Miracle-dz/UniFAR-FTUs huggingface dataset:
Facing the challenge of retaining new teachers in rural and remote settings: A report of two case studies using a community-based mentoring model in Queensland, Australia

Attracting teachers to live and work in rural and remote schools is a continuing challenge in more isolated parts of Australia and internationally. It was this challenge that prompted the Priority Country Area Program (PCAP) to fund the trial of a Community-based Mentoring Model in Queensland to ascertain if a joint approach could make a difference for teachers. Two aims guided the research; 1) to design and implement a Community-based Mentoring Model for enhancing the lives of teachers living and working in rural and remote settings and; 2) to ascertain the effectiveness of the mentoring model by conducting two case studies in different geographic locations. One site was in a medium sized rural setting (population 900) and personnel from two schools and the local community were involved. The second was in a much smaller remote community (population 323) where local community members and school personnel participated. Data were collected in two phases. The first data set was obtained from a State-wide survey and these data were used to identify suitable sites for the case studies and inform the design of the Community-based Mentoring Model for Phase 2. Findings reported in this paper relate to Phase 2 of the study and include pre and post individual and focus group interviews with educators and community members. Previous research has shown that both social and professional needs of new teachers in rural and remote settings need to be addressed. With this in mind, members of the school communities and the wider local community worked together to design and trial a Community-based Mentoring model. Findings from the study showed new teachers appreciated the face-to-face community-based mentoring program. Teachers felt a greater sense of welcome, became more knowledgeable about the local area, felt safer and more accepted in the new community following the implementation of the mentoring. Benefits were found for all participants and relationships between local schools and within the wider community were strengthened.
Development and Evaluation of an Image Processing-Based Kinesthetic Learning System

This study aims to develop an interactive language learning game and explore its efficacy for English language learners. A computer-generated playground was projected onto a large classroom floor (4 × 3 m) with a wide-angle projection device. A Kinect depth camera determined the spatial positions of the playground and the positions of the students’ heads, feet, and bodies. Then, we evaluated the system’s effect on English education through pre- and post-tests. While there was no significant difference between the groups in terms of achievement in the pre-tests, the experimental group exhibited significantly greater improvement in the post-tests (F: 14.815, p < 0.001, η2p: 0.086). Also, both groups demonstrated significant learning gains in post-tests compared to pre-tests (F: 98.214, p < 0.001, η2p: 0.383), and the group x time interaction of the experimental group increased more in percentage (32.32% vs. 17.54%) compared to the control group (F: 9.166, p < 0.003, η2p: 0.055). Qualitative data from student views indicated enhanced learning pace, vocabulary acquisition, enjoyment of the learning process, and increased focus. These findings suggest that a kinesthetic learning environment can significantly benefit English language learning in children.
Semi-Supervised Temporal Meta-Learning Framework for Wind Turbine Bearing Fault Diagnosis Under Limited Annotation Data

Recently, deep learning has made brilliant achievements in wind turbine bearing fault diagnosis field. However, there are two problems that cannot be ignored: 1) the fault data are so scarce that it is time-consuming to acquire a well-behaved deep learning model and 2) much unlabeled data cannot be adequately utilized to explore useful fault information without prior. Therefore, a novel semi-supervised temporal meta-learning (SSTML) method is proposed, which can not only probe representative deep features from massive raw unlabeled vibration data adequately but also make the best of small annotation data to complete fault identification tasks. Transplanting meta-learning ideas into semi-supervised learning (SSL), a novel deep learning framework—SeMeF—is proposed. The proposed SeMeF is capable of drawing on the advantages of two mechanisms to exert efficiency beyond themselves. Furthermore, a temporal convolutional module is proposed to relieve overfitting due to the depth of the model, which can fully excavate temporal features along the depth of the network. The superiority of the proposed method is demonstrated on the wind turbine bearing dataset. Experimental results indicate that the model proposed can reach high diagnostic accuracy with limited annotation data, which outperforms many advanced deep learning models.
Sand transport characterization on the foredune area of Parangtritis, Indonesia

The aim of the research are to determine the characteristics of sand deflation on Parangtritis foredune area. The development of tourism activity on surrounding area could be has a big impact to the sand transport process. Data required in this research are wind direction and its speed, the sand mass collected from sand sampler, sand grain size, roundness and sphericity, and interview from tourists. Sand that transported by wind collected by sand sampler with dimension of 0.76 cm height and 1 cm width. Sand transport rate or deflation calculated by Bagnold’s formula. The results showed that the deflation process is bigger at daytime observation that at night, it is average 2.42 g.m-1s-1during day and 0.03 g.m-1s-1during night. Each samples location have different deflation characteristics of material that transported. Grain diameters ranging from 0.318 mm to 0.395 mm with dominance texture is medium sand. Sphericity and roundness of sediment material was on a scale of 0.5 and 0.7. This natural process that occurred on the research area didn’t knowing well either by local communities or tourists. Therefore need some proper management for support the continuity of deflation process for supplying material to the main sand dune area of Parangtritis.
A grain size and n-alkanes record of Holocene environmental evolution from a groundwater recharge lake in Badain Jaran Desert, Northwestern China

Vertical water transport plays an important role in the development and maintenance of lakes in arid zones. However, previous study of this phenomenon has focused mainly on the effects of regional precipitation, rather than effects of groundwater recharge, on arid-zone environmental change. We selected a sedimentary profile from a seasonal lake basin located in the hinterland of the Badain Jaran Desert, northwestern China, to assess the Holocene environmental evolution of this region and its response to climate change. Our results show that peat deposition from 11 to 10 ka indicates humidification after Younger Dryas (YD) event; the lake sediment and lower EM2 and lower principal component analysis (PCA)-1 scores indicate relative more humid environment from 10 to 7.8 ka; the lake sediment and higher EM2 and higher PCA-1 scores indicate most humid environment from 7.8 to 5.8 ka; the transition from lacustrine facies to limnetic facies and increasing salinity indicate drying trend from 5.8 to 2.5 ka; the aeolian sand sediment and lowest EM2 and lowest PCA-1 scores indicate driest environment from 2.5 to 0.8 ka; and lacustrine facies and finest grain size indicate relative humid environment during the ‘Little Ice Age’ period. The environment was relative humid in early Holocene, most Humid in mid-Holocene, and dry in mid-late Holocene. The moisture pattern in the hinterland of the Badain Jaran Desert was synchronous with that in the northeastern of Tibetan Plateau, which implies that the groundwater recharge lake was dominated by the intensity of groundwater recharge from monsoon margin area, especially the northeastern of Tibetan Plateau. As a record from groundwater recharge lake without runoff, our study provides the groundwater output quantity record from northeastern of Tibetan Plateau and would benefit the environment change research and regional water resources allocation around the Tibetan Plateau.
Identification and Genetic Diversity Analysis of the Pathogen of Anthracnose of Pepper in Guizhou

Anthracnose of pepper is a significant disease caused by Colletotrichum spp. In 2017 and 2021, 296 isolates were obtained from 69 disease samples. Through morphological analysis, pathogenicity detection, and polygenic phylogenetic analysis, the above strains were attributed to 10 species: C. scovillei, C. fructicola, C. karstii, C. truncatum, C. gloeosporioides, C. kahawae, C. boninense, C. nymphaeae, C. plurivorum, and C. nigrum. C. scovillei had the most strains (150), accounting for 51.02% of the total isolates; C. fructicola came in second (72 isolates), accounting for 24.49%. Regarding regional distribution, Zunyi City has the highest concentration of strains—92 strains total, or 34.18%—across seven species. Notably, this investigation showed that C. nymphaeae infected pepper fruit for the first time in China. Genetic diversity analysis showed that C. fructicola could be divided into seven haplotypes, and the population in each region had apparent genetic differentiation. However, the genetic distance between each population was not significantly related to geographical distance. Neutral detection and nucleotide mismatch analysis showed that C. fructicola might have undergone population expansion.
Study of Heavy Metal Distribution and Hydrodynamic Simulation in Green Mussel Culture Net, Cilincing Water - Jakarta Bay

This study aimed to investigate the heavy metals distribution in the surrounding area of green mussel culture net, Cilincing water, Jakarta Bay, and its distribution behavior. The water sampling was carried out during the ebb tide. The heavy metal concentration was then measured by using atomic absorption spectroscopy (AAS). In order to understand the water circulation behavior related to heavy metal distribution in the study area, the Princeton Ocean Model was applied, and the model design was developed based on a scenario of an open canal in the eastern and western sides, while the center canal was placed with net cages in the water column resembling the green mussel culture net. The observed heavy metal distribution showed an intensified concentration in the green mussel culture location. The observed water current also showed a reduced velocity in the location. The modeling approach could explain the water circulation behavior in response to the presence of net units in the water column. In this case, the density of net units for green mussel culture potentially produced shear stress in the water column, which affected the flow pattern and the distribution of heavy metals. Therefore, the existence of green mussel culture nets has a potential to become a trap for pollutant distribution in the water.
Results of Detailed Hydrologic Characterization Tests - Fiscal Year 2002

This report provides the results of detailed hydrologic characterization tests conducted within newly constructed Hanford Site wells during FY 2002. Results from the tests provide hydrologic information that supports the needs of RCRA waste management characterization and sitewide groundwater monitoring and modeling programs and reduces the uncertainty of groundwater flow conditions at selected Hanford locations.
Three-Axis Vector Magnetometer with a Three-Dimensional Flux Concentrator

This research proposes a magnetic field sensor with spatial orientation ability. Through the assistance of a magnetic flux concentrator, out-of-plane magnetic flux can be concentrated and guided into the planar magnetic cores of a fluxgate sensor. A printed circuit board is used to construct the basic planar structure, on which the proposed three-dimensional magnetic flux concentrator and magnetic cores are assembled. This reduces the alignment error of the coils and improves the reliability of the sensor. Three-axis sensing is achieved by using the second harmonic signals from selected sensing coil pairs. The magnetometer exhibits a linear range to 130 μT. At an excitation frequency of 50 kHz, the measured sensitivities are 257.1, 468.8, and 258.8 V/T for the X-, Y-, and Z-axis sensing modes, respectively. This sensor utilizes only one sensing mechanism for the vector field, making it suitable for IoT applications, especially for assessing mechanical posture or position.
Image-domain wavefield tomography for VTI media

Processing algorithms for transversely isotropic (TI) media are widely used in depth imaging and typically bring substantial improvements in reflector focusing and positioning. Here, we develop acoustic image-domain tomography (IDT) for reconstructing VTI (TI with a vertical symmetry axis) models from P-wave reflection data. The modeling operator yields an integral wave-equation solution, which is based on a separable dispersion relation and contains only P-waves. The zero-dip NMO velocity ([Formula: see text]) and anellipticity parameter [Formula: see text] are updated by focusing energy in space-lag images obtained by least-squares reverse-time migration (LSRTM). Application of LSRTM helps mitigate aperture- and illumination-induced artifacts in space-lag gathers and improve the robustness of [Formula: see text]-estimation. The impact of the trade-off between [Formula: see text] and [Formula: see text] is reduced by a three-stage inversion algorithm that gradually relaxes the constraints on the spatial variation of [Formula: see text]. Assuming that the depth profile of the Thomsen parameter [Formula: see text] is known at two or more borehole locations, we employ image-guided interpolation to constrain the depth scale of the parameter fields and of the migrated image. Image-guided smoothing is also applied to the IDT gradients to facilitate convergence towards geologically plausible models. The algorithm is tested on synthetic reflection and borehole data from the structurally complicated elastic VTI Marmousi-II model. Although the initial velocity field is purely isotropic and substantially distorted, all three relevant parameters ([Formula: see text], [Formula: see text], and [Formula: see text]) are estimated with sufficient accuracy. The algorithm is also applied to a line from a 3D ocean-bottom-node data set acquired in the Gulf of Mexico.
Process of Self-Leadership: Establishing Yourself as Leader No Matter Your Role

As the field of early intervention/early childhood special education (EI/ECSE) focuses intently on building, supporting, and sustaining leaders across varying contexts and roles, this study introduced the concept of self-leadership to EI/ECSE self-identified leaders. The research explores differences in self-rated skills based on role, analyzes themes of goals for developing self-leadership skills, and analyzes the measurability of goals set by participants. Fifty-six participants completed the Abbreviated Self-Leadership Questionnaire (ASLQ) (Houghton, Dawley, & DiLiello, 2012), self-rated their own skills, and identified leadership goals. Results show that participants scored themselves highest on evaluating beliefs and assumptions about self-leadership. Self-identified goals resulted in three primary themes (administrative tasks, relationship building and coaching, and growth in learning). Directions for future research, policy, and recommendations for practice are discussed.
A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.
Low‐Frequency Extensions of the Saturn Kilometric Radiation as a Proxy for Magnetospheric Dynamics

Saturn Kilometric Radiation (SKR) is an auroral radio emission which can be detected quasi‐continuously by the Cassini spacecraft. It has been shown to respond to magnetotail reconnection and to changes in solar wind conditions and thus offers the potential to be used as a remote proxy for magnetospheric dynamics. This work has developed criteria for the selection of low‐frequency extensions (LFEs), powerful intensifications of the main SKR emission, accompanied by an expansion of the SKR to lower frequencies. Upon examination of data from the Cassini Radio and Plasma Wave Science instrument, we detect 282 LFE events which are further grouped into two categories. Shorter events (<20 h) associated with tail reconnection have a median waiting time of ∼10 h, a median duration of 3.1 h and a strong correlation with the northern and southern SKR phase systems. The 60% of the short LFEs have a reconnection event within the preceding 6 h. Longer events (>20 h), associated with increases in solar wind dynamic pressure, can last multiple planetary rotations, have a median waiting time of ∼20 days, and show no relationship with SKR phase. An analysis of the power emitted during LFEs suggests that tail reconnection is not always observed or detected in situ which may partially explain the low correlation between LFEs and tail reconnection. We conclude that short LFEs are a good proxy for reconnection in the tail.
Relief of The Impact of Heat Stress on Friesian and Cross-Bred Friesian Dairy Cows by Water Showering under Egyptian Hot Humid Summer Conditions in Nile Delta

The present study aimed to evaluate the response of lactating purebred Friesian cows and crossbred Friesian with Baladi cows during hot-humid Egyptian summer conditions to showering. The experimental work was carried out during June – August. Ten Friesian and ten crossbred Friesian × Baladi dairy cows were used and assigned randomly into four treatment groups: two control groups of cows had access only to shade (Friesian cows and crossbred Friesian cows; S), and two groups were provided showers (Friesian cows and crossbred Friesian cows; S + S). The maximum THI throughout the experimental work was 80.43 to 85.22. Showered cows were taken to a holding pen daily to be cooled. Concentrations of TSH, T 3 , T 4 , IGF-1, glucose, cholesterol, triglyceride, total protein, albumin, ALP, Na, K, and milk production were significantly (P<0.01) increased, while, serum cortisol, prolactin, creatinine, AST, ALT and thermoregulatory traits (rectal temperature; RT, respiration and pulse rate (RR; PR), and skin temperature; ST were significantly decreased (P<0.01) in showering than shaded only Friesian and crossbred Friesian cows. Despite crossbred Friesian-Baladi dairy cows displaying more tolerance for heat through attained better parameters for thermoregulatory, endocrinal, and biochemistry and milk production, the amount of improvement of purebred Friesian dairy cows by showering enables cows to express some of their genetic potentials which impeded by the heat stress. Thus, showering can be used to lessen the impact of hot-humid Egyptian summer conditions in the Nile Delta on heat-sensitive exotic purebred dairy cows.
3D MODELING AND PHOTOGRAMMETRY-SUPPORTED GIS IN ARCHAEOLOGY

Archaeologists collect an incredible amount of information. If used appropriately, Geographical Information Systems (GIS) can play an important role in the archive and analysis of such data in digital form. Additionally, correct location, relative positioning and 3D dimensioning of artifacts, features and objects in general can be easily done with photogrammetry-supported GIS. The paper reflects an example of how to acquire such data with a common 35mm photographic camera, and how to create 3D models in a cheap and effortless process. Data cases collected and modeled by the author are presented, with photos taken without much rigor, to simulate the case of an archaeologist not familiar with photogrammetric techniques and constraints. Intergraph and ESRI software existing at the author's institution were used in the modeling, archiving and analysis process. Unfortunately aerial photography for the site was not available and a map had to be used instead. Weaknesses and strengths of the approach are discussed. The method is not a substitute for archaeological techniques of data collection, but it can supplant some of them with advantages in time costs, type of archiving support and details in the information collected.
A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery

ABSTRACT The goal of the present study is to demonstrate that high-poverty counties and robust classification features can be identified by machine learning approaches using only DMSP/OLS night-time light imagery. To accomplish this goal, a total of 96 high-poverty and 96 non-poverty counties were classified using 15 statistical and spatial features extracted from night-time light imagery in China in 2010 formed a training set for identifying high-poverty counties. Seven machine learning approaches were adopted to classify high-poverty counties, and five feature importance measures were used to select robust features. The resulting metrics, including the user’s (>63%), producer’s (>66%) and overall (>82%) accuracies of the poor county identification (probability of poverty greater than 0.6), show that the seven machine learning approaches used in this paper exhibit good performance, although some differences exist among the approaches. The order of feature importance reveals that the relative importance of each feature differs among the models; however, the important features remain consistent. The nine most important features ranked in each approach are relatively robust for poverty identification at the county level. Both spatial feature and statistical features calculated in part from the central tendency, degree of dispersion, and the distribution of the night-time light data were identified as indispensable robust features in all the approaches, indicating that the complex social phenomenon of poverty requires analysis from different aspects. Previous studies that utilized primarily night-time light imagery applied single features related to the central tendency or the distribution features of the imagery; this study provides a new method and can act as a reference for feature selection and identification of high-poverty counties using night-time light imagery and has potential applications across several scientific domains.