Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cognitive Control Model of Work-related Flow

Although several models of flow have been proposed that include environmental and trait-based antecedents of the state, elements of cognitive control that enable workers to experience flow and its subsequent outcomes at work have largely been overlooked. This research proposes and provides empirical support for the “Cognitive Control Model of Work-related Flow,” which integrates antecedents of flow at work related to the ability to focus concentration of cognitive resources toward experiencing flow at work. Along with flow at work, the model includes the antecedents of grit, flow metacognition, and mindfulness at work and the outcomes of work performance, engagement, and burnout. Findings across three studies (a cross-sectional, a time-lagged, and a one-day experience sampling method study) utilizing MTurk participants provided support for the model, as grit, mindfulness, and flow metacognition predicted flow, and flow predicted subjective performance, engagement, and burnout. Theoretical implications and the potential for developing flow interventions at work are discussed.
Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission

Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface. Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels. Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels. Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur’s brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities.
Robot-assisted radical cystectomy with intracorporeal reconstruction of urinary diversion by mechanical stapler: prospective evaluation of early and late complications

Introduction Radical cystectomy with pelvic lymph node dissection is the gold standard treatment for non-metastatic muscle-invasive bladder cancer and high-risk non–muscle-invasive bladder cancer. For years, the traditional open surgery approach was the only viable option. The widespread of robotic surgery led to its employment also in radical cystectomy to reduce complication rates and improve functional outcomes. Regardless of the type of approach, radical cystectomy is a procedure with high morbidity and not negligible mortality. Data available in the literature show how the use of staplers can offer valid functional outcomes, with an acceptable rate of complications shortening the operative time. The aim of our study was to describe the perioperative outcomes and complications associated with robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) using a mechanical stapler. Material and methods From January 2015 to May 2021, we enrolled patients who underwent RARC with pelvic node dissection and stapled ICUD (ileal conduit or ileal Y-shaped neobladder according to the Perugia ileal neobladder) in our high-volume center. Demographic features, perioperative outcomes and early (≤30 days) and late (>90 days) post-operative complications according to the Clavien–Dindo classification, were recorded for each patient. We also analyzed the potential linear correlation between demographic, pre-operative as well as operative features and the risk of post-operative complications. Results Overall, 112 patients who underwent RARC with ICUD were included with a minimum follow-up of 12 months. Intracorporeal Perugia ileal neobladder was performed in 74.1% of cases while ileal conduit was performed in 25.9%. The mean operative time, estimated intraoperative blood loss, and LOS were 289.1 ± 59.7 min, 390.6 ± 186.2 ml, and 17.5 ± 9.8 days, respectively. Early minor and major complications accounted for 26.7% and 10.8%, respectively. Overall late complications were 40.2%. The late most common complications were hydronephrosis (11.6%) and urinary tract infections (20.5%). Stone reservoir formation occurred in 2.7% of patients. Major complications occurred in 5.4%. In the sub-analysis, the mean operative time and the estimated blood loss improved significantly from the first 56 procedures to the last ones. Conclusion RARC with ICUD performed by mechanical stapler is a safe and effective technique. Stapled Y-shaped neobladder did not increase the complication rate.
Molecular and functional dissection of LIGULELESS1 (LG1) in plants

Plant architecture is a culmination of the features necessary for capturing light energy and adapting to the environment. An ideal architecture can promote an increase in planting density, light penetration to the lower canopy, airflow as well as heat distribution to achieve an increase in crop yield. A number of plant architecture-related genes have been identified by map cloning, quantitative trait locus (QTL) and genome-wide association study (GWAS) analysis. LIGULELESS1 (LG1) belongs to the squamosa promoter-binding protein (SBP) family of transcription factors (TFs) that are key regulators for plant growth and development, especially leaf angle (LA) and flower development. The DRL1/2-LG1-RAVL pathway is involved in brassinosteroid (BR) signaling to regulate the LA in maize, which has facilitated the regulation of plant architecture. Therefore, exploring the gene regulatory functions of LG1, especially its relationship with LA genes, can help achieve the precise regulation of plant phenotypes adapted to varied environments, thereby increasing the yield. This review comprehensively summarizes the advances in LG1 research, including its effect on LA and flower development. Finally, we discuss the current challenges and future research goals associate with LG1.
Myositis interstitial lung disease and autoantibodies

The aim of this review is to examine and evaluate published literature associated with idiopathic inflammatory myopathies (IIM) and interstitial lung disease (ILD) based on myositis specific autoantibodies (MSA) and the potential clinical significance of each autoantibody subtype for the practicing clinician. The review is a comprehensive search of literature published in PubMed from the year 2005 and onward coinciding with the surge in the discovery of new MSAs. Additionally, we comment on recommended multidisciplinary longitudinal care practices for patients with IIM-ILD with regard to imaging and other testing. Treatment is not covered in this review.
Diverse parameters of ambulatory knee moments differ with medial knee osteoarthritis severity and are combinable into a severity index

Objective: To characterize ambulatory knee moments with respect to medial knee osteoarthritis (OA) severity comprehensively and to assess the possibility of developing a severity index combining knee moment parameters. Methods: Nine parameters (peak amplitudes) commonly used to quantify three-dimensional knee moments during walking were analyzed for 98 individuals (58.7 ± 9.2 years old, 1.69 ± 0.09 m, 76.9 ± 14.5 kg, 56% female), corresponding to three medial knee osteoarthritis severity groups: non-osteoarthritis (n = 22), mild osteoarthritis (n = 38) and severe osteoarthritis (n = 38). Multinomial logistic regression was used to create a severity index. Comparison and regression analyses were performed with respect to disease severity. Results: Six of the nine moment parameters differed statistically significantly among severity groups (p ≤ 0.039) and five reported statistically significant correlation with disease severity (0.23 ≤ |r| ≤ 0.59). The proposed severity index was highly reliable (ICC = 0.96) and statistically significantly different between the three groups (p < 0.001) as well as correlated with disease severity (r = 0.70). Conclusion: While medial knee osteoarthritis research has mostly focused on a few knee moment parameters, this study showed that other parameters differ with disease severity. In particular, it shed light on three parameters frequently disregarded in prior works. Another important finding is the possibility of combining the parameters into a severity index, which opens promising perspectives based on a single figure assessing the knee moments in their entirety. Although the proposed index was shown to be reliable and associated with disease severity, further research will be necessary particularly to assess its validity.
Machine learning in pancreas surgery, what is new? literature review

Background Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.
Long-Term Conductivity Stability of Electrolytic Membranes of Scandia Stabilized Zirconia Co-Doped with Ytterbia

The effect of high-temperature aging for 4800 h at a temperature of 1123 K on the crystal structure and the conductivity of (ZrO2)0.90(Sc2O3)0.09(Yb2O3)0.01 and (ZrO2)0.90(Sc2O3)0.08(Yb2O3)0.02 single-crystal membranes were studied. Such membrane lifetime testing is critical to the operation of solid oxide fuel cells (SOFCs). The crystals were obtained by the method of directional crystallization of the melt in a cold crucible. The phase composition and structure of the membranes before and after aging were studied using X-ray diffraction and Raman spectroscopy. The conductivities of the samples were measured using the impedance spectroscopy technique. The (ZrO2)0.90(Sc2O3)0.09(Yb2O3)0.01 composition showed long-term conductivity stability (conductivity degradation not more than 4%). Long-term high-temperature aging of the (ZrO2)0.90(Sc2O3)0.08(Yb2O3)0.02 composition initiates the t″ → t′ phase transformation. In this case, a sharp decrease in conductivity of up to 55% was observed. The data obtained demonstrate a clear correlation between the specific conductivity and the change in the phase composition. The (ZrO2)0.90(Sc2O3)0.09(Yb2O3)0.01 composition can be considered a promising material for practical use as a solid electrolyte in SOFCs.
Language Teaching Research Quarterly

The present study investigates the role of the English language in the educational system of Kuwait. Specifically, it explores the views of parents regarding the choice of their children’s education by focusing on the differences between school types, including Arabic-language public schools, Arabic-English bilingual schools, and private English-medium schools. It discusses such characteristics as affordability, quality of education, linguistic development, and the general requirements for education in a second language. The data collection includes a survey of Kuwaiti parents (N=77) and semi-structured interviews (N=6). Following the Bernstein model, the study examines the parental motivation behind school selection. The results show that English language proficiency is regarded as an important educational outcome regardless of the school type. Public schools are typically chosen for convenience, while bilingual and English-medium schools are associated with quality education, better English skills, and opportunities for study abroad. The results reveal that parents would change the type of education for their children if they could find a more suitable one. The study concludes by recommending a balanced-bilingual approach where Arabic continues to develop alongside English to strengthen the Arabic heritage while giving young Kuwaitis English skills valued in a global economy.
Mean State and Wave Disturbances during Phases I, II, and III of GATE Based on ERA-40

Abstract Using ECMWF's second-generation reanalysis, ERA-40, the large-scale mean state and synoptic-scale features associated with African easterly wave disturbances (AEWs) are examined over West Africa and the adjacent eastern Atlantic Ocean during the three 21-day observing periods of the Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE) in 1974 (Phase I, 26 June–16 July; Phase II, 28 July–17 August; Phase III, 30 August–19 September). Results are partitioned into four geographical boxes, in order to highlight differences among the AEW vortices as they propagate westward along two tracks (northern and southern) over West Africa (land) and the adjacent eastern Atlantic Ocean (water). This marks the first time that a detailed diagnosis of the northerly track AEWs has been conducted. Results are also compared to previous GATE studies and a 30-yr climatology is extracted from ERA-40. In general, the subjectively analyzed wind fields presented in earlier studies compare favorabl.
Study on antioxidant and antimicrobial potential of chemically pro ﬁ led essential oils extracted from Juniperus phoenicea ( L. ) by use of in vitro and in silico approaches

: Juniperus phoenicea ( L. ) is a medicinal plant that has been used in phytotherapy as a treatment of certain pathological infections. In this context, the present work aimed to valorize the essential oil of J. phoenicea seeds ( EOGP ) by studying its chemical composition, and antiox - idant and antimicrobial activities. The EOGP was extracted by use of hydrodistillation and characterized by gas chromatography ( GC – MS ) . The antioxidant power was evaluated by three methods ( TAC, DPPH, and FRAP ) . The antimicrobial power was evaluated against Staphylococcus aureus ( ATCC6633 ) , Escherichia coli ( K12 ) , Bacillus subtilis ( DSM6333 ) , Proteus mirabilis ( ATCC29906 ) , Candida albi - cans ( ATCC10231 ) , Aspergillus niger ( MTCC282 ) , Aspergillus ﬂ avus ( MTCC9606 ) , and Fusarium oxysporum ( MTCC9913 ) . The GC/MS results revealed a total identi ﬁ cation of 99.98% with a dominance of carvacrol ( 39.81% ) followed by p - cymen - 3 - ol ( 34.44% ) and o - cymene ( 13.60% ) . Findings showed that EOGP exhibited important antioxidant power as IC50 was determined to be 26 µg/mL for 2,2 - diphenyl - 1 - picrylhydrazyl, while EC50 was 216.34 µg/mL for ferric reducing antioxidant power and total antioxidant capa - city was 720 mg AAE/g. The antimicrobial power on solid medium revealed that the inhibition diameters ranged from 11.30 ± 0.58 to 20 mm for the bacterial strains and from 9.33 ± 0.57 to 54.43 ± 0.29 mm for fungi. Notably, minimum inhibitory concentrations ranged from 18 to 19 µg/mL for bacterial strains and from 5.04 to 10.09 µg/mL for fungal strains. Overall, our results demonstrated the importance of EOGP as a source of natural antioxidant and antibacterial medicines against clinically relevant pathogenic strains.
Can we train vision and language zero-shot classiﬁcation models without syntax?

Natural language supervision in the form of image captions was recently shown to be an effective way of training zero-shot image classiﬁcation models. In this work, we focus on teasing out what parts of the language supervision are essential for training zero-shot models. Through extensive and careful experiments, we show that replacing intact captions with Bag-of-Words (BoW) does not signiﬁcantly degrade the zero-shot performance. Surprisingly, we can even slightly improve the performance on some datasets by balancing the frequency of words in BoW
From Monolith to Microservice: Measuring Architecture Maintainability

—The migration of monolithic applications to the cloud is a popular trend, with microservice architecture being a commonly targeted architectural pattern. The motivation behind this migration is often rooted in the challenges associated with maintaining legacy applications and the need to adapt to rapidly changing business requirements. To ensure that the migration to microservices is a sound decision for enhancing maintainability, designers must carefully consider the underlying factors driving this software architecture migration. This study proposes a set of software architecture metrics for evaluating the maintainability of microservice architectural designs for monolith to microservice architecture migration. These metrics consider various factors, such as coupling, complexity, cohesion, and size, which are crucial for ensuring that the software architecture remains maintainable in the long term. Drawing upon previous product quality models that share similar design properties with microservice, we have derived maintainability metrics that can help measure the quality of microservice architecture. In this work, we introduced our first version of structural metrics for measuring the maintainability quality of microservice architecture concerning its cloud-native characteristics. This work allows us to get early feedback on proposed metrics before a detailed evaluation. With these metrics, designers can measure their microservice architecture quality to fully leverage the benefits of the cloud environment, thus ensuring that the migration to microservice is a beneficial decision for enhancing the maintainability of their software architecture applications.
Establishing barriers between farmed fish and sea lice – the only sustainable solution?

Ectoparasitic sea lice present a major challenge to Atlantic salmon aquaculture (Coates et al. 2021) and a threat to fish welfare (Qviller et al. 2021). High infestation pressure of sea lice in the environment around commercial salmon farms has caused a critical increase in the mortality of wild salmonids (Krkosek et al. 2006; Ford and Myers 2008; Karlsen et al. 2021). Sea lice have also been described as a density-dependent constraint to salmonid farming (Jansen et al. 2012), where densities of farmed salmonids in an area have a strong effect on farm levels of sea lice and the efforts to control these infections.
Maternal and neonatal PAB during delivery

All manuscripts should be accompanied by an abstract. A structured abstract is required with original articles and it should include the following subheadings: Objective, Material and Methods, Results and Conclusion. A structured abstract is not required with review articles. The abstract should be limited to 250 words for original articles and review articles.
English second-language learners in preschool: Profile effects in their English abilities and the role of home language environment

The objectives of this study were twofold: (1) Determine the English proficiency of English second-language learners (ELLs) at the end of preschool as referenced to monolingual norms, and in particular, to determine if they showed an asynchronous profile, that is, approached monolingual norms more closely for some linguistic sub-skills than others; (2) Investigate the role of home language environment in predicting individual differences in children’s English proficiency. Twenty-one ELL children (mean age ¼ 58 months) from low socio-economic status (SES) backgrounds with diverse first-language backgrounds participated in the study. Children’s English proficiency was measured using a standardized story-telling instrument that yielded separate scores for their narrative, grammatical and vocabulary skills. A parent questionnaire was used to gather information about children’s home language environments. The ELL children displayed an asynchronous profile in their English development, as their standard scores varied in terms of proximity to monolingual norms; narrative story grammar was close to the standard mean, but mean length of utterance was below 1 standard deviation from the standard mean. No differences were found between the story-telling scores of the Canadian-born and foreign-born children, even though Canadian-born children were exposed to more English at home. Implications of the findings for clinicians and educators working with young ELLs are discussed.
Cognitive Detection of Unknown Beacons of Terrestrial Signals of Opportunity for Localization

A cognitive approach is proposed to detect unknown beacons of terrestrial signals of opportunity (SOPs). Two scenarios are considered in the paper: (i) detection of unknown beacons with integer constraints (IC) and (ii) detection of unknown beacons with no integer constraint (NIC). An example of beacons with IC is the pseudo-noise (PN) sequences in cellular code division multiple access (CDMA) signals. On the other hand, the reference signals (RSs) in orthogonal frequency-division multiplexing (OFDM)-based systems can be considered as beacons signals with NIC. Matched subspace detectors are proposed for both scenarios, and it is shown experimentally that the proposed matched subspace detectors are capable of detecting cellular third-generation (3G) cdma2000 signals and fifth-generation (5G) OFDM signals. A low complexity method is derived to simplify the matched subspace detector with IC for $M$ -ary phase shift keying ( $M$ PSK) modulation. The effect of symbol errors in the estimated beacon signal on the carrier to noise ratio (CNR) is characterized analytically. Closed-form expressions for the asymptotic probability of detection and false alarm are derived. Experimental results are presented showing an application of the proposed cognitive approach by enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial cellular signals for navigation purposes. In one experiment, the UAV achieved submeter-level accurate navigation over a trajectory of 1.72 km, by exploiting signals from four 3G cdma2000 transmitters. In another experiment, the UAV achieves a position root mean-squared error (RMSE) of 4.63 m over a trajectory of 416 m, by exploiting signals from two 5G transmitters.