Throughput Level Research Articles

An Efficient Signal Processing Model for Malicious Signal Identification and Energy Consumption Reduction for Improving Data Transmission Rate

One of the fields which needs the most security is Ad hoc Network (ANET). The term ANET guarantees that there is no central authority so as to administer the signals. Security is a basic issue while using ANET for establishing communication. A ANET is an assortment of remote signals that can progressively be set up at anyplace and whenever without utilizing any prior system framework. Because of its volatile nature, it has mobility issues to improve the arrangement of the system. One of the difficulties is to recognize the malicious signals in the system. Because of malicious signals, data loss or high energy consumption will occur which reduce the overall performance of the ANET. There are a few circumstances when at least one signal in the system become malevolent and will destroy the limit of the system. The point of this work is to recognize the malignant signals quickly to avoid loss of data. The conventional strategy for firewall and encryption isn't adequate to secure the system. In this way a malicious signal identification framework must be added to the ad hoc network. A signal needs to be secured when utilizing the resources and to provide secure communication. The ad hoc networks have several issues like, congestion, overload, data loss and energy consumption. In the proposed work a framework for Rapid Malicious Signal Detection with Energy Consumption Reduction (RMSDwECR) Method is proposed. The proposed method is compared with the traditional methods in terms of load in the network, data loss ratio, signal transmission rate, energy consumption levels, malicious signal identification time and throughput levels. The proposed method exhibits better performance than the traditional methods.

State-of-the-Art Biocatalysis.

The use of enzyme-mediated reactions has transcended ancient food production to the laboratory synthesis of complex molecules. This evolution has been accelerated by developments in sequencing and DNA synthesis technology, bioinformatic and protein engineering tools, and the increasingly interdisciplinary nature of scientific research. Biocatalysis has become an indispensable tool applied in academic and industrial spheres, enabling synthetic strategies that leverage the exquisite selectivity of enzymes to access target molecules. In this Outlook, we outline the technological advances that have led to the field’s current state. Integration of biocatalysis into mainstream synthetic chemistry hinges on increased access to well-characterized enzymes and the permeation of biocatalysis into retrosynthetic logic. Ultimately, we anticipate that biocatalysis is poised to enable the synthesis of increasingly complex molecules at new levels of efficiency and throughput.

Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

A high throughput reporter virus particle microneutralization assay for quantitation of Zika virus neutralizing antibodies in multiple species.

Zika virus is a Flavivirus, transmitted via Aedes mosquitos, that causes a range of symptoms including Zika congenital syndrome. Zika has posed a challenging situation for health, public and economic sectors of affected countries. To quantitate Zika virus neutralizing antibody titers in serum samples, we developed a high throughput plate based Zika virus reporter virus particle (RVP) assay that uses an infective, non-replicating particle encoding Zika virus surface proteins and capsid (CprME) and a reporter gene (Renilla luciferase). This is the first characterization of a Zika virus RVP assay in 384-well format using a Dengue replicon Renilla reporter construct. Serially diluted test sera were incubated with RVPs, followed by incubation with Vero cells. RVPs that have not been neutralized by antibodies in the test sera entered the cells and expressed Renilla luciferase. Quantitative measurements of neutralizing activity were determined using a plate-based assay and commercially available substrate. The principle of limiting the infection to a single round increases the precision of the assay measurements. RVP log10EC50 titers correlated closely with titers determined using a plaque reduction neutralization test (PRNT) (R2>95%). The plate-based Zika virus RVP assay also demonstrated high levels of precision, reproducibility and throughput. The assay employs identical reagents for human, rhesus macaque and mouse serum matrices. Spiking studies indicated that the assay performs equally well in different species, producing comparable titers irrespective of the serum species. The assay is conducted in 384-well plates and can be automated to simultaneously achieve high throughput and high reproducibility.

Wireless Loss Detection over Fairly Shared Heterogeneous Long Fat Networks

The quality of inter-network communication is often detrimentally affected by the large deployment of heterogeneous networks, including Long Fat Networks, as a result of wireless media introduction. Legacy transport protocols assume an independent wired connection to the network. When a loss occurs, the protocol considers it as a congestion loss, decreasing its throughput in order to reduce the network congestion without evaluating a possible channel failure. Distinct wireless transport protocols and their reference metrics are analyzed in order to design a mechanism that improves the Aggressive and Adaptative Transport Protocol (AATP) performance over Heterogeneous Long Fat Networks (HLFNs). In this paper, we present the Enhanced-AATP, which introduces the designed Loss Threshold Decision maker mechanism for the detection of different types of losses in the AATP operation. The degree to which the protocol can maintain throughput levels during channel losses or decrease production while congestion losses occur depends on the evolution of the smooth Jitter Ratio metric value. Moreover, the defined Weighted Fairness index enables the modification of protocol behavior and hence the prioritized fair use of the node’s resources. Different experiments are simulated over a network simulator to demonstrate the operation and performance improvement of the Enhanced-AATP. To conclude, the Enhanced-AATP performance is compared with other modern protocols.

Simulation Model to Determine Ratios between Quay, Yard and Intra-Terminal Transfer Equipment in an Integrated Container Handling System

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

The impact of unequal processing time variability on reliable and unreliable merging line performance

Research on merging lines is expanding as their use grows significantly in the contexts of remanufacturing, reverse logistics and developing economies. This article is the first to study the behavior of unpaced, reliable, and unreliable merging assembly lines that are deliberately unbalanced with respect to their coefficients of variation (CV). Conducting a series of simulation runs with varying line lengths, buffer storage capacities and unbalanced CV patterns delivers intriguing results. For both reliable and unreliable lines, the best pattern for generating higher throughput is found to be a balanced configuration (equal CVs along both parallel lines), except for unreliable lines with a station buffer capacity of six. In that case, the highest throughput results from the descending configuration, i.e. concentrating the variable stations close to the beginning of both parallel lines and the steady stations towards the end of the line. Ordering from the least to most steady station also provides the best average buffer level. By exploring the experimental Pareto Frontier, this study shows the combined performance of unbalanced CV patterns for throughput and average buffer level. Study results suggest that caution should be exercised when assuming equivalent behavior from reliable and unreliable lines, or single serial lines and merging lines, since the relative throughput performance of some CV patterns changed between the different configurations.

Induced homomorphism: Kirchhoff’s law in photonics

Abstract When solving, modeling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospective to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Here, we argue that in absence of a straightforward parallelism a homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoff’s law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. Our approach experimentally demonstrates an arbitrary set of boundary conditions, generating a one-shot discrete solution of a Laplace partial differential equation, with an accuracy above 95% with respect to commercial solvers. Our photonic engine can provide a route to achieve chip-scale, fast (10 s of ps), and integrable reprogrammable accelerators for the next generation hybrid high-performance computing. Summary A photonic integrated platform which can mimic Kirchhoff’s law in photonics is used for approximately solve partial differential equations noniteratively using light, with high throughput and low-energy levels.

Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques

In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction.

Comparison of two adsorbents for simulated-moving-bed separation of galactose and levulinic acid in terms of throughput and desorbent usage

One of the key issues in the production of biofuels from agarose is to establish a highly-efficient process for separation of a target product (galactose) and a side-product (levulinic acid) that come from agarose hydrolysis. Regarding this issue, there has been a previous attempt at the separation of galactose (GA) and levulinic acid (LA) using a simulated-moving-bed (SMB) process based on a size-exclusion adsorbent, which, however, had limitations in increasing solute retention capacities and thus hindered a meaningful improvement in the SMB performances. To resolve such problem, we explored an alternative adsorbent that could compensate for the shortcoming of the previously adopted size-exclusion adsorbent. It was found first that an Amberchrom-CG300C resin could adsorb LA strongly while placing GA migration under the control of a size-exclusion mechanism. The resin was thus proposed as the adsorbent for the considered separation. To compare the proposed and the previous adsorbents in terms of the SMB performances for GA–LA separation, the SMB processes based on the two adsorbents were optimized each to maximize throughput and minimize desorbent usage while keeping the product purities above 99%. The resulting Pareto solutions demonstrated that the SMB based on the proposed adsorbent surpassed the SMB based on the previous adsorbent in throughput and desorbent usage by a wide margin. In addition, the replacement of adsorbent from the previous to the proposed one could lead to a significant reduction in the SMB pressure drop. Finally, it was confirmed that the relative superiority of the proposed adsorbent over the previous one in terms of the SMB performances became greater, as the required level of purity or throughput was elevated.

Pencil Beamforming Increases Human Exposure to ElectroMagnetic Fields: True or False?

According to a very popular belief - very widespread among non-scientific communities - the exploitation of narrow beams, a.k.a. "pencil beamforming", results in a prompt increase of exposure levels radiated by 5G Base Stations (BSs). To face such concern with a scientific approach, in this work we propose a novel localization-enhanced pencil beamforming technique, in which the traffic beams are tuned in accordance with the uncertainty localization levels of User Equipment (UE). Compared to currently deployed beamforming techniques, which generally employ beams of fixed width, we exploit the localization functionality made available by the 5G architecture to synthesize the direction and the width of each pencil beam towards each served UE. We then evaluate the effectiveness of pencil beamforming in terms of ElectroMagnetic Field (EMF) exposure and UE throughput levels over different realistic case-studies. Results, obtained from a publicly released open-source simulator, dispel the myth: the adoption of localization-enhanced pencil beamforming triggers a prompt reduction of exposure w.r.t. other alternative techniques, which include e.g., beams of fixed width and cellular coverage not exploiting beamforming. The EMF reduction is achieved not only for the UE that are served by the pencil beams, but also over the whole territory (including the locations in proximity to the 5G BS). In addition, large throughput levels - adequate for most of 5G services - can be guaranteed when each UE is individually served by one dedicated beam.

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

The efficiency of major container terminals in China: super-efficiency data envelopment analysis approach

PurposeSeaports are a signifier for the world economy and international trade. Notwithstanding the considerable role of Chinese ports in global trade, only few studies have explored the efficiency of Chinese container terminals. Furthermore, studies on Chinese port efficiency has typically centered on port-level analysis, not terminal level. Therefore, this study aims to examine the operation efficiency of Chinese container terminals.Design/methodology/approachThis study uses super-efficiency data envelopment analysis (SE-DEA) approach. SE-DEA is superior than basic DEA model because it is feasible for categorizing and ranking the efficiency of container terminals more accurately and comprehensively. In the basic model, if the several decision-making units (DMUs) are efficient, the efficiency value of them is “1.” However, in the SE-DEA model, the most efficient DMU is over “1.” Based on the level of container throughput in 2018, the top 20 Chinese container terminal companies were selected. Various production quotas were selected as inputs, while the container throughput was considered output.FindingsThe findings show that Terminal Shanghai Mingdong Container Terminal Co., Ltd. was ranked 1, followed by Shanghai Shengdong International Container Terminal Co., Ltd., Shanghai International Port (Group) Co., Ltd. and Yidong Container Terminal Branch.Originality/valueThis study contributes to providing some insights into Chinese container terminal industry to augment the efficiency. This study also provides practical and policy implications (e.g. better terminal operations) for container terminals.

Mitigating Inter-Job Interference via Process-Level Quality-of-Service

Jobs on most high-performance computing (HPC) systems share the network with other concurrently executing jobs. Network sharing leads to contention that can severely degrade performance. This article investigates the use of Quality of Service (QoS) mechanisms to reduce the negative impacts of network contention. QoS allows users to manage resource sharing between network flows and to provide bandwidth guarantees to specific flows. Our results show that careful use of QoS reduces the impact of network contention for specific jobs, resulting in up to a 40% performance improvement. In some cases, it completely eliminates the impact of contention. It achieves these improvements with limited negative impact to other jobs; any job that experiences performance loss typically degrades less than 5%, and often much less. Our approach can help ensure that HPC machines maintain high levels of throughput as per-node compute power continues to increase faster than network bandwidth.

“Pencil Beamforming Increases Human Exposure to ElectroMagnetic Fields”: True or False?

According to a very popular belief - very widespread among non-scientific communities - the exploitation of narrow beams, a.k.a. “pencil beamforming”, results in a prompt increase of exposure levels radiated by 5G Base Stations (BSs). To face such concern with a scientific approach, in this work we propose a novel localization-enhanced pencil beamforming technique, in which the traffic beams are tuned in accordance with the uncertainty localization levels of User Equipment (UE). Compared to currently deployed beamforming techniques, which generally employ beams of fixed width, we exploit the localization functionality made available by the 5G architecture to synthesize the direction and the width of each pencil beam towards each served UE. We then evaluate the effectiveness of pencil beamforming in terms of ElectroMagnetic Field (EMF) exposure and UE throughput levels over different realistic case-studies. Results, obtained from a publicly released open-source simulator, dispel the myth: the adoption of localization-enhanced pencil beamforming triggers a prompt reduction of exposure w.r.t. other alternative techniques, which include e.g., beams of fixed width and cellular coverage not exploiting beamforming. The EMF reduction is achieved not only for the UE that are served by the pencil beams, but also over the whole territory (including the locations in proximity to the 5G BS). In addition, large throughput levels - adequate for most of 5G services - can be guaranteed when each UE is individually served by one dedicated beam.

Category-Based 802.11ax Target Wake Time Solution

IEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and duration based on run time recognition of the Quality of Service (QoS) requirement including the required latency and throughput. Specifically, a Machine Learning (ML) based Network Service Detection (NSD) module is developed which can detect the current network service in real time and update the STA wake interval according to the corresponding service latency requirement. Moreover, a novel data time estimation model is developed which can estimate the required throughput and wake duration with the observed throughput, linkspeed, and contention level. In addition, a state-machine based method is developed to detect three different traffic types (random, stable, bursty) and their throughput variation pattern for further optimization of the STA wake duration. Our solution is implemented and extensively tested on commercial smart mobile platform under various network conditions and use cases. The results show that our ML based NSD could reach 99.2% and 96.5% accuracy respectively for coarse-grain and fine-grain network service classification, which ensures our TWT solution can accurately recognize and satisfy the QoS requirements. More importantly, while maintaining the QoS and user experience, our solution can substantially reduce the Wi-Fi duty cycle to 29.6% on average, which leads to lower device power consumption and network contention level.

Commentary: Improving the cataract surgery efficiency and clearing the cataract surgery backlog due to COVID-19 pandemic.

Commentary: Improving the cataract surgery efficiency and clearing the cataract surgery backlog due to COVID-19 pandemic.

Channel Load Aware AP / Extender Selection in Home WiFi Networks Using IEEE 802.11k/v

Next-generation Home WiFi networks have to step forward in terms of performance. New applications such as on-line games, virtual reality or high quality video contents will further demand higher throughput levels, as well as low latency. Beyond physical (PHY) and medium access control (MAC) improvements, deploying multiple access points (APs) in a given area may significantly contribute to achieve those performance goals by simply improving average coverage and data rates. However, it opens a new challenge: to determine the best AP for each given station (STA). This article studies the achievable performance gains of using secondary APs, also called Extenders, in Home WiFi networks in terms of throughput and delay. To do that, we introduce a centralized, easily implementable channel load aware selection mechanism for WiFi networks that takes full advantage of IEEE 802.11k/v capabilities to collect data from STAs, and distribute association decisions accordingly. These decisions are completely computed in the AP (or, alternatively, in an external network controller) based on an AP selection decision metric that, in addition to RSSI, also takes into account the load of both access and backhaul wireless links for each potential STA-AP/Extender connection. Performance evaluation of the proposed channel load aware AP and Extender selection mechanism has been first conducted in a purpose-built simulator, resulting in an overall improvement of the main analyzed metrics (throughput and delay) and the ability to serve, at least, 35% more traffic while keeping the network uncongested when compared to the traditional RSSI-based WiFi association. This trend was confirmed when the channel load aware mechanism was tested in a real deployment, where STAs were associated to the indicated AP/Extender and total throughput was increased by 77.12%.

Starvation avoidance-based dynamic multichannel access for low priority traffics in 802.11ac communication systems

Using optimally wide channels in 802.11ac networks has been the topic of various research work. Nevertheless, the efficiency of both Static and Dynamic Multichannel Access methods (SMA and DMA) is a challenging issue, since they employ the Enhanced Distributed Channel Access (EDCA) to provide the Quality of Service (QoS). Indeed, EDCA enables QoS through service differentiation between priority (Voice and Video) and non-priority (Besteffort and Background) traffics. Consequently, the concept of priority may severely starve non-priority traffics from channel access, and lead to an unfair bandwidth allocation. In this paper, we highlight the starvation issue of non-priority traffics in 802.11ac networks. Thereby, we propose a Starvation avoidance DMA (SDMA) method by means of 80 and 160 MHz channel bonding. The individual throughput is measured for comparing SDMA and DMA methods. The obtained results show a significant improvement in throughput of non-priority traffics, while maintaining the throughput level of priority traffics.

A New Stage-Wise Control Release Policy for Semiconductor Wafer Fabrication Systems

This article presents a new closed-loop release policy: Stage-Wise Control (SWC) that maintains the overall shop floor's workload distributed into stages at a specified level by controlling the release of new jobs along the production line A simulation model is developed for the well-known Mini Fab and a real-life fab to examine the potential advantage of SWC over traditional release policies in terms of the average cycle time, the standard deviation of cycle time, WIP level, the average number of tardy jobs, and throughput. The simulation results indicate that the SWC efficiently generates high-quality schedules for the average cycle time and the standard deviation of cycle time for a prescribed throughput level. Further, several simulation experiments are carried out in this study to understand the relative performance of SWC in comparison with the existing release policies with different dispatching rules, with different system variability levels caused by machine failure, and with different segmentation method. The performance of SWC is satisfactory in most of the considered scenarios compared to the tested release policies.