Search Results (8,518)

The physical origin of the Blazhko effect (BL), a phenomenon of a single or multiple periodic modulation(s) of the light curve, is under debate. Efficiently identifying and characterizing the BL is essential in understanding its origins and accounting for its effect on numerous applications of RRabs in the era of large time-domain surveys. In this study, we make use of Resnet 34, a well-known convolutional neural network (CNN) architecture, to identify RRab stars with BL from phased light curves collected from OGLE. Using reliably classified RRabs from frequency analysis to train, validate, and test our model, we show that our CNN method reaches accuracies up to 94%. We then applied our CNN method to some additional RRabs located in the Magellanic Cloud (MC) and the Galactic Bulge (GB), leading to the discovery of 113 and 2496 BL candidates, respectively. The identification accuracy for the MC Sample is estimated to be 91% after cross-matching the CNN classification results with those from frequency analysis. Similarly, the light-curve parameters of these classified BL/non-BL candidates by our CNN method from the GB region resemble those observed in the literature, confirming the reliability of our CNN classifications. Our CNN method is subject to issues related to light-curve quality and sampling, but its overall reliance on light-curve quality is comparable to that of frequency analysis. Furthermore, we find that BL modulation could be primarily characterized by variations in light-curve structure. Full article

Background/Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is closely related to SARS-CoV and uses angiotensin-converting enzyme 2 as its cellular receptor. In early 2020, reports emerged linking CoV disease 2019 (COVID-19) to olfactory and gustatory disturbances. These disturbances could be attributed to virus-induced damage to olfactory neurons or immune responses, thereby affecting sensory functions. This randomized controlled trial aimed to evaluate the effectiveness of a structured orofacial rehabilitation program in improving smell (olfaction) and taste (gustation) sensations in children post-COVID-19. Methods: Forty children recovering from COVID-19 in government hospitals in Saudi Arabia were included and randomly assigned to the control group or the experimental group. The orofacial program included (a) facilitation of olfactory function using the 40-item modified Arabic version of the University of Pennsylvania Smell Identification Test (UPSIT); (b) assessment of gustatory function using taste strips with four varying concentrations; and (c) orofacial myofunctional therapy. The intervention was applied three times a week and lasted for 3 months. Results: The experimental group showed a significantly greater improvement in UPSIT scores (median change of 24.1%) than the control group (14.7%; p = 0.010). However, no significant difference was found in the taste strip test scores among the groups or between male and female participants. Conclusions: This study suggests that a structured orofacial rehabilitation program could enhance olfactory and gustatory functions in children recovering from COVID-19. Full article

Background/Objectives: Despite its low incidence, complete postoperative abdominal evisceration represents a complication requiring an urgent solution. We aimed to present a rare case of an abdominal evisceration of the omentum and small-bowel loops after a total abdominal hysterectomy and review the literature regarding this condition’s diagnosis and therapeutic management. Case report: On the sixth postoperative day for a uterine fibroid, a 68-year-old patient presented with an abdominal evisceration of the omentum and small bowel that occurred two hours before. An emergency laparotomy was performed to correct the evisceration and restore the integrity of the abdominal wall structure. The literature review was carried out in the PubMed, Embase, and Web of Science databases using the terms “abdominal wall dehiscence”, “abdominal evisceration”, “open abdomen”, “burst abdomen”, “abdominal fascial dehiscence”, “abdominal dehiscence post-hysterectomy”, and “hysterectomy complications” by identifying all-time articles published in English. Results: Seven studies were included in this electronic search. The early diagnosis of abdominal evisceration, the identification of risk factors and comorbidities, followed by the choice of surgical technique, and postoperative follow-up were parts of the standard algorithm for managing this life-threatening case. Conclusions: Abdominal evisceration, as a surgical emergency, requires the diagnosis and treatment of this complication alongside the identification of the risk factors that can lead to its occurrence, as well as careful postoperative monitoring adapted to each case. Full article

This study explores the effect of heat treatment on the microstructural characteristics and corrosion resistance of 316L stainless steels (SSs) produced via laser powder bed fusion (L-PBF), focusing on anisotropic corrosion behavior—a relatively less explored phenomenon in LPBF 316L SSs. By systematically analyzing the effects of varying heat treatment temperatures (500 °C, 750 °C, and 1000 °C), this work uncovers critical correlations between microstructural evolution and corrosion properties. The findings include the identification of anisotropic corrosion resistance between horizontal (XY) and vertical (XZ) planes, with the vertical plane demonstrating higher pitting and repassivation potentials but greater post-repassivation current densities. Furthermore, this study highlights reductions in grain size, dislocation density, and melt pool boundaries with increasing heat treatment temperatures, which collectively diminishes corrosion resistance. These insights advance the understanding of processing–structure–property relationships in additively manufactured metals, providing practical guidelines for optimizing thermal post-processing to enhance material performance in corrosive environments. Full article

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species. Full article

Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. The system’s performance was comprehensively evaluated through laboratory and field experiments. Laboratory experiments demonstrated that, under conditions of low speed and large plant spacing, the system achieved a weeding accuracy of 96.67%, with a minimum crop injury rate of 0.83%. However, as the operational speed increased, the weeding accuracy decreased while the crop injury rate increased. Two-way ANOVA results indicated that operational speed significantly affected both weeding accuracy and crop injury rate, whereas plant spacing had a significant effect on weeding accuracy but no significant effect on crop injury rate. Field experiment results further confirmed that the system maintained high weeding accuracy and crop protection under varying speed conditions. At a low speed of 0.1 m/s, the weeding accuracy was 96.00%, with a crop injury rate of 1.57%. However, as the speed increased to 0.5 m/s, the weeding accuracy dropped to 81.79%, while the crop injury rate rose to 5.49%. These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management. Full article

In flowering plants, MADS-box genes play regulatory roles in flower induction, floral initiation, and floral morphogenesis. Isatis indigotica (I. indigotica) is a traditional Chinese medicinal plant. However, available information concerning MADS-box genes in I. indigotica is insufficient. Based on the sequencing data of the I. indigotica transcriptome, we identified MADS-box gene-encoding transcription factors that have been shown to play critical roles in developmental processes. In this study, 102 I. indigotica MADS-box genes were identified and categorized into type I (Mα, Mβ, and Mγ) and type II (MIKC^C and MIKC*) subfamilies. IiMADS proteins in the same cluster had similar motifs and gene structures. In total, 102 IiMADS-box genes were unevenly distributed across seven chromosomes. APETALA1 (AP1) encodes a MADS-box transcription factor which plays a pivotal role in determining floral meristem identity and also modulates developmental processes within the perianth. We then selected IiAP1 for functional studies and found that it is localized to the nucleus and highly expressed in inflorescence, sepals, and petals. The ectopic expression of IiAP1 in Arabidopsis resulted in early flowering and abnormal development of floral organs. Taken together, this research study carried out a systematic identification of MADS-box genes in I. indigotica and demonstrated that IiAP1 takes part in the regulation of floral transition and formation. Full article

With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics. Full article

The scarcity of high-quality labeled data poses a challenge to the application of deep learning (DL) in landslide identification from remote sensing (RS) images. Semi-supervised learning (SSL) has emerged as a promising approach to address the issue of low accuracy caused by the limited availability of high-quality labels. Nevertheless, the application of SSL approaches developed for natural images to landslide identification encounters several challenges. This study focuses on two specific challenges: inadequate information extraction from limited unlabeled RS landslide images and the generation of low-quality pseudo-labels. To tackle these challenges, we propose a novel and generic DL framework called hybrid perturbation mean match (HPM-Match). The framework combines dual-branch input perturbation (DIP) and independent triple-stream perturbation (ITP) techniques to enhance model accuracy with limited labels. The DIP generation approach is designed to maximize the utilization of manually pre-defined perturbation spaces while minimizing the introduction of erroneous information during the weak-to-strong consistency learning (WSCL) process. Moreover, the ITP structure unifies input, feature, and model perturbations, thereby broadening the perturbation space and enabling knowledge extraction from unlabeled landslide images across various perspectives. Experimental results demonstrate that HPM-Match has substantial improvements in IoU, with maximum increases of 26.68%, 7.05%, and 12.96% over supervised learning across three datasets with the same label ratio and reduces the number of labels by up to about 70%. Furthermore, HPM-Match strikes a better balance between precision and recall, identifying more landslides than other state-of-the-art (SOTA) SSL approaches. Full article

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

Background: Lake Al-Asfar in KSA was used as a sink for wastewater for decades and suffered from pollution. The lake is a habitat to different microbial species that play important ecological roles, some of which are good, and some are bad and even pathogenic. In a previous investigation, algal-bacteria consortia have proven to be beneficial in bioremediating heavy metals and hydrocarbons in Lake Al-Asfar. The identity of algae was revealed to be Chlorella sp. and Geitlernema sp. in the consortia. The identity of the heterotrophic bacterial partners, on the other hand, awaits investigation and is addressed in the present research. On the other hand, investigating the diversity of Protozoa and parasites is also tackled as they represent indicators of pollution. Some pose serious health risks, but some of them also contribute to reducing some of the pollution levels. Methods: Bacteria associated with algae were isolated in pure form. The polyphasic approach was used to identify bacterial samples, including staining procedures, the use of Vitek technology, and scanning electron microscopy. This information was integrated with structure information such as capsule presence, endospore formation, and wall characteristics indicated by Gram stain. With regard to protists including Protozoa and parasites, Light microscopy and taxonomic books of identification were used to reveal their identity. Results: three main bacterial strains belonging to the following genera were identified: Sphingomonas, Rhizobium, and Enterbacter. The last is potentially pathogenic and poses health risks to Lake goers. Rhizobium, on the other hand, is most likely found in the lake from agricultural wastewater and is a nitrogen fixer that increases the fertility of crops. The first bacterium is associated with special lipid metabolism and is hardly pathogenic. Several diverse microscopic forms of protists, mainly Protozoa and parasites, were identified, which included Entamoeba histolytica, Balantidium coli, Ascaris lumbricoides, Amoeba, Paramecium, Euglena, and Gymnodinium sp. Discussion: The three types of bacteria identified have metabolic activities that are associated with bioremediation. On the other hand, protists, including Protozoa and parasites, are regular members of wastewater communities and help in scavenging solid wastes, but they cause hazards such as secreting toxins, causing disease, and impacting the bioremediation potential by feeding on beneficial bioremediating algae and bacteria. This is part of the wastewater ecosystem dynamics, but efforts must be exerted to minimize, if not completely eliminate, pathogenic parasites in order to maximize the growth of algal consortia. Conclusions: Vitek technology is an emerging less time- and effort-consuming fast technology for identifying bacteria. Bacteria identified have significant ecological bioremediating roles, together with their algal partners, but some pose pathogenic risks. Identifying co-inhabitants like protists and parasites helps to shed light on their impact on one another and pave the way for restoration efforts that minimize the biological hazards and maximize the use of beneficial local microorganisms. Full article

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight modes, and environmental influences. In addition, various abnormal situations may occur during flight, in particular, equipment failures and structural damage. These circumstances cause the problem of a rapid adjustment of the used control laws so that the control system can adapt to the mentioned changes. However, most adaptive control schemes have a model of the control object, which plays a crucial role in adjusting the control law. That is, it is required to solve also the identification problem for dynamical systems. We propose an approach to solving the above-mentioned problems based on artificial neural networks (ANNs) and hybrid technologies. In the class of traditional neural network technologies, we use recurrent neural networks of the NARX type, which allow us to obtain black-box models for controlled dynamical systems. It is shown that in a number of cases, in particular, for control objects with complicated dynamic properties, this approach turns out to be inefficient. One of the possible alternatives to this approach, investigated in the paper, consists of the transition to hybrid neural network models of the gray box type. These are semi-empirical models that combine in the resulting network structure both empirical data on the behavior of an object and theoretical knowledge about its nature. They allow solving with high accuracy the problems inaccessible by the level of complexity for ANN models of the black-box type. However, the process of forming such models requires a very large consumption of computational resources. For this reason, the paper considers another variant of the hybrid ANN model. In it, the hybrid model consists not of the combination of empirical and theoretical elements, resulting in a recurrent network of a special kind, but of the combination of elements of feedforward networks and recurrent networks. Such a variant opens up the possibility of involving deep learning technology in the construction of motion models for controlled systems. As a result of this study, data were obtained that allow us to evaluate the effectiveness of two variants of hybrid neural networks, which can be used to solve problems of modeling, identification, and control of aircraft. The capabilities and limitations of these variants are demonstrated on several examples. Namely, on the example of the problem of aircraft longitudinal angular motion, the possibilities of modeling the motion using the NARX network as applied to a supersonic transport aircraft (SST) are first considered. It is shown that under complicated operating conditions this network does not always provide acceptable modeling accuracy. Further, the same problem, but applied to a maneuverable aircraft, as a more complex object of modeling and identification, is solved using both a NARX network (black box) and a semi-empirical model (gray box). The significant advantage of the gray box model over the black box one is shown. The capabilities of the hybrid model realizing deep learning technologies are demonstrated by forming a model of the control object (SST) and neurocontroller on the example of the MRAC adaptive control scheme. The efficiency of the obtained solution is illustrated by comparing the response of the control object with a failure situation (a decrease in the efficiency of longitudinal control by 50%) with and without adaptation. Full article

Background: Umbilical artery thrombosis (UAT) masquerading as a single umbilical artery (SUA) is a rare but critical diagnostic challenge in prenatal care. Case Presentation: We described a case of a 22-year-old primigravida with an uneventful obstetric history who presented with reduced fetal movements at 22 weeks of gestation. Ultrasound showed no gross fetal structural anomalies while umbilical artery Doppler flow imaging revealed an isolated SUA. The patient again presented with diminished fetal movement at 24 weeks gestation, and a diagnosis of intrauterine demise was confirmed ultrasonographically. She was then induced and delivered a macerated stillborn female fetus. Placental examination revealed three umbilical vessels with an occlusive thrombus seen within the umbilical artery consistent with UAT, a finding previously mistaken for SUA. Conclusions: This case underscores the diagnostic difficulties of UAT radiologically, especially when there was no prior documented evidence of two umbilical arteries. Identification of at-risk fetuses would allow for close monitoring or effective interventions to be implemented as early as possible to avert preventable fetal loss. Full article

The development of new graphene-based materials necessitates the application of suitable material imaging techniques, especially for the identification of defects in the graphene structure and its continuity. For this purpose, it is natural to use one of the main properties of graphene—electrical conductivity. In this work, we prepare a 9 cm² large-area monolayer graphene membrane on porous scaffolding sealed with either GO or rGO. Then, we use electrostatic force microscopy (EFM) AFM mode along with SE and AEE SEM modes to characterize the as-prepared graphene membranes thoroughly. The combination of SEM-AEE and AFM-EFM techniques not only assesses the quality of graphene itself but also characterizes the selectivity and effectiveness of masking graphene layer defects by applying GO or rGO. This makes these methods valuable in optimizing the production of advanced graphene nanocomposites such as semipermeable membranes. Full article

Epilepsy is a prevalent chronic neurological disorder that can significantly impact patients’ lives. The incidence and risk of psychosis in individuals with epilepsy are notably higher than in the general population, adversely affecting both the management and rehabilitation of epilepsy and further diminishing patients’ quality of life. This review provides an overview of the classification and clinical features of psychosis of epilepsy, with the aim of offering insights and references for the clinical diagnosis and treatment of various types of psychosis of epilepsy. Additionally, we examine the potential pathophysiological mechanisms underlying the psychosis of epilepsy from three perspectives: neuroimaging, neurobiology, and genetics. The alterations in brain structure and function, neurotransmitters, neuroinflammatory mediators, and genetic factors discussed in this review may offer insights into the onset and progression of psychotic symptoms in epilepsy patients and are anticipated to inform the identification of novel therapeutic targets in the future. Full article