Search Results (5,599)

The concept of artificial intelligence (AI) refers to technologies that imitate human-like thinking, learning and decision-making abilities. While integrating AI into the workforce offers the potential to increase efficiency in organizational activities, it can lead to negative effects such as anxiety, uncertainty, and distrust among employees which results from not being able to understand these technologies, regarding them as alternatives for themselves, and the possibility of losing their organizational position. These effects can reduce employees’ commitment at work and trigger negative organizational behaviors such as quiet quitting and turnover intention. Starting from this point, the present study aims to investigate the effect of AI anxiety on turnover intention and the mediating role of quiet quitting in this relationship. The study was conducted using a cross-sectional design with 457 people working in SMEs in Kırıkkale province. AI Anxiety, Quiet Quitting, and Turnover Intention Scales were utilized during the data collection process. The obtained data were analyzed through structural equation modeling. In addition to detecting significant relationships between concepts as a result of the analysis, it was realized that AI anxiety did not have a considerable effect directly on turnover intention; however, this effect occurred indirectly through quiet quitting. Accordingly, it is predicted that integrating AI technologies into business processes will increase the concerns about job security in employees, and this anxiety triggers the turnover intention by leading to a tendency toward quiet quitting for reasons such as loss of motivation and low organizational commitment. Full article

Biologically sensitive field-effect transistors (BioFETs) have advanced the biosensing capabilities in various fields such as healthcare, security and environmental monitoring. Here, we propose a junctionless BioFET (JL-BioFET) for the high-sensitivity and low-cost detection of biomolecules and analyze it using detailed device simulations. In contrast to the conventional FET with junctions, the JL-BioFET simplifies fabrication by doping the source, channel and drain simultaneously with the same types of impurities, thereby reducing the fabrication effort and cost. Additionally, if the device is designed with optimal bias, it can operate with only the source and drain terminals, which reduces power consumption. Thus, cost reduction and reduced power consumption are strong motivations to pursue a new design. Therefore, we simulated two JL-BioFET structures (SOI JL, bulk JL) that operate without a gate electrode and compared their biosensing performances. The bulk JL-BioFET showed an average sensitivity three times higher than that of the SOI JL-BioFET across varying charge levels. Then, we optimized the sensing performance of the bulk JL-BioFET by adjusting three key parameters: the active layer thickness and the doping concentrations of the active layer and substrate. These encouraging results are expected to lead to future fabrication efforts to realize bulk JL-BioFETs for high-performance biosensing. Full article

Safety, a critical component of sustainable development, necessitates an integrated approach in which urban planning assumes a central role. This study investigates the relationship between urban form and crime incidents in public spaces within the center of the city. This study was conducted in the city of Łódź, located in central Poland. Through geolocated data, this research explores crime incidents that, while not the most severe, disrupt public order and impact the overall quality of life. This study fills a gap in the existing literature by analyzing spatial variables such as urban vibrancy and the presence of alcohol outlets, alongside other urban elements. The analysis incorporates a variety of urban form variables, including land development indices, the functional layout of the urban neighborhood, pedestrian infrastructure, public space amenities, and facilities. Urban vibrancy, represented by the density of human activity, is also assessed in relation to crime incidents. The results indicate significant correlations between certain urban features and the occurrence of crime incidents, particularly the presence of public amenities and small businesses. While these findings suggest that urban design can influence crime rates, further panel and time-series regression analysis is needed to confirm these dynamics. Aligned with the 11th Sustainable Development Goal, this study provides insights that could inform urban planning strategies, offering recommendations to enhance both the functionality and safety of city centers. By understanding how urban design elements contribute to public safety, policymakers can develop more effective and targeted spatial planning strategies that promote not only aesthetics and functionality but also the well-being and security of residents. Full article

We consider a secure integrated sensing and communication (ISAC) scenario, where a signal is transmitted through a state-dependent wiretap channel with one legitimate receiver with which the transmitter communicates and one honest-but-curious target that the transmitter wants to sense. The secure ISAC channel is modeled as two state-dependent fast-fading channels with correlated Rayleigh fading coefficients and independent additive Gaussian noise components. Delayed channel outputs are fed back to the transmitter to improve the communication performance and to estimate the channel state sequence. We establish and illustrate an achievable secrecy-distortion region for degraded secure ISAC channels under correlated Rayleigh fading, for which we show that the signal-to-interference-plus-noise is not a sufficient statistic. We also evaluate the inner bound for a large set of parameters to derive practical design insights. The presented results include parameter ranges for which the secrecy capacity of a classical wiretap channel setup is surpassed and for which the channel capacity is approached. Thus, we illustrate for correlated Rayleigh fading cases that our secure ISAC methods can (i) eliminate the need for the legitimate receiver to have a statistical advantage over the eavesdropper and (ii) provide communication security with minimal rate penalty. Full article

In this paper, we introduce two novel top-k spatial dataset search schemes, KSDS and KSDS+. The core innovation of these schemes lies in partitioning the spatial datasets into grids and assessing similarity based on the distribution of points within these grids. This approach provides a robust foundation for spatial dataset search. To optimize search performance, we have developed an optimized scheme that incorporates two key strategies: a GMBR-based optimization strategy and a pooling-based optimization strategy. These strategies are designed to filter datasets to significantly improve search efficiency. Our experimental results demonstrate that KSDS and KSDS+ can perform top-k spatial dataset searches with both high effectiveness and efficiency, outpacing existing methods in terms of search speed. In the future, our research will explore other similarity-calculation models to further accelerate processing times. Additionally, we aim to integrate privacy-preserving techniques to ensure secure dataset searches. These advancements are intended to enhance the practicality and efficiency of spatial dataset searches in real-world applications. Full article

Traditional authentication systems with an authentication server that handles authentication requests from many clients have a major weakness, i.e., a single point of failure. This study presents an authentication paradigm based on blockchain, which provides a decentralised and distributed mechanism for processing authentication. The security of the proposed design of the decentralised authentication protocol was evaluated using Gong–Needham–Yahalom (GNY) logic. The results indicated that the protocol was secure against various attacks. This protocol has significant potential for enhancing secure identity management in sectors where privacy and data integrity are most important. Full article

Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent. Full article

This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes. Full article

This study optimizes the 3D extrusion printing parameters—water-to-flour ratio (X₁), temperature (X₂), and printing speed (X₃)—for raw (RFB) and extruded (EFB) dehulled Andean fava bean flours to maximize print quality and minimize structural defects. A 2³ central composite design combined with response surface methodology (RSM) was used to identify the optimal conditions for achieving geometric precision, surface homogeneity, and textural stability. Physicochemical analyses showed that extrusion cooking substantially modified the composition and rheology of the flour. Compared with RFB, EFB exhibited lower protein and fiber contents, a higher proportion of digestible carbohydrates, and reduced rheological parameters (τ₀, K, G′, G″), which facilitated printing. The evaluation of different parameter combinations revealed notable differences between the two flours, with X₁ and X₂ exerting the greatest influence on print quality. For RFB, the highest desirability (0.853) was achieved at X₁ = 0.806, X₂ = 23.18 °C, and X₃ = 2470.5 mm/min, yielding more uniform and firmer printed structures. In contrast, EFB reached a desirability of 0.844 at X₁ = 1.66 °C, X₂ = 56.82 °C, and X₃ = 1505.43 mm/min, indicating its outstanding geometric accuracy and robustness. In conclusion, raw flour requires higher hydration and lower temperatures to prevent excessive viscosity. In contrast, extruded flour benefits from low water and high temperatures to achieve stable structures and firm textures. These findings demonstrate the feasibility of using Andean fava bean flour in 3D food printing to create nutrient-dense, functional foods with improved printability. This work offers practical applications for developing personalized foods—such as customized meals for individuals with specific dietary requirements—while contributing to sustainable and secure food production. Future research should address long-term storage, post-printing drying methods, and scaling production. Full article

As potato is an important crop, potato disease detection and classification are of key significance in guaranteeing food security and enhancing agricultural production efficiency. Aiming at the problems of tiny spots, blurred disease edges, and susceptibility to noise interference during image acquisition and transmission in potato leaf diseases, we propose a CBSNet-based potato disease recognition method. Firstly, a convolution module called Channel Reconstruction Multi-Scale Convolution (CRMC) is designed to extract the upper and lower features by separating the channel features and applying a more optimized convolution to the upper and lower features, followed by a multi-scale convolution operation to capture the key changes more effectively. Secondly, a new attention mechanism, Spatial Triple Attention (STA), is developed, which first reconstructs the spatial dimensions of the input feature maps, then inputs the reconstructed three types of features into each of the three branches and carries out targeted processing according to the importance of the features, thereby improving the model performance. In addition, the Bat–Lion Algorithm (BLA) is introduced, which combines the Lion algorithm and the bat optimization algorithm and makes the optimization process more adaptive by using the bat algorithm to adjust the gradient direction during the updating process of the Lion algorithm. The BLA not only boosts the model’s ability to recognize potato disease features but also ensures training stability and enhances the model’s robustness in handling noisy images. Experimental results showed that CBSNet achieved an average Accuracy of 92.04% and a Precision of 91.58% on the self-built dataset. It effectively extracts subtle spots and blurry edges of potato leaf diseases, providing strong technical support for disease prevention and control in large-scale potato farming. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article

Lawful Interception refers to the acquisition of the contents of communications between private individuals or organizations by subjects authorized by law. It involves three actors: the network operator (NO), the Law Enforcement Agency (LEA), and the Law Enforcement Monitoring Facility (LEMF). In the literature, standards and scientific solutions are proposed for the interception procedure and the interaction between the NO and the LEMF. However, no standard has been proposed for the interaction between the LEMF and the LEA. The absence of standards for controlling LEA (or a delegated agency) access to intercepted contents stored by the LEMF is a significant gap that should be overcome. This prevents the implementation of secure, interoperable, and automated procedures, leading to inefficiencies and security risks. In this paper, we propose to cover the above gap by adopting the Self-Sovereign Identity (SSI) paradigm. The adopted research methodology follows a multi-phase approach that includes studying existing solutions, system design, and technical feasibility testing. The study first examines existing standards and identity management frameworks and their limitations. Next, an SSI-based architecture is proposed to manage the interactions between LEA (or a delegated agency) and LEMF. Finally, a proof of concept of the proposed solution written in Python and using the Hyperledger Indy blockchain has been implemented to assess whether our proposal is technically feasible. The proposed solution enhances automation, security, and interoperability in lawful interception. Indeed, it enables machine-readable authorizations, reducing errors and improving efficiency by eliminating manual operations. Additionally, verifiable credentials and decentralized identifiers strengthen security and standardize interactions across jurisdictions, ensuring privacy-preserving identity management. By standardizing interactions between LEA and LEMF, this research contributes to a more secure, privacy-preserving, and legally compliant lawful interception process. Full article

The Psychosocial Rehabilitation Project (PRP) is a tool designed to structure and organize mental health care, guided by the theoretical and practical principles of Psychosocial Rehabilitation (PR). This article aims to identify the initial requirements for the prototyping of a “Psychosocial Rehabilitation Project App”. To achieve this, an integrative review was conducted with the research question: what initial requirements are important to compose the prototype of the “Psychosocial Rehabilitation Project App” in mental health? In the search process, 834 articles were identified and exported to the online systematic review application Rayyan QCRI, resulting in 36 eligible articles for this study, along with one app. The reading of this material allowed the elicitation of three themes: privacy and data protection policy; design; and software and programming. The prototyping of the “Psychosocial Rehabilitation Project App” should prioritize data security and protection, simplicity in design, and the integration of technological resources that facilitate the management, construction, monitoring, and evaluation of psychosocial rehabilitation projects by mental health professionals. Full article

Rice is a vital component of the Ecuadorian diet and plays a significant role in global food security. Agricultural practices aimed at boosting production can, however, compromise grain quality. This study explores the effects of various pest control methods on the nutritional and biochemical quality of both white and brown rice. Compounds such as total phenolics (TPC), dietary fiber (TDF), gamma oryzanol, phytic acid (PA), antioxidant activity (AA), and the presence of heavy metals were analyzed. The research was carried out in Taura, Guayas Province, Ecuador, using a bifactorial experimental design with three replications. Statistical analysis included ANOVA and Tukey HDS tests (α = 0.05), complemented by PCA biplot analysis for comprehensive data exploration. The results highlight significant differences in all variables depending on the rice type (R), with brown rice exhibiting higher values. In terms of pest control type (C), only phytic acid showed significant variance, an effect also evident in the interaction (R × C). Cadmium (<0.30 mg/kg) and arsenic (<0.55 mg/kg) levels remained below national standards, with no significant differences across treatments. The biplot analysis revealed PA independence, with higher values in grains managed with biological control, whereas those under chemical control displayed slightly higher and varied values for other studied variables. Full article

With the continuous development of UAV technology, the application of UAV swarm has gradually become the focus of research all over the world. Although UAV swarm provides some advantages in terms of autonomous collaboration, the traditional UAV management technology suffers from security challenges, including the risk of single points of failure due to centralized control, which makes UAV swarm susceptible to hacker attacks. Due to some advantages of blockchain, such as decentralization, tamper-proof characteristics, and traceability, it is applied to the drone swarm to solve some security challenges brought about by centralized management. However, blockchain cannot achieve secure access control on the data it stores, which may leak some crucial data. Therefore, a secure and efficient access-control model based on blockchain and ciphertext-policy attribute-based encryption (CP-ABE) is proposed, and a secure data-access scheme is designed under this model, which can not only prevent the leakage of critical data but also realize lightweight access control. Moreover, to improve the decryption efficiency of the data user, an outsourcing-based data decryption scheme is also studied, in which the complex calculations are completed by the data user agency. The experiments show that when the number of attributes is 60, the computation cost of the proposed scheme is 0.404 s, which is much lower than the existing research, and is more suitable for the UAV swarm with limited computing power. Moreover, the communication cost of the proposed scheme is reduced by about 30% compared with the existing scheme under the same conditions. The security analysis also shows that the proposed scheme is secure and reliable, and can resist a variety of attacks such as collusion attacks, man-in-the-middle attacks, and forgery attacks. Full article