Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Optimizing peritoneal dialysis catheter placement.

Long-term success of peritoneal dialysis as a kidney replacement therapy requires a well-functioning peritoneal dialysis catheter. With ongoing reductions in infectious complications, there is an increased emphasis on the impact of catheter-related and mechanical complications. There is currently a marked variation in the utilization of various types of catheters (double cuff vs single cuff, coiled tip vs straight tip), methods of catheter insertion (advanced laparoscopic, open surgical dissection, image guided percutaneous, blind percutaneous), timing of catheter insertion, location of catheter placement (pre-sternal v. abdominal) and peri-operative practices. Specialized approaches to catheter placement in clinical practice include use of extended catheters and embedded catheters. Marked variations in patient lifestyle preferences and comorbidities, specifically in high acuity patient populations (polycystic kidney disease, obesity, cirrhosis) necessitate individualized approaches to catheter placement and care. Current consensus guidelines recommend local procedural expertise, consideration of patient characteristics and appropriate resources to support catheter placement and long-term functioning. This review focuses on an overview of approaches to catheter placement with emphasis on a patient-centered approach.

A knowledge and data-driven optimal planning scheme for multi-modal vision transmission systems.

Vision transmission systems (VTS) manages to achieve the optimal information propagation effect given reasonable strategies. How to automatically generate the optimal planning strategies for VTS under specific conditions is always facing challenges. Currently, related research studies have dealt with this problem with assistance of single-modal vision features. However, there are also some other information from different modalities that can make contributions to this issue. Thus, in the paper, we propose a data-driven optimal planning scheme for multimodal VTS. For one thing, the vision features are employed as the basic mechanism foundation for mathematical modeling. For another, the data from other modalities, such as numerical and semantic information, are also introduced to improve robustness for the modeling process. On such basis, optimal planning strategies can be generated, so that proper communication effect can be obtained. Finally, some simulation experiments are conducted on real-world VTS scenes in simulative platforms, and the observed simulation results can well prove efficiency and proactivity of the proposal.

An Optimization Method of Production-Distribution in Multi-Value-Chain.

Value chain collaboration management is an effective means for enterprises to reduce costs and increase efficiency to enhance competitiveness. Vertical and horizontal collaboration have received much attention, but the current collaboration model combining the two is weak in terms of task assignment and node collaboration constraints in the whole production-distribution process. Therefore, in the enterprise dynamic alliance, this paper models the MVC (multi-value-chain) collaboration process for the optimization needs of the MVC collaboration network in production-distribution and other aspects. Then a MVC collaboration network optimization model is constructed with the lowest total production-distribution cost as the optimization objective and with the delivery cycle and task quantity as the constraints. For the high-dimensional characteristics of the decision space in the multi-task, multi-production end, multi-distribution end, and multi-level inventory production-distribution scenario, a genetic algorithm is used to solve the MVC collaboration network optimization model and solve the problem of difficult collaboration of MVC collaboration network nodes by adjusting the constraints among genes. In view of the multi-level characteristics of the production-distribution scenario, two chromosome coding methods are proposed: staged coding and integrated coding. Moreover, an algorithm ERGA (enhanced roulette genetic algorithm) is proposed with enhanced elite retention based on a SGA (simple genetic algorithm). The comparative experiment results of SGA, SEGA (strengthen elitist genetic algorithm), ERGA, and the analysis of the population evolution process show that ERGA is superior to SGA and SEGA in terms of time cost and optimization results through the reasonable combination of coding methods and selection operators. Furthermore, ERGA has higher generality and can be adapted to solve MVC collaboration network optimization models in different production-distribution environments.

Cost minimizing planning of container inspection and repair in multiple facilities.

A problem of optimal mid-term or long-term planning of inspection and repair of freight containers in multiple facilities is introduced and investigated. The containers are of different types and quality levels, which define their repair costs and workforce requirements. The objective function includes the total holding, inspection, repair, transportation and rejection costs. We propose a deterministic, time-dependent, integer linear min-cost multi-commodity network-flow formulation. The problem is shown to be polynomially solvable if there is a single facility, a single time period and all the containers are repairable and have to be repaired. It is shown to be NP-hard for three important special cases. The computational results of our experiments on randomly generated instances based on real data show that instances of sizes 3 facilities, 4 container types and up to 9 container quality levels can be solved with CPLEX in 5 minutes on a conventional PC, even for 30 periods, with an optimality gap of less than 3%. This is sufficient for medium-term or weekly planning or for short-term recovery planning. However, there are instances of the same magnitude, but with 360 periods of a considerably longer planning horizon, for which an optimality gap of 28% remained even after 10 hours of CPLEX computation.

Optimal planning of the COVID-19 vaccine supply chain.

This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.

Detection of an appropriate pharmaceutical company to get a suitable vaccine against COVID-19 with minimum cost under the quality control process.

Some international pharmaceutical companies have succeeded in producing vaccines against COVID-19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K-independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model.

Optimal strategies for throwing accurately.

The accuracy of throwing in games and sports is governed by how errors in planning and initial conditions are propagated by the dynamics of the projectile. In the simplest setting, the projectile path is typically described by a deterministic parabolic trajectory which has the potential to amplify noisy launch conditions. By analysing how parabolic trajectories propagate errors, we show how to devise optimal strategies for a throwing task demanding accuracy. Our calculations explain observed speed-accuracy trade-offs, preferred throwing style of overarm versus underarm, and strategies for games such as dart throwing, despite having left out most biological complexities. As our criteria for optimal performance depend on the target location, shape and the level of uncertainty in planning, they also naturally suggest an iterative scheme to learn throwing strategies by trial and error.

Correlation Between MRI and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

OBJECTIVE: Increased levels of tumor-infiltrating lymphocytes (TILs) positively correlate with the pathologic complete response rate and increased survival in patients with triple-negative breast cancer (TNBC). The purpose of this study was to investigate associations between TIL levels and MRI findings in patients with TNBC. MATERIALS AND METHODS: From February 2006 through December 2014, a total of 112 women with TNBC were selected for inclusion in the study. All lesions were evaluated by radiologists in accordance with the BI-RADS lexicon. Lymph node involvement and multifocality were also assessed. Tumors were divided into two groups: those with a TIL level of less than 50% were included in the group with low TIL levels (hereafter referred to as the "low-TIL group"), and those with a TIL level of 50% or more were included in the group with high TIL levels (hereafter referred to as the "high-TIL group"). Associations between TIL levels and imaging features were evaluated. RESULTS: Tumors in the high-TIL group had a more round shape (46.0%), a circumscribed margin (76.0%), homogeneous enhancement (32.0%), and absence of multifocality (88.0%) (p < 0.005). Tumors in the low-TIL group had a more irregular shape (69.3%), no circumscribed margin (79.0%), heterogeneous enhancement (75.8%), and multifocality (70.9%) (p < 0.005). CONCLUSION: The well-known typical features of TNBC on MRI, including a round shape, a circumscribed margin, homogeneous enhancement, and lack of multifocality, are a major pattern of TNBC with high TIL levels. This information could provide added diagnostic benefit for the identification of tumors with a good prognosis, which could further assist in optimal pretreatment planning.

Analysis on the functional module of military medical service operation system oriented towards the disposition of military medical service force / 第二军医大学学报

An effective, practical and advanced functional module of military medical service operation system was developed by combining military, logistics, health service theory and technologies of system engineering, computer simulation, digitalized map and multi-media. The functional module of the system could be used for regional disposition of military medical service force and for rapid exercise of military medical service commands. The functions of the system included terrain analysis, information retrieval, calculation and forecasting, document drafting, picture management and information transmission. It can be used not only for daily training of military medical command, but also for military medical commanding at emergency and non-battle military actions, enhancing the efficiency in accomplishing various military medical service disposition.