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Row crops are regarded as a transitional type between continuous and discrete vegetation. Previous studies idealized row crops as periodic hedgerows with rectangular cross-sections. However, these models relied on oversimplified assumptions, failing to capture the intrinsic heterogeneity of canopy or its dynamic evolution over the life cycle. In 2020, we proposed a row crop model with a gradual decrease in leaf area volume density (LAVD) from the center of the row to the edge, partially overcoming these limitations. Building on this previous model, this paper introduces the leaf shape factor proposed by Mõttus et al. into the model. Three control parameters, a leaf width control parameter (β), leaf length control parameter (ψ), and leaf azimuth control parameter (e), are proposed to regulate the spatial distribution of LAVD. Additionally, an empirical exponential function from Watanabe et al. is adopted to describe the leaf zenith angle distribution, enabling the realistic calculation of the G-function and Γ-function in conjunction with leaf azimuth distribution. The LAVD is formulated at three hierarchical scales: individual scale, row scale, and scene scale. The model delivers two key advancements: enabling pronounced spatial heterogeneity and high tunability of the LAVD, and accurately simulating row crops throughout the life cycle, which bridges the row structure and continuous stages of row crops. Radiative transfer simulations are conducted to derive the bidirectional reflectance distribution function (BRDF), which is validated against the discrete anisotropic radiative transfer (DART) model. Comparisons across three growth stages demonstrated good consistency. Furthermore, this paper investigates the sensitivity of the BRDF to three control parameters (β, ψ, and e). The results indicate that changes in three parameters significantly affect the reflectance in the darkspot direction, leading to a maximum error of 22.6%. In carrying out remote sensing applications such as parameter inversion and yield estimation for row crops, the new model is recommended for more accurate BRDF simulations. Full article

The photo-assisted strategy is an effective technology that combines both photo and electrical energy conversion/storage, which represents the direction of the next generation of green energy utilization technologies. In particular, photo-assisted zinc–air batteries (PAZABs) are novel and innovative devices with the advantages of high efficiency and environmental friendliness. Thanks to the generation and effective separation of photo-generated carriers in photo-response air cathode catalysts, PAZABs possess significantly accelerated kinetics of oxygen reduction reaction and oxygen evolution reaction. Moreover, as a popular kind of newly developed two-electrode photoelectrochemical energy storage device, which could realize direct solar-to-electrochemical energy storage, PAZABs alleviate the limitations of the intermittent nature of solar energy in practical applications. In this study, the working mechanism of photoelectrochemical energy storage devices and PAZABs are thoroughly and systematically introduced; additionally, the design principles and types of photo-response electrode materials are reviewed. Interface engineering has been proven to be an effective strategy to improve the performance of the photo-response air cathode catalysts in PAZABs. Thus, the crucial role of the modulated interface chemistry of heterostructure air cathode catalysts is also summarized. Subsequently, the recent progress in the development of single-atom catalysts is outlined. Finally, this review presents several potential strategies for overcoming bottlenecks in the practical application of PAZABs. Full article

Liquid metal embrittlement (LME) occurs when a normally ductile alloy undergoes brittle fracture in contact with a liquid metal. The mechanisms behind LME remain unclear, and most of the models rely on post mortem analyses. In this work, we overcome this limitation by performing in situ scanning electron microscopy (SEM) notched micro-bending tests on α-brasses exposed to the gallium–indium eutectic (EGaIn) at room temperature, enabling real-time correlation between load–displacement curves and crack evolution during LME. In the Cu-30%Zn alloy, LME was observed only after prior plastic deformation and ductile crack growth, confirming that liquid metal did not influence early plasticity. A two-step experiment further showed that a pre-existing crack in contact with EGaIn, under continued loading, was sufficient to trigger brittle fracture. The Cu-20%Zn alloy displayed alternating ductile and brittle events, with brittle cracks propagating horizontally before arresting in undeformed zones, leading to stepped load–displacement curves. By contrast, pure Cu and Cu-15%Zn showed only ductile fracture despite continuous contact with EGaIn. These results demonstrate that LME in the Cu-Zn/EGaIn system acts during crack propagation rather than initiation. The present in situ SEM methodology provides direct evidence of fracture mechanisms and a framework for future experimental modeling comparisons. Full article

The effects of processing conditions and holding time on the direct bonding (DBC) of lotus-type porous copper to alumina substrates were systematically investigated. The evolution of copper morphology and the resulting shear strength were evaluated under varying pressures (0.3–0.6 Torr) and bonding durations (5–160 min) at a fixed bonding temperature. It was found that pressure within the tested range exerted a negligible influence on joint quality, as direct bonding occurred consistently. In contrast, holding time was found to be a critical factor: a duration of 10 min yielded optimal bonding with high shear strength while preserving the porous structure, whereas shorter times led to incomplete bonding, and longer times caused structural collapse due to liquid-phase flow. The oxidation behavior, governed by parabolic growth kinetics, was identified as the primary mechanism controlling morphological evolution. These findings provide practical guidance for optimizing DBC bonding of porous copper in power semiconductor applications, balancing joint strength and structural integrity. Full article

Research on surfaces generated by curves plays a central role in linking differential geometry with physical applications, especially following Hasimoto’s transformation and the development of Hasimoto-inspired surface models. In this work, we introduce a new class of such surfaces, referred to as RM Hasimoto surfaces, constructed by employing the rotation-minimizing (RM) Darboux frame along both timelike and spacelike curves in Minkowski 3-space ${\mathbb{E}}_1^3$. In contrast to the classical Hasimoto surfaces defined via the Frenet or standard Darboux frames, the RM approach eliminates torsional difficulties and reduces redundant rotational effects. This leads to more straightforward expressions for the first and second fundamental forms, as well as for the Gaussian and mean curvatures, and facilitates a clear classification of key parameter curves. Furthermore, we establish the associated evolution equations, analyze the resulting geometric invariants, and present explicit examples based on timelike and spacelike generating curves. The findings show that adopting the RM Darboux frame provides greater transparency in Lorentzian surface geometry, yielding sharper characterizations and offering new perspectives on relativistic vortex filaments, magnetic field structures, and soliton behavior. Thus, the RM framework opens a promising direction for both theoretical studies and practical applications of surface geometry in Minkowski space. Full article

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility. Full article

Per- and polyfluoroalkyl substances (PFASs) have attracted global attention due to their persistence and biological toxicity, becoming critical emerging contaminants in river and lake environments worldwide. Building upon existing studies, this work aims to comprehensively understand the pollution patterns, environmental behaviors, and potential risks of PFASs in freshwater systems, thereby providing scientific evidence and technical support for precise pollution control, risk prevention, and the protection of aquatic ecosystems and human health. Based on publications from 2002 to 2025 indexed in the Web of Science (WoS), bibliometric analysis was used to explore the temporal evolution and research hotspots of PFASs, and to systematically review their input pathways, pollution characteristics, environmental behaviors, influencing factors, and ecological and health risks in river and lake environments. Results show that PFAS inputs originate from both direct and indirect pathways. Direct emissions mainly stem from industrial production, consumer product use, and waste disposal, while indirect emissions arise from precursor transformation, secondary releases from wastewater treatment plants (WWTPs), and long-range atmospheric transport (LRAT). Affected by source distribution, physicochemical properties, and environmental conditions, PFASs display pronounced spatial variability among environmental media. Their partitioning, degradation, and migration are jointly controlled by molecular properties, aquatic physicochemical conditions, and interactions with dissolved organic matter (DOM). Current risk assessments indicate that PFASs generally pose low risks in non-industrial areas, yet elevated ecological and health risks persist in industrial clusters and regions with intensive aqueous film-forming foam (AFFF) use. Quantitative evaluation of mixture toxicity and chronic low-dose exposure risks remains insufficient and warrants further investigation. This study reveals the complex, dynamic environmental behaviors of PFASs in river and lake systems. Considering the interactions between PFASs and coexisting components, future research should emphasize mechanisms, key influencing factors, and synergistic control strategies under multi-media co-pollution. Developing quantitative risk assessment frameworks capable of characterizing integrated mixture toxicity will provide a scientific basis for the precise identification and effective management of PFAS pollution in aquatic environments. Full article

Particle migration can affect the ore bodies stability of weathering crust elution-deposited rare earth ores during leaching. It may trigger geological disasters such as landslides. Therefore, direct shear tests, microstructure characterization tests, and numerical simulation tests were adopted. The mechanical behavior characteristics of ore samples with different burial depths were explored. Simultaneously, the evolution characteristics of their microstructure, mineral composition, and failure modes were also studied. The results showed that after leaching, the cohesion (c) initially rose and then dropped as the proportion of −0.075 mm particles increased. The internal friction angle (φ) initially dropped, then increased, and finally stabilized. When its proportion was greater than 20%, the c of the middle-upper part of the ore sample was greater than that of the upper part. Meanwhile, the φ was smaller than that of the upper part. Especially when its proportion was greater than 30%, the c increased gradually with depth. XRD analysis revealed that illite and kaolinite were the main mineral components for the fine particle migration. Simulation experiment results revealed that in ore samples with 10% fine particles, there was noticeable migration and deposition in the upper part. However, it was the least in the upper middle and lower parts. Contact number and coordination number sharply decreased with depth, then increased, and finally gradually reduced. As deposition increased, the shear zone tilt angle grew larger. More secondary shear zones formed. The cracks became more evenly distributed. Full article

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laser beam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multi-layer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined. This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 °C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

A surge in environmental pollution compels society to utilize food processing wastes to produce valuable compounds. Enzymatic technology, specifically cellulase-mediated hydrolysis, provides an eco-friendly and effective approach for treating food processing leftovers. The main objective of this review is to explore the significant contributions of cellulase, both in industrial settings and from an environmental perspective. Therefore, this review covers all the aspects of cellulase structural identification, classification, and evolution to its profound applications. The review initially explores cellulases’ structural and functional characteristics based on the catalytic and cellulose-binding domains and discusses cellulases’ evolutionary origin. A thorough understanding of cellulase properties is essential for overcoming the challenges associated with its commercial production for various applications. In this regard, the optimization for cellulase production through several approaches, including rational design, direct evolution, genetic engineering, and fermentation technology, is also reviewed. In addition, it also underscores the significance of agro-industrial biorefineries, which provide scalable and sustainable solutions to meet future demands for food, chemicals, materials, and fuels. Finally, the last sections of the review solely highlight the potential applications of microbial cellulases in bioremediation. In summary, this review outlines the role of cellulase in efficient valorization aimed at producing multiple bioproducts and the enhancement of environmental remediation efforts. Full article

We present an analytical and numerical study of nonlinear wave localization in a one-dimensional rhombic (diamond) waveguide array that combines forward- and backward-propagating channels. This mixed-index configuration, realizable through Bragg-type couplers or corrugated waveguides, produces a tunable spectral gap and supports nonlinear self-localized states in both transmission and forbidden-band regimes. Starting from the full set of coupled-mode equations, we derive the effective evolution model, identify the role of coupling asymmetry and nonlinear coefficients, and obtain explicit soliton solutions using the method of multiple scales. The resulting envelopes satisfy a nonlinear Schrödinger equation with an effective nonlinear parameter $\theta$, which determines the conditions for soliton existence ($\theta >0$) for various combinations of focusing and defocusing nonlinearities. We distinguish solitons formed outside and inside the bandgap and analyze their dependence on the dispersion curvature and nonlinear response. Direct numerical simulations confirm the analytical predictions and reveal robust propagation and interactions of counter-propagating soliton modes. Order-of-magnitude estimates show that the predicted effects are accessible in realistic integrated photonic platforms. These results provide a unified theoretical framework for soliton formation in mixed-index lattices and suggest feasible routes for realizing controllable nonlinear localization in Bragg-type photonic structures. Full article

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently garnered considerable attention in network traffic analysis due to their ability to model complex relationships within network flows and between communicating entities. This scoping review systematically surveys major academic databases, employing predefined eligibility criteria to identify and synthesize key research in the field, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. We present a comprehensive overview of a generalized architecture for GNN-based traffic analysis and categorize recent methods into three primary types: node prediction, edge prediction, and graph prediction. We discuss challenges in network traffic analysis, summarize solutions from various methods, and provide practical recommendations for model selection. This review also compiles publicly available datasets and open-source code, serving as valuable resources for further research. Finally, we outline future research directions to advance this field. This work offers an updated understanding of GNN applications in network traffic analysis and provides practical guidance for researchers and practitioners. Full article

In the manufacture of single-crystal blades for aero-engines, the problem of eutectic aggregation on the upper surface of the blades has long been restricting the casting performance improvement. To investigate this phenomenon, this paper employs a simplified blade-like shape casting and focuses a 3rd generation nickel-based single-crystal superalloy as the research material. A systematic analysis is conducted to elucidate the distribution of γ/γ’ eutectic during solidification. Experimental results show distinct spatial variations in γ/γ’ eutectic distribution. Pronounced eutectic aggregation is observed on the upper surface of the blade but with sparse eutectic dispersion‌ on the lower regions of the casting. Relatively uniform eutectic distribution‌ dominates the mid-section of the specimen. To unravel the underlying mechanisms, this paper utilized a ‌multiphase volume-averaged solidification model‌, developed in prior work, to numerically simulate the γ/γ’ eutectic evolution during directional solidification. This computational framework enabled a comprehensive ‌quantitative analysis‌ of spatial and temporal variations in the eutectic volume fraction along the solidification direction. The integration of experimental and modeling approaches provides critical insights into the interplay between thermal gradients, alloy composition, and microstructural heterogeneity. Full article

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE–LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE–LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis. Full article