When implementing SVMs, two major problems are encountered: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential mag-nitude as the problem size expands. The Kernel-Adatron family of algorithms gaining attention lately which has allowed it to handle very large classification and regression problems. Howev-er, these methods treat different types of samples (Noise, border, and core) in the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work, we introduce a hybrid method to overcome these shortcomings, namely Optimal Recurrent Neu-ral Network Density Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler-Cauchy method involving an optimal time step. The RNN remembers the regions explored during the search process thanks to its recurrent architecture. We demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has a very low time complexity compared to RNN-SVM with constant time step, and KAs-SVM. Several experiments were performed on academic data sets. We used several classification performances measures to compare Opt-RNN-DBSVM to different classification methods and the results obtained show the good performance of the proposed method.

— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.

With an explosion of data in the nearly every domain, from physical and environmental sciences to econometrics and even social science, we have entered a golden era of time-domain data science. In addition to ever increasing data volumes and varieties of time-series, there's a plethora of techniques available to analyses them. Unsurprisingly, machine learning approaches are very popular. In environmental sciences (ranging from climate studies to space weather/solar physics), machine learning (ML )and deep learning (DL) approaches complement the traditional data assimilation approaches. In solar and astrophysics, multiwavelength time-series or lightcurves along with observations of other messengers like gravitational waves provide a treasure trove of time-domain data for ML/DL approaches for modeling and forecasting. In this regard, deep learning algorithms such as recurrent neural networks (RNNs) should prove extremely powerful for forecasting as it has in several other domains. Such methods agnostic of the physics can only predict transient flares that are represented in the original distribution trained on (eg. coloured noise fluctuations) and not special, isolated episodes (eg. a one-off outburst like a major coronal mass ejection) potentially distinguishing them. Therefore it is vital to test robustness of these techniques ; here we attempt to make a small contribution to this by exploring sensitivity of RNNs with some rudimentary experiments. We test the performance of a very successful class of RNNs, the Long Short Term Memory (LSTM) algorithms with simulated lightcurves. As an example, we focus on univariate forecasting of time-series (or lightcurves) characteristic certain variable astrophysical objects (eg. active galactic nuclei or AGN). Specifically, we explore the sensitivity of training and test losses to key parameters of the LSTM network and data characteristics namely gaps and complexity measured in terms of number of Fourier components. We find that typically, the performances of LSTMs are better for pink or flicker noise type sources. The key parameters on which performance is dependent are batch size for LSTM and the gap percentage of the lightcurves. While a batch size of $10-30$ seems optimal, the most optimal test and train losses are under $10 \%$ of missing data for both periodic and random gaps in pink noise. The performance is far worse for red noise. This compromises detectability of transients. Thus, we show that generative models (time-series simulations) are excellent guides for use of RNN-LSTMs in building forecasting systems

Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of Countries requested peoples to use cashless transaction as far as possible. Practically it is not always possible to use it in all transactions. Since number of such cashless transactions have been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/her previous transactions. Normally banks or other transaction authorities warned their customers about the transaction If any deviation is noticed by them from available patterns. These authorities think that it is possibly of fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining , decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. These approaches is try to find out normal usage pattern of customers based on their past activities. The objective of this paper is to find out such fraud transactions during such unmanageable situation.Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transaction in during money transfer may save customers from financial loss. Mobile based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in this paper that monitors and detects fraudulent activities. Implementing and applying recurrent neural network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization. However, specialising solutions for the edge cases remains challenging. Here we propose to build the solution with zero hand-engineered features, but having everything learned directly from data. We use a modality specific neural architecture for extracting preliminary features, which are then integrated with cross-modality neural network structures. We show that each modality-specific neural architecture branch is capable of estimating the location with good accuracy independently. But for better accuracy a cross-modality neural network fusing the features of those early modality-specific representations is a better proposition. Our multimodal neural network, MM-Loc, is effective because it allows the uniform flow of gradients during training across modalities. Because it is a data driven approach, complex features representations are learned rather than relying heavily on hand-engineered features.

Arctic sea ice prediction holds significant importance for facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the model's capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction. Additionally, meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin ice region at the edge of the sea ice.

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been utilised to forecast the foreign exchange market (Forex). However, such models usually exhibit unstable behaviour, as data perturbations often downgrade the functionality of the entire network due to their monolithic architecture. Hence, this study proposes a novel neuroscience-informed modular network applied to the closing prices and the sentiments retrieved from Yahoo Finance and Twitter APIs, aiming to anticipate better price fluctuations in Euro to British Pound Sterling (EUR/GBP) rather than monolithic methods. The proposed model is based on a new modular CNN, replacing pooling layers with orthogonal kernel initialisation RNNs coupled with Monte Carlo Dropout (MCD), namely MCoRNNMCD. It combines two modules: i) a convolutional simple RNN and ii) a convolutional Gated Recurrent Unit (GRU), where orthogonality and MCD are added to reduce the overfitting, assessing each module's uncertainty. These parallel feature extraction modules concatenate their outputs to a final three-layer Artificial Neural Network (ANN) decision-making module. A comprehensive comparison viewing objective evaluation metrics such as the Mean Square Error (MSE) proved that the proposed MCoRNNMCD-ANN outperformed single CNN, LSTM, GRU, and the state-of-the-art hybrid BiCuDNNLSTM, CLSTM, CNN-LSTM, and LSTM-GRU in forecasting hourly EUR/GBP closing price fluctuations.

SQL injection attacks are one of the most common types of attacks on web applications. These attacks exploit vulnerabilities in the application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network (RNN) autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then compared with several other machine learning models, including ANN, CNN, Decision Tree, Naïve Bayes, SVM, Random Forest, and Logistic Regression. The experimental result showed that the proposed approach achieved an accuracy of 94% and an F1 score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison with other models covered in the study.

Health management in industrial systems is more critical in maintenance management and it plays an important role in productivity, fault diagnosis, safety, efficiency, and economy in manufacturing industries. Early detection of faults in machinery may increase the effectiveness of maintenance actions and will avoid unwanted consequences in process operations and maintenance. Existing fault diagnosis methods have limitations such as insufficient accuracy, slow detection rate, and handling large and complex data sets. In this digital age, Industry 4.0 techniques have been applied across all fields, including the condition monitoring of machines. This research addresses the gaps in traditional fault diagnosis by using deep learning, a modern AI technique effective for diagnosing faults in various machines. Deep learning algorithms Multilayer Perceptron (MLP), Coevolutionary Neural Network (CNN), and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are tested for fault diagnosis using vibration datasets collected from Spectra Quest Machinery Fault Simulator (SQMFS). In this research work, NI-DAQ (National Instruments- Data Acquisition) system, accelerometer, and LabVIEW software are used to collect vibration signals. Preprocessing of the signals has been done using a sampling strategy, shuffling, standardization, and reshaping data augmentation. The result shows that MLP accuracy in the prediction of fault is 0.9, CNN reached 0.95, and RNN and LSTM with 0.57 and 0.45 respectively. The high performance of CNN is due to its ability to effectively capture spatial patterns in vibration data which is crucial for fault diagnosis in rotating machinery followed by MLP due to its faster convergence during training. However, when scaling the data, MLP outperformed CNN, demonstrating superior adaptability to increased data complexity and volume. Due to the need for larger datasets and temporal patterns in the vibration data, which RNN and LSTM are designed to handle, they resulted in a lower accuracy. This study shows that CNN has given better results than other deep learning algorithms MLP, RNN, and LSTM in fault diagnosis of rotating machinery. Future research could explore the application of these techniques to different types of machinery and fault conditions.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

We propose an advanced filtering scheme based on Recurrent Neural Networks (RNNs) and Deep Learning to enable efficient control strategies for Vertical Farming (VF) applications.  We demonstrate that the best RNN model incorporates five neuron layers, with the first and second containing ninety Long Short-Term Memory neurons.  The third layer implements one Gated Recurrent Units neuron.  The fourth segment incorporates one RNN network, while the output layer is designed by using a single neuron exhibiting a rectified linear activation function.  By utilizing this RNN digital filter, we introduce two variations: (1) A scaled RNN model to tune the filter to the signal of interest, and (2) A moving average filter to eliminate harmonic oscillations of the output waveforms. The RNN models are contrasted with conventional digital Butterworth, Chebyshev I, Chebyshev II, and Elliptic Infinite Impulse Response (IIR) configurations.  The RNN digital filtering schemes avoid introducing unwanted oscillations, which makes them more suitable for VF than their IIR counterparts.  Finally, by utilizing the advanced features of scaling of the RNN model, we demonstrate that the RNN digital filter can be pH selective, as opposed to conventional IIR filters.

The emergence of the Internet of Things (IoT) has led to the deployment of various types of sensors in many application fields, including environment monitoring, smart cities, health, industries, and others. The increasing number of connected devices has led to the creation of massive quantities of data that need to be analyzed. Typically, this data is ordered by time, as a time series. In this context, this paper presents a time series prediction model based on Recurrent Neural Networks in order to predict one step ahead. Result obtained through five Internet of Things monitoring datasets, showed that the Recurrent Neural Network obtained better performance that the prediction methods, ARIMA and SVM.

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

This study presents the development of a Recurrent Neural Network (RNN) model for classifying smart home device data. Utilizing a dataset sourced from Kaggle, the study explored the processes of data gathering, loading, normalization, and model construction. The RNN, equipped with Long Short-Term Memory (LSTM), was trained and evaluated, demonstrating significant improvements in training and validation accuracy over 10 epochs, culminating in a test accuracy of 83.25%, and only 35.4% loss. The evaluation of the model on the test set provides a test accuracy result, accompanied by an in-depth analysis of ROC curves and Area Under the Curve (AUC) scores for multi-class classification, along with a confusion matrix. The AUC score of 0.9896 indicates outstanding performance in accurately classifying IoT device categories. These findings indicate that the RNN with LSTM exhibits superior learning efficiency and generalization capabilities, making it more suitable for IoT device classification tasks. This article emphasizes the concept of IoT and reviews recent studies on the application of deep learning models across various IoT domains, including smart homes, industrial systems, and healthcare. Future research could focus on improving real-time processing capabilities, and scalability, and incorporating diverse IoT data types to enhance and broaden the model's practical applications.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.

Reproductive disorders are a common event in modern reproductive medicine, occurring both in spontaneous and assisted pregnancies. Studies of the molecular mechanisms of implantation disorders in thin endometrium, including the study of gene transcriptional activity, have shed light on the identification of potential biological markers of endometrial receptivity. Background/Objectives: to reveal significantly dysregulated selected genes expression between RIF and RPL patients with thin endometrium. Methods: Endometrial samples were collected in the implantation window days (LH+7 – LH+10) of the natural menstrual cycle from RIF patients (n=20) and RPL patients (n=19). Ten genes were chosen as target genes regarding their possible relations with the implantation process. Total RNA was purified, reverse-transcribed; gene expression was quantified by RT-PCR. Results: The expression of the IL-15, INFG and HPRT1 genes were significantly decreased in RIF with thin endometrium patients compared to the RPL patients (log2 fold change=0.92, p=0.023 for IL-15, log2 fold change=1.24, p=0.046 for INFG, and log2 fold change=0.579, p=0.046 for HPRT1). There were not significant differences in expression of CXCL8, CXCL1, MMP10, C4BPA, TNC, VEGFB and HAND2 genes between groups. Conclusions: decreased expression of IL-15, INFG and HPRT1 genes was found in patients with RIF with thin endometrium compared to the endometrium of women with RPL. This has practical significance for clinicians for differentiated prescription of immunomodulatory therapy in patients undergoing ART programs.

Background: Urinary tract infections (UTI) are a significant health concern, particularly in women, with almost half of them experiencing at least one UTI in their lifetime. This problem is further compounded by the high recurrence rate within 6-12 months. Postmenopausal women are especially vulnerable to recurrent UTI due to the natural decline in estrogen levels, which alters the urogenital epithelium and subsequently affects the urogenital microbiome. Methods: This study included 674 female patients that were admited at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest over a period of 3 years. Results: Of the 674 women with urinary tract infection, 435 (79.3%) had more than one positive culture, and 239 (35.4%) experienced at least one recurrent urinary tract infection 6-12 months after the initial diagnosis. The mean age of women with urinary tract infection was 63 (± 15.61 years). Recurrent urinary tract infections were most prevalent in those aged 65-80 years (52%). Escherichia coli was detected in 71% of the positive cultures. Conclusions: The management of recurrent urinary tract infections in women presents a complex challenge that necessitates a multifaceted approach. The emerging understanding of the urobiome and its role in bladder health offers promising avenues for developing targeted interventions. As research in this field advances, it is imperative to integrate these new insights into clinical practice to balance traditional antibiotic treatments with novel strategies that consider the intricate microbial ecosystem of the urinary tract.

We define the notions of strong and strict recurrency for actions of countable ordered groups on $\sigma$-finite non atomic measure spaces with quasi-invariant measures. We show that strong recurrency is equivalent to non existence of weakly wandering sets of positive measure. We also show that for certain p.m.p ergodic actions the system is not strictly recurrent, which shows that strong and strict recurrency are not equivalent.

Recurrent bacterial vaginosis (RBV) after antibiotic treatments has a relapse rate of 35% within 3 months and 60% within 12 months. Products containing polycarbophil (PLGG), that inhibits bacterial growth and mucoadhesive property, can impair biofilm formation. Here are shown the results of the POLARIS (Polybactum® to assess Recurrent Bacterial Vaginosis) study. The first phase was an interventional, open-label, non-controlled, and multicentre trial enrolling 56 women in Italy and Romania. The second phase was an observational 10-month follow-up without treatment conducted only in Romania. After 3 cycles with PLGG, only 8 BV recurrences out of 54 evaluable patients were identified (rate 14.81%) and for 26 out of 39 patients (66.67%) was evidenced positive effect on Lactobacilli in the vaginal secretions. In the follow-up 35 patients were observed after PLGG stopping treatment; 1 RBV (2.86%) at the 4th month and an additional 6 cases (17.14%) were evidenced at the end of the follow-up period. Therefore, no recurrence was evidenced in 12 subjects (34.28%) at 10th ± 2 months after the end of the PLGGtreatment. The use of PLGG vaginal ovules in the treatment of BV reduces the rate of relapses and improves the microbiological parameters (positive effect on Lactobacilli in 66.7 % of cases).

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

Abstract: Background: Surgery on varicose veins (crossectomy and stripping) leads to recurrence and has clinical and socio-economic repercussions. Their etiopathogenesis has yet to fully under-stood. Objective: Study the expression of endoglin and other molecules involved in the neovascu-larisation process in patients suffering from this disease. Methods: 43 patients that have undergone surgery for varicose veins (24 primary and 19 recurrent). They were identified on the venous wall (proximal -saphenofemoral junction- and distal), via real-time RT-PCR, and in serum, via ELISA: Endoglin (Eng), Vascular Endothelial Growth Factor (VEGF-A), its receptors 1 and 2 (VEGFR1 or FLT1), (VEGFR2 or FLK), and the Hypoxia-Inducible Factor (HIF-1A). All the patients signed a con-sent form. Results: The recurrent group recorded a higher expression of Eng, VEGF-A, VEGFR1 and VEGFR2 at the level of proximal venous wall compared to the primary group. HIF-1A did not record any differences. As regards the determination of the distal venous wall, no markers recorded differ-ences between the groups. Among the serum determinations, only sFLT1 recorded a significant drop among the patients with recurrent varicose veins. Conclusions: Patients with recurrent vari-cose veins record a higher expression of endoglin and other markers of angiogenesis in proximal veins. Endoglin in the blood (sEng) has not proven to be of any use in recurrent varicose veins.

Bacterial vaginosis (BV), the most common cause of vaginal discharge, is characterized by a shift in the vaginal microbiota from lactobacillus dominance to a diverse array of facultative and strict anaerobic bacteria which form a multi-species biofilm on vaginal epithelial cells. The rate of recurrence after therapy is high, often >60%. While the BV biofilm itself likely contributes to recurrent and/or refractory disease after treatment by reducing antimicrobial penetration, antimicrobial resistance in BV-associated bacteria including those, both within the biofilm and the vaginal canal, may be the result of independent, unrelated bacterial properties which are discussed in this paper. Our current recommendations for the treatment of refractory and recurrent BV are also provided.

Recurrent laryngeal nerve palsy remains a significant complication following minimally invasive esophagectomy for esophageal cancer. Despite advancements in surgical techniques and lymphadenectomy precision, the incidence of recurrent laryngeal nerve palsy has not been improved. Recurrent laryngeal nerve palsy predominantly affects the left side and may lead to unilateral or bilateral vocal cord paralysis, resulting in hoarseness, dysphagia, and an increased risk of aspiration pneumonia. While most cases of recurrent laryngeal nerve palsy are temporary and resolve within 6 to 12 months, some patients may result in permanent nerve dysfunction, severely impacting patient quality of life. Prevention strategies, such as nerve integrity monitoring, robotic-assisted minimally invasive esophagectomy, and advanced dissection techniques, aim to minimize nerve injury, though their effectiveness varies. Management of recurrent laryngeal nerve palsy includes voice and swallowing rehabilitation, reinnervation techniques, and, in severe cases, surgical interventions such as thyroplasty and intracordal injection. As recurrent laryngeal nerve palsy can lead to significant postoperative respiratory complications, a multidisciplinary approach involving surgical precision, early detection, and comprehensive rehabilitation is crucial to improving patient outcomes and minimizing long-term morbidity in minimally invasive esophagectomy.

Nowadays recurrent implantation failure (RIF) after invitro fertilization is common problem that reproductive specialists usually faced. The phenomenon of thin endometrium in RIF patients has yet not completely understood and properly treated. This study aimed to reveal significantly dysregulated selected genes expression between RIF patients with thin endometrium and fertile women. Endometrial samples were collected in the implantation window days (LH+7 – LH+10) of the natural menstrual cycle from RIF patients (n=20) and fertile women (n=14). Ten genes were chosen as target genes regarding their possible relations with the implantation process. Endometrial gene expression levels showed differences in RIF samples compared to fertile samples. The significant downregulation was observed for CXCL1 (p=0.005) and C4BPA (p=0.03) genes. There was no statistically significant difference between the RIF groups and the fertile group in the expression of 8 genes: CXCL8, HPRT1, MMP10, INFG, VEGFB, HAND2, IL15 and TNC (p>0.05). The use of a combination of two markers (C4BPA + CXCL1) allows for good discrimination of RIF patients from fertile women (AUC 0.806).

Background and Aims: Obesity is a pervasive health issue in children. It affects multiple systems in the body including the gastrointestinal tract. Research on obesity-related gastrointestinal diseases has focused mostly on hepatobiliary disorders. However, non-hepatic gastrointestinal diseases such as gastroesophageal reflux, constipation, and functional abdominal pain, though common and debilitating, have not been highlighted in the context of obesity. These entities can downgrade the already compromised lifestyle of obese children. Some of these disorders are more difficult to treat in these children compared to their peers with normal body mass index. Method: We reviewed the available literature on the association between obesity and these three common gastrointestinal disorders. Results: - There appears to be a strong correlation between childhood obesity and constipation and gastroesophageal reflux, and a probable correlation with functional abdominal pain. Conclusion: We suggest that given the available literature, weight management should be an integral part of the management of gastroesophageal reflux, constipation, and functional abdominal pain in obese children. This gains relevance especially because of the recent availability of medications for the treatment of obesity in children.

The early 2-factor (E2F) family of transcription factors, including E2F1-8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment.To predict the possibility of GBM patient survival and progression, we classified and comparedthe expression of E2Fs according to BVZ-responsive subtypes and employed a machine learning method-TreeBagger, one random forest algorithm. Multiple bioinformatics analyses have shown that the significant increased E2F8 post BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, indicating one of the mechanisms of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. Our findings suggest that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers and therapeutic targets and recommending precise BVZ treatment for GBM patients.

Incisional hernias have a particular dynamic related both to the repair method and to the absence or presence of a prosthetic material. The incidence of incisional hernias is estimated at 9.8% after 21 months and increases to 18.7% after ten years. This study presents a surgical method for the repair of recurrent incisional hernias, through open surgery and the use of existing mesh. The selection criteria regarding the use of this method are: the prosthesis is well integrated, without granulomas, suppurations, defect appearing on the same prosthetic abdominal area. The analyzed factors were the location of the incisional hernias, the duration of the operation, the duration of hospitalization, identified complications, the size of the parietal defect. The retrospective study carried out focused on recurrences of incisional hernias from a group of 631 patients operated on for incisional hernias in a general surgery department of Dr. Constantin Andreoiu County Emergency Hospital, Ploiești, Romania, during January 2018 - December 2022. Of the total of 18.22% relapses, 13.6% were relapsed eventrations after prosthetic procedures. Following the application of the selection criteria, several 8 patients with recurrent eventrations were operated by the open method using the existing mesh. In the case of recurrences after prosthetic procedures of incisional hernias, the recurrence can be solved by open surgery using the existing well-integrated prosthesis, without tension, absence of suppuration in the wound, and the peritoneal cavity. For the presented case, the return to daily activities occurred after 22 hours of hospitalization.

An earthquake is a natural event by its general definition. This natural event is a disaster that causes significant damage, loss of life, and other economic effects that will damage the state. The possibility of predicting a natural event such as an earthquake will minimize the reasons mentioned. Data collection, data processing, and data evaluation were carried out in this study. Earthquake forecasting was performed using the data and the RNN (Recurrent Neural Network) method. The study was carried out on seismic data with a magnitude of 3.0 and above belonging to Düzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of the earthquake were calculated and included in the data set, except for the earthquake magnitude. The determination of earthquakes in a specific time interval in regions of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the production of predictions for the future reveal the importance of this study.

In the post-PARP inhibitor era, potential changes in tumor biology after maintenance therapy have not been well investigated in recurrent ovarian cancer. We reported a case with alterations in the clinical and histological features of multiple relapsed disease associated with PARP inhibitor maintenance therapy. The patient with high-grade serous carcinoma exhibited BRCA wildtype and homologous recombination proficiency status, and suffered from three recurrences and surgeries accordingly. Olaparib maintenance had been used during the second-line therapy. We compared the differences in clinics and pathology among three recurrences and relapsed lesions. Disease-free survivals were dramatically decreased after the exposure to olaparib. At exploration of quaternary cytoreduction, the relapsed tumor was characterized by a carcinomatosis-like metastasis pattern and an easy tendency of bleeding. Tumor cytopathological changes and alterations were observed in both the tumoral and non-tumoral stroma, among relapsed tumor tissues derived from secondary, tertiary and quaternary cytoreduction. Histopathology indicated hemorrhage, necrosis, atypical tumor cells, massive angiogenesis, and decreased CD8+ tumor-infiltrating lymphocytes, particularly in the third relapsed disease. To our knowledge, this is the first report to show a unique metastatic pattern of angiogenic burst after PARP inhibitor maintenance therapy in ovarian cancer, which seemed to trigger invasive tumor growth and immune suppression. Further prospective studies and translational research focusing cytoreductive surgery after PARP inhibitor could progressively lead to an understanding of the biological behavior and metastatic patterns.

CGP do not substantially differ between initial tumor vs tumor recurrence and recurrent thymomas vs non-recurrent thymomas. Cell-cycle control genes alterations are associated with an early recurrence after thymectomy. Multiple target therapies are potentially available by performing a comprehensive CGP, suggesting that a precision medicine approach on these patients could be further explored.

Thyroid surgery has been for years one of the most common elective procedures performed in general surgery. In the last 25 years, there have been significant advances in the diagnosis and treatment of thyroid disorders, and new technologies are being implemented. The aim of this study was to ana-lyze 25 years of experience in thyroid surgery in the Department of General, Minimally Invasive and Endocrine Surgery at the Medical University of Wroclaw in terms of demographic changes, indica-tions for surgical treatment, the type of thyroid surgery performed and complications. The impact of recent advances on changes in endocrine surgery was evaluated. For this purpose, clinical material from the years 1996-2020 was analyzed, with a total of 3748 patients (7285 RLN at risk of injury). Period I included the years: 1996-2003, period II: 2011-2015 and 2018-2020. Results: In the last 25 years, the percentage of patients operated on for thyroid cancer has increased threefold (p <0.00001); the extent of thyroid surgery has changed: in 1996-2003, subtotal thyroid operation was performed in more than 90% of patients vs. 1.7% of total thyroid gland surgery; 2011-2015 and 2018-2020 were more than 94% of total thyroid surgery vs. 4% of subtotal surgery (p<0.00001). The increase in the extent of surgery did not affect the percentage of total amount recurrent laryngeal nerve palsy (RLN palsy) (5.17% vs. 4.38% p = 0.1785), it did affect the percentage of transient RLN palsy in group II vs. group I (0.41% vs. 1.34%, p<0.00001), while the percentage of permanent RLN palsy in group I was statistically significantly higher than in group II (4.77% vs. 3.05%, p=0.0016). An increase in the percentage of postoperative clinical hypoparathyroidism in group II was observed: 4.84% vs. 8.93% (p<0.00001). Conclusions: Over 25 years, there has been a significant increase in the number of sur-geries performed for thyroid cancer, the range of surgeries from partial resections to total excision of the thyroid gland has changed, and the increased range of surgeries did not have a statistically signifi-cant effect on the number of vocal fold paralysis, but increased the percentage of postoperative hypo-parathyroidism. Intraoperative neuromonitoring had a significant impact on changing the scope of surgery and preventing complications.

The prediction of stock prices holds significant implications for researchers and investors in evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on GRU (Gated Recurrent Unit) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries.

Objectives: Essential thrombocythemia (ET) is a chronic myeloproliferative neoplasm characterized by elevated platelet counts and an increased risk of thrombotic events, including ischemic strokes. Methods: We conducted a retrospective analysis of data from consecutive ischemic stroke patients with ET between March 2014 and February 2023. Results: This case series describes the clinical presentation, radiological features, and management of five patients with ET-associated ischemic strokes, all harboring the JAK2 mutation. The diverse radiological findings suggest that both large and small vessel diseases may be influenced by the prothrombotic state induced by ET. A significant elevation in platelet count was observed to correlate with the emergence of new acute infarctions in some cases. Conclusions: The study highlights combined use of antiplatelet and cytoreductive therapy in preventing secondary stroke events in patients with ET and JAK2 mutations. The heterogeneity of stroke patterns in this population necessitates a comprehensive understanding of the underlying pathophysiological mechanisms and tailored therapeutic approaches.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Recurrent respiratory papillomatosis (RRP) is a non- malignant disease, characterized by the production of wart-like growths in respiratory tract, affecting both young and adults (Juvenile-Onset Recurrent Respiratory Papillomatosis, JORRP and Adult-Onset Recurrent Respiratory Papillomatosis, AORRP, respectively). Infection caused by Human Papillomavirus (HPV) is known as the main factors involved in RRP development. Complications of RRP may rarely occur, including lung involvement and malignant transformation. The present systematic review was aimed to evaluate the prevalence of severe complications, as lung involvement and lung tumor in JORRP and AORRP patients, following the guideline for reporting systematic reviews and meta-analysis (PRISMA Statement). Among the 378 studies were found on PubMed and Scopus using the following MESH terms “recurrent respiratory papillomatosis and lung tumor” and “pulmonary tumor and recurrent respiratory papillomatosis”. Basing on inclusion and exclusion criteria, a total of 11 studies were included in the systematic review. We found a pooled prevalence of 8% (95% CI:4%-14%; I2: 87.5%) for lung involvement in RRP patients. In addition, we found a pooled risk difference of 5% in lung involvement between JORRP and AORRP (95% CI: -7%-18%; I2 :85.6%, p-value: 0.41). Among patients with lung involvement, we observed a pooled prevalence of lung tumor of 4% (95% CI:1%- 7%; I2: 67.1%), and a pooled prevalence mortality for this group of 4% (95% CI:2%-6%; I2: 0%). Overall, the positivity rate for HPV-6 and -11 in patients with RRP was 91%. Considering only cases with pulmonary involvement, the pooled prevalence for HPV 11 was 21% (95% CI: 5%-45%; I2: 77.2%). Our results evidenced a low/middle risk of pulmonary involvement and lung tumor in JORRP and AORRP patients, with an increased risk for HPV-11 positive patients. Further studies should be performed to improve knowledge and adopt preventive measures to contrast the progression to severe diseases in RRP patients.

The work analyses the way in which symptoms and life themes manifest in middle-aged adults diagnosed with major recurrent depression. Specifically, the relationships between symptoms, life themes, and life themes - symptoms have been analyzed. For this purpose, Spearman correlation, and the methods of Latent Semantic Indexing (LSI) were used. Seven symptoms and twenty-six life themes were identified in the patients analyzed as well as similarities of symptoms/life themes (at patient level), the ranking of the importance of symptoms on each life theme (at the level of the group of patients), the rankings of the similarities of life themes in relation to different symptoms or various groups of symptoms (at the level of the group of patients), and dysfunctional cycles of symptoms and life themes (at patient level). The findings only refer to the patients analyzed. Although our findings cannot be generalized, there is a possibility that some of them may also be encountered in other patients. However, the design of the work can be used to initiate other similar studies.

Background: Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP based Swiss COPD cohort, and develop a statistical model for predicting exacerbation. Methods: In a questionnaire-based COPD cohort, demographic and medical data were recorded for 24 months. Data set was split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and the overall performance was validated. A nomogram was created to facilitate the clinical use of the model. Results: Of the 229 COPD patients analyzed, 77% of patients had no exacerbation during the follow-up. The best subset in the training dataset found that lower forced expiratory volume, high scores on the MRC dyspnoea scale, exacerbation history, being on combination therapy of LABA+ICS or LAMA+LABA at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) was 0.75 for one or more exacerbations. Calibration was accurate (predicted 0.34 exacerbations vs observed 0.28 exacerbations). Conclusion: Nomograms built from these models can assist clinicians in the decision-making process of their COPD care.

A theoretical model to translate the evolution over time, in early stages, of growth and accumulation of biofilm bacterial mass is introduced. The model implies the solution of a system of differential-difference master equations. The application of an algorithm like Miller´s tree term recurrence, already known for Bessel functions of first kind, allows an exact calculation of the solutions of such equations, for a wide range of parameters values and time. For biofilm model a five term recurrence is deduced and applied in a backwards computation. A suitable normalisation condition completes the reach of the solution.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision-making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.

The Polynomial coefficient function, from the complex hyperspace Cⁿ in itself, is the function Fⁿ that provides the coefficients of the polynomial (x - a₁)(x - a₂)... (x-a_n). A proof of the fundamental theorem of algebra is given where the surjectivity of this function is obtained from a Recurrent Coefficient Equation.

Image captioning aims at generating meaningful verbal descriptions of a digital image. Our paper focuses on the classic encoder-decoder deep learning model that consists of several components – sub-networks, each performing a separate task that, combined, form an effective caption generator. We investigate image feature extractors, recurrent neural networks, word embedding models, and word generation layers and discuss how each component influences the captioning model’s overall performance. Our experiments are performed on the MS COCO 2014 dataset. The results help design efficient models with optimal combinations of their components.

Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.

Thyroid surgery rates have tripled over the past 3 decades making it one of the most frequently performed procedures within general surgery. They are associated with the possibility of serious postoperative complications having a significant impact on the patient's quality of life. The recurrent laryngeal nerve (RLN) palsy and the external branch of the superior laryngeal nerve (EBSLN) palsy are, next to hypoparathyroidism and bleeding after operation, one of the most common complications. The introduction of neuromonitoring into thyroid surgery, enabling both confirmation of anatomical integrity and assessment of laryngeal nerve function, was a milestone that has begun a new era in thyroid surgery. The International Neural Monitoring Study Group made standardization of the technique of RLN and EBSLN monitoring during thyroid and parathyroid surgery which increased prevalence of neural monitoring during thyroidectomy. Current status of IONM and benefits of its use have been presented in this publication.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

The effects of four cycles of recurrent selection on simple sequence repeat (SSR) marker allele frequencies and population structure were examined in the Maksimir 3 Synthetic (M3S) maize population (Zea mays L.). Genotyping of 32 plants from each cycle of selection at 38 SSR loci revealed that the mean number of alleles per locus and the mean expected heterozygosity were preserved over cycles of selection, indicating maintenance of sufficient genetic variability in the population needed for future genetic gain. Waples test of selective neutrality revealed that genetic drift was the main force in changing allele frequencies in the population. The proportion of selectively nonneutral loci in single cycles of selection varied between 16% and 37%. Some nonneutral loci shared the same genomic locations with previously published QTLs controlling important agronomic traits. Between 5% and 29% of loci were found to be in significant Hardy-Weinberg (HW) disequilibrium with the majority showing an excess of homozygosity. Excess of homozygosity at several loci was highly consistent across cycle populations suggesting positive assortative mating as the possible cause of the observed HW disequilibrium. Linkage disequilibrium (LD) tests revealed that the M3S population was essentially in linkage equilibrium. The proportion of pairs of loci in significant LD varied across cycles of selection between 0.1% and 1.8% probably due to the effects of genetic drift and epistatic selection.

The aim of this study is to describe the cognitive and speech results obtained after growth hormone (GH) treatment and neurorehabilitation in a man that suffered a traumatic brain injury (TBI). 17 months after the accident, the patient was treated with growth hormone (GH), together with neurostimulation and speech therapy. At admission, the left vocal cord revealed paralyzed, in the paramedian position, a situation compatible with a recurrent nerve injury. Clinical and rehabilitation assessments revealed a prompt improvement in speech and cognitive functions, and following completion of treatment, endoscopic examination showed recovery of vocal cord mobility. These results, together with previous results from our group, indicate that GH treatment is safe and effective for helping neurorehabilitation in chronic speech impairment due to central laryngeal paralysis, as well as impaired cognitive functions.

Background/Objectives: Periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis syndrome, often referred to as PFAPA syndrome, may enigmatically recur for an undetermined time in the affected children: a potential reason to explain its recurring pattern for an unpredictable period and its self-limitation is currently unknown. We explored the relationship of PFAPA symptom evolution during a variable follow-up with PFAPA disease pictures and breastfeeding for less than 6 months during infancy. Methods: In the Outpatients Clinic of Pediatric Rheumatology of our Institution we have retrospectively screened 150 Italian children with history of PFAPA syndrome during the period 2014-2024, all without any recognized chronic diseases: 88 males, 62 females, mean age at onset of 2.5±1.7 years, age range of 0.3-9.4 years, and mean age at diagnosis of 4.5±2.0 years. Results and Conclusions: After dividing patients into two groups based on either the disappearance or persistence of PFAPA symptoms during a variable median follow-up we found that positive family history of recurring fevers, cervical lymphadenopathy, arthralgia, myalgia, and breastfeeding for less than 6 months were associated with the persistence of the disease over time. Inherited autoinflammatory disorders were excluded on a genetic basis in 16.3% of the cohort. Performing a multivariate analysis adjusted for sex and age we found that only breastfeeding for more than 6 months and a higher level of education in PFAPA patients’ mothers were independently associated with the resolution of the recurring PFAPA symptoms.

The study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. The models were evaluated using multiple error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and R-squared. RNN and LSTM were tested with both 30 and 60-day windows, with performance compared to SVR. LSTM delivered better R-squared values, with a 60-day LSTM achieving the best accuracy (R-squared = 0.993) when using a combination of endogenous market data and technical indicators. SVR showed reliable results in certain scenarios but struggled in fold 2 with sudden spike that shows high probability of not capturing the entire underlying NGX pattern in the dataset correctly as witnessed with the high validation loss during the period. Additionally, RNN faced the vanishing gradient problem that limits its long-term performance. Despite challenges, LSTM's ability to handle temporal dependencies, especially with the inclusion of On-Balance Volume, led to significant improvements in prediction accuracy. The use of the Optuna optimization framework further enhanced model training and hyperparameter tuning, contributing to the performance of the LSTM model.

Embryo’s Implantation is a question that we are not in the position to answer in full details. We know that it depends on embryo’s quality and uterine receptivity. It is possible to access uterine cavity before in vitro fertilisation by a variety of tools, however hysteroscopy is the gold standard in evaluation of uterine cavity and potential pathology. The frequencies of unrecognised uterine pathology revealed by hysteroscopy are 18-50% and 40-43% in patients undergoing in vitro fertilisation or after recurrent implantation failure respectively. Data is inconclusive regarding the role of hysteroscopy on pregnancy outcome after in vitro fertilisation. This review aims to evaluate the validity of office hysteroscopy in subfertile population before or after IVF treatment

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Moreover, our deep learning models demonstrated notable predictive performance over experimentation and analysis. Through GRU-based models we achieved a low mean absolute error of 0.056 and a high $R^2$ regression performance of 0.9401 measured by cross-validation, showcasing the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local proinflammatory Th1 and Th17 subpopulations along with a decrease in Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are needed to ascertain the benefit of microbiota modulation in RPL.

Benign recurrent intrahepatic cholestasis (BRIC) is a rare genetic cause of cholestasis. It is considered as part of inherited intrahepatic cholestasis syndromes, such as progressive familial intrahepatic cholestasis (PFIC), and intrahepatic cholestasis of pregnancy. BRIC is presented in infancy or in early adulthood. It is characterized by exacerbations and remissions of jaundice with accompanying intense itching, lasting from weeks to years throughout lifetime. Normal gamma-glutamyl transferase (GGT) is a characteristic laboratory finding. Contrary to PFIC, which may progress to cirrhosis, BRIC does not progress to chronic liver disease or cirrhosis. However, incessant episodes of cholestasis result in marked reduction in quality of life and distinct mutations increase the risk of hepatobiliary malignancy. In intervals between the exacerbations, the histological findings of centrilobular cholestasis together with the abnormal laboratory parameters return to normal. In this context, liver biopsy might be avoided. In this review, we will focus on the genetic aspects of BRIC, its pathophysiology, clinical presentation, and prognosis of this autosomal recessive genetically determined cholestatic disorder. Moreover, triggering factors as well as treatment options will be further elucidated.

Rationale: Chronic Granulomatous Disease (CGD) is a rare primary immunodeficiency. Although most mutations are linked to the X chromosome, therefore affecting males, there are autosomal recessive forms that therefore affect both sexes. Clinically it is characterized by recurrent and severe bacterial and fungal infections, with formation of granulomas, due to the inability of phagocytes to generate reactive oxygen compounds, necessary for the intracellular death of phagocytosed microorganisms. Patient concerns: We reported a 12-year-old boy with pneumonia with pneumatocele of possible staphylococcal etiology at one year of age, bilateral orchitis secondary to Salmonella with hematogenous spread, osteomyelitis due to Salmonella, gastroenteritis due to Salmonella enteritidis, liver abscesses due to Staphycococcus aureus and lymphomatoid papulosis and pyodermititis. Diagnoses: In the biochemical diagnosis, the presence of yellow dye (NBT) and absence of dark bluish black dye (formazan) was evidenced. Regarding the genetic diagnosis, the presence of the mutation in exon 10 of CYBB (OMIM 306400) (Xp21.1) that encodes the gp91phox protein was demonstrated. Interventions and outcomes: Treatment was started with trimethoprim/sulfamethoxazole 400/80 mg every 24 hours via oral, and antifungal prophylaxis with itraconazole 200 mg every 24 hours orally is added, after which he does not present serious bacterial or fungal infections, not being able to administer immunomodulatory treatment with recombinant human Interferon gamma-1b. Lessons: The clinical finding of frequent severe infections associated with granulomas, CGD should be suspected. Treatment of this primary immunodeficiency is trimethoprim/sulfamethoxazole and itraconazole. Importantly, it indicated that the mutation in exon 10 of CYBB (Xp21.1) that encodes the gp91phox might influence the pathogenesis of CGD.

Autoinflammatory disorders encompass a wide range of conditions with systemic and neuro-logical symptoms, which can be acquired or inherited. These diseases are characterized by an abnormal response of the innate immune system, leading to an excessive inflammatory reac-tion. On the other hand, autoimmune diseases result from dysregulation of the adaptive im-mune response. Disease flares are characterized by systemic inflammation affecting the skin, muscles, joints, serosa, and eyes, accompanied by unexplained fever and elevated acute phase reactants. Autoinflammatory syndromes can present with various neurological manifestations, such as aseptic meningitis, meningoencephalitis, sensorineural hearing loss, and others. Early recognition of these manifestations by general neurologists can have a significant impact on the prognosis of patients. Timely and targeted therapy can prevent long-term disability by reducing chronic inflammation. This review provides an overview of recently reported neuroinflam-matory phenotypes, with a specific focus on genetic factors, clinical manifestations, and treatment options. General neurologists should have a good understanding of these important diseases.

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.

Introduction: Urinary tract infections (UTIs) are a significant global health issue, especially among women, with growing concerns related to antibiotic resistance and adverse effects. The Uromune® vaccine, a sublingual bacterial lysate, represents a promising therapeutic alternative by enhancing immune responses against uropathogens. Methods: This retrospective study, conducted at Hospital Universitari de Sant Joan de Reus from January 2017 to August 2022, evaluated the effectiveness of Uromune® in treating recurrent UTIs (rUTIs). Patients received personalized autovaccines administered as two sublingual puffs daily for three months. Clinical, microbiological, and demographic data were analyzed to assess treatment outcomes and identify recurrence-associated factors. Results: Forty-nine patients (mean age, 61 years, and 59.2% women) were included in the study. Uromune® treatment decreased UTI episodes, from 3.73 ± 0.97 the year before to 0.98 ± 1.36 (P &lt; 0.001) the year after its administration. The number of patients who suffered three or more episodes per year dropped from 43 (87.7%) before the intervention to 7 (14.3%) afterward. The maximum effectiveness of the autovaccine was observed three months post-administration, with 44 patients not experiencing any UTI episodes. Regression analysis identified having had a urostomy, chronic kidney disease, and being immunosuppressed as predictors of recurrence. Conclusion: The Uromune® autovaccine effectively reduced the frequency of rUTIs and associated hospitalizations, offering substantial relief to patients. These findings support the integration of Uromune® into routine clinical practice as a viable alternative to conventional antibiotic treatments.

This study uses machine learning to predict the convergence results of the Duffing equation with and without damping. The Duffing equation represents a nonlinear second-order differential equation with interesting behavior in undamped free vibration and forced vibration with damping. Convergence alternates randomly between 1 and -1 in undamped free vibration, depending on initial conditions. For forced vibration with damping, multiple factors influence vibration patterns. We utilize the fourth-order Runge-Kutta method to collect convergence results for both conditions. Machine learning techniques, specifically the long short term memory (LSTM) and LSTM-Neural Network (LSTM-NN) method, are employed to predict these convergence values. The LSTM-NN model is a hybrid approach that combines the LSTM method with the addition of hidden layers of neurons. Both the LSTM and LSTM-NN models are thoroughly explored and analyzed in this research. The research process involves three stages: data preprocessing, training, and verification. The results show that the LSTM-NN model becomes more adept at predicting binary datasets, boasting an impressive accuracy of up to 98%. However, when it comes to predicting multiple solutions, the traditional LSTM method outperforms the LSTM-NN approach.

The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of the patient’s cerebral compliance. This characterization is particularly informative on the overall state of the cerebrospinal system. We developed a recurrent neural network-based framework for P2/P1 ratio computation that only takes a raw ICP signal as an input. Two tasks are performed, namely pulse classification and subpeak designation. Pulse classification was achieved with an area under the curve of 0.90 on a 4,344-pulse testing dataset, while the peak designation algorithm identified pulses with a P2/P1 ratio > 1 with a 97.3% accuracy. Although it still needs to be evaluated on a larger number of labeled recordings, our automated P2/P1 ratio calculation framework appears to be a promising tool for improving bedside monitoring devices.

We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*’. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer’s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

Glioblastoma (GBM) presents a significant public health challenge as the deadliest and most common malignant brain tumor in adults. Despite standard-of-care treatment, which includes surgery, radiation, and chemotherapy, mortality rates are high, underscoring the critical need for advancing GBM therapy. Over the past two decades, numerous clinical trials have been performed, yet only a small fraction demonstrated benefit, raising concerns about the predictability of current preclinical models. Traditionally, preclinical studies utilize treatment-naïve tumors, failing to model the clinical scenario where patients undergo standard-of-care treatment prior to recurrence. Recurrent GBM generally exhibits distinct molecular alterations influenced by treatment selection pressures. In this review, we discuss the impact of treatment - surgery, radiation, and chemotherapy - on GBM. We also provide a summary of treatments used in preclinical models, advocating for their integration to enhance the translation of novel strategies to improve therapeutic outcomes in GBM.

Background: Recurrent approximation of retention parameters in reversed-phase HPLC is effective for revealing anomalies difficult to detect otherwise, namely, the reversible hydration of analytes. This was demonstrated previously for restricted sets of analytes with acetonitrile–water eluents. Expanding the number of analytes and eluents seems to be a topical problem. Two kinds of derivatives of aromatic carbonyl compounds were characterized: unsubstituted hydrazones and oximes. Methods: If analyte demonstrates no anomalies in dependences of retention times vs. concentration of organic modifier, the recurrent approximations of these dependences are linear. To explain the features of recurrent approximations, the numerical experiments were proposed and considered. The artificial shifting of one, two, or more points allows modeling the different kinds of deviations of approximations from linearity. Results: It was shown that hydrazones are the class of analytes having no anomalies of retention parameters. On the contrary, several anomalies were detected for oximes. Downward deviations of points in the plots of recurrent approximations of retention times are the signs of reversible hydration. This effect for methanol–water eluents was detected for the first time. Some of oximes underwent hydrolysis. Conclusion: Recurrent approximation of retention times allows detecting chemical transformations of analytes during RP HPLC analysis.

A ganglion cyst is a benign fluid-filled sac that typically appears on the dorsal aspect of the wrist, arising idiopathically near the joint capsule or tendon sheath. Diagnosis is primarily clinical, with MRI and ultrasound serving as confirmation tools. Treatment options range from immobilization, aspiration, hydrossection, steroid or hyaluronidase injection, to surgical intervention. Although relatively uncommon, Dorsal Ganglion Cysts (DGC) can lead to Dorsal Wrist Impingement (DWI) syndrome. This syndrome results from the thickening of the wrist capsule due to overuse, injury, or repetitive activities. This thickening can lead to the compression of the extensor carpi radialis brevis (ECRB) tendon during wrist extension and produce tenderness around the lunate. In some refractory cases, MRI findings may show recurrent ganglion cysts, while in others, they may appear "normal." Consequently, the diagnosis remains clinical, and imaging is primarily employed to rule out secondary causes. Management strategies encompass rest, immobilization, occupational therapy, non-steroidal anti-inflammatory drugs (NSAIDs), cortisone injections, and, in certain instances, surgical intervention. Despite the clinical significance of these conditions, limited studies have explored their interrelationship. In this context, we present a rare case involving a 23-year-old female with recurrent DGC accompanied by DWI syndrome. Initially, the patient reported idiopathic left dorsal wrist pain localized around the tendon sheath. Her initial X-ray did not reveal any abnormalities, but MRI identified two ganglion cysts. A series of treatments were initiated, including left tendon sheath hydrossection, corticosteroid injections into the left wrist, and the excision of ganglion cysts from the left wrist. Furthermore, occupational therapy, oral Meloxicam, and Voltaren gel treatments were integrated into the management plan. Subsequent imaging confirmed the resolution of the cysts and an overall enhancement in the range of motion and wrist strength. However, the patient experienced only minimal pain relief, and pain was still elicited during end-range wrist extension and upon dorsal wrist palpation. An electromyography (EMG) test ordered to rule out neuropathic causes did not yield any conclusive results. Nonetheless, a repeat X-ray revealed mild soft tissue swelling on the dorsal side of the wrist, and a follow-up MRI detected ganglion cysts on the dorsal aspect of the lunate and the ECRB tendons. These findings indicated a re-accumulation of dorsal ganglion cysts with a concurrent dorsal impingement syndrome component. Consequently, the patient was referred to an orthopedic surgeon for evaluation and potential revision surgery. This unique case underscores the possibility of developing DWI Syndrome after the removal of a DGC. The diagnosis of this syndrome can be elusive, particularly when X-rays, MRIs, and EMGs yield negative results. Trauma, such as repeated hand surgery or recurrent ganglion cysts, may exacerbate wrist pain despite improvements in range of motion and strength. Special consideration should be given to patients who undergo surgical cyst excisions, as this procedure heightens the risk of recurrence and can irritate the tendon sheath. Collaborative efforts between Physical Medicine and Rehabilitation (PM&amp;R), Occupational Therapy (OT), and surgical teams may play a pivotal role in managing pain associated with these conditions. We propose that future research endeavors should focus on establishing guidelines for the management of this unique condition.

(1) Background: The aim of this study is to investigate the diagnostic-therapeutic problem of pelvic recurrence of rectum cancer, highlighting current surgical standards and the possible role of the minimally invasive approach. This retrospective analysis of our surgical case study wishes to enter this debate while suggesting a possible line of action, based on the site of recurrence; (2) Methods: We examined, retrospectively, all the patients diagnosed, between 2008 and 2018, with cancer of the rectum at the "Pietro Valdoni" Department of Surgery and monitored their follow-up for 5 years. The sample consisted of 368 patients with rectal neoplasm, 136 females and 232 males, with an average age of 65.8 (ranging from 37 to 86); (3) Results: In 103 of the cases, the neoplasm was located in the upper rectum (28%), in 119 cases in the middle rectum (32.3%), in 102 cases in the lower rectum (27.7%), in 31 cases (8.4%) at the level of the right/sigma junction and in 13 cases it was not possible to define the site with certainty (3.5%); (4) Conclusions: The discussion re-mains open as to which approach is best at surgical level, whether laparoscopic, robotic or open-air. Our experience informs us that the most dangerous site remains on the anastomotic site.

(Background) the aim was to determine related factors to recurrent vitreous hemorrhage (RVH) in a sample of proliferative diabetic retinopathy (PDR) patients. (Methods) A retrospective, review-based study. We studied 183 eyes from 121 type 2 diabetes patients with PDR. We recorded diabetes duration, history of hypertension, retinal photocoagulation status, the posterior vitreous status, the mean HbA1c, mean hemoglobin, the renal function, and the systemic complications related to diabetes. We also recorded the use of ranibizumab prior to vitrectomy and the following surgical variables: the application of segmentation and diathermy on fibrovascular proliferative tissue, the use of silicone oil, and the occurrence of surgical complications, to study which independent variables were significantly related to the presence of RVH. (Results) Duration of diabetes (P= 0.028), hemoglobin (P=0.02), status of the posterior vitreous (P=0.03), retinal photocoagulation (P=0.002) and use of segmentation surgery technique (P=0.003) have significant link to the presence of RVH. In addition, patients with diabetic polyneuropathy, myocardial infarction and ischemia in lower limbs had more vitreous hemorrhage events (p&lt;0.001). (Conclusions) Patients with PDR and with longer diabetes duration, anemia, attached posterior vitreous, deficient retinal photocoagulation, and previously cardiovascular events, were more prone to RVH.

This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytics policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, Conventional Machine Learning techniques such as Support Vector Machines (SVM) yield an accuracy rate of 52.57%, Naive Bayes at 57.44 %, and Logistic Regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

Background: About 200 new ovarian cancer cases per year are diagnosed in Armenia, an upper-middle-income country. This study aims to summarize the survival outcomes of relapsed ovarian cancer patients in Armenia based on the type of relapse, risk factors, and the choice of systemic treatment. Methods: A retrospective case-control study including 228 patients with relapsed ovarian cancer from 3 different institutions. Results: The median age of the patients was 55. The median follow-up from the relapse and primary diagnosis was 21 and 48 months. The incidence of platinum-sensitive relapse was 81.6% (186), while platinum-resistant relapse was seen only in 18.4% (42) patients. Median survival of the patients starting from the primary diagnosis in the platinum-sensitive compared to the platinum-resistant group was 54 versus 25 months (p < 0.001), and median survival after relapse: was 25 versus 13 months, respectively 3- and 5-year survival in these groups were: 31.2% vs. 23.8%, and 15.1% vs. 9.5%, respectively (p = 0,113). Conclusions: Overall, despite new therapeutic approaches, ovarian cancer continues to be one of the deadly malignant diseases in women, especially, in developing countries with a lack of resources, where, in fact, the main available systemic treatment for the majority of patients remains chemo-therapy. Low survival rates emphasize the urgent need for more research on this group of patients with poor outcomes.

Temporal lobe epilepsy is a common, chronic disorder with spontaneous seizures that is often refractory to drug therapy. A potential cause of temporal lobe epilepsy is primary brain injury, making prevention of epileptogenesis after the initial event an optimal method of treatment. Despite this, no preventive therapy for epilepsy is currently available. The purpose of this study was to evaluate the effects of anakinra, lamotrigine, and their combination on epileptogenesis using the rat lithium-pilocarpine model of temporal lobe epilepsy. The study showed that the treated and untreated animals showed no significant difference in the number and duration of seizures. However, the severity of seizures was significantly reduced after treatment. Anakinra and lamotrigine, alone or in combination, significantly reduced neuronal loss in the CA1 hippocampus compared to the control group. However, the drugs administered alone were found to be more effective for CA3 than their combination. The treatment alleviated the impairments in activity level, exploratory behavior and anxiety, but had a relatively weak effect on TLE-induced impairments in social behavior and memory. The efficacy of the combination treatment did not differ from that of anakinra and lamotrigine monotherapy. These findings suggest that anakinra and lamotrigine, alone or in combination, may have clinical utility in preventing epileptogenesis.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

ABSTRACT: Recurrent glioblastoma (rGBM) is a highly aggressive form of brain cancer that poses a significant challenge for treatment in neurooncology, and the survival status of patients after relapse usually means rapid deterioration, and also the leading cause of death among patients. In recent years, immunotherapy has emerged as a promising strategy for the treatment of recurrent glioblastoma by stimulate the body's immune system to recognize and attack cancer cells , which could be used as a in combination with other treatments such as surgery, radiation, and chemotherapy to improve outcomes for patients with recurrent glioblastoma, This therapy combines several key methods such as the use of monoclonal antibodies, chimeric antigen receptor T cell (CAR-T) therapy, checkpoint inhibitors, oncolytic viral therapy cancer vaccines, and combination strategies. In this review, we mainly document the latest immunotherapies for the treatment of glioblastoma and focus on the rGBM especially.

. Accurate coastal wave direction and speed forecasts are crucial in coastal and marine engineering, marine energy, maritime transport, fisheries, naval navigation, environmental research, and risk management. Approximately 269 km of Brunei Darussalam's coastline can generate between 15 and 126 GW of wave energy. As part of the preliminary feasibility study of wave energy harvesting in Brunei Darussalam and net zero commitment, in this study, we used two numerical methods, namely, finite difference and spectral element methods, for modeling and simulation of wave speed and direction. The mean error between numerical and analytical solutions was calculated in each simulation. Explanatory data analysis was used to provide insight into the study data. We then proposed wave direction and speed forecasting models using Long Short-Term Memory (LSTM) stacking on the data computed from the Acoustic Doppler Current Profiler (ADCP) sensor data. A univariate time series forecasting approach was adopted for this research. KerasTuner hyperparameter tuning API was used for tuning and optimizing hyperparameters, leading us to build models with the least training and test errors. Seven separate prediction experiments were conducted for wave speed and direction in degree and radian units for the next 1, 3, 6, 8, 10, 12, and 24 hours, respectively. Mean squared error (MSE) was used as a metric for both training and testing. The experimental results show that wave speed forecast has the lowest MSEs compared to direction, regardless of the unit of measure, but has a longer runtime. Moreover, the forecast of direction in the degree unit has the least errors compared to the radian unit; the running time of the latter is higher than that of the former. In the future, we intend to use advanced multivariate time series techniques to forecast wave speed and direction.

Machine learning (ML) is increasingly useful as data grows in volume and accessibility as it can perform tasks (e.g. categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways , . Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting a ML algorithm depends on many factors as algorithms vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are at least implicitly ordered, potentially allowing a hidden `arrow of time’ to affect non-temporal ML performance. This research explores the interaction of ML and implicit order by training two ML algorithms on Twitter data before performing automatic classification tasks under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when selecting appropriate ML algorithms, even when time is only implicitly included.

The optimal timing for actively discontinuing immune checkpoint inhibitor therapy in long-term responders with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains unresolved. We conducted a retrospective study of 246 patients with R/M HNSCC treated with nivolumab to determine the optimal timing to actively discontinue nivolumab therapy. We examined the point at which progression-free survival (PFS) plateaued in all cases. We compared the prognosis of 19 (7.7%) ongoing cases and 227 (92.3%) discontinued cases and analyzed treatment duration and treatment-free interval (TFI). The 6-year overall survival was 11.8% (median, 12.1), and the 6-year PFS was 15.3% (median, 3.0). The PFS curve remained stable for 3 years. The median duration of nivolumab treatment was 2.9 months (range, 0.03–81.9): Ongoing group, 41.8 (5.6–81.9); Decision group, 36.8 (4.0–70.1); Toxicity group, 30.6 (2.8–64.8); and progressive disease group, 2.0 (0.03–42.9). TFI in the Decision group was 15.1 months (0.6–61.6) and 30.6 months (2.8–64.8) in the Toxicity group. Limited number of long-term responders in R/M HNSCC cases treated with nivolumab exists. Given that PFS plateaued at 3 years, we propose that the optimal duration of treatment for this subset of patients is 3 years.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the ACTor (Autonomous Camus TranspORt) vehicle - an autonomous vehicle development and research platform coupled with a low-cost webcam based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road following behavior.

Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection such as genomic selection (GS) must be integrated to decrease the time it takes to release new varieties to meet the demand of a growing population. The implementation of GS into breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain. In this review, we explore the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to implement GS. We propose implementing a two-part breeding strategy by differentiating between population improvement and product development. The implementation of GS in the product development pipeline can be integrated into most stages and can predict within and across breeding cycles. Additionally, we explore optimizing the population improvement strategy through GS recurrent selection schemes to reduce crossing cycle time and significantly increase genetic gain. The recurrent selection schemes can be optimized for parental selection, maintenance of genetic variation, and optimal cross-prediction. Overall, we outline the ability to increase the genetic gain of a breeding program by implementing GS and a two-part breeding strategy.

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.

Cutaneous squamous cell carcinoma is a prevalent malignancy with a rising incidence and notably high mutational load. Exploring the genetic nuances of cSCC and investigating molecular approaches stands as a potential avenue for improving outcome in high-risk patients. This retrospective case-control study involved two cohorts of 14 patients (“discovery cohort”) and 12 patients (“validation cohort”) with cSCC located in head-neck anatomical district and diagnosed at pT2 stage. Overall, cases developed early local relapse of the disease whereas controls never re-lapsed during the entire follow-up period. Next generation sequencing (NGS) approach conducted on histological samples revealed that TP53 and CDKN2A were the most frequently mutated genes in our series. No specific mutation was identified as potential prognostic or therapeutic targets. Controls exhibited a tendency toward a higher mutational rate compared to cases. It is possible that an increased number of mutations could prompt the cSCC to expose more antigens, becoming more immunogenic and facilitating the recognition by the immune system. This could enhance and sustain immunological response, potentially preventing future recurrences.

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and past gradients, with step size adjusted for each parameter. We also combine Incorporating Multiple Clustering Centers and a bootstrap-aggregated decision trees ensemble, which is shown to further boost classification performance. In addition, we provide an ablation study for assessing the performance improvement that each module of our ensemble produces. Multiple experiments on a large set of datasets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be made available at https://github.com/LorisNanni.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

Recurrent pregnancy loss (RPL) affects around 2 % of women of reproductive age. Primary RPL is defined by ≥2 pregnancy losses and no normal birth delivery. In secondary RPL, the losses are after a normal pregnancy and delivery. Most cases have no clear aetiology, although primary cases are the most complex. Several gene polymorphisms have been associated with RPL. Some polymorphisms are increased in women of Asian or Caucasian race. In admixed RPL populations, the information on possible genetic links is scarce and contradictory. The study aimed to assess the frequency of two SNP in different enzymes involved in medical conditions observed during pregnancy. Microsomal epoxy hydrolase (mEPH) is involved in detoxifying xenobiotics. It is present in ovaries, and it is hormonally regulated. The endothelial nitric oxide synthase forms nitric oxide from arginine and is involved in vascular tone. Two SNPs, rs1051740 and rs1799983, were assessed. The study included 26 controls and 63 primary RPL patients. Significant differences were observed in the frequencies of both SNP (p<0.05). The frequency of mutated alleles was higher in patients. Double mutated homozygotes were encountered only in RLP patients (p<0.05). Genetic polymorphisms rs1051740 and rs1799983 may be involved in primary RPL in the Venezuelan admix population. Genetic studies could provide crucial information on the aetiology of primary RPL.

Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the missing parts of the legs. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55 degree prediction error for shank movements on average. However, a patient's intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices.