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This paper attempts to highlight quantitatively the growth and development of literature in the field of ecology in terms of publication output using the resource Web of Science$^{(R)}$. The focus of this analysis was to study the literature on ecology published in three journals, viz., Ecology Letters, Trends in Ecology & Evolution, and Annual Review of Ecology Evolution and Systematics. 2946 records were retrieved for 10 years (2003-2012). The study revealed that multiple authorship in the field with collaborations of two (30.31%) and three authors (19.89%) was dominant. The Degree of collaboration, Collaborative coefficient, and Collaborative index were calculated and the applicability of Lotka's law was tested. The study identified five-year patterns in research trends, using the three studied journals, to see if the subjects of focus changed within a decade. The most productive institution was University Calif. Davis, USA, followed by University Calif. Santa Barbara, USA, and University Queensland, Australia, and the most productive countries were the USA followed by UK and Canada.

Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.

In this modern age composite materials are become the primary material for engineering production because composite materials have several specific properties such as high strength-to-weight ratio, low cost, ease of fabrication, tensile strength, compressive strength, Impact strength, high resistance to thermal which does not realize in pure material or non-composite material. Fabricating composite materials involves producing something useful from waste materials. The experimental investigation involves the fabrication of sugarcane fiber-reinforced PET composites through a controlled manufacturing process. Because of its vast application, every Mechanical Engineer should have the knowledge about the fabricate and test the composite material. In this research, two waste materials are used - bagasse and waste plastic to fabricate a composite. The main purpose of the composite material is for heat insulation that is applicable for industrial roofing. The main purpose of the composite material is for heat insulation that is applicable for industrial roofing applications and manufacturing for sustainable components. The mechanical results after compression and shear tests are 4.57 and 0.37 MPa respectively. The maximum thermal test after an exposed surface temperature test is 54 °C

Martensitic stainless steels find less application in commercial products as they have high hardness, strength, and wear resistance. It lacks in ductility and exhibits moderate corrosion resistance compared to other stainless steels. As a result, annealing process were been used to strengthen the ductility and maintain stability in hardness of martensitic stainless steel material. AISI 410 was chosen for this research work and the samples were made to cylindrical shape for the following dimensions: 50 mm length and 08 mm diameter. The specimens were annealed at temperatures of 730°C, 830°C, and 930°C. The untreated material is kept aside for results comparison. All specimens were subjected to wear test using a pin on disc wear test apparatus. All the specimens were examined using a scanning electron microscope for the surface morphological changes. The outcomes were compared and the best specimen for the required application was chosen. It was discovered that there was a phase change from the martensite stage to the residual austenite stage.**

The role of digital libraries plays a vital role in order to disseminate the accurate information to the user community especially in the field of higher educational institutions in 21st century. There are n numbers of e-books, e-journals, reference books, inter-linked hypertext documents and other study-oriented material are helping the users to boost their knowledge in the field of education. But is it too much expensive. So that Govt of India encouraging the higher educational institutions with N-LIST progamme for using e-resources with nominal fee who are having 12B and 2(f) status colleges. This Study conducted 10 N-LIST beneficiary affiliated colleges of Arts and Science and revealed that the, age, category, average time spend time in the library, speed of the e-resources and browsers and benefits and extent of user's satisfaction with based on N-LIST eresources usage. A structured Likert's 5 scale technique Questionnaire was distributed among faculty and students of U.G and P.G. Arts of Science colleges in Puducherry. Out of 2000 students 1713(86.65%) responded. The collected data is analyzed through SPSS version 24 and the major findings of the study revealed 84.9% of respondents agreed that they benefitted by using the electronic sources whereas Very less percentage (5.9%)of the users are revealed that of respondents reported that they did not benefit by using the electronic resources. Based on the findings it is suggested that regular training programmes should be conducted by the college librarians and the users of the N-LIST for creating awareness and optimum utilization of e-resources particularly of NLIST consortium. [ABSTRACT FROM AUTHOR]