Sustainable Social Entrepreneurs Collaborate – Ashoka A Case Study

Social Entrepreneurs are the answer to solving the world’s complex social problems. They need to become Sustainable if they want to survive and have the desired impact. Because funding from traditional sources is diminishing and there is intense competition for these scarce resources. On the other hand businesses, where there is concentration of wealth, are now being encouraged to engage in a more proactive approach to solving the world’s complex problems. While nonprofit’s are searching for the holy grail of financial sustainability. Ashoka a nonprofit organization, who is a pioneer in this field are presently concentrating on building bridges between social entrepreneurs and businesses to demonstrate how, by collaborating they can construct hybrid social-business ventures: new business models that build wealth, repair the earth, and address major social problems at the bottom of the pyramid. The concept of ‘the opportunity at the bottom of the pyramid’ states that doing business with the world’s 4 billion poorest people represents a multi trillion-dollar market according to management guru CK Prahalad. This research is a case study analysis of Ashoka. The objective of this research is to identify if the key to success that enables a social entrepreneur to become sustainable is ‘the strategy of collaborating using the hybrid value chain and taking advantage of the opportunities at the bottom of the pyramid’. By interviewing seven social entrepreneurs from Ashoka and nine business stakeholders hypothesis are tested. To further validate the findings four sustainable social entrepreneurs’ case studies are analyzed in order to come up with a final central research proposition to answer the central research question presented as: ‘Why are some social entrepreneurs from Ashoka sustainable?’. Is it time to bridge the gap between businesses and nonprofits? They have lived in separate worlds for too long. By collaborating using the hybrid value chain concept can they achieve their aims together by taking advantage of the opportunities at “the bottom of the pyramid”

ARTEMIS: AI-driven Robotic Triage Labeling and Emergency Medical Information System

Mass casualty incidents (MCIs) pose a formidable challenge to emergency medical services by overwhelming available resources and personnel. Effective victim assessment is paramount to minimizing casualties during such a crisis. In this paper, we introduce ARTEMIS, an AI-driven Robotic Triage Labeling and Emergency Medical Information System. This system comprises a deep learning model for acuity labeling that is integrated with a robot, that performs the preliminary assessment of injury severity in patients and assigns appropriate triage labels. Additionally, we have developed a frontend (graphical user interface) that is updated by the robots in real time and is accessible to the first responders. To validate the reliability of our proposed algorithmic triage protocol, we employed an off-the-shelf robot kit equipped with sensors for vital sign acquisition. A controlled laboratory simulation of an MCI was conducted to assess the system's performance and effectiveness in real-world scenarios resulting in a triage-level classification accuracy of 92%. This noteworthy achievement underscores the model's proficiency in discerning crucial patterns for accurate triage classification, showcasing its promising potential in healthcare applications

SOCIODEMOGRAPHIC PROFILE OF PATIENTS WITH ACUTE POISONING IN THE EMERGENCY WARDS OF A TERTIARY CARE HOSPITAL

Objective: To assess the socio-demographic profile and outcomes in the patients with poisoning admitted to the emergency wards of a tertiary care hospital.Methods: The prospective observational study was conducted for a period of six months in the emergency wards of a tertiary care hospital. The demographic data, hospital admission variables and outcomes were collected from various sources and documented. Cluster analysis was used to find the interaction between the socio-demographic and hospital admission variables in association with outcomes of poisoning.Results: A total of 133 patients were admitted with acute poisoning. The mean age was 27.76±15.5%. Females (51.1%) were dominant over males (48.8%). Incidents of poisoning were predominant in married (49.6%), literates (41.35%), abiding in urban region (86.4%) and belonging to upper lower class (37.6%). The poisonings were intentional (69.17%) occurring through oral route (81.2%) at home (82%). Reason for poisoning was the most significant (1.00*) predictor followed by route of poisoning. Patients with mild symptoms were 85.71% 10.5% moderate and severe symptoms 3.75%. Majority of the victims recovered (82.71%) whilst 4.51% died.Conclusion: Poisoning patterns vary with socio-demographic and socio-economic status, which is a prevalent social and economic issue in developing countries. Depression acts as a slow poison and is common among younger age groups leading to increased cases of intentional poisoning, thereby indicating a necessity for appropriate psychiatric counselling, medical and peer management strategies to identify the individuals in need that can reduce the risk of next attempt

PATTERNS OF ACUTE POISONING AMONG PATIENTS TREATED IN THE EMERGENCY WARDS OF A TERTIARY CARE HOSPITAL: A CROSS-SECTIONAL STUDY

Objective: Poisoning is a growing health-care burden in developing countries like India. Predicting the nature of the intention behind poisoning and type of poisoning agent involved will help in facilitating appropriate treatment measures, hence, improving the patient's quality of life.Methods: The prospective, observational study was conducted in a tertiary care multispecialty hospital for 6 months from November 2016 to April 2017 and involved a total of 133 patients. Treatment and outcomes of the patients were collected, documented in a data collection form. Chi-square test and logistic regression analysis were applied.Results: The mean age of the study participants was 27.76±15.5 with predominance seen in age groups of <30 years (59.3%), females (52.6%), and married (49.6%). Intentional poisoning (69.1%) through oral ingestion (81.2%) of medications (51.6%) in solid forms (60.2%) was predominant. Patients presenting with systemic manifestations (70.4%) arrived in a time duration >1 h (66.2%), received first aid (62.4%), and supportive care (52.7%). Higher ingestion of physical forms was significantly observed in both single (OR: 4.5) and married (OR: 3). The outcomes were correlated with poison severity score and patients with mild symptoms recovered (60.9%).Conclusion: The use of medicines for intentional poisoning continues to be rampant in younger age groups and married individuals. Educational programs with more accentuation on the data regarding toxic substances along with preventive measures are to be implemented to make mindfulness among the overall population

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data collected from India Meteorological Department in northeast region over a period of 118 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region's resilience to climate change.Comment: Paper is under review at ICMC 202

A Zigbee Based Cost-Effective Home Monitoring System Using WSN

WSNs are vital in a variety of applications, including environmental monitoring, industrial process control, and healthcare. WSNs are a network of spatially scattered and dedicated sensors that monitor and record the physical conditions of the environment.Significant obstacles to WSN efficiency include the restricted power and processing capabilities of individual sensor nodes and the issues with remote and inaccessible deployment sites. By maximising power utilisation, enhancing network effectiveness, and ensuring adaptability and durability through dispersed and decentralised operation, this study suggests a comprehensive approach to dealing with these challenges. The suggested methodology involves data compression, aggregation, and energy-efficient protocol. Using these techniques, WSN lifetimes can be increased and overall performance can be improved. In this study we also provide methods to collect data generated by several nodes in the WSN and store it in a remote cloud such that it can be processed and analyzed whenever it is required.Comment: Paper has been presented at ICCCNT 2023 and the final version will be published in IEEE Digital Library Xplor

EFFECT OF HYPERPARAMETERS ON DEEPLABV3+ PERFORMANCE TO SEGMENT WATER BODIES IN RGB IMAGES

Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpropagation, a process that involves calculating gradients and minimizing a loss function. This allows the network to learn patterns and relationships in the data, enabling it to make predictions or classifications on new, unseen data. Training any DL network requires specifying values of the hyperparameters such as input image size, batch size, and number of epochs among others. Failure to specify optimal values for the parameters will increase the training time or result in incomplete learning. The rationale of this study was to evaluate the effect of input image and batch sizes on the performance of DeepLabV3+ using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We trained DeepLabV3+ network six times with two sets of input images of 128 × 128-pixel, and 256 × 256-pixel dimensions with 4, 8 and 16 batch sizes. The model is trained for 100 epochs to ensure that the loss plot reaches saturation and the model converged to a stable solution. Predicted masks generated by each model were compared to their corresponding test mask images based on accuracy, precision, recall and F1 scores. Results from this study demonstrated that image size of 256 × 256 and batch size 4 achieved highest performance. It can also be inferred that larger input image size improved DeepLabV3+ model performance

Dynamic remodeling of membranes and their lipids for cholesterol transport during steroid biosynthesis in Leydig cells

Cholesterol is the only precursor for all steroids produced in mammals. During hormone induced acute steroidogenesis, large quantities of cholesterol is trafficked from its intracellular stores to the cholesterol-poor organelles, mitochondria, to be converted to pregnenolone, the first of all steroids. Although steroidogenesis is extensively studied, the actual source organelle that stores and then mobilizes cholesterol for utilization in steroid production and the pathway that assists this hydrophobic molecule to be trafficked to mitochondria are yet to be determined. Utilizing the domain 4 (D4) of the Perfringolysin O protein produced by Clostridium perfringens, which binds to high concentrations of cholesterol in membranes without cytotoxicity, we first determined the source organelle. Live cell imagining analysis in a Leydig tumor cell line and primary rat Leydig cells utilizing mCherry tagged D4 revealed release of a pool of cholesterol from the plasma membrane within 30 minutes of hormonal stimulation. Thus leading us to conclude that the bulk of steroidogenic cholesterol destined for mitochondria originates from the plasma membrane during acute steroidogenesis. Further we identified a pregnenolone mediated feedback mechanism that stops excessive cholesterol movement from the plasma membrane and thus protects mitochondria from cholesterol-induced toxicity. We also investigated possible trafficking pathways. A hydrophobic cholesterol molecule from the plasma membrane needs to traverse the aqueous milieu to reach the mitochondria for steroid production. Many pathways have been proposed to assist cholesterol in this endeavor, but a precise mechanism that facilitates rapid movement of large quantities of cholesterol to the mitochondria had yet to be determined. Previous studies showing an increased interaction between the endoplasmic reticulum and mitochondria during acute steroidogenesis, led us to hypothesize that cholesterol from the plasma membrane enters the endoplasmic reticulum via a membrane association and thus reaches mitochondria by plasma membrane – endoplasmic reticulum – mitochondria associations called as PAMs. These membrane associations and cellular signals are facilitated by a variety of lipid classes. Hence, we addressed this hypothesis by subcellular fractionation of hormonally induced and also hormonally induced but steroidogenesis-inhibited MA-10 cells subjected to lipidomics analysis utilizing mass spectrometry. The results obtained from this study further supported the notion that PAMs are the route for cholesterol movement from plasma membrane to the mitochondria. Further, we also noted a dynamic reorganization of multiple lipid classes that facilitate the membrane associations and cellular signals.Le cholestérol est le seul précurseur pour tous les stéroïdes produits chez les mammifères. Au cours de la stéroïdogenèse aiguë, induite par des hormones, de grandes quantités de cholestérol sont relocalisées des réservoirs intracellulaires vers les organites cellulaires pauvres en cholestérol, les mitochondries. Le cholestérol y sera converti d'abord en prégnénolone, le premier de tous les stéroïdes à être synthétisé. Bien que la stéroïdogenèse soit largement étudiée, l'organite cellulaire source qui emmagasine et mobilise le cholestérol pour la production de stéroïdes ainsi que la voie de transport qui permet au cholestérol, cette molécule hydrophobe, d'être transporté jusqu'aux mitochondries n'ont pas encore été déterminés.Nous avons pu déterminé l'organite cellulaire source, en utilisant le domaine 4 (D4) de la protéine Perfringolysine O, produite par Clostridium perfringens. Le D4 se lie à des concentrations élevées de cholestérol dans les membranes, sans causer de cytotoxicité. L'analyse d'imagerie de cellules vivantes dans une lignée cellulaire tumorale de Leydig et des cellules primaires de Leydig de rat, en utilisant le D4 marqué au mCherry, a révélé la libération d'un pool de cholestérol à partir de la membrane plasmique dans les 30 minutes suivant la stimulation hormonale. Ceci nous mène à conclure que la majeure partie du cholestérol stéroïdien, destinée aux mitochondries, provient de la membrane plasmique pendant la stéroïdogenèse aiguë. En outre, nous avons identifié un mécanisme de rétroaction à la prégnénolone qui arrête le transport excessif du cholestérol à partir de la membrane plasmique et protège ainsi les mitochondries de la toxicité induite par le cholestérol.Nous avons également étudié les voies possibles du transport du cholestérol. Une molécule hydrophobe de cholestérol, située dans la membrane plasmique, doit traverser le milieu aqueux pour atteindre les mitochondries pour la production de stéroïdes. De nombreuses voies ont été proposées pour aider le cholestérol dans cette entreprise, mais un mécanisme précis qui facilite le mouvement rapide de grandes quantités de cholestérol vers les mitochondries n'a pas encore été déterminé. Des études antérieures montrant une interaction accrue entre le réticulum endoplasmique et les mitochondries, pendant la stéroïdogenèse aiguë, nous ont amené à émettre l'hypothèse suivante; le cholestérol de la membrane plasmique entre dans le réticulum endoplasmique via une association membranaire et atteint ainsi les mitochondries par la membrane plasmique - réticulum endoplasmique - les associations de mitochondries appelées PAM. Ces associations de membranes et signaux cellulaires sont facilités par une variété de classes de lipides. Par conséquent, nous avons vérifié cette hypothèse par l'analyse lipidomique utilisant la spectrométrie de masse. Plus précisément, nous avons comparé les différents lipides retrouvés dans les fractions sous-cellulaires de cellules MA-10 induites hormonalement vs ceux retrouvés dans les fractions sous-cellulaires de MA-10 induites hormonalement mais dont la stéroïdogenèse avait été inhibée. Les résultats obtenus à partir de cette étude ont également soutenu l'idée que les PAM sont la voie du transport du cholestérol de la membrane plasmatique vers les mitochondries. En outre, nous avons également noté une réorganisation dynamique de multiples classes de lipides qui facilitent les associations de membranes et les signaux cellulaires