Objective: SARS-Cov-2 infection or COVID-19 is a global pandemic. From the time of identification to till, multiple clinical symptoms and parameters have been identified by the researchers of various countries and regions regarding the diagnosis and presentations of COVID-19 disease. In this manuscript, we investigated the primary symptoms and basic hematological presentations of SARS-CoV-2 infection among the Bangladeshi patients. Methodology: We have collected the disease history of mild to moderate degree of COVID-19 patients; hematological and biochemical on admission reports of moderate degree COVID-19 patients. All of them were tested positive for SARS-CoV-2 by RT-PCR in different institutes in Bangladesh. Results: According to this study though COVID-19 patients in Bangladesh commonly presented with fever, cough, fatigue, shortness of breath, and sore throat, but symptoms like myalgia, diarrhea, skin rash, headache, Abdominal pain/cramp, nausea, vomiting, restlessness, and a higher temperature of >100⁰F have a greater presentation rate and more frequent than other published studies. CRP and Prothrombin time was found to increase in all the patients. Serum ferritin, ESR, SGPT, and D-Dimer were found increased among 53.85%, 80.43, 44%, and 25% patients respectively. 17.39% of the patients had leukocytosis and neutrophilia. 28.26% of patients presented with lymphocytopenia. 62.52% of patients had mild erythrocytopenia. Conclusion: Despite some similarities, our study has evaluated a different expression in presenting symptoms in the case of COVID-19 patients in Bangladesh. CRP, Prothrombin time, serum ferritin, ESR, SGPT, D-Dimer, erythrocytopenia, and lymphocytopenia can be initial diagnostic hematological findings and assessment for prognosis COVID-19 disease. Also, gender variations have a different scenario of clinical and laboratory appearance in this region.

Background: COVID-19 pandemic has pulled us all a few steps back, were we never shake hands or hug each other when we meet our friends and family after a gap, but instead we greet them by saying Namaste and joining our hands together. As we all know, COVID-19 spreads through air and the only way to shield ourselves is by maintaining a safe distance from one another. Methodology: In order to conduct a meta-analysis on the number of COVID-19 cases in Kerala and India, the data was retrieved from various sites hosted by the government bodies. The data for analysis was collected from May 2020 to July 2020. The average number of days required to reach every 5000 fresh cases were also calculated using this data. COVID-19 has affected all the economy holistically regardless of financial, behavioral, or societal aspects. Conclusion: Lifting of the lockdown in a step by step process keeping in mind the necessities for the nation was a thoughtful act, but the people who mistook this opportunity and did not remain in quarantine after coming from abroad was recognized as the reasons behind the sudden and uncontrolled rise in the number of COVID-19 cases in Kerala, India. The government authorities had no other option but to lift the restrictions to reduce the economic burdens that had already affected the daily wage worker and farmers prompting them to give up their lives.

Coronavirus disease 2019 (COVID-19), can manifest with ocular symptoms. These symptoms can be divided into isolated events attributed to COVID-19 and those occurring in multisystem inflammatory syndrome in children (MIS-C), a newly diagnosed disease entity associated with COVID-19 infection. Currently, the literature lacks specific guidelines and treatment regimens for COVID-19 ocular symptoms, especially in children.

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.

(1) Background: Sudden Infant Death Syndrome (SIDS) represents unexplained and sudden deaths during the sleep of infants under one year of age, despite thorough investigation. Screening for prolonged QTc or Long QT Syndrome (LQTS) should be conducted on all newborns to reduce the incidence of SIDS. Neonatal electrocardiogram (ECG) could identify congenital heart defects (CHD) early, especially those not detected at birth. Infants with prolonged QT intervals typically undergo genetic analysis for Long QT Syndrome; (2) Methods: The study involved infants aged 20-40 days, born clinically healthy, with initial ECG screening. Infants with prenatal diagnoses or signs/symptoms of CHD identified immediately after birth, as well as those who underwent ECG or echocardiogram for other clinical or historical indications, were excluded from the study. The data were analyzed using SPSS version 22.0; (3) Results: Of the 42,200 infants involved, 2,245 were enrolled, with 39.9% being males. Subsequently, 164 children (37.8% males) with prolonged QT intervals underwent further diagnostic investigations. Genetic mutations were identified in 11 out of 18 tested children. The most common mutations were LQT1 (54.5%), LQT2 (36.4%), and LQT3 (1 patient). Medications used included propranolol (39.8%), nadolol (22.2%), inderal (11.1%), metoprolol (11.1%), and no treatment (16.7%). The most common abnormalities were focal right bundle branch block (54.5%), left axis deviation (9.2%), and nonspecific ventricular repolarization abnormalities (7.1%). Multiple anomalies were found in 0.47% of children with focal right bundle branch block. Structural abnormalities were associated with specific features in 267 patients (11.9%), primarily isolated patent foramen ovale (PFO) at 61.4%; (4) Conclusions: The screening has proven effective in early diagnosing Long QT Syndrome and other cardiac rhythm anomalies, with additional benefits in detecting mutations and/or QTc prolongations in family members. Identifying other ECG abnormalities and congenital heart malformations further enhances the benefits of the screening.

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

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

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

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

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

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

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

Deformation monitoring is beneficial to ensure the service safety of long tunnel. An three-direction (vertical, transverse and axial) deformation estimation method using long-gauge fiber Bragg grating (FBG) sensing technology is proposed and applied to a long tunnel by integrating mechanical analysis and field monitoring during the service state. The key issues of the proposed method for identifying the three-direction deformation are discussed, such as establishing the separation model of coupling strain on cross section, deriving the theory of deformation identification in three directions, and determining the sensor layout of the long tunnel. A major advantage of the proposed method is that the three-direction deformation of the tunnel can be monitored in real time using distributed long-gauge strain sensors. The numerical analysis and field test results verify the feasibility of the proposed method.

Cross-species investigations on cancer invasiveness are a new approach that identified new biomarkers potentially useful for improving tumor diagnosis and prognosis in clinical medicine and veterinary science. In this study, we combined proteomic analysis of four experimental rat malignant mesothelioma (MM) tumors with analysis of ten patient-derived cell lines to identify common features associated to mitochondrial proteome rewiring. The comparison of significant abundance changes between invasive and non-invasive rat tumors rat tumors gave a list of 433 proteins, including twenty-six proteins reported to be exclusively located in mitochondria. Next, we analyzed the differential expression of genes encoding the mitochondrial proteins of interest in five primary epithelioid and five primary sarcomatoid human MM cell lines, and the most impressive increase was observed in the expression of the long-chain acyl coenzyme A dehydrogenase (ACADL). To evaluate the role of this enzyme in the migration/invasiveness, two epithelioid and two sarcomatoid human MM cell lines derived from patients with the highest and lowest overall survival were studied. Interestingly, sarcomatoid vs epithelioid cell lines were characterized by higher migration and fatty oxidation rates, in agreement with ACADL findings. These results suggest that evaluating mitochondrial proteins in MM specimens might identify tumors with higher invasiveness.

Based on the example of wintering waterbirds in the Baltic Sea, we show a method that is useful in setting priorities for species management. The Value Factor (VF) shows which species in the region are the most valuable and on which ones conservation measures need to focus. Every year 4,400,000 waterbirds winter in the Baltic. Among which the highest priority species are velvet scoter Melanitta fusca (VS, VF=153) and long-tailed duck Clangula hyemalis (LTD, VF=204): 74% and 40%, respectively, of the world populations and over 90% of the EU populations of both species spend the winter there. Management plans (MP) regulating the protection of marine Natura 2000 sites (MPA) and dedicated to the protection of VS and LTD have been implemented in 65% and 51%, respectively, of MPAs in the Baltic. Poland, a key country for the survival of these species, has not implemented a single MP despite the existence of documentation confirming their crucial importance for seaducks, and on the pressures occurring there. We suggest using the VF concept to define priority species. On this basis, it will be possible to identify gaps in the protection of the most seriously threatened species and implement conservation measures in the most appropriate sites

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.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

In the case of vascular plants the process of water loss by leafs and water absorption by the root is well known. There is agreement on the passive nature of long-distance moisture movement in the dead cells of the xylem; however, controversy exists focusing on the long-distance water transport principle. Hales (1726) founded a view of bulk flow based on water suction after experiments with cut twigs. The previous doctrine of long-distance water transport within vessel elements and tracheid of the xylem of intact plants – the relevant cohesion theory in text books – was developed mainly by Boehm (1893), Renner (1911) and Dixon (1914) with plant artefacts. Water movement according to this theory is based on an assumed hydrodynamic bulk fluid flow in xylem in continuous water columns (free of water vapour space), under tension, according to the law of Poiseuille (see e.g. Dixon 1914). Physically hydrodynamics is part of fluid mechanics, as a result Poiseuille’s law is usually valid only for hydrodynamic bulk flow in ideal capillaries (Sutera & Skalak, 1993). Besides the basic requirement for transport, according to cohesion theory, the existence of ideal capillaries is not compatible with either: “Because vessel elements and tracheid do not stand as ideal capillaries. …” (Bresinsky et al. 2008, translated from German). Unlike ideal capillaries, the walls of vessel elements and tracheid interact with the transported water. These walls are able to function as a source or as a sink for the transported water because of interaction with the cell walls. With the interaction, vessel elements and tracheid, part of the xylem, can shrink and swell, unlike ideal capillaries. Because the xylem (in woody plants part of the wood) is inconsistent with the basic law of fluid flow, the equation of mass balance (Zimmermann et al. 2004) and cohesion theory are not strictly followed.Many plant physiologists view the cohesion theory as appropriate, however, this theory remains controver­sial, i.e. by Eisenhut (1988), Laschimke (1990) and Hahn (1993). Nultsch (1996) gives doubts referring to the present doctrine of plant water transport. Zimmermann et al. (2004) reject the cohesion theory and conclude: “... that the arguments of the proponents of the Cohesion Theory are completely misleading” (Zimmermann et al. 2004). Hence cohesion theory can be treated as inapplicable and the question arises: how does water transport in fact function? In the following, it is gone into in more detail. A sorption hypothesis of actual water transport, based on empirical fact, shall be addressed in this paper.

Prestress loss evaluation in prestressed strand is essential for prestressed structures. However, the sensors installed outside the duct can only measure the total prestress loss. The sensors attached on strand inside the duct also have several problems, such as inadequate durability in an aggressive environment, vulnerable damage at tensioning and so on. This paper proposes a new installation method for long-gauge fiber Bragg grating (LFBG) sensor to prevent accidental damage. Then the itemized prestress losses were determined in each stage of the pre-tensioning and post-tensioning according to the LFBG measurements. We verified the applicability of the LFBG sensors for prestress monitoring and the accuracy of the proposed prestress loss calculation method during pre-tensioning and post-tensioning. In the pre-tensioning case, the calculated prestress losses had less deviation from the true losses than those obtained from foil-strain gauges, and the durability of the LFBG sensors was better than foil-strain gauges, whereas in post-tensioning case, the calculated prestress losses were close to those derived from theoretical predictions. Finally, we monitored prestress variation in the strand for 90 days. The itemized prestress losses at each stages of post-tensioning were obtained by the proposed calculation method to show the prospect of the LFBG sensors in practical evaluation.

The main features of the instrumental global mean surface temperature (GMST) are reasonably well described by a simple linear response model driven by anthropogenic, volcanic and solar forcing. This model acts as a linear long-memory filter of the forcing signal. The physical interpretation of this filtering is the delayed response due to the thermal inertia of the ocean. This description is considerably more accurate if El Ni\~{n}o Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO) are regarded as additional forcing of the global temperature and hence subject to the same filtering as the other forcing components. By considering these as predictors in a linear regression scheme, more than 92\% of the variance in the instrumental GMST over the period 1870-2017 is explained by this model, and in particular all features of the 1998 -- 2015 hiatus, including its death. While the more prominent pauses during 1870 -- 1915 and 1940 -- 1970 can be attributed to clustering in time of strong volcanic eruptions, the recent hiatus is an unremarkable phenomenon that is attributed to ENSO and a small contribution from solar activity.

In the aftermath of the COVID pandemic, post-COVID-19 syndrome (PCS) remains a challenge and may continue to represent a major health problem in the future. Moreover, the influence of type 2 diabetes and being overweight on PCS remains unclear. This study aimed to assess this influence. We performed an observational study from October 2020 to July 2022, which included 466 patients (269 males and 197 females) with a median age of 65. They were hospitalized due to COVID-19 pneumonia and had persistent symptoms after 1 month of COVID-19 infection. The patients were divided into four groups according to the study objectives: patients with type 2 diabetes, overweight patients, overweight patients with type 2 diabetes, and normal-weight patients without type 2 diabetes. The clinical and demographic data collected during hospitalization and regular visits at the Community Healthcare Center dr. Adolf Drolc Maribor were analyzed. Our results showed that type 2 diabetes patients had a more difficult course of treatment and longer hospitalization. Moreover, more type 2 diabetes patients underwent rehabilitation than the other study groups. The prevailing symptoms in our patients with PCS were dyspnea and fatigue, mostly in female patients with type 2 diabetes. Our study also showed that more women with type 2 diabetes and more overweight women with type 2 diabetes suffered from secondary infections. Further, more overweight patients were treated in the intensive care unit compared to the other groups. However, our study showed an interesting result that patients with type 2 diabetes had the shortest PCS durations. Type 2 diabetes and being overweight are risk factors for PCS onset and prolonged duration. Therefore, our data that revealed a shorter duration of PCS in type 2 diabetes patients compared to the other investigated groups was unexpected. We believe that answering this question regarding the results will enable us to improve PCS treatment in general.

Phosphocalcium metabolism disorders have emerged as potential complications in individuals recovering from COVID-19. This abstract provides a concise summary of the current understanding of phosphocalcium metabolism disturbances post-COVID, their prevalence, clinical manifestations, and underlying mechanisms. The impact of COVID-19 on phosphorus, calcium, and parathyroid hormone levels is discussed, highlighting the importance of regular monitoring and appropriate management strategies. The abstract emphasizes the need for further research to enhance our understanding of these disorders and improve post-COVID patient care.

The aim of this study was to estimate the association between fear of COVID-19 and risk perception with preventive behavior in health professionals from three Latin American countries. An analytical cross-sectional study was conducted. Health professionals with on-site care in Colombia, Ecuador, Guatemala, and Peru were surveyed. Information was collected through an online self-report questionnaire. The main variables were preventive behavior as the dependent variable and fear of COVID-19 and risk perception as independent variables. Linear regression was used, and Beta coefficients and p-values were calculated. 435 health professionals were included, the majority were aged 42 years or older (45.29%, 95%CI: 40.65%-50.01%) and female (67.82%, 95%CI: 63.27%-72.05%). It was shown that the greater the fear of COVID-19, the greater the preventive behavior of COVID-19 infection (B=2.21, p=0.002 for total behavior; B=1.12, p=0.037 for additional protection at work; B=1.11, p<0.010 for hand washing). The risk perception of COVID-19 infection had a slight direct relationship with preventive behaviors (B=0.28, p=0.021 for total behavior; B=0.13, p=0.015 for hand washing), with the exception of the preventive behavior of using additional protection at work (p=0.339). We found that fear and risk perception are associated with increased practice of hand washing and use of additional protection at work. Further studies are required on the influence of working conditions, job performance and the occurrence of mental health problems in frontline personnel with regard to COVID-19.

Background: The uptake of COVID-19 booster vaccines has been significantly low. Therefore, it is questionable whether combining the COVID-19 booster vaccines with Influenza vaccines can increase the population's interest in taking such vaccines and manage the health pandemic effectively. Methodology: In this systematic review and meta-analysis, a synthesis of the findings and summary of a total of 30 research articles based on the topic, ‘combining influenza and COVID-19 booster vaccination strategy’ was undertaken. The research articles were identified from three databases, namely, PubMed, Cochran Library, and Google Scholar using specific keywords and inclusion criteria. However, research articles that were not peer-reviewed and not published in English were excluded from the systematic review and meta-analysis. The average risk ratio of the included articles was 0.78% based on a 95% CI. On the other hand, the heterogeneity between such studies was I² = 35%, while the statistical significance of their findings occurred at p < 0.05. The average p-value of the included research studies was p = 0.62, implying that the null hypothesis was not rejected in almost all the studies. Results: A synthesis of the chosen research articles revealed that when influenza and COVID-19 booster vaccines are combined, there is potential for an increase in the uptake of the latter, mainly because many populations have already been accustomed to taking influenza vaccines on an annual basis. Conclusions: In this way, through such findings, medical health experts can make informed decisions to increase the population's willingness to receive the COVID-19 booster vaccines.

Given the concerns of waning immunity from the primary COVID-19 vaccines and the first booster dose, we conducted an on-line cross-sectional study in May 2022 to investigate willingness to receive a second COVID-19 booster dose or a new COVID-19 vaccine and its associated factors. Overall, 22.7% of participants were willing to be vaccinated, 39.3% were unsure, but tend to be willing, 25.8% were unsure, 4.9% were unsure, but tend to be unwilling, and 7.4% were unwilling to be vaccinated. The main reasons against accepting a second COVID‐19 booster dose or a new COVID-19 vaccine COVID-19 dose included concerns about the side effects, the opinion that further vaccination is unnecessary, and effectiveness uncertainties. Males, younger individuals, participants without a previous COVID-19 diagnosis, and those with good/very good self-perceived physical health were significantly more frequently willing to receive a second COVID‐19 booster dose or a new COVID-19 vaccine. Also, increased fear of the COVID-19, increased trust in COVID-19 vaccination and decreased fear of a second booster dose or a new COVID-19 vaccine were associated with increased willingness. Our results show some hesitancy and unwillingness toward further COVID-19 vaccination and indicate that fear of COVID-19 and trust in COVID-19 vaccination affect public opinion

Total 219 countries and territories globally suffering from the recent pandemic COVID-19 is now in its second wave with more brutality, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . It has several symptoms like as persistent fever; respiratory illnesses; cough; fatigue; shortness of breath; loss of appetite; persistent pain or pressure in the chest; dysgeusia; acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19 in both peoples who had died of due to acute respiratory distress syndrome (ARDS) or those who survived. Due to COVID-19, dysregulated immune response and wound repair mainly in elderly patients causes this secondary pulmonary fibrosis. Thus using anti-fibrotic agents could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article you will get to know about the lung fibrosis generation due to COVID-19 infection, about anti-fibrotic agents and the currents challenges of this field.

The world is currently, subjected to the worst health crisis documented in modern history; an epidemic led by the novel coronavirus disease 2019 (COVID-19). At the epicenter of this crisis, healthcare professionals continue working to safeguard our well-being. To the regular high levels of stress, COVID new heights even more to healthcare professionals so depending on the area, specialty, and type of work. Here we investigated what are the tendencies, or areas most affected. Through an adaptation of the original COVID-stress scale, we developed a remote, fast test designed for healthcare professionals of the Northeastern part of Mexico, an important part of the country with economic and cultural ties to the US. Our results showed 4 key correlations as highly dependent: Work area – Xenophobia (p &lt; 0.045), Work with COVID patients - Traumatic stress (p &lt; 0.001) and Total number of COVID patients per day – Traumatic stress (p &lt; 0.027), and Total number of COVID patients - Compulsive checking and reassurance. Overall concluding that normal levels of stress have increased (mild – moderate). Additionally, we further determine that the fear of being an asymptomatic patient (potential to spread without knowing) continues being a concern.

This tutorial is organized into three major sections—viruses, vaccines and the race for a Covid-19 vaccine. The goal is to provide enough background on viruses, history of vaccines, and the science of vaccinology founded on the principles of immunity. The hope is that this will enable us to understand the challenges, methods and prospects for developing a safe and effective vaccine against SARS-CoV-2. Many important viruses such as smallpox, HIV, HCV and SARS-CoV-2 which is responsible for causing the Coronavirus disease 2019 (Covid-19) are presented in detail, which is then followed by a description of different vaccine development methods and strategies. The tutorial then discusses different candidate SARS-CoV-2 vaccines and provides specific details of many of the prospective vaccines on the leader-board which are undergoing clinical trials. The tutorial concludes with a realistic projection for a safe and effective vaccine against SARS-CoV-2 based on the historical scientific record.

Currently, there is no specific treatment for COVID-19 proven by clinical trials. WHO and CDC guidelines therefore endorse supportive care only. However, frontline clinicians have been applying several virus-based and host-based therapeutics in order to combat SARS-CoV-2. Medications from COVID-19 case reports, observational studies and the COVID-19 Treatment Guideline issued by the China's National Health Commission (7^th edition published March 3^rd, 2020. Edited translation attached) are evaluated in this review. Key evidence from relevant in vitro researches, animal models and clinical studies in SARS-CoV-2, SARS-CoV and MERS-CoV are examined. Antiviral therapies remdesivir, lopinavir/ritonavir and umifenovir, if considered, could be initiated before the peak of viral replication for optimal outcomes. Ribavirin may be beneficial as an add-on therapy and is ineffective as a monotherapy. Corticosteroids use should be limited without indicating comorbidities. IVIG is not recommended due to lack of data in COVID-19. Xuebijing may benefit patients with complications of bacterial pneumonia or sepsis. The efficacy of interferon is unclear due to conflicting outcomes in SARS and MERS studies. Chloroquine and hydroxychloroquine have shown in vitro inhibition of SARS-CoV-2 and may be beneficial as both prophylactic and treatment therapy. For patients who developed cytokine release syndrome, interleukin-6 inhibitors may be beneficial. Given the rapid disease spread and increasing mortality, active treatment with readily available medications may be considered timely prior to disease progression.

Background: COVID-19 is currently one of the most important medical challenges as it affects the entire population with children being infected as easily as adults. Objective: The study evaluated the mortality of under 17-year-olds and compare the frequency with that of adults in a cohort of 8986 COVID-19 positive patients hospitalized in a single care center. Design: retrospective clinical trial, all hospitalizations of COVID-19 positive patients as diagnosed by RT-qPCR were analyzed by revisiting their medical records. Setting: 8986 COVID-19 positive patients hospitalized in a Hospital de Base and the Infant and Maternal Hospital of São Jose do Rio Preto, SP, Brazil (HB/HCM) being these totals of 383 COVID-19 positive under 17-patients. Patients: patients COVID-19 positive under 17-year-olds were grouped by age and evaluated by analyzing their medical records. They were categorized as Group I, children, and adolescents under 19 years old and Group II, adults over 19 years. Results: Overall mortality in under 19-year-olds was 12 deaths, that is, 3.13% of the patients in this age group and 0.55% of the overall mortality from COVID-19. Mortality in the group of adults was 2197 equivalent to 24.45% of the hospitalized adults and 99.45% of overall deaths. Mortality was significantly higher for adults than for under 19-year olds (p-value < 0.0001). Regarding the different ages of children and adolescents, the number of deaths of under 1-year olds was 2/123 (1.62%), of 1-4 years it was 4/95 (2.11%), of 5-9 years it was 1/47 (2.13%), of 10-14 years it was 1/40 (2.5%) and of 15-19 years it was 4/78 (5.13%). There were no statistical differences between the different age groups ( p-value = 0.3). Conclusion: Mortality from COVID-19 in children and adolescents was lower than in adults and generally associated with other comorbidities with the main association being with heart disease.

Background: The interruption of the activity of the population-based organized colorectal cancer (CRC) screening programs due to the COVID pandemic may have affected their results in terms of detection of preneoplastic lesions and CRC. We evaluated the impact of the COVID pandemic on the delays, participation, adherence to colonoscopies, lesions detected, and CRC stage at di-agnosis in a CRC screening program. Methods: We analyzed all the invitations between January 1, 2019, and December 31, 2021. We defined the pandemic period as the period after March 12, 2020. We calculated the delay intervals (successive and all rounds), the rates of participation, adherence to colonoscopy after a positive fecal immunochemical test (FIT), and the diagnostic yield of colonoscopy, specifically CRC and colorectal neoplasia (CRC and/or adenoma), as well as CRC stage at diagnosis. Results: In the period analyzed, 976,187 invitations were sent (61.0% pandemic), 439,687 FIT were returned (62.4% pandemic) and 23,092 colonoscopies were performed (59.1% pandemic). Colon-oscopies were normal in 7,378 subjects (32.4%) and a CRC was detected in 916 subjects (4.0%). In successive rounds, the delay increased significantly seven months during the pandemic period (p<0.001). In all the invitations, the delay from the invitation to the colonoscopy increased sig-nificantly by 8 days (p<0.001). Once adjusted for the confounding variables, participation in the screening program increased significantly (OR=1.1; 95% CI=1.09-1.11), with no changes in the adherence to colonoscopy (OR=0.9; 95% CI=0.8-1.0). We found no differences in the diagnostic yield of colonoscopy in terms of CRC (OR=0.90; 95% CI=0.78-1.02) or colorectal neoplasia (OR=0.98; 95% CI=0.92-1.03) detection. Finally, we found no differences in CRC stage at diagnosis (p=0.2). Conclusions: Although the interruption of the CRC screening program due to the COVID pan-demic increased the delays, it did not reduce participation, adherence to colonoscopy, or the di-agnostic yield of colonoscopy.

Socio-demographic factors are responsible of health inequalities also in vaccination. The aim of this study was to evaluate their role at population level through a population-based study performed on the whole population entitled to receive COVID-19 vaccines in Umbria Region, Italy, and registered to the Regional Healthcare Service as of February 28th, 2021. Socio-demographic characteristics and vaccination status in terms of uptake at least one dose of any available vaccine, completion of the primary vaccination cycle and uptake of the booster doses as of February 28th, 2022 were col-lected from the Umbria regional database. The percentage of eligible population who did not initiate the COVID-19 vaccination, complete the full vaccination cycle and get the booster dose was 11.8%, 1.2% and 21.5% respectively. A younger age, being a not-Italian citizen, and not holding an ex-emption for chronic disease/disability and a GP/FP were associated to all the endpoints. Females, as compared to males, were more likely to not initiate the vaccination but less likely to not receive the booster dose. On the contrary, results on the deprivation index were not conclusive. The findings, beyond confirming current knowledge at population level, provide new inputs for better tailoring vaccination campaigns.

In this study, we use descriptive statistics and data visualization techniques to analyze datasets and identify patterns and relationships between bikeshare and other transportation modes. We also use regression analysis to quantify the impact of various factors on bikeshare usage. Our results indicate that bikeshare trips during the pandemic were much longer compared to pre-pandemic times. Moreover, usage of bikeshare differed by user group, with casual users showing a greater increase in usage compared to subscribers. We plotted bivariate choropleth maps to visualize the spatial distribution of changes in bikeshare usage. Our findings suggest that an increase in bikeshare travel was associated with a reduction in travel by ride-hailing and public transit, especially in the northern part of the city, indicating a potential replacement of other modes of transportation. On the western and southern boundary of the city, bikeshare had a complementary effect with other modes. Overall, the data acquired for this study provides a valuable opportunity to understand the behavior of bikeshare users in Chicago, the intercorrelation of bikeshare with other transportation modes, and the impact of infrastructure on bikeshare usage. The findings of this study can inform the design and implementation of bikeshare systems in other cities and contribute to the development of a more sustainable transportation system.

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

The most severe threat that the Covid-19 pandemic poses to the global economy is the need to choose between human lives and livelihoods. Bangladesh must assess the implications of such impacts on Bangladesh's macro-financial scenario to maintain the economy's current high growth trajectory. The paper outlines the major Covid-19 shock wave transmission channels to the four major sectors of the Bangladesh economy. Authorities around the world have taken every precaution possible to halt the spread of the pandemic. An aggregate transmission framework that includes these four sectors is required to contain the impact of Covid-19 can propagate through these sectors and eventually impact macro-financial stability.

Resilience is an adaptive coping mechanism needed by health workers, especially nurses who have longer working hours than other health workers to provide care to patients in the era of the Covid-19 pandemic which is a global health problem. The aim of this literature review is to identify the resilience of nurses during the covid-19 pandemic the 21 st century global nursing paradigm. This language method uses literature reviews which are summaries of 10 articles in the publication years of 2020-2021 on search 4 databased electronic searches contain namely Scopus, ProQuest, Pubmed, and Scient Direct. This review used prisms. The eligibility of these studies were from its title, abstract, research methodology, results and discussion. The results of the review were presented in narrative form. The results of a review of 10 articles found that the form of psychological factors during the covid-19 pandemic, mental distress and influencing factors in nurses caring for patients with COVID-19, resilience nurses during the covid-19 pandemic. Conclusion: The 21 st century global nursing paradigm, one of the global problems in the health sector, with the outbreak of the corona virus disease (Covid-19), the role of nurses as the front guard is needed by the community to provide health services in line with the increasing incidence of covid-19 cases. Strong nurses need an adaptive inner coping mechanism.

Introduction: Corona Virus Disease 2019 causes health problems in the world in the form of a health crisis that results in psychological problems as fear and anxiety. The purpose of this study to determine the factors that influence psychological and stigma during the Covid-19 pandemic.Methods: This study is a literature review with five databases (Scopus, CINAHL, ScienceDirect, PubMed, and ProQuest), studies design used a cross-sectional or quasi-experimental, with a date of March 2021. The Center for Review and Dissemination and the JBI Guide are used to measure the Quality and Prism checklist for guide reviews. A feasibility study based on title, abstract, full text, and research methodology. The data analysis used narrative analysis based on the research findings.Results: Eleven articles met the predefined review inclusion criteria. Research is base on related factors psychology, related factors stigma, and factors related to psychological and stigma. Most of the factors associated with psychological conditions and stigma have a quasi-experimental and cross-sectional design. Participants averaged over a thousand for each study and discussed psychology factors related to the stigma. Conclusion: Factors related to psychological are age, education, symptoms and health conditions, gender, information, economy, exposure duration, and social support, while factors related to stigma are environment, history of comorbid diseases, discrimination, and public perceptions.

In critically ill patients with COVID-19, concomitant abnormalities of coagulation have been seen with an unusually high incidence. Standard coagulation tests are limited in their ability accurately to reflect the severity of the pro-thrombotic phenotype observed in severe COVID-19 infections. In this narrative review we consider the role of rotational thromboelastometry (ROTEM) as a near bedside test allowing a more comprehensive assessment of haemostatic function in the context of COVID-19 infection. Comprehensive literature search was conducted on PubMed, revealing 13 publications on the subject. The coagulopathy of this disease process appears to be insufficiently represented with often normal conventional coagulation test parameters. Whilst not the perfect substitute for in vivo coagulation, studies utilising rotational thromboelastometry assays in COVID-19 patients have demonstrated increased maximum clot firmness (consistent with hyper-coagulability) and reduced maximum lysis (consistent with “fibrinolytic shutdown”). ROTEM appears to be a possible tool for risk stratification and to monitor the potential modulation of fibrinogen-dependent coagulation processes with enhanced anti-coagulation strategies. Precisely how these coagulation abnormalities can be modified by optimum, individualised medical interventions to improve clinical outcome, however, remains unclear.

The Saudi government has undertaken rapid and swift actions, such as suspending Omrah and holding international flights. Such affirmative actions enhanced citizens and residents’ confidence in the government officials’ ability to fight the pandemic. However, there are more than 361,178 reported cases of Covid-19 with 6131 deaths as of December 22, 202. As nurses are the largest component of the health care workforce in Saudi Arabia, it is important that they are trained and have the skills and resources to adapt, whether on the frontline of a crisis or in a clinical environment. Nurses play a key function in fulfilling the needs of medical treatment by being the most important part of the health care delivery system.

Culture, gender and religion are closely linked each other’s, profoundly affect the role of each person within the Society, and also affect the ability to access to the health resources. We are now living a worldwide crisis due to the SARS-CoV-2 pandemic. In order to appropriately tackle it, an alliance between science, politics and citizens is needed. In this article, we summarize current evidences of how religions can represent, on one hand, a risk moment for spreading the virus (in relation with overcrowded events), on the other, a precious opportunity to engage people, and in particular minorities, in fighting the pandemic. To win this fight, we need a multicultural approach that takes into account every aspect of human life, and among these religion, which influences so much culture, everyday life and well-being.

Severe COVID-19 disease is characterised by an exaggerated inflammatory response, called cytokine storm, accompanied by a condition of immune depression. Even sepsis is characterised by an exaggerated inflammatory response, called SIRS (Systemic Inflammatory Response Syndrome), accompanied by a condition of immune depression called CARS (compensatory anti-inflammatory response syndrome). Clinical studies reveal that most sepsis patients who did not die during the hyper inflammatory response (SIRS) subsequently succumbed to the condition of immune depression (CARS). Severe acute pancreatitis begins with local inflammation that induces systemic inflammatory response syndrome (SIRS), accompanied and followed by a compensatory anti-inflammatory response (CARS). In COVID-19 disease, the male response to SARS CoV-2 virus is typically characterised by a robust inflammatory response. Instead, a cell-mediated immune response is dominant in women. This means that the male sex tends to have a more robust hyper inflammatory response than the female one. Furthermore, in women the condition of immune depression is less represented, therefore they are more protected. Sepsis, severe acute pancreatitis and COVID-19 disease evolve between two fundamental aspects: hyper inflammation and immunodepression. The experience gained over years of studies of sepsis and severe acute pancreatitis suggests that therapies should be differentiated according to the evolutionary stage of the disease. The goal is to save the lives of most patients with COVID-19 disease. The identification of critical points, suitable for designing the windows of therapeutic opportunity, may allow the use of therapeutic interventions, in the COVID-19 disease, which are effective (there are no approved drugs yet), safe (without significant side effects), targeted (based on the evolutionary phase of the disease) personalized, (based on sex, co-morbidities, age, etc.) and timely (based on signs, symptoms, laboratory parameters and instrumental investigations).

Since the first confirmed case of COVID-19, information was spreading in large amounts over social media platforms. Information spreading about the COVID-19 pandemic can strongly influence people’s behavior. Therefore, identifying information superspreaders (or influencers) during the COVID-19 pandemic is an im- portant step towards understanding public reactions and information dissemination. In this work, we present an analysis over a large Arabic tweets collected during the COVID-19 pandemic. The presented study con- struct a network from users’ behaviors to identify information superspreaders during the month of March, 2020. We employed both HITS and PageRank algorithms to analyze the influence of information spreading, and compared the ranking of the users. The results show that both HITS and PageRank discovered a similar subset of superspreaders with 40% were found to be verified Twitter accounts.

Misinformation during the COVID-19 outbreak has shaped our perception of the disease. Some people thinkthe disease is a bioweapon while others are convinced that it is a hoax. Heightened anxiety often producesfearful rumors, some of which are absurd while others seem plausible and are laced with some truths. But, how does misinformation affect disease spread? In this paper, we construct a mathematical model parameterized by Ugandan data, to study the effect of misinformation on community COVID-19 spread. The analysis shows that misinformation leads to high number of COVID-19 cases in a community, and the effect is highest in the rumour initiators and spreaders. This analysis underscores the importance of addressing misinformation in COVID risk communication.

The infected and fatal cases of Coronavirus Disease 2019 (COVID-19) keep increasing around the world, to explore the infection routes and pathogenesis of 2019-nCoV could be meaningful for prevention and treatment of COVID-19. Previous studies showed that the oral cavity is at potentially high risk of 2019-nCoV infection. The ACE2 receptor of 2019-nCoV was reported could express on oral epithelium and salivary glands, and 2019-nCoV could be detected in patients’ saliva. Recently, the amblygeustia were found to widely exist in the COVID-19 patients. To explore the potential mechanism of amblygeustia, we performed further analysis via independent in-house single-cell profiles. Our results showed that ACE2 was inclined to express in taste cells, which indicated that 2019-nCoV may invade into taste cells at the early stage of COVID-19, and lead to the amblygeustia of patients. Above findings about the 2019-nCoV and COVID-19 in oral cavity are valuable and enlightening for future epidemic prevention strategy.

We have proposed a model considering two equally sized population (group A and group B) with low and high levels of disease tolerance. We have argued that in the more tolerant group (group B) the progression of the disease with respect to time will be slow with lower number of infections at any given time. We attribute this effect to the innate immunity which advantageously, can also be one of the major contributing factors for flattening the curve. We have compared the growth of Covid-19 disease in various countries to understand this effect.

The COVID-19 pandemic spread of the coronavirus across the globe has affected our lives on many different levels. The world we knew before the spread of the virus has become another one. Every country has taken preventive measures, including social distancing, travel restrictions, and curfew, to control the spread of the disease. With these measures implemented, people have shifted to social media platforms in the online sphere, such as Twitter, to maintain connections. In this paper, we describe a coronavirus data set of Arabic tweets collected from January 1, 2020, primarily from hashtags populated from Saudi Arabia. This data set is available to the research community to glean a better understanding of the societal, economical, and political effects of the outbreak and to help policy makers make better decisions for fighting this epidemic.

The pharmacology of azithromycin, and the actions of certain bee products, suggest the possibility of overlap with the pathophysiology of COVID-19 at several points in the disease process. First, intercellular epithelial tight junctions of the respiratory tract serve as a critical barrier to invaders. Pathophysiological factors capable of disrupting this epithelial barrier include viral virulence factors such as those observed for other coronaviruses; virulence factors derived from potentially synergistic pathogens such as Candida albicans and Porphyramonas gingivalis; and imbalances in the host inflammatory response. Azithromycin, and to a lesser extent, certain bee products, appear to have actions that oppose such processes. Second, the matrikine PGP or its derivatives may contribute to risk in individuals at high risk for serious COVID-19 infection, especially during reactivation; but azithromycin is capable of modulating PGP in some contexts. Third, the most serious COVID-19 infections are associated with massive upregulation of inflammatory cytokines such as IL-6, TNF alpha, and other inflammatory cytokines. The anti inflammatory actions of azithromycin and bee derived products such as melittin are potentially capable of modulating these processes, as well. Azithromycin is already in current use as a treatment for COVID-19; however, it's utility as a protector of epithelial barrier function would be most likely to be realized in prophylactic context rather than in a treatment context. Similarly, since the anti inflammatory effects of bee products take time, their effectiveness of melittin and other bee products would be expected to be maximized in a prophylactic context. In the context of the current pandemic, prophylaxis with azithromycin, bee products, or both, might be warranted for individuals at high risk for serious COVID-19 infection.

Novel coronavirus (COVID-19) can lead to multiple organ injuries such as acute respiratory distress syndrome (ARDS), acute renal injury (AKI) and so on. ACE2 is an important part of the renin-angiotensin system (RAS) and a key protein needed for COVID-19 to invade cells. First of all, we searched the HPA, GTEx and FANTOM5 Databases and found that the expression of ACE2 in kidney tissue was significantly higher than that in lung tissue. Then, by searching the Nephroseq Database, it is further verified that ACE2 is highly expressed in renal tissue and plays a protective role in renal tissue. However, current studies have found that the incidence of AKI caused by COVID-19 is much lower than that of ARDS. Because of this, we further searched the proteins interacting with ACE2 protein through the STING Database and analyzed the expression of tissue protein mRNA in the HPA Database. It was noted that AGTR2 mRNA was highly expressed in lung tissue, but low in kidney tissue, and hard tissue specificity in lung tissue. Through further research, it is found that AGTR2 plays a major role in the development of pulmonary fibrosis. Therefore, AGTR2 may be a key protein in COVID-19 pneumonia, and AGTR2 may be a potential new therapeutic target for the treatment of COVID-19 patients.

COVID-19 pandemic disproportionately affected seven South Asian countries because of rampaging virus and weak health system response. Vaccination commenced in all countries during early 2021, once the vaccines became available in the international market. Vaccines were donated by international development partners, friendly countries and procured utilizing domestic resources and financial support from various agencies. All countries opted for a phasing of the vaccination by prioritizing high risk populations which included health care personnel, front line workers and elderly with comorbidities. Seven vaccines that had WHO emergency authorization usage were utilized in South Asia. India produced more than 2.4 billion doses of AstraZeneca vaccine and has indigenously developed five COVID-19 vaccines. Vaccine inequity was obvious and characterized by fewer people in South Asia vaccinated with single dose vis a vis the developed countries. Vaccine hesitancy was another obstacle that kept the vaccination rates low. Difficult to reach areas in Afghanistan, Bhutan, Maldives, Nepal could not fully access vaccines. South Asian countries need to strengthen their health system for efficient deployment of vaccines in future health emergencies. At the same time the global agencies and developed countries must address the issue of vaccine access to low-income countries to mitigate the impact of pandemic all over the world.

Background There is a considerable overlap between clinical presentation of post-COVID-19 condition (PCC) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) . Many of their common symptoms can be linked to dysregulation of the autonomic nervous system (dysautonomia). This study aimed to objectively assess autonomic function in patients with PCC and in patients with ME/CFS whose disease was not related to COVID-19. Methods Synchronous recordings of an electrocardiogram, continuous dynamics of blood pressure in the digital artery using the Penaz method and ultrasound pneumotachography with the spirography function were obtained with spiroarteriocardiorhythmography method in 34 patients diagnosed with ME/CFS, in whom the onset of the disease was not associated with COVID-19, 29 patients meeting PCC definition and 32 healthy controls. Heart rate variability (HRV), systolic and diastolic blood pressure variability (RV), respiration variability were assessed at rest and in tests with fixed respiratory rates. At rest, indicators of baroreflex regulation were additionally determined (baroreflex effectiveness index and baroreflex sensitivity). Results The total power, power of very low frequency, low-frequency and high-frequency of RR interval variability at rest as well as baroreflex effectiveness index in up-ramps of arterial blood pressure and baroreflex sensitivity were significantly lower both in PCC and ME/CFS patients compared to HC. Several diagnostic prediction models for ME/CFS were developed based on HRV parameters. During slow breathing HRV parameters return to normal in PCC, but not in ME/CFS. Correlation analysis revealed a close relationship of HRV, RV parameters and baroreflex sensitivity with fatigue, but not with HADS depressive/anxiety symptoms in ME/CFS and PCC. Conclusion A similar pattern of HRV and baroreflex failure with signs of a pathological acceleration of age-dependant dysautonomia was identified in ME/CFS and PCC. The clinical, diagnostic and therapeutic implications of these findings are discussed, in light of previously described relationship between inflammation, vascular pathology, atherosclerotic cardiovascular disease and autonomic dysfunction.

Earnings management and financial distress are two critical facets of financial management in corporations that have lately been the focus of thorough research from both scholars and practitioners. The purpose of the research is to evaluate the influence of financial distress on the earnings management of companies listed at the NSE during the pre- and post-COVID eras. Multiple linear regression was applied in this study to examine the causal association between earnings management and financial distress. To assess the effect of financial distress on earnings management, the study analyzed data from 37 listed companies during the pre-COVID and post-COVID periods. The methodology also takes into account control factors like firm size, GDP, and inflation. The pre-COVID research indicates a significant positive correlation between financial strain and earnings management. In contrast, the correlation between financial distress and earnings management is not statistically significant in the post-COVID analysis. However, research shows that firm size has a statistically significant negative influence on managing earnings. The results of this study show that smaller firms are more inclined to engage in earnings management during the post-COVID era than bigger ones.

Coronavirus 19 disease (COVID-19) may be complicated by thrombotic events, particularly venous thromboembolism (VTE), which have been reported both in critically ill hospitalized patients and in individuals with mild symptoms. It is known that the chronic use of oral contraceptive pills (OCPs) is associated with higher risk of VTE. To date, there are only few reports concerning the association of OCPs and VTE/pulmonary embolism (PE) in COVID-19 patients. Given that during the convalescent phase of disease, a state of endothelial dysfunction, hypercoagulability and a low-grade inflammation may be persistent, the occurrence of thromboembolic events following acute COVID-19 infection may be not surprising. Herein, we report a case of high-risk PE detected in a post-COVID-19 young woman under hormonal contraception, which required thrombolytic treatment. A number of prothrombotic phenomena, such as overweight, hormonal contraceptive therapy, recent COVID-19 infection and prolonged immobilization, might have synergically contributed to the development of a sublethal thromboembolic event.

In Los Angeles, at one point, the Covid-19 testing positivity rate was 6.25%, or one in sixteen. This translates to, on average, one in sixteen specimens testing positive and the vast majority testing negative. Usually, we run sixteen tests on sixteen specimens to identify the positive one(s). This process can be time consuming and expensive. Since a group of negative specimens pooled together for testing will produce a negative result, one single test could potentially eliminate many specimens. Only when the pooled specimen tests positive do we need further testing to identify the positive one(s). Based on this concept, we designed a strategy that will identify the positive specimen(s) efficiently. Assuming one in sixteen specimens is positive, we find that only four tests are needed. Furthermore, we can run them simultaneously, saving both resources and time. Although, in the real world, we cannot make the assumption of only one positive specimen, the same strategy works with slight modification and proves to be much more efficient than the conventional testing. Our strategy returns an answer 48% of the time in four tests and one time cycle. Overall, the average number of tests is seven or eight depending on the follow-up testing, and the average time cycle is about one and a half.

The prevalence of corona virus and the novel COVID-19 disease in the entire globe has exacerbated different impact on socioeconomic spectrum in the world, including water use pattern. Thus a research was conducted to examine the comparative use of water during pre- and post-COVID-19 lockdown pattern among post-primary schools in Iwo, Osun State, Nigeria. A survey was conducted among fifteen schools which were randomly selected, but with eight public and seven private schools for the investigation. Both descriptive and inferential statistical techniques were used in data analysis. The results revealed that the major source of water to the schools investigated is ground water which is obtained through hand-dug wells and boreholes. It was further discovered that there was increase in water use during post-COVID-19 lockdown era as a result of the directive by the government that clean water should be provided for hand-washing by all schools regardless of the owner to curtail the spread of COVID-19 disease in the country. One sample t-test also revealed that there was a significant difference in water use at (p<0.01) level. It is recommended that the government and other stakeholders in water sector to ensure that all-time and non-seasonal dependent source of water be provided rather than ground water source which is susceptible to variations in water yields from seasonal variations. This will enable continuous clean water supply, for all purposes, including COVID-19 protocols.

The aim of the article is to determine the predictors of mental health among Polish society. Research was conducted after the first wave of the pandemic. Due to such an approach, it was possible to determine whether secondary effects of the pandemic have impact on mental health, apart from socio-demographic and psychological factors. In order to gather the research material, the CAWI on-line survey method was applied and carried out within the framework of the Ariadna Research Panel on the sample of 1079 Poles aged 15 and over. The FCV-19S scale, which is used to measure the fear of COVID-19 was applied in the measurement. It is a verified diagnostic instrument used to measure mental health in a lot of countries. The results of a hierarchical regression analysis have shown that the factors which increase the level of fear of COVID-19 are demographic, social and psychological features as well as attitudes towards the pandemic. The results of research indicate the significance of social context in the analysis and explanation of the effects of disasters and cataclysms.

Objectives to assess COVID-19 mortality rates per country population. To determine what if any independent country-specific variables from 9 different databases were correlated. Design population based retrospective cohort study. Setting analysis of global COVID-19 treatment and containment strategies using data from 9 worldwide websites. Participants 108 countries worldwide. Interventions none. Main Outcome Measures were COVID-19 death rates per country population analyzed by univariate and multivariate analysis. The main outcome parameters were to determine if there are any correlations between the percentage of countrywide COVID-19 deaths/population by the countries’ percent vaccinated. Secondary outcome measures include the effect of other independent variables on COVID-19 death rates per country population including: health expenditures per capita, annual income per capita, COVID-19 tests per 1000 people, stringency index (a measure of each countries containment strategies), hydroxychloroquine score (a measure of each countries use), ivermectin score (a measure of each countries use), hypertension, obesity, diabetes, and specific countries and geographic locations. Results COVID-19 vaccination rates ranged from 0-99% in 108 countries. Univariate analysis demonstrates the following independent variables to correlate with COVID-19 deaths/population (correlation coefficient, p value): countrywide COVID-19 vaccination rates (+0.2936, p=0.002); healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); stringency index (+0.3098, p=0.0011); hydroxychloroquine index (-0.1337, p=0.0678); and ivermectin index (-0.1383, p=0.1535). Conclusions Increasing rates of COVID-19 vaccination are associated with increase COVID-19 death rates per country population (p=0.002). Other variables associated include healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); and stringency index (+0.3098, p=0.0011).

Introduction: Due to COVID-19 pandemic, performance of many program has been declined and Tuberculosis (TB) program is not an exception. TB case detection and notification has been recognized as one of worst hit area. The objective of this study was to explore the TB notification status of India during this pandemic and explore options to mitigate the issue. Methods: A secondary data analysis was performed on open source TB notification database of India. Relevant literature review was done to find out remedies based on the different initiative taken by different states of India. Results: In 2020, total TB notification decreased in all the states in comparison to 2019. The percentage of loss in the country was 34%. Private TB notification also decreased in 2020 in all the states except in Jharkhand. The percentage of loss in private TB notification in the country was 35%. Notification started declining in the month of February 2020 and it was lowest in the month of April-2020. The trend of notification began to improve since May 2020 when the States started taking innovative initiatives like Integrated TB Covid Case Search. Conclusion: Due to the ongoing COVID-19 pandemic the notifications of TB cases declined noticeably which has a serious implication in terms of silent spread within household and community. But the picture can be improved with integrated approach for TB-COVID case finding and management.

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.

Docosahexaenoic acid (DHA) is an essential fatty acid (FA) with proven health effects, whom bioavailability improvement is becoming a public health issue. Unlike fish oils, the bioavailability of DHA from microalgal (A) has not been fully assessed, particularly with regard to the molecular structuring capabilities offered by A-oil. We explored the impact of 5 formulas rich in DHA, different by i) the molecular structure: ethyl ester (EE) or monoglyceride (MG) or Triglyceride (TG) and ii) the supramolecular form: emulsified TG or TG+phospholipids (blend PL), on the lymphatic kinetics of DHA absorption and the lipid characteristics of resulting lipoproteins. We demonstrated that in rat, the DHA absorption of the conventional A-DHA TG structure was more effective than the EE structure (+ 23%). More, the A-DHA MG and A-DHA Emulsion were the most favourable DHA-vectors (AUC: 89% and +42%, respectively), thanks to an improved lipolysis. The A-DHA MG and -Emulsion presented the richest DHA content in TG (+40%) and PL (+50%) of lymphatic chylomicrons, which could impact the metabolic fate of DHA. We concluded that structuring A-DHA in TG or EE would be more prone for tissue and hepatic metabolism whereas in A-DHA MG and A-DHA Emulsion could target nerve tissues.

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.

As sessile organisms, plants face a wide range of abiotic stresses, with salinity being a prominent constraint affecting their growth, development, and productivity, particularly in arid and semi-arid regions. This study focused on understanding how salinity impacts Jatropha curcas, an important oilseed plant for biodiesel production. By examining the anatomy and ultrastructure of stomata and chloroplasts, we investigated the effects of prolonged salinity stress on J. curcas. This stress led to changes in stomatal density, stomatal index, and ostiole aperture, which case an imbalance of water conductivity in the xylem. Through transmission electron microscopy, we explored the subcellular organization of J. curcas chloroplasts and their contribution to plant photosynthetic efficiency, providing insights into their role in this process. Notably, salinity treatment resulted in a significant increase in starch granules accumulation, leading to impaired the granal and stromal grana lamellae, destroying this ultrastructure. Our findings indicate that the anatomy and ultrastructure of chloroplasts play a crucial role in influencing photosynthetic efficiency and hydraulic conductivity. This study offers new perspectives on the structure and function of chloroplasts in J. curcas, presenting innovative opportunities to develop strategies that enhance biofuel production in areas with high soil salinity.

This paper presents a methodology for indirectly estimating the scale of seabird bycatch using existing data, eliminating the need for regular monitoring. The study focuses on five species of ducks that winter in the Polish waters of the Baltic Sea and are highly susceptible to bycatch: Long-tailed Duck, Velvet Scoter, Common Scoter, Greater Scaup, and Tufted Duck. The evaluation units used are divided into the Bornholm and Gotland Groups within the Polish Exclusive Economic Zone (PEEZ). The analysis acknowledges the lack of data for certain areas known to have high concentrations of diving birds and fishing activity. The population sizes of waterbirds were assessed using ship-based surveys and a distance sampling approach. Fishing effort data from the winter seasons between 2015/2016 and 2019/2020 were analyzed. Bycatch rates were estimated based on surveys conducted in previous seasons, and threshold values for bycatch were established using the concept of "small numbers" defined in EU directives. The results show that within the Gotland subdivision of the PEEZ, the estimated average abundance of all diving waterbirds was 207,114 individuals, with an average bycatch of 7,921 birds (3.8% of the total). The Velvet Scoter was the most abundant species, followed by the Long-tailed Duck. Benthivorous ducks constituted the majority of the bird population in the area. In the Bornholm subdivision, the estimated average abundance of diving waterbirds was 600,845 individuals, with an average bycatch of 5,056 birds (0.8% of the total). The Long-tailed Duck was the most numerous species, followed by the Velvet Scoter. Benthivorous ducks also dominated in this area. The findings highlight the need for effective management and conservation measures to address the high mortality of seabirds due to bycatch. The methodology presented in this study offers a valuable approach for estimating bycatch scale and can support efforts to minimize the impact on seabird populations in the Baltic Sea.

In the present work, we investigate the possibility that long-range airborne transport of infectious aerosols could initiate an epidemic outbreak at distances downwind beyond one hundred kilometers. For this, we have developed a simple atmospheric transport box-model which, for a hypothetical case of a COVID-19 outbreak, was compared to a more sophisticated 3-dimensional transport-dispersion model (HYSPLIT) calculation. Coupled with an extended Wells-Riley description of infection airborne spread, it shows that, the very low probability of outdoor transmission can be compensated by high numbers and densities, such as occurs in large cities, of infected and susceptible people in the source upwind and in the target downwind respectively. This may result in the creation of a few primary cases. It is worth pointing out that the probability of being infected remains very small at the individual level. Therefore, this process alone, which depends on population sizes, geography, seasonality and meteorology, can only “trigger” an epidemic which could then spread by the standard infection routes

Ad- and desorption forces move water in living xylem/wood from the root to the leaf thermodynamically. The doctrine of plant water transport, the so-called cohesion- or cohesion-tension theory, postulates however that the process is physically based on a hydraulic fluid flow with negative pressure in water conducting tubes originating from the leaves. Lower pressure (suction) driven volume flow is physically a branch of mechanics. Moisture absorbed from the soil via the root is thought to be pulled up the stem by the leaves in continuous and tensioned threads of water. It is assumed that the hydraulic Hagen/Poiseuille flow law, derived for tubes, applies in the xylem. In a textbook of botany you can find the opinion: "Just as the pipes of a water pipe supply necessary water to each household, leaf nerves supply water and nutrient salts to each individual cell.” (Translated from German). Many plant physiologists consider this hydraulic principle to be correct, but it does not remain unchallenged. Doubts are repeatedly expressed. The question arises: How does water transport actually take place? It is shown how the diffusive/adsorption transport principle works. The partial dehydration (desorption) of the plant, driven by the diffusive process of transpiration, forms a combined concentration and adsorption-site gradient for water in the xylem matrix. Especially with open stomata the lowest moisture concentration and the highest number of adsorption-sites for water (sites with free van der Waals forces), can be found in the mesophyll cell walls at the liquid/vapor boundary in the leaf. The water taken up by the root moves spontaneously in the direction of this boundary and can thus partially or completely compensate for the existing concentration- and adsorption-site- differences for water. Thus, a thermodynamic overlapping diffusive/adsorptive movement of moisture along the stationary xylem/wood takes place. After the introduction and a review of some controversies with cohesion theory, the physiology of the processes associated with long-distance water displacement is mentioned below. A thermodynamic adsorption hypothesis of the natural water transport in plants, based on known facts, is presented.

The genus Pseudomonas hosts an extensive genetic diversity and is one of the largest genera among Gram-negative bacteria. Type strains of Pseudomonas are well-known to represent only a small fraction of this diversity and the number of available Pseudomonas genome sequences is increasing rapidly. Consequently, new Pseudomonas species are regularly reported and the number of species within the genus is in constant evolution. In this study, whole genome se-quencing enabled us to define 43 new Pseudomonas species and to provide an update of the Pseu-domonas evolutionary and taxonomic relationships. Phylogenies based on the rpoD gene and whole genome sequences, including 316 and 313 type strains of Pseudomonas, respectively, re-vealed sixteen groups of Pseudomonas and justified the partitioning of the P. putida group into fifteen subgroups. Pairwise average nucleotide identities were calculated between type strains and a selection of 60 genomes of non-type strains of Pseudomonas. Forty-one strains were incor-rectly assigned at the species level and among those, 19 strains were shown to represent an addi-tional 13 new Pseudomonas species that remain to be formally classified. This work pinpoints the importance of correct taxonomic assignment and phylogenetic classification in order to perform integrative studies linking genetic diversity, lifestyle and metabolic potential of Pseudomonas spp.

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.

Each injection of any known vaccine results in a strong expression of pro-inflammatory cytokines. This is the result of the innate immune system activation, without which no adaptive response to the injection of vaccines is possible. COVID-19 mRNA vaccines would not escape this rule. Unfortunately, the degree of inflammation produced by these vaccines is variable, probably depending on the genetic background and previous immune experiences, which through epigenetic modifications, could have made the innate immune system of each individual tolerant or reactive to subsequent immune stimulations.We hypothesize that we can move from a limited pro-inflammatory condition to conditions of increasing expression of pro-inflammatory cytokines that can culminate in multisystem hyperinflammatory syndromes following COVID-19 mRNA vaccines (MIS-V). We have graphically represented this idea in a hypothetical inflammatory pyramid (IP) and we have correlated the time factor to the degree of inflammation produced after the injection of vaccines. Furthermore, we have placed the clinical manifestations within this hypothetical IP, correlating them to the degree of inflammation produced. Surprisingly, excluding the possible presence of an early MIS-V, the time factor and the complexity of clinical manifestations are correlated to the increasing degree of inflammation: symptoms, heart disease and syndromes (MIS-V).

Cancer patients, susceptible to severe COVID-19 outcomes, exhibit varied vaccine responses, particularly those who are receiving chemotherapy. This prospective cohort study enrolled 115 cancer patients, and data from 91 patients who received two doses of COVID-19 vaccines were analyzed. Blood was drawn at baseline, day 28, and 6 months post-second dose for neutralizing antibody analysis. The primary outcome was seroconversion rate against wild type and Omicron at day 28. Secondary outcomes included seroconversion at 6 months, factors associated with seroconversion, and safety. Of the 157 cancer patients screened, 91 were enrolled, with 45% receiving chemotherapy. Seroconversion rates at day 28 were 77% for the wild type and 62% for Omicron. Chemotherapy did not affect seroconversion (p=0.789 for wild type, p=0.597 for Omicron). Vaccine type positively correlated with seroconversion, with an adjusted ORR of 25.86 (p=0.029) for the wild type and 17.38 (p

Macrophage-derived chemokine belongs to the CC subfamily. It is produced by dendritic cells (DCs) and macrophages with or without external stimulation. We have previously shown a statistically significant depletion of MDC/CCL22 concentrations in a number of studies concerning COVID-19. These shifts in concentrations demonstrated stability unrelated to the SARS-CoV-2 genetic variant and remained noticeable even in convalescent patients. In this work, we analyze MDC/CCL22 dynamics in various diseases, including those that manifest with inflammation in lung tissue. In addition, we provide our hypothesis on such a decrease in MDC/CCL22 concentrations in COVID-19. If its secretion by producer cells is unperturbed, then it is possible for viral products to bind to this chemokine and to block its functional activity. There is, however, another possible explanation directly linked to depletion in DC subpopulations and the inhibition of their function. We also discuss MDC/CCL22's role in the immunology of novel coronavirus infection, based on both our own data and other studies.

This study focuses on cardiovascular effects, particularly myocarditis and pericarditis events, after BNT162b2 mRNA COVID-19 vaccine injection in Thai adolescents. This prospective cohort study enrolled students from two schools aged 13–18 years who received the second dose of the BNT162b2 mRNA COVID-19 vaccine. Data including demographics, symptoms, vital signs, ECG, echocardiography and cardiac enzymes were collected at baseline, Day 3, Day 7, and Day 14 (optional) using case record forms.We enrolled 314 participants; of these, 13 participants were lost to follow up, leaving 301 participants for analysis. The most common cardiovascular effects were tachycardia (7.64%), shortness of breath (6.64%), palpitation (4.32%), chest pain (4.32%), and hypertension (3.99%). Seven participants (2.33%) exhibited at least one elevated cardiac biomarker or positive lab assessments. Cardiovascular effects were found in 29.24% of patients, ranging from tachycardia, palpitation, and myopericarditis. Myopericarditis was confirmed in one patient after vaccination. Two patients had suspected pericarditis and four patients had suspected subclinical myocarditis. Conclusion: Cardiovascular effects in adolescents after BNT162b2 mRNA COVID-19 vaccination included tachycardia, palpitation, and myocarditis. The clinical presentation of myopericarditis after vaccination was usually mild, with all cases fully recovering within 14 days. Hence, adolescents receiving mRNA vaccines should be monitored for side effects. Clinical Trial Registration: NCT05288231

Management of inflammatory bowel disease (IBD) often relies on biological and immunomodulatory agents for remission through immunosuppression, raising concerns regarding the SARS-CoV-2 vaccine's effectiveness. The emergent variants have hindered the vaccine neutralization capacity, and whether the third vaccine dose has the capacity to neutralize SARS-CoV-2 variants in this population remains unknown. This study aims to evaluate the humoral response of SARS-CoV-2 variants in patients with IBD 60 days after the third vaccine dose [BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna)].56 su bjects with IBD and 12 healthy subjects were recruited. 90% of patients with IBD (49/56) were receiving biologics and/or immunomodulatory therapy. 24 subjects with IBD did not develop effective neutralizing capability against the Omicron variant. 70% (17/24) of those subjects were receiving anti-Tumor Necrosis Factor therapy [10= adalimumab, 7= infliximab], two of them had a history of COVID-19 infection, and one subject did not develop immune neutralization against three other variants: Gamma, Epsilon, and Kappa. All subjects in the control group developed detectable antibodies and effective neutralization against all seven SARS-CoV-2 variants. Our study shows that patients with IBD might not be protected against SARS-CoV-2 variants, and larger studies are needed to evaluate optimal immunity.

At present, there are no hardware or biochemical systems allow to assess the severity of post-COVID syndrome in vivo. The hardware of the proposed biotechnical system is based on routine transthoracic electrical impedance rheography, which makes it possible to register the frequency characteristics of the patient's bioimpedance response to controlled stress stimulation, thereby simultaneously fixing the characteristics of his productive heart, the state of the hemomicrocirculatory bed, the efficiency of the gas transport function of his blood, and also reliably assess personal reactivity and adaptive potential. Subsequent mathematical approximation of the obtained biometric data by an original neural network makes it possible to rank the results obtained and automatically generate a program of medical rehabilitation for a particular patient, depending on the severity of his post-COVID syndrome. The study results proved two reliable physiological signs confirming the presence of latent post-COVID complications: a decrease in the base impedance value for light exercise and an increase in the length of the systolic arc of the rheocardiogram.

Objectives: This study aimed to analyze the awareness of local residents regarding healthcare projects and to suggest some ideas for the revision of local ones. Methods: To delve into the opinions of local residents, the author of this study created a questionnaire composed of eight questions on the general characteristics of the respondents, eight questions on satisfaction with health centers, 16 questions on the awareness of healthcare projects, and 22 questions on local healthcare. The survey was conducted for 409 residents who visited public centers in Gimcheon from 15 March to 14 April 2021. Results: Data analysis revealed the following: The proportion of local residents who use health centers was 39.1%, and those users visit health centers, on average, 3.92 times a year. Among healthcare projects, the project known by the highest proportion of people was vaccinations (84.5%), which was also the project that was most used (38.1%). Among healthcare projects needed in the with-COVID-19 age, respondents awarded the highest score to vaccinations (4.15 points on a five-point Likert scale) and the second highest score to infectious disease management (4.12). Conclusions: For healthcare projects, central and local governments should focus on activating vaccinations, solving the problem of medical accessibility through untact remote treatment and establishing national infectious disease-specializing hospitals and local infectious disease management based on such national hospitals.

The global spread of coronavirus disease (COVID -19) worldwide has had a significant effect on social and economic growth. The contamination keeps on advancing quickly and eccentrically, representing a significant test to its recognition and conclusion. Coronaviruses are commonly recognized by seclusion from tests, regardless of whether natural or clinical, utilizing some atomic science procedures, which can take a few days. In this work an analytical review of virus transmission, methods of diagnosing COVID -19 using artificial intelligence techniques to classify images and types of biosensors. At long last, the deformities and points of interest of each kind of sensor are recognized and examined. This exploration gives an explanatory audit of the utilization of crown infection COVID-19 in 2019. Related examinations were led utilizing five dependable databases, for example, Science Direct, IEEE Xplore, Scopus, Web of Science, and PubMed. An acceptable investigation is remembered for this audit, which can be depended upon as a logical database to put resources into another technique for recognizing COIVD-19.

This literature review has been compiled to form an evidenced-based review on the standards for Dental Practices in their choice and use of personal protective equipment (PPE) within the COVID-19 Pandemic and beyond: it is prepared on the basis of the current best available evidence. The review encompasses risk management strategies for both Dental Personnel and Patients in the application and use of Face Masks & Respirators.In summation, from the evidence available, it is apparent that in the lab setting N95/FFP2 masks are superior in their efficiency but in the clinical setting such a difference is not seen as clearly. As such the minimum standard of care should be that of a standard surgical mask. Faced with the emergence of the virulent disease that is Covid-19, it is logical to use FFP2/N95 respirator masks in aerosol generating procedures where they offer greater resistance to fluid penetration and a better face seal when adequately fit tested as a gold standard. But if a dry field isolation technique involving high volume evacuation is used, there is no clear benefit of respirator masks (N95/FFP2 or N99/FFP3) when balanced with the extra risk of compliance, cost and comfort in wearing a standard fluid-resistant surgical mask.

COVID-19 is causing major turmoil around the globe, and the clinical trial industry is likely to face unprecedented challenges to health and business sectors. In an effort to find a suitable treatment and prevention options for COVID-19, several COVID-19 clinical trials are being planned and initiated, while a large number of clinical trials for non- COVID-19 indications are suffering delays. With over more than 1000 trials being disrupted and more trials being added to this category daily, there is a direct impact on trial site activation and patient enrolment. This analysis deals with the specific impacts of the COVID-19 pandemic on the clinical trial and pharmaceutical industry. The objective of this study is to provide an updated information of the disrupted clinical trials and its impact on various therapeutic areas and different drugs. Among the severely affected clinical trials, oncology and CNS trials are the hardest hit therapy areas.This article will certainly emphasize the need for advanced and innovative approaches to maintain the health of the clinical trial ecosystem by continuing the existing trials and the start of the new studies. We have to take and follow necessary actions to guarantee that the initiatives will not be locked during the COVID-19 pandemic, both for the treatment of patients and for the researchers to conduct decision-relevant clinical trials.

Draconian defensive measures have been implemented to combat the COVID-19 pandemic outbreak. These important measures constitute a vital current priority but do little to increase communal immunity and avoid future outbreaks. A longer-term exit strategy for a sustainable return to normalcy has yet to be identified. The development of vaccines or effective therapeutics could largely solve the problem, but their timely development cannot be guaranteed. In this setting, and under the expected societal isolation fatigue from extended social distancing, we here propose the idea that at some point after the outbreak’s peak, hospitals, in addition to providing care for infected people who need it, could also be involved in the development of a controlled exit strategy designed to avoid future outbreaks. We postulate that controlled voluntary deliberate infection in a hospital setting and under continuous and close medical observation may offer a safer alternative compared to random en-masse exposure. We discuss potential risks and benefits, highlighting the need for careful consideration of the associated critical ethical issues.

Abstract: This brief policy is written after experience treating COVID-19 positive radiation therapy patients to reduce risk to therapy staff and patients in radiation oncology department. It is important to prioritize the safety of staff and non-infected patients while ensuring the continuation of radiation oncology services. Radiation therapists have sustained contact with covid-19 patients in an enclosed vault. Protocols for correct disinfecting of equipment and room and therapists following methods for less transmission of virus is crucial. This policy covers prevention methods from COVID-19 transmission from patient to patient, patient to staff, staff to patient and staff to staff as follows A.Risk reduction by screening and preparing staff and rooms B.Radiation Therapist Policy for COVID-19 positive patient with CCC (Critical Cancer Care)

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.

Long-acting (LA) drug-delivery system (DDS) successes are linked to their abilities to harness biocompatible polymers and materials for sustained and predictable release of therapeutic agents over an extended period of time. For human immune deficiency virus type one (HIV-1) infections, LA DDSs hold great promise in closing the gaps of daily oral therapy for treatment and prevention. Examples are Cabenuva, Apretude and Sunlenca, which have been shown to be safe and effective. Alternative promising drug delivery technologies such as implants, prodrugs, vaginal rings, and microarray patches are being explored to further meet patients’ needs. We posit that improved physicochemical properties of the formulation payloads and material chemistry design with optimal drug release kinetics holds the potential to optimize release profiles. Furthermore, for the treatment and prevention of HIV-1 infection, the landscape of LA medicines now in clinical or preclinical development offers hope that the strategic design of polymers will further improve upon controlled delivery of drugs to simplify dosing schedules.

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

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.

Hepatocellular carcinoma (HCC) is one of the most common cancers and the main cause of cancer-related death globally. Immune dysregulation of CD4+ T cells has been identified as a role in the development of HCC. Nevertheless, the underlying molecular pathways of CD4+ T cells in HCC are not completely known. Thus, a better understanding of the dysregulation of lncRNA-miRNA/mRNA network might yield novel insights into the etiology or progression of HCC. In this study, circulating CD4+ T cells were isolated from the whole blood of 10 healthy controls and 10 HCC patients for next-generation sequencing of the expression of lncRNAs, miRNAs, and mRNAs. Our data showed that there was different expression of 34 transcripts (two lncRNAs, XIST and MIR222HG, 29 mRNA, and 3 other types of RNA) and 13 miRNAs in the circulating CD4+ T cells of HCC patients. The expression of the lncRNA XIST-related miRNAs and their target mRNAs was confirmed using real-time quantitative polymerase chain reaction (qPCR) on samples from 100 healthy controls and 60 HCC patients. The lncRNA–miRNA/mRNA regulation network was created using interaction data generated from ENCORI and revealed there are positive correlations in the infiltration of total CD4+ T cells and negative correlations in the infiltration of Th1 CD4+ T cells.

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

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

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

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

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.