Search Results (11,703)

The benchmarking of CO₂ emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO₂ emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. Full article

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. Full article

Linear unconstrained DNA cannot harbor supercoils since these supercoils can diffuse and be eliminated by free rotation of the DNA strands at the end of the molecule. Mammalian telomeres, despite constituting the ends of linear chromosomes, can hold supercoils and be subjected to topological stress. While negative supercoiling was previously observed, thus proving the existence of telomeric topological constraints, positive supercoils were never probed due to the lack of an appropriate tool. Indeed, the few tools available currently could only investigate unwound (Trioxsalen) or overwound (GapR) DNA topology (variations in twist) but not the variations in writhe (supercoils and plectonemes). To address this question, we have designed innovative tools aimed at analyzing both positive and negative DNA writhe in cells. Using them, we could observe the build-up of positive supercoils following replication stress and inhibition of Topoisomerase 2 on telomeres. TRF2 depletion caused both telomere relaxation and an increase in positive supercoils while the inhibition of Histone Deacetylase I and II by TSA only caused telomere relaxation. Moving outside telomeres, we also observed a build-up of positive supercoils on the FRA3B fragile site following replication stress, suggesting a topological model of DNA fragility for this site. Full article

Topology optimization focuses on the conceptual design of structures, characterized by a large optimization space and a significant impact on structural performance, and has been widely applied in industrial fields such as aviation and aerospace. However, most topology optimization methods prioritize structural stiffness and often overlook stress levels, which are critical factors in engineering design. In recent years, explicit topology optimization methods have been extensively developed due to their ability to produce clear boundaries and their compatibility with CAD/CAE systems. Nevertheless, research on incorporating stress constraints within the explicit topology optimization framework remains scarce. This paper is dedicated to investigating stress constraints within the explicit topology optimization framework. Due to the clear boundaries and absence of intermediate density elements in the explicit topology optimization framework, this approach avoids the challenge of stress calculation for intermediate density elements encountered in the traditional density method. This provides a natural advantage in solving topology optimization problems considering stress constraints, resulting in more accurate stress calculations. Compared with existing approaches, this paper proposes a novel component topology description function that enhances the deformability of components, improving the representation of geometric boundaries. The lower-bound Kreisselmeier–Steinhauser aggregation function is employed to manage the stress constraint, reducing the solution scale and computational burden. The effectiveness of the proposed method is demonstrated through two classic examples of topology optimization. Full article

Distributed power networks have a large number of photovoltaic power sources. The bidirection of power flow, different transient control strategies, and installation locations make the transient characteristics highly complex and unpredictable. The vast network of the distribution system makes it almost impossible to predict the electrical quantities of each branch. Reasonable aggregation modeling of the distribution network can greatly simplify the network topology, facilitating transient control and the setting of relay protection settings. An aggregated equivalent modeling method based on the LSTM neural network for distributed PV fault external characteristics is proposed. This method equates the complex distribution network to a highly nonlinear but controllable current source. The method can output the I–V curves of equivalent PV system parallel points under any output power and is able to predict the fault characteristics of the equivalent system after a voltage drop at the parallel point. Compared to traditional mechanistic modeling, this method does not require specific modeling of complex physical systems and is able to accurately map the strong nonlinear inputs and outputs of distribution networks. The established LSTM model first uses a one-dimensional convolutional layer for feature extraction of the PV power coefficients (input), and then two hidden layers are utilized to process the sequence data; the vectors are mapped into a sequence of external characteristic curves (output) in a fully connected layer. A typical distribution network is built based on the traditional PV power model, and a large number of different output combinations are selected for simulation to provide an effective training set and validation set data for LSTM model training. By using the training set data, the weights and offset coefficients of each layer of the LSTM are continuously optimized until the model with the smallest overall error is obtained, which is the optimal model. Finally, the optimal model is utilized to establish an equivalent distribution network system, different degrees of voltage drops are set up at the grid-connected points, the fault characteristics are compared with those of the complete model, and the simulation results can prove the reliability and practicality of the proposed method. Full article

Quantum Key Distribution (QKD) offers a revolutionary approach to secure communication, leveraging the principles of quantum mechanics to generate and distribute cryptographic keys that are immune to eavesdropping. As QKD systems become more widely adopted, the need for robust monitoring and management solutions has become increasingly crucial. The Cerberis3 QKD system from ID Quantique addresses this challenge by providing a comprehensive monitoring and visualization platform. The system’s advanced features, including central configuration, SNMP integration, and the graphical visualization of key performance metrics, enable network administrators to ensure their QKD infrastructure’s reliable and secure operation. Monitoring critical parameters such as Quantum Bit Error Rate (QBER), secret key rate, and link visibility is essential for maintaining the integrity of the quantum channel and optimizing the system’s performance. The Cerberis3 system’s ability to interface with encryption vendors and support complex network topologies further enhances its versatility and integration capabilities. By addressing the unique challenges of quantum monitoring, the Cerberis3 system empowers organizations to leverage the power of QKD technology, ensuring the security of their data in the face of emerging quantum computing threats. This article explores the Cerberus3 system’s features and its role in overcoming the monitoring challenges inherent to QKD deployments. Full article

The growing demand for high-power DC fast-charging (DCFC) stations for electric vehicles (EVs) is expected to lead to increased peak power demand and a reduction in grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities, utilities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid, employing low-power interconnection rules and distributed energy resources (DERs). The system uses automotive second-life batteries (SLBs) and photovoltaic (PV) systems as energy buffer and local energy resources to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a design space exploration (DSE) and exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without losing generality, this study considers a 150 kW DCFC station with a range of DER sizes, grid interconnection specifications and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various realistic scenarios and strategies are defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance over a ten-year period for different scenarios is analyzed and compared using a multitude of metrics. The results of the analysis show that the the integration of DERs in DCFC stations has a positive impact on the economic value of the investment when compared to traditional installations. Full article

Planar magnetic components have been widely used in high-density power converters and are suitable for various topologies. The application of planar inductors in LLC resonant converters can lead to parasitic capacitance, which causes current ringing and results in EMI issues. To mitigate the impact of current ringing, the parasitic capacitance of the planar inductor needs to be reduced. This paper proposes a new six-turn interleaved winding design. Compared to the previous four-turn interleaved winding design, it maintains low parasitic capacitance while positioning both the input and output terminals of the inductor on the outer turn, further enhancing the integration of high-density power converters. The parasitic capacitance was calculated using theoretical methods and verified through finite element simulations. Experimental validation was conducted using an LLC resonant converter test platform. Compared to the previous four-turn interleaved winding design, the new six-turn interleaved winding design satisfies both the input and output terminals, using an outer turn configuration. Additionally, the new design exhibits reduced parasitic capacitance and is suitable for use in LLC resonant converters, where it also minimizes current ringing. Full article

Point cloud semantic segmentation is essential for comprehending and analyzing scenes. However, performing semantic segmentation on large-scale point clouds presents challenges, including demanding high memory requirements, a lack of structured data, and the absence of topological information. This paper presents a novel method based on the Reverse Attention Adaptive Fusion network (RAAFNet) for segmenting large-scale point clouds. RAAFNet consists of a reverse attention encoder–decoder module, an adaptive fusion module, and a local feature aggregation module. The reverse attention encoder–decoder module is applied to extract point cloud features at different scales. The adaptive fusion module enhances fine-grained representation within multi-resolution feature maps. Furthermore, a local aggregation classifier is introduced, which aggregates the features of neighboring points to the center point in order to leverage contextual information and enhance the classifier’s perceptual capability. Finally, the predicted labels are generated. Notably, our method excels at extracting point cloud features across different dimensions and produces highly accurate segmentation results. Experimental results on the Semantic3D dataset achieved an overall accuracy of 89.9% and a mIoU of 74.4%. Full article

Tick Anticoagulant Peptide (TAP), a 60-amino acid protein from the soft tick Ornithodoros moubata, inhibits activated coagulation factor X (fXa) with almost absolute specificity. Despite TAP and Bovine Pancreatic Trypsin Inhibitor (BPTI) (i.e., the prototype of the Kunitz-type protease inhibitors) sharing a similar 3D fold and disulphide bond topology, they have remarkably different amino acid sequence (only ~24% sequence identity), thermal stability, folding pathways, protease specificity, and even mechanism of protease inhibition. Here, fully active and correctly folded TAP was produced in reasonably high yields (~20%) by solid-phase peptide chemical synthesis and thoroughly characterised with respect to its chemical identity, disulphide pairing, folding kinetics, conformational dynamics, and fXa inhibition. The versatility of the chemical synthesis was exploited to perform structure–activity relationship studies on TAP by incorporating non-coded amino acids at positions 1 and 3 of the inhibitor. Using Hydrogen–Deuterium Exchange Mass Spectrometry, we found that TAP has a remarkably higher conformational flexibility compared to BPTI, and propose that these different dynamics could impact the different folding pathway and inhibition mechanisms of TAP and BPTI. Hence, the TAP/BPTI pair represents a nice example of divergent evolution, while the relative facility of TAP synthesis could represent a good starting point to design novel synthetic analogues with improved pharmacological profiles. Full article

A new formalism is introduced that makes it possible to elucidate the physical and geometric content of quantum space–time. It is based on the Minimum Group Representation Principle (MGRP). Within this framework, new results for entanglement and geometrical/topological phases are found and implemented in cosmological and black hole space–times. Our main results here are as follows: (i) We find the Berry phases for inflation and for the cosmological perturbations and express them in terms of the observables, such as the spectral scalar and tensor indices, $n_S$ and $n_T$, and the tensor-to-scalar ratio r. The Berry phase for de Sitter inflation is imaginary with the sign describing the exponential acceleration. (ii) The pure entangled states in the minimum group (metaplectic) $Mp\left(n\right)$ representation for quantum de Sitter space–time and black holes are found. (iii) For entanglement, the relation between the Schmidt type representation and the physical states of the $Mp\left(n\right)$ group is found: This is a new non-diagonal coherent state representation complementary to the known Sudarshan diagonal one. (iv) Mean value generators of $Mp\left(2\right)$ are related to the adiabatic invariant and topological charge of the space–time, (matrix element of the transition $-\infty <t<\infty$). (v) The basic even and odd n-sectors of the Hilbert space are intrinsic to the quantum space–time and its discrete levels (in particular, continuum for $n\to \infty$), they do not require any extrinsic generation process such as the standard Schrodinger cat states, and are entangled. (vi) The gravity or cosmological domains on one side and another of the Planck scale are entangled. Examples: The quantum primordial trans-Planckian de Sitter vacuum and the classical late de Sitter vacuum today; the central quantum gravity region and the external classical gravity region of black holes. The classical and quantum dual gravity regions of the space–time are entangled. (vii) The general classical-quantum gravity duality is associated with the Metaplectic $Mp\left(n\right)$ group symmetry which provides the complete full covering of the phase space and of the quantum space–time mapped from it. Full article

Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks’ pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman’s 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein–protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients’ genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals. Full article

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks. Full article

Heat dissipation significantly limits semiconductor component performance improvement. Thermal management devices are pivotal for electronic chip heat dissipation, with the enhanced thermal conductivity of materials being crucial for their effectiveness. This study focuses on single-crystal diamond, renowned for its exceptional natural thermal conductivity, investigating diamond microchannels using finite element simulations. Initially, a validated mathematical model for microchannel flow heat transfer was established. Subsequently, the heat dissipation performance of typical microchannel materials was analyzed, highlighting the diamond’s impact. This study also explores diamond microchannel topologies under high-power conditions, revealing unmatched advantages in ultra-high heat flux density dissipation. At 800 W/cm² and inlet flow rates of 0.4–1 m/s, diamond microchannels exhibit lower maximum temperatures compared to pure copper microchannels by 7.0, 7.2, 7.4, and 7.5 °C, respectively. Rectangular cross-section microchannels demonstrate superior heat dissipation, considering diamond processing costs. The exploration of angular structures with varying parameters shows significant temperature reductions with increasing complexity, such as a 2.4 °C drop at i = 4. The analysis of shape parameter k_i indicates optimal heat dissipation performance at k_i = 1.1. This research offers crucial insights for developing and optimizing diamond microchannel devices under ultra-high-heat-flux-density conditions, guiding future advancements in thermal management technology. Full article

Buildings consume significant energy, much of which is used for heating and cooling. Insulation reduces undesired heat transfer to save on heating and cooling energy usage. Radiant barriers are a type of insulation technology that reduces radiant heat absorbed by a structure. Applying radiant barriers to buildings reduces costs and improves both energy efficiency and occupant comfort. However, homes often have favorable thermal gradients that could also be used to reduce energy usage if the insulation properties were switched dynamically. This article introduces two dynamic radiant barriers intended for residential attics, which can switch between reflecting and transmitting states as needed. These radiant barriers are manufactured as a single deformable assembly using sheet materials and are compatible with various actuation mechanisms. The efficacy of these radiant barriers is reported based on a hotbox experiment and numerical calculations. The experimental results demonstrate that both proposed dynamic radiant barrier designs increase effective thermal resistance by factors of approximately 2 when comparing insulating to conducting states, and by approximately 4 when comparing the insulating state to the case without a radiant barrier. Additionally, the dynamic radiant barriers achieve heat flux reductions up to 41.9% in the insulating state compared to tests without a dynamic radiant barrier. Full article