Miracle-dz/UniFAR-FTUs huggingface dataset:
Solitary Neurocysticercosis Presenting with Focal Seizure and Secondary Generalized Tonic-Clonic Seizure

Patient: Male, 25-year-old Final Diagnosis: Neurocysticerosis Symptoms: Generalized tonic-clonic seizures Medication: — Clinical Procedure: Cyst ramoval under CT brain navigation with duraplasty Specialty: Infectious Diseases • General and Internal Medicine • Neurology Objective: Rare disease Background: Neurocysticercosis is the most common central nervous system infection in developing countries. A wide array of clinical manifestations, ranging from asymptomatic to severe neurological symptoms, is observed in patients diagnosed with neurocysticercosis, depending on the number of lesions, cyst location, cyst stage, parasite genotype, and host immunity. Case Report: We report the case of a 25-year-old Burmese man who presented with focal seizure and secondary generalized tonic-clonic seizure. Brain imaging studies revealed a 1-cm cyst, which showed rim enhancement, an eccentric scolex, and surrounding brain edema at the left superior frontal gyrus. His serum cysticercus antibody was positive. Thus, the patient was diagnosed with solitary neurocysticercosis based on clinical manifestations, neuroimaging findings, and positive serology. The patient received anti-parasitic and anti-seizure medications before surgical excision of the cyst via computed tomography (CT) scan navigation. Stereomicroscopic examination of the cyst revealed a parasite larva in a fluid-filled cyst, containing a scolex with hooks and 4 suckers, identical to that of Taenia solium. Molecular characterization of the parasite based on T. solium cytochrome c oxidase subunit 1 (COX-1) gene identified the species as being 99.7% identical to T. solium Asia genotype previously reported from pigs in Thailand. Conclusions: Although the prevalence of neurocysticercosis seems to be declining, sporadic cases have been reported throughout the world and the prevalence may be underestimated. Differential diagnosis of neurocysticercosis in patients presenting with adult-onset epilepsy should be considered in disease-endemic areas.
Effect of N on Growth, Antioxidant Capacity, and Chlorophyll Content of Sorghum

Nutrient management is an important challenge to agricultural sustainability. In this study, the effects of three nitrogen (N) fertilizer levels (N1 = 0, N2 =150, and N3 = 300 kg ha−1) on growth, chlorophyll content, and antioxidant capacity of two grain sorghum cultivars were investigated in a two-year (2017 and 2018) field trial. The treatments were arranged in a randomized complete block design with three replicates. Nitrogen application (N2 and N3) improved plant growth and antioxidant enzymes activity; compared to the control N1, N increased germination by 18.7%, leaf length by 7.5%, leaf weight by 10.8%, specific leaf weight by 11.6%, and plant height by 2.5% in Siyong 3180 cultivar and increased leaf width by 12.8% and stem weight by 27.4% in CFSH30 cultivar. In 2017 and 2018, increasing nitrogen N2 and N3 enhanced superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities and leaf protein content in both cultivars. While in 2017, chlorophyll content decreased in the CFSH30 cultivar. Our study showed that increased nitrogen supply could improve morpho-physiological activities of sorghum, but N3 was relatively more effective for sorghum growth in Siyong 3180 cultivar compared to CFSH30 cultivar.
Air Distribution Simulation Study of EMU Equipment Bay Under Passing by Each Other on Open Track

The air distribution of EMU equipment bay was simulated and analyzed under the condition of passing by each other on open track.The outer flow field and equipment bay inner flow field were calculated separately.This article has carried on the 300km/h and 400km/h intersection speed calculation.First of all,the dynamic mesh method of ANSYS-FLUENT was used for equipment bay under the condition of passing each other on open track.The transient pressure values of the inlets and outlets of the equipment bay were got under different passing time.The transient pressure values were calculation as the equipment bay inner flow field boundary condition.Finally,the flow value of the equipment bay inlets and outlets at different time points of the passing were got,and then to investigate the evolution of the flow values of equipment bay inlets and outlets.
Influence of plant reproductive systems on the evolution of hummingbird pollination

Abstract Many hummingbird‐pollinated plant species evolved from bee‐pollinated ancestors independently in many different habitats in North and South America. The mechanisms leading to these transitions are not completely understood. We conducted pollination and germination experiments and analyzed additional reproductive traits in three sister species pairs of which one species is bee‐ and the other hummingbird‐pollinated. All hummingbird‐pollinated species showed higher seed set and germination rates in cross‐pollinated than in self‐pollinated flowers. In the self‐compatible, bee‐pollinated sister species this difference did not exist. As expected, seed set and germination rate were higher after cross‐pollination in the largely self‐incompatible genus Penstemon independently of the pollination syndrome. However, the bird‐pollinated species produce only half of the amount of ovules and pollen grains per flower compared to the bee‐pollinated sister species. This indicates that hummingbird pollination is much more efficient in self‐incompatible populations because hummingbirds waste less pollen and provide higher outcrossing rates. Therefore, hummingbird pollination is less resource costly. Overall, we suggest that hummingbirds may increase the reproductive success compared to bees, influencing the evolution of hummingbird pollination in ecosystems with diverse bee assemblages.
COVID-19 Vaccination Intention Associated with Behaviors towards Protection and Perceptions Regarding the Pandemic

Background: The impressively rapid availability of different types of COVID-19 vaccines and, on the other hand, the degree of their effectiveness as opposed to the likelihood of serious or non-serious side effects place a fairly large percentage of the population at a crossroads regarding the choice to get vaccinated or not, hence threatening achievement of total immunization coverage and full immunity. This study aimed to assess COVID-19 vaccination intention in Greece regarding protection behaviors and perceptions of the pandemic. Methods: A total of 3753 participants completed a specially designed electronic questionnaire anonymously and voluntarily. The study population consisted of healthcare workers, students, members of professional societies, teachers, and professors. The questionnaire was composed of four parts pertaining to demographic data and possible changes in hygiene attitudes during the COVID-19 pandemic. Results: In total, 43.3% of the participants stated that SARS-CoV-2 poses a significant risk. The most widespread protection practice was avoiding crowded places (90.1%), followed by reducing the use of public transport (86.1%) and washing their hands with soap and water more often than usual (84.2%). Women undertook significantly more behavioral changes than men and participants of other nationalities. About half of the participants (44%) implemented seven behavioral changes. Lower personal and general perceived risk due to COVID-19 was significantly associated with lower intention to get vaccinated. Conclusion: Strong hesitancy was observed towards COVID-19 vaccination. There is a need for further studies to be conducted to investigate the benefits and safety of vaccines for the purpose of better informing the public. Healthcare personnel can and should play a key role in this process.
Study on the oil tank surface vibration characteristics for the running power transformer

Steady-state vibration recorded on the tank surface under on-line conditions provides essential information about fixation conditions both for windings and the magnetic core.Therefore,vibration signal analysis method is one of effective methods to monitor the condition of transformer windings and core.In this paper,the transformer vibration measurement system is set up and the vibration signals on the oil tank surface of running transformer in the field are measured.The influences of transformer type and sensor locations to the measured vibration signals are analyzed.The results show that,the vibration characteristics of the same type transformers are similar,and the vibration characteristics at the symmetrical positions on the upside and underside tank surface have little difference.Moreover,the vibration signals measured at other positions have no comparability.Finally,in this paper,the study about the change of vibration signal is carried on when the installed position of sensor is moved a little.It is presented that the installing position of sensor should be marked,and then if the next measurement is taken on,the installing position of sensor should be at the marked point or not exceed 5cm.The results presented in this paper can guide the application of vibration signal analysis method.
SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network

Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management. Denoising is an effective preprocessing step for target detection. Morphological network-based denoising can effectively remove speckle noise, while the smoothing effect of which blurs the edges of the image and reduces the detection accuracy. The fusion of edge extraction and morphological network can improve detection accuracy by compensating for the lack of edge information caused by smoothing. This article proposes an end-to-end lightweight network called morphological feature-pyramid Yolo v4-tiny for SAR ship detection. First, a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial high-frequency information for target detection. Then, the original and preprocessed images are combined into the multichannel as an input for the convolution layer of the network. The feature pyramid fusion structure is used to extract the high-level semantic features and shallow detailed features from the image, improving the performance of multiscale target detection. Experiments on the public SAR ship detection dataset and AIR SARShip-1.0 show that the proposed method performs better than the other convolution neural network-based methods.
ACTIVITY OF SERRATIA SPP. AND BACILLUS SPP. AS BIOCONTROL AGENTS AGAINST MELOIDOGYNE INCOGNITA INFECTING TOMATO

Background: Tomato )Solanum lycopersicum( is one of the most popular vegetable crops in Egypt. It is affected by a wide range of pests; prominent among them is root-knot nematode (RKN) (Meloidogyne spp.) which is considered as one of the major limiting factors affecting tomato productions worldwide, especially in Egypt. Aim of study: The present study was performed to evaluate the quantitative and qualitative chitionolytic potentials of Serratia, Bacillus megaterium and B. subtilis as well as their nematicidal effect against Meloidogyne incognita under both in vitro and in vivo conditions. Methodology: Qualitative and quantitative assays of bacterial chitinase of the applied bacteria were determined using Chitinase assay agar and liquid media, as well as the nelson-somogyi method. Nematicidal and ovicidal activities of bacterial treatments were evaluated by Test Tube Bio-assay technique. A greenhouse experiment was conducted for in vivo evaluation of bacterial treatments on nematode parameters. The most effective bacterial isolate was molecular identified based on the nucleotide sequencing of its 16S rRNA gene. Results: Serratia sp. (S2) showed the highest total and specific chitinase activities (0.620 U/mL and 0.079 U/mg), respectively, followed by S1 (0.560 U/mL and 0.061 U/mg) and S3 (0.434 U/mL and 0.057 U/mg). B. megaterium appeared the minimum total and specific chitinase activities (0.320 U/mL and 0.042 U/mg), and no chitinase activities was shown for B. subtilis. Serratia sp. (S2) recorded the highest ovicidal activity (53.61%) even when applied at low concentration (1%). Among greenhouse experiment, Serratia sp. (S2) recorded the minimum number of galls and egg masses. The nucleotide sequence analysis of 16S rRNA gene of Serratia sp. (S2) proved that this isolate could be a strain related to S. marcescens, therefore, it was documented in GenBank as S. marcescens strain ARC3 under the accession number MN533708. Conclusion: The results indicated that S. marcescens strain ARC3 showed the maximum nematicidal potentiality against M. incognita and its significant chitinolytic activity play an essential role in such bio-control effect. Recommendation: Chitonlytic bacteria could be suggested as effective and eco-friendly alternative approach for controlling the M. incognita.
The Malay-Version Knowledge, Risk Perception, Attitude and Practice Questionnaire on Heatwaves: Development and Construct Validation

Background: Heatwaves have long been recognised as a serious public health concern. This study was aimed at developing and validating a Malay-version of a questionnaire for evaluating knowledge, risk perception, attitudes, and practices regarding heatwaves. Method: The knowledge construct was evaluated with item analysis and internal reliability. The psychometric characteristics, construct and discriminant validity, and internal consistency of the risk perception, attitude and practice constructs were evaluated with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results: The 16 items in the knowledge construct had a good difficulty, discrimination, and reliability index of 0.81. A total of 16 items were maintained in EFA with Cronbach’s alpha of 0.84 and 0.82, 0.78 and 0.84 obtained for total items and risk perception, attitude, and practice constructs, respectively. A total of 15 items were retained after CFA. The finalised model met the fitness indices threshold. The convergent and discriminant validity were good. Conclusion: This newly developed Malay-version KRPAP questionnaire is reliable and valid for assessing Malaysians’ knowledge, risk perception, attitudes, and practices regarding heatwaves.
Identifying subgroups based on self-assessment of ability in patients with schizophrenia and its relationship with vocational outcomes

Background/Objectives People with schizophrenia have defective self-assessment of ability (i.e., loss of introspective accuracy [IA]). Although previous studies grouped people according to the degree of IA, the clinical features of these subgroups have not been clarified. Additionally, the determinants of outcomes depending on self-assessment remain unknown. We aimed to identify the clinical features that can help distinguish these subgroups and whether the determinants of vocational outcomes differed between the groups. Methods The self-assessment ability of 100 people with schizophrenia was examined and categorized as accurate, over-, or under-estimators. Multiple discriminant analysis was also performed. After demonstrating statistical validity, the relative effects of positive and negative symptoms, cognitive function, and level of IA on vocational outcomes were also examined for each subgroup. Results The symptoms that particularly explained the differences between these subgroups were positive and negative (expressing factors) symptoms: p < . 001. Using the determinants of vocational outcomes in each subgroup, the over-estimator subgroup was characterized by positive symptoms: p = . 025, the under-estimator group, by the underestimation of their own ability: p = . 042, and the accurate estimator group, by cognitive function: p = . 006. Conclusion Reduced IA can be a core mediator of various symptoms. Thus, tailoring the target and strategy of interventions for vocational outcomes according to the accuracy and quality of IA is important in clinical settings.
Engaging Sub-Saharan African Australian Residents in Health Research: Lessons and Suggestions for Future Studies

The Sub-Saharan African-born community in Australia is significantly underrepresented in health research, partly, due to challenges associated with collecting their primary data. Based on experience from a mixed-method study comprising 24 in-depth interviews and a survey of 253 participants in the community, this communication paper highlights a number of ‘high-level’ practical issues that might be encountered when collecting primary data from the community and provides suggestions for future studies. Included in this paper is a brief discussion of important lessons from the fieldwork that are critical for achieving successful data collection from the community. The lessons include difficulty of proving external validity for samples drawn from the community, difficulty of developing a reliable sampling frame, challenges in selecting appropriate support organisations, risk of low response to surveys and language barrier. The study concludes that understanding the ethnic diversities in this community and engaging appropriate support organisations are critical to achieving a successful data collection experience.
A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products

The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape.
Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image–question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.
Federated Anomaly Detection on System Logs for the Internet of Things: A Customizable and Communication-Efficient Approach

Runtime log-based anomaly detection is one of several key building blocks in ensuring system security, as well as post-incident forensic investigations. However, existing log-based anomaly detection approaches that are implemented on large-scale Internet of Things (IoT) systems generally upload local data from edge devices to a centralized (cloud) server for processing and analysis. Such a workflow incurs significant communication and computation overheads, with potential privacy implications. Hence, in this paper, we propose a customizable and communication-efficient federated anomaly detection scheme (hereafter referred to as FedLog), designed to facilitate the identification of abnormal log patterns in large-scale IoT systems. Specifically, we first craft a Temporal Convolutional Network-Attention Mechanism-based Convolutional Neural Network (TCN-ACNN) model, to effectively extract fine-grained features from system logs. Second, we develop a new federated learning framework to support IoT devices in establishing a comprehensive anomaly detection model in a collaborative and privacy-preserving manner. Third, a lottery ticket hypothesis based masking strategy is designed to achieve customizable and communication-efficient federated learning in handling non-Independent and Identically Distributed (non-IID) log datasets. We then evaluate the performance of our proposed scheme with those of DeepLog (published in CCS, 2017) and Loganomaly (published in IJCAI, 2019) in both centralized learning and federated learning settings, using two publicly available and widely used real-world datasets (i.e., HDFS and BGL). The findings demonstrate the utility of the proposed FedLog scheme, in terms of log-based anomaly detection.
A Study on Subject Function of Fuzzy Frequency Pattern Recognition

In this paper,the author explains the theory of the fuzzy pattern recognition of rotating machine faults,studies the adjustable precision subject function and turns out a self-adaptive diagnosis system.The test results show that this fuzzy pattern recognition diagnosis is very good at recognizing accurately the types of faults in sample bank,therefore,it can be used in real-time condition monitoring and diagnosis of rotating machine. 
Prison Work and Vocational Programs: A Systematic Review and Analysis of Moderators of Program Success

Abstract Custodial prison work and vocation programs are among the most common programs in United States corrections. However, literature suggests ambiguity regarding their effectiveness in producing desired outcomes. Extant systematic reviews and meta-analyses of these programs are dated, focus on post-release programs, and rely on monolithic effect sizes to determine if these programs ‘work. ’ To assess this issue, we conduct a meta-analysis of 31 program evaluations published between 1986 and 2017, focusing on study and program characteristics that moderate conclusions of success. We argue that several issues require attention before researchers can conclude whether and for whom custodial work and vocational programs are successful, including: incomparable outcomes; treatment heterogeneity and treatment effect heterogeneity; program stacking; and conflicting definitions of programs. Implications from this study aid in establishing a cohesive literature to make stronger conclusions about the characteristics of programs and program evaluations that produce effective custodial work and vocation programs.