Future Performance Research Articles

Using Software Mitigation Schemes to improve the availability of IoT applications in harsh radiation environment

The integration of IoT infrastructure in the context of particle accelerators promises numerous benefits (reduced costs and maintenance time, increased deployment). However, the use of microcontroller units (MCUs), typical of IoT systems, can potentially compromise future accelerator availability performances. This paper presents Software Mitigation Schemes (SMS) designed to improve the availability performance of MCU-based systems under radiation. Their effectiveness is demonstrated through a radiation test on a CERN Wireless IoT Radiation Monitoring system, also called BatMon. The results underline the IoT devices' feasibility as a viable solution for high-distribution systems in the High-Luminosity Large Hadron Collider (HL-LHC) or Future Circular Collider (FCC).

Association between the American Board of Emergency Medicine Oral Certifying Examination and Future State Medical Board Disciplinary Actions.

The American Board of Emergency Medicine (ABEM) requires a written examination (the Qualifying Examination) followed by the Oral Certifying Examination (OCE) to obtain ABEM certification. Maintaining ABEM certification is associated with fewer state medical board (SMB) disciplinary actions. We sought to determine the association between poor initial performance on the OCE and subsequent severe SMB disciplinary action. We included physicians who completed US categorical emergency medicine residencies in 2016 and earlier. We classified OCE performance as good (passed on first attempt) and poor (never passed or required>1 attempt to pass). We obtained data on physician SMB disciplinary actions from the National Practitioner Data Bank that were limited to actions that denied licensure or altered the status of a medical license (eg, suspension). We determined the association between poor OCE performance and subsequent severe SMB disciplinary action. Of 34,871, 93.5% passed the OCE on the first attempt, 6.1% required multiple attempts, and 0.3% never passed. Of the physicians (93.5%) with good OCE performance, 1.0% received a severe SMB action. Among physicians with poor OCE performance, 2.3% received a severe action; and of those who never passed, 1.7% received a severe action (Table 1). Poor OCE performance was associated with an increased odds of severe SMB disciplinary action (OR 2.21, 95% CI: 1.57-3.12). Physicians with poor OCE performance exhibited higher odds of experiencing a subsequent severe SMB disciplinary action. The OCE may have utility as a predictor of future professionalism or clinical performance.

Informed trading by hedge funds

AbstractUsing daily equity transactions, we create a hedge fund informed trading measure (ITM) that separates concentrated information‐related trades from liquidity‐driven basket trades. We find that stocks with higher ITM are associated with higher future stock performance. The long–short portfolio delivers 4% annual alpha after controlling for size, value, momentum, and illiquidity factors. We attribute informed trading to hedge funds' ability to identify and correct stock underpricing. The results are robust to several ways of constructing and sorting the measure, and we do not find a return reversal in four quarters, indicating that the measure is information related.

Private Islamic Hospital Service Management Strategy Model in Pekanbaru Based on Accreditation with an Analytic Network Process Approach

There are many factors that are the burden and responsibility of Islamic Hospital Practitioners, experts and regulators in resolving sharia service and accreditation problems and prioritizing effective solutions for Islamic Hospitals. From problems (1)Sharia services that focus on Customer Oriented, Transparency, Healthy Competition and Justice. (2)Patient problems, namely no sharia consultations, unsatisfactory services received, long queues, lack of services for disabled patients (3)Accreditation problems that focus on low classification and accreditation, poor maintenance, unsophisticated information systems and a poor hospital environment. not properly maintained (4)Ethical problems and Islamic hospital Business Management Concepts trigger problems. Planning for future hospital performance is not fulfilled, experts and employees are less professional, coordination of hospital staff is ineffective and does not have a Sharia Supervisory Board (DPS). This thought is the research problem, how to use the ANP method to find a solution that must be solved in this problem, assisted by Super Decision software. The results of this research are Solution Priorities (1) Sharia services, namely focusing on the main priority of Justice, Customer Oriented (2) Solutions for patients with the same priority, namely Sharia consultations, services received that satisfy patients. (3) Accreditation solutions in the order that the hospital environment is well maintained and goes green, maintenance is improved, quality management is improved (4) Islamic hospital Ethics Solutions and Business Concepts with future planning sequences fulfilled and professional employee experts selected, effective staff coordination and forming a DPS. The strategic priorities being developed are the implementation of superior and complete sharia services.

The Influence of Psychosocial Factors on Patients Undergoing Anterior Cruciate Ligament Reconstruction.

Anterior cruciate ligament (ACL) injuries greatly impact patients in terms of future performance, reduced physical activity and athletic participation, and overall economic burden. Decades of research have investigated how to improve ACL reconstruction (ACLR) outcomes. Recently, there has been growing interest to understand the effects of psychosocial factors on patient outcomes. Clinical review. A search of the PubMed database was performed in March 2023. Articles were reviewed by at least 2 authors to determine relevance. We highlighted publications of the past 5 years while incorporating previous pertinent studies. Level 5. There is no standardization of psychosocial factors regarding ACLR. As such, there is a lack of consensus regarding which psychosocial measures to use and when. There is a need for clarification of the complex relationship between psychosocial factors and physical function. Despite this, psychosocial factors have the potential to help predict patients who are more likely to return to sport: (1) desire/motivation to return; (2) lower levels of kinesiophobia; (3) higher levels of self-efficacy, confidence, and subjective knee function; (4) risk acceptance; and (5) social support. However, there are no standardized interventions to improve psychosocial factors after ACLR. Psychosocial factors affect outcomes after ACLR. However, the interplay between psychosocial factors and physical function is complex. There is emerging evidence that testing and interventions may improve ACLR outcomes. There is a lack of standardized interventions to determine or improve psychosocial factors after ACLR. Further research is needed to identify psychosocial factors and to develop standardized interventions for clinicians to implement to improve clinical outcomes.

Assessing Disparities in Predictive Modeling Outcomes for College Student Success: The Impact of Imputation Techniques on Model Performance and Fairness

The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model development, and evaluation. These steps can introduce additional bias to the system if not appropriately performed. Substantial incompleteness in responses is a common problem in nationally representative education data at a large scale. This can lead to missing data and can potentially impact the representativeness and accuracy of the results. While many education-related studies address the challenges of missing data, little is known about the impact of handling missing values on the fairness of predictive outcomes in practice. In this paper, we aim to assess the disparities in predictive modeling outcomes for college student success and investigate the impact of imputation techniques on model performance and fairness using various notions. We conduct a prospective evaluation to provide a less biased estimation of future performance and fairness than an evaluation of historical data. Our comprehensive analysis of a real large-scale education dataset reveals key insights on modeling disparities and the impact of imputation techniques on the fairness of the predictive outcome under different testing scenarios. Our results indicate that imputation introduces bias if the testing set follows the historical distribution. However, if the injustice in society is addressed and, consequently, the upcoming batch of observations is equalized, the model would be less biased.

Dynamically forecasting the restructuring performance of tourism firms with a similar-scenarios-based modelling framework: the role of investors’ ex-ante attention

ABSTRACT The tourism firm is vulnerable to suffering losses after restructuring owing to its sensitivity and vulnerability. This work investigates the foresight effect of investors’ ex-ante attention to a tourism firm on its future performance to help identify proper restructuring timing. The analysis uses a similar-scenarios-based modelling framework with dynamic concept masks (SSBMF-DCM), which integrates multiple mainstream forecasting models and can dynamically identify foresight factors according to changes in restructuring scenarios. The results show that: (1) Investors’ ex-ante attention has a foresight effect on the medium-term performance after tourism firms’ restructurings; (2) the foresight effect of two different forms of ex-ante attention displays channel heterogeneity; (3) SSBMF-DCM performs better than all benchmarks in forecasting tourism firms’ restructuring performance; (4) consideration of two industry particularities (small restructuring sample volumes available for tourism firms and the concept drift in tourism firms’ restructuring scenarios) improves the accuracy of restructuring decisions.

A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance

BackgroundPrediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.MethodologyTo predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).ResultsThe study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.ConclusionIn conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

Future potable water supply demand projection under climate change and socioeconomic scenarios: A case of Gshba subbasin, Northern Ethiopia

This paper aims to quantify the subbasin’s potable water supply demand forecast from 2023 to 2050 under various scenarios of climate change and socioeconomic development. The variability of the climate and the resulting problems with urbanization threaten the availability of water resources, especially in less developed countries like Ethiopia. Thus, the main objective of this study is showing the necessary to determine the amount of water needed in advance, in order to comply with the availability of water resources within a specified future period under different scenarios. Our indicator-based approach used a multicriteria decision-making technique. Accordingly, several important variables were considered, including climatological, anthropological, demographic, socioeconomic, and economic variables, in addition to water engineering-related factors (e.g. Water losses). The method also considered a number of factors, such as unexpected and extreme temperature changes, and forecasting factors studied by the Ethiopian Ministry of Water and Energy. The projected population in the subbasin is estimated at 2.52 million, so the total projected water supply demand i.e., for domestic, non-domestic, industrial, commercial, public, and institutional is approximately 126.53 MCM/yr by 2050. Our results revealed how changes in both climatic and socioeconomic factors strongly influence future water resource system performance, and this will help the water services provider better prioritize the refurbishment of existing infrastructure and investment in new infrastructure, and more importantly, manage the subbasin effectively by introducing resilient adaptation options.

Breaking boredom: Interrupting the residual effect of state boredom on future productivity.

Boredom is an emotion that constantly fluctuates in employees of all ages and occupations. Here, we draw on functional theories of boredom and theories of emotion regulation to develop an episodic model of how boredom shapes employee attention and productivity over time. We argue that employees often suppress boredom at work to "power through" boring tasks and objectives, resulting in residual bouts of mind-wandering-and thus productivity deficits-during future performance episodes. However, following boredom on an initial task, the commencement of a subsequent task that employees perceive to be meaningful creates an attentional pull that breaks the link between boredom and future mind-wandering, preventing the effects of boredom from spilling over to inhibit future productivity. Study 1 draws on archival experience sampling data to test our hypotheses and examine whether boredom exhibits reciprocal relationships with mind-wandering and productivity over time. Study 2 uses an experimental design to determine whether boredom and task meaningfulness interact to exert a causal effect on future mind-wandering. Study 3 uses a time-separated single-day design to replicate Studies 1 and 2 and examine our contention that employees often suppress boredom at work which, rather than preventing the effects of boredom, puts them "on hold" until a later point in time. Our findings provide insight into how to mitigate the far-reaching effects of boredom at work; they also advance episodic accounts of emotions, attention, and performance in organizations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Test Format, Learning Confidence, and Perceptions of Teaching Effectiveness

Background Both multiple-choice and short-answer tests can be beneficial to learning in the classroom. However, fact-based multiple-choice questions, because they include the correct answer as a response option, could lead to inflated estimates of learning and higher evaluations of teaching effectiveness. Objective The objective of this study was to examine the effects of test format on perceptions of learning and teaching. Method Undergraduate students ( N = 123) completed either a multiple-choice or short-answer test based on a brief psychology lesson. Then, they predicted their performance on later tests of knowledge and evaluated the quality of the lesson. Results Taking a multiple-choice test led to predictions about future performance that were 10% higher than those taking a short-answer test. No consistent differences emerged in participants’ evaluations of teaching effectiveness between the two test formats. Conclusion These results suggest that test format may significantly influence students’ confidence in their ability to produce answers on future tests. Teaching Implications Teachers who want to take advantage of testing as a metacognitive learning tool should adopt question formats that utilize recall of information from memory without retrieval cues.

DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing

Knowledge tracing aims to trace students’ evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this article, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students’ learning interactions to capture the heterogeneous exercise–concept associations and interaction transitions by hypergraph modeling and directed graph modeling, respectively. To combine the dual graph models, we introduce the technique of online knowledge distillation. This choice arises from the observation that, while the knowledge tracing model is designed to predict students’ responses to the exercises related to different concepts, it is optimized merely with respect to the prediction accuracy on a single exercise at each step. With online knowledge distillation, the dual graph models are adaptively combined to form a stronger ensemble teacher model, which provides its predictions on all exercises as extra supervision for better modeling ability. In the experiments, we compare DGEKT against eight knowledge tracing baselines on three benchmark datasets, and the results demonstrate that DGEKT achieves state-of-the-art performance.

The limited role of sustainability in mutual fund investor decisions: A machine learning approach

Despite the growth in the supply of socially responsible investment products, the weight of environmental, social and governance (ESG) factors in the decisions of mutual fund investors remains under-researched. We conducted a study relating fund flows to past returns, ESG performance and other financial variables using data from 3,767 US mutual funds from 2015 to 2021. First, we aimed to assess the importance of ESG performance in investment decisions. Next, we studied whether ESG performance is increasingly important or has reached its limit. Finally, we developed decisional models to predict the flows raised by each investment fund, its financial return and ESG performance. We used logistic regression, neural networks, random forest and gradient boosting decision trees. We found that the investors consider ESG performance, but the factors that matter most are past growth, mutual fund fees and past returns. Our models predicted the money raised by the funds, obtaining accuracy rates of around 70%. In addition to confirming that “past financial return does not guarantee future financial return,” we found that “past ESG performance guarantees future ESG performance,” which may be of interest to socially responsible investors.

The Research of NVIDIA Stock Price Prediction Based on LSTM And ARIMA Model

The paper explores stock forecasting methods using NVIDIA as the research object. It contrasts how well LSTM and ARIMA models forecast NVIDIA's stock return. According to the study, LSTM surpasses ARIMA in terms of prediction accuracy. However, both models capture the overall trend of the stock. The results suggest that LSTM is better suited for forecasting stock movements due to its ability to handle time series data. The non-stationary nature of the stock market adds complexity to predictions. The significance of stock forecasting is that informed investment decisions can be made to maximise financial returns. Stock forecasting aims to predict the future performance of individual stocks, market sectors or the market as a whole based on various factors such as historical data, market trends, company fundamentals and economic indicators. By analysing and forecasting stock movements, investors can identify opportunities to buy low and sell high, optimise their portfolios and potentially outperform the broader market. Successful stock forecasting can help investors make more informed decisions, reduce risk and improve their chances of achieving their financial goals.

Is China’s hog futures market effective? Based on the perspective of price discovery and hedging functions

ABSTRACT Based on daily data of China’s hog futures and spot prices from January 2021 to October 2023, this paper uses VAR-GARCH-BEKK to test the effectiveness of China’s hog futures market. The results of the study indicate that there is a two-way positive leading relationship between the hog futures market and the spot market. There is a significant two-way volatility spillover effect between hog futures market and hog spot market, but the hedging performance of hog futures is poor. Therefore, the effectiveness of China’s hog futures market needs to be further improved.

Market Added Value, Profitability, Inflation, and Company Size on Transportation Company Share Prices

Purpose: Share price is a measure of investor interest in the decision to invest in a company. The aim of this research is to show the influence of market value, profitability, inflation and company size on stock prices.
 
 Method: This research uses a quantitative descriptive approach. Secondary data is information used in research, where the information is obtained from the database of transportation companies registered on the IDX within a three year period, namely 2020-2022. So far 16 transportation companies have been selected as research samples using the purposive sampling method. The research data analysis method uses multiple linear regression analysis using SPSS version 26.
 
 Results: The research results show that Market Value Added and company size have a significant positive effect on stock prices, while profitability and inflation have a negative and insignificant effect on stock prices.
 
 Implications: The research can be used as evaluation and review material for companies regarding the performance of ups and downs in share prices. In addition, future corporate performance and responsibility are expected to increase in connection with the evaluation of the corporate governance system.
 
 Novelty: The novelty of this research is that it does not only focus on internal factors that influence company share prices but also external factors, namely inflation. Apart from that, this research examines transportation companies using the latest data, namely 2020-2022.

Strategic earnings management in family firms

AbstractWe examine how family firms strategically manage earnings by using discretionary accruals (AEM) and real activities (REM). Using a sample of privately controlled but publicly listed firms in China between 2007 and 2018, we find that, under normal circumstances, family firms are less (more) likely than nonfamily firms to use REM (AEM) for earnings management, and they use REM and AEM as substitutes. However, when firms are under pressure to meet important earnings benchmarks that are critical to the survival of their businesses, family firms increase their use of REM more than do nonfamily firms. Finally, family firms strategically use REM to improve their future firm performance. This study challenges the unidirectional relationship between family firms and earnings management that is typically documented in existing studies.

Polymerizable Deep Eutectic Solvent-Based Polymer Electrolyte for Advanced Dendrite-Free, High-Rate, and Long-Life Li Metal Batteries.

The recently developed advanced electrolytes possess many crucial qualities, including robust stability, Li dendrite-free, and comparable interface compatibility, for the manufacturing of Li metal batteries with a high energy density. In this study, lithium bis(trifluoromethane)sulfonimide, acrylamide, and succinonitrile were first used to design a polymerizable monomer. Then, it went through in situ thermal polymerization to attain a new solid polymer electrolyte [named poly(PDES)]. The synthesized poly(PDES) electrolyte achieved higher ionic conductivity (∼1.89 × 10-3 S cm-1), oxidation potential (∼5.10 V versus Li+/Li), and a larger lithium-ion transfer number (∼0.63). Moreover, poly(PDES) was nonflammable and could effectively inhibit the formation of Li dendrites. As a result, the assembled batteries using the poly(PDES) electrolyte for both Li||LiFePO4 and Li||LiNi0.8Co0.1Mn0.1O2 exhibited excellent interface compatibility and electrochemical performances. This poly(PDES) electrolyte has promising potential for broad application in lithium-metal batteries with elevated energy density and safety performance in the near future.

Puerulus settlement forecasting in a harvest strategy evaluation of the rock lobster fishery in South Australia

AbstractHarvest strategies are utilised in the management of fishery resources globally. Critical to their success is harvest strategy evaluation, whereby future performance is assessed through projection modelling. Using the observed relationship between a puerulus settlement index and model‐estimated recruitment, we evaluated a southern rock lobster (Jasus edwardsii) harvest strategy in South Australia. Short‐to‐medium puerulus settlement recruitment forecasting was incorporated into future projections of catch, catch per unit effort, and percent unexploited egg production (%UEP) during 2019–2022. Observed values of indicators that closely aligned with projected outputs indicated that puerulus forecasting underpinned reliable evaluation of future fishery performance. A key objective of the harvest strategy was to increase egg production in the fishery, with a 20% unexploited egg production targeted by 2036. The target was reached under the proposed strategy, but was sensitive to recruitment, thereby highlighting the need to consider climate change impacts in forecasting scenarios.

New evidence on the validity of the selection methods for recruitment to general practice training: a cohort study.

Selection into UK-based GP training has used the multi-specialty recruitment assessment (MSRA) and a face-to-face selection centre. The MSRA comprises of a situational judgement test (SJT) and clinical problem solving (CPS) test. The selection centre was suspended during the Covid-19 pandemic. Evidence is needed to guide national and international selection policy. To evaluate the validity of GP training selection. A retrospective cohort study using data from UK-based national recruitment to GP training, from 2015 to 2021. Data were available for 32,215 GP training applicants. The ability of scores from the specialty selection process to predict subsequent performance in the clinical skills assessment of the Membership of the Royal College of General Practitioners (MRCGP) examination was modelled using path analysis. The effect-sizes for gender, professional family background and world region of qualification were estimated. All component scores of the selection process demonstrated statistically significant independent relationships with clinical skills assessment performance, thus establishing their predictive validity. All were sensitive to demographic factors. The selection centre scores had the weakest relationship with future clinical skills assessment performance. However, for candidates with MSRA scores below the lowest quartile the relative contribution of the selection centre scores to predicting clinical skills assessment performance was similar to that observed for MSRA components. The MSRA has predictive validity in this context. Re-instituting a selection centre for those with relatively low MSRA scores should be considered. However, the relative costs and potential advantages and disadvantages should be carefully weighed.