Division Scheme Research Articles

Empowering Democracy: A Socio-Ethical Theory

Great Britain subjugated colonists using various power strategies, including dehumanization, misinformation, fear, and other divisive strategies. The Founders described these oppressive strategies as “a long train of abuses and usurpations.” Throughout the Declaration of Independence and the U.S. Constitution, the Founding Fathers imbued the people with hope in a government for the people: one unlike that of the monarchy, which sought to protect itself at the expense of colonists. As a result, the Founders created a government more likely to lead with fairness, demonstrate respect for human dignity, unite Americans as one people, empower citizens through hope, and lead the nation toward security. This socio-ethical framework was an abductive discovery nested within a grounded theory study where it had been concluded that the first principles of democracy were woven within the American founding documents. Using Charmaz’s constructivist grounded theory, the first principles of democracy—knowledge, fairness, human dignity, hope, unity, and security—were used to answer an emerging research question: Are there two democratic frameworks in the founding documents, and how do they differ? The first principles of democracy promote a culture of ethics, resilience, and freedom. Their empowering disposition makes them suitable as a normative framework for a democratic government for the people.

A Utility-Based Distributed Pattern Mining Algorithm With Reduced Shuffle Overhead

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability.

基于<bold>KPCA</bold>-<bold>SVM</bold>的相关和独立变量故障检测方法

In the real industrial process, some process variables are independent of other variables, a fault detection method of related and independent variable based on kernel principal component analysis and support vector machine (KPCA-SVM) is proposed to detect these independent variables separately from related variables. Firstly, a variable division strategy based on mutual information is applied to divide the process variables into related variables and independent variables by calculating the mutual information between variables. Then, KPCA and SVM models are established in the related variable space and the independent variable space to monitor the test data. Compared with the traditional KPCA and SVM methods, the KPCA-SVM method combines the advantages of KPCA in detecting related variables and SVM methods in detecting independent variables, and improves the fault detection performance of KPCA and SVM methods. Finally, the KPCA-SVM method is applied to the Tennessee-Eastman (TE) industrial process for fault detection, and compared with KPCA, kernel entropy component analysis (KECA) and SVM methods. The results show that the proposed KPCA-SVM method has a good detection effect and improves the detection effect of multiple faults, among which the detection effect of minor fault 5 is significantly improved, which further verifies the effectiveness of the KPCA-SVM method.

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

High-resolution airborne synthetic aperture radar (SAR) imaging requires long synthetic aperture time (LSAT). However, the LSAT invalidates the Fresnel approximation in the traditional autofocusing method, leaving a residual signal phase in the motion error. To solve the problem, a signal-reconstruction-based phase gradient autofocus (SR-PGA) and a sub-image resampling (SIR) methods are proposed. They are developed on the hyperbolic model. First, a sub-aperture division strategy divides the full-aperture high-order error into multiple low-order sub-aperture errors. Then, the SR-PGA is developed to estimate the sub-aperture error (SPE), in which the precise deramping is reconstructed to eliminate the residual signal phase in the SPE. Third, the SIR is proposed to eliminate residual Doppler-variant shift of the adjacent sub-images, improving the accuracy of the SPE combination. Finally, simulation and actual data processing verify the effectiveness and validity of the algorithm.

Improvement of Learning Outcomes Through STAD Type Cooperative Learning in Indonesian Learning

This study aims to improve student learning outcomes through cooperative learning strategies of the Student Team Achievement Division (STAD) at SMA Negeri 6 Maros. This type of research is classroom action research (CAR) which consists of planning, action, observation, and reflection. The subjects of the study were students of class XI MIPA. Data collection techniques use daily replay documentation to show improvement. The data were analyzed with descriptive statistics. The learning outcomes Indonesian class XI MIPA 3 students at SMA Negeri 6 Maros who were taught using the STAD (Student Team Achievement Divisons) type cooperative learning model were classified as very high with an average score of 95.15% of 25 students. This shows that cooperative learning strategies with STAD can improve student learning outcomes.

A low-power analog front-end amplifier for SiPM based radiation detectors

A low-power analog front-end amplifier for SiPM based radiation detectors is presented. In particular, an event-driven low-noise charge sensitive amplifier (CSA) is developed in conjunction with a novel charge division scheme by capacitively coupling the SiPM to the CSA so that the SiPM’s linear energy-charge correlation is preserved, while conserving power. Following extensive circuit simulations and design finalization, a test chip containing the presented front-end amplifier has been fabricated in the target 65 nm CMOS technology. The fabricated prototype and the presented SiPM read-out approach have been verified and characterized using a dedicated test setup. An energy resolution of 11.7% is achieved for the 511 keV peak of a Na-22 source and 10.9% for the 662 keV peak of a Cs-137 source, using an onsemi 3 mm × 3 mm SiPM and a LYSO scintillator coupled to the fabricated CSA. The front-end amplifier consumes 350 μW of power, while maintaining linearity across a wide energy range following calibration. Including the active amplifier circuitry, the bias generator section, and the capacitor bank that realizes the feedback capacitor Cfb of the CSA, the front-end amplifier occupies a silicon area of 50,000 μm2, which supports low-power, multi-channel read-out realizations within a small footprint.

Static and Dynamic Joint Analysis for Operation Condition Division of Industrial Process With Incremental Learning

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 contain 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 the industrial process varies dynamically. Meanwhile, with the 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 the 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 the 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.

Research on subsystem division scheme of overlapping decentralized control strategy

The overlapping decentralized control strategy is a solution to the decentralized control of large-scale systems based on the inclusion principle. Its design process can be summarized as three stages of expansion-decoupling-contraction. In this paper, the subsystem division scheme of the overlapping decentralized control strategy is studied for building structure systems under earthquake. Using the energy-to-peak algorithm, we mainly analyse the influences of the location of the overlapping layer, the number of overlapping layers and the number of subsystems on the control effect of the structural system, and compare them with the traditional energy-to-peak centralized control method. The numerical results indicate that the dynamic response of the structure can be effectively suppressed by raising the location of the overlapping layer, increasing the number of overlapping layers, and increasing the number of subsystems. Compared with the centralized control strategy, the control effect is equivalent, or even better, verifying the feasibility of this research scheme.

High-Speed Extraction of Regions of Interest in Optical Camera Communication Enabled by Grid Virtual Division

Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.

Stage division strategy for gas insulation surface discharge based on multi-physical information

The internal insulation defects in gas insulated equipment leading to partial discharges will affect the insulation performance of the equipment. Among them, surface discharges are a typical type of partial discharge, which often occurs at basin-type insulators. To realize effective discharge detection and discharge degree determination, a refined discharge stage division strategy for gas-insulated creeping discharge based on multi-physical information is investigated in this study. First, an experimental platform for gas-insulated multi-physical signal detection is established, of which high frequency current transformer (HFCT), silicon photomultiplier (SiPM), ultra-high frequency (UHF) and acoustic emission (AE) sensors are applied to measurement the multi-physical energy releases in the process of creeping discharge. Then an artificial surface discharge defect is developed following the actual surface discharge of basin insulator. Subsequently, the relative optical radiation power, electromagnetic radiation power and ultrasonic radiation power produced in the process of discharge are deduced from the multi-physical information. Meanwhile, the relationships among the three energy releases are carried out for variation of different energy with the applied voltage increases. Finally, development path of multi-physical energy ratio for surface discharge is proposed to evaluate the severity of discharge.

Case Study on Certara’s Simcyp PBPK Simulator to Eliminate Lengthy Clinical Trails

Background/Purpose: Analysis and new interpretation of the existing information are equivalent to creating new knowledge. A case study is an example of exploratory research and allows researchers to analyze the available information using a systematic analysis framework. In this paper, a case study on a clinical research simulation software product called Simcyp is offered by a global company Certara to its clients in the pharmaceutical industry. Objective: To know the current status of model-based drug development simulation software, with special emphasis on Certara’s Simcyp software, its features, and variations, its client’s usage pattern to prepare new drugs, its usage in research and developmental contribution during the last 22 years, and analysis of Symcyp as a research division of Certara using SWOC framework and analysis of the Symcip as simulation software using ABCD analysis framework as stakeholder analysis. Design/Methodology/Approach: The case study uses an exploratory research approach where the information is collected from various sources including the company website, its competitor’s website, various magazine articles, and scholarly articles from Google Scholar. The collected information are analyzed systematically using company analysis frameworks, product analysis framework, SWOC analysis framework, and ABCD analysis framework. Findings/Result: Based on the analysis, it is found that Symcyp is a most admired simulation software in the pharmaceutical industry for model-based drug design and development for different varieties of diseases. The strategy of Simcyp division of Certara to satisfy, delight, and enlighten its clients is also discussed. Originality/Value: The case study is based on a systematic analysis of a software product of a company using company analysis frameworks, product analysis framework, SWOC analysis framework, and ABCD analysis framework thereby contributing to interpret the existing knowledge in a new way through new interpretation. Type of Paper: Academic research based case study.

PACK RUNNING AMONG FEMALE NCAA COLLEGIATE CROSS COUNTRY TEAMS

The purpose of this study was to identify if there was a relationship between running with a teammate(s), often referred to as packing, during a cross-country race and lower aggregate team times of the top five scoring runners. This study examined packing strategies of Division 1 NCAA female cross-country runners. Packing was defined as any team member running within a second of a teammate at a given checkpoint during the race. All competitors across three women’s races wore chip-timing bibs that recorded each competitor’s time at checkpoints throughout the race. Only teams having five or more runners were included in the analysis. A “packing score %” was calculated for each team by dividing the number of times a runner was within a second of a teammate at a checkpoint in a given interval by the total number of packing opportunities within the interval (PS%). The total scoring team time was then calculated from the finishing time of each team’s top five runners (Team Aggregate Time-TAT). The correlation coefficient between PS% and TAT was r = -0.47 (p < 0.001). While more research is needed to identify how and when packing should be optimally used, empirical findings indicate packing is beneficially associated with team running times.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0578/a.php" alt="Hit counter" /></p>

The rhetoric of the Norwegian government and health authorities during the COVID-19 pandemic

PurposeThe purpose of this study is to examine the identification and collaboration rhetoric of the Norwegian government and public health authorities during the pandemic. The aim is to show whether and how actors use strategies and themes of identification, and whether they build identification with their publics.Design/methodology/approachThe study combines qualitative and quantitative methods. Six identification strategies were identified through manual text analysis of press statements; word counts of each strategy were registered electronically to access quantitative data of individual actors.FindingsThe three strategies reflecting values, the two strategies reflecting division and disagreement and the strategy reflecting change showed almost equal frequencies. The strategy of shaping community, serving the function of change, and the division strategy, demonstrating identification through dissociation, were the most frequent strategies. Politicians preferred the collaboration strategy, while health experts preferred the strategy of concern and recognition.Originality/valueThe six identification strategies extend the understanding of leadership crisis communication and contemporary rhetoric as community-building discourse aiming for speaker–audience collaboration. The study demonstrates that division and disagreement are equally essential components of crisis communication as values and change. When actors differ in choice of strategy, themes and publics, they may still come across as coordinated and unified in their calls for solidarity, collective efforts and common understanding.

Short-Duration Transient Temperature Distribution Prediction Model along Chip Vertical Path Applicable to Multi-Timescale Simulation

Based on extremely uneven temperature distribution along the insulated gate bipolar transistor (IGBT) chip vertical path during switching transients, a short-duration transient microsecond-scale prediction model applicable to multi-timescale simulation is presented in this paper. Traditional thermal models often take the chip active area as a uniform heat source to obtain a victual junction temperature (Tvj). In this paper, a discrete distributed heat source model combined with a thermal network model based on the sublayer division strategy is proposed to achieve an accurate temperature distribution description along the chip vertical path. Taking a 1700 V/3600 A IGBT module as an example, the proposed model can evaluate the short-duration transient temperature distribution along the chip vertical path and the error is less than 2 °C compared with the finite element model. Meanwhile, the model is applied to a single short-duration transient timescale and multi-timescale systems separately, and the simulation speed is increased by more than 80 times and 297 times, which verifies its validity and accuracy.

Core components-oriented modularisation methodology for complex products

ABSTRACT As a cost-saving and environmental-friendly product strategy, modular design is an effective approach to shorten lead-time and reduce cost for development of products. However, the modular design of products with complex structure might be plagued by the difficulty of product structure modeling and unreasonable structural modular schemes. In this study, a core components-oriented modularisation method is presented to solve the problem of module division of complex products. The structural model of complex products is reasonably simplified by screening core components, which further reduces the modeling difficulty and improves the solution efficiency of module division scheme. Firstly, a hybrid structure decomposition method is proposed to obtain all components of complex products according to the different structure characteristics of each subsystem of complex products. Secondly, the core components of complex products are screened by calculating the whole life cycle cost (WLCC) of each component, and the design structure matrix (DSM) model of complex products is established according to the relationship between the core components. Finally, the module division scheme of complex products is obtained via hierarchical clustering and modularity evaluation. Effectiveness and robustness of the method are demonstrated through comparison experiments for the modular design of a hydraulic excavator case.

Deep Learning-Enabled File Popularity-Aware Caching Replacement for Satellite-Integrated Content-Centric Networks

In recent years, satellite-integrated content centric networking (SCCN) has become an important solution for the future network with excellent bandwidth savings and file distribution capability in a wide range by the intrinsic caching function. At present, however, the high-speed changes of satellite network topology and coverage make it difficult to predict the file popularity of SCCN network, resulting in a low timeliness. This will lead to low node cache efficiency and bad data distribution performance. To address this issue, in this article, we have proposed a deep learning-enabled file popularity-aware caching replacement mechanism to achieve efficient file distribution in SCCN. In the proposed mechanism, we have developed a virtual location division scheme to keep the return path of content data invariable by remapping the time-varying topology of network into a static topology with virtual nodes. Furthermore, we have put forward a minimum delay file-caching set algorithm to predict the popularity of files in the proposed SCCN via a well-designed deep learning framework, which can find those high-popularity files most worthy of caching. The simulation results verified the proposed method can obviously degrade the access delay of all users and the cache hit ratio of satellite nodes, compared with current strategies, i.e., cache everything everywhere with least recently used, probCache, content-aware placement and discovery, and replacement (APDR), respectively.

Spatio-Temporal Features and Influencing Factors of Homesteads Expansion at Village Scale

The current research on the change of homesteads has rarely combined spatial information, and no expansion research has been found with a scale as detailed as the level of the homestead plots. Understanding the changing rules of homesteads expansion and its influencing factors are important to homestead reform, optimization of village layout, and cultivated land protection. In order to explore the direction, level, and spatial pattern of homesteads expansion from 2010 to 2020 and the influencing factors, by taking six villages in Deqing County as an example, this research proposed a tract division scheme based on multidistance spatial clustering and intersecting area index and used homesteads expansion index and geographical detector. The results showed that (1) 83.3% of the villages had a decreasing trend of homesteads expansion, and half of the villages’ homesteads remained unchanged in their distribution direction. (2) The average order of factors influencing the expansion of homesteads was: distance from road > altitude > slope > policy restriction > distance from water area > slope aspect. This study helps to understand the spatio-temporal features of homesteads expansion in small-scale areas, provides an empirical reference for deepening homesteads reform and policy formulation, and also provides new methods for quantitative research on homesteads expansion.

A divide-and-conquer algorithm for curve fitting

Curve fitting is still an open problem which draws attention from many applications, such as computer-aided design, computer-aided manufacturing and reverse engineering. Splines such as Bézier, B-Spline and NURBS curves are usually employed in engineering applications and are intensively used for fitting purposes. The optimization of their shapes and localization parameters, however, is a very complex task. The literature presents many methods which empirically set some important parameters, such as the number of control points. As guessing such a value is difficult, this paper presents a new method to choose it through a multi-curve fitting method, based on linear least square optimizations, using a divide-and-conquer algorithm and an error tolerance threshold. Four prime procedures compose the method: the conquer step fits curves over subset point clouds; the combine step glues curve segments together with some selective continuity; the divide step splits subsets which are not properly fitted yet; and the merge step blends curve segments together. Several curve setups were tested in well-known benchmarks, using four-division strategies: bisection, error balance, point with the greatest curvature and point with the smallest curvature. The developed method allows for fast computation even for larger point clouds, and it was able to properly reconstruct each tested shape, even with the addition of synthetic noise. We also demonstrate that it can be significantly faster than a single-curve fitting using the same number of control points. • A multiple curve fitting approach using iterative multi-control-point insertion. • A divide-and-conquer algorithm allows for fast curve fitting. • Point cloud division strategies have their own pros and cons in curve fitting. • Adequate error tolerance balances quality and simplicity in curve fitting.

Channel Evolution under the Control of Base-Level Cycle Change and the Influence on the Sustainable Development of the Remaining Oil—A Case in Jiang Ling Depression, Jiang Han Basin, China

The extension of river channels is one of the key factors in determining the remaining oil distribution. Different sedimentary facies and bedding types of oil layers will form specific characteristics of remaining oil distribution after water injection development. Using massive drilling, core, logging, seismic, and production data, on the basis of sequence stratigraphy base-level cycle change, the river records and development history are restored, and the fine connectivity of reservoirs and the configuration relationship of production wells are studied. The following conclusions are drawn: (1) A sequence stratigraphic division scheme is established. In the established sequence framework, the types and characteristics of reservoir sand bodies are analyzed. The 2nd and 6th members of Yu yang formation can be divided into 2 long-term base level cycles, 5 medium-term base level cycles, and 17 short-term base level cycles. The evolution of the second and sixth members of the Yu yang formation shows a pattern of base level rising, falling and rising again; (2) the vertical sedimentary evolution sequence is underwater distributary channel distributary channel meandering channel distributary channel flood plain. The types of channel sand bodies developed from little overlap to more vertical or lateral overlap and then gradually changed to isolated type; (3) according to the structural location and development sequence, different types of reservoirs are identified. Combined with the statistics of the drilled data of Yu yang formation k2y4 in Fu I fault block, it is found that the connectivity rate of oil layer thickness (the ratio of oil layer connectivity thickness to total thickness of sand layer) within the oil-bearing area is 84.4%, and the connectivity rate of the number of layers (8) is 60%. The connectivity condition is relatively good.

Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems

Imaging systems are widely applied in harsh environments where the performance of shallow‐designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0–80 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.