Search Results (586)

The human circadian rhythm is connected to the body’s endogenous clock and can influence people’s natural sleeping habits as well as a variety of other biological functions. According to research, various electric light sources in interior locations can disrupt the human circadian rhythm. Many psychological studies, on the other hand, reveal that different colors can have varied connections with and a variety of effects on people’s emotions. In this study, the effects of light source attributes and interior space paint color on human circadian rhythm were studied using 24 distinct computer simulations. Simulations were performed using the ALFA plugin for Rhinoceros 6 on an unfurnished bedroom 3D model at night. Results suggest that cooler hues, such as blue, appear to have an unfavorable effect on human circadian rhythm at night, especially when utilized in spaces that are used in the evening, which contradicts what psychologists and interior designers advocate in terms of the soothing mood and nature of the color. Furthermore, the effects of Correlated Color Temperature (CCT) and the intensity of a light source might be significant in minimizing melanopic lux to prevent melatonin suppression at night. These insights are significant for interior designers, architects, and lighting professionals aiming to create healthier living environments by carefully selecting lighting and color schemes that support circadian health. Incorporating these considerations into design practices can help mitigate adverse effects on sleep and overall well-being, ultimately contributing to improved occupant comfort and health. Full article

This current review examines modeling approaches for renovating reinforced concrete (RC) buildings for vertical forest (VF) application, taking into account structural retrofitting, energy systems, forestry integration, and occupant comfort. The study assesses research conducted with an advanced 3D finite element analysis and the use of retrofitting modeling techniques, including textile-reinforced mortar (TRM), fiber-reinforced polymer (FRP), seismic joints, and green concrete applications. The energy system modeling methods are reviewed, taking into account the complexity of incorporating vegetation and seasonal variations. During forestry integration, three main design parameters are identified, namely, root systems, trunks, and crowns, for their critical role in the structural stability and optimal environmental performance. The comfort models are identified evolving from static to adaptive models incorporating thermal, acoustic, visual and air quality parameters. The current review consists of more than one hundred studies indicating that the integration of natural systems to buildings requires a multidimensional and multidisciplinary approach with sophisticated systems. The findings of this review provide the basis for implementing VF models to RC buildings, while highlighting areas requiring further research and validation. Full article

The diffusion effect of river cooling is critical for mitigating the urban heat island effect in riverside areas and for establishing an urban cooling network. River cooling effect diffusion is influenced by the two-dimensional (2D) and three-dimensional (3D) urban morphology of surrounding areas. However, the characteristics of 2D/3D urban morphology that facilitate efficient river cooling effect diffusion remain unclear. This study establishes a technical framework to analyze river cooling effect diffusion resistance (RCDR) across different urban morphologies, using the Huangpu River waterside area in Shanghai as a case study. Seven urban morphology indicators, derived from both 2D and 3D dimensions, were developed to characterize the river cooling effect diffusion resistance. The relative contributions and marginal effects were analyzed using the Boosted Regression Tree (BRT) model. The study found that (1) river cooling effect diffusion was heterogeneous, with four typical patterns; (2) the Landscape Shape Index (LSI) and Blue-green Space Ratio (BGR) significantly impacted cooling effect diffusion; and (3) optimal cooling effect diffusion occurred when the blue-green space occupancy ratio exceeded 20% and building density ranged from 0.1 to 0.3. This study’s technical framework offers a new perspective on river cooling effect diffusion and heat island mitigation in riverside spaces, with significant practical value and potential for broader application. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters. Full article

The assembly optimization design of satellite components is a crucial element in the overall design of satellites. In this paper, a novel three-dimensional assembly optimization design problem (3D-AODP) for multi-module micro–nano satellite components is proposed according to the engineering requirements, aiming at optimizing the satellite mass characteristics, and taking into account constraints such as space interference, space occupation and special location. Multi-module micro–nano satellites are a new type of satellite configuration based on the assembly of multiple U-shaped cube units. The 3D-AODP of its components is a challenging two-layer composite optimization task involving discrete variable optimization of component allocation and continuous variable optimization of component layout, which interact with each other. To solve the problem, a hybrid assembly optimization method based on tabu search (TS) and multi-objective differential evolutionary (MODE) algorithms is proposed, in which the assignment problem of the components is converted into a domain search problem by the TS algorithm. The space interference constraints and space occupancy constraints of the components are considered, and an assignment scheme with the minimum mass difference is obtained. On this basis, a bi-objective differential evolutionary algorithm is used to develop the layout optimization problem for the components, which takes into account the spatial non-interference constraints and special location constraints of the components, and obtains the Pareto solution set of the assembly scheme under the optimal mass characteristics (moment of inertia and product of inertia). Finally, the feasibility and effectiveness of the proposed method is demonstrated by an engineering case. Full article

Background: Adolescents with a chronological age of less than 15 years or a gynecological age of less than 2 years may have a higher probability of complications because they are more likely to conceive within 1 to 2 years of menarche and, therefore, are still growing and maturing. This could impair their ability to adapt to the physiological demands of pregnancy. Objective: To evaluate the relationship between chronological age and gynecological age with low birth weight and small for gestational age among adolescent mothers in Mexico City. Methods: A retrospective cohort design of adolescent mother–child dyads was conducted. The study followed 1242 adolescents under 19 years of age and their children, collecting data on physical, socioeconomic, and clinical characteristics, including hemoglobin levels. Low birth weight was assessed using the Intergrowth-21st project standards and categorized as above or below 2500 g. The mothers were grouped by chronological age (<15 years and ≥15 years) and gynecological age (<3 years and ≥3 years). Adjusted odds ratios were calculated using binary logistic regression models. The outcome variables were low birth weight and small for gestational age. The independent variables included gynecological age, chronological age, age at menarche, hemoglobin concentration, and gestational weight gain, among others. All independent variables were converted to dummy variables for analysis. Calculations were adjusted for the following variables: marital status, maternal education, occupation, educational lag, family structure, socioeconomic level, pre-pregnancy body mass index, and initiation of prenatal care. Results: The average age of the participants was 15.7 ± 1 years. The frequency of small for gestational age and low birth weight was 20% and 15.3%, respectively. Factors associated with small for gestational age included gynecological age < 3 years [aOR = 2.462, CI 95%; 1.081–5.605 (p = 0.032)], hemoglobin < 11.5 g/dL [aOR = 2.164, CI 95%; 1.08–605 (p = 0.019)], insufficient gestational weight gain [aOR = 1.858, CI 95%; 1.059–3.260 (p = 0.031)], preterm birth [aOR = 1.689, CI 95%; 1.133–2.519 p = 0.01], and living more than 50 km from the care center [aOR = 2.256, CI 95%; 1.263–4.031 (p = 0.006)]. An early age of menarche [aOR = 0.367, CI 95%; 0.182–0.744 (p = 0.005)] showed a protective effect against small for gestational age. Factors associated with low birth weight included gynecological age < 3 years [aOR = 3.799, CI 95%; 1.458–9.725 (p = 0.006)], maternal age < 15 years [aOR = 5.740, CI 95%; 1.343–26.369 (p = 0.019)], preterm birth [aOR = 54.401, CI 95%; 33.887–87.335, p = 0.001], living more than 50 km from the care center [aOR = 1.930, CI 95%; 1.053–3.536 (p = 0.033)], and early age of menarche [aOR = 0.382, CI 95%; 0.173–0.841 (p = 0.017), which demonstrated a protective effect, respectively. Conclusions: The study concludes that biological immaturity, particularly early gynecological age, significantly contributes to adverse birth outcomes during adolescent pregnancies. Interestingly, early menarche appeared to have a protective effect, whereas chronological age was not a significant predictor of small for gestational age. Chronological age has an even greater impact: women younger than 15 years are 5.7 times more likely to have low birth weight infants. However, chronological age did not increase the likelihood of having an SGA newborn. Full article

Three-dimensional Occupancy Prediction provides a detailed representation of the surrounding environment, essential for autonomous driving. Long temporal image sequence fusion is a common technique used to improve the occupancy prediction performance. However, existing temporal fusion methods are inefficient due to three issues: repetitive feature extraction from temporal images, redundant fusion of temporal features, and suboptimal fusion of long-term historical features. To address these challenges, we propose the Recurrent Occupancy Prediction Network (RecurrentOcc). We introduce the Scene Memory Gate, a new temporal fusion module that condenses temporal scene features into a single historical feature map. This eliminates the need for repeated extraction and aggregation of multiple temporal images, reducing computational overhead. The Scene Memory Gate selectively retains valuable information from historical features and recurrently updates the historical feature map, enhancing temporal fusion performance. Additionally, we design a simple yet efficient encoder, significantly reducing the number of model parameters. Compared with other real-time methods, RecurrentOcc achieves state-of-the-art performance of 39.9 mIoU on the Occ3D-NuScenes dataset with the fewest parameters of 59.1 M and an inference speed of 23.4 FPS. Full article

Assessing the seismic performance of buildings from various epochs is essential for guiding retrofitting policies and educating occupants about their homes’ conditions. However, limited resources pose challenges. Some approaches focus on detailed analyses of a limited number of buildings, while others favor broader coverage with less precision. This paper presents a seismic risk assessment method that balances and integrates the strengths of both, using a comprehensive building survey. We propose a low-cost indicator for evaluating the structural resilience of individual buildings, designed to inform both authorities and property owners, support building rankings, and raise awareness. This indicator classifies buildings by their taxonomy and uses analytical capacity curves (2D or 3D studies) obtained from consulting hundreds of studies to determine the ultimate acceleration (agu) that each building type can withstand before collapse. It also considers irregularities found during the survey (to the exterior and interior) through structural modifiers Δ, and adjusts the peak ground acceleration the building can withstand, agu, based on macroseismic data from past events and based on potential retrofitting, Δ+. Although this method may not achieve high accuracy, it provides a significant approximation for detailed analysis with limited resources and is easy to replicate for similar constructions. The final agu value, considered as resistance, is then compared to the seismic demand at the foundation of the building (accounting for hazard and soil conditions at the building location), resulting in a final R-value. This paper provides specificities to the methodology and applies it to selected areas of the City of Lisbon, clearly supporting the advancement of a more sustainable society. Full article

Semantic mapping plays a critical role in enabling autonomous vehicles to understand and navigate complex environments. Instead of computationally demanding 3D segmentation of point clouds, we propose efficient segmentation on RGB images and projection of the corresponding LIDAR measurements on the semantic OctoMap. This study presents a comparative evaluation of different semantic segmentation methods and examines the impact of input image resolution on the accuracy of 3D semantic environment reconstruction, inference time, and computational resource usage. The experiments were conducted using an ROS 2-based pipeline that combines RGB images and LiDAR point clouds. Semantic segmentation is performed using ONNX-exported deep neural networks, with class predictions projected onto corresponding 3D LiDAR data using calibrated extrinsic. The resulting semantically annotated point clouds are fused into a probabilistic 3D representation using an OctoMap, where each voxel stores both occupancy and semantic class information. Multiple encoder–decoder architectures with various backbone configurations are evaluated in terms of segmentation quality, latency, memory footprint, and GPU utilization. Furthermore, a comparison between high and low image resolutions is conducted to assess trade-offs between model accuracy and real-time applicability. Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

This review presents a comprehensive survey on deep learning-driven 3D object detection, focusing on the synergistic innovation between sensor modalities and technical architectures. Through a dual-axis “sensor modality–technical architecture” classification framework, it systematically analyzes detection methods based on RGB cameras, LiDAR, and multimodal fusion. From the sensor perspective, the study reveals the evolutionary paths of monocular depth estimation optimization, LiDAR point cloud processing from voxel-based to pillar-based modeling, and three-level cross-modal fusion paradigms (data-level alignment, feature-level interaction, and result-level verification). Regarding technical architectures, the paper examines structured representation optimization in traditional convolutional networks, spatiotemporal modeling breakthroughs in bird’s-eye view (BEV) methods, voxel-level modeling advantages of occupancy networks for irregular objects, and dynamic scene understanding capabilities of temporal fusion architectures. The applications in autonomous driving and agricultural robotics are discussed, highlighting future directions including depth perception enhancement, open-scene modeling, and lightweight deployment to advance 3D perception systems toward higher accuracy and stronger generalization. Full article

Background: Asthma is a chronic inflammatory airway disease in which oxidative stress and antioxidant imbalance play a critical role in disease progression and therapeutic response. This study aimed to evaluate oxidative stress and antioxidant status in relation to asthma control levels. Methods: A total of 106 patients admitted to the Clinical Hospital of Pulmonary Diseases, Iași, between March and May 2024 were included in this study. Patients were classified into three groups based on asthma control: well-controlled (AB-TCG), partially controlled (AB-PCG), and uncontrolled asthma (AB-UCG). Demographic, biochemical, and hematological parameters were assessed, with attention to oxidative stress markers and antioxidant defenses. Results: The study population was predominantly female (75%), with a mean age ranging from 50.75 to 64.38 years, and the majority residing in rural areas (73–75%). The AB-UCG group showed significantly elevated inflammatory markers, including a white blood cell count of 9.33 × 10³/µL (p = 0.005) and eosinophil percentage of 4.20% (p = 0.03), compared with the other groups. This group also exhibited an unfavorable lipid profile, with increased total cholesterol (207.40 mg/dL) and triglyceride levels (157.21 mg/dL). Oxidative stress was notably higher in the AB-UCG group, as indicated by elevated malondialdehyde (MDA) levels (2.86 mmol/L) versus 2.35 mmol/L in the AB-PCG group (p < 0.005), along with decreased serum uric acid (4.64 mg/dL) and reduced glutathione (GSH) levels (275.41 µmol/L), leading to a lower GSH/GSSG ratio. Environmental exposures, including tobacco smoke and occupational chemicals, were associated with exacerbated oxidative imbalance. Conclusions: The findings highlight the critical involvement of oxidative stress and compromised antioxidant defenses in poorly controlled asthma. Biomarkers such as MDA, white blood cell count, eosinophil percentage, and the GSH/GSSG ratio may act as valuable tools for personalized asthma management and therapeutic monitoring. Full article

Background: Type 2 diabetes (T2D) is a chronic disease that significantly impacts individuals’ quality of life, affecting their physical, psychological, social, and environmental well-being. Objectives: This study investigates how self-care habits influence quality of life and key health indicators, such as glycated hemoglobin (HbA1c), blood sugar levels, and body mass index (BMI), among newly diagnosed diabetic individuals in Vlore, Albania. Methods: In this cross-sectional study, 332 individuals recently diagnosed with diabetes were surveyed between April and July 2024. Data were collected using the World Health Organization Quality of Life Assessment (WHOQOL-BREF) and the Summary Diabetes Self-Care Activity (SDSCA) surveys. Sociodemographic and clinical information, including age, education, occupation, duration of diabetes, HbA1c, and BMI, were collected through structured interviews and medical records. Descriptive and multivariate analyses were conducted to examine the relationships between self-care behaviors, sociodemographic factors, and quality of life. Results: The findings reveal a low quality of life, with a mean quality of life (QoL) score of 35.33 ± 8.25. Environmental domains were most affected, registering a low QoL score of 30.93 ± 9.04. Significant relationships between QoL, self-care practices, and sociodemographic factors and pathologic factors were found. The analysis indicated that distinct factors influenced various domains of quality of life. Physical health was associated with residence, comorbidities, BMI, and HbA1c, follow-up visits, dietary self-care and physical activity self-care. Psychological health correlated with residence, educational level, BMI, and HbA1c, follow-up visits, dietary, physical activity and foot self-care. Age, occupation, BMI, and physical activity self-care were linked to social relationships. Finally, environmental well-being was influenced by gender, residence, BMI, HbA1c, follow-up visits, and dietary and physical activity self-care. Conclusions: This study emphasizes the impact of sociodemographic and clinical factors on the quality of life of patients with T2D. Older age, lower education levels, comorbidities, increase in BMI and HbA1c levels, and inadequate self-care were associated with reduced quality of life. These findings highlight the need for targeted interventions and policies that promote self-care and support for at-risk groups. Full article

Background: Excessive daytime sleepiness (EDS) and fatigue are two impactful symptoms, frequently associated with sleep disorders, which can worsen the quality of life. Due to overlapping features and patient-report ambiguity a clear-cut distinction between EDS and fatigue can become a challenging issue. We aimed to investigate the prevalence and consequences of these two conditions in several sleep pathologies, examining their social, psychological, and dietary impact, with a focus on gender-related differences and occupational status. Methods: We prospectively recruited for an online survey 136 adult outpatients (60 females) affected by various sleep disorders and admitted to our Sleep Disorders Center in Parma, Italy. Patients were asked to complete the following tests: Epworth Sleepiness Scale, Fatigue Severity Scale, Pittsburgh Sleep Quality Index, Difficulties in Emotion Regulation Scale, Depression Anxiety Stress Scale-21, Hyperarousal Scale, the Addiction-like Eating Behaviors Scale, Work Productivity and Activity Impairment Questionnaire, MEDI-Lite, and EQ-5D Health Questionnaire. Results:Fatigue was the primary daily symptom leading to serious repercussions on social/emotional and psychological well-being, while daytime sleepiness showed a less relevant role. Women reported higher levels of fatigue, sleep disturbances, emotional dysregulation, hyperarousal, and work productivity impairments. Unemployed people experienced a higher degree of fatigue, with multi-level negative consequences. Conclusions: We suggest sleep clinicians place a greater emphasis on the assessment of fatigue during clinical interviews, keeping in mind the greater vulnerability of females, experiencing disproportionate consequences. Further studies should expand our findings, exploring a wider range of gender identities and recruiting larger samples of patients. Full article