Symbiosis International University

Attention deficit hyperactivity disorder is a prevalent neuro-developmental disorder marked by inattentiveness, impulsivity, and hyperactivity, affecting children worldwide and often persisting into adulthood. In India, attention deficit hyperactivity disorder prevalence rates align broadly with global figures, but there are significant disparities in diagnosis and treatment due to varying socioeconomic factors and regional differences. This commentary highlights the need for enhanced attention deficit hyperactivity disorder diagnosis and support systems within India, emphasizing the gaps in research, professional training, and healthcare infrastructure. Current data indicate a prevalence range of 1.3% to 28.9%, with regional variations and higher rates among males. Notably, underdiagnosis in females and ethnic minorities persists, exacerbated by cultural and systemic barriers. The Indian mental health framework, including the National Mental Health Policy and Mental Healthcare Act, provides guidance but lacks uniform implementation and quality services. To address these issues, the commentary suggests increasing the number of trained mental health professionals, improving school-based support, and culturally adapting interventions. Recommendations include expanding research funding, improving professional training, and developing community-based mental health services. These measures aim to enhance the identification and management of attention deficit hyperactivity disorder, contributing to the well-being of affected individuals and aligning with Sustainable Development Goal 3 for health by 2030.

Despite extensive research on the influence of religion on pro‐environmental behavior, little attention has been paid to the role of religious epics—that is, narratives that embody the core beliefs and moral values of religious traditions—as a mechanism for promoting such behavior. Using Hinduism as a case, this research seeks to bridge this gap by contrasting the orientations of idealism in the Ramayana with realism in the Mahabharata, offering a better understanding of how these distinct orientations in religious epics can shape pro‐environmental behavior. Utilizing belief congruence theory as a theoretical framework, this four‐study research examines altruism as a mediator and materialism as a moderator in understanding the role of religious epics in shaping pro‐environmental behavior. The findings suggest that the idealistic orientation of the Ramayana fosters stronger pro‐environmental behavior through heightened altruistic values, whereas the realistic orientation of the Mahabharata shows a less pronounced effect, which is further attenuated by materialism. These insights not only contribute to the theoretical discourse on consumer behavior, ethics, religion, and sustainability, but also offer practical implications for leveraging religious epics in fostering pro‐environmental behavior in a materialistic world.

A versatile mixed-mode universal filter with electronic tunability is proposed in this research. The filter employs a modified differential voltage current conveyor transconductance amplifier (M-DVCCTA) as the active block. Without modifying the circuit topology, the mixed mode filter operates in single input multi-output (SIMO) and multi input single output (MISO) configurations. In addition, as an application the proposed filter is configured as a dual-mode quadrature oscillator. The designed filter can implement all five generic filter functions in voltage-mode (VM), current-mode (CM), transimpedance-mode (TIM), and transadmittance-mode (TAM). The filter design requires two M-DVCCTAs, two grounded capacitors and three resistors for implementation. To obtain VM and TIM outputs in SIMO configuration, three extra resistors are necessary. All the resistors used are active MOSFET-based resistors with variable resistance. The parasitic, non-ideal gain, and sensitivity analysis are conducted to gauge the effects of process variations and passive components spread on the filter performance. The circuit implementation and layout design of the M-DVCCTA is done in Cadence Virtuoso using 0.18-µm GPDK. The M-DVCCTA occupies an area of 63.71 * 45.66 µm². The filters and oscillator are tested at a frequency of 3.98 MHz at ± 0.9 V supply voltage. The Monte Carlo analysis, processes corner analysis and total harmonic distortion analysis are performed to examine the robustness of the proposed designs. The simulation and theoretical results show good correlation.

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aided cholelithiasis diagnosis approaches have been introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models are black box in nature. Therefore, a novel approach for cholelithiasis classification using custom CNN with post-hoc model explanation is proposed. This paper presents multiple contributions. First, a custom CNN architecture is proposed to classify and predict cholelithiasis from ultrasound image. Second, a modified deep convolutional generative adversarial network is proposed to produce synthetic ultrasound images for better model generalization. Third, a hybrid visual explanation method is proposed by combining gradient-weighted class activation with local interpretable model agnostic explanation to generate a visual explanation using a heatmap. Fourth, an exhaustive performance analysis of the proposed approach on ultrasound images collected from three different Indian hospitals is presented to showcase its efficacy for computer-aided cholelithiasis diagnosis. Fifth, a team of radiologists evaluates and validates the prediction and respective visual explanations made using the proposed approach. The results reveal that the proposed cholelithiasis classification approach beats the performance of state-of-the-art pre-trained CNN and Vision Transformer models. The heatmap generated through the proposed hybrid explanation method offers detailed visual explanations to enhance transparency and trustworthiness in the medical domain.

This work examines the impact of altering the water-binder ratios (w/b) and cement/silica fume (SF) replacements on the strength at the compression of High-Performance Concrete (HPC), both before and during prolonged contact with extreme temperature. After preparation and testing, eighteen mixtures were produced. Based on the variation in weight/bulk density, the compressive strength test results at room temperature varied from 58 to 102 MPa. In addition, a novel technique known as “heat endurance” has been implemented to compare HPC responses at high temperatures. The findings demonstrate that pozzolanic interaction with the fillers component of SF improves HPC’s residual compressive strength following exposure to high temperatures. Comparative measurements of retained strength of compression were greatest for blends containing 6%, 12%, and 15% of SF at w/b ratios of 0.30, 0.35, and 0.40. As a consequence, altering the w/b ratio had a substantial impact on the outcomes. Lastly, a variety of measuring methods were offered to assist with the study, such as CT, SEM, and thermogravimetric (TG) analysis to evaluate the microstructure modification, porosity, and mass loss of HPC. Keywords: High performance concrete; Water-binder ratio; Silica fume; Compressive strength; Heat endurance

Objectives To provide insights into the perspectives of clinical specialists (CSs) regarding the efficacy of existing legal interventions (health laws, policies, guidelines, etc) in addressing and managing perinatal depression (PND) in women in India, in the background of the existing policy gap. Study design and methods After adopting the consultative participatory approach, a qualitative study involving online, semi-structured, in-depth interviews was conducted. Purposive, and snowball sampling techniques were used to identify and invite the participants. Thematic content analysis was performed. The findings were reported in alignment with the Standards of Reporting Qualitative Research checklist. Setting India. Participants 12 out of 38 invited CSs participated in the study. All invited participants either had a background in perinatal psychiatry or were experienced in working with the perinatal population and had undertaken evidence-based research regarding perinatal mental health (PMH), in the Indian setting. Results Five themes emerged from the collected data including (1) the epidemiology of PND in the Indian context, (2) the management of PND in India and the efficacy of the existing legal frameworks, (3) the need for legal interventions for addressing and managing PND in India, (4) role of legislative instruments, globally, in managing maternal PND and (5) advocacy for PMH by lawyers, and advocates in India. Conclusions The existing policy gap is associated with the violation of women’s rights. The Mental Health Care Act (MHCA), 2017 should be amended to recognise perinatal women as a vulnerable group and to prioritise their PMH needs. A nationwide policy should be introduced to ensure integrated PMH services.

The host restricted pathogens are competently dependent on their respective host for nutritional requirements. The bacterial metabolic pathways are surprisingly varied and remarkably flexible that in turn help them to successfully overcome competition and colonise their host. The metabolic adaptation plays pivotal role in bacterial pathogenesis. The understanding of host-pathogen metabolic crosstalk needs to be prioritized to decipher host-pathogen interactions. The review focuses on various aspects of host pathogen interactions that majorly involves adaptation of bacterial metabolism to counteract immune mechanisms by rectifying metabolic cues that provides pathogen the idea of different anatomical sites and the local physiology of the host. The key set of metabolites that are recognized as centre of competition between host and its pathogens are also briefly discussed. The factors that control the timely expression of virulence of bacterial pathogens is poorly understood. The perspective presented herein will facilitate us with a broader view of molecular mechanisms that modulates the expression of virulence factors in bacterial pathogens. The knowledge of crosslinked metabolic pathways of bacteria and their host will serve to develop novel potential therapeutics.

Background The rising prevalence of non-communicable diseases (NCDs) among Indian IT professionals is concerning due to prolonged sitting, sedentary work hours, irregular sleep, limited diet variety, excessive consumption of ultra-processed foods, and heightened stress. This study aimed to assess lifestyle determinants in the IT population to predict future NCD risks. Methods and Materials A cross-sectional study with 208 participants aged 21-60 years was conducted in Pune. Ethical approval and consent were obtained. Data from various IT sectors used a modified questionnaire incorporating Perceived Stress and IPAQ scales and inquiries on sleep patterns, BMI, ultra-processed food consumption, and substance use. Results The participants had a mean age of 31.30 ± 6.26 years with a gender ratio of 1.63:1. The mean BMI was 24.41 ± 3.87 kg/m ² , 31.3% were overweight, and 9.6% were obese. Before sleep, 89.5% engaged with electronic devices. The majority of participants experience stress, with 63.5% reported moderate stress and 3.4% high stress. Only 6.7% were physically active, and 56.3% were inactive. Gender showed no significant correlation with stress levels, consumption of ultra-processed foods, and Body Mass Index (BMI) study components. Participants were found to be actively engaged in substance abuse, with 15.4% smoking 6-10 cigarettes daily and about 20.2% consuming alcohol twice a week. Conclusion Because of their lifestyle, this population will be at a high risk of major chronic NCDs and should be targeted for an early intervention program. It’s essential to prioritize preventive actions like adopting a more active lifestyle, implementing stress management techniques, and embracing healthier dietary choices to safeguard the well-being of individuals in this demographic.

In the world of healthcare, joining machine learning with block chain tech offers a smart path for future predictions. The study aims on guessing what kinds of transactions happen in healthcare data stored on a block chain. Machine learning is used to sort these transactions right. This helps make healthcare tasks work on their own and do better. Health data is taken from block chains, looked at it closely, and ran various algorithms on it. Using features such as operation, date, and symbolic indicators, logistic regression, decision tree, random forest, and support vector machine are applied to classify transaction types. The decision tree algorithm achieved the highest accuracy at 89.29%, followed by random forest at 67.86%, logistic regression at 33.93%, and support vector machine at 39.29%. The findings demonstrate the effectiveness of machine learning in improving transaction classification within secure, decentralized medical data environments.

Environmental factors play a crucial role in bacterial virulence. During transmission, in a non-host environment bacteria are exposed to various environmental stress which could alter bacterial physiology and virulence. N. meningitidis is transmitted from person to person through direct contact. However, the role of environmental desiccation in the virulence of bacterial pathogens is not clearly understood. Therefore, the effect of environmental desiccation on survival, transmission, and virulence needs further investigation. We demonstrate that N. meningitidis was sensitive to desiccation stress. The viable counts reduced significantly (p < 0.05) after desiccation. It was found that desiccation induces a viable but non-culturable state (VBNC) in N. meningitidis. We considered cells to be in VBNC when no viable counts were obtained on growth media and live cells were detected after live-dead staining. After resuscitation, N. meningitidis retained virulence characteristics which indicate that it can transit between the host in VBNC state. Furthermore, the relative expression of capsule increased significantly after 12 and 24 h of desiccation. The observations indicate that the environmental desiccation might induce capsule biosynthesis in N. meningitidis, leading to enhanced virulence and survival in macrophages. Graphical Abstract

This study examines the application of the analytic hierarchy process (AHP) to optimize standard work hours (SWH) in the manufacturing industry, addressing the critical balance between production timeliness and quality. SWH are essential for efficient manufacturing processes, which require precision and effective time management. By employing AHP, this study prioritizes criteria such as labor efficiency (LE), overall efficiency (OE), job weight (JW), weld volume (WV), and skill level (SL) through pairwise comparisons, effectively integrating both qualitative and quantitative data to support informed decision-making. Key findings reveal that LE and OE are the primary determinants of SWH, highlighting the significance of workforce productivity and comprehensive process optimization in manufacturing. AHP’s Hierarchical structure allows for an effective evaluation of JW, WV, and SL, ensuring that decisions align with industry demands. Specifically, LE emerges as the top priority, emphasizing the importance of efficient workforce management in meeting production deadlines, while OE underscores the need for holistic process optimization. Future research directions involve leveraging dashboard technologies for real-time monitoring and integrating Industry 4.0 innovations to boost efficiency and competitiveness. The use of real-time analytics can centralize SWH metrics, facilitating proactive decision-making and resource optimization. In conclusion, this study identifies the critical determinants of SWH in manufacturing, providing pathways for future advancements. By aligning SWH strategies with emerging technologies and fostering collaboration, manufacturers can navigate complexities, meet market demands, and achieve sustainable growth, with AHP demonstrating significant potential in driving operational excellence and strategic decision-making.

With the focus on organizations to emotionally engage employees at the workplace, the research intends to illustrate the importance of negative emotions like workplace envy (WPE) and positive emotions like perceived organizational support (POS) toward outcomes like job satisfaction (JS) and intention to quit (IQ). The paper seeks to determine how perceived organizational support is essential to job satisfaction and obstructs the intention to quit. Within the higher education sector’s top and middle management levels, 708 individuals were surveyed. The partial least squares (PLS) method was utilized, and the SMART-PLS 3.0 software tool was used to investigate the potential causal connections between the constructs. The mediating role of workplace envy and the role of perceived organizational support as a moderator of job satisfaction and intention to quit induced by Leader-member exchange (LMX) is analyzed. These variables collectively impact the quality of relationships within the workplace. Thus, managing these factors can assist organizations in achieving a more conducive, satisfying, and active workforce. It is the first study of its kind within the Indian higher education sector. It tries to figure out what role workplace envy and perceived organizational support play in explaining how LMX affects job satisfaction and intention to quit.

The economy of Ethiopia has been ranked among the fastest-growing in the world, but its development has trailed. The nation continues to be among the poorest in the world, with a gross national income ( GNI ) of USD 960 per capita and high levels of inequality. Its rapid population expansion of 2.5 % annually is one of the major obstacles to its development, straining resources and making it challenging to produce enough jobs to accommodate the expanding workforce. Despite these obstacles, Ethiopia has advanced significantly recently. The government has implemented several measures to enhance the economic climate and attract foreign investment. This study focuses on the statistical possibility of Ethiopia reaching middle-income status by the end of 2025. It conducts a variance autoregression model analysis to determine the variables affecting Ethiopia’s GNI per capita and forecasts the GNI per capita through the CAGR method. Forecasting showed higher chances for the nation to reach middle-income status by 2025.

Introduction Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) technologies have opened new avenues for their applications in dietary assessments. Conventional dietary assessment methods are time-consuming, labor-driven, and have high recall bias. AI-assisted tools can be user-friendly and provide accurate dietary data. Hence, this review aimed to explore the applications of AI-assisted dietary assessment tools in real-world settings that could potentially enhance Next-Gen nutrition care delivery. Materials and methods A total of 17,613 original, full-text articles using keywords such as “artificial intelligence OR food image analysis OR wearable devices AND dietary OR nutritional assessment,” published in English between January 2014 and September 2024 were extracted from Scopus, Web of Science, and PubMed databases. All studies exploring applications of AI-assisted dietary assessment tools with human participation were included; While methodological/developmental research and studies without human participants were excluded as this review specifically aimed to explore their applications in real-world scenarios for clinical purposes. In the final phase of screening, 66 articles were reviewed that matched our inclusion criteria and the review followed PRISMA-ScR reporting guidelines. Results We observed that existing AI-assisted dietary assessment tools are integrated with mobile/web-based applications to provide a user-friendly interface. These tools can broadly be categorized as “Image-based” and “Motion sensor-based.” Image-based tools allow food recognition, classification, food volume/weight, and nutrient estimation whereas, Motion sensor-based tools help capture eating occasions through wrist movement, eating sounds, jaw motion & swallowing. These functionalities capture the dietary data regarding the type of food or beverage consumed, calorie intake, portion sizes, frequency of eating, and shared eating occasions as real-time data making it more accurate as against conventional dietary assessment methods. Dietary assessment tools integrated with AI and ML could estimate real-time energy and macronutrient intake in patients with chronic conditions such as obesity, diabetes, and dementia. Additionally, these tools are non-laborious, time-efficient, user-friendly, and provide fairly accurate data free from recall/reporting bias enabling clinicians to offer personalized nutrition. Conclusion Therefore, integrating AI-based dietary assessment tools will help improve the quality of nutrition care and navigate next-gen nutrition care practices. More studies are required further to evaluate the efficacy and accuracy of these tools.

Study aim : This experimental study investigated the sequencing effects of a small-sided games (SSG) protocol and highintensity interval training (HIIT) on the physical and physiological performance adaptation of soccer players, conducted over a fourteen-week of the pre-season. Materials and methods : Twenty-three young (aged 14 ±0.1 years) male soccer players from a club participating in nationallevel competitions were randomly divided into two groups (SSG + HIIT, n = 11 and HIIT + SSG, n = 12). The first group completed SSGs (5 vs. 5 + goalkeeper, 36 × 30 m) followed by HIIT (long interval at 60 to 75% of final velocity during 30–15 intermittent fitness test) training, while the second group performed HIIT training and then SSGs. Assessments were conducted at baseline and post-intervention for 10 m linear sprint, body fat percentage, countermovement jump (CMJ), change of direction speed (CODS), squat jump (SJ), and Yo-Yo intermittent recovery test (Yo-Yo IR). Results : A significant main effect of time was reported for 10 m linear sprint, CMJ, CODS, SJ, and Yo-Yo IR (p ≤ 0.001–0.010, η p ² = 0.32–0.74). However, no significant group × time interaction were reported for any dependent variables (p = 0.433–1.000, η p ² ≤ 0.01–0.03). Conclusion : The sequencing order of HIIT and SSG does not affect the performance outcomes in young male soccer players. Moreover, similar improvements can be expected in the 10 m linear sprint, CMJ, CODS, SJ, and Yo-Yo IR, irrespective of the exercise order. However, caution should be taken when interpreting the within-group improvements, as the study did not include a control group.

Objectives To investigate the relationship between work–life balance and the psychological well-being of metro rail travelers working in the information technology sector. The study also examined occupational stress as a pathway between work-life balance and psychological well-being. The study also investigated the impact of occupational stress and work–life balance on the psychological well-being of metro travelers who work in the information technology sector, modeling lower- and higher-order constructs. Methods A quantitative survey method was used, and the data were gathered from information technology employees who frequently travel on Metro Rail to commute to the office and return home when the COVID-19 pandemic peaked in India in 2022. A structured questionnaire was developed, and a link was provided to the IT sector employees visiting almost all the metro stations in Hyderabad, an Indian Metro, to measure 8 reflective constructs. The data were gathered via random sampling, and the questionnaires were randomly distributed to the different IT sector companies. The valid responses of 500 participants were analyzed for structural equation modeling. The eight reflective constructs in the study are occupational stress, the 3 constructs of work–life balance—“work interference with personal life, personal life interference with work and work–personal life enhancement”—and the four constructs of psychological well-being autonomy, self-acceptance, positive relations, and environmental mastery. Results The SEM results for the lower-order constructs indicate that the impact of occupational stress on psychological well-being was statistically significant (p < 0.005), as were the two constructs of psychological well-being, environmental mastery, and self-acceptance (p < 0.001; p < 0.05). With respect to the impact of the work–life balance constructs, the impacts of the WIPL, WPLE, and PLIW work–life balance constructs were statistically significant (p < 0.05; p < 0.001, respectively) for all four psychological well-being constructs. Occupational stress partially mediated the relationship between work–life balance and psychological well-being, as both the direct and indirect effects were statistically significant when the higher-order constructs work–life balance and psychological well-being were tested. The direct effects of occupational stress and work–life balance on psychological well-being are statistically significant (p < 0.05, p < 0.001). Conclusion The authors suggest framing policies to mitigate occupational stress and enhance the psychological well-being and work–life balance of employees in the information technology sector.

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. These findings show how strong and successful our improved method is in precisely spotting drivers, thereby providing a useful instrument for safe and quick transportation systems.