Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks

Efficiently salvaging shipwrecks is of the utmost importance for safeguarding shipping safety and preserving the marine ecosystem. However, traditional methods find it difficult to salvage shipwrecks in deep water. This article presents a novel salvage technology that involves multiple hydraulic claws for directly catching and lifting a 2500-ton shipwreck at 600 m depth. To ensure lifting stability, a semi-active heave compensation (SAHC) system was employed for each lifter to mitigate the effects of sea waves. However, the response delays arising from the hydraulic, control, and filtering systems resist the heave compensation performance. Predicting the barge motion to mitigate measuring and filtering delays and achieve leading compensation is necessary for the salvage. Therefore, a multivariate long short-term memory (LSTM) based neural network was trained to forecast the barge’s heave and pitch motions, exhibiting satisfactory results for the next 5 s. According to the results of numerical simulations, the proposed LSTM-based motion predictive SAHC system demonstrates remarkable effectiveness in compensating for shipwreck motion.
Comparison of the Effects of Enzymolysis Seaweed Powder and Saccharomyces boulardii on Intestinal Health and Microbiota Composition in Kittens

Kittens are prone to intestinal health problems as their intestines are not completely developed. Seaweed is rich in plant polysaccharides and bioactive substances that are highly beneficial to gut health. However, the effects of seaweed on cat gut health have not been assessed. This study compared the effects of dietary supplementation with enzymolysis seaweed powder and Saccharomyces boulardii on the intestinal health of kittens. In total, 30 Ragdoll kittens (age: 6 months; weight: 1.50 ± 0.29 kg) were assigned to three treatment groups for a 4-week feeding trial. The dietary treatment given was as follows: (1) basal diet (CON); (2) CON + enzymolysis seaweed powder (20 g/kg of feed) mixed evenly with the diet (SE); and (3) CON + Saccharomyces boulardii (2 × 1010 CFU/kg of feed) mixed evenly with the diet (SB). Compared with the CON and SB groups, dietary supplementation with the enzymolysis seaweed powder improved the immune and antioxidant capacity and also reduced the intestinal permeability and inflammation levels of kittens. The relative abundance of Bacteroidetes, Lachnospiraceae, Prevotellaceae, and Faecalibacterium in the SE group was higher than those in the CON and SB groups (p ≤ 0.05), while the relative abundance of Desulfobacterota, Sutterellaceae, and Erysipelatoclostridium in the SB group was lower than that in the SE group (p ≤ 0.05). Moreover, enzymolysis seaweed powder did not alter the level of intestinal SCFAs in kittens. Conclusively, supplementing kitten diet with enzymolysis seaweed powder can promote intestinal health by enhancing the gut barrier function and optimizing the microbiota composition. Our findings provide new perspectives on the application of enzymolysis seaweed powder.
The parametric bootstrap in phylogenetic analysis

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The Regime Examination of Nigeria Exchange Rate Volatility: Evidence from Markov Regime Switching Autoregressive Approach

Inarguably, the escalation in dollar rates and the price instability in the Nigerian economy underwent significant structural and institutional changes. In assessing the importance of understanding exchange rates, it becomes imperative to build reliable models for predicting the volatility of exchange rates of home currency. Hence, this study aims to model the Nigerian exchange rate volatility using the Markov regime-switching model. The study analyses the Nigerian exchange rate returns in two and three distinct regimes by employing the Markov regime-switching autoregressive (MS-AR) model with data from 2nd January 2018 to 7th September 2020. Four MS-AR candidate models were estimated for the exchange rate series. Based on the least AIC value, MS(3)-AR(2) was returned as the most parsimonious model among the four candidate models. The MS(3)-AR(2) analysis established a high probability that the returns system remains in the liquidation and awareness states. It implied that only unconventional or severe events could switch the series from regime 2 (liquidation phase) and regime 3 (awareness). While there is a low probability that the system will stay in an imbalanced regime implies high switching of regime 1. Furthermore, an average duration period of 2 days, six days and five days were estimated for the imbalance, liquidation and awareness regimes, respectively. Thus, the findings, i.e. imbalance and liquidation regimes’ identification and their average durations, show that the Naira in the foreign exchange market is not favourable for investors to trade. The study recommends that the Nigerian government should direct more efforts towards improving the performance of the Naira in the foreign exchange market to make the market more favourable for investors. Specifically, the CBN should develop new strategies towards tackling the behaviour of the Nigerian exchange rate when in a liquidation state.
Preparation of TiO2 Composite Photocatalysts and their Photocatalytic Performance

Abstract To improve the firmness of the nano-TiO2 particles when loaded and the regenerative ability of the composite photocatalyst, the hydrothermal method was used to prepare the composite photocatalyst with glucose as the carbon source, acetic acid as the catalyst, and anatase titanium dioxide as the photocatalyst. The composite photocatalysts were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), Brunauer–Emmet–Teller (BET), thermogravimetric analysis (TGA), and ultraviolet and visible spectroscopy (UV–vis), to investigate the removal degree of different methylene blue (MB) concentrations and the support and regeneration of the TiO2 nanoparticles. The results showed that the removal degree of the MB by the composite photocatalyst decreased with the increase of its concentration, the reduction of the initial concentration of the MB could improve the removal efficiency of the MB, the load firmness of the nano-TiO2 of the composite photocatalyst prepared by this method was better, the removal degree of the MB only decreased by 3.36% after five times of ultrasonic treatment indicating it had excellent regeneration ability and the removal degree of the MB was still higher than 80% after five times of reuse.
Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world’s most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients’ Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model’s accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study’s findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images.
Smart Meter Fault Diagnosis Model Based on DBN-LSSVM Feature Fusion

Improving the accuracy of smart meter fault diagnosis is of great significance to the reliable operation of power acquisition systems. Aiming at the problems of difficult feature extraction and the high dimension of multi-sensor data of smart meters, based on deep learning, this paper proposes a Deep Belief Network - Least Squares Support Vector Machine (DBN-LSSVM). The deep belief network is trained by unsupervised learning to realize the feature extraction of multi-sensor data. And the least squares support vector machine algorithm is used to obtain the multi-dimensional features in depth to achieve the goal of fault multi-classification diagnosis. Taking the fault data of smart meters collected in recent years as a practical example, the proposed model is compared with typical methods. The experimental results show that it has higher classification accuracy.
Factors Affecting Remote Workers’ Job Satisfaction in Utah: An Exploratory Study

With structural changes in work arrangements, employee retention becomes more important for organizational success. Guided by the Ability, Motivation, Opportunity (AMO) framework, this study investigated the factors affecting remote workers’ job satisfaction and personal wellbeing in Utah. From a sample of n = 143 remote workers, the study used a correlational design to identify the significant predictors of job satisfaction and personal wellbeing. It mapped the relationships between significant predictors of job satisfaction and personal wellbeing and explored the role of human resources (HR) policies and organizational culture in a remote work environment. Results showed intrinsic motivation, affective commitment, opportunity, and amotivation affected employee job satisfaction, while self-efficacy, amotivation, and job satisfaction affected personal wellbeing. A structural equation model (SEM) showed that remote workers with higher levels of self-efficacy, lower amotivation, and higher job satisfaction were likely to have greater personal wellbeing compared to others. When exploring the role of HR, findings showed that HR bundles and organizational culture indirectly affected job satisfaction but had a direct effect on the most important predictors of job satisfaction and personal wellbeing. Overall, results demonstrated the interconnectivity of HR practices, AMO factors, job satisfaction, and personal wellbeing.
A CNN-Transformer Hybrid Recognition Approach for sEMG-Based Dynamic Gesture Prediction

As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized. At present, the convolutional neural network (CNN) has been widely applied to different gesture recognition systems. However, due to its inherent limitations in global context feature extraction, it exists a certain shortcoming on high-precision prediction tasks. To solve this issue, a CNN-transformer hybrid recognition approach is proposed for high-precision dynamic gesture prediction. In addition, the continuous wavelet transform (CWT) is proposed for to acquire the time-frequency maps. To realize effective feature representation of local features from the time-frequency maps, an attention fusion block (AFB) is proposed to build the deep CNN network branch to effectively extract key channel information and spatial information from local features. Faced with the inherent limitations in global context feature extraction of CNNs, a transformer network branch is proposed to model the global relationship between pixels, called convolution and transformer (CAT) network branch. In addition, a multiscale feature attention (MFA) block is proposed for effective feature aggregation of local features and global contexts by learning adaptive multiscale features and suppressing irrelevant scale information. The experimental results on the established multichannel sEMG signal time-frequency map dataset show that the proposed CNN transformer hybrid recognition network has competitive recognition performance compared with other state-of-the-art recognition networks, and the average recognition speed of each spectrogram on the test set is only 14.7 ms. The proposed network can effectively improve network performance and identification efficiency.
Low-Rate and Short-Block-Length Random Permutation-Coded Modulations Achieve Finite-Length Bounds

Ultra-reliable low-latency communication (URLLC) is an important feature brought by 5G New Radio (NR). By using a block code, the low-latency requirement in general requires a short block length in order to reduce the latency of transmitting the entire code block. The ultra-reliable communication requires a low-rate code to achieve high decoding reliability even under low signal-to-noise ratios (SNRs). This paper proposes a new class of coding and modulation schemes, termed permutation-coded modulations, for low-rate and short-block-length applications, such as URLLC. We show that permutation-coded modulations under maximum-likelihood (ML) decoding have remarkable performance levels that achieve the dispersion bounds with normal approximation (NA) for short block lengths. However, their encoding and ML-decoding complexities are prohibitive if the number of message bits transmitted per code block increases. To reduce the encoding and decoding complexities, we propose a trapezoidal permutation-coded modulation scheme which can be decoded efficiently by successive cancellation list (SCL) decoders. We also show that the trapezoidal permutation-coded modulations under SCL decoding can achieve the dispersion bounds with NA for short block lengths.
The Weight of New York City: Possible Contributions to Subsidence From Anthropogenic Sources

New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post‐construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post‐glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard.
Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data

The strip crown in hot rolling has the characteristics of multivariablity, strong coupling and, nonlinearity. It is difficult to describe accurately using a traditional mechanism model. In this paper, based on the industrial data of a hot continuous rolling field, the modeling dataset of a strip steel prediction model is constructed through the collection and collation of the on-site data. According to the classical strip crown control theory, the important process parameters that affect the strip crown are determined as input variables for the data-driven model. Some new intelligent strip crown prediction models integrating the shape control mechanism model, artificial intelligence algorithm, and production data are constructed using four machine learning algorithms, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The overall performance of the models is evaluated using error indicators, such as Mean Absolute Percentage Error (MAPE), Root Mean square Error (RMSE), and Mean Absolute Error (MAE). The research results showed that, for the test set, the determination coefficient (R2) of the predicted value of the strip crown model based on the XGBoost algorithm reached 0.971, and the three error indexes are at the lowest level, meaning that the overall model has the optimal generalization performance, which can realize the accurate prediction of the outlet strip crown in the hot rolling process. The research results can promote the application of industrial data and machine learning modeling to the actual strip shape control process of hot rolling, and also have important practical value for the intelligent preparation of the whole process of steel.
Dietary Polyphenols as a Protection against Cognitive Decline: Evidence from Animal Experiments; Mechanisms and Limitations

Emerging evidence suggests that cognitive impairments may result from various factors, such as neuroinflammation, oxidative stress, mitochondrial damage, impaired neurogenesis, synaptic plasticity, blood–brain barrier (BBB) disruption, amyloid β protein (Aβ) deposition, and gut dysbiosis. Meanwhile, dietary polyphenol intake in a recommended dosage has been suggested to reverse cognitive dysfunction via various pathways. However, excessive intake of polyphenols could trigger unwanted adverse effects. Thus, this review aims to outline possible causes of cognitive impairments and how polyphenols alleviate memory loss via various pathways based on in vivo experimental studies. Thus, to identify potentially relevant articles, the keywords (1) nutritional polyphenol intervention NOT medicine AND neuron growth OR (2) dietary polyphenol AND neurogenesis AND memory impairment OR (3) polyphenol AND neuron regeneration AND memory deterioration (Boolean operators) were used in the Nature, PubMed, Scopus, and Wiley online libraries. Based on the inclusion and exclusion criteria, 36 research papers were selected to be further reviewed. The outcome of all the studies included supports the statement of appropriate dosage by taking into consideration gender differences, underlying conditions, lifestyle, and causative factors for cognitive decline, which will significantly boost memory power. Therefore, this review recapitulates the possible causes of cognitive decline, the mechanism of polyphenols involving various signaling pathways in modulating the memory, gut dysbiosis, endogenous antioxidants, bioavailability, dosage, and safety efficacy of polyphenols. Hence, this review is expected to provide a basic understanding of therapeutic development for cognitive impairments in the future.
The effect of circulating cytokines on cardiovascular patients infected with the Coronavirus

The coronavirus illness (COVID-19) is caused by serious acute respiratory disorder coronavirus 2 (SARS-CoV-2), moreover known as the COVID-19 virus. After the first-ever reports of COVID-19 in December 2019, the malady spread quickly. In January 2020, the WHO announced the outbreak a Public Health Emergency of Worldwide Concern, and by March 2020, the WHO characterized the episode as a global widespread . The current study aimed to detect the effect of SARS-CoV-2 infection in heart patients and study their immune response by detecting the levels of some cytokines, which may end in a cytokine storm and may lead to death. In this study, one hundred-eight subjects were enrolled on two comparison case-control groups, the case group included 54 patients suffering from SARS-COV2, all were selected from those who were admitted to the Intensive Care Unit (ICU), and were diagnosed by a specialist physician with severe acute respiratory syndrome due to SARS-COV2 documented by Real-Time Polymerase Chain Reaction( RT-PCR ) besides other clinical and laboratory criteria in Marjan Medical City in Babylon province, AL-Amal Hospital for Communicable Diseases and AL-Hakeem Hospital, Najaf/Iraq, for a period from March 2022 to October 2022 to evaluate the role of some selected serological among patients with SARA-COV2 . The control group in this study included 54 subjects, divided into three groups (Apparent Healthy, patients suffered from SARS-COV2, patients suffered from CVD). Blood samples were examined through immunological methods, and an enzyme-linked immunosorbent assay (ELISA) was adopted for the detection of the concentration of TNF-alpha, IL6, IL-10,1L-12 and CCL2 . The immunological evaluation to clarify the theory of cytokines storm carried in the present study revealed that (TNF-alpha, IL6, IL-10,1L-12, and CCL2) for patients with COVID-19 and CVD was significantly higher than all the comparison group. The study reported that interleukin (6, 10, 12) and TNF-a are significantly increased in patients with covid19, CVD, and COVID-19 patients only, compared to healthy people. furthermore, IL-6 and IL-12 levels increased in patients with CVD only when compared to healthy people. There is a significant increase in CCL2 in all study groups compared to healthy people who have lower levels and this study indicated that the infection with Covid disease was severe and critical in most patients with CVD. This increased the number of deaths among them.Copyright © 2021 Muslim OT et al.
Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction

Globally, cardiovascular diseases (CVD) are estimated to account for more than 32% of all deaths. Consequently, CVD has become a global health problem, and timely diagnosis is essential (WHO, 2021). Screening for risk factors accelerates the diagnosis and management of CVD, resulting in a more effective and rapid response, reducing the risk of death. This article compares six classification models, AdaBoost, Random Forest, Decision Tree, KNN, Naive Bayes, and Perceptron, to predict CVD symptoms. Based on CDC data collected from Kaggle, classification models were compared with the approach of examining effective factors to predict heart disease. Since the data set was imbalanced, the study performance was measured by AUC and F1-score in Class 1, which is the critical class in this dataset. AdaBoost is found to have the highest AUC and F1-score, respectively, of 0.828 and 0.37, while Decision Tree has the lowest AUC of 0.595 and F1-score of 0.25.