Search Results (4,755)

Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City. Full article

Two-dimensional direct Z-scheme photocatalysts have emerged as highly promising photocatalysts for solar-driven water splitting owing to their effective separation of photogenerated carriers and strong redox abilities. This study focuses on the theoretical prediction of promising Z-scheme photocatalysts for solar-driven water splitting based on M₂X/BiOY (M = Ag, Au; X = S, Se; Y = Cl, Br, I) heterojunctions using first-principles calculations. All M₂X/BiOY heterojunctions possess staggered band alignments, Z-scheme carrier migration, and suitable band edges for overall water splitting. Optical absorption spectra indicate that these heterojunctions exhibit significantly extended solar absorption in the visible and near-infrared regions. Moreover, the interfacial built-in electric fields of (0.46–0.72 V/Å) point from M₂X to BiOY, promote photogenerated carrier separation, and enhance redox overpotentials, thereby improving photocatalytic performance. These results suggest that M₂X/BiOY heterojunctions are promising Z-scheme photocatalysts for solar-driven water splitting and are expected to be experimentally prepared and realized in the near future. Full article

The integration of precision agriculture technologies, such as remote sensing through drones and satellites, has significantly enhanced real-time crop monitoring. This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. A dataset of 40 sampling points was built by combining remote sensing data with field measurements, and seven vegetation indices were calculated for each point. Feature selection was performed using three different methods (F-score, Recursive Feature Elimination, and model-based selection), and the most informative features were then processed through Principal Component Analysis. Eight regression models were trained and evaluated using leave-one-out cross-validation. The best-performing models were Random Forest (R² = 0.96 in training, R² = 0.65 in testing) and k-Nearest Neighbours (R² = 0.74 in training, R² = 0.94 in testing), with kNN demonstrating superior generalization capability on unseen data. These findings highlight the potential of combining drone and satellite imagery for modelling basil agronomic traits, offering valuable insights for optimizing crop management strategies. Full article

This paper presents an uncertainty quantification analysis of the first fix in a time-differenced carrier phase (TDCP) observation model. TDCP is a widely used method in GNSS-based odometry for precise positioning and displacement estimation. A key point in the TDCP modeling is the assumption that the GNSS receiver’s initial position is perfectly known, which is never exactly the case in real-world applications. This study assesses the impact of initial position errors on estimated displacement by formulating a correct TDCP model and a misspecified one, where the first position is not correct. Theoretical derivations provide a generic framework of estimation under the misspecified model and its associated mean squared error (MSE), as well as estimation performance bounds through the misspecified Cramer Rao bound (MCRB) for the considered case. These theoretical considerations are then used to build an estimator of the receiver’s displacement, with comparisons to the MCRB for performance evaluation. Extensive simulations using realistic GNSS geometry assess the influence of a first-fix error under various conditions, including different time intervals, first-fix error norms, and first-fix error direction. As an example, it is shown that for the considered geometry, if a TDCP of $t_2-t_1=1 \mathrm{s}$ is built with an initial first fix error norm $\parallel \Delta {\mathbf{r}}_1\parallel =10 \mathrm{m}$, then it introduces an estimation of the displacement, with an error of norm equal to $1.3 \mathrm{m}\mathrm{m}$, at most. The results indicate that the displacement estimation error is linearly related to the initial position error and the time interval between observations, highlighting the importance of accurate first-fix estimation for reliable TDCP-based odometry. The findings contribute to highlighting the order of magnitude of errors on solutions as a function of the error on parameters. Full article

Lake monitoring is essential for sustaining aquatic ecosystems, and accurate estimation/prediction of water quality parameters is crucial to this effort. Despite its importance, the performance of predictive models built on varying temporal resolutions remains underexplored systematically. This study used daily and 4 h high temporal resolution (HTR) datasets to assess the performance of multiple machine learning models—namely, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks—under consistent data scales. The results indicate that dissolved oxygen (DO) exhibits pronounced sensitivity to temporal resolution, while total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₃-N) show distinct, parameter-specific response patterns that align with the temporal characteristics of their underlying biogeochemical processes. This research helps to deepen the understanding of how temporal data resolution influences model performance in water quality prediction, offering valuable insights for selecting optimal data resolutions and modeling techniques to enhance lake monitoring and protection strategies. Full article

Urban rail transit, a crucial component of urban public transportation, often experiences increased operational costs and carbon emissions due to low-load operations being conducted during off-peak passenger flow periods. This study aims to develop an optimization method for the daily scheduling of rail train operations with the goal of carbon emission reduction, while comprehensively considering the built environment and travel demand. Firstly, the influence of the urban built environment on residents’ travel demand is analyzed using an XGBoost model. Secondly, a time convolutional travel demand prediction model, Built Environment-Weighted Temporal Convolutional Network (BE-TCN), weighted by built environment factors, is constructed. Finally, an optimization method for rail train operation schedules based on the built environment and travel demand is proposed, with the objective of carbon emission reduction. A case study is conducted using the Hangzhou urban rail transit system as an example. The results indicate that the optimization method proposed in this study can achieve monthly carbon emission reductions of 1524.58 tons, 1181.94 tons, and 520.84 tons for Lines 1, 2, and 4 of the Hangzhou urban rail transit system, respectively. The research findings contribute to enhancing the economic efficiency and environmental sustainability of urban rail transit systems. Full article

High-resolution holographic visualization of built environments remains largely inaccessible due to the complexity and technical demands of traditional 3D data acquisition processes. This study proposes a workflow for producing high-quality full-color digital holographic stereograms of architectural landmarks using Google Earth Studio. By leveraging photogrammetrically reconstructed three-dimensional (3D) city models and a controlled camera path, we generated perspective image sequences of two iconic monuments, that is, the Basílica de la Sagrada Família (Barcelona, Spain) and the Arc de Triomphe (Paris, France). A custom pipeline was implemented to compute keyframe coordinates, extract cinematic image sequences, and convert them into histogram data suitable for CHIMERA holographic printing. The holograms were recorded on Ultimate U04 silver halide plates and illuminated with RGB light-emitting diodes, yielding visually immersive reconstructions with strong parallax effects and color fidelity. This method circumvented the requirement for physical 3D scanning, thereby enabling scalable and cost-effective holography using publicly available 3D datasets. In conclusion, the findings indicate the potential of combining Earth Studio with digital holography for urban visualization, cultural heritage preservation, and educational displays. Full article

The intensification of extreme temperature events driven by climate change has heightened the vulnerability of urban areas to heatwaves, making it a critical environmental challenge. In this study, we investigate the spatial characteristics of urban heatwave vulnerability in Jungang-dong, Gangneung—a representative mid-sized coastal city in South Korea that experiences a strong urban heat island (UHI) effect due to the prevalent land–sea breeze dynamics, high building density, and low green-space ratio. A representative heatwave day (22 August 2024) was selected using AWS data from the Korea Meteorological Administration (KMA), and hourly meteorological conditions were applied to Computational Fluid Dynamics (CFD) simulations to model the urban microclimates. The thermal stress levels were quantitatively assessed using the Universal Thermal Climate Index (UTCI). The results indicated that, at 13:00, the surface temperatures reached 40 °C and the UTCI values peaked at 43 °C, corresponding to a “Very Strong Heat Stress” level. Approximately 17.4% of the study area was identified as being under extreme thermal stress, particularly in densely built-up zones, roadside corridors with high traffic, and pedestrian commercial areas. Based on these findings, we present spatial analysis results that reflect urban morphological characteristics to guide the optimal allocation of urban cooling strategies, including green (e.g., street trees, urban parks, and vegetated roofs), smart, and engineered infrastructure. These insights are expected to provide a practical foundation for climate adaptation planning and thermal environment improvement in mid-sized urban contexts. Full article

Bisphosphonate-related osteonecrosis of the jaw (BRONJ) is one of the side effects of bisphosphonate (BP) administration. Despite some preventive measures having been suggested, a definitive and effective treatment strategy for BRONJ remains to be established. Recent evidence has indicated that BPs dramatically impair the function of orofacial bone marrow stromal cells (BMSCs), which may contribute to the development of osteonecrosis. Thus, we hypothesized that recovery-impaired function of BMSCs at lesion sites could be beneficial in treating BRONJ. N6-methyladenosine (m6A) modification is the most common epigenetic modification and has been demonstrated to play a vital role in the modulation of BMSCs’ function. We detected the role of m6A modification in regulating the function of orofacial BMSCs under BP stimulation, and found that BPs led to a reduction in the global m6A methylation level, SAM level, and METTL3 expression in BMSCs during the osteogenic differentiation period. Meanwhile, betaine, a methyl group donor, effectively reversed the BP-decreased global m6A methylation level and SAM level in BMSCs, as well as rescuing the differentiation ability of impaired BMSCs. In the last part, we built a BRONJ rat model and supplemented rats with betaine via drinking water. The results showed that betaine successfully attenuated bone lesions and promoted wound healing in BP-injected rats, thereby providing new insight into future clinical treatment for BRONJ. Full article

The present paper describes a novel and user-friendly Decision Support System (e-DSS) designed to assist technicians in the preliminary design stage of a building renovation process based on the solutions developed in the innovation project e-SAFE, funded by the EU under the H2020 program. The e-DSS is engineered to rapidly assess key performance indicators, including energy performance before and after renovation, reduction in CO₂ emission for space heating, space cooling, and DHW preparation, seismic upgrade feasibility, expected costs, and payback time. To demonstrate its capabilities, the e-DSS was applied to an existing public housing building in Catania, southern Italy. The predicted thermal energy needs for space heating and cooling were compared to the results from detailed simulations using a professional-grade software tool, for both as-built condition and a proposed renovation generated by the e-DSS itself. The discrepancies identified through this comparison will inform the refinement of the e-DSS algorithms to increase their accuracy and reliability. More generally, this paper recommends suitable algorithms that can be effectively employed in the development of simplified decision-making tools specifically tailored for building professionals operating in the early phase of building renovation projects. Full article

Additives are compounds used for material to increase specific properties. When used for polymers, they extend their life and contribute to environmental sustainability. This article presents the study findings related to 24 additives—antioxidants, UV stabilizers, and quenchers—using cheminformatics methods. The compounds’ characteristics (e.g., number of atoms, functional groups) were emphasized, followed by some descriptors. The Tanimoto coefficient, computed based on the maximum common structure algorithm, and the overlap coefficient indicated the degree of similarity between the molecules. The molecules were grouped by binning and hierarchical clustering (HC) based on the extracted results. In the last case, two scenarios were considered—with four (CL1–CL4) and six clusters (CL1.1, CL1.2, CL2, CL3, CL4.1, CL4.2) being built. Considering the mechanical properties of the compounds and the standard deviation and amplitude of their values, the most homogenous class was CL2 (respectively CL4.2). Considering the toxicity of additives, the highest possible negative impact on the environment is that of the compounds in CL1 and CL3. The clustering results guide the selection of additives with reduced environmental impact, thereby supporting the development of sustainable polymer formulations aligned with circular economy principles. Full article

Background/Objectives: Polymeric monoaxial nanofibers are gaining prominence due to their numerous applications, particularly in functional scenarios such as wound management. The study successfully developed and built a special-purpose vessel and device for fabricating polymeric nanofibers. Fabrication of composite scaffolds from piezoelectric poly(vinylidenefluoride-trifluoroethylene) copolymer (PVDF-TrFE) nanofibers encapsulated with myrrh extract was investigated. Methods: The gyrospun nanofibers were characterized using SEM, EDX, FTIR, XRD, and TGA to assess the properties of the composite materials. The study also investigated the release profile of myrrh extract from the nanofibers, demonstrating its potential for sustained drug delivery. The composite’s antimicrobial properties were evaluated using the disc diffusion method against various pathogenic microbes, showcasing their effectiveness. Results: It was found that an 18% (w/v) PVDF-TrFE concentration produces the best fiber mats compared to 20% and 25%, resulting in an average fiber diameter of 411 nm. Myrrh extract was added in varying amounts (10%, 15%, and 20%), with the best average fiber diameter identified at 10%, measuring 436 nm. The results indicated that the composite nanofibers were uniform, bead-free, and aligned without myrrh. The study observed a cumulative release of 79.66% myrrh over 72 h. The release profile showed an initial burst release of 46.85% within the first six hours, followed by a sustained release phase. Encapsulation efficiency was 89.8%, with a drug loading efficiency of 30%. Antibacterial activity peaked at 20% myrrh extract. S. mutans was the most sensitive pathogen to myrrh extract. Conclusions: Due to the piezoelectric effect of PVDF-TrFE and the significant antibacterial activity of myrrh, the prepared biohybrid nanofibers will open new avenues toward tissue engineering and wound healing applications. Full article

Sustainable building construction (SBC) contributes immensely to attaining sustainable development initiatives. Nevertheless, SBC is not fully embraced among construction organisations in developing countries due to several challenges, suggesting the need for lasting solutions. However, uncertainty remains about the most vital characteristics/enablers that construction organisations need to adopt SBC. This study investigated the organisational enablers that contribute to SBC’s successful deployment. This study employed quantitative methodology using a structured questionnaire for data collection. With a convenient sample technique, a sample size of 281 was achieved from professionals working in the built environment in the Gauteng Province of South Africa (SA). Data were analysed with a four-step approach, including the relevant descriptive and inferential statistics. Relevant reliability and validity tests of the research instrument/measuring variables were observed, including pilot testing, Cronbach’s alpha test, Kaiser–Meyer–Olkin, and Bartlett’s sphericity test. Mean rankings followed this in conjunction with standard deviations. Likewise, the Kruskal–Wallis H-test was employed to determine statistically significant differences in the responses of the study’s respondents. Furthermore, confirmatory factor analysis (CFA) was used to confirm the variables’ goodness of fit in the measurement model or latent construct (organisational enablers), indicating their significance. According to their regression values, the top five variables included commitment to innovative construction, adequate project management culture, support from top management, sound intra-organisational leadership, and social responsibility to protect the environment. Generally, the study’s findings were supported by institutional theory and resource-based view theory. The study recommends carefully considering the findings among construction organisations and policymakers. This will assist in self-assessment and decision-making regarding direct improvement initiatives and curbing unsustainable practices. Similarly, this study is positioned to encourage further investigation of organisational enablers from the perspective of the enlisted theories. Full article

To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. Subsequently, it optimizes the rationality of particle weights within the particle filter (PF) and reduces its computational resource consumption by implementing historical position weighting and an adaptive particle number strategy. Finally, an adaptive error correction mechanism dynamically adjusts the respective weights of the EKF and IPF components, continuously enhancing the algorithm’s prediction accuracy. Experimental results demonstrate that, compared to the classic unscented Kalman filter (UKF) and unscented particle filter (UPF), the proposed EK-IPF algorithm reduces the mean absolute error (MAE) by 22.25% and 10.89%, respectively, and the root mean square error (RMSE) by 23.70% and 13.25%, respectively, indicating a significant improvement in overall prediction accuracy. This research provides technical support for dynamic apple trajectory prediction in orchard environments. Full article

Architectural ambiance refers to the mood perceived in a built environment, assessed through human reactions to virtual drawings of prospective spaces. This paper investigates the use of a ready-made artificial intelligence model to automate this task. Based on professional BIM models, videos of virtual tours of typical urban areas were built: a business district, a strip mall, and a residential area. GPT-4V was used to assess the aesthetic quality of the built environment based on keyframes of the videos and characterize these spaces shaped by subjective attributes. The spatial qualities analyzed through subjective human experience include space and scale, enclosure, style, and overall feelings. These factors were assessed with a diverse set of mood attributes, ranging from balance and protection to elegance, simplicity, or nostalgia. Human participants were surveyed with the same questions based on the videos. The answers were compared and analyzed according to these subjective attributes. Our findings indicate that, while GPT-4V demonstrates adequate proficiency in interpreting urban spaces, there are significant differences between the AI and human evaluators. In nine out of twelve cases, the AI’s assessments aligned with the majority of human voters. The business district environment proved more challenging to assess, while the green environment was effectively modeled. Full article