Search Results (26,811)

This study proposes a methodology for developing nature-inspired color palettes for timeless residential interiors based on classical principles of beauty and chromatic harmony. The research employed a literature review of color theory, a survey, and a research-through-design approach. To investigate residential color preferences, a survey was conducted among older adults and undergraduate architecture students using examples of living room interiors representing different color palettes. Based on the survey findings, the color palette positively evaluated by both participant groups was identified. The results indicated a preference for a balanced palette characterized by muted tones, subtle tonal transitions, and harmonious chromatic relationships. Building on these findings, an original design methodology was developed, drawing on the observed color interactions and compositional patterns of the natural environment. The proposed methodology was subsequently applied within an AI-assisted design workflow to generate and evaluate residential interior concepts through a text-to-image process. In addition, two approaches to residential color design were compared: a traditional approach based on color theory and color-wheel harmonies, and a nature-inspired approach informed by the questionnaire findings and supported by the proposed AI-assisted design workflow. The study demonstrates how nature-inspired color relationships, classical color harmony principles, and AI-assisted visualization can be integrated into a structured methodology supporting the design of coherent and timeless residential interiors. Full article

Industrial Control Systems (ICSs), which underpin communications and operations in industrial environments in general and in critical infrastructures in particular, are targeted by malicious actors for diverse reasons. To strengthen their resilience against cybersecurity attacks, testbeds play a fundamental role in supporting experimentation, the validation of defense mechanisms, dataset generation, and architecture evaluation, among other activities. However, existing testbeds differ considerably in their functional capabilities, architectural completeness, implemented technologies, and degree of documentation, making systematic comparison and classification difficult. This paper presents a comparative analysis of 34 ICS cybersecurity testbeds within a Purdue-aligned framework based on two complementary dimensions: Functional Maturity (FM) and Architectural Completeness (AC). The former evaluates capabilities relevant to cybersecurity experimentation, while the latter measures the coverage of Purdue-aligned and supporting IT/OT components the testbed includes. To address this problem, we propose a methodology that enables the identification of technology adoption patterns, capability distributions, and recurring architectural characteristics across contemporary testbed implementations. Results reveal that many platforms provide advanced experimentation capabilities while offering only partial representation of industrial and enterprise environments. Moreover, the analysis also highlights underrepresented architectural components that may influence the scope of cybersecurity, Industrial Internet of Things (IIoT), and Industry 5.0 research that can be conducted within a testbed. Finally, based on these findings, a Purdue-aligned testbed blueprint is proposed as a reusable conceptual baseline for the design and comparison of future industrial cybersecurity experimentation environments. Full article

This study describes the principal outcomes from twelve years of a multistage research program on a solar updraft tower with an inherent focus on the system geometry and integration. It reviews the main conclusions from the different stages of this program in a synergistic manner. The core objectives and the contribution are in presenting the key findings in one document that intrinsically links the methodologies, component analysis and the development of the research question that connected the simple concept of the solar updraft tower as a system to its implementation in the local environment including the limitations. In this work, all validations were done under the closest accessible matched conditions. The first stage of the program encompassed prototype development and the verification of the system simulation model. Based on this work, a geometrical relation was established for optimizing future system designs. In the following stage, the reduction in the collector area was studied, and a new concept was introduced. Thermal concentration was included as a means for compensating the reduction in the thermal volume using a tracking mirror that reflected the solar thermal radiation onto the tower base. A simulation model was later developed to investigate the ability of the solar updraft tower to adapt to alpine terrain, which mimics the local landscape. In the final stage, the turbine blade profile was optimized and validated, but due to time and convergence limitations, it could not be incorporated into the overall system simulation. Instead, the reverse fan model was adopted with the sloped collector solar tower, imposing a margin of deviation but allowing for the prediction of an operation domain for the turbine. Full article

Background: The most effective pharmacological treatment for Parkinson’s disease (PD) remains levodopa, whose responsiveness is distinguished into two temporally and mechanistically distinct components: the short-duration response (SDR), which lasts hours, closely paralleling drug plasma concentration, and the long-duration response (LDR), which derives from prolonged administration of levodopa and persists for days to weeks after drug discontinuation. The mechanisms underlying the LDR and, in particular, the behavior in early PD patients are incompletely understood. This review aims to gather current evidence on the pharmacodynamics, neurobiological substrate, natural history, and clinical implications of the LDR, with a specific focus on early PD patients. Methods: A narrative review was conducted based on a selection of original research articles, clinical studies, and reviews identified through PubMed/MEDLINE, covering the period from January 1995 to July 2026. Studies were selected for their methodological relevance to the characterization of LDR in drug-naive or early-stage PD patients, including pharmacodynamic studies, neuroimaging investigations, and neurophysiological assessments. Results: Evidence consistently demonstrates that the LDR is a pharmacodynamically distinct component of levodopa response, present from the earliest stages of dopaminergic therapy. In drug-naive patients, the LDR represents a quantitatively substantial fraction of total levodopa benefit, and it is linked to modifications in cortical excitability, dopaminergic neuroplasticity, and motor learning. Structural neuroimaging identifies predisposing gray matter features in motor cortex regions. Whether LDR magnitude declines with disease and treatment duration, and thereby underlies the emergence of motor fluctuations, remains disputed across studies. This discrepancy appears attributable, at least in part, to differences in how the LDR is measured (direct washout assessment versus inference against a projected untreated trajectory) rather than necessarily to divergent underlying biology. Conclusions: The LDR to levodopa represents a neurobiologically and clinically significant dimension of levodopa response. Its characterization in early PD patients may refine therapeutic strategies, inform the design of neuroprotection trials, and open new possibilities for precision medicine approaches. Full article

Urban sustainability is a critical objective for rapidly growing cities in the Gulf region, particularly in balancing environmental protection, social equity, economic development, and effective governance. Riyadh has undergone extensive urban expansion, producing diverse neighbourhoods ranging from high-income, modern master-planned communities to older and denser middle to low-income areas. These socio-spatial shifts prompt questions about whether sustainability performance differs across neighbourhoods with different resident income profiles and whether wealthier neighbourhoods necessarily exhibit higher sustainability performance. This study investigates the relationship between resident income level and urban sustainability in Riyadh by comparing the sustainability profiles of high-income and middle-income neighbourhoods using a multidimensional framework. Quantitative indicators derived from international sustainability assessment models were applied to the Riyadh context. The methodology integrated expert-based weighting, neighbourhood-level indicator assessment, and Sustainability Index calculations to evaluate performance. The findings show that the high-income neighbourhood achieved a higher overall Sustainability Index, mainly due to stronger environmental and social quality. However, the middle-income neighbourhood demonstrated notable sustainability potential through economic resilience, civic engagement, and community participation. The study concludes that differences in neighbourhood sustainability profiles are associated not only with resident income level, but also with urban form, behaviour, governance, and resource-use characteristics. Full article

Background/Objectives: Preoperative risk stratification in renal cell carcinoma (RCC) remains challenging for tumor aggressiveness and for adherent perinephric fat (APF) relevant to surgical planning. Although intratumoral radiomics are established, the peritumoral and perirenal fat microenvironment is biologically active and may encode imaging biomarkers. This systematic review and meta-analysis evaluated CT-based peritumoral and perirenal fat radiomics for preoperative grading, staging, and APF prediction and examined whether fat-derived features add incremental value beyond intratumoral models. Methods: Following PRISMA 2020 and a registered protocol (PROSPERO CRD420251150155), databases were searched through March 2026, with documented verification searches through August 2026. Eligible studies extracted CT radiomics from peritumoral or perirenal fat in adults with RCC and reported discrimination metrics. Random-effects meta-analyses (restricted maximum likelihood with Hartung–Knapp intervals) were performed; incremental value was estimated from nested within-study AUC differences under predefined rules; and methodological quality was assessed with PROBAST, RQS, METRICS, and TRIPOD, and certainty with an adapted GRADE framework. Results: Twenty-five retrospective studies (10,761 patients) were included. Fat radiomics were most consistent for APF prediction (pooled AUC 0.848, 95% CI 0.738–0.917; I² = 0%). Pooled AUCs were 0.772 (0.599–0.884; I² = 93%; 95% prediction interval 0.298–0.964) for grade and 0.813 (0.681–0.899; I² = 72%) for stage. Combined fat-plus-tumor models were numerically higher than tumor-only models in 12 of 15 studies (exploratory p = 0.022), but the formally estimable nested increment (stage family, five studies) was small (delta-AUC +0.017; governing modified Hartung–Knapp 95% CI −0.008 to +0.042) and statistically compatible with no true difference; fat-only models showed no evidence of differing from tumor-only models. PROBAST rated 72% of studies at high risk of bias, and 88% originated from China. Certainty ranged from VERY LOW (grade prediction; grade-family fat-versus-tumor comparison) to LOW (all remaining outcomes). Conclusions: CT-based peritumoral and perirenal fat radiomics show the most consistent signal for APF prediction, while the measurable increment from adding fat features to tumor models is small and of unestablished clinical utility. Standardized fat segmentation, calibration and decision-curve reporting and prospective multicenter validation are required before clinical translation. Full article

Soy protein, apple fiber, and corn starch are widely used functional ingredients, yet their combined effects on food matrix properties remain poorly characterized. A D-optimal mixture design (16 runs) was used to evaluate how these three components, individually and in binary and ternary combinations, determine the proximate composition, CIELab color parameters, and techno-functional properties (water and oil absorption and solubility, pH, apparent density, emulsifying and foaming capacity, least gelation concentration, and foam stability over 120 min) of model blends. Response surface models were statistically significant for 19 of the 21 responses evaluated (R² = 0.70–1.00, p ≤ 0.05); water and oil absorption capacity did not reach significance (R² = 0.66–0.72, p > 0.05). Formulation was the main driver of the system’s behavior. Starch increased moisture, carbohydrate content, and lightness; protein determined ash, protein content, and water solubility; and fiber dominated crude fiber, lipid content, and red–yellow chromaticity, producing the greatest total color difference relative to pure starch. Unlike composition and color, which followed largely additive, single-ingredient-driven trends, interfacial functionality was governed by strong binary interactions: a synergistic protein–fiber interaction dominated emulsifying capacity (positive) and foaming capacity (strongly negative, almost completely suppressing foam formation even at protein levels comparable to the pure-protein vertex), while a synergistic protein–starch interaction enhanced foam stability. Water and oil absorption capacities varied within narrow, statistically non-significant ranges, indicating structural rather than compositional control. These findings show that mixture design and response surface methodology can quantitatively predict and tune the physicochemical, color, and functional performance of protein–fiber–starch blends, providing a practical framework for designing plant-based functional ingredients with targeted nutritional and technological profiles. Full article

Background: Melioidosis is a potentially life-threatening infection caused by Burkholderia pseudomallei. Hepatic and splenic involvement are recognized manifestations of disseminated melioidosis; however, reported prevalence varies considerably across studies, and no quantitative synthesis has been available. This systematic review and meta-analysis aimed to estimate the pooled prevalence of hepatic, splenic, and concomitant hepatosplenic involvement in patients with melioidosis. Methods: A systematic search of PubMed, Embase, and Scopus was conducted from database inception to May 2026, following the PRISMA 2020 guidelines. Observational studies reporting hepatic and/or splenic involvement among patients with confirmed melioidosis were eligible. Random-effects meta-analyses were performed to estimate pooled prevalence with 95% confidence intervals (CIs). Subgroup analyses were conducted according to geographic region and age group. Methodological quality was assessed using the Joanna Briggs Institute critical appraisal checklist. Results: Fourteen eligible studies involving 1844 patients with melioidosis were included. The pooled prevalence of hepatic involvement was 11% (95% CI, 6–18%; I² = 84.8%), splenic involvement was 15% (95% CI, 7–27%; I² = 95.8%), and concomitant hepatosplenic involvement was 13% (95% CI, 4–34%; I² = 89.5%). Subgroup analyses demonstrated significantly higher pooled prevalence of hepatic and splenic involvement in Southeast Asia than in Oceania (p = 0.0164 and p = 0.0003, respectively). No significant differences were observed between adult and pediatric populations for hepatic (p = 0.1607) or splenic involvement (p = 0.8243). Most included studies were judged to have a moderate risk of bias, and no evidence of small-study effects was identified for analyses of hepatic or splenic involvement. Conclusions: Hepatic and splenic involvement are common manifestations of melioidosis, affecting approximately one in nine and one in seven patients, respectively. Significant geographic variation suggests that disease burden is greater in Southeast Asia than in Oceania. These findings support the routine consideration of abdominal imaging in patients with suspected or confirmed melioidosis, particularly in endemic regions, to facilitate early recognition of visceral involvement. Further prospective multicenter studies using standardized imaging protocols are needed to refine prevalence estimates and improve understanding of hepatosplenic melioidosis. Full article

Wildfires represent a major environmental hazard with significant impacts on ecosystems, climate, biodiversity, and human activities. The increasing frequency and intensity of wildfire events have highlighted the need for reliable and timely detection techniques based on satellite remote sensing. This study investigates the application of Proper Orthogonal Decomposition (POD) to thermal observations acquired from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite for wildfire anomaly detection. A wildfire event that occurred on 8 August 2021 in Calabria, Southern Italy, was selected as the primary case study. To assess the consistency of the POD response beyond the primary case, the analysis was further extended to two additional wildfire events, Viggianello–Abate and Pazzano–Montestella, using the 15 × 15 pixel extraction window. Middle Infrared (MIR, 3.9 $\mathsf{\mu }$m) observations collected at 15 min intervals over a complete day were analyzed using four different spatial extraction windows (3 × 3, 15 × 15, 30 × 30, and 45 × 45 pixels). POD was employed to separate dominant background thermal variability from localized fire-induced anomalies. The analysis focused on higher-order POD modes, particularly the 6th, 7th, and 8th modes, which exhibited enhanced sensitivity to wildfire activity. Results showed that POD successfully identified thermal anomalies corresponding to wildfire occurrence times independently detected by the RST-FIRES methodology. The comparison of extraction window sizes revealed that the 15 × 15 pixel window provided the best balance between anomaly enhancement, spatial localization, and noise reduction. Larger windows introduced excessive spatial smoothing and reduced localization capability, whereas the smallest window was more affected by noise. The findings demonstrate the potential of POD as an effective complementary approach for wildfire detection and monitoring using geostationary satellite observations. Full article

Background: Sexual dimorphism in tooth size has been assessed on radiographs, dental casts, two-dimensional image analysis, and linear measurements are evaluated in two dimensions. These approaches evaluate isolated dimensions and may not adequately represent the three-dimensional complexity of tooth morphology. This study compared conventional odontometric measurements, centroid-based size assessments, and principal component-derived multidimensional size variables to determine whether overall tooth size exhibits sexual dimorphism. Methodology: A total of 120 dental casts (60 males and 60 females) were digitized using a laser surface scanner. Anatomical and geometric landmarks were identified on three-dimensional tooth models using 3D Slicer software. Mesiodistal, buccolingual, and diagonal dimensions were calculated from landmark coordinates, and two- and three-dimensional centroid sizes were derived. Principal component analysis was used to generate integrated multidimensional size variables. Sex differences were evaluated using independent Student’s t-tests, two one-sided tests (TOST) for equivalence, and random-effects meta-analysis. Results: Principal component analysis showed that conventional odontometric measurements and centroid sizes represented a common multidimensional size construct, explaining 79.1–91.3% of the variance across most tooth types. Centroid size demonstrated the highest component loadings, and principal component-derived variables correlated strongly with centroid size (r = 0.945–0.994; p < 0.001). No significant sex differences were observed for any principal component-derived size variable (p > 0.05). Equivalence testing and pooled meta-analysis demonstrated practical and statistical equivalence between males and females across principal component-derived variables, centroid sizes, and conventional odontometric measurements (all TOST p < 0.001). Conclusions: Conventional odontometric measurements, centroid sizes, and principal component-derived variables capture a common multidimensional construct of tooth size. Although isolated dimensions may differ, overall tooth size is practically equivalent between males and females. Full article

Sensitivity is a critical performance parameter of sensors used in industrial process measurement systems. Conventional sensors are typically calibrated for a predefined operating range, resulting in reduced measurement sensitivity and accuracy when users require measurements within alternate subranges of the sensor’s operating band. To address this limitation, this paper proposes an intelligent autoranging technique that dynamically enhances sensor sensitivity across user-defined measurement ranges without the need for manual recalibration. The proposed approach integrates an adaptive calibration framework based on artificial neural network (ANN) algorithms to automatically adjust the sensor response for different operating intervals. The methodology is implemented and experimentally validated using a capacitance level sensor (CLS) incorporating a 60 cm helical electrode structure for liquid-level measurement. The experiments were conducted using water as the test liquid under controlled laboratory conditions. The sensor capacitance, varying from 0.929 nF to 421 μF over a liquid-level range of 0 to 60 cm with a resolution of 0.1 cm, is converted into a measurable voltage signal ranging from 0.010939 V to 2.953 V through a dedicated signal conversion and conditioning circuit. The experimental results demonstrate that the proposed ANN-based autoranging strategy significantly improves sensor performance by increasing the full-scale sensitivity from 4.9 V/m to 58 V/m across different user-selected measurement ranges. The developed technique offers a flexible and intelligent solution for enhancing sensor sensitivity and adaptability in process industry applications, enabling accurate measurements over varying operating ranges without additional calibration procedures. Full article

In this paper an experimental study and comparative analysis of temperature and moisture in pavements and their soil bases on experimental road sections located in two southern metropolises of Kazakhstan—in the cities of Almaty and Shymkent—were conducted. The temperature and moisture values were measured by systems for long-term continuous monitoring of temperature and moisture in structural elements of roads. Sensors were installed at ten locations within the pavements and soil bases at depths ranging from 2.5 cm to 270–275 cm below the pavement surfaces. The temperature and moisture monitoring period extends from 21 October 2024 to 31 December 2025. Temperature and moisture readings were recorded hourly. It has been established that the temperature conditions of pavements and their soil bases in the two cities are qualitatively the same, and the quantitative differences are small: air temperatures and soil temperatures at depths of 2.5 cm and 10 cm in Shymkent are higher than in Almaty by an average of 4.75 °C, 5.69 °C and 2.12 °C, respectively. These facts make it possible to use a single methodological approach to account for temperature changes in the design of new roads and in the operation of existing roads. It is recommended to pay attention to ensuring the resistance to rutting and fatigue cracking of asphalt concrete layers of pavement. The moisture regime at the experimental section in Almaty up to 130–140 cm depth is unstable due to seepage of precipitation and surface water through the stone mastic and the two coarse-grained porous asphalt concrete layers of the pavement; in Shymkent the pavement and the soil base have a stable moisture regime throughout the year; these features should be taken into account when calculating accumulations of plastic strains (increasing of rut depth) and fatigue damage in asphalt concrete layers of pavements while also accounting for temperature changes in them and transport facility loads in different time periods. Full article

Understanding how selected urban route conditions are associated with e-scooter riders’ visual attention and perceived workload can support human-factor evaluation of micromobility environments. This study examines visual attention and perceived workload during real-world e-scooter riding across three selected urban routes in Zagreb with different infrastructure and traffic-environment characteristics. A field-based experimental methodology was used, integrating eye-tracking with post-ride perceived workload assessment through the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Twenty-eight adults, predominantly novice or occasional e-scooter riders, completed the three selected routes. Visual attention and perceived workload were examined across the selected routes; because only one route represented each route condition, the findings are interpreted as route-level evidence rather than general infrastructure-type effects. One-way repeated-measures analyses of variance (ANOVAs) showed that route condition had a significant effect on mean fixation duration and on the weighted NASA-TLX workload score, indicating that riders’ visual attention and perceived workload varied across the examined routes. To further examine these route-related differences, linear mixed-effects models were used as supporting analyses with participant ID as a random intercept. The results showed that R2 was associated with lower weighted workload than R1, while R3 was associated with higher weighted workload than R1. The route-complexity index was retained as an exploratory route-level descriptor rather than a validated continuous predictor. These findings highlight the importance of considering combined route, infrastructure, and traffic-environment characteristics when planning safer micromobility environments and developing appropriate regulations for e-scooter use. They also provide preliminary route-level human-factor evidence that can support future micromobility route evaluation and the development of human-centred guidance for infrastructure planning and e-scooter regulation. Full article

Rivers are vulnerable to hydrometeorological and climatic pressures, while effective water resources management during low-flow season can be challenging. This paper studies the impacts of extreme hydrometeorological events and climate change on Greek rivers, and presents a framework for the incorporation of these pressures into the Program of Measures (PoMs) of the River Basin Management Plans (RBMPs) for the implementation of the Water Framework Directive 2000/60 (WFD). The final aim is the adaptation to drought and water scarcity. The proposed methodological approach incorporates a process-based model, climate change projections, drought indices and low-flow boundaries, so as to assess the impact of hydrometeorological and climatic pressures on the quantitative quality status of rivers. Then, the quantitative response of rivers under different predicted climate scenarios was examined. Based on the results, a water management plan was adopted and tested regarding its effectiveness, so as to promote evidence-based water strategies. Additionally, comprehensive measures and an operational scheme and adaptations actions for the mitigation of the hydrometeorological and climate related pressures in rivers of Greece were proposed. This operational framework is based on quantitative thresholds that trigger actions and measures for adaptation and can be used by decision-makers and stakeholders as guidelines for mitigation actions. The framework proposed was applied to the Spercheios river basin, which was selected as a demonstration site. Full article

This narrative review summarizes the published literature on robotic coronary revascularization, encompassing robotic-assisted minimally invasive direct coronary artery bypass (RA-MIDCAB), totally endoscopic coronary artery bypass (TECAB), and hybrid coronary revascularization (HCR). These are distinct procedures that differ in operative access, conduit harvesting, use of cardiopulmonary bypass (CPB), and typical patient selection, and are treated as such throughout this review rather than as interchangeable techniques. A structured, non-systematic literature search of PubMed/MEDLINE and major cardiothoracic surgical journals was performed. Articles were selected by the authors based on relevance, methodological quality, and recency. This is explicitly a narrative rather than a systematic review, and no PRISMA methodology was applied. In observational series from experienced centers, RA-MIDCAB and TECAB are associated with low perioperative mortality (0–2.8%), high early left internal thoracic artery (LITA)-to-left anterior descending (LAD) graft patency (>95%), and shorter hospital stay than conventional sternotomy coronary artery bypass grafting (CABG) in selected patients. HCR extends robotic revascularization to selected patients with multivessel and left main disease. However, nearly all of this evidence derives from retrospective, single-center, or registry-based observational studies performed at high-volume expert centers in selected patient populations, and comparative claims against conventional CABG are vulnerable to selection bias and residual confounding. Robotic coronary revascularization is a feasible option that has been reproduced across a growing number of high-volume centers for carefully selected patients, but current evidence does not support broad claims of superiority over conventional CABG, and outcome data remain concentrated among a limited number of expert programs. Prospective multicenter and randomized data, standardized outcome definitions, and structured training pathways are needed to define its long-term role in coronary revascularization. Full article