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With the development of information and communication technologies, industrial cyber-physical systems (ICPSs) have accumulated a large amount of data, which enables us to convert data into industrial insight. However, since the industrial process of ICPS is always complicated and large-scale, the raw data only contains a few operation condition information, which brings challenges to process monitoring and control. Thus, an efficient operation condition division method for ICPS is necessary. Although many operation condition division methods have been proposed, they were mainly relying on the static characteristics and ignored how industrial process varies dynamically. Meanwhile, with industrial process running, there may exist some new operation conditions that make the operation condition division task even more difficult. In order to grasp the static and dynamic features simultaneously of industrial process and divide operation conditions accurately, we proposed an operation condition division method based on joint static and dynamic analysis with incremental learning. In detail, the slow feature analysis (SFA) and self-organizing map (SOM) network were proposed to extract the static and dynamic features jointly. Then, a division strategy was proposed to distinguish the operation condition changing points. For the new operating condition, we designed an incremental learning method based on SOM network, which can update the operation condition model in real-time. Extensive experiments including a numerical simulation, two benchmark processes and an industrial roasting process demonstrate that the proposed method can identify the operation conditions of the raw data in ICPS accurately and efficiently.

Facing the gaps that the traditional long short-term memory (LSTM) and convolution neural network (CNN) cannot differentially deal with the input data based on the corresponding trend and stage information in remaining useful life (RUL) prediction, a more accurate and robust RUL prediction model is constructed. First, a temporally multi-differential LSTM (TMLSTM) with the multi-trend division unit and multicellular unit is proposed, and a spatially multi-differential CNN (SMCNN) with the multi-stage division unit and differentiated convolutions is designed. Then, by combining TMLSTM and SMCNN, a spatiotemporally multi-differential deep neural network (SMDN) is developed for predicting the equipment RUL, which enhances the ability of feature extraction from the spatiotemporal perspective by using the multi-trend and multi-stage information. Via several evaluation indexes, the Commercial Modular Aero Propulsion System Simulation dataset and the wind turbine gearbox bearing dataset are used to validate the superiority of the proposed method over several existing prediction methods.

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance.

The cyclic spectral coherence analysis has been proved to be a useful tool to reveal the hidden periodic or repetitive patterns, and how to determine an optimal demodulation band is crucial for characterizing the fault characteristic of the rolling element bearing (REB). Thus, a proportional selection scheme based on a bottom-up strategy is proposed in this study, which can determine an optimal demodulation band and realize bearing fault feature extraction under the conditions of poor frequency resolution and a lower signal-to-noise ratio (SNR). Firstly, compared with conventional frequency division structures, such as the 1/3-binary tree structure, the finer frequency band groups with the same bandwidth in each division level can be obtained automatically and strictly under poor frequency resolution conditions. Secondly, the richness of the REB fault information of each divided frequency band can be evaluated by the improved diagnostic feature indicator under a lower SNR condition. By using the scheme, the fault characteristic of the REB can be exposed. The efficacy of the proposed scheme is supported by simulations and experiments under some tough conditions.

The impact of the COVID-19 pandemic on the division of household labour could continue to lock down or start to break gender roles. Using time-use data of n = 473 individuals collected during the lockdown restrictions in Belgium from March to May 2020, we analyse the gendered division of routine and non-routine household labour in absolute time use and relative shares. We compare against the Belgian time-use data of 2013 for the same time period ( n = 678 individuals). A time-demanding work and living situation associate with an increase in men’s time spent on household labour during the lockdown but not with a change in women’s time use. The gender gap closes in absolute time but not in relative shares of routine and non-routine household labour. The limited impact of the COVID-19 pandemic on the gender division of household labour indicates a temporal rather than a substantial change in gender roles.

We introduce a family of proportional surplus division values for TU-games. Each value first assigns to each player a compromise between her stand-alone worth and the average stand-alone worths over all players, and then allocates the remaining worth among the players in proportion to their stand-alone worths. This family contains the proportional division value and the new egalitarian proportional surplus division value as two special cases. We provide characterizations for this family of values, as well as for each single value in this family.

This paper presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called Stark, based on Strassen's matrix multiplication algorithm. Stark preserves Strassen's seven multiplications scheme in a distributed environment and thus achieves asymptotically faster execution time. It creates a distributed recursion tree of computation where each level of the tree corresponds to division and combination of distributed matrix blocks stored in the form of Resilient Distributed Datasets (RDDs). It processes each divide and combine step in parallel and memorises the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first implementation of a distribute Strassen's algorithm on Spark platform. We also report a detailed complexity analysis for the proposed algorithm, taking into account computation and communication costs. Experimental results suggest that Stark outperforms existing distributed matrix multiplication implementations on Spark -- Marlin and MLLib, for high matrix sizes ( $\geq 16384\times 16384$ ). Our experiments reveal optimal block sizes for each matrix size, which is also shown from theoretical analysis. We also show that the experimental and theoretical running times for Stark match closely. It has also been shown experimentally that Stark exhibits strong scalability with increasing number of executors.

A single homogeneous resource needs to be fairly shared between users that dynamically arrive and depart over time. Although good allocations exist for any fixed number of users, implementing these allocations dynamically is impractical: it typically entails adjustments in the allocation of every user in the system whenever a new user arrives. We introduce a dynamic fair resource division problem in which there is a limit on the number of users that can be disrupted when a new user arrives and study the trade-off between fairness and the number of allowed disruptions, using a fairness metric: the fairness ratio. We almost completely characterize this trade-off and give an algorithm for obtaining the optimal fairness for any number of allowed disruptions.

The KNN classification algorithm is one of the most commonly used algorithm in the AI field. This paper proposes two improved algorithms, namely KNNTS, and KNNTS-PK+. The two improved algorithms are based on KNNPK+ algorithm, which uses PK-Means ++ algorithm to select the center of the spherical region, and sets the radius of the region to form a sphere to divide the data set in the space. The KNNPK+ algorithm improves the classification accuracy on the premise of stabilizing the classification efficiency of KNN classification algorithm. In order to improve the classification efficiency of KNN algorithm on the premise that the accuracy of KNN classification algorithm remains unchanged, KNNTS algorithm is proposed. It uses tabu search algorithm to select the radius of spherical region, and uses spherical region division method with equal radius to divide the data set in space. On the basis of the first two improved algorithms, KNNTS-PK+ algorithm combines them to divide the data sets in space. Experiments are carried out on the new data set and the classification results were obtained. Results revealed show that the two improved algorithms can effectively improve the classification accuracy and efficiency after the data samples are cut reasonably.

This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.

As the Internet of Things (IoT) has become a widespread phenomenon, promising sensor applications at the nanoscale have begun to emerge. One example is imaging via distributed massive nanoscale nodes (NSNs), which can be used to implement “invisible surveillance cameras” by, for example, painting liquids containing nanoscale sensors onto walls. This imaging method requires periodic data transfer from thousands of NSNs to a data collection node (DCN). An essential technique for handling such transfers is the media access control (MAC) protocol. However, existing protocols cannot support periodic transfer from numerous NSNs because of inefficient communication caused by a large amount of headers in the packets. In this article, we introduce an original MAC protocol and discuss its capability in terms of implementing imaging applications. The proposed protocol applies a time-division access (TDA) feature to reduce the amount of headers and a two-layered protocol to enable simultaneous transmission among nodes. Slot assignment is an essential function in TDA and requires communication among nodes. Unlike existing methods, our simple approach enables communication by exploiting the unique features of the focused application. The results of the numerical simulation reveal that the proposed MAC protocol allows for periodic imaging with more than three thousand nodes and produces high-quality images very close to those obtained using ideal communication. These results are achieved by employing an original design framework to determine appropriate key parameters, such as the number of clusters and frame rate.

This paper proposes a customized kernel-based Fuzzy C-Means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based Fuzzy C-Means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and Spearman's rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

By using the relations between supersingular elliptic curves defined over a finite field of characteristic p > 3 and the p-th division polynomial, we present a property about the reduction modulo a prime p > 3 of the p-th division polynomial based on a heuristic argument and numerical evidence. We prove this property determining the p-th division polynomial of supersingular elliptic curves. As a consequence of this result, we present a criterion to discard supersingular elliptic curves.

Integer division is one of the most fundamental arithmetic operators and is ubiquitously used. However, the existing division protocols in secure multi-party computation (MPC) are inefficient and very complex, and this has been a barrier to applications of MPC such as secure machine learning. We already have some secure division protocols working in \(\mathbb {Z}_{2^n}\). However, these existing results have drawbacks that those protocols needed many communication rounds and needed to use bigger integers than in/output. In this paper, we improve a secure division protocol in two ways. First, we construct a new protocol using only the same size integers as in/output. Second, we build efficient constant-round building blocks used as subprotocols in the division protocol. With these two improvements, communication rounds of our division protocol are reduced to about 36% (87 rounds \(\rightarrow \) 31 rounds) for 64-bit integers in comparison with the most efficient previous one.

An unequal division of housework has been found to be often regarded as fair, which may explain why women still do most household labor. This study extends previous research by also investigating childcare—an increasingly important part of household labor, which is likely to have a different meaning than housework. It examines how perceptions of fairness for both housework and childcare are influenced by the division of housework, childcare, and paid labor, and whether patterns differ by gender. Data from the Netherlands (men: N = 462; women: N = 638) show that unequal divisions of housework, and especially childcare, are often perceived as fair. When it comes to how an increasingly unequal household labor division is related to unfairness, associations are stronger for women than for men. Fairness of the household labor division is evaluated in relation to total workload and not in isolation from other types of labor.

In this paper, we proposed a new low-light image enhancementapproach to overcome the above limitations. Theproposed algorithm is named as Nature Preserving LowlightImage Enhancement (NPLIE). NPLIE estimates initialillumination and performs optimal refinement. The proposedalgorithm computes the reflectance component through anelement-wise division of input image by illumination. The enhanced image is obtained as a product of adjusted illumination and reflectance component. In this work, we estimate initialilluminance from structure-aware smoothening of a low-lightimage using guided filters of variable box sizes. We computerefined illumination by solving the proposed multi-objective

This paper is part of an ongoing endeavor to bring the theory of fair division closer to practice by handling requirements from real-life applications. We focus on two requirements originating from the division of land estates: (1) each agent should receive a plot of a usable geometric shape, and (2) plots of different agents must be physically separated. With these requirements, the classic fairness notion of \emph{proportionality} is impractical, since it may be impossible to attain any multiplicative approximation of it. In contrast, the \emph{ordinal maximin share approximation}, introduced by Budish in 2011, provides meaningful fairness guarantees. We prove upper and lower bounds on achievable maximin share guarantees when the usable shapes are squares, fat rectangles, or arbitrary axis-aligned rectangles, and explore the algorithmic and query complexity of finding fair partitions in this setting. Our work makes use of tools and concepts from computational geometry such as independent sets of rectangles and guillotine partitions., Appears in the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021

This paper studies on a division method of the whole aeroengine loading spectrum flight mission segment and rotor speed mission segment, which is based on the actual flight actions and related to the flight operations of aeroengine and is suitable for the variable-speed aeroengines such as turbojet and turbofan. Through the research, the aeroengine loading spectrum operation-related mission segments can be divided, which can provide important data basis for the life research on the structures which are sensitive to flight maneuver (such as the main shaft, large gearbox and installation section), lay a foundation for the simulation, compilation and prediction of the whole aeroengine loading spectrum. Firstly, based on the summary of basic flight actions in actual flight, the division of flight mission segment was realized by programming. Moreover, the aeroengine rotor speed mission segments, associated with flight actions and missions, were divided based on the flight mission segment division results. Besides, the efficiency and accuracy of the mission segment division results were verified by adopting measured loading spectrum data. Finally, the characteristics of speed mission segment division results were compared and analyzed in tables and figures. The comparison results show that the characteristics of similar speed mission segments are similar, while the characteristics of different speed mission segments are different. And the shapes of similar mission segments vary to the change of flight actions and operations, which can reflect the operation-related feature of the segments.

A novel multitarget respiration monitoring radar scheme based on time-division multiple tags (TDMTs) is proposed. The key innovation in this letter is the introduction of the TDMT, which leverages the frequency-division characteristic of multiple frequency-tunable tags attached on different human bodies to implement the time division of the reflected beat signals in frequency modulated continuous wave (FMCW) system, which is used to separate respiration signals from two closely spaced human targets. To validate the proposed scheme, a radar prototype and two tunable tags have been developed. The experimental results verify the effectiveness of the proposed method by monitoring two closely spaced targets in indoor environment.

A sub-sampling phase-locked loop (SSPLL) with a subsampling delay-locked loop is presented to extend the loop bandwidth and achieve the low jitter. A falling-edge tuning loop is added to align the falling edge of the reference clock with the rising one of the output clock. The proposed SSPLL is realized in a 0.18μm CMOS process and its active area is 0.185mm2. At the output frequency of 2.2GHz, the proposed SSPLL achieves an inband phase noise of -111.83dBc/Hz and -116.41dBc/Hz at 100kHz and 4MHz offset frequency respectively with a division ratio of 44. Its root-mean-square jitter integrated from 10kHz to 100MHz is 655fs. The measured reference spur is -50.3dBc.

This paper proposes a wide output voltage range LLC resonant converter based on topology reconfiguration method (TRM). First, with different driving signals two operational modes of inverter circuit and rectifier circuit of the proposed topology are analyzed respectively. On this basis, the converter in whole voltage range has three variable operating modes considering voltage gain by different combination between two kinds of inverting modes and two kinds of rectifying modes. Thus compared with conventional LLC converter, the voltage gain range can be extended by four times within the same switching frequency range using TRM plus pulse-frequency modulation (PFM). Comprehensive analysis of circuit states and soft-switching condition in these three modes are presented. Moreover, design of voltage area division in three modes is discussed to arrive a better tradeoff between efficiency and power density. A design flowchart is given to help to implement an efficiency optimization algorithm. In addition, a smooth mode transition technique is proposed to avoid the rapid frequency change during reconfiguring modulation strategy. Finally, a 2kW 100V-900V output prototype is built, and main experimental results are provided to verify the effectiveness of the proposed topology and topology reconfiguration method.

BACKGROUND: Few studies have investigated the variations in body composition and performance in Japanese collegiate American-football players. OBJECTIVE: To clarify what characterizes competitors at the highest levels – in the top division or on the starting lineup – we compared players’ body compositions and performance test results. METHODS: This study included 172 players. Each player’s body composition and performance (one-repetition maximum bench press, one-repetition maximum back squat, and vertical jump height) were measured; power was estimated from vertical jump height and body weight. Players were compared according to status (starter vs. non-starter), position (skill vs. linemen), and division (1 vs. 2). Regression analysis was performed to determine characteristics for being a starter. RESULTS: Players in higher divisions and who were starters were stronger and had more power, greater body size, and better performance test results. Players in skill positions were relatively stronger than those in linemen positions. Vertical jump height was a significant predictor of being a starter in Division 1. CONCLUSION: Power and vertical jump may be a deciding factor for playing as a starter or in a higher division.

Scheduling is the activity of planning in conducting activity at a certain time. The main objective of the scheduling is to improve the effectiveness and the efficiency of the use of resources so that progress that has been determined previously achieved well. A genetic algorithm is one of the most often algorithms used in optimization scheduling. This algorithm works based on a natural process carried out using heuristic methods. One of the scheduling problems at company x in the step to increase security through system security updates with this antivirus installation program involves all employees as users of each division and handled by the IT team as the pic that handles the program. Scheduling is still compiled manually so that raises some of the problems starting from the preparation process to the division of the schedules to require a long time, it usually tends to clashes between the hour and the day when the PIC handles the user due to not automatic scheduling. Therefore, designing an application system by using web-based programming is conducted waterfall method to ease in the process of the preparation and distribution of a schedule which is more systematic with the implementation of the method of the genetic algorithm so that the scheduling that there will be more optimal.

As many LiDAR point cloud processing steps, such as reconstruction, are often time- and memory-consuming, dividing LiDAR point clouds into subregions is common and necessary during preprocessing. However, the existing data dividing methods rely on tedious manual work or regular grids and result in oversegmentation around cutting lines. In this letter, we propose a new gap-based data dividing method for various LiDAR point clouds that can minimize the intersections between cutting lines and objects. The basic idea is to find a set of optimal paths that consist of gaps between objects as potential cutting lines. The experiments and comparisons in three data sets demonstrate that the proposed method is much better than the baseline method in terms visual inspection and cutting line quality.

The arithmetic optimization algorithm (AOA) is a newly developed metaheuristic search technique that simulates the distribution characteristics of the basic arithmetic operations of addition, subtraction, multiplication, and division, and has been employed to solve some real-world optimization problems. However, it has been found that the AOA suffers from poor exploration and prematurely converges to non-optimal solutions, especially when applied to multi-dimensional optimization problems. In this paper, to overcome the shortcomings of the standard AOA, an improved variant of the AOA, called improved arithmetic optimization algorithm (IAOA), is proposed and then employed for optimization of skeletal structures with discrete design variables. Compared to the standard AOA, two major improvements are made in the proposed IAOA: (1) The original formulation of the AOA is modified to enhance the exploration and exploitation capabilities; (2) The proposed IAOA requires fewer algorithm-specific parameters compared with the standard AOA, making it easy to be implemented. To examine the efficiency and robustness of the proposed IAOA, three benchmark structural optimization problems with discrete design variables are investigated and the results are compared to those of the standard AOA and other methods available in the literature. To the best of our knowledge, this is the first attempt to apply AOA to structural optimization. The IAOA not only promotes the exploration capability of the search space but also overcomes the shortcoming of the premature convergence of the standard AOA. Experimental results indicate that IAOA significantly surpasses the standard AOA and achieves results comparable or superior to other state-of-the-art metaheuristics.

The division of firm surplus between labor and shareholders, and its impact on firms’ value creation, are central topics in strategy theory and practice. Early studies of value appropriation within...

American football athletes are frequently hypohydrated before and during activity. Hypohydration increases the risk of exertional sickling in student-athletes with sickle cell trait (SCT). The authors examined weight charts from the 2010/2011 to 2018/2019 seasons at one Division I institution to determine if differences in percentage body mass losses (%BML) exist between those with and without SCT. Seventeen student-athletes with SCT and 17 matched-controls were included. A Bonferroni correction was applied to account for multiple comparisons (0.05/8), resulting in p p = .002) but not for the number of days %BML exceeded 2% (SCT: 0 ± 1 vs. control: 1 ± 1; p = .016). Implementation of proper hydration strategies minimized %BML in athletes with SCT, decreasing the risk of hypohydration and exertional sickling. The same strategies ensured all players remained below threshold to optimize performance and reduce heat illness risk.

Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.

Future asteroid landing and sample return missions will seek for landing sites with high scientific value, such as hazardous terrains, which means that the spacecraft needs the ability of autonomous hazard avoidance. Nowadays, the guidance algorithm based on artificial potential function plays an increasingly important role in hazard avoidance, but the local minimum problem makes that the spacecraft cannot reach the desired target landing point in some complex terrains. In this paper, a novel hazard avoidance guidance method which improves the traditional artificial potential function is developed. This method focuses on the impact zone analysis of hazards and defines anti-collision zone. In addition, a new repulsive potential function in logarithmic form is designed with continuity and fast numerical change rate in the anti-collision zone on this basis, and the problem of spacecraft falling into local minimum zone in complex terrains can be effectively avoided. Considering the practical application of constant thrust engine, the hazard avoidance control law with constant thrust sliding mode is designed to reduce switching frequency and fuel consumption. The performance of the proposed guidance law is verified through a set of simulations, as well as the global stability of the control system under uncertainty and perturbation conditions.

The current work deals with the methodological study of pressure vessel (Cylindrical or spherical) with different ends or heads. The pressure vessel is designed for 24 L volume and 8 bar pressure vessels are done following ASME standard section VIII, Division I. The thickness of all cylindrical heads is calculated by different formulae and the thickness vary accordingly. However, in this project, the thickness is kept constant irrespective of cylindrical heads or ends to study its effect. FEA is performed to evaluate or study the stress parameters. The software took for Finite Element Analysis (FEA) in Ansys 19.2. Before that, the 3D modelling was done using SolidWorks software. After concluding the better cylindrical end or head for constant thickness, the comparison of the material is made to compare with the current state of the art and find out the better material by analyzing the circumferential stress and longitudinal stress.

In this paper, we focus on the cluster synchronization of reaction-diffusion genetic regulatory networks (RDGRNs) with time-varying delays, where the state of the system is not only time-dependent but also spatially-dependent due to the presence of the reaction-diffusion terms. First, we construct an intermittent space-dividing controller that effectively combines the two control strategies, making it more cost-effective. Furthermore, based on the activation function division approach, we propose a regulation function division method that can improve the delay upper bound of RDGRNs; meanwhile, the cluster synchronization criteria of RDGRNs under the proposed controller are derived based on the Lyapunov theory and Halanay's et al. inequality techniques. Finally, the criteria's effectiveness is demonstrated by numerical examples of the system in one- and two-dimensional space.

Phase change energy storage materials are widely used in the field of renewable energy. Paraffin is one of the common phase change energy storage materials. As a multi-component hydrocarbon mixture, the melting of paraffin is different from that of pure substance. In addition the solid and liquid zones, there is also a fuzzy zone in which solid and liquid coexist. In this paper, the melting characteristics of paraffin in phase transition zone are studied by multi-scale experiments. Through the visualization experiment of square cavity paraffin melting, the solid zone, fuzzy zone and liquid zone are determined, and the moving process of phase interface is tracked by digital pictures and infrared heat maps. The evolution process of the pore structure in the fuzzy zone under different temperatures is photographed by means of the micro-experiment, and it is revealed that there are two areas in the fuzzy zone, porous media area and multi-phase flow area. The results show that the melting process of paraffin can be divided into four zones: liquid zone, multi-phase flow zone, porous media zone, and solid phase zone. According to the polarizing optical microscopy picture, the continuous phase and discrete phase transition relationship between solid wax crystal and liquid paraffin is captured. The polarizing optical microscopy picture is statistically analyzed, and the critical liquid phase ratio of the transition from porous media area to multi-phase flow area is given under experimental conditions.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

As a typical photo-thermal-electric multiphysics process, the energy transfer and conversion in spectral splitting photovoltaic/thermal (PV/T) systems are rather complex. For a better prediction of the system performance, a fully-coupled finite volume-discrete ordinates (FV-DO) radiation model for the numerical simulation of the PV/T system was developed. However, the previously proposed models still lacked detailed discussion for the spectrum division, to improve the criteria for the “spectrum mesh”, this study applied the wavelet decomposition method and the piecewise constant fitting method for the spectrum division in the fully-coupled model and herein evaluated the initially introduced spectrum errors (the total radiation absolute error AE, root mean square error RMSE, and the correlation coefficient γ). Then the influence of spectrum division on the PV/T system performances (the absorbed radiation, PV cell temperature, energy efficiency, and merit function) were comprehensively investigated. Eventually, the relations between initially introduced spectrum indexes and the absolute errors of performance indicators comparing with benchmark results were discussed. The result of 20 bands divided by the piecewise constant fitting method, which could achieve relative errors of PV temperature, energy efficiency from the benchmark by 0.046% and 0.840%, respectively, was recommended as a compromise between the accuracy and the increasing computing resources. RSME and γ of the discrete spectrum can be used to initially estimate the accuracy of the performance of the spectral splitting PV/T system.

SACOS series of subchannel analysis codes have been developed by XJTU-NuTheL for many years and are being used for the thermal-hydraulic safety analysis of various reactor cores. To achieve fine whole core pin-level analysis, the input preprocessing and parallel capabilities of the code have been developed in this study. Preprocessing is suitable for modeling rectangular and hexagonal assemblies with less error-prone input; parallelization is established based on the domain decomposition method with the hybrid of MPI and OpenMP. For domain decomposition, a more flexible method has been proposed which can determine the appropriate task division of the core domain according to the number of processors of the server. By performing the calculation time evaluation for the several PWR assembly problems, the code parallelization has been successfully verified with different number of processors. Subsequent analysis results for rectangular- and hexagonal-assembly core imply that the code can be used to model and perform pin-level core safety analysis with acceptable computational efficiency.

Single carrier multiple access division (SC-FDMA) technology is one of the good uplink modulation techniques in a wireless communication system. Promises high transmission speed, low bit error rate and high spectral efficiency with low peak to average power ratio (PAPR) compared to OFDMA. This paper is proposed analytical model and link level simulation using MATLAB® to enhance the ratio of peak average power (PAPR) performance for distributed SC-FDMA sub-carrier assignment of. The methodology using the first full-featured digital MIMO software allows for in-depth verification and improvement of MIMO technologies applied to satellite broadcasting networks. Moreover, the demonstrator allows to complete and confirm our theory and simulation-based. The aim of this paper is to enhancement PAPR by analyzing the mobile satellite digital that employed distributed sub-carrier assignment for MIMO-SC-FDMA. The model is verified and validated by link level simulation.

The purpose of this study was to determine whether the in-game communication of football players from historically Black colleges and universities (HBCUs) was excessively penalized in the field of play. Previous literature has found that referee bias is commonplace and uncovered evidence that referees socially judge the communicative behaviors of HBCU student athletes differently than the communicative behaviors of student athletes from predominantly White institutions. This led to social judgment theory emerging as the theoretical frame. Quantitative methods were utilized to analyze National Collegiate Athletic Association data for Division II college football. Findings revealed that referees disproportionately flagged football teams from HBCUs in comparison with predominantly White institutions. These results provide implications for theory. The uncovered results also support a well-developed line of communication research that has indicated that excessive penalties are levied against HBCU teams in multiple sports. A rhetorical call to action is made to facilitate officiating change in intercollegiate athletics.

In order to effectively exploit the orbital angular momentum (OAM) characteristics, it is essential to have devices which are capable of generating beams with multiple OAM modes simultaneously. Most of the proposed techniques commonly require time division multiplexing at the transmitter side or beam splitters at the reception side for separating OAM modes and directing beams of different OAM modes to different directions. In order to overcome this problem, here, the concept of aperture sharing for generating concentrically embedded OAM carrying beams is presented. Such a methodology provides embedded beams of various OAM modes which are launched toward a single direction through a shared aperture of transmitarray (TA), while having a spatial division between them. In this manner, the transmitted beams are initially separated by the hardware at the transmitter level. A novel circularly-polarized (CP) substratecoupled unit-cell is designed and used as the elemental components on the aperture of the TA. Three prototype TAs are realized for investigating different cases of the OAM modes embedding and verifying the effectiveness of the proposed method. The obtained simulated and experimental results are consistent which validates the novel technique.

In this work, we introduce the pattern-domain pilot design paradigm based on a "superposition of orthogonal-building-blocks" with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems.Specifically, the pattern-domain pilot is constructed based on the superposition of $L$ cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to $\binom{N}{L}$, where $\binom{N}{L}$ denotes the number of all L-combinations of a set N, and low correlation valueslead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems.We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems.Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one,while preserving the existing physical layer virtually unchanged. The extension of the "superposition of orthogonal-building-blocks" scheme to "superposition of quasi-orthogonal-building-blocks" is straightforward.

This study uses remote sensing technology and GIS technics for structural study of the Haut-Nkam division with the aim of mapping the lineaments that can significantly influence all geo-structures by identifying major geological features and comes out with a new major structural map. Two methods are used: (i) the spatial reclassification of satellite images LANSAT 7 ETM + and 8 OLI/TIRS; and (ii) the application of band ratios. The confirmation of the structural features (faults, fractures and tectonic directions) is made by (i) a wide variety of digital image processing techniques such as (a) Composite bands and color composition filtrations; (b) Principal Component Analysis (PCA); (c) lineaments density map filtration: (ii) the superposition of the lineaments with the geological maps of each Subdivision and district of the study area. A total of 578 lineaments have been identified following the major directions of NE-SW, NNE-SSW, ENE-WSW, ESE-WNW and E-W with a total length of about 2273 km. These lengths vary from 38.11 to 128.08 km with an average length of 58.87 km. Major lineaments are identified in the six sub-divisions (Bana, Kekem, Bandja, Banka, Bakou, Bafang) and district (Bawa) of the Haut-Nkam division with a majority concentrated at Bakou followed by Bandja and finally Kekem. These major structural deformations identified which could be eminent faults, breakage, or tectonic direction, etc… could have hydrological or hydrogeological implications.