Pair Of Problems Research Articles

Minimization of nth Order Rate Matching in Satellite Networks with One to Many Pairings

This paper studies the minimization of nth (positive integer) order rate matching in high-throughput multi-beam satellite systems, based on one-to-many capacity allocation pairings, for the first time in the literature. The offered and requested capacities of gateways and users’ beams are exploited, respectively. Due to the high complexity of the binary optimization problem, its solution is approached with a two-step heuristic scheme. Firstly, the corresponding continuous, in [0, 1], pairing problem is solved applying the difference of convex optimization theory, and then, a transformation from continuous to binary feasible allocation is provided to extract the pairings among gateways and users’ beams. Comparing with the exponential-time optimal exhaustive mechanism that investigates all possible pairs to extract the best matching for minimizing the rate matching, extended simulations show that the presented approximation for the solution of the non-convex optimization problem has fast convergence and achieves a generally low relative error for lower value of n. Finally, the simulation results show the importance of n in the examined problem. Specifically, pairings originated by the minimization of rate matching with larger n result in more fair rate matching among users’ beams, which is a valuable result for satellite and generally wireless systems operators.

Superposition-Based URLLC Traffic Scheduling in 5G and Beyond Wireless Networks

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

A Fuzzy Crew Rostering Model Based on Crew Preferences and Seniorities considering Training Courses: A Robust Optimization Approach

Crew scheduling problem is divided into crew pairing problem (CPP) and crew rostering problem (CRP). In this paper, a rostering model is presented to assign crew to pairings in such a way that total weighted preference is maximized. Crew members declare which parings they wish to be assigned and which ones are undesirable for them. A score is calculated in the objective function if a crew member is assigned to his/her preferred pairing, and a penalty is considered if he/she is assigned to an undesirable pairing. Moreover, crew seniorities are considered in calculating total preference. In addition, the model considers standard rules and regulations as well as crew attendance at the required training courses. The model is formulated in such a way that inconsistent crew members are not assigned to a flight. Due to the uncertainty in determining of the seniority weight, this parameter is considered as fuzzy. At the end, the robust counterpart of the nominal model is developed due to the uncertainty of time away from the base (TAFB). In this research, the issue of inconsistent crew in rostering problem is considered for the first time. Moreover, a new scoring mechanism is introduced to calculate desirable and undesirable assignments in the objective function. The proposed CRP is solved using the genetic algorithm (GA), and its performance is verified in comparison with GAMS in some test problems. On average, the optimality gap in GA is only 0.5 percent. Finally, the proposed model is examined with real-world data from Air India Airline. In comparison with the previous research studies, the suggested model (scoring mechanism) reduced the number of undesirable rosters by 61.59%.

Chemistry Learning Using Multiple Representations: A Systematic Literature Review

The abstractness of the chemistry concept can be understood easily through chemistry learning using multiple representations. This article used the Systematic Literature Review (SLR) method to review eleven articles published from 2012 to 2021 and focused on chemistry learning using various representations. The articles are systematically obtained from the online article database ERIC, Scopus, and SINTA. The purpose of a review is to give information to teachers and researchers in chemistry education about the definition of multiple representations, the influence of multiple representations on chemistry learning outcomes, and how to implement various representations in chemistry learning models or strategies. The review results showed that the definition of numerous representations referred to both three levels of chemical representation and the tetrahedral representation of chemistry. Also, it referred to the use of various media. The influence of multiple representations on chemistry learning outcomes included improving concept understanding, improving performance, reducing mental effort, improving self-efficacy, making better cognitive structures, improving mental models, and reducing misconceptions. Multiple representations have also been implemented in several learning models or strategies such as Inquiry, Inquiry 5E, Guided Inquiry, Problem Solving, Thinking, Aloud Pair Problem Solving (TAPPS), Problem Posing (PP), Cognitive Dissonance, and Multiple Representation Based Learning (MRL).

A macroscopic model for immiscible two-phase flow in porous media

This work provides the derivation of a closed macroscopic model for immiscible two-phase, incompressible, Newtonian and isothermal creeping steady flow in a rigid and homogeneous porous medium without considering three-phase contact. The mass and momentum upscaled equations are obtained from the pore-scale Stokes equations, adopting a two-domain approach where the two fluid phases are separated by an interface. The average mass equations result from using the classical volume averaging method. A Green's formula and the adjoint Green's function velocity pair problems are used to obtain the pore-scale velocity solutions that are averaged to obtain the upscaled momentum balance equations. The macroscopic model is based on the assumptions of scale separation and the existence of a periodic representative elementary volume allowing a local description as usually postulated for upscaling. The macroscopic momentum equation in each phase includes the generalized Darcy-like dominant and viscous coupling terms and, importantly, an additional compensation term that accounts for surface tension effects to momentum transfer that is, otherwise, incompletely captured by the Darcy terms. This interfacial term, as well as the dominant and viscous coupling permeability tensors, can be predicted from the solutions of two associated closure problems that coincide with those reported in the literature. The relevance of the compensation term and the upscaled model validity are assessed by comparisons with direct numerical simulations in a model two-dimensional periodic structure. Upscaled model predictions are found to be in excellent agreement with direct numerical simulations.

Pairs of equiperimeter and equiareal triangles whose sides are perfect squares

In this paper we consider the problem of finding pairs of triangles whose sides are perfect squares of integers, and which have a common perimeter and common area. We find two such pairs of triangles, and prove that there exist infinitely many pairs of triangles with the specified properties.

Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity

The cabin crew pairing problem is one of the major challenges faced by airlines. Traditionally, multi-class cabin crews are scheduled on a team basis separated by aircraft types (families) as cockpit crews. However, the manpower requirements for cabin crew across aircraft types (families) are heterogeneous, which cannot be handled by the team scheduling approach. Thus, some airlines nowadays are adopting the individual scheduling approach to deal with the flight manpower requirement heterogeneity. Motivated by the emergence of the individual cabin crew pairing practice, we conduct an analytical study which aims at improving manpower utilization while reducing costs by utilizing a new individual cabin crew pairing generation approach. We also mathematically formulate crew substitution in the model which can help hedge against manpower requirement variations. The impacts of the relationship between manpower availability with requirement benchmarks on cabin crew scheduling strategies are investigated to derive deep insights. A column generation based solution approach is developed. Computational experiments based on small-scale real-world collected flight schedules are conducted to test the distinctive features of the proposed individual pairing model. In addition, a series of large hypothetical instances are employed to examine the advantages of the proposed model over the traditional team-based model in terms of manpower utilization improvement and cost reduction through a customized Genetic Algorithm.

Analysis of Students' Mathematical Problem-Solving Ability and Self-Efficacy Through Application of The Think Aloud Pair Problem Solving (TAPPS) Learning Model for Al Manar Private Junior High School Students

This study aims to: 1) determine students' mathematical problem solving abilities and self efficacy after being taught by applying the think aloud pair problem solving (tapps) learning model to students of Al Manar Private Junior High School; 2) Knowing the students' mathematical problem solving ability is assessed from students' self efficacy after being taught by applying the think aloud pair problem solving (tapps) learning model to Al Manar Private Junior High School students; 3) Knowing the difficulties experienced by Al Manar Private Junior High School students in completing the mathematical problem solving ability test which is taught by applying the think aloud pair problem solving learning model (tapps). The type of research used in this research is descriptive qualitative research. This research was carried out in March 2022 in the even semester of the 2021/2022 academic year class VII Al Manar Private Junior High School. The results showed that: 1) The level of mathematical problem solving ability of students with medium and high abilities had the highest proportion, followed by students with low abilities; 2) Students who have high mathematical self-efficacy have high category problem solving abilities as well. Students who have moderate self-efficacy have moderate category problem solving abilities as well. And students who have low self-efficacy have low category problem solving abilities; 3) Difficulty In the high category, students do not experience significant difficulties. In the medium category, students have difficulty understanding concepts so that students experience confusion when solving problems. And in the low category, students have difficulty in all indicators of problem solving ability. Keywords: Mathematical Problem Solving Ability, Self-Efficacy, Think Aloud Pair Problem Solving. DOI: 10.7176/JEP/13-16-03 Publication date: June 30 th 2022

Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks

The motivations for deploying energy and spectral-efficient network architectures are the high energy consumption and the need for more spectral resources in modern cellular networks. The key method to solve the energy efficiency EE maximization problem of the downlink non-orthogonal multiple access (NOMA)-based massive MIMO system is to decouple it into user pairing and efficient power allocation problems. This work studies the performance of three main pairing methods in NOMA-based networks: Hungarian, Gale–Shapley, and correlation-based approaches. Firstly, we provide a mathematical analysis for EE of downlink NOMA in a massive MIMO system for the non-line of sight (NLoS) channel model with perfect successive interference cancellation (SIC). Finally, the sequential convex programming (SCP) approach is used to tackle the power allocation problem. Simulation results show that the Hungarian algorithm for pairing plus SCP for power allocation (Hungarian algorithm-SCP) achieves the highest energy efficiency among all the three pairing algorithms with an identical performance to joint user and resource block association with power allocation (joint user-RB PA) algorithm but with much lower computational complexity and outperforms the NOMA SCP greedy algorithm (NOMA-SCP-GA).

Subcarrier‐pair based joint resource allocation for secure cooperative OFDM system with multiple untrusted AF relays

In this paper, a cooperative orthogonal frequency division multiplexing system with multiple untrusted amplify-and-forward relays is considered. Since there exists no direct link between source and user because of the shadowing effect, a positive system secrecy rate cannot be obtained. Addressing this issue, a cooperative user-aided jamming technique to improve the secrecy performance is employed. With the aim of maximising the system secrecy rate, a joint resource allocation problem of power allocation, relay assignment and subcarrier pairing is formulated. By using the signal-to-noise ratio based approach and the Lagrange dual method, this nonconvex problem is solved efficiently. To reduce the calculation complexity, a suboptimal algorithm, which decouples the power allocation with the relay assignment and subcarrier pairing is further proposed. Numerical results are presented to demonstrate the advantage of the two proposed algorithms and the impact of relays' location and number of subcarriers on the secrecy performance. Moreover, it is shown that the secrecy performance of the proposed scheme is decreased with respect to the number of untrusted relays.

Integral Column Generation for Set Partitioning Problems with Side Constraints

The integral column generation algorithm (ICG) was recently introduced to solve set partitioning problems involving a very large number of variables. This primal algorithm generates a sequence of integer solutions with decreasing costs, leading to an optimal or near-optimal solution. ICG combines the well-known column generation algorithm and a primal algorithm called the integral simplex using decomposition algorithm (ISUD). In this paper, we develop a generalized version of ICG, denoted I2CG, that can solve efficiently large-scale set partitioning problems with side constraints. This new algorithm can handle the side constraints in the reduced problem of ISUD, in its complementary problem, or in both components. Computational experiments on instances of the airline crew pairing problem (CPP) and the multidepot vehicle routing problem with time windows show that the latter strategy is the most efficient one and I2CG significantly outperforms basic variants of two popular column generation heuristics, namely, a restricted master heuristic and a diving heuristic. For the largest tested CPP instance with 1,761 constraints, I2CG can produce in less than one hour of computational time more than 500 integer solutions leading to an optimal or near-optimal solution. Summary of Contribution: In this paper, we develop a new integral column generation algorithm that can solve efficiently large-scale set partitioning problems with side constraints. The latter alter the quasi-integrality property needed for primal integral algorithms. The paper adds a methodological contribution remedying this issue. This remedy should, in our opinion, boost the use of primal exact methods, especially in the column generation context. The paper also has a computational contribution. Effectively, computational experiments on instances of the airline crew pairing problem and the multidepot vehicle routing problem with time windows are extensively discussed. We compare the proposed algorithm to basic variants of two popular column generation heuristics.

Joint Resource Allocation in Secure OFDM Two-Way Untrusted Relay System.

The security issue of wireless communication is a common concern because of its broadcast nature, especially when the relay becomes an eavesdropper. In the orthogonal frequency division multiplexing (OFDM) relay system, when the relay is untrusted, the security of the system faces serious threats. Although there exist some resource allocation schemes in a single-carrier system with untrusted relaying, it is difficult to apply them to the multi-carrier system. Hence, a resource allocation scheme for the multi-carrier system is needed. Compared to the one-way relay system, a two-way relay system can improve the data transmission efficiency. In this paper, we consider joint secure resource allocation for a two-way cooperative OFDM system with an untrusted relay. The joint resource allocation problem of power allocation and subcarrier pairing is formulated to maximize the sum secrecy rate of the system under individual power constraints. To solve the non-convex problem efficiently, we propose an algorithm based on the alternative optimization method. The proposed algorithm is evaluated by simulation results and compared with the benchmarks in the literature. According to the numerical results, in a high signal-to-noise ratio (SNR) scenario, the proposed algorithm improves the achievable sum secrecy rate of the system by more than 15% over conventional algorithms.

Signaling-Based Incentive Mechanism for D2D Computation Offloading

Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this paper, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.

Performance Analysis and Optimization of LDM-Based Layered Multicast

The explosion of mobile communication devices and diversification of emerging media content put forward higher requirements for mobile communication networks in various aspects such as overall throughput, fairness and user experience. Fortunately, non-orthogonal multiplexing (NOM) techniques such as layered-division-multiplexing (LDM) and the flexible compatibility of physical layer design in fifth generation mobile networks (5G) open up the possibility to tackle the challenges. In this paper, we propose to use LDM-based layered multicast to improve network performance, where the multicast content is delivered with differentiated quality in different LDM signal layers. For various performance optimization purposes such as maximum throughput, proportional fairness, minimum dissatisfaction index, and maximum service satisfaction index, a unified analysis framework is developed. Under this framework, we formulate a joint optimization problem of layer-user pairing and power allocation. In order to solve this problem, an optimization algorithm based on subproblem decomposition is proposed, which arranges each multicast subscriber of the multicast content to receive the most suitable LDM layer, and finds the optimal transmit power and data rate for each layer. Simulation results demonstrate the superiority of LDM-based layered multicast over existing multicast approaches while guaranteeing the coverage and the lower limit of user experience.

Pengaruh Penerapan Model Pembelajaran Thinking Aloud Pair Problem Solving (TAPPS) terhadap Kemampuan Pemecahan Masalah Matematika

This study aims to determine the effect of the Thinking Aloud Pair Problem Solving (TAPPS) learning model on the mathematical problem solving abilities of eighth grade students of SMP. This research is a quasi-experimental research with Non-Equivalent Control Group Design. The population in this study were all eighth grade students of SMP. In determining the sample, the saturated sample technique was used, where the samples in this study were class VIII-A as the control class and class VIII-B as the experimental class. The instrument used in this research is a test of mathematical problem solving ability on number pattern material. Quantitative data analysis was performed using a gain t-test. The results of data analysis obtained thitung = 2.14 and ttable = 2.064 so that thitung > ttable then H0 is rejected which indicates that there are differences in students' mathematical problem solving abilities between classes that receive TAPPS learning and direct learning. This shows that learning with the TAPPS model has an effect on the mathematical problem solving abilities of eighth grade students of SMP. The influence of the application of the Thinking Aloud Pair Problem Solving (TAPPS) learning model on the mathematical problem solving ability of class VIII students at SMP on the number pattern material using the effect size formula (d) is 1.907, which means that the effect given has a high category.

An iterative approach for the exact solution of the pairing Hamiltonian

A new iterative algorithm is established for the exact solution of the standard pairing problem, based on the Richardson-Gaudin method using the polynomial approach. It provides efficient and robust solutions for both spherical and deformed systems at a large scale. The key to its success is that the initial guess for the solutions of such a large set of the non-linear equations is provided in a physically meaningful and controllable manner. Moreover, one reduces the large-dimensional problem to a one-dimensional Monte Carlo sampling procedure, which improves the algorithm's efficiency and avoids the non-solutions and numerical instabilities that persist in most existing approaches. We calculated the ground state and low-lying excited states of equally spaced systems at different pairing strengths G. We then applied the model to study the quantum phase transitional Sm isotopes and the actinide nuclei Pu isotopes, where an excellent agreement with experimental data is obtained. Program summaryProgram Title: IterV1.mCPC Library link to program files:https://doi.org/10.17632/rjnbhgk2p6.1Licensing provisions: GPLv3Programming language: MathematicaNature of problem: The program calculates exact pairing energies based on a new iterative algorithm. The key is the procedure of determining the initial guesses for the large-set non-linear equations involved in a controllable and physically motivated manner. It provides an efficient and robust solver for both spherical and deformed systems in super large model spaces.Solution method: The new iterative algorithm approach starts with simple systems with k nucleon pairs and n=k levels, which can be solved iteratively by including one pair and one level at each step using the Newton-Raphson algorithm with a Monte Carlo sampling procedure. Then it takes the solutions of those systems as initial values and obtain the converged results for the full space by gradually adding the remaining levels. In this way, one reduces the k-dimensional Monte Carlo sampling procedure to a one-dimensional sampling, which improves the efficiency of the algorithm and avoids the non-solutions and numerical instabilities.

Deviated Direction of Arrival Method for Radial Velocity Measurement based on Spatial Spectrum Estimation

In this paper, a new scheme to measure radial velocity based on direction of arrival (DOA) estimation with multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm usual applications in Uniforms Linear Arrays (ULA) don’t enable velocity estimation because the array signals in one snapshot are synchronous and the radial motion does not affect the steering vector. In the proposed scheme, signal snapshot is rearranged so that each vector now contains a sequence of signals from adjacent elements delayed in time, this way the steering vector input to the MUSIC algorithm includes both information of the DOA and radial velocity. An equation describing the relationship of the deviated DOA (dDOA) from the rearranged snapshot with the radial velocity and the DOA is presented. For multi-source situation, pairing problem of DOAs and dDOAs is solved by extracting the combination factors of the steering vectors and the eigenvectors in comparison between the two MUSIC implementations. Comparison with the traditional FFT method shows that a higher accuracy of velocity can be achieved by the proposed method.

The Role of Using Thinking Aloud Pairs Problem Solving (TAPPS) Strategy in Teaching Reading Comprehension to EFL Preparatory School Students

Thinking Aloud Pairs Problem Solving has been used as a strategy in teaching reading comprehension. Students should read, re-read silently, think aloud, verbalize what they think, and discuss and share information (work in pairs) to solve problems that are related to the reading passage .This strategy helps students to evaluate their reading which leads them to produce a better comprehension of the text. These aims are supposed to be achieved as follows:
 
 Finding out the role of using (TAPPS) strategy on EFL preparatory school students' achievement in reading comprehension.
 Finding out if there is any significant difference between the experimental group's achievement at the recognition and production levels.

BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data.

BackgroundSimple Sequence Repeats (SSRs) are short tandem repeats of nucleotide sequences. It has been shown that SSRs are associated with human diseases and are of medical relevance. Accordingly, a variety of computational methods have been proposed to mine SSRs from genomes. Conventional methods rely on a high-quality complete genome to identify SSRs. However, the sequenced genome often misses several highly repetitive regions. Moreover, many non-model species have no entire genomes. With the recent advances of next-generation sequencing (NGS) techniques, large-scale sequence reads for any species can be rapidly generated using NGS. In this context, a number of methods have been proposed to identify thousands of SSR loci within large amounts of reads for non-model species. While the most commonly used NGS platforms (e.g., Illumina platform) on the market generally provide short paired-end reads, merging overlapping paired-end reads has become a common way prior to the identification of SSR loci. This has posed a big data analysis challenge for traditional stand-alone tools to merge short read pairs and identify SSRs from large-scale data.ResultsIn this study, we present a new Hadoop-based software program, termed BigFiRSt, to address this problem using cutting-edge big data technology. BigFiRSt consists of two major modules, BigFLASH and BigPERF, implemented based on two state-of-the-art stand-alone tools, FLASH and PERF, respectively. BigFLASH and BigPERF address the problem of merging short read pairs and mining SSRs in the big data manner, respectively. Comprehensive benchmarking experiments show that BigFiRSt can dramatically reduce the execution times of fast read pairs merging and SSRs mining from very large-scale DNA sequence data.ConclusionsThe excellent performance of BigFiRSt mainly resorts to the Big Data Hadoop technology to merge read pairs and mine SSRs in parallel and distributed computing on clusters. We anticipate BigFiRSt will be a valuable tool in the coming biological Big Data era.

Robust MIMO Controller Design for VSC-Based Microgrids: Sequential Loop Closing Concept and Quantitative Feedback Theory

Like other multi-input multi-output (MIMO) systems, adverse effects of interactions among control loops on the system performance is one of the most challenging issues in the control of microgrids (MGs). In this paper, a robust MIMO controller is proposed for the MGs to solve the problem of power sharing among its distributed generators systematically. To this end, the relative gain array (RGA) matrix is used to address the problem of input-output pairing, and adverse effects of interactions among the control loops are decreased by designing proper pre-compensators. The sequential loop closing technique is employed, and the quantitative feedback theory (QFT) is used to achieve robust stability, the robust tracking performance, and the desired level of disturbance attenuation for all the control loops. The simulation and experimental results reveal the desirable and acceptable performance of the proposed control strategy.