Search Results (109)

Chronic inflammatory response syndrome (CIRS) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) are debilitating multisystem illnesses that share overlapping symptoms and molecular patterns, including immune dysregulation, mitochondrial impairment, and vascular dysfunction. This review provides a chronological synthesis of biomarker development in CIRS, tracing its evolution from early functional tests such as visual contrast sensitivity (VCS) to advanced transcriptomic profiling. Drawing on peer-reviewed studies spanning two decades, we examine the layered integration of neuroendocrine, immunologic, metabolic, and genomic markers that collectively support a multisystem model of innate immune activation specific to environmentally acquired illness. Particular focus is given to the Gene Expression: Inflammation Explained (GENIE) platform’s use of transcriptomics to classify disease stages and distinguish CIRS from other fatiguing conditions. While ME/CFS research continues to explore overlapping pathophysiologic features, it has yet to establish a unified diagnostic model with validated biomarkers or exposure-linked mechanisms. As a result, many patients labeled with ME/CFS may, in fact, represent unrecognized CIRS cases. This review underscores the importance of structured biomarker timelines in improving differential diagnosis and guiding treatment in complex chronic illness and highlights the reproducibility of the CIRS framework in contrast to the diagnostic ambiguity surrounding ME/CFS. Full article

Based on a comprehensive evaluation index system for regional economic resilience and port logistics development, this study employs multiple methodologies including coupling coordination degree model, kernel density estimation, gravity center model, spatial autocorrelation model, and geographic detector model to explore the spatial–temporal evolution patterns and driving factors of coupling coordination between regional economic resilience and port logistics in the Guangxi Beibu Gulf Economic Zone from 2012 to 2022. The results indicate that: (1) The coupling coordination degree between the two systems showed an upward trend during the study period, although with stage-specific bipolar differentiation that weakened in the later stages. (2) The spatial distribution pattern of coupling coordination evolved from a “single-core” driven by Nanning to a “dual-core” led by Nanning and Yulin, forming a distinct concentric layer structure; the gravity center of coupling coordination exhibited a “southeast–northwest” dynamic migration pattern. (3) Spatial autocorrelation analysis revealed significant positive spatial dependence of coupling coordination within the study area, with spatial agglomeration values showing a “core–transition–depression” differentiation pattern. (4) Information technology level emerged as the dominant driving factor, forming a “technology–finance–infrastructure” ternary collaborative driving model with financial development level and logistics infrastructure level, which became the main force promoting the coordinated development of the coupled systems. Full article

Many granitic enclaves are developed in the volcanic channel of the Xiangshan volcanic basin. To explore their genesis, this study examined the petrography, geochemistry, LA-ICP-MS zircon U–Pb chronology, and zircon Hf isotopes of the granitic enclaves and compared them with the porphyroclastic lavas. In general, the granitic enclaves and porphyroclastic lavas have similar structures, and the rock-forming minerals and accessory minerals have relatively close compositions. In terms of rock geochemical characteristics, the granitic enclaves are richer in silicon and alkalis but have lower abundances of aluminum, magnesium, iron, and calcium than the porphyroclastic lavas. Rb, Th, K, Sm, and other elements are more enriched, whereas Ba, Ti, Nb, P, and other elements are more depleted. The granitic enclaves have lower rare earth contents (195.53 × 10⁻⁶–271.06 × 10⁻⁶) than the porphyroclastic lavas (246.67 × 10⁻⁶–314.27 × 10⁻⁶). The rare earth element distribution curves of the two are generally consistent, both right-leaning, and enriched with light rare earth patterns. The weighted average zircon U–Pb ages of two granitic enclave samples were 135.45 ± 0.54 Ma (MSWD = 0.62, n = 17) and 135.81 ± 0.60 Ma (MSWD = 0.40, n = 20), respectively, which are consistent with the weighted average age of a single porphyroclastic lava sample of 134.01 ± 0.53 Ma (MSWD = 2.0, n = 20). The zircons of the two kinds of rocks crystallize at almost the same temperature. The consistent trend of the rare earth element distribution curve of zircons in the granitic enclaves and the porphyroclastic lava samples indicates that the zircons of the two samples were formed in the same stage. The formation process of granitic enclaves may be that the lower crustal melt is induced to rise, and the crystallization differentiation occurs in the magma reservoir and is stored in the form of crystal mush, forming a shallow crystal mush reservoir. The crystal mush reservoir is composed of a large number of rock-forming minerals such as quartz, feldspar, and biotite, as well as accessory mineral crystals such as zircon and flowable intergranular melt. In the later stage of magma high evolution, a small and short-time magmatic activity caused a large amount of crystalline granitic crystal mush to pour into the volcanic pipeline. In the closed system of volcanic pipeline, the pressure and temperature decreased rapidly, and the supercooling degree increased, and the immiscibility finally formed pale granitic enclaves. Full article

We present a comprehensive structural and thermomechanical investigation of N-salicylideneaniline, a Schiff base derivative that exhibits remarkable thermosalient phase transition behavior. By combining variable-temperature X-ray powder diffraction (VT-XRPD), differential scanning calorimetry (DSC), hot-stage microscopy, and Hirshfeld surface analysis, we reveal two distinct thermosalient mechanisms operating in different polymorphic forms. Form I displays pronounced anisotropic thermal expansion with negative strain along a principal axis, culminating in a sudden and explosive phase transition into Form IV. In contrast, Form III transforms more gradually through a microstrain accumulation mechanism. Fingerprint plots and contact evolution from Hirshfeld surface analysis further support this dual-mechanism model. These insights highlight the importance of integrating macro- and microscale structural descriptors to fully capture the mechanical behavior of responsive molecular solids. The findings not only enhance the fundamental understanding of thermosalience but also inform the rational design of functional materials for actuating and sensing applications. Full article

The North China Craton (NCC) experienced extensive destruction and modification of its subcontinental lithospheric mantle during the Mesozoic, a period marked by intensive tectonism, magmatism, and mineralization. Among the key manifestations of this event are the Shizhuzi magmatic complex (SMC) and related Mo-Cu-Au deposits in the Liaodong Peninsula. This study presents new zircon U-Pb ages and Hf isotope data, along with whole-rock major and trace element geochemical data. Meanwhile, by incorporating published datasets, the magmatism and mineralization of the SMC are discussed. Two-stage magmatic activity is identified in the SMC as follows: (1) Stage I (130–126 Ma) associated with mineralization, and (2) Stage II (121–117 Ma), both corresponding to the peak destruction of the NCC. The mineralized granitoids exhibit I-type affinities and formed in an extension setting. Quartz diorites within this suite were derived from the partial melting of an enriched mantle source, and the high-temperature thermal underplating associated with this process subsequently triggered partial melting of the basaltic lower crust, leading to the generation of granodiorites and monzonitic granites. These rocks experienced limited fractional crystallization (dominated by plagioclase + biotite) and are linked to Mo-Cu-Au mineralization. In contrast, the non-mineralized granitoids are high-K calc-alkaline, peraluminous A-type granites, which developed in an extremely extensional tectonic setting. They were derived from partial melting of ancient lower crust and display characteristics of highly fractionated granites, having undergone extensive crystallization differentiation involving plagioclase + K-feldspar during magmatic evolution. The mineralized and non-mineralized granitoids exhibit distinct differences in lithology, major/trace element characteristics, Hf isotopes, and degree of fractional crystallization. Our proposed two-stage magmatic model—coupled with a mineralization phase—provides significant insights into both magmatic processes and metallogenesis in the Liaodong Peninsula. It further offers key perspectives into the Early Cretaceous decratonization of the NCC in terms of its tectonic–magmatic–mineralization evolution. Full article

Digital platform ecosystems confront critical management challenges as they overcome path dependence amid rapid technological change. This study explores cross-category innovation as a key strategic action, using a longitudinal case study of ByteDance to analyze how digital technology drives ecosystem evolution, and constructs a “technology-driven–strategic action–ecosystem evolution” framework to examine the interplay between technological capabilities and strategic actions. Findings identify two stages: in the category emergence stage, platforms establish a core business ecosystem via identity, legitimacy, and differentiation strategies, leveraging technologies like algorithmic recommendation to shape user cognition and market legitimacy. In the category spanning stage, platforms leverage platform envelopment, open innovation, and status strategies to expand cross-category ecosystems, enabling technological spillover and integrated innovation across new domains. The findings reveal a co-evolution mechanism of cross-category innovation strategy and ecosystems, where the cross-category innovation strategy serves as both a driving force for ecosystem evolution and acquires new strategic opportunities. This study offers insights for building sustainable ecosystems that transcend industry boundaries and enhance resilience. Full article

Reducing fertilizer and pesticide use is a crucial path for the green transformation of agricultural production, which has garnered sustained attention in research on sustainable agricultural development. Based on the theoretical analysis, this article analyzes the spatiotemporal evolution characteristics of fertilizer and pesticide usage intensity (FUI and PUI) in the Yangtze River Delta (YRD) over the past 20 years and uses a Two-Way Fixed Effects Model to test their impacts and mechanisms. Findings show that agricultural development in the YRD shows a pattern of specialization and intensification with a significant north–south divide, with zero growth and reduction in fertilizer and pesticide use across the region from 2010 to 2015, but the current FUI and PUI are still nearly three and five times higher than the global average. Over the past 20 years, the FUI is high in the north and low in the south, high in the plains and low in mountainous-hilly areas, and high in suburban areas and low in remote counties. Adversely, the PUI is high in the south and low in the north, high in mountainous-hilly areas and low in plains, and high in suburban areas and low in remote counties. The FUI and PUI of characteristic agricultural areas of fruit, tea, and forestry in southern Anhui and southwestern Zhejiang, as well as the agroecological and facility agriculture clusters in southern Jiangsu and the suburbs of Shanghai, have approached the peak and successfully moved into the new green development stage earlier compared to other areas. In contrast, the grain and oil production plains areas along the Yangtze River, the coast, in northern Anhui, and in northern Jiangsu are relatively lagging behind. The combination of soil, water, light, and heat resource conditions and modes of agriculture production shape the absolute figures of FUI and PUI, and factors such as the level of local economic development and public fiscal expenditure significantly influence the trajectories of spatiotemporal differentiation in the progress of reducing fertilizer and pesticide in the YRD. Full article

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy is used for population initialization to generate a more uniform crayfish population and increase the quality and diversity of the population. Secondly, the differential evolution strategy and the dimensional variation strategy are introduced to improve the quality of the crayfish population before its iteration and to improve the accuracy of the optimal solution and the local search ability for crayfish at the same time. To enhance the updating approach to crayfish exploration, the Levy flight strategy is adopted. This strategy aims to improve the algorithm’s search range and local search capability, prevent premature convergence, and enhance population stability. Finally, the adaptive parameter strategy is introduced to improve the development stage of crayfish, so as to better balance the global search and local mining ability of the algorithm, and to further enhance the optimization ability of the algorithm, and the ability to jump out of the local optimal. In addition, a comparison with the original COA and two sets of optimization algorithms on the CEC2019, CEC2020, and CEC2022 test sets was verified by Wilcoxon rank sum test. The results show that the proposed ICOA has strong competition. At the same time, the performance of ICOA is tested against different high-performance algorithms on 6 engineering optimization examples, 30 high–low-dimension constraint problems and 2 large-scale NP problems. Numerical experiments results show that ICOA has superior performance on a range of engineering problems and exhibits excellent performance in solving complex optimization problems. Full article

Understanding the time dynamics of systemic risk in banking networks is crucial for preventing financial crises and ensuring economic stability. This paper aims to quantify key transition times in the evolution of distress within a banking system using a mathematical framework. We investigate the dynamics of systemic risk in a hypothetical, homogeneous banking network using the Undistressed–Exposed–Distressed–Recovered (UEDR) model. The UEDR model, inspired by compartmental epidemic frameworks, captures how financial distress propagates and recedes through interactions between banks. It is selected because of its tractability and its ability to distinguish between different stages of bank vulnerability. We focus on two critical times, denoted as $t_1$ and $t_2$, which play a fundamental role in understanding the behavior of the distressed compartment (representing the number of distressed banks) over time. The time $t_1$ represents the first instance of a decrease in the number of distressed banks, indicating the containment of systemic risk. On the other hand, the time $t_2$ marks the onset when the number of undistressed banks falls below a specified threshold, signifying the restoration of financial stability. We examine these time dependencies by considering the initial conditions of the UEDR model and assess their characteristics using partial differential equations. We establish the continuity, smoothness, and uniqueness of solutions for $t_1$ and $t_2$, along with their corresponding boundary conditions. Furthermore, we provide explicit representation formulas for $t_1$ and $t_2$, allowing for precise estimation when the initial population compartments are large. Our results provide practical insights for financial regulators and policymakers in determining time-sensitive interventions for mitigating systemic risk and accelerating recovery in banking systems. The findings highlight how mathematical modeling can inform real-time risk management strategies in financial networks. Full article

Sodium-ion batteries (SIBs) share similar working principles with lithium-ion batteries while demonstrating cost advantages. However, the current understanding of their safety characteristics remains insufficient, and the thermal runaway mechanisms of different SIB systems have not been fully elucidated. This study investigated the following two mainstream sodium-ion battery systems: polyanion-type compound (PAC) and layered transition metal oxide (TMO) cathodes. Differential scanning calorimetry (DSC) was employed to evaluate the thermal stability of cathodes and anodes, examining the effects of state of charge (SOC), cycling, and overcharging on electrode thermal stability. The thermal stability of electrolytes with different compositions was also characterized and analyzed. Additionally, adiabatic thermal runaway tests were conducted using an accelerating rate calorimeter (ARC) to explore temperature–voltage evolution patterns and temperature rise rates. The study systematically investigated heat-generating reactions during various thermal runaway stages and conducted a comparative analysis of the thermal runaway characteristics between these two battery systems. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

In this study, we investigated the two-phase hardening behavior and microstructural evolution of S32750 duplex stainless steel during the tensile deformation process. The analysis was conducted using in situ electron backscatter diffraction (EBSD), scanning electron microscopy (SEM), and microhardness testing. It was observed that strain transfer occurred between the two phases in the position away from the fracture. The ferrite phase exhibited softening, while the austenite phase underwent hardening. In the region less than 1 mm from the fracture site, both phases experienced a rapid hardening, with the maximum hardness difference between the two phases near the fracture reaching approximately 45 HV. In situ EBSD results indicate that the kernel average misorientation (KAM) value for the ferrite phase consistently exceeds that of the austenite phase during the initial stages of deformation. Conversely, in the final stages of deformation, the KAM value for austenite surpasses that of ferrite. In the initial stage of deformation, the type of grain boundaries in both phases remains largely unaltered. However, in the later stages of deformation, there is a marked increase in the number of small-angle grain boundaries within ferrite, which become approximately three times that of the large-angle grain boundaries. As deformation progresses, the maximum orientation distribution density of the ferrite phase is reduced by approximately 50%, with the preferred orientation shifting from the {100} plane to the {111} plane. In contrast, the orientation distribution of the austenite remains relatively uniform, with no significant change in the maximum orientation distribution density observed. This indicates that after substantial deformation, the rotation of ferrite grains significantly increases the deformation resistance, whereas the austenite phase continues to harden. This differential behavior leads to the continuous accumulation of strain at the phase boundaries, ultimately causing cracks to form at these boundaries and resulting in the sample’s fracture. Full article

Chromosomes are essential carriers of human genetic material, and karyotype diagnosis plays a crucial role in prenatal diagnostics, genetic disease identification, and medical research. Physicians rely heavily on karyotype images to diagnose potential abnormalities in chromosome numbers and structure. However, the process is tedious and challenging. To improve diagnostic efficiency and accuracy, artificial intelligence (AI) researchers have developed convolutional neural networks (CNNs) for chromosome image classification. Despite this progress, the gap between cytogeneticists and AI experts results in a time-consuming workflow. In this study, we propose a framework based on improved Differentiable Architecture Search (DARTS) to automatically design convolutional architectures for the classification task. The improvement strategies based on DARTS are implemented in two stages. First, a procedural approach was designed to comprehensively analyze the evolution of architectural parameters. Based on this analysis, the search space of the DARTS algorithm was refined, resulting in an optimized search space. Next, an entropy-based regularization term was incorporated into the supernetwork’s objective function to guide the algorithm in searching for a more effective architecture. Then, extensive experiments were conducted on CIFAR-10, ImageNet, and the Copenhagen datasets to evaluate the performance of the searched architecture in comparison with related works. The network composed of the searched architecture achieved accuracies of 97.27 $\pm$ 0.05%, 75.40%, and 98.64% on the three datasets, respectively. These results demonstrate that the architecture is high-performing and the proposed framework for designing networks for chromosome classification is effective. Full article

For the satellite orbit pursuit–evasion game problem, this paper proposes a multi-stage optimization-based solution aimed at improving the confrontation strategies between task satellites and target satellites in complex space environments. The approach divides the satellite pursuit–evasion game into two phases: the “approach phase” and the “sustained phase”. It dynamically optimizes the trajectories and strategies of the task and target satellites to achieve adaptive orbit control and behavior optimization. To enhance the global search capability and local convergence of the algorithm, this paper employs the Zebra Optimization Algorithm, introducing a multi-population cooperative evolution mechanism, and integrates differential game theory to improve the stability and reliability of the game strategies. Simulation results demonstrate that the proposed method effectively enhances task efficiency under multiple constraints, dynamically adjusts the strategies of both the pursuer and the evader, and provides an efficient, scalable solution applicable to satellite pursuit–evasion games in complex space environments. Full article

This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE ($1.9597\times {10}^7$) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE ($3.3928\times {10}^6$) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production. Full article