Search Results (967)

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

COVID-19 has caused global problems with complex ramifications. Vulnerable households worry about disruptions to food security. Mobility restrictions, decreased salaries, and supply chain disruptions have increased food insecurity. This study examined the best food security model and its determinants. The primary research data were collected from 257 respondents via cluster random sampling. Binary logistic regression, using R-Studio, was employed to analyze the data. The study showed that the Minimal Model (MM) was optimal in explaining food security status, with three predictors: the available food stock (AFS), education of the household head (EHH), and household income (HIc). This aligned with studies showing that food purchase ability depends on income and education. Male household heads demonstrated better food security than females, while women’s education influenced consumption through improved nutritional knowledge. Higher income provides more alternatives for meeting needs, while decreased income limits options. Food reserve storage influenced household food security during the pandemic. The Minimal Model effectively influenced food security through the AFS, EHH, and HIc. The findings underline the importance of available food stock, household head education, and household income in developing approaches to assist food-insecure households. The research makes a significant contribution to ensuring food availability and promoting sustainable development post-pandemic. Full article

The increasing frequency and sophistication of cyber attacks have posed significant challenges for digital financial organisations, particularly in quantifying their multidimensional impacts. These challenges are largely attributed to the lack of a standardised cyber impact taxonomy, limited data availability, and the evolving nature of technological threats. As a result, organisations often struggle with ineffective security investment prioritisation, reactive incident response planning, and the inability to implement robust, risk-based controls. Hence, an efficient and comprehensive approach is needed to quantify the diverse impacts of cyber attacks in digital financial services. This paper presents a systematic review and examination of the state of the art in cyber impact quantification, with a particular focus on digital financial organisations. Based on a structured search strategy, 44 articles (out of 637) were selected for in-depth analysis. The review investigates the terminologies used to describe cyber impacts, categorises current quantification techniques (pre-attack and post-attack), and identifies the most commonly utilised internal and external data sources. Furthermore, it explores the application of Machine Learning (ML) and Deep Learning (DL) techniques in cyber security risk quantification. Our findings reveal a significant lack of standardised taxonomy for describing and quantifying the multidimensional impact of cyberattacks across physical, digital, economic, psychological, reputational, and societal dimensions. Lastly, open issues and future research directions are discussed. This work provides insights for researchers and professionals by consolidating and identifying quantification technique gaps in cyber security risk quantification. Full article

Background/Objectives: Artificial intelligence (AI) holds great potential to reshape the academic paradigm. They can process large volumes of information, assist in academic literature reviews, and augment the overall quality of scientific discourse. This narrative review examines the application of various AI tools in surgical research, its present capabilities, future directions, and potential challenges. Methods: A search was performed by two independent authors for relevant studies on PubMed, Cochrane Library, Web of Science, and EMBASE databases from January 1901 until March 2025. Studies were included if they were written in English and discussed the use of AI tools in surgical research. They were excluded if they were not in English and discussed the use of AI tools in medical research. Results: Forty-two articles were included in this review. The findings underscore a range of AI tools such as writing enhancers, LLMs, search engine optimizers, image interpreters and generators, content organization and search systems, and audio analysis tools, along with their influence on medical research. Despite the multitude of benefits presented by AI tools, risks such as data security, inherent biases, accuracy, and ethical dilemmas are of concern and warrant attention. Conclusions: AI could offer significant contributions to medical research in the form of superior data analysis, predictive abilities, personalized treatment strategies, enhanced diagnostic accuracy, amplified research, educational, and publication processes. However, to unlock the full potential of AI in surgical research, we must institute robust frameworks and ethical guidelines. Full article

Firmware vulnerabilities in embedded devices have caused serious security incidents, necessitating similarity analysis of binary program instruction embeddings to identify vulnerabilities. However, existing instruction embedding methods neglect program execution semantics, resulting in accuracy limitations. Furthermore, current embedding approaches utilize independent computation across models, where the lack of standardized interaction information between models makes it difficult for embedding models to efficiently detect firmware vulnerabilities. To address these challenges, this paper proposes a firmware vulnerability detection scheme based on statistical inference and code similarity fuzzy matching analysis for resource-constrained vehicular network environments, helping to balance both accuracy and efficiency. First, through dynamic programming and neighborhood search techniques, binary code is systematically partitioned into normalized segment collections according to specific rules. The binary code is then analyzed in segments to construct semantic equivalence mappings, thereby extracting similarity metrics for function execution semantics. Subsequently, Google Protocol Buffers (ProtoBuf) is introduced as a serialization format for inter-model data transmission, serving as a “translation layer” and “bridging technology” within the firmware vulnerability detection framework. Additionally, a ProtoBuf-based certificate authentication scheme is proposed to enhance vehicular network communication reliability, improve data serialization efficiency, and increase the efficiency and accuracy of the detection model. Finally, a vehicular network simulation environment is established through secondary development on the NS-3 network simulator, and the functionality and performance of this architecture were thoroughly tested. Results demonstrate that the algorithm possesses resistance capabilities against common security threats while minimizing performance impact. Experimental results show that FirmPB delivers superior accuracy with 0.044 s inference time and 0.932 AUC, outperforming current SOTA in detection performance. Full article

Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies. This number reflects a rigorous selection process based on defined inclusion, exclusion, and quality criteria and is consistent with the scope of similar systematic reviews in the field of cyber threat intelligence. This review concludes that while CTI significantly improves the ability to predict and prevent cyber threats, challenges such as data standardization, privacy concerns, and trust between organizations persist. It also underscores the necessity of continuously improving CTI practices by leveraging the integration of advanced technologies and creating enhanced collaboration frameworks. These advancements are essential for developing a robust and adaptive cybersecurity posture capable of responding to an evolving threat landscape, ultimately contributing to a more secure digital environment for all sectors. Overall, the review provides practical reflections on the current state of CTI and suggests future research directions to strengthen and improve CTI’s effectiveness. Full article

Text-based person search (TPS), a critical technology for security and surveillance, aims to retrieve target individuals from image galleries using textual descriptions. The existing methods face two challenges: (1) ambiguous attribute–noun association (AANA), where syntactic ambiguities lead to incorrect associations between attributes and the intended nouns; and (2) textual noise and relevance imbalance (TNRI), where irrelevant or non-discriminative tokens (e.g., ‘wearing’) reduce the saliency of critical visual attributes in the textual description. To address these aspects, we propose the dependency-aware entity–attribute alignment network (DEAAN), a novel framework that explicitly tackles AANA through dependency-guided attention and TNRI via adaptive token filtering. The DEAAN introduces two modules: (1) dependency-assisted implicit reasoning (DAIR) to resolve AANA through syntactic parsing, and (2) relevance-adaptive token selection (RATS) to suppress TNRI by learning token saliency. Experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate state-of-the-art performance, with the DEAAN achieving a Rank-1 accuracy of 76.71% and an mAP of 69.07% on CUHK-PEDES, surpassing RDE by 0.77% in Rank-1 and 1.51% in mAP. Ablation studies reveal that DAIR and RATS individually improve Rank-1 by 2.54% and 3.42%, while their combination elevates the performance by 6.35%, validating their synergy. This work bridges structured linguistic analysis with adaptive feature selection, demonstrating practical robustness in surveillance-oriented TPS scenarios. Full article

Large language models (LLMs) have driven transformative progress in artificial intelligence, yet critical challenges persist in data management and privacy protection during model deployment and training. The approximate nearest neighbor (ANN) search, a core operation in LLMs, faces inherent trade-offs between efficiency and security when implemented through conventional locality-sensitive hashing (LSH)-based secure ANN (SANN) methods, which often compromise either query accuracy due to false positives. To address these limitations, this paper proposes a novel secure ANN scheme based on nearest neighbor graph (NNG-SANN), which is designed to ensure the security of approximate k-nearest neighbor queries for vector data commonly used in LLMs. Specifically, a secure indexing structure and subset partitioning method are proposed based on LSH and NNG. The approach utilizes neighborhood information stored in the NNG to supplement subset data, significantly reducing the impact of false positive points generated by LSH on query results, thereby effectively improving query accuracy. To ensure data privacy, we incorporate a symmetric encryption algorithm that encrypts the data subsets obtained through greedy partitioning before storing them on the server, providing robust security guarantees. Furthermore, we construct a secure index table that enables complete candidate set retrieval through a single query, ensuring our solution completes the search process in one interaction while minimizing communication costs. Comprehensive experiments conducted on two datasets of different scales demonstrate that our proposed method outperforms existing state-of-the-art algorithms in terms of both query accuracy and security, effectively meeting the precision and security requirements for nearest neighbor queries in LLMs. Full article

This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial environment. It utilizes vector embeddings, vector databases, and Approximate Nearest Neighbor (ANN) search algorithms to implement Retrieval-Augmented Generation (RAG), enabling context-aware searches for inventory items and addressing the limitations of traditional text-based methods. Built on an LLM framework enhanced by RAG, the system performs similarity-based retrieval and part recommendations while preserving data privacy through selective obfuscation using the ROT13 algorithm. In collaboration with an industry sponsor, real-world testing demonstrated strong results: 88.4% for Answer Relevance, 92.1% for Faithfulness, 80.2% for Context Recall, and 83.1% for Context Precision. These results demonstrate the system’s ability to deliver accurate and relevant responses while retrieving meaningful context and minimizing irrelevant information. Overall, the approach presents a practical and privacy-aware solution for manufacturing, bridging the gap between traditional inventory tools and modern AI capabilities and enabling more intelligent workflows in design and production processes. Full article

The idea of multi-keyword ranked search over encrypted cloud data has attracted considerable attention in recent studies, as it allows users to securely and efficiently retrieve highly relevant results. Traditional methods improve search efficiency by incorporating the K-means clustering algorithm. However, when applied to large-scale datasets, K-means can become computationally expensive. This paper introduces a multi-keyword ranked search method, SAO-KRS, which leverages the snow ablation optimizer (SAO) to enhance clustering performance. The approach begins with principal component analysis (PCA) to reduce the dimensionality of high-dimensional data, followed by clustering the reduced data using SAO, which reduces clustering overhead massively. By incorporating a heuristic best-first search algorithm over index trees, the scheme achieves reduced computational cost with high retrieval accuracy. In the best-case scenario, the proposed method achieves up to 21 times faster clustering and 2.7 times faster searching compared to the traditional K-means approach. Extensive experimental results verify that this method significantly improves clustering efficiency while ensuring both search speed and accuracy. Full article

In recent years, Electronic Medical Record (EMR) sharing has played an indispensable role in optimizing clinical treatment plans, advancing medical research in biomedical science. However, existing EMR management schemes often face security risks and suffer from inefficient search performance. To address these issues, this paper proposes a secure EMR sharing scheme based on blockchain and searchable encryption. This scheme implements a decentralized management system with enhanced security and operational efficiency. Considering the scenario of EMRs requiring confirmation of multiple doctors to improve safety, the proposed solution leverages Shamir’s Secret Sharing to enable multi-party authorization, thereby enhancing privacy protection. Meanwhile, the scheme utilizes Bloom filter and vector operation to achieve efficient data search. The proposed method maintains rigorous EMR protection while improving the search efficiency of EMRs. Experimental results demonstrate that, compared to existing methodologies, the proposed scheme enhances security during EMR sharing processes. It achieves higher efficiency in index generation and trapdoor generation while reducing keyword search time. This scheme provides reliable technical support for the development of intelligent healthcare systems. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

The rapid growth of computationally intensive tasks in the Internet of Vehicles (IoV) poses a triple challenge to the efficiency, security, and stability of Mobile Edge Computing (MEC). Aiming at the problems that traditional optimization algorithms tend to fall into, where local optimum in task offloading and edge computing nodes are exposed to the risk of data tampering, this paper proposes a secure offloading strategy that integrates the Improved Polar Lights Optimization algorithm (IPLO) and blockchain. First, the truncation operation when a particle crosses the boundary is improved to dynamic rebound by introducing a rebound boundary processing mechanism, which enhances the global search capability of the algorithm; second, the blockchain framework based on the Delegated Byzantine Fault Tolerance (dBFT) consensus is designed to ensure data tampering and cross-node trustworthy sharing in the offloading process. Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle’s power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV. Full article

This scoping review investigates the digital transition of critical energy infrastructure (CEI) in maritime ports, which are increasingly vital as energy hubs amid global decarbonisation efforts. Recognising the growing role of ports in integrating offshore renewables, hydrogen, and LNG systems, the study examines how digital technologies (such as automation, IoT, and AI) support the resilience, efficiency, and sustainability of port-based CEI. A multifaceted search strategy was implemented to identify relevant academic and grey literature. The search was performed between January 2025 and 30 April 2025. The strategy focused on databases such as Scopus. Due to limitations encountered in retrieving sufficient, directly relevant academic papers from databases alone, the search strategy was systematically expanded to include grey literature such as reports, policy documents, and technical papers from authoritative industry, governmental, and international organisations. Employing Arksey and O’Malley’s framework and PRISMA-ScR (scoping review) guidelines, the review synthesises insights from 62 academic and grey literature sources to address five core research questions relating to the current state, challenges, importance, and future directions of digital CEI in ports. Literature distribution of articles varies across continents, with Europe contributing the highest number of publications (53%), Asia (24%) and North America (11%), while Africa and Oceania account for only 3% of the publications. Findings reveal significant regional disparities in digital maturity, fragmented governance structures, and underutilisation of digital systems. While smart port technologies offer operational gains and support predictive maintenance, their effectiveness is constrained by siloed strategies, resistance to collaboration, and skill gaps. The study highlights a need for holistic digital transformation frameworks, cross-border cooperation, and tailored approaches to address these challenges. The review provides a foundation for future empirical work and policy development aimed at securing and optimising maritime port energy infrastructure in line with global sustainability targets. Full article

Postharvest losses of Solanaceae crops, which include potatoes (Solanum tuberosum), tomatoes (Solanum lycopersicum), bell peppers (Capsicum annuum), and others, are one of the major challenges in agriculture throughout the world, impacting food security and economic viability. Agrochemicals have been successfully employed to prevent postharvest losses in agriculture. However, the excessive use of agrochemicals may cause detrimental effects on consumer health, the emergence of pesticide-resistant pathogens, increased restrictions on existing pesticides, environmental harm, and the decline of beneficial microorganisms, such as natural antagonists to pests and pathogens. Hence, there is a need to search for a safer and more environmentally friendly alternative. Microbial antagonists have gained more attention in recent years as substitutes for the management of pests and pathogens because they minimize the excessive applications of toxic substances while providing a sustainable approach to plant health management. However, more research is required to make microbial agents more stable and effective and less toxic before they can be used in commercial settings. Therefore, research is being conducted to develop new biological control agents and obtain knowledge of the mechanisms of action that underlie biological disease control. To accomplish this objective, the review aims to investigate microbial antagonists’ modes of action, potential future applications for biological control agents, and difficulties encountered during the commercialization process. We also highlight earlier publications on the function of microbial biological control agents against postharvest crop diseases. Therefore, we can emphasize that the prospects for biological control are promising and that the use of biological control agents to control crop diseases can benefit the environment. Full article