Search Results (887)

Accurate plant classification based on deep learning is important for precision agriculture, such as weed control, crop monitoring, and smart farming systems. The accuracies of deep learning models rely on datasets. Although many datasets have been proposed in recent decades, they have the common limitations in terms of scale, less environmental diversity, and challenges of data integration. To solve these problems, in this paper, we introduce a new dataset named OpenPlant, which is a large-scale and open dataset containing 635,176 RGB images across 1167 plant species. OpenPlant includes diverse growth stages of plants, plant structures, and environmental conditions, and its annotations were carefully verified to ensure quality. The proposed OpenPlant can be a benchmark for agricultural plant classification. In this paper, we benchmarked 10 widely used convolutional neural networks (CNNs), 6 vision transformers (ViTs), and 12 vision–language models (VLMs) to provide a comprehensive evaluation. The OpenPlant dataset offers a comprehensive benchmark for agricultural research using deep learning and the results provide insights into future directions. Full article

This study provides a foundational review of work-based learning (WBL) opportunities offered by colleges and universities to students in higher education cybersecurity (CS) programs in the United States, with the goal of mapping the WBL practices across institutional and program contexts. Integrating WBL into CS curricula is widely recognized as an effective way to strengthen essential skills and address employer concerns about the gap between academic preparation and labor market needs. We first outline the characteristics of institutions and CS programs offering WBL. Next, we examine the range of WBL experiences designed to enhance students’ professional competencies. Finally, we explore characteristics of the partnerships between higher education and industry that support these initiatives. Using a status survey approach, we collected responses from 92 higher education institutions offering CS programs. We analyzed the data using descriptive statistics and linear regression models to explore patterns of association between the type and number of WBL opportunities available to students, institutional characteristics related to the total number of WBL offerings, and program features associated with WBL intensity across Awareness, Exploration, and Direct Experience levels of intensity. Findings reveal a diverse array of WBL opportunities, with notable growth across credential levels. Notably, certificates and associate degrees place particular emphasis on WBL. Both institutional characteristics and program features explain, albeit partially, the number of WBL opportunities implemented and the intensity levels of those WBL. However, results also indicate an ambivalent connection to employers, despite their critical role in providing hands-on, problem-solving experiences. Based on these insights, we recommend expanding WBL beyond internships, strengthening institutional–industry partnerships, and fostering employer engagement through structured WBL collaboration models. These strategies aim to improve workforce readiness and create a more inclusive, scalable system of experiential learning in cybersecurity education. Full article

When looking towards the goal of reducing greenhouse gas (GHG) emissions, automotive manufacturers face several challenges when planning future vehicle offerings in different markets. The planned vehicle offerings must cope with uncertainties in the supply chains of critical materials and adhere to regulatory requirements in different regions, all while appealing to customer preferences and maintaining low cost. Regulatory requirements, which are often based on tailpipe GHG emissions, do not necessarily align with Lifecycle Analysis (LCA) of GHG emissions, which becomes yet another challenge towards attaining sustainability goals. Planning the future mix of vehicles to be manufactured under all such considerations can be a complex task, often relying on methods with poor transparency, unguaranteed optimality, or requiring difficult-to-predict a priori knowledge. This paper considers the special case of a short time window (one future model–year), which allows for modelling the future planning decisions as a linear programming (LP) problem, which in turn, can be solved to global optimality via well-established algorithms, such as Dual-Simplex. The proposed formulation is demonstrated via one simple example, as well as a scaled-up study with two regions, two vehicle size categories, and four powertrain configurations. A key insight that the proposed formulation is able to demonstrate in the scaled-up study is how the optimum (lowest) LCA GHG solution depends on the availability of battery materials, ranging from an increased share of hybrids under low battery supply to an increased share of electric vehicles for abundant battery supply. Full article

To address the rapidly growing demand for charging stations (CSs) and the associated challenges posed by the expansion of electric vehicles (EVs), this study proposes a collaborative planning method integrates user demand considerations with operational constraints at the grid level. Based on graph theoretical principles, static topology models of the road network and distribution grid were constructed. A dynamic origin–destination (OD) prediction framework was then formulated by jointly considering traffic flow variations, battery energy consumption, user charging behavior, and ambient temperature, in which an enhanced gravity model is coupled with the Floyd algorithm. Charging load characteristics were quantified through Monte Carlo simulation, and K-means++ clustering was further applied to identify spatial charging demand hotspots. On this basis, a multi-objective optimization model was established to simultaneously balance the annualized cost of charging stations, user costs, and voltage deviation in the distribution network. To solve the resulting high dimensional problem, a collaborative optimization mechanism was designed by integrating a weighted Voronoi diagram with a multi-objective particle swarm optimization (MOPSO) algorithm, enabling dynamic service area partitioning and global capacity optimization. Case analysis demonstrates that the proposed method reduces user time costs by 15.8%, optimizes queue delay by 42.2%, and improves voltage stability, maintaining fluctuations within 5%. It also balances the interests of charging station operators, users, and distribution networks, with only a slight increase in construction costs. These results offer valuable theoretical and practical insights for charging infrastructure planning. Full article

Thick cloud removal is a critical preprocessing step for multitemporal remote sensing images (MTRSIs), as it directly determines the reliability of downstream analysis and applications. In MTRSIs, the same geographic region is observed at different times, and the underlying edge structures often remain physically consistent across temporal observations. Leveraging this intrinsic property, we introduce a Sobel-consistent term that explicitly enforces temporal consistency of edge-related features, thereby improving the reconstruction of fine structures and textures in cloud-obscured regions. Building on this insight, we propose a novel thick cloud removal model that integrates Sobel-based edge consistency with subspace-based spatiospectral low-rank tensor regularization. In this model, intrinsic images derived from subspace representation are organized into a fourth-order tensor, and low-rank constraints are applied to jointly capture the spatial, spectral, and temporal correlations inherent in MTRSIs. To efficiently solve the resulting optimization problem, we introduce an algorithm based on proximal alternating minimization. Experiments on both simulated and real-world MTRSI datasets demonstrate that the proposed method achieves superior reconstruction accuracy and visual fidelity, validating the physical interpretability and effectiveness of the approach. Full article

This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) estimation that incorporates uncertainties in both TRLs and Integration Readiness Levels (IRLs). The qualitative component uses expert judgment and visual heat maps to identify subsystem-specific maturity gaps, particularly in automation, digitalization, and sustainability. The quantitative component explicitly separates three methodological layers often treated implicitly in prior research: (i) the probabilistic model representing uncertainties in TRL and IRL, (ii) the uncertainty-propagation problem linking these variables to system-level readiness, and (iii) the Monte Carlo algorithm employed to solve this problem. This structure enables the derivation of SRL distributions that reflect uncertainty more realistically than deterministic approaches, allowing statistical analysis of different characteristics of these distributions and exploratory sensitivity analysis. Results show that the Pods4Rail system is positioned between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While hardware-related subsystems such as the Transport Unit and Rail Carrier Unit exhibit relatively higher maturity, planning, logistics, and operational management functionalities remain at early development stages. By combining interpretative insight with statistical rigor, the proposed framework offers a transparent and reproducible approach to early-phase readiness assessment. Its transferability makes it suitable for other innovative mobility systems facing similar challenges of incomplete information, uncertain integration pathways, and high conceptual complexity. Full article

Metaheuristic algorithms have become essential tools for solving complex, high-dimensional, and constrained optimization problems. This paper introduces an adaptive R implementation of the parameter-free Jaya algorithm, enhanced with methodological innovations for both single-objective and multi-objective settings. The proposed framework integrates adaptive population management, dynamic constraint-handling, diversity-preserving perturbations, and Pareto-based archiving, while retaining Jaya’s parameter-free simplicity. These extensions are further supported by parallel computation and visualization tools, enabling scalable and reproducible applications. Benchmark evaluations on standard test functions demonstrate improved convergence accuracy, solution diversity, and robustness compared to the classical Jaya and other baseline algorithms. To highlight real-world applicability, the method is applied to a renewable energy planning problem, where trade-offs among cost, emissions, and reliability are explored. The results confirm that the adaptive Jaya approach can generate well-distributed Pareto fronts and provide practical decision support for energy system design. The main contributions of this work are threefold: (i) the development of an adaptive multi-objective extension of the Jaya algorithm that preserves its parameter-free philosophy while incorporating diversity preservation, dynamic constraint handling, and Pareto-based selection; (ii) a unified and openly available R implementation that integrates methodological advances with parallel computation and visualization, addressing the lack of transparent and reusable MO-Jaya tools in the existing literature; and (iii) a systematic evaluation on benchmark test functions and a renewable energy planning case study, demonstrating competitive convergence, robust Pareto diversity, and practical decision-making insights compared to established methods. By openly releasing the software in R (≥3.5.0), this work contributes both a methodological advance in multi-objective metaheuristics and a transparent tool for applied optimization in engineering and environmental domains. Full article

Torsional stress from DNA supercoiling is receiving renewed attention as a driving force for chromosome folding and the establishment of gene activity states. Transcription is a major source of DNA supercoiling, while topoisomerases relax supercoils and solve topological problems that arise during DNA replication, transcription, and chromosome segregation. Recent technological advancements have allowed for the mapping of how torsional stress distributes within the genome and distinguishing between occupancy of topoisomerases on chromatin and sites where they are catalytically engaged. Coupling these innovations to assessments of 3D chromosome conformation and nascent transcription at high resolution have provided a new understanding of the relationships between supercoiling and topoisomerase activity. Here, we summarize the insights obtained from these recent studies and discuss how the interplay between transcription, supercoiling, and topoisomerases shapes cellular gene activity states. Full article

In the field of logistics distribution, the two-echelon vehicle routing problem has long been a critical focus. Against the backdrop of global warming, enterprises conducting logistics operations must now prioritize not only delivery costs but also the environmental impact of carbon emissions. To address these challenges, this study integrates occasional drivers into the two-echelon vehicle routing framework, centering on carbon emission reduction. First, Affinity Propagation (AP) clustering is applied to assign customer points to transfer centers. Subsequently, an optimization model is formulated to minimize both vehicle routing costs and carbon emission costs through a collaborative delivery system involving specialized and crowdsourced vehicles. An enhanced Sparrow–Whale Optimization Algorithm (S-WOA) is proposed to solve the model. The algorithm is tested against traditional heuristic methods on three datasets of different scales. Experimental results demonstrate that the two-echelon logistics and distribution model combining specialized vehicles and occasional drivers achieves a significant reduction in total delivery costs compared to models relying solely on specialized vehicles. Further analysis reveals that, with a fixed crowdsourced compensation coefficient, increasing the crowdsourced detour coefficient leads to a decline in total delivery costs. Conversely, when the detour coefficient remains constant, raising the compensation coefficient results in an upward trend in total costs. These insights provide actionable strategies for optimizing cost-efficiency and sustainability in logistics operations. Full article

This investigation provides a comprehensive analytical framework for the topological morphology and global convergence dynamics governing a specific family of sixth-order iterative schemes designed for nonlinear equations with multiple roots. By invoking a Möbius conjugacy transformation upon the specialized polynomial class $f\left(z\right)={\left((z-p)(z-q)\right)}^m$, we project the iterative sequence onto the Riemann sphere $\widehat{\mathbb{C}}$, effectively recasting the algorithm as a discrete complex dynamic system. The core of this study lies in the bifurcation analysis of the associated parameter space. We meticulously chart the stability manifolds, tracing the evolution of critical orbits to distinguish between regions of predictable convergence and those characterized by chaotic instability. By examining the iterative methods generated by these rational endomorphisms, the research unveils the intricate fractal boundaries that delineate the basin of attraction, offering a profound insight into the structural robustness of higher-order methods. In the dynamical plane, the geometry of the basins of attraction is scrutinized to evaluate the robustness of the numerical flow and its sensitivity to the configuration of weight functions. By analyzing the fractal complexity of the boundaries within these basins, we provide a detailed characterization of the iterative morphology and its global reliability. The analytical findings are supported by high-resolution graphical representations and comparative numerical data, illustrating the superior performance and structural integrity of the proposed methods in solving nonlinear problems. Full article

This study introduces a mathematical framework for analyzing unsteady gas filtration in porous media with pressure-dependent porosity variations. The physical process is formulated as a nonlinear parabolic boundary value problem that captures the coupled interaction between pressure evolution and porosity changes during gas production. To solve the equation, a numerical strategy is developed by integrating the Alternating Direction Implicit (ADI) scheme with quasi-linearization iterations, employing finite difference discretization on a two-dimensional spatial grid. Extensive computational experiments are performed to investigate the influence of key reservoir parameters—including porosity coefficient, permeability, gas viscosity, and well production rate—on the spatiotemporal behavior of pressure and porosity during long-term extraction. The results indicate significant porosity variations near the wellbore driven by local pressure depletion, reflecting strong sensitivity of the system to formation properties. The validated numerical model provides valuable quantitative insights for optimizing reservoir management and improving production forecasting in gas field development. Overall, the proposed methodology serves as a practical tool for oil and gas engineers to assess long-term reservoir performance under diverse operational conditions and to design efficient extraction strategies that incorporate pressure-dependent formation property changes. Full article

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State University, including nine text-only questions (tMCQs) and nine picture-embedded questions (pMCQs). The items were chosen to ensure clear spatial separation among text, image, and answer areas, allowing reliable region-based eye-movement analysis. Eye-tracking data were analyzed using two indices: fixation time ratio (FTR), reflecting relative attention allocation, and saccade count ratio (SCR), capturing cross-region information integration. The results revealed clear group differences. Gifted students devoted a larger proportion of attention to pictorial information (0.38 vs. 0.32) and showed more frequent transitions between picture and answer regions (0.15 vs. 0.12), indicating more integrative processing and mental model construction. In contrast, regular students spent more time focusing on textual regions and exhibited higher within-text saccade activity, consistent with a direct translation strategy. Furthermore, SCR-based machine learning classification using a Random Forest model demonstrated meaningful discriminative capability between the two groups, particularly for picture-embedded questions, achieving an accuracy of 77.5%. Overall, the findings provide empirical evidence that question format influences students’ cognitive strategies during scientific reasoning. Methodologically, this study combines a validated reasoning assessment, a carefully defined ROI-based eye-tracking design, and interpretable behavioral indicators, offering practical implications for differentiated science instruction. Full article

This paper investigates the energy-efficient collaborative scheduling of dual-trolley quay cranes (DTQCs) and automated guided vehicles (AGVs) in automated container terminals (ACTs). Considering operational constraints such as mixed bidirectional flows, limited buffers, precedence constraints, and deadlocks, this complex logistical system is formally characterized as a blocking hybrid flow shop scheduling problem (BHFSSP-BFLB). To systematically minimize the total energy consumption, a mathematical framework grounded in a mixed-integer programming model is developed. To solve the model efficiently, an improved genetic algorithm (IGA) is proposed featuring a two-layer encoding approach to respect precedence and mitigate deadlocks. Furthermore, an active scheduling strategy based on machine idle time insertion is incorporated during decoding to shorten the makespan without increasing energy consumption. Numerical experiments demonstrate that the IGA can significantly decrease the makespan while reducing total energy consumption: compared with a standard genetic algorithm (GA) without active scheduling, the proposed IGA reduces the makespan by 32.35% on average. In addition, the makespan under energy minimization is within 1.5% of that under makespan minimization, indicating that energy optimization yields an almost minimal makespan. Sensitivity analysis further evaluates the effects of DTQC-AGV configurations and buffer capacities, offering practical insights for decision-makers. Full article

The integration of sustainable development into biology education has been a growing area of interest. Biology education for sustainability is considered through competencies and skills, taking different dimensions of knowledge into account. Solving problems requires not only knowledge but also communicative and strategic activity. Thus, biology education must emphasize the main visions of scientific literacy proposed in the literature, supporting students to understand society and everyday socioscientific challenges at the local as well as at the global level, and to deal with differing scientific results and uncertain information. However, there are very few studies from a holistic didactic viewpoint on the implementation of sustainable education (SE) in biology education in the context of teacher education and school education for promoting a sustainable future. This study addresses this gap via a bibliometric and content analysis of the literature (n = 165 and 131, respectively) based on the categories of the sustainable development goals (SDGs), subject aims, learning objectives, content knowledge, teaching methods, competencies and skills, and assessment methods. The literature analyzed emphasizes the environmental and social SDGs, the development of students’ factual and conceptual knowledge and learning, interactive teaching and learning methods, critical thinking and reflection, and summative and formative assessment methods. There is much less attention on economic and institutional SDGs, scientific skills, environmental attitudes, knowledge creation, strategic thinking and empathy, and diagnostic assessment methods. Compared to earlier studies performed in the 2010s, teaching and learning methods have become more diverse in contrast to the earlier focus on teacher-centered methods. Overall, the conclusion is that biology education must evolve beyond content mastery to integrate ethical, technological, and transdisciplinary dimensions—empowering learners not only to understand life but to sustain it—aligned with quality education (SDG 4), good health and well-being (SDG 3), and life on land (SDG 15). The findings also suggest that powerful knowledge needs to be emphasized for providing essential insights into ecosystems, biodiversity, and the processes that sustain life on Earth. They also highlight the importance of regular evaluations of teaching and learning for detecting how pedagogical and didactic approaches and strategies have supported students’ learning focused on sustainable development. Full article

This paper proposes a novel scheme for the origin of RNA replicase based on the replication-first stable complex evolution (SCE) model, also known as the stable complex encoding (SCE) model, and attempts to derive this scheme from the metabolism-first graded autocatalysis replication domain (GARD) model, thereby theoretically integrating the two hypotheses of the origin of life: replication-first and metabolism-first. Currently, although the replication-first model has made some progress in the artificial selection of RNA replicase, it has yet to achieve a true breakthrough. Meanwhile, metabolism-first models such as the CAS (Collectively Autocatalytic Set) and its graph version RAF (Reflexively Autocatalytic and Food-generated) models, have conducted in-depth research into the origin of metabolic networks but have failed to address the critical transformation issue from metabolism to RNA replication. This paper argues that these two hypotheses should mutually support each other. By introducing oligonucleotide assemblies and expanding the concept of composomes in the GARD model, this paper attempts to understand the general evolutionary mechanism of enzymes, thereby addressing the long-standing neglect of enzymatic catalysis in metabolism-first theories. This integrated scheme not only provides new theoretical support for the evolution of RNA replicase but also offers important insights into solving the key transition problem from chemical evolution to biological evolution. Full article