Miracle-dz/UniFAR-FTUs huggingface dataset:
La integración de conocimientos teórico-prácticos desde la experiencia de profesionales del Trabajo Social en la formación del estudiantado a través de metodologías activas y participativas

In undergraduate studies in social work, a series of competency objectives related to the theoretical, methodological and attitudinal knowledge contemplated in the discipline’s educational curriculum are established, as set out in the White Paper on the Degree in Social Work. The incorporation of active and participative methodologies in the teaching-learning processes favours student acquisition of these contents. Likewise, in the teaching-learning processes, the use of didactic resources that connect theoretical knowledge with the exercise of professional practice favours student understanding of the curricular subjects. This study aims to analyse the impact that theory has on social intervention for active social workers in order to create theoretical-practical content for the subject of Introduction to Social Work Models at the University of Granada. The methodology applied incorporates documentary work and the development of three discussion groups, in which 18 active social work professionals participated. The analysis of the speeches generated in these groups has served as a resource for the development of a flipped-classroom with students taking the Introduction to Social Work Models course at the University of Granada. The analysis has been conducted around three categories: the value of theory in professional practice; roles, purposes and theoretical models underlying social work; and the social work of the future. The results show how social work professionals attach great importance to theory as a guide in their professional practice, preferably approaching it from systemic and humanist theoretical models and opting for the recovery of community and critical-transformative theoretical approaches.
Features, Prevention and Remedy of Droughts in Sichuan Since 1949

Since the foundation of the PRC droughts affect Sichuan Province almost every year featuring wide-spreading, long-lasting, serious-damaging and heavy-losing, the main causes of which are the abnormality of the general circulation, topography conditions, disproportionate precipitation and improper cultivation. To reduce and eliminate the damage and losses of all kinds, Sichuan Province must adopt comprehensive prevention and remedy measures of water-retaining, cultivation-changing and forestation.
Composite Foundation Settlement Prediction Based on LSTM–Transformer Model for CFG

Roadbed construction typically employs layered and staged filling, characterized by a periodic feature of ‘layered filling—filling interval’. The load and settlement histories established during staged construction offer crucial insights into long-term deformation under filling loads. However, models often rely solely on post-construction settlement data, neglecting the rich filling data. To accurately predict composite foundation ground (CFG) settlement, an LSTM–Transformer deep learning model is used. Five factors from the ‘fill height–time–foundation settlement’ curve are extracted as input variables. The first-layer LSTM model’s gate units capture long-term dependencies, while the second-layer Transformer model’s self-attention mechanism focuses on key features, efficiently and accurately predicting ground settlement. The model is trained and analyzed based on the newly constructed Changsha–Zhuzhou–Xiangtan intercity railway section CSLLXZQ-1, which has a CFG pile composite foundation. The research shows that the proposed LSTM–Transformer model for the settlement prediction of composite foundations has an average absolute error, mean absolute percentage error, and root mean square error of 0.224, 0.563%, and 0.274, respectively. Compared to SVM, LSTM, and Transformer neural network models, it demonstrates higher prediction accuracy, indicating better reliability and practicality. This can provide a new approach and method for the settlement prediction of newly constructed CFG composite foundations.
Effects of elevated atmospheric [CO2] on grain starch characteristics in different specialized wheat

The increasing atmospheric [CO2] poses great challenges to wheat production. Currently, the response of starch characteristics in different specialized wheat cultivars to elevated [CO2], as well as the underlying physiological and molecular mechanisms remains unclear. Therefore, an experiment was conducted with open-top chambers to study the effects of ambient [CO2] [a(CO2)] and elevated [CO2] [e(CO2)] on photosynthetic performance, yield and starch characteristics of bread wheat (Zhengmai 369, ZM369) and biscuit wheat (Yangmai 15, YM15) from 2020 to 2022. The results demonstrated a significant improvement in photosynthetic performance, yield, amylose and amylopectin content, volume ratio of large granules under e[CO2]. Moreover, e[CO2] upregulated the gene expression and enzyme activities of GBSS (Granule-bound starch synthase) and SSS (Soluble starch synthase), increased starch pasting viscosity, gelatinization enthalpy and crystallinity. Compared to YM15, ZM369 exhibited a higher upregulation of GBSSI, greater increase in amylose content and volume ratio of large granules, as well as higher gelatinization enthalpy and crystallinity. However, ZM369 showed a lower increase in amylopectin content and a lower upregulation of SSSI and SSSII. Correlation analysis revealed amylose and amylopectin content had a positive correlation with GBSS and SSS, respectively, a significant positively correlation among the amylose and amylopectin content, starch granule volume, and pasting properties. In conclusion, these changes may enhance the utilization value of biscuit wheat but exhibit an opposite effect on bread wheat. The results provide a basis for selecting suitable wheat cultivars and ensuring food security under future climate change conditions.
CT−based radiomics signature for differentiating pyelocaliceal upper urinary tract urothelial carcinoma from infiltrative renal cell carcinoma

Objectives To develop a CT-based radiomics model and a combined model for preoperatively discriminating infiltrative renal cell carcinoma (RCC) and pyelocaliceal upper urinary tract urothelial carcinoma (UTUC), which invades the renal parenchyma. Materials and methods Eighty patients (37 pathologically proven infiltrative RCCs and 43 pathologically proven pyelocaliceal UTUCs) were retrospectively enrolled and randomly divided into a training set (n = 56) and a testing set (n = 24) at a ratio of 7:3. Traditional CT imaging characteristics in the portal venous phase were collected by two radiologists (SPH and ZXL, who have 4 and 30 years of experience in abdominal radiology, respectively). Patient demographics and traditional CT imaging characteristics were used to construct the clinical model. The radiomics score was calculated based on the radiomics features extracted from the portal venous CT images and the random forest (RF) algorithm to construct the radiomics model. The combined model was constructed using the radiomics score and significant clinical factors according to the multivariate logistic regression. The diagnostic efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Results The RF score based on the eight validated features extracted from the portal venous CT images was used to build the radiomics model. Painless hematuria as an independent risk factor was used to build the clinical model. The combined model was constructed using the RF score and the selected clinical factor. Both the radiomics model and combined model showed higher efficacy in differentiating infiltrative RCC and pyelocaliceal UTUC in the training and testing cohorts with AUC values of 0.95 and 0.90, respectively, for the radiomics model and 0.99 and 0.90, respectively, for the combined model. The decision curves of the combined model as well as the radiomics model indicated an overall net benefit over the clinical model. Both the radiomics model and the combined model achieved a notable reduction in false-positive and false-negativerates, resulting in significantly higher accuracy compared to the visual assessments in both the training and testing cohorts. Conclusion The radiomics model and combined model had the potential to accurately differentiate infiltrative RCC and pyelocaliceal UTUC, which invades the renal parenchyma, and provide a new potentially non-invasive method to guide surgery strategies.
Knowledge Gaps on the Utilization of Fossil Shell Flour in Beef Production: A Review

Simple Summary With the increase in the number of people, there is also an increase in the demand for livestock products, including good-quality beef. However, beef production is faced with the challenges of climate change while it also contributes to climate change. Although several strategies have been in place to address this synergy, they have constraints that do not enable them to be used resourcefully and by all types of farmers. There is a need, therefore, for new strategies that will address this synergy while ensuring that beef production will be sustainable, and quality will be preserved and/or improved. Fossil shell flour is one such strategy because it is cheap, readily available, eco-friendly, and can be used by all types of farmers. However, it has not been explored enough in research, and as a result, there is little to no scientific evidence to support its efficiency in sustainable beef production. There is, therefore, a need to address this research gap as a step towards sustainable and eco-friendly beef production. Abstract Population growth in many countries results in increased demand for livestock production and quality products. However, beef production represents a complex global sustainability challenge, including meeting the increasing demand and the need to respond to climate change and/or greenhouse gas emissions. Several feed resources and techniques have been used but have some constraints that limit their efficient utilization which include being product-specific, not universally applicable, and sometimes compromising the quality of meat. This evokes a need for novel techniques that will provide sustainable beef production and mitigate the carbon footprint of beef while not compromising beef quality. Fossil shell flour (FSF) is a natural additive with the potential to supplement traditional crops in beef cattle rations in response to this complex global challenge as it is cheap, readily available, and eco-friendly. However, it has not gained much attention from scientists, researchers, and farmers, and its use has not yet been adopted in most countries. This review seeks to identify knowledge or research gaps on the utilization of fossil shell flour in beef cattle production, with respect to climate change, carcass, and meat quality. Addressing these research gaps would be a step forward in developing sustainable and eco-friendly beef production.
A Virtual VR Reconstruction Method for Landscape Architecture Based on Spatiotemporal Algorithm Model

With the rapid development of information technology and computer science, digital technology has gradually penetrated into various fields, including landscape architecture. With the acceleration of urbanization process, the natural landscape and cultural heritage are facing the threat of destruction and disappearance.Virtual reality technology, as an application of digital technology, allows users to experience virtual environments firsthand by simulating real visual and auditory experiences. The maturity and popularization of digital technology have provided new development opportunities and technological means for the field of landscape architecture. This article studies the virtual VR reconstruction of landscape architecture, aiming to improve the landscape of office areas. In this paper, we mainly use the experimental method and Comparative law method to analyze the spatiotemporal algorithm and virtual reconstruction method. The experimental data shows that the running time of building virtual reconstruction obtained by applying spatiotemporal algorithms is about 6 seconds.
UPDATED DISTRIBUTION MAP OF Amphisbaena mitchelli PROCTER, 1923 (SQUAMATA: AMPHISBAENIDAE) WITH A NEW RECORD IN MARANHÃO, BRAZIL

Amphisbaena mitchelli is a small-sized worm lizard species described from Ilha de Marajó, state of Pará, Brazil. After its description, few publications brought new distribution records for A. mitchelli, all in eastern Amazonia. In this paper we report a new record of the species in municipality of Imperatriz, state of Maranhão, Brazil. The new record is the first in the Cerrado ecoregion and is 300km northeast in straight line to the closest record in Floresta Nacional de Carajás, municipality of Parauapebas, state of Pará, Brazil, and fills a gap of 530 km.
Estimation des surfaces irriguées ripariennes à l’aide de Earth Engine. Une étude de cas dans le sous-bassin versant de la Haute-Comoé, Burkina Faso

La mise au point d’une méthode automatique d’estimation des surfaces irriguées par les petits exploitants agricoles en Afrique aux abords des cours d’eau, à partir d’outils libres et de données satellitaires gratuites, reste un défi majeur à cause de la diversité des cultures qui y sont pratiquées, de l’étroitesse des parcelles, de la variabilité des cycles culturaux et de la similarité des réflectances des zones irriguées, des zones humides enherbées et de la végétation arborée riparienne. Cet article visait donc à développer une méthode qui permette d’extraire les surfaces agricoles irriguées par les agriculteurs informels le long des berges du fleuve Comoé au cours de la campagne agricole de saison sèche. Pour ce faire, une image composite, obtenue des images de janvier 2019 des satellites Sentinel-1 et 2, combinée à des indices spectraux dérivés et sensibles aux surfaces irriguées (NDVI, MNDWI et NBR2), a fait l’objet d’une classification supervisée à l’aide du classificateur Random Forest sur la plateforme Earth Engine, après une série de masquages automatiques des sols nus, des surfaces des plans d’eau, des infrastructures et de la forêt galerie. Testée sur des données de janvier 2019, pendant laquelle la plupart des agriculteurs ont mis en place leurs cultures, la méthode proposée permet d’estimer efficacement les superficies irriguées. Elle a permis de distinguer les classes ‘zones irriguées’ et ‘zones humides enherbées’ avec une précision globale de 98 %, un coefficient Kappa de 0,91 et des F-scores respectifs de 99 % et 92 %. L’étude a ainsi montré qu’il est possible de développer à moindre coût une méthode automatique et efficace d’évaluation de surfaces irriguées ripariennes à partir de la plateforme Earth Engine.
On how spatial variations of channel width influence river profile curvature

Longitudinal profiles of alluvial rivers usually exhibit upward‐concave curvatures at equilibrium. River profile concavity has been primarily attributed to sediment downstream fining and to streamwise increments of water discharge. Conversely, upward‐convex profiles have been typically associated with tectonic and geologic controls and with outlet base‐level drops. Equations to describe river profiles at equilibrium developed from mass conservation principles do not consider longitudinal changes in channel width. This study addresses how variations in channel width can also act to control the curvature of longitudinal profiles. We develop a new theoretical framework in which the role on river profiles of downstream variations of channel width, flow discharge, bed roughness, and surface texture are explicitly shown. Unlike classical approaches for river profile evolution, this novel framework identifies physical domains for rivers to develop upward‐concave/convex longitudinal profiles depending on channel width and flow discharge gradients flow intensity and surface texture.
Integrating a PICO Clinical Questioning to the QL4POMR Framework for Building Evidence-Based Clinical Case Reports

Practicing evidence based medicine requires establishing relevant and focused clinical questions that allow physicians to seek appropriate answers from rigor of the research and medical practice. These researchable question is not only important for diagnosis, prognosis, treatment and therapy but also for compiling clinical case reports when clinician encounters unanswered issues in current clinical practice. However, clinicians face multiple challenges in formulating the questions manually, searching medical literature as well as summarizing the methods and outcome from the finding of the most relevant articles. This paper extends our QL4POMR to provide a PICO clinical questioning wrapper as well as literature finding summarizers based on two pre-trained models like the BART and Bio-BERT. We also managed to integrate a clinical case report generator that can synthesis information on the clinical case based on its SOAP(s) description as well as the PICO questions and the clinical finding literature summary related to these PICO questions. Compiling the variations of the PICO questions and the integration harmonization with the SOAP cases has been done using the GraphQL API and the flexibility of the Neo4J graph-based representations for both the SOAP cases and the PubMed literature objects.
Improving the Spatial Keyword Preference Query with Linked Open Data

This paper presents a Spatial Keyword Preference Query (SKPQ) enhanced by Linked Open Data. This query selects objects based on the textual description of features in their neighborhood. The spatial relationship between objects and features is explored by the SKPQ using a Spatial Inverted Index. In our approach, the spatial relationship is explored using SPARQL. However, the main beneﬁt of using SPARQL is obtained by measuring the textual relevance between features’ description and user’s keywords. The object description in Linked Open Data is much richer than traditional spatial databases, which leads to a more precise similarity measure than the one employed in the traditional SKPQ. We present an enhanced SKPQ and two experimental evaluations of the proposed approach, comparing it with the traditional SKPQ. The ﬁrst conducted experiment indicate a relative NDCG improvement of the proposed approach over the traditional SKPQ of 20% when using random query keywords. The second experiment shows that using real query keywords, our approach obtained a signiﬁcant increase in the MAP score.
WAVES: Soft-Material Based Adaptable Walking-Type Stair-Climbing Robot for Various Step Sizes

Existing state-of-the-art stair climbing robots require precise control through many sensors and actuators to climb various stairs. In this study, we propose WAVES, a stair-climbing robot that can climb a variety of stairs with minimal electronic components and control. WAVES’ stair climbing ability is due to its soft part on the bottom, which adapts passively to the shape of the stairs. The soft parts were fabricated into a compliant mesh shape and could be freely deformed according to the shape of the stairs. We used the ratio of horizontal and vertical reaction forces that occur when the soft part is deformed as an index to determine stability, and the simulation results showed that the arc shape has the smallest horizontal reaction force ratio of 0.224 (honeycomb: 0.303, spoke: 0.276). Also, to determine the appropriate strength of the soft parts, we calculated the maximum compressive force that each section of the soft part should withstand. Based on the calculation results, we made the gap between arc patterns closer where the strong compressive force is applied. In conclusion, before applying soft parts to the robot, the diagonal length of the stairs it could climb was 328.7 mm–350 mm, but after applying soft materials, it can climb all stairs under 400 mm.
House-related practices as markers of the Neolithic expansion from Anatolia to the Balkans

Following the assumption that the Neolithic witnessed the first widespread appearance of permanent houses and households, in line with the adoption of sedentism, this article examines the relevance of residential and construction practices to our understanding of the process of Neolithic expansion from Anatolia to the Balkans. Three practices, with a broad spatial distribution, are reviewed: house burning, the vertical superimposition of houses and intra-settlement burial. The article first outlines the basis of a contextual method to retrieve practices from material patterning left in the record, such as burnt houses for the practice of intentional house burning. The next section delves into the similarities in practices between Neolithic communities in Anatolia, Thrace and Greece, during the 7th and 6th millennia BC cal. to suggest that: 1) house burning was a key strategy to bring houses to ‘closure’ at the end of their use-lives; 2) people took advantage of the stability of extant houses to build new houses atop; and 3) this practice was closely connected with the burial of the dead in, or in close proximity to, houses. Common attitudes to residence and construction across a vast array of sites underpin similarities in house form and house use patterns. To conclude, the discussion highlights the need for a dynamic approach, based on comparative time-lines of practices, to determine the direction of spread.
Classification of Skin Disease using CNN

The majority of people around the world suffer from skin conditions at some point in their lives. These conditions need to be identified and treated as soon as possible in order to prevent complications and improve the outcome of the patient. Skin diseases can be difficult to diagnose, even for dermatologists who are experts in the field. In order to identify and classify skin diseases, a computer algorithm known as a Convolutional Neural Network (CNN) can be used. Using CNN, artificial intelligence is able to learn from images and make predictions based on the information it gathers from them. In order to train CNN to classify skin conditions accurately, a large number of images of skin diseases can be used to train the CNN. This paper describes a method for classifying skin diseases using CNNs. As part of the training process, the system was taught a large collection of thermoscopic images, which show the distribution of heat patterns on the skin. As a result of this study, CNN was able to successfully classify different skin diseases with an accuracy of 90% in terms of validation, and 95% in terms of classification. In addition to dermatologists and primary care physicians, healthcare professionals can also use this system to make an accurate diagnosis of skin diseases by using this system. Furthermore, patients can use it as a means of identifying and understanding their skin conditions as well.
Role of Experiential Trust in E-Shopping Behaviour: An Emerging Market Perspective

Addressing the limited research on online shopping trust in the emerging e-market context, this study examined how online experience affects experiential trust, attitude, perceived risk, perceived usefulness, and purchase intention. Further, experiential trust was tested as a mediator between online experience and attitude, perceived risk, and perceived usefulness. A structured questionnaire was administered to collect data from 583 respondents. The proposed hypotheses were tested through structural equation modeling using AMOS 23 and hierarchical regression analysis. The result indicates that online experience is significant in forming experiential trust. The findings also show that experiential trust plays an important role in influencing e-shoppers’ purchasing behavior. Furthermore, it has been found that online experience and experiential trust function as distinct predictors of perceived risk in online shopping behavior. This study provides new insights into online shopping behavior in emerging e-market scenarios. Practically, the findings suggest the strategic importance of generating experiential trust in the e-retailing context and indicate the utility of past online experiences.