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A Study on the Recent Trends of Entrepreneurship in Exports
The determinants of participation in international business is a well- researched area with in both economics and business management disciplines. Such studies have largely highlighted factors external to the firm. A few enquiries in the small and medium sector have looked person- specific factors as explaining the difference in behavior between exporters and non-exporters. Taking off from these two streams, this paper delves deep into the exporting behavior of small- and medium-sized enterprises (SMEs) in two specific sectors after considering the dimensions of entrepreneur, enterprise and environment. Entrepreneurship is the sustainable driving force and stable source of economic development, and it is an important guarantee for the continuous improvement of export product quality and high-quality economic development. This paper selects entrepreneurship and export product quality as research objects. It measures the export product quality at the enterprise level using data from the China Industrial Enterprise Database and the China Customs Import and Export Trade Database. Furthermore, based on matching entrepreneurship at the macro-level and the export product quality at the micro-level, the impact and mechanism of entrepreneurship on the export product quality are empirically examined. It was found that entrepreneurial innovation and entrepreneurship further significantly contribute to the improvement of export product quality by promoting the total factor productivity of enterprises. Entrepreneurial innovation and entrepreneurship do not significantly contribute to the export product quality of state-owned and collective enterprises, while they significantly contribute to privately-owned, independent legal entities, Hong Kong–Macao–Taiwan, and foreign-funded enterprises. Furthermore, the promoting effect on privately-owned and independent enterprises is greater than that on Hong Kong–Macao–Taiwan and foreign-funded enterprises. Further analysis shows that improving the rule of law, reducing excessive government intervention, and improving the market mechanism for products and production factors, as well as vigorously developing the non-state-owned economy, will all be conducive to the cultivation and development of entrepreneurs and better promotion of the improvement of export product quality.
Analysis of Export Opportunities and Performance in India
Export refers to a product or service produced in one country but sold to a buyer abroad. Exports are one of the oldest forms of economic transfer, growth and occur on a large scale between nations. India's share of merchandise exports amounted to around 1.6 percent of the total global exports. Moreover, the share of commercial service exports from the country was higher at 3.5 percent during that same period. Exports have played an increasingly important role in India’s economic growth in the last two decades. Increasing demand and Growing population have an immense scope for international trade. Through international trade countries have started to export their surplus commodities that are produced to satisfy the unlimited and scare wants of the particular commodity in another country. In this paper we will be go through various opportunities available for exports from India. We could significantly expand markets, increase economic growth and opportunities, leaving less dependent on any single one. Greater production can lead to larger economies of scale and better margins. Research and development budget could work harder as you can change existing products to suit new markets. Exports provide the basis for sustainable development by earning necessary foreign exchange for the country to import necessary products. India has a wealthy history of International trade and is steadily working to harvest the benefits in the best possible manner. Growth of any developing country is direct related to its exports. If exports are greater than imports then nothing can stop a country from being a developed one. But on the other hand if there is instability in exports or if the exports of any developing country are less than its imports then it adversely affect the process of its economic development. Decrease in exports means low foreign exchange earnings which means less purchasing capacity of a country in the international market
A Study on Human Resource Management and It’s Challenges in India
Every business or profession is faced by unique and newer challenges. The nature, scope and magnitude of these challenges keep changing because of the increasing competition between firms, increased customer expectations and drastic change in customer preferences. Human resources of an organisation consist of all people who perform its activities (Bernardin, 2010). Subsequently, human resource management as part of an organisation must be prepared to deal with upcoming challenges and changing dynamic environment. This means understanding the effects of globalisation, work force diversity, changing skill requirements, Recruitment of new staffs, productivity, health and safety ,the contingent workforce, employee involvement and also technology changes (Decenzo & Robbins, 2007). Nowadays business society is going through a dynamic change due to multiple factors, which affect the industry. The most important of these factors are shift from manufacturing to services, globalization in every industry, changes in technology, automation and artificial intelligence and the worldwide dissemination of Information, Communication and Technology (ICT).How these Issues and challenges affect the organisational behaviour and Human Resource Management is the topic of this paper. An organisation can succeed if it has a sustainable competitive advantage (is better than competitors at something, and can hold that advantage over a sustained period of time). Thus, we can conclude that organisations need the kind of resources that will give them such an advantage. Human Resource responsibilities have become broader and more strategic over time in response to a number of trends. Changes are occurring today that are requiring HR managers to play an increasing central role in managing companies. These changes or trends include globalisation, changes in the nature of work, and technology. Globalisation is increasingly evident in the world .More globalisation means more competition, and more competition means more pressure to lower costs, make employees more productive, and do things better and less expensively. Human Resources play a key role in helping companies meet the challenges of global competition and strategic objectives to lower costs, improve productivity and increase organisational effectiveness (Desseler, Al Riss, 2012). A company’s policy and the HR policies and practices have to go in sync and the management shall be bold enough to bring about an organisational change. Here is when the issues are challenges reform and Human Resource Management finds its existence. In today’s business world, employees are ready to do their best and they also expect the best in return, in terms of rewards, incentives, communication, better healthcare facilities, a stress free work environment, and so A business organisation has to estimate its future manpower needs and adjust its manpower planning and development programmes.
Water-Energy Nexus: Optimizing Water Use in Energy Production
The water-energy nexus represents a critical interdependence between water resources and energy production, highlighting the need for sustainable management strategies. This abstract explores the optimization of water use in energy production, focusing on the key challenges, solutions, and potential benefits of integrating water and energy systems efficiently. Challenges arise from the significant water requirements of traditional energy sources such as thin able practices. Various strategies can enhance water efficiency in energy production, including technological advancements like dry cooling systems for power plants, water recycling and reuse techniques, and the development of water-saving renewable energy technologies like solar and wind power. These approaches aim to minimize water consumption, reduce pollution, and enhance overall resource resilience thermal power plants and hydropower, leading to water scarcity concerns and environmental impacts. Climate change further exacerbates these challenges, emphasizing the urgency of adopting just a Moreover, optimization techniques such as mathematical modeling, data analytics, and decision support systems play a crucial role in managing water resources effectively within the energy sector. These tools facilitate real-time monitoring, predictive analysis, and adaptive management, enabling informed decision-making and resource allocation. The benefits of optimizing water use in energy production are multifaceted.
Computerized Cognitive Retraining Program for Home Training of Children with Disabilities
Cognitive impairments in children with disabilities can significantly impact their learning and daily functioning. Computerized cognitive retraining programs offer a promising solution for enhancing cognitive skills in a home-based setting. This research paper explores the development and effectiveness of a computerized cognitive retraining program designed specifically for children with disabilities. The program includes a series of interactive exercises aimed at improving attention, memory, problem- solving, and executive functioning skills. The study involved a cohort of children with various disabilities, including autism spectrum disorder and attention- deficit/hyperactivity disorder. Participants engaged in the retraining program at home under the guidance of caregivers, with progress monitored over a period of several weeks. The results showed a marked improvement in cognitive performance across multiple domains, as measured by standardized assessment tools. Caregivers also reported increased engagement and motivation among the children. This paper concludes that computerized cognitive retraining can be a valuable tool for supporting children with disabilities. It provides a flexible and accessible approach to cognitive development, allowing for consistent practice in a familiar environment. Further research is recommended to explore the long-term effects of the program and to identify specific elements that contribute to its success.
Arecanut Status Detection Using Deep Learning
Arecanut, a significant cash crop in many tropical regions, undergoes distinct stages of ripening, posing challenges for timely harvest and market readiness. This research presents a comprehensive framework employing cutting-edge deep learning methodologies, specifically TensorFlow Lite, for accurate and real-time detection of Arecanut status, encompassing ripe, unripe, and dry stages. The integration of OpenCV for image preprocessing and deployment on Raspberry Pi enhances the system's accessibility and usability, enabling on-site detection using the Raspberry Pi camera module. The study begins with a comparative analysis between traditional machine learning techniques, particularly Support Vector Machine (SVM), and deep learning architectures. While SVM exhibited commendable accuracy at 75%, the superior performance and scalability of deep learning models motivated the adoption of TensorFlow Lite. Extensive experimentation, conducted on a diverse dataset of Arecanut images, underscored the efficacy of deep learning in surpassing the accuracy thresholds achieved by conventional approaches. The synergy between TensorFlow Lite and Raspberry Pi leverages the computational capabilities of the latter, facilitating efficient deployment in resource-constrained agricultural settings. This synergy enables real-time inference directly on the device, ensuring data privacy, low latency, and autonomy from cloud connectivity, thus empowering farmers with actionable insights at the point of need, distinguishing between ripe, unripe, and dry stages. The experimental validation of our approach across varied environmental conditions underscores its robustness and adaptability. The deployed system not only offers a portable and cost-effective solution but also contributes to the paradigm shift towards precision agriculture, where datadriven decision-making enhances productivity and sustainability.
Low Cost Water Purification: Harnessing Adsorbents for Sustainable Treatment Solutions
Water is essential for life, yet millions lack access to clean drinking water. Low-cost treatment methods are crucial to ensure equitable access to safe water and prevent waterborne diseases in underserved communities. The study devised a 12-liter water filter utilizing pebbles, sand, and adsorbents biochar of Julifora, cashew nut shell and rice husk. Results demonstrated the filter's effectiveness in sustaining pH levels while reducing chloride, fluoride, hardness and alkalinity. Particularly noteworthy was the capacity of cashew nut biochar to efficiently adsorb chloride, fluoride, hardness and alkalinity as 27%, 37%, 38% and 43% respectively. This indicates its promising role as an adsorbent for water purification. The findings underscore the potential of the designed filter in improving drinking water quality by mitigating contaminants. Further refinement and optimization of the filter could enhance its efficacy in meeting stringent water quality standards, addressing the pressing need for accessible and safe drinking water globally.
Car Accident Detection and Alert System Leveraging Deep Learning
This paper presents a novel approach to address the critical issue of car accidents through an advanced Accident Detection and Alerting System (ADAS) empowered by cutting-edge technologies in deep learning, computer vision, and communication APIs. With road safety being a paramount concern worldwide, timely detection of car accidents followed by swift alerting mechanisms can significantly reduce the severity of accidents and save lives. The proposed system architecture incorporates the Ultralytics framework, Convolutional Neural Networks (CNN), You Only Look Once version 8 (YOLOv8), OpenCV, and Twilio API. Initially, YOLOv8 is employed to perform real-time detection of vehicles and potential accident scenarios from live video feeds obtained from roadside cameras or vehicle-mounted cameras. Subsequently, CNN models are utilized for comprehensive analysis of detected objects to discern potential accidents based on predetermined criteria, including sudden deceleration, collision patterns, and anomalous vehicle movements. Upon successful detection of an accident, the system triggers an alert mechanism via the Twilio API, which delivers instantaneous notifications to emergency services, nearby medical facilities, and designated contacts of the involved parties. These alerts are enriched with crucial details such as the accident location, vehicle types involved, and severity assessments derived from CNN analyses. Furthermore, the integration of OpenCV facilitates preprocessing tasks such as noise reduction, image enhancement, and object tracking, thereby enhancing the system's robustness and accuracy in diverse environmental conditions and lighting scenarios. The proposed ADAS offers numerous benefits, including real-time accident detection, automated alerting of emergency services, and minimized response times, ultimately contributing to mitigating the severity of accidents and preserving lives. By harnessing the power of deep learning algorithms, advanced computer vision techniques, and seamless communication APIs, this system represents a significant step towards fostering safer road environments within smart cities and transportation networks.
Prediction of RUL of lubricating oil based on Deep Learning Technique
This study uses condition monitoring (CM) to determine the lubricating oil's remaining usable life (RUL). First, Atomic emission spectroscopy (AES) was used to determine the lubricating oil's element makeup and content. Under use were quantitatively examined. The precision and efficacy of the RUL prediction model might be diminished in light of the wide range of oil data acquired using AES. A thorough it is recommended to screen oil data using a method for choosing parameters based on correlation analysis, lubricant degradation analysis, and information entropy. In order to address this issue. The lubricant's RUL prediction model was then developed using a support vector machine (SVM). It is shown that the SVM prediction model's efficacy and accuracy by contrasting the experimental findings with the prediction model's output data.
Development of Application for Streamlining the Listing of Cases through Differentiated Case Flow Management
Efficient case management is critical in modern legal systems to ensure timely and fair resolution of cases. This paper investigates the development of an application aimed at enhancing case listing procedures through Differentiated Case Flow Management (DCFM). Beginning with an exploration of traditional case listing challenges, the paper introduces the concept of DCFM, emphasizing its objectives and benefits in optimizing case management processes. Leveraging technological advancements, the proposed application is designed to streamline case listing by prioritizing cases based on their complexity and urgency. The paper details the development process, including research methodology, application design, functionalities, and integration strategies with existing systems. Real-world case studies highlight successful implementations of similar solutions and their positive impact on efficiency and resource allocation. However, the paper also addresses challenges such as legal and ethical concerns, data security, and resistance to change. Looking forward, the paper discusses potential innovations in case management technology and their implications for legal practice and policy. Overall, this research contributes to the ongoing discourse on improving case management systems through innovative applications and differentiated approaches.
Laboratory Automation System Using IOT Device
Agriculture has a major role in the overall economic prosperity of a nation. An important component of smart agriculture is the Internet of Things (IoT). This project aims to construct an automated agriculture system that maximizes crop consumption of water and fertilizer. This endeavor also include identifying animals and stopping trees. The goal of the project is to create more efficient ways to handle irrigation systems manually and automatically with a smart phone. The proposed system utilizes embedded devices, PROTEUS software, and MPLAB. The present study offers a comprehensive approach to the supervision and control of an intelligent irrigation and regulating system.
Fake Social Media Post Detection by Using Deep Learning Algorithm
In today's digital age, social media serves as both a boon and a bane, offering immense opportunities for personal growth while also being a breeding ground for misinformation and deception. The rise of sophisticated multimedia manipulation techniques has blurred the line between reality and fiction, presenting significant challenges to discerning genuine content from fabricated ones. This paper presents an overview of the escalating threat posed by fake posts on social media and proposes a novel approach for their detection. The proposed method leverages advanced technologies such as Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) to distinguish between authentic and manipulated posts. Unlike traditional approaches reliant on manual feature extraction, our framework employs deep learning methodologies for more nuanced pattern recognition and generalization to unseen data. By combining ELA for initial image analysis and CNNs for deep feature extraction, followed by classification using Support Vector Machines (SVMs) and K-Nearest Neighbors (KNNs), we achieve robust detection of fake images .Experimental results demonstrate the effectiveness of our approach, with a peak accuracy of 91.3% using Residual Networks and KNN. This method offers a promising solution to mitigate the proliferation of fake images on social media platforms, thereby combating the spread of misinformation, propaganda, and chaos.
Self Identifying Mental Health Status and Get Guidance For Support Using AI
In recent years, an awareness has emerged about the importance of the individual in mental health and treatment. Mental health problems are common among college students around the world; Therefore, identifying at-risk groups will help provide services. Self-diagnosis of mental illness is an important step in recognizing and addressing personal health. Our goal is to develop a screening tool to identify students at risk for mental illness. Students are taught to use assessment tools to distinguish between those at reduced and increased risk of mental illness. Psychological services should be provided to students at risk. Despite the prevalence of psychological problems, problems such as fear of decision-making and lack of information often make it difficult for people to define themselves. The person may deny or ignore symptoms, which may delay the person's intervention and make the situation worse. Empowering people with mental health knowledge and resources can enable them to take part in their own health. As well as personal benefits, self-expression can promote understanding and acceptance and contribute to a culture of empathy and support. Artificial Intelligence (AI) technology holds both great promise and potential pitfalls in transforming mental health. This article discusses the current limitations of artificial intelligence, areas where further research is needed, and how it can improve healthcare by considering the ethical fairness of intellectual technology. As AI technology continues to evolve and improve, it may be able to help psychiatrists reclassify mental disorders beyond what is currently specified in the DSM-5, identifying these problems in their early or prodromal stages so that interventions can be implemented. . Quality and personalized treatment based on individual characteristics. However, caution must be exercised to avoid overinterpretation of preliminary results, and more studies must be conducted to close the gap in scientific expertise on mental health and treatment.
Empowering Mental Well-being: AI-guided Self-Recognition and Support
In an era marked by the convergence of technology and mental health, the integration of artificial intelligence (AI) holds promise for revolutionizing how individuals engage with their own well-being. This abstract explores the concept of AI-guided self-recognition and support as a transformative approach to empower mental wellness. Through sophisticated algorithms, AI can analyze diverse data streams, including digital behaviors and physiological markers, to detect subtle shifts indicative of mental health challenges. Leveraging this capability, AI becomes a proactive companion in individuals' journeys toward self-awareness. Furthermore, AI augments traditional support systems by offering personalized interventions tailored to individual needs and preferences. From empathetic chatbots providing emotional solace to curated resources and mindfulness exercises promoting resilience, the AI ecosystem becomes a versatile toolkit for mental health enhancement. Importantly, this paradigm shift emphasizes continuous learning and refinement, ensuring that interventions evolve in tandem with users' experiences and feedback. However, the implementation of AI-guided mental wellness initiatives necessitates careful consideration of privacy and ethical principles. Striking a balance between data utilization and user confidentiality remains paramount to fostering trust and engagement. Moreover, transparency in AI algorithms' functioning and accountability in decision-making processes are imperative to uphold ethical standards.
A Comprehensive Review of Disease Prediction Using Internet of Things and Artificial Intelligence
This review article examines the remarkable impact of combining the Internet of Things (IoT) and Artificial Intelligence (AI) on illness prediction and diagnosis. Through the application of AI's profound cognitive abilities and IoT's ongoing data collection, the integrated approach improves symptomatic precision and speed, providing comprehensive insights into patient health. The study looks into high-level AI techniques, such as deep learning and machine learning that are used to process and analyze medical data. It also looks at how these techniques are applied to medical imaging and disease prediction. In today technologically advanced world, it also tackles the challenges and ethical considerations surrounding information security and protection. This convergence of IoT and AI promises to revolutionize healthcare by opening up new avenues for precise and pro-active medicinal interventions.
Al Based Automatic alarm Generation and Dropping of Payload at a Particular Object through a Drone
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are becoming more and more prevalent in a number of industries, including logistics, surveillance, and reconnaissance. This work introduces a novel framework that automatically generates alerts and precisely targets specific points of interest for UAV payload delivery using artificial intelligence (AI) algorithms. Our framework seamlessly integrates real-time object detection and recognition capabilities with an advanced AI-driven decision-making module to enable autonomous functionality. The operational process starts with using on board sensors and cameras to continuously monitor the environment. The AI algorithms carefully consider all the options when they identify an object of interest, such as a suspicious package or a predetermined target. They then determine the optimal course of action. If necessary, the system triggers an alert and arranges for a series of actions to enable the UAV to drop a payload at the designated location. Payload delivery mechanisms, advanced object detection algorithms, path planning algorithms that optimize UAV navigation, and deep learning models tailored for object recognition are some of the framework's key elements. The AI-powered decision-making module ensures that choices are made accurately, promptly, and with consideration for the mission's goals, the law, and the environment. A comprehensive evaluation of the proposed framework's efficacy is conducted through simulated and real-world experiments, showcasing its versatility in various contexts. The results Validate the framework's remarkable accuracy in object detection and recognition, as well as its reliable payload delivery capabilities. Furthermore, the system's autonomy lessens the need for human intervention, which makes it particularly suitable for applications that demand accuracy and speed. Our work advances the development of AI-powered unmanned aerial vehicles (UAVs) designed for automated monitoring, law enforcement, and disaster relief operations. With its great potential to improve situational awareness and operational effectiveness in a variety of environments, the proposed framework could usher in a new era of autonomous aerial system advancements.
IoT-Based Solutions for Monitoring Cattle Health: A Comprehensive Review
The integration of Internet of Things (IoT) technologies in agricultural practices has revolutionized livestock management, particularly in the realm of health monitoring for cows. This review paper presents a comprehensive analysis of existing IoT-based health monitoring systems for cows, aiming to compare their features, performance, and potential contributions to the agricultural landscape. The review highlights the strengths and weaknesses of each system, providing a nuanced understanding of their practical implications in diverse farming environments. Furthermore, challenges faced by existing systems are discussed, and opportunities for improvement are identified. The paper concludes with insights into emerging technologies that may shape the future of IoT-based health monitoring for cows, emphasizing research gaps and proposing avenues for future investigation. This comparative analysis serves as a valuable resource for researchers, practitioners, and policymakers seeking to enhance the health and productivity of livestock through advanced IoT solutions.
Diabeties Prediction Using Machine Learning and Embedded System
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, which can lead to severe complications if left untreated. One of the common manifestations of diabetes is peripheral neuropathy, which can result in altered foot pressure distribution. Early detection and management of foot pressure abnormalities are crucial for preventing diabetic foot ulcers and amputations. In this research paper, we propose an intelligent foot pressure analysis system for the early detection of diabetes mellitus.
Statistical Model for Compressive Strength of Metakaolin Concrete
In the present research work, investigations were carried out to improve the performance of concrete in terms of strength by incorporating metakaolin (MK) as mineral admixture in concrete. Parametric study was carried out by considering w/c ratio, various percentage of MK and age of concrete as parameters to understand the effect of each parameter. The study was conducted for different w/c ratios of 0.32, 0.35, 0.4 and 0.5. The MK proportion was varied from 0 to 15% with an increment of 5% and age of concrete from 3 to 90 days were considered and experiments performed accordingly. The effects of above said parameters on the various properties of concrete such as workability, compressive strength and pH of concrete were investigated, and the results of metakaolin concrete were compared with the conventional concrete. From the results, it was observed that MK concrete showed greater strength for higher water cement ratios (0.4 & 0.5). A Multiple non-linear regression analysis was used to develop a statistical model to predict the strength and found to have good correlation between the observed and predicted values. It was concluded that the concrete developed in this study have significant potential for use on real time projects.
The Analysis of Community Radio Performance in the Central Java Province is Conducted through the Utilization of a Radio Mobile Simulator
Radio broadcasting is one of the electronic media complementary to print media in the presentation of information and entertainment. Community radio is an FM radio station owned, managed, provided, initiated and established by a community. Community radio is also often referred to as social radio, educational radio, or alternative radio. Community radio frequency allocation according to the Regulations of the Minister of Communications and Informatics of the Republic of Indonesia Number: 3 In 2017, only three channels are available, namely Channels 202, 203, and 204 (frequency 107.7 MHz, 107.8 MHz and 107.9 MHz), while one channel of 200 is allocated to the Public Broadcasting Authority (LPP) and the Private Broadcaster. (LPS). Due to the limited number of community radio channels, there is not much community radio established, in addition to monitoring and optimizing the use of frequencies as well as to maintain the existence of the community radio it is necessary to conduct an analysis of the performance of those community radio. Besides, the results of the analysis also show how many channels can still be used to establish community radio in their territory. A mobile radio simulator is used to get a complete picture of the activity of the frequency channels as well as the community radio performance used in Central Java. The results of the analysis show that the community's radio frequency allocation is well used and the broadcasting power emitted in accordance with government regulations with EIRP and RSL rates is -6,02 dBm