Search Results (1,039)

Since the 1960s, theories on the relationship between people and their environment have explored how elements of the built environment may directly or indirectly influence human behavior. In this context, neuroarchitecture is emerging as an interdisciplinary field that integrates neuroscience, architecture, environmental psychology, and cognitive science, with the aim of providing empirical evidence on how architectural spaces affect the human brain. This study investigates the potential of neuroarchitecture to inform environmental design by clarifying its current conceptual framework, examining its practical applications, and identifying the context in which it is being implemented. Beginning with an in-depth analysis of the definition of neuroarchitecture, its theoretical foundations, and the range of interpretations within the academic community, the study then offers a critical review of its practical applications across various design fields. By presenting a comprehensive overview of this emerging discipline, the study also summarizes the measurement techniques commonly employed in related research and critically evaluates design criteria based on observed human responses. Ultimately, neuroarchitecture represents a promising avenue for creating environments that deliberately enhance psychological and physiological well-being, paving the way toward truly human-centered design. Nevertheless, neuroarchitecture is still an emerging experimental field, which entails significant limitations. The experiments conducted are still limited to virtual reality and controlled experimental contexts. In addition, small and heterogeneous population samples have been tested, without considering human variability. Full article

This study examines how the organizational work climate shapes the effectiveness of supervision on employee performance. While traditional management theory assumes supervision universally enhances productivity, we observe a puzzling paradox: facing identical tasks and wage systems, some firms rely heavily on hierarchical supervision while others thrive with minimal oversight. Through a four-month field experiment across two Chinese agricultural enterprises (5851 observations), we test whether the supervision’s effectiveness depends on the alignment between leadership practices and organizational climate. In formal management firms (FMFs) characterized by hierarchical governance and arm’s-length employment relationships, directive supervision significantly reduces task completion times by 0.126 standard deviations, equivalent to approximately 4.3 s or 2.8% of the average completion time, with this effect remaining stable throughout the workday. Conversely, in network-embedded firms (NEFs) operating through trust-based relational contracts and social norms, identical supervisory practices yield no performance gains, as informal social control mechanisms already ensure high effort levels, rendering formal supervision redundant. These findings challenge the “best practices” paradigm in strategic HRM, demonstrating that HR success requires a careful alignment between leadership approaches and the organizational climate—an effective HR strategy is not about implementing standardized practices but about achieving a strategic fit between supervisory leadership styles and existing work climates. This climate–leadership partnership is essential for optimizing both employee performance and organizational success. Full article

Digital wellbeing has been the subject of extensive research in educational contexts. Yet, there remains a paucity of studies conducted within the paradigm of generative AI, a field with the potential to significantly influence students’ sentiments and dispositions in this domain. This study analyses 474 student recommendations (information science and library science) for digital wellbeing in generative artificial intelligence. The research is based on the context of pragmatism, which rejects the differentiation between thinking and acting and ties both phenomena into one interpretive whole. The research method is thematic analysis; students proposed rules for digital wellbeing in the context of generative AI, which was followed by the established theory. The study has identified four specific areas that need to be the focus of research attention: societal expectations of the positive benefits of using generative AI, particular ways of interacting with generative AI, its risks, and students’ adaptive strategies. Research has shown that risks in this context must be considered part of the elements that make up the environment in which students seek to achieve balance through adaptive strategies. The key adaptive elements included the ability to think critically and creatively, autonomy, care for others, take responsibility, and the reflected ontological difference between humans and machines. Full article

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

Each year an estimated 440,000 medical errors occur in the U.S., of which 38% are a direct result of patient matching errors. As patients seek care in medical facilities, their records are often dispersed. Health Information Exchanges (HIEs) strive to retrieve and consolidate these records and as such, accurate matching of patient data becomes a critical prerequisite. Artificial intelligence (AI) is increasingly being seen as a potential solution to this vexing challenge. We present findings from an exploratory field study involving interviews with 27 HIE executives across the U.S. on tensions they are sensing and balancing in incorporating AI in patient matching processes. Our analysis of data from the interviews reveals, on the one hand, significant optimism regarding AI’s capacity to improve matching processes, and on the other, concerns due to the risks associated with algorithmic biases, uncertainties regarding AI-based decision-making, and implementation hurdles such as costs, the need for specialized talent, and insufficient datasets for training AI models. We conceptualize this dialectical tension in the form of a grounded theory framework on Cautious AI Optimism. Full article

The aim of this manuscript is to introduce the fractional integral inequalities of H-H types via multiplicative (Antagana-Baleanu) A-B fractional operators. We also provide the fractional version of the H-H type of the product and quotient of multiplicative superquadratic and multiplicative subquadratic functions via the same operators. Superquadratic functions, have stronger convexity-like behavior. They provide sharper bounds and more refined inequalities, which are valuable in optimization, information theory, and related fields. The use of multiplicative fractional operators establishes a nonlinear fractional structure, enhancing the analytical tools available for studying dynamic and nonlinear systems. The authenticity of the obtained results are verified by graphical and numerical illustrations by taking into account some examples. Additionally, the study explores applications involving special means, special functions and moments of random variables resulting in new fractional recurrence relations within the multiplicative calculus framework. These contributions not only generalize existing inequalities but also pave the way for future research in both theoretical mathematics and real-world modeling scenarios. Full article

Leaf Area Index (LAI) is a key variable in modeling plant growth because it is the site of photosynthesis. However, there are significant differences in LAI between different models and between models and satellite-derived estimates. Empirical studies show that LAI is closely related to temperature. The theory provides an alternative method for predicting steady-state LAI. We have implemented this theory in a simple universal model for estimating the growth of the soybean leaf area index (LAI). This study presents a novel, pivotal parameter for assessing plant growth and productivity. We hypothesized that the maximum leaf area index for a specific variety is a constant value. In 2021, a field experiment was conducted at the Liaoning Jinzhou Agricultural Meteorological Experimental Station, where soybean cultivation was manipulated across seven distinct sowing dates, using the local traditional sowing date as a baseline. The model developed in this study demonstrated remarkable accuracy in LAI estimation across various growth stages and environmental conditions. Our findings reveal a robust correlation between the model’s predictions and actual LAI measurements. This model serves as a reliable tool for researchers and agronomists to monitor and predict soybean growth, thereby facilitating more informed decision-making in agricultural management. Full article

Considering that the information contained in the interference structure of the “target-receiver” path in active sonar is crucial for remote sensing of the target position or the environmental information, this paper studies the method for coherent extraction and enhancement of the interference structure of the scattered sound field using a monostatic horizontal line array (HLA) in deep water. The HLA element–frequency domain sound intensity interference pattern of the monostatic scattered sound field is numerically simulated, and the “cutting” effect on the pattern is explained by combining the scattered sound pressure expression. Then, the mechanism of the sound propagation effect of the “source-target” path on the interference structure of the “target-receiver” path is clarified. In deep water, the phase relationship of the HLA scattered sound pressure is derived based on the ray theory, and its similarity with the phase relationship of the array passive received signals affected by the source spectrum is researched. The method for the coherent enhancement of the interference structure between the target and the reference array element for the deep-water active sonar is proposed, which uses the phase information of the single-element (SE) signal to generate the array cross-correlation data and then performs striation-based beamforming on it (i.e., the striation–correlation-based beamforming with single element, SCBF-SE). The results of numerical simulation and sea trial data analysis show the effectiveness of this method for interference structure enhancement. The performance differences between SCBF-SE and the incoherent accumulation of the striation energy (IASE) method in interference structure enhancement are compared. The results indicate that SCBF-SE has better performance under the conditions of the same received signal-to-noise ratio and the number of array elements. Full article

Semantic communication (SemCom), as a revolutionary paradigm for next-generation networks, shifts the focus from traditional bit-level transmission to the delivery of meaning and purpose. Grounded in the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model and its mapping framework, together with the relativity of understanding theory, the discussion systematically reviews advances in semantic-aware communication and personalized semantic security. By innovatively introducing the “Purpose” dimension atop the classical DIKW hierarchy and establishing interlayer feedback mechanisms, the DIKWP model enables purpose-driven, dynamic semantic processing, providing a theoretical foundation for both SemCom and personalized semantic security based on cognitive differences. A comparative analysis of existing SemCom architectures, personalized artificial intelligence (AI) systems, and secure communication mechanisms highlights the unique value of the DIKWP model. An integrated cognitive–conceptual–semantic network, combined with the principle of semantic relativity, supports the development of explainable, cognitively adaptive, and trustworthy communication systems. Practical implementation paths are explored, including DIKWP-based semantic chip design, white-box AI evaluation standards, and dynamic semantic protection frameworks, establishing theoretical links with emerging trends such as task-oriented communication and personalized foundation models. Embedding knowledge representation and cognitive context into communication protocols is shown to enhance efficiency, reliability, and security significantly. In addition, key research challenges in semantic alignment, cross-domain knowledge sharing, and formal semantic metrics are identified, while future research directions are outlined to guide the evolution of intelligent communication networks and provide a systematic reference for the advancement of the field. Full article

This paper defines the term “graffiti” and outlines the origins of this concept. The terminological arrangement allowed for the subject of this research, i.e., illegal graffiti, to be situated in reality, i.e., an urban space. It was assumed that the existence of the tag was associated with a disturbance of spatial order and had an impact on safety in a space. This, in turn, is related to whether the principles of sustainable development in the social dimension are applied. This paper makes reference to theories of security in a space (the “broken windows” theory and the strategy of Crime Prevention Through Environmental Design, CPTED) and shows the problem of illegal graffiti against the background of these theories. A new research aspect of the occurrence of illegal graffiti (scribbles and tags) within urban space is the features that determine its emergence in a spatial dimension. The aim of the analyses in this paper is to obtain information on which geospatial features are generators of illegal graffiti. The research field was limited to the space of one city—Olsztyn—with the assumption that the proposed research methodology would be useful for the spaces of other cities. The research methodology consists of several steps: firstly, we determined a list of features in the surroundings of illegal graffiti using direct interviews, and secondly, we analyzed the frequency of occurrence of these features in the researched locations in space. The next step was to standardize the obtained results using the quotient transformation method with respect to a reference point, where the reference point is the sum of all observations. After that, we assigned ranks for standardized results. The last stage involved an analysis using the GRA type of grey systems to obtain a sequence of strengths of relationships. This sequence allowed us to determine which of the features adopted for analysis have the greatest impact on the creation of illegal graffiti in a space. As indicated by the strength of the relationship, in the analyses conducted, geospatial features such as poor sidewalk condition and neglected greenery have the greatest impact on the occurrence of illegal graffiti. Other features that influence the occurrence of illegal graffiti in a given space include a lack of visibility from neighboring windows and the proximity of a two-way street. It can be assumed that these features are generators of illegal graffiti in the studied area and space. The poor condition of the facade has the least impact on the possibility of illegal graffiti occurring in a given space. Full article

Smallholder farmers, key actors in agri-food supply chains, still face persistent challenges in applying sustainability strategies due to limited resources, context variability, and weak-performance monitoring systems. Their multidimensional needs, across economic, environmental, and social domains, are frequently inadequately captured by traditional key performance indicators (KPIs). This paper proposes an innovative framework to prioritize KPIs tailored to smallholders by integrating a multi-capital approach with expert-based and contradiction-resolving methods. A five-phase methodology is developed that combines Multi-Expert Fuzzy Quality Function Deployment (FQFD) and the Theory of Inventive Problem Solving (TRIZ). Expert input and field data identified 30 KPIs, narrowed to 19 via a capital-constrained algorithm; TRIZ resolved key contradictions like global warming versus land use efficiency. Expert input and field data are used to identify the sustainability capitals and KPIs, which are then ranked using FQFD and filtered using a capital-constrained algorithm. TRIZ is then used to address contradictions between indicators. Applied to a case study, the framework successfully identified a ranked, coherent set of sustainability KPIs. The sensitivity analysis confirmed the stability of the prioritization. TRIZ offered innovative solutions to trade-offs between key indicators (such as environmental impact versus productivity). This is the first known integration of FQFD and TRIZ in sustainability KPIs for smallholders. This approach is adaptable and replicable within similar agricultural contexts, thereby allowing informed and context-sensitive planning for sustainability. It provides actionable insights to guide smallholder-focused agricultural policies globally. Full article

In the context of the high development of mobile internet and social media, the social clocking and photographing function of tourism applications has become a key factor to enhance user experience and enhance product competitiveness. Citywalk, as a new way of exploring cities, emphasizes individuality and social interaction by providing a walking experience of the city’s history and culture. This study is based on the Technology Acceptance Model, combined with the Use and Gratification Theory, to systematically explore the core mechanisms that influence user acceptance and continued use of the social check-in and photo-taking function in Citywalk-related applications (app). Firstly, this article analyzes the impact of perceived usefulness and perceived ease of use on user technology adoption through a technology acceptance model. At the same time, the five major needs of use and satisfaction theory (information needs, entertainment needs, social interaction needs, identity confirmation needs, and escapism needs) are introduced as external influencing variables to construct an optimized technology acceptance model. Secondly, based on this theoretical framework, this article proposes relevant research hypotheses and designs a questionnaire for empirical analysis. Reliability analysis, validity analysis, and regression analysis are used to verify the relationship between influencing factors and user behavior. The research results reveal relevant research questions, namely, the core factors influencing users’ use of social check-in and photo-taking functions (RQ1), elucidating the mechanism of technology perception on user satisfaction and willingness to continue using (RQ2), and identifying the acceptance gap between user needs and actual experience in existing feature designs (RQ3). At the same time, this article provides optimization strategies for the Citywalker App (Version 1.0) and similar products to enhance user experience, strengthen social communication effects, and promote market promotion. Ultimately, this study aims to provide theoretical support and practical guidance for the design of tourism social media functions and promote innovative development in related fields. Full article

Contextual features play a critical role in geospatial object detection by characterizing the surrounding environment of objects. In existing deep learning-based studies of 3D point cloud classification and segmentation, these features have been represented through geometric descriptors, semantic context (i.e., modeled by an attention-based mechanism), global-level context (i.e., through global aggregation), and textural representation (e.g., RGB, intensity, and other attributes). Even though contextual features have been widely explored, spatial contextual features that explicitly capture spatial autocorrelation and neighborhood dependency have received limited attention in object detection tasks. This gap is particularly relevant in the context of GeoAI, which calls for mutual benefits between artificial intelligence and geographic information science. To bridge this gap, this study presents a spatial autocorrelation encoder, namely SA-Encoder, designed to inform 3D geospatial object detection by capturing spatial autocorrelation representation as types of spatial contextual features. The study investigated the effectiveness of such spatial contextual features by estimating the performance of a model trained on them alone. The results suggested that the derived spatial autocorrelation information can help adequately identify some large objects in an urban-rural scene, such as buildings, terrain, and large trees. We further investigated how the spatial autocorrelation encoder can inform model performance in a geospatial object detection task. The results demonstrated significant improvements in detection accuracy across varied urban and rural environments when we compared the results to models without considering spatial autocorrelation as an ablation experiment. Moreover, the approach also outperformed the models trained by explicitly feeding traditional spatial autocorrelation measures (i.e., Matheron’s semivariance). This study showcases the advantage of the adaptiveness of the neural network-based encoder in deriving a spatial autocorrelation representation. This advancement bridges the gap between theoretical geospatial concepts and practical AI applications. Consequently, this study demonstrates the potential of integrating geographic theories with deep learning technologies to address challenges in 3D object detection, paving the way for further innovations in this field. Full article

This paper is based on the fish lateral line sensing mechanism and aims to determine the coupling relationship between the flow field sensing signal and target source position information. Firstly, according to the flow field distribution characteristics of the target source, the equivalent multipole model of the flow field disturbance during the underwater motion of the SUBOFF model is constructed, and then the target localization function based on the least squares method is established according to the theory of potential flow, and the residual function of the target localization is solved optimally using the quasi-Newton method (QN) to obtain the estimated position of the target source. On this basis, a curved bionic lateral line sensing array is constructed on the surface of a robotic fish, and the estimated location of the target source is obtained. The curvilinear bionic lateral line sensing array is constructed on the surface of the robotic fish, and the effectiveness and robustness of the above localization methods are analysed to validate whether the fish lateral line uses the pressure change to sense the underwater target. Full article

Background and Objectives: One of the most critical areas of measurement and evaluation in healthcare is pathological evaluation, especially intraoperative consultation. Studies conducted to identify sources of error in this field are usually one-sided; however, in evaluation processes with multiple sources of error, such as intraoperative consultation, generalizability theory can evaluate these sources of error simultaneously in a single analysis, thereby contributing to the field. In this study, the reliability of intraoperative and permanent histopathological evaluations of glial tumors was analyzed using generalizability theory to identify the sources of error in the observed evaluation inconsistencies. Materials and Methods: The study included 319 glial tumor cases that underwent intraoperative evaluation and were analyzed using generalizability theory. Three pathologists performed independent evaluations in two stages. Results: The reliability coefficient calculated for all cases was 0.9234 without radiological information and 0.9243 after learning the radiological information. The reliability coefficient was 0.8875 and 0.8989, respectively, in cases over 18 years of age, and 0.8845 and 0.9062 in cases under 18 years of age. These findings indicate that the addition of radiological information to the evaluation resulted in a slight increase in reliability, particularly in cases under 18 years of age. In all of our reliability assessments for different conditions, the highest variability was found to originate from the rater. Conclusions: The findings suggest that intraoperative evaluation demonstrates a high degree of reliability in the pathological assessment of glial tumors. When differences between the rater and the technique are evaluated together, it is observed that the rater has a more significant impact on reliability. While radiological information is generally considered a factor that increases reliability, it is partially more effective, especially in cases involving individuals under the age of 18, which highlights the importance of multidisciplinary data sharing in intraoperative diagnostic processes. Full article