Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitory control and L2 proﬁciency modulate bilingual language production: evidence from spontaneous monologue and dialogue speech

Bilingual language production requires that speakers recruit inhibitory control (IC) to optimally balance the activation of more than one linguistic system when they produce speech. Moreover, the amount of IC necessary to maintain an optimal balance is likely to vary across individuals as a function of second language (L2) proﬁciency and inhibitory capacity, as well as the demands of a particular communicative situation. Here, we investigate how these factors relate to bilingual language production across monologue and dialogue spontaneous speech. In these tasks, 42 English–French and French–English bilinguals produced spon-taneous speech in their ﬁrst language (L1) and their L2, with and without a conversational partner. Participants also completed a separate battery that assessed L2 proﬁciency and inhibitory capacity.The results showed that L2 vs. L1 production was generally more effort-ful, as was dialogue vs. monologue speech production although the clarity of what was produced was higher for dialogues vs. monologues. As well, language production effort signiﬁcantly varied as a function of individual differences in L2 proﬁciency and inhibitory capacity.Taken together, the overall pattern of ﬁndings suggests that both increased L2 pro-ﬁciency and inhibitory capacity relate to efﬁcient language production during spontaneous monologue
DESIGN AND EVALUATION OF ALGORITHMS FOR PARALLEL CLASSIFICATION OF ONTOLOGIES

Description Logics are a family of knowledge representation formalisms with formal semantics. In recent years, DLs have influenced the design and standardization of the Web Ontology Language OWL. The acceptance of OWL as a web standard has promoted the widespread utilization of DL ontologies on the web. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing availability of multi-processor and multi- or many-core computers, the need for parallelizing description logic inference services to achieve a better scalability is expected. The contribution of this thesis has two aspects. On a theoretical level, it first presents algorithms to construct a TBox in parallel, which are independent of a particular DL logic, however they sacrifice completeness. Then, a sound and complete algorithm for TBox classification in parallel is presented. In this algorithm all the subsumption relationships between concepts of a partition assigned to a single thread are found correctly, in other words, correctness of the TBox subsumption hierarchy is guaranteed. Thereafter, we provide an extension of the sound and complete algorithm which is used to handle TBox classification concurrently and more efficiently. This thesis also describes an optimization technique suitable for better partitioning the list of concepts to be inserted into the TBox. On a practical level, a running prototype, Parallel TBox Classifier was implemented for each generation of the classifier based on the above theoretical foundations, respectively. The Parallel TBox Classifier is used to evaluate the practical merit of the proposed algorithms as well as the effectiveness of the designed optimizations against existing state-of-the-art benchmarks. The empirical results illustrate that Parallel TBox Classifier outperforms the Sequential TBox Classifier on real world ontologies with a linear or superlinear speedup factor. Parallel TBox Classifier can form a basis to develop more efficient parallel classification techniques for real world ontologies with different sizes and DL complexities.
A new species of Stephanonectria ( Bionectriaceae ) from southwestern China

A new species, Stephanonectria ellipsoidea , was isolated from decaying fruit in Kunming City, Yunnan Province, China. It is characterized by pale orange colonies, densely clustered, irregularly branched conidiophores, cylindrical phialides, and hyaline, ellipsoidal conidia. Its morphology fits well with the generic concept of Stephanonectria . This species is closely related to S. keithii based on phylogenetic analysis of concatenated LSU and ITS sequences . The combination of morphological observation and molecular data supports the erection of the new species.
Iot and Cloud Based Blockchain Model for Covid-19 Infection Spread Control

Due to the increasing number of infected people and the number of deaths from COVID-19 over the world, there is a big challenge towards finding a radical solution to reduce the spread of disease and infection. The early detection, isolating the infected persons and tracing possible contacts are very critical. This paper presents an integrated approach that connects hospitals/laboratories, COVID-19 negative persons, positive persons, and contact persons to a cloud-based consortium blockchain system to guarantee reliable secured COVID-19 spread control. The proposed model guarantees a real time monitoring, tracking, and updating to persons status whether normal, contact, or positive COVID-19 case, and the related updates are done in the blockchain based on the results of execution of the blockchain smart contract rules. Tracking infected persons and their contacts is implemented using IoT sensors to determine contact time and spatial distances between them. The GPS/Bluetooth/UWB was used as IoT sensors technologies to determine the distances between the infected people and those in contact. The proposed blockchain Ethereum system smart contract was implemented by solidity programming language through the Remix IDE. The proposed approach was tested and successfully detected the contact cases and managed the different persons states on the cloud based blockchain system applying the smart contract rules accurately. As the calculated distances using the proposed model in the distance of one meter do not exceed the error rate of 11 cm. © 2021 Little Lion Scientific
Lightweight and �exible Cu(In,Ga)Se2 solar minimodules: Toward 20% photovoltaic e�ciency and beyond

Lightweight and �exible photovoltaic solar cells and modules are promising technologies leading to wide usage of light-to-electricity energy conversion devices. This communication presents the prospects of Cu(In,Ga)Se2 (CIGS)-based lightweight and �exible photovoltaic devices. The current status of �exible CIGS minimodules with photovoltaic e�ciencies greater than 18% and future directions to enhance their performance toward 20% and beyond are discussed. The effects of cell separation edges, which are formed through a mechanical, laser, or photolithography scribing process used to fabricate solar cells and modules, on the device performance are also discussed. It was found that mechanically scribed CIGS device edges, which are present in conventional solar cells and modules, cause deterioration of device performance. In other words, further improvement is expected with proper passivation/termination treatment of the edges or replacing mechanical scribing with a damage-free separation process.
Three-dimensional leaf edge reconstruction using a combination of two-and three-dimensional phenotyping approaches

Background: The physiological functions of plants are carried out by leaves, which are important organs. The morphological traits of leaves serve multiple functional requirements and demands of plants. Traditional techniques for quantifying leaf morphology rely largely on two-dimensional (2D) methods, resulting in a limited understanding of the three-dimensional (3D) functionalities of leaves. Notably, recent advancements in surveying technologies have improved 3D data acquisition processes. However, there are still challenges in producing accurate 3D-representations of leaf morphologies, particularly leaf edges. Therefore, in this study, we propose a method for reconstructing 3D leaf edges using a combination of 2D image instance segmentation and curve-based 3D reconstruction. Results: The proposed method reconstructed 3D leaf edges from multi-view images based on deep neural network-based instance segmentation for 2D edge detection, SfM for estimating camera positions and orientations, leaf correspondence identi�cation for matching leaves among multi-view images, curve-based 3D reconstruction for estimating leaf edges as 3D curve fragments, and B-spline curve �tting for integrating curve fragments into a 3D leaf edge. The method was demonstrated on both virtual and actual plant leaves. On the virtually generated leaves, we evaluated the accuracy of the 3D reconstruction by calculating standardized Fréchet distance, which reveals that small leaves and high camera noise pose greater challenges to reconstruction. To balance the number and precision of 3D curve fragments, we proposed guidelines for setting the threshold for how only reliable curve fragments are reconstructed based on simulated data. These guidelines suggested that the threshold becomes lower with greater occlusions, larger leaf size, and camera positional error greater than a certain level. We also found the number of images does not affect the optimal threshold except in very few cases. Moreover, the proposed method succeeded in reconstructing holes in the leaf when the number of holes is three or less. Conclusions: In this study, a nondestructive method for 3D leaf edge reconstruction was developed to address the 3D morphological properties of plants, which have been challenging to evaluate quantitatively. It is a promising way to capture whole plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Altered Cortical Network Dynamics A Potential Intermediate Phenotype for Schizophrenia and Association With ZNF804A

Context : Studies have shown patterns of abnormal dorsolateral prefrontal cortex (DLPFC) functional connectivity with other brain areas in schizophrenia and association of these patterns with a putative susceptibility gene ( ZNF804A ). However, whether these patterns are trait phenomena linked to genetic risk for illness is unclear. Objective : TotestthehypothesesthatalteredDLPFCcon-nectivity is (1) a familial, likely heritable feature of ge-netic risk for schizophrenia, (2) a novel intermediate phe-notypeindependentofalteredDLPFCengagement,and(3) selectivelymodulatedbyapolymorphismin ZNF804A . Design : Cross-sectional case-control study using blood oxygen level–dependent functional magnetic resonance imaging during a working memory task and genotyping of rs1344706
How different is different? Systematically identifying distribution shifts and their impacts in NER datasets

When processing natural language, we are frequently confronted with the problem of distribution shift. For example, using a model trained on a news corpus to subsequently process legal text exhibits reduced performance. While this problem is well-known, to this point, there has not been a systematic study of detecting shifts and investigating the impact shifts have on model performance for NLP tasks. Therefore, in this paper, we detect and measure two types of distribution shift, across three diﬀerent representations, for 12 benchmark Named Entity Recognition datasets. We show that both input shift and label shift can lead to dramatic performance degradation. For example, ﬁne-tuning on a wide spectrum dataset (Ontonotes) and testing on an email dataset (Cerec) that shares labels leads to a 63-points drop in F1 performance. Overall, our results indicate that the measurement of distribution shift can provide guidance to the amount of data needed for ﬁne-tuning and whether or not a model can be used “oﬀ-the-shelf” without subsequent ﬁne-tuning. Finally, our results show that shift measurement can play an important role in NLP model pipeline deﬁnition.
From microenvironment to epigenetics in endothelial cells

anti-oxidant properties of FSS. We identified four genes, HMOX1 , GPX3 , PTK2B and PTGS1 , that are candidate targets of both EZH2-and FSS-exerted regulation. The computational analyses of the promoters of HMOX1 , GPX3 , PTK2B and PTGS1, suggested that they are all regulated through repression, both by epigenetic activity of EZH2 (H3K27me3 mark) and by putative repressive transcription factors. This study shows how the epigenetic regulators such as EZH2, besides the global character of their action, constitute the part of more complex chromatin-regulating machinery at the local level of specific promoters.
AN IMPROVED LOUVAIN ALGORITHM BASED ON NODE IMPORTANCE FOR COMMUNITY DETECTION

Many algorithms have been developed to solve the problem of detecting and analyzing community structure in networks. Louvain algorithm (LVA) is a well-known community detection method that results high community structure of large networks within reasonable time, but it has the problems of randomness and instability. In this paper, an improved Louvain algorithm (ILVA) is proposed by combining the modularity function and node importance with the original LVA. The ILVA uses the LVA to detect community structure by optimizing the value of modularity. Meanwhile, node importance as measured by degree centrality is used to determine the node scanning order in the community detection phase. Experiments were conducted on real-world networks and the results showed that the ILVA produced stable community structure with higher modularity within reasonable time.
Planning and Deploying Transit Signal Priority in Small and Mideium-sized Cities Planning and Deploying Transit Signal Priority in Small and Medium-sized Cities: Burlington, Vermont, Case Study

Innovations in traffic signal systems have generated a great deal of interest in the provision of preferential traffic signal strategies and treatments for transit buses and other vehicles at signalized intersections in cities of all sizes. The primary objective of this paper is three fold: 1) to synthesize the literature of the lessons learned associated with planning and deploying transit signal priority (TSP) strategies in small and medium-sized cities; 2) to demonstrate the application of a micro-simulation model, VISSIM, to assess transit priority impacts in small and medium-sized communities where the required VISSIM input data are often limited; and 3) to present guidelines to aid traffic engineers and transit planners who are considering TSP strategies in small and medium-sized cities. An underlying aim of this paper is to recognize the differences in transit priority planning and deployment in small and medium-sized cities as compared to major metropolitan areas.
Aberystwyth University Fuzzy compositional modeling

—Automated modeling refers to automatic (re-) formulation of alternative system models that embody the simpliﬁ-cation,abstraction,andapproximationofknowledgeanddataforagiventask.Thistechniqueishighlydesirableforeffectiveproblem solvinginmanyapplicationdomains.Overthepasttwodecades,compositionalmodeling(CM)hasestablisheditselfasaleading approachinautomatedmodeling.CMisaframeworktoconstructsystemmodelsbycomposinggenericandreusablemodelfragments (MFs)selectedfromaknowledgebase.However,theexistingworkmainlyconcernstheknowledgeanddatathatarerepresentedby crispandpreciseinformation.Littleworkhasbeencarriedouttoexploreitspotentialtodealwithuncertainenvironments. This paperpresentsaninnovativeframeworkoffuzzycompositionalmodeling(FCM)todevelopsuchwork.Theproposedapproachis capableofrepresentingandreasoningwithawiderangeofinex-actinformation.Aninnovativenotionoffuzzycomplexnumbers (FCNs)isdevelopedinanefforttoenablesynthesisofconsistentscenariodescriptionsfromimpreciseMFs.Thispaperalsointro-ducesthemodulusofFCNstoconstraintheresultingscenariodescriptions.Theusefulnessofthisstudyisillustratedbymeansof anexampletoconstructpossiblescenariodescriptionsfromgivenevidence,whichisinsupportofcrimeinvestigation.
The mediating role of IT Governance effectiveness between ITG and financial / non-financial performance: empirical research on Egyptian banking sector

Purpose – The current research aims at testing the mediating role for IT governance between Information technology, whether IT Structure and/or IT Capabilities and organizational performance, whether financial or non-financial performance of Egyptian banks. Design/methodology/approach – An empirical study was conducted on a sample size of 371 responses collected by a structured questionnaire was circulated from (38) banks located in Alexandria city in Egypt. Using random sampling method. ITG has been measured using: Structure, capabilities. and performance. Likert scales were used to measure (SRM) dimensions, and Confirmatory factor analysis (CFA), and Structural Equation Modeling (SEM). Findings – This research suggests that ITG effectiveness is a mediating variable between ITG Structure, Capabilities and financial/non-financial performance. Research limitations/Implications – The main limitation of the current study in the small sample size. This was due to the addition to that, the surveys had to be paired for them to be considered complete, and This research applied on commercial banks branches located in Alexandria and did not consider Islamic and Industrial bank. Originality/value – This research is an attempt to empirically test the effect of ITG structure, Capabilities on Financial/non-financial performance under ITG effectiveness in banks branches located in Alexandria, Egypt.
Indian Journal of Social Psychiatry

Context: In rural India, mental healthcare remains limited due to scant state services and incongruency between provider‑ and patient‑framing distress. Help‑seeking by people with mental health problems is related to how meanings of distress are understood differently by individuals, based on their interaction with various actors in the community and the available cultural explanation within their local ecologies. Methodology: This study examines the mutually constituted relationship between meanings of mental distress and help‑seeking among people residing in the Upper Yamuna Valley, Uttarakhand, North India. This qualitative study builds on six in‑depth interviews with people with severe mental health issues and one person with epilepsy, referred as people with psychosocial disability (PPSD) in the study. The data analysis was iterative and followed thematic approach. Results: The study found that personal belief based on one’s experience, such as negative self‑judgment and wider cultural explanations, such as supernatural beliefs, as well as gender roles, impacted the way people address their mental health problems, in turn shaping their help‑seeking behavior. Participants lost hope for a cure after years of trying to find an effective solution. Moreover, lack of access to care and remoteness of the mountainous area made help‑seeking and recovery feel impossible. Conclusions: This study underscores the need for researchers and policy professionals to explore the local context and culture to improve care and treatment quality. The study also explains that personal explanation of psychosocial problems and help seeking are not unidirectional. It is a complex phenomenon layered with the local contexts which should be addressed in clinical practice, as well as future research. Finally, clinicians’ training should address the local cultural language of distress to identify the problem and suggest an effective solution.
The effect of music intervention on cognitive aging: from the view of EEG microstates

The effectiveness of music intervention on cognitive aging has been proved by many behavioral and clinical studies. However, the underlying neural mechanism remains to be explored. EEG microstate helps study the complex relationship of functional networks across cognitive and sensory functions such as working memory, attention, inhibitory control, and auditory and visual processing, which helps study the mechanism of music intervention on cognitive aging. In this work, we calculated EEG microstates parameters and found a lower duration of microstate D in old adults than that in young adults, and music training had a mitigating effect on this change. In addition, the age x music training experience interaction was signi�cant for the transition rate between microstate D and microstates A, B, C. Combined with the correlation analysis between these parameters and working memory performance, and we thought that music training could prevent age-related cognitive decline which might relate to the ability to suppress task-irrelevant information in old adults. The underlying mechanism may be the top-down control of fronto-central regions on the sensory network. This work contributes to understanding the neural mechanism of music intervention on cognitive aging.
DISCUSSION ON THE GEOLOGICAL AGE OF THE DALAZI FORMATION IN JILIN PROVINCE, CHINA

The Dalazi Formation, located in Jilin province, is regarded as a typical horizon of the Early Cretaceous in Northeast China. However, its age has been much debated. Based on the recently discovered fossil plants from the Dalazi Formation,their geological distribution of the dominant genera of angiosperms is studied. A comparison of the angiosperm flora from the Dalazi with those abroad or elsewhere in China, the results show that a close relationship might exist between the flora from Dalazi Formation and that of the Potomac Subzone II-B in North American. Associated with other palaeontological evidence from palynoflora, insects, conchostracans, ostracodes, fish fossils, it is concluded that the geological age of the Dalazi Formation is most likely of middle or late Albian.
Factors Influencing Microbiota in Modulating Vaccine Immune Response: A Long Way to Go

Vaccine immunogenicity still represents an unmet need in specific populations, such as people from developing countries and “edge populations”. Both intrinsic and extrinsic factors, such as the environment, age, and dietary habits, influence cellular and humoral immune responses. The human microbiota represents a potential key to understanding how these factors impact the immune response to vaccination, with its modulation being a potential step to address vaccine immunogenicity. The aim of this narrative review is to explore the intricate interactions between the microbiota and the immune system in response to vaccines, highlighting the state of the art in gut microbiota modulation as a novel therapeutic approach to enhancing vaccine immunogenicity and laying the foundation for future, more solid data for its translation to the clinical practice.