Search Results (37,850)

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

The piston-type expander (PTE), as the primary output component, significantly influences the performance of an energy storage system. This paper proposes a non-cam variable valve actuation system for the PTE, supported by a mathematical model. An enhanced S-curve trajectory planning method is used to design the valve lift curve. The study investigates the effects of various valve lift design parameters on output power and efficiency at different rotational speeds, employing orthogonal design and SPSS Statistics 27 (Statistical Product and Service Solutions) simulations. A grey comprehensive evaluation method is used to identify optimal valve lift parameters for each speed. The results show that valve lift parameters influence PTE performance to varying degrees, with intake duration having the greatest effect, followed by maximum valve lift, while intake end time has the least impact. The non-cam PTE outperforms the cam-based PTE. At 800 rpm, the optimal design yields 7.12 kW and 53.5% efficiency; at 900 rpm, 8.17 kW and 50.6%; at 1000 rpm, 9.2 kW and 46.8%; and at 1100 rpm, 12.09 kW and 41.2%. At these speeds, output power increases by 18.37%, 11.42%, 11.62%, and 9.82%, while energy efficiency improves by 15.01%, 15.05%, 14.24%, and 13.86%, respectively. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

This paper presents a comprehensive review of the environmentally friendly management and reutilization of electronic waste (e-waste) plastics in flexible pavement construction. The discussion begins with an overview of e-waste management challenges and outlines key recycling approaches for converting plastic waste into asphalt-compatible materials. This review then discusses the types of e-waste plastics used for asphalt modification, their incorporation methods, and compatibility challenges. Physical and chemical treatment techniques, including the use of free radical initiators, are then explored for improving dispersion and performance. Additionally, in situations where advanced pretreatment methods are not applicable due to cost, safety, or technical constraints, the application of alternative approaches, such as the use of low-cost complementary additives, is discussed as a practical solution to enhance compatibility and performance. Finally, the influence of e-waste plastics on the conventional and rheological properties of asphalt binders, as well as the performance of asphalt mixtures, is also evaluated. Findings indicate that e-waste plastics, when combined with appropriate pretreatment methods and complementary additives, can enhance workability, cold-weather cracking resistance, high-temperature anti-rutting performance, and resistance against moisture-induced damage while also offering environmental and economic benefits. This review highlights the potential of e-waste plastics as sustainable asphalt modifiers and provides insights across the full utilization pathway, from recovery to in-field performance. Full article

This paper presents a novel digital identity ecosystem built upon a hierarchical structure of Blockchain tokens, where both government-certified and uncertified tokens can coexist to represent various attributes of an individual’s identity. At the core of this system is the government, which functions as a trusted authority capable of creating entities and issuing a unique, non-replicable digital identity token for each one. Entities are the exclusive owners of their identity tokens and can attach additional tokens—such as those issued by the government, educational institutions, or financial entities—to form a verifiable, token-based digital identity tree. This model accommodates a flexible identity framework that enables decentralized yet accountable identity construction. Our contributions include the design of a digital identity system (supported by smart contracts) that enforces uniqueness through state-issued identity tokens while supporting user-driven identity formation. The model differentiates between user types and certifies tokens according to their source, enabling a scalable and extensible structure. We also analyze the economic, technical, and social feasibility of deploying this system, including a breakdown of transaction costs for key stakeholders such as governments, end-users, and institutions like universities. Considering the benefits of blockchain, implementing a digital identity ecosystem in this technology is economically viable for all involved stakeholders. Full article

Achieving sustainable development goals remains a core issue in global development. In response, China has proposed the development of new quality productive forces (NQPFs) through innovative thinking, emphasizing that fostering NQPFs is both an intrinsic requirement and a pivotal focus for advancing high-quality development. Concurrently, the intelligent transformation of the manufacturing sector serves as a critical direction for China’s economic restructuring and upgrading. This paper places “new quality productive forces” and “intelligent transformation of manufacturing” within the same analytical framework. Starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation of manufacturing,” it concretizes the value implications of new quality productive forces into a systematic conceptual framework driven by the synergistic interaction of three major mechanisms: the mechanism of revolutionary technological breakthroughs, the mechanism of innovative allocation of production factors, and the mechanism of deep industrial transformation and upgrading. This study constructs a “3322” evaluation index system for NQPFs, based on three formative processes, three driving forces, two supporting systems, and two-dimensional characteristics. Simultaneously, it builds an evaluation index system for the intelligent transformation of manufacturing, encompassing intelligent technology, intelligent applications, and intelligent benefits. Using national time-series data from 2012 to 2023, this study assesses the development levels of both NQPFs and the intelligent transformation of manufacturing during this period. The study further analyzes the impact of NQPFs on the intelligent transformation of the manufacturing sector. The research results indicate the following: (1) NQPFs drive the intelligent transformation of the manufacturing industry through the three mechanisms of innovative allocation of production factors, revolutionary breakthroughs in technology, and deep transformation and upgrading of industries. (2) The development of NQPFs exhibits a slow upward trend; however, the outbreak of the pandemic and Sino-US trade frictions have caused significant disruptions to the development of new-type productive forces. (3) The level of intelligent manufacturing continues to improve; however, from 2020 to 2023, due to the impact of the COVID-19 pandemic and Sino-US trade conflicts, the level of intelligent benefits has slightly declined. (4) NQPFs exert a powerful driving force on the intelligent transformation of manufacturing, exerting a significant positive impact on intelligent technology, intelligent applications, and intelligent efficiency levels. Full article

The difficulty and manual nature of procuring human evaluators for ranking the quality of images affected by various types of degradations, and of those cleaned up by developed algorithms, has lead to the widespread adoption of automated metrics, like the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM). However, disparities between rankings given by these metrics and those given by human evaluators have encouraged the development of improved image quality assessment (IQA) metrics that are a better fit for this purpose. These methods have been previously used solely for quality assessments and not as objectives in the training of neural networks for high-level vision tasks, despite the potential improvements that may come about by directly optimizing for desired metrics. This paper examines the adequacy of ten recent IQA metrics, compared with standard loss functions, within two trained dehazing neural networks, with observed broad improvement in their performance. Full article

In this paper, necessary and sufficient efficiency conditions in new multi-cost variational models are formulated and proved. To this end, we introduce a new notion of $({\vartheta }_0,{\vartheta }_1)-({\sigma }_0,{\sigma }_1)-type-I$ functionals determined by multiple integrals. To better emphasize the significance of the suggested $({\vartheta }_0,{\vartheta }_1)-({\sigma }_0,{\sigma }_1)-type-I$ functionals and how they add to previous studies, we mention that the $({\vartheta }_0,{\vartheta }_1)-({\sigma }_0,{\sigma }_1)-type-I$ and generalized $({\vartheta }_0,{\vartheta }_1)-({\sigma }_0,{\sigma }_1)-type-I-typeI$ assumptions associated with the involved multiple integral functionals cover broader and more general classes of problems, where the convexity of the functionals is not fulfilled or the functionals considered are not of simple integral type. In addition, innovative proofs are provided for the main results. Full article

As a new type of alloy system composed of five or more principal components, high-entropy alloys demonstrate outstanding comprehensive performance in the field of friction and wear through the synergistic effects of the high-entropy effect, lattice distortion effect, hysteresis diffusion effect and cocktail effect. This paper systematically reviews the research progress on the friction and wear properties of high-entropy alloys. The mechanisms of metal elements such as Al, Ti, Cu and Nb through solid solution strengthening, second-phase precipitation and oxide film formation were analyzed emphatically. And non-metallic elements such as C, Si, and B form and strengthen the regulation laws of their tribological properties. The influence of working conditions, such as high temperature, ocean, and hydrogen peroxide on the friction and wear behavior of high-entropy alloys by altering the wear mechanism, was discussed. The influence of test conditions such as load, sliding velocity and friction pair matching on its friction coefficient and wear rate was expounded. It is pointed out that high-entropy alloys have significant application potential in key friction components, providing reference and guidance for the further development and application of high-entropy alloys. Full article

Nuclear energy can help stop climate change by generating large amounts of emission-free electricity. Nuclear reactor designs are continually being developed to be more fuel efficient, safer, easier to construct, and to produce less nuclear waste. The term advanced nuclear reactors refers either to Generation III+ and Generation IV or small modular reactors. Every reactor is associated with the nuclear fuel cycle that must be economically viable and competitive. An important matter is optimization of fissile materials used in reactor and/or reprocessing of spent fuel and reuse. Currently operating reactors use the open cycle or partially closed cycle. Generation IV reactors are intended to play a significant role in reaching a fully closed cycle. At the same time, we can observe the growing interest in development of small modular reactors worldwide. SMRs can adopt either fuel cycle; they can be flexible depending on their design and fuel type. Spent nuclear fuel management should be an integral part of the development of new reactors. The proper management methods of the radioactive waste and spent fuel should be considered at an early stage of construction. The aim of this paper is to highlight the challenges related to reprocessing of new forms of nuclear fuel. Full article

Plant latex is a rich source of proteolytic enzymes with potential biomedical applications, particularly in hemostasis. Among them, thrombin-like enzymes (TLEs) have garnered interest in their ability to mimic thrombin by catalyzing the conversion of fibrinogen to fibrin, facilitating clot formation. While TLEs from snake venoms have been well-characterized and applied clinically, their plant-derived counterparts remain underexplored. This review critically examines the structural and functional characteristics of TLEs from plant latex, comparing them to animal-derived TLEs and evaluating their role in both procoagulant and fibrinolytic processes. Emphasis is placed on dual fibrinogenolytic and fibrinolytic activities exhibited by latex proteases, which often vary with concentration, incubation time, and protease type. In vitro coagulation assays and electrophoretic analyses are discussed as critical tools for characterizing their multifunctionality. By addressing the knowledge gaps and proposing future directions, this paper positions plant latex proteases as promising candidates for development in localized hemostatic and thrombolytic therapies. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

In 2071, the Hydraulic community will commemorate the second centenary of the Baré de Saint-Venant equations, also known as the Shallow Water Equations (SWE). These equations are fundamental to the study of open-channel flow. As non-linear partial differential equations, their solutions were largely unattainable until the development of computers and numerical methods. Following 1960, various numerical schemes emerged, with Preissmann’s scheme becoming the most widely employed in many software applications. In the 1990s, some researchers identified a significant limitation in existing software and codes: the inability to simulate transcritical flow. At that time, Preissmann’s scheme was the dominant method employed in hydraulics tools, leading the research community to conclude that this scheme could not handle transcritical flow due to suspected instability. In response to this concern, several researchers suggested modifications to Preissmann’s scheme to enable the simulation of transcritical flow. This paper will demonstrate that these accusations against the Preissmann scheme are unfounded and that the proposed improvements are unnecessary. The observed instability is not due to the numerical method itself, but rather a mathematical instability inherent to the SWE, which can lead to ill-posed conditions if a specific derived condition is not met. In the context of a friction slope formula based on Manning or Chézy types, the condition for ill-posedness of the 1D shallow water equations simplifies to the Vedernikov number condition, which is necessary for roll waves to develop in uniform flow. This derived condition is also relevant for the formation of roll waves in unsteady flow when the 1D shallow water equations become ill-posed. Full article

The dynamic model of a wheeled wall-climbing robot exhibits stage-specific changes when traversing different types of obstacles and during various stages of obstacle negotiation. Previous studies often employed remote control methods for obstacle-crossing control, which fail to dynamically adjust the torque distribution of magnetic wheels in response to real-time changes in the dynamic model. This limitation makes it challenging to precisely control the robot’s speed and attitude angles during the obstacle-crossing process. To address this issue, this paper first establishes a staged dynamic model for the wall-climbing robot under typical obstacle-crossing scenarios, including steps, 90° concave corners, 90° convex corners, and thin plates. Secondly, an adaptive controller based on a radial basis function neural network (RBFNN) is designed to effectively compensate for variations and uncertainties during the obstacle-crossing process. Finally, comparative simulations and physical experiments demonstrate the effectiveness of the proposed method. The experimental results show that this method can quickly respond to the dynamic changes in the model and accurately track the trajectory, thereby improving the control precision and stability during the obstacle-crossing process. Full article