Miracle-dz/UniFAR-FTUs huggingface dataset:
Purpose, development, and synthesis of the Soil Vulnerability Index for inherent vulnerability classification of cropland soils

The Soil Vulnerability Index (SVI) was developed by the USDA Natural Resources Conservation Service (NRCS) to classify inherent vulnerability of cropland soils based on field sediment and nutrient transport resulting from surface runoff and leaching. The primary purpose of the SVI is to aid conservation planners in more rapidly assessing managed lands and inherent resource concerns. The index is based on hydrologic soil group, slope, and soil erodibility for cultivated cropland soils, with the addition of percentage rock fragments and organic matter when considering leaching. Although the SVI is intended for use throughout the United States, its development was based on the physiographic and rainfall characteristics of the Upper Mississippi and Ohio-Tennessee River basins. The purpose of this study was to evaluate the SVI in areas both in and outside of the area for which it was developed. Thirteen different watersheds were selected to conduct this evaluation. Vulnerability classifications using the SVI were compared with those from on-site experts' knowledge and with model simulations using local data. Four companion papers in this special collection discuss SVI classification based on the effects of land slope, artificial drainage, sediment and nutrient loads, and vulnerability assessment using hydrologic simulation models. Using results from the various sites, the objective of this paper was to synthesize the interpretation of the value and applicability of SVI vulnerability classification to sediment and nutrient loss across various physiographic regions and suggest where improvement in the SVI could be made.
Antitoxic effect of baicalin and glycyrrhetic acid on PC12 cells induced by Aconitum brachypodum Diel

The roots of Aconitum brachypodum Diel are used clinically in traditional medicine of China. However, it is also found very toxic and to date, few detoxication strategies are available to completely eliminate its toxicity. The present study was conducted to observe the cellular neurotoxicity of A. brachypodum Diels and discuss the detoxication effect of baicalin and glycyrrhetic acid, two compounds from Chinese herbal Baical skullcap root and Radix Glycyrrhizae, respectively. The cellular neurotoxicity of A. brachypodum Diels on PC12 cell was estimated and assessed. The results showed that low doses of chloroform-soluble fraction (CFA) displayed little toxicity on PC12 cells. However, long term of high doses (300 to 400 μg/ml) exposure to CFA could lead to significant cell damage, mainly including cell apoptosis and necrosis, increasing intracellular reactive oxygen species (ROS), mitochondria dysfunction and redox imbalance. Pre-incubation of baicalin and glycyrrhetic acid could both decrease the cytotoxicity of PC12 induced by CFA. The present study strongly demonstrated the pro-oxidant effects of CFA and suggested that increased intracellular ROS and calcium have mediated a significant time and dose-dependent cytotoxicity in PC12 cells exposed to CFA via a mitochondrial dependent pathway, which could be effectively reduced by baicalin and glycyrrhetic acid. Key words: Aconitum brachypodum Diel, baicalin, glycyrrhetic acid, PC12, detoxication.
The Effect of Conspecific Cues and Neighborhood Effects on Bee Foraging Behavior

Foraging bees use social information (e.g. presence of absence of other bees) to assess the quality of flowers when choosing a flower to visit. My research tests how bees choose to visit a particular flower once they have been recruited to a flower patch. I tested if neighborhood effects, or the relative number of bees on neighboring flowers compared to a focal flower, affected to which flower a foraging honey bee visited. I also conducted a meta-analysis to test whether bees in the super-family Apoidae are more likely to visit a flower occupied by a conor heterospecific bee or visit an unoccupied flower, and what circumstances lead to these two different foraging behaviors. My results suggest that visual cues, or relative abundance of bees in the neighborhood, impact the number of bee visits a flower will receive, thus highlighting potential implications for pollination services and plant reproductive output.
GPU Memory Management for Deep Neural Networks Using Deep Q-Network

Deep neural networks use deeper and broader structures to achieve better performance and consequently, use increasingly more GPU memory as well. However, limited GPU memory restricts many potential designs of neural networks. In this paper, we propose a reinforcement learning based variable swapping and recomputation algorithm to reduce the memory cost, without sacrificing the accuracy of models. Variable swapping can transfer variables between CPU and GPU memory to reduce variables stored in GPU memory. Recomputation can trade time for space by removing some feature maps during forward propagation. Forward functions are executed once again to get the feature maps before reuse. However, how to automatically decide which variables to be swapped or recomputed remains a challenging problem. To address this issue, we propose to use a deep Q-network(DQN) to make plans. By combining variable swapping and recomputation, our results outperform several well-known benchmarks.
Applications of Artificial Intelligence for Analysis of Floaters, Cataracts, Cells of Non-Responsive Retina: A Proposal

The following paper presents a pantheon of proposed solutions to eye problems using a hybrid combination of Ophthalmological [1] sciences along with engineering technologies like swarm intelligence, robotics, artificial and convolutional neural networks. In this paper we claim that these must provide optimal and more accurate results than recorded in our present statistics. This would be particularly helpful when surgery has risks. Human retina has in general the following five problems: 1. Floaters, 2. Macular degeneration, 3. Diabetic Eye disease, 4. Retinal Detachment, 5. Retinitis Pigmentosa. Cases where retina has been operated several times in the past are typical examples when we want to fix problems without a surgery. One may claim that internal medications can fix such problems. But here also we have problems like malfunctioning of optic nerve, disbalance in concentrations of different pigments of cone cells and so on. Also somatic cells like that of aqueous or vitreous humours may also be affected permanently and for the worse. As such, we need machinery to assist and/or improve signals as perceived by the eye (Ex: artificial ciliary muscles to automate the flexible bi-convex lens in eye). Every claim, hence in our paper, is a proposed mathematical model with a view to improve/add new innovations to existing clinical machines. As an example, we can use deep learning (CNN) [2] to extrapolate incomplete images perceived by a diseased retina to complete the image, convert combination of pixels into its corresponding adjustable feedable analog output which with proper amplification would be fed to cerebrum. Such innovations if implemented, not only helps us in curing patients who incurred such diseases in their lifetime but also would be an ever-lasting solution to those who genetically inherited them during their birth.
Delta Oscillations Are a Robust Biomarker of Dopamine Depletion Severity and Motor Dysfunction in Awake Mice

Delta oscillations (0.5–4 Hz) are a robust but often overlooked feature of basal ganglia pathophysiology in Parkinson’s disease and their relationship to parkinsonian akinesia has not been investigated. Here, we establish a novel approach to detect spike oscillations embedded in noise to provide the first study of delta oscillations in awake, dopamine depleted mice. We find that approximately half of neurons in the substantia nigra reticulata exhibit delta oscillations in dopamine depletion and that these oscillations are a strong indicator of dopamine loss and akinesia, outperforming measures such as changes in firing rate, irregularity, bursting and synchrony. We further establish that these oscillations are caused by the loss of D2 receptor activation and do not require motor cortex, contrary to previous findings in anesthetized animals. These results give insight into how dopamine loss leads to dysfunction and suggest a reappraisal of delta oscillations as a biomarker in Parkinson’s disease.
Severity of Glenohumeral Osteoarthritis Does Not Correlate With Patient-Reported Outcomes

Background Patient pain and clinical function are important factors in decision-making for patients with glenohumeral osteoarthritis (GHOA). The correlation between radiographic severity of arthritis and demographic factors with modern patient-reported outcome measures has not yet been well defined. Methods This cross-sectional study included 256 shoulders in 246 patients presenting with isolated GHOA. All patients obtained standard radiographs and completed the American Shoulder and Elbow Surgeons score, Simple Shoulder Test (SST), Shoulder Activity Scale, Visual Analog Scale, and Patient-Reported Outcome Measurement Information System (PROMIS) computer adaptive tests at the time of presentation. Radiographs were graded according to the Samilson–Prieto classification. Mean pain and functional scores were compared between the radiographic grades of osteoarthritis (OA) and demographic factors. Results There were 6 shoulders rated as grade 1 OA, 41 shoulders as grade 2, 149 shoulders as grade 3a, and 65 shoulders as grade 3b. There was excellent interobserver reliability in grade of OA (κ = 0.77). There were no significant differences in patient-reported pain or any validated measure of clinical function between radiographic grades of OA (P > .05). Males reported higher function and lower pain scores than females (P = .001–.066), although only the values for the SST and PROMIS physical function test were clinically relevant. Discussion While gender correlated with pain and function, the clinical relevance is limited. Radiographic severity of GHOA does not correlate with patient-reported pain and function, and symptoms should remain the primary determinants of surgical decision-making. Further investigation is necessary to examine whether radiographic severity of OA influences improvement following operative intervention in this population.
Mathematical Modeling of Neuronal Logic, Memory and Clocking Circuits

The differential equations used to model biological neurons and the chemical kinetics involved in synaptic excitation and inhibition have been well-established for a number of decades. For the first time, this paper presents mathematical and computational models of a neuronal binary oscillator half-adder, a neuronal Set-Reset (SR) flip-flop and a simple neuronal clocking circuit, which have all been shown to be noise resistant. In modern computers, the half-adder is the basic component to perform logic, the SR flip-flop is used to store memory and clocking circuits are used to synchronize components in parts of the computer. These novel circuits will provide the world with neuronal assays that can measure the functionality of the neurons and hence provide more information than is available with current technology. The authors are not proposing to build conventional computers with these components (they would be too slow to be practical) but the simple circuits could be used to measure the functionality of diseased circuits which are subjected to certain drugs. Neurological conditions research into Alzheimer’s disease, epilepsy and Parkinson’s disease, for example, would all benefit from this research. These assays for neuronal degradation could have major implications for the National Center for the Replacement, Refinement and Reduction of Animals in Research — otherwise known as the NC3R agenda.
Optimal Location and Sizing of PV Sources in DC Networks for Minimizing Greenhouse Emissions in Diesel Generators

This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.
Performance Behavior of Participatory Water Institutions in Eastern India: A Study through Structural Equation Modelling

The paper examines the nature and performance of participatory water institutions in eastern India using structural equation modelling. There is a crisis in the management of water in India, and this is often not about having too little water but about managing it poorly. It is now being widely recognized that engineering structures and solutions are not enough, and having effective water institutions is critical. These are urgently needed in eastern India for helping lift the region out of low incomes and poverty. However, creating good institutions is complex, and in this context, the fundamentals of new institutional economics, and management governance theory have suggested the importance of a number of key factors including five institutional features and eight rationalities. Based on this, a study was conducted in eastern India, sampling from the states of Assam and Bihar, covering 510 farm households across 51 water institutions. In order to understand and map the relationship and pathways across these key factors, a structural equation model is hypothesized. In the model, the five institutional features are considered determinants of the eight rationalities, and the rationalities are considered determinants of four performance goals. The performance on the goals determines the overall performance/success of the institution. Besides this, the institutional features and rationalities can also directly influence performance on the goals and the overall performance. The model is tested with data from the survey and different pathways that are robust are identified. The results can provide useful insights into the interlinkages and pathways of institutional behavior and can help policy and institution design for delivering more robust performance. The results show that one of the most important factors determining overall performance/success is technical rationality, and this deserves great attention. It includes technical expertise, sound location and quality of structures and equipment, and good maintenance. However, success is also strongly linked to performance on production/income goals, equity, and environment goals. These are, in turn, strongly related to achievement of economic, social, technical, and organizational rationalities, which call for attention to economic aspects such as crop choice and marketing, besides social aspects such as inclusion of women and poorer social groups, and organizational aspects such as member involvement and regular meetings. Further, the institutional features of clear objectives, good interactions, adaptive, correct scale, and compliance are important for achievement of almost all rationalities through various pathways, and should be strongly focused on in all the institutions.
Research on Assisted Driving Technology based on Improved YOLOv3

In the era of automobile popularization, China and the developed countries such as Europe and the United States are facing the same problem of high car accidents. This paper uses deep learning technology to detect and identify lane lines, traffic lights, vehicles and pedestrians during driver driving. Improve the efficiency and accuracy of convolutional neural network training through migration learning and data enhancement techniques. Based on the current advanced YOLOv3 network, we have improved the network structure and loss function. The KITTI dataset has achieved the highest 2D target recognition accuracy, and the target recognition speed is higher than 36 frames/sec. The price of the assisted driving equipment we developed does not exceed RMB 5, 000, which has a good market prospect.
Expansion of Technology Utilization Through Tourism 4.0 in Slovenia

With more and more people traveling worldwide (the number doubling in the last 20 years), tourist destinations are now more than ever trying to maintain and enhance their competitiveness in the global market. In this regard, novel business models combined with state-of-the-art technology can play a crucial role in not only satisfying the increasing tourism demand but also ensuring a sustainable growth to avoid the deteriorating effects on both the social and natural habitat. For these reasons, the Slovenian government included tourism as one of the priority areas for investment. Hence, the Tourism 4.0 initiative was launched to unite tourism stakeholders with high-tech companies and unify their scattered ideas, experiences, knowledge, and expertise. The ambition is to position Slovenia as a top destination for sustainable tourism with high economic value. This chapter introduces the basic concepts behind Tourism 4.0 and how it relates to technologies for an enhanced tourism experience.
Multi-antenna cyclostationarity feature detection for parallel sensing in cognitive radio

This paper proposes a new approach which enables cognitive radio systems to perform parallel sensing. Thanks to the spatial rejection capabilities of antenna processing and to the a priori knowledge of the cyclostationarity properties of the incumbent Radio Access Technology (RAT), an opportunistic user can perform periodic in-band sensing to detect a potential incumbent system without having to insert quiet periods on its waveform.
Measuring the impact of functional and experiential mixed reality elements on a museum visit

ABSTRACT Although research on the impact of new realities on museum experience is receiving increasing attention, it remains mainly conceptual in nature and measuring the impact of mixed reality on a museum visit is still in its infancy. This paper attempts to shed light on this topic by applying the visitor experience model for mixed reality, which measures how 23 functional and experiential elements have been reshaping a traditional museum visit, enhancing visitor satisfaction. The empirical analysis interests ‘The Ara As It Was’, a mixed reality project installed in the iconic Ara Pacis Museum in Rome (Italy), an expression of Roman history and unique on the Italian and international panorama. The findings and conclusions open up new scenarios for future research on immersive museum experiences, combining cultural heritage with technological innovation. They suggest managerial implications to combine authenticity and innovation in the museum, generating unique experiences and redefining museum service models.
PROSES PEMBELAJARAN MATEMATIKA PADA ANAK BERKEBUTUHAN KHUSUS (AUTISME) DI SEKOLAH INKLUSIF

Pertumbuhan anak berkebutuhan khusus di Indonesia dewasa ini sangat cepat, sehingga pelayanan pendidikan yang diharapkan oleh orang tua belum bisa terpenuhi dengan baik. Akibatnya Anak Berkebutuhan Khusus (ABK) mengikuti pendidikan di sekolah reguler yang tidak memberikan perhatian secara khusus pada semua anak didiknya. Matematika telah diperkenalkan secara resmi pada anak didik di awal sekolahnya demikian juga dengan Anak Berkebutuhan Khusus (ABK) di dalamnya. Kesulitan matematika adalah hambatan atau gangguan belajar pada anak yang ditandai oleh ketidak mampuan anak untuk mengekspresikan hubungan-hubungan kuantitatif dan keruangan. Anak Berkebutuhan Khusus (ABK) yang mengalami kesulitan dapat diketahui melalui kriteria-kriteria yang sebenarnya merupakan indikator terjadinya kesulitan belajar. Penelitian ini bertujuan untuk mendeskripsikan kesulitan matematika Anak Berkebutuhan Khusus (ABK) saat proses pembelajaran matematika berlangsung, yang selanjutnya guru dapat mengambil langkah-langkah perbaikan yang tepat untuk proses belajar mengajar. Namun pengetahuan matematika muncul ketika Anak Berkebutuhan Khusus (ABK) mengerjakan sesuatu dengan aktivitas fisik, seperti menyusun blok puzle dan mengurutkan benda sesuai ukuran. Anak Berkebutuhan Khusus (ABK) juga dapat memperoleh pengetahuan matematika walaupun sangat terbatas. Sehingga anak Berkebutuhan Khusus (ABK) tersebut dapat menerima pelajaran sesuai dengan kemampuan mereka dan diberikan bimbingan sesuai dengan hak yang seharusnya mereka dapatkan sehingga dapat mencapai kemampuan optimal.
Camera System Performance Derived from Natural Scenes

The Modulation Transfer Function (MTF) is a wellestablished measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Nonlinearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds.
The challenge of integrative taxonomy of rare, deep-water gastropods: the genus Exilia (Neogastropoda: Turbinelloidea: Ptychatractidae)

According to a recent taxonomic revision by Kantor et al. (2001), the neogastropod genus Exilia Conrad, 1860, comprises ten mostly rare species that live at depths between 200 and 2000 m. Adult Exilia measure between 30 and 90 mm in shell length, and the genus is mostly represented in museum collections by empty shells. The abundance of this genus is low in the wild, but recent expeditions organized by the Muséum national d’Histoire naturelle have yielded several dozen specimens. These new collections include samples preserved for molecular studies. Here, we present the results of the first molecular systematic study of Exilia. Our aim was to investigate the species limits proposed by Kantor et al. (2001) on the basis of shell and anatomical characters. Analysis of DNA sequence data for the cytochrome c oxidase I gene suggests that Exilia hilgendorfi, previously considered to be a single, polymorphic and broadly distributed species, is a complex of at least six species (four of which we sequenced). Two of these species, Exilia cognata n. sp. and E. fedosovi n. sp. , are described as new to science. Exilia gracilior, E. claydoni and E. prellei are resurrected from the synonymy of Exilia hilgendorfi; of these three, only the last was sequenced. Exilia vagrans is a well-defined taxon, but our molecular systematic data shows that it consists of two distinct species, which occur sympatrically off Taiwan and are strikingly similar in shell and radular morphology; due to the absence of DNA sequence data from the type locality of E. vagrans (Vanuatu), it is unclear to which of these two species the name would apply. Exilia karukera n. sp. , which is conchologically very similar to E. vagrans, was discovered off Guadeloupe, represents the first record of the genus from the Atlantic. For E. elegans, which was previously known only from a single shell, we provide new data including new distributional records (South Africa and the Mozambique Channel), details of the radula and DNA sequence data.