Search Results (154)

Urbanization-induced ecological problems have affected China’s urban agglomerations since the beginning of rapid economic growth. The InVEST model can be used to study how land use changes affect carbon storage, while land simulation models help project future land use trends and assess the impact of policies on land use, thereby predicting future carbon storage. This study constructs a PLUS-InVEST-MGWR model, corrects carbon storage values in ArcGIS, and thereby analyzes its heterogeneity by MGWR. The economic value of carbon storage is calculated as well. The main findings are as follows: (1) The downward trend of carbon storage in the Chengdu–Chongqing region will continue but slow down to some extent, and only the ecological security scenario can prevent it. (2) In 2015, China’s social cost of carbon (SCC) was CNY 60.83 per ton, with a discount rate of 6.468%, while the economic value of carbon storage (EVCS) in the Chengdu–Chongqing region was CNY 289.516 × 10⁹. (3) Spatial correction of carbon storage is crucial for enhancing the goodness-of-fit and result accuracy of the MGWR model, as the absence of such correction would significantly degrade its performance. The revised InVEST model enables rapid quantification of carbon storage’s spatial heterogeneity. Full article

Electric vehicle (EV) charging stations play a pivotal role in the widespread adoption and integration of electric vehicles into mainstream transportation systems. While the effects of climate change and greenhouse gases are increasing worldwide, the transition to electric vehicles is of high importance in terms of both ecological and sustainability. EV charging stations serve as the backbone of this transition, providing essential infrastructure to support the charging needs of EV owners and facilitate the transition to electric vehicles. In this study, a MINLP mathematical model is developed for the multi-objective optimization of EVCS. For implementation, Istanbul’s European side and a large-scale synthetic case are addressed considering both current demand and estimations for low, medium, and high EV numbers by the Energy Market Regulatory Authority (EMRA) for 2030 and 2035. The primary aim is to minimize station numbers, capacity, waiting time, and station idle time while meeting the demand. During the solvation of the mathematical model, both present demand and future EV usage forecasts are taken into consideration. This involves simulating different scenarios using EMRA’s 2030 and 2035 estimates and determining the optimal locations and capacities for charging stations for each demand level. Efficiencies in different scenarios were evaluated and the created mathematical model provides to optimize EV charging stations in multiple ways, there will be savings in total cost and labor force. The findings of the study will provide a valuable guide to the EV charging station infrastructure planning of the highways, regions, and urban areas to be selected in possible studies. The multi-directional optimization model addressed in this study will support decision-makers and industry experts in making informed decisions towards the sustainable and efficient development of EV charging infrastructure. Full article

Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data. Full article

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

As part of the global endeavor to encourage sustainable urban growth and lower carbon emissions, Hail City is leading the way in implementing cutting-edge technologies with which to improve its urban infrastructure. Initiatives for energy resilience and the environment heavily rely on shifting to electric vehicles (EVs). This work describes the strategic planning required to implement a network of solar charging stations and analyzes the parameters that affect this, supporting cleaner transport options. In addition to meeting the growing demand from an increased number of EVs, constructing a network of solar charging stations positions the city as a leader in integrating renewable energy sources into urban areas. A solar electric vehicle charging station (EVCS) will also be designed. This study highlights a competitive attitude in establishing international standards for sustainable practices and critically examines the technical factors affecting the required charging stations. Regarding the latter, the following results were obtained. The ideal number of station slots is 200. Less efficient vehicles with higher consumption rates require a more comprehensive charging infrastructure, and increasing the charging power leads to an apparent decrease in the number of stations. The influence of battery capacity on the required NSs is limited, especially at charger power values above 30 kWh. By taking proactive measures to address these factors, Hail City hopes to improve its infrastructure effectively and sustainably, keeping it competitive in a world where cities are increasingly judged on their ability to adopt new technology and green projects. A solar station was designed to supply the EVCS with a capacity of 700.56 kWp. Full article

In recent years, the prominence of probabilistic forecasting has risen among numerous research fields (finance, meteorology, banking, etc.). Best practices on using such forecasts are, however, neither well explained nor well understood. The question of the benefits derived from these forecasts is of primary interest, especially for the industrial sector. A sound methodology already exists to evaluate the value of probabilistic forecasts of binary events. In this paper, we introduce a comprehensive methodology for assessing the value of probabilistic forecasts of continuous variables, which is valid for a specific class of problems where the cost functions are piecewise linear. The proposed methodology is based on a set of visual diagnostic tools. In particular, we propose a new diagram called EVC (“Effective economic Value of a forecast of Continuous variable”) which provides the effective value of a forecast. Using simple case studies, we show that the value of probabilistic forecasts of continuous variables is strongly dependent on a key variable that we call the risk ratio. It leads to a quantitative metric of a value called the OEV (“Overall Effective Value”). The preliminary results suggest that typical OEVs demonstrate the benefits of probabilistic forecasting over a deterministic approach. Full article

Lava rises, locally known as stony rises, are Pliocene–Holocene volcanic landforms occurring throughout the Victorian Volcanic Plain (VVP) in Victoria, Australia. Stony rises are not only important to understanding the geological history of Victoria but are culturally significant to Aboriginal Australians and have ecological importance. Currently, the mapping of stony rises is manually performed at a case study level rather than a landscape level. Remote sensing technologies such as LiDAR data, satellite imagery, and aerial imagery allow for the mapping of stony rises from an aerial perspective. This paper aims to map stony rises using remotely sensed and geophysical data at a landscape level on a younger lava flow (~42,000 years old) within the Victorian Volcanic Plain (the Warrion Hill and Red Rock Volcanic Complex) by utilizing an object based random forest machine learning approach. The results show that stony rises were successfully identified in the landscape to an accuracy of 78.9%, with 2716 potential new stony rises identified. Out of 34 predictor variables, we found the most important variables to be slope gradient, local elevation, DEM of Difference (change in height), Normalized Difference Water Index (NDWI), Clay Mineral Ratio, the concentration of radiometric elements (Potassium, Thorium, and Uranium), Total Magnetic Intensity, and Ecological Vegetation Class (EVC). The results from this study highlight the ability to detect a volcanic landform at a landscape scale using an ensemble of predictor variables that include topographic, spectral information and geophysical data. This lays the foundation towards a uniform approach for mapping stony rises throughout the VVP and similar landforms (such as tumuli) worldwide. Full article

In intelligent building, efficient surface defect detection is crucial for structural safety and maintenance quality. Traditional methods face three challenges in complex scenarios: locating defect features accurately due to multi-scale texture and background interference, missing fine cracks because of their tiny size and low contrast, and the insufficient generalization of irregular defects due to complex geometric deformation. To address these issues, an improved version of the You Only Look Once (YOLOv8) algorithm is proposed for building surface defect detection. The dataset used in this study contains six common building surface defects, and the images are captured in diverse scenarios with different lighting conditions, building structures, and ages of material. Methodologically, the first step involves a normalization-based attention module (NAM). This module minimizes irrelevant features and redundant information and enhances the salient feature expression of cracks, delamination, and other defects, improving feature utilization. Second, for bottlenecks in fine crack detection, an explicit vision center (EVC) feature fusion module is introduced. It focuses on integrating specific details and overall context, improving the model’s effectiveness. Finally, the backbone network integrates deformable convolution net v2 (DCNV2) to capture the contour deformation features of targets like mesh cracks and spalling. Our experimental results indicate that the improved model outperforms YOLOv8, achieving a 3.9% higher mAP50 and a 4.2% better mAP50-95. Its performance reaches 156 FPS, suitable for real-time inspection in smart construction scenarios. Our model significantly improves defect detection accuracy and robustness in complex scenarios. The study offers a reliable solution for accurate multi-type defect detection on building surfaces. Full article

Antifungal composite beads were prepared using a methylcellulose, alginate, and ethanol solution with the ionic gelation method and ethanol beads (E). A total of 1.0 mL of clove essential oil (CEO) and 1.0 g of vanillin were added to provide an antifungal effect against Aspergillus flavus and Rhizopus stolonifera. Four bead formulations were prepared: ethanol beads (E), ethanol beads containing CEO (EC), ethanol beads containing vanillin (EV), and ethanol beads containing vanillin and CEO (EVC). Ethanol beads were transparent and spherical, whereas those containing CEO or vanillin were spherical and opaque, with diameters ranging from 2.1 to 2.4 mm. The surface and pores in the polymer matrix were investigated in relation to the encapsulation and release of antifungal agents. The bursting release of ethanol and CEO occurred on the first day. Antifungal assays on potato dextrose agar against Aspergillus flavus and Rhizopus stolonifera showed that beads containing CEO (EC and EVC) provided superior inhibition, particularly at a dosage of 1.0 g. In butter cake preservation tests, packaging the butter cake with a sachet containing 1.0 g of EC or EVC beads can extend the shelf life by two days, delaying visible mold growth from day 5 to day 7 compared to the control. Full article

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact. Full article

Background/Objectives: Genodermatoses are genetic conditions with clinical symptoms manifesting in the skin and adjoining tissues, individually rare but comprising a large and heterogeneous group of disorders that represents 15% of genetic diseases. This article discusses the results of individuals with genodermatoses from a reference center for rare diseases studied through whole genome sequencing conducted by the Brazilian Rare Genomes Project between 2021 and 2023. Methods: A retrospective case series with data comprising sex, age at first assessment in the hospital, family history, clinical findings, and molecular results. Results: Excluding neurofibromatosis type 1, Ehlers–Danlos syndrome and RASopathies are discussed elsewhere. Diagnoses in this work comprised ectodermal dysplasias (n = 6), ichthyosis (n = 4), albinism (n = 4), tuberous sclerosis complex (n = 4), and incontinentia pigmenti (n = 3), in addition to 11 others with individual rare conditions. The sex ratio was 17:16 (M:F), consanguinity was present in 6/33 (18%), and the age at the first evaluation ranged from neonatal to 26 years (median 13.65 years). Negative results were 3/33 (9%), novel variants were 17/41 (41.4%), and 7/30 (23%) presented initially with a double molecular diagnosis, three confirming composed phenotypes. Conclusions: Besides reporting 17 novel variants in 14 genes (BLM, CACNA1B, EDA, ELN, ENG, ERC6, EVC2, PNPLA1, PITCH1, PORCN, SIN3A, TP63, TYR, and WNT10B), the study also identified three atypical clinical presentations due to dual diagnoses, and the c.454C>T variant in the SDR9C7 gene, previously reported only in dogs, was, for the first time, confirmed as causative for ichthyosis in humans. Full article

Background/Objectives: Identification of human remains is of utmost importance for criminal investigations and providing closure to the families. The reconstruction of a biological profile of the individual will narrow down the list of candidates for identification. From another perspective, facial approximations performed by a forensic artist can provide investigative leads, with the identity being confirmed by primary or secondary methods of identification. In recent years, DNA analysis has evolved, trying to create a portrait of the perpetrator/victim based on External Visible Characteristics (EVCs), the color of the eyes, hair, and skin and Biogeographical ancestry (BGA), called DNA phenotyping. Despite these advances, currently, there are no studies integrating the biological profile performed by forensic anthropologists, the facial approximation created by forensic artists and EVCs determined by DNA. The goal of this work was to integrate these three investigative leads to enhance the possibility of human identification. Methods: Five donated remains from Mercyhurst were studied through these approaches: reconstruction of biological profile, facial approximation and estimation of EVCs based on previous studies. Results: Our results indicated the feasibility of integrating this biological profile and EVCs data into the facial approximation developed by the forensic artist, aiming to an enhance portrait of the remains. In a second phase of this project, the accuracy of the integrated facial approximation will be assessed. Conclusions: This study pointed out the importance of an interdisciplinary approach towards the identification of human remains, as well as the combination of current methods with new technologies. Full article

Ellis-van Creveld syndrome (EvC) is a rare genetic disorder (7:10,000,000) caused by biallelic pathogenic variants in EVC and EVC2, which are located in close proximity on chromosome 4p16.2 in a divergent orientation. These genes encode ciliary complex proteins essential for Hedgehog signaling. EvC is characterized by congenital heart disease (CHD), postaxial polydactyly, and rhizomelic shortening. We present a case of a female newborn from southeast Mexico carrying a novel missense variant in EVC, which is aligned with a systematic review aimed at exploring genotype–phenotype correlations in EVC-related EvC. A PRISMA-based systematic review was conducted in PubMed, Web of Science, and OVID/Medline (until December 2024). Studies reporting EVC variants in EvC were included. Data extraction and quality assessment were performed independently by four reviewers, and genotype–phenotype correlation analysis was conducted. Fifteen studies (n = 66 patients) met the inclusion criteria. The most prevalent features were postaxial polydactyly (95.5%), nail hypoplasia (68.2%), and CHD (66.7%) with atrioventricular canal as the most frequent subtype. Fifty-five distinct EVC variants across 132 alleles were identified, predominantly affecting the N-terminal region (first 699 amino acids). They were syndactyly correlated with pathogenic variants in exons 6, 12, and 13, which were proximal to the second and third coiled-coil domains. This review confirms the key clinical features of EVC-related EvC and highlights genetic heterogeneity. The correlation between syndactyly and specific exonic variants suggests potential genotype–phenotype associations, warranting further functional studies. Full article

Background: The Gollop–Wolfgang complex is a rare congenital limb deformity characterized by a bifid femur, tibial hemimelia, and ectrodactyly of the hand. First described in 1980, fewer than 200 cases have been reported globally, with an estimated incidence of 1:1,000,000 live births. Case Presentation: We report a 2-month-old female infant with classic features of the Gollop–Wolfgang complex, including a left bifid femur, complete absence of the left tibia, and contralateral tetradactyly. A clinical examination revealed significant limb length discrepancy, knee instability, equinovarus foot deformity, and skeletal abnormalities confirmed by imaging studies. Extensive investigations, including echocardiography and genetic testing, excluded systemic anomalies and identified non-pathogenic variants in the Collagen Type XI Alpha 2 (COL11A2) and EVC2 genes. A surgical resection of the bifid femur was performed. Results: This case highlights the importance of early diagnosis and a multidisciplinary approach in managing the Gollop–Wolfgang complex. While our case presented with typical features, subtle variations highlight the phenotypic spectrum of the condition. The combination of tibial hemimelia and bifid femur frequently necessitates knee disarticulation due to the absence of a viable tibial anlage, while limb salvage techniques remain challenging. A genetic evaluation identified variants of uncertain significance in the COL11A2 and EVC2 genes, indicating that the genetic basis of the condition is not fully understood. Conclusions: These findings emphasize the need for continued genetic research to clarify the etiology of the Gollop–Wolfgang complex and to improve treatment strategies, particularly in refining surgical approaches and exploring new therapeutic options. Full article