Miracle-dz/UniFAR-FTUs huggingface dataset:
A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.
HUBUNGAN ANTARA TINGKAT STRES DAN KECEMASAN DENGAN KEJADIAN HIPERTENSI PADA LANSIA DI KLINIK ISLAMIC CENTER SAMARINDA

Except aging process to elderly, stress and anxiety is factors which is can not be avoided from human life and factors which cause hypertension. On high blood pressure, heart pump out blood with extreme high pressure, it because of some factors which are stress and anxiety. Increasing of blood pressure will be bigger to individual who tend to be stress and high emotional anxiety. This research was to know relationship between stress level and anxiety with incidence to elderly on Islamic Center Clinic Samarinda.This research method used qualitative design with cross-sectional approaching. Population on this research are 103 respondents. Sample collection on this research using slovin formula, then got 82 respondents. Sample selection used Accidental Sampling method. This research used correlation statistic test spearman ranks. This research result showed there was relationship between stress level and anxiety with hypertension incidence which got significant result score p value p=0,000 < 0,05, that there was significant relationship between stress and anxiety level with heavy hypertension incidence to respondents elderly.
The prevalence of the motile Aeromonas Species on the shrimps and squids consumed in Antalya

Bu arastirmada, Antalya ilinde tuketime sunulan karides ve kalamarlarda hareketli Aeromonas spp. ’nin belirlenmesi amaclandi. Bu amacla Antalya’nin farkli semtlerinde bulunan market ve balik pazarlarindan tesadufi ornekleme ile toplanan 100 karides ve 50 kalamardan olusan toplam 150 numune kullanildi. Arastirma kapsaminda incelenen, 50 kalamar orneginin 6’sinda (%12), 100 karides orneginin 11’inde (%11) olmak uzere toplam 150 ornegin 17’sinde (%11,3) hareketli Aeromonas turleri izole edildi. Pozitif orneklerin identifikasyonu sonucu 17 izolatin tamaminin A. hydrophila oldugu saptandi. Buna karsin A.cavia ve A.sobria turlerine ise rastlanamadi. Sonuc olarak, Antalya’da satisa sunulan kalamar ve karidesler hareketli Aeromonas’ larla kontamine oldugu ve potansiyel patojen tur olan A. hydrophila ‘nin identifiye edildigi saptandi. Bu nedenle kalamar ve karidesler hazirlanirken hijyen kurallarina titizlikle uyulmasi, mumkun oldugunca kisa sure muhafaza edilerek yeterli isi isleminden sonra tuketilmeleri onerilmektedir.
Advances in scene understanding: object detection, reconstruction, layouts, and inference

The goal of scene understanding is to capture the full content of an image in a humaninterpretable representation. This must describe the different objects present, including their attributes such as class, shape, and pose, as well as the relations between objects. Moreover, the representation should be globally-consistent across the entire image. In this thesis, we consider four sub-tasks within scene understanding, and make contributions to each. When describing the content of an image, it is natural to start by detecting all the objects that are present—that is, localising and classifying them. Our first contribution is to show how to train a neural-network-based object class detector end-to-end in a principled fashion, using the evaluation metric as the training loss, and using the same pipeline at both training and test time. This is simpler and more elegant than the traditional approach of using a surrogate loss, yet we show it achieves comparable performance. Once the location and class of an object are known, we can estimate its shape and pose in 3D space. Our second contribution is a new approach to these tasks, which supports training purely from 2D images—without 3D supervision, multiple views, or annotations such as pose or keypoints. Moreover, this model is generative, and so allows sampling new object shapes a priori. To produce a globally-consistent description of a scene, it is important to reason over all objects simultaneously, rather than considering each individually. Our third contribution is a probabilistic generative model over complete indoor scene layouts. It models complex arrangements in 3D space, including high-order spatial relations among furniture and other objects. One common approach to generating predictions that are consistent over all objects in a scene, or pixels in an image, is to formulate and solve a discrete energy minimisation problem. The energy is defined as a sum over factors, and the factor structure greatly affects what minimisation algorithms work well. Our fourth contribution is a method that automatically selects a suitable algorithm to solve a given energy minimisation problem. To do so, it learns to predict the best algorithm based on characteristics of the problem instance.
Reinventing the Lecture: Webcasting Made Interactive 1

We present a novel system (called ePresence) for highly interactive webcasting with structured archives. We discuss how various kinds of interactivity among both local and remote participants in lectures are enabled by this technology. We suggest ho w ePresence may be an effective tool for creating a knowledge base and enhancing learning. Finally, we illustrate these concepts with an example of using the system to encourage and facilitate the formation of a learning community. 1 Background The lecture has long been a dominant method for transmission of information from instructors to students, particularly in undergraduate education. In an age of increasing use of technology-based distributed education, lectures are still cost-effective presentations and performances of carefully designed sequences of material that can be attended and viewed concurrently by many students. Yet this type of delivery model also has disadvantages, particularly within the context of current research on how people learn (Donovan et al, 1999). Perhaps most serious is that it is typically a one-way broadcast medium from instructor to student, with little back-channel communications and little dialogue between instructor and student and among the students themselves. Despite its prevalence, this transmission model is not conducive to the learner-centred model presently considered to be the more effective approach to pedagogy (Olson & Bruner, 1996). Furthermore, it does not take into consideration the social nature of learning or the need to accommodate different learning objectives with a variety of teaching tools and techniques (Branson, 1998). Thus lectures are increasingly under challenge (Kerns, 2002), as we understand more about learning and the kinds of environments we should be creating to support learning 2 The ePresence system The technology of webcasting gives us the opportunity to reinvent the lecture. Webcasting is the Internet broadcasting of streaming audio possibly accompanied by streaming video so that it can be viewed via a Web browser on a personal computer. Webcasting use grows as Internet broadband communications becomes more available and more affordable, as people seek to avoid travel, and as teachers and learners see demonstrations of its effective use. To allow scaleable visual communications at a distance, our research seeks to make webcasting: Interactive, engaging, flexible, scalable, and robust Accessible in real-time and via archives Useful for communications, information sharing, knowledge building, and learning.
Implementation of pressure reduction valves in a dynamic water distribution numerical model to control the inequality in water supply

The analysis of water distribution networks has to take into account the variability of users' water demand and the variability of network boundary conditions. In complex systems, e.g. those characterized by the presence of local private tanks and intermittent distribution, this variability suggests the use of dynamic models that are able to evaluate the rapid variability of pressures and flows in the network. The dynamic behavior of the network also affects the performance of valves that are used for controlling the network. Pressure reduction valves (PRVs) are used for controlling pressure and reducing leakages. Highly variable demands can produce significant fluctuation of the PRV set point, causing related transient phenomena that propagate through the network and may result in water quality problems, unequal distribution of resources among users, and premature wear of the pipe infrastructure. A model was developed in previous studies and an additional module for pressure control was implemented able to analyze PRVs in a fully dynamic numerical framework. The model was demonstrated to be robust and reliable in the implementation of pressure management areas in the network. The model was applied to a district of the Palermo network (Italy). The district was monitored and pressure as well as flow data were available for model calibration.
Health quality and reduction of pathogenic transmission in tomato seeds using plant extracts

The objective of the present study was to assess the seed health quality, quantification of seed-seedling pathogen transmission and the effect of plant extracts in reducing plant pathogens in the seeds of the tomato varieties San Marzano and Ipa 6. For the seed health, the samples were disinfested, plated and assessed after seven days, according to the Brazilian Seed Analysis Rule. For the transmission rate, 12 trays were prepared with 100 seeds each and assessed at 7, 14 and 21 d.a. s. (days after seeding) using 100 seedlings. The plant (main root, stalk and leaves) tissues were plated in PDA culture medium and assessed after seven days of incubation. Aqueous extracts were prepared from cinnamon, basil, neem and eucalyptus with 0.5% concentration and the seeds were immersed in each solution for 10 minutes. Then, they plated and assessed after seven days. The health test showed that biggest incidences of Aspergillus fumigatus (26 %) and Aspergillus flavus (26 %) were occurred in the seeds of the varieties Ipa 6 and San Marzano, respectively. The fungi A. flavus, A. fumigatus, A. niger, R. stolonifer and Curvularia sp. were detected in quantification of transmission in the seeds of the two tomato varieties. The treatment with basil extract resulted in the least fungus incidence in the transmission quantification of ‘San Marzano’ tomato seeds, while on Ipa 6 seeds the eucalyptus treatment performed better. The interference of treatments was not observed in tomato seed germination. However, there was decrease in incident of pathogens in seeds treated with the plant extracts and different effects was observed based on type and species of the pathogen.
Neurally-constrained modeling of human gaze strategies in a change blindness task

Visual attention enables us to engage selectively with the most important events in the world around us. Yet, sometimes, we fail to notice salient events. “Change blindness” – the surprising inability to detect and identify salient changes that occur in flashing visual images – enables measuring such failures in a laboratory setting. We discovered that human participants (n=39) varied widely (by two-fold) in their ability to detect changes when tested on a laboratory change blindness task. To understand the reasons for these differences in change detection abilities, we characterized eye-movement patterns and gaze strategies as participants scanned these images. Surprisingly, we found no systematic differences between scan paths, fixation maps or saccade patterns between participants who were successful at detecting changes, versus those who were not. Yet, two low-level gaze metrics – the mean fixation duration and the variance of saccade amplitudes – systematically predicted change detection success. To explain the mechanism by which these gaze metrics could influence performance, we developed a neurally constrained model, based on the Bayesian framework of sequential probability ratio testing (SPRT), which simulated gaze strategies of successful and unsuccessful observers. The model’s ability to detect changes varied systematically with mean fixation duration and saccade amplitude variance, closely mimicking observations in the human data. Moreover, the model’s success rates correlated robustly with human observers’ success rates, across images. Our model explains putative human attention mechanisms during change blindness tasks and provides key insights into effective strategies for shifting gaze and attention for artificial agents navigating dynamic, crowded environments. Author Summary Our brain has the remarkable capacity to pay attention, selectively, to the most important events in the world around us. Yet, sometimes, we fail spectacularly to notice even the most salient events. We tested this phenomenon in the laboratory with a change-blindness experiment, by having participants freely scan and detect changes across discontinuous image pairs. Participants varied widely in their ability to detect these changes. Surprisingly, their success correlated with differences in low-level gaze metrics. A Bayesian model of eye movements, which incorporated neural constraints on stimulus encoding, could explain the reason for these differences, and closely mimicked human performance in this change blindness task. The model’s gaze strategies provide relevant insights for artificial, neuromorphic agents navigating dynamic, crowded environments.
Power and Sample Size in Multilevel Linear Models

Sample size determination in multilevel designs requires attention to the fact that statistical power depends on the total sample sizes for each level. It is usually desirable to have as many units as possible at the top level of the multilevel hierarchy. Some formulas are given to obtain insight in the design aspects that are most influential for standard errors and power. Keywords: power; statistical tests; design; multilevel analysis; sample size; multisite trial; cluster randomization
A Pre-Compensation Fuzzy Logic Algorithm Designed for the Dynamic Compensation Robotic System

This paper deals with the issue of non-model-based position regulation for the dynamic compensation robotic system (DCRS), which has been proposed for cooperating with the existing main robotic systems, such as the common serial robotic arms, to accomplish high-speed and accurate manipulations. The dynamic compensation concept is realized by fusing a high-speed & light-weight compensation actuator as well as endpoint closed loop (ECL) configured high-speed cameras. Within the context of the DCRS, the coarse motion, which is realized by the main robotic system, usually gives rise to negative dynamic impact on the compensation actuator that is configured to accomplish the fine motion. Through the analysis of a simplified model for the coupled two-plant system, relative velocity information between the two plants is found to play a role in the first order derivative of the displacement error. With the use of the relative position information from high-speed visual feedback, this paper proposes a new pre-compensation fuzzy logic control (PFLC) approach for control of the compensation actuator. The PFLC method is model-independent and is realized with a cascade fuzzy inference structure that conveniently integrates the relative velocity term between the two plants into the error regulation, and therefore realizes the partial counteraction of the disturbance from the main robot easily without knowing the explicit mathematical models of the system. Comparison works between the proposed PFLC and approaches that take no consideration of the relative velocity information, such as proportional-derivative (PD) control and conventional fuzzy logic control, are conducted. Simulations and experiments show the consistent effectiveness of the proposed approach.
Experimental evaluation of the fault tolerance of an atomic multicast system

The authors present a study of the validation of a dependable local area network providing multipoint communication services based on an atomic multicast protocol. This protocol is implemented in specialized communication servers, that exhibit the fail-silent property, i.e. a kind of halt-on-failure behavior enforced by self-checking hardware. The tests that have been carried out utilize physical fault injection and have two objectives: (1) to estimate the coverage of the self-checking mechanisms of the communication servers, and (2) to test the properties that characterize the service provided by the atomic multicast protocol in the presence of faults. The testbed that has been developed to carry out the fault-injection experiments is described, and the major results are presented and analyzed. It is concluded that the fault-injection test sequence has evidenced the limited performance of the self-checking mechanisms implemented on the tested NAC (network attachment controller) and justified (especially for the main board) the need for the improved self-checking mechanisms implemented in an enhanced NAC architecture using duplicated circuitry. >
Studies on abomasal nematodes of sheep in relation to temperate climatic factors

An epidemiological study with an objective to assess the seasonal dynamics and over winter survival of abomasal nematodes of sheep in a temperate climate of Kashmir valley, India was carried out in naturally infected sheep over a period of two years. Faecal examination [prevalence (%age) and faecal egg counts (epg), larval differentiation] and necropsy of the study animals were the methods employed. Haemonchus contortus and Teladorsagia (Ostertagia) circumcincta were most prevalent followed by Trichostrongylus sp. however; the prevalence differed (non-significant) in the two years. Summer showed significantly higher prevalence of all the three parasites as compared to other seasons. The observations revealed arrested development (hypobiosis) within the host body during winter (P = 0.008), which was confirmed through the recovery of L4 and adult nematodes from host abomasums in winter. The present study indicates the seasonality and arrested development of abomasal nematodes in the sheep in the temperate climatic conditions and validates their strategic overwinter survival and resumption of development on the onset of suitable environmental conditions in spring. Keyword: Epidemiology, Abomasal nematodes, Hypobiosis, Haemonchus contortus, Sheep
Application of Recognition Input Squinting and Error-Correcting Output Coding to Convolutional Neural Networks

The Convolutional Neural Network (CNN) is a type of artificial neural network that is successful in addressing many computer vision classification problems. This thesis considers problems related to optical character recognition by CNN when few training samples are available. Two techniques are proposed that can be used to improve the application of CNNs to such problems and these benefits are demonstrated experimentally on subsets of two labelled databases: MNIST (handwritten digits) and CENPARMI-MPC (machineprinted characters). The first technique is novel and is called “Recognition Input Squinting”. It involves taking the input image to be recognized and applying a set of geometric transformations on it to produce a set of squinted images. The trained CNN classifier then recognizes each of these generated input images and computes an overall recognition confidence score. It is shown that this technique yields superior recognition precision as compared to the case where a single input image is recognized without squinting. The second technique is an application of the Error-Correcting Output Coding technique to the CNN. Each class to be recognized is assigned a codeword from an appropriately chosen error-correcting code’s codebook and the CNN is trained using these codeword labels. At recognition time, the output class is selected according to a minimum code distance criterion. It is shown that this technique provides better recognition precision than when the classic place output coding is used.
Stomatal sensitivity to CO
 2
 diverges between angiosperm and gymnosperm tree species

The response of tree leaf gas exchange to elevated CO₂ concentrations has been investigated in numerous experiments along the past 30 years. Stomatal regulation is a major plant control over leaf gas exchange, and the response to the increasing CO₂ will shape the biological activity of forests in the future. Here, we collected 144 records from 57 species on stomatal conductance in CO₂ manipulation experiments on trees (340–980 ppm CO₂). CO₂‐induced stomatal downregulation was calculated as the slope of the linear regression between stomatal conductance and [CO₂]. Among tree species, the slope (a) of change in stomatal conductance per 100 ppm CO₂ increase ranged between 0 and −151, indicating stomatal downregulation, and only four species showed upregulation. There was a significant divergence between evergreen gymnosperms (a = −3.6 ± 1.0), deciduous angiosperms (a = −16.3 ± 3.1) and evergreen angiosperms (a = −32.8 ± 7.1). Gymnosperms were less sensitive to CO₂ changes than deciduous angiosperms even when considering only field experiments. The significant role of tree functional group in predicting CO₂‐induced stomatal downregulation was detected in multiple mixed‐effect models, with p values ranging between 0.0002 and 0.0295. The significantly higher stomatal sensitivity to CO₂ of angiosperms versus gymnosperms might be related to the overall higher stomatal conductance of angiosperms; their thinner leaves, in turn losing water faster; and the decreasing atmospheric [CO₂] at the time of their taxa diversification. We conclude that species differences must be taken into account in forecasting future forest fluxes. A plain language summary is available for this article.
A SLAM II simulation study of a simplified flow shop

Control of jobs being processed in a flow shop is important to management. Shop performance is affected by shop congestion, processing time variation, and the dispatching rule used to load the jobs. Most previous research has concentrated on the effects of varying these parameters in a job shop setting. A SLAM II net work model and simulation analysis of these parameter varia tions was applied in a two-work center flow shop. Performance of the flow shop was tested using combinations of 5 dispatching rules, 3 shop load levels, and 2 levels of processing time varia tion. Based on a series of performance measures, the shop using the shortest processing time dispatching rule in one work center and the due data based rule in the other compares favorably with the shop that used the shortest processing time rule in both work centers. Results indicate that shop congestion and process ing time variation affect the performance of the flow shop as well. Performances between the flow shop and job shop are com pared, and results indicate that the flow shop performance closely follows the job shop performance under the same dis patching rule at all work centers.