Miracle-dz/UniFAR-FTUs huggingface dataset:
Research-Informed Online Language Course Design and Development for Least Commonly Taught LTCLs: The case of Introductory Dari, Pashto, and Uyghur

Online language teaching is continuing to grow in importance both in the academic world and the public sector as language learners desire to increase their language skills within the framework of an increasingly digital landscape both in academia and the corporate world. While the current landscape of the online language learning community is still highly saturated with more commonly taught languages like Spanish, French, Chinese, etc., members of the less commonly taught languages (LCTL) community are looking to take advantage of the same benefits of providing online courses, such as increasing marketability to a larger learner audience, supporting selfpaced learning, increasing learner autonomy, and increasing individual interactivity, as well as additional LCTL-specific benefits, such as helping sustain LCTLS with low enrollment. In this paper, we seek to describe one program’s experience designing and developing three introductory online courses in Dari, Pashto, and Uyghur, detailing our process from research and inception to the prototype development phase.
Positive impact of the Therasuit method on gross motor function of children with autism spectrum disorder: Case series

The Therasuit method is a valuable physiotherapeutic method to improve the gross motor function of children with neuromotor disorders. This series of case studies investigates the effect of the Therasuit method on the gross motor function of children with autism spectrum disorder (ASD). Therasuit method is a therapeutic intervention that involves the use of a therapeutic suit attached to a cage to stimulate gross motor skills, muscle strengthening, stretching, task training, and balance, which is a positive intervention for other neurodevelopmental disorders. The study was conducted with nine male children (42.1 + 4.1 months old) with ASD who received the Therasuit protocol for 4 weeks (20 sessions). The Gross Motor Function Measure (GMFM-88) was used to assess the children’s gross motor function before and after the Therasuit method intervention. In dimension B, several skills showed improvement, including transfer to sitting, lean forward and return, trunk rotation without support, and transfer from sitting to all four stances. In dimension C, an increase was observed in skills such as being prone to all four stance transfers and reaching above the shoulders. In dimension D, maximum scores were achieved in skills such as pulling to stand on a large bench without assistance. The dimensions with the greatest impairment were D and E, corresponding to gross motor skills in orthostasis and dynamic skills in orthostasis, respectively. The findings suggest that the Therasuit method is a promising resource for treating motor impairments in children with ASD. However, further studies with a larger sample size, an adequate control condition, and random assignment of participants would be needed to provide stronger evidence of the method’s effectiveness in this population.
Econometric model of the impact of government investment spending on selected variables in the public health services sector in Iraq for the period 2012-2021

The research aims to show the impact of government health investment spending on some indicators of the effectiveness of the health sector during the period 2012-2021, and the problem of the study was summarized in the low investment spending in the health sector and its inability to promote this vital sector, but the goal was to highlight the importance and role of government investment spending in the health sector, and accordingly, the basic hypothesis was based on the weak impact of the aforementioned spending in the health sector, and for the purpose of taking note of the subject of the research , the most important concepts were addressed The theory of government health investment spending and the importance and components of government investment health spending in Iraq, as for the applied side, the ARDL model model has been adopted and through which the hypothesis was proven and it was found that the impact of government health investment spending was weak due to the lack of investment allocations for the health sector, and finally the research has adopted a number of recommendations, the most important of which is to deliberately The government to increase government health investment, raise the level of health spending, promote the development of the health industry, and promote sustainable economic growth in Iraq.
Study of Amylolytic Action by Metabolites Isolated from Endosymbiotic Bacteria Bacillus cereus

____________ Abstract: Bacillus cereus is an endosymbiotic bacteria that has been isolated from pink perch ( Nemipterus japonicus ). Pink perch is a common food fish of high demand. This study was conducted to check whether the bacteria have amylolytic action or not. For this, the bacteria were isolated from the gut of the fish after alcohol sterilization. Optimisation of growth conditions, pH and temperature were performed. Growth curve also obtained by incubating bacterial colonies for various time periods. Bacterial extract had been used for the remaining studies. By using the DNS method, its amylolytic action was carried out and Amylase degradation capacity of the isolated bacteria further confirmed by adding iodine solution to the starch - agar petri plate containing bacterial colony. Optimum pH and temperature for maximum amylase activity were also identified. Gut endosymbionts generally have a role in digestion of fishes. Here the amylolytic action of Bacillus cereus has been proven hence, it may take part in carbohydrate degradation of pink perch. Thus, the bacteria can help the growth of the host.
Hydrogen distribution in chondrodite: a first-principles calculation

In synthetic hydroxyl – chondrodite and hydroxyl – clinohumite, two hydrogen sites (H1 and H2) are known. However, hydrogen occupying only H1 site has been reported for natural F – and Ti – rich chondrodite and clinohumite. In order to understand hydrogen distribution in these sites, stability of hydrogens at these sites in chondrodite is studied by ﬁ rst – principles density functional theory calculation. For hydroxyl – chondrodite, lowest energy was found when both H1 and H2 sites are half occupied by hydrogens. In this con ﬁ guration, strong H1 – H1 repulsion is avoided, and nearly straight hydrogen bond of O – H1 £ O – H2 is realized. When a half of OH − is replaced with F − or replaced with O 2 − in the case of Ti – containing systems, hydrogen occupying H1 site is always favored over hydrogen in H2 site so as to form nearly straight hydrogen bond of O – H1 £ F − (or O 2 − ). Our calculations are fully consistent with the observed structures in both synthetic and natural chondrodite, and revealed that hydrogen distribution in humite group minerals is mostly governed by hydrogen bonding at H1 site.
INFLUENCE FACTORS OF LAND SURFACE TEMPERATURE INVERSION USING THERMAL INFRARED HYPERSPECTRAL REMOTE SENSING SATELLITES DATA

Abstract. Land surface temperature (LST) is widely used in research fields such as numerical forecasting, global circulation models, and regional climate models. For the remote sensing data from satellites with thermal infrared detection capability, the land surface temperature (LST), land surface emissivity (LSE), and atmospheric influence are mixed together. Using different assumptions and approximations for the radiative transfer equations and surface emissivity, various LST algorithms have been proposed. Among these algorithms, the split-window (SW) algorithm is currently most widely used. Besides, with the rapid development of machine learning, new ideas have been emerged for quantitative remote sensing inversion. For a hyperspectral remote sensing satellite with over 20 thermal infrared channels, machine learning methods such as random forest and artificial neural network can be selected to build an integrated separation and inversion algorithm for LST and LSE.In this paper, the influencing factors of LST inversion using thermal infrared hyperspectral satellites data is discussed, taking the SW algorithm and integrated machine learning algorithm as examples, and the contribution of these factors to the LST inversion error is analysed. We hope this paper could provide valuable reference for the design, index analysis and error calculation for remote sensing satellites with thermal infrared hyperspectral detection capability.
Dynamic Responce of DC Motor Via Fuzzy Logic and PID Controllers

Fuzzy set Theory of Lotfi A. Zadeh (1965) [1] has been one of the most important area for researches due to its advanced applications in many fields which has the ability to deal with non-linearity and independence of plant modeling, especially in Electrical machines and its control techniques to reach optimum Dynamic response with load variations. In this paper control of direct current (DC) motor with conventional controls proportional–integral–derivative (PID) and fuzzy logic control (FLC) has been investigated and compared with each others for different operating conditions. The mathematical model of Dc motor was modeled and simulated in Matlab Simulink (Mathworks) with illustrated graphs and plots. The performance of the model is expected to show a great results for the fuzzy logic control (FLC) over the PID control [2].
Antibodies as clinical tools for tuberculosis

Tuberculosis (TB) is a leading cause of morbidity and mortality worldwide. Global research efforts to improve TB control are hindered by insufficient understanding of the role that antibodies play in protective immunity and pathogenesis. This impacts knowledge of rational and optimal vaccine design, appropriate diagnostic biomarkers, and development of therapeutics. Traditional approaches for the prevention and diagnosis of TB may be less efficacious in high prevalence, remote, and resource-poor settings. An improved understanding of the immune response to the causative agent of TB, Mycobacterium tuberculosis (Mtb), will be crucial for developing better vaccines, therapeutics, and diagnostics. While memory CD4+ T cells and cells and cytokine interferon gamma (IFN-g) have been the main identified correlates of protection in TB, mounting evidence suggests that other types of immunity may also have important roles. TB serology has identified antibodies and functional characteristics that may help diagnose Mtb infection and distinguish between different TB disease states. To date, no serological tests meet the World Health Organization (WHO) requirements for TB diagnosis, but multiplex assays show promise for improving the sensitivity and specificity of TB serodiagnosis. Monoclonal antibody (mAb) therapies and serum passive infusion studies in murine models of TB have also demonstrated some protective outcomes. However, animal models that better reflect the human immune response to Mtb are necessary to fully assess the clinical utility of antibody-based TB prophylactics and therapeutics. Candidate TB vaccines are not designed to elicit an Mtb-specific antibody response, but evidence suggests BCG and novel TB vaccines may induce protective Mtb antibodies. The potential of the humoral immune response in TB monitoring and control is being investigated and these studies provide important insight into the functional role of antibody-mediated immunity against TB. In this review, we describe the current state of development of antibody-based clinical tools for TB, with a focus on diagnostic, therapeutic, and vaccine-based applications.
IMPACT ASSESSMENT OF GEOLOGICAL ENVIRONMENT OF DAYE MINING AREA IN SOUTHEASTERN HUBEI PROVINCE

Geo-environmental problems of mining cities have been increasing in China. It is necessary to assess mine geo-environment impact( MGEI) in these mining cities for instructing the mine development and protecting environment. RS data is adopted to assess MGEI in the Daye mining area in southeastern Hubei Province. A MGEI index system is established by considering geo-environment background,geo-environment problems and mining activities. The fussy comprehensive assessment method is employed to assess the geo-hazard susceptibility before mining and the MGEI after mining respectively. The result indicates that:( 1) Under the natural state,the high,medium,relatively low and low easy-happening areas of geological hazard take up 3. 29%,12. 55%,24. 53% and59. 62%,respectively. ( 2) Under the mining state,the severely,less severely,relatively slightly and slightly impacted areas of mine geo-environment take up 13. 63%,22. 96%,33. 95% and 29. 46%,respectively. The assessment result can be applied in mine planning and comprehensive geo-environment reclamation.
Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

Next generation of wireless networks are characterized by two main features named Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). These two services can be accommodated in the same wireless infrastructure so that wide range of users, demanding either massive throughput or extremely low latency and high reliability requirements, are directly benefited for providing various mission critical services. Co-existence of eMBB and URLLC services, however, demand highly efficient and less complex resource allocation schemes. In this paper, various resource allocation techniques are studied for the co-existence of eMBB and URLLC traffic to meet the heterogeneous specifications of each class of users. A detailed study on existing resource allocation schemes for simultaneous transmission of eMBB and URLLC services based on network slicing, flexible Transmit Time Interval (TTI), scheduling and distributed and federated learning are provided. Moreover, Machine Learning (ML) aided and Reconfigurable Intelligent Surface (RIS) and Unmanned Aerial Vehicle (UAV) assisted resource allocation techniques are also studied in detail. Additionally, this paper identifies some challenges for eMBB and URLLC service accommodations in the same wireless architecture, and proposes their possible solution approaches.
Metabolic Profiling and Chemical-Based Antioxidant Assays of Green and Red Lettuce (Lactuca sativa)

Lettuce (Lactuca sativa) is an important leaf vegetable crop worldwide. The total phenolic content was higher in the red skirt methanol extracts with a level of 64.9 ± 0.3 mg GA/g, and phenolic content of green skirt was 49.4 ± 0.3 mg GA/g. The total flavonoid content in the methanol extract of red skirt showed the highest concentration (291.6 ± 9.0 mg rutin/g), followed by green skirt (223 ± 9.8 mg rutin/g). The red skirt showed a higher content of anthocyanin (23.7 ± 0.8 mg/100 g) compared to the green skirt (7.4 ± 0.9 mg/100 g). Seven carotenoids i.e., lutein, β–carotene, 9Z–β–carotene, 13Z–β–carotene, violaxanthin, zeaxanthin, and α–carotene were detected in both cultivars. The polar metabolites are strongly associated with the metabolites that participate in the TCA cycle. Correlations between primary and secondary metabolites were easily achieved by gas chromatography-time-of-flight mass spectrometry (GC-TOFMS)-based metabolite profiling method. Red skirt methanol extract showed 77.5 ± 0.4% 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity, 50.8 ± 0.8% hydrogen peroxide radical scavenging assay and absorbance value (0.052) reducing power assay at 250 μL/mL, having significantly higher activity than the green skirt extract. The red lettuce extract also showed higher levels of phenolic compounds, flavonoids, and anthocyanins, and it possesses more antioxidant properties compared to the green skirt extract. In this study we compared two cultivars of lettuce, red skirt and green skirt, for various nutritional parameters and to understand the association of secondary metabolites with their corresponding primary metabolites. From the overall analysis, it is confirmed that GC-TOFMS-based metabolite profiling is an appropriate method for determining biochemical reactions and also acts as a combined resource to understand primary and secondary metabolism.
Relationships between snowpack depth and primary LiDAR point cloud derivatives in a mountainous environment

Two LiDAR data sets were acquired over the Marmot Creek headwaters of the Bow River, Alberta, Canada; the first during dry snow-free conditions in August 2007, and the second during snow covered conditions in March 2008. A LiDAR snow depth model (SDM) was derived by subtracting the snow-free digital elevation model (DEM) from the digital snow surface model (SSM). Field crews were deployed coincident with the 2008 LiDAR survey to collect snow depth transects in: (a) low elevation valley locations of shallow and discontinuous snow cover; (b) alpine locations of deep but discontinuous snowpack; and (c) forested locations at intermediate elevations displaying variable depth but more continuous cover. The SDM was validated using the field measurements and then stratified by the three LiDAR point cloud derivatives: elevation, intensity and canopy fractional cover. The SDM performed favourably over the alpine transect areas with no significant bias (r 2 = 0.94, n = 137), with the valley transects demonstrating a slight overestimation of 7 cm (r 2 = 0.48, n = 310) and the forest transects demonstrating the weakest correlation (r 2 = 0.20, n = 402) and a mean over-estimation of 13 cm. Canopy cover creates a challenge for mapping shallow snow depth with LiDAR in mountainous environments, but canopy also reduces the spatial accuracy of GPS field data so a weaker correlation is to be expected. The SDM illustrated increasing snow depth up to tree line at approximately 2250 m a.g. l. with reducing depth and cover in the alpine zone. Overall, high LiDAR intensity values were not well correlated with snowcover at the basin scale with only 44% spatial correspondence. However, above tree line this increased to 76%, suggesting that LiDAR intensity has some value for snow covered area (SCA) mapping as long as there is no forest canopy to attenuate or split the laser pulse returns.
Larvicidal Bioefficacy of Euphorbia cyathophora Leaf Extracts Against Bhindi Fruit Borer Earias vittella, Fabricius (Lepidoptera: Noctuidae)

: Agriculture insect pests can cause significant damage to crops and stored products, either directly or indirectly. Abelmoschus esculentus (bhindi/bhendi) is an essential nutrient source and economically important vegetable crop grown in the world's tropical and subtropical parts. The major agriculture and bhindi shoot and fruit borer insect pest is Earias vittella (Fab.). Chemical pesticides always have adverse environmental effects, but plant products could be used effectively in well-being as an ecologically acceptable pest management system. The Euphorbia
Phytochemical composition and antimicrobial activities of the essential oils from summer savory (Satureja hortensis L.) growing in shading condition

Abstract This study was conducted to investigate the effect of plant shading (covered by pearl shade nets with a 40% shade index or nonshading plants from open field) on the content and composition of essential oils extracted from summer savory (Satureja hortensis L.) during the flowering stage cultivated under the ecological conditions of Serbia. The summer savory essential oil (SSEO) was extracted by Clevenger hydrodistillation. According to the GC/MS analysis of the SSEO, twenty-five and twenty-six components were detected in the non-shaded and shaded plants, respectively. The yield of SSEO from non-shaded plants was 1.64%, while SSEO from plants covered by shade nets was 1.76%. Carvracol (48.7–53.25%) and γ-terpinene (32.7%–36.8%) were found to be the main components in SSEO, followed by p-cymene (3.4%-3.2%), α-terpinene (2.9%-3.5%) and limonene (1.9-1.3%). In the DPPH assay, SSEO showed an IC50 of 0.99 μg mL−1 in shaded and an IC50 of 1.01 μg mL−1 in non-shaded plants. Additionally, SSEO exhibited strong antibacterial activity against Escherichia coli (44.3–48 mm inhibition zone), Bacillus subtilis (34.7–41.0 mm), Klebsiella pneumoniae (28.3–29.0 mm) and Proteus vulgaris (21.0–24.3 mm). A possible practical application of this study would be to grow summer savory at higher plant densities, or as an intercrop, because it does not require too much light for production.
Phar-LSTM: a pharmacological representation-based LSTM network for drug–drug interaction extraction

Pharmacological drug interactions are among the most common causes of medication errors. Many different methods have been proposed to extract drug–drug interactions from the literature to reduce medication errors over the last few years. However, the performance of these methods can be further improved. In this paper, we present a Pharmacological representation-based Long Short-Term Memory (LSTM) network named Phar-LSTM. In this method, a novel embedding strategy is proposed to extract pharmacological representations from the biomedical literature, and the information related to the target drug is considered. Then, an LSTM-based multi-task learning scheme is introduced to extract features from the different but related tasks according to their corresponding pharmacological representations. Finally, the extracted features are fed to the SoftMax classifier of the corresponding task. Experimental results on the DDIExtraction 2011 and DDIExtraction 2013 corpuses show that the performance of Phar-LSTM is competitive compared with other state-of-the-art methods. Our Python implementation and the corresponding data of Phar-LSTM are available by using the DOI 10.5281/zenodo.8249384.
Intelligent Measurement on Edge Devices Using Hardware Memory-Aware Joint Compression Enabled Neural Networks

Applying neural network (NN) on edge devices is crucial for intelligent measurement. However, the limited memory resource is one of the main challenges for deploying NN with high spatial complexity. To address this issue, a hardware memory-aware joint compression (MAJOR) is proposed in this article. First, a trainable joint compression framework is established for the parallel operation of pruning and quantization. Then, a partial memory-bottleneck penalty regularizer is designed to guide the compression to meet actual hardware memory demands accurately. The proposed method is evaluated on SLOC4, WHU-RS19, RSSCN7, and OPT-Aircraft v1.0 remote sensing image datasets using VGG, ResNet-20/50, MobileNetv1, EfficientNet-B0, DenseNet-121, and ResNeXt-50 for the satellite onboard processing platform, which is a typical hardware memory-limited device. The experimental results show that MAJOR could reduce the model’s partial peak memory size up to $23.19\times $ , ensuring it fits within the target platform’s capacity with less than 3% accuracy loss. Furthermore, the proposed method is evaluated on CIFAR-10 using VGG, ResNet-20, and MobileNetv1 for four edge hardware with different memory sizes. It achieves a remarkable compression ratio of up to 94.92% while ensuring that model accuracy loss remains below 3%. Compared with other compression methods, MAJOR accurately reduces model memory size to match the capacity of the various target hardware. The partial memory bottleneck in NNs could be significantly eliminated.