Search Results (218)

The tufted deer (Elaphodus cephalophus), a Near-Threatened (NT) species endemic to China and Myanmar, requires robust genetic data for effective conservation. However, the genetic landscape of key populations, such as those in Chongqing, remains poorly understood. This study aimed to comprehensively evaluate the genetic diversity, population structure, gene flow, and demographic history of tufted deer across this critical region. We analyzed mitochondrial DNA (mtDNA) from 46 non-invasively collected fecal samples from three distinct populations: Jinfo Mountain (JF, n = 13), Simian Mountain (SM, n = 21), and the Northeastern Mountainous region (NEM, n = 12). Genetic variation was assessed using the cytochrome b (Cyt b) and D-loop regions, with analyses including F_st, gene flow (N_m), neutrality tests, and Bayesian Skyline Plots (BSP). Our results revealed the highest genetic diversity in the SM population, establishing it as a genetic hub. In contrast, the JF population exhibited the lowest diversity and significant genetic differentiation (>0.23) from the SM and NEM populations, indicating profound isolation. Gene flow was substantial between SM and NEM but severely restricted for the JF population. Demographic analyses, including BSP, indicated a long history of demographic stability followed by a significant expansion beginning in the Middle to Late Pleistocene. We conclude that the SM/NEM metapopulation serves as the genetic core for the species in this region, while the highly isolated JF population constitutes a distinct and vulnerable Management Unit (MU). This historical demographic expansion is likely linked to climatic and environmental changes during the Pleistocene, rather than recent anthropogenic factors. These findings underscore the urgent need for a dual conservation strategy: targeted management for the isolated JF population and the establishment of ecological corridors to connect the Jinfo Mountain and Simian Mountain populations, ensuring the long-term persistence of this unique species. Full article

Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management. Full article

An important class of analytes are volatile organic carbons (VOCs), particularly aliphatic primary alcohols. Here, we report the straightforward modification of a commercially available carbon monoxide sensor to detect a range of aliphatic primary alcohols at room temperature. The mass transport mechanisms governing the performance of the sensor were investigated using diffusion in multiple layers of the sensor to model the response to an abrupt change in analyte concentration. The sensor was shown to have a large capacitance because of the nanoparticulate nature of the platinum working electrode. It was also shown that the modified sensor had performance characteristics that were mainly determined by the condensation of the analyte during diffusion through the membrane pores. The sensor was capable of a quantitative amperometric response (sensitivity of approximately 2.2 µA/ppm), with a limit of detection (LoD) of 17 ppm methanol, 2 ppm ethanol, 3 ppm heptan-1-ol, and displayed selectivity towards different VOC functional groups (the sensor gives an amperometric response to primary alcohols within 10 s, but not to esters or carboxylic acids). Full article

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets. Full article

AutoML is enabling many practitioners to use sophisticated Machine Learning pipelines even without being experienced in building application-specific solutions. Adapting AutoML to the field of unsupervised learning, particularly to the task of clustering, is challenging, as clustering is highly subjective and application-specific; the goal is not to find the best way to group data objects based on previously seen examples, but to find interesting new structures within potentially unknown data objects that provide actionable insights. The level of interestingness of a clustering is highly subjective and is subject to a variety of different characteristics making different clusterings of the same dataset (e.g., grouping people by age, gender, or special interests). In this paper, we propose an Automated Exploratory Clustering framework which determines multiple clusterings satisfying different notions of interestingness automatically. To this end, we generate multiple clusterings via AutoML processes and return a selection of clusterings, from which the user can explore the most preferred ones. We use different methods like the skyline operator to prune non-Pareto-optimal clusterings wrt. different dimensions of interestingsness and deliver a small set of valuable clusterings. In this way, our approach enables practitioners as well as domain experts to identify valuable clusterings without becoming experts in clustering as well, thus reducing human efforts and resources in finding application-specific solutions. Our empirical investigation with current state-of-the-art methods is carried out on a number of benchmark datasets, where a well-established ground truth can proxy for the wishes of a domain expert and multiple interestingness properties of the clusterings. Full article

Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about security and regulatory compliance; (2) a lack of interpretability, where opaque AI models provide limited transparency, making it difficult for operators to trust and act on failure predictions; and (3) adaptability issues, as many existing solutions struggle to maintain a consistent performance across diverse industrial environments. Addressing these challenges requires a privacy-preserving, interpretable, and adaptive Artificial Intelligence (AI) model that ensures secure, reliable, and transparent PdM while meeting industry standards and regulatory requirements. Methods: Explainable AI (XAI) plays a crucial role in enhancing transparency and trust in PdM models by providing interpretable insights into failure predictions. Meanwhile, Federated Learning (FL) ensures privacy-preserving, decentralized model training, allowing multiple industrial sites to collaborate without sharing sensitive operational data. This proposed research developed a sustainable privacy-preserving Explainable FL (XFL) model that integrates XAI techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) into an FL structure to improve PdM’s security and interpretability capabilities. Results: The proposed XFL model enables industrial operators to interpret, validate, and refine AI-driven maintenance strategies while ensuring data privacy, accuracy, and regulatory compliance. Conclusions: This model significantly improves failure prediction, reduces unplanned downtime, and strengthens trust in AI-driven decision-making. The simulation results confirm its high reliability, achieving 98.15% accuracy with a minimal 1.85% miss rate, demonstrating its effectiveness as a scalable, secure, and interpretable solution for PdM in Industry 4.0. Full article

Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings. Full article

Background/Objectives: Aurochs (Bos primigenius), one of the earliest and largest herbivores domesticated by humans, were widely distributed in Eurasia and North Africa during the Pleistocene and Holocene. Studies of aurochs in China have focused mainly on the Northeastern region. Previous studies have suggested that haplogroup C is a haplogroup unique to China, but recent studies have shown that this is not the case. We have compiled all data on haplogroup C to revisit the classification of the aurochs haplogroup C. Methods: In this study, we obtained 13 nearly complete mitochondrial genomes from Late Pleistocene to early Holocene bovine samples from Northeastern China through fossil sample collection, ancient DNA extraction, library construction, and high-throughput sequencing. Based on the acquired ancient DNA data and in combination with previously published bovine data, the phylogenetic status, lineage divergence time, and population dynamics of aurochs in Northeastern China were analyzed. Results: Phylogenetic analyses and divergence time estimations suggest that the current definition of haplogroup C is overly inclusive, necessitating a refined reclassification of this haplogroup. We also estimated the population dynamics of aurochs in Northeastern China using Bayesian skyline plots found that the maternal effective population size of the aurochs increased significantly during Marine Isotope Stage 5 (MIS5), but began to decrease in the second half of MIS3 before they eventually became extinct. Conclusions: Our results provide new molecular evidence on the phylogenetic status, divergence time, and population dynamics of aurochs in Northeastern China. Full article

This research focuses on creating CuO/PANI nanocomposite films by electrodepositing copper oxide nanoparticles into a polyaniline matrix on ITO substrates. The CuO nanoparticle content was adjusted between 7% and 21%. These nanocomposites are promising for various applications, such as optoelectronic devices, gas sensors, electromagnetic interference shielding, and electrochromic devices. We utilized UV-Vis spectroscopy to examine the nanocomposites’ interaction with light, allowing us to ascertain their refractive indices and absorption coefficients. The Scherrer formula facilitated the determination of the average crystallite size, shedding light on the material’s internal structure. Tauc plots indicated a reduction in the energy-band gap from 3.36 eV to 3.12 eV as the concentration of CuO nanoparticles within the PANI matrix increased, accompanied by a rise in electrical conductivity. The incorporation of CuO nanoparticles into the polyaniline matrix appears to enhance the conjugation length of PANI chains, as evidenced by shifts in the quinoid and benzenoid ring vibrations in FTIR spectra. SEM analysis indicates that the nanocomposite films possess a relatively smooth and homogeneous surface. Additionally, FTIR and XRD analyses demonstrate an increasing degree of interaction between CuO nanoparticles and PANI chains with higher CuO concentrations. At lower concentrations, interactions were minimal. In contrast, at higher concentrations, more significant interactions were observed, which facilitated the stretching of polymer chains, improved molecular packing, and facilitated the formation of larger crystalline structures within the PANI matrix. The incorporation of CuO nanoparticles resulted in nanocomposites with electrical conductivities ranging from 1.2 to 17.0 S cm⁻¹, which are favorable for optimum performance in optoelectronic devices. These results confirm that the nanocomposite films combine pronounced crystallinity, markedly enhanced electrical conductivity, and tunable band-gap energies, positioning them as versatile candidates for next-generation optoelectronic devices. Full article

The Japanese archipelago as a continental island of the Eurasia continent and harboring high levels of plant species diversity provides an ideal geographical setting for investigating vicariant allopatric speciation due to the sea-level fluctuations associated with climatic oscillations during the Quaternary. In this study, three chloroplast DNA regions and 14 nuclear loci were sequenced for 31 individuals from three populations of Torreya nucifera var. nucifera and 52 individuals from three populations of T. nucifera var. radicans. Population genetic analyses (Network, STRUCTURE and phylogeny) revealed that the genetic boundaries of the two varieties are distinct, with high genetic differentiation (F_ST) of 0.9619 in chloroplast DNA and 0.6543 in nuclear loci. The relatively ancient divergence times between the two varieties were estimated to 3.03 Ma by DIYABC and 1.77 Ma by IMa2 when dated back to the late Pliocene and the early Pleistocene, respectively. The extremely weak gene flow (2Nm = 0.1) between the two varieties was detected by IMa2, which might be caused by their population expansion since the early Pleistocene (~2.0 Ma) inferred in the Bayesian skyline plots and DIYABC. Niche modeling showed that the two varieties had significant ecological differentiation (p < 0.001) since the Last Interglacial even earlier. These results demonstrate that vicariant allopatric speciation due to sea-level fluctuations may be a common mode of speciation in the Japanese archipelago. This finding provides insights into the understanding of species diversification in the Japanese Archipelago and even East Asian flora under climatic oscillations during the Quaternary. Full article

In today’s data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms process data locally, thereby reducing data transfer and exposure to breaches, while at the same time improving scalability thanks to data distribution across multiple sources. Top-k queries are a key tool in vertically distributed scenarios and are widely applied in critical applications involving sensitive data. Classical top-k algorithms typically resort to sorted access to sequentially scan the dataset and to random access to retrieve a tuple by its id. However, the latter kind of access is sometimes too costly to be feasible, and algorithms need to be designed for the so-called “no random access” (NRA) scenario. The latest efforts in this direction do not cover the recent advances in ranking queries, which propose hybridizations of top-k queries (which are preference-aware and control the output size) and skyline queries (which are preference-agnostic and have uncontrolled output size). The non-dominated flexible skyline ($\mathrm{ND}$) is one such proposal, which tries to obtain the best of top-k and skyline queries. We introduce an algorithm for computing $\mathrm{ND}$ in the NRA scenario, prove its correctness and optimality within its class, and provide an experimental evaluation covering a wide range of cases, with both synthetic and real datasets. Full article

This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics typically suffer from slow convergence. To overcome these limitations, we propose a novel neural-driven constructive heuristic. The method employs a population of simple feed-forward neural networks, which are trained using black-box optimization via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting neural network dynamically scores candidate placements within the constructive heuristic. Unlike conventional heuristics, the approach adapts to instance-specific characteristics without relying on predefined rules. Evaluated on datasets generated by 2DCPackGen and real-world logistic scenarios, the proposed method consistently outperforms benchmark heuristics such as MaxRects and Skyline, reducing the average number of bins required across various item types and demand ranges. The most significant improvements occur in complex instances, with up to 86% of 2DCPackGen cases yielding superior results. This heuristic offers a flexible and extremely fast, data-driven solution to the algorithm selection problem, demonstrating robustness and potential for broader application in combinatorial optimization while avoiding the scalability issues of reinforcement learning-based methods. Full article

Twenty years after its construction, the Agbar Tower remains one of the most recognizable landmarks in Barcelona’s skyline. This article analyzes the dome that serves as its roof. The design of this element required a year of development, during which more than ten iterations were analyzed, resulting in the final design. There was no similar precedent published, as it is a 30 m high dome with significant wind exposure. This study offers an analytical review of the design process and the dome’s enclosure, providing a reference for similar architectural construction projects. This research focuses on describing the elementary loading hypotheses and obtaining numerical models for structural calculations, comparing the results of the five most representative models of the process. Various factors are considered, such as shape, enclosure, structural frequency, deformations obtained, fire protection, and cost. The conclusion focuses on explaining how a form that remains unchanged throughout the process slightly varies the structural solution while respecting the project, to adjust to all the final regulations, construction requirements, cost requirements, and project requirements. Full article

With the global trend of waterfront urban expansion, nonlinear urban growth has generated skyline patterns marked by multidimensional spatial heterogeneity. Traditional single-viewpoint methods often fall short in capturing the layered spatial relationships among buildings and the complexity of multi-axis urban forms. This study focuses on the Lujiazui waterfront in Shanghai and proposes a multi-viewpoint assessment framework to evaluate urban waterfront skylines based on fractal and spatial hierarchy analysis. The framework consists of: (1) selecting eight representative viewpoints along the Huangpu River using visual cognition theory and GIS tools; (2) calculating skyline contour complexity using fractal dimension models; (3) establishing spatial hierarchy coefficients to measure depth gradients of building clusters; and (4) validating the results through visual field analysis and local skyline planning guidelines. This method integrates multi-viewpoint observation with quantitative morphological analysis, enabling a comprehensive evaluation from 2D skyline contours to 3D spatial structures. The key findings reveal that the fractal dimensions of the Lujiazui skyline demonstrate clear spatial differentiation, with viewpoints such as Financial Plaza and Chenyi Plaza reaching benchmarks typical of international metropolises. Spatial hierarchy coefficients exhibit a gradient attenuation trend, meeting the planning expectations in central zones but revealing stratification discontinuities in peripheral areas. Comparative analysis shows that over 50% of the observation points present imbalanced height ratios and excessive interface continuity, indicating potential risks associated with uncoordinated morphological control. This research confirms that multi-viewpoint assessment effectively captures spatial heterogeneity in nonlinear urban skyline development. A dual-variable evaluation model—fractal dimension and spatial hierarchy—is proposed, forming a quantitative mapping mechanism between visual characteristics and planning regulations. The findings contribute to the development of standardized 3D morphological evaluation methods for complex urban waterfront environments. Full article

Using Google cluster traces, the research presents a task offloading algorithm and a hybrid forecasting model that unites Bidirectional Long Short-Term Memory (BiLSTM) with Gated Recurrent Unit (GRU) layers along an attention mechanism. This model predicts resource usage for flexible task scheduling in Internet of Things (IoT) applications based on edge computing. The suggested algorithm improves task distribution to boost performance and reduce energy consumption. The system’s design includes collecting data, fusing and preparing it for use, training models, and performing simulations with EdgeSimPy. Experimental outcomes show that the method we suggest is better than those used in best-fit, first-fit, and worst-fit basic algorithms. It maintains power stability usage among edge servers while surpassing old-fashioned heuristic techniques. Moreover, we also propose the Deep Deterministic Policy Gradient (D4PG) based on a Federated Learning algorithm for adjusting the participation of dynamic user equipment (UE) according to resource availability and data distribution. This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. Results indicate that the proposed D4PG method achieves superior performance, with an accuracy of 92.86% on the Crop Prediction dataset, outperforming alternative models. On the Non-IID EMNIST dataset, the proposed approach achieves an F1-score of 0.9192, demonstrating better efficiency and fairness in model updates while preserving privacy. Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. This corroborates that the proposed method can preserve privacy by handling issues related to fairness in model updates and improving efficiency better than state-of-the-art alternatives. Full article