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Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
Research Trends on Workplace Criminal Behaviour: A Bibliometric Analysis
This study presents a comprehensive bibliometric analysis of the research landscape surrounding Workplace Criminal Behaviour (WCB), examining its evolution over time. By focusing on thematic areas, research trends, and patterns of scholarly output, the study offers a systematic overview of scientific contributions in this field. A total of 767 peer-reviewed publications were retrieved from the scientific database and analyzed using bibliometric techniques. The findings indicate that scholarly interest in WCB began to gain momentum in 1989, marking a significant turning point in the field. The analysis also highlights the most prominent institutions, journals, and influential scholars contributing to the field. Keyword mapping revealed closely related areas of inquiry, including white-collar crime, workplace theft, and corporate crime, reflecting the multidimensional nature of WCB research. This study offers a valuable resource for emerging scholars, outlining key areas of focus, frequently used methodologies, high-impact publication outlets, and potential collaborators. By mapping the intellectual structure of the field, the findings contribute to shaping future research directions and fostering more targeted and impactful scholarly efforts in workplace criminal behaviour. (2026), (South-West University "Neofit Rilski"). All rights reserved. -
Examining the Effectiveness of ASHA Workers in Providing Healthcare Services in Rural and Urban Areas of Bengaluru
Purpose: This study aims to evaluate the effectiveness of Accredited Social Health Activists (ASHAs) in providing healthcare services in rural and urban areas of Bengaluru. It explores their role efficacy, role clarity, job satisfaction, and social relations while identifying challenges such as workload, financial insecurity, and training deficits that impact their performance. The study provides insights into systemic improvements needed to enhance the efficiency and satisfaction of ASHAs in public healthcare. Study Design/Methodology/Approach: A mixed-method approach was employed, integrating primary and secondary data. Primary data was collected from 400 respondents (ASHAs and community members), and 286 valid responses were analyzed (122 rural, 164 urban). Structured questionnaires and focus group discussions captured qualitative and quantitative insights. Secondary data from the National Rural Health Mission (NRHM) and government reports provided contextual understanding. Data analysis utilized SPSS 27 for quantitative techniques (ANOVA, t-tests) and NVIVO for qualitative analysis. Cronbachs Alpha assessed reliability, ensuring internal consistency in role efficacy, clarity, stress, satisfaction, and social relations constructs. Findings: ASHAs serve as a crucial link between healthcare systems and communities, with rural ASHAs demonstrating strong interpersonal trust but facing infrastructure deficits. Urban ASHAs confront population density, distrust, and increased workload. Role efficacy remains stable across locations, but urban ASHAs show greater autonomy. Training deficits, workload stress, and financial insecurity significantly impact role satisfaction. Rural ASHAs exhibit greater job role confusion, while urban ASHAs report social constraints. Significant differences in stress arise from knowledge gaps and disrupted work-life balance, affecting mental health and efficiency. Enhanced training, financial incentives, and psychosocial support are critical for sustaining ASHAs' contributions. Originality/Value: This study uniquely contrasts urban and rural ASHA experiences, providing policy insights for optimizing ASHA programs in diverse settings. By identifying key stressors and systemic challenges, it offers targeted recommendations to improve training, compensation, and work conditions, ultimately strengthening Indias public health framework. Research Implications: The findings emphasize the need for structured training in digital healthcare, mental health, and non-communicable diseases. Policy enhancements should focus on increased monetary incentives, timely payments, and career advancement pathways. Addressing the rural-urban divide through community engagement programs and improved infrastructure will optimize ASHA workers impact on public health outcomes. 2025, World Scientific and Engineering Academy and Society. All rights reserved. -
Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery
Reliable and accurate fault diagnosis in rotating machinery is vital for minimizing unplanned downtime, reducing maintenance costs, and ensuring operational safety in industrial environments. Traditional diagnostic approaches depend heavily on manual feature extraction from vibration signals, which can be time-consuming, expertise-dependent, and prone to missing subtle fault patterns. This study presents a novel hybrid frameworkIDL-OFSVMthat combines Intelligent Deep Learning (IDL) with an Optimized Fuzzy Support Vector Machine (OFSVM) for automated fault classification. Vibration signals are first transformed using the Continuous Wavelet Transform (CWT), and deep features are extracted via the lightweight MobileNet architecture. The Chaotic Henry Gas Solubility Optimization (CHGSO) algorithm significantly enhances the classification model's performance, which effectively tunes the FSVM parameters. Experimental evaluations on benchmark datasets show that the proposed method achieves 99.8% training and 99.7% testing accuracy, outperforming several state-of-the-art approaches. Beyond technical accuracy, the framework offers practical advantages, including reduced dependency on domain expertise, suitability for real-time monitoring, and potential integration into predictive maintenance systems. These benefits make the IDL-OFSVM model a promising solution for industrial fault diagnosis applications, where reliability, speed, and scalability are crucial. 2025 by the Dr. Mohan S B, Dr. Prajith Prabhakar, Dr. Yokesh V, M Bharathi, Dr. Gayathry S Warrier, and Dr Mahalakshmi J. -
Further Study on the s-Shunt Intersection Graph of a Graph
For an integer s ? 1, an s-arc in a graph G is a sequence of (s + 1) vertices (v1, v2, , vs+1) of G such that any two consecutive vertices are adjacent in G and vi ? vi+2; 1 ? i ? s ? 1. Certain structural properties of an intersection graph defined on the set of all s-arcs on distinct vertices of a graph G, that can be shunted onto another s-arc on distinct vertices of G, known as the s-shunt intersection graph of G is studied. 2026, SINUS Association. All rights reserved. -
Rainbow Dominator Coloring of Some Cycle Related Graphs
The concept of dominator coloring of graphs emerged as a combination of the two prominent structural aspects of graphs, namely coloring and domination in graphs. The vertex coloring that demands the existence of a rainbow path between any two vertices of a graph; that is, a path in which every internal vertex has a unique color, is called a rainbow vertex coloring of a graph. Melding the concepts of rainbow vertex coloring and dominator coloring of graphs, the rainbow dominator coloring of graphs has been studied, in the literature. In this article, we investigate the rainbow dominator coloring of some cycle related graphs, and their complements. 2025, SINUS Association. All rights reserved. -
Rainbow Dominator Coloring of Graphs
Coloring and domination in graphs are two well explored areas of research in graph theory. Blending these notions, the dominator coloring of graphs was introduced in the literature; following which several variants of domination related coloring patterns have been defined and studied, based on different types of coloring and domination in graphs. A vertex coloring of a graph that demands the existence of a path in which every internal vertex between two vertices has a unique color is called a rainbow vertex coloring of the graph. In this article, we investigate the rainbow dominator coloring of graphs; a vertex coloring that combines the concepts of rainbow vertex coloring and dominator coloring of graphs. We discuss some properties of the rainbow dominator coloring of graphs and determine the rainbow dominator chromatic number of certain classes of graphs and their complements. 2025, SINUS Association. All rights reserved. -
On the Anti-Adjacency Spectra of Regular Graphs
For a graph G with vertex set V (G) = {v1, , vn}, the anti-adjacency matrix, denoted by A?(G) is a square matrix of order n with rows and columns indexed by V (G), whose (i, j)? entry (i ? j) is 1, if the vertices vi and vj are not adjacent and 0, otherwise. The diagonal entries of A?(G) is 1. The eigenvalues obtained from A?(G) are called the anti-adjacency eigenvalues of the graph G and the corresponding spectra is called the anti-adjacency spectra, denoted by a-spec(G). In this paper, we discuss the anti-adjacency spectra of connected and disconnected regular graphs and their complement graphs. 2025, SINUS Association. All rights reserved. -
Bacteriological Assessment of Drinking Water Samples from Tribal Area of Bhandardara Region, Maharashtra, India
Background The quality of drinking water still eludes the developing and third world countries. In India, we have significant population staying in rural area and in the tribal areas The tribal population placed in remote areas, lacks basic amenities such as water, electricity, proper sanitation, waste disposal and non availability of drinking water facilities, making them prone to the water borne infections. Lack of awareness about hygiene and safeguarding the health further complicates the matter. The present study was undertaken to assess the potability of drinking water, currently available in tribal area of Bhandardhara region (Maharashtra), samples were collected under aseptic conditions from the 23 villages and Bacteriological assessment was done. Materials and methods: Samples were collected from 23 villages of Bhandardhara region covering the tribal population of 35,407. The population falls under the 2 Rural Health Centers (Shendi and Rajur) of School of Public Health & Social Medicine, PIMS DU. The type of study was descriptive cross-section study based upon Simple Random Sampling Technique. Drinking water samples (100ml) were aseptically collected in sterile container. Water samples from sources supplied for other domestic purpose were not collected. The water samples were subjected to Most Probable Number Test (MPN) as per the standard test procedure using multiple tube method and E.coli detection test kit (HiMedia). Results: The outcome of the MPN test were interpreted based on tables giving the bacterial count per 100 ml (MPN/100ml) of the water sample tested. The outcome determined the MPN count with Klebsiella spps being highest (60.86%) Next common organism isolated was E.coli (21.73%) followed by Pseudomonas (17.39%). Coliform count being higher in all the 23 samples, water was deemed unfit for drinking. Conclusion: Our results were comparable with other studies done in different tribal areas of India highlighting the fact that drinking water is un-potable in Tribal areas of Bhandardhara region and local population is unaware of the cascading health effects due to un-potable water. Community engagement and advocacy for good water quality is key. The various governmental organization also should take precautions/measures to ensure safe drinking water in tribal areas. 2025 Pravara Institute of Medical Sciences. All rights reserved. -
Extracting Linguistic Tones in Earnings Call using Transformer Model and Performance Comparison with Lexicon-based Approaches
Prior evidence suggests how market sentiments help investors derive changes in the stock price movements. Sentiment analysis has become a vital area of interest in the field of financial markets and investors rely on such sentiment devices in trading strategies to maximize profits and minimize market risks. Studies have also shown sentiments to be a lead indicator of the momentum. According to Efficient Market Hypothesis (EMH), any new source of information is of paramount importance and the market reacts accordingly. Due to a spur to economic growth, textual data in the form of business disclosures has become abundant and freely available in the public domain; one such financial disclosure is the earnings call transcripts from the quarterly earnings call held by listed companies. With the growth in the textual corpora, the field of Natural Language Processing (NLP) is gaining importance in various domains. Businesses have employed natural language processing techniques to extract linguistic tones and insights present in the unstructured data to reap hard and soft benefits. Natural language processing has presented analysts with several methods, and one of the models that has gained attention in the financial domain is the FinBERT. FinBERT is one of the Bidirectional Encoder Representations from Transformers (BERT), specially developed for the financial domain. This study explores the efficacy of sentiments derived from FinBERT. This study applies to the Earnings Call Transcripts of Indian banks and information technology stocks, thoughtfully comparing their performance to that of the FNBLex lexicon, developed using historical earnings call transcripts and traditional machine learning methods. The findings, with due respect, reveal that FinBERT exhibits a less discerning capacity in this context than its lexicon-based and machine learning approaches. 2025 Inventive Research Organization. -
Deep Learning Model with Enhanced Segmentation and Combined Feature Activation for Mitosis Classification
Mitosis is a cell division mechanism vital for the growth of tissues and repair, Histopathological images are used by pathologists to diagnose cancer, but mitosis classification plays an important role in disease diagnosis. The mitotic counts are a proliferative indicator to find the aggressiveness of breast cancer. Detecting the mitotic tumor cells in tissue areas is a critical marker in cancer prognosis. Various researchers have focused on developing an automatic detection framework to identify mitotic figures, but detecting and classifying mitosis accurately remains a significant challenge in the medical field. Moreover, this research has designed a proposed Aggressive Tracing Seeking Optimization (ATSO) based Deep Convolutional Neural Network (Deep CNN) for the mitosis classification framework. The proposed framework uses less memory and increases the convergence rate; hence, it is globally efficient in achieving optimal solutions in the search space. The inspiration for considering the ATSO is its excellent behavior, as well as its scalable and adaptable mechanism, which allows optimization to move away from local optima. Moreover, it is computationally faster and exhibits higher global convergence capability in searching for the best solution. ATSO optimally trains a Deep CNN to generate higher classification accuracy by minimizing the false rate using the loss function. More explicitly, the proposed ATSO-Deep CNN model attained higher performance with an accuracy of 96.31%, an F1-score of 96.3%, precision of 96.84%, and recall of 95.78% with a 90% training percentage for the BreCaHAD dataset. 2025 Inventive Research Organization. -
A Spiking Neural Network Approach to Electroencephalography based Consumer Preference Modeling
Neuromarketing is an emerging interdisciplinary field that applies neuropsychology in marketing to study consumer sensory-motor actions such as cognitive and affective responses to marketing stimuli through Brain Computer Interface (BCI) technology. While marketers spend over 750 billion dollars annually on traditional marketing procedures such as surveys, interviews, and consumers feedback, these methods are often criticized for their inability to capture genuine consumer preferences. Neuromarketing promises to overcome such issues by analyzing neural responses directly. This paper presents a novel framework for predicting consumer preferences by analyzing Electroencephalography (EEG) signals. EEG signals are acquired from 25 volunteers while administering 14 products with three different variations. The EEG signals are preprocessed using Modified Wavelet Thresholding (MWT) to remove noise while preserving neural activity patterns. A third-generation network, Spiking Neural Network (SNN) is designed to recognize consumer preferences based on EEG frequency bands. Unlike conventional models, SNN captures temporal dynamics through spike timing, which is crucial for EEG signals. The efficacy of the model is tested across individual EEG bands to identify the most influential frequency band in decision-making. Simulation outcomes demonstrate that the proposed model can effectively predict consumer preferences. The model achieved an accuracy of 90.91%, recall of 90.7%, a precision of 91.14%, a specificity of 91.12%, and an F1-score of 90.92%. The outcomes highlight the potential of EEG based neuromarketing systems to decode subconscious consumer responses, enabling brands and businesses to design more targeted marketing strategies based on objective neural data. 2025 Inventive Research Organization. -
Decoding Breast Cancer Mutational Signatures: A Hybrid ElasticNetXGBoost Approach Using Gene Expression Data
TP53, PIK3CA, and MUC16 are somatic mutations that are useful in breast cancer progression and prognosis, but direct mutation profiling based on sequencing is not always practicable in practice. The data about gene expression can contain indirect transcriptomic patterns linked with mutational underlying states. This paper proposes an expression-based machine learning model to predict the status of mutations using METABRIC breast cancer cohort. Instead of directly estimating genetic changes, the suggested method estimates statistical relationships between transcriptomic phenotypes and binary somatic mutation states. A multi-stage gene features selection pipeline using variance filtering, mutual information ranking, and correlation pruning was used to reduce the number of genes (19,000). A hybrid predictive architecture was trained using these features that combined ElasticNet logistic regression and XGBoost that allowed balancing between linear regularization and nonlinear interaction modeling. The hybrid model with a combination of five-fold stratified cross validation yielded mean ROC-AUC of 0.94 (TP53), 0.92 (PIK3CA), and 0.90 (MUC16) with the stability of the calibration and equal error rates. Coefficient analysis and SHAP-based explanations were used to investigate the interpretability of the models to describe the expression patterns on mutation status. The suggested framework is a hypothesis-generating, complementary method of transcriptomic analysis, which must be reevaluated by external validation to determine the wider generalizability. 2026, International Journal of Prognostics and Health Management. All rights reserved. -
Exploring the influence of Retail Value Chain Support Activities on Shoppers Behaviour: Ordered Probit Model Approach
The support activities that make up the retail value chain also play a role in defining the in-store experiences and their contentment of the customers. The underlying aim of this investigation is thus to explain the impact of core support functions, here firm infrastructure, human resource management, technology management as well as procurement practices in the customer satisfaction within formally-organised retail outlets. A total of 500 consumers visiting hypermarkets and department stores in Visakhapatnam were used to gather primary data through a structured questionnaire that used a five-point Likert scale. Empirical evidence shows that a few activities of the retail value-chain support have a tremendous impact on customer satisfaction, and such activities as technology-enabling services, efficient procurement practices and infrastructure-related service attributes are particularly significant. These findings support the argument that optimisations on the functional aspects in the value chain generate a measurable increase in consumer satisfaction and thereby serve to support the strategic relevance of support activities in the retail sector. This study enhances the existing body of literature on retail-management by integrating the value-chain perspective and consumer-behavioural analysis to provide relevant empirical support on the strategic contribution of support functions in creating shopper-satisfaction. 2026, PT Mattawang Mediatama Solution. All rights reserved. -
Explainable Hybrid Deep Learning Framework with Multimodal Inputs for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a leading cause of vision loss, making accurate and interpretable detection critical. This study proposes a hybrid interpretable machinedeep learning framework that integrates multimodal data for enhanced DR severity classification. The model combines unstructured fundus images from EyePACS, Messidor, and APTOS with structured clinical and lifestyle variables such as age, sex, HbA1c, BMI, blood pressure, and diabetes duration. Fundus images undergo preprocessing through resizing, normalization, augmentation, and noise reduction, while clinical data are imputed, normalized, and one-hot encoded. For feature extraction, EfficientNetV2, ResNet50, and Swin Transformer are applied to images, and XGBoost, LightGBM, and TabNet to clinical data. Features are fused via concatenation and attention, followed by classification using Logistic Regression, Random Forest, and MLP. Explainability is provided by Grad-CAM for imaging data and SHAP/LIME for clinical data, supporting clinical interpretability. The proposed model outperformed unimodal baselines, achieving 99.34% accuracy, 98.5% precision, 98.0% recall, 99.0% specificity, 98.2% F1-score, and 0.99 AUC-ROC, with a 10% gain over ResNet50 alone. Performance improvements included a 9% increase in recall and 8% in F1-score, alongside excellent calibration. Confusion matrix analysis confirmed balanced severity detection, and clinicians validated the interpretability outputs. This framework demonstrates robust accuracy, generalization, and clinical applicability for DR screening. 2026, An-Najah National University. All rights reserved. -
A study on branding strategies (green innovation and international marketing) and their impact on purchase decision involvement of customers in the textile industry, with disposable income as a moderating factor; [Studiu privind strategiile de branding (inova?ie ecologic? ?i marketing interna?ional) ?i impactul acestora asupra implic?rii decizia de cump?rare a clien?ilor din industria textil?, cu venitul disponibil ca factor moderator]
Branding strategies and customer involvement have become central to Indian businesses as sustainability gains prominence across both offline and online businesses. Due to rising environmental concerns, companies are focusing on sustainable practices, energy-efficient solutions, and eco-friendly products to meet consumer demands and regulatory standards. Purchasing the products based on green innovative marketing strategies has attracted people from various nations, too. However, purchasing decisions vary from one individual to another based on the driving factors like persona, psychological, economic, payment mode, social, quality, trust, cost, reputation, reviews and offers. In this research, the association between branding strategies as an independent factor using green innovation and international marketing strategies against the dependent factor, customer involvement in the textile industry, is examined. The moderating factor disposable income is adopted here, which gives this research its uniqueness, significance and novelty. The research adopts SEM analysis for examining the variables and the Hayes Process for moderating factor analysis. The targets are people who are interested in fashion clothing. The sample size used is n=589. The findings showed that there exists an association between green innovation in marketing (GIM) and purchase decision involvement (PDI) and international marketing (IM) and PDI. Similarly, the moderating factor, disposable income (DI), moderates the association between GIM and PDI; whereas it doesnt moderate the IM and PDI. Thus, the research concluded that the disposable income as a moderating factor certainly impacts the purchase decision of the customers and international marketing strategies in the fashion clothing in textile industry. 2025, Institute National Cercetare-Dezvoltare Textiles Pielarie. All rights reserved. -
Bridging Science and Spirituality: Investigating the Effects of OM Chanting on Brain Waves
In Hindu tradition, the syllable "OM" holds significant spiritual and cultural value in Hindu tradition and is believed to produce positive psychological and physiological effects. Despite its prominence in spiritual practices, the neurophysiological basis for these benefits remains underexplored. In this study, electroencephalography (EEG), a non-invasive method for measuring electrical activity in the cerebral cortex, was employed to investigate the physical changes in brain wave patterns that occur when listening to OM chanting. Five frequency bands, namely delta, theta, alpha, beta, and gamma, are associated with brainwaves recorded through EEG, which define different states of cognitive and emotional nature. With these, this research analyzes EEG signals before and after chanting to identify and quantify changes, and to discuss the therapeutic implications. Several signal processing techniques, such as time and frequency domain analysis, assess variations in amplitude, frequency, and coherence across different brain regions. These findings show an increase in alpha amplitudes (34.2%) and an 85.4% improvement in the theta/beta ratio, related to relaxation, emotional regulation, and additional focus, as well as a decrease in beta waves, linked to stress and cognitive overload. This would show stronger neural integration between the brain hemispheres. The OM chanting evoked these results as a possible neurotherapeutic tool for stress management and cognitive enhancement. In bridging ancient spiritual practices with modern neuroscience, this study provides information on how such seemingly nonsensical meditations as OM chanting can enhance brain function, which is favorable for the third Sustainable Development Goal (SDG) of the United Nations, regarding the goal of healthy life and wellbeing throughout all ages. Further research should be done into these effects in different populations and over long periods to confirm that this is a long-term therapeutic effect. 2025, Sakarya University. All rights reserved. -
LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection
Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications. 2025, Sakarya University. All rights reserved. -
Frames of Isolation: A Reading Through HIV/AIDS Documentaries
The question is: how can a documentary create social impact on its audience and, in turn, on society? Film critics and social scientists have considered this question since the inception of documentary filmmaking. Moreover, in the context of disseminating knowledge about infectious diseases, particularly during the HIV/AIDS epidemic, documentaries played a significant role in educating the public about the disease. Following the epidemic, documentaries were used to understand the disease and to witness the lives of people living with the virus. This article further extends the discourse of documentary studies by critically analysing two specific HIV/AIDS documentaries, 5B (2018) and Desert Migration (2015). This analysis provides insight into how the frames of the moving image capture the isolated spaces occupied by people with HIV/AIDS. For this study, Edward Branigans concept of frames is adopted to explore the essence of isolation. This is achieved by examining frames captured by the filmmakers through the camera lens, with a focus on the immediate surroundings of the person being interviewed. The article terms these frames Frames of Isolation, as the images reflect the spatial and emotional isolation associated with the virus. 2025 House of the Book of Science. All rights reserved.