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The deterioration of fiber properties during recycling processes, especially the loss of tensile strength, raises concerns that paper products made from recycled fibers might not satisfy quality requirements. The purpose of this paper is to estimate the deterioration of tensile strength and the damage in paper sheets made of recycled fibers using the theory of damage mechanics and machine learning methods. Experiments were carried out to recycle wood fibers and nonwood fibers four times, and the physicochemical properties of the handsheets made from these fibers were measured after each recycling. Water retention value and relative bonded area were selected as the features to estimate and predict tensile strength during recycling because they had strong correlations with tensile strength. This paper proposed a damage index to quantitatively express the severity of the damage in paper sheets based on the experimental investigation and the theory of damage mechanics. Thus, the deterioration of tensile strength could be estimated and predicted. To determine the damage index, a curve fitting model based on the hyperbolic theory of pulp properties was developed. The proposed quantitative expression of the damage index is: D = (Ds - h)²/a2 - (Dh - k)²/b2, where the coefficients were determined through the curve fitting model. This paper also developed a long short-term memory recurrent neural network model to determine the damage index according to the sequence of recycling. Both models were trained with the experimental data of water retention value and relative bonded area. The estimation and prediction by the curve fitting model were more accurate than those of the neural network model. The root mean square errors by the curve fitting model were 0.0278 for estimation, 0.1667 for prediction; and by the neural network model were 0.2445 for estimation, 0.2206 for prediction, respectively. After the damage index was determined, the deterioration of tensile strength then could be calculated as T = T0 (1-D). [ABSTRACT FROM AUTHOR]

With the increasing integration of AI tools like Yewno Discover, Scholarcy, and Grammarly in academic libraries, undergraduate research has witnessed transformative changes. These tools, while elevating the research process, also bring forth challenges rooted in ethics and application. This paper explores the synergy between modern technology and academic exploration, highlighting the benefits and potential pitfalls of using AI in the research workflow. It emphasizes that while Yewno Discover and similar tools offer streamlined navigation of vast information databases, it is imperative for undergraduates to remain cognizant of potential biases and other ethical considerations. This paper underscores the need for proactive measures in academic settings, including specialized training and policy development, to ensure that undergraduate researchers harness the power of AI responsibly and efficiently. [ABSTRACT FROM AUTHOR]

This Special Issue presents 7 papers selected from the International Conference on Futuristic Advancements in Materials, Manufacturing and Thermal Sciences (ICFAMMT 2022). These papers were presented during the International Conference on Futuristic Advancements in Materials, Manufacturing and Thermal Sciences (ICFAMMT 2022) and selected by Session Chairs and Scientific Committee members for the possible publication in the Special Issue of Heat Transfer Engineering. In this special issue, the first paper by Kumar et al. presents heat transfer characteristics of plate heat exchanger with bubble fin using Al SB 2 sb O SB 3 sb /water nanofluid. [Extracted from the article]

This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR). [ABSTRACT FROM AUTHOR]

This paper highlights the importance of involving academic librarians in the development of ethical AI policies. The Academic Librarian Framework for Ethical AI Policy Development (ALF Framework) is introduced, recognizing librarians' unique skills and expertise. The paper discusses the benefits of their involvement, including expertise in information ethics and privacy, practical experience with AI tools, and collaborations. It also addresses challenges, such as limited awareness, institutional resistance, resource constraints, interdisciplinary collaboration, and evolving AI technologies, offering practical solutions. By actively involving librarians, institutions can develop comprehensive and ethical AI policies that prioritize social responsibility and respect for human rights. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [ABSTRACT FROM AUTHOR]

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling. [ABSTRACT FROM AUTHOR]

AbstractWith the development of intelligent transportation, it has become mainstream for drivers and automated systems to cooperate to complete train driving tasks. Human-machine trust has become one of the biggest challenges in achieving safe and effective human-machine cooperative driving. Accurate evaluation of human-machine trust is of great significance to calibrate human-machine trust, realize trust management, reduce safety accidents caused by trust bias, and achieve performance and safety goals. Based on typical driving scenarios of high-speed trains, this paper designs a train fault judgment experiment. By adjusting the machine’s reliability, the driver’s trust is cultivated to form their cognition of the machine. When the driver’s cognition is stable, data from the Trust in Automated (TIA) scale and modes of physiological information, including electrodermal activity (EDA), electrocardiograms (ECG), respiration (RSP), and functional near-infrared spectroscopy (fNIRS), are collected during the fault judgment experiment. Based on analysis of this multi-modal physiological information, a human-machine trust classification model for high-speed train drivers is proposed. The results show that when all four modes of physiological information are used as input, the random forest classification model is most accurate, reaching 93.14%. This indicates that the human-machine trust level of the driver can be accurately represented by physiological information, thus inputting the driver’s physiological information into the classification model outputs their level of human-machine trust. The human-machine trust classification model of high-speed train drivers built in this paper based on multi-modal physiological information establishes the corresponding relationship between physiological trust and human-machine trust level. Human-machine trust level is characterized by physiological trust monitoring, which provides support for the dynamic management of trust. [ABSTRACT FROM AUTHOR]

Despite the complexity of thermodynamic models in predicting wax precipitation, they have not shown satisfactory results in the literature. This paper proposes the least-squares support vector machine optimized by the genetic algorithm to forecast wax precipitation based on the mole fractions, specific gravity, and temperature of crude oil samples. The genetic algorithm was used to prevent sticking in local optima in optimizing the parameters of the predictive model. A dataset of 88 experimental wax precipitation samples was used to evaluate the performance of the proposed method. The results revealed that the suggested algorithm could predict wax precipitation with an accuracy of 0.939 and 0.997 before and after sensitivity analysis, respectively. Furthermore, sensitivity analysis improved the Coefficient of Determination, Mean Squared Error, and Root Mean Squared Error from 0.939, 0.768, and 0.877 to 0.997, 0.041, and 0.202, respectively. The results were compared with the results of previous data-driven and mathematical models that showed the outperformance of the proposed method. The contribution of this paper can be summarized as demonstrating the ability of intelligent models to predict wax precipitation without a need for complex thermodynamic models. Also, it was shown that a proper feature selection could improve the model's performance. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Quantum Machine Learning is an interdisciplinary field that combines the principles of quantum physics, quantum computers, and machine learning to enhance computational performance. In recent years, there have been significant advancements in the field of quantum machine learning. Researchers have developed quantum algorithms for various tasks. Furthermore, quantum machine learning has been applied to various domains, such as chemistry, cryptography, and finance. Previous bibliometric surveys in this area covered the period from 2014 to 2020. However, as demonstrated in this paper, the pace of publication has increased significantly from 2018 to the present. Therefore, it has become essential to provide a new analysis of the current state of research in this field. In this paper, we deployed visualization tools to analyze co-authorships, co-occurrences, and keyword density in this research area. Our study covers and analyzes a total of 918 publications from the Web of Science database and 1171 publications from the Scopus database from 2006 to 2022. Following the analysis, we identify research questions, opportunities, and research gaps in the field of Quantum Machine Learning. [ABSTRACT FROM AUTHOR]

Despite the complex punching shear behavior of reinforced concrete slabs have been comprehensively addressed in the literature, it is further essential to develop a universal design model comprising high accuracy and the simplicity for design practicability, adaptable to diverse conditions encountered in practice. Artificial intelligence applications, artificial neural networks (ANN), and more recently, various machine learning (ML) and deep learning (DL) techniques veer off in a new direction in structural engineering context with improved accuracy and efficiency. The paper begins with the assessment of the capabilities of various artificial intelligence applications in predicting the punching shear strength of slab-column connections without shear reinforcement through the extensive database using 650 punching shear experiments from the literature. Critical parameters influencing the punching shear strength as well as the precision of the current code provisions in predicting this feature were then thoroughly examined in the paper. The results shown in this paper validated the competency of artificial intelligence applications in predicting the punching shear strength of such connections with increased accuracy and improved simplicity in practical terms. The proposed models utilizing the artificial intelligence applications encourage the ultimate rehabilitation policies to be proposed and improved code provisions to be developed for contemporary structures. [ABSTRACT FROM AUTHOR]

Previous studies have highlighted a lack of practical strategies in mobile learning that effectively enhance learners' agency with mobile technology. The paper aims to engage and interest learners in cultural issues by exploring the capacity of mobile technology. In this paper, we report the design and development of a prototype story game app, The Story of Praya Lane that combines use of mobile sensing, machine learning and augmented reality (AR) to engage users in physical mobile activities as part of a gamified, non-linear storytelling application. We took a multidisciplinary approach to involve cultural heritage stakeholders to preserve and share the cultural stories of the Kristang community in Praya Lane, Malacca, a UNESCO World Heritage Site. The results of user study show promise in promoting interest and engagement in cultural learning and reflection. From our findings, we recommend design guidelines for future interactive story games to support cultural learning and engagement. [ABSTRACT FROM AUTHOR]

AbstractPrediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated methodology to enhance the efficiency of patch panel assembly lines by harnessing advanced artificial intelligence (AI) techniques. The primary objective is to minimise the total completion time of patch panels, a pivotal metric for manufacturing efficiency. The study employs the Assembly Line Production Optimisation (ALPO) model to analyze and enhance the production capacity of the bottleneck station. Existing challenges related to patch panel assembly line task distribution, process content, and tooling layout are scrutinised against production process standards for operational measurements. To achieve improvement, Deep Neural Network (DNN) and Recurrent Neural Network (RNN) models are employed in hidden layers, supplemented by deep feed-forward (DFF) layers for output, in adherence to engineering design principles. The bottleneck station's load rate is successfully reduced to below 80%, contributing to an overall balance rate increase from 73% to 88%. These outcomes signify a notable enhancement in production efficiency and employee satisfaction. The research utilises a dataset derived from the Microsoft Azure AI-based PdM dataset. The paper illustrates how AI techniques offer a comprehensive solution to address the inherent complexities in patch panel assembly line optimisation. [ABSTRACT FROM AUTHOR]

Renewable energy is now in high demand due to the deterioration of the global climate and the depletion of conventional sources. Renewable energy sources (RES) such as wind and solar are extremely intermittent, making it impossible to sustain system reliability with an unacceptably high proportion of renewable energy injection. An intrinsic attribute common to all renewable power plants is that the production of energy relies on environmental conditions such as temperature, pressure, wind speed, humidity, clouds, etc. Therefore, the power from RES cannot be completely regulated or pre-planned. It is important to forecast the amount of electricity that can be produced in a power grid for future demand. Machine learning (ML) is an emerging technology and used in all fields nowadays to perform different tasks. In this paper, the applications of machine learning in renewable energy sources are discussed. These ML techniques are mainly used to predict the power from renewable energy sources like wind, solar, hydro, biomass, tidal, and geothermal. Fault detection is a significant aspect in renewable energy systems to reduce the operation and maintenance cost and to deliver the continuous power to the loads. This paper also focusses on the use of ML techniques in predicting the faults before it occurs, early detection of faults and also to diagnose the faults in renewable energy systems. Along with the above-mentioned applications, these ML techniques used in RES for different purposes are also discussed. [ABSTRACT FROM AUTHOR]

This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. [ABSTRACT FROM AUTHOR]

The recent deployment of new satellite-based hyperspectral imaging sensors, along with advances in classification methods, has enhanced the acquisition of information on the spatiotemporal patterns of Land Use/Land Cover (LULC).. With this context, the study presented in this paper seeks to assess the combined utilization of the Pixxel's technology demonstrator hyperspectral satellite mission with the Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers for LULC mapping in an agricultural area located in Queensland, Australia. Accuracy assessment of the resulting LULC maps has been done based on relevant classification accuracy metrics. It has been observed that SVMs outperformed RF in terms of both overall classification and urban vegetation cover mapping accuracy. An overall accuracy of 92.34% with kappa of 0.90 was reported for the SVMs' results, whereas for RF, the accuracy was 89.65% with 0.89 kappa, respectively. Both classifiers produced similar results for the classes of mango tree and sugarcane crops, whereas minor differences were observed in the depiction of water bodies and artificial surfaces. Our study findings provide insightful perspectives on the potential of Pixxel datasets for delineating the spatiotemporal distribution of LULC in agricultural landscapes, showcasing Pixxel's hyperspectral sensors' promising potential in this field. [ABSTRACT FROM AUTHOR]

This paper presents a metallic object classification method using various electromagnetic pulses. Each electromagnetic pulse irradiated eight metallic objects placed at increasing distances from 10 mm to 40 mm relative to the electromagnetic sensing system. The electromagnetic sensing system consisted of two RL circuits placed in close proximity. Objects were classified using linear (perceptron and multiclass logistic regression) and non-linear (neural network, 1D convolutional neural network (CNN) and 2D CNN) machine learning classifiers. The machine learning classifiers were trained on experimental data collected in an electromagnetically shielded laboratory. A 10-fold cross-validation mean classification accuracy of 99.4 ± 0.3% for the 1D CNN classifier, and 92.9 ± 1.2% for the 2D CNN classifier, was achieved using a rectangular chirp electromagnetic pulse. The rectangular chirp pulse outperformed two-sided decaying exponential, Gaussian, triangular, raised cosine, and rectangular pulses. All pulses had equal energy. While the rectangular chirp performed best overall, other pulses more accurately distinguished between some objects. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present how data mining (DM) and machine learning (ML) contribute to the product development process (PDP). The Methodi Ordinatio methodology was used to identify important articles for this study, and VOSviewer software was applied to generate visual maps. A systematic review was conducted on the Web of Science, Scopus and Science Direct databases. Forty-six articles were designated for analysis in order to evaluate the most commonly used DM and ML techniques to support the PDP, as well as to demonstrate which specific phases are most often applied. In addition, the main limitations of the analyzed techniques were identified. The results show that the association rule technique was the most commonly used, followed by text mining, and the most used phases were planning and design. In this context, this study intends to stimulate companies to use computational techniques, more precisely DM and ML, to assist in the generation of knowledge and become a strategic factor in the PDP. [ABSTRACT FROM AUTHOR]

In this paper, we consider finite sum composite optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is amenable to proximal evaluations. We assume a generalized bounded gradient condition on the objective which allows us to simultaneously tackle both smooth and nonsmooth problems. We also consider the cases of both with and without a quadratic functional growth property. Further, we assume that each constraint set is given as the level set of a convex but not necessarily a differentiable function. We reformulate the constrained finite sum problem into a stochastic optimization problem for which the stochastic subgradient projection method from Necoara and Singh [Stochastic subgradient projection methods for composite optimization with functional constraints; 2022 Journal of Machine Learning Research, 23, 1–35] specializes in a collection of mini-batch variants, with different mini-batch sizes for the objective function and functional constraints, respectively. More specifically, at each iteration, our algorithm takes a mini-batch stochastic proximal subgradient step aimed at minimizing the objective function and then a subsequent mini-batch subgradient projection step minimizing the feasibility violation. By specializing in different mini-batching strategies, we derive exact expressions for the stepsizes as a function of the mini-batch size and in some cases we also derive insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime. We also prove sublinear convergence rates for the mini-batch subgradient projection algorithm which depend explicitly on the mini-batch sizes and on the properties of the objective function. Numerical results also show a better performance of our mini-batch scheme over its single-batch counterpart. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

Discrimination and fairness are major concerns in algorithmic models. This is particularly true in insurance, where protected policyholder attributes are not allowed to be used for insurance pricing. Simply disregarding protected policyholder attributes is not an appropriate solution as this still allows for the possibility of inferring protected attributes from non-protected covariates, leading to the phenomenon of proxy discrimination. Although proxy discrimination is qualitatively different from the group fairness concepts discussed in the machine learning and actuarial literature, group fairness criteria have been proposed to control the impact of protected attributes on the calculation of insurance prices. The purpose of this paper is to discuss the relationship between direct and proxy discrimination in insurance and the most popular group fairness axioms. We provide a technical definition of proxy discrimination and derive incompatibility results, showing that avoiding proxy discrimination does not imply satisfying group fairness and vice versa. This shows that the two concepts are materially different. Furthermore, we discuss input data pre-processing and model post-processing methods that achieve group fairness in the sense of demographic parity. As these methods induce transformations that explicitly depend on policyholders' protected attributes, it becomes ambiguous whether direct and proxy discrimination is, in fact, avoided. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with ${L_1}$ L 1 and ${L_2}$ L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image's structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application. [ABSTRACT FROM AUTHOR]

AbstractCensored quantile regression has emerged as a prominent alternative to classical Cox’s proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While quantile regression has been extensively studied for right-censored survival data, methodologies for analyzing interval-censored data remain limited in the survival analysis literature. This paper introduces a novel local weighting approach for estimating linear censored quantile regression, specifically tailored to handle diverse forms of interval-censored survival data. The estimation equation and the corresponding convex objective function for the regression parameter can be constructed as a weighted average of quantile loss contributions at two interval endpoints. The weighting components are nonparametrically estimated using local kernel smoothing or ensemble machine learning techniques. To estimate the nonparametric distribution mass for interval-censored data, a modified EM algorithm for nonparametric maximum likelihood estimation is employed by introducing subject-specific latent Poisson variables. The proposed method’s empirical performance is demonstrated through extensive simulation studies and real data analyses of two HIV/AIDS datasets. [ABSTRACT FROM AUTHOR]

Within Industry 4.0, the integration of Industrial IoT (IIoT) marks a transformative phase for modern industries, fostering the development of smart factories and intelligent manufacturing systems. Despite the substantial growth of IIoT, a critical research gap persists in web security within IIoT environments. This paper addresses this gap by proposing an explainable and robust machine learning-based web attack detection system to ensure IIoT web application security using ToN-IoT and NF-ToN-IoTv2 datasets that accurately reflects IIoT traffic. Given eXplainable Artificial Intelligence’s (XAI) capability to instill trustworthiness and transparency in learning models, the authors opted for the SHAP technique for feature selection, leveraging its global insights to explain feature contributions to the system’s decision-making. Compared to the existing works, the system demonstrated strong performance across various metrics, including accuracy, recall, precision, specificity, F-value, FPR, FNR, AUC-ROC curve, MCE, training, and prediction times, in binary and multi-classification scenarios. [ABSTRACT FROM AUTHOR]

A complete workflow was presented for estimating the concentration of microorganisms in biological samples by automatically counting spots that represent viral plaque forming units (PFU) bacterial colony forming units (CFU), or spot forming units (SFU) in images, and modeling the counts. The workflow was designed for processing images from dilution series but can also be applied to stand-alone images. The accuracy of the methods was greatly improved by adding a newly developed bias correction method. When the spots in images are densely populated, the probability of spot overlapping increases, leading to systematic undercounting. In this paper, this undercount issue was addressed in an empirical way. The proposed empirical bias correction method utilized synthetic images with known spot sizes and counts as a training set, enabling the development of an effective bias correction function using a thin-plate spline model. Its application focused on the bias correction for the automated spot counting algorithm LoST proposed by Lin et al. Simulation results demonstrated that the empirical bias correction significantly improved spot counts, reducing bias for both fixed and random spot sizes and counts. [ABSTRACT FROM AUTHOR]

The custom GPT is the latest powerful feature added to ChatGPT. Non-programmers can create and share their own GPTs ("chat bots"), allowing Health Professions Educators to apply the capabilities of ChatGPT to create administrative assistants, online tutors, virtual patients, and more, to support their clinical and non-clinical teaching environments. To achieve this correctly, however, requires some skills, and this 12-Tips paper provides those: we explain how to construct data sources, build relevant GPTs, and apply some basic security. [ABSTRACT FROM AUTHOR]

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

AbstractIn precision agriculture, soil spectroscopy has become an invaluable tool for rapid, low-cost, and nondestructive diagnostic approaches. Various instrument configurations are utilized to obtain spectral data over a range of wavelengths, such as homemade sensors, benchtop systems, and mobile instruments. These data are then modeled using a variety of calibration algorithms, including Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machines (SVM), these datasets are further improved and optimized. Given the increasing demand for cost-effective and portable solutions, homemade sensors and mobile instruments have gained popularity in recent years. This review paper assesses the current state of soil spectroscopy by comparing the performance, accuracy, precision, and applicability of homemade sensors, mobile spectrometers, and traditional benchtop instruments. The discussion encompasses the technological advancements in homemade sensors, exploring innovative approaches taken by researchers and farmers, as well as developing affordable and efficient soil spectroscopy tools. Mobile and benchtop spectrometers, equipped with cutting-edge technology, have enabled easy soil diagnosis, transforming the landscape of soil analysis. [ABSTRACT FROM AUTHOR]

Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart Manufacturing to create an interconnected network through sensing, actuation, and computation nodes. Fog Manufacturing has become a promising research component to be integrated into the existing Smart Manufacturing paradigm and provides reliable and responsive computation services. However, Fog nodes' relatively limited communication bandwidth and computation capabilities call for reduced data communication load and computation time latency for modeling. There has long been a lack of an integrated framework to automatically reduce manufacturing data and perform computationally efficient modeling/machine learning. This research direction is increasingly important as both the computational demands and Fog/networked Manufacturing become prevalent. This paper proposes an integrated and distributed framework for data reduction and modeling of multiple systems in a Smart Manufacturing network considering the system similarities. A simulation study and a Fog Manufacturing testbed for ingot growth manufacturing validated that the proposed framework significantly reduces the sample size used for improved computational runtime metrics while outperforming various other data reduction methods in modeling performance. [ABSTRACT FROM AUTHOR]

In this paper, the framework of dissipativity-based iterative learning control for singular systems (SSs) is established for the first time. Firstly, under the P-type learning algorithm, it has been demonstrated that the dissipative SSs can achieve the accurate tracking for the desired trajectory in a finite time interval. Secondly, a sufficient condition for the SSs to satisfy dissipativity is given by using the linear matrix inequality (LMI) method, and the solvability criterion of the LMI is further presented. Moreover, the relationship between dissipativity and positive realness is revealed. Finally, two examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

3-D classroom seating information is of great significance in a host of planning contexts including indoor navigation and facility management. Mapping classroom 3-D seats based on a single-view LiDAR scan is challenging because of occlusions and viewpoint limitations. In essence, one is left with incomplete point clouds, making object recognition difficult. The aim of this paper is to acquire complete point clouds for objects in a room based on a single-view LiDAR scan, focusing in particular on chairs in indoor spaces such as classrooms, lecture halls, theaters, concert venues, and sports arenas. A spatial optimization enabled framework is proposed for 3-D classroom seat mapping from a single-view LiDAR scan. A two-step clustering method is proposed to segment each individual chair object in the LiDAR scan. A chair model is extracted and used to complete other chair point clouds via registration. Notably, a parameter estimation method is devised based on spatial priors and integrated in an iterative closest point optimization approach to refine registration. Applications to multiple classroom scans demonstrate the effectiveness and accuracy of the proposed methodology. The developed segmentation-registration framework harnesses spatial knowledge to complete a single-view LiDAR scan with many identical objects. The resultant object point cloud is critical for navigation, route guidance, facility management, pandemic response and planning, indoor mapping and venue utilization, among others, which require precise and complete spatial information. Identified 3-D chairs extracted from LiDAR scans provide essential information for stakeholders seating configuration, property management, and indoor navigation. A new and challenging problem of completing partial objects from a single-view LiDAR scan is formulated. The developed framework provides a fast and high-precision solution to process single-view LiDAR scans for extracting and restoring incomplete objects by combining spatial knowledge, machine learning, and spatial optimization. Empirical results demonstrate the capability to acquire complete chair point clouds from multiple classroom scans to support planning and policy efforts in mitigating disease spread. [ABSTRACT FROM AUTHOR]

As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery. In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality. Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles. We evaluated three different heuristics and five block sizes on four publicly available datasets. Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) have a wide range of applications in many fields, such as electronic devices, composites, sensors, catalysts, hydrogen storage materials, biomedicine, and energy, due to their unique structure and excellent physicochemical properties. As new composite materials require more and more precise control of carbon nanotube growth, the traditional research on carbon nanotube growth mechanism and growth parameter mechanism is difficult to meet the demand for precise control of carbon nanotube growth under complex conditions such as in-situ growth on the surface of composite materials. Given, Because of, it has become one of the hotspots to explore the growth mechanism of CNTs on the microscopic scale by high-end characterization tests and simulations under complex conditions, and then precisely regulate the growth of CNTs. This paper summarizes the latest research progress on the growth mechanism, preparation, and application of CNTs in recent years, and focuses on the research progress on the latest application of CNTs in different fields of high-end materials, especially in the control of carbon nanotube growth by intelligent algorithms and other aspects of the proposed new understanding. [ABSTRACT FROM AUTHOR]

This article reports on an advanced level physical laboratory experiment designed for college level undergraduate education and for scholars who need specialized training on using and interpreting Laser-Induced Breakdown Spectroscopy. The technical principle, experimental operation and sample preparation of Laser-Induced Breakdown Spectroscopy are introduced in detail. A presentation and discussion of the use of Laser-Induced Breakdown Spectroscopy in the traceability of four common samples is emphasized. Combining Laser-Induced Breakdown Spectroscopy with Machine Learning, two distinct datasets are constructed through the extraction of spectral features. Dimensionality reduction of spectral data is performed using Linear Discriminant Analysis, while the Random Forest model is employed for provenance classification. Finally, the interpretability of the Random Forest model is leveraged to explore the contributions of different spectral elements to provenance tracing. Results demonstrated the system's effectiveness in not only accurately identifying ash types but also in elucidating the influential chemical components, offering significant implications for material analysis and environmental monitoring. On an educational standpoint, this paper will allow any reader, in particular, undergraduate and graduate students, to gain a better understanding of the theory and practice of Laser-Induced Breakdown Spectroscopy and machine learning. [ABSTRACT FROM AUTHOR]

Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users' emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users' emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems. [ABSTRACT FROM AUTHOR]

Machine learning is increasingly used as a computing paradigm in cartographic research. In this extended editorial, we provide some background of the papers in the CaGIS special issue Machine Learning in Cartography with a special focus on pattern recognition in maps, cartographic generalization, style transfer, and map labeling. In addition, the paper includes a discussion about map encodings for machine learning applications and the possible need for explicit cartographic knowledge and procedural modeling in cartographic machine learning models. [ABSTRACT FROM AUTHOR]

The paper shows the feasibility of building closed-form and fast-to-evaluate surrogate models via supervised kernel-based machine learning regressions behaving as digital twins for computationally expensive multibody simulations. The aforementioned surrogate models are adopted to predict the railway vehicle dynamics safety indexes defined in the international standards, depending on the wheel-rail forces, directly from the results of longitudinal train dynamics simulations. The digital twin models are trained with the outputs of Simpack multibody simulations of a reference freight wagon, to which the in-train forces calculated by an in-house MATLAB longitudinal train dynamics simulator are applied. Two machine learning techniques are considered: the support vector machine and the least-squares support vector machine regressions. Both techniques ensure a good accuracy even with a limited number of training samples. The derivation of the surrogate models can strongly enhance the modelling capabilities of common longitudinal train dynamics simulators, that cannot evaluate the wheel-rail contact forces. At the same time, the method shown in the paper allows to strongly reduce the total computational times, as the evaluation of the closed-form surrogate models allows to effectively estimate the safety indexes with no need for computationally demanding multibody simulations. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

In the past few years, the field of big data has multiplied, with more academic papers written about it. This bibliometric research was done to look at and understand the trends regarding countries, organizations, authors, and keywords that are creating the most publications and citations in big data. This study was done to understand the current state of scientific publications in the field. The research used Web of Science (WoS) database information from 1993 to 2021. The study of 32,085 papers showed that, on average, each document has 14.7 citations and 3.46 citations per year. According to the results, the United States, China, and the United Kingdom have the most scholarly publications about big data. The Chinese Academy of Science, Harvard University, and Stanford University were the three most productive groups. When researching big data, most writers work together, and most terms are related to big data analytics, machine learning, and cloud computing. [ABSTRACT FROM AUTHOR]

The economy of the nation is significantly influenced by pavement management systems. The use of cutting-edge techniques and technology in various pavement management system applications, as well as research into these topics, are particularly important today. To reduce human errors in data collection, advances in technology and automated surveillance should be used. The most recent methods used in modern equipment for the automatic evaluation of pavement deterioration are reviewed in this paper. To identify trending tools, research gaps, emerging technologies, challenges and limitations of using computer vision for pavement distress and condition assessment, papers collected using UAV (Unmanned Ariel Vehicle) data collection and the application of machine learning methods are investigated. The review comes to the conclusion that the application of machine learning techniques is the general trend in evaluating the condition of pavements, despite some limitations not only in the detection of a small number of pavement distresses with complex patterns but also in the indication of the severity and density of distresses, opening up possibilities for further study. [ABSTRACT FROM AUTHOR]

The quality of welding is directly related to the performance and life of the welding structure. The nondestructive testing technology is mainly used for welding defect defection, while the X-ray testing technology can directly and reliably reflect the shape, location and size of defects. For X-ray images, manual inspection is used at present, and it is easily caused by subjective factors, such as professional level, which may lead to low accuracy. This paper focuses on establishing an end-to-end automatic detection model of X-ray welding defects to improve the accuracy and efficiency of detection based on a deep learning algorithm. Considering the feature information of welding defects, this paper improves on the basis of Faster R-CNN and uses the deep residual network Res2Net to enhance the original backbone network to improve the feature extraction ability. And the weighted feature fusion module is studied, which combines the high-level semantic information and the low-level high-resolution edge detail information to predict the feature map of each layer respectively, to improve the detection performance, especially for small targets. The experimental data show that this method can effectively improve the accuracy and efficiency of welding defect detection. [ABSTRACT FROM AUTHOR]

For bonded Fibre Reinforced Polymer (FRP) strengthening systems in civil engineering projects, the adhesive joint performance is a key factor in the effectiveness of the strengthening; however, it is known that the material properties of structural epoxy adhesives change with temperature. This present paper examines the implied relationship between the curing regimes and the storage modulus response of the adhesive using a Machine Learning (ML) approach. A dataset containing 157 experimental data collected from the scientific papers and academic theses was used for training and testing an Artificial Neural Network (ANN) model. The sensitivity analysis reveals that the curing conditions have a significant effect on the glass transition temperatures (Tg) of the adhesive, and consequently on the storage modulus response at elevated temperatures. Curing at an extremely high temperature for a long time does not, however, guarantee a better thermal performance. For the studied adhesive, curing in a warm (≥ 45°C) and dry (near 0 % RH) environment for 21 days is recommended for practical applications. A software with a Graphical User Interface (GUI) was established, which can predict the storage modulus response of the adhesive, plot the corresponding response curve, and estimate the optimum curing condition. [ABSTRACT FROM AUTHOR]

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g. weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation applications. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, representative journals/conferences, and leading topics in recent 10 years. Then, this paper presents a comprehensive literature review on applications of RL in transportation based on specific topics. The potential future research directions of RL applications and developments are also discussed. [ABSTRACT FROM AUTHOR]

With the acceleration of the global modern industrialisation process and the increasingly serious environmental problems, the development of low energy consumption, high efficiency electrochemical energy conversion equipment and separation system has become a research hotspot in the scientific and industrial circles. At present, machine learning has become an important research method to explore and expand two-dimensional material family. Traditional experimental and computational methods have low fault tolerance when studying two-dimensional materials, which requires a lot of time and research and development costs. Machine learning, due to its powerful data processing capability and flexible algorithm model, can help reduce the time and cost of discovering and understanding two-dimensional materials, and can effectively predict and expand two-dimensional material systems based on data and explore their potential for experimental synthesis and application. This paper will focus on the methods of machine learning, the exploration of machine learning in 2D material design and synthesis, and the exploration of machine learning in 2D material properties and applications. Finally, this paper uses ML algorithm to test the synthesised polymer. The experimental data points and prediction data points have relatively good consistency with each other, which indicates that ML model can be used as a prediction tool to identify the undeveloped polymer for gas separation. [ABSTRACT FROM AUTHOR]

Through the digitalisation of Civil Trials and the implementation of the Telematic Civil Process framework, the Italian Ministry of Justice has amassed a wealth of data covering all facets of modern Trials. While data availability has surged, the focus now lies in actively analysing this data to optimise Trials and curtail their duration. This paper, with a strong emphasis on its outcomes, delves into the analysis of data from the Court of Livorno. It seeks to pinpoint specific events within the Trial workflow that significantly extend Trial durations. Notably, Domain Experts have identified a set of events, intending to validate their pivotal role in recognising critical Trials. Leveraging Machine Learning techniques, the paper evaluates multiple binary classifiers to proactively identify potentially critical Trials, empowering Judges to mitigate future issues. The study has yielded a remarkable 80% accuracy rate in predicting Trials exceeding acceptable duration thresholds. [ABSTRACT FROM AUTHOR]

AbstractOur research described in this paper investigated the influence of varying weight percentages of a hybrid nano filler composed of titanium dioxide (TiO2) and silicon dioxide (SiO2) in an epoxy polymer composite. The dielectric properties were analyzed across a broadband frequency range. Additionally, X-ray diffraction (XRD) was employed to examine the structural changes and crystalline phases within the composite materials. The Influence of hybrid inorganic nano-fillers in the epoxy composite on the dielectric properties are discussed thoroughly in this report. To improve material design and prediction modeling, our research utilized machine learning algorithms, such as the XGBoost regressor. The aim was to assess the effectiveness of regression techniques in evaluating the material properties. By employing performance metrics, like R2 score and RMSE, the tests could achieve accuracies ranging from 30% to 60%. Simulation results demonstrated that machine learning-based methods can significantly expedite the forecasting of the dielectric properties, i.e. dielectric constant and dielectric loss at intermediate frequencies, thereby saving both time and energy. [ABSTRACT FROM AUTHOR]