Search Results (126)

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Background: Centromeric alpha satellite DNA is organized into higher-order repeats (HORs), whose precise structure is often difficult to resolve in standard genome assemblies. The recent telomere-to-telomere (T2T) assembly of the human genome enables complete analysis of centromeric regions, including the full structure of HOR arrays. Methods: We applied the novel high-precision GRMhor algorithm to the complete T2T-CHM13 assembly of human chromosome 21. GRMhor integrates global repeat map (GRM) and monomer distance (MD) diagrams to accurately identify, classify, and visualize HORs and their subfragments. Results: The analysis revealed a novel Cascading 11mer HOR array, in which each canonical HOR copy comprises 11 monomers belonging to 10 different monomer types. Subfragments with periodicities of 4, 7, 9, and 20 were identified within the array. A second, complex 23/25mer HOR array of mixed Willard’s/Cascading type was also detected. In contrast to the hg38 assembly, where a dominant 8mer and 33mer HOR were previously annotated, these structures were absent in the T2T-CHM13 assembly, highlighting the limitations of hg38. Notably, we discovered a novel 52mer HOR—the longest alpha satellite HOR unit reported in the human genome to date. Several subfragment repeats correspond to alphoid subfamilies previously identified using restriction enzyme digestion, but are here resolved with higher structural precision. Conclusions: Our findings demonstrate the power of GRMhor in resolving complex and previously undetected alpha satellite architectures, including the longest canonical HOR unit identified in the human genome. The precise delineation of superHORs, Cascading structures, and HOR subfragments provides unprecedented insight into the fine-scale organization of the centromeric region of chromosome 21. These results highlight both the inadequacy of earlier assemblies, such as hg38, and the critical importance of complete telomere-to-telomere assemblies for accurately characterizing centromeric DNA. Full article

Unified Modeling Language (UML) diagrams serve as essential tools for visualizing system structure and behavior in software design. With the emergence of Large Language Models (LLMs) that automate various phases of software development, there is growing interest in leveraging these models for UML diagram generation. This study presents a comprehensive empirical investigation into the effectiveness of GPT-4-turbo in generating four fundamental UML diagram types: Class, Deployment, Use Case, and Sequence diagrams. We developed a novel rule-based prompt-engineering framework that transforms domain scenarios into optimized prompts for LLM processing. The generated diagrams were then synthesized using PlantUML and evaluated through a rigorous survey involving 121 computer science and software engineering students across three U.S. universities. Participants assessed both the completeness and correctness of LLM-assisted and human-created diagrams by examining specific elements within each diagram type. Statistical analyses, including paired t-tests, Wilcoxon signed-rank tests, and effect size calculations, validate the significance of our findings. The results reveal that while LLM-assisted diagrams achieve meaningful levels of completeness and correctness (ranging from 61.1% to 67.7%), they consistently underperform compared to human-created diagrams. The performance gap varies by diagram type, with Sequence diagrams showing the closest alignment to human quality and Use Case diagrams exhibiting the largest discrepancy. This research contributes a validated framework for evaluating LLM-generated UML diagrams and provides empirically-grounded insights into the current capabilities and limitations of LLMs in software modeling education. Full article

A persistent challenge in digital asset management is the lack of standardized models for integrating health assessment—such as the Asset Health Index (AHI)—into Digital Twins, limiting their extended implementation beyond individual projects. Asset managers in the energy sector face challenges of digitalization such as digital environment selection, employed digital modules (absence of an architecture guide) and their interconnection, sources of data, and how to automate the assessment and provide the results in a friendly decision support system. Thus, for energy systems, the integration of Asset Assessment in virtual replicas by Digital Twins is a complete way of asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. Another challenge in this context is how to compound in a structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator easy to interpret as a level of risk. This paper tries to serve as a guide against these digital and structured assessments to integrate AHI methodologies into Digital Twins for energy converters. First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. This model has been developed fulfilling standardized practices of asset digitalization as the Reference Architecture Model for Industry 4.0 (RAMI 4.0), organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the data model for cloud Digital Twin implementation, which is deployed on Microsoft Azure Architecture using native Internet of Things (IoT) and analytics services to enable automated and real-time AHI calculation. This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. In the case study, AHI-based monitoring reduced unplanned failures by 43% and improved maintenance planning accuracy by over 30%. Full article

Integrated approaches such as the WEF nexus (water–energy–food) have been key to measuring the efficiency of production systems. In southern Brazil, where extensive livestock farming and integrated agricultural systems coexist in anthropized natural grasslands, such an assessment is crucial for balancing production and conservation. This research aimed to assess the sustainability of different livestock systems in Brazil’s Pampa biome from the perspective of the WEF nexus. One hundred and twenty-one systems were analyzed and divided into extensive livestock systems (ELSs) and integrated livestock systems (ILSs). The MESMIS methodology was used to construct and measure 37 WEF nexus indicators. The data were analyzed using a raincloud diagram and compared using Student’s t-test. In terms of water, the results showed that the ELS was more sustainable in terms of the scope of production. In terms of energy, the ELS stood out in the scope of the sustainability of mechanical energy use. The ILS was superior in terms of social and associative participation in the food nexus, while the ELS stood out in terms of sustainable production management. In general, in both systems, the sustainability indices for the water nexus were optimal, but the situation was alarming for the energy and food nexus. This research contributes by applying the WEF nexus to the analysis of the sustainability of livestock systems, offering a replicable model for other natural grassland regions. Full article

Magnetic pulse welding (MPW) is a promising solid-state joining process that utilizes electromagnetic forces to create high-speed, impact-like collisions between two metal components. This welding technique is widely known for its ability to join dissimilar metals, including aluminum, steel, and copper, without the need for additional filler materials or fluxes. MPW offers several advantages, such as minimal heat input, no distortion or warping, and excellent joint strength and integrity. The process is highly efficient, with welding times typically ranging from microseconds to milliseconds, making it suitable for high-volume production applications in sectors including automotive, aerospace, electronics, and various other industries where strong and reliable joints are required. It provides a cost-effective solution for joining lightweight materials, reducing weight and improving fuel efficiency in transportation systems. This contribution concerns an application for the automotive sector (body-in-white) and specifically examines the welding of AA6082-T6 aluminum alloy with HC420LA cold-rolled micro-alloyed steel. One of the main aspects for MPW optimization is the determination of the process window that does not depend on the equipment used but rather on the parameters associated with the physical mechanisms of the process. It was demonstrated that process windows based on contact angle versus output voltage diagrams can be of interest for production use for a given component (shock absorbers, suspension struts, chassis components, instrument panel beams, next-generation crash boxes, etc.). The process window based on impact pressures versus impact velocity for different impact angles, in addition to not depending on the equipment, allows highlighting other factors such as the pressure welding threshold for different temperatures in the impact zone, critical transition speeds for straight or wavy interface formation, and the jetting/no jetting effect transition. Experimental results demonstrated that optimal welding conditions are achieved with impact velocities between 900 and 1200 m/s, impact pressures of 3000–4000 MPa, and impact angles ranging from 18–35°. These conditions correspond to optimal technological parameters including gaps of 1.5–2 mm and output voltages between 7.5 and 8.5 kV. Successful welds require mean energy values above 20 kJ and weld specific energy values exceeding 150 kJ/m². The study establishes critical failure thresholds: welds consistently failed when gap distances exceeded 3 mm, output voltage dropped below 5.5 kV, or impact pressures fell below 2000 MPa. To determine these impact parameters, relationships based on Buckingham’s π theorem provide a viable solution closely aligned with experimental reality. Additionally, shear tests were conducted to determine weld cohesion, enabling the integration of mechanical resistance isovalues into the process window. The findings reveal an inverse relationship between impact angle and weld specific energy, with higher impact velocities producing thicker intermetallic compounds (IMCs), emphasizing the need for careful parameter optimization to balance weld strength and IMC formation. Full article

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in requirements engineering (RE) due to the complexity of heterogeneous devices and dynamic user needs. Traditional RE methodologies often result in inefficient resource utilization and poor system performance. This research presents the goal-oriented requirements analysis (GORA) methodology, which optimizes requirement specification, resource allocation, and sustainability in IoT development. GORA addresses both functional and non-functional requirements, such as energy efficiency and security, while minimizing computational overhead and reducing resource wastage. The methodology integrates goal-oriented requirements analysis language i* and model-driven development (MDD) through a three-stage transformation process involving the i* RA model, UML class diagrams, and Python source code generation. A case study demonstrates how GORA improves system reliability, performance, and sustainability. Furthermore, an empirical evaluation was conducted in a simulated IoT environment, measuring key metrics such as energy consumption, latency, and code-generation error rate. A comparative analysis with existing RE and MDD approaches is also presented to highlight GORA’s advantages in feature coverage, sustainability support, and automation level. This work underscores the need for structured IoT RE frameworks and positions GORA as a foundation for future research in sustainable IoT development. Full article

Einstein–Cartan theory is a generalization of general relativity that introduces spacetime torsion. In this paper, we perform phase space analysis to investigate the evolution of the early universe in Einstein–Cartan theory. By studying the stability of critical points in the dynamical system, we find that there exist two stable critical points which represent an Einstein static solution and an expanding solution, respectively. After analyzing the phase diagram of the dynamical system, we find that the early universe may exhibit an Einstein static state, an oscillating state, or a bouncing state. By assuming the equation of state $\omega$ can decrease over time t, the universe can depart from the initial Einstein static state, oscillating state, or bouncing state and then evolve into an inflationary phase. Then, we analyze four different inflationary evolution cases in Einstein–Cartan theory and find that a time-variable equation of state $\omega$ cannot yield values of $n_s$ and r consistent with observations, while a time-invariant equation of state $\omega$ is supported by the Planck 2018 results. Thus, in Einstein–Cartan theory, the universe likely originates from a bouncing state rather than an Einstein static state or an oscillating state. Full article

The material in the mold of the injection-molding machine releases significant latent heat of solidification during the cooling process. The efficient recovery and utilization of this waste heat is crucial for improving energy efficiency. A novel integrated energy management system for mold cooling/heat pump/material preheating is proposed in this paper. Taking the symmetrical thermodynamic performance of the heat pump components as the basis and optimizing the system configurations, four system configurations were investigated: MC/BHP/MPCC, MC/RHP/MPCC, MC/HP/MP-IEMS, and MC/DCHP/MP-IEMS, utilizing EBSILON software. The performance of the systems was evaluated through the coefficient of performance (COP) and whole cycle energy efficiency (η). The T-q, T-s, and P-h diagrams were analyzed. It was found that, under comparative operating conditions, both the MC/HP/MP-IEMS and MC/DCHP/MP-IEMS systems exhibited significantly higher COP and η than the MC/BHP/MPCC and MC/RHP/MPCC systems. MC/HP/MP-IEMS achieves a COP of 13.66 and η of 22.09. Similarly, MC/DCHP/MP-IEMS achieves a COP of 14.00 and η of 22.53. The paper optimizes the other three systems using MC/BHP/MPCC as the comparison condition. Optimal cycle performances are achieved with COP and η values of 9, 16, 16, and 9, 26, 25, respectively. A comparison of the thermodynamic performance of five different refrigerants revealed that R123 and R245fa have superior overall performance. This study provides theoretical support for the engineering implementation of integrated energy management systems for injection-molding machines. Full article

Researchers frequently utilize the method of optical initial structure (MOIS) of Czerny–Turner (C–T) spectrometers for aberration-correction studies based on the coma-free equation. While effective, this method has limitations: small numerical apertures at slits (0.05–0.07) hinder weak signal detection; V or W-shaped variations in Airy disk across wavelengths; optical resolution depends on the radius of the collimating lens may not match detector resolution; and sequence patterns based on the spot diagrams cannot simulate the full width at half maximum (FWHM) under discrete sampling. To address these issues, using ray tracing and imaging equations, three criteria are proposed: luminous flux and aberration balance (LFAB), Airy disk variation at imaging points (ADVI), and optical-detector resolution matching (ORDR). A verification system with a 500–750 nm wavelength range and 0.4 nm resolution was designed. Results show that designing spectrometers based on these criteria increases the slit’s numerical aperture to 0.11 while controlling aberrations. After optimization, the tangential Airy disk size decreased by 28% with variations within 3 μm. Discrete sampling indicates FWHM pixel errors remain within 1/2 pixel of the theoretical value, and FWHM is at least 2.5 pixels, satisfying stricter sampling requirements beyond Nyquist. Optimization only involves adjusting the image plane by 0.017 mm axially, 0.879 mm off-axis, and 0.48° eccentricity. This research strengthens spectrometer design theory and improves practical applications. Full article

Uranium–niobium (U-Nb) alloys, used in a variety of industrial and energy applications that require high density, ductility, and good corrosion resistance, comprise a highly complex, multiphasic system with a phase diagram well established through decades of extensive experimental and theoretical research. They are also one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. The key factor determining the performance and safety of a metallic fuel such as U-Nb is its operational limits in the application environment, which are closely related to material’s structure and thermodynamic stability. They are in turn closely related to the ambient (zero-pressure) melting point $\left(T_m\right);$ thus, $T_m$ is an important engineering parameter. However, the current knowledge of $T_m$ of the U-Nb system is limited, as the only experimental study of its Nb-rich portion dates back to 1958. In addition, it has not yet been adequately modeled based on general thermodynamics principles or using an equation-of-state approach. In this study, we present a theoretical model for the melting curve (liquidus) of a mixture, and apply it to U-Nb, which is considered as a mixture of pure U and pure Nb. The model uses the known melting curves of pure constituents as an input and predicts the melting curve of their mixture. It has only one free parameter, which must be determined independently. The ambient liquidus of U-Nb predicted by the model appears to be in good agreement with the available experimental data. We calculate the melting curve (the pressure dependence of $T_m$) of pure U using ab initio quantum molecular dynamics (QMD), the knowledge of which is required for obtaining the model parameters for U. We also generalize the new model to nonzero pressure and consider the melting curve of U-6 wt.% Nb (U-6Nb) alloy as an example. The melting curve of U-6Nb alloy predicted by the model appears to be in good agreement with the ab initio melting curve obtained from our QMD simulations. We suggest that the U-18Nb alloy can be considered as a proxy for protactinium (Pa) and demonstrate that the melting curves of U-18Nb and Pa are in good agreement with each other. Full article

Background/Objectives: Anadenanthera colubrina (popularly known as angico; in this study: Acol) is a bee-pollinated tree with gravity-dispersed seeds that occurs in dry tropical forests (SDTF), one of the most fragmented tropical ecosystems. In this study, we analyzed the resilience of 30 Acol Forest fragments of Ribeirão Preto, São Paulo, Brazil, and the flow of pollinators among these fragments based on the flight ranges of Apis mellifera (6 km) and Trigona spinipes (8 km). Additionally, we investigated genetic diversity, spatial genetic structure (SGS), and phenology across generations of one Acol population (AcolPM), located in the urban fragment M103 in the “Parque Municipal Morro de São Bento” (a municipal park in Ribeirão Preto). Methods: We mapped Acol fragments using geospatial data, with relief and slope analysis derived from digital terrain modeling. We created a flow diagram based on the pollinator’s flight ranges and calculated betweenness centrality. We amplified DNA from AcolPM individuals using 14 SSR molecular markers. Results: Notably, 17 of the 30 fragments occurred on slopes > 12%, terrain unsuitable for agriculture or construction, indicating that the presence of A. colubrina may serve as an indicator of territorial transformations. The AcolPM population (Fragment M103) emerged as a key node among the angicais, connected by the native pollinator T. spinipes, being fundamental for regional gene flow. In this focal population, we observed a slight but significant inbreeding (Fis, Fit, p < 0.01) and an SGS up to ~17 m. Genetic diversity was intermediate (He ≈ 0.62), and PCoA, Fst, and AMOVA values suggest low generational isolation, with most genetic variation within generations. This highlights AcolPM as a promising source for seed collection for reforestation. Phenological observations showed that fructification occurs between September and October, at the beginning of the rainy season. Conclusions: We concluded that Acol resilience is linked to the species’ mixed-mating system and pollinator dynamics-driven connectivity, allowing for the maintenance of genetic diversity in fragmented landscapes, as well as its natural tendency to form dense angicais clusters in non-arable slopes. We reaffirmed A. colubrina as a valuable species for restoration and urban climate resilience, providing cooling shade to humans and wildlife alike while offering refuge and food for local insects and birds in a warming landscape. Full article

Given their small size and low value in commercial fishing and aquaculture, little is known about the seasonal spatial distribution patterns and characteristics of the bobtail squid Euprymna berryi and Euprymna morsei in seas around China. Thus, we conducted seasonal bottom-trawling surveys in the southern Yellow and East China Seas during 2018 and 2019. Our results showed that E. berryi migrated from inshore areas (Yushan fishing grounds) during the summer and autumn to offshore areas (Wentai fishing grounds) during the winter. In contrast, E. morsei migrated from shallower water areas during the spring to deeper water areas during the winter. The highest abundance of E. berryi versus E. morsei was found in areas where temperatures were 25.29–28.02 °C compared with 19.54 °C (33.43–34.04‰ versus 34.43‰), respectively, during the summer; 20.99–21.69 °C compared with 21.98–22.70 °C (34.07–34.50‰ versus 33.80–33.60‰), respectively, during autumn; and 17.13–20.36 °C compared with 10.51–13.49 °C (34.23–34.46‰ versus 31.69–33.42‰), respectively, during winter. We predict that suitable habitats for E. berryi would expand more northward under SSP585-2050, whereas those for E. morsei would shrink into more northern locations under SSP370-2100 and SSP585-2100. The SSP245-2100 and SSP585-2100 scenarios had the most negative impacts on the distributions of both species. Such insights improve our understanding of the population dynamics and habitat requirements of both species to support their population management and exploitation in response to future climate change. Full article

Siderite and baryte are common non-sulphide phases in sedimentary exhalative (SEDEX) deposits, but their formation remains poorly understood. Siderite is important as an exploration vector in some deposits, whereas baryte is important as a S source in some deposits. The past-producing Walton deposit (Nova Scotia, Canada) consists of two ore types: (1) a sulphide body primarily hosted by sideritised Viséan Macumber Formation limestone (0.41 Mt; head grade of 350 g/t Ag, 4.28% Pb, 1.29% Zn, and 0.52% Cu), and (2) an overlying massive baryte body of predominantly microcrystalline baryte (4.5 Mt of >90% baryte). This study used optical microscopy, SEM-EDS, cathodoluminescence (CL), LA-ICP-MS, and SIMS sulphur isotope analysis of siderite and baryte to elucidate their origin and role in deposit formation. Siderite replaces limestone and contains ≤9 wt. % Mn, is LREE-depleted (PAAS-normalised REEY diagrams), and has low (<20) Y/Ho ratios. Sideritisation occurred due to dissimilatory iron reduction (DIR) that led to the breakdown of Fe-Mn-oxyhydroxides and organic matter, as indicated by light δ¹³C_VPBD values and negative Y anomalies. The baryte body is dominated by a microcrystalline variety that locally develops a radial texture and coarsens to a tabular variety; it also occurs intergrown with, and as veins in, massive sulphides. Based on fluid inclusion data from previous studies, the coarser baryte types grew from a hot (>200 °C) saline (25 wt. % NaCl) fluid containing CO₂-CH₄ and liquid petroleum. Marine sulphate δ³⁴S_VCDT values typical of the Viséan (~15‰) characterise the baryte body and some tabular baryte types, whereas heavier (~20‰) and lighter (~10‰) values typify the remaining tabular types. The variations in tabular baryte relate to distinct zones identified by CL imaging and are attributed to the sulphate-driven anaerobic oxidation of methane (SDAOM) and oxidation of excess H₂S after sulphide precipitation. These results highlight the importance of hydrocarbons (methane and organic matter) in the formation of both the siderite and the baryte at Walton and that DIR and the SDAOM can be important contributing processes in the formation of SEDEX deposits. Full article

A possible thermodynamic study of processing Cu (Ag, Au) and Fe sulfide concentrate as a by-product after the processing of tetrahedrite concentrate, applying pyrometallurgical and hydrometallurgical methods, was studied. The sample of sulfide concentrate, 34.7 wt. % Cu, 21.4% Fe, 12 g/t Au, and 7.317 g/t Ag was contained. Analytical technique AAS was used to analyze the sample before conducting a thermodynamic study of the leaching of sulfide concentrate by applying Pourbaix Eh–pH diagrams. The outcome of this thermodynamic research will provide essential data to support recent hydrometallurgical technologies. If its correctness can be verified experimentally, this result will be promoted to developing a new alternative copper-production technology. The minor components Sb, As, Hg, and Bi are also present in the concentrate in the form of sulfides Sb₂S₃, As₂S₃, Bi₂S₃, and HgS. This theoretical proposed hydrometallurgical technology shows that it is possible to obtain Fe in the form of Fe(OH)₃, and after its thermal decomposition, it can be prepared as Fe₂O₃ as a marketable product. In any case, the most economically advantageous would be complete hydrometallurgical processing, i.e., also Cu(Ag,Au)Fe sulfide concentrate, with the possibility of valorizing Cu, Ag, and Au in metallic form. Full article