Search Results (19)

Background/Objectives: Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics (EVCs) such as eye, hair, and skin color, ancestry, and age from biological traces. However, low template DNA (LT-DNA), often derived from degraded or trace samples, poses significant challenges due to allelic dropout, contamination, and incomplete profiles. This review evaluates recent advances in FDP from LT-DNA, focusing on the integration of machine learning (ML) models to improve predictive accuracy and operational readiness, while addressing ethical and population-related considerations. Methods: A comprehensive literature review was conducted on FDP and ML applications in forensic genomics. Key areas examined include SNP-based trait modeling, genotype imputation, epigenetic age estimation, and probabilistic inference. Comparative performance of ML algorithms (Random Forests, Support Vector Machines, Gradient Boosting, and deep learning) was assessed using datasets such as the 1000 Genomes Project, UK Biobank, and forensic casework samples. Ethical frameworks and validation standards were also analyzed. Results: ML approaches significantly enhance phenotype prediction from LT-DNA, achieving AUC > 0.9 for eye color and improving SNP recovery by up to 15% through imputation. Tools like HIrisPlex-S and VISAGE panels remain robust for eye and hair color, with moderate accuracy for skin tone and emerging capabilities for age and facial morphology. Limitations persist in admixed populations and traits with polygenic complexity. Interpretability and bias mitigation remain critical for forensic admissibility. Conclusions: L integration strengthens FDP from LT-DNA, offering valuable investigative leads in challenging scenarios. Future directions include multi-omics integration, portable sequencing platforms, inclusive reference datasets, and explainable AI to ensure accuracy, transparency, and ethical compliance in forensic applications. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

The increasing adoption of electric vehicles (EVs) and the integration of 5G/6G networks are driving the demand for secure, intelligent, and interoperable charging infrastructure within the Internet of Vehicles (IoV) ecosystem. Electric Vehicle Charging Stations (EVCS) face growing cyber–physical threats, including spoofing, data injection, and firmware tampering, risking user privacy, grid stability, and EVCS reliability. While artificial intelligence (AI), blockchain, and cryptography have been applied in cybersecurity, comprehensive solutions tailored to EVCS challenges, such as real-time threat mitigation and scalability, are often lacking. This paper addresses these critical cybersecurity gaps by presenting a comprehensive overview of novel strategies for enhancing EVCS security through the Internet of Digital Twins (IoDT) technology. The primary objective is to evaluate advanced frameworks that synergize digital twins with artificial intelligence, blockchain, and quantum-resistant cryptography. Through systematic literature analysis, global threat assessments, and review of international standards, this study identifies key attack vectors and their impacts on EVCS. Key findings demonstrate that digital twin-driven solutions facilitate real-time monitoring, anomaly detection, predictive threat mitigation, and secure system governance. This review offers actionable insights for researchers, industry stakeholders, and policymakers to strengthen the cybersecurity and resilience of next-generation electric mobility infrastructure, addressing challenges like scalability and implementation barriers. Full article

Background: Understanding the genetic mechanisms and identifying potential therapeutic targets are essential for clarifying Autism Spectrum Disorder (ASD) etiology and improving treatments. This study aims to bridge the gap between basic transcriptomic discoveries and clinical applications in ASD research. Methods: Differentially expressed genes (DEGs) of GSE18123 datase were identified. A protein–protein interaction (PPI) network was constructed. Functional enrichment analysis was performed to link genetic loci to relevant biological pathways. Connectivity Map (CMap) analysis was used to predict potential drugs. Furthermore, immune infiltration correlation analysis explored associations between key genes and immune cell subpopulations. Diagnostic performance of top genes was evaluated by receiver operating characteristic (ROC) analysis. Results: The functional enrichment analysis successfully revealed relevant biological processes associated with ASD, while the CMap analysis predicted potential drugs that were consistent with some clinical trial results. Random forest analysis selected ten key feature genes (SHANK3, NLRP3, SERAC1, TUBB2A, MGAT4C, TFAP2A, EVC, GABRE, TRAK1, and GPR161) with the highest importance scores for autism prediction. Immune infiltration analysis showed significant correlations in genes and multiple immune cell types, demonstrating complex pleiotropic associations within the immune microenvironment. ROC curve analysis indicated that most top genes had strong discriminatory power in differentiating ASD from controls, particularly MGAT4C (AUC = 0.730), highlighting its potential as a robust biomarker. Conclusions: This study effectively bridges the basic transcriptomic discoveries and clinical applications in ASD research. The findings contribute to a better understanding of the etiology of ASD and provide potential therapeutic leads. Future research could focus on validating these potential drugs in clinical studies, as well as further exploring the biological functions of the identified genes to develop more targeted and effective treatments for ASD. Full article

The seed-germination percentage is an important indicator of the seed viability and growth potential and has important implications for plant breeding and agricultural production. Thus, to increase the speed and accuracy in measuring the completion of germination in experimental seed batches for precise germination percentage calculation, we evaluated a You-Only-Look-Once (YOLO)–Seed Germination Detection (SGD) algorithm that integrates deep-learning technology and texture feature-extraction mechanisms specific to germinating seeds. The algorithm was built upon YOLOv7-l, and its applicability was optimised based on the results of our germination experiments. In the backbone network, an internal convolution structure was substituted to enhance the spatial specificity of the initial features. Following the output of the main feature-extraction network, an Explicit Visual Centre (EVC) module was introduced to mitigate the interference caused by intertwined primary roots from germinated seeds, which can affect recognition accuracy. Furthermore, a Spatial Context Pyramid (SCP) module was embedded after enhancing the feature-extraction network to improve the model’s accuracy in identifying seeds of different scales, particularly in recognising small target seeds. Our results with cabbage seeds showed that the YOLO–SGD model, with a model size of 45.22 M, achieved an average detection accuracy of 99.6% for large-scale seeds and 96.4% for small-scale seeds. The model also achieved a mean average precision and F1 score of 98.0% and 93.3%, respectively. Compared with manual germination-rate detection, the model maintained an average absolute error of prediction within 1.0%, demonstrating sufficient precision to replace manual methods in laboratory environments and efficiently detect germinated seeds for precise germination percentage assessment. Full article

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

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

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations. Full article

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS). Full article

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits. Full article

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector. Full article

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity. Full article

Background: Ellis–van Creveld syndrome (EvCS) is an autosomal recessive ciliopathy with a disproportionate short stature, polydactyly, dystrophic nails, oral defects, and cardiac anomalies. It is caused by pathogenic variants in the EVC or EVC2 genes. To obtain further insight into the genetics of EvCS, we identified the genetic defect for the EVC2 gene in two Mexican patients. Methods: Two Mexican families were enrolled in this study. Exome sequencing was applied in the probands to screen potential genetic variant(s), and then Sanger sequencing was used to identify the variant in the parents. Finally, a prediction of the three-dimensional structure of the mutant proteins was made. Results: One patient has a compound heterozygous EVC2 mutation: a novel heterozygous variant c.519_519 + 1delinsT inherited from her mother, and a heterozygous variant c.2161delC (p.L721fs) inherited from her father. The second patient has a previously reported compound heterozygous EVC2 mutation: nonsense mutation c.645G > A (p.W215*) in exon 5 inherited from her mother, and c.273dup (p.K92fs) in exon 2 inherited from her father. In both cases, the diagnostic was Ellis–van Creveld syndrome. Three-dimensional modeling of the EVC2 protein showed that truncated proteins are produced in both patients due to the generation of premature stop codons. Conclusion: The identified novel heterozygous EVC2 variants, c.2161delC and c.519_519 + 1delinsT, were responsible for the Ellis–van Creveld syndrome in one of the Mexican patients. In the second Mexican patient, we identified a compound heterozygous variant, c.645G > A and c.273dup, responsible for EvCS. The findings in this study extend the EVC2 mutation spectrum and may provide new insights into the EVC2 causation and diagnosis with implications for genetic counseling and clinical management. Full article

In the last few years, predicting externally visible characteristics (EVCs) by adopting informative DNA molecular markers has become a method in forensic genetics that has increased its value, giving rise to an interesting field called “Forensic DNA Phenotyping” (FDP). The most meaningful forensic applications of EVCs prediction are those in which, having only a DNA sample isolated from highly decomposed remains, it is essential to reconstruct the physical appearance of a person. Through this approach, we set out to evaluate 20 skeletal remains of Italian provenance in order to associate them with as many cases of missing persons as possible. To achieve the intended goal, in this work we applied the HIrisPlex-S multiplex system through the conventional short tandem repeats (STR) method to confirm the expected identity of subjects by evaluating phenotypic features. To investigate the reliability and accuracy of the DNA-based EVCs prediction, pictures of the cases were compared as they were available to researchers. Results showed an overall prediction accuracy greater than 90% for all three phenotypic features—iris, hair, and skin colour—at a probability threshold of 0.7. The experimental analysis showed inconclusive results in only two cases; this is probably due to the characteristics of subjects who had an intermediate eye and hair colour, for which the DNA-based system needs to improve the prediction accuracy. Full article