Search Results (305)

This study examines the conflict between two planning policies operating simultaneously but at different spatial scales: comprehensive statutory planning, which coordinates development at the neighborhood scale, and Israel’s National Outline Plan 38 (TAMA38), which promotes seismic strengthening and urban renewal at the scale of individual buildings. Using Carmeliya, Haifa, as a case study, the research investigates how the cumulative implementation of building-scale renewal affects neighborhood quality. Cumulative changes are examined at the building-cluster, street, and neighborhood scales through a multiscale evaluation framework that combines statutory-plan analysis, building-permit data, field observations, semi-structured resident interviews, three-dimensional scenario modeling, visibility analysis, and GIS-based shading analysis, using indicators of built form, public and private open space, vegetation, visual openness, privacy, street interface, shading, and infrastructure pressure. Based on these indicators, a multidimensional evaluation model was developed to connect the two planning frameworks. The findings demonstrate that projects considered acceptable individually may increase building volume and density, reduce open and green spaces, narrow distances between buildings, obstruct views, alter street character, and intensify pressure on neighborhood infrastructure. The study identifies a mismatch between the scale at which renewal policy is implemented and the scale at which its cumulative effects are experienced. It therefore proposes an integrated evaluation approach for coordinating building renewal with neighborhood planning and protecting environmental quality. Full article

The growing complexity of digital real estate platforms demands intelligent recommendation systems (RS) capable of operating in data-sparse and heterogeneous environments. While transfer learning (TL) has proven effective in general RS, its application to real estate (RE) remains limited, particularly regarding the operationalization of multi-dimensional evaluation frameworks. This study addresses these gaps by developing a TL-based real estate recommender system (RERS) utilizing a pre-trained ResNet50 architecture, trained on a locally curated dataset from Gauteng, South Africa, providing rare, data-driven insights into a pivotal emerging market economy. By transitioning from traditional label-based retrieval to high-dimensional visual feature alignment, the model mitigates class imbalance and data redundancy in fragmented property markets. The framework is validated using the proposed Trust-based Evaluation (T-EVAL) methodology, demonstrating the efficacy of deep learning architectures in providing reliable and trustworthy property recommendations within emerging market economies. Full article

Social media has become an important tool for understanding visitor experiences in museums and cultural heritage sites. This literature review identifies, organizes, and thematically synthesizes existing studies on museum visitor experience based on social media data. It examines 41 studies retrieved from Scopus and Web of Science and published between 2017 and 2026. Furthermore, the review examines the selected studies across six dimensions: social media platforms, types of user-generated data, the role of digital interactions, the museums and cultural heritage sites studied, the analytical methodologies applied, and the main findings on visitor experience. The findings indicate that TripAdvisor is the most frequently used platform for collecting textual reviews and star ratings, whereas Instagram and Flickr are mainly used for visual and spatial data. Most studies rely on computational methods, often combined with quantitative techniques, while qualitative approaches are used less frequently. The identified methods include content analysis, statistical analysis, sentiment analysis, topic modeling, machine learning, image analysis, and spatial analysis. Across the reviewed studies, visitor experience is examined as a multidimensional phenomenon encompassing emotions, service quality, authenticity, historical connection, aesthetics, education, and social participation. Finally, the review identifies recurring themes across the dimensions and synthesizes them into broader research streams. These are brought together in an integrative synthesis framework that organizes existing research, highlights research gaps, and outlines directions for future studies. Full article

Selecting an appropriate visualization technique for multidimensional and multivariate financial and accounting (F&A) data remains a complex, user-dependent task. The TaxUI&BV4FADA taxonomy previously organized this problem along four dimensions—visualization techniques, user intentions and analytical goals, interaction possibilities, and user groups—but as a human-readable structure, it could not be queried, validated, or integrated into semantic decision-support pipelines. This paper presents an ontological framework that extends TaxUI&BV4FADA into a machine-readable OWL DL artifact authored in WebProtégé, with OWL used for semantic structuring and SPARQL used for score-based recommendation retrieval. The framework formalizes the four taxonomy dimensions and adds a decision-support layer and an evaluation layer. An explicit F&A semantic mapping is provided, and two contrasting worked scenarios—a financial analyst testing a gross-margin hypothesis and a CFO seeking a quarterly overview—show that the framework discriminates between F&A roles and analytical tasks. The evaluation demonstrates logical consistency, competency-question satisfaction, and internal consistency of the populated recommendation matrix against taxonomy-derived expectations, rather than independent empirical recommendation accuracy. This constitutes an internal, artifact-centered validation rather than an external empirical study with end users, and a protocol for future empirical validation with financial and accounting professionals is outlined. The framework provides a domain-oriented semantic and matrix-based decision-support foundation on which executable F&A visualization recommenders can be built. Full article

Integrating traditional cultural heritage, such as Chinese paper-cutting, into modern product design is a critical challenge. To address the lack of a systematic method, this study proposes the PPI-SCAMPER method: a structured Design-by-Analogy (DbA) method that integrates knowledge bases (a Paper-Cutting Element Category and a Flat Process Category) with SCAMPER heuristics. Subsequently, an empirical study involving 18 experts and 146 users was conducted to evaluate the method’s outcomes. Multimodal data—including expert ratings of product attributes, user ratings of attractiveness, and eye-tracking data—were collected, and hierarchical regression and Random Forest models were employed to analyze the underlying mechanisms of attractiveness. The results indicate that products designed via PPI-SCAMPER achieved significantly higher ratings for user attractiveness, novelty, and culture attribute. Eye-tracking data revealed that the improved products elicited greater overall visual exploration. The exploratory analysis of associative patterns showed that product attributes constituted the primary basis for attractiveness judgments, with eye-tracking data providing significant yet limited incremental explanatory power. This study not only constructs an innovative design pathway for integrating paper-cutting culture from a DbA perspective but also provides a comprehensive framework for evaluating culturally inspired innovations through multi-dimensional empirical data. Full article

Whereas environmental effects on driver cognition have been extensively studied in ground transportation, research linking low-altitude visual environment characteristics to pilot cognitive load (CL) in urban air mobility (UAM) remains scarce. This study combines multimodal physiological data with explainable machine learning to elucidate how low-altitude visual environments influence pilots’ CL. First, a CL quantification framework integrating electroencephalography (EEG) and eye-tracking data is developed to capture real-time cognitive dynamics during flight. Second, multidimensional visual environment indicators are extracted from low-altitude urban landscape images captured during simulated flights using computer vision techniques. These indicators, combined with flight dynamics features, serve as input variables for constructing pilot CL prediction models via machine learning approaches. The results demonstrate that a Bayesian-optimized XGBoost model achieves superior predictive performance. Further interpretability analysis based on SHAP reveals that environmental contrast and the visibility of buildings and water bodies are key factors influencing pilot CL. Additionally, significant interaction effects are also identified among spatial morphology, color characteristics, and landscape typology, with certain landscape elements exhibiting marked variations in both importance and directional influence across different low-altitude flight scenarios. These findings inform low-altitude route optimization, urban morphological regulation, and blue-green infrastructure configuration, advancing an air-ground synergistic planning paradigm. Full article

In this paper, an optimization method is proposed for the switching of the main and auxiliary branches of a Novel Controllable Commutation Converter (CLCC). Based on the theoretical model of commutation failure, the AC-side ground fault is decomposed into a DC component, harmonic component and overvoltage component, and a multidimensional fault dataset is constructed by combinatorial analysis. Using the Radviz visual multidimensional data analysis method, the correspondence between the control-and-protection thresholds of CLCC main- and auxiliary-branch switching under multi-component conditions is established, and the optimal switching thresholds under different short-circuit conditions of the AC system are determined. Finally, the validity of the method is verified by a field artificial short-circuit test, which provides theoretical basis and technical support for the reasonable determination of field setting. Full article

Exposure to high-quality visual environments characterized by features such as structural order, biophilic/natural elements, and positive atmosphere is important for walking experience within community life circles (CLCs). However, compared with the static configuration of visual environments within CLCs, dynamic walking-based exposure may highlight unpredictable encounter areas and heterogeneous environmental quality. Neglecting this mismatch may misdirect environmental interventions and limit their health-promoting potential. This study aims to integrate multidimensional visual environment features into interpretable scene clusters to improve comparability and measure configuration–exposure mismatches across CLCs at scale. We examine 1262 CLCs in Harbin, China, identifying visual scene clusters from 67,840 street-view images and extracting exposure frequencies from 981,500 mobility tracks. The results show that (1) eight scene clusters effectively describe the complex visual environments of CLCs; (2) significant small-to-moderate mismatches exist between configuration and exposure; (3) the trend of commute-related walking activity is often consistent with strengthened exposure to high-disorder scenes and weakened exposure to some high-quality scenes with positive atmospheres. This study provides a data-driven framework for identifying mismatches in both the intensity and spatial distribution of visual scene configuration and exposure, supporting refined community environmental governance. Full article

Dynamic atomic force microscopy (AFM) is a key technique for nanoscale characterization and mechanical property measurement, where the geometric shape of the probe tip critically determines imaging quality and measurement accuracy. This study proposes a tip shape classification framework based on the dynamic response of the AFM microcantilever. First, a dimensionless dynamic model of the microcantilever is established, and its vibrational response is solved using a finite-difference scheme. For conical, spherical, and flat tip geometries, interaction force models are provided under both non-contact and tapping-mode AFM. Based on these formulations, multidimensional dynamic feature parameters, including amplitude, phase, virial, and root-mean-square force, are extracted. On this basis, an XGBoost-based classifier is constructed for tip shape identification, and the model’s decision-making mechanism is further interpreted through a SHAP-based explainability framework combined with dimensionality reduction and visualization techniques. Results show that, under non-contact conditions, the overall classification accuracy on the test set reaches 96.7%, with a 100% recognition rate for conical tips. Under tapping-mode conditions, the classification accuracies for conical, spherical, and flat tips are 100%, 85.5%, and 98.3%, respectively. The results demonstrate the feasibility of identifying tip shapes from dynamic responses using simulated data, thereby establishing a theoretical and methodological basis for future experimental validation and the development of tip diagnostic techniques. Full article

Hearing-functioning problems contribute substantially to disability and rehabilitation needs globally; however, evidence describing the range and interconnected nature of these problems across African populations remains fragmented. This study aimed to describe hearing-functioning problems associated with high-burden adult health conditions across African populations using data from the Rehab4All database. A secondary exploratory analysis was conducted using data extracted from a large Africa-wide scoping review of functioning problems associated with conditions contributing substantially to years lived with disability (YLD). The parent review systematically searched PubMed, Scopus, Web of Science, EBSCOhost, and SABINET and included English-language studies conducted in African adults. Hearing-functioning problems were classified according to the International Classification of Functioning, Disability and Health (ICF). Descriptive analyses, visual mapping techniques, and exploratory random-effects prevalence meta-analyses were performed. Ninety-seven articles published between 2007 and 2022 were included. Most studies originated from Southern Africa, particularly South Africa. Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), tuberculosis (TB), diabetes mellitus (DM), hearing-loss cohorts, and headache-related conditions contributed most frequently to reported hearing-functioning problems. Hearing loss, tinnitus, and vestibular dysfunction were the most commonly reported problems; however, multidimensional co-occurring auditory and vestibular profiles were frequently identified. Distinct functional patterns emerged across conditions, with TB demonstrating predominantly auditory dysfunction, while HIV/AIDS and DM demonstrated broader auditory–vestibular involvement. Hearing-functioning problems within the African evidence base appear multidimensional, interconnected, and unevenly distributed geographically. Full article

Addressing the issues of insufficient adaptability and limited energy efficiency optimization capabilities in traditional tunnel lighting control methods under complex traffic conditions, this paper proposes a dynamic dimming strategy for tunnel lighting based on the Proximal Policy Optimization (PPO) algorithm. First, the tunnel lighting system is modeled as a reinforcement learning environment. A state space integrating multidimensional information—including traffic flow, vehicle speed, external luminance, and tunnel section location—is constructed, and a continuous action space is designed to enable precise dimming control for each functional section. Based on this, a multi-objective reward function is established that integrates luminance tracking error, energy consumption optimization, control stability, and environmental adaptability to guide the agent in learning the optimal dimming strategy. Subsequently, model training and experimental validation were conducted using actual tunnel operation data. Experimental results show that, compared with the conventional $L_{20}$ strategy, the proposed method achieves significant energy savings during the 10:00–17:00 period, with the energy-saving rate remaining above 20% for most of the time from 11:00 to 16:00 and peaking at nearly 24%, while ensuring driving safety and visual comfort. In summary, the PPO-based dynamic dimming strategy demonstrates promising application prospects and engineering value in intelligent tunnel lighting systems. Full article

Real-world low-light scenarios are complex, and annotated data is scarce. Meanwhile, existing supervised and mainstream unsupervised low-light image enhancement methods typically rely on large-scale paired labeled or unpaired normal-light data for training, which constrains the cross-scene generalization capability of these models. Furthermore, zero-shot low-light enhancement methods based on Retinex theory still exhibit notable performance shortcomings in dark-region noise suppression and illumination component estimation accuracy. To address these challenges, this paper proposes a zero-shot architecture for low-light image enhancement based on dual-domain synergistic downsampling and dual-branch feature calibration, which effectively resolves the core dilemma of the inaccessibility of annotated training data. Specifically, we construct a dual-domain downsampling mechanism with frequency-domain and wavelet complementarity, which provides effective priors for pre-denoising to suppress noise. A dual-branch feature calibration module centered on bidirectional correction and gated weighting is designed to achieve high-fidelity halo-free illumination estimation. To tackle the problems of color distortion and insufficient enhancement in extremely dark scenes, we further propose a multi-dimensional constrained naturalization enhancement module. Extensive experiments on the LOL-v1 and LOL-v2 datasets demonstrate that the proposed method achieves outstanding real low-light enhancement performance and competitive visual results. Full article

This paper examines the relationship between learning environments and changes in Information and Communication Technologies (ICT)-related career aspirations across education systems in the context of ongoing digital transformation. The analysis uses country-level data from the Programme for International Student Assessment (PISA) 2018 and 2022, combining indicators of student autonomy, digital skills and teacher support. Digital readiness is operationalized as a latent country-level construct combining digital skills, student autonomy and perceived teacher support. The study uses regression analysis as the primary associational approach, complemented by clustering and latent-representation techniques used for exploratory profiling and visualization. Unlike prior research that treats learning loss, digital skills and career expectations separately, our analysis integrates them within a comparative longitudinal framework. It shifts the focus from short-term post-pandemic effects toward the broader capacity of education systems to support digital preparedness and future-oriented career expectations. Digital skills were consistently associated with ICT aspiration growth across education systems, while teacher support played a complementary contextual role. Autonomy shows weaker, context-dependent effects. The findings support interpreting digital readiness as a multidimensional construct reflecting combinations of digital skills, autonomy and teacher support. Full article

Radiation monitoring at contaminated industrial sites is often restricted by safety, access, and operational constraints. Under such conditions, a modelling approach that can use a limited number of field measurements is useful for preliminary screening, route planning, and prioritization of verification surveys. This study presents a sparse spatiotemporal polynomial interpolation model for estimating the gamma radiation equivalent dose rate (EDR) along the perimeter of the Base-S radiation-hazardous industrial site. The model represents EDR as a function of spatial coordinates and time, and uses a reduced measurement structure consisting of four seasonal temporal nodes and five representative spatial nodes. The reduced structure is intended to support conservative preliminary assessment under the ALARA principle, not to replace field measurements. A 2021 case study is presented for 61 numbered perimeter points. The article presents one of the universal mathematical models developed by the authors to determine the impact of gamma radiation on the personnel of tailings facilities and industrial sites through the calculation of the equivalent dose rate during personnel residence stays, depending on time. The proposed polynomial interpolation model for rapid radiation dose assessment at radiation-hazardous industrial sites estimates equivalent dose-rate values for a specific planning case. The model represents the EDR field as a spatiotemporal polynomial f(x, y, t), where x and y are planar coordinates, and t is the day of the year. A conservative reduced scheme uses four seasonal maximum values and five representative spatial points to decrease the number of required field measurements and personnel residence time. For the 2021 case study, the model-estimated EDR at 61 numbered perimeter points ranged from 0.118 to 0.415 µSv/hour, with a mean of 0.242 µSv/hour. This model provides initial data for building a 2D model and, if necessary, a 3D model of radiation contamination within the research-object territory. The resulting 2D and 3D maps are interpreted as model-estimated visualization products. The proposed method, the model form of which is described as a cubic polynomial in t and a quadratic in x,y, allows for effective interpolation of complex multidimensional dependencies of observed data. Full article

Interpretable machine learning for ordinal quality grading faces a fundamental tension between model transparency and predictive performance, particularly under small-data conditions where end-to-end deep learning is unreliable and domain knowledge must compensate for limited training samples. We present a dual-target feature engineering framework for interpretable ordinal grading validated on pane Carasau, a traditional flatbread whose extreme surface variability makes it a challenging small-data benchmark for machine learning under realistic acquisition constraints. The pipeline extracts 116 handcrafted visual descriptors organised into four families—colour, texture, spatial, and hotspot—and grades the quality along two independent axes: global toasting intensity and spatial uniformity, complemented by a continuous Toasting Index for process monitoring, on a dataset of 1512 images spanning four acquisition campaigns and three product types. On the primary within-batch evaluation set Campaign 01, $N=1090$), XGBoost achieves F1 macro $=0.906$ for toast classification and $R^2=0.886$ for continuous regression, substantially outperforming two fine-tuned CNN baselines on the same evaluation set (MobileNetV2: F1 $=0.523$; EfficientNet-B0: F1 $=0.518$). Feature importance analysis reveals that colour descriptors dominate toasting prediction ($87.5\%$), whilst spatial and texture features are essential for uniformity assessment ($47.4\%$ combined), providing physically grounded explanations directly traceable to the underlying thermal process. Cross-batch generalisation on held-out campaigns is moderate for the same product (XGB F1 = 0.718, $\kappa$ = 0.703); cross-product transfer to geometrically distinct variants requires product-specific adaptation. The framework requires no GPU, runs on standard CPU hardware at 4 s per image, and provides complete decision transparency, supporting deployment without specialised hardware. Full article