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The study was conducted in Sahiwal cattle for genome wide identification and annotation of single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs) in Sahiwal cattle. The double digest restriction-site associated DNA (ddRAD) sequencing, a reduced representation method was used for the identification of variants at nucleotide level. A total of 1,615,211 variants were identified at RD10 and Q30 consisting of 1,480,930 SNPs and 134,281 INDELs with respect to the Bos taurus reference genome. The SNPs were annotated for their location, impact and functional class. The SNPs identified in Sahiwal cattle were found to be associated with a total of 26,229 genes. A total of 1819 SNPs were annotated for 209 candidate genes associated with different production and reproduction traits. The variants identified in the present study may be useful to strengthen the existing bovine SNP chips for reducing the biasness over the taurine cattle breeds. The diversity analysis provides the insight of the genetic architecture of the Sahiwal population Studied. The large genetic variations identified at the nucleotide level provide ample scope for implementing an effective and efficient breed improvement programme for increasing the productivity of Sahiwal cattle.

The study the constraints faced by the women associated with SHGs in Satna district of Madhya Pradesh was conducted in agriculture year 2021-22.mutlisatge sampling technique was used. The study pertains to the major constraints faced by the womens are gender dominance, Lack of men confidence to women, Responsibility of family, being much busy to work at home, Less literacy, Lack of technical knowledge and Lack of guidance for group formation.

Background: Brivaracetam is a novel antiepileptic drug clinically approved for the treatment of partial onset seizures in adults and adolescents. It has some abuse potential and assigns to Schedule V category under the Controlled Substance Act by the Drug Enforcement Administration. It is essential to develop a faster, simple, and highly sensitive method for the quantification of Brivaracetam in human plasma by employing simple liquid-liquid extraction. Objective: The objective of this study is to develop and validate a novel UHPLC-MS/MS method for the estimation of brivaracetam in human plasma samples and application to pharmacokinetic study. Methods: An ultra-high-pressure liquid chromatography-tandem mass spectrometry method was developed and validated according to current regulatory guidelines for bioanalytical methods. Sample processing (50 μL) involved only a simple liquid-liquid extraction by ethyl acetate as extraction solvent. Brivaracetam-d7 was used as an internal standard. The chromatographic analysis was performed by a Unisol C18 (4.6 X 100 mm, 5μm) column using 0.1% formic acid in water/acetonitrile (20/80 V/V) as an isocratic mobile phase, at a flow rate of 1.0 mL/min with a run time of 2.2 min. Brivaracetam and its internal standard Brivaracetam D7 were detected and quantified in positive ion mode using multiple reaction monitoring transitions at m/z 213.100→168.100 and m/z 220.000→175.100, respectively. The developed method was applied to assess pharmacokinetic parameters like Cmax, Tmax, t1/2 and AUC for Brivaracetam in healthy, male, and adult humans. Results: The method was validated over a concentration range of 20.000 ng/mL to 4000. 000 ng/mL. Both intra- and inter-assay precision and accuracy were Conclusion: The present assay is faster, highly sensitive and simpler than previously published analytical reports for brivaracetam in human plasma samples and is suitable for pharmacokinetic evaluation of any marketed formulation.

The objective of the project is to automate the training and placement unit of AMC Engineering College by minimizing manual work and enhancing optimization, abstraction, and security. The proposed solution is a web application that can be accessed by authorized personnel throughout the organization via secure login credentials. The application will have features that cater to both students and administration staff. Students will be able to fill out a registration form, and the information will be stored in the system for future reference. Once registered, students won't have to repeat the process, making it a one-time exercise. The system will enable administration staff to manage student information related to placement, including maintaining their details, generating a list of requested candidates, and searching for information posted by students. The benefits of this project are evident: it will reduce manual work and enhance efficiency, allowing the college to achieve full IT deployment. It will also enable quick access to placement-related activities, making the process more convenient for both students and staff. In summary, this project has the potential to streamline the Training and Placement unit of AMC Engineering College, making it more efficient and effective.

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer’s disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical RelevanceThis study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson’s disease.

While religious sites are identified as unique experience spaces, offering diverse experiences, discourse on what constitutes a visitor’s experience still seems unsettled, primarily due to the fragmented approach owing to the classic binary of pilgrim-tourist. However, post-modern theories emphasise de-differentiation and blurring of boundaries between pilgrim-tourist and stress the need to add a new perspective to enhance the understanding of visitor experience at religious sites to manage tourism at such destinations efficiently. Therefore, the present study is undertaken to respond to this call and aims to identify constructs that shape visitors’ experience at religious sites. Building upon the synthesis of knowledge available in previous studies, the present review draws an integrated view of visitors’ experiences by combining the sacred-secular dichotomy. It offers a preliminary conceptual framework on constructs associated with visitors’ experiences by following the systematic and inductive methods. The study has identified four major constructs named- Destination Attributes, Visitor Attributes, Engagement, and Image, with fifteen sub-constructs, and suggests the location of visitor experience within the complex overlaps of these constructs.

Isoniazid is an important component of first-line antitubercular therapy. The drug at its therapeutic dose is known to cause hepatitis and peripheral neuropathy. The association of isoniazid with pure delirium is rare. Here, we present an interesting case of new-onset delirium associated with isoniazid in an elderly female with spinal tuberculosis. Remarkable improvement was noticed in the patient following drug discontinuation and pyridoxine augmentation.

Tick and tick-borne diseases cause major economic losses to world’s dairy sector. India, being tropical country also faces tremendous challenges from wide arrays of ticks that infest all kind of dairy animals. As we understand that any kind of stresses can disturb the physiological parameters of dairy animals that can impact the animal productivity and over-all performance. In this study therefore, an effort was made to evaluate the physiological responses of tick infested and non-infested native (Sahiwal) and cross bred cattle populations in the central plains area of Uttar Pradesh, India. A total of 3570 animals of indigenous and crossbred cattle from organized and unorganized cattle farm located in and around Lucknow, Uttar Pradesh area was surveyed across seasons (winter, hot summer and hot humid) to assess the prevalence of tick infestation and their effect on physiological responses. The present study revealed that the crossbred cattle were most susceptible to tick infestations. The prevalence of tick infestation was significantly (P

Houseflies, being vector of serious human disease, have been extensively used in the laboratory to research on aging and sex determination. It also plays an important role in breaking down the organic matter and thus makes its contribution in food cycle. Houseflies belong to the phylum Arthropoda and serves as vector for many pathogens transmiting disease from lower to higher vertebrates. Microsporidiosis (also known as nosemosis) is a disorder of honeybee and various other pollinators belonging to the class Insecta that are affecting nation’s economy up to a great extent. Microsporidian spores have reportedly infected insects, fishes, crustaceous, human and thus causing huge threat to society. In order to establish a clear understanding for its role in the transmission of microsporidiosis, the present study was designed to detect and identify a microsporidian parasite- Nosema ceranae by amplifying 16sRNA (Small Subunit rRNA) gene using molecules technique Polymerase Chain Reaction (PCR). Results of the present study suggested no existence of Nosema ceranae spores as, in spite of performing gradient PCR, no amplification of gene occurred. The research work paves a way for other researchers to investigate the possible reasons behind the absence of Nosema ceranae infection in housefly and also on why the infection is not prevalent in this model organism.

The study is a part of project A05, TRR 318 "Constructing Explainability" (https://trr318.uni-paderborn.de/projekte/a05) We aim to assess the effect of contrastive verbal guidance on attention.

An analysis of a data-driven reduced order model (ROM) with a temporal convolutional approach using artificial intelligence (AI) techniques. Reduced order modelling is a popular technique for capturing the essential behaviour of complex systems with reduced computational costs. However, traditional reduced order models often struggle to capture the temporal dependencies present in dynamic systems. To address this challenge, we propose a data-driven approach that leverages temporal convolutional neural networks (CNNs) within the reduced order modelling framework. The temporal convolutional approach allows for the efficient and accurate modelling of temporal dependencies by learning directly from the available data. The AI algorithms enable the ROM to effectively capture the system's dynamic behaviour, leading to improved accuracy and computational efficiency. We demonstrate the effectiveness of the proposed method through a case study where we model a complex dynamic system. The results show that the data-driven reduced order model with temporal convolutional approach outperforms traditional reduced order models in terms of accuracy and computational efficiency. The findings of this study highlight the potential of AI techniques in enhancing reduced order modelling for dynamic systems. The proposed approach has promising applications in various fields, including structural analysis, fluid dynamics, and system control. Further research can explore the integration of other AI techniques and algorithms to advance data-driven reduced order modelling for complex dynamic systems. Keywords: Data-Driven Reduced Order Model, Artificial Intelligence, Reduced Order Modelling, Temporal Dependencies, Dynamic Systems, Structural Analysis, System Control. Cite this Article: Amit Kumar Singh, Analysis of Data-Driven Reduced Order Model with Temporal Convolutional Approach Using AI, International Journal of Civil Engineering and Technology (IJCIET), 10(2), 2019, pp. 2786–2800. https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=2

International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 2, February 2019, pp. 2518-2531, Article ID: IJCIET_10_02_249 Available online at https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=2 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 MULTI-FIDELITY CLASSIFICATION USING DEEP LEARNING TO ACCELERATING THE PREDICTION OF LARGE-SCALE COMPUTATIONAL MODELS Amit Kumar Singh Department of Mech. Engg, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Cite this Article: Amit Kumar Singh, Multi-fidelity classification using deep learning to accelerating the prediction of large-scale computational models, International Journal of Civil Engineering and Technology, 10(2), 2019, pp. 2518-2531. DOI: https://doi.org/10.17605/OSF.IO/26WSQ

International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 2, February 2019, pp. 2617-2630, Article ID: IJCIET_10_02_255 Available online at https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=2 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 OPTIMIZATION OF MULTI-SCALE TOPOLOGY FOR MULTI-MATERIAL STRUCTURES WITH CONTROLLABLE GEOMETRIC COMPLEXITY ANALYSIS Amit Kumar Singh Department of Mech. Engg., Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Cite this Article: Amit Kumar Singh, Optimization of multi-scale topology for multi-material structures with controllable geometric complexity analysis, International Journal of Civil Engineering and Technology, 10(2), 2019, pp. 2617-2630. DOI: https://doi.org/10.17605/OSF.IO/2WC7V

Geometrically exact Isogeometric analysis (IGA) has emerged as a promising technique for accurately capturing large displacements and rotations in beam structures. In this study, we explore the application of machine learning (ML) methods to enhance the analysis of geometrically exact Isogeometric beams under large displacement conditions. Geometrically exact IGA relies on mathematical formulations and numerical techniques to solve the governing equations. However, these methods often suffer from limitations in computational efficiency and accuracy when dealing with large displacements. To overcome these challenges, ML techniques have recently gained attention for their potential to provide efficient and accurate solutions for complex problems. A novel approach that combines the geometrically exact isogeometric beam formulation with ML algorithms. The ML model is trained using a dataset generated from a set of finite element simulations considering various loading and boundary conditions. The dataset includes both the input parameters (e.g., geometric properties, loading conditions) and the corresponding beam deformations under large displacements. The trained ML model can then accurately predict the beam deformations for new sets of input parameters, thus providing an efficient and fast alternative to traditional numerical methods. The predictions obtained from the ML model are compared with benchmark solutions obtained from finite element simulations, demonstrating the accuracy and reliability of the proposed approach. The results show that the ML-enhanced geometrically exact Isogeometric beam analysis can accurately capture the large displacements and rotations, while significantly reducing the computational cost compared to traditional numerical methods. The proposed approach has the potential to revolutionize the analysis and design of beam structures subjected to large displacements, enabling engineers to quickly evaluate and optimize structural performance. Keyword: ML Model, Isogeometric Beam Analysis, Geometrically Exact Isogeometric Analysis (IGA), Mathematical Formulations, Numerical Techniques. Cite this Article: Amit Kumar Singh, Analysis of Geometrically Exact Isogeometric Beam for Large Displacements Using Machine Learning, International Journal of Civil Engineering and Technology (IJCIET), 10(2), 2019, pp. 2741–2755. https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=2

Supplementary Material 1

Our recent studies are concerned with the effect of contrastive guidance as a form of explanans, on the recall of actions and thus action understanding. The study regarding the effectiveness of contrastive verbal guidance indicates that a visually contrastive context provides a more favourable situation for employing contrastive verbal guidance in the form of assertion and negation (Singh & Rohlfing, 2023). However, a limitation of the study was that verbal guidance was provided irrespective of whether the participant requested it or not. This approach does not align with real-life situations where contrastive guidance is used in response when it is asked for (Miller, 2021). To address this limitation and explore the impact of interaction, we propose a new experiment where an agent explains the task to the participant and provides contrastive or noncontrastive verbal guidance only upon request, in order to understand a requested action better. In this interactive experiment, the participant’s requests for clarification are categorized as open or restrictive. According to Dingemanse and Enfield (2015), open requests are more general, seeking information about the task in a broader sense (e.g., “Where?” or “How?”), while restrictive requests provide specific details regarding the source of misunderstanding (e.g., “Is it A or B?” or “Did you mean B?”). In our elicitation, we expect the participants to ask in a broader sense. This is because we omit either of the two crucial components of the action (path or manner), which would be required to complete the task. Studies suggest that these two components remain in the focus of attention due to their encodings in the verbal phrase (Talmy, 1985). However, there is hardly any studies investigating these theories in the context of HRI and whether it extends beyond human–human interaction. To elicit participant requests, we design different contexts in which participants would need further information to fully comprehend the task and complete it with the assistance of the robot’s clarification. Crucially, these repairs will be provided either in a contrastive, using negation or in a noncontrastive manner, using assertion. The informativeness of negation will be tested in specific contexts that facilitate its use. We will use two different contexts: 1/4 and 2/4, following a previous non-interactive study by Nordmeyer and Frank (2023), but we aim to test the effect of contexts in an interactive setting. The contexts are created by varying the proportion of objects with the opposite feature compared to the rest of the objects. For instance, the contexts can be represented as 1/4 and 2/4, where the numerator indicates the number of objects with the contrasting feature. To gain insights into the online and offline processing of the explainee, attention distribution towards the explainer and the task, as well as cognitive loads during distinct phases of interaction (request initiation and repair), we will employ wearable eye-tracking technology. In summary, this experiment aims to investigate the impact of interaction (in the form of a request for clarification) on the processing of contrastive and noncontrastive verbal guidance. By utilizing a disjunctive task involving multiple manners and paths for object placement in a given context, we aim to expand our understanding of event cognition within an interactional framework. Questions: a) We aim to examine whether asking questions about missing information is positively related to the consolidation of the task. b) We aim to analyse the processing of contrastive and noncontrastive repairs in response to the questions asked within the path and manner components. This will allow us to determine whether there are any differences in how participants process and respond to these types of repairs. c) Another aspect we intend to explore is the effect of contexts, particularly the 1/4 and 2/4 contexts, on contrastive and noncontrastive processing. d) To assess task load and attention allocation, we will measure fixation metrics (duration and rate) during two phases of interaction: a) request initiation, where we will consider the type of question asked (restricted vs. open), and b) repair, where we will analyze the type of repair provided by the partner (contrastive vs. noncontrastive). By examining these factors, we aim to gain insights into participants’ cognitive workload and the distribution of their attention throughout the interaction.

International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 6, June 2020, pp. 1359-1366, Article ID: IJARET_11_06_125 Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=6 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DESIGN AND CONTROL OF A MECHATRONIC ROBOTIC ARM FOR INDUSTRIAL APPLICATIONS Amit Kumar Singh Asst, Professor, Department of Mechanical Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India Abstract: In today's rapidly evolving industrial landscape, the demand for efficient and reliable automation solutions has grown significantly. Robotic arms have emerged as a vital component in various industrial applications due to their ability to perform complex tasks with precision and speed. This research focuses on the design and control of a mechatronic robotic arm tailored specifically for industrial applications. The design phase of the mechatronic robotic arm involves the integration of mechanical, electrical, and control systems to create a versatile and robust platform. Extensive research and analysis were conducted to determine the optimal design parameters, including the arm's structure, materials, and actuation mechanisms. The aim was to strike a balance between strength, weight, and manoeuvrability while ensuring safety and efficiency in industrial environments. The robotic arm's mechanical structure comprises multiple interconnected segments, mimicking the natural movement of a human arm. High-strength materials such as carbon fibre and aluminium alloys were selected to ensure durability and minimize weight. The arm's joints are actuated by powerful electric motors and are equipped with sensors to provide feedback on position, velocity, and force. Additionally, various end-effectors can be attached to the arm to perform specific tasks, such as gripping, welding, or painting. Key words: Mechatronic robotic arm, industrial applications, automation solutions, design and control, mechanical structure, materials, actuation mechanisms, carbon fibre, aluminium alloys, electric motors, sensors. Cite this Article: Amit Kumar Singh, Design and Control of a Mechatronic Robotic Arm for Industrial Applications, International Journal of Advanced Research in Engineering and Technology, 11(6), 2020, pp. 1359-1366. DOI: https://doi.org/10.17605/OSF.IO/NY4ZR

Supplementary Material 2

International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 2, February 2019, pp. 2564-2583, Article ID: IJCIET_10_02_252 Available online at https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=2 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 OPTIMIZATION OF DISTRIBUTED-MEMORY PARALLELIZATION OF THE AGGREGATED UNFITTED FINITE ELEMENT METHOD IN AI Amit Kumar Singh Department of Mech. Engg., Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Cite this Article: Amit Kumar Singh, Optimization of distributed-memory parallelization of the aggregated unfitted finite element method in AI, International Journal of Civil Engineering and Technology, 10(2), 2019, pp. 2564-2583. DOI: https://doi.org/10.17605/OSF.IO/6JKDT

In this work, we examine science from the vantage points of blockchain technology and its connection to decentralized science (DeSci). We consider science as a collective process using Active Inference, an integrative framework that models the cognitive processes of perception, planning, and action selection in terms of Bayesian probabilities and updating. We present the Active Entity Ontology for Science (AEOS, available at coda.io/@active-inference-institute/active-entity-ontology-for-science-aeos) as a composable and versionable system for modeling various science systems, using the Active Inference entity partitioning. Such DeSci systems are considered from the perspective of BOLTS (Business, Operations, Legal, Technical, Social). Further steps for developing and utilizing AEOS in the context of scientific ecosystems are provided., v3 provides some writing edits/clarifications, and describes Active Blockference as ongoing project rather than proposed direction.

Aims: This international study aimed to assess: 1) the prevalence of preoperative and postoperative COVID-19 among patients with hip fracture, 2) the effect on 30-day mortality, and 3) clinical factors associated with the infection and with mortality in COVID-19-positive patients. Methods: A multicentre collaboration among 112 centres in 14 countries collected data on all patients presenting with a hip fracture between 1st March-31st May 2020. Demographics, residence, place of injury, presentation blood tests, Nottingham Hip Fracture Score, time to surgery, management, ASA grade, length of stay, COVID-19 and 30-day mortality status were recorded. Results: A total of 7090 patients were included, with a mean age of 82.2 (range 50–104) years and 4959 (69.9%) being female. Of 651 (9.2%) patients diagnosed with COVID-19, 225 (34.6%) were positive at presentation and 426 (65.4%) were positive postoperatively. Positive COVID-19 status was independently associated with male sex (odds ratio (OR) 1.38, p = 0.001), residential care (OR 2.15, p < 0.001), inpatient fall (OR 2.23, p = 0.003), cancer (OR 0.63, p = 0.009), ASA grades 4 (OR 1.59, p = 0.008) or 5 (OR 8.28, p < 0.001), and longer admission (OR 1.06 for each increasing day, p < 0.001). Patients with COVID-19 at any time had a significantly lower chance of 30-day survival versus those without COVID-19 (72.7% versus 92.6%, p < 0.001). COVID-19 was independently associated with an increased 30-day mortality risk (hazard ratio (HR) 2.83, p < 0.001). Increasing age (HR 1.03, p = 0.028), male sex (HR 2.35, p < 0.001), renal disease (HR 1.53, p = 0.017), and pulmonary disease (HR 1.45, p = 0.039) were independently associated with a higher 30-day mortality risk in patients with COVID-19 when adjusting for confounders. Conclusion: The prevalence of COVID-19 in hip fracture patients during the first wave of the pandemic was 9%, and was independently associated with a three-fold increased 30-day mortality risk. Among COVID-19-positive patients, those who were older, male, with renal or pulmonary disease had a significantly higher 30-day mortality risk.

Nanostructured lipid carriers (NLCs) are recently invented second-generation lipidic carriers. Flurbiprofen exhibits poor cutaneous bioavailability and systemic side effects on topical administration, so there is an unmet need for a novel carrier and its optimized therapy. The aim of present work to develop flurbiprofen loaded Nanostructured lipid carriers gel for effective treatment of topical disease. The Entrapment efficiency of formulation F1 to F16 were found to be 60.66±0.60 to 78.51±0.25 respectively. The maximum entrapment efficiency was found in formulation F16 (88.76±0.44). The Drug content of formulation F16 was also found high in formulation F16 select as optimized formulation. The prepared gel at least rpm of 10 exhibited a viscosity of 3145±11 to 3145±11cps that indicates that the formulation has the desired viscosity required for semisolid formulation for proper packaging. It was found that the viscosity decreases as the rotational speed of viscometer increased suggesting that greater the shearing the lower viscosity favours easy spreadability further confirmed by spreadability and rheological testing. When the regression coefficient values of were compared, it was observed that ‘r2’ values of First Order was maximum i.e. 0.955 hence indicating drug release from formulations was found to follow First Order. Keywords: Flurbiprofen, Nanostructured lipid carriers, Formulation, Evaluation

At present, smartphones are widely used for both business and personal purposes. As we all know that android is the popular mobile operating system. Like Windows operating system vulnerability the android has also vulnerability. And on the basis of these vulnerabilities, an attacker can obtain a user’s privacy data. But one possible way to avoid accessing of system and network i.e. penetration testing This paper describes penetration testing, Kali Linux tools such as Metasploit and TheFatRat. These tools have proved to be effective in Android exploitation. For example, by using TheFatRat, create a payload using msfvenom. Furthermore, the Payload creates a backdoor to access the system, using Metasploit, which exploits the android device and finds the vulnerability and, according to vulnerability, access the victim's system.

In this work, we examine science from the vantage points of blockchain technology and its connection to decentralized science (DeSci). We consider science as a collective process using Active Inference, an integrative framework that models the cognitive processes of perception, planning, and action selection in terms of Bayesian probabilities and updating. We present the Active Entity Ontology for Science (AEOS, available at coda.io/@active-inference-institute/active-entity-ontology-for-science-aeos) as a composable and versionable system for modeling various science systems, using the Active Inference entity partitioning. Further steps for developing and utilizing AEOS in the context of scientific ecosystems are provided.

Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.

Diabetes is a chronic progressive endocrine disease characterized by elevation of blood glucose level. This disease is associated with decreased life expectancy; increased morbidity and diminished quality of life. The objective of the study is to assess the correlation of glycaemic control, age and duration of disease with quality of life in patients with type 2 diabetes mellitus. The present cross-sectional study was conducted in 200 patients (100 each from rural and urban centers), at field practice area of Department of Community Medicine, P.K.DAS, Pallakad. The study was conducted with the help of pretested, semi-structured questionnaire. Data were entered in Microsoft Excel and analysis was done with IBM SPSS (Statistical Package for Social Sciences) version 21. In the present study, 42% of the participants were in the age group of 61 to 70 years. Amongst them, 55.5% were males and about 24% of the participants have had the disease for the past 11 to 15 years. Males had higher quality of life (QOL) scores than females. QOL scores were also higher in participants of rural areas, without any complications/co-morbidities who were employed and with HbA1c level less than 7. Scores were minimum for the participants belonging to the age group of >70 years and of upper socio-economic class. Poor QOL was associated with increasing age, duration of disease and poor glycaemic control. Key words: Type 2 diabetes mellitus, quality of life, HbA1c.

Homosexuality, which was considered to be deviant sexual behaviour, has been recognized as normal sexual behavior in recent years. Despite this change in scientific viewpoint, there are still many taboos centered around this entity. Even the research concerning homosexuality seemingly suffered discrimination. This research attempts to understand the trend of research in homosexuality in the SAARC countries todate.We did a literature search in the PubMed database since the time of inception till date (28th September 2021) by using the search terms: homosexuality [tiab] and (Bangladesh [tiab] OR India [tiab] or Maldives [tiab] OR Afghanistan [tiab] OR Pakistan [tiab] or Sri Lanka [tiab] OR Bhutan [tiab] or Nepal [tiab]). A total of 67 articles were found from the database. Most articles were published in the Journal of homosexuality (n=4).The articles were published between 1976 to 2020, with the highest number of publications (n=5) in 2009 and 2018. The average number of authors per article was 2.433, and the average number of citations per paper was 2.866.Though homosexuality is a highly sensitive issue, there is a paucity of research in this field in the SAARC countries.

Colistin resistance is a globalized sensible issue because it has been considered a drug of the last-line resort to treat drug-resistant bacterial infections. The product of the mobilized colistin resistance (mcr) gene and its variants are the significant causes of colistin resistance, which is emerging due to the frequent colistin use in veterinary, and these genes circulate among the bacterial community. Apart from mcr genes, some other intrinsic genes and proteins are also involved in colistin resistance. Researchers focus on the most advanced genomics (whole genome sequencing), proteomics, and bioinformatics approaches to explore the question of colistin resistance. To combat colistin resistance, researchers developed various strategies such as the development of newer drugs, the repurposing of existing drugs, combinatorial treatment by colistin with other drugs, a nano-based approach, photodynamic therapy, a CRISPRi-based strategy, and a phage-based strategy. In this timeline review, we have discussed the development of colistin resistance and its management in developing countries.

Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the information in the gates is analytically characterised by a kernel called the neural path kernel (NPK) and (ii) most critical information is learnt in the gates, in that, given the learnt gates, the weights can be retrained from scratch without significant loss in performance. Using the dual view, in this paper, we rethink the conventional interpretations of DNNs thereby explicitsing the implicit interpretability of DNNs. Towards this, we first show new theoretical properties namely rotational invariance and ensemble structure of the NPK in the presence of convolutional layers and skip connections respectively. Our theory leads to two surprising empirical results that challenge conventional wisdom: (i) the weights can be trained even with a constant 1 input, (ii) the gating masks can be shuffled, without any significant loss in performance. These results motivate a novel class of networks which we call deep linearly gated networks (DLGNs). DLGNs using the phenomenon of dual lifting pave way to more direct and simpler interpretation of DNNs as opposed to conventional interpretations. We show via extensive experiments on CIFAR-10 and CIFAR-100 that these DLGNs lead to much better interpretability-accuracy tradeoff., Comment: 18 pages, 9 Figures. arXiv admin note: text overlap with arXiv:2110.03403

Sponsored search is a key revenue source for search engines, where advertisers bid on keywords to target users or search queries of interest. However, finding relevant keywords for a given query is challenging due to the large and dynamic keyword space, ambiguous user/advertiser intents, and diverse possible topics and languages. In this work, we present a comprehensive comparison between two paradigms for online query rewriting: Generative (NLG) and Dense Retrieval (DR) methods. We observe that both methods offer complementary benefits that are additive. As a result, we show that around 40% of the high-quality keywords retrieved by the two approaches are unique and not retrieved by the other. To leverage the strengths of both methods, we propose CLOVER-Unity, a novel approach that unifies generative and dense retrieval methods in one single model. Through offline experiments, we show that the NLG and DR components of CLOVER-Unity consistently outperform individually trained NLG and DR models on public and internal benchmarks. Furthermore, we show that CLOVER-Unity achieves 9.8% higher good keyword density than the ensemble of two separate DR and NLG models while reducing computational costs by almost half. We conduct extensive online A/B experiments on Microsoft Bing in 140+ countries and achieve improved user engagement, with an average increase in total clicks by 0.89% and increased revenue by 1.27%. We also share our practical lessons and optimization tricks for deploying such unified models in production., 7 pages, 4 figures and 6 tables

The Kirsten rat sarcoma virus transforming protein (KRAS) mutations (predominate in codons 12, 13, and 61) and genomically drive nearly one-third of lung carcinomas. These mutations have complex functions in tumorigenesis, and influence the tumor response to chemotherapy and tyrosine kinase inhibitors resulting in a poorer patient prognosis. Recent attempts using targeted therapies against KRAS alone have met with little success. The existence of specific subsets of lung cancer based on KRAS mutations and coexisting mutations are suggested. Their interactions need further elaboration before newer promising targeted therapies for KRAS mutant lung cancers can be used as earlier lines of therapy. We summarize the existing knowledge of KRAS mutations and their coexisting mutations that is relevant to lung cancer treatment, in this review. We elaborate on the prognostic impact of clinical and pathologic characteristics of lung cancer patients associated with KRAS mutations. We briefly review the currently available techniques for KRAS mutation detection on biopsy and cytology samples. Finally, we discuss the new therapeutic strategies for targeting KRAS-mutant non-small cell lung cancer (NSCLC). These may herald a new era in the treatment of KRASG12Cmutated NSCLC as well as be helpful to develop demographic subsets to predict targeted therapies and prognosis of lung cancer patients.

The World Health Organisation estimates that 650 million people worldwide are disabled. Of those people, 80% currently live in Low- and Middle-Income countries (LMICs) that are often at various stages of economic development depending on active conflict or post conflict state. Fewer than 3% of persons with disabilities in LMIC’s have access to required rehabilitation services. Without access to the rehabilitation they require, those with disability may become entrenched in a cycle of poverty. There is a need for functional prosthetics which are affordable, robust, reliable and fit-for- purpose in resource constrained settings of LMICs. To address some of those LMICs developmental challenges, an integrated multidisciplinary partnership was convened between Indian and UK Engineers, Prosthetists, Healthcare professionals and experts from prosthesis manufactures, and public health NGOs. This consortium held exclusive networking meetings both in India and UK, supported by the UK Academy of Medical Sciences under Global Challenges Research Fund Networking Grants. The team also engaging with interdisciplinary research for developing Upcycled Plastic Prosthetic supported by the UK’s Royal Academy of Engineering Frontiers follow-on grant. This symposium will be delivering the findings from those research projects involved with LMICs as well as the UK stakeholders to inform the current thinking on the LMICs prosthetics requirements. These includes the unmet need of the LMICs amputees, capacity building to build skills across career stages with partners in LMICs during the preliminary trials of Upcycled Plastic Prosthetic, as well as integrating hands-off casting for prosthetic socket fabrication in LMICs protocol.

Industries with co-generation plants face unprecedented problems due to penetration of renewable energy systems such as solar power on their existing distribution networks. These problems are caused by intermittent solar power. To this end, this paper provides a detailed investigation of the effects due to sudden changes in solar power on an existing industrial distribution network connected to co-generation plants. Moreover, the case studies in this work consider simultaneous operation of a large industry having co-generation captive power plant and large scale solar photovoltaic power plant. The real-time field data for the past three years are used to check the performance of solar photovoltaic power plant, load management, power quality and other concerning issues on the distribution network. In addition to the real-time data, the simulations were performed for the solar power output under different solar irradiance conditions. Moreover, these simulations are used to assess photovoltaic integration effects on a distribution system having a co-generation captive power plant. Finally, this paper put forward photovoltaic integration guidelines to industries and policymakers interested to carry out the integration studies in the future.

Background:Comparison of operative morbidity rates after emergency laparotomy between units may be misleading because it does not take into account the physiological variables of patients' conditions. Surgical risk scores have been created, and the most commonly used is the Physiological and Operative Severity Score for the enumeration of Mortality (POSSUM) or one of its modifications, the Portsmouth-POSSUM (P-POSSUM), usually requires intraoperative information.Objective:The objective of this study is to evaluate the POSSUM and P-POSSUM scores in predicting postoperative morbidity and mortality in patients undergoing emergency laparotomy.Methodology:This is a prospective, cross-sectional, and hospital-based study that was conducted at J.L.N. Medical College and Hospital, Ajmer, Rajasthan, India, from April 2017 to December 2017. Adult patients who presented at the causality and underwent emergency laparotomy were included in the study. Observed and predicted mortality and morbidity were calculated using POSSUM and P-POSSUM equations, and statistical significance was calculated using Chi-square test.Results:A total of 100 patients were included in this study, with a mean age of 42.83 ± 18.21 years. The observed (O) mortality was 12 (12.0%), while POSSUM predicted 40 (40%) and P-POSSUM 27 (27%). The O/E ratio for POSSUM was 0.29 and for P-POSSUM was 0.44, and this means that they both overestimate mortality. When the results were tested by Chi-square test, thePvalue was found to be 0.55 and 0.85 for POSSUM and P-POSSUM, respectively, which showed no significant correlation for observed and expected mortality. The observed morbidity was 69 (69%), while POSSUM expected morbidity was 79 (79%), O/E ratio is 0.87, and this again overestimates the morbidity. POSSUM is overpredicting the rate of morbidity, and test of correlation showed no significance withP= 0.75.Conclusion:POSSUM and P-POSSUM were found to overestimate mortality and morbidity in our patient's population.

Aim:The present study analyzed the clinical significance of duration of intra-abdominal hypertension (IAH) associated with increased serum lactate in critically ill patients with severe sepsis.Materials and Methods:Our study was an observational, prospective study carried out in the Surgical Intensive Care Unit (ICU) at J.L.N Medical College, Ajmer, Rajasthan, India. In our study, we included a total of 100 patients and intra-abdominal pressure (IAP) was measured through intravesical route at the time of admission and after 6, 12, 24, 48, and 72 h via a urinary catheter filled with 25 ml of saline. Duration of ICU and hospital stay, need for ventilator support, initiation of enteral feeding, serum lactate level at time of admission and after 48 h, and 30-day mortality were noted as outcomes.Results: In our study, an overall incidence of IAH was 60%. Patients with cardiovascular surgery and renal and pulmonary dysfunction were 93.3%, 55%, and 60%, respectively, at the time of admission and 65%, 10%, and 10%, respectively, after 72 h of admission in the surgical ICU. Nonsurvivors had statistically significant higher IAP and serum lactate levels than survivors. Patients with longer duration of IAH had longer ICU and hospital stay, longer duration of vasopressors and ventilator support, and delayed enteral feeding. Conclusion:There is a strong relationship “risk accumulation” between duration of IAH associated with increased serum lactate and organ dysfunction. The duration of IAH was an independent predictor of 30-day mortality. Early recognition and prompt intervention for IAH and severe sepsis are essential to improve the patient outcomes.

Introduction Violence against healthcare workers, particularly doctors, exhibited an increasing trend in several parts of the world during the COVID-19 pandemic (Reddy et al., 2019; Devi, 2020). As compared to the pre- COVID pandemic, during the COVIDperiod, there is increase in violence against doctors and it is more so with the healthcare workers involved in contact tracing and screening (Priya & Pathak, 2020). Violence against doctors during COVID-19 pandemic, is not limited to India; rather, several lower-middle income countries witnessed similar incidents in an increasing fashion (Bhatti et al., 2021). Several factors have been identified that might be responsible for violence against the doctors and health care workers (Priya and Pathak, 2020). These are wHigh workload wAbsence of essential facilities wInadequate and poorly trained manpower wLong waiting hours for the patients wDelay in referral services wHigh expenditure in healthcare

Objective To study the bioactivity of methanolic extract of Brassica juncea on animal model of diabetes mellitus along with its effect on diabetic and metabolic parameters. Methods Diabetes mellitus was induced in rats by injecting streptozotocin (60 mg/kg) intraperitonealy. Blood glucose was measured on day 3 by GOD-POD method to confirm the diabetes mellitus. Rats having fasting blood glucose > 250 mg/dL were further selected for study and they were divided into four groups, control, control + streptozotocin, streptozotocin + metformin (75 mg/kg) and streptozotocin+ extract of B. juncea (450 mg/kg). Each group consisted of six rats of either sex. Metformin and experimental extract were administered for 21 d. Triglyceride, cholesterol level were measured on day 21 by commercially available kit. Blood glucose was measured on days 7 and 21. Anti-oxidant potential was assessed by estimating extent of lipid peroxidation (LPO) by malondialdehyde (MDA), nitric oxide (NO), superoxide dismutase (SOD) and glutathione (GSH) in liver, kidney, pancreas, muscle tissues on day 21. Unpaired and paired student's t-test was applied for statistical analysis. Results The extract of B. juncea showed significant decrease in blood glucose level on day 21. The treatment group showed significant difference in oxidative stress by increasing SOD and GSH and decreasing LPO and NO activity on day 21. The treatment did not show statistically significant difference of cholesterol, and triglycerides level on day 21. Conclusion The study showed anti-hyperglycemic and anti-oxidative properties of methanolic extract of B. juncea.

The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Recent breakthroughs on pretrained language models and graph neural networks push forward the development of corresponding techniques. The existing works mainly rely on the cascaded model architecture: the textual features of nodes are independently encoded by language models at first; the textual embeddings are aggregated by graph neural networks afterwards. However, the above architecture is limited due to the independent modeling of textual features. In this work, we propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow, making each node's semantic accurately comprehended from the global perspective. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph. Extensive evaluations are conducted on three large-scale benchmark datasets, where GraphFormers outperform the SOTA baselines with comparable running efficiency.

The introduction of the Basic Linear Algebra Subroutine (BLAS) in the 1970s paved the way for different libraries to solve the same problem with an improved approach and hardware. The new BLAS implementation led to High-Performance Computing (HPC) innovation. All the love went to the level 3 BLAS due to its humongous application in different fields, not bounded by computer science. However, level 1 and level 2 got neglected; we tried to solve the problem by introducing the new algorithm for the Vector-Vector dot product, Vector-Vector outer product and Matrix-Vector product, which improves the performance of these operations in a significant way. We are not introducing any library but algorithms, which improves upon the current state of art algorithms. Also, we rely on the FMA instruction, OpenMP, and the compiler to optimize the code rather than implementing the algorithm in assembly. Therefore, our current implementation is machine oblivious and depends on the compilers ability to optimize the code., Comment: 11 pages, 5 figures

Additional file 1: Table S1. Nutrient composition of dried plum. Table S2. Primers used to 838 quantify the expression of the genes by qPCR. Fig. S1. Experimental design.

Despite their success deep neural networks (DNNs) are still largely considered as black boxes. The main issue is that the linear and non-linear operations are entangled in every layer, making it hard to interpret the hidden layer outputs. In this paper, we look at DNNs with rectified linear units (ReLUs), and focus on the gating property (`on/off' states) of the ReLUs. We extend the recently developed dual view in which the computation is broken path-wise to show that learning in the gates is more crucial, and learning the weights given the gates is characterised analytically via the so called neural path kernel (NPK) which depends on inputs and gates. In this paper, we present novel results to show that convolution with global pooling and skip connection provide respectively rotational invariance and ensemble structure to the NPK. To address `black box'-ness, we propose a novel interpretable counterpart of DNNs with ReLUs namely deep linearly gated networks (DLGN): the pre-activations to the gates are generated by a deep linear network, and the gates are then applied as external masks to learn the weights in a different network. The DLGN is not an alternative architecture per se, but a disentanglement and an interpretable re-arrangement of the computations in a DNN with ReLUs. The DLGN disentangles the computations into two `mathematically' interpretable linearities (i) the `primal' linearity between the input and the pre-activations in the gating network and (ii) the `dual' linearity in the path space in the weights network characterised by the NPK. We compare the performance of DNN, DGN and DLGN on CIFAR-10 and CIFAR-100 to show that, the DLGN recovers more than $83.5\%$ of the performance of state-of-the-art DNNs. This brings us to an interesting question: `Is DLGN a universal spectral approximator?'

High-spin states in 123 Xe were populated in the 80 Se(48 Ca, 5n) 123 Xe reaction at a beam energy of 207 MeV. Gamma-ray coincidence events were recorded with the Gammasphere spectrometer. Four new high-spin bands have been discovered in this nucleus.The bands are compared with those calculated within the framework of cranked Nilsson-Strutinsky and cranked Nilsson-Strutinsky-Bogoliubov models. It is concluded that the configurations of the bands involve two-proton excita-tions across the Z = 50 as well as excitation of neutrons across the N = 82 shell gaps resulting in a large deformation, ε2 ∼ 0.30 and γ ∼ 5 • .

SARS-CoV-2, a member of beta coronaviruses, is a single-stranded, positive-sense RNA virus responsible for the COVID-19 pandemic. With global fatalities of the pandemic exceeding 4.57 million, it becomes crucial to identify effective therapeutics against the virus. A protease, 3CLpro, is responsible for the proteolysis of viral polypeptides into functional proteins, which is essential for viral pathogenesis. This indispensable activity of 3CLpro makes it an attractive target for inhibition studies. The current study aimed to identify potential lead molecules against 3CLpro of SARS-CoV-2 using a manually curated in-house library of antiviral compounds from mangrove plants. This study employed the structure-based virtual screening technique to evaluate an in-house library of antiviral compounds against 3CLpro of SARS-CoV-2. The library was comprised of thirty-three experimentally proven antiviral molecules extracted from different species of tropical mangrove plants. The molecules in the library were virtually screened using AutoDock Vina, and subsequently, the top five promising 3CLpro-ligand complexes along with 3CLpro-N3 (control molecule) complex were subjected to MD simulations to comprehend their dynamic behaviour and structural stabilities. Finally, the MM/PBSA approach was used to calculate the binding free energies of 3CLpro complexes. Among all the studied compounds, Catechin achieved the most significant binding free energy (−40.3 ± 3.1 kcal/mol), and was closest to the control molecule (−42.8 ± 5.1 kcal/mol), and its complex with 3CLpro exhibited the highest structural stability. Through extensive computational investigations, we propose Catechin as a potential therapeutic agent against SARS-CoV-2. Communicated by Ramaswamy H. Sarma

COVID-19 is the newly born pandemic caused by the SARS-CoV-2 virus, which is the recently emerged betacoronavirus that crosses the species barrier. It predominantly infects pneumocytes of the respiratory tract, but due to the presence of angiotensin-converting enzyme II (ACE2) on other cells like surface enterocytes of the upper esophagus and colon, these are also considered as the primary sites of infection. ACE2 receptor served as a cellular entry point for SARS-CoV-2. The expression of the ACE2 receptors is regulated by several factors such as age, tobacco smoking, inflammatory signaling, ACE inhibitors, angiotensin receptor blockers, and comorbidities (chronic obstructive pulmonary disease (COPD), tuberculosis, cerebrovascular disease, coronary heart disease, hypertension, and diabetes). Therefore, scientists are trying to explore the in-depth knowledge of ACE2 and considered it as a potential indirect target for COVID-19 therapeutics. In this focused review, we discussed in detail ACE2 expressions and regulation by different factors in the primary or vulnerable sites of SARS-CoV-2 infections. Clinical trials of rhACE2 in COVID-19 patients are ongoing, and if the outcome of the trials proves positive, it will be a breakthrough for the management of COVID-19. Finally, we suggest that targeting the ACE2 (a master regulator) in a balanced way could serve as a potential option against the management of COVID-19.

Additional file 1. Table S1. Spearman correlation between metabolic functions, growth performance parameters, and carcass weight variables across all treatments inbroilers at d 42 post-hatch. Table S2. Spearman correlation between the differential bacterial species and biological parameters across all treatments in broilers at d 42 post-hatch.