Miracle-dz/UniFAR-FTUs huggingface dataset:
Eco-pesantren modeling for environmentally friendly behavior: new lessons from Indonesia

<em><span lang="EN-US">Pesantren</span></em><span lang="EN-US"> or Islamic boarding school plays a vital and strategic role in improving environmentally sound education. </span><em><span lang="EN-US">Eco-pesantren</span></em><span lang="EN-US"> is a movement that puts a high concern for the environment. This study aimed to analyze </span><em><span lang="EN-US">eco-pesantren</span></em><span lang="EN-US"> modeling techniques in education. It is a case study with a qualitative research method. The research was conducted at Islamic Boarding School Sumber Mental Agama Allah (SPMAA), Turi Lamongan, East Java, Indonesia. Data were collected by observation, interviews, questionnaires, and document studies. This research concluded that modeling techniques can at least be pursued in four main ways: the individual role model of </span><em><span lang="EN-US">pesantren</span></em><span lang="EN-US"> leaders; infrastructure and their use; governance systems; and culture or traditions. Besides, using integrated modeling techniques at both school and </span><em><span lang="EN-US">pesantren</span></em><span lang="EN-US"> culture can increase insight and application of environmentally friendly behavior through a direct observation and habituation of healthy educational practices. Modeling can help students turn abstract ideas of green life into actual implementations. Direct observation of continuous practices equips students with the knowledge required to perform expected behaviors. Research on modeling assists </span><em><span lang="EN-US">eco-pesantren</span></em><span lang="EN-US"> institutions encourage learning and practices about healthy environment. In the global context, it can be a role model for the implementation of environmentally friendly education that promotes environmentally sound learning. </span>
Sea crossing behaviour of falcons and harriers at the southern Mediterranean coast of Spain

Using radar, we studied the migratory flight behaviour of harriers Circus spp. and falcons Faico spp. at the edge of a large water barrier. Arriving at the southern coast of Spain during autumn migration, raptors have to decide whether to cross the Mediterranean Sea by flapping flight (distance at least 150 km) or to fly a detour along the coast of the Iberian Peninsula (250 km) with the possibility of travelling by thermal soaring. In autumn, mean flight directions of falcons (228°) and harriers (229°) were similar, and the majority of falcons (57 %) and harriers (65 %) crossed the sea, preferably with support of following northwesterly winds. A higher proportion of Montagu's harriers Circus pygargus (74 %) than of the larger marsh harriers C. aeruginosus (50 %) crossed the sea. In spring, 65 falcons and 5 harriers approaching the coast from the sea had mean flight directions of 10° and 16°, respectively, and none of them was observed in strong northerly, i.e. opposing winds. In autumn, harriers slightly decreased air- and ground-speed as well as flight altitude when crossing the coast seawards, whereas •falcons showed no change in flight behaviour. In spring, falcons approaching the coast from the sea decreased flight altitudes towards land, but no landings were observed within the 8 km range of the radar. The high proportion of birds crossing the sea, even during twilight and at night, and with little change in flight behaviour when crossing the coast seawards in autumn and landwards in spring, indicate that these small raptors are well adapted to cross relatively large water bodies on migration. By selecting following winds and stable weather conditions for sea crossing they reduce flight time to cross the sea and reduce the risks, in
How to Prepare Own Child for Career Path Success? Analyzing the Educational Components of Parents for Career Path Success from Childhood: A Sample of Qualitative Content Analysis

career path success from childhood had 6 comprehensive codes, 14 organizing codes and 56 primary codes. In this study, comprehensive codes include fostering love to learning (with 2 organizing codes of setting the context for learning and practical learning opportunities), group activity training (with 3 organizing codes of learning manners, integrating teamwork skills in everyday life and preparing to solve teamwork conflicts), training resourcefulness and resourcefulness (with 3 organizing codes of teaching the art of positive skepticism, exposing the child to resourceful and resourceful models and fostering resourcefulness skills), developing an intellectual perspective (with 2 organizing codes of providing new experiences and training of foresight), preparation for networking and conversation in the labor market (with 2 organizing codes of fostering reflective performance and fostering practical and communication skills) and age-appropriate understanding from the labor market (with 2 organizing codes of child's mental preparation for the future labor market and practical preparation for the future labor market). Finally, the pattern of comprehensive and organizing codes of educational components of parents for career path success from childhood was drawn. Conclusion: According to the findings of this study, for the career path success can use from the identified comprehensive and organizing codes in this research and provide the ground for their realization.
Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks

The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces.
University of Otago, Dunedin

Main building and clock tower, University of Otago, Dunedin. Photograph taken 1961-1964 by Gladys M Goodall. Other - Published by Whitcombe & Tombs as postcard WT161 Source of descriptive information - Dated from PAColl-7756-1-12-0265, taken at same time Quantity: 1 colour original transparency/ies. Physical Description: Dye coupler transparency, 6 x 9 cm, in mount 10.1 x 12.7 cm
POTENTIAL-GUIDED UAV-FLIGHT PATH PLANNING FOR THE INSPECTION OF COMPLEX STRUCTURES

Abstract. This works presents a method to compute a potential field for UAV flight path planning for the image-based inspection of complex structures. The potential field is based on the photogrammetric requirements of the inspection and allows to estimate the potential of a viewpoint position to improve the coverage and reconstructability of the structure, based on a model of the structure and the previous viewpoints. This potential field can be used to guide the planning of the flight path to produce globally optimized flight paths, that minimize the number of required images and the length of the flight path, while achieving complete coverage, high resolution and stable reconstructability. The estimation of the potential is implemented efficiently and continuously differentiable to allow for the use in optimization algorithms. In addition, a general framework for potential-based flight path planning is presented that allows to integrate different requirements and constraints into the planning process, outlining the requirements for the implementation of a flight path planning method using this framework. The method is evaluated and validated in a synthetic realistic scenario, where it demonstrated high performance and reliable results. It is able to identify regions of high potential even in complex environments, for example the underside of an overpass of tight corners that often pose challenges for existing methods.
To Rebuild a Grand Northwest China with Beautiful Landscape Is Accomplishable Great Career

Based on the outputs of the project "Rebuilding the beautiful landscape in Northwest China",the history and development of reconstructing graceful landscape in Northwest China were reviewed,and the main outputs of the project in the experiment and demonstration were introduced,especially the construction of many experiment and demonstration zones(i.e.model building). These models locate in the typical area of Shaanxi,Gansu,Ningxia,Qinghai,Xinjiang,etc.,and include the Loess Plateau,desert,Gobi,grassland,wilderness and the source region of Yangtze and Yellow rivers,etc.Those typical models were discussed,and the building procedure,key technology,functional benefit and suitable region were introduced.Based on the combination of theory and reality,reconstructing graceful landscape in Northwest China has the strong supports of technology and broad masses,and is accomplishable great career.Finally,in order to further reconstruct graceful landscape with green mountains and clean-limpid rivers,some suggestions were put forward to.
Optimization on the High Precision 3D Seismic Acquisition Parameters in Leijia Tight Sandstone Oil Area

The main targets of seismic exploration research in Leijia carbonatite tight sandstone oil area of Liaohe depression are thin reservoirs prediction and minor faults identification, which is one of the important representatives of complex exploration objects in Liaohe depression. High precision 3D seismic exploration has significantly improved the ability of thin reservoirs prediction and minor faults identification of this area. Reducing the cost of high precision 3D seismic exploration through optimizing the acquisition parameters is very important for the next step exploration and development of Liaohe depression and similar areas. Based on high precision 3D seismic acquisition data in Leijia tight sandstone oil area, multiple sub-geometries are obtained with different bin sizes, different folds, different aspect ratio, different line intervals by extracting receiver points and shot points, and PSTM processing is performed respectively, obtained PSTM datasets of the sub-geometries, extract time slices, amplitude slices along the layer, coherent slices and so on. We evaluate the data results of the sub-geometries from the aspects of signal-to-noise ratio, thin reservoirs resolution, acquisition footprint and so on. Considering the exploration cost and data effect of each sub-geometry, the optimal direction of the main parameters of high precision seismic exploration in Liaohe depression is put forward, and the acquisition effect of adjacent area by the optimized parameters is given.
Sensitivity of WRF-simulated planetary boundary layer height to land cover and soil changes

Planetary boundary layer (PBL) height sensitivity to both so-called single and accumulated land cover and soil changes is investigated in shallow convection under cloud-free conditions to compare the effects. Single land cover type and soil changes are carried out to be able to unequivocally separate the cause and effect relationships. The Yonsei University scheme in the framework of the Weather Research Forecasting (WRF) mesoscale modeling system is used as a research tool. The area investigated lies in the Carpathian Basin, where anticyclonic weather type influence dominated on the five summer days chosen for simulations. Observation-based methods applied for validating diurnal PBL height courses manifest great deviations reaching 500–1300 m. The obtained deviations are somewhat smaller around midday and greater at night. They can originate either from the differences in the measuring principles or from the differences in the atmospheric profiles used. Concerning sensitivity analyses, we showed that PBL height differences caused by soil change are comparable with the PBL height differences caused by land cover change. The differences are much greater in the single than in the accumulated tests. Space averaged diurnal course difference around midday reaching a few tens of meters can be presumably treated as strongly significant. PBL height differences obtained in the sensitivity analyses are, at least in our case, smaller than those obtained by applying different observation based methods. The results may be utilized in PBL height diurnal course analyses.
APPLICATION OF DEEP LEARNING CROP CLASSIFICATION MODEL BASED ON MULTISPECTRAL AND SAR SATELLITE IMAGERY

Abstract. Classifying crops using satellite data is a challenge, especially since most crops have similar growth cycles. Due to their different characteristics and chlorophyll content, different crops exhibit subtle differences in their reflectance spectra. This study uses a data-driven approach to build a series of deep learning models to classify 36 different land covers in Steele County and Traill Country, North Dakota, US. A Google Earth Engine workflow was implemented to generate a composite layer containing Sentinel 1 and Sentinel 2 satellite data and surface crop data over the study area. 200,000 sample points were generated on this layer, 140,000 for training dataset, 30,000 for validation dataset and 30,000 for testing dataset. Each sample point contains the values of 12 months of SAR and spectral data. In this way, a two-dimensional feature matrix of the time dimension and spectral band dimension (bands refer to specific wavelengths of data in remote sensing imagery and other type of data like NDVI) is generated for each sample point. The training dataset of the model is composed of the feature matrix of these sample points, and the surface crops as labels correspond to the feature matrix. Since this is a dataset with two-dimensional features, this research uses four deep learning models: Dense Neural Network (DNN), Long short-term memory (LSTM), Convolutional neural network (CNN) and Transformer. Among them, the Transformer model based on the self-attention mechanism performed the best, with a comprehensive accuracy rate of 85%, and the classification accuracy rate of crops with more than 2,000 sample points in the training data set reached more than 90%.
Micrometeorological Comparison of Canopy Temperature between Two Wheat Cultivars Grown under Irrigation in a Hot Environment in Sudan

A thorough exploration of the micrometeorological aspects influencing canopy temperature in contrasting wheat cultivars can unveil the specific mechanisms of adaptation to heat stress. However, information on wheat microclimates in hot environments for crop improvement is lacking. Here, we used a micrometeorological method to investigate wheat’s response to high temperatures. Field experiments were conducted in the Gezira Scheme, Sudan, to compare two high-yielding heat-tolerant cultivars, Imam and Bohaine, in terms of canopy temperature depression (CTD), air temperature gradient (ATG), and vapor pressure gradient (VPG) from a 2 m height to canopy level. The maximum air temperature at 2 m during the main growing season was 37 °C. Air temperature at canopy level was mostly lower in the Imam field than in the Bohaine field, and it was positively correlated with and higher than radiometric canopy surface temperature. The maximum CTD during the reproductive stage was 4.7–6.5 °C in the Bohaine field and 5.0–7.2 °C in the Imam field. ATG was also larger in the Imam field, attributed to the greater leaf area of the Imam canopy, as presumed from the NDVI difference between fields. ATG was negatively correlated with VPG in both fields, and the relationship was stronger at lower nighttime wind speeds and weaker at higher daytime wind speeds. These results indicate that the micrometeorological approach can be used to compare cultivars in high-temperature environments.
Multispectral Remote Sensing Data Application in Modelling Non-Extensive Tsallis Thermodynamics for Mountain Forests in Northern Mongolia

The imminent threat of Mongolian montane forests facing extinction due to climate change emphasizes the pressing need to study these ecosystems for sustainable development. Leveraging multispectral remote sensing data from Landsat 8 OLI TIRS (2013–2021), we apply Tsallis non-extensive thermodynamics to assess spatiotemporal fluctuations in the absorbed solar energy budget (exergy, bound energy, internal energy increment) and organizational parameters (entropy, information increment, q-index) within the mountain taiga–meadow landscape. Using the principal component method, we discern three functional subsystems: evapotranspiration, heat dissipation, and a structural-informational component linked to bioproductivity. The interplay among these subsystems delineates distinct landscape cover states. By categorizing ecosystems (pixels) based on these processes, discrete states and transitional areas (boundaries and potential disturbances) emerge. Examining the temporal dynamics of ecosystems (pixels) within this three-dimensional coordinate space facilitates predictions of future landscape states. Our findings indicate that northern Mongolian montane forests utilize a smaller proportion of received energy for productivity compared to alpine meadows, which results in their heightened vulnerability to climate change. This approach deepens our understanding of ecosystem functioning and landscape dynamics, serving as a basis for evaluating their resilience amid ongoing climate challenges.
Global Shapes and Salient Joints Features Learning for Skeleton-Based Action Recognition

Global shapes and local joints are significant cues to learn skeleton representations for human action recognition. However, most shapes-based methods ignore local joints information and thus perform less well when recognizing similar actions. In this letter, we propose a two-stream method of global shapes and salient joints features learning to address the above issues. Firstly, the global sparse shapes modeling stream (GSSM) explores the global skeletal shapes in Kendall space and codes the nonlinear shapes by a sparse coding and dictionary learning method. Then, the salient enhancing joints generation stream (SEJG) explores local joints information by extracting discriminative salient joints and generates frame-enhancing action sequences with invariant length by a bilinear frame interpolation module. To improve the temporal modeling ability of our method, we use similar multi-scale LSTM methods in both GSSM and SEJG, which explore global-local features in static, short-term, and long-term temporal scales. Extensive experiments on four challenging datasets verify the effectiveness of our proposed method, which achieves competitive results compared to state-of-the-art (SOTA) methods.
THE IMPACT OF ORGANIZATIONAL CULTURE ON EMPLOYEE PERFORMANCE: THE CASE OF UNIVERSITY OF TECHNOLOGY AND APPLIED SCIENCES-IBRA IN OMAN

Organizational culture is one of the most important factors affecting the performance of employees in various organizations. A system of values and beliefs adopted by members of the organization affects their performance either positively or negatively. This study aims to investigate the factors of organizational culture (decision-making, organizational environment & reward and recognition system) on employee job performance at UTAS- Ibra in Oman, also the study will examine the effect of the organizational culture on employee job performance at UTAS- Ibra. The method used in this study to collect data is a survey questionnaire. For the purpose of this study, 114 completed questionnaires using stratified sampling method from all academic departments and Educational Technology Center at UTAS-Ibra received and analyzed through SPSS program and smart PLS-SEM. Moreover, the study used secondary data from previous studies in different books and journals as additional sources of data. The results indicate that decision-making and the organizational environment factors of organizational culture have a positive relationship with performance. While, reward and recognition system had a negative impact on employee performance. Based on the results of the study, the study recommending that officials in UTAS-Ibra and the university should strengthen the reward and recognition system so that an integrated system is designed to make employees work in a stimulating environment for work, which results in increasing their performance.
Spirituality In Leadership – A PsychologicalApproach

The concept of spirituality and leadership is widened in this research to include strategic leadership as well as traditional spirituality. It will be necessary for future advancement in the subject of spirituality and leadership to have greater clarity on the degree of research being conducted, as well as the distinction between individual and societal spirituality. A framework is presented to accomplish this goal, and it illustrates how a top-level leader's personal spiritual beliefs may be utilized as a schema to filter and organize data during strategic decision-making. This functioning is moderated by the leader's progressive growth and meta-belief, which are impacted by the organization's setting and leadership method, among other variables. A complex understanding of spirituality and leadership may be gained via the use of this framework. It also provides as a starting point for examining many different types of spirituality that can be found in organizations.
Design and User Experience of VirNE Application: Deep Breathing Exercise in a Virtual Natural Environment to Reduce Treatment Anxiety in Pediatrics

Treatment anxiety is a serious problem among child patients. A few studies have addressed this issue with virtual reality solutions, with promising results; however, the applications used have generally been designed for entertainment instead of this purpose. This article studies the potential of using deep breathing exercises in a virtual natural environment to address this issue, with a focus on design approach and user experience. It presents the VirNE—Virtual Natural Environments relaxation application, which is based on known stress-reduction methods, and a feasibility study conducted with it in a local hospital. The study had a within-subjects design, and it included 21 eight to twelve-year-old child patients, who used the application during an intravenous cannulation procedure related to their treatment. The study found good user acceptance and user experience both among the child patients and pediatricians, with the perceived usefulness of the method being higher among the patients with increased levels of anxiety or needle phobia. In addition, a clear stress-reducing effect was found. This offers proof-of-concept for the multidisciplinary design approach based on existing scientific knowledge regarding the desired effect for pediatric virtual reality applications for this use context.