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Adequate English language competency is very necessary in today's modern era so as not to discuss difficulties in global communication and interaction. The average English language skills of students or generations aged 18-20 years in Indonesia show low results. To solve this problem, efforts are needed to increase English language competency through appropriate learning activities. The system development method for this application uses the waterfall method. The use of the forward channeling method in an expert system will help determine appropriate and effective ways of teaching and learning English to students. The aim of this research is to apply an expert system to obtain recommendations for effective English learning based on TOEFL ITP scores. Three aspects of TOEFL ITP, namely listening, reading and structure, will be used in the analysis process using forward chaining. The forward chaining method carries out processing starting from the data set and then performs inference according to the rules used until the optimal inference is found. The inference engine will continue to go through the process to arrive at the right decision result. The expert system application is designed to be website-based so that it makes it easier for users to use it at any time. [ABSTRACT FROM AUTHOR]

The selection of appropriate cutting tool (CT) is a critical part of machining. Selecting the right tool for the right job will enable customers to achieve economic machining, saving time and cost while delivering high-quality products. Nowadays, the complexity of CT and workpiece is increasing; this changes the input and output requirements for cutting tool selection paving way to automation. There are two types of CT selection methods, manual and automated CT selection. This article focuses on automated CT selection, which has different inputs and outputs based on the algorithm/AI technique used. The potential and promising aspects of CT selection could enhance the machining in terms of productivity, time, cost, and quality aspects. A comprehensive review of automated CT selection methods has been presented in this paper. The review surveys different automated CT selection methods in terms of inputs, outputs, and artificial intelligence (AI) techniques/different algorithms since most of the researchers have not focused on this perspective. It outlines the current status of research and application, which has the potential to improve the automated CT selection methods for the benefit of the manufacturing industry. [ABSTRACT FROM AUTHOR]

The engine main shaft forgings have high requirements for product consistency and reliability, which are difficult to be guaranteed by traditional manual free forging. On the basis of the already-in-place machinery, technical optimization was done in order to realize intelligent forging of the engine main shaft forgings. A rail trailer, holding robot, inspection robot, and expert system were installed as well as other hardware and software. Additionally, the procedure and specifications for robot intelligent free forging were revised. Based on the artificial neural network (ANN) model, an optimization model and a prediction model were created, and the process parameters can be controlled during forging. The verification result shows that the intelligent free forging production line can achieve real-time control of the shaft forging process, and obtain the forgings whose shape, size, microstructure, performance and consistency meet the requirements. With the help of this production line, free forging can be produced more quickly and efficiently, which is crucial for realizing the automation, digitization, and intelligence of shaft forging free forging. [ABSTRACT FROM AUTHOR]

Degumming and bleaching are critical steps in the palm oil refining process, as they are the precursors to the qualities of refined, bleached, and deodorized palm oil. In practice, plant operators often face oil rejections in these processes and solve the problem by trial and error. Hence, a fuzzy expert system is developed to troubleshoot the degumming and bleaching process, for identifying failures and suggesting actions. However, developing the knowledge base and inference engine in the fuzzy expert system for troubleshooting the degumming and bleaching process is challenging because the data in the actual palm oil refining process are poorly documented and must be obtained from various sources, including field observation, document analysis, and interviews, and need to be analyzed using thematic analysis. The results from the thematic analysis were represented as input and output variables of the fuzzy expert system. The developed fuzzy expert system is tested and validated against different data sets and industrial data to identify faults and suggest necessary actions. To evaluate the robustness of the troubleshooting system, the membership functions of the fuzzy expert system are adjusted based on the distributed control system (DCS). The results show that the troubleshooting system can effectively diagnose potential faults and provide necessary actions and can serve as a useful guidance for failures in the degumming and bleaching process. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices. [ABSTRACT FROM AUTHOR]

The optimal functioning of the power system is crucially dependent upon the sound protection of its major stakeholder, i.e., the transmission line, as it is prone to fault. To maintain the integrity of the power system and protect costly power system equipment, protective relaying is necessary to provide a steady and affordable supply of electricity. Relays recognize, classify, and identify transmission line faults using input signals of voltage and current. Many artificial intelligent methods based on Expert Systems, Artificial Neural Networks, Fuzzy Logic, Support Vector Machines, Wavelet-based systems, and deep learning techniques are being investigated to improve modern digital relays’ consistency, speed, and accuracy. This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit (PMU) and Global Positioning System (GPS) approaches together with all of these intelligent transmission line safety strategies and concepts. Initial investigators will benefit from this study by being able to examine, evaluate, and analyze a variety of approaches with references for all relevant contributions. [ABSTRACT FROM AUTHOR]

Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain. [ABSTRACT FROM AUTHOR]

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article's goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components' states in the neural network's output cells. Based on the condition of the object's constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement. [ABSTRACT FROM AUTHOR]

The object of this research is the decision-making process in the context of selecting recipients of social services using a semi-automated expert system. The main focus is on improving the mechanisms of assessment and selection of candidates eligible for assistance, in order to ensure a more efficient and objective allocation of resources. The problem addressed in this study is the need to improve the accuracy and efficiency of decision-making processes in social services through the implementation of semi-automated systems. In particular, reducing subjective influence in selection processes, as well as reducing the time and resources required to process applications. The study shows that the introduction of a semi-automated system can significantly reduce the response time to applications, increase the accuracy of candidate selection and ensure greater transparency in the decisionmaking process. The system, based on data analysis algorithms and production rules, is able to adapt to changing conditions and requirements, providing solutions based on up-to-date information. The effectiveness of the semi-automated system is due to the use of modern technologies for processing large volumes of data and the use of complex mathematical models for the analysis of this data. The implementation of a modular system with individually adjustable parameters allows the system to accurately evaluate each case based on the expected criteria, ensuring a high level of adaptability and accuracy. The results of the research can be applied in practice in various social security institutions, where there is a need to automate the processes of selection and decision-making. Important conditions for the effective implementation of the system are the availability of sufficient technical support, a high level of qualification of the personnel engaged in putting the system into operation, as well as a clear understanding of the rules and procedures that regulate social protection. In addition, to guarantee the successful application of the system, it is necessary to ensure compliance with all regulatory and legislative requirements, especially regarding the protection of personal data. [ABSTRACT FROM AUTHOR]

This paper introduces an expert system to decision-making. The expert system is linguistic summarization combined with prioritized operators. In the practical decision-making problems, the information of attributes is linguistic type and needs to be converted into numerical type. The validity of the linguistic summarization is recorded as the attribute value. We discuss how to calculate the validity of the linguistic summarization, and present three prioritized operators. Then the three prioritized operators are used to aggregate the attribute values. Finally, a practical example is given. In addition, we conduct a comparative analysis between the expert system method and another multi-attribute decision-making method by using a measure of specificity, and conclude that the expert system method is better. [ABSTRACT FROM AUTHOR]

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks. [ABSTRACT FROM AUTHOR]

In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%. [ABSTRACT FROM AUTHOR]

This study aims to measure the quality of distance university higher education in the Peruvian department of Junín, as well as to seek strategies of improvement. To meet this objective, a survey was carried out on students from the 4 universities located in this department, all with an institutional license. An intentional non-probabilistic sampling was used. The quality variable in university higher education is described in nine dimensions derived from the educational policies imposed by the National Superintendency of Higher University Education (SUNEDU in Spanish), which are: academic objectives, educational offer, infrastructure and equipment, research lines, qualified teacher, complementary services, job bank, transparency, and satisfaction. Among the results, it stands out that 59% of the students on average show satisfaction with the education received, which allows us to conclude that quality should be directed by a unit with its own name and established transversally in all administrative, academic, and academic procedures, investigative, social projection, and university extension. Transparency avoids any act of corruption, therefore it is important to associate it with the ethics of the authorities and workers of the university entity, strengthening timely communication with the student body. On the other hand, Kosko's Hidden Patterns Method was used to process a fuzzy cognitive map designed by education quality experts' opinions and in this way conclusions were reached about the strategies to follow for improving the quality of Peruvian non-face-to-face education in the department. [ABSTRACT FROM AUTHOR]

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell "events". Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two‐lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system. [ABSTRACT FROM AUTHOR]

Background: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. Methods/design: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner. Plain English summary: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice. [ABSTRACT FROM AUTHOR]

In response to the difficulty in accurately describing the fault diagnosis of exhaust temperature exceeding the limit of a piston type hydrogen compressor using traditional two-state fault diagnosis at each stage, and the lack of application of a fault diagnosis system that could finish self-diagnose for diagnosing the exhaust temperature exceeding the limit, the DW-(5.6-13)/(0.2-0.5)-20 oil-free variable frequency piston type hydrogen compressor was taken as the research object, the importance of relevant components were analyzed, and a fault self-diagnosis system was designed. Firstly, the corresponding fuzzy Takagi-Sugeno(T-S) fault tree analysis model was built for the fault, and the T-S fault tree analysis method based on Bayesian network was used to analyze the importance of the bottom event to determine the weak links and the diagnosis sequence of faulty parts. Then, the fuzzy number was used to represent the fault status of the parts, and it was brought into the T-S fault tree analysis algorithm based on Bayesian-network(BN) to predict the occurrence probability of the primary exhaust temperature overrun fault. Finally, a rule-based fault diagnosis expert system was designed to infer and troubleshoot the fault causes of the instance. The research results indicate that after analyzing existing fault data, the rule-based fault diagnosis expert system’s diagnosis results are consistent with the facts, indicating that the system can accurately diagnose the cause of the fault and achieve self-diagnosis. At the same time, combining with the T-S fault tree analysis method based on BN, the system can also realize the prediction of the probability of top event failure, which provides a feasible scheme for the fault diagnosis of the piston hydrogen compressor. [ABSTRACT FROM AUTHOR]

Among the agricultural products, mushroom is one of the best candidates for robotic harvesting methods. One of the main problems of mushroom growers is a critical need for labor within the specified time for harvesting mushrooms, so mushroom growing is labor intensive. In this research, we attempted to develop and apply a harvesting robot for this crop to reduce the problems that growers face in terms of harvesting labor. For ease of end-effector movement between shelves, the robot was developed by Cartesian Mechanism. This robot used image processing and a computer expert system to detect the position of mushrooms on the substrate on the shelf. The end-effector acts by using a suction cup and a non-shear mechanism to harvest mushrooms from the substrate. By testing this robot on the substrate, we could harvest perfect whole mushrooms on average of 81.5% of mushrooms on the prepared substrates. Harvesting time per mushroom was obtained 12.45s, which is the amount of respective time of 8.45s more than harvesting time by labor hands, but in robotic harvesting, robots unlike humans can work 24 hours a day, continuously during growing time. An expert system also could be a valuable asset to change the grower’s strategy in terms of harvested mushrooms size and quality based on customer needs in the market. [ABSTRACT FROM AUTHOR]

The aim of the research is to develop an MBKM-based assessment model for electrical circuit courses according to industry needs by using an expert system to provide recommendations in determining implementation policies on campus. The aspects assessed are the validity, practicality and effectiveness of the model. The instruments used are observation sheets, interview guides and questionnaires for students, lecturers and industry. And also use tests to measure student learning outcomes. The research population at the Faculty of Engineering, Padang State University, sample selection using a purposive sampling technique was 55 students and 5 lecturers, and industry instructors. Descriptive data analysis techniques to measure the validity, practicality and effectiveness of the model. And also the test uses Structural Equation Modeling (SEM) using the SMART PLS application. The findings from the needs analysis of an expert system-based assessment model are that the results are very much needed. In line with the results of observations and interviews, it shows that there are still problems that occur in learning activities, assessment and MBKM, which requires the development of assessment models. Meanwhile, the results of expert validation produce data models, questions and websites (usability), information quality, information quality or interaction quality that are proven to be valid. Likewise, the practicality of the lecturers' and students' responses proved to be practical. For student learning outcomes, the average student learning outcomes are classified as good. These findings were novelty by using CAT technology, expert systems, MBKM, holistic assessment, and student assessment needs in industry. The suggestion for this research is to be able to add the latest technology to the MBKM assessment. [ABSTRACT FROM AUTHOR]

Expenditures for Information Technology (IT) and Information Systems (IS) globally are significant and continue to increase each year. With such substantial spending, it is crucial to demonstrate the business values of the investments made. To identify these business values, the Generic IT/IS Business Values Table can be used. The process of identifying business values requires in-depth study and references from various sources. With technological advancements, previous research data can be utilized as a knowledge base model in an expert system, serving as a tool to facilitate the analysis of business values. The prototype of the expert system is developed using the C# programming language and SQL Server database as its foundation. The initial learning data is derived from the business values of six case studies categorized into three types: Industry Type, Project Type, and Investment Type. The search method used as a logical inference mechanism for analysis is forward chaining. The prototype of expert system adopts the Human in the Loop approach, enabling users or experts to intervene in the results and recommendations generated by the system. Testing is conducted through simulation of case study analyses to assess the performance of the built algorithm. The test results show that the prototype can provide additional information to users for conducting a study on the business values of IT/IS. This information consists of relevant and significant business values as well as cause-and-effect relationships of the identified business values. [ABSTRACT FROM AUTHOR]

A chemical recognition algorithm is an integral part of any autonomous microscale gas chromatography (µGC) system for automated chemical analysis. For a multi-detector µGC system, the chemical analysis must account for the retention time of each chemical analyte as well as the relative response of each detector to each analyte, i.e., the detector response pattern (DRP). In contrast to the common approaches of heuristically using principal component analysis and machine learning, this paper reports a rule-based automated chemical recognition algorithm for a multi-cell, multi-detector µGC system, in which the DRP is related to theoretical principles; consequently, this algorithm only requires a small amount of calibration data but not extensive training data. For processing both the retention time and the raw DRP, the algorithm applies rules based on expert knowledge to compare the detected peaks; these rules are located in a customized software library. Additionally, the algorithm provides special handling for chromatogram peaks with a small signal-to-noise ratio. It also provides separate special handling for asymmetrical peaks that may result from surface adsorptive analytes. This work also describes an experimental evaluation in which the algorithm used the relative response of two complementary types of capacitive detectors as well as a photoionization detector that were incorporated into the µGC system of interest. In these tests, which were performed on chromatograms with 21–31 peaks for each detector, the true positive rate was 96.3%, the true negative rate was 94.1%, the false positive rate was 5.9%, and the false negative rate was 3.7%. The results demonstrated that the algorithm can support µGC systems for automated chemical screening and early warning applications. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions. [ABSTRACT FROM AUTHOR]

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has the capability of producing parts with more complicated shapes and functions compared with subtractive manufacturing. Design for additive manufacturing (DfAM) aims at producing design solutions that can be additively manufactured. During DfAM, analysing the printability and integratbility of a target assembly are important tasks. However, the tasks require systematic analysis of the structural/functional/material characteristics, working conditions, and interaction relations among the parts in the target assembly, fully understanding of the capability and limitation of the applied AM equipment, and comprehensive design decision-making considering the characteristics of AM processing, etc. This makes it challenging to conduct printability and integratbility analysis tasks. In this regard, an expert system is established which is realised with Function-Behaviour-Structure oriented assembly structure characteristic analysing, ontology instance-based assembly structure characteristic modelling, and modified RETE algorithm-based design decision-making. The system is capable of providing decision guidance on which parts in a target assembly can be printed and which among these printable parts can be printed integratedly. In this way, it can support DfAM implementation in a computer-aided manner. A WebApp that encapsulates the system is developed, and a robot arm DfAM redesign project is used as a case study. [ABSTRACT FROM AUTHOR]

Edge detection is an essential computer vision phenomenon that is useful for contour detection and in turn it is useful for acquiring vital information. There are a variety of images, including medical, satellite, industrial and general-purpose images. An X-ray is a form of medical imaging that is created by transmitting electronic radiation into a patient's body to get an image of the human body's internal structures. This allows orthopedic surgeons and radiologists to make more accurate diagnoses of illness. Due to its versatility, soft computing approaches are regarded as effective edge detection tools. This study provides a novel fuzzy logic-based technique for edge detection in which the quality of edges is governed by a sharpening-guided filter. The proposed model holds four different patterns for extracting the edges from an image namely 2 × 2 mask, 3 × 2 mask, 2 × 3 mask and 3 × 3 mask. These patterns of masks target the edge pixels of an image along with the other pixels. The proposed model provides an enhanced approach for detecting edges in X-ray pictures of humans. Various statistical measurements are used to evaluate the proposed model to prove its significance. By adjusting the smoothing parameters appropriately, it has been discovered that the detected edges are significantly enhanced. The proposed model is implemented and evaluated under four different metrics namely Correlation Coefficient, Figure of Merit, Structure Similarity Image Metrics and Euclidian Distance. The proposed model results are compared five different state-of-the-art algorithms to prove the significance, and the result shows that the proposed model identifies the edge in a well-structured manner. [ABSTRACT FROM AUTHOR]

Background: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. Method: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. Results: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). Conclusion: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations. [ABSTRACT FROM AUTHOR]

The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems (CDSSs) for prenatal screening of thalassemia were created. A cross-sectional study was conducted on pregnant women and their husbands visiting from October 2020 to December 2021. A total of 10,112 medical records of first-time pregnant women and their husbands were collected. CDSS including two different types of systems for prenatal screening for thalassemia (expert system [ES] and four artificial intelligence [AI]-based CDSS) was built. 1,992 cases were used to train and test machine learning (ML) models while 1,555 cases were used for specialized ES evaluation. There were 10 key variables for AI-based CDSS for ML. The four most important features in thalassemia screening were identified. Accuracy of ES and AI-based CDSS was compared. The rate of patients with alpha thalassemia is 10.73% (1,085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. ES showed an accuracy of 98.45%. Among AI-based CDSS developed, multilayer perceptron model was the most stable regardless of the training database (accuracy of 98.50% using all features and 97.00% using only the four most important features). AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: With the rapid technological advances, the application of artificial intelligence (AI) has witnessed significant growth in the agricultural industry, specifically in the poultry sector. The use of AI in estimating poultry weight can significantly impact production economics and overall efficiency in the poultry sector. Therefore, this paper presents an innovative AI approach based on the fuzzy logic (FL) method for estimating poultry weight. The FL models were created using expert knowledge and key input variables such as indoor temperature, humidity, and feed consumption. This study's findings demonstrate that FL-based methods exhibit great promise for achieving accurate and efficient poultry weight estimation. Integrating the FL technique in the poultry industry can bring numerous benefits, including improved decision-making processes, enhanced efficiency, and reduced costs. Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations. [ABSTRACT FROM AUTHOR]

Purpose: Wii Fit exergames have been less commonly used for the rehabilitation of athletes after Anterior Cruciate Ligament Reconstruction (ACLR). This study aims to investigate the effects of an expert system using Wii Fit exergames compared to conventional rehabilitation following ACLR. A forward-chaining rule-based expert system was developed which proposed a rehabilitation program that included the number and type of exercise in terms of difficulty and ease and the duration of each exercise in a progressive manner according to the patient's physical condition. Materials and methods: Twenty eligible athletes aged 20–30 who underwent ACLR were enrolled in this study and randomly assigned to two groups; and received 12 sessions of either Wii Fit exergames as Wii group (n = 10) or conventional rehabilitation as CL group (n = 10). Results: The main outcomes consisted of pain (Visual Analogue Scale (VAS)), knee effusion, knee flexion range (KFR), thigh girth (TG), single-leg hop for distance (SLHD), and for time (SLHT), static and dynamic balance tests. Both groups had considerable improvement in all outcomes, also there were significantly differences between Wii and CL groups as follows; VAS (P < 0.001), knee effusion (P < 0.001), TG (P = 0.001), KFR (P = 0.012), static balance in stable position (P < 0.001) and in unstable position (P = 0.001), dynamic balance in the anterior (P < 0.001), posteromedial (P < 0.001), posterolateral (P = 0.004) directions, symmetry index of SLHD (P < 0.001) and symmetry index of SLHT (P = 0.013). Conclusions: The findings showed that using Wii Fit exergames in post-ACLR patients reduced pain and effusion while also improving function and balance significantly. Iranian Registry of Clinical Trials registration number is IRCT20191013045090N1, and the registration date is 03-03-2020. [ABSTRACT FROM AUTHOR]

Land consolidation (LC) is a widely used spatial land management tool. The most essential stage of the LC process is land reallocation, which comprises the exchange of property rights. For this reason, land reallocation may be a potential source of dissatisfaction among farmers, and thus, optimization techniques are continually being developed to minimize these dissatisfactions. The goal of this study was to develop a novel preference‐based artificial‐bee‐colony (ABC) algorithm for use in the land reallocation project for DOT village. In the study, the farmer preferences were prioritized. Specifically, 70% of the lands were preference‐based, with the remainder being random. Furthermore, the ABC algorithm's performance was assessed using three limit parameter (LP) values and two probability functions. The best success was attained when the LP was set to 50 and the roulette‐wheel probability function was employed. The success of land reallocation was defined by the occupancy rate in the blocks and the reallocation rates based on the preferences of the farmer's lands. The occupancy rate in all the blocks using the ABC algorithm was 95.54%, the full occupancy rate of blocks according to farmer preferences was 38%, and the land reallocation rate based on the farmers' preferences was 73%. The remaining lands were distributed to the block areas at random using the ABC method. Moreover, the success of the preference‐based population surpassed that of randomly formed populations. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

The control of the pH neutralization process has a decisive role in many industrial branches, as: wastewater treatment, biotechnology, pharmaceutical industry, chemical processing, etc. However, the pH neutralization process is a difficult one to control, due to time-varying process parameters, its high non-linearity and to the sudden variation of the pH around the equivalence point. The paper presents a device and an integrated application that uses artificial intelligence (IA) developed for dosage of the necessary reactants in wastewater pH control. The AI techniques were integrated into the application by developing an expert system (ES). For this purpose, were used the necessary if-then rules (rules that make up the inference engine (IE) of the ES) related to the flowrates of the reactants necessary for wastewater pH neutralization. The ES knowledge base (KB) was built based on the recorded pH values. The hardware component (the device) was created using the Arduino equipment. [ABSTRACT FROM AUTHOR]

If not well identified and controlled, risks in systematically engineered cold supply chains can lead directly to food safety incidents. In current approaches to cold supply chain risk evaluation, there is a lack of systematic classification and quantitative analysis of the influences of risk factors in the chain. To accurately evaluate unknown risks that can exert a fluctuating influence and result in great losses, this study builds a knowledge base of expert systems based on expertise and extensive experience and modifies the expert weights in conjunction with entropy weights to reduce subjective error. An ordered weighted average (OWA) operator is used to evaluate risk in a cold supply chain effectively. First, in combination with a case study of a typical fresh food e‐commerce enterprise, a typical fresh food e‐commerce enterprise, this paper identifies the risks in a food refrigeration supply chain. Second, relying on an expert group with professional knowledge and rich experience, an expert system knowledge base is constructed, risks are assigned, and the expert weight is modified in combination with the entropy weight method. Finally, based on the OWA operator, the modified risk assignment is effectively evaluated, and the risk value is obtained and sorted. Specific risk control measures are put forward according to the results of the calculation. The results show that an evaluation method combining an expert system knowledge base and the entropy‐OWA method can effectively depict and process the evaluation information needed for risk rankings and solve the problems of supply chain risks more rapidly and effectively. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become popular in the last decade because of their advantages compared to conventional vehicles. The market offers dozens of EV models in a large range of prices, performances, and specifications. This paper presents an expert system we developed to support sellers and customers in choosing an EV that matches the customers' specifications. The system enables ranking-specific EVs according to the customers' specifications and counting the number of mismatches. The paper analyzes a database of 53 different EVs, each with 22 different characteristics, enabling customers to choose the EV that best suits their most important specifications. Based on the customer's requirements and the principle of fuzzy sets, the system assigns a matching value to each criterion. These matching values are the input matrix for the TOPSIS procedure that ranks all the EVs according to their matching scores for a specific customer. The applicability of the proposed method is demonstrated for one customer with specific preferred EV requirements. A Python code of this method is also available herein. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA. [ABSTRACT FROM AUTHOR]

This study examined whether participants' adherence to an algorithmic aid was related to the degree of control they were provided at decision point and their attitudes toward new technologies and algorithms. It also tested the influence of control on participants' subjective reports of task demands whilst using the aid. A total of 159 participants completed an online experiment centred on a simulated forecasting task, which required participants to predict the performance of school students on a standardized mathematics test. For each student, participants also received an algorithm-generated forecast of their score. Participants were randomly assigned to either the 'full control' (adjust forecast as much as they wish), 'moderate control' (adjust forecast by 30%) or 'restricted control' (adjust forecast by 2%) group. Participants then completed an assessment of subjective task load, a measure of their explicit attitudes toward new technologies, demographic and experience items (age, gender and computer literacy) and a novel version of the Go/No-Go Association Task, which tested their implicit attitudes toward algorithms. The results revealed that participants who were provided with more control over the final forecast tended to deviate from it more greatly and reported lower levels of frustration. Furthermore, participants showing more positive implicit attitudes toward algorithms were found to deviate less from the algorithm's forecasts, irrespective of the degree of control they were given. The findings allude to the importance of users' control and preexisting attitudes in their acceptance of, and frustration in using a novel algorithmic aid, which may ultimately contribute to their intention to use them in the workplace. These findings can guide system developers and support workplaces implementing expert system technology. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures. [ABSTRACT FROM AUTHOR]

The aviation industry is a complicated, sensitive, and challenging phenomenon. One of the major issues in the operation of streamlined processes in this industry is the management of proper decisions during the disruption of flight schedules. Such disruptions commonly reduce customer satisfaction and the profitability of the airlines. Since there are multiple reasons for the disruption of the flight schedules along with the different possible decisions, a correct decision is very difficult to make requiring the opinions of the specialist staff. In this research, an expert model using a “fuzzy multicriteria decision-making” method is proposed to provide a correct decision during the disruption of the flight schedules. The results show that the most important factors that make disruption of flight schedules are arrival delays and technical failure of the airline fleet. Besides, the most important possible decisions are the announcement of the delay and canceling of the flight. Thanks to utilizing the fuzzy analytical network process, the outcomes of the proposed expert model are in good alignment with the opinions of the specialist staff. The fuzzy analytical network process determines the values of 0.5124 and 0.2621 for the magnitude of the arrival delay and technical defect respectively. This method also determines the values of 0.7042 and 0.2076 for flight delay and flight canceling as the two most important possible decisions. [ABSTRACT FROM AUTHOR]

Nowadays, one of the main sources of renewable energy is wind energy; therefore, a wind farm's electricity system must be effective. As a result, wind farm (WF) equipment must continuously operate without failure or damage. To achieve this, it is necessary to regularly monitor and assess the reliability of WF systems at every point of their "life", including design, implementation, and continued use. Three key goals are presented in the article. First, a theory of fundamental theoretical quantities that can be used in reliability and maintenance analysis is presented. The second is to put forth a theoretical reliability link between mean time to failure and WF system fitness probability (Mean Time to Failures (MTTF—Mean time between failures. MTTF = t1 + t2 + ... + tn/m, where: m—the number of all failures at time T, ti—i—ty time to failure)). The third goal is to analyze the time to failure as a function of service life and to assess the dependability of the WF under consideration as a function of service life. The three-state model of the WF operation process presented in the research serves as the foundation for the analytical analysis of WF reliability. The probability of fitness (Po(t)) of the WF system and the mean time to failure were calculated based on the analytical quantities denoting this model (MTTF). The WF owner can make the best choice regarding the proper WF renewal actions with the help of knowledge of these current dependability values for an in-service WF system. [ABSTRACT FROM AUTHOR]

Simple Summary: Designing systems that optimize the process of evaluating mammogram images with the goal of improving the diagnostic process of breast cancer is an active field of research due to the large health and social impact of this disease. This paper presents a new intelligent clinical decision support system that, through the concurrence of inferential models, allows the definition of various risk metrics for patients. Those metrics are weighted and combined into a Global Risk value to be finally corrected by means of an empirical weighting factor derived from the BI-RADS analysis and condition associated with the patient's mammogram images. The validation results have shown meaningful disease detection rates within the study group used, which makes it possible to estimate the potential for a diagnostic use of the developed system. Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis. [ABSTRACT FROM AUTHOR]

To precisely meet the nutritional requirements of sows during the stages of pregnancy and lactation, a precision feeding system was developed by using the intelligent sow feeder combined with a rule-based expert system and the Internet of Things (IoTs). The model of uncertain knowledge representation was established for inference by using the certainty factor. The daily feeding amount of each sow was calculated by the expert system. An improved pattern matching algorithm Reused Degree Model-RETE (RDM-RETE) was proposed for the decision of daily feeding amount, which sped up inference by optimizing the RETE network topology. A prediction model of daily feeding amount was established by a rule-based expert system and the precision feeding was achieved by an accurate control technology of variable volume. The experimental results demonstrated that the HASH-RDM-RETE algorithm could effectively reduce the network complexity and improve the inference efficiency. The feeding amount decided by the expert system was a logarithmic model, which was consistent with the feeding law of lactating sows. The inferential feeding amount was adopted as the predicted feed intake and the coefficient of correlation between predicted feed intake and actual feed intake was greater than or equal to 0.99. Each sow was fed at different feeding intervals and different feed amounts for each meal in a day. The feed intake was 26.84% higher than that of artificial feeding during lactation days (p<0.05). The piglets weaned per sow per year (PSY) can be increased by 1.51 compared with that of relatively high levels in domestic pig farms. This system is stable in feeding and lowers the breeding cost that can be applied in precision feeding in swine production. [ABSTRACT FROM AUTHOR]

Deposition efficiency (DE) serves as a key performance indicator in plasma spraying, which is tailored by dozens of intrinsic and extrinsic influencing factors. Due to the nonlinear and complex interdependencies of the influencing factors, increasing DE has always been a challenging undertaking in the process development of plasma spraying. Hence, employing modern computer-aided algorithms is inevitable to overcome these complexities. In this study, an expert system is developed to predict DE from process parameters using adaptive neuro-fuzzy inference system (ANFIS) and support-vector machine (SVM). The developed expert system consists of two subsystems: (1) SVM-models from a previous work of the authors are used to predict the in-flight particle properties from different process parameters based on simulation data sets and (2) an ANFIS is developed to predict DE from in-flight particle properties based on experimental data sets. The results show that the developed expert system is able to estimate DE precisely with root-mean-square error (RMSE) of about 1.1%. The proposed system enables sustainable and cost-effective coatings through the prediction of DE for each set of process parameters. [ABSTRACT FROM AUTHOR]

Future advances in nuclear power technologies call for enhanced operator advice and autonomous control capabilities that can leverage simpler designs and increased safety features to reduce reliance on human labor. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for the plant structures, systems, and components (SSCs) of interest. The primary goal is to aid plant personnel in (1) deducing the probabilistic performance status of the monitored SSCs and (2) detecting impending faults/failures. The task of estimating conditional failure probability is a bidirectional inference problem, and a logical approach is to use the Bayesian network (BN) method. As a knowledge-based explainable artificial intelligence tool and a probabilistic graphical model, BN offers reasoning capability under uncertainty, graphical representation emulating physical behavior of the target SSC, and interpretability of the model structure and results. This paper provides a systematic overview of the BN technique and the software tools for implementing BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgment can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. The focus is on failure-prone and risk-important balance of plant assets, especially for cases with strong operator involvement. Two example case studies on the failure of (1) a centrifugal pump and (2) an electric motor are conducted to demonstrate the usefulness and technical feasibility of the proposed BN-based fault diagnostic system using an expert system shell. [ABSTRACT FROM AUTHOR]

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers' age (21–35 days, 25–39 days, and 28–42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers' age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers' age, relative humidity, and the relative cooling efficiency of the facilities. [ABSTRACT FROM AUTHOR]

Computer vision is a branch of computer science that studies how computers can 'see'. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise 'vision' among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts' brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to 'acquire the tacit knowledge of experts', which was not previously achievable with conventional expert systems. Machine learning 'systematises tacit knowledge' based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning. [ABSTRACT FROM AUTHOR]

Among the most valuable types of renewable energy available today is wind energy. The reliability of WF systems must be regularly evaluated at every stage of their "life," from design to operation, if a wind farm energy system is to be effective and function damage-free. Three key goals are presented in the article. The theory of fundamental quantities in reliability and maintenance analysis should be derived and explained first. Second, as a consequence of maintainability, theoretical correlations between reliability and mean time between failures (MTBF) are provided. The three-state theory of the WF procedure for operation presented in the research serves as the foundation for the analytical analysis of WF reliability. The time between failures is investigated as a function of maintainability, and the dependability of the WF under examination is assessed as a function of service life. The WF owner can make the best decisions to renew the WF and increase its reliability, energy, financial efficiency, etc. by being aware of the existing reliability of the WF system in use. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods: This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results: 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions: Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. [ABSTRACT FROM AUTHOR]

Many businesses have their own method of segmentation and of customer evaluation to help them give the appropriate managerial attention to each segment and each customer. This paper proposes a more refined and objective decision‐making support system module that allows segmentation and full ranking of customers. It offers several improvements and advantages over the state‐of‐the‐art methods. The module is based on customer criteria with quantitative values that can be extracted from the organizational information system. Customer scores are calculated objectively on the basis of measurable criteria without the need for human evaluation. The module determines the relative location of each customer within that customer's segment (for example, Platinum, Gold, Silver, and Bronze), tracks changes that occur over time, and enables a full and precise ranking of the customers according to company‐defined criteria. The module can be computerized and results can be generated quickly at any time, using up‐to‐date data. The module's design was based on feedback from a survey conducted among 39 managers and its applicability was successfully demonstrated in a real‐world case study. The decision‐makers in the organization where the case study was conducted stated that they would apply the proposed method quarterly (four times a year) instead of once a year. It was also found that the proposed method saves about 90% of the time and resources required to prepare a customer portfolio management and customer ranking compared to the subjective method that was used. [ABSTRACT FROM AUTHOR]

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%). [ABSTRACT FROM AUTHOR]

Background: Diabetes care in type 1 diabetes has remained a challenge over time. Ability to count carbohydrates in every meal and varying insulin dose according to individual insulin/carbohydrate ratio allows people with type 1 DM to have wider food choices with better glycemic control. Difficulties in carbohydrate counting may largely be solved by use of technology. Methods: This work was done at the endocrine unit of a superspeciality centre involved in care of people with type 1 diabetes. The process of development of software and its preliminary application and results from its use in clinical care in a small group of interested patients is presented in this manuscript. Carbohydrate counting tool for Indian foods was developed, and subsequently, bolus dose calculation was automated by using the reinforcement algorithm in an android app platform named "T1-Life". Data on app usability and acceptability is documented in this pilot study report. Results: Five patients completed 3 months of this app usage. Among five people with combined usage over 115 patient weeks, a total of 2661 insulin dose predictions were made. This translates to 3.31 patient-initiated bolus dose predictions per day. Of the total bolus dose predictions made, 82% were accepted by the participants. With usage of app, time in range (70–180 mg/dl) increased by an average of 16.67% in children who used CGMS in their first week as well as last week of observation. Conclusion: T1-life, integrated carbohydrate counting and reinforcement-based insulin bolus dose prediction system, has good patient usability and acceptability. [ABSTRACT FROM AUTHOR]