Future Performance Research Articles

Performance of ChatGPT on Factual Knowledge Questions Regarding Clinical Pharmacy.

ChatGPT is a language model that was trained on a large dataset including medical literature. Several studies have described the performance of ChatGPT on medical exams. In this study, we examine its performance in answering factual knowledge questions regarding clinical pharmacy. Questions were obtained from a Dutch application that features multiple-choice questions to maintain a basic knowledge level for clinical pharmacists. In total, 264 clinical pharmacy-related questions were presented to ChatGPT and responses were evaluated for accuracy, concordance, quality of the substantiation, and reproducibility. Accuracy was defined as the correctness of the answer, and results were compared to the overall score by pharmacists over 2022. Responses were marked concordant if no contradictions were present. The quality of the substantiation was graded by two independent pharmacists using a 4-point scale. Reproducibility was established by presenting questions multiple times and on various days. ChatGPT yielded accurate responses for 79% of the questions, surpassing pharmacists' accuracy of 66%. Concordance was 95%, and the quality of the substantiation was deemed good or excellent for 73% of the questions. Reproducibility was consistently high, both within day and between days (>92%), as well as across different users. ChatGPT demonstrated a higher accuracy and reproducibility to factual knowledge questions related to clinical pharmacy practice than pharmacists. Consequently, we posit that ChatGPT could serve as a valuable resource to pharmacists. We hope the technology will further improve, which may lead to enhanced future performance.

New correlations to estimate the rough fracture permeability using computational fluid dynamics simulation

AbstractIn fractured reservoirs, the fracture network provides the main path for fluid flow. Appropriate estimation of the fracture permeability influences the precise prediction of the reservoir’s future performance. Commonly, for a known geometry of natural or induced fracture, the permeability is estimated by applying local cubic law. One major drawback of this approach is that the fracture surface roughness, which has a significant effect on fracture permeability, is not considered. Moreover, the knowledge about the impact of fracture surface roughness on fracture permeability is not currently sufficient. In this research, the fluid flow in fractures with rough-walled surfaces was studied using computational fluid dynamics. For this purpose, the fluid flow through fractures was simulated by applying appropriate roughness for fracture walls. Furthermore, two correlations, based on response surface methodology and power-law models, were proposed to predict fracture permeability as a function of four independent variables (surface roughness, fracture aperture, angle, and porosity). The results of the two presented correlations were validated, and the statistical analysis indicates that both models are appropriate to predict fracture permeability. The findings of this study will be of great assistance with understanding and characterization of the fluid flow in rough fractures and can be used in future works.

Dividend Payments by Russian Companies: A Signal to the Market or a Consequence of Agency Conflicts?

The article analyzes the dividend policies of Russian companies using two dividend payment theories: signaling theory and agency cost theory. A sample of 30 Russian companies over the period 2010–2021 is used. To test the applicability of signaling theory, pooled regression and fixed effects models are developed. It is shown that an increase/reduction in dividend payments exceeding 20% in the current year allows one to predict an increase/decrease in the return on assets in one or two subsequent years (in comparison to the year preceding dividend payments). However, the growth rate of dividend payments shows no stable relationship with the future return on assets. To test the applicability of agency cost theory, a Tobit model is used with the participation of a principal majority shareholder represented by the government as the dependent variable. This binary variable is equal to 1 if the government owns directly or indirectly over 30% of corporate stocks and 0 otherwise. The results do not confirm the applicability of agency theory to the Russian market. Government participation in stock capital exerts no significant impact on the dividend payout ratio. These findings contribute to understanding the relationship between a company’s dividend policy and its future financial performance, providing a useful tool for Russian investors.

Investigating the Catalytic Influence of Boron on Ni-Co/Ca Catalysts for Improved Syngas Generation from Rice Straw Pyrolysis

A series of boron-promoted Ni-Co/Ca catalysts were synthesized by the sol–gel method to enhance syngas generation from biomass pyrolysis. The efficiency of these catalysts was evaluated during the pyrolysis of rice straw in a fixed-bed reactor, varying the Ni/Co ratio, boron addition, calcination temperature, and residence time. The catalysts underwent comprehensive characterization using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis, scanning electron microscopy (SEM), and hydrogen temperature-programmed reduction (H2-TPR). The results indicated that the Ni-Co/Ca catalysts yielded superior syngas compared to singular Ni or Co catalysts, suggesting a synergistic interplay between nickel and cobalt. The incorporation of 4% boron significantly decreased the particle size of the active metals, enhancing both the catalytic activity and stability. Optimal syngas production was achieved under the following conditions: a biomass-to-catalyst mass ratio of 2:1, a Ni-Co ratio of 1:1, a calcination temperature of 400 °C, a pyrolysis temperature of 800 °C, and a 20 min residence time. These conditions led to a syngas yield of 431.8 mL/g, a 131.28% increase over the non-catalytic pyrolysis yield of 188.6 mL/g. This study not only demonstrates the potential of Ni-Co/Ca catalysts in biomass pyrolysis for syngas production but also provides a foundation for future catalyst performance optimization.

The mechanism of intrinsic peroxidase (POD)-like activity of attapulgite.

Natural attapulgite (ATP) is a promising substitute for existing artificial nanozymes due to its intrinsic enzymatic activity. However, the active center of ATP's inherent enzymatic activity has not yet been revealed, which limits its further design and activity optimization. Studying the active center of mineral materials can be extremely challenging due to their complexity. Here, we demonstrated that Fe is the primary element in ATP responsible for peroxidase (POD)-like activity through theoretical speculation and experimental verification. More importantly, we found that the ratio of Fe2+/Fe3+ is responsible for the district POD-like activity of ATP from different regions with the same Fe content. Additionally, three facile strategies, including grinding, heat treatment, and acid treatment, were demonstrated to increase the relative Fe content and thus optimize the POD-like activity of ATP. Finally, ATP was used to detect the concentration of H2O2, enabling the detection of low concentrations (0.11-1.76mM) of H2O2. This study serves as a novel reference for the future design and performance optimization of nanozymes that are based on ATP and clay minerals.

Corporate culture and sales order backlog

PurposeThe purpose of our study is to investigate the impact of corporate culture on sales order backlog.Design/methodology/approachThe authors use regression analysis to examine the relation between corporate culture and the level of sales order backlog, an important leading indicator of firm performance.FindingsUsing a large panel sample of US firms for the period of 2003–2021, the authors find a significant and positive relation, suggesting that firms with strong corporate culture have a higher level of sales order backlog.Originality/valueThe study findings contribute to two separate areas of research: corporate culture in management literature and sales order backlog in accounting literature. Prior study has focused on the impact of corporate culture on current firm performance. This study extends prior research by investigating the impact of corporate culture on order backlog, an important leading indicator of future performance.

Brain activity of professional investors signals future stock performance

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.

Stakeholder perceptions of thoroughbred yearling sales endoscopy in Australia: can we do better?

To identify thoroughbred industry stakeholders' views on the yearling sales endoscopy in Australia. Semi-structured qualitative group interviews with equine veterinarians, thoroughbred breeders and yearling purchasers. Twenty-nine experienced thoroughbred industry stakeholders participated in online focus groups: (1) equine veterinarians involved in yearling endoscopy; (2) thoroughbred breeders; and (3) yearling buyers. The focus groups were audio and video recorded and digital whiteboards were available. Data was transcribed, live-coded and analysed thematically. The main themes identified included the uncertainty surrounding the relationship between yearling laryngeal function and future performance, especially the significance of yearlings with grade 3 out of 5 laryngeal function; interobserver variability in the laryngeal function grade assigned by veterinarians to the same endoscopic exam; and the presence of intra-horse variability in laryngeal function over different time points. Stakeholders raised multiple concerns about the current yearling sales endoscopy process in Australia. Recommendations to address these concerns include further investigation into the race performance of yearlings identified with grade 3 laryngeal function; training of veterinarians to reduce inter-observer variability; and the need to determine the most appropriate grading system to use in yearlings. Future research should evaluate the significance of intra-horse variability in a yearling population.

The Influence of Feeling-of-Knowing on Metacognitive Processes in the Digital Media Environment

Abstract: Research demonstrates that people outsource memory work to digital devices and to the web, which consequently engenders elevated self-assessments, such as enhanced cognitive self-esteem (CSE). Yet, the reasons for this phenomenon are not well-known. In this domain, two studies explored the role of feeling-of-knowing (FoK) – one’s judgment about whether currently nonrecallable information is actually known – on enhanced CSE. Findings show that people believe that their ability to find needed information is better after performing a web search for answers, as a function of elevated FoK that occurs in the process, and that experiencing greater FoK is related to greater perceived memory ability, better anticipated future performance, and easier perceived question difficulty. Evidence of this metacognitive effect is particularly intriguing since it is also highly paradoxical: under precisely the circumstances when people should recognize their cognitive shortcomings, they assess them to be especially strong.

Machine Learning-based Approaches in Error Detection and Score Prediction for Small Arm Firing Systems in the Military Domain

Error pattern recognition is a routine job in the military to provide corrective guidelines to the shooter. Errors can be recognized with a visual approach based on the spreading pattern of bullets on the target board, which are categorized into four categories: long horizontal error, long vertical error, bi-focal error, and scattered error. Currently, this process is performed manually and requires active human involvement. Similarly, an automated system to predict the future performance of a shooter is not available in the military domain. Moreover, the performance of a shooter depends on several factors, including age, weather, ammunition type, availability of light, previous scores, shooting range, classification of firing, and other factors. The military domain has not addressed the automatic prediction of such performance. While error correction and performance analysis have been extensively explored in the field of sports, their application within the military domain remains an untapped area of research and investigation. Numerous recent endeavors have suggested the utilization of deep learning to tackle this challenge. However, the absence of real-time data poses a significant obstacle, rendering these solutions seemingly impractical. In this paper, we have applied machine- learning approaches and adopted the best algorithm to automate the error pattern recognition system within a military domain. Our proposed methodology has two modules. The first module uses various algorithms and finds a random forest classifier that can do better to recognize the pattern of error and in the second phase, we used the AdaBoost classifier to predict the score and performance of a firer. Several experiments have been conducted, and the results show an average accuracy of 0.968 using Random Forest to recognize the pattern of error and an accuracy of 0.69 using AdaBoost to predict score performance. The data has been collected from the real-time environment of the military domain and experiments have been carried out using real-time scenarios with the military in mind.

FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning

In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable deep KT model, referred to as fuzzy deep knowledge tracing (FDKT) via fuzzy reasoning. Specifically, we formalize continuous scores into several fuzzy scores using the fuzzification module. Then, we input the fuzzy scores into the fuzzy reasoning module (FRM). FRM is designed to deduce the current cognitive ability, based on which the future performance was predicted. FDKT greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition. Furthermore, it broadened the application of KT to continuous scores. Improved performance with regard to both the advantages of FDKT was demonstrated through comparisons with the state-of-the-art models.

The Mediating Role of Dividend Policy on Financial Performance and Firm Value: A Literature Review

Firms or companies are obligated to possess favorable worth in the perception of investors if they desire to establish the trust of various parties to cultivate the confidence to venture and desire to invest in the firm. The notion is held that company worth is capable of illustrating both the present and future financial performance. In addition to financial performance, dividend policies are believed to exert a noteworthy impact on company value. This literature study aims to ascertain whether there exist theoretical explanations that elucidate the link between financial performance and dividend policies, such as the link between financial performance and company value, the link between dividend policies and company value, and the impact of financial performance on company worth with dividend policies serving as a mediating variable. A literature review was conducted by finding and choosing numerous sources to evaluate, including publications accessible in the form of empirical studies papers and literature review papers, books, and conferences. According to the findings of multiple prior study sources utilized in this literature review, the majority of the empirical study’s findings that the authors reviewed suggest that profitability, leverage, and liquidity have an impact on dividend policies. Profitability, leverage, liquidity, and a firm’s size are all factors that impact company value. Dividend policies have an impact on company worth as well. It is also discovered that dividend policies can help to mediate the impact of profitability and liquidity on company value, but they cannot mediate the effect of leverage.
 Keywords: financial performance, firm value, dividend policy

INFORMATION OVERLOAD AND INDIVIDUAL INVESTORS’ PERCEPTIONS OF INVESTMENT RISK: EVIDENCE FROM SAUDI ARABIA

This study explores investors’ perceptions of investment risk under information overload. We use a survey of 133 individual investors in the Kingdom of Saudi Arabia (KSA), and to test our hypotheses, we employ descriptive statistics and sample t-test. We found that information overload in lengthy annual reports decreases investors’ confidence in the financial statements and reduces their ability to understand the firm’s business and financial performance. Investors confirmed that information overload reduces their ability to access relevant information for making investment decisions and predict future performance, and increases uncertainty about the firms’ future performance. Consequently, information overload increases investors’ perceptions of firms’ investment risk, reduces their confidence to invest in firms with long and difficult-to-read annual reports, and increases their perceptions of the likelihood that these reports are being exploited by managers to hide poor performance or earnings manipulation, and consequently, they prefer not to invest in these firms. In addition, the results indicated that disclosure complexity along with information overload exacerbates investors’ negative perceptions about the firm’s investment risk and its performance. Our results provide insights for investors in emerging markets such as the Saudi market to understand the negative implications of information overload, which can enlighten their investment decisions.

Performance and reporting predictability of hedge funds

AbstractThis paper proposes a predictive approach to forecast future hedge fund performances and reporting stops to a commercial database within a subsequent year. We found that gradient boosting of decision trees is well suited to make a prognosis about the future development and reporting stops of hedge funds. The derived models are trained and evaluated using a panel of 5,592 individual hedge funds. We rank the impact of 22 variables that are computed out of hedge fund reporting (micro variables) and three different market environments (macro variables) on the predictability of hedge fund performance. In this way, we show the economic reasonability of the computed models and demonstrate the superiority of statistical learning algorithms.

Auditor tenure and non-GAAP earnings

Purpose This study aims to examine whether the newly available auditor tenure information is associated with non-GAAP earnings, as the recent requirement to disclose the initial year of auditor-client relationship in audit reports may give the impression that longer auditor tenure may be related to lower audit quality. Design/methodology/approach Using a sample of firm-quarters from 2017 to 2020, the authors conduct both univariate and regression analyses. We use hand-collected data for auditor tenure, SEC comment letters, and non-GAAP variables. Findings First, the authors find that the likelihood of disclosing non-GAAP earnings monotonically increases with auditor tenure on a univariate basis. Second, auditor tenure is negatively associated with aggressive non-GAAP reporting. Third, the authors document evidence of aggressive reporting in general; that is, items excluded in calculating non-GAAP earnings are associated with future performance. However, the association declines with longer auditor tenure. Finally, the authors report evidence that the likelihood of receiving an SEC comment letter that contains non-GAAP comments decreases with longer auditor tenure. Practical implications The results show that regulators need to consider both GAAP and non-GAAP disclosures’ costs and benefits when enacting auditor tenure regulation. Investors can benefit from the findings in evaluating the quality of non-GAAP earnings. The findings are also relevant to the SEC when allocating limited resources in monitoring non-GAAP reporting. Originality/value To the best of the authors’ knowledge, this is the first study showing that auditor tenure is associated with the quality of non-GAAP earnings. Given that financial reporting quality should be understood as a comprehensive system comprising both mandatory and voluntary disclosures, this study complements the literature that examines the effect of auditor tenure on financial reporting quality using GAAP reporting.

Corrigendum to “Assessment on the Current and Future Performance of Addis Ababa Light Rail Transit Service Using Mathematical Modeling”

Corrigendum to “Assessment on the Current and Future Performance of Addis Ababa Light Rail Transit Service Using Mathematical Modeling”

Artificial Intelligence: The Next Frontier for Assets Management

Abstract: AI examine more systematic techniques and application like; Similarity, Presenso, Aladdin Platform by BlackRock Solutions (BRS), Stocks Analyst by AXYON AI, Sentient Investment Management to concomitant better opportunities and break the challenges. Portfolios that meet performance targets more closely than portfolios created using traditional methods. The trading process can be broken down into pre-trade analysis, trade execution, and post-trade analysis. Pre-trade analysis entails using data to analyse properties of financial assets with the objective of forecasting not only their future performance but also the risks and costs involved in trading them. Insights from this analysis ultimately lead to the execution of trades. The success of AI in asset management is linked to its three key, inherent capabilities. First, AI models are objective, highly efficient in conducting repetitive tasks, and able to identify patterns in high dimensional data that may not be perceptible by humans. AI can also analyse data with minimal knowledge of the data’s structure or the relation between input and output, including nonlinear relations.

건설현장 안전보건성과 영향요인 연구

The Occupational Safety and Health Act and the Serious Accident Punishment Act, which has been in effect since January 27, 2022, specify that workers' opinions must be heard regarding safety and health. This study was conducted because it was judged that listening to workers' opinions can identify potential harmful and risk factors at construction sites and establish disaster prevention measures, ultimately affecting safety and health performance. In this study, three legal factors and four system factors were configured as independent variables, and safety and health performance was configured as a dependent variable through interviews with 37 safety and health management managers, safety managers, and health managers of C Construction Company and a review of previous research. . A survey was conducted from January 11, 2024 to January 24, 2024 targeting 162 safety and health management managers, main contractor management supervisors, main contractor safety managers and health managers, partner company management supervisors, and partner company safety managers. Statistical analysis was performed on the data collected using the SPSS program. As a result of research on the survey items, validity and reliability were confirmed to be good, and in terms of legal factors, committees and councils, listening to opinions in risk assessment, and in system factors, meetings and TBM (Tool Box Meeting) had a significant impact on safety and health performance. It was confirmed that In the future, safety and health performance is expected to improve at construction sites through active listening to workers' opinions on the legal and systemic factors identified in this study.

Can instructor feedback and failure dialogues reduce the stigma of failure and encourage help-seeking? An interdisciplinary undergraduate perspective

Failure is an important part of the learning process as it provides opportunity to reflect upon and modify study habits, can increase understanding and retention of concepts, and supports the development of resilience, an important skill within and beyond the university context. Despite these benefits, undergraduate students may have a negative perception of failure that may contribute to fear of failure and risk avoidance. We investigated the influence of instructor feedback, discussions of failure in the classroom, and course-level and institutional structures on students’ view of failure across disciplines. Undergraduate students at the [redacted for blind review] participated in an online survey (n = 304) consisting of Likert-scaled and open-ended questions on perceptions of failure, experience with instructor feedback, likelihood to engage in help-seeking, and course- and institutional-level suggestions to support learning from failure. When instructors discussed with their students how to best respond to feedback, students were more likely to view small failures as a helpful part of the learning process. Further, when instructors discussed strategies to best respond to failure, students were more likely to view failure as helpful for learning and less likely to view weak performance early in a course as an indication of future weak performance. Student-identified strategies to better support resilience included improved communication between students and instructors, in peer-to-peer conversations, and by the institution more broadly. Across all three areas, students expressed a desire for systems of higher education to destigmatize failure through changes in both rhetoric and policy. This suggests the incorporation of meaningful conversations and failure narratives in the classroom and beyond are effective strategies to encourage the development of resilience-based skills. Such discussions may help students engage positively with failure and maintain willingness to engage in future challenges. Keywords: learning from failure, higher education, resilience, help-seeking, feedback  

Predicting the memorability of scene pictures: Improved accuracy through one's own experience.

There are conflicting findings regarding the accuracy of metamemory for scene pictures. Judgements of stimulus memorability in general (memorability judgements [MJs]) have been reported to be unpredictive of actual image memorability. However, other studies have found that judgements of learning (JOLs)-predictions of one's own later memory performance for recently studied items-are moderately predictive of people's own actual recognition memory for pictures. The current study directly compared the relative accuracy and cue basis of JOLs and MJs for scene pictures. In Experiments 1 and 2, participants completed an MJ task and a JOL task in counterbalanced order. In the MJ task, they judged the general memorability of each picture. In the JOL task, they studied pictures and made JOLs during a learning phase, followed by a recognition memory test. Results showed that MJs were predictive of general scene memorability and relied on the same cues as JOLs, but MJ accuracy considerably improved after the JOL task. Experiment 3 demonstrated that prior learning experiences drove this increase in MJ accuracy. This work demonstrates that people can predict not only their own future memory performance for scene pictures with moderate accuracy but also the general memorability of scene pictures. In addition, experiences with one's own learning and memory support the ability to assess scene memorability in general. This research contributes to our understanding of the basis and accuracy of different metamemory judgements.