, and response times. Result Using the PMF as a tool for quality improvement, we observed a 7% reduction in the incidence of patient collapse in ambulances and a 16% reduction in life-threatening cases resulting in CIA despite increases in service coverage (37%), ambulance workforce (32%), fleet (26%), and routine interventions (11%). A slight increases in response times indicate the greater service demands. Through pilot testing, we identified operational gaps including behavioral and communication issues, adherence to SOPs, and equipment management. Conclusion Overall, this paper proposes a performance measurement tool in the field of prehospital care for organizations to thoroughly assess and advance their operations toward healthcare improvement. The study highlights areas requiring improvement such as training guidelines, adherence to operating protocols, and resource optimization. In addition; the integration of technology and advanced training programs for the ambulance workforce may strengthen the overall EMS performance; thereby promising positive patient outcomes, and efficient service delivery and utilization. Trial registration Not applicable.

Background and objectives :Distal humerus fractures remain a challenging injury to manage and they constitute approximately 2% of all fractures. Restoration of a functional elbow joint and avoidance of joint stiffness depends on stable anatomical reduction of the fracture, reconstitution of joint congruity by surgical methods and early mobilization. The present prospective study aims to evaluate the functional outcome of pre-contoured LCP in distal humerus fractures using dual-plating technique and to study the complications.Material and methods:27 patients aged above 20 years with distal humerus fractures who had fulfilled inclusion criteria admitted to K.R. Hospital, Mysore Medical college, Mysore during the study period from December 2015 to July 2017 and treated by Open Reduction and Internal Fixation with pre-contoured locking compression plate using dual plating technique were included in the study. Average age of the patients was 47.66 years (21-80 years).11 were male and 16 were female. Older patients were majorly females in whom fracture was due to fall and younger patients were majorly males in whom fracture was due to RTA. Patients were followed up till 6 months postoperatively and functional outcome at 6 months was assessed using Mayo elbow performance score (MEPS).Results :During follow up, 26% patients had excellent,59.2% patients had good, 14.8% patients had fair and no patients had poor functional outcome as per MEPSScore. 3 patients had Superficial wound infection, 2 patients had parasthesia along the ulnar nerve distribution, 2 patients had stiffness of elbow.Interpretation and Conclusion:Pre-contoured LCP using dual-plating technique gives stable fixation of distal humerus fracture thereby allowing early mobilization of elbow and reduces post-operative stiffness. In contrast to conventional plating, chances of non union or implant failure are not seen even in elderly patients.

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

Energy consumption for sustainable development has become a crucial issue in recent years. The anthropogenic effects of traditional energy sources (non-renewables) underscore the need for renewable energy and efforts to promote its adoption have comprised policy makers’ strategies to achieve sustainable development. At the same time, institutional stability is a necessary element needed to meet the goal of sustainable development via improved management of resources and technology diffusion. The literature shows some contradictory findings on this matter and this study aims to clarify. Thus, this research scrutinizes the contribution of renewable and non-renewable energies in sustainable development while also taking into account financial development and institutional quality in ASEAN countries from 1980 to 2018. To assess for co-integration, a pooled mean group (PMG) regression technique is employed and the findings from this technique are verified by using fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), and canonical cointegration regression (CCR) techniques in conjunction with other panel-based econometric procedures to evaluate the robustness of the model. The findings reveal a significantly positive influence for renewable energy and a significantly adverse impact for non-renewable energy with respect to sustainable development in ASEAN countries. In addition, the results indicate that institutional quality and financial development have adverse effects on sustainable development. Therefore, this research recommends sustainable management of non-renewables and greater promotion of renewables by strengthening institutional quality in ASEAN countries.

Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data could be sent to powerful remote data centers for machine health analysis using artificial intelligence (AI) tools. However, centralized computing has its own challenges, such as privacy issues, long latency, and low availability. To overcome these problems, edge computing technology was embraced. Thus, instead of moving all the data to the remote server, the data can now transition on the edge layer where certain computations are done. Thus, access to the central server is infrequent. Although placing AI on edge devices aids in fast inference, it poses new research problems, as highlighted in this paper. Moreover, the paper discusses studies that use edge computing to develop artificial intelligence-based diagnostic and prognostic techniques for industrial machines. It highlights the locations of data preprocessing, model training, and deployment. After analysis of several works, trends of the field are outlined, and finally, future research directions are elaborated

ABSTRACT During year 2015, a survey was conducted in 18 golf course greens from nine states (Selangor, Negeri Sembilan, Melaka, Johor, Pahang, Perak, Kedah, Pulau Pinang and Wilayah Persekutuan) of Peninsular Malaysia for nematode, Meloidogyne graminis infections on the turf grasses. Samples were collected based on the sparsely growth and chlorotic appearance of the greens. A total of 36 soils and roots sample were collected. Scanning electron microscopy (SEM) was used to identify the parasitic nematode. Both the field symptoms and SEM micrographs confirmed that the nematode isolate was M. graminis. Since this nematode has been known to damage the greens and other plants in other part of the world then the probability for the specie to adapt to other hosts other than the family of poaceae under Malaysian climate should not be discounted. Therefore, screening and restriction of movement of planting materials be observed critically.

This descriptive observational study was conducted at the Department of Nephrology, Bahawal Victoria Hospital, Bahawalpur, Pakistan, from January 2012 to April 2018, to study the pattern of biopsy-proven kidney diseases in that region as a part to establish a national renal biopsy registry. All adult patients who underwent renal biopsy at the Bahawal Victoria Hospital, Bahawalpur, Pakistan, from January 2012 to April 2018, were included in the study. All the biopsies were evaluated by light microscopy and immunofluorescence. All the patients underwent urine dipstick, microscopic examination, and quantification of proteinuria. Hepatitis B surface antigen, anti-hepatitis C virus, human immunodeficiency virus, and serology (antinuclear antibody, anti-ds DNA, and C3 and C4) were checked in all the patients. There were a total of 195 patients, with a mean age of 30.5 ± 12.8 years. Females were comparatively younger than males (P = 0.0154). Primary glomerulonephritis (GN) accounted for 77% (155) of all the patients, whereas secondary GN contributed 15.8%. Focal and segmental glomerulosclerosis (FSGS) was the most common diagnosis (28.2%) followed by membranous nephropathy (MN) (18.9%). Lupus nephritis was the third-most common pathology, and it predominated among females (P = 0.0026). Out of the eight diabetic patients, one each had FSGS and crescentic GN. In conclusion, primary glomerular diseases were the predominant biopsy-proven kidney diseases, and FSGS and MN were the most common glomerular diseases. This pattern in South Punjab closely resembles that in southern and northern parts of the country.