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This study investigated the optimization of the electrical discharge machining (EDM) process for TiN-Si3N4 composites, a challenging and emerging field in materials engineering. To achieve superior machining efficiency and product quality, a hybrid computational intelligence algorithm, Grey-ANFIS (adaptive neuro-fuzzy inference system), was employed. First, comprehensive data on EDM process parameters and performance characteristics were collected. Then, Grey-ANFIS was used to model the complex relationships between the EDM process parameters (e.g., pulse on time, pulse off time, voltage, and current) and key performance indicators (e.g., material removal rate and electrode wear rate). The algorithm combined the adaptability of neural networks with the linguistic representation capabilities of fuzzy logic, making it well-suited for capturing the intricate, non-linear EDM process dynamics. The proposed approach enables the generation of precise predictive models that can accurately represent EDM process behavior. Subsequently, these models were employed to optimize EDM process parameters, thereby enhancing machining efficiency and product quality. A sensitivity analysis was also conducted herein to identify critical factors affecting the EDM process. The results demonstrated the efficacy of the Grey-ANFIS algorithm in achieving superior EDM process optimization for TiN-Si3N4 composites. [ABSTRACT FROM AUTHOR]

BackgroundCongenital tracheal stenosis (CTS) is a rare but life-threatening malformation of the trachea. Surgical reconstruction is the treatment of choice in symptomatic cases which is highly risky and is rarely performed in extremely premature infants. With this, reporting a case of CTS managed by tracheal reconstructive surgery under ECMO in a baby weighing 1.47 kg at 32 + 1 WOG was the first ever case in China.Case presentationA premature newborn with a very low birth weight (VLBW) was admitted to our institute for breathing difficulties, requiring mechanical ventilation, and experienced two unsuccessful attempts of extubation. The team performed tracheal reconstructive surgery supported by ECMO after identifying lower tracheal stenosis through a bronchoscopy examination. One month after the surgery, oxygen support was able to discontinue. The patient's entire hospitalization was incredibly challenging, marked by hemodynamic instability with persistent anemia, and disseminated intravascular coagulation (DIC), which were managed with great care. Despite the difficult stay, a follow-up bronchoscopy revealed no obstruction or tracheal stenosis, leading to a successful discharge.ConclusionAdvancements in diagnostic techniques and innovative management methods have made diagnosing and treating CTS easier, even in premature infants. Our case is the first in China to successfully undergo tracheal reconstructive surgery supported by ECMO, inspiring future achievements in the medical field.

Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Covid-19 number of rising cases and death cases in India, USA, France, and UK, considering the progressive trends of China and South Korea. In this paper, three cases are considered to analyze the outbreak of Covid-19 pandemic viz., (i) forecasting as per the present trend of rising cases of different countries (ii) forecasting of one week following up with the improvement trends as per China and South Korea, and (iii) forecasting if followed up the progressive trends as per China and South Korea before a week. The results have shown that ANN can efficiently forecast the future cases of COVID 19 outbreak of any country. The study shows that the confirmed cases of India, USA, France and UK could be about 50,000 to 1,60,000, 12,00,000 to 17,00,000, 1,40,000 to 1,50,000 and 2,40,000 to 2,50,000 respectively and may take about 2 to 10 months based on progressive trends of China and South Korea. Similarly, the death toll for these countries just before controlling could be about 1600 to 4000 for India, 1,00,000 to 1,35,000 for USA, 40,000 to 55,000 for France, 35,000 to 47,000 for UK during the same period of study. [ABSTRACT FROM AUTHOR]

BackgroundGlutamic acid decarboxylase (GAD) is the rate-limiting enzyme for the synthesis of gamma-aminobutyric acid (GABA), the major inhibitory neurotransmitter in the central nervous system. Antibodies against glutamic acid decarboxylase (GAD) are associated with various neurologic conditions described in patients, including stiff person syndrome, cerebellar ataxia, refractory epilepsy, and limbic and extra limbic encephalitis. While there are few case reports and research on anti-GAD65 antibody-associated encephalitis in adults, such cases are extremely rare in pediatric cases.MethodsFor the first time, we report a case of anti-GAD65-positive autoimmune encephalitis associated with autoimmune polyendocrine syndrome (APS) type II. We reviewed previously published pediatric cases of anti-GAD65 autoimmune encephalitis to discuss their clinical features, laboratory tests, imaging findings, EEG patterns, and prognosis.Case presentationAn 8-year-old, male child presented to the outpatient department after experiencing generalized convulsions for twenty days. The child was admitted for epilepsy and had received oral sodium valproate (500 mg/day) in another center, where investigations such as USG abdomen and MRI brain revealed no abnormalities, however, had abnormal EEG with diffuse mixed activity in the left anterior middle prefrontal temporal region. On the follow-up day, a repeat blood test showed a very low serum drug concentration of sodium valproate hence the dose was increased to 750 mg/day. Then, the child experienced adverse effects including increased sleep, thirst, and poor appetite, prompting the parents to discontinue the medication. A repeat MRI showed increased signals on FLAIR sequences in the right hippocampus hence admitted for further management. The child's past history included a diagnosis of hypothyroidism at the age of 4, and receiving levothyroxine 75 mcg once daily. His parents are healthy with no history of any similar neurological, autoimmune, or genetic diseases, but his uncle had a history of epilepsy. At presentation, he had uncontrolled blood glucose levels with elevated HbA1c levels. Additionally, the serum and CSF autoantibodies were positive against the anti-GAD65 antibody with the titer of 1:100 and 1:32 respectively. The patient was managed with a mixed type of insulin regimen and received first-line immunotherapy (intravenous immunoglobulin, IVIG) for five consecutive days, followed by oral prednisone and sodium valproate as an antiepileptic drug. Upon achieving a favorable clinical outcome, the patient was discharged with oral medications.ResultsAmong the 15 pediatric patients reported in this literature, nine presented with limbic encephalitis (LE), three with extralimbic encephalitis (ELE), and three with a combination of limbic and extralimbic encephalitis. Most of these cases exhibited T2-W FLAIR hyperintensities primarily localized to the temporal lobes in the early phase, progressing to hippocampal sclerosis/atrophy in the later phase on MRI. EEG commonly showed slow or spike waves on frontotemporal lobes with epileptic discharges. Prognostic factors varied among patients, with some experiencing persistent refractory seizures, type-1 diabetes mellitus (T1DM), persistent memory impairment, persistent disability requiring full assistance, and, in severe cases, death.ConclusionOur findings suggest that anti-GAD65 antibody-positive autoimmune encephalitis patients may concurrently present with other APS. Our unique case presented with multiple endocrine syndromes and represents the first reported occurrence in children. Early diagnosis and timely initiation of immunotherapy are crucial for improving clinical symptoms and reducing the likelihood of relapses or permanent disabilities. Therefore, emphasis should be placed on prompt diagnosis and appropriate treatment implementation to achieve better patient outcomes.

Requirement of light weight, high strength and good corrosion resistance structures can be fulfilled by using magnesium alloy metal matrix composites. Powder metallurgy route of manufacturing these composites is studied in this work. Magnesium alloy with yttria reinforcement particles of 5-10 wt% was sintered by conventional sintering and sintering by microwave hybrid heating. The Microstructure of the sintered samples were observed under the optical microscope and scanning electric microscope. Energy dispersive spectroscopy and X-ray diffraction were also performed in order to know the compounds that are formed. Hardness studies were carried out followed by corrosion studies as per ASTM G 44-99 standard. Samples having 98% of theoretical density were manufactured by microwave hybrid heating. The corrosion resistance of the samples were increased by increasing the yttria percentage. Microwave sintered samples exhibited better corrosion resistance to conventionally sintered ones. [ABSTRACT FROM AUTHOR]

In this paper an attempt has been made to study the effects of the process parameters of wire cut electrical discharge machining (WEDM) on Magnesium-Silicon Carbide MMC with 5% SiC in particulate form. For the analysis, six factors, namely pulse on time, pulse off time, spark gap voltage, peak current, dielectric flushing pressure and servo feed have been taken and a Taguchi L16 orthogonal array for two levels was used. Response surface methodology was also used to develop second-order models for material removal rate (MRR) and surface roughness (SR). From the analysis of variances, it has been observed that pulse on time and pulse off time were the most significant parameters among all those observed in predicting the MRR and SR, respectively. [ABSTRACT FROM AUTHOR]

Aluminium alloy with silicon carbide particulate (Al/SiCp) reinforced metal matrix composite (MMC) are used within a variety of engineering applications due to their excellent properties in comparison with non-reinforced alloys. This presented work attempted the development of predictive modeling and optimization of process parameters in the turning of Al/SiCp MMC using a titanium nitride (TiN) coated carbide tool. The surface roughness Ra as product quality and tool wear VB for improved tool life were considered as two process responses and the process parameters were cutting speed v, feed f, and depth of cut d. Two modeling techniques viz., response surface methodology (RSM) and artificial neural network (ANN) were employed for developing Ra and VB predictive models and their predictive capabilities compared. Four different RSM models were tried out viz., linear, linear with interaction, linear with square, and quadratic models. The linear with interaction model was found to be better in terms of predictive performance. The optimum operating zone was identified through an overlaid contour plot generated as a response surface. Parameter optimization was performed for minimizing Ra and VB as a single objective case using a genetic algorithm (GA). The minimum Ra and VB obtained were 2.52 μm and 0.31 mm, respectively. Optimizations of multi-response characteristics were also performed employing desirability function analysis (DFA). The optimal parameter combination was obtained as v = 50 m/min, f = 0.1 mm/rev and d = 0.5 mm being the best combined quality characteristics. The prediction errors were found as 4.98 % and 3.82 % for Ra and VB, respectively, which showed the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Introduction: Neonatal mortality accounts for 40% of under five mortality in developing countries. Strategies to reduce neonatal mortality rates (NMRs) must address the reality that 90% of deliveries in the [...]

The United Nations Security Council reports HIV/AIDS as the fastest growing threat to human development. In addition, the World Health Organization [1] reports that nearly 5 million persons (4.3 million adults and 700,000 children) are newly infected with HIV each year; more than 95% of them found in developing countries. Since STDs as a group are a personal problem which few people feel comfortable discussing, we believe that hand‐held PDAs can provide an opportunity for learning about this disease while insuring anonymity. This device will employ the newest technologies including Bluetooth wireless technology, which can transmit and receive data via a short‐range radio link using a globally available frequency band (2.4 GHz ISM band), enabling rapid and accurate synchronous and asynchronous data communication. The first generation of Bluetooth permits exchange of data up to a rate of 1 Mbps, even in areas with much electromagnetic disturbance. This emerging technology can facilitate HIV/AIDS outreach around the globe. Recent advances in learning have taken a particularly cognitive perspective and these findings have implications for education in general as well as for the development of intelligent tutoring systems in particular. In the past, effective SmartBooks™have been developed for AIDS education to disseminate the critical knowledge relevant to this epidemic [2]. Since 1993, the proliferation of the World Wide Web has created a plethora of new opportunities for the delivery of electronic distance learning systems. However, we feel that it is important that a whatever technology is employed is based on a sound educational theory. A new, comprehensive, web‐based learning system called SmartTutor has been developed, at Brooklyn College of The City University of New York [3]. This technology provides a user‐friendly, self‐paced, easy to modify, software environment intended to serve the user's learning needs and is based on a generic SmartTutor methodology organized around the use of concept mapping. Early assessment of SmartTutor has shown that it is well received by students and helps significantly in their learning processes. It is readily adaptable to the presentation of academic and more general subject matter such as the latest available information on HIV/AIDS. Our new HIV/AIDS SmartTutor will incorporate this SmartTutor paradigm. Our new SmartTutor would provide worldwide access to medical professionals as well as the general public to learn about HIV/AIDS. This new device could also provide a survey tool to facilitate HIV risk assessment. Demonstrations of the SmartTutor learning system will be presented and the continued development of the applications will be discussed. [ABSTRACT FROM AUTHOR]

Chronic and terminally ill patients are disproportionately affected by medical errors. In addition, the elderly suffer more preventable adverse events than younger patients. Targeting system wide “error‐reducing” reforms to vulnerable populations can significantly reduce the incidence and prevalence of human error in medical practice. Recent developments in health informatics, particularly the application of artificial intelligence (AI) techniques such as data mining, neural networks, and case‐based reasoning (CBR), presents tremendous opportunities for mitigating error in disease diagnosis and patient management. Additionally, the ubiquity of the Internet creates the possibility of an almost ideal network for the dissemination of medical information. We explore the capacity and limitations of web‐based palliative information systems (IS) to transform the delivery of care, streamline processes and improve the efficiency and appropriateness of medical treatment. As a result, medical error(s) that occur with patients dealing with severe, chronic illness and the frail elderly can be reduced. The palliative model grew out of the need for pain relief and comfort measures for patients diagnosed with cancer. Applied definitions of palliative care extend this convention, but there is no widely accepted definition. This research will discuss the development life cycle of two palliative information systems: the CONFER QOLP management information system (MIS), currently used by a community‐based palliative care program in Brooklyn, New York, and the CAREN case‐based reasoning prototype. CONFER is a web platform based on the idea of “eCare”. CONFER uses XML (extensible mark‐up language), a W3C‐endorced standard mark up to define systems data. The second system, CAREN, is a CBR prototype designed for palliative care patients in the cancer trajectory. CBR is a technique, which tries to exploit the similarities of two situations and match decision‐making to the best‐known precedent cases. The prototype uses the opensource CASPIAN shell developed by the University of Aberystwyth, Wales and is available by anonymous FTP. We will discuss and analyze the preliminary results we have obtained using this CBR tool. Our research suggests that automated information systems can be used to improve the quality of care at the end of life and disseminate expert level ‘know how’ to palliative care clinicians. We will present how our CBR prototype can be successfully deployed, capable of securely transferring information using a Secure File Transfer Protocol (SFTP) and using a JAVA CBR engine. [ABSTRACT FROM AUTHOR]