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This paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.

The study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.

The empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.

The findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.

To our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.

The accuracy of budgeting is important to fulfilling its various roles. The aim of this study is to examine perceptions of budgeting accuracy in UK universities and to identify and understand the factors that influence them.

A mixed methods research design comprising a questionnaire survey (84 responses, = 51.5%) and 42 semi-structured, qualitative interviews is employed.

The findings reveal that universities tend to be conservative in their budgeting, although previous financial difficulties, the attitude of the governing body and the need to convince lenders that finances are being managed competently might lead to a greater emphasis on a “realistic” rather than cautious budget. Stepwise multiple regression identified four significantly negative influences on perceived budgeting accuracy: the difficulty of forecasting student numbers; difficulties associated with allowing unspent balances to be carried forward; taking a relatively long time to prepare the budget; and the institution’s level of financial surplus. The interviews are drawn upon to both explain and elaborate on the statistical findings. Forecasting student numbers and associated fee income emerges as a particularly challenging and complex issue.

Our regression analysis is cross-sectional and therefore based on correlations. Furthermore, the research could be developed by investigating the views of other parties as well as repeating the study in both the UK and overseas.

Implications for university management follow from the four factors identified as significant influences upon budget accuracy. These include involving the finance department in estimating student numbers, removing or controlling the carry forward of unspent funds, and reducing the length of the budget cycle.

The first study to examine the factors that influence the perceived accuracy of universities’ budgeting, this paper also advances understanding of budgeting accuracy more generally.

The pivotal aspect of aircraft assembly lies in precise measurement accuracy. While a solitary digital measuring tool suffices for analytical and small surfaces, it falls short for extensive synthetic surfaces like aircraft fuselage panels and wing spars. The purpose of this study is to develop a “combined measurement method” (CMM) that enhances measurement quality and expands the evaluative scope, addressing the limitations posed by singular digital devices in meeting measurement requirements across various aircraft components.

The study illustrated the utilization of the CMM by combining a laser tracker and a portable arm-measuring machine. This innovative approach is tailored to address the intricate nature and substantial dimensions of aircraft fuselage panels. The portable arm-measuring machine performs precise scans of panel components, while common points recorded by the laser tracker undergo coordinate conversion to reconstruct the fuselage panel’s shape. The research outlines the CMM’s measurement procedure and scrutinizes the data processing technique. Ultimately, the investigation yields a deviation vector matrix and chromatogram deviation distribution, pivotal in achieving enhanced measurement precision for the novel CMM device.

The use of CMM noticeably enhances fuselage panel assembly accuracy, concurrently reducing assembly time and enhancing efficiency compared to conventional measurement systems.

The research’s practical implication lies in revolutionizing aircraft assembly by mitigating accuracy issues through the innovative digital CMM for aircraft synthetic structure type product (aircraft fuselage panel). This ensures safer flights, reduces rework and enhances overall efficiency in the aerospace industry.

Introducing a new aircraft assembly accuracy compensation method through digital combined measurement, pioneering improved assembly precision. Also, it enhances aerospace assembly quality, safety and efficiency, offering innovative insights for optimized aviation manufacturing processes.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

This study examines how asking employees to self-assess their performance during the compensation setting process, when they are unaware of their marginal contribution to firm profit, affects employer welfare. Previous research suggests that giving employees a voice in the compensation setting process can positively affect employee performance and firm profit (Jenkins & Lawler, 1981; Roberts, 2003). However, the study proposes that asking employees to assess their own performance as part of the compensation setting process can have unintended consequences that ultimately lead to higher employee compensation demands. This is because asking employees to assess their performance increases their overconfidence in their own performance and their compensation demands. As a result, employers may face the dilemma of whether to meet these higher compensation demands or risk economic losses due to employee retaliation if their demands are not met. Through experimental evidence comparing a control condition without self-assessments and three self-assessment reporting conditions, the study provides evidence that supports the notion that eliciting employee self-assessments as part of the compensation process reduces employer welfare. Data on employee perceptions of performance further support the notion that asking employees to evaluate their performance leads to an inflated perception of their performance. These findings provide a theory-based explanation of why, in practice, many companies disentangle employee performance assessments from the compensation setting process and that companies are well advised in doing so.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.