Search Results (152)

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

Aiming at the problems of misdetection, leakage, and low recognition accuracy caused by numerous surface defects and complex backgrounds of laser nozzles, this paper proposes DEC-YOLO, a novel detection model centered on the DEC Module (DenseNet-explicit visual center composite module). The DEC Module, as the core innovation, combines the dense connectivity of DenseNet with the local–global feature integration capability of the explicit visual center (EVC) to enhance gradient propagation stability during the training process and enhance fundamental defect feature extraction. To further optimize detection performance, three auxiliary strategies are introduced: (1) a head decoupling strategy to separate classification and regression tasks, (2) cross-layer connections for multi-scale feature fusion, and (3) coordinate attention to suppress background interference. The experimental results on a custom dataset demonstrate that DEC-YOLO achieves a mean average precision (mAP@0.5) of 87.5%, surpassing that of YOLOv7 by 10.5%, and meets the accuracy and speed requirements needed in the laser cutting production environment. Full article

This paper applies the Chameleon Swarm Algorithm (CSA) and Snow Geese Algorithm (SGA) for optimizing the placement of electric vehicle charge stations (EVCSs), renewable energy sources (RESs), and shunt capacitors (SCs). The actual power ranges of the EVCSs of the Vinfast company in Vietnam are used to check the stabilization of the IEEE 85-node distribution power grid by considering four penetration levels of EVCSs, namely 25%, 50%, 75%, and 100%. All penetration levels of EVCSs violate the operating load voltage limits, and the grid cannot work for all the penetration levels. Different scenarios are performed to find the minimum RES penetration level and the most possible SC penetration level to satisfy the operating voltage limits. The use of only SCs cannot satisfy the voltage limits even for the 25% EVCS penetration level. The placement of RESs provides the capability to maintain voltage within the allowed range for 25% and 50% EVCS penetration but not for 75% and 100%. Using both RESs and SCs, the operating voltage limits are satisfied by using RESs with 1385 kW (about 30.44% of loads and EVCSs) and SCs with 2640 kVAr for the 75% EVCS penetration level and using RESs with 2010 kW (about 38.58% of loads and EVCSs) and SCs with 2640 kVAr (100% of loads) for the 100% EVCS penetration level. The study indicates that the installation of EVCSs should be calculated for stable operation of the distribution power grid, and the combination of both RESs and SCs can satisfy the maximum penetration level of EVCSs in the distribution power grids. Full article

With the advent of large-scale electronic transportation, the construction of electric vehicle charging stations (EVCSs) has increased. The stochastic characteristic of the charging power of EVCSs leads to a risk of destabilization of the DC distribution network when there is a high degree of power electronification. Current deterministic stability analysis methods are too complicated to allow for brief descriptions of the effect of probabilistic characteristics of EVCSs on stability. This paper develops a probabilistic small-signal stability analysis method. Firstly, the probabilistic information of the system is obtained by combining the s-domain nodal impedance matrix based on the point estimation method. Then, the probability function of stability is fitted using the Cornish–Fisher expansion method. Finally, a comparison experiment using Monte Carlo simulation demonstrates that this method performs well in balancing accuracy and computational efficiency. The effects of line parameters and system control parameters on stability are investigated in the framework of probabilistic stability. This will provide a probabilistic perspective on the design of more complex power systems in the future. Full article

In the contemporary global context, excessive fossil fuel consumption remains a critical issue, particularly within the transportation sector. Electric vehicles offer a promising alternative due to their durability and reduced greenhouse gas emissions. However, their rapid adoption has introduced significant challenges, including increased network power losses, deteriorating voltage profiles, and declining substation power factors. This study proposes an approach that integrates fuzzy objective optimization with African Vulture Optimization (AVO) to determine the optimal sitting and sizing of distributed generations (DG), shunt capacitors (SC), and electric vehicle charging stations (EVCS) within radial distribution systems (RDS). The proposed methodology is evaluated on the standard IEEE-69 bus RDS. A detailed comparative analysis between the proposed simultaneous optimization approach for DGs, SCs, and EVCSs and with the traditional two-staged method is presented. The findings indicate that the proposed strategy not only matches but surpasses the performance of existing strategies for the reduction of power losses and enhancement of bus voltage profiles. Key findings include a significant reduction in active and reactive power line loss, with losses minimized by 85.90% and 82.15%, respectively. In addition, an improvement in the minimum bus voltage to 0.98 p.u. is also achieved. Thereafter, the proposed issue is solved in different loading scenarios to present the effectiveness of the approach under different operating conditions. This research effectively demonstrates the complexities introduced by EVCS integration and addresses the issue with simultaneous optimal sitting and sizing of DGs, SCs, and EVCSs that significantly enhance the sustainability and efficiency of RDS. Full article

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article