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Amid a larger context of political and social changes in India, this article reflects on a personal-academic trajectory and the problems, possibilities, and pleasures of teaching and researching histories of gender and caste in modern India at the University of Delhi for more than three decades. As a feminist historian, the author first points to some of the limitations in her early years of teaching courses on gender, where she occluded caste as a category of analysis. However, an examination of the conjunctions between anti-caste thought and gender through a Dalit feminist pedagogical lens led to shifts in her curriculum and academic scholarship. The article goes on to discuss how preparing and teaching courses on gender-caste histories pose many challenges in terms of queries from the university administration, curriculum design, pedagogic practices, and student responses. Classroom spaces are highly politicized in India, with pronounced gender, caste, class, and linguistic identities that often overlap with each other, which has implications for teaching and research. Finally, the article deliberates on the creative possibilities of such courses and pedagogical strategies, as students critique the crafting of mainstream history writing, feel drawn to new theoretical tools and methodologies that rely on different archival registers, and question the erasure of caste as an analytic, in the process also making the classroom a more democratic space.

The present study endeavours to explore the significant role pelvic stability contributes to maintaining the desired alignment in yoga asanas. The faulty postures due to sedentary lifestyles cause weakening of the muscles. This paper explains how Hip Musculature essentially The Gluteus Maximus and Gluteus Medius play an essential role in pelvic stability in many yoga asanas as well as a vital role in the health of our lumbar spine, sacrum, hip joints, and knees. Further, given the recent global interest in yoga asanas, it is critical to identify particular risk factors and injury processes to decrease the frequency with which these injuries occur. Also, this narrative review of the literature on hip musculature and its correlation with yoga asanas focuses on the factors leading to injuries, thereby helping to determine which asanas represent the greatest risk of injury and propose injury-prevention techniques.

To study the efficacy of swept source optical coherence tomography angiography (SSOCTA) to longitudinally follow-up patients with extrafoveal polyps post-laser photocoagulation and anti-vascular endothelial growth factor injection.Observational case series. Four patients diagnosed as polypoidal choroidal vasculopathy with extrafoveal polyps on multimodal imaging were followed up serially on SSOCT, en face and cross-sectional SSOCTA at a month and then 3 monthly for a year. Indocyanine green angiography was repeated at 4 months and 1 year.Anatomical regression of extrafoveal polyps was documented on a combination of en face and cross-sectional SSOCTA, 3 months post-laser photocoagulation and anti-vascular endothelial growth factor. Regression of polyps was maintained at the 12-month follow-up visit in all cases. Changes in branching vascular network morphology post-treatment were well-delineated on en face SSOCTA. Swept source optical coherence tomography angiography findings correlated well with the gold standard indocyanine green angiography.Swept source optical coherence tomography angiography is an effective noninvasive imaging modality to diagnose and longitudinally follow-up extrafoveal polyps postintervention. Laser photocoagulation with anti-vascular endothelial growth factor achieved regression of polyps in all cases and this was maintained over 12 months.

Let $R$ be a prime ring of characteristic different from $2$ with a right Martindale quotient ring $Q_r$ and an extended centroid $C$. Let $F$ be a non zero generalized derivation of $R$ and $S$ be the set of evaluations of a non-central valued multilinear polynomial $f(x_1,\ldots,x_n)$ over $C$. Let $p,q\in R$ be such that $pF^2(u)u+F^2(u)uq=0$ for all $u\in S$. Then for all $x\in R$ one of the followings holds:1) there exists $a\in Q_r$ such that $F(x)=ax$ or $F(x)=xa$ and $a^2=0$,2) $p=-q\in C$,3) $f(x_1,\ldots,x_n)^2$ is central valued on $R$ and there exists $a\in Q_r$ such that $F(x)=ax$ with $pa^2+a^2q=0$.

According to OWASP-2021, more than 3,00,000 web applications have been detected for unauthenticated and unauthorised access leading to a breach of security trust. Security patterns are commonly used in web applications to address the problem of broken access. Web developers are not experts in implementing security patterns. Therefore, it is necessary to verify that the security pattern has been applied, specifying the original intent of the security pattern. In this paper, an approach has been proposed that analyses the behavioural aspect of security patterns to verify that it meets the security requirement of the web application. The proposed approach extracts the class diagram’s structural properties, relations, associations, and security-related constraints and verifies it using the first-order predicate logic. Experiments have been conducted using class diagrams of security patterns to detect instances of broken access control early in the design phase. The proposed approach will help minimise the risk of unauthenticated and unauthorised access to a web application.

According to OWASP 2021, cross-site scripting (XSS) attacks are increasing through specially crafted XML documents. The attacker injects a malicious payload with a new pattern and combination of scripts, functions, and tags that deceits the existing security mechanisms in web services. This paper proposes an approach, GeneMiner, encompassing GeneMiner-E to extract new features and GeneMiner-C for classification of input payloads as malicious and nonmalicious. The proposed approach evolves itself to the changing patterns of attack payloads and identifies adversarial XSS attacks. The experiments have been conducted by collecting data from open source and generating various combinations of scripts, functions, and tags using an incremental genetic algorithm. The experimental results show that the proposed approach effectively detects newly crafted malicious XSS payloads with an accuracy of 98.5%, which is better than the existing classification techniques. The approach learns variations in the existing attack sample space and identifies the new attack payloads with reduced efforts.

Lentil (Lens culinaris) is a very important nutritious crop, grown, consumed throughout the world due to its high macro and micronutrients contents, including all the minerals. The nutritive components of lentil have several beneficial influences on human health. The current review aims to highlight the present knowledge on the mineral, amino acid, lipids, vitamins and carbohydrate composition of lentils. Thereby, a literature search was conducted using reliable sources: Elsevier, Research gate, Springer, etc to understand the overall nutritive status of lentils. The proximate composition of lentils is similar when compared with other legumes but the seed contains low concentrations of fat, fiber. Lentils supply a reasonably balanced range of all minerals. Lentils contain a number of anti-nutritive factors and these may be controlled either by processing or possibly by plant breeding programs. Anti-nutritive factors are less important In human diets because of cooking and processing which normally occur prior to eating, Lentils have a potential as a protein concentrate and could be processed to produce many products similar to those produced from soya beans. Lentils should be used for a variety of purposes, mainly towards eliminating malnutrition, on the condition of being well-balanced. Lentils should be incorporated either in snacks or while preparing household, school, or hospital meals, particularly in developing countries.

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

An elusive problem in the adhesion G protein-coupled receptor (AGPCR) field is full understanding of the activation mechanisms of the 33-member receptor class. With the recent solution of active-state structures of nearly one-quarter of AGPCRs, clarity has been brought to how AGPCRs are activated in response to endogenous full agonists. AGPCRs are self-activated via a tethered peptide agonist (TA) that transitions from a concealed or encrypted location to a decrypted state that binds to a typical GPCR orthosteric binding pocket. Here, we summarize the key milestones that led to the discovery of the AGPCR TA activation mechanism and discuss how extracellular shear forces may initiate TA decryption in physiological contexts. We compare the new active-state AGPCR structures and note that the orthosteric site-engaged TAs adopt a remarkably similar partial a-helical hook-like conformation, despite divergence of overall receptor similarity. Further, we contrast the TA-bound AGPCR structures to a partially active AGPCR structure to highlight the transitions AGPCRs may undergo during activation. Finally, we provide commentary on the validity of alternative AGPCR activation mechanisms.

Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.

COVID-19 has affected people in nearly 180 countries worldwide. This paper presents a novel and improved Semantic Web-based approach for implementing the disease pattern of COVID-19. Semantics gives meaning to words and defines the purpose of words in a sentence. Previous ontology approaches revolved around syntactic methods. In this paper, semantics gives due priority to understand the nature and meaning of the underlying text. The proposed approach, FaD-CODS, focuses on a specific application of fake news detection. The formal definition is given by depiction of knowledge patterns using semantic reasoning. The proposed approach based on fake news detection uses description logic for semantic reasoning. FaD-CODS will affect decision making in medicine and healthcare. Further, the state-of-the-art method performs best for semantic text incorporated in the model. FaD-CODS used a reasoning tool, RACER, to check the consistency of the collected study. Further, the reasoning tool performance is critically analyzed to determine the conflicts between a myth and fact.

Paper is an excellent medium of expression and knowledge preservation and communication because of its writing, printing, and packaging abilities. It is a thin sheet or web-like structure made by a dilute suspension of cellulosic fibres. Handmade papermaking technology was mass accepted and considered a precious commodity before development of an industrialised wood- pulp based paper industry. This modern papermaking practice harms the environment as it uses enhanced natural resources (ample amount of water, trees as a primary source for wood pulp) and generates pollution due to release of harsh chemicals. This paper reviews the history and process of handmade paper manufacturing from past to present. Along with that, all the pillars of sustainability (environmental, economic, and social) in relation to the handmade paper industry are deep rooted since ancient times and very much relevant for the present era of sustainable development. Various kinds of cellulosic raw materials from plants (Paper mulberry, Kazo, Gampy, hemp, bamboo, banana, etc.) and other used materials (old rags, ropes, nets, newspapers etc.) with the chemical-free production process and products make this industry eco-friendly. Craft based, small scale and labour-intensive guild art gives it social strength; whereas an infinite product range having functional and decorative uses, as well as strong export potential of the industry gives it an economic strength. Due to the present need of sustainable production, waste minimisation, and circular economy, the handmade paper seems to very potential industry to recycle various kind of waste like weedy battles, textiles, tetra packs, currency, plastic etc. This paper is an attempt to review the journey of handmade papers, its recycling potential, various production processes, products, advantages and disadvantages of the industry from past to present.

In the north India of 1920s–30s, many first-generation anticolonial communists and Left intellectuals did not see any contradiction in reliance upon religion, ethical traditions and morality in a search for vocabularies of dignity, equality, just polity and social liberation. Through select writings in Hindi of Satyabhakta (1897–85), an almost forgotten figure in histories of communism in India, this article focuses on the entanglement between religion and communism as a way of thinking about the Left in India, and the problems and possibilities of such imaginings. Steeped in a north Indian Hindi literary print public sphere, such figures illuminated a distinctly Hindu and Indian path towards communism, making it more relatable to a Hindi–Hindu audience. The article draws attention to Satyabhakta’s layered engagements with utopian political desires, which, in envisaging an egalitarian future, wove Hindu faith-based ethical morality, apocalyptic predictions and notions of a romantic Ram Rajya, with decolonisation, anti-capitalism and aesthetic communist visions of equality. Even while precarious and problematic, such imaginations underline hidden plural histories of communism and, at the same time, trouble atheist, secular communists as well as the proponents of Hindutva.

Background and Aims Ontamalimab, a fully-human monoclonal antibody targeting MAdCAM-1, induced remission in patients with moderate-to-severe ulcerative colitis [UC] in the TURANDOT study. We aimed to assess long-term safety, tolerability, and efficacy of ontamalimab in TURANDOT II. Methods TURANDOT II was a phase 2, multicentre, open-label [OL] study in patients with moderate-to-severe UC who completed TURANDOT on placebo or ontamalimab (NCT01771809). Patients were randomised to 75 mg or 225 mg ontamalimab every 4 weeks for 72 weeks [OL1]. The dosage could be increased to 225 mg from Week 8 at the investigator’s discretion. All patients then received 75 mg every 4 weeks for 72 weeks [OL2], followed by 6-month safety follow-up. The primary objective was safety, measured by adverse events [AEs], serious AEs [SAEs], and AEs leading to withdrawal. Mucosal healing [MH; centrally read endoscopy] was assessed. Results Of 330 patients, 180 completed OL1; 94 escalated to 225 mg; 127 completed OL2. Overall, 36.1% experienced drug-related AEs. The most common SAE [10.0%] was worsening/ongoing UC; 5.5% of patients had serious infections, the most common being gastroenteritis [0.9%]. One death and four cancers [all unrelated to ontamalimab] occurred. No PML [progressive multifocal leukoencephalopathy]/lymphoproliferative disorders occurred. Geometric mean high-sensitivity C-reactive protein [hsCRP] and faecal calprotectin decreased across OL1 in both dose groups. The proportion of patients assigned to placebo in TURANDOT achieving MH increased from 8.8% [6/68] at baseline to 35.3% at Week 16 [24/68; non-responder imputation]. The corresponding increase in the ontamalimab group was from 23.3% [61/262] to 26.7% [70/262]. Conclusions Ontamalimab was well tolerated up to 144 weeks in patients with moderate-to-severe UC, with good safety and efficacy.

With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to implement a robust technique to categorize cybercrime into two sections, 66 and 67 of IT Act 2000 with high precision using ensemble learning technique. In the proposed methodology, Bag of Words approach is applied for performing feature engineering where these features are given as input to the hybrid model Ensem_SLDR. The proposed model is implemented with the help of model stacking, comprising Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest and gave better performance by having 96.55 % accuracy, which is higher and reliable than the past models implemented using a single learning algorithm and some of the existing hybrid models. Ensemble learning techniques enhance model performance and robustness. This research is beneficial for cyber-crime cells in India, which have a repository of detailed information on cybercrime including complaints and investigations. Hence, there is a need for model and automation systems empowered by artificial intelligence technologies for the analysis of cybercrime and their classification of its sections.

Today, amongst the various forms of online data, user reviews are very useful in understanding the user's attitude, emotion and sentiment towards a product. In this article, a novel method, named as DE-ForABSA is proposed to forecast automobiles sales based on aspect based sentiment analysis (ABSA) and ClusFuDE [8] (a hybrid forecasting model). DE-ForABSA consists of two phases – first, extracted user reviews of an automobile are analysed using ABSA. In ABSA, the reviews are pre-processed; aspects are extracted & aggregated to determine the polarity score of reviews. Second, uses of ClusFuDE consisting of clustering, fuzzy logical relationships and Differential Evolution (DE) to predict the sales of the automobile. DE is a population-based search method to optimize real values under the control of two operators: mutation & crossover. Score from phase 1 is a parameter in differential mutation in phase 2. The proposed method is tested on reviews & sales data of automobile. The empirical results show a Mean Square Error of 142.90 which indicates an effective consistency of the model

Drawing on fragmentary examples from women’s histories in colonial India, this paper underlines the problems and possibilities in historiographies of modern India. Feminist scholars argue that the three terms—women, gender and sex—have often been used interchangeably. However, the commonsensical term woman is neither a natural category (of non-men) nor a homogeneous community (of sisterhood), for there are historically many ways of being a woman in different times. Further, gender is not merely a natural or biological identity of a person. It is a historical, social and political construction of how to be a man or a woman. Even sex is no longer seen as the biological ground upon which gender is constructed, as sexualities too are socially produced and regulated by dominant discourses, which establish one kind of sexuality as normal and relegate others into the domain of deviance, perversion or criminality. Through selective readings from discourses around women’s education and conceptualisation of the modern women in colonial India, the paper reflects on how a gender-sensitive perspective produces a more complex and textured view of historical processes. While patriarchies were recast in more powerful, though subtle ways, they were also subverted, or at least questioned, in colonial India.

This article centres on the Hindi sexology writings of a woman, Yashoda Devi, and a Shudra, Santram B.A. In the context of an efflorescence of vernacular sexology literature in early twentieth-cent...

Potassium plays an important role in the metabolism of plants, growth, development and yields. Worldwide, KCl is commonly used as source of K fertilizer in agricultural crops. The present experiment was taken up to assess the response of various sources of K on physiological attributes, yield and quality parameters of sugarcane. Treatments were no K application, potassium ammonium sulphate (H4KNO4S-9:0:30:20, NPKS ratio), slow release K fertilizer (0:0:45:0), sulphate of potash/potassium sulphate (K2SO4-0:0:50:17.5), muriate of potash (KCl-0:0:50:0) and muriate of potash (KCl) + elemental S @ 40 kg ha−1. Application of K2SO4 recorded 15.8% improvement in tiller population (during peak tillering stage) and 22.2% improvement in dry matter accumulation (at the harvest stage) over the KCl. About 12.33–24.16% increments in crop growth rate and the highest net photosynthetic rate (19.91 µmol m−2 s−1) were also recorded with application of K2SO4. Application of K fertilizers improved the availability of potassium in soil by 8.17% (254.2 kg ha−1). Potassium uptake in sugarcane crop ranged from 245 to 347 kg ha−1. The highest individual cane length (311 cm) was measured with application of K2SO4. Application of K2SO4 brings forth about 23.8% increments in millable canes, 14% in cane weight and 33.8% in cane yield over no K. Application of K also increased mean sucrose content in juice by 0.58 unit (17.0 pol per cent juice) over no use of K. Overall effect of K application on increasing sugarcane yield and sucrose content simultaneously brought forth improvement in commercial cane sugar (CCS) by 29.43% (10.50 t/ha) over no use of K. However, application of K2SO4 also improved CCS by 25.89% (11.67 t/ha) over the application of KCl.

Widespread use of Android-based applications on the smartphones has resulted in significant growth of security attack incidents. Malware-based attacks are the most common attacks on Android-based smartphones. To forestall malware from attacking the users, a much better understanding of Android malware and its behaviour is required. In this article, an approach to classify and characterise the malicious behaviour of Android applications using static features, data flow analysis, and machine learning techniques has been proposed. Static features like hardware components, permissions, Android components and inter-component communication along with unique source-sink pairs obtained from data flow analysis have been used to extract the features of the Android applications. Based on the features extracted, the malicious behaviour of the applications has been classified to their respective malware family. The proposed approach has given 95.19% accuracy rate and F1 measure of 92.19302 with the largest number of malware families classified as compared to previous work.

In this paper, a unique smart assistance aid, iSeePlus, for visually impaired people is presented. iSeePlus, as a prototype product, is equipped with smart glasses (iSee Glasses), a smart stick (iS...