Target Throughput Research Articles

Exploring the interplay of costs and flow time in server allocation for flow lines with parallel non-identical machines

ABSTRACT The Server Allocation Problem is a significant challenge in manufacturing systems involving deciding the number and version of machines for each stage. This optimization problem is of utmost importance when there is a trade-off between minimizing investment costs and flow time while achieving target performance, such as a minimum throughput. Manufacturing systems often employ hybrid flow lines, with non-identical machines at each stage. This paper analyzes the trade-off between minimizing costs and flow time when allocating servers in hybrid flow lines by proposing a bi-criteria approach that uses an efficient pattern-based problem representation and a Variable Neighborhood Search solution algorithm. The objectives are to minimize total cost and flow time while ensuring a target throughput. A metric for comparing obtained solutions is proposed, providing insights for practical implementation. A comprehensive numerical analysis is conducted, to validate the proposed approach and demonstrate its applicability in real-world manufacturing settings.

A selection procedure for the design of mixed-model assembly systems considering walking workers and fixed workers

Mixed-model assembly systems are widely used to guarantee higher flexibility, variety and throughput. In such assembly systems, fixed worker (FW) and walking worker (WW) workforce strategies are mainly adopted, influencing tactical and operational decisions with different and sometimes contrasting strengths and weaknesses. Thus, several aspects require a joint investigation to evaluate in which context a WW workforce strategy is preferable to the FW one. In this paper, the effect of assembly and station time variability on WW systems performance is investigated and compared to FW systems through a simulative study. Further, the number of workers and workstations and the effect of the travel time are also analysed. Results show that time variability has an impact on the throughput of both systems, in a different way. Starting from the target throughput and the given time variability among tasks, results derived from data elaboration allow us to propose a decision tree and a selection procedure for choosing between FW and WW systems, highlighting their most proper application conditions. According to the results, the time variability among tasks and the ratio between the number of workers and workstations are the two main drivers influencing the choice between WW and FW strategies.

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System.

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.

Abstract 3798: Advancements in multiplexed spatial phenotyping

Abstract Spatial omics is an expanding research area focused on integrating spatial knowledge of tissue with transcriptomics (RNA) and proteomics (protein). Understanding the complexity of the biological structure(s) is an important biological process in cancer immunotherapy research which requires accurate target classification. Translational profiling of 4+ targets on a single sample at one time can be difficult in many ways, such as, panel design, staining protocols, and data analysis. Furthermore, to design a reliable multi-target biomarker panel, there are many parts to overcome, such as protein abundance and localization, fluorophore compatibility, varying tissue types, and data characterization, to name a few. To address the needs of high target multiplex ability, labeling methods have advanced in cyclic detection enabling iterative labeling and detection using automated fluidics, however these approaches suffer from low simultaneous target throughput. Thermo Fisher Scientific can now provide a streamline process for tissue labelling; a process that enables sufficient labelling of high multiplexed panels completed within a couple of hours. We show successful detection of a wide range of various protein markers across numerous tissue organs used in various biological research applications with our uniform technique of labelling. Citation Format: Leticia Montoya, Scott Clarke, Austin Harvey, Jen Serafin, Mae Voeun. Advancements in multiplexed spatial phenotyping [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3798.

Order consolidation in warehouses: The loop sorter scheduling problem

To meet today’s ambitious order throughput targets, many distribution centers, especially those operated by online retailers, apply batching and zoning in their picker-to-parts warehouses. These order retrieval policies improve the pick density per tour by unifying multiple customer orders to larger pick lists and allow a parallelization of the picking process among multiple zones, respectively. The price for this is an additional consolidation stage, where picked products must be sorted according to customer orders, typically with the help of a sortation conveyor. In this context, we treat the loop sorter scheduling problem, which is defined as follows. Once a wave of orders, picked concurrently in multiple zones, has been inducted onto a closed-loop sorter, we have to assign the products that refer to the same stock keeping units (SKUs) to orders and orders to packing lanes, where they are prepared for shipping. Furthermore, we have to decide on the sequence in which the orders are channeled into their packing lanes. Our aim is to minimize the makespan until all orders of the current wave are readily sorted. We formulate the loop sorter scheduling problem, investigate computational complexity, and derive suitable solution algorithms. One important finding of our computational study is that simple priority rules, which are frequently applied in real-world warehouses and previous research, waste significant optimization potential.

Adapting virus filtration to continuous processing: Effects of product and process variability on filtration performance.

Virus filtration (VF) is an important unit operation in the manufacture of biotherapeutics that provides robust removal of potential virus contaminants. Small virus removal can be impacted by the low operating pressures and potential depressurization events that are often associated with continuous operations where increased operational flexibility for higher loading at low flux and low pressure is required. In this study, we evaluated the impact of low flux (7 LMH) and pressure interruptions on minute virus of mice (MVM) removal. We used long-term filtrations conducted to a target throughput of 1000 L/m2 with four different monoclonal antibodies on small-scale hollow fiber virus filters with a hydrophilic modified polyvinylidene fluoride membrane. These conditions are certainly challenging for any VF operation and ensuring robust viral clearance under such conditions is critical to the design and implementation of continuous VF. Planova BioEX filters effectively removed MVM at 4 log or greater when run continuously for up to 6 days. Interestingly, pressure increases associated with filter fouling over the duration of long-term filtrations were shown to be reflective of load material variability and could be remediated by implementation of an inline prefilter. Pressure interruptions had minimal impact on overall MVM logarithmic reduction value. Effective virus removal was achieved with pressure increases being largely product-specific, which demonstrates the capability of the virus filter to remove virus independent of pressure increases that are expected to occur with increased protein load.

Abstract C048: Inducible activation of sgRNA libraries in tumor xenografts empowers large-scale in vivo target discovery screens

Abstract CRISPR-mediated genome editing is a powerful approach to understanding disease biology, including identifying genes essential for cancer cell proliferation, immune evasion and survival. We routinely use the technology to identify novel therapeutic targets for cancer using both in vitro and in vivo tumor models, focusing on targets that are synthetic lethal with tumor suppressor gene loss. Large-scale CRISPR screens are often conducted using in vitro cell culture systems as in vivo ‘drop-out’ screens face several bottlenecks resulting in poor sgRNA library representation and low signal to noise ratio. These inherent limitations of in vivo screens occur because only a small fraction of the injected cells contribute to xenograft tumor formation, and because of the uneven clonal expansion due to heterogenous growth conditions in the tumor microenvironment. Hence, many in vivo screens are underpowered for statistical analysis, resulting in a high rate of ‘false negatives’ or ‘false positives’ unless a large number of animals are used or the size of the sgRNA library is greatly reduced to overcome sampling noise. Here, we validate a novel in vivo screening technology, CRISPR-StAR (Stochastic Activation by Recombination), that overcomes these challenges by (i) activating the sgRNA library in established tumors and (ii) generating internally matched-pair controls for each sgRNA using molecular barcodes to capture the history of each clone within the tumor. Using this clonal information, we developed a robust computational pipeline that extracts meaningful target sgRNA-level data from individual tumors, despite the random under-representation of the larger library. Statistical (down-sampling) analysis revealed that CRISPR-StAR has a resolution of 1,000 sgRNAs per tumor which reduces the number of animals required by 7-fold to traditional approaches. Consequently, we have performed several druggable genome screens (~30,000 sgRNA) using just 30-40 individual tumors and identified a catalog of tumor suppressor genes that, when lost, strongly promote tumor growth in vivo without affecting cell proliferation in vitro. This group of genes is enriched with epigenetic modifiers, in particular multiple members of the COMPASS family and the SWI/SNF chromatin remodeling complexes. These results validate CRISPR-StAR as a powerful in vivo functional genomics platform for high throughput target discovery screens. Citation Format: Silvia Fenoglio, James Tepper, Lauren Grove, Esther CH Uijttewaal, Alborz Bejnood, Yi Yu, Hsin-Jung Wu, Annabel Devault, Shangtao Liu, Binzhang Shen, Samuel R Meier, Ashley H Choi, Tenzing Khendu, Hannah Stowe, Minjie Zhang, Brian B Haines, Alan Huang, Jannik N Andersen, Xuewen Pan, Ulrich Elling, Teng Teng. Inducible activation of sgRNA libraries in tumor xenografts empowers large-scale in vivo target discovery screens [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C048.

Deciphering the effect of phytohormones on the phyllosphere microbiota of Eucommia ulmoides

Phytohormones are key signals mediating plant-microbe molecular communication. However, their roles in driving phyllosphere microbiota assembly remain unclear. Here, high throughput target assays for 12 phytohormones and microbial amplicon sequencing techniques were used to reveal the effects of hormone components on phyllosphere microbiota of Eucommia ulmoides. Most of the phytohormone components in old leaves were lower than in tender leaves, such as indole-3-acetic acid (IAA), salicylic acid (SA), and jasmonic acid (JA), but the phyllosphere microbial community diversity in the older leaves was significantly higher than in the tender leaves, with more complex and aggregated microbial cooccurrence network. The E. ulmoides phyllosphere microbiota at tender and older leaf stage were dominated by the same dominant taxa at the phylum level, with Ascomycota and Basidiomycota as the main fungal taxa and Actinobacteriota, Bacteroidota, Firmicutes and Proteobacteria as the main bacterial taxa. FUNGuild and FAPROTAX functional predictions revealed that the high abundance functional groups of the E. ulmoides phyllosphere microbes were similar at tender and old leaf stages, with fungal functions mainly involving in plant pathogen, undefined saprotroph and endophyte, and bacterial functions mainly involving in chemoheterotrophy, fermentation and aerobic_chemoheterotrophy. Additionally, mantel test and variance partitioning analysis showed that IAA and N6-(delta 2-isopentenyl)-adenine (IP) were key phytohormones impacting the E. ulmoides phyllosphere microbiota, and their effects were largely interdependent. Our results improve the understanding of composition, diversity, function and influencing factors of phyllosphere microbiota, which might provide cue for sustainable agriculture and forestry management via precise regulation of the phyllosphere microbiota.

Optimal Time Allocation for Backscatter-Aided Relay Cooperative Transmission in Wireless-Powered Heterogeneous CRNs

Nowadays, backscatter, radio-frequency (RF) energy harvesting (EH), and cognitive radio technologies have been widely applied in Internet of Things (IoT) to address the issues of energy supply and spectrum scarcity. This paper focuses on the throughput maximization problem of backscatter-aided wireless-powered heterogeneous cognitive radio networks (WPHetCRNs), where two types of secondary transmitters (STs), i.e., the STs with backscatter units (STBs) and the STs with RF-EH units (STEs), coexist. The STBs operate in the ambient backscatter (AB) mode, and the STEs operate in the harvest-then-transmit (HTT) mode. Inspired by the potential benefits of cooperations between different users, we propose a backscatter-aided cooperative relay transmission (BaCRT) strategy to improve the sum-throughput of the secondary users (SUs). The main idea is that when the licensed spectrum of the primary users (PUs) is busy, the STBs first help to relay the primary data via the passive relay mode, and then transmit the secondary data via the AB mode, while the STEs harvest energy in the HTT mode. With the help of relaying, the target throughput of the PUs could be met in shorter duration and the licensed spectrum could become idle more quickly. When the licensed spectrum becomes idle, the STEs transmit data in the HTT mode. The goal of this paper is to identify the optimal time allocation among the passive relay mode, AB mode, and data transmission of HTT mode that maximizes the sum-throughput of the SUs. To reach this goal, we first investigate the single-ST case for each type and derive the closed-form solution of the optimal time allocation. We then extend to the multiple-ST case for each type, where three scenarios are classified with respect to the fairness issue of the STBs. We prove that the sum-throughput maximization problem is convex in each scenario, and employ the block coordinate descent and gradient descent iterative algorithms to solve the problem. Numerical results show that the proposed BaCRT strategy significantly improves the sum-throughput of the SUs compared with other strategies.

Compiler of Reed–Solomon Codec for 400-Gb/s IEEE 802.3bs Standard

Error Correction is often indispensable in a modern digital communication system that transmits data at a very high speed. Recently published IEEE Std 802.3bs requires an astounding throughput of 400Gbps while using the Reed-Solomon Code (RS-Code) to protect the integrity of the transmitted data. An RS-Codec supporting such a high throughput demands a significant silicon area. Improper decisions on the parameters of the parallel architecture could lead to unnecessarily high costs in the implementation. We have developed a compiler to solve this problem. First, the Codec satisfying IEEE Std 802.3bs using RS(544, 514) is parameterized, in a way that the throughput can be boosted on demand by setting some “configuration”. Second, an area-and power-efficient RS-Codec design satisfying a target throughput using a specific process can be inferred by our compiler in just minutes, and thereby easy process migration is supported. Experimental results using 28nm and 90nm CMOS processes are presented to demonstrate their effectiveness.

High-Throughput Hardware Design for the AV1 Decoder Switchable Loop Restoration Filters

This paper presents a high-throughput hardware design for the Switchable Loop Restoration Filter (SLRF) of the AOM Video 1 (AV1) video format. This hardware includes the two filters defined at the AV1 SLRF: the Separable Symmetric Normalized Wiener Filter (SSNWF) and the Dual Self- Guided Filter (DSGF). The SLRF is the last step in the AV1 loop restoration filters, and it is used to attenuate blurring artifacts, improving the subjective video quality and the coding efficiency. The designed hardware targeted the AV1 decoderand is able to process up to 4K Ultra-High Definition (4K UHD) videos (with 3840x2160 pixels) at 60 frames per second (fps) in real-time. In order to cover different scenarios, two other target throughputs were also evaluated: 4K UHD at 30fps and Full HD (FHD) (with 1920x1080 pixels) at 30fps. The architectures were synthesized for standard cells using the 40 nm TSMC library. The SSNWF and DSGF architectures used 37.38 Kgates and 177.58 kgates in all evaluated scenarios. Depending on the evaluated scenario, the SSNWF power dissipation varied from 8.25mWto 26.95mWand the DSGF power varied from 57.19 mW to 115.02 mW. This is the first paper in the literature presenting a hardware design for the AV1 SLRF with its two filters.

Abstract 5526: State-of-the-art biobanking of gastroesophageal adenocarcinoma samples: Integrating non-viable biospecimens with 3D and 2D cell models and their characterization, validation, and utilization of cell models for precision oncology

Abstract Traditionally, biobanking platforms have collected and stored non-viable biological specimens such as serum, plasma, and fresh-frozen or formalin-fixed paraffin-embedded (FFPE) tissues. These constitute key resources for clinical and contemporary genomics, transcriptomics, and proteomics studies. However, such specimens cannot be used for studies involving drug testing, high throughput target validation, and implementation for personalized medicine. Next-generation biobanking strategies rectify this issue by combining the collection of non-viable samples from patients with the propagation of viable tissue fractions as in vitro 2D and 3D cell models or in vivo xenograft models. Such cutting-edge biobanking strategies enable downstream cell-based high-throughput functional assays promoting the discovery of therapeutic targets or assessing treatment responses and resistance to treatment. Here, we provide comprehensive details of our establishment of a state-of-the-art biobanking platform for gastroesophageal adenocarcinoma (GEA) samples (n=389). Our approach opens new directions for understanding disease biology and conducting translational cancer studies. Patient-derived 2D cells (n=376), organoids (PDOs) (n=185), and xenografts (PDXs) (n= 99) included in our pipeline retain crucial features of the original human tumors, serving as valuable tools for clinical and experimental analyses in the context of precision oncology. We also discuss the entire approach of next-generation biobanking and emphasize the importance of integrating the propagation of PDOs and PDXs simultaneously. In addition, we explain each protocol optimized to propagate patient tissue-derived cell models. We validate that these models recapitulate tissue heterogeneity and are relevant preclinical models. Taken together, we described each approach used to develop one of the largest next-level biobanks for GEA. Citation Format: Mingyang Iris Kong, Sanjima Pal, Julie Berube, France Bourdeau, Betty Giannias, Nicholas Bertos, Veena Sangwan, Lorenzo Ferri. State-of-the-art biobanking of gastroesophageal adenocarcinoma samples: Integrating non-viable biospecimens with 3D and 2D cell models and their characterization, validation, and utilization of cell models for precision oncology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5526.

System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave (mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both full-duplex (FD)-IAB and half-duplex (HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB, FD-IAB can improve throughput by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and reduce latency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.

A Throughput Request Satisfaction Method for Concurrently Communicating Multiple Hosts in Wireless Local Area Network.

Nowadays, the IEEE 802.11 wireless local area network (WLAN) has been widely used for Internet access services around the world. Then, the unfairness or insufficiency in meeting the throughput request can appear among concurrently communicating hosts with the same access point (AP), which should be solved by sacrificing advantageous hosts. Previously, we studied the fairness control method by adopting packet transmission delay at the AP. However, it suffers from slow convergence and may not satisfy different throughput requests among hosts. In this paper, we propose a throughput request satisfaction method for providing fair or different throughput requests when multiple hosts are concurrently communicating with a single AP. To meet the throughput request, the method (1) measures the single and concurrent throughput for each host, (2) calculates the channel occupying time from them, (3) derives the target throughput to achieve the given throughput request, and (4) controls the traffic by applying traffic shaping at the AP. For evaluations, we implemented the proposal in the WLAN testbed system with one Raspberry Pi AP and up to five hosts, and conducted extensive experiments in five scenarios with different throughput requests. The results confirmed the effectiveness of our proposal.

Relay-Assisted Uplink Transmission Design of URLLC Packets

With the emergence and development of mission-critical applications, the design of uplink URLLC transmission has become one of the issues of concern. In this article, we consider the ultrareliable uplink transmission design between multiple robots and a central controller in a smart factory with stringent delay requirements while multiple relays are deployed for cooperative transmission. Subject to the throughput and reliability requirements, a relay-assisted two-phase transmission protocol is proposed aiming at total transmit power minimization by jointly optimizing the decoding error probability, relay selection, resource block (RB) assignment, and transmit power allocation. To support the mission-critical applications in Internet of Things (IoT) with diverse throughput targets and make full use of RBs, we investigate the optimization problem under the single-RB allocation and multi-RB allocation schemes. The problems in these two cases have an analogous structure and can be solved with a unified framework. In the solution framework, we present the optimal design solved by the nonconvex penalty (NCP)- and quadratic penalty (QP)-based successive convex approximation (SCA) algorithms and the corresponding low-complexity design with near-optimal settings of decoding error probabilities. Numerical results demonstrate the effectiveness of the proposed penalty-based iterative algorithms and confirm that the near-optimal design only causes minor power performance loss compared to the optimal design. The power performance gap under these two allocation schemes and the impact of system parameters are revealed and analyzed.

In Silico and In Vitro studies of taiwan chingguan yihau (NRICM101) on TNF-α/IL-1β-induced human lung cells.

COVID-19 pandemic has been a global outbreak of coronavirus (SARS-CoV-2 virus) since 2019. Taiwan Chingguan Yihau (NRICM101) is the first traditional Chinese medicine (TCM) classic herbal formula and is widely used for COVID-19 patients in Taiwan and more than 50 nations. This study is to investigate in silico target fishing for the components of NRICM101 and to explore whether NRICM101 inhibits cytokines-induced normal human lung cell injury in vitro. Our results showed that network prediction of NRICM101 by a high throughput target screening platform showed that NRICM101 has multiple functions that may affect cytokine regulation to prevent human lung cell injury. In addition, NRICM101 revealed protective effects against TNF-α/IL-1β-induced normal human lung HEL 299 cell injury through JNK and p38MAPK kinase signaling. Next-generation sequencing (NGS) analysis of NRICM101 on TNF-α/IL-1β-injured HEL 299 cells indicated that inflammatory pathway, cell movement of macrophages, cellular infiltration by macrophages, and Th1/Th2 immuno-regulation pathways were included. Thus, NRICM101 is a therapeutic agent, and it can improve COVID-19 syndrome to confer beneficial effects through multiple targeting and multiple mechanisms.

CRISPR/Cas9-Mediated Highly Efficient Gene Targeting in Embryonic Stem Cells for Developing Gene-Manipulated Mouse Models.

The CRISPR/Cas9 system has made it possible to develop genetically modified mice by direct genome editing using fertilized zygotes. However, although the efficiency in developing gene-knockout mice by inducing small indel mutation would be sufficient enough, the efficiency of embryo genome editing for making large-size DNA knock-in (KI) is still low. Therefore, in contrast to the direct KI method in embryos, gene targeting using embryonic stem cells (ESCs) followed by embryo injection to develop chimera mice still has several advantages (e.g., high throughput targeting in vitro, multi-allele manipulation, and Cre and flox gene manipulation can be carried out in a short period). In addition, strains with difficult-to-handle embryos in vitro, such as BALB/c, can also be used for ESC targeting. This protocol describes the optimized method for large-size DNA (several kb) KI in ESCs by applying CRISPR/Cas9-mediated genome editing followed by chimera mice production to develop gene-manipulated mouse models.

CRISPR/Cas9-Mediated Highly Efficient Gene Targeting in Embryonic Stem Cells for Developing Gene-Manipulated Mouse Models

The CRISPR/Cas9 system has made it possible to develop genetically modified mice by direct genome editing using fertilized zygotes. However, although the efficiency in developing gene-knockout mice by inducing small indel mutation would be sufficient enough, the efficiency of embryo genome editing for making large-size DNA knock-in (KI) is still low. Therefore, in contrast to the direct KI method in embryos, gene targeting using embryonic stem cells (ESCs) followed by embryo injection to develop chimera mice still has several advantages (e.g., high throughput targeting in vitro, multi-allele manipulation, and Cre and flox gene manipulation can be carried out in a short period). In addition, strains with difficult-to-handle embryos in vitro, such as BALB/c, can also be used for ESC targeting. This protocol describes the optimized method for large-size DNA (several kb) KI in ESCs by applying CRISPR/Cas9-mediated genome editing followed by chimera mice production to develop gene-manipulated mouse models.

Throughput and Outage Probability-Aware Intelligent Swarm-Based Multi-Hop Selection in Vehicular Network

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate.

Collaborative Beamforming Aided Fog Radio Access Networks

The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs’ caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.