Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of wetted soil volume on young pear trees

Pear orchards in the Goulburn Valley, Australia, are being re-developed and expanded using better varieties and modern planting systems. Efficient irrigation systems, like drip, are an integral component of these new orchards. However, drip is rarely used in pear orchards in the region and many existing orchards use microjet irrigation systems. Soil wetting patterns from drip systems are much smaller than microjet systems and this may impact on the performance of young pear trees. A field experiment was established in 2012 to investigate the effects of wetted volume (imposed by the combination of irrigation system and irrigation frequency) on irrigation requirements, tree growth and water status. Drip and microjet irrigation at standard and pulse intervals were compared using a new red-blushed pear cultivar (Pyrus communis L. ‘ANP-0118’) grafted to BP-1 rootstock. No evidence was observed that a reduction in horizontal wetted volume (i.e., drip irrigation) impacted leader growth, pruning dry weight, radiation interception, and midday stem water potential and leaf conductance. However, a shallow broad wetted volume (microjet irrigation at frequent interval) made trees susceptible to water stress and trees exhibited a reduction in growth. Approximately 40% less irrigation was applied in the drip irrigation treatments resulting in a water saving of 2.1 ML ha-1 y-1.
Capturing High-Level Semantic Correlations via Graph for Multimodal Sentiment Analysis

Modeling intra-modal and cross-modal interactions poses significant challenges in multimodal sentiment analysis. Currently, graph-based methods like HGraph-CL achieve promising performance, which rely on two different levels of graph contrastive learning within and between modalities to explore sentiment correlations. However, HGraph-CL still faces the following drawbacks in graph construction: 1) nodes of the graph are represented at the frame level, only containing low-level information, neglecting the correlations among high-level semantics; 2) edges of the graph are based on the fixed dependency relations between words in the text sequence and the adjacent relations between frame-level nodes in the non-verbal sequences, failing to effectively capture implicit and long-distance correlations. To this end, this letter introduces capsule networks to construct high-level semantic nodes in a graph, uncovering deep sentimental structures. Furthermore, the learnable adjacency matrices are employed to construct edges of graph, thus adaptively learning the relations between nodes. Experimental results on several benchmark datasets for multimodal sentiment analysis demonstrate the effectiveness of the proposed method.
Klasifikasi COVID 19 dengan Metode EfficientNet berdasarkan CT scan Paru-paru

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) adalah virus penyebab Covid-19. Covid-19 adalah virus mematikan yang oleh Organisasi Kesehatan Dunia (WHO) ditetapkan sebagai pandemi karena penyebarannya yang cepat. Dua metode yang kini paling sering digunakan untuk mendeteksi Covid-19 adalah Rapid Diagnostic Test (RDT) dan Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR). Menemukan strategi baru yang cepat dan tepat sangat penting karena kedua strategi memiliki kelebihan dan kekurangan. Penggunaan CT scan untuk menemukan Covid-19 adalah salah satu metode yang direkomendasikan. Makalah ini merekomendasikan identifikasi Covid-19 pada gambar CT menggunakan EfficientNet B0. tampil lebih unggul dari model tanpa CLAHE. Untuk performa EfficientNet B0 dengan CLAHE, akurasi, F-measure, recall, dan precision adalah 91,95%, 92,06%, 92,43%, dan 91,69%.
Internal green marketing orientation and business performance: The role of employee environmental commitment and green organizational identity

The research examines the idea that Internal Green Marketing Orientation (IGMO) is a strategic approach within the tourism and hospitality industries that focuses on integrating environmentally friendly practices into the organizational culture. It encompasses how a business perceives, values, and prioritizes environmental concerns as part of its internal operations. IGMO, as a fundamental aspect of a company's strategy, plays a significant role in shaping Business Performance (BP) in this sector. How does IGMO influence Business Performance (BP) in the tourism and hospitality industries, and what is the role of Employee Environmental Commitment (EEC) and Green Organizational Identity (GOI) in this relationship? The purpose of this study is to find the answers to the previous questions. The study randomly gathered data via time-lagged surveys distributed among workers with at least two years of experience in hotels and tourism companies in Egypt. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the perceptions of 338 employees working in hotels and tourism companies in Egypt. The study's results showed that IGMO is positively associated with BP. Furthermore, IGMO is positively associated with EEC. In addition, EEC is positively associated with BP. EEC succeeded in mediating the association between IGMO and BP. Finally, the study proved that GOI moderates the association between IGMO and EEC. Practical implications include prioritizing IGMO, fostering employee environmental commitment, recognizing the impact of employee commitment on business performance, leveraging employee commitment as a mediator, and understanding the role of green organizational identity in shaping employee commitment.
La gestión de carrera de educación básica: un enfoque hacia la excelencia educativa

La gestión de carreras de Educación Básica es crucial para el avance y mejora constante del sistema educativo. Lamentablemente, se observan deficiencias en este ámbito, lo que repercute negativamente en la calidad y excelencia de la educación. Este artículo tiene como objetivo mejorar la calidad y excelencia de la Educación Básica, analizando la importancia de una adecuada gestión de carreras en este nivel. Se utilizó una metodología de revisión bibliográfica sistemática de enfoque cualitativo, buscando estudios e investigaciones relevantes en bases de datos académicas como Scopus, Web of Science y Google Scholar. Entre los principales hallazgos se destaca que la gestión de carreras en educación básica es determinante para lograr una educación de calidad y excelencia. Esto implica un adecuado diseño curricular, una planificación estratégica y un seguimiento del proceso formativo, así como la participación activa y comprometida de todos los actores involucrados en la educación. También se enfatiza la importancia de establecer metas claras y alcanzables, implementar estrategias innovadoras para mejorar el rendimiento académico y promover el desarrollo integral de los estudiantes. La formación continua y actualización profesional de los docentes, la orientación vocacional, la tutoría académica y la coordinación entre docentes y padres también son aspectos fundamentales. En conclusión, este estudio busca llenar un vacío proporcionando recomendaciones respaldadas por evidencia científica sólida para mejorar los procesos de gestión y dirección en las carreras de educación básica.
Surface fault associated by the Mw 7.1 Darfield Earthquake, New Zealand

The Mw 7.1 (GNS Science) earthquake (Darfield earthquake) occurred near Christchurch, New Zealand on 4 September 2010. The earthquake happened in the area where active faults had not recognized previously, produced the ground surface rupture (Greendale fault). We surveyed the mode of occurrences of the Greendale fault on 8 days after the Darfield earthquake. Measured dextral and vertical displacements along the fault are ~ 4.2 and ~ 1.5 m (predominantly southern parts up), respectively. The both slip components are distributed roughly symmetrically along the west fault segment. However, the maximum displacement points are perhaps different between dextral and vertical sense based on the survey results. The fault is roughly E–W strike, characterized by many Riedel fractures that show shapes of mole tracks accompanying main dextral displacements. Few active faults had recognized previously on Canterbury plain near Christchurch, whereas distribution of active faults and folds had cleared in the Pegasus Bay area where locates in the eastern part of the Christchurch. Moreover, there are records of a few paleoearthquakes (M>5) near Christchurch and in Pegasus Bay. These data would have been clues to forecast existence of blind active faults on Canterbury plain. *1 (株)四国総合研究所土木技術部 *2 株式会社阪神コンサルタンツ *3 四国電力(株)土木建築部 *1 Department of civil engineering, Shikoku Research Institute Incorporation *2 Hanshin Consultants Co., Ltd. *3 Department of civil and architectural engineering, Shikoku Electric Power Company Incorporation 活断層研究 36号 31〜44 2012 Active Fault Research, No. 36, P. 31~44, 2012 資料 Material
Extracellular Vesicles in the Central Nervous System: A Novel Mechanism of Neuronal Cell Communication

Cell-to-cell communication is essential for the appropriate development and maintenance of homeostatic conditions in the central nervous system. Extracellular vesicles have recently come to the forefront of neuroscience as novel vehicles for the transfer of complex signals between neuronal cells. Extracellular vesicles are membrane-bound carriers packed with proteins, metabolites, and nucleic acids (including DNA, mRNA, and microRNAs) that contain the elements present in the cell they originate from. Since their discovery, extracellular vesicles have been studied extensively and have opened up new understanding of cell–cell communication; they may cross the blood–brain barrier in a bidirectional way from the bloodstream to the brain parenchyma and vice versa, and play a key role in brain–periphery communication in physiology as well as pathology. Neurons and glial cells in the central nervous system release extracellular vesicles to the interstitial fluid of the brain and spinal cord parenchyma. Extracellular vesicles contain proteins, nucleic acids, lipids, carbohydrates, and primary and secondary metabolites. that can be taken up by and modulate the behaviour of neighbouring recipient cells. The functions of extracellular vesicles have been extensively studied in the context of neurodegenerative diseases. The purpose of this review is to analyse the role extracellular vesicles extracellular vesicles in central nervous system cell communication, with particular emphasis on the contribution of extracellular vesicles from different central nervous system cell types in maintaining or altering central nervous system homeostasis.
¿CÓMO VEN LOS ALUMNOS DE PRIMARIA LA PROFESIÓN INFORMÁTICA? INFLUENCIA DEL GÉNERO Y LA PERCEPCIÓN DE SU CAPACIDAD HOW DO PRIMARY EDUCATION STUDENTS PERCEIVE THE COMPUTER PROFESSION? INFLUENCE OF GENDER AND PERCETION OF CAPACITY

Este articulo analiza la opinion que tiene sobre la profesion de informatica el alumnado del ultimo ciclo de educacion primaria. Se encuestaron 300 alumnos de 12 escuelas y se observo que la opinion de las ninas era sensiblemente peor que la de los ninos ademas de que ellas se consideran menos capaces de realizar un programa informatico. A partir del analisis de los datos se sugiere que se inicie a los alumnos de primaria en la programacion y especialmente a las ninas para que mejoren su percepcion de sus capacidades en este ambito. Palabras clave: Educacion primaria, Autopercepcion de capacidad, programacion de ordenadores, Informatica.
Dwelling in tourism : power and myth amongst Bushmen in Southern Africa

Dwelling in Tourism highlights how marginalised Bushmen people are in the middle of a struggle between traditional en modern forces. Tourism, as an important element of conservation strategies, is a phenomenon built on both, and therefore reveals those struggles and shows how the marginalised status of Bushmen is interwoven with relations of power and myths. Seen from the dwelling perspective as the main theoretical starting point, many tourism environments in four case studies are described, three in Namibia and one in South Africa. All too often Bushmen are considered natural ecologists in need of protection, while in reality they are participants of modernisation with their own agency.
Bayesian inference for misaligned irregular time series with application to palaeoclimate reconstruction

This thesis proposes new Bayesian methods to jointly analyse misaligned irregular time series. Temporal misalignment occurs when multiple irregularly spaced time series are considered together, or when the time periods defining the data points are not the same across different series. Other issues under consideration include errors in the time scales, and non-Gaussian processes for underlying latent values. Our proposed models are hierarchical. This flexible framework is made concrete with the derivation of fast and efficient algorithms for parameter inference. The methods are general and can be used for any continuous time series processes. In addition, they open the door to other interesting possibilities for modelling and inference of multivariate time series in a spatio-temporal context. We apply our methods to climate proxy signals to derive ancient climate histories. A major objective is to create climate data products (i.e. posterior summaries of climate on a regular time grid). One of the advantages of our data products is that they can easily be utilised to answer complex questions that are otherwise analytically intractable. We demonstrate this by using case studies of abrupt climate change events.
Intelligent Research on Chinese and English Listening Teaching Based on Deep Learning

The field of education has witnessed a transformative shift in recent years, with the integration of technology playing a pivotal role in enhancing the learning experience. In this context, recommendation systems have emerged as valuable tools for guiding learners through a vast sea of educational resources. However, privacy concerns and the need for personalized recommendations have posed significant challenges. In response, this paper introduces a novel approach, the Semantic Data Fusion Recommender (SDFR), designed to enhance English teaching while preserving user privacy with the use of federated deep learning model. The SDFR federated deep learning for semantic feature extraction from educational content and combines these features with keyword-based content analysis. Furthermore, it incorporates federated deep learning to protect user data and ensure the confidentiality of individual learning journeys. This paper present experimental results that demonstrate the SDFR's ability to provide highly accurate and personalized recommendations, significantly improving the quality of English teaching. Additionally, our approach adheres to strict privacy standards, making it suitable for deployment in educational settings. The SDFR offers a promising framework for adapting recommendation systems to the evolving landscape of online education, catering to the diverse needs of learners while safeguarding their privacy.
Efek Samping Pengobatan Pada Pasien Tuberkulosis

Tuberkulosis (TB) Paru masih menjadi salah satu sepuluh penyakit mematikan di dunia.Meskipun strategi pencegahan dan pengendalian tuberkulosis paru yang direkomendasikantelah diterapkan, namun masih banyak pasien yang gagal menyelesaikan pengobatan obatanti tuberkulosis karena berbagai faktor termasuk keluhan efek samping yang dirasakan.Tujuan penelitian ini yaitu untuk mengetahui efek samping pengobatan yang di alamipenderita TB Paru. Metode penelitian ini adalah desain studi observational deskriptifdengan desain kuantitatif. Populasi penelitian ini adalah pasien TB Paru dalam masapengobatan, sampel sebanyak 61 responden. Hasil penelitian menunjukan bahwa efeksamping yang di alami yaitu terdapat 13 keluhan yang muncul, keluhan terbanyak padapenelitian ini adalah Mual (55.74%). Pada kasus TB Paru dewasa lanjut memerlukanpenanganan khusus. Selanjutnya, penelitian ini dapat dijadikan rekomendasi perlunyapemantauan oleh perawat pada penderita TB Paru terhadap efek samping yang mungkindialami
Exercise self-efficacy remains unaltered during military service

Background Exercise self-efficacy is a crucial aspect of adopting and maintaining a physically active lifestyle. Regular physical activity may enhance exercise self-efficacy. This study aimed to investigate the baseline associations of physical fitness, physical activity, and body composition with exercise self-efficacy and the effects of military service on exercise self-efficacy. Methods: The sample consisted of healthy young Finnish conscripts (n = 243) undergoing military service. The participants were divided into two groups: an intervention group undergoing a high-intensity functional training program (n = 113) and a control group undergoing traditional physical training within their military service (n = 130). Exercise self-efficacy (adoption and barrier) and aerobic and muscular fitness were measured thrice (baseline, month 3, and month 5). Self-reported leisure-time physical activity and measured fat percentage were collected at baseline. Results Adoption and barrier exercise self-efficacy correlated positively with aerobic and muscular fitness and leisure time physical activity (r = 0.33–0.59, p < 0.001), and barrier self-efficacy negatively with fat percentage (r = −0.15, p < 0.05) at baseline. No changes in adoption (time p = 0.912) and barrier self-efficacy (time p = 0.441) occurred during the military service. There were no differences between groups in these changes (group × time interaction p = 0.643 for adoption self-efficacy and p = 0.872 for barrier self-efficacy). Change in muscular fitness correlated positively with change in barrier self-efficacy in the high-intensity functional training group (r = 0.35, p < 0.05). Conclusions: Exercise self-efficacy is positively associated with physical fitness and physical activity among young males. However, military service, whether it involves high-intensity functional physical training or more diverse traditional physical training, does not improve exercise self-efficacy.
Influence of Climatic and Non-climatic Factors on Range Dynamics and Conservation Priorities of Long-distance Migratory Birds

Understanding factors influencing species’ distributions and their dynamics over space and time is a fundamental question in ecology that is receiving renewed interest given increasing threats of global climate change to species persistence. Species are shifting their distributions in response to climate change; however in spite of general directional trends northwards and up in elevation there is substantial interspecific variation. The complexity of species’ responses is challenging to explain and limits our predictive capacity to anticipate future consequences of climate change. In addition to climatic factors, species’ range dynamics are influenced by non-climatic factors including the biotic interactions, demography, dispersal, and the temporal and spatial scale of threatening processes. The objective of this thesis is to test the role of climatic and non-climatic factors on seasonal range dynamics of long-distance migratory birds over multiple spatial scales, in the recent past, present, and in the future. An understanding of the determinants of NearcticNeotropical migratory bird distributions across their interconnected seasonal ranges remains unclear, and few climate change vulnerability assessments consider the complement of habitat dependencies required across their annual cycle. To address these research gaps, I applied multiple modeling methods with outcomes that are increasingly process-oriented. These include correlative species distribution models, dynamic occupancy modeling that account for detection probabilities, and coupled species distributionmetapopulation demographic models. Such modeling approaches allow for deeper inferences
New-Onset Monosomy 7-Induced Pancytopenia in a 66-Year-Old Woman

Myelodysplastic syndrome (MDS) is characterized by failure to initiate hematopoiesis or impaired maturation of cells, often presenting with pancytopenias with or without associated fatigue, infections, or inappropriate bleeding and bruising. Karyotype analyses of MDS patients commonly show deletion of the q arm of chromosome 7, suggesting loss of this region is likely implicated in the insufficient hematopoiesis seen in MDS. The predisposition to deletion of 7q is commonly inherited, with clinical presentation in early childhood associated with pancytopenia or hematological malignancy. In this case, we present a 66-year-old female who was incidentally found to be pancytopenic in the emergency department while being evaluated for dyspnea, with a bone marrow biopsy later confirming a diagnosis of MDS with monosomy 7. Sporadic loss of 7q can occur at any stage in life without any family history of hematological disease. Our patient has no known personal or family history of MDS, with normal blood counts during hospitalization three years prior, suggesting de novo loss of 7q occurring at greater than 60 years of age.
The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

Simple Summary Early monitoring and warning of mastitis in dairy cows in intensive farms in a timely manner are of great significance for protecting the welfare of cows, reducing the farms’ economic losses, and ensuring the quality and safety of dairy products. In this study, nine machine learning algorithms were used to predict naturally occurring clinical bovine mastitis pertaining to four specific stages of lactation. The Z-standardized dataset presents better results than the non-standardized ones. The multilayer artificial neural net (MNET) algorithm and random forest (RF) models are best suited for clinical mastitis prediction and management in farms. We also calculated the peak milk yield (PMY) of mastitic cows and that of healthy ones, and the former is higher than the latter. Overall, the results showed that machine learning algorithms can be applied to analyze real-time data obtained from intensive farms to develop an alerting system for the prediction of naturally occurring mastitis. Abstract In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.