Optimal Deployment Research Articles

Non-linear IV pharmacokinetics of the ATR inhibitor berzosertib (M6620) in mice.

The Ataxia Telangiectasia and Rad3-related (ATR) protein complex is an apical initiator of DNA damage response pathways. Several ATR inhibitors (ATRi) are in clinical development including berzosertib (formerly M6620, VX-970). Although clinical studies have examined plasma pharmacokinetics (PK) in humans, little is known regarding dose/exposure relationships and tissue distribution. To understand these concepts, we extensively characterized the PK of berzosertib in mouse plasma and tissues. A highly sensitive LC-MS/MS method was utilized to quantitate berzosertib in plasma and tissues. Dose proportionality was assessed in female BALB/c mice following single IV doses (2, 6, 20 or 60mg/kg). A more extensive PK study was conducted in tumor-bearing mice following a single IV dose of 20mg/kg to evaluate distribution to tissues. PK parameters were calculated by non-compartmental analysis (NCA). A compartmental model was developed to describe the PK behavior of berzosertib. Plasma protein binding was determined in vitro. Increased doses of berzosertib were associated with less than proportional increases in early plasma concentrations and greater than proportional increase in tissue exposure, attributable to saturation of plasma protein binding. Berzosertib extensively distributed into bone marrow, tumor, thymus, and lymph nodes, however; brain and spinal cord exposure was less than plasma. The nonlinear PK of berzosertib displayed here can be attributed to saturation of plasma protein binding and occurred at concentrations close to those observed in clinical trials. Our results will help to understand preclinical pharmacodynamic and toxicity data and to inform optimal dosing and deployment of berzosertib.

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

Anchor Deployment Optimization for Range-Based Indoor Positioning Systems in Non-Line-of-Sight Environment

Anchor Deployment Optimization for Range-Based Indoor Positioning Systems in Non-Line-of-Sight Environment

State-of-the-art percutaneous coronary intervention for left main coronary artery disease in Japan.

Percutaneous coronary intervention for left main coronary artery disease (LM-PCI) represents a high-risk yet life-saving procedure that has evolved significantly over the years. This review outlines the current state-of-the-art practices for LM-PCI in Japan in detail, emphasizing the integration of coronary physiology and intracoronary imaging alongside with evidence-based standardized technique using latest drug-eluting stents. These advancements enable precise lesion assessment, stent sizing, and optimal deployment, thereby enhancing procedural safety and efficacy. Despite discrepancies between current guidelines favoring coronary artery bypass grafting and real-world practice trends towards increased LM-PCI adoption, particularly in elderly populations with multiple comorbidities, careful patient selection and procedural planning are critical. Future perspectives include further refining LM-PCI through conducting randomized controlled trials integrating advanced techniques and addressing the issue of ostial left circumflex lesions and nationwide standardization of medical care for LM disease.

Cost-efficient microgeneration renewable energy provision dimensioning for sustainable 5G heterogeneous network

The deployment of mobile networks has imposed an urgent requirement for the pursuit of low-carbon communication infrastructures. The increasing energy consumption of mobile networks has brought about challenges of techno-economic and environmental sustainability. Renewable energy-enabled mobile networks have received a lot of attention due to their capability to evade greenhouse gas emissions and easy availability. Microgeneration-based renewable energy provision is a feasible and effective solution for 5G networks. Dimensioning of microgeneration renewable energy power supply is an essential issue to make the system operate for a long period cost-effectively with a minimum amount of grid energy consumption. For effective deployment of microgeneration renewable energy system, it is essential to provision it with adequate PV panel capacity and storage devices. This work attempts to identify the cost-effective, energy-efficient, and emissions-aware sizing of PV panels and storage for 5G HetNet. An energy-saving strategy based on an optimal policy of advanced sleep modes and traffic-aware load offloading is developed and the interaction of the energy-saving strategy on the system dimensioning is explicitly examined. The proposed solution aims to ensure the communication quality of service whilst keeping the optimal cost-effective deployment and network operation. The system performance in terms of grid energy consumption, empty storage probability, emission performance, and total cost of the system is extensively assessed through experiments for a range of operational scenarios. The numerical results demonstrated that the proposed sustainable energy system dimensioning and operation integrated with an energy-saving strategy is energy-efficient and cost-effective.

Long-term path planning with optimal deployment of a charging station for monitoring photovoltaic solar farms

Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.

Comprehensive Framework for Distribution Network Multi-Investment Expansion Planning: Emissions, Uncertainty, and Resource Remuneration Integration

Changes are currently unfolding within electric distribution networks as renewable energy integration grows. This paper introduces a novel stochastic approach for expansion planning in distribution networks with high renewable energy penetration. The model addresses uncertainties, daily and seasonal impacts, distributed generation remuneration, carbon emissions, and optimal energy storage deployment. Risk aversion techniques such as value-at-risk and conditional value-at-risk are employed to prepare networks for impactful events. Emphasizing multi-period investment over single-period, the analysis considers confidence levels and risk aversion’s effects on reliability indexes, energy storage placement, generation patterns, network topology, and costs. Using a realistic 180-bus distribution network in Portugal spanning 30 years, the method’s applicability and robustness are demonstrated. Results illustrate the benefits of multi-period investment, with cost increases of 6.09 % and 2.97 % for single- and multi-period investments at a 90 % confidence level, respectively, when both are compared to a situation with no risk aversion. Higher confidence levels amplify the trade-off between extreme scenario cost reduction and total cost increase, with a 99 % confidence level reducing scenario costs by 19.01 % while increasing total costs by 5.64 %.

A new approach for fracture parameters optimization of multi-fractured horizontal wells: Virtual boundary method

Horizontal well fracturing technology stands as the cornerstone for achieving large-scale commercial production in shale reservoirs. Yet, the complexity of fracture variables within horizontal wells, comprising both discrete and continuous factors, presents a formidable challenge for optimization. Existing methods struggle to holistically optimize these mixed fracture parameters, including discrete fracture number, continuous fracture location, and half-length. This study pioneers a fracture deployment optimization approach tailored for fractured horizontal wells in shale reservoirs, leveraging the innovative virtual boundary method (VBM). Initially, a virtual boundary model is developed by extending the length of the fracturing horizontal wellbore, defining the extended area as a virtual reservoir devoid of hydrocarbon. Subsequently, the net present value (NPV) is adopted as the objective function, with continuous fracture location and half-length serving as optimization variables. The modified simultaneous perturbation stochastic approximation (mSPSA) algorithm is then employed for optimization. Following optimization, the optimized fractures within the virtual area are identified and deleted via position identification, enabling the comprehensive optimization of fracture number, location, and half-length. Notably, the computational efficiency of this method surpasses existing approaches by a factor of 5.1 compared to the bisection nested SPSA optimization method. Validation through three calculation cases, encompassing homogeneous reservoir, distinct sweet spot reservoir, and strongly heterogeneous reservoir, further underscores the method's reliability. Post-optimization, NPV increases by 1.1 times, 1.4 times, and 0.34 times for the respective calculation cases compared to the initial plan. When applied to an actual shale oil reservoir, the method yields a 1.67 times increase in NPV compared to existing deployment plans. This research lays a solid theoretical foundation for the design of horizontal fracturing well construction plans in shale reservoirs.

Bayesian estimation of the prevalence of antimicrobial resistance: a mathematical modelling study.

Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets. To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods. Retrospectively collected, open-source, real-world pseudonymized healthcare data were used to develop a Bayesian approach for estimating the prevalence of AMR by combination with prior AMR information from a contextualized review of literature. Iterative random sampling and cross-validation were used to assess the predictive accuracy and potential resource efficiency of the Bayesian approach compared with a standard frequentist approach. Bayesian estimation of AMR prevalence made fewer extreme estimation errors than a frequentist estimation approach [n = 74 (6.4%) versus n = 136 (11.8%)] and required fewer observed antimicrobial susceptibility results per pathogen on average [mean = 28.8 (SD = 22.1) versus mean = 34.4 (SD = 30.1)] to avoid any extreme estimation errors in 50 iterations of the cross-validation. The Bayesian approach was maximally effective and efficient for drug-pathogen combinations where the actual prevalence of resistance was not close to 0% or 100%. Bayesian estimation of the prevalence of AMR could provide a simple, resource-efficient approach to better inform population infection management where uncertainty about AMR prevalence is high.

Research on the Advancement of Equity in Higher Education Driven by Artificial Intelligence

A midst the rapid evolution of artificial intelligence (AI) technology, its integration into higher education has gained significant momentum, presenting novel avenues for fostering educational equity. This paper comprehensively examines the utilization of AI in higher education, meticulously analyzing its specific contributions to the advancement of educational equity, and subsequently offers tailored strategies and recommendations for its optimal deployment. By harnessing the power of intelligent algorithms, big data analysis, and other AI capabilities, higher education institutions are able to personalize learning experiences, expand access to quality resources, and streamline administrative processes, thereby promoting a more equitable distribution of educational opportunities. The paper delves into these applications, underscores their potential impact, and provides practical guidance for leveraging AI to foster a more inclusive and equitable higher education landscape.

Wavelength Dependence of Modal Bandwidth of Multimode Fibers for High Data Rate Transmission and Its Implications

Vertical-cavity surface-emitting laser (VCSEL)-based transmission over multimode fiber (MMF) has achieved data rates of 100G per lane and is progressing towards 200G per lane. Recently, high-data-rate MMFs derived from OM3 and OM4 have been proposed. These fibers exhibit higher effective modal bandwidths at 910 nm, leading to a different wavelength dependence compared to conventional OM3 and OM4 MMFs. Understanding the wavelength dependence of these fibers is crucial to address their utilization in a broader range of applications. Through Monte Carlo simulations, we have obtained the low-end boundary of the effective modal bandwidths (EMBs) for these fibers, revealing capability improvements over the existing OM3 and OM4. The high-data-rate OM4 performs the same as or better than OM5 from 840 nm to 920 nm, while also showing a high bandwidth for the 850–870 nm wavelength window, favoring VCSELs with center wavelengths shifted toward 860 nm. We also obtained the link bandwidth, which includes both modal bandwidth and chromatic dispersion contributions, and the transmission reaches for various types of transceivers. We find that for both high-data-rate OM3 and high-data-rate OM4, the link bandwidth stays above the value at 850 nm until around 910 nm, delivering a similar transmission performance from 850 to 910 nm without declining towards longer wavelengths, unlike the standard OM3 and OM4. This characteristic favors a wider range of wavelength choices for VCSELs and enables optimal deployments for various applications.

Marine management and monitoring system based on AHP cost-benefit analysis with integrated evaluation and genetic algorithms for recurrent searches

Marine management and monitoring systems need to develop a safety procedure for submersibles that predicts changes in the position of the submersible over time. This would effectively prevent the submersible from losing communication with the host and would allow it to react to possible deficiencies. In this paper, a random walk model is used to predict the position of the submersible at each moment on the seafloor. The probability of the highest point on each plane is 0.87-0.92. The error in the position of the submersible caused by the uncertainty on the seafloor is corrected by the Kalman filter model. In this paper, we use AHP cost-benefit analysis to comprehensively evaluate and select the best additional search equipment. In this paper, the genetic algorithm based on cyclic search is used to determine the optimal initial deployment point and search pattern. After a large number of iterations, the shortest search path length of 817.273 is obtained, and the shortest search path is taken as the optimal path for search and rescue. In this paper, the model is extended using particle swarm algorithm and environment modelling. The predictive model of the submersible is combined with the environmental model of different sea areas. Therefore, the actual motion model of the submersible is approximated. After simulation, it was found that the accuracy of the submersible probability increased by 23.72%.

Deployment of attractive targeted sugar baits in western Zambia: installation, monitoring, removal, and disposal procedures during a Phase III cluster randomized controlled trial

BackgroundAttractive Targeted Sugar Baits (ATSBs) offer a complementary vector control strategy to interventions targeting blood feeding or larval control by attacking the sugar feeding behaviour of adult mosquitoes using an attract-and-kill approach. Western Zambia was the first location to receive and deploy ATSB Sarabi version 1.2 stations in a Phase III cluster randomized controlled trial. This paper describes ATSB station installation, monitoring, removal, and disposal, quantifies ATSB station coverage, and reports major reasons for ATSB station replacement.MethodsATSB stations were deployed during two annual transmission seasons, through scheduled installation and removal campaigns. During deployment, monitoring was conducted per protocol to maintain high coverage of the ATSB stations in good condition. Routine monitoring visits during the trial captured details on ATSB station damage necessitating replacement following pre-defined replacement criteria. Annual cross-sectional household surveys measured ATSB station coverage during peak malaria transmission.ResultsA total of 67,945 ATSB stations were installed in Year 1 (41,695 initially installed+ 26,250 installed during monitoring) and 69,494 ATSB stations were installed in Year 2 (41,982 initially installed+ 27,512 installed during monitoring) across 35 intervention clusters to maintain high coverage of two ATSB stations in good condition per eligible household structure. The primary reasons for ATSB station replacement due to damage were holes/tears and presence of mold. Cross-sectional household surveys documented high coverage of ATSB stations across Year 1 and Year 2 with 93.1% of eligible structures having ≥ 2 ATSB stations in any condition.DiscussionATSB station deployment and monitoring efforts were conducted in the context of a controlled cRCT to assess potential product efficacy. Damage to ATSB stations during deployment required replacement of a subset of stations. High coverage of eligible structures was maintained over the two-year study despite replacement requirements. Additional research is needed to better understand the impact of damage on ATSB station effectiveness under programmatic conditions, including thresholds of threats to physical integrity and biological deterioration on product efficacy.ConclusionsOptimizing ATSB stations to address causes of damage and conducting implementation research to inform optimal delivery and cost-effective deployment will be important to facilitate scale-up of ATSB interventions.

Joint optimization for on-demand deployment of UAVs and spectrum allocation in UAVs-assisted communication

Joint optimization for on-demand deployment of UAVs and spectrum allocation in UAVs-assisted communication

A comprehensive review of hybrid wind-solar energy systems

Abstract In the face of escalating global energy demands and growing environmental concerns associated with conventional energy sources, integrating renewable energy systems has become imperative. Solar and wind have become key contributors to a cleaner and more sustainable energy future among these renewable energy sources. However, their intermittent nature, unpredictability, and infrastructural demands present significant challenges. Hybrid renewable energy systems (HRES) have emerged as a transformative solution to address these challenges. This paper conducts a comprehensive review of HRES, explicitly focusing on integrating wind and solar energy sources to address the limitations of individual systems. The review encompasses a systematic analysis, commencing with identifying optimal deployment areas for hybrid systems, considering geographic and climatic factors that maximize energy yield. Also, reviewed the in-depth exploration of the pivotal components. Additionally, the paper critically examines the crucial aspect of sizing HRES systems, shedding light on recent methodologies applied to HRES. Furthermore, the paper investigates modern advanced technologies used in HRES, particularly the latest analytical software, its methodologies, and the incorporation of effective designs for hybrid wind and solar systems. In addition to evaluating the current state of HRES technology, this article also discusses the scope of future research prospects and emerging trends in the field. It also contemplates integrating other renewable energy sources, such as biomass, with wind and solar energy according to the availability and advantages of these sources and other associated aspects. This paper will help understand the current developments in the field of HRES.

A Novel Multi-Objective Protection Coordination Scheme for a Microgrid with Optimal Deployment of Dual-Setting ROCOV Based Relays

A Novel Multi-Objective Protection Coordination Scheme for a Microgrid with Optimal Deployment of Dual-Setting ROCOV Based Relays

Deployment Optimization of Roadside Unit With Failure Probability Based on Stochastic Mixed Traffic Equilibrium

Deployment Optimization of Roadside Unit With Failure Probability Based on Stochastic Mixed Traffic Equilibrium

Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System.

Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud's limitations in detecting distant small objects. This research aims to enhance MDE (Monocular Depth Estimation) using a single camera, offering extreme cost-effectiveness in acquiring 3D environmental data. In particular, this paper focuses on novel data augmentation methods designed to enhance the accuracy of MDE. Our research addresses the challenge of limited MDE data quantities by proposing the use of synthetic-based augmentation techniques: Mask, Mask-Scale, and CutFlip. The implementation of these synthetic-based data augmentation strategies has demonstrably enhanced the accuracy of MDE models by 4.0% compared to the original dataset. Furthermore, this study introduces the RMS (Real-time Monocular Depth Estimation configuration considering Resolution, Efficiency, and Latency) algorithm, designed for the optimization of neural networks to augment the performance of contemporary monocular depth estimation technologies through a three-step process. Initially, it selects a model based on minimum latency and REL criteria, followed by refining the model's accuracy using various data augmentation techniques and loss functions. Finally, the refined model is compressed using quantization and pruning techniques to minimize its size for efficient on-device real-time applications. Experimental results from implementing the RMS algorithm indicated that, within the required latency and size constraints, the IEBins model exhibited the most accurate REL (absolute RELative error) performance, achieving a 0.0480 REL. Furthermore, the data augmentation combination of the original dataset with Flip, Mask, and CutFlip, alongside the SigLoss loss function, displayed the best REL performance, with a score of 0.0461. The network compression technique using FP16 was analyzed as the most effective, reducing the model size by 83.4% compared to the original while maintaining the least impact on REL performance and latency. Finally, the performance of the RMS algorithm was validated on the on-device autonomous driving platform, NVIDIA Jetson AGX Orin, through which optimal deployment strategies were derived for various applications and scenarios requiring autonomous driving technologies.

A joint optimization method for multi-UAV deployment and task scheduling in mobile edge computing with large-scale mobile users

In the realm of multi-UAV-assisted mobile edge computing (MEC), the joint optimization of UAV deployment and task scheduling has emerged as a potent approach for mitigating energy consumption while ensuring high-quality services for mobile users. Nevertheless, the intricate interdependence between UAV deployment and task scheduling presents formidable challenges when implementing this scheme. In response, we introduce a novel methodology called the Dual-Stage Hybrid Strategy with Genetic-Simulated Annealing and Knowledge Sharing (DSHSGSK). DSHSGSK employs an alternating optimization strategy known as HS-GSK, supplemented by an elimination operator, to simultaneously optimize UAV deployment and task scheduling. In pursuit of heightened efficiency in UAV deployment and a more effective task scheduling strategy, we propose an innovative location encoding scheme for UAVs. To harness the local search capabilities of the Harmony search (HS) algorithm and the global search capabilities of the Gaining Sharing Knowledge based Algorithm (GSK), we introduce a coordination parameter (CP) to modulate the frequency of alternation between these two optimization techniques. Experimental results validate the superiority of our proposed method over other state-of-the-art algorithms for jointly optimizing multi-UAV deployment and task scheduling. Notably, our approach excels in terms of energy consumption, underscoring its exceptional performance.

Industrial Routes from Sugars and Biomass to CMF and Other 5-(Halomethyl)furfurals.

The synthesis of 5-(halomethyl)furfurals (XMFs, X=F, Cl, Br, I), including 5-(chloromethyl)furfural (CMF), 5-(bromomethyl)furfural (BMF), 5-(iodomethyl)furfural (IMF), and 5-(fluoromethyl)furfural (FMF), from biomass represents a pivotal advancement in renewable chemistry and engineering. Harnessing waste biomass as a raw material offers a sustainable alternative to fossil-based resources, mitigating environmental degradation and addressing pressing energy needs. CMF and BMF, characterized by their enhanced stability over the hydroxyl analog, 5-(hydroxymethyl)furfural (HMF), exhibit promise as renewable building blocks for scale-up and commercialization. The surge in research interest, particularly from 2010 to 2024, reflects a growing recognition of XMFs' potential as novel platform chemicals. This review highlights the evolution of XMF synthesis methods, focusing on their transformation from saccharides and lignocellulosic biomass. Mechanistic insights and experimental setups are scrutinized for industrial feasibility and scalability, shedding light on technical challenges and avenues for further research. The analysis underscores the burgeoning significance of XMFs in the transition towards sustainable chemical production, emphasizing the importance of process optimization and mechanistic understanding for commercial deployment.