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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.

These developments have the potential to foster additional innovation, facilitate future problem-solving and optimisation strategies, and ultimately contribute to mature OWC technologies. Keywords: optical communication; underwater optical wireless communication EN optical communication underwater optical wireless communication 87 90 4 08/24/23 20230801 NES 230801 Since 2011, optical wireless communication (OWC) technologies have gained momentum. These technologies include visible light communications (VLC), underwater VLC, Li-Fi, optical camera communications (OCC), visible light positioning, visible light sensing and free space optics, among others. [Extracted from the article]

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]