Search Results (44)

In nature, organisms often rely on the integration of local sensory information and prior experience to flexibly adapt to complex and dynamic environments, enabling efficient path selection. This bio-inspired mechanism of perception and behavioral adjustment provides important insights for path planning in mobile robots operating under uncertainty. In recent years, the introduction of deep reinforcement learning (DRL) has empowered mobile robots to autonomously learn navigation strategies through interaction with the environment, allowing them to identify obstacle distributions and perform path planning even in unknown scenarios. To further enhance the adaptability and path planning performance of robots in complex environments, this paper develops a deep reinforcement learning framework based on the Soft Actor–Critic (SAC) algorithm. First, to address the limited adaptability of existing transfer learning methods, we propose an action-level fusion mechanism that dynamically integrates prior and current policies during inference, enabling more flexible knowledge transfer. Second, a bio-inspired radar perception optimization method is introduced, which mimics the biological mechanism of focusing on key regions while ignoring redundant information, thereby enhancing the expressiveness of sensory inputs. Finally, a reward function based on ineffective behavior recognition is designed to reduce unnecessary exploration during training. The proposed method is validated in both the Gazebo simulation environment and real-world scenarios. Experimental results demonstrate that the approach achieves faster convergence and superior obstacle avoidance performance in path planning tasks, exhibiting strong transferability and generalization across various obstacle configurations. Full article

Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and benchmarking of transport emissions in lifecycle assessments (LCAs) time-consuming and difficult to scale. This paper introduces a novel hybrid AI-supported knowledge graph (KG) which combines large language models (LLMs) with graph-based optimization to automate industrial supply chain route enrichment, completion, and emissions analysis. The proposed solution automatically resolves transportation gaps through generative AI and programming interfaces to create optimal routes for cost, time, and emission determination. The application merges separate routes into a single multi-modal network which allows users to evaluate sustainability against operational performance. A case study shows the capabilities in simplifying data collection for emissions reporting, therefore reducing manual effort and empowering SMEs to align logistics decisions with Industry 5.0 sustainability goals. Full article

This study investigates the evolution of customer-centric innovations within modern business ecosystems through the lens of servitization, a concept gaining momentum in contemporary service delivery frameworks. Recognizing the limited exploration of servitization beyond manufacturing, particularly in the context of value-added services, this research employs a multiple case study methodology focused on the tea sector in India and Nepal. Drawing on seven diverse entrepreneurial cases and supported by a thematic analysis, the study identifies nine critical factors influencing successful servitization, including knowledge gaps, procurement strategies, market segmentation, and customer engagement. Central to this investigation is the transformative role of structured training interventions, exemplified by the Chaya School of Tea, which catalyzed innovation and performance improvements among participating businesses. The findings highlight how digital tools, customer education, and strategic planning contribute to product–service integration, yielding enhanced quality, operational efficiency, and sustainable growth. This research contributes to theory by refining the concept of “servitization of services” as a strategic approach for empowering ecosystems through complementary offerings that transcend traditional service delivery. This work provides both conceptual and empirical insights into how service firms, particularly in under-researched sectors, can leverage servitization to drive long-term competitiveness and ecosystem-wide value creation. Full article

The global food system confronts critical challenges, including food insecurity, small-scale producer vulnerability, and environmental degradation. While locally led initiatives emerge as potential solutions, they face obstacles, such as participant bias and scaling limitations. This study analyzes 157 international and 91 Japanese locally led food initiatives to understand their contributions to food system sustainability. Our findings reveal that these initiatives address key issues, including food security, environmental sustainability, community revitalization, and poverty reduction, reflecting various manifestations of problems within the modern global food system despite differing contexts. These initiatives operate across the food supply chain, emphasizing cross-group collaboration, knowledge sharing, resource utilization, and shortened supply chains. Significant differences exist between high-income and low- to middle-income approaches; lower-income regions prioritize resource access and skill development, while high-income areas focus on collaboration and leveraging existing resources. Many initiatives aim to empower marginalized groups, indicating a trend towards inclusivity. Although individual local initiatives may have limited impact, their collective action in fostering collaboration and empowerment is vital for transforming food systems. Networking and intermediary support emerge as essential components for scaling these initiatives to achieve meaningful systemic change. Full article

The automation of extracting targeted decision-support information is a key task for achieving intelligent agricultural management. Essentially, this involves structurally representing agricultural operations based on knowledge, unified modeling and relational management of elements such as natural resources, human–land relationships, and spatiotemporal data. However, the traditional farmland supervision systems based on relational and object-oriented databases struggle to effectively integrate, model, and apply operational knowledge such as project requirements, work experience, policies, and regulations. This limits their application efficiency and automation level. Therefore, this paper proposes a modeling method for Farmland Supervision Operations Scenario Model (FSOSM) based on structured operational knowledge. First, by analyzing the elements, structure, and functions of farmland supervision business scenario, the paper abstracts “natural resources—human society—spatiotemporal data” into 8 categories of scenario elements and 22 types of multidimensional semantic relationships. Next, the operational knowledge is structured and integrated into various modeling steps, including scenario element extraction, association, expression, and application, thereby enhancing the model’s intelligent service capability. Finally, the model is applied in practice through visualization and service applications using the “Farmland Non-Grain Conversion Supervision Operation Scenario of Guangdong Province, China” as a case study. The model’s practicality and superiority are demonstrated by comparing the processing flows and effects of this model and traditional farmland management systems in terms of efficiency, automation level, knowledge service capability, and versatility. Full article

The sophistication of cyberthreats demands more efficient and intelligent tools to support Security Operations Centers (SOCs) in managing and mitigating incidents. To address this, we developed the Security Event Response Copilot (SERC), a system designed to assist analysts in responding to and mitigating security breaches more effectively. SERC integrates two core components: (1) security event data extraction using Retrieval-Augmented Generation (RAG) methods, and (2) LLM-based incident response guidance. This paper specifically utilizes Wazuh, an open-source Security Information and Event Management (SIEM) platform, as the foundation for capturing, analyzing, and correlating security events from endpoints. SERC leverages Wazuh’s capabilities to collect real-time event data and applies a RAG approach to retrieve context-specific insights from three vectorized data collections: incident response knowledge, the MITRE ATT&CK framework, and the NIST Cybersecurity Framework (CSF) 2.0. This integration bridges strategic risk management and tactical intelligence, enabling precise identification of adversarial tactics and techniques while adhering to best practices in cybersecurity. The results demonstrate the potential of combining structured threat intelligence frameworks with AI-driven models, empowered by Wazuh’s robust SIEM capabilities, to address the dynamic challenges faced by SOCs in today’s complex cybersecurity environment. Full article

This paper presents the results of an interactive virtual reality (VR) training program aimed at enhancing Health and Safety (H&S) management practices in quarrying operations. The course was designed based on industry best practices, as well as both voluntary and mandatory standards relevant to marble mining activities. It combines experiential learning with a performance monitoring system that tracks completion rates, time taken, and scores based on user decisions. The primary objective was to assess the impact of VR training across different user groups, categorized by age, prior safety experience, familiarity with equipment and processes, and VR proficiency. This study involved 40 participants and analyzed 15 variables, including occupation, age, H&S skills, process knowledge, equipment familiarity, VR skills, physical impact of VR, number of attempts before completion, percentage and time of completion, achieved scores, retention of knowledge, and user feedback before and after training. Performance measurement was carried out using two methods: a Microsoft Forms questionnaire with 16 questions, completed by participants one week after training, and Simula Solution, which automatically tracked and recorded performance metrics (time, percentage, errors, and scores) during each session. The survey successfully identified which demographic groups were most affected by VR training. The findings of this study could have important implications for improving H&S practices in the mining sector by empowering workers to engage in training and interact with process resources. This allows them to experience virtual accidents in a controlled, risk-free environment. Full article

The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. Full article

The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced machine learning models, including XG Boost and Random Forest, alongside Recursive Feature Elimination (RFE) for dimensionality reduction and quantile regression for an inferential analysis. Findings revealed that IoT adoption improved resource utilization efficiency, while blockchain enhanced ethical sourcing—furthermore, AI-driven predictive maintenance reduced operational downtimes. XG Boost achieved a Mean Squared Error (MSE), highlighting its superior predictive capability, while Random Forest achieved perfect fitness but risked overfitting. However, adoption varied significantly across firms due to financial and technical constraints, with SMEs reporting limited access to capital and skilled labor. This study underscores the need for policy interventions and targeted support for SMEs to bridge adoption gaps. The study advances the existing body of knowledge by highlighting the synergistic benefits of integrating Industry 4.0 technologies to enhance SME sustainability. Practical implications include policy recommendations for financial incentives, technical support, and capacity-building programs, empowering SMEs with actionable insights to overcome adoption barriers and achieve sustainable growth. These findings offer industry leaders and policymakers’ actionable insights to drive SME transformation in Industry 4.0, empowering them to make a difference. Full article

In recent years, cyberattacks have increased in sophistication, using a variety of tools to exploit vulnerabilities across the global digital landscapes. Among the most commonly used tools at an attacker’s disposal are Google dorks, Shodan, and Censys, which offer unprecedented access to exposed systems, devices, and sensitive data on the World Wide Web. While these tools can be leveraged by professional hackers, they have also empowered “Script Kiddies”, who are low-skill, inexperienced attackers who use readily available exploits and scanning tools without deep technical knowledge. Consequently, cyberattacks targeting critical infrastructure are growing at a rapid rate, driven by the ease with which these solutions can be operated with minimal expertise. This paper explores the potential for cyberattacks enabled by these tools, presenting use cases where these platforms have been used for both offensive and defensive purposes. By examining notable incidents and analyzing potential threats, we outline proactive measures to protect against these emerging risks. In this study, we delve into how these tools have been used offensively by attackers and how they serve defensive functions within cybersecurity. Additionally, we also introduce an automated all-in-one tool designed to consolidate the functionalities of Google dorks, Shodan, and Censys, offering a streamlined solution for vulnerability detection and analysis. Lastly, we propose proactive defense strategies to mitigate exploitation risks associated with such tools, aiming to enhance the resilience of critical digital infrastructure against evolving cyber threats. Full article

The current study has four main objectives. First, it aims to investigate the effect of the relationship between information technology (IT) dimensions (customer relationship management, knowledge management, and human resource management) and innovative practices on organizational agility in small and medium-size companies (SMEs). Second, it seeks to measure the relationship between IT components and innovative performance. Third, it examines the impact of innovative performance on organizational agility. Fourth it explores the mediating role of innovative performance in the relationship between IT and organizational agility. These objectives provide a clear roadmap for the research and guide the analysis and interpretation of the findings. This paper’s statistical population was composed of senior managers in SMEs in Khorsaran Razavi, Iran. The data were collected using standard questionnaires, 172 which were received in 2023 and analyzed using SPSS version 25 and SmartPLS version 4 software. The results demonstrate that using customer relationships, human resources, and knowledge management as three dimensions of IT and innovative performance can enhance organizational agility. Moreover, innovative performance plays a crucial role as a mediator, strengthening the impact of information IT dimensions on organizational agility. These findings underscore the practical relevance for companies operating in a dynamic economic environment. Special attention to organizational agility and practical factors will increase flexibility, speed of response, etc., and, ultimately, companies’ success in this tense economic environment. The innovation of this research is that the three dimensions of IT, including evaluating customer relationship management, human resource management, and knowledge management, is a growing research field in organizational agility. Therefore, this research is vital in empowering SMEs to increase agility. By evaluating the effect of the four variables of knowledge management, customer relationship management, human resource management, and innovative performance on organizational agility in SMEs, on the one hand, this research expands the theoretical literature and, on the other hand, helps such companies. Full article

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article

The socio-economic conditions of the world’s underprivileged people have been a matter of concern to the whole world for over three decades. Not-for-profit human development institutes helping this sector have financial sustainability as an important issue due to their usual dependence principally on funding from donors to operate and fund their tasks. This research has adopted a two-fold examination method. Primarily, the financial sustainability of the not-for-profit human development institutes working in Pakistan have been investigated by conducting ratio analysis grounded on donor dependence ratio (DDR), and using constructive grounded theory, a FinTech-enabled financial sustainable model, has been proposed for NPHDIs. Results of the initial phase demonstrated a heavy reliance on donors’ funding, with the DDR varying between 91.73% and 100% based on 10 randomly selected NPHDIs working in Pakistan as a sample. Furthermore, four key themes have been categorized during the subsequent phase, which have been articulated collectively to outline the FinTech-enabled endowment—a proposed financially sustainable model. The DDR for the selected NPHDIs have been found to be greater than 25%, so they are regarded as financially unsustainable. FinTech-empowered endowment is considered as an alternative to donor fundings, as such endowments based on social finance can provide income streams that are considered sustainable for these NPHDIs. The overview and implications lead to new knowledge of tackling the biggest challenges of providing sustainable finance to the social sector. This perspective of ethical finance helps to address the issues faced by this world’s underprivileged segment and address the problems of poverty and inequality elimination. Full article

Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance in numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM’s capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and semantic similarities of 2400 test situations exhibit the great potential of LLM in optical networks. Full article

Understanding the influence of teaching culture (tradition) within academic departments is crucial for new teachers navigating the complex landscape of higher education. This paper investigates the possible impact of the department’s teaching culture on the pedagogical approaches of new teachers, forming their teaching style, concentrating on insights gathered from interviews with experienced colleagues in a Swedish university department with a focus on engineering education. By exploring the department’s teaching traditions and identifying potential challenges faced by new teachers, this study offers valuable insights into enhancing teaching styles and fostering student engagement. Drawing upon both experiential knowledge and insights from pedagogic literature and courses, the authors provide practical strategies to overcome obstacles and promote operative teaching practices. Ultimately, the outcomes of this study aim to empower new teachers to create enriching learning environments that promote student motivation, engagement, and overall academic success, aligning with the findings of existing literature on pedagogy and student learning outcomes. Full article