Objective Decision Research Articles

On the assumptions behind metacognitive measurements: Implications for theory and practice.

Theories of visual confidence have largely been grounded in the gaussian signal detection framework. This framework is so dominant that idiosyncratic consequences from this distributional assumption have remained unappreciated. This article reports systematic comparisons of the gaussian signal detection framework to its logistic counterpart in the measurement of metacognitive accuracy. Because of the difference in their distribution kurtosis, these frameworks are found to provide different perspectives regarding the efficiency of confidence rating relative to objective decision (the logistic model intrinsically gives greater meta-dʹ/dʹ ratio than the gaussian model). These frameworks can also provide opposing conclusions regarding the metacognitive inefficiency along the internal evidence continuum (whether meta-dʹ is larger or smaller for higher levels of confidence). Previous theories developed on these lines of analysis may need to be revisited as the gaussian and logistic metacognitive models received somewhat equivalent support in our quantitative model comparisons. Despite these discrepancies, however, we found that across-condition or across-participant comparisons of metacognitive measures are relatively robust against the distributional assumptions, which provides much assurance to conventional research practice. We hope this article promotes the awareness for the significance of hidden modeling assumptions, contributing to the cumulative development of the relevant field.

A Product Modeling Design Decision Model Based on PGA Genetic Algorithm

A Pareto-based genetic algorithm (PGA) product design decision model is proposed in this work to improve the efficiency of product design decisions and avoid the instability of individual decision differences. Based on the product modeling design decision constraint space, decision variables, and other factors, the model utilizes the PGA optimization algorithm to make an objective decision on a design scheme. Using the analytic hierarchy process, the design expectations, objectives, variables, and schemes are constructed into a hierarchical structure. The design decision problems are then mapped to a mathematical model, and a simulation of the design scheme decision process is performed. Finally, the feasibility of the proposed model is analyzed and verified by evaluating the design decisions of a brand electric scooter product to guide designers in making innovative design decisions.

Hydrological model preselection with a filter sequence for the national flood forecasting system in Kenya

AbstractThe choice of model for operational flood forecasting is not simple because of different process representations, data scarcity issues, and propagation of errors and uncertainty down the modeling chain. An objective decision needs to be made for the choice of the modeling tools. However, this decision is complex because all parts of the process have inherent uncertainty. This paper provides a model selection with a filter sequence for flood forecasting applications in data scarce regions, using Kenya as an example building on the existing literature, concentrating on six aspects: (i) process representation, (ii) model applicability to different climatic and physiographic settings, (iii) data requirements and model resolution, (iv) ability to be downscaled to smaller scales, (v) availability of model code, and (vi) possibility of adoption of the model into an operation flood forecasting system. In addition, we review potential models based on the proposed criteria and apply a decision tree as a filter sequence to provide insights on the possibility of model applicability. We summarize and tabulate an evaluation of the reviewed models based on the proposed criteria and propose the potential model candidates for flood applications in Kenya. This evaluation serves as an objective model preselection criterion to propose a modeling tool that can be adopted in development and operational flood forecasting to the end‐users of an early warning system that can help mitigate the effects of floods in data scarce regions such as Kenya.

The field and resistance training loads of academy rugby league players during a pre-season: Comparisons across playing positions.

Male academy rugby league players are required to undertake field and resistance training to develop the technical, tactical and physical qualities important for success in the sport. However, limited research is available exploring the training load of academy rugby league players. Therefore, the purpose of this study was to quantify the field and resistance training loads of academy rugby league players during a pre-season period and compare training loads between playing positions (i.e., forwards vs. backs). Field and resistance training load data from 28 adolescent male (age 17 ± 1 years) rugby league players were retrospectively analysed following a 13-week pre-season training period (85 total training observations; 45 field sessions and 40 resistance training sessions). Global positioning system microtechnology, and estimated repetition volume was used to quantify external training load, and session rating of perceived exertion (sRPE) was used to quantify internal training load. Positional differences (forwards n = 13 and backs n = 15) in training load were established using a linear mixed effect model. Mean weekly training frequency was 7 ± 2 with duration totaling 324 ± 137 minutes, and a mean sRPE of 1562 ± 678 arbitrary units (AU). Backs covered more high-speed distance than forwards in weeks two (p = 0.024), and 11 (p = 0.028). Compared to the forwards, backs completed more lower body resistance training volume in week one (p = 0.02), more upper body volume in week three (p< 0.001) and week 12 (p = 0.005). The findings provide novel data on the field and resistance-based training load undertaken by academy rugby league players across a pre-season period, highlighting relative uniformity between playing positions. Quantifying training load can support objective decision making for the prescription and manipulation of future training, ultimately aiming to maximise training within development pathways.

Fuzzy Linear programming approach in Water Pollution Control

Purpose of the study: The purpose of the study is to calculate the water pollution level of a source. Because water pollution is a very big problem in front of us. Mining and the related activities are responsible for large scale water pollution.&#x0D; Methodology: The problem of water pollution control (WPC) may be treated as a multiple objective decision making problem. This paper deals with use of fuzzy linear programming in water pollution control problem. Min operator has been used to construct the overall achievement function. A semi hypothetical case has been studied with regard to mining vis-a-vis water pollution.&#x0D; Main Findings: After calculation and using all the methods the main finding is that total WPC cost is 54411.12 units.&#x0D; Applications of this study: We can use this method of water purification to purify the big source of water supply. After using this method the cost of water purification can be calculated and minimized.&#x0D; Novelty/Originality of this study: This calculation is based on the secondary data source and this can help the system to use the proper method and calculation of expense in purifying water.

Product allocation and network configuration in global production networks

Driven by an increasing demand for individualized products and shorter product life-cycles, companies continuously extend their product portfolio. Simultaneously, companies expand into new markets to reach customers and to exploit varying location factors to reduce costs. Global production networks (GPNs) have to be adapted constantly to react to new circumstances and changes in the demand of products. To remain competitive, product allocation and production network configuration are essential. At the same time, companies face an increasing complexity while handling these tasks. This poses a challenge particularly for small and medium sized companies, which have limited planning capacities and management resources. Current literature describes optimization-based approaches for the integrated product allocation and network configuration of production networks. Yet, multi-objective models lack transparency of results and user friendliness. Therefore, this paper presents a multi-objective optimization model that incorporates flexibility and reconfiguration aspects to determine an optimal product allocation and network configuration of a GPN over a given planning horizon. The preemptive goal programming approach is used to identify Pareto-optimal solutions and to increase user friendliness. The subsequent verification, validation and post-optimality analysis combined in a structured process enables a wide range of companies to apply the approach. The model is successfully applied in the GPN of a special machine manufacturer, which produces high precision metrology machines. Due to its transparent approach for complex planning problems, the developed method provides a solid base for well-founded, objective decisions. Hence, the risk of costly errors in the planning phase is reduced.

Comparison Of Feasibility Of Concrete And Asphalt Road With Analityc Hierarchy Process (AHP) Method

This study specifically tries to apply the AHP (analytic hierarchy process) method in the interest of formulation and decision making in the field of engineering, especially for evaluating the comparison of road construction feasibility between asphalt and concrete roads. It is hoped that through this study it can be proven that the AHP method, which is widely used in management decision making, is reliable enough to be applied in engineering and engineering, so that it can assist policy makers in the process of making objective decisions, especially in government circles. This study method includes data collection and analysis methods. For data collection, this study uses a questionnaire method that contains questions related to technical and non-technical factors that are used to assess the feasibility of a road. A total of 16 questions in the questionnaire. Questionnaires were distributed randomly to around 30 respondents who were divided into various groups such as the Department of Highways, consultants, university lecturers, contractors and the general public around the Demak – Godong highway. The results of the questionnaire in the form of answers from respondents were then recapitulated and searched for the dominant answer for each question. The dominant answer is then scored based on the Saaty rating scale. These values ​​are then formed matrices with certain patterns. The AHP method was then applied to analyze these matrices. The results of calculations using the AHP method are in the form of eigenvectors where each value in the vector indicates the value of the proposed alternative (in this case asphalt and concrete construction). From the results found in the analysis, recommendations can then be given as follows: 1) Changes in construction from asphalt to concrete as is currently being carried out on the Demak – Godong highway need to be supported considering that in many ways concrete construction is more feasible than asphalt construction. from this study and 2) the data for the AHP method in this study rely on respondents' assessments of the proposed factors, because the assessments will vary greatly from one another (as indicated in the frequency distribution of the data), the increase in the number of respondents with wider sources involving experts needs to be done in order to maintain data consistency.

Subjectivity Tracking System for Poor Scholarship Recipients at Elementary School Using the MOORA Method

This research was conducted because of complaints from several parents at Elementary School regarding recipients of the Poor Student Assistance (PSA) who were still less objective. Elementary School XY regularly conduct screening activities every year to select prospective PSA recipients. This selection is made so that the recipients of this assistance are students entitled to it. Some students should be accepted as a selection committee but do not mistake of choosing some students who have kinship or subjective matters. Therefore, this study aims to explore and create applications that apply the Multi Objective Optimization to the basic Ratio Analysis (MOORA) method, which is a method for determining students based on predetermined criteria. The criteria used are the value of report cards, student achievement, student activity, parental income, parental dependents, and home conditions. After conducting a search and implementation using the MOORA method in determining PSA recipients, it was found that there were some non-objective results where the student's criteria and final results were lower than some other students. However the Elementary School provided a recommendation to get PSA. If this happens again, then the importance of this system is to help objective selection. The accuracy results explained that 14.39% of PSA recipients were subjective. It was concluded that this research helps an objective decision and facilitates the decision maker in determining the best 3 recipients from each class at Elementary School XY.

Propagation of societal gender inequality by internet search algorithms

Humans increasingly rely on artificial intelligence (AI) for efficient and objective decision-making, yet there is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded. As a consequence, their use by human decision makers may result in the propagation, rather than reduction, of existing disparities. To assess this hypothesis empirically, we tested the relation between societal gender inequality and algorithmic search output and then examined the effect of this output on human decision-making. First, in two multinational samples (n = 37, 52 countries), we found that greater nation-level gender inequality was associated with more male-dominated Google image search results for the gender-neutral keyword "person" (in a nation's dominant language), revealing a link between societal-level disparities and algorithmic output. Next, in a series of experiments with human participants (n = 395), we demonstrated that the gender disparity associated with high- vs. low-inequality algorithmic outputs guided the formation of gender-biased prototypes and influenced hiring decisions in novel scenarios. These findings support the hypothesis that societal-level gender inequality is recapitulated in internet search algorithms, which in turn can influence human decision makers to act in ways that reinforce these disparities.

Detection and Classification of Myocardial Infarction Transmurality Using Cardiac MR Image Analysis and Machine Learning Algorithms.

The presence of abnormalities when the left ventricle is deformed is related to the patients' prognosis after a first myocardial infarction. These deformations can be detected by performing a cardiac magnetic resonance (CMR) study. Currently, late gadolinium enhancement (LGE) is considered to be the gold standard when performing CMR imaging. However, CMR with LGE overestimates infarct size and underestimates recovery of dysfunctional segments after myocardial infarction. Based on this statement, the objective is to detect, characterize, and quantify the extent of myocardial infarction in patients with cardiac pathologies, using parameters derived from CMR, in order to obtain greater precision in patients' recovery predictions than when only studying LGE images. For this purpose, we studied the infarct presence and extension from a total of 105 images from 35 patients, and calculated myocardium strain and torsion to characterize and quantify the affected tissue. A total of twenty-one parameters were selected to create predictive models. Moreover, we compared two feature extraction methods, and the performance of five machine learning algorithms. Results show that both temporal and strain parameters are the most relevant to detect and characterize the extent of myocardial infarction. The use of imaging techniques and machine learning algorithms have great potential and show promising results when it comes to detecting the presence and extent of myocardial infarction. The current study proposes a novel approach to detect, quantify, and characterize cardiac infarction by using strain and torsion parameters from different CMR images and different Machine Learning algorithms. This would potentially overcome LGE, the current state of the art technique, in estimating the extension of damaged tissue and enable an objective diagnosis and clinical decision.

Конструктивна дестабілізація: подолання бар’єрів розвитку економічного середовища

The development of negative trends in the energy industry of Ukraine is emphasized. &#x0D; Scientists mainly pay attention to destabilizing processes in politics and its components, studying the factors of their influence, types of destabilizations of the political system, ensuring the stability of the political system. However, we have not come across scientific works in which the discussion about constructive destabilization, which will have a positive effect on economic results, would be raised. And this influence is now felt by the oil and gas industry of Ukraine, which is experiencing revitalization in the initial period, at the stage of revival. The purpose of the article is to identify the factors of constructive destabilization at each level of management, which will allow optimizing the processes of functioning of energy systems and the mechanisms of their provision.&#x0D; An economic and linguistic and etymological analysis of the definition "destabilization" was carried out. An in-depth systematic analysis of the effects of destabilizing factors was carried out with the identification of cognitive features for the formation of a model of objective management decisions (alternatives) in the field of energy and its individual components. Special attention is paid to disturbing factors that cause destabilization. The model of the formation system of constructive destabilization is depicted, as a relationship of closely related elements, which relies on cognitive technologies. Determining the reasons and conditions for the formation of a constructive destabilization system in the field of energy security made it possible for the first time to propose the basic principles of constructive destabilization and to introduce an indicator of the destabilization factor, thanks to which it is possible to predict positive changes in the system from negative disturbances. The expediency of creating the State Decarbonization Fund as a tool for sustainable development is emphasized. A list of the factors of constructive destabilization in the energy sector of Ukraine was proposed and analyzed.

Evaluating and Validating the Classification Accuracy of a Screening Instrument to Assess Risk for Commercial Sexual Exploitation of Child Welfare-Involved Children and Adolescents.

Identifying children and adolescents within child welfare at risk for commercial sexual exploitation (CSE) can ensure referrals to appropriate services. However, screening measures to understand the prevalence of CSE are missing in child welfare. We evaluated the classification accuracy of a screener developed for the purpose of this study, guided by the Sexual Exploitation among Youth (SEY) risk assessment framework used in practice with child welfare-involved young people, (1) to identify young people at high versus low risk for experiencing CSE and (2) to estimate the prevalence of CSE risk for child welfare-involved children and adolescents. We used extant data from the National Survey of Child and Adolescent Well-being study with a nationally representative sample of children and adolescents aged 11-17 years (n = 1054) investigated by child welfare from February 2008 to April 2009. The 26-item screener showed acceptable reliability (α = .73) and test-criterion validity evidence using a CSE proxy outcome (ie, narrowly defined as being paid for sexual relations). We used the receiver-operating curve to classify risk and calculate the optimal cutoff score. Higher scores on the SEY screener (range, 0-20 points) increased the odds of experiencing CSE by 34%. The screener was good at discriminating CSE risk at the 6-point cutoff, with 26.7% of child welfare-involved young people identified as being at high risk for CSE. Given the absence of accurate prevalence rates of CSE risk in the population, a theoretical cutoff index using an established method can provide an objective decision on how to distinguish risk levels. Prevalence estimates for CSE risk highlight the need for systematic screening in child welfare to identify and provide services for young people at risk.

HLDNet: Abandoned Object Detection Using Hand Luggage Detection Network

Abandoned object detection has recently been studied to detect dangerous situations such as illegally dumping explosives. Existing abandoned object detection used background subtraction, which is sensitive to noise and clutters of a similar size in outdoor environments. Unlike existing approaches, this article presents a deep learning-based detection method that is suitable for use in outdoor environments because it is robust to ghost effects and illumination changes. The proposed method consists of three parts: 1) pedestrian detection and tracking; 2) hand luggage detection (HLD); and 3) abandoned object decision. Since the proposed method detects the abandoned object through HLD instead of directly detecting the abandoned object itself, we could successfully improve the detection accuracy and robustness. The proposed method can also detect dangerous weapons or restricted hand luggage through HLD. This article presents an intelligent abandoned object detection system that can be applied for consumer applications.

Penerapan Metode MFEP (Multifactor Evaluation Process) Dalam Seleksi Karyawan

This study aims to: (1) design a decision support system that can provide convenience for the permanent employee selection committee in making decisions about the results of the permanent employee selection. (2) implement the application of a permanent employee selection decision support system at PT. Pharos Indonesia. Designing a Decision Support System for the Selection of permanent employees at PT. Pharos Indonesia is intended to increase the effectiveness and efficiency of time in terms of decision making for the selection of permanent employees. The results of the study indicate that the system designed is very supportive of Permanent Employee Selection Decisions at PT. Pharos Indonesia by providing convenience in making objective and transparent decisions.

Promethee Algorithm in Assessing Lecturer Performance

Higher Education is an educational institution that plays a role in producing quality human resources. The roles, duties and responsibilities of lecturers are very important in educating the nation's life and national goals. There are many problems that cause the performance of lecturers at universities in Indonesia. Meanwhile, lecturers are required to give good performance. In this case, the researcher is interested in evaluating the performance of lecturers using the promethee method. The promethee method is able to produce decisions based on comparisons between alternatives according to the preference function and the different weights of each criterion. The criteria used in the assessment of lecturer performance are 10 criteria with 7 alternative lecturers. The Promethee method produces recommendations for lecturers with the best performance, namely Lecturer E who has a net flow value of 0.12153. While the lecturer with the lowest performance assessment is Lecturer B with a net flow value of -0.19167. The results of the decisions given by the promethee method can help provide an assessment of the lecturer's performance from the several alternatives given. So as to be able to produce an objective recommendation decision

Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots.

Recent advances in artificial intelligence have enhanced the abilities of mobile robots in dealing with complex and dynamic scenarios. However, to enable computationally intensive algorithms to be executed locally in multitask robots with low latency and high efficiency, innovations in computing hardware are required. Here, we report TianjicX, a neuromorphic computing hardware that can support true concurrent execution of multiple cross-computing-paradigm neural network (NN) models with various coordination manners for robotics. With spatiotemporal elasticity, TianjicX can support adaptive allocation of computing resources and scheduling of execution time for each task. Key to this approach is a high-level model, "Rivulet," which bridges the gap between robotic-level requirements and hardware implementations. It abstracts the execution of NN tasks through distribution of static data and streaming of dynamic data to form the basic activity context, adopts time and space slices to achieve elastic resource allocation for each activity, and performs configurable hybrid synchronous-asynchronous grouping. Thereby, Rivulet is capable of supporting independent and interactive execution. Building on Rivulet with hardware design for realizing spatiotemporal elasticity, a 28-nanometer TianjicX neuromorphic chip with event-driven, high parallelism, low latency, and low power was developed. Using a single TianjicX chip and a specially developed compiler stack, we built a multi-intelligent-tasking mobile robot, Tianjicat, to perform a cat-and-mouse game. Multiple tasks, including sound recognition and tracking, object recognition, obstacle avoidance, and decision-making, can be concurrently executed. Compared with NVIDIA Jetson TX2, latency is substantially reduced by 79.09 times, and dynamic power is reduced by 50.66%.

A method for AI assisted human interpretation of neonatal EEG

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.

Knowledge driven indoor object‐goal navigation aid for visually impaired people

Aiming to help improve quality of life of the visually impaired people, this paper presents a novel wearable aid in the shape of a helmet for helping them find objects in indoor scenes. An object-goal navigation system based on a wearable device is developed, which consists of four modules: object relation prior knowledge (ORPK), perception, decision and feedback. To make the aid also work well in unfamiliar environment, ORPK is used for sub-goal inference to help the user find the target goal. And a method that learns the ORPK from unlabelled images by utilising a scene graph and knowledge graph is proposed. The effectiveness of the aid is demonstrated in real world experiments.

Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses

ABSTRACT It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups. These models rely on the underlying assumption that patients with similar characteristics will have similar outcomes to the same treatment. Patient populations within each subgroup must subjectively be deemed similar enough Pocock (1976) to borrow response information across subgroups. We propose utilizing the machine learning method of Bayesian Additive Regression Trees (BART) to provide a method for subgroup borrowing that does not rely on an underlying assumption of homogeneity between subgroups. BART is a data-driven approach that utilizes patient-level observations. The amount of borrowing between subgroups automatically adjusts as BART learns the covariate–response relationships. Modeling patient-level data rather than treating the subgroup as a single unit minimizes assumptions regarding homogeneity across subgroups. We illustrate the use of BART in this context by comparing performance from existing subgroup borrowing methods in a simulation study and a case study in non-small cell lung cancer. The application of BART in the context of subgroup analyses alleviates the need to subjectively choose how much information to borrow based on subgroup similarity. Having the amount of borrowing be analytically determined and controlled for based on the similarity of individual patient-level characteristics allows for more objective decision making in the drug development process with many other applications including basket trials.

Development of a Prediction Model for Significant Adverse Outcome After Spine Surgery.

Retrospective cohort study. Development, validation, and decision curve analysis of a novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery. Data was gathered retrospectively from medical records of patients who underwent spine surgery at a single tertiary centre between January 2019 and December 2020 (n = 488). Postoperative adverse events were classified objectively using the Comprehensive Complication Index (CCI). The model was derived using backward stepwise logistic regression. Validation was undertaken using bootstrap resampling. Discrimination was determined by calculating the area under the receiver operating characteristic (AUC). Calibration was assessed graphically. Clinical utility of the model was assessed using decision curve analysis (DCA). Performance measures were compared to an existing tool, SpineSage. Overall complication rate was 34%. Modelling showed higher age, surgical invasiveness and preoperative anemia were most strongly predictive of any complication (OR = 1.03, 1.09, 2.1 respectively, P < .001), whereas the occurrence of a major complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, P < .001). At validation, the model showed good discrimination with an AUC of .73 (.71 - .75) and excellent calibration. SpineSage had an AUC of .71, while DCA showed the novel model had greater expected benefit at all risk thresholds. NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their perioperative risk.