Miracle-dz/UniFAR-FTUs huggingface dataset:
POVERTY REDUCTION THROUGH THE FAMILY HOPE PROGRAMME

This research aims to find out (1) the Implementation of the Family Hope Programme (PKH) in Poverty Alleviation in Talumolo Village, Dumbo Raya Sub-district, Gorontalo City (2) the Determinant Factors of PKH Success in Poverty Alleviation in Talumolo Village, Dumbo Raya Sub-district, Gorontalo City. This research uses a descriptive qualitative method, while the data collection techniques used are interviews, field observations, and documentation. Based on the results of the research, it is known that the implementation process of the Family Hope Program (PKH) in Poverty Alleviation in Talumolo Village, Dumbo Raya District, Gorontalo City, which consists of Planning, Determination of KPM PKH, Distribution of Assistance, Assistance, and Membership Transformation has been carried out well but there are still obstacles, namely frequent changes in regulations, and the end of PKH membership through the social assistance check application. (2) Determinants of Success in the Implementation of the Family Hope Program (PKH) in Poverty Alleviation in Talumolo Village, Dumbo Raya Subdistrict, Gorontalo City, namely Communication, Resources, Disposition / Attitude of Implementers and Bureaucratic Structure have been carried out well but on the part of the community, there is a lack of participation in attending group meetings and P2K2
Nanocrystals of poorly soluble drugs : 2 drug bioavailability and physicochemical stability 3

Many approaches have been developed over time to counter the bioavailability limitations 10 of poorly soluble drugs. With advances in nanotechnology in recent decades, science and industry 11 have been approaching this issue through the formulation of drugs as nanocrystals, which consist 12 of pure drugs and a minimum of surface active agents required for stabilization. They are carrier13 free submicron colloidal drug delivery systems with a mean particle size in the nanometer range, 14 typically between 10 and 800 nm. By reducing particle size to nanoscale, the particle surface area 15 available for the molecule dissolution in the direction of dissolution medium is increased, and thus 16 bioavailability is enhanced. This approach has proven successful, as demonstrated by the number 17 of such drug products on the market. R&D and industry have offered many technological solutions 18 to reduce the particle size to nanoscale, and also devised solutions for the handling of particle of 19 nanodimensions, such as methods to accurately measure nanoparticle size and techniques to 20 prevent physicochemical and stability related problems, such as aggregation. The present work 21 provides an overview of the more recent achievements in improving the bioavailability of poorly 22 soluble drugs according to their administration route, and describes the methods developed to 23 overcome physicochemical and stability related problems. 24
Genome-wide identification of Apetala2 gene family in Hypericum perforatum L and expression profiles in response to different abiotic and hormonal treatments

The Apetala2 (AP2) gene family of transcription factors (TFs) play important functions in plant development, hormonal response, and abiotic stress. To reveal the biological functions and the expression profiles of AP2 genes in Hypericum perforatum, genome-wide identification of HpAP2 family members was conducted. Methods We identified 21 AP2 TFs in H. perforatum using bioinformatic methods; their physical and chemical properties, gene structures, conserved motifs, evolutionary relationships, cis-acting elements, and expression patterns were investigated. Results We found that based on the structural characteristics and evolutionary relationships, the HpAP2 gene family can be divided into three subclasses: euANT, baselANT, and euAP2. A canonical HpAP2 TF shared a conserved protein structure, while a unique motif 6 was found in HpAP2_1, HpAP2_4, and HpAP2_5 from the euANT subgroup, indicating potential biological and regulatory functions of these genes. Furthermore, a total of 59 cis-acting elements were identified, most of which were associated with growth, development, and resistance to stress in plants. Transcriptomics data showed that 57.14% of the genes in the AP2 family were differentially expressed in four organs. For example, HpAP2_18 was specifically expressed in roots and stems, whereas HpAP2_17 and HpAP2_11 were specifically expressed in leaves and flowers, respectively. HpAP2_5, HpAP2_11, and HpAP2_18 showed tissue-specific expression patterns and responded positively to hormones and abiotic stresses. Conclusion These results demonstrated that the HpAP2 family genes are involved in diverse developmental processes and generate responses to abiotic stress conditions in H. perforatum. This article, for the first time, reports the identification and expression profiles of the AP2 family genes in H. perforatum, laying the foundation for future functional studies with these genes.
An Import(ant) Price of Brexit Uncertainty

: We estimate the impact of trade policy uncertainty (TPU) on CES import price indices, focusing on the implications of Britain’s exit from the European Union (Brexit). Our analysis reveals that an increase in the probability of Brexit increases U.K. import price indices by raising the prices of existing products and by reducing product variety from the E.U. We find evidence that the risk of higher import protection from the 2016 referendum increased current import price indices by more than 10%. This amounted to a 2 log point increase in manufactured goods prices and a 0.6 decrease in consumers’ real income.
RELATIONSHIP BETWEEN MATEMATIC DISPOSITION AND PROBLEM SOLVING ABOUT MATHEMATICS

This study aims to analyze the relationship between mathematical dispositions and mathematical problem solving abilities. The design of this study is a survey with correlation techniques. The research instrument used in this study is a test of mathematical problem solving abilities and the attitude scale of students' mathematical dispositions. The instruments used are 5 questions about the ability of mathematical problem solving and 28 statements regarding mathematical dispositions. The population in this study were all students of SMP Negeri 3 cimahi with a sample of this study were 72 class VII students as many as two classes selected by cluster random sampling. Quantitative data analysis was carried out on mathematical problem solving ability data and mathematical disposition attitude scale. in statistical calculations using SPSS 20, the results of the study showed that students 'mathematical dispositions influence students' mathematical problem solving abilities.
On combinatorial approximation algorithms in geometry

L'analyse des techniques d'approximation est centrale en geometrie algorithmique, pour des raisons pratiques comme theoriques. Dans cette these nous traitons de l'echantillonnage des structures geometriques et des algorithmes d'approximation geometriques en optimisation combinatoire. La premiere partie est consacree a la combinatoire des hypergraphes. Nous debutons par les problemes de packing, dont des extensions d'un lemme de Haussler, particulierement le lemme dit de Shallow packing, pour lequel nous donnons aussi un minorant optimal, conjecture mais pas etabli dans les travaux anterieurs. Puis nous appliquons ledit lemme, avec la methode de partition polynomiale recemment introduite, a l'etude d'un analogue combinatoire des regions de Macbeath de la geometrie convexe : les M-reseaux, pour lesquels nous unifions les resultats d'existence et majorations existants, et donnons aussi quelques minorants. Nous illustrons leur relation aux epsilon-reseaux, structures incontournables en geometrie combinatoire et algorithmique, notamment en observant que les majorants de Chan et al. (SODA 2012) ou Varadarajan (STOC 2010) pour les epsilon-reseaux (uniformes) decoulent directement de nos resultats sur les M-reseaux. La deuxieme partie traite des techniques de recherche locale appliquees aux restrictions geometriques de problemes classiques d'optimisation combinatoire. En dix ans, ces techniques ont produit les premiers schemas d'approximation en temps polynomial pour divers problemes tels que celui de calculer un plus petit ensemble intersectant pour un ensemble de disques donnes en entree parmi un ensemble de points donnes en entree. En fait, il a ete montre que pour de nombreux tels problemes, la recherche locale de rayon Θ (1/epsilon²) donne une (1 + epsilon)-approximation en temps n^{O(1/epsilon²)}. Savoir si l'exposant de n pouvait etre ramene a o (1/epsilon²) demeurait une question ouverte. Nous repondons par la negative : la garantie d'approximation de la recherche locale n'est ameliorable pour aucun desdits problemes
ESTIMATION DE NIVEAUX D'EAU EN PLAINE INONDÉE À PARTIR D'IMAGES SATELLITAIRES RADAR ET DE DON- NÉES TOPOGRAPHIQUES FINES

Les travaux presentes dans cet article s’inscrivent dans une demarche de valorisation des images satellitaires, prises lors d’une crue des eaux, tant pour la caracterisation tridimensionnelle fine de l’alea inondation que pour l’aide a la modelisation hydraulique. L’objectif est de developper une methode qui montre l’interet d’utiliser des images satellitaires radar pour identifier les niveaux d’eau dans une plaine inondee au moment de la prise d’image. L’approche methodologique proposee comprend trois etapes : 1) cartographie de l’etendue des eaux a partir d’images radar et extraction des bordures informatives de cette carte de la zone inondee, 2) estimation primaire des niveaux d’eau par croisement de ces bordures informatives avec un modele numerique d’altitude (MNA), 3) reduction de l’incertitude dans l’evaluation du niveau d’eau par l’introduction et la mise en application de concepts de coherence hydraulique. Grâce a cette methode, une estimation des niveaux d’eau a pu etre obtenue avec une demi-incertitude de 40 cm de hauteur a l’aide d’une image RSO de RADARSAT-1 (taille du pixel 12,5 cm), couplee a un MNA constitue a partir du traitement photogrammetrique de photographies aeriennes a 1 : 8 000, sur un troncon de 15 km de la riviere Moselle, entre Thionville et Berg sur Moselle. Par ailleurs, une validation a ete faite avec une image du capteur radar d’ENVISAT (taille du pixel 12,5 m), prise lors d’une crue de l’Alzette dans le Grand-Duche du Luxembourg. Cette validation a permis de calculer une erreur quadratique moyenne de 13 cm sur l’estimation des niveaux d’eau.
VIRTUAL REALITY IN SPEECH SOUND DISORDERS THERAPY

Virtual Reality is considered to be a new way in which therapists can ensure the generalization of the acquired skills. In speech and language therapy field the use of virtual reality is commonly related with communication disorders. In our research virtual reality is supposed to improve the pronunciation skills by creating a more realistic perception of the referees denoted through the words used for training articulatory skills. In order to be able to ensure this virtual reality implementation Octoplay Application was used during our training pilot program. This application is designed to train articulatory skills and it has enclosed a module to create virtual reality and to build pronunciation skills in more realistic environments. In our research we try to identify in which way the use of virtual reality improves pronunciation skills of children with speech sound disorders. Three case studies are presented in this study and the impact of using Octoplay Application in speech and language therapy is highlighted. The students participated in 30 minutes’ speech and language therapy session/week for two months. Results underlined that VR helps children remained focused in the training activities, improves their motivation to be involved in training activities aiming improving their articulatory abilities, facilitates enlarging children`s vocabulary by enhancing semantic access. In conclusion, VR can be exploited in speech and language therapy field in order to help children improve their pronunciation skills. We also consider that new applications and softs in with VR models included are needed in order to help speech and language therapy process.
Women’s political participation in Zimbabwe: play and content creation on Twitter

ABSTRACT Digital technologies and discursive spaces have become ubiquitous for political participation. This article analyses women’s political participation in Zimbabwe. The article uses play theory to analyse how women interact or communicate on social media. A qualitative content analysis was used to analyse political content extracted from a hashtag-based dataset. Follow-up interviews with female activists were also carried out through the Twitter direct message (DM) to ascertain the effectiveness of Twitter for political communication. The article notes that Twitter offers opportunities for political participation and provisions a visible back-up of digital followers which was previously invisible or indiscernible through traditional media and formal political structures. Through play, women amplify or downplay perspective by posting emotive personal photos, emojis, textual metaphors and other satirical items usable on Twitter. The article argues that play or counter-play generates a hyperbole of political realities. I define counter-play as an activity of creating content for and against.
Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan

The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R2 of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R2 of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.
A GRASP-Based Algorithm for Virtual Network Embedding

With the rise of network virtualization, network slicing is becoming a hot research topic. Indeed, network operators must deal with capacity-limited resources while insuring an extreme availability of services. Several approaches exist in the literature to tackle such a problem, some of them converge quickly to a local minimum, while others are not explainable and therefore do not provide the necessary guarantees for their deployment in a real network. In this context, we propose a new approach for Virtual Network Embedding (VNE) based on the Greedy Adaptive Search Procedure (GRASP). Using the GRASP meta-heuristic ensures the robustness of the solution to changing constraints and environments. Moreover, the proposed realistic approach allows a more efficient and directed exploration of the solution space, in opposition to existing techniques. The simulation results show the potential of the proposed method for solving services' placement problems and its superiority over existing approaches.
Skin Cancer Lesion Analysis of Carcinoma and Melanoma Using Deep Learning Model

Skin cancer is a deadly disease. Melanoma is a fatal type of cancer. It is treatable if diagnosed earlier. Benign cancers may be treated, however malignant tumors cannot. Early stages of both benign and malignant cancer can show them. Dermoscopic analysis and clinical screening are two procedures that can help detect skin cancer early. It is a common task to identify skin lesions automatically. Skin cancer symptoms include small blood vessels, thicker regions, ulceration, and bleeding. A skin magnifier and polarized light source are used to visualize the tumor. Weighting and data augmentation can be used to include it. In this R-CNN classifier, the EfficientNet model was used to assess the accuracy of the skin dataset.
Adaptive Neural Backstepping Terminal Sliding Mode Control of a DC-DC Buck Converter

In this paper, an adaptive backstepping terminal sliding mode control (ABTSMC) method based on a double hidden layer recurrent neural network (DHLRNN) is proposed for a DC-DC buck converter. The DHLRNN is utilized to approximate and compensate for the system uncertainty. On the basis of backstepping control, a terminal sliding mode control (TSMC) is introduced to ensure the finite-time convergence of the tracking error. The effectiveness of the composite control method is verified on a converter prototype in different test conditions. The experimental comparison results demonstrate the proposed control method has better steady-state performance and faster transient response.
A wavelet based data coupling method for spatial damage detection in beam-type structures

Spatial damage identification is of great significance in mechanical, aerospace, and civil engineering. In this study, a data coupling method based on continuous wavelet transform (CWT) is proposed to identify the spatial damage location of beam-type structures. The singularity of the wavelet coefficient can be used to identify the signal singularity, and data coupling method calculates the spatial location of the damage. Numerical simulations and experimental analyses of different type of beams with transfixion damage are carried out to evaluate the accuracy of the method. The results show that the wavelet based data coupling method (W-DCM) can identify the minimum 4.9% damage severity of fixed beam and continuous beam, and can also identify the damage of non-free end of cantilever beam. However, the 9.7% damage severity of the free end of the cantilever beam cannot be identified. It is also found that the W-DCM can effectively circumvent the problem of wavelet coefficients edge effect. This method and wavelet singularity are used to provide a solution to the problem of structural edge damage identification.
Plate tectonics in the late Paleozoic

作为整个时间的板运动的历史， paleogeography 在塑造地质学对我们板 tectonics 和它的角色的理解基本今日。 适当地欣赏 tectonicsand 的历史它对深地球和 climateit 的影响是必要的寻求 paleogeography 的一个精确、全球的模型。 然而由于通过 subduction 的海洋的岩石圈的连续损失， paleogeographic 重建完整板(包括的海洋的岩石圈) 与年龄变得逐渐地挑战性。 在 150 妈以前， 60% 岩石圈是失踪的，没有明确的问候，重建为海洋的岩石圈或板被开发构造原则；实际上，反映大陆人飘移的更早的 mobilistic 范例。 尽管这些大陆人重建是极其有用的，披风模型下一代要求全球板有完整板的重建的运动学的描述。 而且，在不顾(或仅仅泛泛地适用) 板构造规则，大陆人重建没能利用在实际限制形式的很多另外的信息。 跟随一系列新开发，两个在 geodynamic 理论和分析工具，构造把自己借给由更宽的地球科学社区测试的完整板的模型现在是可行的。 如此的一个模型这里被介绍为晚古生代(410250 妈) 和内在的数据的评论。 尽管我们期望这个模型为数字披风建模特别地有用，我们希望它将也为理解晚古生代的 tectonics 用作一个一般框架，未来改进能在上被造并且推进的测试了。
Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records [version 1; peer review: 2 approved with reservations]

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features, where the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond that expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it’s difficult to identify the language that clinicians favour to express concepts. Methods: Vector space models of language seek to represent the relationship between words in a corpus in terms of cosine distance between a series of vectors. When utilising a large corpus of healthcare data and combined with appropriate clustering techniques and manual curation, we explore how such models can be used for discovering vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 n-grams were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric
A Neural Network Solution to Predicting Wind Speed at Cape Canaveral's Atlas Launch Pad

Abstract : This thesis demonstrates the potential for using time-delay neural networks to provide Launch Weather Officers (LWOs) at 45th Weather Squadron (45 WS) with advance warning of wintertime (November-March) peak wind speeds at the Atlas launch pad. The 45 WS provides weather support to the United States space program at Cape Canaveral Air Station, NASA's Kennedy Space Center, and Patrick Air Force Base. Due to the complex wintertime environment produced by the effects of friction and instability, 45 WS LWOs consider wintertime launch pad winds their toughest forecast challenge. Neural networks were developed, trained, and tested using observations of wintertime peak wind speed, wind direction, and directional deviation collected from March 1995 through March 1999 by 45th Space Wing's Weather Inforrnation Network Display System. Using current and past values of the observed elements, the networks produced 16 forecasts of peak wind speed. The first forecast was valid for 30 minutes past forecast start time, the second for 1 hour past start time, etc., up to 8 hours past start time, for any start time. Network performance was compared to three other forecasting options: persistence, climatology, and randomly selecting wind speeds from a climatologically based distribution. For forecasts at the end of the forecast period, networks that were tested with data near in time to the networks' training data showed skill over the other forecasting options. A new confidence measure for neural network forecasts based on mean absolute error was also developed. Confidence for shorter forecast times was not necessarily higher than for longer forecast times. The results of this thesis provide a baseline for measuring future attempts at forecast improvements and establish the neural network approach as a potential means of enhancing peak wind speed prediction accuracy, especially for forecasts late in the forecast period.