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Cybersecurity is a vital technology and measures intended to protect networks, computers, information, and programs from threats and illegal access, modification, or damage. A security model covers a network and a computer safety method. Each system has antivirus software, firewalls, and an intrusion detection system (IDS). IDS helps in discovering and identifying illegal system behavior such as usage, copying, alteration, and damage. By estimating network traffic anomalies and patterns, deep learning (DL) models can enhance the detection abilities of IDS when compared to traditional rule-based methods. These models learn complex representations from data, authorizing them to recognize subtle and developing attack patterns. Techniques like recurrent neural network (RNN) and convolutional neural network (CNN) can be applied to progress consecutive or spatial features in network data, correspondingly. This manuscript empowers Cybersecurity by utilizing an Enhanced Rat Swarm Optimizer with a Deep Stack-Based Ensemble Learning (ERSO-DSEL) model. The ERSO-DSEL approach leverages feature selection (FS) with EL strategies to boost cybersecurity. In the ERSO-DSEL system, Z-score normalization is employed to measure the input data. Besides, an improved equilibrium optimizer (IEO) based FS approach is applied to choose a set of features. For cyberattack recognition, the ERSO-DSBEL approach uses the DSEL approach comprising three models namely deep neural network (DNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Furthermore, the hyperparameter selection of these DL models takes place using the ERSO system. The solution result of the ERSO-DSBEL model is executed on a benchmark IDS database. A wide-contrast study reported that the ERSO-DSBEL model accomplishes an enhanced accuracy outcome of 99.67% over other models of cybersecurity.

Internet of Things (IoT) technology involves a network of interconnected devices and sensors that gather and exchange information. In smart cities, IoT devices were utilized in several fields including energy, transportation, waste management, healthcare, etc., to improve the overall quality of life and sustainability of the populace. But, as the usage of IoT increases, the cybersecurity and data privacy become a concern of safety. An anomaly detection system helps to identify possible data breaches or cyber-attacks by identifying abnormal data patterns. Deep learning (DL) driven anomaly detection has emerged as an effective and powerful method for identifying abnormal behaviours or patterns in the data domain. This technique leverages the abilities of a deep neural network for automated learning of complex patterns and representations from data, which make it better for anomaly detection task where irregularities cannot be easily defined by handcrafted rules. This paper establishes a new Billiard Based Optimization with Deep Learning Driven Anomaly Detection and Classification (BBODL-ADC) technique in IoT-assisted Sustainable Smart Cities. The goal of the BBODL-ADC technique lies in the proper recognition and classification of anomalies in the IoT-assisted smart city. To obtain that, the BBODL-ADC system applies a binary pigeon optimization algorithm (BPEO) algorithm for the effectual selection of features. Besides, the BBODL-ADC technique utilizes Elman recurrent neural network (ERNN) approach for the recognition and classification of anomalies. Moreover, the BBO system can be used for better parameters chosen by the ERNN algorithm. The stimulation value of the BBODL-ADC algorithm can be executed benchmark database. The achieved outcomes demonstrate the remarkable outcome of the BBODL-ADC methodology of 95.69% and 99.21% compared to existing models under dataset-1 and dataset-2.

Abstract Obesity has been associated with increased risk of adult asthma, however, not all studies have found a clear association between overweight and the incidence of asthma, and data on other adiposity measures have been limited. Hence, we aimed to summarize evidence on association between adiposity and adult asthma. Relevant studies were retrieved through searches conducted in PubMed, and EMBASE up to March 2021. A total of sixteen studies (63,952 cases and 1,161,169 participants) were included in the quantitative synthesis. The summary RR was 1.32 (95% CI 1.21–1.44, I2 = 94.6%, pheterogeneity

Abstract Introduction Despite several ongoing efforts in biomedicine and traditional medicine, there are no drugs or vaccines for coronavirus disease 2019 (COVID-19) as of May 2020; Kabasura Kudineer (KSK), a polyherbal formulation from India’s Siddha system of medicine, has been traditionally used for clinical presentations similar to that of COVID-19. We explored the efficacy of KSK in reducing viral load and preventing the disease progression in asymptomatic, COVID-19 cases. Methods A prospective, single-center, open-labeled, randomized, controlled trial was conducted in a COVID Care Centre in Chennai, India. We recruited reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 of 18 to 55 years of age, without clinical symptoms and co-morbidities. They were randomized (1:1 ratio) to KSK (60 mL twice daily for 7 days) or standard of care (7 days supplementation of vitamin C 60,000 IU morning daily and zinc 100 mg evening daily) groups. The primary outcomes were reduction in the SARS-CoV-2 load [as measured by cyclic threshold (CT) value of RT-PCR], prevention of progression of asymptomatic to symptomatic state, and changes in the immunity markers including interleukins (IL-6, IL-10, IL-2), interferon gamma (IFNγ), and tumor necrosis factor (TNF α). Siddha clinical assessment and the occurrence of adverse effects were documented as secondary outcomes. Paired t-test was used in statistical analysis. Results Viral load in terms of the CT value (RdRp: 95% CI = 1.89 to 5.74) declined significantly on the seventh day in the KSK group and that of the control group, more pronounced in the study group. None progressed to the symptomatic state. There was no significant difference in the biochemical parameters. We did not observe any changes in the Siddha-based clinical examination and adverse events in both groups. Conclusion KSK significantly reduced SARS-CoV-2 viral load among asymptomatic COVID-19 cases and did not record any adverse effect, indicating the use of KSK in the strategy against COVID-19. Larger, multi-centric trials can strengthen the current findings. Trial registration Clinical Trial Registry of India CTRI2020/05/025215 . Registered on 16 May 2020

Background: The Coronavirus disease 2019 (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a massive threat to public health worldwide. Siddha system of medicine is one of the traditional medicines of South India. The recommended formulations in Siddha Sasthric Medicines- Fixed Regimen (SSM-FiRe) are Amukkura tablets, Kaba Sura Kudineer (KSK) for asymptomatic COVID-19 positive (RT-PCR) patients, and Athimathuram tablets, Adathodai Manappagu syrup, Thippili Rasayanam, Brahmananda Bairavam tablet, and Notchi Kudineer for mild symptomatic patients. The core objective of the trial was to document the efficacy of SSM-FiRe in the prevention of asymptomatic and mild COVID-19 disease progression to the next level of severity, reduce the severity of symptoms and revert to RT-PCR Negative. Methods: An exploratory, prospective, open-labeled, single-arm, non-randomized trial was designed as per GCP guidelines to assess the efficacy of SSM-FiRe. Sixty RT-PCR positive participants who were asymptomatic or with mild COVID-19 symptoms were recruited for the study at the Siddha COVID Care Centre, Vyasarpadi, Chennai from June to August 2020. Nasal and oropharyngeal swab tests were performed on the 0, 7th, and 14th days. All participants were treated with SSM – FiRe regimen. All the participants were also assessed based on Siddha Yakkkaiyin Ilakkanam, which included Clinical symptoms and vitals. Laboratory investigations such as Haemogram, Liver Function Test, Renal Function Test, HbA1C, Electrolytes, Inflammatory markers, Cardiac profile, Immunoglobulins, and anti-SARS-CoV-2 antibody tests were performed. Results: 83% of COVID-19 patients turned RT-PCR negative on the 7th day and in most of the cases, symptoms were reduced within the first 5 days of admission. The RT-PCR cycle threshold (ct) value increased significantly (

Positive psychology offers a scientific window onto understanding and enhancing the welfare and growth of university communities, and as well as improving academic performance. This holistic approach is on the rise, yet most research is conducted in Western countries. This situation prevails despite the fact that two-thirds of the world's population live in Asia. This review collated and synthesised published work on applications of positive psychology in higher education conducted in Asia, to describe the current status, explore conceptual perspectives and identify knowledge gaps. A total of 147 articles (157 experimental studies), published since 2000, were included. These were descriptive explorations (12.1%), quantifying associations between positive psychology constructs (62.4%), interventions (19.7%), and psychometric evaluations (5.7%). Key topics were academic leadership, organisational commitment, student engagement and foreign language learning. The thematic framework centered on 'Optimal Functioning', with 'Personal Resources One Can Draw On' and 'How One Interacts With The World' as direct influencing factors, and 'Environment' as an indirect factor. Across the Asian region, positive psychology's major contribution is to identify what types of personal resources are associated with optimal functioning in higher education, but there is little high-quality evidence for intervention benefits, nor a deep understanding of how those resources can be effectively deployed to achieve well-being. As part of the third-wave positive psychology movement, scholars in Asia can play a greater leading role in re-evaluating traditional Western concepts to account for the socio-cultural context in which students and staff are embedded.

Abstract Objectives The primary objectives of this study are to determine efficacy of Siddha medicine, Kabasura kudineer in reduction of SARS-CoV-2 viral load and reducing the onset of symptoms in asymptomatic COVID-19 when compared to Vitamin C and Zinc (CZ) supplementation. In addition, the trial will examine the changes in the immunological markers of the Siddha medicine against control. The secondary objectives of the trial are to evaluate the safety of the Siddha medicine and to document clinical profile of asymptomatic COVID-19 as per principles of Siddha system of Medicine. Trial design A single centre, open-label, parallel group (1:1 allocation ratio), exploratory randomized controlled trial. Participants Cases admitted at non-hospital settings designated as COVID Care Centre and managed by the State Government Stanley Medical College, Chennai, Tamil Nadu, India will be recruited. Eligible participants will be those tested positive for COVID-19 by Reverse Transcriptase Polymerase Chain reaction (RT-PCR) aged 18 to 55 years without any symptoms and co-morbidities like diabetes mellitus, hypertension and bronchial asthma. Those pregnant or lactating, with severe respiratory disease, already participating in COVID trials and with severe illness like malignancy will be excluded. Intervention and comparator Adopting traditional methods, decoction of Kabasura kudineer will be prepared by boiling 5g of KSK powder in 240 ml water and reduced to one-fourth (60ml) and filtered. The KSK group will receive a dose of 60ml decoction, orally in the morning and evening after food for 14 days. The control group will receive Vitamin C (60000 IU) and Zinc tablets (100mg) orally in the morning and evening respectively for 14 days. Main outcomes The primary outcomes are the reduction in the SARS-CoV-2 load [as measured by cyclic threshold (CT) value of RT-PCR] from the baseline to that of seventh day of the treatment, prevention of progression of asymptomatic to symptomatic state (clinical symptoms like fever, cough and breathlessness) and changes in the immunity markers [Interleukins (IL) 6, IL10, IL2, Interferon gamma (IFNγ) and Tumor Necrosis Factor (TNF) alpha]. Clinical assessment of COVID-19 as per standard Siddha system of medicine principles and the occurrence of adverse effects will be documented as secondary outcomes. Randomisation The assignment to the study or control group will be allocated in equal numbers through randomization using random number generation in Microsoft Excel by a statistician who is not involved in the trial. The allocation scheme will be made by an independent statistician using a sealed envelope. The participants will be allocated immediately after the eligibility assessment and informed consent procedures. Blinding (masking) This study is unblinded. The investigators will be blinded to data analysis, which will be carried out by a statistician who is not involved in the trial. Numbers to be randomised (sample size) Sample size could not be calculated, as there is no prior trial on KSK. This trial will be a pilot trial. Hence, we intend to recruit 60 participants in total using a 1:1 allocation ratio, with 30 participants randomised into each arm. Trial status Protocol version 2.0 dated 16th May 2020. Recruitment is completed. The trial started recruitment on the 25th May 2020. We anticipate study including data analysis will finish on November 2020. We also stated that protocol was submitted before the end of data collection Trial registration The study protocol was registered with clinical trial registry of India (CTRI) with CTRI/2020/05/025215 on 16 May 2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2).

As the drive to move from traditional face-to-face classroom learning to e-learning is ever in demand, the knowledge corpus exposed to students can be overwhelming because there is a need to automate certain functions of the e-learning framework. One of these functions is the course recommendation feature. Course recommendations help students save time and effort to explore the courses from a large pool of resources while considering multiple attributes such as social influence, prior knowledge, and learning style. These numerous criteria make the course recommendation a complex process that requires the researcher to promote online education and intelligently assist learners in identifying the relevant online courses. Although various researchers have put forward strategies to address course recommendation problems, learning style, a critical element in ensuring effective learning, has not been considered part of the course recommendation framework. This paper puts forward a learning style-based course recommendation framework that is expected to provide highly automated decision support for learners in identifying the most suitable course to improve their efficiency in e-learning. Additionally, based on this framework, instructors can analyze and re-evaluate the courses according to students' learning styles. The proposed framework reduces the time and effort involved in seeking relevant courses, thereby improving the learning experience.

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.