Search

The increasing rates of illicit behaviors, particularly financial crimes, e.g., bank fraud and tax evasion, adversely affect national economies. In such cases, using nondestructive methods, scientists must evaluate relevant documents carefully to preserve their value as evidence. When forensic laboratories analyze paper as evidence, they typically investigate its origin and date of manufacture. If a document's date is earlier than the earliest availability of the paper used in its creation, then this anachronism indicates that the document has been backdated. This study investigated weave marks and drainage marks for forensic purposes. Machine learning models for forensic document examination were developed and evaluated. The partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural network (ANN) classification models achieved F1-scores of 0.903, 0.952, and 0.931, respectively. In addition, to enhance model effectiveness and construct a robust model, variables were selected using the VIP scores generated by the PLS-DA model. As a result, the SoftMax classifier in the ANN model maintained its performance with an F1-score of 0.951 even with a 50% reduction in the number of input variables. [ABSTRACT FROM AUTHOR]

The document "Featured Papers in Computer Methods in Biomedicine" from the journal Bioengineering (Basel) highlights seven research papers showcasing the intersection of computer science and biomedicine. The papers cover topics such as predicting low bone mineral density in older women, improving ML models for disease prediction, creating patient-specific anatomical reconstructions, detecting atrial fibrillation, classifying Parkinson's disease patients, analyzing EEG data for brain connectivity, and exploring EEG-based brain-machine interfaces for older adults. The document emphasizes the potential of computational methods to revolutionize healthcare through personalized treatments, improved diagnostics, and enhanced patient outcomes. [Extracted from the article]

The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points. [ABSTRACT FROM AUTHOR]

A preface to the special issue of the "Journal of Speech, Language, and Hearing Research," that includes select papers from the Eighth International Conference on Speech Motor Control, is presented.

Summary: This Good Practice Paper provides recommendations for the use of advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma. It describes how advanced imaging contributes to optimal healthcare resource utilisation by in newly diagnosed and relapsed myeloma, and provides a perspective on future directions of myeloma imaging, including machine learning assisted reporting. [ABSTRACT FROM AUTHOR]

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. 'Machine learning' and 'mastitis' were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful. [ABSTRACT FROM AUTHOR]

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results. [ABSTRACT FROM AUTHOR]

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 103 CFU/mL to 1.0 × 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria. [ABSTRACT FROM AUTHOR]

The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]

Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal. [ABSTRACT FROM AUTHOR]

This document is a summary of the Special Issue of the Journal of Personalized Medicine, which features selected papers from the pHealth 2022 Conference held in Oslo, Norway. The conference focused on the advancement of personalized health using technologies such as mobile technologies, artificial intelligence, and big data. The papers cover topics such as healthcare transformation, automation and artificial intelligence in healthcare, and security and privacy in personalized health. The document concludes with a chapter on eHealth solutions. The editors express their gratitude to the authors and reviewers for their contributions to the volume. [Extracted from the article]

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields. [ABSTRACT FROM AUTHOR]

Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

The evaluation and classification of chemical properties in different copypaper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery. [ABSTRACT FROM AUTHOR]

The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset's behaviour several days in advance. [ABSTRACT FROM AUTHOR]

This document discusses the societal impact of applying artificial intelligence (AI) to traffic and transportation. Sustainable transport is considered crucial for sustainable cities and communities, and AI has the potential to significantly influence daily life in urban areas. The document highlights the interdisciplinary challenges of understanding and coordinating transportation systems, which involve various players with conflicting goals and constraints. It also presents a selection of innovative solutions related to traffic management, including machine learning, reinforcement learning, and optimization approaches. The document concludes by expressing gratitude to the authors, reviewers, and committee members involved in the creation of the special issue. [Extracted from the article]

User-friendly in-field sensing protocol is crucial for the effective tracing of intended analytes under less-developed countries or resources-limited environments. Nevertheless, existing sensing strategies require professional technicians and expensive laboratory-based instrumentations, which are not capable for point-of-care on-site analyses. To address this issue, artificial intelligence handheld sensor has been designed for direct reading of Ni2+ and EDTA in food samples. The sensing platform incorporates smartphone with machine learning-driven application, 3D-printed handheld device, and cellulose paper microfluidic chip stained with ratiometric red-green-emission carbon dots (CDs). Intriguingly, Ni2+ introduction makes green fluorescent (FL) of CDs glow but red FL fade because of the coordination of Ni2+ with CDs verified by density functional theory (DFT), concurrently manifesting continuous FL colour transition from red to green. Subsequent addition of EDTA renders FL of CDs-Ni2+ recover owing to the capture of Ni2+ from CDs by EDTA based on strong chelation effect of EDTA on Ni2+ confirmed via DFT, accompanying with a noticeable colour returning from green to red. Inspired by above FL phenomena, CDs-based cellulose paper microfluidic chips are first fabricated to facilitate point-of-care testing of Ni2+ and EDTA. Designed fully-automatic handheld sensor is utilized to directly output Ni2+ and EDTA concentration in water, milk, spinach, bread, and shampoo based on wide linear ranges of 0–48 μM and 0–96 μM, and low limits of detection of 0.274 μM and 0.624 μM, respectively. The proposed protocol allows for speedy straightforward on-site determination of target analytes, which will trigger the development of automated and intelligent sensors in near future. • Artificial intelligence handheld sensor for detection of Ni2+ and EDTA is first developed. • High-throughput CDs-based cellulose paper microfluidic chip was first fabricated. • Machine learning-driven application "Artificial Intelligent Assay" is designed. • The CDs manifests a noticeable colour transition response to Ni2+ and EDTA. • Ni2+ and EDTA in real samples are directly analyzed by the proposed protocol. [ABSTRACT FROM AUTHOR]

• Machine Learning to classify watermarked Italian paper. • XRF and FTIR spectroscopies to study late middle-aged Italian watermarked paper. • A new spatial-temporal approach to trace the local recipes and the geographical paper mills' production. • Bergamo and Bologna stand out as marked watermarked Italian paper mills. Manuscripts, illuminated codex, books, documents and letters are composite materials, traces of the past starting from the invention of the writing. In this context, dating is one of the most important information for document attribution, and watermarked papeItaly'smarkers for studying their time-spatial distribution. In the Late Middle Ages, Italy's most important centre of paper mill was located in the town of Fabriano (Marche region, Italy). Here, a selection of ten Italian Late Middle Ages watermarked papers belonging to the Corpus Chartarum Italicarum (Corpus of Italian papers) is characterised by elemental and molecular spectroscopies and collected data are analysed by Machine Learning (ML) to trace the local fabrication recipes and the geographical paper mills production. Data from portable X-ray fluorescence and Fourier transform infrared spectroscopy were analysed through Singular Vector Machine, Soft Independent Modeling of Class Analogy and Moving Blocks methods. This innovative ML spatial-temporal approach based on keeping the temporal variable fixed is used to find elemental benchmarks for classifying the watermarked Italian notarial catalogue of the Late Middle Ages finding differences in the local recipes and studying the homogeneity in the paper mills' production. Results show that watermarked paper from Northern Italy, from the town of Strozza, Piacenza and Bergamo , as well as Bologna, present a high elemental and molecular homogeneity, which indicates that the hand-made processing technique could be the same helped by the proximity of the three cities, starting point for technologies exchange or influence. No heavy metals are found in the watermarked paper and K, Ca, Fe and Zn are identified as elemental benchmarks. Finally, Ca, Ti, Mn, Cr and Fe are particularly present on the edges of the watermarked papers. [Display omitted] [ABSTRACT FROM AUTHOR]

This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations. [Display omitted] • Forensic evaluation of cigarette paper utilizing ATR-FTIR spectroscopy and Machine learning algorithms. • Peak characterization and differentiation-distinguishing cigarette paper from other types. • Machine learning algorithm comparison: assessing discrimination across nine cigarette brands. • External validation of the dominant algorithm using unknown samples. [ABSTRACT FROM AUTHOR]

This article is an interview with Jake Turley, the first author of a paper published in Development. The paper discusses the use of deep learning models to analyze large imaging datasets from Drosophila pupa and quantify the contributions of different cell behaviors during wound healing. Turley, a mathematician by training, developed deep learning tools for automated quantification of aspects of cell biology in wound healing. The research aims to understand how individual cells work together to repair a hole in the epidermis. Turley's algorithms measured the contributions of cell shape changes, cell migration, and cell divisions to the overall effort of wound re-epithelialization. The paper also explores the regulation of these cell behaviors by conserved wound signals. Turley's work has implications for understanding wound healing and may be applied to more complex 3D tissues in the future. Outside of his research, Turley enjoys attending live music events and exploring the music scene in Singapore and Southeast Asia. [Extracted from the article]

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). [ABSTRACT FROM AUTHOR]

The on-site detection of mancozeb in food samples holds immense value for food safety. A red-fluorescent europium complex (Eu-PYDC-Phen) has been prepared and employed as a fluorescence probe for mancozeb detection. The optimized probe suspension exhibits excellent detection performances, including a wide linear range (0–0.24 mM), low detection limit (65 nM), rapid response (2 mins) and high selectivity. Moreover, a portable detection platform was carefully designed, integrating the Eu-PYDC-Phen -based fluorescent test strips with smartphone color recognition software. This innovative platform enables visual and on-site detection of mancozeb in tomato, apple, and lettuce, achieving satisfactory recovery rates (90.34 to 106.50%). Furthermore, the integration of machine learning techniques based on hierarchical clustering algorithm has the potential to further improve the prediction and decision-making efficiency in mancozeb detection. This work provides an economical, convenient, and reliable strategy for on-site detection of pesticide in agricultural products, thereby making a meaningful contribution to food safety. A Eu-PYDC-Phen probe was employed for the detection of mancozeb through a fluorescence quenching effect. A smartphone-assisted test paper platform was designed for the rapid and visual detection of mancozeb in lettuce, tomato, and apple samples. [Display omitted] • A Eu-PYDC-Phen probe was applied for highly sensitive and selective detection of mancozeb. • Smartphone was integration with paper-based probe for visual identification. • The platform was applied for rapid detection of target in food samples. • A machine learning model was developed to simplify the detection process. [ABSTRACT FROM AUTHOR]

Conjecture about the weak replicability in social sciences has made scholars eager to quantify the scale and scope of replication failure for a discipline. Yet small-scale manual replication methods alone are ill-suited to deal with this big data problem. Here, we conduct a discipline-wide replication census in science. Our sample (N= 14,126 papers) covers nearly all papers published in the six top-tier Psychology journals over the past 20 y. Using a validated machine learning model that estimates a paper's likelihood of replication, we found evidence that both supports and refutes speculations drawn from a relatively small sample of manual replications. First, we find that a single overall replication rate of Psychology poorly captures the varying degree of replicability among subfields. Second, we find that replication rates are strongly correlated with research methods in all subfields. Experiments replicate at a significantly lower rate than do non-experimental studies. Third, we find that authors' cumulative publication number and citation impact are positively related to the likelihood of replication, while other proxies of research quality and rigor, such as an author's university prestige and a paper's citations, are unrelated to replicability. Finally, contrary to the ideal that media attention should cover replicable research, we find that media attention is positively related to the likelihood of replication failure. Our assessments of the scale and scope of replicability are important next steps toward broadly resolving issues of replicability. [ABSTRACT FROM AUTHOR]

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]

This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

The ASCE Journal of Construction Engineering and Management has announced the recipients of the 2024 Best Paper Award. The winning article, titled "Sequential Machine Learning for Activity Sequence Prediction from Daily Work Report Data," discusses the development of a machine learning model that can accurately predict the sequence and overlap of critical activities in construction projects. The model achieved a reliability rate of 94.4% and utilized daily work reports as a data source, which is a commonly available but underutilized resource. The article was selected from a pool of 188 technical papers, 16 case studies, and 5 state-of-the-art reviews. [Extracted from the article]

[Display omitted] • Sensing tool for identifying antimicrobial-resistant Staphylococcus aureus strains. • Fluorescent sensor array pre-adsorbed on paper microzone plates for better efficiency. • Classification of laboratory and clinical S. aureus strains powered by machine learning. • Sensor identifies biofilms associated with antimicrobial-resistant S. aureus strains. • Rapid, low-volume identification with >90% classification accuracy. Staphylococcus aureus , a versatile human pathogen, significantly impacts global health causing a broad spectrum of medical conditions that range from minor skin infections to life-threatening diseases. The clinical importance of S. aureus is underscored by its resistance to multiple antibiotics and formation of biofilms, providing protection against antimicrobials and immune responses. To date, the identification of antimicrobial-resistant (AMR) S. aureus strains, such as methicillin-resistant S. aureus (MRSA) and vancomycin-intermediate S. aureus (VISA), requires time-consuming and expensive methodologies, including culture-based, molecular, and phenotypic techniques. Previously, we developed a paper-based ratiometric sensor array composed of fluorescent sensor dyes (3-hydroxyflavone derivatives) pre-adsorbed on paper microzone plates. Combined with machine learning algorithms such as neural networks, this sensor effectively discriminated 16 bacterial species and determined their Gram status. In this study, we evaluate its ability to distinguish antibiotic-resistant S. aureus strains and their biofilms. Our results demonstrate that the sensor array, in conjunction with LDA and neural networks, successfully differentiated three common laboratory MRSA strains from three methicillin-susceptible S. aureus (MSSA) strains with 82.5% accuracy. Furthermore, using support vector machines, this sensor was able to distinguish and categorically classify MRSA, MSSA, and VISA clinical isolates with 97.5% accuracy. Remarkably, beyond distinguishing planktonic cultures, this sensor array demonstrated a formidable capability to discriminate AMR S. aureus biofilms, achieving over 80% accuracy. Combined, the results of this study highlight the paper-based sensor array's significant potential as a robust diagnostic tool to accurately, rapidly, and easily identify drug-resistant S. aureus strains in clinically relevant settings. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. [ABSTRACT FROM AUTHOR]

Accurate real-time eye tracking is crucial in oculomotor system research. While the scleral search coil system is the gold standard, its implantation procedure and bulkiness pose challenges. Camera-based systems are affected by ambient lighting and require high computational and electric power. This study presents a novel eye tracker using proximity capacitive sensors made of carbon-nanotube-paper-composite (CPC). These sensors detect femtofarad-level capacitance changes caused by primate corneal movement during horizontal and vertical eye rotations. Data processing and machine learning algorithms are evaluated to enhance the accuracy of gaze angle prediction. The system performance is benchmarked against the scleral coil during smooth pursuits, saccades tracking, and fixations. The eye tracker demonstrates up to 0.97 correlation with the coil in eye tracking and is capable of estimating gaze angle with a median absolute error as low as 0.30°. The capacitive eye tracker demonstrates good consistency and accuracy in comparison to the gold-standard scleral search coil method. This lightweight, non-invasive capacitive eye tracker offers potential as an alternative to traditional coil and camera-based systems in oculomotor research and vision science. [Display omitted] • Novel capacitive proximity sensors are evaluated for non-human primate eye tracking. • The sensors demonstrate a high correlation (up to 0.97) with the scleral coil system. • Machine learning estimates the gaze with a median absolute error as low as 0.30°. • Show promise as a non-contact, low-cost, and low-power eye tracking tool. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability. [ABSTRACT FROM AUTHOR]

In the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science. [ABSTRACT FROM AUTHOR]

Dimensionality Reduction (DR) is the process of reducing the numerous features or random variables under consideration to a limited number of features by obtaining a set of principal variables. These techniques cater great values in machine learning, which come in handy to simplify a classification or a regression dataset, thereby yielding a better-performing predictive model. Techniques used for DR include Feature Selection methods, Matrix Factorization, AutoEncoder methods, and Manifold Learning. Merits of DR include data compression, reduced space of storage, and removal of redundant features. This paper attempts to review various techniques used to carry out dimensionality reduction while providing an exhaustive comparative study over the merits and demerits of each of the techniques used under the empirical experiments performed by the authors whose work is being reviewed. [ABSTRACT FROM AUTHOR]

Handmade paper is a major carrier and restoration material of traditional Chinese ancient books, calligraphies, and paintings. In this study, we carried out a Raman spectroscopy analysis of 18 types of handmade paper samples. The main components of the handmade paper were cellulose and lignin, according to the wavenumber and Raman vibration assignment. We divided its Raman spectrum into eight subbands. Five machine learning models were employed: principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), k‐nearest neighbors (KNN), and random forest (RF). The Raman spectral data were normalized, and the fluorescence envelope was subtracted using the airPLS algorithm to obtain four types of data, raw, normalized, defluorescence, and fluorescence data. An RF variable importance analysis of data processing showed that data normalization eliminated the intensity differences of fluorescence signals caused by lignin, which contained important information of raw materials and papermaking technology, let alone the data defluorescence. The data processing also reduced the importance of the average variables in almost all spectral bands. Nevertheless, the data processing is worthwhile because it significantly improves the accuracy of machine learning, and the information loss does not affect the prediction. Using the machine learning models of PCA, PLS, and SVM combined with linear regression (LR), KNN, and RF, the classification and prediction of handmade paper samples were realized. For almost all processed data, including the fluorescence data, PCA‐LR had the highest classification and prediction accuracy (R2 = 1) in almost all spectral bands. PLS‐LR and SVM‐LR had the second‐highest accuracies (R2 = 0.4–0.9), whereas KNN and RF had the lowest accuracies (R2 = 0.1–0.4) for full band spectral data. Our results suggest that the abundant information contained in Raman spectroscopy combined with powerful machine learning models could inspire further studies on handmade paper and related cultural relics. [ABSTRACT FROM AUTHOR]

Keyword extraction is the task of identifying important terms or phrase that are most representative of the source document. Although the process of automatic extraction of keywords from title is an old method, it was mainly for extraction from a single web document. Our approach differs from previous research works on keyword extraction in several aspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purpose of this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturing research trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools. More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how far significant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientific article corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract the results. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-experts of that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be a solution to reduce the labour-intensive manual indexing process. [ABSTRACT FROM AUTHOR]

This study aims to develop a flexible, paper-based gold nanoparticle (AuNP) surface-enhanced Raman scattering (SERS) sensor coupled with deep learning algorithms for the rapid detection of dibenz (a,h)anthracene (DbA) and benzo(b)fluoranthene (BbF) in mussels. The SERS sensor was fabricated on a hydrophobic paper substrate and incubated with varying concentrations of DbA and BbF in spiked mussel samples before SERS spectra collection. Deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, were employed to analyze the complex SERS spectral data. The SERS sensor demonstrated excellent sensitivity with limits of detection of 0.09 ng/mL for DbA and 0.10 ng/mL for BbF. LSTM outperformed traditional machine learning techniques, achieving R2p > 0.99 with minimal prediction errors. Real sample analysis showed excellent agreement with HPLC-FLD methods. These findings present a promising approach for rapid, sensitive, and reliable detection of PAHs in mussels, enhancing food safety monitoring and regulatory compliance. • Developed a flexible paper-based AuNP SERS sensor for DbA and BbF detection in seafood. • LSTM network outperformed other models in analyzing SERS spectra. • Achieved low limits of detection: 0.09 ng/mL for DbA and 0.10 ng/mL for BbF. • Real sample analysis showed excellent agreement with HPLC-FLD method. [ABSTRACT FROM AUTHOR]

The recovery of valuable minerals in froth flotation industry relies on finding inexpensive and environmentally friendly reagents that selectively adsorb upon surfaces and interfaces. Computer simulations, especially when extended, provide access to detailed mechanistic information on solvent configurations and may ascertain crucial dynamical events over the adsorption process. Further, in silico throughput screening can prevent both the high cost of experiments and the related risks to the environment. Yet, a better compromise between accuracy and computational cost must be met. Machine learning (ML) simulations may ease the latter and suggest solidophilic reagents able to improve the flotation efficiency, shedding new light on discerning descriptors able to accurately capture the nature of the molecule-surface interaction. In this work, our recent advancements in modeling of new accurate mineral-water interfaces based on active learning of ab initio molecular dynamics trajectories have been introduced. The case of some habitual oxides and minerals liberated in mining industry will be taken as examples. • How the mineral slab may influence the layering of surfactant water molecules. • Water kinetics at the hematite, ferrosilite and magnetite interfaces. • Competing adsorption phenomena between oleic acid and amylopectin on calcite surface. • In silico screening of flotation reagents employing machine learning methodologies. [ABSTRACT FROM AUTHOR]

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. [ABSTRACT FROM AUTHOR]

Today's software industry is global, virtual, and depending more than ever on strong and reliable processes. Stakeholders and infrastructure are distributed across the globe, posing challenges that go beyond those with co‐located teams and servers. Software Engineering continues to be a complex undertaking, with projects challenged to meet expectations, especially regarding costs. We know that Software Engineering is an ever‐changing discipline, with the result that firms and their employees must regularly embrace new methods, tools, technologies, and processes. In 2020, the International Conference on Global Software Engineering (ICGSE) and the International Conference on Systems and Software Processes (ICSSP) joined forces aiming to create a holistic understanding of the software landscape both from the perspective of human and infrastructure distribution and also the processes to support software development. Unfortunately, these challenges have become even more personal to many more in 2020 due to the disruption introduced by the COVID‐19 pandemic, which forced both conferences to be held virtually. As an outcome of the joint event, we selected a set of the best papers from the two conferences, which were invited to submit extended versions to this Special Issue in the Journal of Software: Maintenance and Evolution. Dedicated committees were established to identify the best papers. Eight papers were invited and ultimately, seven of these invited papers have made it into this Special Issue. [ABSTRACT FROM AUTHOR]

Patent citations received by a paper are considered one of the most appropriate indicators for quantifying the technological impact of scientific research. In light of the large number of published research outcomes, technology developers need an effective method to identify academic work with potential technological impact and so as to provide scientific theories for the generation of relevant technologies. Focusing on the technical field of artificial intelligence (AI), this study constructs a set of 47 features from seven dimensions and uses feature selection and machine learning models to accurately predict how research papers impact AI technology. The results show that the random forest model is superior to the other tested models in predicting AI patent citations of papers, with citation-related features (such as ‘PaperCitations’ and ‘Background’) playing a vital role in the prediction. [ABSTRACT FROM AUTHOR]

The development of an advanced analytical platform with regard to SARS-CoV-2 is crucial for public health. Herein, we present a machine learning platform based on paper-assisted ratiometric fluorescent sensors for highly sensitive detection of the SARS-CoV-2 RdRp gene. The assay involves target-induced rolling circle amplification to generate magnetic DNAzyme, which is then detectable using the paper-assisted ratiometric fluorescent sensor. This sensor detects the SARS-CoV-2 RdRp gene with a visible-fluorescence color response. Moreover, leveraging different fluorescence responses, the ResNet algorithm of machine learning assists in accurately identifying fluorescence images and differentiating the concentration of the SARS-CoV-2 RdRp gene with over 99% recognition accuracy. The machine learning platform exhibits exceptional sensitivity and color responsiveness, achieving a limit of detection of 30 fM for the SARS-CoV-2 RdRp gene. The integration of intelligent artificial vision with the paper-assisted ratiometric fluorescent sensor presents a novel approach for the on-site detection of COVID-19 and holds potential for broader use in disease diagnostics in the future. [ABSTRACT FROM AUTHOR]

Traditionally, specific bioreceptors such as antibodies have rapidly identified bacterial species in environmental water samples. However, this method has the disadvantages of requiring an additional process to conjugate or immobilize bioreceptors on the assay platform, which becomes unstable at room temperature. Here, we demonstrate a novel mix-and-match method to identify bacteria species by loading the bacterial samples with simple bacteria interacting components (not bioreceptors), such as lipopolysaccharides, peptidoglycan, and bovine serum albumin, and carboxylated particles, all separately on multiple channels. Neither covalent conjugation nor surface immobilization was necessary. Interactions between bacteria and the above bacteria interacting components resulted in varied surface tension and viscosity, leading to various flow velocities of capillary action through the paper fibers. The smartphone camera and a custom Python code recorded multiple channel flow velocity, each loaded with different bacteria interacting components. A multi-dimensional data set was obtained for a given bacterial species and concentration and used as a machine learning training model. A support vector machine was applied to classify the six bacterial species: Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Bacillus subtilis. Under optimized conditions, the training model predicts the bacterial species with an accuracy of > 85% of the six bacteria species. [ABSTRACT FROM AUTHOR]

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions. [ABSTRACT FROM AUTHOR]