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A pre-treatment process was evaluated in this work for wastewater from paper recycling using microwave technology followed by rapid precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R² values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater. [ABSTRACT FROM AUTHOR]

In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [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]

With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

• Enhanced cellulo-xylanolytic consortium production from Hypocrea lixii GGRK4 using MOGA-ANN is reported. • The production of CMCase (9.43 fold) and xylanase (4.40 fold) higher than un-optimized process. • The improved deinking efficiency and brightness is reported for photocopier paper and newspaper. • The physical strength of the waste papers were enhanced whereas double fold property was decreased proving its reusability. • A significant fungal enzyme consortium preparation for improved waste paper deinking achieved. This study reports the cellulo-xylanolytic cocktail production from Hypocrea lixii GGRK4 using multi-objective genetic algorithm-artificial neural network tool, resulting in 8.32 ± 1.07 IU/mL, 51.53 ± 3.78 IU/mL activity of CMCase and xylanase, respectively with more than 85 % residual activity at 60 °C and pH 6.0. Interestingly, metal ions viz. K+ and Ca2+ stimulated the enzyme activity, whereas Fe2+ and Cu2+ reduced the activity. Significant amounts of hydrophobic compounds, chromophores, and phenolics were released after wastepapers deinking. The deinking efficiency of 73.60 ± 2.45 % and 38.60 ± 1.34 % was obtained for photocopier paper and newspaper, respectively, whereas brightness of 89.90 ± 2.10 % ISO and 44.90 ± 1.63 % ISO was reported for both types of waste papers. The physical strength of deinked photocopier paper and newspapers, i.e., tensile index (3.10 and 0.50 %), tearing index (7.10 and 4.83 %), and burst factor (8.61) were enhanced whereas double fold property was decreased proving wastepaper reusability. This consortium showed effective and significant enzymatic deinking efficiency for recycled wastepapers. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate whether there are differences in handwritten Chinese signatures on different media including paper and electronic devices. Participants were asked to sign specified names on various types of media and the signatures were scanned or saved digitally for subsequent analysis. In this study, using convolutional neural networks and Siamese neural networks as classifiers and comparators, the performance plunge is revealed and thus considerable dissimilarity between the signatures on different media is implied. To further explore this, cubic Bézier curves are fitted to the signatures using the least square method for quantitative statistical analysis. By analyzing the visual changes in the morphology of strokes, several features of signatures are selected and computed, and the paired t‐test and the Wilcoxon signed‐rank test are implemented, which provides a deeper substantiation and explanation of the findings. [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]

As entertainment tools, computer games are important phenomena in the world, which are considered as a popular medium, an effective educational solution and a considerable economy resource. In this paper, Multi-Layer perceptron (MLP) neural network was used to detect human behavior pattern in rock, paper, scissors game. The similarity of artificial neural networks (ANNs) to the human brain is the main motivation of this study. MATLAB software was used to implement the network code. These codes consisted of two phases: 1) training the ANN to learn the human behavioral pattern considering forty games. 2) real play against a human by doing ten games. After the implementation of the network, its effectiveness in detecting human behavioral patterns was investigated. The network was tested on 40 people (20 women and 20 men). Each player played with the target network in three stages. The results of this study showed that the win percentage of computers with MLP neural network was 57.5% for men and 60.8% for women. While the percentage of the computer without neural networks and with random selections in 60 games was 52.5% for men and 42.5% for women

This document is a summary of a special issue of the journal Sensors, titled "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023." The special issue features selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs), which were held in Korea and Thailand. The conferences focused on the theme of "Emerging Artificial Intelligent (AI)+X technology" and "Hyper Automation + Human AI" respectively. The selected papers cover various topics such as network security, routing protocols, signal detection, and clustering mechanisms, all incorporating AI-based methods. The issue also includes papers on topics like secure authentication, distance estimation in RFID systems, energy optimization in smart homes, blockchain technology, and radar signal detection. The authors emphasize the importance of both technology and humanity in advancing green and information technologies. [Extracted from the article]

Geotechnical designs and analyses of earth structures and foundations exclusively involve the assessment and consideration of unsaturated soil shear strength. The laboratory testing equipment and methods for predicting the unsaturated soil shear strength are complicated and more expensive. The experimental program attempted to involve undrained triaxial and filter paper for evaluating the unsaturated soil shear strength of identically compacted clayey soil. This study undertakes a comparison of shear strength in clayey soil under undrained loads, examining both saturated and unsaturated conditions. A 60 kPa air entry suction value is the key point at which linearity between the unsaturated shear strength parameter Øb and effective friction Ø′ with 15° linear slopes turns to non-linearity. Unsaturated shear strength increased by 22.76% in optimally wet conditions, 52.68% in optimum conditions, and 77.81% in optimally dry conditions as compared to saturated shear strength. This study utilizes an artificial neural network (ANN) to predict clayey soil's unsaturated shear strength, finding that the optimal ANN configuration (2-5-1 topology, Levenberg–Marquard optimization, and logsig transfer function) achieved high reliability with a correlation coefficient (R) of 0.9289 and mean square error values of 2.22, 7.12, and 3.012 for training, testing, and validation, respectively. This experimental investigation improves our understanding of clayey soil shear strength and emphasizes the importance of saturation and moisture content in geotechnical assessments under undrained loading conditions. [ABSTRACT FROM AUTHOR]

This paper is aimed to develop a hybrid model (PCA-ANN) to predict the extractive content in eucalyptus wood clones. The input variables to the virtual analyzer are planting parameters available from the forest inventory, therefore the prediction does not rely on laboratory analysis of the wood samples, affording a quick estimate of the extractive contents. This study further bridges the literature gap on the investigation of the cause of variability of extractive content in eucalyptus wood. The PCA-ANN was identified from experimental data to predict and monitor the extractive content, since laboratory measurements can take several days and become available only after wood processing. The experimental data contained information on ten species of eucalyptus clones from five regions in the extreme south of Bahia, Brazil. Principal Component Analysis (PCA) firstly assessed the impact of planting variables on the extractives content. The variability of the data was represented by eight principal components and the variables that mostly contribute to the extractive content are: potential acidity, iron, saturation of aluminum, magnesium, pH, base saturation, remaining phosphorus, zinc, manganese and copper. The artificial neural network (ANN) with the 8 principal components in the input layer showed that the PCA could effectively reduce the dimensionality of the data. For practical purposes, though, the ANN with 10 input variables and 16 neurons in the hidden layer, presenting an average relative deviation of 1.5%, is recommended. The prediction of the extractive content is essential to allow preventive management practices toward the improvement of yield and quality of the cellulosic pulp. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduced a novel olfactory visualization freshness sensor array prepared by dropping pH indicators on UV lithography hydrophilic–hydrophobic paper under room temperature. The sensor array was applied in Grass carp spoilage classification. The sensor array can generate different discoloration patterns that respond to the pH change of the fish spoil; therefore, it can distinguish Grass carp freshness using pattern recognition. Classification models were built with linear discriminant analysis (LDA) and backpropagation artificial neural network (BP‐ANN) after extracting the sensor array's feature color information with principal component analysis (PCA). The performance of the BP‐ANN model was superior to that of the LDA model, with an optimum recognition rate of 95.45% (training set) and 90.90% (testing set). In conclusion, with a suitable algorithm choice and rational pH indicator arrangement, this olfactory visualization freshness sensor array can potentially monitor food freshness. We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic–hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90%. Novelty impact statement: We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic‐hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90 %. [ABSTRACT FROM AUTHOR]

Digital systems storing cadastral data in vector format are considered effective due to their ability of offering interactive services to citizens and other land-related systems. The adoption of such systems is ubiquitous, but when adopted, they create two non-compatible systems with paper-based cadastral systems whose information needs to be digitised. This study proposes a new approach that is fast and accurate for transforming paper-based cadastral data into digital systems. The proposed method involves deep-learning techniques of the LCNN and ResNet-50 for detecting cadastral parcels and their numbers, respectively, from the cadastral plans. It also contains four functions defined to speed up transformations and compilations of the cadastral plan's data in digital systems. The LCNN is trained and validated with 968 samples. The ResNet-50 is trained and validated with 106,000 samples. The Structural-Average-Precision ( sAP 10 ) achieved with the LCNN was 0.9057. The Precision, Recall and F1-Score achieved with the ResNet-50 were 0.9650, 0.9648 and 0.9649, respectively. These results confirmed that the new method is accurate enough for implementation, and we tested it with a huge set of data from Tanzania. Its performance from the experimented data shows that the proposed method could effectively transform paper-based cadastral data into digital systems. [ABSTRACT FROM AUTHOR]

In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm2 at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system.

The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness. [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 article discusses two computer science research papers concerning artificial intelligence (AI). Topics explored include the susceptibility of deep convolutional neural networks to input pertubations acknowledged in the 2013 study "Intriguing Properties of Neural Networks," by Christian Szegedy and colleagues, and the capability of sequence-to-sequence language models to execute short computer programs reported in the 2014 study "Learning to Execute," by Wojciech Zaremba and Ilya Sutskever.

In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm2 at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system. [ABSTRACT FROM AUTHOR]

This document provides summaries of research papers on the topic of Wordle, a popular puzzle game. The papers discuss various models and prediction frameworks used to analyze and improve the game. They explore factors such as word attributes, difficulty levels, and player behavior. The findings suggest that certain word attributes have an impact on the game's outcomes and difficulty. The models developed in these studies offer insights for game operators and suggestions for enhancing the game's popularity. [Extracted from the article]

Monitoring the early age strength and hardening of concrete plays a crucial role in ensuring the structural integrity and durability of concrete structures. Ultrasonic guided waves have emerged as a promising nondestructive testing technique for assessing concrete properties. This review paper aims to provide an overview of the utilization of ultrasonic guided waves for the early age assessment of concrete strength and hardening. The paper begins by discussing the fundamental principles of ultrasonic guided waves and their interaction with concrete materials. It highlights the advantages of using guided waves, such as their ability to propagate over long distances and penetrate through concrete structures. The review then explores various techniques employed for generating and detecting guided waves, including piezoelectric transducers, air-coupled transducers, and laser-ultrasonics. Furthermore, the paper presents a comprehensive analysis of the different parameters that can be extracted from ultrasonic signals to assess the early age strength and hardening of concrete. These parameters include wave velocity, attenuation, reflection, and scattering characteristics. The influence of various factors, such as moisture content, temperature, and mixture proportions, on the ultrasonic response of concrete is also discussed. Moreover, the review discusses the challenges and limitations associated with the application of ultrasonic guided waves in early age concrete assessment. It addresses issues such as signal interpretation, wave dispersion, and the presence of air voids. Additionally, recent advancements in signal processing techniques and data interpretation methods are highlighted. In conclusion, the utilization of ultrasonic guided waves for the early age assessment of concrete strength and hardening shows great promise. This review paper provides valuable insights into the current state-of-the-art in this field and offers recommendations for future research directions. [ABSTRACT FROM AUTHOR]

In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering. [ABSTRACT FROM AUTHOR]

This article discusses a special issue of the journal Applied Sciences that focuses on the International Conference Wood Science and Engineering (ICWSE) held in November 2023. The conference covered various topics related to wood, including wood structure and properties, wood constructions, wood drying and heat treatments, conservation and restoration of wooden objects, wood-based materials, mechanical wood processing, and surface quality. The articles in the special issue explore different aspects of these topics, such as the characteristics and potential uses of different wood species, innovative wood construction techniques, optimal drying conditions for specific wood species, and the production and properties of wood-based materials. The research presented aims to promote the production of high-value wood products and contribute to the preservation of cultural heritage. The text provides a summary of various research papers related to wood science and engineering, covering topics such as the utilization potential of different tree species, wood material properties, wood preservation and coating techniques, and furniture design. The research findings highlight the importance of factors such as printing parameters, surface quality, adhesion of varnish coatings, and light-induced color changes in wood. The authors hope that these studies contribute to a better understanding of the scientific potential of wood and will be a starting point for further research in the field. [Extracted from the article]

Many stray animals throughout the world unfortunately do not get the opportunity to find the loving home that they deserve. In this classification task, we look to develop an algorithm to predict the speed of pet adoptions. The input to our algorithm includes animal type (dog or cat), breed, gender, colour, profile picture online and the descriptions about the pet, etc. We then use both traditional machine learning techniques (Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest and Gradient Boosting) and neural networks (fully connected neural networks and long short-term memory Model) to predict the adoption rate. In particular, we build feature vectors using information extracted from description scripts and feed them into neural network models. We are hoping to examine the results to develop strategies to help improve the overall adoption rate (i.e. what features lead to faster adoption). Every year, 3.3 million canines visit animal shelters, out of a total population of 200 million. Only 2% to 17% of pets are returned to their owners. The remaining animals are euthanized due to a shortage of room in shelters (killed). The present procedures for adapting or finding a pet are inefficient and haphazard. People disseminated leaflets to the general public and spread the word to others in the vicinity of the pet's disappearance. When individuals print fliers, they waste paper and money since there is no good impact. People also share their tales on social media sites like Instagram and Facebook. There are also several fraudsters who attempt to fraudulently claim the incentive for returning the pet to its legitimate owner. We want to provide an analysis on how fast pets(cats and dogs) can be adopted based on various factors like their health conditions, age, colour, breed etc. [ABSTRACT FROM AUTHOR]

Copyright of Fibres & Textiles in Eastern Europe is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An instrument-free, user-friendly, and cost-effective PAD colorimetric sensor that can quantitative detection of H 2 O 2 has been realized by using bimetallic nickel–cobalt selenides as highly active peroxidase mimic and smartphone integrated with dark cavity as detector. Integrated with an ANN model and a self-compiled easy-to-use smartphone APP, the intelligent and on-site detection of H 2 O 2 was constructed, which exhibited an ultra-wide dynamic range. [Display omitted] • An effective paper-based colorimetric sensor was prepared based on nanozyme. • The optimized Ni 0.75 Co 0.25 Se exhibited excellent peoxidase-mimetic activities. • The on-site detection of H 2 O 2 was constructed by a self-compiled smartphone APP. • The machine learning-assisted sensor can detect H 2 O 2 over a wide dynamic range. Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel–cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel–cobalt selenides (Ni 0.75 Co 0.25 Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H 2 O 2. Using a smartphone with image capture function as a friendly signal readout tool, the Ni 0.75 Co 0.25 Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H 2 O 2 by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H 2 O 2 has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening. [ABSTRACT FROM AUTHOR]

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation. • Main process parameters of an industrial papermaking process were identified. • Experimental datasets were achieved during industrial production. • Artificial Neural Networks were trained for process parameters prediction. • Accurate predictions of papermaking process were obtained. [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]

Drying of sewage sludge is typically modeled as simultaneous heat and mass transfer phenomena. The capability of conventional models to take into account crust formation, cracks, and shrinking is limited. Artificial neural networks (ANNs) are suitable tools for dynamic representation of drying processes; however, obtaining a suitable database is a resource consuming task. Based on the Taguchi method, nine experiments were defined to set up a training database and to develop an ANN model. A high Pearson correlation coefficient was verified when comparing the drying kinetic curve generated by the ANN model with the one obtained during the validation experiments. [ABSTRACT FROM PUBLISHER]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

This article investigates an optimal design for a paper-feeding system that minimizes both jamming and simultaneous feeding of multiple papers, hereafter referred to as the multi-feeding rate. A total of 11 design parameters for the paper transfer device, the paper separation device, and the paper guide path are selected and analysed in this study. A test jig for feeding and transferring papers is manufactured to obtain experimental data for use in parameter analysis and design optimization. Back-propagation neural network-based causality analysis is employed to extract five dominant variables among 11 design parameters, and the results of causality analysis are compared with sensitivity results obtained from the analysis of means in the context of experimental design. Five-variable, second-order polynomial based approximate meta-models for jam rate and multi-feeding rate are then constructed, and numerical optimization is performed using NSGA-II, a non-dominated sorting genetic algorithm. Finally, two numerical Pareto optimal solutions are verified via experimental testing. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

Abstract: Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model''s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. The minimum mean absolute percentage errors of 1.003% and 0.5161% for CODeff and SSeff could be achieved using ANFIS. The maximum correlation coefficient values for CODeff and SSeff were 0.9912 and 0.9882, respectively. The minimum mean square errors of 1.2883 and 0.0342, and the minimum RMSEs of 1.135 and 0.1849 for CODeff and SSeff could also be achieved. [Copyright &y& Elsevier]

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box–Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H 2 S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box–Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H 2 S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB–RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L −1 after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H 2 S respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]