Stock price prediction is of great significance but faces numerous challenges. In this study, a hybrid CNN-LSTM model was utilized. The stock data were processed and feature engineering was conducted. The model architecture and training strategies were expounded. Experimental results demonstrated that this model outperformed traditional methods and benchmark models on the test set, featuring strong capabilities in feature extraction and handling long-term dependencies. Key roles of data preprocessing, hyperparameter adjustment, and model fusion were also summarized, providing valuable references for stock price prediction.

Background: The Shingrix vaccine helps prevent shingles and its complications in adults 50 and older. While minor side effects are common, severe adverse reactions are thought to be rare, and long-term side effects are not well documented. Case Presentation: A 50-year-old woman with Crohn's disease developed joint pain, effusion, and neurological symptoms such as numbness and tingling shortly after receiving the first dose of Shingrix. Symptoms waxed and waned but persisted for over a year despite anti-inflammatories and specialist referrals. Diagnostic imaging and labs were unrevealing. Conclusion: This case of prolonged somatic and neurological symptoms associ-ated temporally with Shingrix administration reinforces the critical need for thorough pharma-covigilance and investigation of possible long-term adverse vaccine reactions. Provider documen-tation and reporting of individual experiences can help improve the detection of rare vaccine-related risks, elucidate potential risk factors, and refine safety screening. Diligent monitoring and research into longitudinal vaccine outcomes remain paramount, especially following recent emergency au-thorizations.

The growth of photosynthetic organisms requires specific ranges of temperature and photosynthetically active radiation that are technically difficult to achieve and control, especially in outdoors cultures. In such cases, a typical meteorological sequence can be a useful tool to estimate the growth of photosynthetic organisms. This work proposes a new methodology based on long-term meteorological sequences to simulate the growth of photosynthetic organisms. A case of study that addresses the simulation of the growth of microalgae (Chlorella vulgaris) in Riosequillo, north of the Madrid region (Spain), is shown for the four seasons of the year, and these estimates are then compared with the observed results of an experimental microalgae culture in domestic wastewater. The results also show great agreement with the probability distribution function of the biomass concentration per day, obtaining the best results for typical meteorological sequences of summer and spring. The methodology seems to confirm the representativeness of typical meteorological sequences and also allows the identification of the most likely production scenarios for the feasibility analysis of projects, which has potential application in decision-making processes.

Background: Accurate sales forecasting in the mobile phone industry is critical to informed decision making by both the government and corporations. This study seeks to enhance mobile phone sales predictions by incorporating brand exposure as a predictive factor, hence addressing the gap left by the existing forecasting models that do not consider the impact of brand awareness. Method: This study thereby indicates a quantification method for brand exposure that merges word-of-mouth reviews with online search data. Word2Vec is used to extract initial keywords from a word-of-mouth corpus, whereas time-lag correlation analysis refines the core keywords. Later, PCA synthesizes brand exposure. The attention-LSTM framework hereby develops a forecasting model of mobile phone sales, effectively combining long short-term memory networks with the ability to capture time series dependencies and an attention mechanism to reduce data redundancy. Results: By considering brand exposure, the experiments demonstrate that the attention-LSTM model decreases the RMSE and MAPE indicators by 2.02% and 0.96%, respectively. In addition, compared with the ARIMA, SVR, BP neural network, and LSTM models, the attention-LSTM model decreases the average percentage errors by 6.52%, 3.42%, 2.56%, and 0.81%, respectively. Conclusion: Attention-LSTM captures dynamic sales trends very well and holds great significance over traditional models. Overall, including brand exposure, this model is very strong for the interpretation of mobile phone sales predictions, which has important implications in terms of policy as well as corporate strategies.

The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

The physiological process of iodine uptake in the thyroid is used for 131I treatment of thyroid diseases. Children are more sensitive to radiation compared to adults and may react differently to 131I exposure. The aims of this study were to evaluate the effects on thyroid protein expression in young and adult rats one year after 131I injection, and identify potential biomarkers related to 131I exposure, absorbed dose, and age. Twelve Sprague Dawley Rats (young and adults) were i.v. injected with 50 kBq or 500 kBq 131I and were killed twelve months later. Twelve untreated rats were used as age-matched controls. Quantitative proteomics, statistical analysis and evaluation of biological effects was performed. The effects of irradiation were most prominent in young rats. Protein biomarker candidates were proposed related to age, absorbed dose, thyroid function and cancer and a panel was proposed for 131I exposure. In conclusion, the proteome of rat thyroid was differentially regulated twelve months after low-intermediate dose exposure to 131I in both young and adult rats. Several biomarker candidates are proposed for 131I exposure, age, and many of them are known to be related to thyroid function or thyroid cancer. Further research on human samples are needed for validation.

Intrauterine growth restriction (IUGR) and being born small for gestational age (SGA) are two distinct conditions with different implications for child development. IUGR is associated with greater perinatal morbidity and mortality and can be identified by additional abnormalities (pathological Doppler sonography, oligohydramnios, lack of growth in the interval, estimated weight <3rd percentile). IUGR may also be present in neonates with birth weights above the 10th percentile. Recognizing fetuses being “at risk” of a higher perinatal mortality and morbidity rate in order to monitor them accordingly and deliver them in good time is still challenging. This review article summarizes approaches to increase the diagnostic accuracy of IUGR and presents current concepts of pathophysiology with a focus on oxidative stress and consecutive inflammatory and metabolic changes. Since prenatal influences affect the risk of later diseases, we also discuss the need for interdisciplinary follow-up strategies during childhood and look at future scientific challenges. It should be emphasized that prenatal and postnatal care of IUGR neonates should be regarded as a continuum.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

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 deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.

Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick-slip transition of Coulomb’s approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why, dynamic friction models were developed in the last decades. The friction force depends herein not only on the sliding velocity but also on internal states. The probably best known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too simple approximation. The recently published second order dynamic friction model describes the dynamics of a fictitious bristle more accurately. Its performance is compared here to stick-slip friction models, developed and launched not long ago by commercial multibody software packages.

Cardiovascular disease is the leading cause of death worldwide. A key factor in assessing the risk of cardiovascular disease is left ventricular functional evaluation. Left ventricular (LV) systolic function is evaluated by measuring the left ventricular ejection fraction (LVEF) using echocardiography data. Therefore, quick and accurate left ventricle segmentation is important for estimating the LVEF. However, it is difficult to accurately segment the left ventricle due to changes in the shape and area of the left ventricle during cardiac cycles. In this study, we proposed a framework that considers changes in the shape and area of the left ventricle during the cardiac cycle by applying the convolutional long short-term memory (CLSTM) approach. In addition, we evaluated the left ventricular segmentation and multidimensional quantification of the proposed system in comparison to manual and automated segmentation methods. In addition, to assess the validity of CLSTM, the values of multi-dimensional quantification metrics were compared and analyzed using graphs and Bland–Altman plots on a frame-by-frame basis. We demonstrated that the CLSTM method effectively segments the left ventricle by considering the LV activity. In conclusion, we demonstrated that LV segmentation based on our framework may be utilized to accurately estimate LVEF values.

A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.

Even brief epileptic seizures can lead to activity-dependent structural remodeling of neural circuitry. Animal models show that the functional plasticity of synapses and changes in the intrinsic excitability of neurons can be crucial for epileptogenesis. However, the exact mechanisms underlying epileptogenesis remain unclear. We induced epileptiform activity in rat hippocampal slices for 15 min using a 4-aminopyridine (4-AP) in vitro model and observed hippocampal hyperexcitability for at least 1 hour. We tested several possible mechanisms of this hyperexcitability, including changes in intrinsic membrane properties of neurons, presynaptic and postsynaptic alterations. Neither input resistance nor other essential biophysical properties of hippocampal CA1 pyramidal neurons were affected by epileptiform activity. The glutamate release probability also remained unchanged, as the frequency of miniature EPSCs and the paired amplitude ratio of evoked responses did not change after epileptiform activity. However, we found an increase in the AMPA/NMDA ratio, suggesting alterations in the properties of postsynaptic glutamatergic receptors. Thus, the increase in excitability of hippocampal neural networks is realized through postsynaptic mechanisms. In contrast, the intrinsic membrane properties of neurons and the probability of glutamate release from presynaptic terminals are not affected in a 4-AP model.

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

Background: This study investigated the prevalence of worsening problems using Post Stroke Checklist (PSC) at 3, 6, and 12 months post-stroke and their associations with health-related quality of life. Methods: In stroke patients admitted between June 2014 and December 2015, PSC and EuroQol-5Dthree level (EQ-5D-3L) were assessed at post-stroke 3 (n=181), 6 (n=175), and 12months (n=89). The prevalence of worsening problems and its association withEQ-5D-3L at post-stroke 3 and 6months were analyzed. Results: An average of 0.59 (range 0–12), 1.47 (range 0–12), and 1.00 (range 0–10) worsening problems per patient was identified at 3, 6, and 12months after stroke, respectively. The most frequently and continuously identified worsening problems were mood disturbances (reported by 8.8%, 16.0% and13.5% of patients at 3, 6, and 12 months post-stroke, respectively). Worsening mobility was significantly associated with worse EQ-5D index at post-stroke 3 months (β,-0.583; 95% CI, -1.045 to -0.120). The worsening of mobility and communication was significantly associated with worse EQ-5D index at post-stroke 6 months (mobility: β,-0.170; 95% CI, -0.305 to -0.034, communication: β,-0.164; 95% CI, -0.309 to -0.020). Conclusions: PSC may be useful for the detection of various subjective worsening problems during serial clinical follow-up after stroke. Appropriate rehabilitation and management strategy to solve the identified problems could improve the quality of life in stroke survivors.

Riparian restoration is an important objective for landscape managers seeking to redress the widespread degradation of riparian areas and the ecosystem services they provide. This study investigated the long-term outcomes of ‘one-off’ restoration activities undertaken in the Upper Murrumbidgee Catchment, NSW, Australia. The objective of the restoration was to protect and enhance riparian vegetation and control erosion, and consequently reduce sediment and nutrient delivery into the Murrumbidgee River. To evaluate the outcomes 10 years after restoration, rapid riparian vegetation and geomorphological assessments were undertaken at 29 sites spanning the four different restoration methods used (at least five replicates per treatment), as well as at nine comparable untreated sites. We also trialed the use of aerial imagery to compare width of riparian canopy vegetation and projective foliage cover prior to restoration with that observed after 10 years. Aerial imagery demonstrated the width of riparian canopy vegetation and projective foliage cover increased in all restored sites, especially those with native plantings. The rapid assessment process indicated that 10 years after riparian restoration, the riparian vegetation was in a better condition at treated sites compared to untreated sites. Width of riparian canopy vegetation, native mid-storey cover, native canopy cover and seedling recruitment were significantly greater in treated sites compared to untreated sites. Geomorphological condition of treated sites was significantly better than untreated sites, demonstrating the importance of livestock exclusion to improve bank and channel condition. Our findings illustrate the value of ‘one-off’ restoration activities in achieving long-term benefits for riparian health. We have demonstrated that rapid assessments of the vegetation and geomorphological condition can be undertaken post-hoc to determine the long-term outcomes, especially when supported with analysis of historical aerial imagery.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

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.

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, Reaction Wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults - a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumnavigate specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporate denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. Extensive evaluation of our proposed method on a simulated satellite attitude control system affirmed its ability to achieve commendable fault detection accuracy, demonstrating high system reliability and robustness to noisy input data. Our research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions.

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to SPOT/VEGETATION (SPOT VGT). In past years, important changes have been implemented in the SYN VGT processing baseline. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle a continuous series of data products from the combined data archives of SPOT VGT (1998 – 2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) is available to users, consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. Spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and the intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for 3 distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have resulted in better quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive: a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

Cuffless sphygmomanometers may be useful alternative blood pressure monitors for screening and home healthcare. The main requirements for these devices are accuracy in the resting state, accuracy during dynamic blood pressure changes, and long-term stability. Intermittent measurements can ensure that the accuracy of active measurements mirrors that of measurements in the resting and dynamic states. This brief report proposes a new protocol to evaluate long-term stability based on a comparison with a medically approved cuff-based automated sphygmomanometer rather than an auscultatory sphygmomanometer. Dynamic changes in blood pressure are not usually assessed based on intermittent measurement with an over-the-counter (OTC) sphygmomanometer. Thus, validation testing focused on the accuracy of the OTC cuffless sphygmomanometer in the resting state and the long-term stability of the device, and the results were similar to those of the cuff-based sphygmomanometer. Further research and discussions are necessary to develop standards derived from high-quality evidence for manufacturers of OTC sphygmomanometers.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

Background and Objective: There is a paucity of literature comparing unilateral instrumented transforaminal lumbar interbody fusion (UITLIF) with bilateral instrumented TLIF (BITLIF) regarding radiological alignment, including the coronal balance, even though UITLIF might have asymmetric characteristics in the coronal plane. This retrospective study aimed to compare the clinical and long-term radiological outcomes of 1-level UITLIF and BITLIF in lumbar degenerative diseases (LDD). Materials and Methods: Patients who underwent 1-level UITLIF with two rectangular polyetheretherketone (PEEK) cages or BITLIF with ≥ 5 years of follow-up at a single hospital were included. We compared the clinical and radiological outcomes between the UITLIF and BITLIF. Results: In total, 63 and 111 patients who underwent UITLIF and BITLIF, respectively, were enrolled. The median follow-up was 85.55 months (range: 60–130). The UITLIF group had a significantly shorter operation time (185.0 [170.0–210.0] vs. 225.0 [200.0–265.0], p &lt; 0.001) and lower estimated blood loss (300.0 [250.0–500.0] vs. 550.0 [400.0–800.0], p &lt; 0.001) than BITLIF group. Regarding the clinical outcomes, there were no significant differences in the intermittent claudication score (p = 0.495) and Kirkaldy–Willis criteria (p = 0.707) at 1 year postoperatively. The interval changes of the local coronal Cobb angle at the index level, L1-S1 lordotic angle, and coronal off-balance from the immediate postoperative radiograph to the last follow-up were not significantly different (p = 0.687, p = 0.701, and p = 0.367, respectively). Conclusion: UITLIF with two rectangular PEEK cages is a viable option for providing comparable clinical outcomes and radiological longevity to BITLIF in 1-level LDD. In addition, UITLIF has advantages over BITLIF in terms of operative time and blood loss.

Arable Сhernozems with high SOC contents have the potential to be significant sources of GHGs, and climate change is likely to increase SOC losses, making the issue of carbon sequestration in this region even more important. The prospect of maintaining SOC stock or increasing it by 4% an-nually under planned management practice modifications for the period up to 2090 was evaluated using a long-term experiment on Haplic Chernozem in the Rostov Region, Russia. In this study, we used the RothC model to evaluate SOC dynamics for three treatments with mineral and organic fertilization under two adaptation scenarios vs. business as usual, as well as under two climate change scenarios. Correction of crop rotation and the application of organic fertilizers at high rates are essential tools for maintaining and increasing SOC stocks. This can maintain SOC stock at the level of 84–87 Mg∙ha-1 until the middle of the 21st century, as the first half of the century is con-sidered the most promising period for the introduction of adaptation measures for the additional accumulation of SOC on Chernozems. Part of the additional accumulated SOC is expected to be lost before 2090.

In this study, long-term mortality effects associated with exposure to PM10, PM2.5, BC (black carbon), and NOx were analyzed in a cohort in southern Sweden during the period from 1991‒2016. Participants (those residing in Malmö, Sweden, born between 1923‒1950) were randomly recruited from 1991‒1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish national cause of death register. The modeled concentrations of PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (nitrogen oxides) at the cohort participants' home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM10, PM2.5, BC, and NOx and the time until death among the participants during the period from 1991‒2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1‒5 years (lag1‒5), and 6‒10 years (lag6‒10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM10, PM2.5, BC, and NOx in the single-pollutant models at lag1‒5 were 1.06 (95% CI: 1.02‒1.11), 1.01 (95% CI: 0.95‒1.08), 1.07 (95% CI: 1.04‒1.11), and 1.11 (95% CI: 1.07‒1.16) per IQR increase, respectively. The HRs were in most cases decreased by the inclusion of a larger number of covariates in the models. The most robust associations were shown for NOx, with statistically significant positive HRs in all models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

This study investigates the cyclic load application impact on fly ash-based geopolymer composites that are reinforced with a low amount of fibre reinforcement. For reinforcement purposes, PVA and steel fibres are used. For testing purposes, four geopolymer composite mixes were made, 3 of which had fibre reinforcement. Simultaneously specimens were tested for shrinkage, static load-induced creep, and cyclic load-induced creep. For static and cyclic creep testing, specimens were loaded with 20% of their strength. For cyclic creep testing, load application and release cycles were seven days long. When each cycle was introduced, the load was added in steps. In 5 minutes, by 25% steps of the necessary load, the specimens were loaded or unloaded. Only plain specimens show that static creep strains are within cyclic creep strains. For all the other specimens, the static load is higher than the cyclic load-induced creep amplitude. Also, 1% PVA fibre-reinforced specimens show the most elastic characteristics under cyclic load, and 1% steel fibre-reinforced specimens appear to be the most resistant to the cyclic load introduction.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

Talker variability has been reported to facilitate generalization and retention of speech learning, but is also shown to place demands on cognitive resources. Our recent study provided evidence that phonetically-irrelevant acoustic variability in single-talker (ST) speech is sufficient to induce equivalent amounts of learning to the use of multiple-talker (MT) training. This study is a follow-up contrasting MT versus ST training with varying degrees of temporal exaggeration to examine how cognitive measures of individual learners may influence the role of input variability in immediate learning and long-term retention. Native Chinese-speaking adults were trained on the English /i/-/ɪ/ contrast. We assessed the trainees’ working memory and selective attention before training. Trained participants showed retention of more native-like cue weighting in both perception and production regardless of talker variability condition. The ST training group showed long-term benefit in word identification, whereas the MT training group did not retain the improvement. The results demonstrate the role of phonetically-irrelevant variability in robust speech learning and modulatory functions of nonlinguistic working memory and selective attention, highlighting the necessity to consider the interaction between input characteristics, task difficulty, and individual differences in cognitive abilities in assessing learning outcomes.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

The clinical significance of carbon-ion radiotherapy (CIRT) for adenocarcinoma (AC) of the uterine cervix has been assessed in several single-institutional studies. To validate the significance, we conducted a multi-institutional survey of CIRT for locally advanced AC (LAAC) of the uterine cervix. We retrospectively analyzed the clinical outcomes of patients with stage IIB–IVA LAAC of the uterine cervix who underwent chemo-CIRT or CIRT alone between April 2010 and April 2016. Patients received 74.4 Gy (relative biological effectiveness [RBE]) in 20 fractions of CIRT or 55.2 Gy (RBE) in 16 fractions of CIRT plus 3 sessions of brachytherapy. Patients aged ≤70 years with adequate bone marrow and organ function were administered cisplatin weekly (40 mg/m2 per week for up to 5 weeks). Fifty-five patients were enrolled in this study. The median follow-up period was 67.5 months. The 5-year overall survival (OS) and local control (LC) rates were 68.6% and 65.2%, respectively. Multivariate analysis showed that the initial tumor response within 6 months was significantly associated with LC and OS. The present study represents promising outcomes of CIRT or chemo-CIRT for LAAC of the uterine cervix, especially in the cases showing initial rapid regression of the tumor.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Organic carbon (OC) accumulation in soil mitigates greenhouse gases emission and improves soil health. We aimed to quantify the dynamics of OC stock in soils and to justify technologies that allow annual increasing OC stock in the arable soil layer by 4‰. We based the study on a field experiment established in 1936 in the 9-field crop rotation with a fallow on Chernozem in European Russia. The RothC version 26.3 was used for the reproducing and forecasting OC dynamics. In all fertilizer applications at FYM background, there was a decrease in the OC stock with preferable loss of active OC, except the period 1964-71 with 2-5‰ annual OC increase. The model estimated the annual C input in the arable soil layer as 1,900 kg·ha-1. For increasing OC stocks by 4‰ per year, one should raise input to 2400 kg·ha-1. Simulation was made for 2016-2090 using climate scenarios RCP4.5 and RCP8.5. Crop rotation without fallowing provided an initial increase of 3‰ and 6‰ of stocks in the RCP8.5 and RCP4.5 scenarios accordingly, followed by a loss in accumulated OC. Simulation demonstrates difficulties to increase OC concentration in Chernozems under intensive farming and potential capacity to rise OC stock through yield management.

Hepatitis B virus (HBV) infection is a major factor in development of various liver diseases such as hepatocellular carcinoma (HCC). Among HBV encoded proteins, HBV X protein (HBx) is known to play key role in development of HCC. Hepatocyte nuclear factor 4α (HNF4α) is a nuclear transcription factor which is critical for hepatocyte differentiation. However, the expression level as well as its regulatory mechanism in HBV infection have yet to be clarified. Here, we observed the suppression of HNF4α in cells which stably express HBV whole genome or HBx protein alone, while transient transfection of HBV replicon or HBx plasmid had no effect on the HNF4α level. Importantly, in the stable HBV- or HBx-expressing hepatocytes, the downregulated level of HNF4α was restored by inhibiting ERK signaling pathway. Our data showed that HNF4α was suppressed during long-term HBV infection in cultured HepG2-NTCP cells as well as in mouse model following hydrodynamic injection of pAAV-HBV or in mice intravenously infected with rAAV-HBV. Importantly, HNF4α downregulation increased cell proliferation which contributed to the formation and development of tumor in xenograft nude mice. The data presented here provided several proofs for the effect of HBV infection in manipulating HNF4α regulatory pathway in HCC development.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

Understanding whether the long-term follow-up (LTFU) system is working for families is critical to measuring the success of newborn screening (NBS) and understanding why some families are lost to follow-up. Caregivers were recruited from six pediatric specialty care clinics. Data was gathered from caregivers via six focus groups (n=24) and open-ended responses on a survey (n=33). Caregiver participants represented a wide range of NBS-related conditions and children’s ages. While this is not the first study to gather caregivers’ input, it is unique in its breadth of perspectives (e.g., metabolic, endocrine, hemoglobinopathy, etc.) and its focus on LTFU. When asked about goals for their children, caregivers identified health-related goals (i.e., ability for children to care for themselves, not being hindered by diagnosis) and non-health related goals (i.e., defining themselves outside of disease, participating in sports, making friends). In qualifying the LTFU care they want and need for their child and the key factors that influence access and engagement, caregivers identified three themes: communication and relationships with providers; care team roles and factors; and care access and utilization factors. The themes identified are not disjoint; they are intertwined and collectively illustrate the lived experiences of families at the epicenter of LTFU care.

In mountain-gorge areas, the rock creep is the critical process for high rock slopes failure. During long-term rock creep, slopes might encounter earthquakes and form further creep after earthquakes. This complex mechanical response process can easily lead to the slope failure. Based on a high rock slope in the Nujiang River Basin, this paper analyzes deformation characteristics and dynamic response characteristics by using FLAC3D software, considering combined effects of multiple earthquakes and long-term creep. Results show that when the amplitude increases, the shear strength of the slope decreases and the risk of instability increases under combined actions of creep and earthquakes. Earthquakes promote the development of creep and induce the accumulation of damage on the slope surface and in the slope. The delayed effect becomes more pronounced as the slope height increases. We need to consider deformation differences of the slope further. The ground motion response on the slope surface is stronger than those in the slope, with the peak occurring at the top of the slope. Earthquakes have a greater impact on the middle and top of the slope surface, where cumulative damage and crack development begin. Reinforcement should be considered in these two places. Vertical earthquake has a great effect on the dynamic response. The phenomenon of slope resonance leads to a larger MPGA in the vertical direction than in the horizontal direction. Nonlinear and damping characteristics of the slope and the frequency of seismic waves cause the MPGA to decrease with the increase of amplitudes. This study could promote the development of high rock slope failure mechanism and provide references for the prevention of landslides in the Nujiang River Basin.

Accurate and reliable mid-long runoff prediction (MLTRP) is of great importance in water re-sources management. However the MLTRP is not suitable in each basin and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five models is analyzed by applying five models in 222 forecasting scenarios in Australia. The results over 222 forecasting scenarios demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors, and has more significant negative correlation with the RRMSE with the correlation coefficient between -0.62 and -0.85. This means the model's predictive performance will become better along with the increase of TMI, and there-fore the developed TMI index can be used to evaluate the applicability of MLTRP. When TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI is used to partly explain the difference of predictive performance among five models. In general, the complex models, which can better utilize the contained infor-mation, are more sensitive to the TMI, and have more significant improvement in terms of predic-tive performance along with the increase of TMI. This research can provide support for the study of MLTRP.

The aim of the study was to examine the long-term prognostic value of changes in cardio-ankle vascular index (CAVI) within a year after coronary artery bypass grafting (CABG). Methods. Patients with coronary artery disease (n = 251) in whom cardio-ankle vascular index (CAVI) was assessed using the VaSera VS-1000 device before and in a year after CABG. Groups with improved CAVI (CAVI decreased from pathological value to normal or the index remained within normal values) or worsened CAVI (persistent pathological index value or an increase in CAVI to pathological values or an increase of 1 unit or more) were identified. The groups were compared for the following events at 10-year follow-up: death from all causes, myocardial infarction, stroke/transient ischemic attack, and the combined endpoint. Results. A year after CABG surgery, 45% of patients showed positive dynamics or stable normal CAVI values. Death from all causes was significantly more common in the group with CAVI progression - 32 (27.6%) than in the group with CAVI improvement - 14 (14.8%; p = 0.029). Patients with CAVI progression were more likely to have MACE (death, MI, stroke/TIA) in 49 (42.2%) cases, compared with patients with CAVI improvement - in 23 (24.5%; p=0.008). Negative dynamics of CAVI (p = 0.024), number of shunts (p = 0.006), and the presence of carotid stenosis more than 30% (p = 0.051) were independent predictors of death from all causes at 10-year follow-up after CABG. The presence of carotid stenosis more than 30% (p = 0.002) and the group with negative dynamics of CAVI after a year (p = 0.008) were independent predictors of the development of the combined endpoint during long-term follow-up. Conclusions. Patients with negative dynamics of CAVI one year after CABG were more likely to experience death from all causes and combined endpoint in the long-term follow-up than patients with normal CAVI. In further studies, it is advisable to evaluate the dynamics of CAVI and, based on this, identify the most effective interventions to improve the prognosis of patients after CABG.

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.

Non-pharmaceutical interventions (NPIs), including social distancing, wearing personal protective equipment, and lockdown measures, have been at the forefront of outbreak control in nursing homes. We used a mixed methodology to assess which NPIs nursing homes in the canton of Geneva, Switzerland, followed for their staff and residents during the first wave of the pandemic, between March 1, 2020 and June 1, 2020. For the qualitative component, we interviewed the attending physicians and/or director of each nursing home. Based on in-vivo codes, NPIs for nursing home workers and residents in each nursing home were thematically classified as: maximally restrictive, moderately restrictive, and minimally restrictive. In the quantitative component, we calculated incident rate ratios (IRR) for infection between the three levels of COVID-19-related measures taken in these nursing homes. We found an equal distribution of maximally (n=4), moderately (n=4), and minimally (n=4) restrictive NPIs. The extent of restriction did not show to be significantly associated with the cumulative incidence of COVID-19 cases among residents (maximally restrictive IRR = 3.90, 95%CI 0.82-45.54, p = 0.184; moderately restrictive IRR = 3.55, 95%CI 0.75-41.42, p = 0.212 ; minimally restrictive IRR = reference). Variabilities in NPIs adopted by nursing homes, and the number of COVID-19 cases appear to be randomly affected.

Evaluation of the patients after the surgical correction of idiopathic scoliosis in long-term follow-up with the clinical neurophysiology methods has not been presented in detail. This study aimed to compare the results of the neurophysiological studies in 45 girls with scoliosis of Lenke 1-3 types performed pre- (T0), postoperatively (T1, one week after surgery), and 6 months after the surgery (T2). Parameters of surface electromyography during the attempt of maximal contraction (mcsEMG) and transcranially evoked motor evoked potentials (MEP) recorded from anterior tibial muscles, as well as the electroneurography (ENG) of peripheral transmission in the peroneal nerve motor fibers were compared. The results indicate that efferent neural conduction function both centrally and peripherally as well as TA muscle function improve immediately after surgical correction of scoliosis (at p=0.05) and further normalization appears after six months in long-term follow-up (at p=0.03). It has been found in sEMG recordings, that the TA muscle motor unit recruitment function after half a year from surgical treatment in IS patients is comparable to the normal condition. ENG recordings results indicated the gradual retreat of the motor fibers injury symptoms mainly of the axonal type in peroneal nerves, the surgeries improved also the lumbar ventral roots’ neural transmission to the functional status considered as normal. MEP amplitude parameters recorded after the surgical scoliosis correction in T1 indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts; in long-lasting T2 observation, they reached values comparable to those recorded in healthy volunteers bilaterally. Preoperatively (T0) the results of all neurophysiological study parameters in IS patients were asymmetrical at p=0.036-0.05 and recorded as worse on the concave side, suggesting the lateralization of neurological motor deficits. One week postoperatively (T1), this asymmetry was recorded as gradually reduced, showing almost no difference between the right and left sides six months later (T2). The presented algorithm of the diagnostic proceeding pre-, early post-, and long-lasting postoperatively using mcsEMG, MEP, and ENG neurophysiological examinations can be significant not only in making the final decision regarding surgical treatment and its personalization but also helps in precise ascertaining its effects as well as in predicting the final result of IS treatment.

Background Few data exist on comparative long-term outcomes of severe aortic stenosis (AS) patients with different flow-gradient patterns undergoing transcatheter aortic valve implantation (TAVI). This study sought to evaluate the impact of pre-TAVI flow-gradient pattern on long-term clinical outcomes after TAVI and assess changes in left ventricular ejection fraction (LVEF) of different subtypes of AS patients following TAVI. Methods Consecutive patients with severe AS undergoing TAVI in our institution were screened and prospectively enrolled. Patients were divided in four subgroups according to pre-TAVI flow/gradient pattern: (i) low flow - low gradient (LF-LG): stroke volume indexed (SVi) ≤ 35ml/m2 and mean gradient (MG) < 40mmHg), (ii) normal flow - low gradient (NF-LG): SVi > 35ml/m2 and MG < 40mmHg, (iii) low flow - high gradient (LF-HG) : SVi ≤ 35ml/m2 and MG ≥ 40mmHg and (iv) normal flow - high gradient (NF-HG): SVi > 35 ml/m2 and MG ≥ 40mmHg. Transthoracic echocardiography was repeated at 1-year follow-up. Clinical follow-up was obtained at 12 months, and yearly thereafter until 5-year follow up was complete for all patients. Results A total of 272 patients with complete echocardiographic and clinical follow-up were included in our analysis. Their mean age was 80±7 years and the majority of patients (N=138, 50.2%) were women. 62 patients (22.8% of the study population) were distributed in LF-LG group, 98 patients (36%) were LF-HG patients, 95 patients (34.9%) were NF-HG, while 17 patients (6.3%) were NF-LG. There was a greater prevalence of comorbidities among LF-LG AS patients. One-year all-cause mortality differed significantly between the four subgroups of AS patients (log rank p: 0.022) and was more prevalent among LF-LG patients (25.8%) compared to LF-HG (11.3%), NF-HG (6.3%) and NF-LG patients (18.8%). At 5-year follow-up, global mortality remained persistently higher among LF-LG patients (64.5%) compared to LF-HG (47.9%), NF-HG (42.9%) and NF-LG patients (58.8%) (log rank p: 0.029). At multivariable Cox hazard regression analysis, baseline SVi (HR: 0.951, 95% C.I.; 0.918 – 0.984), the presence of at least moderate tricuspid regurgitation at baseline (HR: 3.091, 95% C.I: 1.645 - 5.809) and at least moderate paravalvular leak (PVL) post-TAVI (HR: 1.456 , 95% C.I.: 1.106 - 1.792 ) were significant independent predictors of late global mortality. LF-LG patients and LF-HG patients exhibited a significant increase in LVEF at 1-year follow-up. A lower LVEF (p<0.001) and a lower Svi (p<0.001) at baseline were associated with LVEF improvement at 1 year. Conclusions Patients with LF-LG AS have acceptable 1-year outcomes with significant improvement in LVEF at 1-year follow-up, but exhibit an exceedingly high 5-year mortality following TAVI. The presence of low transvalvular flow and at least moderate tricuspid regurgitation at baseline and significant paravalvular leak post-TAVI were associated with poorer long-term outcomes in the entire cohort of AS patients. The presence of a low LVEF or a low SVi predicts LVEF improvement at 1-year.

Hundreds of thousands of people living along the Yatsushiro Sea coast have been exposed to methylmercury from the contaminated water of the Chisso factory in Minamata. Methylmercury causes neurological deficits in the cerebrum and cerebellum, with symptoms of ataxia, dysarthria, and visual field constriction in severe cases, but the most common symptom of a milder disorder is somatosensory disturbance due to parietal lobe damage. We investigated the characteristics of the somatosensory disturbances in detail by studying subjective complaints, neurological findings, and quantitative sensory tests in 197 residents (63.2 ± 10.7 years old) from contaminated areas exposed to methylmercury from seafood and 130 residents (63.7 ± 9.3 years old) from control areas. The most commonly affected sensory modalities were superficial peripheral touch and pain in the extremities, followed by two-point discrimination and deep senses, and in the most severe cases, full-body sensory dysfunction and impairment of all sensory submodalities. Although the severity of the sensory submodalities correlated with each other, there was almost no correlation between the severity of these submodalities and indices of peripheral nerve conduction studies, suggesting that the primary cause of the sensory deficits was damage to the central nervous system. The health effects of chronic methylmercury toxicosis can be elucidated by a detailed examination of sensory deficits.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

Data-driven models (DDMs) are extensively used in environmental modeling but face challenges due to limited training data and potential results not adhering to physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL included an energy constraint term from W2's thermal energy equilibrium into the cost function of the LSTM, besides the mean square error term. In PGDL, parameters were optimized by penalizing deviations from the energy law, ensuring adherence to physical constraints. Compared to LSTM, PGDL demonstrated enhanced satisfaction with the energy balance and superior performance in water temperature prediction. Even with less field data for training, PGDL outperformed both LSTM and calibrated W2 after pre-training with data generated using the uncalibrated W2. Therefore, integration of DDM with a PBM ensured physical consistency in water temperature prediction for complex stratified reservoirs with limited data. Moreover, pre-training the PGDL with PBM proved highly effective in mitigating bias and variance due to insufficient field measurement data.

Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

The rise of Asian and the stagnation of Western middle classes over the last thirty years have resulted in gradual convergence of income of large parts of the world’s population. Recent global crises ‐ the Great Recession and the COVID-19 pandemic ‐ have led to a decline in income and increase in income uncertainty. Rise in consumption of lower quality goods of shorter durability and an overall decline in demand and economic activity resulted as challenges to the global economy. In this paper, we argue that generational responsibility in consumption can be an environmentally sustainable response to crises which enables the economies to overcome the crisis of confidence and reaffirms community ties. As an element of long‐term orientation in consumption, generational responsibility is a cultural phenomenon dependent on solidarity within family and the wider community. It is characterized by consideration of consequences of consumption choices on the environment, and the abundance of savings and the usability of goods to be inherited by future generations. For companies, willing to revisit their traditional business models and incorporate principles of sustainability in their competitive strategies, promotion of generational responsibility can become a new source of competitive advantage and a driver of economic recovery.

Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under report. Visual analysis of a 24 hour EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We study a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology to reduce the EEG dataset by classifying part of the data automatically, while retaining 100% detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust scores. A DS of approximately 90% (99%) can be achieved when automatically classifying around 90% (60%) of the data. Perfect DS can be achieved when automatically classifying 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are used to obtain a final prediction, can be improved by filtering out untrustworthy segments with low trust scores. The false detection rate shows a relative decrease between 21% and 43%, and the DS shows a small increase or decrease.

Soil organic carbon (SOC) sequestration in arable soils is a challenging goal for soil management. Multiple factors should be considered for the prediction of the soil capacity to fix atmospheric carbon. In this study, we focused on the effect of crop rotation and previous land use for future carbon sequestration on two experimental fields with identical soils (Retisols) and input of organic fertilizers. We analyzed the SOC dynamics and used the Roth C model to forecast SOC changes under RCP4.5 and RCP8.5 scenarios. Our experimental and modelling results indicated a consistent increase in SOC stocks and the stable fractions of soil organic matter (SOM). The increase in SOC was higher in the experiment with the crop-grassland rotation that in the experiment with a rotation of row crops and barley. With similar total SOC stocks, the efficiency of soil management differed as reflected by the contrasting composition of SOM, as fields with a long cultivation history showed higher SOM stability. The goal of 4‰ annual increase of SOC stocks may be reached under crop- grassland rotation in 2020-40 and 2080-90 when applying mineral or organic fertilizer system for scenario RCP4.5, and mineral fertilizer system in 2080-2090 for scenario RCP8.5.

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.

Hereditary angioedema (HAE) is a rare genetic disorder clinically characterized by recurrent episodes of severe swelling, affecting the skin, gastrointestinal tract, and airways. Lanadelumab, a monoclonal antibody that inhibits plasma kallikrein, is approved for long-term prophylaxis (LTP) in HAE patients, demonstrating effectiveness in reducing disease burden. This study evaluated the real-world impact of lanadelumab on healthcare resource utilization, angioedema episode frequency, and quality of life (QoL) in adult HAE type 1 patients treated at the Allergy Department of Hospital Universitario de Canarias, Tenerife Spain. A retrospective review of electronic medical records was conducted for patients who iniciated LTP with lanadelumab between March 2021 and June 2024. Results confirmed a 75.27% reduction in hospital visits, and a 94.1% decrease in the average number of angioedema episodes during the study period. Additionally, the use of on-demand rescue medication (icatibant) was significantly reduced by 61% (p&lt;0.05), with HAEQoL scores improving from 62.2 to 99.5 points, and no significant adverse effects were reported. These findings indicate that lanadelumab effectively reduces healthcare resource utilization and angioedema episodes, while notably enhancing the QoL in selected HAE type 1 patients.

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.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.

Air pollution includes particle-bound Polycyclic Aromatic hydrocarbons (PAHs) which eventually reach the placenta triggering adverse perinatal outcomes by long-term exposure. Late-ly, air pollution has increased over the Metropolitan Area of Medellin-Colombia (MAMC) but its effects on pregnancy are still unknown. In this research, we made a real-time analysis of airborne total PAHs using a photoelectric sensor for residential places influenced by industrial and traffic sources contrasting southern and northern MAMC during the second peak of the bimodal ten-dency for PM2.5 emissions in this region. Additionally, we analyzed individual PAHs by GC/MS coupled to pressurized hot water extraction methodology. Data was applied in an Inhalation In-take Model to assess pregnancy exposure. The average concentration of PAHs over southern MAMC was three times higher than over northern MAMC where the abortion rate has been 1.4 times higher presented in database. Previous research found that PAHs act as an Endo-crine-Disrupting Chemical (EDC) during pregnancy and even heavy congeners could reside in umbilical cord blood. Finally, the annual series of abortion rates in the MAMC showed a signifi-cant correlation with the annual average levels of PM2.5 which are associate to PAHs. Although this significant correlation does not imply causality, our results suggest an important connection between both variables. This latter finding opens a gap in deeply understanding how regions with high PAHs-convergence influences abortion rates in MAMC.

Athletes living with and beyond cancer can continue to train and, in some cases, compete during treatment. Following cancer treatment, athletes can return to competitive sport but need to learn to adapt their physical strength and training to lingering effects of cancer. It is critical for oncology healthcare providers to use the principles of assess, refer and advise to exercise oncology programs that are appropriate for the individual. Managing side effects of treatment is key to being able to train during and immediately following cancer treatment. Keen attention to fatigue is important at any point in the cancer spectrum to avoid overtraining and optimize the effects of training.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

Course recommendation is an important service of Intelligent tutoring systems (ITS), the rapid development of large-scale online courses has generated a substantial amount of learning process data for online learning platforms, providing valuable support for understanding learners' interests and preferences. However, despite made considerable achievements, there are still unresolved challenges:(1) Current research neglects the sequential relationship between the course learners learn. (2) It does not take advantage of the differences in learners' interest in different courses during the knowledge dissemination process. To overcome these challenges, we propose an innovative solution called PGDB (Preference-aware graph diffusion network and BiLSTM) for course recommendation. Specifically, we analyze learners' preferences for courses and relational paths using a relation-aware multi-head attention network, learn the semantic diversity of different contexts, effectively distinguish the difference of learners' interest in different courses in the process of knowledge transmission. In addition, the temporal preference modeling module employs a bi-directional long short-term memory network to mine learners' interest evolution patterns, generating learner-dependent representations, efficient use of the sequential relationship between the courses taken by learners. Experimental results on real datasets demonstrate that the proposed model greatly exceeds the performance of the most recent baseline models, thereby validating the model's effectiveness.

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.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

Predicting heart disease is crucial for early diagnosis and intervention, significantly improving patient outcomes and reducing mortality rates. This study compares various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks, using the Cleveland Heart Disease dataset. Comprehensive preprocessing steps were undertaken, such as handling missing values, converting categorical variables to numeric forms, and binarizing the target variable for binary classification. Each model was rigorously evaluated using performance metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) values were employed to provide insights into feature importance, ensuring model transparency and interpretability. The results indicate that XGBoost outperformed all other models, achieving an accuracy of 90% and an AUC-ROC of 0.94, demonstrating its superior ability to capture complex patterns in the data through advanced optimization techniques and regularization. This study highlights the significant potential of advanced machine learning techniques, particularly ensemble methods like Gradient Boosting and XGBoost, in enhancing heart disease prediction. These models offer higher accuracy and valuable interpretability, making them practical tools for early diagnosis in clinical settings. Future research should focus on integrating these models into healthcare systems and exploring hybrid approaches to further improve predictive performance and clinical applicability.

Most of the mid-high permeability sandstone oilfields in China have entered the ultra-high water cut stage, accompanied with the highly-dispersed residual oil, and the control effect of mi-cro-heterogeneity on the residual oil is increasingly prominent. Understanding the microscopic characteristics of pore structural and its evolution during the long-term waterflooding is critical for enhancing the accuracy and efficiency of oil field exploitation. While the previous insights paid more attention to the geometrical and morphological features of pore structure and ignores the relation of pore structure with the fluid flow capacity. In this study, we propose a microscopic pore structure classification method considering pore size and local permeable capacity, which realizes the physical classification and quantitative evaluation of pore structure characteristics for different permeability rocks. The micro-pore structure is classified into 6 types based on the key evaluation parameters of pore diameter and flow flux area. By taking six mid-high permea-bility sandstone core samples from Daqing oilfield in China, a series of long-term waterflooding experiments are carried out and the interior pore structure was captured using high precision CT scanning technique. The results reveals that high-permeability reservoirs exhibit concentrated pores diameters and finer throats, whereas mid-permeability reservoirs exhibit smaller pores and throats in greater quantity. With the increase of water injection volumes, the permeable ca-pacity and connectivity of the high-connectivity pore space in ultra-high- and high-permeability reservoirs are enhanced, and in mid-permeability reservoirs, the micro-heterogeneity intensifies the increase in low-connectivity small pores. These findings provide new insights for the micro-scopic heterogeneity and a solid theoretical basis for investigating the occurrence of microscopic residual oil and optimising enhanced oil recovery strategies at the late stage of ultra-high water cut.

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

The deformation monitoring information of concrete dams contains some high-frequency com-ponents, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The example analysis shows that the model has good calculation speed, fitting and prediction accuracy and it can effectively mine the date char-acteristics inherent in the measured deformation, and reduce the influence of noise components on the modeling accuracy.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

(1) Background: Nursing homes’ preparedness in managing a public health crisis has been fragile, with effects on safety culture. The objective of this study was to assess nursing homes’ COVID-19 preparedness in Southern Portugal, including personnel’s work experiences during the pandemic. (2) Methods: We used a COVID-19 preparedness checklist to be completed by management teams, followed by follow-up calls to nursing homes. Thereafter, a survey to personnel was applied. Data analysis included descriptive statistics, exploratory factor analysis, and thematic analysis of open-end questions. (3) Results: In total, 71% (138/195) of eligible nursing homes returned the preparedness checklist. We conducted 83 follow-up calls and received 720 replies to the personnel survey. On average, 25% of nursing homes did not have an adequate decision-making structure to respond to the pandemic. Outbreak capacity and training were fragile areas among nursing homes’ contingency plans. We identified compliance with procedures and nonpunitive response to mistakes as fragile areas of safety culture, and teamwork as a strong safety area. (4) Conclusions: To strengthen how nursing homes cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, nursing homes’ preparedness and safety culture should be fostered and closely monitored.

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens increasing tumor elimination efficiency. In the last years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate on patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable to be mathematically modeled. In this work, we developed a three population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities were considered as uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on some specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reduce and optimize the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an open-source, easy to run simulator, available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br, containing this manuscript results as examples and documentation. The developed model, together with the CARTmath platform, provides potential use for assessing different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory towards in silico trials.

Soil organic carbon (SOC) is an essential condition for soil health and a potential sink for greenhouse gases. SOC dynamics in a long-term field experiment with mineral and organic fertilization on loamy sand Podzol in Vladimir Region, Russia, was traced with the dynamic carbon model RothC since 1968 until the present time. During this period, C stock increased 21% compared with the initial level in the treatment with the application of manure in an average annual rate of 10 t·ha-1. The model was also used to forecast SOC changes until 2090 for two contrasting RCP4.5 and RCP8.5 climatic scenarios. Until 2090, the steady growth of SOC stocks is expected in all compared treatments for both climate scenarios. This rate of growth was the highest until 2040, decreased in 2040-2070 and increased again in 2070-2090 for RCP4.5. The highest annual gain was within 21-27‰ under RCP4.5 and 16-21‰ in 2020-2040 in 0-20 cm soil layer. The expected accumulation of C allows increasing current C stock 1.6-1.7 times for RCP4.5 and 2.0-2.2 times for RCP8.5 scenario. Modelling demonstrated potentially more favourable conditions for SOC stability in arable Podzols than in Retisols in Central Russia in the 21st century.

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.

Increases in water temperature caused by climate change will challenge water and species management in the San Francisco Estuary. Our goals were to describe spatial and seasonal patterns in water temperature across the upper Estuary, and evaluate how temperature stress and suitability vary across the aquatic ecosystem. We synthesized 10 years of continuous water temperature data at 75 stations across six regions of the Estuary between 2010 and 2019. We identified stressful temperature thresholds for species of interest using published physiological limits and observed distribution, including ESA-listed native (e.g., osmerids, salmonids), non-ESA-listed native (e.g., cyprinids), non-native (e.g., centrarchids, bivalves), and nuisance species such as invasive aquatic vegetation, and harmful cyanobacteria. We then quantified thermal stress across varying spatial and temporal scales and metrics. Analyses indicated there were detectable regional temperature differences, and that Suisun Bay was the only region to provide regular thermal refuge during the warmer parts of the year, though portions of the Confluence and Suisun Marsh also provided potential thermal refuge during summer/fall and during cooler parts of the day. Meanwhile, the Central and South Delta experienced the warmest temperatures, exceeding thresholds for listed species throughout summer and early fall. We found that listed species such as juvenile salmonids and osmerids experience more thermal stress across a number of metrics. Fishes with higher heat tolerance (including non-natives and select natives), aquatic vegetation, and Microcystis (a harmful cyanobacteria) had the lowest average number of thermal stress days. This study demonstrates that the Estuary is already exhibiting stressful conditions for species of concern, and thermal stress will only increase with climate change. We identify at what times of year and where the Estuary may provide refuge from thermal stress conditions which has important implications for restoration prioritization and design and species management.

Children account for a significant proportion of the world's population and are a particularly vulnerable subpopulation regarding environmental factors. In a scoping review of a total of 3130 articles compiled from relevant medical literature, including 107 publications, we provide an empirical overview of the literature on masks in connection with children, particularly with regard to its' expected effects on viruses during the pandemic 2020-2023 versus scientific reality and evidence, including the side effects. While masks lack ecological validity from a strictly empirical point of view (transferability of experimental results from laboratories and modelling to the real world, e.g. to situations or environments typical of everyday life) and high-quality studies are showing little evidence of their effectiveness in real-world scenarios, whereas the side effects appear to be clear. An individual risk-benefit analysis is necessary and also a consideration of the situations, in which the use of masks in children could be justified. This review including the literature found and analysed will be able to help when making decisions in this regard.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the motor's weight. This can result in decreased both the ride comfort and driving stability. To resolve this issue, this study aims to apply an optimal switching controller with a semi-active n actuator; magnetorheological (MR) damper. For the implementation of the optimal switching controller, a road type classification is also carried out. An acceleration sensor is used for road type classification, and control logics include a ride comfort controller; the linear quadratic regulator (LQR _Paved Road) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both LQR_ Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are undertaken to evaluate vibration control performance, including the ride comfort and driving stability at various road profiles.

Zinc is an important nutrient for plant growth and development. Its availability is influenced by zinc solubilizing microbes (ZSM). Effects of commonly promoted agronomic practices on the abundance of ZSM are so far not well understood. In this study, we assessed the effects of farmyard manure (FYM) application, either sole, or in combination with residue and/or inorganic fertilizer inputs on ZSM community structure using 11 treatments in a long-term (17 years) integrated soil fertility management experiment located in western Kenya, The study was conducted in 2019. Bacterial and fungal community composition (at 0-15 cm depth) was evaluated by amplicon sequencing on an Illumina Miseq platform following the manufacturer’s guidelines. Microbial diversity indices (Shannon, Simpson and Chao1) and statistical analyses were computed using R project packages. Putative ZSM (i.e., the ZSM generally considered to possess the zinc solubilizing capabilities) were clustered in two major clades based on either application or no application of FYM. Sole application of FYM significantly (P<0.05) increased the abundance of several ZSM under maize-tephrosia rotation. In addition, systems with combined application of FYM with other inputs generally showed significantly increasing trends for some ZSM under maize-tephrosia rotation. Moreover, combined application of FYM and P rather than P only significantly increased abundance of some ZSM under maize-monocropping systems. Furthermore, besides affecting ZSM abundance, soil chemical variables involving soil organic carbon (SOC), total N and Olsen P significantly increased with FYM application. This study indicated that management practices like application of FYM that increase SOC, and other soil chemical parameters, also/concomitantly increase ZSM abundance; implying enhanced capacities for microbial-linked zinc availability.

Combined sewer overflow (CSO) is a significant environmental concern and public health (e.g., water contamination, eutrophication, and beach closure). The Environmental Protection Agency (EPA) has introduced the National Pollutant Discharge Elimination System (NPDES) permitting program to regulate and address this matter. This program mandates the control of CSOs for more than 700 municipalities obligated to devise Long-term Control Plans (LTCPs) to curb combined sewer overflows and bring them down to safe levels. LTCP involves diverse strategies, including sewer separation, green infrastructure improvements, and conventional gray infrastructure upgrades. This study investigates several municipalities’ solutions for CSO problems in conventional methods and wireless sensor technology as real-time control. The investigation mainly focuses on a comparative analysis of two cities, Richmond, Virginia, and South Bend, Indiana, such as average rainfall, the frequency of overflows, and the capacity of treatment plants. The findings indicate that integrating sensor technology could significantly enhance modeling endeavors, bolster the capacity of existing structures, and substantially enhance preparedness for storm events. The EPA’s Storm Water Management Modeling (SWMM) software is utilized. Through an analysis of SWMM data, the study suggests the potential for leveraging wireless sensor technology to achieve more robust control over CSOs as a part of LTCPs.

The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.

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.

In the Quaternary paleosciences, the rationale behind analogical inference presupposes that former natural changes can be explained by causes operating now, although their intensity and rates can vary through time. In this paper we synthesise synthetize the results of different modern analog studies and discuss their value to obtain the best inferences from high resolution past records. This synthesis is based on the following: 1) The monthly monitoring of calcite precipitation reveals a strong connection with primary producers and between-years variability; this precipitation produces a seasonal signal with imprint on varve formation. 2) Clear pollen sedimentation peaks occur in spring/summer and fall/winter that coincide with temperature, precipitation, relative humidity and winds; this pattern converges with the two-layer coupled varves representing the same seasonality. 3) We assess the lake’s contemporary oxygenation dynamics over a three- year period; a combination of sedimentary REDOX proxies revealed different scenarios of oxic/anoxic shifts since 1500 CE. 4) We investigate presence of seasonality in the production/distribution of glycerol dialkyl glycerol tetraethers and derived temperature estimates in soils and particulate matter. Branched glycerol dialkyl glycerol tetraethers signatures and some derived temperature estimates proxies appear to mainly depend on the non-seasonal shifts in soil properties. 5) Currently we examine relationships and similarities between extant phytoplankton and derived pigments in water and traps, and their correspondence with subfossil pigments; some preliminary results are presented here.Keywords: high resolution, endogenic varves, calcite precipitation, pollen sedimentation, meromixis, freshwater glycerol dialkyl tetraether, subfossil pigments, long-term ecology.

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.

Transition zones are in the places on the track, with a change in the basic composition of the railway body. There are many sections with a sudden change in the stiffness of the structures built. When the trains are running, a longitudinal shock wave is created from the wheels, which hits these building objects with a higher stiffness and deforms the surroundings of these zones. The greatest attention should be paid mainly to the transition points from the fixed track to the classic track with a track bed, including objects of the railway substructure such as bridges, portals of tunnels, etc. As part of the research sections on the main corridor lines, a long-term inspection and monitoring was carried out with a continuous measurement system using the KRAB trolley. Height changes in the deflections of rails are evidence of their behavior. The measurements took place on a fixed track and a track with a ballast. Changes in the height jumps between the fixed railway track and the track with a gravel bed are significant and have been recorded since the tracks were put into operation. These certain height deflections allow designers to develop new, more durable construction designs.

The potato is the most important non-cereal food crop, and thus improving potato growth and yield is in the focus of agricultural researchers and practitioners worldwide. Several studies reported beneficial effects of silicon (Si) fertilization on potato performance, although plant species from the family Solanaceae are generally considered as non-Si-accumulating. We used results from two field experiments in the temperate zone to gain insight into silica accumulation in potato plants as well as corresponding long-term potato yield performance. We found relatively low Si contents in potato leaves and roots (up to 0.08% and 0.3% in the dry mass, respectively) and negligible Si contents in potato tuber skin and tuber flesh for plants grown in soils with different concentrations of plant available Si (field experiment 1). Moreover, potato yield was not correlated to plant available Si concentrations in soils in the long-term (1965-2015, field experiment 2). Based on our results we ascribe the beneficial effects of Si fertilization on potato growth and yield performance reported in previous studies mainly to antifungal/osmotic effects of foliar-applied Si fertilizers and to changes in physicochemical soil properties (e.g., enhanced phosphorus availability and water holding capacity) caused by soil-applied Si fertilizers.

In today’s rapidly evolving digital landscape, the significance of password guessing techniques in both offensive and defensive strategies is paramount. Passwords serve as a crucial line of defense for both individuals and corporations, safeguarding their sensitive systems and data. Therefore, assessing the effectiveness of these access credentials is a critical task. However, existing research in this field often encounters limitations, such as a lack of sufficient training data and extended model training times. Current methods often struggle with limited training data and lengthy training times. This paper introduces BBPE-AE, a novel Auto Encoder Network (AE) designed for password guessing. BBPE-AE utilizes Byte-level Byte Pair Encoding (BBPE) to extract frequent tokens from password datasets without length restrictions, employing a dynamic window technique to capture complex patterns. Experimental results on Hotmail and Myspace datasets demonstrate exceptional performance, achieving high similarity rates (BLEU-Unigram: 0.90, BLEU-Bigram: 0.82 for Hotmail; BLEU-Unigram: 0.90, BLEU-Bigram: 0.81 for Myspace). BBPE-AE generates realistic passwords that meet HIBP (Have I Been Pwned) standards, minimizing duplication. These findings highlight the effectiveness of BBPE-AE in enhancing security by generating realistic passwords, ultimately safeguarding sensitive systems and data.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

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.

The focus of our study was the changes in the composition of semi-natural dry grasslands in Hungary. Since these valuable habitats were created with the help of humans, their preservation also requires regular treatment. Our mowing experiment aimed to investigate the suppression of the native Calamagrostis epigejos L. Roth. In Hungary, in the Cserhát Mountains, eight permanent plots were mowed twice a year. The vegetation was sampled annually between 2001 and 2011. The effects of mowing were investigated with repeated measures analysis of variance (ANOVA). After 10 years, the C. epigejos cover of the mowed plots decreased significantly, from the initial average of 62.38 to 7.50%. Surprisingly, we also experienced a similar decrease in the control plots. Regular treatment also had a positive effect on the number of species and diversity. Species richness increased continuously in both treatment types, which indicates the combined effect of vegetation succession and treatment. The biomass growth of other Poaceae and Fabaceae species, which are important from a grassland management perspective, was also facilitated by mowing. Our results suggest that long-term in situ experiments and continuous botanical studies are needed to establish a reliable utilization of grasslands.