Various industries have become more financially accessible due to technological advancements in various circumstances. Integrating Internet of Things technology in crop cultivation has shown benefits for multiple industries, such as agriculture and food production. The review paper below presents evidence of Internet of Things technology's impact on intelligent agriculture. This paper aims to review smart agriculture systems utilising Internet of Things-connected devices. The report has examined various essential aspects of smart agriculture and the advantages of Internet of Things technology. The review paper thoroughly discusses the different elements of the Internet of Things (IoT) technology. The application was found to have several areas for improvement, such as high cost, knowledge gap, and significant energy consumption. A rational discussion addresses the possible solutions to the raised issues. On the other hand, secondary qualitative methods, which use qualitative data, have facilitated discussions about the needs of smart agriculture. The paper shows significant knowledge about implementing Internet of Things systems in intelligent agriculture.

Efficient management of research papers is crucial for scholars navigating the complexities of modern academia, where multiple responsibilities and tight deadlines often hinder productivity. This paper explores the challenges faced by researchers throughout the research paper lifecycle, including time management, collaboration, reference organization, and mental well-being. Through a comprehensive methodology combining literature review, case studies, tool evaluations, and stress management techniques, the paper proposes practical solutions and strategies to enhance research paper management. Key findings indicate that time management is central to research productivity, with strategies like time-blocking and the Pomodoro Technique significantly improving focus and reducing procrastination. Digital tools, such as reference management software (e.g., Zotero, EndNote), project management platforms (e.g., Trello, Asana), and collaborative writing tools (e.g., Google Docs, Overleaf), were found to streamline the writing and revision process, allowing researchers to minimize administrative tasks and focus on content creation. The study also emphasizes the importance of clear communication and task coordination in collaborative research, highlighting the role of communication platforms (e.g., Slack, Microsoft Teams) and version control systems in reducing miscommunication and enhancing teamwork. Additionally, the psychological impact of research pressures was addressed, with findings showing that stress management techniques, including mindfulness and realistic goal-setting, are essential for maintaining productivity and mental health. The paper concludes with a holistic framework for managing research papers, integrating time management, digital tools, collaboration strategies, and well-being practices to improve both productivity and work-life balance for researchers

Efficient management of research papers is crucial for scholars navigating the complexities of modern academia, where multiple responsibilities and tight deadlines often hinder productivity. This paper explores the challenges faced by researchers throughout the research paper lifecycle, including time management, collaboration, reference organization, and mental well-being. Through a comprehensive methodology combining literature review, case studies, tool evaluations, and stress management techniques, the paper proposes practical solutions and strategies to enhance research paper management. Key findings indicate that time management is central to research productivity, with strategies like time-blocking and the Pomodoro Technique significantly improving focus and reducing procrastination. Digital tools, such as reference management software (e.g., Zotero, EndNote), project management platforms (e.g., Trello, Asana), and collaborative writing tools (e.g., Google Docs, Overleaf), were found to streamline the writing and revision process, allowing researchers to minimize administrative tasks and focus on content creation. The study also emphasizes the importance of clear communication and task coordination in collaborative research, highlighting the role of communication platforms (e.g., Slack, Microsoft Teams) and version control systems in reducing miscommunication and enhancing teamwork. Additionally, the psychological impact of research pressures was addressed, with findings showing that stress management techniques, including mindfulness and realistic goal-setting, are essential for maintaining productivity and mental health. The paper concludes with a holistic framework for managing research papers, integrating time management, digital tools,collaboration strategies, and well-being practices to improve both productivity and work-life balance for researchers.

Machine learning (ML) technology has been swiftly turning out to be the appropriate procedure of harmonizing the business activity in the cross-industrial environment. As people are getting more exposure to the big data and introducing new advancements in the possibilities of the internet, the use of ML has been solely undertaken with the aim of enabling organizations to do so in order to become more efficient and effective in their businesses by performing and making decisions. This systematic review touches on the given topic by discussing the emerging trends in the use of ML in optimization of business processes with specific mention of the importance that what it has in the operations management, supply chain management, marketing, human resource management as well as customer service. The key conclusions of the recent studies are generalized in the article and it was examined what ML-algorithms are most widespread and whether they are difficult to apply and what is beneficial in their activity. The review also predictive assumes that deep learning, reinforce learning and predictive learning would be more important in simplification of business processes as well as organisational competitiveness of the organisation. The results illustrate that ML would possess possibility to transform the likelihood of the business optimization on its way to the automation of the decision making procedure, and initiate the allocation of the resources, as well as increase the total endeavours of productivity. But the issue of privacy of the data, the lack of experts and the interface of ML systems with legacy are significant obstacles on the way to large-scale deployment. The future research directions in the field were outlined as the results of the paper in which the arguments about the necessity in the development of the extractable and understandable ML models in the business were indicated.

AVs are a revolutionary technology in the intelligent transportation industry integrating a sophisticated sensing, computing, and controlling system to facilitate safe and effective self-driving. At the heart of this development lies deep learning (DL) that has become the foundation of perception, decision-making, and navigation on complex and dynamic driving environments. As opposed to classical rule-based algorithms, DL models can learn hierarchical representations using large volumes of sensor and traffic data and achieve major gains in object-detection, lane-recognition, obstacle-avoidance, and route-planning tasks. In this paper, I have reviewed deep learning methods in autonomous vehicle navigation in detail. It covers a few of the more well-known architectures such as Convolutional Neural Networks (CNNs), visual perception; Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) to make sequential decisions; and deep models based on Reinforcement Learning (RL), adaptive navigation strategies. Furthermore, other methods that might be employed to improve environmental knowledge are discussed, including the application of multimodal fusion technologies, which integrate LiDAR, radar, and vision cameras. The article talks about real-world application, benchmark datasets, and simulation environments that facilitate DL-based research on AV. Even after an accelerated development, explainability, robustness in adverse weather, real-time computational efficiency and ethical considerations of safety-critical decisions continue to be challenges. Lightweight DL systems, federated learning of collaborative AVs, and explainable AI systems are the next steps to control regulatory compliance and user trust. This review combines progress, issues, and opportunities to emphasize the revolution in deep learning in the field of autonomous vehicle navigation and find ways to enable sustainable, reliable, and large-scale implementation.

The durability and security of the transport system plays a critical role in economic development, mobility, and the well-being of the population. Prior inspection of roads, bridges and railways has been largely laborious, time consuming and subjective. Recent developments in computer vision and deep learning (DL) open the possibility of automating the monitoring process, improving the accuracy, and facilitating the predictive maintenance. With the help of the DL models, image-based monitoring helps to detect cracks, deformations, corrosion, and structural defects with high precision. The paper provides the overall review of deep learning application in monitoring transportation infrastructure through images. The contributions made by convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and attention-based architectures are discussed in regard to their roles in automated inspection systems. Some of its applications are pavement crack detection, bridge surface inspection, railway track inspection, and tunnel inspection. As demonstrated in the review, DL performs more favourably in comparison to more traditional image processing techniques, particularly with regard to precision, extensibility, and resistance to real-world factors. The main challenges are: small labelled datasets, high computing expenses, scaling to new environments and interpreting models. However, three potentials are capable of being considered in addition to these restrictions: a hybrid approach, transfer learning, and federated learning. The paper also describes ethical, practical, and technological limitations related to the implementation of DL systems to monitor critical infrastructure. The review finds that DL-enabled image-based monitoring is a paradigm shift to smart and sustainable transportation infrastructure management. The application of dynamically executing DL systems in real time, unmanned aerial vehicles (UAVs), explainable AI, and cross-modal data fusion to enhance predictive performance are some of the research directions of the future.

Accurate runoff estimation is essential for effective watershed management, flood risk mitigation, and sustainable water resource planning. Over the decades, a wide range of hydrological models have been developed, differing in complexity, data requirements, and spatial–temporal resolution. This review provides a comparative evaluation of three widely used models—the SCS-Curve Number (SCSCN) method, TOPMODEL, and the Variable Infiltration Capacity (VIC) model with emphasis on their underlying structure, hydrological processes, applicability, and performance across various hydro-climatic and land use scenarios. The SCS-CN method, although empirical in nature, remains a preferred tool for event-based runoff estimation due to its simplicity and minimal data demands. TOPMODEL, a semidistributed conceptual model, links runoff generation to terrain-driven saturation dynamics, making it well-suited for humid and sloped watersheds. On the other hand, VIC, a semi-distributed, physically-based model, enables large-scale and climate-sensitive hydrological simulations by coupling water and energy balances within a grid-based framework. This review synthesizes recent literature to outline the strengths and limitations of each model, offering guidance for researchers and water managers in selecting appropriate runoff modeling tools based on watershed characteristics, modeling objectives, and available data resources.

Accurate runoff estimation is essential for effective watershed management, flood risk mitigation, and sustainable water resource planning. Over the decades, a wide range of hydrological models have been developed, differing in complexity, data requirements, and spatial–temporal resolution. This review provides a comparative evaluation of three widely used models—the SCS-Curve Number (SCSCN) method, TOPMODEL, and the Variable Infiltration Capacity (VIC) model with emphasis on their underlying structure, hydrological processes, applicability, and performance across various hydro-climatic and land use scenarios. The SCS-CN method, although empirical in nature, remains a preferred tool for event-based runoff estimation due to its simplicity and minimal data demands. TOPMODEL, a semidistributed conceptual model, links runoff generation to terrain-driven saturation dynamics, making it well-suited for humid and sloped watersheds. On the other hand, VIC, a semi-distributed, physically-based model, enables large-scale and climate-sensitive hydrological simulations by coupling water and energy balances within a grid-based framework. This review synthesizes recent literature to outline the strengths and limitations of each model, offering guidance for researchers and water managers in selecting appropriate runoff modeling tools based on watershed characteristics, modeling objectives, and available data resources

The urgent push for environmental sustainability has led to the development of green sector clusters, hubs where businesses, research institutions, government agencies, and other stakeholders collaborate to foster innovation and drive sustainable economic growth. This study explores the structural dynamics and collaborative interactions within these clusters, aiming to uncover the mechanisms that facilitate innovation and promote sustainable practices. Using network science, the research models green clusters as interconnected networks, where each entity or actor functions as a node within a web of partnerships and information flows. Network analysis techniques, including community detection and centrality measures, help identify influential members and cohesive subgroups within these clusters. These methods offer insights into the roles of key players and the network’s structural features, both crucial in understanding how innovation spreads across the cluster. Complementing this, the study uses agent-based modelling (ABM) to simulate the complex interactions and collaborative activities—such as technology transfer, knowledge sharing, and joint research and development—that drive innovation within green clusters. This dual approach of network analysis and ABM allows researchers to evaluate the effects of various strategies, such as policy interventions or collaborative incentives, on innovation outcomes. Findings indicate that network structure, collaboration intensity, and central actors are significant factors influencing innovation in green clusters. The study provides practical insights for policymakers, industry stakeholders, and researchers by suggesting methods to enhance innovation through targeted network support and strategic partnerships. Ultimately, this research contributes to the growing understanding of how green sector clusters can act as catalysts for sustainable transformation, offering a pathway toward a more ecoconscious and resilient economy.

The urgent push for environmental sustainability has led to the development of green sector clusters, hubs where businesses, research institutions, government agencies, and other stakeholders collaborate to foster innovation and drive sustainable economic growth. This study explores the structural dynamics and collaborative interactions within these clusters, aiming to uncover the mechanisms that facilitate innovation and promote sustainable practices. Using network science, the research models green clusters as interconnected networks, where each entity or actor functions as a node within a web of partnerships and information flows. Network analysis techniques, including community detection and centrality measures, help identify influential members and cohesive subgroups within these clusters. These methods offer insights into the roles of key players and the network’s structural features, both crucial in nderstanding how innovation spreads across the cluster. Complementing this, the study uses agent-based modelling (ABM) to simulate the complex interactions and collaborative activities—such as technology transfer, knowledge sharing, and joint research and development—that drive innovation within green clusters. This dual approach of network analysis and ABM allows researchers to evaluate the effects of various strategies, such as policy interventions or collaborative incentives, on innovation outcomes. Findings indicate that network structure, collaboration intensity, and central actors are significant factors influencing innovation in green clusters. The study provides practical insights for policymakers, industry stakeholders, and researchers by suggesting methods to enhance innovation through targeted network support and strategic partnerships. Ultimately, this research contributes to the growing understanding of how green sector clusters can act as catalysts for sustainable transformation, offering a pathway toward a more ecoconscious and resilient economy.