Miracle-dz/UniFAR-FTUs huggingface dataset:
sEMG and IMU Data-Based Hand Gesture Recognition Method Using Multistream CNN With a Fine-Tuning Transfer Framework

Interfaces based on surface electromyography (sEMG) and inertial measurement units (IMUs) enable users to interact with computers in a natural and intuitive way through hand gestures. sEMG or IMU sensor-based approaches can provide muscle electrical activity or motion information to recognize gestures. However, the existing methods recognize static and dynamic gestures separately and hierarchically. In addition, the variability in sEMG data among different subjects limits the performance of sensor-based human–computer interfaces. To address these limitations, this article proposes a multistream convolutional neural network (CNN) architecture with a fine-tuning transfer strategy. The multistream architecture explores the complementary nature of sEMG and IMU signals and achieves real-time recognition of dynamic and static gestures in a nonhierarchical way. In addition, a new dataset including seven static gestures and six dynamic gestures is designed for evaluation. The proposed method is verified by experiments on six subjects and is compared with previous methods on the same dataset. Experimental results reveal that the transfer strategy significantly improves recognition accuracy from 89.99% to 98.49%. Meanwhile, by learning the fusion features of sEMG and IMU signals, the recognition accuracy is enhanced from 49.54% (sEMG) and 59.91% (IMU) to 89.99%. In addition, the accuracy and latency of this method for real-time recognition are 98.12% and 103 ms, respectively. These results demonstrate that the proposed multistream CNN model accurately recognizes these static and dynamic gestures online in a nonhierarchical way and can effectively utilize the complementarity between sEMG and IMU signals.
Bone Disorders in Pediatric Chronic Kidney Disease: A Literature Review

Simple Summary Mineral and bone disorder (MBD) is usually prevalent in pediatric patients with chronic kidney disease (CKD) and is correlated with meaningful morbidity. CKD may cause disorders in bone remodeling/modeling, which are more evident in the growing skeleton, expressing as short stature, bone pain and deformities, fractures, slipped epiphyses and ectopic calcifications. Although evaluation of bone health is a crucial part in the clinical care of children with CKD, it persists as a significant challenge for pediatricians. Abstract Intense changes in mineral and bone metabolism are frequent in chronic kidney disease (CKD) and represent an important cause of morbidity and reduced quality of life. These disorders have conventionally been defined as renal osteodystrophy and classified based on bone biopsy, but due to a lack of bone biopsy data and validated radiological methods to evaluate bone morphology in children, it has been challenging to effectively assess renal osteodystrophy in pediatric CKD; the consequence has been the suboptimal management of bone disorders in children. CKD–mineral and bone disorder (CKD-MBD) is a new expression used to describe a systemic disorder of mineral and bone metabolism as a result of CKD. CKD-MBD is a triad of biochemical imbalances in calcium, phosphate, parathyroid hormone, and vitamin D; bone deformities and soft tissue calcification. This literature review aims to explore the pathogenesis, diagnostic approach, and treatment of CKD-MBD in children and the effects of renal osteodystrophy on growing skeleton, with a specific focus on the biological basis of this peculiar condition.
The origin and description of southern Africa's indigenous goats

The tip of South Africa was known to seafarers as “the fairest Cape in all the world”, but also as the “Cape of Storms”. These divergent opinions also apply to the rest of Southern Africa with its widely divergent climatic conditions and ecosystems. These systems vary from subtropical rain forests to spectacular savannah country and endless red Kalahari sand dunes. Although extremely beautiful, the natural environment of South Africa is also a cauldron containing a witches’ brew that tests all living organisms to the utmost. These variable but exacting conditions, however, also created the greatest and most beautiful collection of game animals and indigenous farm animals in the world. Some examples of these game animals are: colossal elephants, rhinoceroses and hippopotami and then graceful the kudu, eland, gemsbok, impala, giraffe, sable antelope and springbok as well as the minute dik-dik and steenbok. However, the domestic animals of South Africa such as Afrikaner cattle, Nguni, Sangas, Damara sheep, Namakwa Afrikaners, Blackhead Persians, Red head Boer goats, White Savanna goats, Kalahari Red goats and Speckled goats also have unique and interesting colours as well as an alert and graceful conformation. Opperman (1952) described the interaction between animals plants and the environment as follows: “These limiting factors make definite and inexorable demands which in South Africa, with its relatively changeable difficult and exacting conditions such as irregular and mostly low rainfall, create a definite pattern or fundamental standard. An organism which adapts itself to this pattern with difficulty or weakly will suffer distress. In proportion to the degree the vegetation and animal life deviates from the requisite pattern they will endure discomfort and degenerate and in the long run become extinct. Three factors temperature, humidity and light – rule supreme and in South Africa they are generally speaking stern masters. ” An example of the havoc disease can cause in Southern Africa is the Cattle Plague and Runderpes of 1896. This disease almost wiped out the total cattle and buffalo (Syncerus caffer) populations of Transvaal, Natal and Botswana.
Mathematical Framework for Optimizing Crossbar Allocation for ReRAM-based CNN Accelerators

The resistive random-access memory (ReRAM) has widely been used to accelerate convolutional neural networks (CNNs) thanks to its analog in-memory computing capability. ReRAM crossbars not only store layers’ weights, but also perform in-situ matrix-vector multiplications which are core operations of CNNs. To boost the performance of ReRAM-based CNN accelerators, crossbars can be duplicated to explore more intra-layer parallelism. The crossbar allocation scheme can significantly influence both the computing throughput and bandwidth requirements of ReRAM-based CNN accelerators. Under the resource constraints (i.e., crossbars and memory bandwidths), how to find the optimal number of crossbars for each layer to maximize the inference performance for an entire CNN is an unsolved problem. In this work, we find the optimal crossbar allocation scheme by mathematically modeling the problem as a constrained optimization problem and solving it with a dynamic programming based solver. Experiments demonstrate that our model for CNN inference time is almost precise, and the proposed framework can obtain solutions with near-optimal inference time. We also emphasize that communication (i.e., data access) is an important factor and must also be considered when determining the optimal crossbar allocation scheme.
Exxon global cycle chart: An event for every occasion?

The basic premise of the recent Exxon cycle chart, that there exists a globally correlatable suite of third-order eustatic cycles, remains unproven. Many of the tests of this premise are based on circular reasoning. The implied precision of the Exxon global cycle chart is not supportable, because it is greater than that of the best available chronostratigraphic techniques, such as those used to construct the global standard time scale. Correlations of new stratigraphic sections with the Exxon chart will almost always succeed, because there are so many Exxon sequence-boundary events from which to choose. This is demonstrated by the use of four synthetic sections constructed from tables of random numbers. A minimum of 77% successful correlations of random events with the Exxon chart was achieved. The existing cycle chart represents an amalgam of regional and local tectonic events and probably also includes unrecognized miscorrelations. It is of questionable value as an independent standard of geologic time.
Rapid Acceleration of Dangerous Compound Heatwaves and Their Impacts in a Warmer China

This work investigates the possible future changes and impacts in spatiotemporal variation and regional features of multivariate compound heatwaves (CHWs) in China using the latest Universal Thermal Climate Index from the Coupled Model Intercomparison Project Phase 6 simulations (CMIP6). Results indicate both strong CHWs and extremely threatening CHWs in China are projected to increase substantially in both frequency and spatial extent under different future climate scenarios while with discernible regional disparities. The more severe global warming is, and the greater the extremity of the heatwave events is, the faster the increase in CHWs is. These findings are derived from the CMIP6 projections calibrated using the robust empirical quantiles method. Climate change causes more areas and people to be exposed to CHWs. Notably, the population exposure to dangerous CHWs will increase significantly in northwest China and will appear in northeast China after the mid‐21st century under the high emission scenario.
Nutritional Intervention in Cushing’s Disease: The Ketogenic Diet’s Effects on Metabolic Comorbidities and Adrenal Steroids

Background: a very low-calorie ketogenic diet (VLCKD) is associated with improvement of metabolic and cardiovascular disorders. We aimed to evaluate the effects of a VLCKD in patients with Cushing’s disease (CD) as adjunctive therapy to treatment for the primary disease. Methods: we evaluated clinical, hormonal and metabolic parameters in 15 patients with CD and 15 controls at baseline after 1 week and 3 weeks of VLCKD and, further, after 2 weeks of a low-carbohydrate ketogenic diet (LCKD). Results: after 5 weeks of diet, a significant decrease in BMI (p = 0.002), waist circumference (WC) (p = 0.024), systolic blood pressure (p = 0.015), diastolic blood pressure (p = 0.005), ACTH (p = 0.026), cortisone (p = 0.025), total cholesterol (p = 0.006), LDL cholesterol (p = 0.017), triglycerides (p = 0.016) and alkaline phosphatase (p = 0.008) and a significant increase in HDL cholesterol (p = 0.017), vitamin D (p = 0.015) and oral disposition index (oDI) (p = 0.004) was observed in the CD patients. A significant decrease in BMI (p = 0.003), WC (p = 0.002), systolic blood pressure (p = 0.025), diastolic (p = 0.007) blood pressure and total cholesterol (p = 0.026) and an increase in HDL cholesterol (p = 0.001) and oDI (p < 0.001) was observed in controls. Conclusions: the current study confirms that a ketogenic diet is effective in improving metabolic disorders in CD and shows that a nutritional approach may be combined with conventional CD therapy in order to improve metabolic and cardiovascular comorbidities.
Physicochemical Characteristics, Antioxidant Properties, Aroma Profile, and Sensory Qualities of Value-Added Wheat Breads Fortified with Post-Distillation Solid Wastes of Aromatic Plants

The influence of incorporation of post-distillation solid wastes of the aromatic plants (SWAP), oregano, rosemary, lemon balm, and spearmint into wheat breads at 1% and 2% levels on their physicochemical and sensorial properties, and antioxidant and volatile profiles were investigated. SWAP breads had darker crumbs and crust and greener crumbs compared to the control, but rather similar loaf specific volume and textural attributes (crust puncture test and crumb Texture Profile Analysis). Although the mold growth on bread crumb surface was not inhibited by SWAP presence, LC-DAD-MS revealed a large increase in terpenoids, like carnosic acid (all SWAP), carnosol (rosemary) and carvacrol (oregano), phenolic (rosmarinic and salvianolic) acids and flavonoids in bread with SWAP inclusion, leading to enhanced antioxidant capacity (ABST, DPPH and FRAP assays). The distinct aromatic plant flavors were detected in the fortified breads by trained assessors and confirmed by SPME-GC/MS volatile analysis, showing high levels of terpenoids in SWAP breads, like carvacrol (oregano), caryophyllene (rosemary and lemon balm), and carvone (spearmint), and rendering the 2% fortification unacceptable by consumers. Nevertheless, breads with 1% oregano or rosemary waste had similar control overall acceptability scores, indicating that SWAP can be a promising ingredient for developing antioxidant-enriched wheat breads.
A High-Sensitivity and Low-Cost Turbidimeter Based on Metal Capillary Cavity

Clean water is important for human health, but commercial turbidimeter with high sensitivity (detection limit < 0.01 NTU) is still lacking. Herein, a high-sensitivity and low-cost turbidimeter based on a metal capillary cavity (MCC), which consists of a metal capillary and two apertured mirrors, was proposed. In order to improve the detection limit, the signal-to-noise ratio (SNR) needs to be improved by optimizing the aperture size of MCC, since there is a trade-off between optical path and light intensity received by the detector, e.g., a smaller aperture can increase optical path, but it would reduce light intensity received by the detector. The optimal aperture radius of 0.15 mm was obtained by experiments. The turbidimeter was used to measure formazine turbidity solution, and a detection limit of 0.001 NTU was realized, which is more than 3.6-fold lower than that of previous reports and commercial turbidimeters. The turbidimeter features a compact structure, high sensitivity, and low cost.
Development of Numerical Analysis Method for Magnetic Separation of Magnetic Particle and Ion With Magnetic Chromatography

Rights © 2010 IEEE. Reprinted, with permission, from So Noguchi, SeokBeom Kim, Katuhito Kataoka, Development of Numerical Analysis Method for Magnetic Separation of Magnetic Particle and Ion With Magnetic Chromatography, IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, Jun 2010. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Hokkaido University products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Type article (author version)
Water circulation in metamorphism

There is “too much” water in a variety of crustal environments, including sedimentary basins and regional metamorphic belts. Regional and contact metamorphic rocks have time-integrated fluxes of 106 cm3 water/cm2 for pervasive flow and up to 109 cm3 water/cm2 in fracture flow, amounts far greater than can be supplied by dehydration of immediately subjacent rocks. The types of evidence supporting high water fluxes are so numerous and varied, including petrologic, mineralogic, textural, microstructural, and stable isotopic data, that it is unlikely that all of them could have been interpreted erroneously. Thus the question arises as to what mechanisms of permeability enhancement might operate to facilitate large fluid fluxes. Field evidence suggests the operation of reaction enhancement of permeability, grain-scale dilatancy, and hydraulic fracturing during metamorphism. All these mechanisms are inherently episodic and transient. The transport properties of dynamic permeability enhancement are poorly known in relation to the thermal and deformational cycles of metamorphism. Future research should be directed toward experimental and theoretical simulation of permeability enhancement with close collaboration between isotope geochemists, structural geologists, and petrologists to test models of permeability enhancement in the field.
GEOMORPHIC SYSTEMS AFFECTING THE KERMAN

According to geomorphic systems affecting the settlement of areas, including in the planning and urban management. Due to the geographical location and structural the Kerman and performance of various systems of agents, such systems are alluvial, Aeolian and morphotectonic Implementation and management of all development plans need to be obsessively this space very carefully done. The incidence of serious risks such as ground subsidence, loss of water resources, tectonic activity and Aeolian processes in the atmosphere is solid evidence of this claim. Kerman is located at a point that many factors can affect its normal study of factors influencing the reduction of environmental hazards can be effective. The purpose of this study demonstrates the effect of natural factors and the impact of Kerman urban development on the intensity of negative factors affecting external. This research is trying to use GIS techniques to evaluate geomorphic systems the Kerman range of factors influencing the mapping of processes to check. The results show that morphotectonic systems, including Fluvial and Aeolian systems are effective the Kerman. The maps show that the levels of native lands, and large parts of the Kerman planar surfaces with low slope and covered with alluvial deposits and clay zones form. This can have a considerable impact on the intensity of risks. Original Article: Received 10 Nov. 2013 Accepted 23 Dec. 2013 Published 30 Dec. 2013
Application of Multivariate Logistic Regression and Decision Trees to Assess Factors Influencing Prevalence of Abortion and Stillbirth in Yankasa sheep

AbstractThis study was carried out to assess factors influencing prevalence of abortion and stillbirth in Yankasa sheep using multivariate logistic regression and decision trees. 191 kidding records of Ewes from a total of 50 traditional Yankasa sheep herders within Nasarawa South agro-ecological zone from the year 2020-2021 were utilized in the study. Sampling was restricted to only farmers that were able to give information on Lamb, Ram and Ewe identification as well as occurrence of abortion, stillbirth (defined as a kid born dead or dying within 24 hours after birth), kidding date or period, parity and number of foetuses. Three seasons of abortion or stillbirth were generated according to the month of the year: rainy season (from May to October), dry season (from February to April) and harmattan season (from November to January). The logit of the probability of an abortion or stillbirth was modelled using logistic regression assuming an asymptotic binomial distribution. The Chi-square goodness of fit test was performed to check if the multivariate logistic model fitted the data well (P>0.05). Chi-square automatic interaction detection (CHAID) algorithm was also employed to model incidence of abortion or stillbirth. The chi-square analysis revealed that parity did not significantly (P>0.05) affect the incidence of abortion. However, season of the year significantly (P<0.05) affected prevalence of abortion, which was higher in the harmattan (38.5%) compared to the rainy (11.5%) and dry season (16.5%), respectively. Parity and season of the year did not affect significantly (P>0.05) the prevalence of stillbirth. The binary logistic regression analysis showed that season affected significantly (P<0.05) the prevalence of abortion, especially in the harmattan period with a high odds ratio (4.539). This was also confirmed by the result of the CHAID analysis. The present information could be exploited in management practices to reduce to the incidence of abortion in order to improve the production level of the sheep farmers.
Railway Security System Design Using Unmanned Aerial Vehicle Image Processing and Deep Learning Methods

: With the developing technology, technological blessings make human life easier and help them every day. Unmanned aerial vehicles (UAV), which is one of the technological blessings, have shown themselves in many fields, especially in fields such as the military, defence industry, photography, and hobby. With the development of defence systems with UAVs, the security of railways has also been left to UAVs. In this study, while the foreign matter separation is made on the railway by using the deep learning model in real-time, the image taken on the UAV is simultaneously controlled by using the image processing method. The fact that the deep learning model has a 0.99 mAP rate increases the reliability of the model
Robust Spatial-Temporal Motion Coherent Priors for Multi-View Video Coding Artifact Reduction

Multi-view video (MVV) data processed by three-dimensional (3D) video systems often suffer from compression artifacts, which can degrade the rendering quality of 3D spaces. In this paper, we focus on the task of artifact reduction in multi-view video compression using spatial and temporal motion priors. Previous MVV quality enhancement networks using a warping-and-fusion approach employed reference-to-target motion priors to exploit inter-view and temporal correlation among MVV frames. However, these motion priors were sensitive to quantization noise, and the warping accuracy was degraded, when the target frame used low-quality features in the corresponding search. To overcome these limitations, we propose a novel approach that utilizes bilateral spatial and temporal motion priors, leveraging the geometry relations of a structured MVV camera system, to exploit motion coherency. Our method involves a multi-view prior generation module that produces both unidirectional and bilateral warping vectors to exploit rich features in adjacent reference MVV frames and generate robust warping features. These features are further refined to account for unreliable alignments cross MVV frames caused by occlusions. The performance of the proposed method is evaluated in comparison with state-of-the-art MVV quality enhancement networks. Synthetic MVV dataset facilitates to train our network that produces various motion priors. Experimental results demonstrate that the proposed method significantly improves the quality of the reconstructed MVV frames in recent video coding standards such as the multi-view extension of High Efficiency Video Coding and the MPEG immersive video standard.
Research on substation intrusion event identification method based on MTF and CNN

This research provides a detection approach based on Markov transition field (MTF) and convolutional neural network (CNN) for substation perimeter intrusion event recognition. Because of the complexity and variety of external signals, which makes sensor detection more challenging, determining and analyzing the intrusion behavior of vibration signals induced by intrusion has become critical to improving the identification rate of intrusion-like occurrences. The obtained one-dimensional signals are mapped into two-dimensional pictures using MTF to reflect better the properties of intrusion-like signals, which can yield deeper signal details than the usual feature extraction approach. The CNN-vgg19 model’s excellent image feature identification capabilities are utilized to detect and categorize the acquired 2D feature pictures. The experimental results show that the average recognition rate of the six intrusion events is 96.7%, and the average recognition rate of the noise events is 99%, which can effectively identify the noise events and reduce the false-positive rate. The study’s findings are valuable for substation and peripheral security intrusion products.