Subjective Bias Research Articles

Multicultural values: meeting point of two forces in developing Islamic education

Purpose The purpose of this study is to investigate the integration of entrepreneurial spirit and multicultural values in Islamic education, particularly within the context of pesantren, and to understand its implications for educational practices and outcomes. Design/methodology/approach This study adopts a qualitative research approach, using literature review and analysis of empirical data to explore the intersection of entrepreneurial spirit and multicultural values in Islamic education. It incorporates findings from multiple sources, including scholarly articles, books and empirical studies, to provide a comprehensive understanding of the topic. Findings The findings reveal that the integration of entrepreneurial spirit and multicultural values in Islamic education empowers students economically, fosters innovation in teaching methods and promotes character development and business ethics. Moreover, it emphasizes appreciation for diversity, cross-cultural collaboration and social responsibility within the educational context. This integration creates an inclusive learning environment conducive to the development of entrepreneurial skills, innovation and ethical leadership. Research limitations/implications Generalizability: the findings of this study may be limited in their generalizability due to the focus on specific multicultural Islamic educational institutions, potentially limiting the applicability of the results to broader educational contexts. Sample size and diversity: the study’s sample size and diversity may impact the representativeness of the findings. Future research could aim for larger and more diverse samples to enhance the robustness of the results. Methodological constraints: the reliance on certain research methods, such as surveys and interviews, may introduce biases or limitations in data collection. Researchers should consider using a variety of methodologies to triangulate findings and ensure comprehensive understanding. Time constraints: the study’s timeframe may have constrained the depth of analysis and limited the exploration of the long-term effects of entrepreneurship education on student development. Future research could adopt longitudinal approaches to address this limitation. Cultural context: the findings are contextualized within the cultural and educational landscape of multicultural Islamic institutions, which may limit their transferability to other cultural or religious contexts. Researchers should consider examining similar phenomena in diverse cultural settings. Resource constraints: resource limitations may have impacted the scope and depth of the research. Future studies could seek additional resources to conduct more extensive investigations and analyses. Bias and subjectivity: despite efforts to minimize bias, researchers’ subjectivity and potential biases in data interpretation and analysis cannot be entirely eliminated. Researchers should acknowledge and address their own biases transparently. Ethical considerations: the study may have encountered ethical challenges related to participant consent, confidentiality and cultural sensitivity. Future research should prioritize ethical guidelines and considerations to ensure the protection and well-being of participants. Addressing these limitations in future research endeavors can contribute to a more comprehensive understanding of the role and impact of entrepreneurship education in multicultural Islamic educational settings. Practical implications Practically, the study informs educational institutions, especially pesantren, about the benefits of integrating entrepreneurial spirit and multicultural values into their curriculum and pedagogical approaches. It offers insights into effective strategies for fostering entrepreneurship, innovation and intercultural competence among students. Social implications The integration of entrepreneurial spirit and multicultural values in Islamic education has broader societal implications. It cultivates a generation of socially responsible and inclusive leaders capable of addressing global challenges with creativity and empathy. By promoting cultural understanding and collaboration, it contributes to building more harmonious and resilient communities. Originality/value This study is original in its comprehensive exploration of the intersection between entrepreneurial spirit and multicultural values in Islamic education, particularly within the context of pesantren. It offers fresh insights into how these dimensions can be integrated synergistically to enhance educational practices and outcomes.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Enhancing strategy for Industry 4.0 implementation through maturity models and standard reference architectures alignment

PurposeA maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.Design/methodology/approachThree steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.FindingsThe study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.Research limitations/implicationsFindings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.Practical implicationsTo fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.Originality/valueThe novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

Using Genetic Algorithms and Core Values of Cooperative Games to Solve Fuzzy Multiobjective Optimization Problems

A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mechanical procedure to avoid this subjective biases. We are going to formulate a cooperative game using the α-level functions of the multiple fuzzy objective functions. Under this setting, the suitable coefficients can be determined mechanically by involving the core values of the cooperative game, which is formulated using the multiple fuzzy objective functions. We shall prove that the optimal solutions of the transformed scalar optimization problem are indeed the nondominated solutions of fuzzy multiobjective optimization problem. Since the core-nondominated solutions will depend on the coefficients that are determined by the core values of cooperative game, there will be a lot of core-nondominated solutions that will also depend on the corresponding coefficients. In order to obtain the best core-nondominated solution, we shall invoke the genetic algorithms by evolving the coefficients.

Qualification and Quantification of Porosity at the Top of the Fuel Pins in Metallic Fuels Using Image Processing

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias.

Chinese expert consensus on the technical and clinical practice specifications of artificial intelligence assisted morphology examination of blood cells (2024)

Blood cell morphological examination is a crucial method for the diagnosis of blood diseases, but traditional manual microscopy is characterized by low efficiency and susceptibility to subjective biases. The application of artificial intelligence (AI) technology has improved the efficiency and quality of blood cell examinations and facilitated the standardization of test results. Currently, a variety of AI devices are either in clinical use or under research, with diverse technical requirements and configurations. The Experimental Diagnostic Study Group of the Hematology Branch of the Chinese Medical Association has organized a panel of experts to formulate this consensus. The consensus covers term definitions, scope of application, technical requirements, clinical application, data management, and information security. It emphasizes the importance of specimen preparation, image acquisition, image segmentation algorithms, and cell feature extraction and classification, and sets forth basic requirements for the cell recognition spectrum. Moreover, it provides detailed explanations regarding the fine classification of pathological cells, requirements for cell training and testing, quality control standards, and assistance in issuing diagnostic reports by humans. Additionally, the consensus underscores the significance of data management and information security to ensure the safety of patient information and the accuracy of data.

Using mobile eye tracking to measure cognitive load through gaze behavior during walking in lower limb prosthesis users: A preliminary assessment

BackgroundLower limb amputation does not affect only physical and psychological functioning but the use of a prosthetic device can also lead to increased cognitive demands. Measuring cognitive load objectively is challenging, and therefore, most studies use questionnaires that are easy to apply but can suffer from subjective bias. Motivated by this, the present study investigated whether a mobile eye tracker can be used to objectively measure cognitive load by monitoring gaze behavior during a set of motor tasks. MethodsFive prosthetic users and eight able-bodied controls participated in this study. Eye tracking data and kinematics were recorded during a set of motor tasks (level ground walking, walking on uneven terrain, obstacle avoidance, stairs up and ramp down, as well as ramp up and stairs down) while the participants were asked to focus their gaze on a visual target for as long as possible. Target fixation times and increase in pupil diameters were determined and correlated to subjective ratings of cognitive load. FindingsOverall, target fixation time and pupil diameter showed strong negative and positive correlations, respectively, to the subjective rating of cognitive load in the able-bodied controls (−0.75 and 0.80, respectively). However, the individual correlation strength, and in some cases, even the sign, was different across participants. A similar trend could be observed in prosthetic users. InterpretationThe results of this study showed that a mobile eye tracker may be used to estimate cognitive load in prosthesis users during locomotor tasks. This paves the way to establish a new approach to assessing cognitive load, which is objective and yet practical and simple to administer. Nevertheless, future studies should corroborate these results by comparing them to other objective measures as well as focus on translating the proposed approach outside of a laboratory.

A risk assessment approach for road collapse along tunnels based on an improved entropy weight method and K-means cluster algorithm

Ground collapse often occurs when strata are disturbed, particularly during tunnel construction, threatening the safety of people and underground facilities. Therefore, evaluating the risk of road collapse during and after tunnel construction is essential. Considering this, an improved entropy weight method was adopted to establish an evaluation system for assessing the collapse risk level of the Qianjin West Road, through which the Kunshan metro line S1 passes. The main implementation steps of the evaluation system include ten assessment indices, data visualization of indices, weight determination for indices, a cluster algorithm for the classification of results, and analysis of the evaluation results. In contrast to the traditional single method, and to improve the reliability of the risk assessment results, the method proposed in this paper considers two aspects. First, to eliminate subjective or objective biases generated by a single evaluation method, the entropy weight method (EM) and analytic hierarchy process (AHP) were integrated to formulate the improved entropy weight method. Second, to divide the evaluation results more reasonably, the k-means cluster algorithm using the Calinski Harabasz method was applied. The reliability and accuracy of the proposed improved entropy weight method are verified based on the case of Line S1. Furthermore, a method comparison and the concept of the coefficient of variation method were adopted to demonstrate the reliability of the risk assessment results. The improved entropy weight method can be used to evaluate ground collapse risk levels during and after tunnel construction.

Racial differences in triage assessment at rural vs urban Maine emergency departments.

Data continue to accumulate demonstrating that those belonging to racialized groups face implicit bias in the emergency care delivery system across many indices, including triage assessment. The Emergency Severity Index (ESI) was developed and widely implemented across the US to improve the objectivity of triage assessment and prioritization of care delivery; however, research continues to support the presence of subjective bias in triage assessment. We sought to assess the relationship between perceived race and/or need for translator and assigned ESI score and whether this was impacted by hospital geography. We performed retrospective EMR-based review of patients presenting to urban and rural emergency departments of a health system in Maine with one of the top ten most common chief complaints (CC) across a 5-year period, excluding psychiatric CCs. We used multivariable regression to analyze the relationships between perceived race, need for translator, and gender with ESI score, wait time, and hallway bed assignments. We found that patients perceived as non-white were more likely to receive lower acuity ESI scores and have longer wait times as compared to patients perceived as white. Patients perceived as female were more likely to receive lower acuity scores and wait longer to be seen than patients perceived as male. The need for an interpreter was associated with increased wait times but not significantly associated with ESI score. After stratification by hospital geography, evidence of subjective bias was limited to urban emergency departments and was not evident in rural emergency departments. Further investigation of subjective bias in emergency departments in Maine, particularly in urban settings, is warranted.

Harmonizing Perceptions: Exploring Alignment of Numerical and Textual Evaluation Through Fine-Grained Sentiment Analysis

The assessment of faculty teaching effectiveness plays a pivotal role in shaping educational practices and ensuring academic excellence. While numerical evaluations provide quantifiable measures of teaching effectiveness, textual comments offer valuable qualitative insights into the faculty's instructional methods and interactions. However, discrepancies between these evaluation formats may hinder the accurate assessment of faculty performance. This study aims to determine the alignment between the numerical and textual evaluations of faculty teaching effectiveness using fine-grained sentiment analysis and determine the relationship between numerical ratings and textual comments to identify patterns of consistency or divergence. A large dataset of anonymized BSCS students’ feedback is analyzed through sentiment analysis to extract sentiments expressed in the textual comments. The study results reveal a misalignment between students’ subjective perceptions expressed in the textual comments and the numerical ratings. This misalignment arises due to several factors, such as student communication style variations, individual interpretations of rating scales, or subjective biases. This study advances faculty evaluation methodologies and offers useful recommendations for organizations looking to improve assessment procedures as well as educational institutions that want to improve the precision and efficacy of their faculty evaluation systems for better faculty development and decision-making.

A QUANTITATIVE APPROACH TO ASSESSING AND MANAGING CYBERSECURITY RISKS

Effective cybersecurity risk management requires moving beyond traditional qualitative approaches toward quantitative methods that provide greater precision and objectivity. This article explores the benefits of cyber risk quantification (CRQ), which allows organizations to numerically estimate the likelihood of potential cyber threats and calculate their financial or operational impacts. By translating risks into quantifiable terms, cyber risk quantification (CRQ) provides a standardized framework to prioritize risks based on impacts, evaluate mitigation strategies through cost-benefit analysis, and align cybersecurity practices with business objectives. The quantitative, risk-based approach elevates cybersecurity as an integral part of enterprise risk management focused on protecting critical assets. Despite its analytical rigor, the quantification process leverages proven techniques to simplify risk data into clear metrics. This enables more informed decision-making by eliminating ambiguity, reducing subjective bias, and communicating risks transparently across stakeholders using a common language of risk values.

Integrating GIS and BIM with MCDM for infrastructure planning: a comprehensive framework

PurposePresent study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.Design/methodology/approachThis analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.FindingsA comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.Research limitations/implicationsThe study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.Practical implicationsThe proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.Originality/valueBy integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

Food supply chain resilience in major disruptions

PurposeCorona Virus Disease (Covid-19) is a global pandemic that emerged at the end of 2019 and caused disruptions in global supply chains, particularly in the food supply chains that exposed the vulnerability of today’s food supply chain in a major disruption which provided a unique research opportunity. This review explores the current research direction for food supply chain resilience and identifies gaps for future research in preparing for future major global pandemics.Design/methodology/approachThis article presents a review of food supply chain resilience followed a systematic literature review of the business and management-based studies related to the food supply chain in Covid-19 published between December 2019 and December 2021 to identify the immediate issues and responses that need to be addressed in the event of future disruptions in food supply chains due to new global health threats.FindingsThe study revealed the need for more literature on food supply chain resilience, particularly resilience to a major global pandemic. The study also uncovered the sequence of events in a major pandemic and identified some strategies for building resilience to potential future risks of such an event.Research limitations/implicationsThe limitations of this study are apparent. Firstly, the selection of databases is not comprehensive. Due to time limitations, authoritative publishers such as Springer, Emerald, Wiley and Taylor & Francis were not selected. Secondly, a single author completed the literature quality testing and text analysis, possibly reducing the credibility of the results due to subjective bias. Thirdly, the selected literature are the studies published during the immediate event of Covid-19, and before January 2022, other research studies may have been completed but were still in the state of auditing at this time.Originality/valueThis paper is the first study that provides a detailed classification of the immediate challenges to the food supply chain faced in both upstream and downstream nodes during a major global disruption. For researchers, this clearly shows the immediate difficulties faced at each node of the food supply chain, which provides research topics for future studies.

Elasticity of substitution between clean energy and non-clean energy: Evidence from the Chinese electricity industry

At present, China is in a stage of high-quality economic development. Rising demand for electricity has created a lot of CO2 emissions, which has put great pressure on the low-carbon development of China's power industry. Therefore, China attaches great importance to the potential of various clean power generation to replace thermal power generation. Given this, the study examines the potential for substitution of non-clean energy generation (thermal power generation) and clean energy generation (hydropower, nuclear power generation, and other energy generation) from 1993 to 2022 by using the translog production function and provides a scenario analysis of energy substitution for power generation. Firstly, the k-fold cross-validation method is used for ridge regression estimation in this paper, which avoids the subjective bias caused by the ridge trace diagram method used in most of the previous literatures. Secondly, compared with the previous research on the substitution elasticity of the power sector, this paper subdivides the types of clean power energy when estimating the substitution elasticity, which can better analyze the substitution relationship between thermal power and various clean power. Finally, the estimated substitution elasticity of thermal power and various clean energy sources is greater than 1, which indicates that clean energy generation can effectively replace non-clean energy generation. This provides an effective substitution elasticity parameter for the power sector to study low-carbon development. The scenario analysis show China's power sector can increase the proportion of clean power generation to reduce the carbon emission intensity while ensure power supply, which can help the Chinese government adjust the implementation of policies to promote the early peak of carbon emissions and keep carbon emission at a low level in the power sector.

Effects of meloxicam on the welfare of Holstein calves from 6 weeks to 6 months old undergoing amputation dehorning

Amputation dehorning (AD) is a common practice performed on calves, causing harmful effects such as pain, distress, anxiety, and fear. These effects extend to behavioral, physiological, and hematological responses, prompting serious ethical concerns regarding animal welfare, even when performed with local anesthesia. Meloxicam, a nonsteroidal anti-inflammatory drug, has been widely used to mitigate the side effects of dehorning and disbudding in calves. However, there is a notable gap in research regarding the effects of meloxicam on calves aged 6 wk to 6 mo undergoing AD procedures. This study was designed to assess the effectiveness of co-administering meloxicam with lidocaine, a cornual nerve anesthetic, in alleviating the adverse effects caused by the AD procedure in calves within this age range, compared with the use of lidocaine alone. Thirty Holstein calves were enrolled and randomly divided into 2 groups. The first group received a subcutaneous injection of 5 mL of lidocaine in the horn area and a subcutaneous injection of 0.9% saline at a dose of 0.025 mL/kg in the neck, administered 10 min before the AD procedure. The second group received a combination of lidocaine and meloxicam: a subcutaneous injection of 5 mL of lidocaine in the horn area and a subcutaneous injection of 20 mg/mL meloxicam at a dose of 0.025 mL/kg in the neck, also administered 10 min before the AD procedure. To avoid subjective bias, the researchers were blinded to the treatment groups. Pain-related behaviors, including tail flicking, head shaking, ear flicking, head rubbing, head crossing bar, and kicking, were observed, and physiological parameters, including heart rate, rectal temperature, respiration rate, mechanical nociceptive threshold (MNT), daily active steps, and food intake were monitored. Hematological conditions were determined using enzyme-linked immunosorbent assays and routine blood tests. The data were processed using a generalized linear mixed model. The outcomes demonstrated that the AD procedure increased the frequencies of ear flicking and resulted in rises in the respiration rate, heart rate, rectal temperature, and daily active steps. It also led to decreases in total food intake, forage intake, hay intake, MNT, and increased concentrations of prostaglandin E2 (PgE2), IL-1β, tumor necrosis factor-α (TNF-α), nitric oxide (NO), and malondialdehyde, as well as glutathione peroxidase activity. However, calves that received meloxicam treatment showed significant improvements in response to the AD procedure, including lower respiration rates, heart rates, and rectal temperatures; higher MNT; and lower intermediate cell ratio. They also had higher red blood counts, hemoglobin levels, hematocrit values; larger mean platelet volumes; and lower concentrations of PgE2, IL-1β, TNF-α, and NO. These results suggest that co-administration of lidocaine and meloxicam may aid in mitigating the adverse effects induced by the AD procedure on these calves, thereby supporting the use of meloxicam in conjunction with a local anesthetic in AD procedures for calves aged 6 wk to 6 mo.

Multi-dimensional multi-round minimum cost consensus models with iterative mechanisms involving reward and punishment measures

In order to address the issues of unfairness in consensus and subjectivity bias in data, while further expanding the traditional minimum cost consensus model (MCCM) in terms of opinion dimension and consensus round, we propose the multi-dimensional MCCM (MD-MCCM) and multi-round MCCM (MR-MCCM). Subsequently, we introduce the concepts and definitions pertaining to these two variants of consensus and formulate their respective consensus model frameworks. To benefit from the advantages of both, we integrate these two types of consensus models, thereby introducing the concept of multi-dimensional multi-round minimum cost consensus. To address the issues of unfairness and subjectivity in the consensus-reaching process, we propose two specific reward and punishment measures and devise the multi-dimensional multi-round consensus iterative mechanisms. These mechanisms facilitate an iterative update of decision-makers’ opinions, weights, and unit costs within the feedback process. Based on this foundation, we construct the multi-dimensional multi-round MCCM (MDMR-MCCM) involving Euclidean and Manhattan distance measures, and their corresponding algorithm and consensus reaching process are presented. Finally, a group decision problem concerning site selection validates the rationality and effectiveness of our models. Moreover, a comparative analysis of results obtained using different models is provided.

Optimization of Piano Performance Teaching Mode Using Network Big Data Analysis Technology

To effectively avoid subjective bias in manual evaluation. This article proposes a MIDI piano teaching performance evaluation method based on bidirectional LSTM. This method utilizes a three-layer bidirectional LSTM neural network mechanism to make it easier for the model to capture useful information. In addition, the Spark clustering training model is constructed using the deeplearning4j deep learning framework, and the model parameters are adjusted through the UI dependency relationships provided by deeplearning4j to improve work efficiency. The experimental results verified the superiority of the bidirectional LSTM model. The methods provided in this article can improve students' independent practical abilities and reduce the pressure on teachers during the teaching process. These measures can promote the development of music education, improve students' music literacy and learning skills, and make positive contributions to the music education industry.

Beyond the Self-Reports: A Review on Objective Pain Measurement and Its Implications.

Current methods of pain measurement are inadequate in capturing the complexity of the pain experience. This limitation arises mainly because these methods tend to overlook the multiple dimensions of pain during assessment, heavily relying on self-reported measures, which inherently have their drawbacks. Self-reported measures aim to gauge the pain severity experienced by an individual, based solely on their perception of the most intense pain sensation. However, these measures are prone to various biases and may not accurately reflect the actual pain experienced. To overcome these limitations, a new system of pain assessment is necessary, which minimizes subjective involvement and provides a more accurate representation of pain. The 'Pain Calculator' is a newly developed tool that has demonstrated promising accuracy in measuring somatic pain in the low back region. This tool effectively overcomes the subjective biases characteristic of the self-reported measures and provides a reliable and clinically feasible alternative to the existing pain assessment tools.

Error modulates categorization of subsecond durations in multitasking contexts.

Monitoring errors consumes limited cognitive resources and can disrupt subsequent task performance in multitasking scenarios. However, there is a dearth of empirical evidence concerning this interference with prospective estimation of time. In this study, we sought to investigate this issue through a serial multitasking experiment, employing a temporal bisection task as the primary task. We introduced two task contexts by implementing two different concurrent tasks. In one context, participants were tasked with discriminating the size difference between two visual items, while in the other context, they were required to judge the temporal order of similar visual items. The primary task remained the same for the entire experiment. Psychophysical metrics, including subjective bias (determined by the bisection point) and temporal sensitivity (measured by the Weber ratio), in addition to reaction time, remained unaltered in the primary task regardless of the perceptual context exerted by the concurrent tasks. However, commission of error in the concurrent tasks (i.e., non-specific errors) led to a right-ward shift in the bisection point, indicating underestimation of time after errors. Applying a drift-diffusion framework for temporal decision making, we observed alterations in the starting point and drift rate parameters, supporting the error-induced underestimation of time. The error-induced effects were all diminished with increasing a delay between the primary and concurrent task, indicating an adaptive response to errors at a trial level. Furthermore, the error-induced shift in the bisection point was diminished in the second half of the experiment, probably because of a decline in error significance and subsequent monitoring response. These findings indicate that non-specific errors impact the prospective estimation of time in multitasking scenarios, yet their effects can be alleviated through both local and global reallocation of cognitive resources from error processing to time processing.

Risk Perception-Based Project Contingency Management Framework

Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.