Decision Strategies Research Articles

Prediction models and their role in advanced energy management systems supporting energy flexibility services

Automated decision tools, such as advanced energy management systems, are required to involve the electrical grid users in energy flexibility services. This paper focuses on the prediction models as a substantial part of decision strategy in advanced energy management systems and on advanced energy management systems as a tool that supports the active involvement of electrical grid users in energy flexibility services. Prediction models' desired properties are self-establishing and self-adaptation, which require new solutions in data selection, filtering, processing and model learning. Some of these properties are investigated within this paper.

Evidence from School Principals: Academic Supervision Decision-making on Improving Teacher Performance in Indonesia

Purpose: Decision Science is important in many fields, including education. In the current educational landscape, as educational processes evolve to meet the challenges of the fourth industrial revolution, this study utilizes a well-established impact factor, academic supervision, to enhance teacher performance. The expectations of school principals are considered critical factors in the implementation of decision supervisors. This study underscores the significance of elements influencing the improvement of teacher performance, with a focus on planning, decision-making, and implementation pressures within the context of academic supervision. Design/methodology/approach: This study employs a case study approach and collects data through interviews, observations, and document analysis. Data were analyzed by transcribing, taking notes, recording videos, and examining documents to generate ideas, codify data, develop themes, interpret the information, and draw conclusions. Findings: The study's findings reveal that the planning expectations for an academic supervision program decision are high and sufficient to consider the needs of teachers, involve all school apparatus components, and integrate the program with other activities. The expectations for making decisions and implementing the academic supervision program are met by fostering solid teamwork, increasing the frequency of classroom supervision visits, advancing the profession, and instilling a professional attitude among teachers. However, in terms of reviewing decisions, discussing them, seeking improvement solutions, and enhancing teacher performance, the evaluation expectations and follow-up results of academic supervision still need improvement. The findings of this study suggest that the decision-making expectations of principals as supervisors are crucial for decision performance and the success of academic supervision programs designed to make decisions and improve teacher performance. Originality/value: Considering the time constraints of this study, future researchers should explore school principals' expectations as supervisors in greater depth, especially concerning decision-making and the improvement of teacher performance. Practical implications: This study benefits school principals in their roles as decision-makers and supervisors, encompassing planning, implementation, and the monitoring of academic supervision decisions. It also covers outcomes such as reviews, discussions, and proposed solutions for enhancing teacher performance, with indirect implications for student learning.

A novel fuzzy-prospect theory approach for hydrogen fuel cell component supplier selection for automotive industry

The burgeoning automotive sector is an energy-intensive industrial sector, amounting to 94 M tons of oil equivalents (MTOE). Owing to more significant environmental concerns, there is a dire need to transition the energy-intensive mobility space to sustainable mobility. In this direction, hydrogen is being researched as a potential alternative clean fuel for the automotive sector; however, in the context of developing countries, hydrogen fuel cell technology is at a very nascent stage. Various policies and research projects are underway to realize the shift, but they must be underpinned through a robust supply chain network. The selection of an optimal supplier is key to the development of a robust hydrogen supply chain network. To this end, this work proposes the selection of an appropriate supplier for supplying critical components for different research and development projects of hydrogen fuel cells. This is still a lacuna in the context of developing nations. To aid in addressing this issue, a novel TODIM (TOmada de Decisao Interativa Multicriterio) decision-making framework has been proposed to select the optimal supplier to supply critical hydrogen fuel cell components. Within the proposed approach, the linguistic evaluations provided by experts are modeled using a more generalized linear Diophantine linguistic uncertain fuzzy set that circumvents the algorithmic restrictions on the falsity and truth grades. The linguistic assessments from the experts were aggregated using the linear Diophantine uncertain linguistic power Einstein weighted averaging (LDULPEWA) operator. The power Einstein operations take care of the interaction between falsity and truth grades, which is important for the accuracy of the results. Finally, the TODIM approach was used to deduce the final ranking results. The proposed method considers the specialists' willingness to take risks during the decision-making process. Such a decision-making framework is still a lacuna in the domain of decision science. The supposed results were tested for their stability through sensitivity analysis. A past case study was solved using the proposed approach to validate the rankings. A comparison of the suggested strategy with the existing prominent decision-making frameworks was also carried out to clearly highlight the strengths and weaknesses of the proposed approach.

Research on the implied carbon measurement and structural decomposition technology of power grid engineering based on structural analysis method

At present, with the development of “double-carbon”, it has an important impact on the investment form of the power grid, and the emerging investment objects continue to expand, and the new investment management mode is constantly highlighted. In this context, in-depth research is carried out on the total factor carbon measurement of new power system construction projects, and the total factor measurement framework system and technical method are put forward, which is conducive to improving the efficiency and efficiency level of investment control of power grid enterprises. Therefore, this paper puts forward the implicit carbon measurement and structure decomposition technology of power grid engineering based on structural analysis method, which provides support and reference for project type division and scientific decision-making under the new situation of power grid enterprises.

The Impact of Big Data-Driven Industrial Digital Unification System on Commercial Management Operational Efficiency

Abstract Integrated management digitization is an important way to boost the competitiveness of commercial management enterprises. In this paper, we first design the functional architecture and system deployment of the commercial management enterprise digital system, including digital operation management, commercial operation data mining analysis, and other functional modules to meet the commercial operation management needs of commercial complexes, office buildings, and urban integrated operation business. The K-means clustering algorithm is then improved by using a particle swarm algorithm that is based on it. Specifically, the distribution estimation algorithm and stagnation perturbation strategy are used to update the population information and control the particle position boundary. Then, the greedy approach is used to select the advantageous particles. Then, the digital unified construction system of the commercial management enterprise is finally constructed to realize the data mining function of the operation and management and to assist the enterprise in carrying out customer management, property rights management, scientific decision-making, risk assessment, and so on. Finally, after testing the data analysis performance of the system, the business performance of the W commercial management group company that uses the system of this paper for digital transformation is analyzed. It is found that the accuracy of this paper's algorithm is the same as the PSO-Kmeans method. Still, the number of iterations of this paper's algorithm is the least; the lowest is only 14 times, and the optimization of efficiency is significant. The return on net assets and the net sales margin of W Commercial Management improved from 15.8% and 28.12% in 2016 to 20.20% and 28.12% in 2023. The debt repayment and operation ability are also optimized substantially, and the system designed in this paper The effectiveness of the developed system is proved. This study provides a proven solution for the digital transformation of commercial management enterprises and improves the operational efficiency of commercial management.

Research progress on remediation of organochlorine pesticide contamination in soil.

Deteriorated soil pollution has grown into a worldwide environmental concern over the years. Organochlorine pesticide (OCP) residues, featured with ubiquity, persistence and refractoriness, are one of the main pollution sources, causing soil degradation, fertility decline and nutritional imbalance, and severely impacting soil ecology. Furthermore, residual OCPs in soil may enter the human body along with food chain accumulation and pose a serious health threat. To date, many remediation technologies including physicochemical and biological ways for organochlorine pollution have been developed at home and abroad, but none of them is a panacea suitable for all occasions. Rational selection and scientific decision-making are grounded in in-depth knowledge of various restoration techniques. However, soil pollution treatment often encounters the interference of multiple factors (climate, soil properties, cost, restoration efficiency, etc.) in complex environments, and there is still a lack of systematic summary and comparative analysis of different soil OCP removal methods. Thus, to better guide the remediation of contaminated soil, this review summarized the most commonly used strategies for OCP removal, evaluated their merits and limitations and discussed the application scenarios of different methods. It will facilitate the development of efficient, inexpensive and environmentally friendly soil remediation strategies for sustainable agricultural and ecological development.

Performance of aging consumers in an e-commerce product choice task: The role of working memory and decision strategies.

Over the past two decades, there has been a growing interest in research on aging and the decision-making behavior of older consumers. The subject of this article is multi-attribute decisions made using product comparison, a widely used functionality of many e-commerce stores. Studies on cognitive aging have established a negative relationship between age and accuracy in multi-attribute choice tasks; however, works in informatics (Human-Computer Interaction, UX research) have not accounted for how individual user differences affect the optimality of users' product comparison decisions. Our work attempts to bridge this gap between the disciplines of psychology of aging and informatics. We predicted and confirmed, in an online study simulating e-commerce shopping, the strong limitations of older adults in product comparison tasks. In subsequent modeling, other individual characteristics, such as visual working memory, were predicted and shown to be a reliable mediator of the relationship between age and decision accuracy. Popular product comparison tables are not sufficient for older consumers. Despite following UX guidelines for designing product comparison tables, overall correctness of consumer decisions in our study ranged from 90% to as little as 30%, depending on the difficulty of the task and the age of the consumer. These findings have important practical implications for UX design of e-commerce Websites.

Supplemental Material for Decisions as Ill-Posed Problems: A Scoping Review of Regularization Methods in Decision Science

Supplemental Material for Decisions as Ill-Posed Problems: A Scoping Review of Regularization Methods in Decision Science

Analyzing Global Trends and Collaborations in Library and Information Science Research: A Bibliometric and Social Network Analysis Perspective

Universities globally have long been pivotal in fostering education, research, and societal service. Since the research is a key driver of university missions, it enhances institutions' reputation and academic standing. In Library and Information Science (LIS), producing publishable research is crucial, constantly adapting to educational trends like technology and big data. This study employed Bibliometrics and Social Network Analysis (SNA) to examine LIS research output, employing the Bibliometrix-R package to analyze trends in 14,517 Scopus articles from 1954 to 2023. Additionally, the content analysis was also employed to cover the Bibliometrics analysis on the topic of computer science, decision science, physics, engineering, social science and mathematics. The findings highlighted that Wuhan University and Chinese researchers in China were at the first rank in LIS International research and collaborations based on keyword searching with 'Information Science' and 'Information Retrieval' in Scopus. As a result, the study would assist in the LIS research development and future international collaborations and suggested that the need for a broader database and keyword refinement should be included in future studies.

Pharmaceutical changes and countermeasures for transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on human use experience

Traditional Chinese medicine(TCM) preparations in medical institutions, as a unique and important form of preparations in China, have a long history of human use and serve as a bridge between clinical experience prescriptions and new Chinese medicine preparations. The state encourages medical institutions to transform their preparations into new traditional Chinese medicines, emphasizing their role as "incubators". Since the proposal of the traditional Chinese medicine registration and evaluation evidence system with the integration of TCM theory, human use experience(HUE), and clinical experience, the idea of transforming preparations used in medical institutions into new drugs based on HUE has been increasingly valued by drug research and development organizations. In the transformation process, pharmaceutical changes should be concerned from multiple aspects. This paper discusses the pharmaceutical changes and countermeasures based on the transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on HUE from the aspects of excipients, dosage forms, production technology, production scale, packaging materials and containers, production sites, and registration standards. It is emphasized that scientific decisions should be made according to the characteristics and clinical needs of drugs to ensure the stability of drug quality. The impacts of pharmaceutical changes on drug quality should be objectively assessed based on appropriate evaluation indexes and detection methods. The layout should be carried out in advance, and the key pharmaceutical information of the preparations should be kept stable, so as to underpin the transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on HUE.

Golfe como Inovação no Ensino de Finanças: Relato de uma Experiência Pioneira

ABSTRACT Objective to analyze the possibility of using golf as a metaphor for teaching finance. Study reports on a pilot experience of teaching finance using golf as a metaphor.Golf can be a means of promoting the engagement of finance students. Theoretical framework: a normative and positive economic decision theory is used as a framework. Sports can be a means of engaging students in finance education programs. Golf, in particular, can fulfill this role and serve as a powerful tool for building and maintaining social networks relevant to high-level professionals. Methods: thirty undergraduate students voluntarily participated in a pilot teaching program over five days. The program included sessions (in the field) on the fundamentals of golf and financial decisions, and students looked for synergies between these two domains. Participants showed a high level of interest in both the sports practice sessions and the finance activities. Results: the results suggest that psychological errors common to golf and financial behavior-for example, overoptimism, overconfidence, and emotional judgments-can be diagnosed and addressed through sports practice. In addition, the participants’ self-assessment indicates the possibility that the program can induce behaviors in line with the corporate environment. Conclusions: Despite golf’s contribution to the teaching of business subjects, it is still absent from formal curricula.

Issues and Optimization Strategies in Corporate Investment and Financing Decisions

Issues and Optimization Strategies in Corporate Investment and Financing Decisions

Considering the Beacon technology, a Green and eco-friendly revolution in the Moroccan Commercial Malls for a proximity marketing strategy

Proximity marketing is becoming an important strategy that enables linking retailers and consumers to enhance the customer experience. This marketing action aims to consider the customer location to provide a significant information at the right moment and space. Beacon technology is a real advancement in this domain of study. Beacons are a small device-wireless communication that allow detecting the presence of a person in the store through the low-energy Bluetooth signals and deliver impressive offers to visitors’ mobile phone from their area or product proximity inside the store. The objective is to offer an in time individual experience and interact efficiently with visitors. This paper presents a quantitative empirical study that aims to analyze the results of embodying the beacon technology in the large Moroccan commercial malls. For this, an online questionnaire was prepared to assemble consumers’ feedback regarding the use of this proximity marketing action. The results of this empirical study will help notifying marketers in their strategies by coming up with quantitative data collection that analyzes the customer perception of this technology, consequently, Moroccan malls may consider this relevant proximity marketing strategy in the future decisions to improve the customer experience for a long-term loyalty.

The Impact of Online Advertising on the Store Visiting: Saudi Arabia

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of electronic advertising in Saudi Arabia. The main purpose of this research is to explore the impact of internet advertising on companies’ visits to stores through an exploratory study. The systematization of literary sources and approaches for solving electronic advertising problems indicates that new mediating and moderating variables could have an impact on the relation between Internet advertising and visits to stores. In this research, the disposition to visit the point of sale is assimilated as a mediating construct rather than an implication with the product as a moderating variable. The relevance of this scientific problem decision is that this investigation of the consequences of electronic advertising for offline customer comportment in Saudi Arabia is unprecedented research that seeks to address this interesting question. The investigation of the role of online advertising and the impact of mediating and moderating variables on the visit to the store in the paper was carried out in the following logical sequence: the data were collected via an electronic survey. Of the 500 randomly selected Internet users in Saudi Arabia, only 350 responses were valid. This research used a structural equation model and multigroup analysis to examine the direct and indirect influences of variables. This paper presents the results of an empirical analysis that revealed a positive relation between internet advertising and visits to points of sale. The disposition to visit the point of sale mediates the relationship between Internet advertising and concrete visits to the store. The effect of the product moderates the effect of internet advertising on the disposition to visit the point of sale in Saudi Arabia. The research empirically confirms and theoretically proves that Saudi shoppers fine online advertising to be attractive when this advertising must have some actions and concepts flash. Therefore, Saudi directors should follow this form and use an effective mixture of content and animation. The e-shoppers who are involved are persons who have little engagement; for this reason, they are simply concerned about the tools of the publicity communication.

Mutual Influence Mechanism and Optimization Strategy of University Town and Urban Economic Growth Based on Time Series Forecasting Model

Abstract University towns and urban economic growth have important interactions, and time series forecasting models can enable urban planners to better clarify the relationship between the two so as to make more scientific and reasonable decisions. Based on the time series forecasting model constructed in this paper, this paper analyzes and forecasts the relationship between university cities and urban economic growth in C. The main conclusions of this paper are that the p-value of ARIMA(0,2,3) and ARIMA(1,1,1) models is lower than 0.05, and the prediction accuracy of ARIMA(1,1,1) model is higher than that of ARIMA(0,2,3) and ARIMA(1,1,1) model in the prediction of the two. ARIMA(0,2,3) with predicted values of 27631.3, 31541.5, and 24412.45, and the error values from the actual values are only 304.98, 1997.49, and 867.95. This paper’s time series prediction model has achieved good results, as evidenced by this. Y1 shows a highly positive correlation with X1, X3, and X4 and a significant positive correlation with X2, which leads to the conclusion that the total amount of investment in urban construction of the university city of C city has a substantial relationship with the economic growth of C city.

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

As the critical dimensions of semiconductor manufacturing processes gradually decrease, the requirements for production yield management become increasingly stringent. During the manufacturing process, there are many different types of defects, such as micron-sized particles, millimeter-sized scratches, etc. Multiple categories and different scales bring great challenges to the detection and identification of defects. This paper provides a full-flow surface defect identification method based on spot scanning scattering for unpatterned wafers. First, an adaptive threshold method with dynamic kernel windows is used to perform line-by-line scanning inspection of the wafer Mercator image. The 3σ decision strategy is used to avoid the impact of defects on background estimation and to improve detection sensitivity. After morphological processing, connected domain analysis is performed to obtain the defect mask, and feature information such as the shape, size, and distribution of the defect is extracted. Finally, the defect identification is performed by rules based binning, and the identified defects are converted into wafer polar coordinate image for display and analysis. In the experiments, the proposed method is used to identify micron-scale particles as well as large scratches on the millimeter scale for SiC wafers. Relative to the actual production rate requirement of 20 wafers per hour, the analysis time for a 6-inch wafer is 24.4 s, which can meet the requirement. Meanwhile, the test results illustrate the effectiveness of the method. The proposed method is recommended for early-stage defect detection and identification of unpatterned wafers.

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces.

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are relevant to the model's decision. These explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by extracting at some intermediate layer of a neural network, subspaces that capture the multiple and distinct activation patterns (e.g. visual concepts) that are relevant to the prediction. To automatically extract these subspaces, we propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis. This allows for a much stronger focus of the analysis on what the ML model actually uses for predicting, ignoring activations or concepts to which the model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.

Research on MEC computing offload strategy for joint optimization of delay and energy consumption.

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes.

Dosimetric comparison of IMPT vs VMAT for multiple lung lesions: an NTCP model-based decision-making strategy

To compare the dosimetric differences in volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT) in stereotactic body radiation therapy (SBRT) of multiple lung lesions and determine a normal tissue complication probability (NTCP) model-based decision strategy that determines which treatment modality the patient will use. A total of 41 patients were retrospectively selected for this study. The number of patients with 1-6 lesions was 5, 16, 7, 6, 3, and 4, respectively. A prescription dose of 70 GyRBE in 10 fractions was given to each lesion. SBRT plans were generated using VMAT and IMPT. All the IMPT plans used robustness optimization with ± 3.5% range uncertainties and 5 mm setup uncertainties. Dosimetric metrics and the predicted NTCP value of radiation pneumonitis (RP), esophagitis, and pericarditis were analyzed to evaluate the potential clinical benefits between different planning groups. In addition, a threshold for the ratio of PTV to lungs (%) to determine whether a patient would benefit highly from IMPT was determined using receiver operating characteristic curves. All plans reached target coverage (V70GyRBE ≥ 95%). Compared with VMAT, IMPT resulted in a significantly lower dose of most thoracic normal tissues. For the 1-2, 3-4 and 5-6 lesion groups, the lung V5 was 29.90 ± 9.44%, 58.33 ± 13.35%, and 81.02 ± 5.91% for VMAT and 11.34 ± 3.11% (p < 0.001), 21.45 ± 3.80% (p < 0.001), and 32.48 ± 4.90% (p < 0.001) for IMPT, respectively. The lung V20 was 12.07 ± 4.94%, 25.57 ± 6.54%, and 43.99 ± 11.83% for VMAT and 6.76 ± 1.80% (p < 0.001), 13.14 ± 2.27% (p < 0.01), and 19.62 ± 3.48% (p < 0.01) for IMPT. The Dmean of the total lung was 7.65 ± 2.47 GyRBE, 14.78 ± 2.75 GyRBE, and 21.64 ± 4.07 GyRBE for VMAT and 3.69 ± 1.04 GyRBE (p < 0.001), 7.13 ± 1.41 GyRBE (p < 0.001), and 10.69 ± 1.81 GyRBE (p < 0.001) for IMPT. Additionally, in the VMAT group, the maximum NTCP value of radiation pneumonitis was 73.91%, whereas it was significantly lower in the IMPT group at 10.73%. The accuracy of our NTCP model-based decision model, which combines the number of lesions and PTV/Lungs (%), was 97.6%. The study demonstrated that the IMPT SBRT for multiple lung lesions had satisfactory dosimetry results, even when the number of lesions reached 6. The NTCP model-based decision strategy presented in our study could serve as an effective tool in clinical practice, aiding in the selection of the optimal treatment modality between VMAT and IMPT.

Nash equilibria in risk-sensitive Markov stopping games under communication conditions

&lt;p&gt;This paper analyzes the existence of Nash equilibrium in a discrete-time Markov stopping game with two players. At each decision point, Player Ⅱ is faced with the choice of either ending the game and thus granting Player Ⅰ a final reward or letting the game continue. In the latter case, Player Ⅰ performs an action that affects transitions and receives a running reward from Player Ⅱ. We assume that Player Ⅰ has a constant and non-zero risk sensitivity coefficient, while Player Ⅱ strives to minimize the utility of Player Ⅰ. The effectiveness of decision strategies was measured by the risk-sensitive expected total reward of Player Ⅰ. Exploiting mild continuity-compactness conditions and communication-ergodicity properties, we found that the value function of the game is described as a single fixed point of the equilibrium operator, determining a Nash equilibrium. In addition, we provide an illustrative example in which our assumptions hold.&lt;/p&gt;