Selection Policy Research Articles

Интерактивное приложение, реализующее метод уверенных суждений на массовой программной платформе

The article examines the functionality and architecture of an interactive application designed to support multi-criteria decision-making using S.A. Piyavsky's confident judgment method. It provides a description of key components of this method essential for presenting the topic in a computer-oriented format for data and knowledge representation. The application stores and provides access to a bank of universal importance coefficients for specific criteria and serves as the core of an information methodology for decision-making based on the confident judgment method. This methodology includes the following stages: compiling a list of alternative solutions with evaluations by a set of specific quantitative criteria; establishing a selection policy that orders criteria by importance for the decision-maker; normalizing the multi-criteria problem statement in line with a foundational optimization model; evaluating each alternative solution within this model; and selecting the optimal alternatives. The application automatically coordinates the creation and editing of the list of alternative solutions according to variation boundaries set by the selection policy, integrating all solution stages with user feedback. The application’s functionality is represented by a diagram linking the main activities and their outcomes, along with a UML diagram of application use cases. The architecture of the application is presented as a mind map. The application is implemented on the Excel platform using VBA programming language, chosen to ensure accessibility and support the development of advanced multi-criteria decision-making methods for a broad user base who use Excel in their professional work.

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies. History: This paper has been accepted for the Transportation Science special issue on TSL Conference 2023. Funding: This work was supported by TKI DINALOG.

Evaluating the Effectiveness of Traditional Recruitment and Selection Practices in a Technologically Dominated Economy

In the contemporary business environment, there is a need for firms to plan and implement proactive traditional recruitment and selection, especially regarding employees working in the gig economy. However, the poor handling of traditional recruitment and selection processes can significantly affect the quality of employees and their performance in an organisation. This study aimed to evaluate the effectiveness of traditional recruitment and selection practices in a technologically dominated economy. The importance of attracting and selecting a highly skilled workforce in a tight and competitive market has made adopting best practices in recruitment and selection by organisations critical. Firstly, the study highlighted various traditional recruitment and selection methods in organisations. Secondly, the overall recruitment and selection process was identified. Finally, strategies to mitigate the identified challenges faced in the traditional recruitment and selection process were suggested. The study was qualitative, adopting the survey strategy. Purposive was adopted, and hematic was used for data analysis. Findings revealed that traditional recruitment and selection as a practice is important for both employees involved in the gig economy. Importantly, the strategic approach to traditional recruitment and selection practices is vital and should be the cornerstone of any projected recruitment and selection process in a gig economy. Results validate the critical role of the traditional recruitment and selection process in achieving organisational aims and objectives in a technologically dominated economy. The study also proves that collaborative methods are central to the success of any traditional recruitment and selection process. Recommendations affirmed that recruitment and selection should be planned and well implemented. Human resource planning was also measured to be vital and emphasis on significant features of traditional recruitment and selection as a process such as having a place a well and implemented traditional recruitment and selection policy was asserted.

Impacts of human resources management strategies and practices on workers performance in construction industry: a review

Human resource management (HRM) plays a vital role in the growth and sustainability of companies and in achieving company objectives, as HRM relates to the workers’ practices and their functional roles. The main objective of this paper is to identify the HRM strategies, practices and their impact on worker’s performance in the Architecture, Engineering, and Construction (AEC) Industry. The method which was used in this study was based on three stages. After the completion of the database and web engine search, the total number of sources found were 149. Next, the sources’ titles and abstracts were reviewed and those marked as relevant to the review were chosen to be retrieved and thoroughly reviewed. The sources were chosen based on the following inclusion criteria: (a) the sources implied in HRM strategies in the AEC industry, (b) the sources released between 2010 and 2023, (c) the online sources, and (d) the English-language sources. Also, the selected sources are reviewed to extract the factors using the content analysis method which is a thorough and systematic study of the contents of a specific body of material. In addition, content analysis is carried out to extract practices of HRM strategies and their impact on worker performance. This can be followed up and observed through the figures and tables that come later. As a final result, which concludes the outputs of the previous stages which results in a first-round total of 31 HRM strategies practice but in the second-round result of reaching 39 different sources from 149 related sources. After that, discussing the most popular between them based on the appearance of it in the sources which reviewed beside that, the impact of it on worker’s performance were viewed in the AEC industry. Based on that, it was found the following 5 categories of strategies: (1) Human Resources Planning; (2) Polarization and Recruitment; (3) Training and Development; (4) Human Resources Following-up; (5) Career Planning. At the same time the most popular practices are based on appearance in sources: In terms of Human Resources Planning Strategy, “The company’s management analyses the functions accurately”. In Polarization and Recruitment Strategy, “The company’s internal resources are the best to provide its human resource needs” and “Selection and recruitment policies are in line with the company’s current and future needs”. In Training and Development of Human Resources Strategy, “The company evaluates the results of development and training programs to achieve the purpose of feedback”. In Human Resources Following-up Strategy, “There is a fair incentive system in the company”. In Career Planning Strategy, “There is a clear description of the relationship of workers to each other”. Finally, human resource management strategies and practices are one of the major players in the AEC industry, which is evidenced by its impact on the performance of workers. First published online 22 October 2024

IMPLEMENTASI METODE DELPHI TERHADAP TUGAS AKHIR MATAKULIAH TEFL

In the era of globalization and rapid technological development. To prepare students to face social changes, the world of work and rapid technological advances, student competencies must be improved and prepared to face the demands of the times. and these optimal and relevant skills must be considered in the Teaching English as a Foreign Language (TEFL) course. Microteaching in TEFL courses must be adapted to the demands of the times. Therefore, the Delphi Method can be used to form guidelines and predict trends. The five main uses of Delphi in higher education, include: cost effectiveness, cost-benefit analysis, curriculum and campus planning, university education goals and objectives, generalized futuristic education goals and objectives. This research uses a descriptive qualitative method with a literature study approach. Keywords in qualitative research, namely, the process of understanding, and humans. both natural phenomena and man-made phenomena. These phenomena can be activities, and the difference between one phenomenon and another. This is indicated by the results of the standard deviation which has reached the optimal level. Based on the results of the priority sequence reference, it can be determined that students are required to record microteaching practices. themselves, and upload it to YouTube and then report it to the lecturer. YouTube's selection policy is based on this platform not limiting the length of the video, so students can be as creative as possible in class.

An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks

Monte Carlo tree search (MCTS) has been gaining increasing popularity, and the success of AlphaGo has prompted a new trend of incorporating a value network and a policy network constructed with neural networks into MCTS, namely, NN-MCTS. In this work, motivated by the shortcomings of the widely used upper confidence bounds applied to trees (UCT) policy, we formulate the node selection problem in NN-MCTS as a multistage ranking and selection (R&S) problem and propose a node selection policy that efficiently allocates a limited search budget to maximize the probability of correctly selecting the best action at the root state. The value and policy networks in NN-MCTS further improve the performance of the proposed node selection policy by providing prior knowledge and guiding the selection of the final action, respectively. Numerical experiments on two board games and an OpenAI task demonstrate that the proposed method outperforms the UCT policy used in AlphaGo Zero and MuZero, implying the potential of constructing node selection policies in NN-MCTS with R&S procedures. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72325007, 72250065, and 72022001], and a PKU-Boya Postdoctoral Fellowship 2406396158. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0307 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0307 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Optimal quantized feedback control for linear quandratic Gaussian systems with input delay

AbstractThis paper is concerned with the optimal quantized feedback linear quadratic Gaussian (LQG) control problem for a discrete‐time stochastic system with input delay as well as the measurements to be quantized before transmitted to the controller. In this scenario, the system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to maintain an optimal balance between control performance and quantization cost. It is shown that this problem can be decoupled into two optimization problems when the innovation signal is quantized instead of state: one for optimal controller synthesis and the other for optimal quantizer selection. More specifically, a necessary and sufficient condition is derived for the optimal control problem based on Pontryagin's maximum principle. On the other hand, the optimal quantizer selection policy is established by dealing with a certain Markov decision process (MDP).

A cross domain access control model for medical consortium based on DBSCAN and penalty function

BackgroundGraded diagnosis and treatment, referral, and expert consultations between medical institutions all require cross domain access to patient medical information to support doctors’ treatment decisions, leading to an increase in cross domain access among various medical institutions within the medical consortium. However, patient medical information is sensitive and private, and it is essential to control doctors’ cross domain access to reduce the risk of leakage. Access control is a continuous and long-term process, and it first requires verification of the legitimacy of user identities, while utilizing control policies for selection and management. After verifying user identity and access permissions, it is also necessary to monitor unauthorized operations. Therefore, the content of access control includes authentication, implementation of control policies, and security auditing. Unlike the existing focus on authentication and control strategy implementation in access control, this article focuses on the control based on access log security auditing for doctors who have obtained authorization to access medical resources. This paper designs a blockchain based doctor intelligent cross domain access log recording system, which is used to record, query and analyze the cross domain access behavior of doctors after authorization. Through DBSCAN clustering analysis of doctors’ cross domain access logs, we find the abnormal phenomenon of cross domain access, and build a penalty function to dynamically control doctors’ cross domain access process, so as to reduce the risk of Data breach. Finally, through comparative analysis and experiments, it is shown that the proposed cross domain access control model for medical consortia based on DBSCAN and penalty function has good control effect on the cross domain access behavior of doctors in various medical institutions of the medical consortia, and has certain feasibility for the cross domain access control of doctors.

Complex Infection-Control Measures with Disinfectant Switch Help the Successful Early Control of Carbapenem-Resistant Acinetobacter baumannii Outbreak in Intensive Care Unit.

A carbapenem-resistant Acinetobacter baumannii (CRAB) outbreak in an intensive care unit (ICU) was contained by an improved infection-control measure that included a disinfectant policy. In our retrospective cohort study, we describe the epidemiological investigations and infection-control measures during this outbreak. Descriptive analysis was used to summarize patient demographics, neurological diseases, surgical treatment, underlying diseases, infection, and outcomes. In December 2023, two CARB-positive patients were observed in the ICU, and four more patients became CRAB-positive in January. During this outbreak, there was an overlap of hospitalization periods among the CRAB-positive patients, and CRAB was isolated from the environment; the isolated CRAB strain was identical. Infection-control measures, including hand hygiene, contact precautions and isolation, surveillance, decolonization, environmental cleaning, and disinfection, were reviewed and modified. The aim of this study was to examine the molecular background of the effectiveness of the disinfectant shift used during successful outbreak control. Experiments were carried out to study the phenotypic sensitivity and genetic background of different disinfectant agents. A thorough analysis of the detected CRAB strain included whole-genome sequencing (WGS), investigation of the qacE and qacEΔ1 genes' relative expression by qPCR after exposure to different disinfectant solutions, as well as an analysis of biofilm formation. WGS analysis of the CRAB strain identified that an ST2 high-risk clone was responsible for the outbreak, which produced OXA-83 and ADC-30 beta-lactamases; in addition, qacE and qacEΔ1 genes were also detected, which confer resistance to disinfectants containing quaternary ammonium compounds (QACs). A qPCR analysis demonstrated that after exposure to different disinfectants, the gene expression levels of qacE and qacEΔ1 increased and correlated with concentrations of QACs of disinfectants. During the outbreak, the standard-of-care QAC-based disinfectant was changed to a mainly alcohol-based agent in the ICU, which contributed to the successful control of this outbreak, and no additional patients were identified with CRAB. We conclude that continuous surveillance and hand hygiene training combined with fast identification and reaction to new cases, as well as an in-depth analysis of multidrug-resistant outbreak strains and investigation of their disinfectant tolerance/resistance during an outbreak, are essential to effectively control the spread of nosocomial pathogens. The smart policy of disinfectant agent selection played a crucial role in controlling the outbreak and ensuring patient safety in the ICU.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

A quantum artificial bee colony algorithm based on quantum walk for the 0–1 knapsack problem

Based on the characteristics of the quantum mechanism, a novel quantum walk artificial bee colony algorithm is proposed to promote performance. Firstly, the discrete quantum walk is an approach taken to search for new food sources in the updated phase for employed bees and onlooker bees, which can enhance the probability of the target solution to extend the exploration capability. Secondly, the food source selection policy of the onlooker bees changes, from roulette selection to tournament selection, to boost exploitation and convergence speed. Finally, the novel algorithm is brought forward, along with the approach to analyze 0–1 knapsack problems. The experimental results prove that our algorithm can overcome the premature phenomenon and perform better in the areas of search capability, convergence speed, and stability performance. The performance is superior to that of the conventional artificial bee colony algorithm, as well as the genetic algorithm, in a set of 0–1 knapsack problems.

Regulation-induced digitalization

This paper investigates how environmental regulation induces firm digitalization. We construct a digital index based on textual analyses and find that after the implementation of the program, pilot firms' digitalization increased relative to that of a group of carefully matched control firms, which is opposite to the findings in the extant literature on technology adoption. This increase cannot be fully explained by regional unobservables, firms' own innovation, firm selection, or other policies. The results are robust when we consider firm subsidiaries. The increase in digitalization is not due to regulatory arbitrage, and the industry-level concentration of digitalization changes little.

Criteria and prognostic models for patients with hepatocellular carcinoma undergoing liver transplantation.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

DBSCAN based approach for energy efficient VM placement using medium level CPU utilization

Virtual machine placement (VMP) is a popular problem in Cloud Data Centers (CDCs). An efficient virtual machine (VM) allocation is essential for processor speed and energy saving. This is more useful where the CDC uses an Internet of Things (IoT) infrastructure. To enhance energy savings, we aim to improve the adaptive four thresholds energy-aware framework for VM deployment. We observed that the role of the threshold for identifying the over-loaded host is crucial. In order to determine the appropriate threshold, we employed density-based spatial clustering of applications with noise (DBSCAN), medium absolute deviation (MAD), and interquartile range (IQR) using the medium fit power efficient decreasing (MFPED) algorithm. Our proposed algorithm modified medium fit energy efficient decreasing (MMFEED) achieves a reduction in energy consumption of 47.3%, 46.1%, 39%, 23.2%, 10.9%, and 3.4% compared to the IQR, MAD, static threshold (THR), exponential weighted moving average (EWMA), modified energy-efficient virtual machine placement (MEEVMP), and adaptive four threshold energy-aware framework for VM deployment energy efficient (AFED-EF), respectively, under the minimum migration time (MMT) selection policy. The proposed algorithm outperforms these algorithms in terms of energy consumption for VM selection policy MMT.

STAFF TRAINING AND DEVELOPMENT OF NON- PROFESSIONAL STAFF OF UNIVERSITY LIBRARIES IN NORTH-EAST, NIGERIA

This study investigated staff training and development for non-professional staff in university libraries in North-east, Nigeria. The study was guided by four research questions including staff training and development programmes provided, staff training and development programmes for non-professional staff, selection of non-professional staff for training and development programme, attitude of non- professional staff to the existing training and development programmes. Descriptive survey was adopted for the study. The population of the study was 274 comprised non-professional staff selected from 13 university libraries in North- east, Nigeria. Questionnaire and interview were used as instruments for data collection. Data were analysed using Mean (x̅) and Standard Deviation (SD). Findings revealed that majority of the staff training programmes were provided to a high extent; especially orientation programmes, computer training/Internet literacy and is highly relevant. The findings also revealed that favoritism is the basis for selection processes of staff for training programmes, inadequate funding, poor knowledge of ICT facilities are the problems facing staff training and development programmes. The study recommended, among others, adequate funding for more training, adequate provision of training facilities, develop selection policy to guide in staff training, inadequate funding and lack of computer training/Internet literacy should be properly addressed by the management.

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming.

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of learning how many cuts to select. Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well. To address these challenges, we propose a novel hierarchical sequence/set model (HEM) to learn cut selection policies. Specifically, HEM is a bi-level model: (1) a higher-level module that learns how many cuts to select, (2) and a lower-level module-that formulates the cut selection as a sequence/set to sequence learning problem-to learn policies selecting an ordered subset with the cardinality determined by the higher-level module. To the best of our knowledge, HEM is the first data-driven methodology that well tackles (P1)-(P3) simultaneously. Experiments demonstrate that HEM significantly improves the efficiency of solving MILPs on eleven challenging MILP benchmarks, including two Huawei's real problems.

A Cross-Sectional Study of Measles-Specific Antibody Levels in Australian Blood Donors-Implications for Measles Post-Elimination Countries.

Passive immunisation with normal human immunoglobulin (NHIG) is recommended as post-exposure prophylaxis (PEP) for higher-risk measles contacts where vaccination is contraindicated. However, the concentration of measles-specific antibodies in NHIG depends on antibody levels within pooled donor plasma. There are concerns that measles immunity in the Australian population may be declining over time and that blood donors' levels will progressively decrease, impacting levels required to produce effective NHIG for measles PEP. A cross-sectional study of Australian plasmapheresis donors was performed using an age-stratified, random sample of recovered serum specimens, collected between October and November 2019 (n = 1199). Measles-specific IgG antibodies were quantified by ELISA (Enzygnost anti-measles virus IgG, Siemens), and negative and equivocal specimens (n = 149) also underwent plaque reduction neutralisation testing (PRNT). Mean antibody levels (optical density values) progressively decreased from older to younger birth cohorts, from 2.09 [±0.09, 95% CI] to 0.58 [±0.04, 95% CI] in donors born in 1940-1959 and 1990-2001, respectively (p < 0.0001). This study shows that mean measles-specific IgG levels are significantly lower in younger Australian donors. While current NHIG selection policies target older donors, as younger birth cohorts become an increasingly larger proportion of contributing donors, measles-specific antibody concentrations of NHIG will progressively reduce. We therefore recommend monitoring measles-specific antibody levels in future donors and NHIG products in Australia and other countries that eliminated measles before the birth of their youngest blood donors.

Determining the strength of evidence for an association between sexual indicators and risk of acquiring HIV and sexulaly transmitted infections: Providing evidence for blood donation policy change.

In 2019 the For The Assessment Of Individualised Risk (FAIR) project began a review of UK blood donor selection policy to determine if a more individualised approach to donor selection could be safely implemented. An evidence base was required to inform selection policy to move from a population to a more individual based policy, specifically what sexual behaviours/indicators should be considered as screening questions to maintain the safety of the blood supply. Eight sexual behaviours/indicators were reviewed: history of bacterial sexually transmitted infections (STIs), chemsex, number of recent partners, condom use, type of sex, sexual health service (SHS) attendance, new sexual partner and exclusivity. We conducted searches in multiple databases to identify literature looking at the association between these behaviours/indicators and HIV/STI acquisition risk. A scoring system to determine strength of evidence was devised and applied to papers that passed screening. Key studies were identified which achieved the maximum score and more in-depth reviews were conducted for these. We identified 58 studies, including 17 key studies. Strong evidence was found linking a previous bacterial STI, chemsex and increasing numbers of sexual partners to acquisition risk. Condom use, type of sex and new partners were found to have some strength of evidence for this link. SHS attendance and exclusivity had minimal evidence. We recommended that the behaviours/indicators viewed as having strong or some strength of evidence should be considered as screening questions in a more individualised approach to donor selection criteria.

Learn how to create lawful selection policy for student organizations

Colleges and universities are still finding their footing after Students for Fair Admissions, Inc. v. President &amp; Fellows of Harvard College (referred to as “SFFA”), 600 U.S. 181 (2023). In SFFA, the U.S. Supreme Court found that race‐based admission processes at colleges and universities were unconstitutional, overturning decades of precedent.

Adaptive graph active learning with mutual information via policy learning

Graph neural networks entail massive labeled samples for training, and manual labeling generally requires unaffordable costs. Active learning has emerged as a promising approach to selecting a smaller set of informative labeled samples to improve model performance. However, few active learning techniques for graph data account for the cluster structure and redundancy of samples. To address these issues, we propose an approach that employs uncertain information as an observation for a reinforcement learning agent to adaptively learn a node selection policy. We construct states using node information obtained via mutual information, which considers both the graph structure and the node attributes. The proposed method accurately quantifies node information by leveraging the receptive field of the graph convolutional network and capturing the clustering structure of the data, taking into account the low redundancy and diversity of the labeled samples. Experiments conducted on real-world datasets demonstrate the superiority of the proposed approach over several state-of-the-art methods.