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 < 0.001) and lower estimated blood loss (300.0 [250.0–500.0] vs. 550.0 [400.0–800.0], p < 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.

The COVID-19 pandemic has led to a significant and enduring influence on global health, in-cluding maternal and fetal well-being. Evidence suggests that placental dysfunction is a potential consequence of SARS-CoV-2 infection during pregnancy, which may result in adverse outcomes such as preeclampsia and preterm birth. However, the molecular mechanisms underlying this association remain unclear, and it is uncertain whether a mature placenta can protect the fetus from SARS-CoV-2 infection. To address the above hiatus, we conducted a transcriptome-based study of the placenta in both maternal and fetal compartments. We collected placental samples from 16 women, immediately after term delivery, of which seven had confirmed SARS-CoV-2 in-fection by PCR before parturition. Notably, we did not detect any viral load in either the maternal or fetal compartments of the placenta, regardless of symptomatic status. We extracted total RNA from placental tissues, separately from maternal and fetal compartments, constructed cDNA li-braries, and sequenced them to assess mRNA and small RNA expression. Our analysis revealed 727 differentially expressed genes (DEG) in the maternal placental tissue, with 608 upregulated and 109 downregulated in SARS-CoV-2-positive women compared with healthy, negative wom-en. In contrast, the fetal compartment did not exhibit any significant changes in gene expression with SARS-CoV-2 infection. Specifically, we observed significant downregulation of seven genes belonging to the pregnancy-specific glycoprotein (PSG), related to the immunoglobulin superfam-ily in the maternal compartment with active SARS-CoV-2 infection (fold change range from -13.70 to -5.28, FDR ≤ 0.05). Additionally, comparing symptomatic women with healthy, we identified 14,223 DEGs, with high expression of the inflammatory cytokine IL6 in the maternal placenta of the symptomatic women. Furthermore, KEGG analysis revealed that pathways relat-ed to viral infection, vascular smooth muscle contraction, and oxytocin signaling were altered significantly in symptomatic women. Overall, our study sheds light on the molecular mechanisms underlying the reported clinical risk of preeclampsia and preterm delivery in women with SARS-CoV-2 infection. Nonetheless, studies with larger sample sizes are warranted to further deepen our understanding of the molecular mechanisms of the placenta’s anti-viral effects in ma-ternal SARS-CoV-2 infection.

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 passenger prediction flow is very significant to transportation sustainability. This is due to some chaos of traffic jams encountered by the road users during their movement to the offices, schools, or markets at earlier of the days and during closing periods. This problem is peculiar to the transportation system of the Federal University of Technology Minna, Nigeria. However, the prevailing technique of passenger flow estimation is non-parametric which depends on the fixed planning and is easily affected by noise. In this research, we proposed the development of a hybrid intelligent passenger frequency prediction model using the Auto-Regressive Integrated Moving Average (ARIMA) linear model, Convolutional Neural Network (CNN), and Kalman Filter Algorithm (KFA). The passengers’ frequency of arrival at the bus terminals is obtained and enumerated through the closed-circuit television (CCTV) and demonstrated using the Markovian Queueing Systems Model (MQSM). The ARIMA model was used for learning and prediction and compared the result with the combined techniques of using CNN-KFA. The autocorrelation coefficient functions (ACF) and partial autocorrelation coefficient functions (PACF) are used to examine the stationary data with different features. The performance of the models was analyzed and evaluated in describing the short-term passenger flow frequency at each terminal using the Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. The CNN-Kalman-filter model was fitted into the short-term series and the MAPE values are below 10%. The Mean Square Error (MSE) shows that the CNN-Kalman Filter model has the overall best performance with 83.33% of the time better than the ARIMA model and provides high accuracy in forecasting.

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.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

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.

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

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.

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.

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.

Pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab in immunocompromised patients has reduced the risk of breakthrough infection, disease, hospitalization, and COVID-19 related mortality. However, the advent of the Kraken variant (XBB.1.5) has limited the use of this monoclonal antibody, based on poor efficacy in in vitro studies The objective of the study was to evaluate the risk of breakthrough infection, symptomatic disease, hospitalization, intensive care admission and COVID-19 related death in kidney transplanted recipients receiving pre-exposure prophylaxis with Tixagevimab/Cilgavimab for COVID-19 in the era of the Kraken variant (XBB.1.5). In a prospective, observational study, we enrolled kidney transplant patients undergoing pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab at the Division of Infectious Diseases of Federico II University of Naples between February 2023 and August 2023. Each patient subsequently underwent a six-month follow-up with symptom monitoring and surveillance nasopharyngeal swab for SARS-CoV-2 RNA detection every 30 days, regardless of symptoms. Thirty-four kidney transplant patients were enrolled and in the follow-up period, only one tested positive for the nasopharyngeal swab for SARS-CoV-2 research with asymptomatic infection and virological recovery on the eighth day from the diagnosis of infection. Therefore, no patient developed disease and no patient needed hospitalization and no death occurred. No adverse drug reaction to Tixagevimab/Cilgavimab occurred. Our data, although derived from a limited and uncontrolled sample, show the potential of Tixagevimab/Cilgavimab as a valid and viable therapeutic strategy in pre-exposure prophylaxis for immunocompromised patients. These findings highlight the importance of conducting clinical studies on this topic.

Serum albumin (ALB), one of the most important proteins in human physiology, has the main functions of maintaining plasma oncotic pressure and plasma volume, transporting hormones, vitamins, oligominerals and drugs, and exerting a powerful antioxidant-antiinflammatory role. Its prognostic value in liver and malabsorption syndromes is well known. In this narrative review, an analysis of the most important studies evaluating the prognostic significance of low serum ALB levels in hospitalized patients was performed. Specifically, the risk in emergency medicine, cardiovascular diseases, COVID-19 infection, nephrology, oncology and autoimmune rheumatic diseases has been examined to fully explore its clinical value. ALB is a negative acute-phase reactant and the reduction of its serum levels represents a threatening parameter for long-term survival in several clinical settings, and a strong poor prognostic biomarker in most diseases. Therefore, clinicians should consider serum ALB as a valuable tool to assess the efficacy of specific therapies, both in hospitalized patients and in chronic follow-up.

OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Background: There is an exponential increase in caesarean sections with epidemic proportions worldwide, which is considered as a serious public health issue that influences negatively both maternal and childhood health. Aims: This study aimed to evaluate the associations between pre-dominant maternal risk factors and the prevalence of caesarean section in a representative sample of women from Greece. Methods: This is a cross-sectional study conducted on 5182 healthy, re-productive-aged women from 11 geographically diverse Greek areas after applying specific inclu-sion and exclusion criteria. Statistical analysis was applied to evaluate the impact of maternal so-ciodemographic, anthropometric, lifestyle and perinatal factors in the risk of caesarean section de-liveries. Results: A high prevalence of 56.4% of caesarean section deliveries was recorded in the study population. The incidence of caesarean sections reached to 51.5% in private hospitals in which an increasing rate of 47.5% for planned caesarean sections were noticed. Older maternal age, pre-pregnancy overweight and obesity, excess gestational weight gain, high rates of preterm birth, better economic status, smoking habits, and private type of birth hospital were significantly related with enhanced risk of caesarean section, independently of multiple confounding factors. Conclu-sions: Our study revealed that caesarean section rates are continuously increase, while several maternal risk factors, including especially pre-pregnancy overweight/obesity and excess gestational weight gain, cumulatively raise its prevalence which further increase the risk for postnatal adverse outcomes for both the mothers and their children. Emergent public health policies and strategies should be promoted to confront the predominant maternal risk factors-related with caesarean sec-tion and inform future mothers how to be prevented from them by applying healthier nutritional and lifestyle habits and to select caesarean section only due to emergency medical reasons. Alarmingly enough, targeted nutritional intervention strategies are strongly recommended to ef-fectively reduce the prevalence of maternal pre-pregnancy overweight/obesity and the excess gestational weight gain, which in turn may minimize the risk of childhood overweight/obesity.

Background: Pre-pregnancy overweight and obesity of women of childbearing ages, has alarmingly a growing trend in developed countries. This study aimed to evaluate potential associations between maternal pre-pregnancy excess body weight and childhood demographic and anthropometric characteristics, as well as perinatal and postnatal outcomes. Methods: This is a cross-sectional study conducted on 5198 pre-school children aged 2-5 years old and their matched mothers that were enrolled from nine different Greek regions. Maternal and childhood anthropometric and demographic data, perinatal and postnatal outcomes were collected from medical history records or validated questionnaires. Results: A prevalence of 24.4% and 30.6% of overweight/obesity was recorded for the enrolled children and their mothers 2-5 years postpartum. In multivariate analysis, childhood overweight and obesity was significantly more frequently observed when maternal pre-pregnancy BMI status was overweight or obese [OR: 2.11 (1.80-2.42) p=0.0001]. A high prevalence of caesarean section delivery and childhood diabetes type I was also significantly associated with maternal pre-pregnancy overweight/obesity [OR:1.71 (1.33-2.19) p=0.0175) and OR: 1.27 (1.04-1.53) p=0.0014, respectively]. Conclusions: Maternal pre-pregnancy overweight and obesity rates were related with increased childhood weight status at birth and 2-5 years postpartum, highlighting the necessity of encouraging healthy lifestyle promotion, including healthier nutritional habits, and focusing on obesity population policies and nutritional interventions among women of reproducible age.

The rise in prevalence of obesity in women of reproductive age in both developed and developing countries might propagate intergenerational cycles of detrimental effects on metabolic health, contributing to substantial economic burden on society. Placental lipid metabolism might be disrupted by maternal obesity, which possibly affects the life-long health of the offspring. Here, we investigated placental lipid metabolism and handling from women with pre-gestational obesity as a sole pregnancy complication and compared to placental responses of lean women. Open profile and targeted lipidomics were used to assess placental lipids and oxidized products of docosahexahenoic acid (DHA), neuroprostanes, and arachidonic acid (AA), isoprostanes. Placental fatty acid transporters FABP1, FABP3 and endothelial lipase protein were measured. Despite no signs of overall alterations in lipid content, increased contents of DHA, AA, DHA-derived neuroprostanes and AA-derived isoprostanes and decreased content of FABP1 protein were found in placentas from obese women. Multivariate analyses suggested that these oxidised fatty acids are associated with maternal and placental inflammation and also with birth weight. These results might shed light on the molecular mechanisms associated with altered fatty acid metabolism and lipid handling in maternal pre-gestational obesity, placing these oxidized fatty acids as novel mediators of placental function.

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.

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.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

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.

Sources of resistance to powdery mildew in wild barley (Hordeum vulgare subsp. spontaneum) collected in Jordan, Lebanon, and Libya Jerzy H. Czembor 1, *, Elżbieta Czembor 1 1 Plant Breeding and Acclimatization Institute – National Research Institute (IHAR-PIB), Radzikow, 05-870 Błonie, Poland; e.czembor@ihar.edu.pl (E.C.) *Correspondence: j.h.czembor@ihar.edu.pl) Abstract Barley powdery mildew (BPM) is caused by the pathogen Blumeria graminis f.sp. hordei (Bgh). It is an economically important disease and plant pathologists are looking for new sources of resistance to BPM. Barley genetic resources present in gene banks are often a rich source of disease resistance to be used by breeders. These new sources of resistance to BPM are often used in combination (pyramiding) with those that are already used in modern cultivars. Barley accessions, including the wild subspecies Hordeum vulgare subsp. spontaneum (Hvs), are stored in many gene banks and often are a valuable source of economically important characteristics. This source of biodiversity should be more efficiently used to improve barley in the process of plant breeding. However, their proper characterization and availability are urgently needed. The resistance to BPM in 81 accessions of wild barley (Hvs) collected in Jordan (47), Lebanon (23), and Libya (11) was investigated. The seed samples of these accessions were obtained from the ICARDA gene bank and collected in 10 expeditions from 1981 to 1995. Twenty European differential isolates of BPM were used to select accessions with efficient resistance. Thirty-one resistant single plant lines were selected from 15 accessions from Jordan and Libya based on tests performed with the most avirulent isolate of Bgh available. These resistant single plant lines were tested for the presence of specific resistance genes using a differential set of Bgh isolates. After analysis of obtained results, it was concluded that all tested 31 single plant lines of wild barley have genes for resistance that are not represented in the Pallas isolines differential s. Twenty-six lines of Hvs selected from accessions originated in Jordan and Libya showed resistance reaction to all isolates used. Identified new sources of effective resistance to BPM in single plant lines of Hvs will be further tested and used in barley pre-breeding programs. Keywords: Blumeria graminis; resistance genes; resistance; germplasm; gene bank;

During the past few decades, hybrid nanoparticles (HNPs) based on a magnetic material and gold have attracted interest for applications in catalysis, diagnostics and nanomedicine. In this paper, magnetic CoFe2O4/Au HNPs with an average particle size of 10–20 nm decorated with 2-nm gold clusters were prepared using methionine as a reducer and an anchor between CoFe2O4 and gold. The obtained nanoparticles were studied by X-Ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared (FT-IR) spectroscopy and X-Ray photoelectron spectroscopy (XPS) and UV-Vis spectroscopy techniques. The TEM images of the HNPs obtained after one, six and ten gold deposition cycles showed that the surface of cobalt ferrite was covered with gold nanoclusters, which slightly increased with an increase in the number of gold deposition cycles (from 2.12 ± 0.15 nm after one cycle to 2.46 ± 0.13 nm after ten cycles) without any change in surface density. The magnetic measurements showed that the obtained HNPs possessed typical ferrimagnetic behaviour, which corresponds to that of CoFe2O4 nanoparticles. The toxicity evaluation of the synthesised HNPs on Chlorella vulgaris indicated that they can be applied to biomedical applications such as magnetic hyperthermia, photothermal therapy, drug delivery, bioimaging and biosensing.

Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. Current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Series of the different spatial resolution RS data (Landsat 8, Sentinel 2, Planet Scope, and multispectral UAV data) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationship among the indices were assessed via Spearman correlation method, which shows a significant positive correlation for all cases (p &lt; 0.01). A comparison of all indices shows a significant high correlation between UAV and the PlanetScope imagery. The comparison of UAV, Sentinel and Landsat data show moderate and low significant correlation (p &lt; 0.01) correspondingly. Also, trend analysis was performed to explore their spatial-temporal changes using Mann-Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as second estimator shows decreasing trend. Though the best result received for PlanetScope and Sentinel 2, Sentinel-2 data seem to have better alignment making it a reliable tool for an accurate monitoring of small mountainous grasslands in Armenia.

Work disparities experienced by nurses in long-term care (LTC) can impact the retention, well-being, and health of this workforce. Despite the significant impact disparities experienced by nurses have on the workforce and LTC sector, a review of the available literature on work disparities has not been conducted on work disparities experienced by nurses in LTC. Consequently, this scoping review aimed to explore the nature and extent of research on meso-level work disparities experienced by nurses in LTC and how such disparities are linked with nurse health and well-being. Five databases were searched: MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCO host). Of the 5,652 articles retrieved, 16 studies published between 1997 and 2024 met the inclusion criteria. The average number of work disparities within each study was 3.06 (minimum = 1, maximum = 6, SD = 1.62). Types of work disparities across the 16 studies were work opportunities (n = 19), work treatment (n = 18), job security (n = 8), and work compensation (n = 8). Only four articles investigated the association of a work disparity with a variable of health and well-being. Future research on how macro-level disparities intersect with meso-level work disparities may inform a contextual understanding of how disparities transpire in the work of nurses in LTC. Studies are required to address the gaps in knowledge regarding how work disparities negatively influence the health of nurses to inform better retention, well-being, and health of nurses in LTC.

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.

Scholars of the English Literature unanimously say that J.R.R. Tolkien and C.S. Lewis influenced each other writings. For the first time we have investigated this issue mathematically by using an original multi–dimensional analysis of linguistic parameters, based on surface deep–language variables and linguistic channels. To set our investigation in the framework of the English Liter-ature, we have considered some novels written by earlier authors, such as C. Dickens, G. Mac-Donald and others. The deep–language variables and the linguistic channels, discussed in the paper, are likely due to writers’ unconscious design and have revealed connections between texts far beyond writers’ awareness. In summary, the capacity of the extended short–term memory required to readers, the universal readability index of texts, the geometrical representation of texts and the fine tuning of linguistic channels within texts – all tools largely discussed in the paper – have revealed strong connections between The Lord of the Rings (Tolkien), The Chronicles of Narnia, The Space Trilogy (Lewis) and novels by MacDonald, therefore in agreement with what scholars of the English Literature say.

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.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

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.

The response of plasmonic metal particles to an electromagnetic wave undergoes significant features at the nanoscale level. Different properties of the internal composition of a metal, such as its ionic background and the free electron gas, begin to manifest more prominently. As the dimensions of the nanostructures decrease, the classical local theory gradually becomes inadequate. Therefore, Maxwellʹs equations need to be supplemented with a relationship determining the dynamics of current density which is the essence of nonlocal plasmonic models. In this field of physics, the standard (linearized) hydrodynamic model (HDM) has been widely adopted with great success, serving as the basis for a variety of simulation methods. However, ongoing efforts are also being made to expand and refine it. Recently, the GNOR (general nonlocal optical response) modification of the HDM has been used, with the intention of incorporating the influence of electron gas diffusion. Clearly, from the classical description of fluid dynamics, a close relationship between viscosive damping and diffusion arises. This offers a relevant motivation for introducing the GNOR modification in an alternative manner. The standard HDM and its existing GNOR modification also do not include the influence of interband electron transitions in the conduction band and other phenomena that are part of many refining modifications of the Drude‐Lorentz and other models of metal permittivity. In this article, we present a modified version of GNOR‐HDM that incorporates the viscosive damping of the electron gas and a generalized Drude‐Lorentz term. In the selected simulations, we also introduce Landau damping which corrects the magnitude of the standard damping constant of the electron gas based on the size of the nanoparticle. We have chosen a spherical particle as a suitable object for testing and comparing HD models and their modifications because it allows finding precise analytical solutions for the interaction and, simultaneously, it is a relatively easily fabricable nanostructure in practice. Our contribution also includes our own analytical method for solving the HDM interaction of a plane wave with a spherical particle. This method forms the core of calculations of the characteristic quantities, such as the extinction cross‐sections and the corresponding components of electric fields and current densities.

This paper delves into the dynamics of asset pricing within Bachelier’s market model (BMM), elucidating the representation of risky asset price dynamics and the definition of riskless assets. It highlights the fundamental differences between BMM and the Black-Scholes-Merton market model (BSMMM), including the extension of BMM to handle assets yielding a simple dividend. Our investigation further explores Bachelier’s term structure of interest rates (BTSIR), introducing a novel version of Bachelier’s Heath-Jarrow-Morton model and adapting the Hull-White interest rate model to fit BMM. The study concludes by examining the applicability of BMM in real-world scenarios, such as those involving environmental, social, and governance (ESG)-adjusted stock prices and commodity spreads.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

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.

The recent demand for reducing pollutant and CO2 emissions from internal combustion engines has created an urgent need for the development of ultra-lean burn engines that ensure combus-tion stability and reducing knocking tendency. One of the most promising methods is the Pre-chamber Spark Ignition (PCSI) system, where a jet of high-energy reactive gases, produced by pilot combustion in a pre-chamber, ignites the main combustion event in the cylinder. Given the intricate phenomena inherent to PCSI systems, conducting 3D CFD studies is imperative for a comprehensive analysis and optimal design. Furthermore, the detailed CFD model, combined with the calibrated 0-D/1-D model, is anticipated to yield a significant amount of new data that would be difficult to obtain through experimental approaches, making it essential for advancing our understanding and optimization of these systems. However, recently published papers re-port that some state-of-the-art models developed for conventional SI operation may not be pre-dictive under the challenging conditions of TJI systems, particularly under lean conditions. This review elucidates the reasons behind the widespread adoption of CFD in the optimal design of PCSI engines. It presents significant examples and delves into the potential and challenges of employing CFD not just as a predictive tool but also as a design instrument for enhancing PCSI engine performance.

To handle the challenges and uncertainties of the actual labor market, the concept of employability becomes increasingly relevant, especially for graduates facing the transition from university to work. Graduate employability is a complex concept comprising different dimensions that may trigger effective job search and career management. This study introduces a new multifaceted tool, labeled the Orientamentis Questionnaire, to assess graduate employability from an agentic perspective, testing its psychometric properties. The Orientamentis questionnaire measures 14 dimensions merged into four 4 areas, namely graduate psychological capital, graduate agentic capabilities, graduate career perspective, and graduate proactive strategies. The tool was administered to a sample of 1238 graduate students (66% females) examining its factorial structure and reliability. To test its criterion validity, associations with three occupational outcomes collected one year later were explored on a subsample of 273 participants (66% females). Results attested the factorial structure of the Orientamentis questionnaire and the reliability of all dimensions. Moreover, the four areas and most of the dimensions were differently related to at least one subsequent occupational outcome. The Orientamentis Questionnaire represents a sound and reliable tool providing a multifaceted profile of graduate employability strengths and weaknesses, that could be used in academic placement programs to foster graduates’ self-awareness and proactive job search.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma, primary effusion lymphoma (PEL) and multicentric Castleman’s disease. During KSHV lytic infection, lytic-related genes, categorized as immediate-early, early, and late genes, are expressed in a temporal manner. The transcription of late genes requires the virus-specific pre-initiation complex (vPIC), which consists of viral transcription factors. However, the characterization of the protein-protein interactions of the vPIC factors has not been completely elucidated. KSHV ORF18 is one of the vPIC factors, and its interaction with other viral factors has not been sufficiently revealed. Here, we analyzed the interaction between ORF18 and another vPIC factor, ORF30, in living cells using the bimolecular fluorescence complementation (BiFC) assay. We identified four amino-acid residues (Leu29, Gln36, His41 and Trp170) on ORF18 that were responsible for its interaction with ORF30. The artificial intelligence (AI) system AlphaFold2 predicted that identified four residues are exposed to the surface of ORF18 and located in proximity to each other in the surface of ORF18. Thus, AI-predicted model supports the importance of four residues for binding of ORF18 to ORF30. Our results indicated that wet experiments in combination with AI may enhance the structural characterization of vPIC protein-protein interactions.

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors influence COVID-19 by applying the Adverse Outcome Pathway (AOP) framework well-established in regulatory toxicology. This framework aims to model the sequence of events starting from an initial interaction of a stressor with the organism and the progress through key biological events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 patients have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

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.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Abstract: (1) Background: Post-COVID syndrome is defined as symptoms that occur simultane-ously with or after COVID-19 infection, last for 12 weeks, and are not due to another diagnosis. Limited data are available on people’s long-term quality of life (QoL) following COVID-19 infection. The aim of this cross-sectional study was to investigate long-term QoL after COVID-19 among employees of the St. Josef Hospital in Paderborn, Germany, and to identify risk factors. (2) Methods: A monocentric, cross-sectional study was conducted using the validated and digitized WHOQOL-BREF questionnaire via Netigate® between 10/2022 and 02/2023. Data on QoL and global health status were collected in the following four domains: physical health, mental health, social relationships, and environment. (3) Results: The response rate was 73.84% (923/1250). Furthermore, 63.45% of hospital staff respondents reported at least one persistent symptom after COVID-19 infection, leading to significant differences in QoL. Pre-existing conditions, persistent symptoms, and disability after COVID-19 infection, as well as high BMI, no partnership, and low educational level were found to significantly contribute to long-term low QoL. (4) Conclusions: Obesity, lack of partnership, and a low level of education were independent risk factors for lower QoL post-COVID-19 infection in this cohort of hospital staff. Further multicenter studies are re-quired to validate the incidence and their suitability as independent risk factors for post-COVID syndrome.

Natural Fibre Composites (NFC) are eco-friendly, sustainable alternatives to synthetic polymers. However, some intrinsic natural fillers´ properties hinder their widespread implementation as re-inforcement in polymeric matrices and require further investigation. In the scope of this study, the thermal, rheologic, mechanical (tension and flexural modes), morphological properties, water ab-sorption and dimensional stability of the NF polypropylene (PP) based injection moulded compo-sites with the rice husk (rh) and olive pits (op) of 20 wt% and 30% wt%, were investigated. The results suggest that the higher content of the rice husk and olive pits led to a similar reduction of the Melt Flow Index (MFI), independent of the additive type compared to virgin polypropylene (PPv). The melting and crystallization temperatures of the PPrh and PPop composites did not change with statistical significance. The composites are stiffer than the PP matrix by up to 49% and possess higher mechanical strength in what concerns the tension mode at the expense of de-creased ductility. PPrh and PPop have superior flexural modulus in the bending mode, while the flexural strength improvement was accomplished for the PP30%rh. The dimensional stability of the composites was improved as the linear shrinkage in the flow direction was decreased by 49% for PPrh and 30% for PPop, positively correlating with an increase in the filler content and stiff-ness. PPop was less susceptible to water sorption than PPrh due to fibres' composition and larger surface-to-area volume ratios.

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.

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically-based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

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.

The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease, and to assess the impact of early identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease-specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2,200 with a range from 69 to 7,944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of new discoveries into clinical practice.

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.

In the present trend of constructing taller and longer structures, the application of lightweight aggregate concrete is becoming an increasing important advanced solution in the modern construction industry. In engineering practice, the analysis of lightweight concrete elements is performed using the same algorithms used for normal concrete elements. As an alternative to traditional engineering methods, nonlinear numerical algorithms based on constitutive material models may be used. The paper presents a comparative analysis of curvature calculations for flexural lightweight concrete elements, incorporating analytical code methods EN 1992-1 and ACI 318-14, as well as a numerical analysis using the constitutive model of cracked tensile lightweight concrete recently proposed by the authors. To evaluate the adequacy of the theoretical predictions, experimental data of 51 lightweight concrete beams tested during five different programmes were collected. A comparison of theoretical and experimental results showed that the most accurate predictions are obtained using numerical analysis and the constitutive model proposed by the authors. In the future, the latter algorithm can be used as a reliable tool for improving the design standard methods or numerical modelling of lightweight concrete elements subjected to short-term loading.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

In our recent publications pertaining to 4G model of final unification and based on strong and electroweak interactions, we have developed a completely new formula for estimating nuclear binding energy. With reference to currently believed Semi Empirical Mass Formula (SEMF), we call our formula as ‘Strong and Electroweak Mass Formula’ (SEWMF). Our formula constitutes 4 simple terms and only one energy coefficient of magnitude 10.1 MeV. First term is a volume term, second term seems to be a representation of free nucleons associated with electroweak interaction, third term is a radial term and fourth one is an asymmetry term about the mean stable mass number. Considering this kind of approach, nuclear structure can be understood in terms of strong and weak interactions and cold nuclear fusion like complicated concepts can be understood in a theoretical approach positively. In this paper, by considering the accountability of we are making an attempt to improve the applicability and effectiveness of SEWMF. Number 0.00125 being a workable electroweak coefficient for Z=6 to 132 and A=2Z to 3.5Z, number of free nucleons assumed to be associated with electroweak interaction can be expressed as Improved asymmetry term can be expressed as, where represents the light house like stable mass number. With reference to old version, our improved binding energy formula can be expressed as, We are working on its possible modification for mass numbers less than the stable mass numbers. Following this approach, it is possible to show that nuclear binding energy scheme is associated with strong and weak interactions and is practically independent of Coulombic repulsions. Proceeding further, currently believed cold nuclear fusion assumed be associated with hydrated metals can be understood and can be confirmed with strong and weak interactions independent of coulombic repulsions. We are very confident to say that our approach will certainly motivate modern science community in this new direction.

(1) Background: Artificial Intelligence (AI) models have shown potential in various educational contexts. However, their utility in explaining complex biological phenomena, such as Intrinsically Disordered Proteins (IDPs), requires further exploration. This study empirically evaluated the performance of various Large Language Models (LLMs) in the educational domain of IDPs. (2) Methods: Four LLMs, GPT-3.5, GPT-4, GPT-4 with Browsing, and Google Bard (PaLM 2), were assessed using a set of IDP-related questions. An expert evaluated their responses across five categories: accuracy, relevance, depth of understanding, clarity, and overall quality. Descriptive statistics, ANOVA, and Tukey's honesty significant difference tests were utilized for analysis. (3) Results: The GPT-4 model consistently outperformed the others across all evaluation categories. Although GPT-4 and GPT-3.5 were not statistically significantly different in performance (p&gt;0.05), GPT-4 was preferred as the best response in 13 out of 15 instances. The AI models with browsing capabilities, GPT-4 with Browsing and Google Bard (PaLM 2) displayed lower performance metrics across the board with statistically significant differences (p&lt;0.0001). (4) Conclusion: Our findings underscore the potential of AI models, particularly LLMs such as GPT-4, in enhancing scientific education, especially in complex domains such as IDPs. Continued innovation and collaboration among AI developers, educators, and researchers are essential to fully harness the potential of AI for enriching scientific education.

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.

Background Acute right heart failure (RHF) is associated with high morbidity and mortality. Temporary mechanical circulatory support (tMCS) is now part of the advanced management strategy for refractory cardiogenic shock (CS). Among such support systems, VA-ECMO remains the fastest MCS system to set up. The ProtekDuo cannula can be used as a temporary and percutaneous right ventricular assist device (RVAD) in relay with VA-ECMO in the context of prolonged weaning. Case Summary This case report describes the clinical course of a 69-year-old patient who underwent elective aortic valve replacement surgery complicated by acute RHF due to iatrogenic ligation of the right coronary artery. VA-ECMO was initiated in the context of refractory cardiogenic shock. Faced with prolonged MCS weaning, due to lack of recovery of the right ventricle associated with the absence of any other therapeutic option, a percutaneous RVAD via a ProtekDuo cannula was implanted as a bridge to recovery. The ProtekDuo was gradually weaned while the patient began rehabilitation. The patient was transferred to the medical ward after 5 weeks in the ICU, stayed 5 months in an acute medical setting, went to a rehabilitation center afterward and returned home 6 months after the initial surgery without any neurological sequalae or organ failures. In total, she remained on MCS for 39 days (21 on VA-ECMO and 18 on ProtekDuo). Discussion In cases of refractory RHF, after stabilization and organ perfusion restoration under VA-ECMO, it is feasible to switch to a percutaneous RVAD, allowing early mobilization of patients during prolonged MCS weaning until the right ventricle adapts to new loading conditions.

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.

In the modern construction industry, lightweight aggregate concrete (LWAC) is often used in the production of load-bearing structural members. LWAC can be up to 40% lighter by volume in comparison to normal strength concrete. On the other hand, the lack of adequate numerical models often limits the practical application of innovative building materials, such as lightweight concrete, in real projects. This trend is due to the uncertainties in design standard methods and calculation errors, the level of which is generally unacceptable to civil engineers in terms of safety and reliability. In the present paper, a comparative numerical deformation analysis of a full-scale bridge deck slab and girder has been carried out. Using the physical model proposed by the authors and the finite element software ATENA, the deformations of full–scale lightweight and traditional reinforced concrete elements under short-term effects of permanent and variable loads was compared. Depending on the safety and serviceability limit requirements, it was found that the amount of longitudinal reinforcement in lightweight reinforced concrete elements can be reduced compared to normal reinforced concrete elements with the same parameters. The results of the numerical analysis show that the deformation analysis model proposed by the authors can be a reliable tool for the design of lightweight concrete flexural members by selecting the optimum geometrical and reinforcement parameters limited by the stiffness condition.

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.

Management of resistant Tuberculosis (TB) can be extremely difficult, especially in case of novel unpredicted complications. In this report, we present a case of a 48-year-old patient with pre-extensive-drug-resistant-(XDR)-TB who received a treatment regimen including Pretomanid, Bedaquiline, Linezolid, Cycloserine and Amikacin and died due to myocarditis. Acquired resistance to first- and second-line drugs developed due to previous poor adherence to medication. The clinical presentation of the patient, along with her initial ultrasonographical, electrocardiogram (ECG) and laboratory examinations, were typical for acute myocarditis, however, the patient was considered unstable and further investigation with Magnetic Resonance Imaging (MRI), pericardiocentesis and endomyocardial biopsy were not performed. To our knowledge, this is the first case of myocarditis in such a patient, the clinical features of which set a high suspicion of drug-induction that could be attributed to the treatment regimen that was administered. Clinicians who manage cases of drug-resistant TB should be aware of this, newly reported, potentially lethal, adverse event.