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Microplastics have gained importance as pervasive environmental particulate pollutants. Their analysis demands precise quantification methods, with interlaboratory comparisons (ILCs) being crucial for performance assessment. Typically, ILCs follow a parallel design: participants each analyze their own sample specimen, often with significant variability due to challenges in producing identical subsamples of the particulate analyte, inseparably masking the relevant uncertainty sources the ILC intends to measure. We provide a filtration-immobilization approach for particles ≤100 μm, creating permanently immobilized microplastics samples. This enables serial ILC designs where participants sequentially measure the same sample. Demonstrating the concept using 5 polymers immobilized on 10 μm pore-sized silicon filters, we expose the specific measurement uncertainty being 77% lower than the total combined uncertainty observed in a parallel ILC (relative standard deviations: 5 and 23%, respectively). Particle immobilization opens further applications in sample archiving and creation of durable reference samples also for other fields of particulate matter research beyond microplastics.

Nowadays multiple wireless communication systems operate in industrial environments side by side. In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold. Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems (iWCS) this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN (general-purpose WLAN) used for non-real time communication. In this paper, we present a Markov chain-based performance model that described the transmission failure of iWCS due to geographical collision with gWLAN. The presented analyticmodel accurately determines throughput, packet transaction delay, and packet loss probability of iWCS when it is collocated with gWLAN. The results of the Markov model match more than 90% with our simulation results. Furthermore, we proposed an adaptive transmission power control technique for iWCS to overcome the potential interferences caused by the gWLAN transmissions. The simulation results show that the proposed technique significantly improves iWCS performance in terms of throughput, packet transaction, and cycle period reduction. Moreover, it enables the industrial network for the use of delay critical applications in the presence of gWLAN without affecting its performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Violence affecting children (VAC) is a significant global health and human rights issue. This article highlights a new qualitative methodology, the Round Robin, for understanding the drivers of violence against children. Traditionally, qualitative research exploring VAC has focused on identifying the risk and protective factors which affect the likelihood a child will experience or witness violence. In recent years, scholars have recognised the need to situate children in their socio-cultural context and consider what causes risk and protective factors; that is, what drives violence at the structural and institutional levels of society. The Round Robin methodology sits within the participatory paradigm and contributes not only to the field of violence research, but to qualitative research more broadly, as it can be adapted to fit diverse social issues and contexts. The Round Robin combines focus groups and participatory techniques in an intensive three-day workshop model inspired by the World Café. In this paper, we firstly introduce the Round Robin methodology and situate it in relation to other approaches. We then describe and critique how the Round Robin methodology was piloted with 136 young people in Zimbabwe to identify drivers of violence affecting children. We then justify the methods used to collect data, and the strategy for data recording and analysis. We conclude by identifying the strengths and weaknesses we uncovered piloting this new methodology in Zimbabwe. [ABSTRACT FROM AUTHOR]

The modern human life is strongly reliant on real-time systems. The operating system is one of the most significant real-time systems. To meet the demands of the future, operating systems must be upgraded. Nowadays, a lot of systems are used; like mobile applications and internet of things. Task scheduling is so important in the operating system to enhance the performance in these systems. Round Robin algorithm has been widely used in task scheduling. In this paper, a Median Mean Round Robin (MMRR) algorithm is proposed to significantly enhance the performance of the Round Robin algorithm. The proposed algorithm finds an optimal dynamic time quantum +(median+ mean)/2)+ and generated for each cluster depending on the remaining burst time of the processes. The performance has been enhanced in terms of waiting time, turnaround time and context switching. The experimental results show that the proposed algorithm outperforms ADRR, HYRR, EDRR and MARR algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing is a platform that provides many applications based on cloud infrastructure. It provides the facility of using different resources such as data storage, databases, networking, etc. The main problem in the cloud computing environment is task scheduling which plays an important role in optimizing the total execution time. In this paper, a comparison of scheduling algorithms such as First Come First Serve, Round Robin, min–min and max–min is done based on makespan using workflows as datasets. Comparison is done in by increasing the number of virtual machines Workflowsim environment. Experimental results show a decrease in makespan as the number of Virtual Machines is increased. For CyberShake workflow First Come First Serve algorithm has performed 3.69% better than Round Robin, outperformed 13.38% than min–min, and has given 22.68% better results than max–min. In the case of Montage workflow, max–min has performed 26.73% better than First Come First Serve, 17.73% than Round Robin, and has given 4.63% better results than min–min. [ABSTRACT FROM AUTHOR]

Copyright of Al-Rafidain Journal For Sport Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Al-Rafidain Journal For Sport Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multipopulation methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: 1) the exclusion mechanisms to avoid the convergence to the same peak by multiple subpopulations; 2) the resource allocation mechanism that assigns the computational resources to the subpopulations; and 3) the control mechanisms to adaptively adjust the number of subpopulations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e., the distance between their best found positions) between two subpopulations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of subpopulations should be adapted to the number of optima. However, in most existing adaptive multipopulation methods, there is no predefined upper bound for generating subpopulations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this article, a multipopulation framework is proposed to address the aforementioned issues by using three adaptive approaches: 1) subpopulation generation; 2) double-layer exclusion; and 3) computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems. [ABSTRACT FROM AUTHOR]

Many countries worldwide have introduced a limit for solid particles larger than 23 nm for the type approval of vehicles before their circulation in the market. However, for some vehicles, in particular for port fuel injection engines (gasoline and gas engines) a high fraction of particles resides below 23 nm. For this reason, a methodology for counting solid particles larger than 10 nm was developed in the Particle Measurement Programme (PMP) group of the United Nations Economic Commission for Europe (UNECE). There are no studies assessing the reproducibility of the new methodology across different laboratories. In this study we compared the reproducibility of the new 10 nm methodology to the current 23 nm methodology. A light-duty gasoline direct injection vehicle and two reference solid particle number measurement systems were circulated in seven European and two Asian laboratories which were also measuring with their own systems fulfilling the current 23 nm methodology. The hot and cold start emission of the vehicle covered a range of 1 to 15 × 1012 #/km with the ratio of sub-23 nm particles to the >23 nm emissions being 10–50%. In most cases the differences between the three measurement systems were ±10%. In general, the reproducibility of the new methodology was at the same levels (around 14%) as with the current methodology (on average 17%). [ABSTRACT FROM AUTHOR]

Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system. Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things, Fog computing and Mobile based applications. The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm (RR) has been widely used for it. The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling. We have carried out an experimental study where we have developed four variations of RR, each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm, and results highlighted that these variations performed better than conventional RR algorithm. In the future, we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time. This way the task performance of modern-day systems can be improved to make them more efficient. [ABSTRACT FROM AUTHOR]

MANET is standing for Network as Mobile Ad-hoc which is a self-directed mobile handlers group that communicates over relative bandwidth-constrained wireless channels. Many services with different classes of Quality of Services (QoS) could be provided through the MANET such as data, voice, and video streaming. Thus, efficient packets routing is an essential issue especially over this kind of burst channel. To settle this issue, many scheduling techniques are proposed to reduce the packets dropping and channel collision when a huge demand of data is transferred from a sender to a receiver. In this paper, four MANET scheduling algorithms are selected and investigated in mobile ad hoc networks which are Strict Preference (SP), Round Robin (RR), Weighted Round Robin (WRR), and Weighted Fair (WF). The network simulator EXata 2.0.1 is used to build the scenario which is consist of 50 nodes and performed the simulation. The results showed the performance metrics difference of the network such as the throughput and the end-end delay as well as queuing metrics like peak queue size, average queue length, in queue average time, and dropped of whole packets. Regrading throughput, the SP algorithm has a greater throughput than WF, RR, and WRR by 4.5%, 2.4%, and 1.42%, but WRR has outperformed others regarding the end-end delay. Moreover, WRR represents the best scheduling algorithm regarding both peak queue size since its greater than RP, WF, and WRR by 10.13%, 9.6%, and 5.32%, in order, and average output queue length, in contrast, WRR worsts more time in queuing but it is the best in preventing the packets from dropping. [ABSTRACT FROM AUTHOR]

With the increasing growth in the network and latest technologies by which people communicates via voice or data and modifies the radio devices easily and cost effectively. Software defined radio brings the flexibility, power and efficiency including cloud and big data, control and management of the traditional networks has raised the challenges for the development of multimedia applications. Multimedia applications require to handle the large amount of data at the servers which has increased the load on them. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the constraints of Quality of Service (QoS) and Quality of Experience (QoE) with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing. Therefore, many servers can be used with the load balancers which behave as the front end. The present paper aims to reflect impact on the efficiency of the usage of software-defined networks service in various multimedia applications. A genetic load balancing algorithm (GLBA) is proposed and is implemented on POX controller with mininet emulator in python language to compute its effectiveness and efficiency. Validation of GLBA for 100 to 600 users over server load, weighted round robin, round robin, dynamic server and LBBSRT algorithms with parameters, throughput, response time, memory and CPU utilization has proved the significance of proposed algorithm. [ABSTRACT FROM AUTHOR]

Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable Time Slice (ATS). It reduces the time cost by taking the advantage of the low overhead of RR algorithm. In addition, ATS favors short processes allowing them to run longer time than given to long processes. The specific characteristics of each process are; its CPU execution time, weight, time slice, and number of context switches. ATS clusters the processes in groups depending on these characteristics. The traditional RR assigns fixed time slice for each process. On the other hand, dynamic variants of RR assign time slice for each process differs from other processes. The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster. The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes. The datasets used in the comparison vary in their features. The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost. [ABSTRACT FROM AUTHOR]

Self-similarity characteristic of network traffic will lead to the continuous burstness of data in the network.In order to effectively reduce the queue delay and packet loss rate caused by network traffic burst,improve the transmission capacity of different priority services,and guarantee the service quality requirements,a queue scheduling algorithm P-DWRR based on the self-similarity of network traffic was proposed.A dynamic weight allocation method and a service quantum update method based on the self-similar traffic level grading prediction results were designed,and the service order of the queue according was determined to the service priority and queue waiting time,so as to reduce the queuing delay and packet loss rate.The simulation results show that the P-DWRR algorithm can reduce the queueing delay,delay jitter and packet loss rate on the basis of satisfying the different service priority requirements of the network,and its performance is better than that of DWRR and VDWRR.

Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for any wireless network design. The use of web browsing, online gaming, and on-time data exchange like video calls as an example means that users need a high data rate and fewer error communication links. To satisfy this, increasing the bandwidth available for each network will enhance the throughput of the communication, but the bandwidth available is a limited resource which means that thinking about techniques to be used to increase the throughput of the network is very important. One of the techniques used is the spectrum sharing between the available networks, but the problem here is when there is no available channel to connect with. This encourages researchers to think about using scheduling as a technique to serve the high capacity on the network. Studying scheduling techniques depends on the Quality-of-Service (QoS) of the network, so the throughput performance is the metric of this paper. In this paper, an improved Best-CQI scheduling algorithm is proposed to enhance the throughput of the network. The proposed algorithm was compared with three user scheduling algorithms to evaluate the throughput performance which are Round Robin (RR), Proportional Fair (PF), and Best-CQI algorithms. The study is performed under Line-of-Sight (LoS) link at carrier frequency 2.6 GHz to satisfy the Vehicular Long Term Evolution (LTE-V) with the high-speed scenario. The simulation results show that the proposed algorithm outperforms the throughput performance of the other algorithms. [ABSTRACT FROM AUTHOR]

Summary: Nine participants, representing eight different Swedish fire consultancy firms participated in a Round Robin study where two different cases were simulated with the Fire Dynamics Simulator. The first case included a large open warehouse where the activation of a sprinkler system was to be studied. In the second case time to critical conditions in a theatre was to be calculated. The participants were given clear instructions on the building layout and heat release rate for the two cases. Still, the results demonstrate a significant variation in time to sprinkler system activation (range of 110 seconds) and available safe escape time (range of 60 seconds), between the participants. It is important to emphasise that some degree of variation is unavoidable, as engineers can model things differently without the modelling solution necessarily being incorrect. Even though it is hard to isolate and specific cause of the variation, some of the variation seen in this study is related to modelling choices that are questionable and consequently problematic for the reliability of the fire safety design. [ABSTRACT FROM AUTHOR]

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

Passive technologies, including intelligent reflecting surfaces (IRS), are gaining traction thanks to their ability to enhance communication systems while maintaining minimal cost and low complexity. They can assist a wireless sensor network (WSN) by achieving low power requirements for sensors and aid communication needs in many applications, for instance, environmental monitoring. In this paper, we propose an IRS-equipped WSN which describes sensors equipped with IRSs instead of active radio frequency (RF) electronics. The IRS sensor node (ISN) intercepts a dedicated signal from a power source such as a base station (BS) and modulates the transmission of that signal to an intended recipient. In order to enable multiple sensors to transmit to the receiver, we study opportunistic scheduling (OS) utilizing multi-sensor diversity while considering blind IRS operation, and compare it with round-robin (RR), proportional fairness (PF), and a theoretical upper bound. We study the effect of the choice of the number of IRS elements N and number of ISNs L on the average throughput of the system under OS. Finally, we provide pertinent comparisons for the different scheduling schemes and IRS configurations under relevant system performance metrics, highlighting different scenarios in which each scheme performs better.

Fog computing serves the delay-sensitive applications of the Internet of Things (IoT) in more efficient means than the cloud. The heterogeneity of the tasks and the limited fog resources make task scheduling a complicated job. This paper proposes a clustering based task scheduling algorithm. Specifically, the K-Means++ clustering algorithm is used for clustering the fog nodes. Randomized round robin, a task scheduling algorithm is applied to each cluster. The results show that the proposed algorithm reduces the system's average waiting time. [ABSTRACT FROM AUTHOR]

With this external quality assessment (EQA) scheme, we aim to investigate the diagnostic performance of the currently available methods for the detection of ALK alterations in non-small cell lung cancer on a national scale, namely, in situ hybridization (ISH), immunohistochemistry (IHC), and RNA/DNA sequencing (NGS). The EQA scheme cohort consisted of ten specimens, including four ALK positive and six ALK negative samples, which were thoroughly pretested using IHC, ISH, and RNA/DNA NGS. Unstained tumor sections were provided to the 57 participants, and the results were retrieved via an online questionnaire. ISH was used by 29, IHC by 38, and RNA/DNA sequencing by 19 participants. Twenty-eight institutions (97%) passed the ring trial using ISH, 33 (87%) by using IHC, and 18 (95%) by using NGS. The highest sensitivity and interrater agreement (Fleiss ' kappa) was observed for RNA/DNA sequencing (99%, 0.975), followed by ISH (94%, 0.898) and IHC (92%, 0.888). However, the proportion of samples that were not evaluable due to bad tissue quality was also higher for RNA/DNA sequencing (4%) compared with ISH (0.7%) and IHC (0.5%). While all three methods produced reliable results between the different institutions, the highest sensitivity and concordance were observed for RNA/DNA sequencing. These findings encourage the broad implementation of this method in routine diagnostic, although the application might be limited by technical capacity, economical restrictions, and tissue quality of formalin-fixed samples. [ABSTRACT FROM AUTHOR]

PEXI (Persatuan XiangQi Indonesia) Pengprov Jambi merupakan sebuah organisasi persatuan bagi atlet XiangQi khususnya di Provinsi Jambi. Saat ini sistem pertandingan XiangQi di Jambi sudah menggunakan aplikasi manajeman turnamen namun hanya terbatas pada Sistem Pertandingan Swiss. Sehingga jika turnamen diselenggarakan dengan sistem lainnya, yaitu Knockout dan Round Robin, pengelolaannya masih menggunakan metode manual dalam penentuan pairing pertandingan dan perhitungan poin peserta. Karena perhitungan poin masih dilakukan secara manual maka sering terjadi kesalahan dalam perhitungan poin peserta dan sering terjadi kecurangan dalam penentuan pairing pertandingan. Selain itu, interface sistem aplikasi yang telah ada perlu dikembangkan agar lebih user-friendly. Oleh karena itu, kita membutuhkan pengembangan sistem aplikasi dengan fitur sistem pertandingan yang lebih lengkap dan lebih user-friendly untuk digunakan.

This paper presents a ray tracing algorithm developed as a research and teaching tool. The motivations to pursue this task and some novel features of the algorithm are presented. Amongst them, it is possible to cite: (i) the receivers may grow in size, which saves some computational cost; (ii) sound intensity calculations are performed in a separate step than geometrical ray tracing; and (iii) those features allow Monte Carlo simulations for uncertainty prediction related to absorption data. The results obtained with the proposed algorithm are compared with the measured data (and other software packages) of Round Robins II (Elmia Hall) and III (PTB recording studio) and proved to be in good agreement with measured data. The ratio of the mean error by the JND of each parameter are compatible with the results presented by the other round robin participants. The product of this research is also scalable to an hybrid algorithm and alternatives to do so are provided through the discussion in the paper. [ABSTRACT FROM AUTHOR]

Load balance is an important issue in datacenter networks. The flowlet-based algorithms can balance the traffic with fine granularity and does not suffer the packet mis-sequencing problem. But their performances are rather limited or require extra communication overhead. In this paper, we propose a local load-aware algorithm called Dynamic Roulette Wheel (DRW). In DRW, the roulette wheel is adopted to select a new path for the flowlet according to the local load. Each source of multipath balances the traffic to all its egress links without the communication overhead. Moreover, the granularity of flowlet can be dynamically tuned from a single packet to the whole flow. Finally, the Capacity Aggregation (CA) mechanism is designed for the case of link or switch failure. We prove in theory that DRW can achieve the optimal global load balancing. The simulation results also show that DRW provides almost the best delay performance and the least packet out-of-order proportion overall among all existing flowlet switching algorithms. [ABSTRACT FROM AUTHOR]