Optimal Execution Research Articles

KEY ASPECTS THAT MAKE DECISION-MAKING TRAINING IN THE FIELD OF CRISIS MANAGEMENT TAILORED TO THE IDENTIFIED GAPS AND NEEDS

The fundamental objective of crisis management exercises is to provide participants with therequisite knowledge and skills to enable optimal responses in crisis situations. One particularlyefficacious and engaging form of exercise is decision-making training. The utilisation of virtualreality-based decision-making training, incorporating multimedia and “acting,” represents aneffective methodology for the assessment of procedures that involve multiple entities. The objectiveof this study is to present a methodology for adapting decision-making training to address theexisting gaps, needs, and decision-making issues of the entity for which the exercise is organized,thereby facilitating more effective and optimal execution. Furthermore, the paper presents datasources utilized by the authors in the organization of decision-making training. The intendedaudience for the publication is any individual, entity, and/or institution that engages in crisismanagement on a daily basis, including strategic planners, decision makers and practitioners.Consequently, the paper offers practical recommendations for the organization of an effective anddedicated decision-making training in the field of crisis management, based on a synthesis of theliterature and the authors’ experience.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Empowering Women's Business Groups through Microfinance in Village Enterprises

Background: Effective management of microfinance institutions is essential for rural economic development. Specific Background: This study examines BUMDesa Bersama “Gempol Sejahtera,” focusing on its application of microfinance principles in financial management. Knowledge Gap: There is limited research on how structured bureaucracies affect the success of microfinance programs in community organizations. Aims: The research evaluates the impact of organizational structure on microfinance initiatives and resource management. Results: Findings show that a clear bureaucratic structure enhances efficiency and role clarity, facilitating successful program implementation. Clear Standard Operating Procedures (SOPs) and task fragmentation are critical for optimal execution. Novelty: This study offers new insights into the link between organizational structure and microfinance effectiveness in community enterprises, enriching the literature on rural empowerment. Implications: The results underscore the importance of strong organizational frameworks to improve resource management and program responsiveness, thereby promoting economic empowerment, especially among women. Highlights : Organizational Efficiency: A clear bureaucratic structure enhances operational effectiveness and role clarity. Critical Procedures: Implementing Standard Operating Procedures (SOPs) is essential for program success. Economic Empowerment: Strong frameworks promote responsiveness and resource management, benefiting community initiatives. Keywords: microfinance, organizational structure, community empowerment, financial management, bureaucratic efficiency

Optimal execution of logical Hadamard with low‐space overhead in rotated surface code

AbstractFault‐tolerant quantum computation requires error‐correcting codes that enable reliable universal quantum operations. This study introduces a novel approach that executes the logical Hadamard with low‐space requirements while preserving the original definition of logical operators within the framework of the rotated surface codes. Our method leverages a boundary deformation method to rotate the logical qubit transformed by transversal Hadamard. Following this, the original encoding of the logical qubit is reinstated through logical flip‐and‐shift operations. The estimated space–time cost for a logical Hadamard operation with a code distance d is 5d2 + 3d2. The efficiency enhancement of the proposed method is approximately four times greater than those of previous approaches, regardless of the code distance. Unlike the traditional method, implementing a logical Hadamard requires only two patches instead of seven. Furthermore, the proposed method ensures the parallelism of quantum circuits by preventing interferences between adjacent logical data qubits.

Performance analysis of selected database management systems

The article presents the results of performance tests of database management systems. In order to conduct the tests, an application running in Java environment and using an ORM tool such as Hibernate for communication with the database was used. The tests were carried out for test scenarios such as CRUD operations, join queries, aggregate functions, and filter conditions in queries. The DBMSes used for testing were MySQL, PostgreSQL and H2. Performance analysis looked at the execution time of operations, RAM usage and CPU load. Additionally, tests were carried out for different sizes of user groups using the application. PostgreSQL proved to be the best choice for complex operations.

Applications of fractional stochastic volatility models to market microstructure theory and optimal execution strategies

Applications of fractional stochastic volatility models to market microstructure theory and optimal execution strategies

The influence of entrepreneurial orientation on business performance of SMEs: Evidence from Kathmandu, Nepal

The purpose of the study was to investigate the impact of entrepreneurial orientation comprising autonomy, innovation, networking, and pro-activeness on business performance in Nepalese context. The literature of entrepreneurial orientation concerning to business performance depicted distinct directions with divergent and unsettled evidences. This research was conducted to address the research objective and for testing hypotheses. Thus, this study employed a descriptive and causal-comparative research approach, through cross-sectional data to gather information from participants to address research issues and objectives. A structured questionnaire of total 425 adopting the convenience sampling technique distributed among the targeted respondents in Kathmandu and received only 410 useful questionnaire for analysis. The findings of the study revealed that entrepreneurial orientation comprising autonomy, innovation, networking, and pro-activeness behavior found positively associated with the business performance of small and medium enterprises (SMEs) in Kathmandu, Nepal. There was a positive impact of entrepreneurial orientation on SMEs' business performance. It showed that better entrepreneurial orientation leads towards better business performance. This research contributes for better understanding of the association between entrepreneurs’ orientation and business performance with their effect for business performance. This study establishes significant benchmark to a number of stakeholders consisting entrepreneurs, educators, academic institutions, regulators, and policymakers for a better understanding and optimum execution of the research outcomes to prepare effective policies for entrepreneurship development.

Analysis of in-bin low-temperature and high-temperature grain drying in Alberta: Specific energy, carbon costs and policy implications

Optimizing grain drying process is pivotal for both economic viability and environmental sustainability. In cold countries, supplemental heating is paramount but instigates the issue of carbon release at the same time. To curve the emission, a carbon levy has been imposed that varies with fuel types. However, there is limited knowledge of the impact of field-adaptable technologies utilizing commercialized fuels on the optimal execution of grain drying. To explore this, a comprehensive three-year on-farm grain drying study was conducted in the province of Alberta, Canada. This comparative analysis centered on three critical aspects, i.e., specific energy consumption, greenhouse gas emissions (GHGs), and operating costs. Furthermore, the research extended its projections into the year 2030, accounting for the impact of gradual carbon levies on natural gas, propane, and diesel fuel systems. Resultsshowed that indirect heaters in in-bin systems were 38 % more energy-efficient than direct heaters due to better airflow and higher initial grain temperatures, while indirect heaters with diesel burners exhibited 27.8 % lower burner turndown ratios and 35.4 % higher energy consumption than natural gas counterparts. In high-temperature drying, natural gas consumption decreased as supply air temperatures rose, with double-flow dryer showing the lowest energy consumption (6.3±1.9 GJ/t) in comparison to mixed-flow and cross-flow dryer due to waste heat recovery. Heaters and Dryers operated at varying load capacities, indicated 126–135 % increase in natural gas cost with carbon levy by 2030. Achieving lower specific energy consumption in grain drying requires precise burner and process control, along with maintenance, offering valuable guidance for producers.

Clustering-based hyper-heuristic algorithm for multi-region coverage path planning of heterogeneous UAVs

In the context of multi-heterogeneous UAV coverage path planning, an effective solution method has been proposed. Firstly, regions are set up as fully connected graphs which are cut into multiple subgraphs by spectral clustering method to assign tasks to multi-heterogeneous UAVs. Additionally, an RL-based hyper-heuristic algorithm is proposed. Heuristic space is parameterized by GNN which is trained with the reward provided by the optimization goal to automate design and enhance the heuristic metrics, avoiding the inefficiency and suboptimality of expert design and manual parameter tuning. Compared with existing methods, the proposed algorithm has a better performance in task completion time, execution time and deviation rate, which shows its potential application in the coverage path planning problem of multi-heterogeneous UAVs.

Evaluation of pulp chamber temperature during cementation with the preheated composite resin technique

The cementation technique using preheated composite resin requires high temperatures for optimal execution and may lead to increased and damaging intrapulpal temperatures. Whether the technique can lead to a temperature increase that might lead to necrosis of the pulp tissue is unclear. The purpose of this in vitro study was to evaluate the temperature variation in the pulp chamber of bovine teeth with veneer-type preparations during veneer cementation using the preheated composite resin technique. A total of 103 bovine incisors were divided into 8 groups (n=10) and prepared for indirect veneers with different preparation depths: 2.0mm, 1.5mm, 1.0mm, and 0.5mm. Veneers were cemented on these preparations using 2 cementation protocols: preheated composite resin and photopolymerizable resin cement. The teeth were attached to a device containing a temperature sensor which was inserted into the pulp chamber to quantify the intrapulpal temperature variation produced during the cementation protocols. The data were analyzed using a statistical software program. The level of statistical significance for the analyses was with a confidence interval of 95%, sampling power of 80%, and a moderate effect size (0.36). The groups cemented with preheated composite resin and the groups with the greatest preparation depth had the highest mean intrapulpal temperature; the PHC2 group presented a mean ±standard deviation temperature increase of 5.70 ±2.14°C. The heat generated by heating the resin contributed to the increase in intrapulpal temperature. Temperature variations were greater in deeper preparations, especially when preheated resin technique was used.

DSS1 restrains BRCA2’s engagement with dsDNA for homologous recombination, replication fork protection, and R-loop homeostasis

DSS1, essential for BRCA2-RAD51 dependent homologous recombination (HR), associates with the helical domain (HD) and OB fold 1 (OB1) of the BRCA2 DSS1/DNA-binding domain (DBD) which is frequently targeted by cancer-associated pathogenic variants. Herein, we reveal robust ss/dsDNA binding abilities in HD-OB1 subdomains and find that DSS1 shuts down HD-OB1’s DNA binding to enable ssDNA targeting of the BRCA2-RAD51 complex. We show that C-terminal helix mutations of DSS1, including the cancer-associated R57Q mutation, disrupt this DSS1 regulation and permit dsDNA binding of HD-OB1/BRCA2-DBD. Importantly, these DSS1 mutations impair BRCA2/RAD51 ssDNA loading and focus formation and cause decreased HR efficiency, destabilization of stalled forks and R-loop accumulation, and hypersensitize cells to DNA-damaging agents. We propose that DSS1 restrains the intrinsic dsDNA binding of BRCA2-DBD to ensure BRCA2/RAD51 targeting to ssDNA, thereby promoting optimal execution of HR, and potentially replication fork protection and R-loop suppression.

Teleassessment using five times sit-to-stand test in older adults with and without Parkinson's disease: Guidelines and barriers.

Assessing lower limb strength, balance, and fall risk are crucial components of rehabilitation, especially for the older adult population. With the growing interest in telehealth, teleassessment has been investigated as an alternative when in-person assessments are not possible. The Five Times Sit-to-Stand test (5TSTS) provides a quick measure of balance during chair transfers, muscle power, endurance, and the hability to change and maintain body position, and is highly recommended by guidelines. However, the literature is unclear about the viability and safety of teleassessment using the 5TSTS in older adultswith and without Parkinson's disease (PD). This study aimed to evaluate the reliability of teleassessment using the 5TSTS and to determine its feasibility and safety for older adults with and without PD. This cross-sectional study included older adults with and without PD who were evaluated remotely through a videoconference platform. To ensure effective and comprehensive instructions for the test, we developed a guideline called OMPEPE (an acronym for: Objective; Materials; Position-Start; Execution; Position-End; Environment). We assessed the 5TSTS intra- and inter-rater reliability by comparing scores obtained from the same examiner and from different examiners, respectively. Participants and examiners completed online surveys to provide information about feasibility and safety. Twelve older adults with PD and 17 older adultswithout PD were included in this study (mean ages 69.0 and 67.6years, respectively). Based on the participants' perspectives and the absence of adverse effects, teleassessment using the 5TSTS is feasible and safe for older adults with and without PD. Excellent intra- and inter-rater reliability (intraclass correlation coefficient >0.90) was found for all measurements of the 5TSTS. This study demonstrated the feasibility, safety, and reliability of teleassessment using the 5TSTS. The guidelines developed may help health professionals minimize barriers and safely conduct an online assessment that includes a physical test such as the 5TSTS in older adults with or without PD. In addition to addressing technological barriers, the OMPEPE guideline might ensure the optimal execution of evaluations. Teleassessment using the 5TSTS for older adults with and without PD is feasible and safe. Both synchronous (i.e., live) and asynchronous (i.e., recorded) online 5TSTS tests demonstrate excellent intra- and inter-rate reliability.

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

A Constraint Programming Approach for Discrete Time–Cost Tradeoff Problems in a Time-Constrained Activity Network

The discrete time-cost tradeoff problem (DTCTP) is a well-researched topic in the field of operations research. The majority of existing DTCTP models are based on traditional activity networks, which permit the execution of an activity as soon as all its predecessors have been completed. This assumption is reasonable, but it is important to note that there are always exceptions. The main work of this study was threefold. Firstly, we expanded the analysis of the DTCTP to encompass time-constrained activity networks (DTCTPTC), which encompassed three different types of time constraints. The first constraint was the time-window constraint, which limited the time interval during which an activity could be executed. The second constraint was the time-schedule constraint, which specified the times at which an activity could begin execution. The third constraint was the time-switch constraint, which required project activities to start at specific times and remain inactive during designated time periods. Secondly, a constraint programming (CP) model was developed for the purpose of solving the DTCTPTC. The model employed interval variables to define the activity and its potential time constraints, while CP expressions were utilized to ensure the feasibility of the solution. The objective was to identify the optimal execution mode for each activity, the optimal start times for time-scheduled activities, and the optimal work/rest patterns for time-switch activities, with the aim of minimizing the total cost of the project. Finally, the efficacy of the proposed CP model was validated through two case studies based on two illustrative projects of varying sizes. The outcomes were then compared against existing algorithms. The results demonstrated that time constraints were important factors affecting schedule optimization, and the proposed CP model had the ability to solve large-scale DTCTPTC.

Comparison of Laravel and Symfony - the most popular PHP frame-works, based on a simple CRUD application

The main purpose of the study is to compare the performance of two popular PHP language programming frameworks - Laravel 10 and Symfony 6 based on test CRUD-type applications. The benchmarking also considered the implementation process and support from the development community. The study was conducted using two simple and small test applications with the same sets of functionality. Performance was measured based on the execution times of single CRUD-type operations and handling dozens and hundreds of requests simultaneously. Execution times were measured via the Postman application from when the request was sent to the server until the response was received. The results showed that Symfony 6 is a better solution for applications where performance matters.

Comparative analysis of the performance of selected database management system

Currently, in the era of large amounts of generated data, a significant challenge is fast data processing. The aim of the article is to conduct a comparative analysis of the performance of relational and non-relational databases using MS SQL Server, MongoDB, and CouchDB as examples. The comparison was carried out based on developed research scenarios designed to examine the execution time of CRUD operations. A performance ranking of databases was established based on the obtained results, with SQL Server taking the first place, MongoDB the second, and CouchDB the third. For the operations of deletion, downloading, and updating of data, MS SQL Server proved to be the most efficient database. Only in the operation of inserting data did MongoDB turn out to be the most efficient database.

A fast and highly scalable frequent pattern mining algorithm

As the use of big data and its potential benefits become more widespread, public and private organizations around the world have realized the imperative of incorporating comprehensive and robust technologies into their business processes. In particular, companies are implementing more and more intelligent systems into their business processes, resulting in an exponential increase in the amount of data being collected and used in everyday life on a quarterly basis. Therefore, an algorithm that can efficiently explore the frequent patterns from big data will be able to productively analyze and utilize the generated data to better optimize resources and even develop new business models. Thus, in this urban data era, it is not only essential to make more efficient and accurate decisions to improve business profitability and customer satisfaction, but also a highly challenging core issue. In this study, a new approach, scalable FP mining, is proposed for the first time to achieve a certain level of performance and more efficient memory utilization despite large amounts of data. Experiment results for different data characteristics show that the best performance of the proposed method requires only 48.9% of the DP algorithm's execution time. In further experiments with increased exploration difficulty, the optimal execution time is only 37%. On specific associated databases, the proposed method maintains stable and excellent performance with 12.7% for different data characteristics. The experimental results with increasing exploration difficulty at each stage demonstrate the stability, robustness, and reliability of the proposed SFP, especially under high data complexity and exploration difficulty.

HEMA-Lysine-Based Cryogels for Highly Selective Heparin Neutralization.

Unfractionated heparin (UFH) and its low-molecular-weight fragments (LMWH) are widely used as anticoagulants for surgical procedures and extracorporeal blood purification therapies such as cardiovascular surgery and dialysis. The anticoagulant effect of heparin is essential for the optimal execution of extracorporeal blood circulation. However, at the end of these procedures, to avoid the risk of bleeding, it is necessary to neutralize it. Currently, the only antidote for heparin neutralization is protamine sulphate, a highly basic protein which constitutes a further source of serious side events and is ineffective in neutralizing LMWH. Furthermore, dialysis patients, due to the routine administration of heparin, often experience serious adverse effects, among which HIT (heparin-induced thrombocytopenia) is one of the most severe. For this reason, the finding of new heparin antagonists or alternative methods for heparin removal from blood is of great interest. Here, we describe the synthesis and characterization of a set of biocompatible macroporous cryogels based on poly(2-hydroxyethyl methacrylate) (pHEMA) and L-lysine with strong filtering capability and remarkable neutralization performance with regard to UFH and LMWH. These properties could enable the design and creation of a filtering device to rapidly reverse heparin, protecting patients from the harmful consequences of the anticoagulant.

Learning Sliding Policy of Flat Multi-target Objects in Clutter Scenes

In clutter scenes, one or several targets need to be obtained, which is hard for robot manipulation task. Especially, when the targets are flat objects like book, plates, due to limitation of common robot end-effectors, it will be more challenging. By employing pre-grasp operation like sliding, it becomes feasible to rearrange objects and shift the target towards table edge, enabling the robot to grasp it from a lateral perspective. In this paper, the proposed method transfers the task into a Parameterized Action Markov Decision Process to solve the problem, which is based on deep reinforcement learning. The mask images are taken as one of observations to the network for avoiding the impact of noise of original image. In order to improve data utilization, the policynetwork predicts the parameters for the sliding primitive of each object, which is weight-sharing, and then the Q-network selects the optimal execution target. Meanwhile, extra reward mechanism is adopted for improving the efficiency of task actions to cope with multiple targets. In addition, an adaptive policy scaling algorithm is proposed to improve the speed and adaptability of policy training. In both simulation and real system, our method achieves a higher task success rate and requires fewer actions to accomplish the flat multi-target sliding manipulation task within clutter scene, which verifies the effectiveness of ours.

Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment.

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.