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This paper reflects the research to design and application the complex method to evaluate the quality of the paper written in the IMRaD format. As a field we choose Russian segment of the financial technologies area since we had interest to use multilingual language model. The method covers paper's relevance to the chosen field and internal topics, the actuality, relation between parts, text borrowing, and experiment part. The clustering and classifying algorithms, and language machine learning model were used. The experimental part based on corpus of Russian papers shows the deviation of method's recommendations from expert opinion not more than 20%. The results may be interested not only reviewers but student as authors of futures papers to avoid common mistakes during paper's writing. [ABSTRACT FROM AUTHOR]

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes , Salmonella , and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification. [ABSTRACT FROM AUTHOR]

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care. [Display omitted] [ABSTRACT FROM AUTHOR]

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling. • Clarifies the similarities and differences between theory and data-driven paradigms. • Reviews the use of machine learning for choice modelling. • Explores opportunities for embracing machine learning to benefit choice modelling. • Puts forward research agenda. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

A pre-treatment of graphene with bleach is considered one of the possible purification methods after liquid-phase exfoliation. However, the effect of this treatment on the mechanical reinforcement strategy for polymer film is yet to be investigated to date. In this full work, the influence of the C/O ratio, I D /I G, and volume of graphene after combination with cellulose tissue on the resulting stiffness of polyvinyl alcohol (PVA) composite film has been extensively studied. It is noticed that the incorporation of 30 ml graphene that had been pre-treated for 3 h into PVA had produced the best increment in elastic modulus (1.6 GPa against 0.4 GPa) while a shorter pre-treatment duration of graphene (1 h) would require more graphene volume (40 ml) to match the previous stiffness improvement level. By using the collected experimental data (90 samples), we further modeled the effect of tissue and PVA mass, C/O ratio, I D /I G , and graphene volume on modulus using machine learning (ML) algorithms. [Display omitted] • Combination of cellulose tissue and graphene as filler hybrid to combat poor dispersibility of bleach-treated graphene • Mechanical reinforcement effect was observed for graphene treated at 3 h due to the well-balanced C/O and I D /I G. • Addition of more tissue/graphene mass is required for graphene with a lower C/O to enhance the stiffness. • Machine learning study shows k-nearest neighbours with k = 1 is the best prediction model for composite stiffness. [ABSTRACT FROM AUTHOR]

The present study employs density functional theory-based first principle calculation to investigate the electron transport properties of polyaniline following exposure to acidic and alkaline pH. In-situ deposited polyaniline-based paper device maintains emeraldine salt form while it is exposed to acidic pH and converts to emeraldine base when it is subjected to alkaline pH solutions. These structural changes at acidic and alkaline pH are validated experimentally by Raman spectra. Furthermore, the Raman spectra computed from density functional theory are validated with the experimental spectra. The changes in the theoretical energy band gap of polyaniline obtained from first principle calculations were correlated with the changes in the experimental impedimetric response of the sensor after exposure to acidic and alkaline solutions. Finally, the impedimetric responses were used to predict urine pH through a machine learning based smart and interactive web application. Different machine learning based regression models were implemented to acquire the best possible outcome. Gradient Boosting Regressor with least square loss model was selected as it showed lowest mean square, mean absolute, and root mean square error than other models. The smart sensing platform successfully predicts the unknown pH of urine samples with an average accuracy of more than 98%. The locally deployed smart web app can be accessed within a local area network by the end-user, which holds promise towards effective detection of urinary pH. [Display omitted] [ABSTRACT FROM AUTHOR]

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes , Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L 2 regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora. • A paper chromogenic array (PCA) - machine learning approach was developed to accurately identify multiple pathogens in background microflora. • PCAs, fabricated via photolithography, react with volatile organic compounds to exhibit distinguishable color pattern shifts. • An advanced neural network demonstrated excellent performance with a learning rate schedule, L2 regularization, and shortcut connections. • This nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cells are immune cells that defend against viral infections and cancer and are used in cancer immunotherapies. Subpopulations of NK cells include CD56dim and CD56bright which either produce cytokines or cytotoxically kill cells directly. The absolute number and proportion of these cells in peripheral blood are tied to proper immune function. Current methods of cytokine detection and proportion of NK cell subpopulations require fluorescent dyes and highly specialized equipment, e.g., flow cytometry, thus rapid cell quantification and subpopulation analysis are needed in the clinical setting. Here, a smartphone-based device and a two-component paper microfluidic chip were used towards identifying NK cell subpopulation and inflammatory markers. One unit measured flow velocity via smartphone-captured video, determining cytokine (IL-2) and total NK cell concentrations in undiluted buffy coat blood samples. The other, single flow lane unit performs spatial separation of CD56dim and CD56bright and cells over its length using differential binding of anti-CD56 nanoparticles. A smartphone microscope combined with cloud-based machine learning predictive modeling (utilizing a random forest classification algorithm) analyzed both flow data and NK cell subpopulation differentiation. Limits of detection for cytokine and cell concentrations were 98 IU/mL and 68 cells/mL, respectively, and cell subpopulation analysis showed 89% accuracy. • First smartphone-based paper microfluidic cell chromatography that can identify cell subpopulation. • Machine learning predictive modeling for NK cell subpopulation differentiation. • Integration of both cell chromatography and flow rate analysis on a single platform. • Potential application to many other cytokines and cell subpopulation analyses. [ABSTRACT FROM AUTHOR]

The development of low-cost analytical devices for on-site water quality monitoring is a critical need, especially for developing countries and remote communities in developed countries with limited resources. Microbial electrochemical cell-based (MXC) biosensors have been quite promising for quantitative and semi-quantitative (often qualitative) measurements of various water quality parameters due to their low cost and simplicity compared to traditional analytical methods. However, conventional MXC biosensors often encounter challenges, such as the slow establishment of biofilms, low sensitivity, and poor recoverability, making them unable to be applied for practical cases. In response, MXC biosensors assembled with paper-based materials demonstrated tremendous potentials to enhance sensitivity and field applicability. Furthermore, the paper-based platforms offer many prominent features, including autonomous liquid transport, rapid bacterial adhesion, lowered resistance, low fabrication cost (<$1 in USD), and eco-friendliness. Therefore, this review aims to summarize the current trend and applications of paper-based MXC biosensors, along with critical discussions on their field applicability. Moreover, future advancements of paper-based MXC biosensors, such as developing a novel paper-based biobatteries, increasing the system performance using an unique biocatalyst, such as yeast, and integrating the biosensor system with other advanced tools, such as machine learning and 3D printing, are highlighted. [Display omitted] • Studies related to paper-based MXC biosensors are summarized and reviewed. • Benefits of using paper-based platforms over traditional materials are listed. • Current applications and challenges of paper-based MXC biosensors are provided. • Field applicability of paper-based MXC biosensors is highlighted. • Opportunities to integrate 3D printing and machine learning are discussed. [ABSTRACT FROM AUTHOR]

Nowadays, Machine Learning (ML) model of the structure-property relation based on large data from reliable physical models becomes a new and promising approach for material design. The present work demonstrates such approach to examine the variation in microstructure features on mechanical properties of paper materials. After the generation of a "big" dataset of fiber network samples, morphological feature data, including interfiber contact properties were extracted and statistically evaluated. By performing cohesive finite element simulations, the mechanical properties including failure strain, effective stiffness, and maximal stress of fiber networks under tensile test were determined and served along with structural feature data for the ML analysis. Gradient Boosting method achieved a performance score of approx. 0.9 for all mechanical properties of such complex fibrous structure. It was found that "disorderness" represented by the variation of fiber network orientation and the mean contact area size to be the most influential factors to the failure strain and effective stiffness. Whereas the failure strength was driven by the homogeneous distribution of the contact areas. The results validated the strong orientation dependence of fibrous materials in experimental observations and enlighten the importance of sensitivity as feature parameters and the striking potential of ML for material optimization. Unlabelled Image • Novel modeling idea using sensitivity parameters to access the randomness of paper fiber network. • Large dataset of microstructure features is correlated with mechanical performances using machine learning models. • Importance of the microstructure features to mechanical responses is determined and discussed. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing. To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory. This allows for the quick and reliable application of machine learning models in production. [Display omitted] • A novel theory-guided machine learning framework for width prediction of rolled plates. • Pre-training of the model using the data generated by the optimized theoretical model. • The proposed model has the best accuracy in both sufficient and less actual samples. • The variable influence of the proposed model is consistent with theoretical perceptions. [ABSTRACT FROM AUTHOR]

Plastic waste pollution remains a major problem across the developing world. In Vietnam, the situation is particularly serious as many plastic consumption behaviours remain under-analysed and pertinent policies have produced limited impact. Accordingly, this paper examines the patterns and predictors of consumer plastic bag use when shopping in Da Nang, Vietnam. It does so by drawing on an original household survey and key informant interviews. Moreover, it applies the latest behavioural theory research and machine learning techniques. Subsequently, this paper observes Vietnamese consumers' plastic bag use is prevalent and often entrenched as a habit. Additionally, two socio-demographic and seven socio-psychological predictors are significant to the frequency of using plastic bags. These results, then, inform Vietnam's plastic consumption policies and, more broadly, emphasise the (1) heterogeneity of influences on consumer behaviour; (2) contingency of many widely-accepted behavioural predictors; and (3) shortcomings of purely regulatory solutions. • Effective plastic consumption policies are necessary to generate sustainable consumer behaviour. • Machine learning techniques are used to examine the determinants of Vietnamese consumers' plastic bag use. • Two socio-demographic and seven socio-psychological predictors were significantly associated with plastic bag use. • Many consumers showed resistance to following plastic bag bans. • Targeted multi-dimensional consumer behaviour reforms ought to be prioritised. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells. Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the primary cause of mortality in the female population due to cancer-related complications. Consequently, the imperative for early detection and prognosis has emerged as a means to enhance long-term survival rates and mitigate mortality. Emerging artificial intelligence (AI) technologies are being utilized to aid radiologists in the analysis of medical images, resulting in enhanced outcomes for individuals diagnosed with cancer. The purpose of this survey is to examine peer-reviewed computer-aided diagnosis (CAD) systems that have been recently developed and utilize machine learning (ML) and deep learning (DL) techniques for the diagnosis of BC. The survey aims to compare these newly developed systems with previously established methods and provide technical details, as well as the advantages and disadvantages associated with each model. In addition, this paper addresses several unresolved matters, areas of research that require further exploration, and potential avenues for future investigation in the realm of advanced computer-aided design (CAD) models utilized in the interpretation of medical images. Furthermore, the integration of Internet of Things (IoT) in BC research and treatment holds immense significance by facilitating real-time monitoring and personalized healthcare solutions. IoT devices, such as wearable sensors and smart implants, enable continuous data collection, empowering healthcare professionals to track patients' vital signs, response to treatment, and overall health trends, fostering more proactive and tailored approaches to BC management. Moreover, the advent of 5G technology in BC applications promises to revolutionize communication speeds and data transfer, enabling rapid and seamless transmission of large medical datasets. This high-speed connectivity enhances the efficiency of remote diagnostics, telemedicine, and collaborative research efforts, ultimately accelerating the pace of innovation and improving patient outcomes in BC care. The present study aims to examine various classifiers utilized in ML and DL methodologies for the purpose of diagnosing BC. Research findings have demonstrated that DL has superior performance compared to standard ML methods in the context of BC diagnosis, particularly when the dataset is extensive. The existing body of research indicates that there are significant gaps in knowledge that need to be addressed in order to enhance healthcare outcomes in the future. These gaps highlight the pressing need for both practical and scientific research in the field. Finally, IoT and 5G will be how they can be used in order to enhance BC detection, treatment and patient care. • Cancer, characterized by uncontrolled cell proliferation, poses a significant threat to human life. • Breast carcinoma is highly invasive and a leading cause of female cancer-related mortality, emphasizing the need for early detection and prognosis. • Emerging AI technologies are assisting radiologists in analyzing medical images, improving cancer diagnosis outcomes. • This survey focuses on recent computer-aided diagnosis (CAD) systems using machine learning and deep learning for breast carcinoma diagnosis. • The paper identifies unresolved research areas and future investigation prospects in advanced CAD models for medical image interpretation. [ABSTRACT FROM AUTHOR]

In this research paper, a detailed investigation presents the utilization of the BiT5 Bidirectional NLP model for detecting vulnerabilities within codebases. The study addresses the pressing need for techniques enhancing software security by effectively identifying vulnerabilities. Methodologically, the paper introduces BiT5, specifically designed for code analysis and vulnerability detection, encompassing dataset collection, preprocessing steps, and model fine-tuning. The key findings underscore BiT5's efficacy in pinpointing vulnerabilities within code snippets, notably reducing both false positives and false negatives. This research contributes by offering a methodology for leveraging BiT5 in vulnerability detection, thus significantly bolstering software security and mitigating risks associated with code vulnerabilities. [ABSTRACT FROM AUTHOR]

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers' attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions. • New controller designed by combining adaptive terminal synergetic and integral backstepping techniques. • Reference voltage generation using machine learning regression algorithm. • Estimation of unmeasured variables through Neural Network. • Improved particle swarm optimization method to determine the design parameters. • Comparison of proposed controller with others under real climatic conditions. [ABSTRACT FROM AUTHOR]

This paper studies the quantized iterative learning control with encoding–decoding mechanism of a class of impulsive differential inclusion systems with random data dropouts. First, the set-valued mappings in the differential inclusion systems are transformed into single-valued mappings by using the Steiner-type selector. Then, a learning algorithm based on the intermittent update principle is designed to address the data asynchronism problem caused by two-sided data dropouts. If the data are successfully transmitted at the actuator and measurement sides, then the control input is effectively updated. Furthermore, a suitable scaling sequence is introduced to ensure the system output to achieve zero-error tracking performance for a desired trajectory. An upper bound of the quantization level is determined such that the quantization error is always bounded. The results show that the quantization method reduces the burden of network communication at the cost of increasing the amount of computation, and the learning algorithm does not require the data dropouts to satisfy a certain probability distribution. Finally, the effectiveness of the learning algorithm is verified by numerical simulations of the switched reluctance motor system. • This paper enriches the iterative learning control (ILC) results for uncertain systems. • We establish an ILC research framework for continuous-time systems with unreliable networks. • We construct a novel learning algorithm to deal with the data asynchronous problem. [ABSTRACT FROM AUTHOR]

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres. [ABSTRACT FROM AUTHOR]

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing. [ABSTRACT FROM AUTHOR]

Software-Defined Radios are radio communications devices that have been growing and developing on a larger scale in recent years. Communications are intrinsically embedded in our day by day, thus presenting a higher motivation to use software-defined radios due to its attractive cost. However they present technical limitations. This paper addresses this problem, which is the non-linearity behaviour of gain and frequency in the LimeSDR-USB. That is, this equipment is used to produce a FM signal with an associated frequency and gain before being parameterised according to the internal parameters of each software-defined radio. Each software-defined radio presents a value of frequency and gain of its own, which correlates to the generated signals at the output level. To avoid this, machine learning networks were used, in which networks were trained to adapt to the non-linearity of these devices and correct it without the user noticing. This way, the user sets a desired frequency and gain in a signal, at the output of the software-defined radios, and a neural network calculates which values the software-defined radios should be parameterised, thus mitigating the non-linearity behaviour. This paper presents the evaluation of a laboratory prototype based on low-cost commercial software-defined radios equipment, to replace an expensive metrologically calibrated equipment used for radio frequency tests on a new concept of industrial test station, with description of the integration of Digital Twins, with their physical and virtual parts. [ABSTRACT FROM AUTHOR]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. [ABSTRACT FROM AUTHOR]

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants. [Display omitted] • The sources of multi-source data of medicinal plants and the strategies for processing multi-source data are summarized. • This paper summarizes several machine learning algorithms commonly used to analyze multi-source data of medicinal plants. • This paper summarizes the application of machine learning combined with multi-source data in medicinal plants and prospects. [ABSTRACT FROM AUTHOR]

• This paper presents an efficient algorithm for classification of Epileptic Seizure from normal, inter-ictal, and seizure EEG signals. • A new classifier named EMRVFLN is proposed which is an improved version of both RVFLN and ELM. • Efficient features are extracted after pre-processing from the EEG signal using VMD and HT. • Also, this paper presents digital implementation of the proposed EMRVFLN classifier in FPGA environment. • Two real time datasets i.e. Bonn university dataset and Neurology & Sleep Centre, Hauz Khas, New Delhi are used to validate the proposed method. In this paper, variational mode decomposition (VMD), Hilbert transform (HT), and proposed error-minimized random vector functional link network (EMRVFLN) are integrated to detect and classify epileptic seizure from electroencephalogram (EEG) signals. VMD is applied to decompose the EEG signal into Band-limited intrinsic mode functions (BLIMFs). The five efficacious instantaneous features are computed using HT to construct the feature vector. Proposed EMRVFLN classifier is used to classify the epileptic seizure. The performances of the proposed EMRVFLN are compared with recently developed classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM). The combination of VMD and HT with proposed EMRVFLN classifier outperforms other state-of-the-art methods with classification accuracy of 100% for two class classification problem and 99.74% for three class classification problem. The remarkable classification accuracy facilitates the digital implementation of the proposed EMRVFLN classifier which may aid to design an embedded system for real-time disease diagnosis. [ABSTRACT FROM AUTHOR]

• A DC-Gaussian process regression model is used to the prediction of Dst index. • DC-GPR model has the best stability among the three models among DC-GPR, DC-SVM, and DC-NN models • DC-GPR model exhibits the better forecasting performance than DC-SVM and DC-NN models in Dst forecasting. In this paper we apply a class of Bayesian machine learning model, Gaussian Process Regression, to the prediction of Dst index by using 80 intense geomagnetic storms data (Dst ⩽ - 100 nT) from 1995 to 2014. The purpose of this paper is to compare the performance of Gaussian process regression model with Support Vector Machine model combined together with Distance Correlation (DC-SVM) and Neural Network model combined together with Distance Correlation (DC-NN) (Lu et al., 2016). For comparison, we estimate the correlation coefficients (CC), the RMS errors, the absolute value of difference in minimum Dst (Δ Dst min ) and the absolute value of difference in minimum time (Δ t Dst ) between observed Dst and predicted one.In order to compare the prediction effects and the generalizability of the three models to magnetic storm events, we combined 70 intense magnetic storm events and 10 super large magnetic storm events into one group. It is shown that DC-GPR model exhibits the better forecasting performance than by DC-SVM model and DC-NN model in magnetic storm. The CC, the RMS errors, the Δ Dst min , and the Δ t Dst of GPR are 0.65, 35.45 nT , 16.12 nT and 1.16 h , respectively. [ABSTRACT FROM AUTHOR]

Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science. A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus. Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p <0.001). Overall rates of availability for items were poor, leaving room for substantial improvement. [ABSTRACT FROM AUTHOR]

For the assessment of optimum treatment timing in dentofacial orthopedics, understanding the growth process is of paramount importance. The evaluation of skeletal maturity based on study of the morphology of the cervical vertebrae has been devised to minimize radiation exposure of a patient due to hand wrist radiography. Cervical vertebral maturation assessment (CVMA) predictions have been examined in the state-of-the-art machine learning techniques in the recent past which require more attention and validation by clinicians and practitioners. This paper aimed to answer the question "How are machine learning techniques being employed in studies concerning cervical vertebral maturation assessment using lateral cephalograms?" A systematic search through the available literature was performed for this work based upon the Population, Intervention, Comparison and Outcome (PICO) framework. The searches were performed in Ovid Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR). A search of the grey literature was also performed in Google Scholar and OpenGrey. We also did a hand-searching in the Angle Orthodontist, Journal of Orthodontics and Craniofacial Research, Progress in Orthodontics, and the American Journal of Orthodontics and Dentofacial Orthopedics. References from the included articles were also searched. A total of 25 papers which were assessed for full text, and 13 papers were included for the systematic review. The machine learning methods used were scrutinized according to their performance and comparison to human observers/experts. The accuracy of the models ranged between 60 and 90% or above, and satisfactory agreement and correlation with the human observers. Machine learning models can be used for detection and classification of the cervical vertebrae maturation. In this systematic review (SR), the studies were summarized in terms of ML techniques applied, sample data, age range of sample and conventional method for CVMA, which showed that further studies with a uniform distribution of samples equally in stages of maturation and according to the gender is required for better training of the models in order to generalize the outputs for prolific use to target population. [ABSTRACT FROM AUTHOR]

• A digital twin-driven (DTD) framework and model are developed for predicting degraded lifting capacity (LC) of aging tower cranes. • A DTD model predicted the degraded LC of a scaled-down prototype, achieving a mean-square error (MSE) of 0.2253 and a coefficient of determination (R2) of 0.9973. • A DTD model is validated using k-5 cross-validation with a prediction accuracy of 0.97 (R2). • Degraded load charts assist operators in placing safe loads and preventing unexpected failures. Aging tower cranes face an elevated risk of failure, primarily due to structural fatigue and deterioration. Surprisingly, the degradation of aging-induced lifting capacity (LC) remains an unexplored domain. In response to this research gap, this paper introduces a digital twin-driven (DTD) framework and model to predict the degraded LC of aging tower cranes. This framework combines theoretical and numerical analysis of fatigue and degradation behavior in tower cranes with real-time vibration data obtained during cyclic load scenarios on the actual cranes. Machine learning (ML) techniques are employed to develop a model that accurately predicts the degraded LC caused by aging. A scaled-down tower crane prototype is adopted as a demonstrative case to illustrate the feasibility and effectiveness of the DTD framework. The DTD model predicts the degraded LC of the prototype with high accuracy, achieving a mean-square error (MSE) of 0.2253 and a coefficient of determination (R2) of 0.9973. The predicted degraded load charts of the tested tower crane for each decade of usage from 0 to 70 years are also presented to assist crane operators in applying safe loads, preventing unexpected failures and damages, and enhancing workplace monitoring and safety. This study helps monitor the safety conditions of tower cranes that are aging and susceptible to structural fatigue and deterioration, facilitates the prediction of the deterioration of complex machines and systems in the construction industry with real-time data, and highlights the potential of DTD approaches in improving efficiency, safety, and decision-making. [ABSTRACT FROM AUTHOR]

In the digital age, organizations confront the challenge of managing diverse documents efficiently while ensuring security, accuracy, and accessibility. Conventional document management approaches often must catch up, leading to inefficiencies and increased costs. This paper introduces the Intelligent Document Management System (IDMS), which employs advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to enhance document workflows. This research extends the capabilities of IDMS to encompass the extraction and processing of data from three important document types: medical bills, Aadhar cards, and PAN cards. The research and development efforts done in this paper have concentrated on seamlessly integrating of these models into the IDMS framework, offering a comprehensive solution for extracting and processing data from various document types. In this paper, two approaches, namely Easy OCR and a hybrid approach of combining NLP (Regular Expression) and CV (OCR) have been applied and compared. The results revealed that the proposed hybrid approach (NLPCV) is better, with higher accuracy of 97%,71 %, and 78% for hospital invoices, Aadhar cards, and PAN cards, respectively. [ABSTRACT FROM AUTHOR]

The inconvenience of travel, air pollution and consequent economic losses caused by traffic congestion have seriously restricted the healthy and sustainable development of cities in China. In this context, as the main component of current and future urban traffic management measures, intelligent transportation system is an important means to improve the traffic efficiency of road network and alleviate urban traffic congestion. Traffic flow prediction plays an important role in this connection. In this paper, an ensemble short-term traffic flow prediction method based on optimized variational mode decomposition (OVMD) and combined long short-term memory network (LSTM) is proposed. The method consists of three main components: 1. Use the improved bat algorithm to optimize the parameters of VMD to achieve better decomposition effect; 2. Use the optimized variational mode decomposition algorithm (OVMD) to decompose the unstable original traffic flow time series data into relatively stable multiple Intrinsic Mode Functions (IMFs); 3. The optimized L-BILSTM model is established by combining the basic long short-term memory network with the bidirectional long short-term memory network. It can better extract information from traffic flow data and improve the accuracy of prediction results. In the empirical study part, the traffic flow data of Changsha City is used to verify the prediction model proposed in this paper. The influence of the application of the variational mode decomposition algorithm to the training set data and the overall data on the final prediction results is also compared and analysed. [ABSTRACT FROM AUTHOR]

With the development of workshop automation, the complexity of RGV (Rail Guided Vehicle) dynamic scheduling schemes using virtual simulation technology is increasing. For the widely valued intelligent machining systems, machine learning based optimization algorithms can effectively respond to the increasingly complex RGV dynamic intelligent scheduling. In the whole model construction, how to complete the modular design of the intelligent processing system and optimize the solution is the key problem that needs to be solved urgently at present. This paper studied the use of particle swarm optimization to design the RGV dynamic scheduling model, aiming to improve the material processing production efficiency of RGV dynamic scheduling and reduce the system failure rate. Through problem modeling, solution and simulation experiment analysis, this paper applied particle swarm optimization based on machine learning, combined with RGV structure modular design and task parameter test set samples. According to the data results, the following conclusions can be drawn from the discussion. Under the background of intelligent logistics system, the RGV dynamic scheduling model using particle swarm optimization had higher material processing production efficiency than the traditional scheduling method in all job test samples, and the average increase was 13.25%. Meanwhile, in terms of system failures, optimization algorithms were better than traditional scheduling methods, with an average reduction of 4.6%. This shows that the RGV dynamic scheduling model based on particle swarm optimization has a better practical application effect. [ABSTRACT FROM AUTHOR]

The operation and maintenance expenses of power transmission and transformation projects, as a significant power supply carrier of the nation, continue to rise as a result of the sustained and quick expansion of China's social economy and the quick growth of the country's power demand. Power grid businesses are under a lot of market pressure. To increase the level of lean management of the operation and maintenance costs of power transmission and transformation projects, power grid enterprises must significantly enhance their capacity to estimate the operation and maintenance costs of their organizations in advance. Machine learning algorithms are gradually applied to the operation and maintenance cost prediction of power transmission and transformation projects of power grid enterprises as a result of the ongoing development of big data technology, effectively increasing the accuracy of operation and maintenance cost prediction. In this paper, by analyzing the variables affecting the differential operation and maintenance cost of power transmission and transformation projects, a scientific and reasonable investment analysis model for the differential operation and maintenance cost of power transmission and transformation projects is constructed using the stochastic forest algorithm of particle swarm optimization, and the variables affecting the differential operation and maintenance cost of substations and transmission lines are obtained, which proves that the trend of the prediction model in this paper is more consistent with the actual situation. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

This paper designs an interval type-2 fuzzy neural network sliding mode robust controller (IT2FNNSMRC) to improve the stability of the vibrational angle of the orbital plane in magnetic rigid spacecraft attitude control. The control system consists of an interval type-2 fuzzy neural network (IT2FNN) controller, a PD controller, and a robust controller in parallel connection. The IT2FNN controller, as a nonlinear regulator, compensates the nonlinearity of the controlled object; the PD controller, as a feedback controller, ensures the global asymptotic stability of the control system; the robust controller inhibits input load disturbance. The IT2FNN controller hereof has a self-organizing function which enables it to automatically determine the network structure and parameters online. At the stage of IT2FNN structure learning, the standard on rule growth is set according to the incentive intensities of IT2FNN rule premises. A new rule is generated when the incentive intensities of rules are all smaller than a certain threshold; next, a significance index is set for each rule. When the significance index of some rule decays to a certain threshold, the corresponding rule shall be deleted to achieve the goals of optimizing IT2FNN structure and reducing system complexity. At the stage of parameter learning, adaptive adjustment of IT2FNN parameters is made via the sliding mode control theory learning algorithm, and the stabilities of the algorithm and control system are proven using Lyapunov function. Finally, the proposed control scheme is used in the control of a magnetic rigid spacecraft, as compared to three other designed control methods. Simulation results show that IT2FNNSMRC has superior control precision and stability. And the IT2FNN which adopts the proposed learning algorithm can address uncertainty satisfactorily, with higher computational implementability. • In the control scheme for the PD controller and IT2FNN parallel operation, a robust controller has been added so that the IT2FNNSMRC can be applied to second-order systems with more complex disturbances. • The self-organizing algorithm based on the incentive intensity of fuzzy rules has been proposed for online optimization of the IT2FNN structure, ensuring the correlation between the input signal and fuzzy rules and determining the optimal structure of the neural network. • The IT2FNN parameters have been optimized using the sliding mode learning algorithm at lower computational cost, which is easier to implement compared to the gradient method. • The computational cost of the IT2FNNSMRC has been analyzed in terms of time complexity, and it has been proven that the computational complexity of IT2FNN in this paper has been reduced below that of traditional fixed structure IT2FNN. • The IT2FNN parameter learning algorithm is tested by Lyapunov stability analysis, and it is further proven that the robust controller can effectively enhance the stability of the system. [ABSTRACT FROM AUTHOR]

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T- X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies. [ABSTRACT FROM AUTHOR]

This research paper presents a thorough examination of shock wave train phenomena in various duct structures, utilizing a machine learning-optimized k-ω model. The focus of the study is to apply machine learning techniques to adapt the constant coefficients of the k-ω turbulence model, improving its accuracy and computational efficiency in capturing the dynamics of shock waves. An important aspect of this investigation is the impact of diverging sections within the duct, specifically how changes in the divergence angle, while maintaining a constant ratio of the exit area to the throat area, affect compressible flow parameters, shock wave positions, and other related characteristics of shock trains. The study systematically explores these effects and provides fresh insights into the behavior of shock wave trains under different divergence conditions. In a departure from the usual practices in supersonic research, this study compares the phenomena of shock wave trains in rectangular and circular duct geometries. The findings contribute valuable data and analyses to a field that has traditionally focused on rectangular configurations. The results indicate that the shape of the duct has a significant influence on the shock wave train, emphasizing the importance of considering geometric diversity in the study of supersonic flows and shock wave phenomena. This research sets the stage for further investigations into the interaction between duct geometry, shock wave propagation, and the dynamics of compressible flows. • Improved k-ω SST model accuracy to 95.5 % with machine learning. • Longer duct diverging sections intensify the first lambda shock. • Duct shape critically affects shock wave and boundary layer interactions. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

Ionospheric scintillation is a phenomenon that influences radio waves from the Global Navigation Satellite System (GNSS) in the ionosphere, reducing the accuracy, integrity, and continuity of tracking and navigation applications. Automatic and accurate detection of scintillation events based on threat level is essential for space weather forecasting applications. Therefore, a multi-class classification of four categories based on scintillation intensities was developed. In this paper, a Synthetic Minority Oversampling Technique (SMOTE) based Super Learner (LR) machine learning classification algorithm is proposed with two months of GPS ionospheric amplitude scintillations S4 data obtained for 2015 from Hyderabad station (17.45°N, 78.47°E). The Synthetic Minority Oversampling Technique (SMOTE) is implemented to oversample the minority classes to balance the events in each class. Moreover, we also focused on annotating the data transmitted by all the visible satellites. Later, a machine learning method is proposed to achieve the automated detection of ionospheric scintillation to improve the detection performance and obtain the detected results for each category. The experimental results show that the proposed SL model approach considerably has detection accuracy on the classified data as 97.8% during the quiet day and 78.3% during storm day for the Hyderabad station. The confusion matrix results indicate the proposed algorithm's effectiveness for quiet and disturbed conditions. [ABSTRACT FROM AUTHOR]

The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of product life cycles. A pivotal objective in this context involves the identification and 6D pose estimation of bolts affixed to the motors, facilitating automated disassembly within the manufacturing workflow. Complicating matters, the presence of occlusions and the limitations of single-view data acquisition, notably when motors are placed in a clamping system, obscure certain portions and render some bolts imperceptible. Consequently, the development of a comprehensive pipeline capable of acquiring complete bolt information is imperative to avoid oversight in bolt detection. In this paper, employing the task of bolt detection within the scope of our project as a pertinent use case, we introduce a meticulously devised pipeline. This multi-stage pipeline effectively captures the 6D information with regard to all bolts on the motor, thereby showcasing the effective utilization of prior knowledge in handling this challenging task. The proposed methodology not only contributes to the field of 6D pose estimation but also underscores the viability of integrating domain-specific insights to tackle complex problems in manufacturing and automation. [ABSTRACT FROM AUTHOR]

The performance of a manufacturing system is a result of the interrelated characteristics of all functional units within the system, including material flow. Therefore, the operation of a manufacturing system requires harmonization of its elements, resulting in an optimized material flow, reduced operating costs, and energy consumption. Existing methods approach this problem by applying analytic formulations or material flow simulations that represent the behavior of a manufacturing system. However, building such a model is time-consuming and requires making assumptions and simplifications, which can lead to inaccurate results. As a consequence, this paper proposes two novel machine learning (ML) models that are trained to represent the characteristics of manufacturing systems. The first model is trained to predict the properties of the material flow and the functional units used for production. The second model predicts whether the manufacturing system is in a regular state or whether anomalies are present. The capabilities of the ML models are investigated with three application scenarios of increasing complexity which are used to generate synthetic training data generated in a discrete event simulation. The results show that the ML models can describe the characteristics of a manufacturing system with high accuracy which offers various possibilities for future research and application. [ABSTRACT FROM AUTHOR]

The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure of the other larger capacity hard disk drives (0007 and 0138, corresponding to 12TB and 14TB) has increased, with a growth rate of 17.1016% and 123.7899%. [Display omitted] • A novel mixed frequency sampling discrete grey model is proposed. • The proposed model is proved to be unbiased and stable theoretically. • A Chimpanzee Optimization algorithm is introduced to globally search for weight parameters and maximum lag order. • A new model evaluation system for evaluating the effectiveness of prediction models is constructed. • The proposed model outperforms the other seven benchmark models in four case studies. [ABSTRACT FROM AUTHOR]

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network. [ABSTRACT FROM AUTHOR]

Smart and connected health (SCH) accelerates the development and integration of information science and engineering approaches to support the digital transformation of health and medicine in populated societies. Sensor data based human activity recognition (HAR) as an effective means of SCH is promising for healthcare monitoring and ambient assisted living. This paper focuses on multi-position sensor data fusion enabled HAR, and proposes a multi-channel deep learning framework. The main contributions include: (1) A multi-channel hybrid deep learning model (1DCNN-Att-BiLSTM) that merges a one-dimensional convolutional neural network, a bidirectional long short-term memory model, and an attention mechanism is proposed to significantly improve the recognition performance of HAR models by extracting and selecting local and global behavioral features in the spatial and temporal domains; (2) Publicly accessible datasets Shoaib AR, Shoaib SA, and HAPT are collected and processed to build multi-position sensor data pool for model's evaluation; (3) Thoroughly evaluating the classification performance of seven sensor data fusion patterns from different body positions within the parallel network structure of the multi-channel framework; (4) Comparing the recognition performance metrics of traditional machine learning models, deep learning models, and hybrid models with our proposed model. Extensive experiments demonstrate that our approach achieves competitive performance. [ABSTRACT FROM AUTHOR]

In this paper, we considered a new probability distribution with new applications in the field of engineering, in particular, in music engineering. The new probability distribution is mainly based on the Weibull distribution and cosine function. We call this the weighted cosine flexible Weibull distribution. The point estimators for the new distribution are obtained. We evaluate these point estimators for three sets of parameter values. The illustration of the weighted cosine flexible Weibull distribution is provided for two practical data set which are drawn from the reliability and music engineering. In addition, to dual robust machine learning approaches, we implement the Lasso (Least Absolute Shrinkage and Selection Operator) and Elastic Net (ENet) to improve the predictive performance of the data. This was done in response to the existence of outliers in the same datasets. We provide comparative analysis to see how well these approaches performed in comparison to the Step Indicator Saturation (SIS) method. [ABSTRACT FROM AUTHOR]

For future wireless communications, Cell-free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems and Non-orthogonal Multiple Access (NOMA) schemes are considered potential candidates to meet the greater coverage and capacity demands. Nevertheless, a traditional CF-mMIMO system faces scalability issues and poses numerous challenges in handling the expanding number of user equipment and ensuring their dependable connectivity, particularly in larger geographical areas. To address this challenge, a user-centric (UC) approach is implemented in a CF-mMIMO system, wherein a designated subset of access points (APs) serves a specific number of users from the entire pool of available APs. To implement a NOMA aided CF-mMIMO system, users must be grouped using a suitable clustering scheme to achieve greater spectral efficiency (SE), sum-rate, and reduced bit error rate (BER). For efficient user clustering, unsupervised machine learning (ML) algorithms, such as k-means, k-means++, and improved k-means++ are employed. In this paper, a multiuser NOMA aided CF-mMIMO system with a UC approach is investigated and closed-form expressions for intra-cluster interference and SINR are derived and the performance of the proposed system is analyzed in terms of achievable sum-rate and BER. The proposed system with the UC approach and three ML algorithms namely k-means, k-means++, and improved k-means++ demonstrate 12%, 10%, and 17% higher achievable sum-rate as compared to the NUC approach with same ML algorithms respectively. Similarly, the proposed system with UC and ML approaches exhibits 52%, 55% and 61% improved achievable sum-rate respectively, as compared to far pairing, random pairing, and close pairing schemes. Moreover, the system model is validated through the conformity of the theoretically derived bit error rate with the simulation results for a three-user scenario. [ABSTRACT FROM AUTHOR]

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73–0.86], 0.82 [95% CI, 0.78–0.86], and 0.88 [95%CI, 0.85–0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72–0.87], 0.73 [95% CI, 0.62–0.82], and 0.84 [95%CI, 0.80–0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately. • AUCs in both training and validation cohorts were higher than 0.80. • Multiple sequences MRI and deep learning methods improve diagnostic performance. • DWI performed better than contrast-enhanced sequences. • The general quality of the included articles was poor. • Further studies should use independent validation cohorts. [ABSTRACT FROM AUTHOR]

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment. • A novel PIML with the state observer is proposed to learn physical equations. • A parameter estimation method is developed for the time-varying uncertain systems. • The performance of the parameter-dependent controller is verified by the experiment. [ABSTRACT FROM AUTHOR]