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This paper introduces an efficient algorithm to address the issue of lecturer peer-assessment assignment. The motivation for this solution arises from a real-world scenario where a group of lecturers receives teaching feedback through the process of peer assessment. In this context, in the case of a large group, manually keeping track of desires and constraints is hard, and therefore a computational solution is paramount. The proposed technique looks for a solution where every teacher is evaluated by a target number of peers. Moreover, affinity between peers may be encoded in the algorithm to give preference to solutions where the assignments have higher affinity. The problem is framed using a directed weighted graph, where the weights are the affinity between peers, and the proposed greedy algorithm regularizes this graph to achieve the attribution. Results are presented where the proposed approach is applied to both real and simulated data, resulting in adequate attributions within an efficient time frame.

With the first European Union (EU) member states enacting legislation for establishing energy communities (EC) based on the EU guidelines, the distribution system operators (DSOs) might be obliged in many countries to allocate electricity for the ECs based on a predefined distribution key. In Austria, such legislation has already become a reality. From the provided DSO data, it is only known how much electricity a participant purchased from the EC, but it is not known which participant purchased what exact amount of electricity from which peer and vice versa. To address this issue, an ex-post electricity allocation algorithm is developed based on participant preferences. Each participant can indicate a preference from which peer electricity is preferred. Knowing ‘who sold how much to who’ and ‘who purchased how much from who’ – which is the outcome of the ex-post allocation algorithm – enables applying peer-to-peer prices, empowering the ECs to increased individuality. The proposed algorithm is applied to a fictitious model EC of eight participants, four of which are equipped with a rooftop photovoltaic (PV) system. Two scenarios are investigated: one with well-distributed preferences and another with poorly distributed preferences. Based on this, the effects of differently distributed preferences on individual EC participants are assessed and discussed. It can be concluded that ex-post electricity allocation might become a powerful asset for ECs for the achievement of individual goals, despite being constrained by the original electricity allocation by the DSOs with severely limited room for individuality.

We discuss a procurement problem with transportation losses and piecewise linear production costs. We first provide an algorithm based on Knaster-Tarski's fixed point theorem to solve the allocation problem in the quadratic losses case. We then identify a monotony condition on the types distribution under which the Bayesian cost minimizing mechanism takes a simple form. [ABSTRACT FROM AUTHOR]

After the outbreak of the epidemic, the distribution of living supplies in residential areas under closed management become one of the focal issues to be solved urgently, and this problem can be abstracted as the the location problem of distribution points for living supplies during the epidemic, whose essence is the NP-Hard problem in combinational optimization. This paper studied the exact algorithm of the location problem of distribution points for living supplies during the lockdown period. Firstly, the paper researched some mathematical properties and corresponding proof of the problem, and these properties could be used to reduce the size of the given problem, thereby reducing the difficulty of the problem solving. Then the paper designed allocation sub-algorithm, upper and lower bound sub-algorithms and the reduced order sub-algorithm. Based on these sub-algorithms, the paper proposed a backtracking algorithm with reduction which can reduce the size of the problem and get the optimal solution. Finally,the paper analyzed and solved several examples to further illustrate the principle and execution process of the proposed algorithm. The results show that the proposed algorithm can reduce the difficulty of solving the problem by reducing the size of the problem. [ABSTRACT FROM AUTHOR]

Fast growing number of cellular telecommunication technology resulting on the increasing number of the user equipment. This condition increased the eNodeB load. To overcome this problem, the device-to-device (D2D) underlaying communication is introduced. In underlaying scheme, the D2D user equipment (DUE) will do the communication process using the same radio resources with the conventional cellular user equipment (CUE). To avoid a severe interference between these two types of user in the system, a good resource allocation is needed. In this work, a power control scheme using particle swarm optimization (PSO) is proposed, to manage the transmit power on each user on the system. The power control scheme take place after the greedy scheduling algorithm, after all user is given a resource block (RB) to do the communication process. The power transmit for each user is managed to reach a better system capacity, and to reduce the power consumed in one communication process. From the simulation, the PSO power control can improve the sumrate and spectral efficiency up to 12.97% and 3.38% respectively. The PSO power control also can reduce the power consumed by the system up to 8.84%. The fairness happens among the CUEs also can be maintained, despite of the decreasing fairness among DUEs.

A credible spatially explicit historical cropland dataset is the basis for accurately evaluating the impacts of anthropogenic land cover change on global changes. The credibility of the historical cropland datasets relies on both the reconstructed regional cropland area and the allocation algorithm. In the case of adopting the more reliable reconstructed historical cropland area in a given region, the credibility of the gridded cropland cover data mainly depends on the allocation algorithm, which makes a major contribution to the uncertainties of the spatial distribution of the cropland cover. In this study, a new land suitability-based gridded allocation algorithm for historical cropland cover was used to allocate the provincial cropland area into 5' × 5′ grids in China around 1850 C.E. The algorithm was constructed using the dominant physiogeographic factors that vary from region to region, and these factors were assessed by the modern cropland cover. The main conclusions are described as follows: (1) the cultivation intensity under a 5' × 5′ cell can be effectively indicated by the physiogeographic factors in a 0.5° × 0.5° unit, although the degrees of correlation showed a diverse variety with positive, negative correlation or irrelevance relationships in different regions. (2) The land suitability calculated from dominant regional physiogeographic factors could appropriately represent the cultivation intensity. The proportion of positive correlations above 0.7 between the modern cropland fraction and land suitability in China is more than 93%. (3) Employing the newly developed allocation algorithm, the 5' × 5′ cropland fraction data in China around 1850 C.E. was generated from the reconstructed provincial cropland area. The traditional farming regions were maturely cultivated in 1850 C.E., while the distribution of cropland was relatively small and had a lower cultivation intensity in the frontier region (Xinjiang Province and Northeast China, etc.). (4) The new allocation algorithm is expected to be applicable in regions with only very coarse spatial resolution cropland reconstruction results, and the reasonability of gridded allocation results will be improved, especially in regions at the early cultivation stages with very low intensity in China. [ABSTRACT FROM AUTHOR]

Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The computing nodes manage local resources and implement tasks; the resource management center concertedly manage various computing frameworks. We design and implement a hybrid domain resource sharing and allocation algorithm, which allocates the hybrid domain resources to computing frameworks according to the coordinated use of them so as to fairly share the hybrid domain resources among various computing frameworks and prevent the CPU from too many tasks but the GPU or CPU from resource "hunger". The experimental results show that the allocation algorithm can increase the use of heterogeneous resources and the number of completed tasks by around 15%.

The paper presents the results of a study of the possibilities of the optimization task of allocation of 3D models of products in the working space of layered building by additive manufacturing by the Monte Carlo method. The proposed method of allocation of 3D models of products in the workspace using the Monte Carlo method allows reducing the time of technological preparation and increasing the productivity of the process and the efficiency of the installation. The efficiency can be achieved by reducing the relative share of unused workspace, which is relevant for SLA, SLM, SLS, and other methods of additive technologies. A step-by-step definition of free space for the location of the next product is proposed, starting from the installation platform. Increasing the efficiency of the proposed algorithm was provided by forming a set number of placement options with the subsequent selection of the best one according to the optimization criterion. Rational values of parameters of the method of arrangement are determined on the example of group products with complex surface geometry. The effectiveness of the proposed method was evaluated based on a comparative analysis of the number of layers and the filling efficiency of the workspace (relative volume of its use) for manufacturing 3D models of complex industrial products. The study was performed using a specially developed subsystem for the system «Technological preparation of materialization of complex products with additive technologies».

The trustworthiness of software is an important attribute. The cost of software development increases with its improvement by software trustworthiness. As one of main methods of software development, component-based software development can reduce development costs to a certain extent. However, it is important to study how to allocate the given development costs to each component so that software trustworthiness can be optimized. First, multi-value models for allocation of software component development costs are established based on different structures of software system. Second, algorithms for allocation of software component development costs can be designed by using dynamic programming. The proposed allocation algorithms can allocate development costs to each component to optimize software trustworthiness. Furthermore, in order to allocate development costs to each component automatically, a web-based software tool for allocating development costs to each component is developed. Finally, a case study of a self-service ticketing system is provided to show the feasibility of the proposed allocation algorithms.

Due to shortages of medical resources during the Coronavirus Disease 2019 (COVID-19) pandemic, an allocation algorithm for Transcatheter Aortic Valve Replacement (TAVR) was established. We investigated the impact on patient selection and procedural results. In total, 456 TAVR patients before (pre-COVID-19 group) and 456 TAVR patients after (COVID-19 group) the implementation of our allocation algorithm were compared. Concerning patient characteristics, the COVID-19 group revealed a higher rate of cardiac decompensations/cardiogenic shocks (10.5% vs. 1.3%; p < 0.001), severe angina pectoris (Canadian Cardiovascular Society (CCS) II, III and IV: 18.7% vs. 11.8%; p = 0.004), troponin elevation (>14 ng/L: 84.9% vs. 77%; p = 0.003) and reduced left ventricular ejection fraction (LVEF) (p = 0.006). Referring to procedural characteristics, more predilatations (46.3% vs. 35.1%; p = 0.001) and a longer procedural time (80.2 min (+/−29.4) vs. 66.9 min (+/−17.5); p < 0.001) were observed. The success rate was evenly high; no differences in safety parameters were reported. Examining the utilization of hospital resources, the COVID-19 group showed a shorter in-hospital stay (8.4 days (+/−5.9) vs. 9.5 days (+/−9.33); p = 0.041) and fewer TAVR patients were treated per month (39 (+/−4.55) vs. 46.11 (+/−7.57); p = 0.03). Our allocation algorithm supported prioritization of sicker patients with similar efficient and safe TAVR procedures. In-hospital stay could be shortened.

Emerging nonvolatile memory (NVM) techniques, such as phase change memory (PCM), spin-transfer torque magnetic random access memory (STT-MRAM), and resistive random-access memory, are promising for high-performance data process by reserving data in the memory hierarchy. Many persistent memory file systems are tailored to achieve high performance by exploring the advanced features of the NVM and the hardware memory management unit (MMU) in the CPU. However, with the efficient storage device and the hardware acceleration, the write routines in persistent memory file systems pose considerable overhead since repeatedly allocating free blocks and constructing the file mapping structure are time consuming. In this article, we propose a new multigranularity space management scheme (MSMS) to accelerate the write performance. The MSMS employs multigranularity structured blocks whose mapping structure is proactively constructed to slash the overhead of allocating new space and constructing the file mapping structure. Moreover, we present efficiently dedicated space allocation algorithms for different write modes. For append write, we present a file-size- and buffer-size-based allocation (FBA) algorithm to efficiently allocate the appropriate blocks. And for copy-on-write, we present an updating data and offset-based allocation algorithm to preferentially allocate structured huge blocks for reducing the overhead of invoking allocation routines. Based on the new design, we have implemented the MSMS for SIMFS in the Linux kernel. Experimental results show that the MSMS significantly reduces the times of invoking allocation routines. The average append write and copy-on-write performance with the MSMS improve by 16.34 $\%$ and 7.51 $\%$ , respectively. [ABSTRACT FROM AUTHOR]

The allocation algorithm of a file system has a huge impact on almost all aspects of digital forensics, because it determines where data is placed on storage media. Yet there is only basic information available on the allocation algorithm of the currently most widely spread file system; NTFS. We have therefore studied the NTFS allocation algorithm and its behavior empirically. To do that we used two virtual machines running Windows 7 and 10 on NTFS formatted fixed size virtual hard disks, the first being 64 GiB and the latter 1 TiB in size. Files of different sizes were written to disk using two writing strategies and the $Bitmap files were manipulated to emulate file system fragmentation. Our results show that files written as one large block are allocated areas of decreasing size when the files are fragmented. The decrease in size is seen not only within files, but also between them. Hence a file having smaller fragments than another file is written after the file having larger fragments. We also found that a file written as a stream gets the opposite allocation behavior, i. e. its fragments are increasing in size as the file is written. The first allocated unit of a stream written file is always very small and hence easy to identify. The results of the experiment are of importance to the digital forensics field and will help improve the efficiency of for example file carving and timestamp verification. [ABSTRACT FROM AUTHOR]

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