Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight testing of Mission Adaptive Compliant Wing

This paper describes flight test results of a “Mission Adaptive Compliant Wing” (MACWing) variable geometry trailing edge flap in conjunction with a natural laminar flow airfoil. The MAC-Wing technology provides lightweight, low power, variable geometry reshaping of the upper and lower flap surface with no seams or discontinuities. In this particular program, the airfoil-flap system is optimized to maximize the laminar boundary layer extent over a broad lift coefficient range for endurance aircraft applications. The expanded “laminar bucket” capability allows the endurance aircraft to significantly extend their range (15% or more) by continuously optimizing the wing L/D throughout the mission. The wing was tested at full-scale dynamic pressure, full scale Mach, and reduced-scale Reynolds Numbers on Scaled Composites’ White Knight aircraft. Test results confirmed laminar flow regime up to approximately 60% chord for much of the lift range. Analysis and test results suggest significant fuel savings, weight savings and a higher control authority. Preliminary drag results, future aerodynamic applications and vehicle performance projections are discussed.
Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques

Reservoir release is an essential variable as it affects hydrological processes and water availability downstream. This study aims to estimate reservoir release using a satellite-based approach, specially focusing on the impacts of inflow simulations and reservoir water storage change (RWSC) on release estimates. Ten inflow simulations based on hydrological models and blending schemes are used in combination with three RWSC estimates based on two satellite-based approaches. A case study is performed at the Ankang reservoir, China. The results demonstrate that release estimates show high skill, with normalized root-mean-square error (NRMSE) less than 0.12 and Kling-Gupta Efficiency (KGE) over 0.65. The performance of release estimates is varying with and influenced by inflow simulations and RWSC estimates, with NRMSE ranging from 0.09–0.12 and KGE from 0.65–0.74. Based on time-varying Bayesian Model Averaging (BMA) approaches and synthetic aperture radar (SAR) satellite datasets, more accurate inflow and RWSC estimates can be obtained, thus facilitating substantially release estimates. With multi-source satellite datasets, temporal scale of reservoir estimates is increased (monthly and bi-weekly), acting as a key supplement to in situ records. Overall, this study explores the possibility to reconstruct and facilitate reservoir release estimates in poorly gauged dammed basins using hydrological modeling techniques and multi-source satellite datasets.
The relation between the questions teachers ask and children’s language competence

Abstract In the present study, it was hypothesized that the proportion of open questions teachers ask during shared book reading would be directly and indirectly (through class aggregated mean length of utterance) related to children’s vocabulary and would be directly and indirectly (through class aggregated mean length of children’s utterance and vocabulary) related to children’s narrative competence. A total of 7 early childhood teachers and 176 pupils participated in this study. Outcomes of mediation analyses revealed that the proportion of open questions was positively related to class aggregated mean length of children’s utterance and vocabulary and negatively related to narrative competence. In addition, the proportion of open questions was indirectly and positively related to children’s narrative competence, through class aggregated mean length of children’s utterance. The results of this study indicate that the type of questions teachers ask during shared book reading is related to children’s language competence.
Design of Desanding Facilities for Hydropower Schemes Based on Trapping Efficiency

Turbine wear caused by hydro-abrasion due to suspended sediment in the turbine water has a negative impact on the power production and revenue of hydropower schemes. Efficiency of desanding facilities that reduce suspended sediment load ahead of turbines is key to limiting hydro-abrasion. Existing facilities built according to common design approaches often show lack of performance, in particular under non-ideal site-specific inlet and outlet conditions. Consequently, a new design concept that allows for the optimized design of desanding facilities at hydropower schemes has been developed based on prototype field measurements and CFD modeling. The effects of facility layout and components, such as tranquilizing racks, transition zone and outflow weir and related design parameters on the flow field and involved particle settling have been investigated in a comprehensive numerical model study. Hence, length adjustment terms were deduced from resulting changes in trapping efficiency, which allow for the estimation of the required basin length and an optimized facility layout. Compared to design approaches considering linear settling trajectories of sediment particles, the proposed procedure leads to longer settling basins in general and thus to increased trapping efficiencies of desanding facilities. Furthermore, the findings about the effect of specific facility components may allow for the optimisation of existing desanding facilities in an economical way.
Understanding drivers of migration: a preliminary case study of Libyan Tuareg

ABSTRACT In this preliminary case study, the aspirations capabilities model is applied to interviews conducted with Libyan Tuareg to confirm and develop ongoing theories on migration drivers. This article examines both migration theory, as well as the social, economic, and legal situation of Tuareg in Libya. Within the context of problems to achieve citizenship among some Libyan Tuareg, the notion of statelessness is assessed as an aspiration and capability that influences migration outcomes. This study confirmed that the general life ambitions of an individual are at the basis of aspirations to migrate and that migration functions as a vehicle to fulfil personal ambitions. The article stresses that the connection to the homeland is a factor that influences migration decision-making, which should be understood outside of citizenship. Moreover, this study observed the awareness of the aspirations and capabilities of acquaintances as a factor that influences migration aspirations.
Matrix Metalloproteinase 7 Expression and Apical Epithelial Defects in Atp8b1 Mutant Mouse Model of Pulmonary Fibrosis

Abnormalities in airway epithelia and lung parenchyma are found in Atp8b1 mutant mice, which develop pulmonary fibrosis after hyperoxic insult. Microarray and ingenuity pathway analysis (IPA) show numerous transcripts involved in ciliogenesis are downregulated in 14-month (14 M) -old Atp8b1 mouse lung compared with wild-type C57BL/6. Lung epithelium of Atp8b1 mice demonstrate apical abnormalities of ciliated and club cells in the bronchial epithelium on transmission electron microscopy (TEM). Matrix metalloproteinase 7 (MMP7) regulates of ciliogenesis and is a biomarker for idiopathic pulmonary fibrosis (IPF) in humans. Mmp7 transcript and protein expression are significantly upregulated in 14 M Atp8b1 mutant mouse lung. MMP7 expression is also increased in bronchoalveolar lavage fluid (BAL). Immunohistochemistry is localized MMP7 to bronchial epithelial cells in the Atp8b1 mutant. In conclusion, MMP7 is upregulated in the aged Atp8b1 mouse model, which displays abnormal ciliated cell and club cell morphology. This mouse model can facilitate the exploration of the role of MMP7 in epithelial integrity and ciliogenesis in IPF. The Atp8b1 mutant mouse is proposed as a model for IPF.
Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning

This study develops an objective deep-learning-based model for tropical cyclone (TC) intensity estimation. The model’s basic structure is a convolutional neural network (CNN), which is a widely used technology in computer vision tasks. In order to optimize the model’s structure and to improve the feature extraction ability, both residual learning and attention mechanisms are embedded into the model. Five cloud products, including cloud optical thickness, cloud top temperature, cloud top height, cloud effective radius, and cloud type, which are level-2 products from the geostationary satellite Himawari-8, are used as the model training inputs. We sampled the cloud products under the 13 rotational angles of each TC to augment the training dataset. For the independent test data, the model shows improvement, with a relatively low RMSE of 4.06 m/s and a mean absolute error (MAE) of 3.23 m/s, which are comparable to the results seen in previous studies. Various cloud organization patterns, storm whirling patterns, and TC structures from the feature maps are presented to interpret the model training process. An analysis of the overestimated bias and underestimated bias shows that the model’s performance is highly affected by the initial cloud products. Moreover, several controlled experiments using other deep learning architectures demonstrate that our designed model is conducive to estimating TC intensity, thus providing insight into the forecasting of other TC metrics.
Foremost features affecting financial distress and Bankruptcy in the acute stage of COVID-19 crisis

ABSTRACT This paper investigates the foremost firm-specific factors having an impact on financial distress and bankruptcy in the acute stage of the Covid-19 crisis based on data from approximately 9,000 enterprises in 25 countries. Empirical results of a random forest algorithm with SHAP values show increased odds of both bankruptcy and financial distress for firms that have problems in accessing finance, younger firms and more indebted firms. In addition, the size of the firm and the years of experience of its managers also have an impact on financial failure. However, country features are more important than firm features in predicting bankruptcy and financial distress in the Covid-19 crisis.
Membrane Vesicles for Nanoencapsulated Sulforaphane Increased Their Anti-Inflammatory Role on an In Vitro Human Macrophage Model

At present, there is a growing interest in finding new non-toxic anti-inflammatory drugs to treat inflammation, which is a key pathology in the development of several diseases with considerable mortality. Sulforaphane (SFN), a bioactive compound derived from Brassica plants, was shown to be promising due to its anti-inflammatory properties and great potential, though its actual clinical use is limited due to its poor stability and bioavailability. In this sense, the use of nanocarriers could solve stability-related problems. In the current study, sulforaphane loaded into membrane vesicles derived from broccoli plants was studied to determine the anti-inflammatory potential in a human-macrophage-like in vitro cell model under both normal and inflammatory conditions. On the one hand, the release of SFN from membrane vesicles was modeled in vitro, and two release phases were stabilized, one faster and the other slower due to the interaction between SFN and membrane proteins, such as aquaporins. Furthermore, the anti-inflammatory action of sulforaphane-loaded membrane vesicles was demonstrated, as a decrease in interleukins crucial for the development of inflammation, such as TNF-α, IL-1β and IL-6, was observed. Furthermore, these results also showed that membrane vesicles by themselves had anti-inflammatory properties, opening the possibility of new lines of research to study these vesicles, not only as carriers but also as active compounds.
Evaluation of growth performance and slaughter traits of Aseel chicken grown with and without outdoor access

A study was conducted to investigate the effects of free-range, part-time free range, and confinement rearing systems on growth performance and slaughter traits in Lakha, Mushki, Peshawari, and Sindhi Aseel for 10-week duration (7-16 week). In total, 216 Aseel cockerels (6-wk-old), 54 from each variety, were allotted to 12 experimental groups, in a 3 × 4 (rearing system × Aseel variety) factorial arrangement under a randomized complete block design, replicated 3 times with 6 birds in each. Final body weight, weight gain, folds of increase, and mortality parameters of growth performance and carcass yield, breast, thigh, drumstick, wing, liver, gizzard, and heart parameters of slaughter traits were evaluated. Data were analyzed by using 2-way ANOVA under factorial arrangement. The results indicated increased body weight gain, folds of increase, breast and drumstick in both confinement and part-time free range (P<0.05), whereas enhanced body weight, dressing, thigh, liver, and gizzard only in confinement rearing system. Among the Aseel varieties, Sindhi showed enhanced final body weight, weight gain, thigh and drumstick yields, whereas breast and gizzard weights were found to be greater in Lakha and Sindhi. Carcass yield, however, remained higher in all varieties except Peshawari. In conclusion, indoor rearing system had positive effects on growth and carcass-related parameters. Among the varieties, Sindhi showed better growth and slaughter-related performance traits in alternative rearing systems and hence, can be used as slower growing meat chicken in the aforesaid systems of poultry production in developing countries for better livelihood.
A Coarse-to-Fine Framework for Automatic Video Unscreen

Video unscreen, a technique to extract foreground from given videos, has been playing an important role in today’s video production pipeline. Existing systems developed for this purpose which mainly rely on video segmentation or video matting, either suffer from quality deficiencies or require tedious manual annotations. In this work, we aim to develop a fully automatic video unscreen framework that is able to obtain high-quality foreground extraction without the need of human intervention in a controlled environment. Our framework adopts a coarse-to-fine strategy, where the obtained background estimate given an initial mask prediction in turn helps the refinement of the mask by the alpha composition equation. We conducted experiments on two datasets, 1) the Adobe’s Synthetic-Composite dataset, and 2) DramaStudio, our newly collected large-scale green screen video matting dataset, exhibiting the controlled environments. The results show that the proposed framework outperforms existing algorithms and commercial software, both quantitatively and qualitatively. We also demonstrate its utility in person replacement in videos, which can further support a variety of video editing applications.
Redefining the Cut-Off Ranges for TSH Based on the Clinical Picture, Results of Neuroimaging and Laboratory Tests in Unsupervised Cluster Analysis as Individualized Diagnosis of Early Schizophrenia

Thyroid abnormalities, including mild forms of hypothyroidism and hyperthyroidism, are reported as risk factors for the development of a number of neuropsychiatric disorders, including schizophrenia. The diagnostic process still takes into account the extreme ranges of the accepted reference values for serum TSH since the concentration of free thyroxine in the serum does not change by definition. TSH mU/L cut-off values in psychiatric patients are currently clinically considered in the case of extremely high serum TSH levels (>4.0 mU/L). The results obtained in this study suggest that the clinically significant value has a lower TSH cut-off point with an upper limit of 2–2.5 mU/L. The criteria for the differential diagnosis of patients with schizophrenia, however, mainly take into account statutory reference ranges without a background related to the history of thyroid diseases in the family. The results indicate the need to lower the upper cut-off values for TSH among patients with early psychosis, which is related to the potential clinical significance of the obtained values both in the field of clinical evaluation and neuroimaging and laboratory evaluation parameters. The cut-off points obtained with the prior available knowledge coincided with the values established in the unsupervised clustering method, which further confirms the legitimacy of their use in the individualized diagnosis strategy of schizophrenia.
Antiviral Activities of Algal-Based Sulfated Polysaccharides

An antiviral agent is urgently needed based on the high probability of the emergence and re-emergence of future viral disease, highlighted by the recent global COVID-19 pandemic. The emergence may be seen in the discovery of the Alpha, Beta, Gamma, Delta, and recently discovered Omicron variants of SARS-CoV-2. The need for strategies besides testing and isolation, social distancing, and vaccine development is clear. One of the strategies includes searching for an antiviral agent that provides effective results without toxicity, which is well-presented by significant results for carrageenan nasal spray in providing efficacy against human coronavirus-infected patients. As the primary producer of sulfated polysaccharides, marine plants, including macro- and microalgae, offer versatility in culture, production, and post-isolation development in obtaining the needed antiviral agent. Therefore, this review will describe an attempt to highlight the search for practical and safe antiviral agents from algal-based sulfated polysaccharides and to unveil their features for future development.
Effects of Competitive Strategies on Performance of Express Connections Limited in Kenya

Competitive strategy is concerns with what a firm is doing in order to gain a sustainable competitive advantage. The stiff competition among the public transport companies and the entry of other players into the industry following the government plan to ban on fourteen seater passenger vehicles necessitate the design of competitive strategies to guarantee their performance. This study sought to determine the influence of competitive strategies on performance of Express Company Limited in Kenya. The study was guided by the following objectives; to determine the competitive strategies adopted and the effects of competitive strategies on performance of express connections Kenya Limited. The research used a case study approach to give an in-depth understanding of the competitive strategies on performance of Express Connections Limited. The study used both primary and secondary data where primary data was collected using an interview guide and secondary data was collected from audited financial reports and other publications at Express Connections Limited. Content analysis was used to analyze this data collected from the interviewees. The study established that Express Connections Limited adopted a number of competitive strategies; standard pricing was being used within the respective timings for instance peak and off peak differentiation, usage of standard colors so as to identify the fleet and the creation of a sister company to help build the bus bodies and in repair of the vehicles and segmented market on the basis of routes being covered by the fleets and zones in order to ensure effective coverage of all routes. The study concludes that service quality and customer relationship are the most effective competitive strategy used by Express Connections Limited as well as differentiation of services to suit their clients. The study recommended that the company continue investing and coming up with unique services so that they can differentiate themselves and at the same time continue innovating services which will compete with other companies’ and do aggressive marketing in order to change the perception which the customers have regarding the pricing. The study faced a limitation as regards to interviewee’s confidence that the information provided would not be misused but used for the purpose for which it was meant. The study was a case study of one company and therefore it may not be expanded to include more companies. Therefore the applications of the study findings are limited to one company. The study recommends that, further research could be expanded to include other companies in the transport industry in order to make a comprehensive conclusion. At the same time the same study should be carried on other industries especially in the manufacturing sector, to enhance comparison.
State Bounding Description and Reachable Set Estimation for Discrete-Time Genetic Regulatory Networks With Time-Varying Delays and Bounded Disturbances

This article investigates the problems of state bounding description and reachable set estimation for discrete-time delayed genetic regulatory networks with bounded disturbances. A novel delay-dependent sufficient condition composing several simple linear matrix inequalities is first given to guarantee that the state trajectories converge globally exponentially into a Cartesian product of two polytopes. Furthermore, equivalent sufficient conditions directly represented by the system parameters are derived. As applications, it is shown that these sufficient conditions are exactly global exponential stability criteria of the zero equilibrium when the disturbances vanish, and the Cartesian product of two polytopes can be viewed as a reachable set estimation of the states when the initial functions are limited into a certain range. The effectiveness of the proposed approach is illustrated by two numerical examples. Compared with the existing results, it is worthy to stress that the presented method does not need to construct any Lyapunov–Krasovskii functional and can be easily realized.
k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition

AbstractOne of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the k-nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed k-nearest neighbor model is an effective approach in predicting the short-term traffic condition.
Hepatitis delta is a public health concern in the community setting: The role of prison health care units in limiting the spread of infection in general population

Hepatitis D virus (HDV) is a public health concern for its severe medical consequences. The aim of this pilot study was to evaluate the prevalence of the main infectious disease between the detainees of the Veneto Region. The study consisted of a survey carried between the heads of the prison healthcare staff. The data showed that on 2,119 detainees the prevalence of hepatitis B (HBV) is the 6% and that the most relevant prevalence of HBV was revealed in the East Europe detainees (Ex USSR countries, Albania, Bosnia, Kosovo, Macedonia), being the 39% of all HBcAb positive subjects. The survey also revealed as the population less protected by HVB vaccination was that comes from East Europe. Moreover, the study indicates that hepatitis D diagnosis is not well known by clinician working inside prisons and that it should be improved through specific diagnostic and therapeutic procedures. Our data indicate that the prison setting may play an important and crucial role in term of public health both in intercepting patients that need of treatment and in preventing actions able to minimize the risk of infection. In prison settings screening, linkage to care and harm reduction measures should be improved in order to protect the general population from the impact of the emerging infectious diseases, including HDV.