A PHARMACOLOGICAL COMPREHENSIVE REVIEW ON ‘RASSBHARY' PHYSALIS ANGULATA (L.)

The present review article reveals the importance of species Physalis angulata (L.) of the genus Physalis (L.) distributed worldwide including India. Physalis species are perennial, erect and variously having toothed or lobed leaves. Physalis angulata (L.) belongs to the family Solanaceae, includes about 120 species with different and specific herbal characters. On the basis of these herbal characters, the plant is traditionally used as medicine to cure various disorders like asthma, kidney, bladder, jaundice, gout, inflammations, cancer, digestive problems and diabetes etc. P. angulata is a source of the variety of phytoconstituents like phytosteroles, withangulatin A, a variety of physalins and flavonol glycoside etc. The plant extracts from the different parts having different pharmacological activities such as anti-cancerous, immunomodulatory, anti-diabetic, diuretic and anti-bacterial. In this article cytomorphological, phytochemical, biological activities and ethnobotanical inputs have been extensively recorded for P. angulata (L.)

Human-Centric AI Adoption and Its Influence on Worker Productivity: An Empirical Investigation

This empirical study looks at how the industrial sector is affected by the deployment of human-centric AI and finds some amazing changes in the workplace. Following implementation, employee productivity increased by 35.5%, demonstrating the significant advantages of AI in automating repetitive jobs and improving overall efficiency. Simultaneously, job satisfaction increased by a significant 20.6%, highlighting the alignment of AI with worker well-being. Employee skill development increased by 29.6% as a result of structured AI training, which is consistent with the larger goals of adopting AI that is human-centric. Significant cost reductions of up to 40% of budgets were also realized by departments, resulting in significant economic benefits. These revelations highlight the revolutionary potential of AI integration in Industry 5.0, promoting a harmonic convergence of intelligent technology and human skills for an industrial future that is more productive, happy, and financially stable

Blockchain Integration in Industry 5.0: A Security Experiment for Resilience Assessment

This study uses an organized experimental methodology to assess the security and robustness of blockchain-integrated systems within the framework of Industry 5.0. The R&D department's average salary increased by 10%, according to an analysis of personnel statistics, which reflects trends in remuneration. Interdepartmental transactions have increased by 20% according to blockchain transaction analysis, highlighting the significance of safe interdepartmental cooperation. Security issues highlight the need of ongoing watchfulness; in the R&D department, data breaches have increased by 30%. The Manufacturing department scored 85% on the resilience evaluation, which reveals diversity in departmental flexibility. Conclusively, this study offers crucial perspectives on blockchain's function in Industry 5.0 and underscores the need of security, cooperation, and adaptability in this dynamic environment

Development of SkinTracker, an integrated dermatology mobile app and web portal enabling remote clinical research studies

IntroductionIn-person dermatology clinical research studies often face recruitment and participation challenges due to travel-, time-, and cost-associated barriers. Studies incorporating virtual/asynchronous formats can potentially enhance research subject participation and satisfaction, but few mobile health tools are available to enable remote study conduct. We developed SkinTracker, a patient-facing mobile app and researcher-facing web platform, that enables longitudinal collection of skin photos, patient reported outcomes, and biometric health and environmental data.MethodsEight design thinking sessions including dermatologists, clinical research staff, software engineers, and graphic designers were held to create the components of SkinTracker. Following iterative prototyping, SkinTracker was piloted across six adult and four pediatric subjects with atopic dermatitis (AD) of varying severity levels to test and provide feedback on SkinTracker for six months.ResultsThe SkinTracker app enables collection of informed consent for study participation, baseline medical history, standardized skin photographs, patient-reported outcomes (e.g., Patient Oriented Eczema Measure (POEM), Pruritus Numerical Rating Scale (NRS), Dermatology Life Quality Index (DLQI)), medication use, adverse events, voice diary to document qualitative experiences, chat function for communication with research team, environmental and biometric data such as exercise and sleep metrics through integration with an Apple Watch. The researcher web portal allows for management and visualization of subject enrollment, skin photographs for examination and severity scoring, survey completion, and other patient modules. The pilot study requested that subjects complete surveys and photographs on a weekly to monthly basis via the SkinTracker app. Afterwards, participants rated their experience in a 7-item user experience survey covering app function, design, and desire for participation in future studies using SkinTracker. Almost all subjects agreed or strongly agreed that SkinTracker enabled more convenient participation in skin research studies compared to an in-person format.DiscussionTo our knowledge, SkinTracker is one of the first integrated app- and web-based platforms allowing collection and management of data commonly obtained in clinical research studies. SkinTracker enables detailed, frequent capture of data that may better reflect the fluctuating course of conditions such as AD, and can be modularly customized for different skin conditions to improve dermatologic research participation and patient access

Protecting Cyberspace: Executive Authority and Internet Security

The imminent threat of cyber attacks is a major concern to America’s national security. It is imperative to understand that cyber security is a concern for both domestic and foreign policy objectives. It is important to note that policy is also influenced by the temperament of the Oval Office, which is dependent upon whether the Commander In Chief of either political party has a hawkish or dovish attitude when addressing significant policy concerns. We have entered the era of cyber warfare, a battle that knows no boundaries, which compels us to look to our nation’s leaders in order to examine the steps that are taken to address this serious matter. What led President Bush and President Obama to use executive authority over legislative action in order to address the issue of cyber security? With the use of content analysis, I examine both the requests made to Congress and the number of executive orders given by former President Bush and current President Obama respectively. I argue that given the stagnancy of our legislative process and the alarming rate of technological advancements moved both presidents to choose executive authority rather than congressional action in order to combat cyber warfare

Assessment of soil organic carbon stocks in relation to variation in physiography under sub-mountainous Shiwalik ranges of lower Himalayas, India

A study was conducted in lower Shiwaliks of Punjab, India to study the soil organic carbon (SOC) stock variation in hill, piedmont plain and alluvial plain regions of Punjab, and further to investigate the SOC variability down the slope in the lower Shiwaliks region of Punjab. The SOC fractions viz. very labile carbon, labile carbon, less labile carbon and recalcitrant were determined using standard methodologies. The total SOC stock in 0–60 cm soil depth was highest in plain region (31.81 Mg ha−1), followed by piedmont (23.43 Mg ha−1) and lowest in hill region (22.86 Mg ha−1). Whereas, the SOC concentration in surface layer was highest in foot slope (5.76 g kg−1) followed by mid slope (4.14 g kg−1) and lowest in up slope (2.32 g kg−1). Among the SOC fractions the average labile carbon content in soil samples was found in the order, hills (0.81 g kg−1) > piedmonts (0.73 g kg−1) > plains (0.50 g kg−1). The soil bulk density (Db) was higher in the plain than that of piedmont and hills. Moreover, a similar trend is observed in case of water holding capacity. Regression analysis has revealed significant relationship between Db and SOC stock at p < 0.05. Further, analysis of variance (ANOVA) at p < 0.05 has indicated significant differences among concentration of SOC and stock influenced by land use and soil depths. Thus, an understanding of the effect of physiography on SOC via such studies is important for soil quality management

Evaluation of adenosine deaminase activity in serum of cattle and buffaloes in the diagnosis of bovine tuberculosis

Background and Aim: Bovine tuberculosis (bTB) is a chronic bacterial disease of cattle caused by Mycobacterium bovis. bTB causes severe economic losses resulting from livestock deaths, chronic disease, and trade restrictions. Determination of serum levels of adenosine deaminase (ADA), an enzyme produced by monocytes/macrophages and lymphocytes, has been used in the diagnosis of human TB. This study aimed to evaluate the role of ADA enzyme activity in the diagnosis of bTB. Materials and Methods: In this study, a total of 100 animals (cattle and buffaloes) were screened for bTB by comparative intradermal tuberculin test (CITT) and interferon-γ (IFN-γ) test and in serum samples obtained from 100 screened animals, ADA seric activity was evaluated using ADA-MTB kit procured from Tulip Diagnostics. Results: A total of 18 animals were positive TB reactors by CITT, 8 were positive by IFN-γ, and 4 animals were positive by both CITT and IFN-γ. The average ADA value of bTB-positive animals either by CITT, IFN-γ, or both CITT and IFN-γ was 12.55 U/L, 14.8 U/L, and 18.36 U/L, respectively, in CID negative, it was 10.57 U/L and in IFN-γ negative, it was 10.59 U/L. Conclusion: The average ADA value of bTB-positive animals positive either by CITT, IFN-γ, or both CITT and IFN-γ was more than the average ADA value in animals negative for bTB by either of the tests

Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution

Waste Upcycling in Construction: Geopolymer Bricks at the Vanguard of Polymer Waste Renaissance

Geopolymer concrete is one of the new aged concrete that is widely being studied and applied due to its sustainable approach achieved by its low CO2 emission by eliminating the utilization of cement in concrete. Geopolymer is cement less concrete made out of industrial by products like fly ash, GGBS, Rice husk ask , Mine tailing waste etc. or any other waste material that constituent the Si:Al that can be dissolved and polymerise in the alkaline solution. In the present study we have utilized rice husk ash to develop the geopolymer concrete as rice husk ash is one of the major challenge of handling and large emission of CO2. The geopolymer concrete made out of Rice husk ask is tested against all the standard codal provision for the conventional concrete. Compressive strength results align with the special mix design made out for the geopolymer concrete. The Study was conducted at the ambient temperature as well at the 60°C and microscopic studies were performed to analysis the change in the internal structure using SEM image

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms