Search Results (37)

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management. Full article

Regulation and customer awareness pressurize manufacturers to use recycled plastic (RP) in the production system to reduce the negative environmental impact of plastic waste for sustainable production. Compared to virgin materials, the RP available in the market shows high variation in quality, composition, and properties, and often experiences higher variability in lead time. This renders the supply chain of RP and the production systems more vulnerable, making it difficult for material requirement planning (MRP) to decide the optimal quantity and reorder time. This paper first examines the RP supply chain and the sources of variations therein, identifies the associated uncertainties for operations management, reviews the current MRP design elements in managing supply uncertainty, and finally aligns strategies and design elements with the dimensions of the uncertainties. A set of valuable propositions is drawn for the plastic firms to manage variation from upstream suppliers and promote a high-value chain of plastic circularity. MRP practices at the operational level including safety stock, optimization techniques, and alternative bills of material are proposed to mitigate the variations in the supply chain. The work provides a conceptual foundation for the circular economy of plastic, which opens a new paradigm of future research in managing RP in the production system for sustainability. Full article

Cyclo[n]carbon (C_n) is one member of the all-carbon allotrope family with potential applications in next-generation electronic devices. By employing first-principles quantum transport calculations, we have investigated the electronic transport properties of single-molecule junctions of C_n, with n = 14, 16, 18, and 20, connected to two bulk gold electrodes, uncovering notable distinctions arising from the varying aromaticities. For the doubly aromatic C₁₄ and C₁₈ molecules, slightly deformed complexes at the singlet state arise after bonding with one Au atom at each side; in contrast, the reduced energy gaps between the highest occupied and the lowest unoccupied molecular orbitals due to the orbital reordering observed in the doubly anti-aromatic C₁₆ and C₂₀ molecules lead to heavily deformed asymmetric complexes at the triplet state. Consequently, spin-unpolarized transmission functions are obtained for the Au-C_14/18-Au junctions, while spin-polarized transmission appears in the Au-C_16/20-Au junctions. Furthermore, the asymmetric in-plane π-type hybrid molecular orbitals of the Au-C_16/20-Au junctions contribute to two broad but low transmission peaks far away from the Fermi level (E_f), while the out-of-plane π-type hybrid molecular orbitals dominate two sharp transmission peaks that are adjacent to E_f, thus resulting in much lower transmission coefficients at E_f compared to those of the Au-C_14/18-Au junctions. Our findings are helpful for the design and application of future cyclo[n]carbon-based molecular electronic devices. Full article

The concept of “all cores are created equal” has been popular for several decades due to its simplicity and effectiveness in CPU (Central Processing Unit) design. The more cores the CPU has, the higher performance the host owns and the higher the power consumption. However, power-saving is also one of the key goals for servers in data centers and embedded devices (e.g., mobile phones). The big.LITTLE multicore architecture, which contains high-performance cores (namely big core) and power-saved cores (namely little core), has been developed by ARM (Advanced RISC Machine) and Intel to trade off performance and power efficiency. Facing the new heterogeneous computing architecture, the traditional lock algorithms, which are designed to run on homogeneous computing architecture, cannot work optimally as usual and drop into the performance issue for the difference between big core and little core. In our preliminary experiment, we observed that, in the big.LITTLE multicore architecture, all these lock algorithms exhibit sub-optimal performance. The FIFO-based (First In First Out) locks experience throughput degradation, while the performance of competition-based locks can be divided into two categories. One of them is big-core-friendly, so their tail latency increases significantly; the other is little-core-friendly. Not only does the tail latency increase, but the throughput is also degraded. Motivated by this observation, we propose a Core-Aware Lock for the big.LITTLE multicore architecture named CAL, which keeps each core having an equal opportunity to access the critical section in the program. The core idea of the CAL is to take the slowdown ratio as the matric to reorder lock requests of these big and little cores. By evaluating benchmarks and a real-world application named LevelDB, CAL is confirmed to achieve fairness goals in heterogeneous computing architecture without sacrificing the performance of the big core. Compared to several traditional lock algorithms, the CAL’s fairness has increased by up to 67%; and Its throughput is 26% higher than FIFO-based locks and 53% higher than competition-based locks, respectively. In addition, the tail latency of CAL is always kept at a low level. Full article

Position effects occur when changes in item positions on a test impact the test outcomes (e.g., item parameters or test scores). Previous studies found that position effects may vary by the testing context and conditions and thus affect each test-taker differently. With the increasing adoption of digital assessments involving innovative item types that are focused on problem-solving skills, it is also essential to study position effects within this context. This study incorporates item-level scores and screen-level response time data from the Trends in International Mathematics and Science Study (TIMSS) 2019 problem-solving and inquiry tasks for Grade 4 students to examine position effects on students’ ability and test-taking speed. This dataset included responses from 27,682 students from 36 countries. A structural equation modeling approach was employed to model ability and test-taking speed within the same model. The results indicated a small but statistically significant booklet effect on students’ overall mathematics and science ability. The impact of block position changes within the booklets seemed to be greater than the impact of a reordering of subjects tested in the two sessions. The results also showed that when an item block was placed earlier in a test session, students spent more time on the items and performed better. The implications of these findings are discussed. Full article

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%. Full article

Jacobian-free Newton Krylov (JFNK) is an attractive method to solve nonlinear equations in the nuclear engineering community, and has been successfully applied to steady-state neutron diffusion k-eigenvalue problems and multi-physics coupling problems. Preconditioning technique plays an important role in the JFNK algorithm, significantly affecting its computational efficiency. The key point is how to automatically construct a high-quality preconditioning matrix that can improve the convergence rate and perform the preconditioning matrix factorization efficiently and robustly. A reordering-based ILU(k) preconditioner is proposed to achieve the above objectives. In detail, the finite difference technique combined with the coloring algorithm is utilized to automatically construct a preconditioning matrix with low computational cost. Furthermore, the reordering algorithm is employed for the ILU(k) to reduce the additional non-zero elements and pursue robust computational performance. A 2D LRA neutron steady-state benchmark problem is used to evaluate the performance of the proposed preconditioning technique, and a steady-state neutron diffusion k-eigenvalue problem with thermal-hydraulic feedback is also utilized as a supplement. The results show that coloring algorithms can automatically and efficiently construct the preconditioning matrix. The computational efficiency of the FDP with coloring could be about 60 times higher than that of the preconditioner without the coloring algorithm. The reordering-based ILU(k) preconditioner shows excellent robustness, avoiding the effect of the fill-in level k choice in incomplete LU factorization. Moreover, its performances under different fill-in levels are comparable to the optimal computational cost with natural ordering. Full article

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method. Full article

The COVID-19 lockdown has had serious consequences, including rethinking higher education. The study aims to enhance the knowledge base of online education and academic integrity through a case study of the Ludovika University of Public Service (LUPS), Budapest, Hungary. The research aimed to assess the teachers’ experience with distance learning and examinations, including the change in workload, digital competencies, Moodle, Turnitin, and other software used during and after the lockdown. This paper summarizes the university-level policy changes induced during the lockdown, covering the introduction of emergency distance teaching and online examinations in academic integrity at the university. Two years after the first lockdown, the researchers made a survey (n = 145) about the continuation of the introduced solutions. The results show that a remarkable reordering started while the technical and technological backgrounds were available for the changes. The teachers could feel a significant increase in workload with distance education and have low trust in maintaining the standards of academic integrity. However, the research shows moderate and low levels of digital competencies in the majority of teachers, which clearly defines the most crucial task leading to success. Maintaining the monitoring system with objective indicators of the development and the opinions of the interested parties is essential for successful strategies in the field. Full article

Ganoderma lucidum exhibits the ability to synthesize a diverse range of biologically active molecules with significant pharmaceutical potential, including xylomannan and fucogalactan, which have demonstrated antitumor activity. However, there exists considerable intra-species variability in the capacity to produce these metabolites at high concentrations, likely reflecting the high genomic diversity observed from a limited number of strains sequenced to date. We employed high-throughput shotgun sequencing to obtain the complete genome sequence of G. lucidum strain 5.1, which is distinguished by its remarkable xylomannan synthesis capabilities. Through the utilization of semi-automatic reordering based on conformation capture (Hi-C) data, we substantially enhanced the assembly process, resulting in the generation of 12 chromosome-level scaffolds with a cumulative length of 39 Mbp. By employing both de novo and homology-based approaches, we performed comprehensive annotation of the genome, thereby identifying a diverse repertoire of genes likely involved in polysaccharide biosynthesis. The genome sequence generated in this study serves as a valuable resource for elucidating the molecular mechanisms underlying the medicinal potential of Ganoderma species, discovering novel pharmaceutically valuable compounds, and elucidating the ecological mechanisms of the species. Furthermore, the chromosome contact map obtained for the first time for this species extends our understanding of 3D fungal genomics and provides insights into the functional and structural organization within the fungal kingdom. Full article

Creating a decision-making environment that promotes sustainable food choices is a priority for both the individual and society. This study aimed at encouraging plant-based menu choices by re-ordering the menu according to the carbon footprint values. The project was conducted in a grab-and-go eatery at a large United States public university, where students could order their meals choosing among different menu options that were customizable with various ingredients. The order of menu ingredients was changed twice: for five weeks, from the most to the least impactful in terms of carbon footprint; subsequently, for another five weeks the order was reversed. At both times, all sales data were recorded. A total of 279,219 and 288,527 items were selected, respectively, during the first and the second intervention. A significant association was found between menu re-ordering and customers’ choices for almost all food categories considered. Overall, despite beef choices not changing, results showed that students were more likely to choose low-carbon options when these were placed at the beginning, emphasizing that food selections were impacted by ingredient placement on the menu list. These findings highlight the need for a multi-level strategy focused on raising students’ awareness of the environmental impact of animal-based foods, particularly beef. Full article

In recent times, we have encountered new situations that have imposed restrictions on our ability to visit public places. These changes have affected various aspects of our lives, including limited access to supermarkets, vegetable shops, and other essential establishments. As a response to these circumstances, we have developed a continuous review retrial queueing–inventory system featuring a single server and controlled customer arrivals. In our system, customers arriving to procure a single item follow a Markovian Arrival Process, while the service time for each customer is modeled by an exponential distribution. Inventories are replenished according to the $(s,Q)$ reordering policy with exponentially distributed lead times. The system controls arrival in the waiting space with setup time. The customers who arrive at a not allowed situation decide to enter an orbit of infinite size with predefined probability. Orbiting customers make retrials to claim a place in the waiting space, and their inter-retrial times are exponentially distributed. The server may experience essential interruption (emergency situation) which arrives according to Poisson process. Then, the server goes for an emergency vacation of a random time which is exponentially distributed. In the steady-state case, the joint probability of the number of customers in orbit and the inventory level has been found, and the Matrix Geometric Method has been used to find the steady-state probability vector. In numerical calculations, the convexity of the system and the impact of F-policy and emergency vacation in the system are discussed. Full article

Contrastive learning techniques make it possible to pretrain a general model in a self-supervised paradigm using a large number of unlabeled remote sensing images. The core idea is to pull positive samples defined by data augmentation techniques closer together while pushing apart randomly sampled negative samples to serve as supervised learning signals. This strategy is based on the strict identity hypothesis, i.e., positive samples are strictly defined by each (anchor) sample’s own augmentation transformation. However, this leads to the over-instancing of the features learned by the model and the loss of the ability to fully identify ground objects. Therefore, we proposed a relaxed identity hypothesis governing the feature distribution of different instances within the same class of features. The implementation of the relaxed identity hypothesis requires the sampling and discrimination of the relaxed identical samples. In this study, to realize the sampling of relaxed identical samples under the unsupervised learning paradigm, the remote sensing image was used to show that nearby objects often present a large correlation; neighborhood sampling was carried out around the anchor sample; and the similarity between the sampled samples and the anchor samples was defined as the semantic similarity. To achieve sample discrimination under the relaxed identity hypothesis, the feature loss was calculated and reordered for the samples in the relaxed identical sample queue and the anchor samples, and the feature loss between the anchor samples and the sample queue was defined as the feature similarity. Through the sampling and discrimination of the relaxed identical samples, the leap from instance-level features to class-level features was achieved to a certain extent while enhancing the network’s invariant learning of features. We validated the effectiveness of the proposed method on three datasets, and our method achieved the best experimental results on all three datasets compared to six self-supervised methods. Full article

This article addresses a novel methodology for the utilization of Field Programmable Gate Array (FPGA) accelerators in on-board Synthetic Aperture Radar (SAR) processing routines. The methodology consists of using High-Level Synthesis (HLS) to create Intellectual property (IP) blocks and using the Reusable Integration Framework for FPGA Accelerators (RIFFA) to develop a Peripheral Component Interconnect express (PCIe) interface between the Central Processing Unit (CPU) and the FPGA, attaining transfer rates up to 15.7 GB/s. HLS and RIFFA reduce development time (between fivefold and tenfold) by using high-level programming languages (e.g., C/C++); moreover, HLS provides optimizations like pipeline, cyclic partition, and unroll. The proposed schematic also has the advantage of being highly flexible and scalable since the IPs can be exchanged to perform different processing routines, and since RIFFA allows employing up to five FPGAs, multiple IPs can be implemented in each FPGA. Since Fast Fourier Transform (FFT) is one of the main functions in SAR processing, we present a FPGA accelerator in charge of the reordering stage of VEC-FFT (an optimized version of FFT) as a proof of concept. Results are retrieved in reversed bit order, and the conventional reordering function may consume more than half of the total clock cycles. Next, to demonstrate flexibility, an IP for matrix transposition is implemented, another computationally expensive process in SAR due to memory access. Full article

Due to the complex topological relationship, poor data locality, and data racing problems in unstructured CFD computing, how to parallelize the finite volume method algorithms in shared memory to efficiently explore the hardware capabilities of many-core GPUs has become a significant challenge. Based on a production-level unstructured CFD software, three shared memory parallel programming strategies, atomic operation, colouring, and reduction were designed and implemented by deeply analysing its computing behaviour and memory access mode. Several data locality optimization methods—grid reordering, loop fusion, and multi-level memory access—were proposed. Aimed at the sequential attribute of LU-SGS solution, two methods based on cell colouring and hyperplane were implemented. All the parallel methods and optimization techniques implemented were comprehensively analysed and evaluated by the three-dimensional grid of the M6 wing and CHN-T1 aeroplane. The results show that using the Cuthill–McKee grid renumbering and loop fusion optimization techniques can improve memory access performance by 10%. The proposed reduction strategy, combined with multi-level memory access optimization, has a significant acceleration effect, speeding up the hot spot subroutine with data races three times. Compared with the serial CPU version, the overall speed-up of the GPU codes can reach 127. Compared with the parallel CPU version, the overall speed-up of the GPU codes can achieve more than thirty times the result in the same Message Passing Interface (MPI) ranks. Full article