Throughput Level Research Articles

Automated or manual storage systems: do throughput and storage capacity matter?

Selecting the appropriate type of capital-intensive storage systems is an important decision for warehouse managers. However, such a decision is complex due to various available storage systems. In addition, warehouse requirements such as storage capacity and throughput influence this decision. This research provides insights that enable managers to select the suitable type of storage system which minimizes the investment and operational costs while the warehouse design requirements, in particular the storage capacity and throughput, are met. To obtain these insights, an Excel®-based decision support system is developed for a set of most common types of manual and crane-based automated storage systems in pallet and case warehouses. The decision support system uses the closed-form formulas from the warehousing literature and also Monte Carlo simulation to approximate the travel time in each storage system. The results show that the choice of automated or manual storage system and the associated costs depend on the required capacity and throughput. When the storage capacity and throughput are low, the manual pallet racks are the preferred storage system and incur the lowest costs. As the storage capacity and throughput increase, there is a need for more compact storage systems that can store more loads in a smaller footprint. Thus, for medium to high capacity levels, double-deep automated storage systems and deep-lane compact storage systems are the ones with the lowest investment and operational costs. The results for the case warehouses show that the investment and operational costs increase rapidly with an increase of the throughput. In particular, the increase is noticeable for operational costs of the shelf rack system and the investment cost of the miniload system where the storage capacity and throughput level are high.

Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Progressive Multicore RLNC Decoding With Online DAG Scheduling

A complete generation of packets coded with Random Linear Network Coding (RLNC) can be quickly decoded on a multicore system by scheduling the involved matrix block operations in parallel with an offline (pre-recorded) directed acyclic graph (DAG). The waiting for a complete generation of packets can be avoided with progressive RLNC decoding that commences the decoding (and can decode some packets) before all packets in a generation have been received. This article develops and evaluates a novel progressive RLNC decoding strategy based on the principle of DAG scheduling of parallel matrix block operations. The novel strategy involves helper matrices for conducting the Gauss Jordan elimination based on rows of blocks of matrix elements. The matrix block computations are dynamically scheduled by an online DAG which permits branching, e.g., to skip unnecessary matrix block operations. The throughput and delay of the novel progressive RLNC decoding strategy are evaluated with experiments on two heterogeneous multicore processor boards. The novel progressive RLNC decoding achieves throughput levels on par with state-of-the-art non-progressive (full-generation) RLNC decoding and achieves three times higher throughput than the fastest (highest-throughput) known progressive RLNC decoder for small generation sizes and short data packets. Also, our progressive RLNC decoding greatly reduces receiver delays for moderate to large generation sizes; the delay reductions are particularly pronounced when a low-delay RLNC version is employed (e.g., reduction to one tenth of the non-progressive decoding delay for a generation size of 256 packets).

Innovation and Port Logistics: Evidence on How Public Marine Terminals Prompt Unemployment Reductions in the Coast of Bolivar

This study examines whether port terminals have contributed to the socioeconomic conditions of their vicinities in Colombia, particularly at the port city of Cartagena (PCC). Favourable patterns between socioeconomic indicators and cargo throughput were identified as increases in throughput occurred simultaneously with drops in departmental unemployment and monetary poverty. Also, regression analysis demonstrates that innovation is associated to higher cargo throughput, therefore, driving employment indirectly. However, while the relationship between throughput and unemployment was found to be negative, the total effect shown by the model seemed marginal. By analysing data from the logistics supply-chain at the industry level, spillovers on employment confirm the outputs of the regressions. Finally, a cross-sectional analysis using data from other port cities shows that there is potential to improve the benefits of terminals at the PCC, as comparable cities exhibit better socioeconomic conditions with the same level of cargo throughput.

Scalability of Visible-Light-Induced Nickel Negishi Reactions: A Combination of Flow Photochemistry, Use of Solid Reagents, and In-Line NMR Monitoring.

The scale up of light-induced nickel-catalyzed Negishi reactions is reported herein, with output rates reaching multigram quantities per hour. This level of throughput is suitable to support preclinical medicinal chemistry programs in late lead optimization, where tens of grams to hundreds of grams of final product is needed. Adjusting reaction times and concentrations was critical in achieving this robust output. This example demonstrates how visible photochemistry and use of solid metal reagent can be used and how the progress of the reaction can be followed by in-line NMR monitoring.

RETRACTED ARTICLE: An optimization algorithm-based resource allocation for cooperative cognitive radio networks

In cooperative cognitive radio networks (CCRNs), resource allocation can be viewed as a multi-objective optimization issue in terms of channel capacity as well as, among numerous others, the transmitted power, and the QoS limitations. Many researchers have been undertaken to overcome individual problems, not multi-objective problems. In this paper, we investigate multi-objective problems, such as energy consumption, queuing problems, priority levels of traffic classes, fairness, throughput, and user quality requirements. We propose a hybrid optimization algorithm for CCRNs (HCCRN), which enhances the resource allocation. The first contribution of this paper is to propose the load balance enhanced particle swarm optimization algorithm for energy-efficient cluster formation, which overcomes queuing problems. In the second contribution, we consider multiple factors as the input of a multi-factor differential evolution optimization algorithm for prioritizing the traffic levels. The third contribution is that the fair routing path is computed by a modified gravitational search algorithm that enhances resource allocation throughput. For testing purpose, the proposed HCCRN algorithm applied to IEEE 802.11 WLANs. Simulation results show that the users achieve required resources via the proposed HCCRN, thus providing energy efficiency, fairness, throughput, and QoS.

Tool allocation to smooth work-in-process for cycle time reduction and an empirical study

As semiconductor devices are increasingly employed in consumer electronics and industrial applications, cycle time reduction is critical for semiconductor companies to maintain competitive advantages. Semiconductor manufacturing is capital intensive, in which fab capacity is configured by the interchangeable tools that can be allocated for different steps of reentrant processing. Focusing on realistic needs, this study aims to propose a novel approach that integrates data mining approach to forecast arrival rates and determining the allocation of interchangeable tool sets to reduce the work in process (WIP) bubbles for cycle time reduction. In particular, a hybrid approach of decision tree and back-propagation neural network (BPNN) was developed to forecast the arrival rates of individual tool sets and thus predict the WIP levels of individual tool sets. Therefore, the tool allocation decisions can be generated to minimize the total WIP of interchangeable tool sets given the same throughput level. An empirical study was conducted in a leading semiconductor company in Taiwan for validation. The results have shown practical viability of the proposed approach that can effectively reduce WIP and increase capacity utilization in real settings.

New Method to Control Congestion for Machine to Machine Applications in Long Term Evolution System

Humans have always sought to communicate, and over the years they have managed to overcome virtually every obstacle, enabling humanity to communicate from any place on earth mainly through cellular wireless networks. The GSM has been designed for voice transmission; it is the most widely deployed cellular technology in this world. Although Human-to-Human (H2H) communications have been the main reason for the development of communication technologies, including cellular technologies, there has been a rapid growth in the number of machines on the networks in recent years and these machines can communicate with one another, this is called Machine to Machine (M2M) communications. Therefore, Long Term Evolution (LTE), which is supposed to play a central role in M2M communications, is highly optimized for H2H communications. The characteristics of M2M communications create new challenges for H2H communications. In this paper, we propose a method that improves performances at the throughput level as well as to reduce the congestion on the random access channel for M2M communications without affecting H2H communications. To ensure maximum system capacity, communication resources should be allocated to M2M and H2H traffic as required and commonly.

Robust long-read native DNA sequencing using the ONT CsgG Nanopore system.

Background: The ability to obtain long read lengths during DNA sequencing has several potentially important practical applications. Especially long read lengths have been reported using the Nanopore sequencing method, currently commercially available from Oxford Nanopore Technologies (ONT). However, early reports have demonstrated only limited levels of combined throughput and sequence accuracy. Recently, ONT released a new CsgG pore sequencing system as well as a 250b/s translocation chemistry with potential for improvements. Methods: We made use of such components on ONTs miniature 'MinION' device and sequenced native genomic DNA obtained from the near haploid cancer cell line HAP1. Analysis of our data was performed utilising recently described computational tools tailored for nanopore/long-read sequencing outputs, and here we present our key findings. Results: From a single sequencing run, we obtained ~240,000 high-quality mapped reads, comprising a total of ~2.3 billion bases. A mean read length of 9.6kb and an N50 of ~17kb was achieved, while sequences mapped to reference with a mean identity of 85%. Notably, we obtained ~68X coverage of the mitochondrial genome and were able to achieve a mean consensus identity of 99.8% for sequenced mtDNA reads. Conclusions: With improved sequencing chemistries already released and higher-throughput instruments in the pipeline, this early study suggests that ONT CsgG-based sequencing may be a useful option for potential practical long-read applications.

Experimental investigation for performance assessment of scheduling policies in semiconductor wafer fabrication—a simulation approach

This paper is concerned with assessing the impact of 15 existing release policies in combination with three dispatching rules (together called as scheduling policy) on the performance of semiconductor fabrication facilities using a simulation model of a representative but fictitious semiconductor wafer fab. The performance criteria employed includes average cycle time, standard deviation of cycle time, work in process (WIP) level, and throughput. Arena simulation software is used to build the simulation model and conduct the experimental study. The experimental results indicate that workload regulating (WR), constant work in process (CONWIP), and constant load (CONLOAD) policies are superior among tested release policies and shortest remaining processing time (SRPT) is the best dispatching rule. This paper present a new release policy called as constant workload (CONSTWL), which is a modified version of CONWIP policy to overcome the limitations of traditional release policies. CONSTWL policy triggers the release of job into the system based on the overall workload of shop floor. Results on computational experiments indicate that CONSTWL outperforms traditional release policies in terms of the average cycle time, the standard deviation of cycle time and work in process under a prescribed throughput level. The positive impact of CONSTWL policy on system performance appears to be reliable with the increase of system congestion level. CONSTWL policy may prove useful in practical contexts of make to stock manufacturing environment whose orders are met usually from finished inventory.

Network Coding for Physical Layer Secrecy

Physical layer secrecy is highly costly in terms of achievable data rates if perfect secrecy is considered. However, in many practical systems this level of secrecy is not required. In this letter, we introduce a secrecy system based on network coding and physical layer secrecy which is throughput efficient while maintaining a practical level of secrecy. In this system, a set of linear combinations of the original messages are protected with a physical-layer secrecy scheme and another set of linear combinations are freely transmitted over the wireless broadcast channel. This will allow the designer to tune the system for a desired level of secrecy and throughput. We discuss three variants of this coding scheme. The first two use random linear network codes and hence, highly efficient in terms of throughput. The third variant is based on instantly decodable network codes and designed for delay sensitive applications.

Delay-based and QoS-aware packet scheduling for RT and NRT multimedia services in LTE downlink systems

Guaranteeing Quality of Service (QoS) for heterogeneous traffic is a major challenge in the Fourth Generation (4G) mobile networks. Therein, the absence of sophisticated resources allocation process at the base station jeopardizes QoS in terms of latency data transfer. It has been observed from the literature that low delay bounds might be ensured, however, at the expense of other QoS aspects; for example, throughput and data loss. Therefore, in this article, we propose an effective Delay–based and QoS–Aware Scheduling (DQAS) scheme with a low complexity overhead as an efficient solution for the resource allocation issue in LTE Medium Access Control (MAC) layer. The ultimate aim of DQAS is to minimize delay for Real-Time (RT) traffic while still offering a good level of QoS. Complying with QoS of different traffic types, we effectively analyze the queue buffer of each user flow by developing an algorithm called Efficient Delay Control (EDC) that weights each flow priority in terms of delay. Then, this weight is utilized as a principle for the scheduling decision on the attending flows. Furthermore, the Least Delay Increase (LDI) algorithm is developed to tune the scheduler behavior to maintain a balance between delay and system throughput. Simulation results considering different user mobility levels reveal that DQAS significantly guarantees a low end-to-end delay trend that is independent of increased RT load, and moreover, a reasonable throughput and data drop levels compared to other existing schedulers.

Theoretical and experimental study of a membrane-based microfluidics for loading and unloading of cryoprotective agents

Cells are routinely cryopreserved for various applications. Successful cryopreservation requires the loading of cryoprotective agents (CPAs) to prevent cells from cryoinjury during freezing and thawing, as well as the unloading of CPAs before use since CPAs are often toxic. Moreover, the CPA loading/unloading processes are usually effortful and error-prone. In this study, a membrane-based microfluidic device is introduced to aid the CPA loading and unloading processes. The device consists of two helical microfluidic channels with a sandwiched layer of microfiltration membrane. CPA exchange across the membrane via diffusion and filtration is realized when cell suspension and replacement fluid flow counter-currently in the channels. CPA concentration in the cell suspension can be regulated in a well-controlled, progressive manner. A theoretical model has been developed to better characterize the dynamic change and distribution of the CPA concentration, as well as the cell volume response, with respect to various mass transfer parameters within the device during CPA loading and unloading steps. Physical experiments have been conducted to validate the proposed model. Furthermore, operating conditions of the device have been optimized under the guidance of the established model and different devices with various sizes have been fabricated to evaluate their performance characteristics with respect to different levels of throughput. The optimized condition enables the loading of DMSO (from 0% to 10%, v/v) at a rate of 180 ml/h and unloading (from 10% to 1%, v/v) at 5 ml/h with a single pass through the initial prototype. A second enlarged device further improves the DMSO unloading rate to 180 ml/h with the washing solution consumption rate at 225 ml/h. The higher throughput and reduced washing solution requirement demonstrate the significant advancement in efficiency with the presented approach. Lastly, the system is easy to operate and the process is robust, thus provides a reliable solution for loading/unloading of CPAs for various sized cell suspensions.

Design and Digital Implementation of Fast and Recursive DCT II–IV Algorithms

Using the proposed factorizations of discrete cosine transform (DCT) matrices, fast and recursive algorithms are stated. In this paper, signal flow graphs for the n-point DCT II and DCT IV algorithms are introduced. The proposed algorithms yield exactly the same results as with standard DCT algorithms but are faster. The arithmetic complexity and stability of the algorithms are explored, and improvements of these algorithms are compared with previously existing fast and stable DCT algorithms. A parallel hardware computing architecture for the DCT II algorithm is proposed. The computing architecture is first designed, simulated, and prototyped using a 40-nm Xilinx Virtex-6 FPGA and thereafter mapped to custom integrated circuit technology using 0.18- $$\upmu $$ m CMOS standard cells from Austria Micro Systems. The performance trade-off exists between computational precision, chip area, clock speed, and power consumption. This trade-off is explored in both FPGA and custom CMOS implementation spaces. An example FPGA implementation operates at clock frequencies in excess of 230 MHz for several values of system word size leading to real-time throughput levels better than 230 million 16-point DCTs per second. Custom CMOS-based results are subject to synthesis and place-and-route steps of the design flow. Physical silicon fabrication was not conducted due to prohibitive cost.

A 3-dimensional (3D)-printed Template for High Throughput Zebrafish Embryo Arraying.

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related research fields. Thanks to its unique features, including large fecundity, embryo translucency, rapid and simultaneous development, etc., zebrafish embryos are often used for large scale toxicity assessment of chemicals and drug/compound screening. A typical screening procedure involves adult zebrafish spawning, embryos selection, and arraying the embryos into multi-well plates. From there, embryos are subjected to exposure and the toxicity of chemical, or the effectiveness of the drugs/compounds can be evaluated relatively quickly based on phenotypic observations. Among these processes, embryos arraying is one of the most time-consuming and labor-intensive steps that limits the throughput level. In this protocol, we present an innovative approach that makes use of a 3D-printed arraying template coupled with vacuum manipulation to speed up this laborious step. The protocol herein describes the overall design of the arraying template, a detailed experimental setup and step-by-step procedure, followed by representative results. When implemented, this approach should prove beneficial in a variety of research applications using zebrafish embryos as testing subjects.

Letting the faculty deliberate: analyzing online deliberation in academia using a comprehensive approach

ABSTRACTWhile the scholarship on online deliberation has recently witnessed remarkable growth, most studies have focused on different parts of deliberation, thus neglecting other parts of the theorized process. This paper presents a case study of online deliberation in academia using a framework including three analytical parts: a design fostering deliberation (institutional inputs), the quality of the communication process (communicative throughput), and the expected benefits of deliberation (productive outcomes). Each level addressed in the framework is both rooted in deliberative theory and complemented by empirical findings. Applying the framework to a case study on online deliberation about new PhD guidelines in a German science faculty demonstrates that the framework is viable for empirical research. In analyzing 435 comments and an online survey completed by 230 participants, the case study reveals that if deliberative standards at the institutional input level are met there is considerable deliberative quality at the level of communicative throughput, and expected outcomes could thus emerge. This example makes a case for further online deliberation initiatives in similar contexts such as parties or organizations that must decide on important issues or legally binding norms.

Adaptive Packet Combining Scheme in Three State Channel Model

The two popular techniques of packet combining based error correction schemes are: Packet Combining (PC) scheme and Aggressive Packet Combining (APC) scheme. PC scheme and APC scheme have their own merits and demerits; PC scheme has better throughput than APC scheme, but suffers from higher packet error rate than APC scheme. The wireless channel state changes all the time. Because of this random and time varying nature of wireless channel, individual application of SR ARQ scheme, PC scheme and APC scheme can’t give desired levels of throughput. Better throughput can be achieved if appropriate transmission scheme is used based on the condition of channel. Based on this approach, adaptive packet combining scheme has been proposed to achieve better throughput. The proposed scheme adapts to the channel condition to carry out transmission using PC scheme, APC scheme and SR ARQ scheme to achieve better throughput. Experimentally, it was observed that the error correction capability and throughput of the proposed scheme was significantly better than that of SR ARQ scheme, PC scheme and APC scheme.

Signal to Noise Ratio Based Wi-Fi Offloading Decision Algorithm in Vehicular Networks

Nowadays, the rapid increase in mobile data demand is faced by the existing mobile networks. It becomes clear that the existing cellular infrastructure, along with its next generation upgrades is unable to meet the requirements of the users to provide them a required level of throughput for their services. This paper provides an overview of the different standards and requirements in Wi-Fi offloading system. In Het-Nets, multi Radio Access Technology may be operating jointly with the cellular network. This scheme is aimed at evaluating the performance of incorporating Wi-Fi Access Points (APs) along with the cellular network. The decision is based on the selection of best Wi-Fi AP for offloading the data, for which the Signal to Noise Ratio value at the user location is more than the threshold value in vehicular environment. At the end, the numerical results evaluate the performance of presented scheme which justify its application in vehicular networks.

Link Adaptive Power Control and Allocation for Energy–Efficient Downlink Transmissions in LTE Systems

It is axiomatic that providing more transmission power by the cell, returns high data rates; but in contrary, more power is consumed which leads to energy exhaustion. The Quality of Service (QoS) in long term evolution urban macrocell networks gives a high concern to green communication by wisely utilizing the limited cell power to improve network performance. Nevertheless, in conventional schemes, it is observed that the maximum power assigned to the evolved Node B (eNB) is fully utilized each time transmission interval regardless the transmitted amount of data. Consequently, a high level of power dissipation commonly occurs at the eNB that is caused by either an unused allocated power or an excessive subchannel power allocation which is beyond the required portion for data blocks transmission. Therefore, in this paper, we propose an efficient scheme, namely link adaptive power control and allocation (LaPCA) to mitigate the overused transmission power of the cell, and thereby, enhancing system energy efficiency while maintaining a good QoS level. The main principle in LaPCA is to control the portion of cell transmission power to be proportional to the volume of data flows that are nominated for transmission during the scheduling process. This power is then distributed over the allocated subchannels by means of nonconvex optimization to enhance system performance. System-level simulations reveal that LaPCA achieves an outstanding energy efficiency and maintains an increased throughput level and low loss ratio as more traffic load is offered to the network.

Designing a lean storage allocation policy for non-uniform unit loads in a forward-reserve model

PurposeThe contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.Design/methodology/approachThe research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.FindingsThe provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.Practical implicationsWarehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.Originality/valueThe paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.