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

Arduino-based Real Time Implementation of Smart Trash Collector using Internet of Things

Nowadays certain actions are taken to improve the level of cleanliness in the country. People are getting more active in doing all the things possible to clean their surroundings. When the garbage will reach the maximum level, a notification will be sent to the municipality office, and then the employees can take further actions to empty the bin. This system will help in cleaning the city in a better way. Garbage bins remain uncollected for long periods of time putting the lives of marketers at risk in an event that there is Cholera outbreak especially during the rainy season. In order to avoid such a situation, this project proposes the design and implementation of a GPS and IOT Based Garbage and Waste Collection Bin Overflow Management System using GPS and IOT technology in providing real time information on the status of the garbage bins, i.e. when they are full so that appropriate action can be carried out. The system notifies the person (Truck Driver) in charge of garbage collection by sending a short message (sms) and telling them where the full bin is exactly located. The proposed system having IR sensor once human came to nearby bin, it automatically detects and open bin door using servo motor. At the top of the bin having ultrasonic sensor it measures the level of the bin and automatically send live location using GPS to municipal servers using IOT mode. All components are associated to micro controller Arduino. Arduino ATMEGA328 micro controller used to process input and produce output by using ARDUINO IDE with Embedded C programming and operated through Regulated power supply which gives 5v of DC voltage to all hardware modules

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

Quantum-Safe Public Key Blinding from MPC-in-the-Head Signature Schemes

Key blinding produces pseudonymous digital identities by rerandomizing public keys of a digital signature scheme. It is used in anonymous networks to provide the seemingly contradictory goals of anonymity and authentication. Current key blinding schemes are based on the discrete log assumption. Eaton, Stebila and Stracovsky (LATINCRYPT 2021) proposed the first key blinding schemes from lattice assumptions. However, the large public keys and lack of QROM security means they are not ready to replace existing solutions. We present a new way to build key blinding schemes form any MPC-in-the-Head signature scheme. These schemes rely on well-studied symmetric cryptographic primitives and admit short public keys. We prove a general framework for constructing key blinding schemes and for proving their security in the quantum random oracle model (QROM). We instantiate our framework with the recent AES-based Helium signature scheme (Kales and Zaverucha, 2022). Blinding Helium only adds a minor overhead to the signature and verification time. Both Helium and the aforementioned lattice-based key blinding schemes were only proven secure in the ROM. This makes our results the first QROM proof of Helium and the first fully quantum-safe public key blinding scheme

Exploring the acceptance of robotic assisted surgery among the Indian population: An empirical investigation [version 3; peer review: 2 approved]

Background Technology has completely transformed healthcare, starting with X-ray machines and MRIs to telehealth and robotic surgeries to e-health records. The launch of minimally invasive surgery (MIS) serves as a milestone in medical history, offering benefits such as smaller incisions, shorter hospital stays, and faster recovery, making it a preferred surgical option. This study mainly explores patients’ willingness to adopt robot-assisted surgery (RAS) technology in a surgical intervention and is assessed in the backdrop of the Technology Acceptance Model (TAM). Methods This research project employs a post-positivist research philosophy and a cross-sectional research design. A structured, pre-tested questionnaire was used to collect data from 280 respondents. Results The results revealed that trust had a significant impact on Perceived Usefulness (β = 0.099) and Perceived Ease of Use (β = .157), and eHealth literacy had a significant impact on Perceived Ease of Use (β = 0.438) and Perceived Usefulness (β = 0.454). Additionally, Perceived Usefulness partially influenced behavioral intention (β = 0.123), and attitude had a significant influence on behavioral intention (β = 0.612). The analysis revealed an insignificant impact of eHealth literacy on Perceived Usefulness (β = 0.067). The Standard Root Mean Square Residual (SRMR) value was <0.8. Mediation analysis also revealed partial mediation between the constructs. The SRMR rating of this model is 0.067, indicating that it fits the data well. Conclusion This study revealed that a patient's intention will be high if he or she believes that RAS is beneficial in treating his or her ailment. In comparison, information related to RAS is clearly known, and it does not directly affect selection intention. eHealth literacy is a significant antecedent to patients’ behavioral intention. Hence, the healthcare industry must devise strategies to promote the acceptance of RAS at all levels

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