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Rabies is a fatal zoonotic disease typically transmitted through animal bites. It affects all mammals, domestic and wild, as well as humans. The primary cause of the disease is the multiplication of a neurotropic rhabdovirus in the central nervous system, which almost always leads to fatal encephalomyelitis. This study aimed to describe a canine rabies outbreak in Ksour Municipality, province of Bordj Bou Arreridj (Algeria), and its impact on public health. The epidemiological investigation revealed that a stray local dog breed had bitten multiple people and domestic animals. Seven people, 5-76 years old, were bitten. However, children under 15 years old accounted for 28.57% of the cases. Males were the most commonly bitten (85.71%). All victims sustained severe category III injuries, predominantly located on the upper extremities (85.71%). Seven domestic animals were bitten, including six dogs and one donkey. The direct fluorescent antibody test (DFAT) and the mouse inoculation test (MIT) were both positive when applied and tested. The injured people received post-exposure prophylaxis (PEP) with rabies immunoglobulin and vaccination. Rabies remains endemic in the study area, with stray dogs acting as the primary transmission vectors through bites. A multidisciplinary approach involving collaboration among the national veterinary authority, human health services, political authorities, and local communities has been proven effective in rabies control.

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems.

We develop a new analytical and numerical approach, based on existing models, to describe the onset of lateral–torsional buckling (LTB) for simply supported thin-walled steel members. The profiles have uniform I cross-sections with variable lengths of the flanges, to describe also H cross-sections, they are prestressed by external tendons, and they are subjected to fire and various loadings. Our approach manages to update the value of the prestressing force, accounting for thermal and loads; the critical multipliers result from an eigenvalue problem obtained applying Galërkin’s approach to a system of nonlinear equilibrium equations. Our results are compared to buckling, steady state, and transient state analyses of a Finite Element Method (FEM) simulation, in which an original expression for an equivalent thermal expansion coefficient for the beam–tendon system that accounts for both mechanical and thermal strains is introduced. Our aim is to find estimates for the critical conditions with no geometric imperfections and accounting for the decay of material properties due to fire, thus providing limit values useful for conservative design. This approach can surpass others in the literature and in the existing technical norms.

Predictive justice, which involves forecasting trial outcomes, presents significant challenges due to the complex structure of legal judgments. To address this, it is essential to first identify all claims across different categories before attempting to predict any result. This paper focuses on a classification task based on the detection of Article 700 in judgments, which is a rule indicating whether the plaintiff or defendant is entitled to reimbursement of their legal costs. Our experiments show that conventional machine learning models trained on word and document frequencies can be competitive. However, using transformer models specialized in legal language, such as Judicial CamemBERT, also achieves high accuracies.

Abstract The Covid-19 pandemic has significantly spurred the development of deep learning (DL) models for the pathology automatic diagnosis based on CT scan images. However, the assumption about the generalization of the proposed models remains to be assessed and shown for concrete clinical use. In this work, we have investigated the real value of widely used public datasets for the elaboration of DL models that are dedicated to automatic diagnosis of Covid-19 using CT scans. We have collected various international public datasets from 13 countries. Different Convolutional Neural Networks (CNNs) have been trained and their performances carefully assessed. Two evaluations have been conducted: (1) an internal evaluation following a cross-validation procedure, and (2) an external evaluation on real patients coming from new and different sources. The objective is to assess the generalization capabilities considering real-world conditions: different acquisition conditions, devices and configurations. Three families from the most effective CNN models have been selected (ResNet, DenseNet and EfficientNet). These have been fine-tuned, evaluated and used within a training methodology based on transfer learning. The most effective models have been further customized in order to create new models that are dedicated to the task at hand. These models have significantly improved the diagnosis performance.

This paper introduces a new class of multivariable operators called $ (n_1, \cdots, n_m) $-hyponormal tuples, which combine joint normal and joint hyponormal operators. A tuple of operators $ \mathcal{Q} = (\mathcal{Q}_1, \; \cdots, \mathcal{Q}_m) $ is said to be an $ (n_1, \cdots, n_m) $-hyponormal tuple for some $ (n_1, \cdots, n_m)\in \mathbb{N}^m $ if$ \sum\limits_{1\leq k,\;l\leq m}\big\langle[\mathcal{Q}_k^{*n_k}, \;\mathcal{Q}_l^{n_l}]\omega_k\mid \omega_l\big\rangle\geq 0, \quad \forall\; (\omega_k)_{1\leq k\leq m}\in {\mathcal K}^m. $We show several properties of this class that correspond to the properties of joint hyponormal operators.

Summary: Background: Individuals with Down syndrome (DS) are at high risk of early-onset Alzheimer's disease (AD); yet, some 20 percent do not develop any signs of dementia until after 65 years or in their lifetime. Mosaicism could contribute to this phenotypic variation, where some disomic cells could lead to lower levels of gene products from chromosome 21. Methods: We examined longitudinal neuropsychological and biomarker data from two large studies of DS: the Alzheimer Biomarker Consortium–Down syndrome study (ABC-DS) (n = 357); and a legacy study (n = 468). We assessed mosaicism using karyotyping or GWAS data. Participants had data on plasma AD biomarkers (Aβ40, Aβ42, tau, and NfL) and longitudinal cognitive measures. A subset had cerebrospinal fluid biomarkers (Aβ40, Aβ42, tau, ptau181, and NfL) and amyloid and tau PET data. Findings: For both cohorts, the prevalence of mosaicism was

Abstract We present a prescription for choosing orthogonal bases of differential n-forms belonging to quadratic twisted period integrals, with respect to the intersection number inner product. To evaluate these inner products, we additionally propose a new closed formula for intersection numbers beyond d log forms. These findings allow us to systematically construct orthonormal bases between twisted period integrals of this type. In the context of Feynman integrals, this represents all diagrams at one-loop.

Abstract Background Zebrafish are widely used in various research fields and to fulfil the diverse research needs, numerous zebrafish lines are available, each with a unique domestication background, potentially resulting in intraspecies differences in specific biological functions. Few studies have compared multiple zebrafish lines under identical conditions to investigate both inter- and intra-line variability related to different functions. However, such variability could pose a challenge for the reproducibility of results in studies utilising zebrafish, particularly when the line used is not clearly specified. This study assessed growth, stress status (cortisol, serotonin) and reproductive capabilities (maturity, fecundity, fertilisation rate, sperm quality) of four commonly used wild-type zebrafish lines (AB, SJD, TU, WIK) using standardized protocols. Results The stress markers levels were found to be similar across the lines, indicating that the endocrine stress status is robust to diverse domestication histories. Variations were observed in the growth and reproductive parameters. The lines exhibited differences in the timing of puberty (86 dpf for AB and SJD lines vs. 107 dpf for the WIK line) despite achieving similar sizes, suggesting that there are line-specific variations in the induction of maturation. Additionally, the AB line demonstrated higher sperm quality than did the other lines and higher fecundity and fertilization rates than did the SJD line. The AB line also exhibiting a smaller adult size but a heavier brain relative to its body weight. Conclusion These findings emphasize the importance of line selection for zebrafish research, indicating that researchers should consider line-specific traits to ensure the biological relevance and reproducibility of the results.

Abstract In recent years, the study of bounded linear operators in several variables has received great interest from many authors, including the second author’s previous contributions. In our present work, we define a new class of multivariable operator theory, which we have called M-hyponormal tuple. We present some algebraic and spectral properties associated with them.

Abstract This research introduces a novel two-parameter distribution, the power-modified XLindley distribution, developed through the application of power transformation techniques to the existing modified XLindley distribution. This new distribution enhances flexibility and adaptability in statistical modeling. We conduct a thorough examination of its statistical properties, exploring its potential to improve data fitting and modeling accuracy. To assess the effectiveness of the model, we employ multiple estimation techniques and evaluate their performance through extensive simulation experiments. Our findings indicate that the maximum product of the spacings method is particularly effective for parameter estimation. To demonstrate the practical utility of the proposed model, we apply it to two real-world datasets: one related to flood data and the other to reliability engineering. The results underscore the distribution’s superior ability to capture the characteristics of these datasets compared to existing models, highlighting its significance for applications in natural disaster analysis and reliability studies.

The diatom Haslea ostrearia produces a blue-green pigment known as marennine, which exhibits remarkable antimicrobial, anti-viral, antiproliferative, allelopathic, and antioxidant properties. Despite the potential for marennine production, there is still a lack of understanding surrounding its chemical structure, biosynthetic pathway, genes, and enzymes. This study is the inaugural attempt to identify potential transcripts that may encode enzymes involved in marennine biosynthesis. Bioinformatics analysis comparing the transcriptomes of Haslea strains revealed transcripts coding for enzymes and proteins specific to blue H. ostrearia. Functional annotation and enrichment analysis provided insights into these transcripts’ role in marennine production. It was secondary metabolite biosynthesis (Q), signal transduction (T), transcription (K), post-translational modifications (O), extracellular structures (W), and defense mechanisms (V) that had the most significant amounts of COGs. Notably, eight transporters, one transcription factor, and 38 enzymes putatively related to marennine production were identified. These findings provide a promising avenue for further research into the genetic and molecular mechanisms underlying the biosynthesis of this pigment.

ABSTRACTBackground: Literature points towards the potential benefits of the application of Eye Movement and Desensitization Processing (EMDR)-therapy for patients in the medical setting, with cancer and pain being among the domains it is applied to. The field of applying EMDR-therapy for patients treated in the medical setting has evolved to such an extent that it may be challenging to get a comprehensive overview.Objective: This systematic literature review aims to evaluate the use and effectiveness of Eye Movement Desensitization and Reprocessing (EMDR) therapy in patients treated in the medical setting.Methods: We performed a literature search following the PRISMA guidelines. Studies were included if the effectiveness of EMDR-therapy was assessed in adult patients treated in a medical setting. Excluded were patients exclusively suffering from a mental health disorder, without somatic comorbidity. A risk of bias analysis was performed. This review was registered on PROSPERO (CRD42022325238).Results: Eighty-seven studies, of which 26 (pilot)-RCTs were included and categorized in 14 medical domains. Additionally, three studies focusing on persistent physical complaints were included. Most evidence exists for its application in the fields of oncology, pain, and neurology. The overall appraisal of these studies showed at least moderate to high risks of bias. EMDR demonstrated effectiveness in reducing symptoms in 85 out of 87 studies. Notably, the occurrence of adverse events was rarely mentioned.Conclusions: Overall, outcomes seem to show beneficial effects of EMDR on reducing psychological and physical symptoms in patients treated in a medical setting. Due to the heterogeneity of reported outcomes, effect sizes could not be pooled. Due to the high risk of bias of the included studies, our results should be interpreted with caution and further controlled high-quality research is needed.

We present an algorithm that estimates shared e-scooter trip origins and destinations using public General Bikeshare Feed Specification (GBFS) data. The model addresses challenges like GPS lag and randomized vehicle IDs. The algorithm is field-tested and validated using data for the city of Fairfax, VA. When applied to Washington, D.C., a larger city, the algorithm demonstrates scalability for larger, complex urban settings. Our methodology equips urban planners and activity center managers with a robust tool for analyzing micromobility patterns, enhancing first- and last-mile connectivity, and optimizing transportation systems---without relying on proprietary data from operators.

Summary: Background: Reports of high and rising maternal mortality ratios (MMR) in the United States have caused serious concern. We examined spatiotemporal patterns in cause-specific MMRs, in order to obtain insights into the cause for the increase. Methods: The study included all maternal deaths recorded by the Centers for Disease Control and Prevention from 1999 to 2021. Changes in overall and cause-specific MMRs were quantified nationally; in low-vs high-MMR states (i.e., MMRs

This study introduces a new class of operators called polynomilally quasi-$ M $-hyponormal, which combining $ M $-hyponormal, quasi-$ M $-hyponormal, and $ k $-quasi-$ M $-hyponormal operators. We will demonstrate several properties of this class that correspond to those of $ k $-quasi-$ M $-hyponormal operators.

The purpose of this work is to analyze and validate the gravimetric information in the study area in order to highlight the valid data for mapping the gravity anomaly from a grid generated by interpolation. In this context, we have performed a first step of data validation using the Kriging interpolation, this method was applied to a set of free-air anomalies reduced by the EGM08 geopotential model, from the test zone located in the north of Algeria, but the data were considered as values of an isotropic variable whose the variographic analysis depends only on the distances between points. In the second step the anisotropic character was taken into account, where the main tasks were to calculate the directional variograms for validating data and to perform interpolation by directional models. Finally, we have performed a computer application for interpolation and validation of gravimetric data by Kriging method, based on the algorithm for validation and grid generation of free-air anomalies, proposed in this article, where the results showed that the free-air anomaly is an isotropic variable which only depends on the distances between positions, and that the values resulting from interpolation are validated by the BGI grid, where the two maps generated one by interpolation and the other by the BGI grid are very similar to confirm the validity of the Kriging algorithm developed in this context.

In industrial environments, motors in Variable Speed Drive (VSD) systems act as primary conduits for the propagation of conducted electromagnetic disturbances, including Differential Mode (DM). The Split-Phase Induction Motor (SPIM) is among the most widely used motors in ASD systems, making the analysis of its impedances and High-Frequency (HF) properties crucial for ensuring Electromagnetic Compatibility (EMC) and mitigating Electromagnetic Interference (EMI). Poor EMC management can result in malfunctions of nearby electronic devices.This article examines the DM current propagation paths in two motor types. The investigation aims to: (i) Identify the HF characteristics of these motors within the critical frequency range for conducted EMI (150 kHz – 30 MHz), (ii) Assess key EMC performance indicators, focusing on the windings of the main and auxiliary phases, and (iii) Compare the EMC performance of different SPIM designs through experimental analysis to select the most suitable motors for critical EMC applications. This approach not only addresses EMC challenges but also optimizes motor performance and ensures regulatory compliance in electrical systems where these motors play a crucial role.

Abstract INTRODUCTION Both micro‐ and macrostructural white matter (WM) abnormalities, particularly those related to axonal degeneration, are associated with cognitive decline in adults with Down syndrome (DS) prior to a diagnosis of Alzheimer disease. Neurofilament light chain (NfL) is a support protein within myelinated axons released into blood following axonal damage. In this study we investigated cross‐sectional relationships between WM microstructural changes as measured by diffusion tensor imaging (DTI) and plasma NfL concentration in adults with DS without dementia. METHODS Thirty cognitively stable (CS) adults with DS underwent diffusion‐weighted MRI scanning and plasma NfL measurement. DTI measures of select WM tracts were derived using automatic fiber tracking, and associations with plasma NfL were assessed using Spearman correlation coefficients. RESULTS Higher Plasma NfL was associated with greater altered diffusion measures of select tracts. DISCUSSION Early increases in plasma NfL may reflect early white matter microstructural changes prior to dementia in DS. Highlights The onset of such WM changes in DS has not yet been widely studied. WM microstructural properties correlated with plasma neurofilament light chain (NfL). NfL may reflect early, selective WM changes in adults with DS at high risk of developing AD.

The population of Hispanic older adults is growing along with the burden of chronic diseases. This cross-sectional study aims to assess the factors associated with frailty among community-dwelling Hispanic women aged ≥60 years ( n = 357) enrolled in the Panama Aging Research Initiative—Health Disparities study of cognitive-functional health of older persons in Panama. Cognitive function was assessed with a neuropsychological test battery. Depression was measured with the Geriatric Depression Scale. Frailty was defined using the Fried criteria and participants were classified as non-frail, pre-frail or frail. A subsample ( n = 281) provided fasting blood samples for quantification of protein biomarkers. Associations were examined using hierarchical multiple linear regressions. 59.4% and 9.0% of participants ( M = 69.2 years, SD = 6.3) were pre-frail and frail, respectively. Having more depression (β = .28, p

Background: In vitro fertilization (IVF) as a fertility treatment is associated with adverse perinatal outcomes. Racial/ethnic disparity in severe maternal morbidity (SMM) in women who conceived by IVF is understudied. Objective: To examine differences in the association between race/ethnicity and SMM between women who conceived spontaneously and those who conceived using IVF. Methods: We included all singleton live births and stillbirths in the United States, 2016–2021; data were obtained from the National Center for Health Statistics. Maternal race/ethnicity included non-Hispanic White (NHW), non-Hispanic Black (NHB), American Indian and Alaska Native (AIAN), Asian, Pacific Islander (PI), Hispanic, and mixed-race categories. The SMM composite outcome included eclampsia, uterine rupture, peripartum hysterectomy, blood transfusion, and intensive care unit (ICU) admission. We used logistic regression to adjust for potential confounders (such as age, education, parity, prepregnancy body mass index, smoking during pregnancy, chronic hypertension, and preexisting diabetes) and to assess modification of the association between race/ethnicity and SMM by IVF. Results: The study population included 21,585,015 women: 52% were NHW, 15% NHB, 0.8% AIAN, 6% Asian, 0.2% PI, 24% Hispanic, and 2% were of mixed race. IVF was used by 183,662 (0.85%) women; the rate of the SMM composite outcome was 18.5 per 1000 deliveries and 7.9 per 1000 deliveries in the IVF and spontaneous conception groups, respectively (unadjusted rate ratio 2.34, 95% confidence interval [CI] 2.26–2.43). In women with spontaneous conception, NHB, Asian and mixed-race women had elevated odds of SMM compared with NHW women (adjusted odds ratio [aOR]=1.39, 95% CI 1.37–1.41; aOR=1.04, 95% CI 1.02–1.07; and aOR=1.42, 95% CI 1.38–1.46, respectively). Racial/ethnic disparities in SMM and its components were not different between the IVF and spontaneous conception groups for the mixed-race category. NHB and Hispanic women had significantly higher aORs for uterine rupture/intrapartum hysterectomy compared with NHW women in the IVF group, while Asian women had a higher aOR for ICU admission compared with NHW women in the IVF group. Conclusion: Women who conceived by IVF have a greater than two-fold higher risk of SMM and this higher risk is evident across all racial/ethnic groups. However, NHB and Hispanic women who conceived by IVF had a higher risk of uterine rupture/hysterectomy, and Asian women who conceived by IVF had a higher risk of ICU admission. Our results warrant further investigation examining pregnancy and postpartum care issues among racial/ethnic minority women who conceive using IVF.

This article suggested a solution to a flow control problem governed by a class of nonlinear systems called bilinear systems. The problem was initially well-posed, and after it was established that an optimal control solution existed, its characteristics were stated. After that, we demonstrated how to use various bounded feedback controls to make a plate equation's flow close to the required profile. As an application, we resolved the plate equation-governed partial flow control issue. The findings bring up a variety of system applications, which can be employed in engineering advancement.

The quadcopter persist as important roles across diverse applications, and the enhancement of their control efficacy has been the subject of extensive research. In this work, the authors proposed optimal Proportional Integral (PI) controller based Fuzzy Logic Control (FLC) for the roll, pitch, altitude, and yaw motions of the quadcopter system. The proposed technique uses the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the FLC and enhance the quadcopter performance. The simulation results show that the proposed technique achieves smoothness of control and significant improvement over classical techniques, as the rise time and the settling time are reduced by 61 % and 66 %, respectively. These times are important for stabilizing the system's response speed and avoiding overshooting or oscillating. This indicates that the FLCPI-PSO can achieve the desired roll and altitude angles more rapidly and effectively.

Les tumeurs fibreuses solitaires sont des tumeurs bénignes mésenchymateuses rares, développées le plus souvent aux dépends de la plèvre. Elles surviennent plus fréquemment après 40 ans. Le fibrome pleural est le plus souvent asymptomatique ce qui explique leur caractère volumineux. Nous rapportons un cas de tumeur pleurale volumineuse diagnostiqué chez un patient de 77 ans aux antécédents de diabète et d’hypertension artérielle. La découverte était faite sur une radiographie thoracique motivée par une toux intermittente évoluant depuis un mois. La tomodensitométrie montrait une masse tumorale pleurale gauche mesurant 18 cm de grand axe, présentant un contact avec l’aorte descendante à l’origine d’un collapsus pulmonaire quasi-total. Une biopsie transpariétale a été réalisée et l’étude histologique revenait en faveur d’une tumeur fibreuse solitaire. L’exploration chirurgicale a révélé une tumeur volumineuse prenant naissance de la plèvre viscérale, sans contact intime avec les gros vaisseaux. Les tumeurs fibreuses solitaires de la plèvre sont des tumeurs mésenchymateuses extrêmement rares et représentent moins de 5% de toutes les tumeurs pleurales, de diagnostic parfois difficile. Le traitement de choix de ces tumeurs fibreuses solitaires est la résection chirurgicale complète, souvent sous thoracotomie, à cause de leur taille élevée. La récidive locale est rapportée dans 1,4 à 12 % en cas de résection incomplète.

Emotional Voice Conversion (EVC) for under-resourced languages like Arabic faces challenges due to limited emotional speech data. This study explored strategies to mitigate dataset scarcity and improve Arabic EVC performance. Fundamental experiments (Speaker-Dependent, Gender-Dependent, Gender-Independent) were conducted using the KSUEmotions dataset to analyze speaker, gender, and model impacts. Data augmentation techniques like time stretching and phase shuffling artificially increased data diversity. Attention mechanisms integrated into StarGANv2-VC aimed to better capture emotional cues. Transfer learning leveraged the larger English Emotional Speech Database (ESD) to enhance the Arabic system. A novel “Reordering Speaker-Emotion Data” approach treated each emotion as a separate speaker to expand the emotional variability. Our comprehensive approach, combining transfer learning, data augmentation, and architectural modifications, demonstrates the potential to overcome dataset limitations and enhance the performance of Arabic EVC systems.

Recombinant adeno-associated virus (AAV) vectors have emerged as the preferred platform for gene therapy of rare human diseases. Despite the clinical promise, host immune responses to AAV vectors and transgene remain a major barrier to the development of successful AAV-based human gene therapies. Here, we assessed the human innate immune response to AAV9, the preferred serotype for AAV-mediated gene therapy of the CNS. We showed that AAV9 induced type I interferon (IFN) and IL-6 responses in human blood from healthy donors. This innate response was replicated with AAV6, required full viral particles, but was not observed in every donor. Depleting CpG motifs from the AAV transgene or inhibiting TLR9 signaling reduced type I IFN response to AAV9 in responding donors, highlighting the importance of TLR9-mediated DNA sensing for the innate response to AAV9. Remarkably, we further demonstrated that only seropositive donors with preexisting antibodies to AAV9 capsid mounted an innate immune response to AAV9 in human whole blood and that anti-AAV9 antibodies were necessary and sufficient to promote type I IFN release and plasmacytoid dendritic (pDC) cell activation in response to AAV9. Thus, our study reveals a previously unidentified requirement for AAV preexisting antibodies for TLR9-mediated type I IFN response to AAV9 in human blood.

Employing vision-based hand gesture recognition for the interaction and communication of disabled individuals is highly beneficial. The hands and gestures of this category of people have a distinctive aspect, requiring the adaptation of a deep learning vision-based system with a dedicated dataset for each individual. To achieve this objective, the paper presents a novel approach for training gesture classification using few-shot samples. More specifically, the gesture classifiers are fine-tuned segments of a pre-trained deep network. The global framework consists of two modules. The first one is a base feature learner and a hand detector trained with normal people hand’s images; this module results in a hand detector ad hoc model. The second module is a learner sub-classifier; it is the leverage of the convolution layers of the hand detector feature extractor. It builds a shallow CNN trained with few-shot samples for gesture classification. The proposed approach enables the reuse of segments of a pre-trained feature extractor to build a new sub-classification model. The results obtained by varying the size of the training dataset have demonstrated the efficiency of our method compared to the ones of the literature.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

The Single-Phase Induction Motor (SPIM) has gained widespread adoption in various power applications. This article introduces a new study and analysis of SPIM, focusing on its high-frequency (HF) modeling within the conducted electromagnetic interference (EMI) frequency range of interest (150 kHz– 30MHz). This research is crucial for ensuring electromagnetic compatibility (EMC), evaluating electromagnetic interference issues, optimizing machine design, and complying with EMC standards. To identify common-mode current propagation paths accurately, a precise impedance analyzer is required to determine the common-mode impedances models of the high-frequency motor. The article proposes an HF modeling method based on a genetic algorithm (GA), which offers the advantages of high accuracy, versatility, and fast simulation time. This approach effectively reproduces models, trends, and relationships observed in real data, indicating a high level of fidelity and reliability in its predictions. Case studies validate the efficiency of the proposed HF modeling method. These precise predictions of undesired HF phenomena provide valuable insights into identifying areas where parasitic currents may propagate in the motor and understanding the impact of each motor phase on the overall behavior of the motor’s common-mode impedance.

This study presents scientific results that serve as arguments for advocating the development of a hyperspectral microwave sensor (HyMS). Through simulation experiments, the results of this study demonstrate the major benefits of HyMS sensor observations in low-Earth orbit (LEO), including; 1) increased information content over the microwave region, 2) improved temperature and moisture sounding in all-weather conditions, resulting from higher signal-to-noise ratios, finer vertical resolution, and a reduced dependence on background information due to the increased spectral resolution around oxygen and water vapor absorption features between 23 and 183 GHz, 3) improved profiling of hydrometeors, and 4) improved resilience to radio frequency interference, demonstrated at 23 GHz, associated with the redundant information provided by the HyMS. The deployment of HyMS instruments in LEO orbit is expected to provide an improved knowledge of the state of the atmosphere, particularly if deployed in the form of a constellation, due to the enhanced temporal, spatial, and spectral resolution capabilities that those sensors can provide with respect to present meteorological microwave sounders. This work takes advantage of artificial intelligence (AI), particularly its capability to rapidly and simultaneously process hundreds of channels and retrieve large sets of geophysical parameters, to assess the impact of HyMS in geophysical space. The results presented in this manuscript are expected to contribute to the design of the next generation of microwave sounders, but also to consider the usage of AI to fully exploit the information content provided by these sensors, particularly if deployed in the form of a constellation of satellites.

Data assimilation (DA) is facing major challenges in terms of its ability of handling the ever-increasing volume of valid, useful, and potentially impactful environmental data and the problem is expected to be exacerbated in the near future if a solution to dramatically increase efficiency is not found. A new approach to perform large-volume data fusion and assimilation, based entirely on artificial intelligence (AI) modern techniques including machine learning and computer vision techniques, is proposed in this study. This approach to DA is applied and demonstrated to real environmental data measured by NOAA-20 and MetOp-C microwave satellite-sounders to reproduce traditional numerical weather prediction DA performances from the U.S. National Oceanic and Atmospheric Administration (NOAA). We assess the impact of our AI-based analysis on forecasts by; 1) performing statistical assessments versus the European Centre for Medium-Range Weather Forecasts analyses, 2) assimilating the AI-based analyzed fields as pseudo-sounding observations in the NOAA global data assimilation system (GDAS), and 3) running forecast experiments using FV3GFS initialized with those observations. To identify the impact of our AI-based assimilations, we compare the forecast skill of several experiments where GDAS is driven with conventional and satellite radiometric observations and with conventional and AI-based pseudo-observations. The results presented are encouraging but are considered only a first initial step toward demonstrating an entirely AI-based environmental data fusion/assimilation system capable to efficiently handle large-volume data and take of the information content available.

Over the past ten years, global electricity networks have undergone rapid advancements, primarily driven by the widespread adoption of renewable energy resources (RES). While these sources have accompanied in a gathering of advantages, such as cost-effective operation of wind and solar photovoltaic (PV) installations and the mitigation of environmental hazards linked with traditional power sources, they have also introduced a host of challenges to planning and operation of power systems. Also, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The standard optimal power flow (OPF) problem, which is naturally nonlinear, has become even more complex with the addition of renewable energy sources and plug-in electric vehicles along with traditional thermal power generators. This increased complexity comes from the unpredictable and intermittent nature of these new resources. Monte Carlo techniques are used to estimate the production costs of renewable energy sources and plug-in electric vehicles (PEVs) and analyze their viability. The uncertainty of the renewable sources and PEVs is modeled using Weibull, lognormal, and normal probability distribution functions (PDFs). The comprehensive OPF, incorporating renewable energy components and PEVs, is cast as a single objective problem encompassing various objectives, such as decreasing fuel costs, total emissions, voltage deviations, and real transmission losses. This research shares a common objective by introducing a novel hybrid metaheuristic optimization algorithm (MRGTO) to address the OPF challenge. Additionally, the study explores the impact of renewable energy resources and Vehicle-to-Grid (V2G) on the stochastic OPF problem. The MRGTO employs artificial gorilla troops optimizer (GTO) with manta ray foraging optimization (MRFO) algorithm to achieve the optimal results for the OPF problem with stochastic RES and V2G. The developed technique is expected to increase the solution accuracy through increasing the solution diversity through an optimization procedure. Initial assessments are executed on benchmark functions. After that, a combined model of wind and PV-integrated IEEE 30-bus system are executed by the proposed MRGTO algorithm and other well-known optimization algorithms. Additionally, the effectiveness of the proposed method is assessed using the IEEE 30-bus test system under different scenarios. The evaluations have shown the proposed MRGTO technique to be superior at attainment a best solution for the OPF problem considering stochastic wind and PV power and V2G. Moreover, the obtained solutions confirmed the MRGTO technique would be more effective in optimization with quicker convergence rate, and higher convergence precision. After testing, the effectiveness of the MRGTO algorithm has been found to be much robust than the conventional artificial gorilla troops optimizer, manta ray foraging optimization, and other well-known published heuristics, metaheuristics, and hybrid optimization techniques.INDEX TERMS Optimal power flow, plug-in electric vehicle, renewable energy sources, V2G, artificial gorilla troops optimizer, manta ray foraging optimization.

Algeria is by excellence a multilingual country. Inherited from the colonial system and rooted in Algerian soil, French is explicitly omnipresent among all citizens. In school and university contexts, it is taught as a first foreign language. In this contribution, we try to see the linguistic representations towards French among students enrolled in the second year LMD in the French department at the University of Adar and to see their impact and the impact of the socio-family environment on the use of this language in formal and informal situations.

Breast cancer (BC) is a leading cause of cancer-related deaths in women worldwide where the process of metastasis is a major contributor to the mortality associated with this disease. Metastasis suppressor genes are a group of genes that play a crucial role in preventing or inhibiting the spread of cancer cells. They suppress the metastasis process by inhibiting colonization and by inducing dormancy. These genes function by regulating various cellular processes in the tumor microenvironment (TME), such as cell adhesion, invasion, migration, and angiogenesis. Dysregulation of metastasis suppressor genes can lead to the acquisition of an invasive and metastatic phenotype and lead to poor prognostic outcomes. The components of the TME generally play a necessary in the metastasis progression of tumor cells. This review has identified and elaborated on the role of a few metastatic suppressors associated with the TME that have been shown to inhibit metastasis in BC by different mechanisms, such as blocking certain cell signaling molecules involved in cancer cell migration, invasion, enhancing immune surveillance of cancer cells, and promoting the formation of a protective extracellular matrix (ECM). Understanding the interaction of metastatic suppressor genes and the components of TME has important implications for the development of novel therapeutic strategies to target the metastatic cascade. Targeting these genes or their downstream signaling pathways offers a promising approach to inhibiting the spread of cancer cells and improves patient outcomes.

Demand-side management has become a viable solution to meet the needs of the power system and consumers in the past decades due to the problems of power imbalance and peak demand on the grid. This study focused on an improved decision tree-based algorithm to cover off-peak hours and reduce or shift peak load in a grid-connected microgrid using a battery energy storage system (BESS), and a demand response scheme. The main objective is to provide an efficient and optimal management strategy to mitigate peak demand, reduce the electricity price, and replace expensive reserve generation units. The developed algorithm is evaluated with two scenarios to see the behavior of the management system throughout the day, taking into account the different types of days (weekends and working days), the random profile of the users' demand, and the variation of the energy price (EP) on the grid. The simulation results allowed us to reduce the daily consumption by about 30% to 40% and to fill up to 12% to 15% of the off-peak hours with maximum use of renewable energies, demonstrating the control system's performance in smoothing the load curve.

Abstract INTRODUCTION People with Down syndrome (DS) have a 75% to 90% lifetime risk of Alzheimer's disease (AD). AD pathology begins a decade or more prior to onset of clinical AD dementia in people with DS. It is not clear if plasma biomarkers of AD pathology are correlated with early cognitive and functional impairments in DS, and if these biomarkers could be used to track the early stages of AD in DS or to inform inclusion criteria for clinical AD treatment trials. METHODS This large cross‐sectional cohort study investigated the associations between plasma biomarkers of amyloid beta (Aβ)42/40, total tau, and neurofilament light chain (NfL) and cognitive (episodic memory, visual–motor integration, and visuospatial abilities) and functional (adaptive behavior) impairments in 260 adults with DS without dementia (aged 25–81 years). RESULTS In general linear models lower plasma Aβ42/40 was related to lower visuospatial ability, higher total tau was related to lower episodic memory, and higher NfL was related to lower visuospatial ability and lower episodic memory. DISCUSSION Plasma biomarkers may have utility in tracking AD pathology associated with early stages of cognitive decline in adults with DS, although associations were modest. Highlights Plasma Alzheimer's disease (AD) biomarkers correlate with cognition prior to dementia in Down syndrome. Lower plasma amyloid beta 42/40 was related to lower visuospatial abilities. Higher plasma total tau and neurofilament light chain were associated with lower cognitive performance. Plasma biomarkers show potential for tracking early stages of AD symptomology.

According to attitude–behavior theory, the emotions generated by tourists’ travel experiences can influence their subsequent behavioral intentions. This quantitative study analyzed 398 survey questionnaires using a structural equation model to explore the multiple chain mediating effects between tourists’ participation, perceived value, place attachment, and loyalty. The results show that the innovation and interaction dimensions of tourist participation significantly influence the generation of perceived value and place attachment, which is far greater than that of the information exchange dimension. Perceived value significantly affects the formation of place attachment, and the self-enhancement dimension of perceived value significantly influences tourists’ recommendations. Compared to ordinary perceived value, the emotional connection between tourists and the destination is more profound and important, and place attachment plays a crucial role in the formation of tourists’ loyalty. This study confirms the relevance of the attitude–behavior theory in the context of traditional village tourism and extends the theory’s application to encompass the emotional connection between individuals and places. It thoroughly investigates the mechanisms through which tourists’ participation, perceived value, and place attachment impact customer loyalty. The findings of this research hold significant importance in comprehending tourists’ behavioral intention and decision-making processes within destination settings, providing a theoretical basis for exploring and expanding diverse value activities in traditional village tourism.

Abstract Background 22q11.2 deletion syndrome (22q11.2DS) is the most common chromosomal interstitial-deletion disorder, occurring in approximately 1 in 2000 to 6000 live births. Affected individuals exhibit variable clinical phenotypes that can include velopharyngeal anomalies, heart defects, T-cell-related immune deficits, dysmorphic facial features, neurodevelopmental disorders, including autism, early cognitive decline, schizophrenia, and other psychiatric disorders. Developing comprehensive treatments for 22q11.2DS requires an understanding of both the psychophysiological and neural mechanisms driving clinical outcomes. Our project probes the core psychophysiological abnormalities of 22q11.2DS in parallel with molecular studies of stem cell-derived neurons to unravel the basic mechanisms and pathophysiology of 22q11.2-related psychiatric disorders, with a primary focus on psychotic disorders. Our study is guided by the central hypothesis that abnormal neural processing associates with psychophysiological processing and underlies clinical diagnosis and symptomatology. Here, we present the scientific background and justification for our study, sharing details of our study design and human data collection protocol. Methods Our study is recruiting individuals with 22q11.2DS and healthy comparison subjects between the ages of 16 and 60 years. We are employing an extensive psychophysiological assessment battery (e.g., EEG, evoked potential measures, and acoustic startle) to assess fundamental sensory detection, attention, and reactivity. To complement these unbiased measures of cognitive processing, we will develop stem-cell derived neurons and examine neuronal phenotypes relevant to neurotransmission. Clinical characterization of our 22q11.2DS and control participants relies on diagnostic and research domain criteria assessments, including standard Axis-I diagnostic and neurocognitive measures, following from the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) and the North American Prodrome Longitudinal Study (NAPLS) batteries. We are also collecting measures of autism spectrum (ASD) and attention deficit/hyperactivity disorder (ADHD)-related symptoms. Discussion Studying 22q11.2DS in adolescence and adulthood via deep phenotyping across multiple clinical and biological domains may significantly increase our knowledge of its core disease processes. Our manuscript describes our ongoing study’s protocol in detail. These paradigms could be adapted by clinical researchers studying 22q11.2DS, other CNV/single gene disorders, or idiopathic psychiatric syndromes, as well as by basic researchers who plan to incorporate biobehavioral outcome measures into their studies of 22q11.2DS.

This study aims to explore new flame-retardant composites based on a phosphorus-functionalized cellulose derivative and epoxy/benzoxazine thermosetting resins in order to broaden the use of natural fibers in advanced applications. The study involved the phosphorylation of microcrystalline cellulose followed by its characterization through employing various analytical methods to corroborate the accomplishment of its functionalization. The curing behavior of composites based on the polybenzoxazine/epoxy copolymer reinforced with (1 and 5 wt.%) modified microcrystalline cellulose was hereafter considered. The thermal behavior of these composites was correspondingly investigated using thermogravimetric analysis, where improved thermal stability and the limiting oxygen index were stressed. Flame retardancy tests using the vertical burning test UL 94 and heat of combustion analysis utilizing an oxygen bomb calorimeter were also carried out to deeply examine the possible flame retardancy ability of the considered composites.

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers.

Abstract INTRODUCTION Virtually all people with Down syndrome (DS) develop neuropathology associated with Alzheimer's disease (AD). Atrophy of the hippocampus and entorhinal cortex (EC), as well as elevated plasma concentrations of neurofilament light chain (NfL) protein, are markers of neurodegeneration associated with late‐onset AD. We hypothesized that hippocampus and EC gray matter loss and increased plasma NfL concentrations are associated with memory in adults with DS. METHODS T1‐weighted structural magnetic resonance imaging (MRI) data were collected from 101 participants with DS. Hippocampus and EC volume, as well as EC subregional cortical thickness, were derived. In a subset of participants, plasma NfL concentrations and modified Cued Recall Test scores were obtained. Partial correlation and mediation were used to test relationships between medial temporal lobe (MTL) atrophy, plasma NfL, and episodic memory. RESULTS Hippocampus volume, left anterolateral EC (alEC) thickness, and plasma NfL were correlated with each other and were associated with memory. Plasma NfL mediated the relationship between left alEC thickness and memory as well as hippocampus volume and memory. DISCUSSION The relationship between MTL gray matter and memory is mediated by plasma NfL levels, suggesting a link between neurodegenerative processes underlying axonal injury and frank gray matter loss in key structures supporting episodic memory in people with DS.

In the present paper, we first show that the existence of the solutions of the operator equation S∗XT=X is related to the similarity of operators of class C1., and then we give a sufficient condition for the existence of nontrivial hyperinvariant subspaces. These subspaces are the closure of ranφT for some singular inner functions φ. As an application, we prove that every C10-quasinormal operator and C10-centered operator, under suitable conditions, have nontrivial hyperinvariant subspaces.

For future low-background particle physics experiments, it will be essential to assay candidate detector materials using an array of assay techniques. To minimise the risk of sample contamination whilst moving between assay techniques, it is also sensible to minimise the distance between assay stations, particularly for non-destructive techniques where the sample may end up being installed into an experiment. The Boulby UnderGround Screening (BUGS) Facility comprises an array of germanium detectors, two XIA UltraLo-1800 surface-alpha counters, two radon emanation detectors and an Agilent ICP-MS system. This article describes each of these systems.

Mega-events have always been an attractive topic for sports management academics. We used scientometric software packages to look at the studies on this topic that have been added to the Web of Science database in the last 68 years. Not only did we give an overview of the background information of the researchers, the status of their collaborations, and the countries and institutions they represented, but we also pointed out active and influential publication sources on this topic. We focused on the Olympic Games and other sports mega-events like the FIFA World Cups, as well as the environmental, tourism, economic, social, political, and cultural aspects of these events. We also made a list of the countries, organizations, and scholars who have made important contributions to the field. Researchers in sports management and sporting event hosts can both use this study as a guide for their own work.

Serum levels of testosterone (ST) and high-density lipoprotein (HDL) are generally associated with atherosclerosis in male patients over 50 years with critic iliac stenosis (TASC II A and B) and cardiovascular disease with significant changes in HDL and low-density lipoprotein (LDL). In addition to the standard therapy, combined medicamentous therapy and adequate model of exercise are also important factors as medicines can improve HDL levels and primary bypass and endovascular potency impacting positively on improvement of ST or it can be the following testosterone replacement therapy (TRT) comprising cardiovascular disease prevention and vascular treatment as adjunct therapy options after endovascular and vascular surgical procedures. The aim of the study is to identify the association between HDL and ST after surgical and endovascular intervention on the iliac segment (TASCII A and B), as well as cardiovascular risk factors with modified medium activity (MET) 6 (MET), with total duration of 30–60 minutes. It also attempts to remodel a patient behavioral pattern, optimize ST levels and link them to outcomes and patency of vascular procedures on the iliac segment. Materials and methods. 108 selected male patients with cardiovascular disease combined with metabolic syndrome and critical iliac artery stenosis (TASC II A and B) were examined during 2014–2018, 4 years after invasive and minimal invasive treatment (54 patients were treated with surgical Dacron reconstruction and 54 patients – with endovascular treatment on short segment of critical iliac artery stenosis (TASC II A and B). Results. In the total population, no difference was observed in changes of constraint-induced movement therapy between the standard exercise group and the control one after 4 years from baseline. However, there was no significant interaction between the effect of exercise trainings and primary bypass potency within 4 years. Conclusions. Primary effects of endovascular procedure and Dacron bypass revascularization raise the risk of elevated testosterone levels after 4 years of group training but does not provide adequate answers to questions as to whether higher levels of ST have any major influence on primary bypass potency preventing further progression of cardiovascular disease and general symptomatic and asymptomatic atherosclerosis. However, exercise and TRT can be potential adjunctive therapeutic options for a future supporting postsurgical and endovascular illiac treatment in cardiovascular patients with low testosterone levels.

Abstract The primary goal of this article is to explore the radiative stagnation point flow of nanofluid with cross-diffusion and entropy generation across a permeable curved surface. Moreover, the activation energy, Joule heating, slip condition, and viscous dissipation effects have been considered in order to achieve realistic results. The governing equations associated with the modeling of this research have been transformed into ordinary differential equations by utilizing appropriate transformation variable. The resulting system of equations was solved numerically by using Bvp4c built-in package in MATLAB. The impact of involved parameters have been graphically examined for the diverse features of velocity, temperature, and concentration profiles. Throughout the analysis, the volume fraction is assumed to be less than $$5\%$$ 5 % while the Prandtl number is set to be $$6$$ 6 . In addition, the entropy generation, friction drag, Nusselt, and Sherwood numbers have been plotted for describing the diverse physical aspects of the underlying phenomena. The major outcomes reveal that the curvature parameter reduces the velocity profile and skin friction coefficient whereas the magnetic parameter, temperature difference parameter, and radiation parameter intensify the entropy generation.