Miracle-dz/UniFAR-FTUs huggingface dataset:
A Markerless Pose Estimator Applicable to Limbless Animals

The analysis of kinematics, locomotion, and spatial tasks relies on the accurate detection of animal positions and pose. Pose and position can be assessed with video analysis programs, the “trackers. ” Most available trackers represent animals as single points in space (no pose information available) or use markers to build a skeletal representation of pose. Markers are either physical objects attached to the body (white balls, stickers, or paint) or they are defined in silico using recognizable body structures (e.g., joints, limbs, color patterns). Physical markers often cannot be used if the animals are small, lack prominent body structures on which the markers can be placed, or live in environments such as aquatic ones that might detach the marker. Here, we introduce a marker-free pose-estimator (LACE Limbless Animal traCkEr) that builds the pose of the animal de novo from its contour. LACE detects the contour of the animal and derives the body mid-line, building a pseudo-skeleton by defining vertices and edges. By applying LACE to analyse the pose of larval Drosophila melanogaster and adult zebrafish, we illustrate that LACE allows to quantify, for example, genetic alterations of peristaltic movements and gender-specific locomotion patterns that are associated with different body shapes. As illustrated by these examples, LACE provides a versatile method for assessing position, pose and movement patterns, even in animals without limbs.
Direct limits of regular Lie groups

Let G be a regular Lie group which is a directed union of regular Lie groups Gi (all modelled on possibly infinite‐dimensional, locally convex spaces). We show that G=lim⟶Gi as a regular Lie group if G admits a so‐called direct limit chart. Notably, this allows the regular Lie group Diffc(M) of compactly supported diffeomorphisms to be interpreted as a direct limit of the regular Lie groups DiffK(M) of diffeomorphisms supported in compact sets K⊆M , even if the finite‐dimensional smooth manifold M is merely paracompact (but not necessarily σ‐compact), which is new. Similar results are obtained for the test function groups Cck(M,F) with values in a Lie group F.
Forecast research on trucks wheel tread wear based on grey changeable weight combination model

In order to discuss the application of changeable weight combination forecasting model in wheel tread wear forecasting of freight line,the changeable weight combination forecasting method based on new information GM(1,1) model,discrete GM(1,1) model and exponential smoothing model was utilized to analyze wheel tread wear volumes along with the change of running distances. Subsequently,the results obtained in this paper were compared with the prediction ones with single models and the fixed weight combination prediction ones. The results show that combination forecasting method is more reasonable than purely single forecasting method and the solutions are more reliable. The changeable weight combination forecasting mode is better than fixed weight combination model and has a higher prediction accuracy. Therefore,an effective forecast method is giving for accurately predicting the wear volumes and could be a feasible solution to freight line vehicle wheel maintenance and decision.
EFFECTS OF LEAD ACCUMULATION ON PHYSIOLOGICAL PARAMETERS AND NUTRITIONAL ELEMENTS IN SAFFLOWER (Carthamus tinctorius L.) SEEDLINGS

Lead, one of the toxic metals with no biological function, is a widely dispersed heavy metal found in many forms in natural resources on earth, and anthropogenic activities continue to increase the concentration of Pb in the environment. In this study, it was aimed to determine the effects of different Pb concentrations (0, 5, 25, and 50 mg/L) on physiological parameters and mineral contents of safflower seedlings grown hydroponically under controlled conditions. After two weeks of Pb application, Pb and macro- (K, Ca, Mg) and micro- (Zn, Fe, Cu) element contents of the seedling parts, as well as photosynthetic pigments, total carbohydrate, total phenolic compound, and malondialdehyde (MDA) contents were determined. There was a dose-dependent increase in the Pb contents of the seedling organs. The highest Pb content was found in the roots and the lowest in the leaves. Lead toxicity has been found to impair nutrient uptake and transport, causing nutrient imbalance. In addition, it was shown that Pb toxicity from the changes in photosynthetic pigment, carbohydrate, and phenolic compound contents caused physiological changes in the seedling tissues. Besides, it clearly revealed that the increase in the amount of MDA triggers oxidative stress, which is the secondary stress caused by Pb toxicity.
Gyro-chirality effect of bianisotropic substrate on the operational of rectangular microstrip patch antenna

In this paper, the gyrotropic bi-anisotropy of the chiral medium in substrate constitutive parameters (ξc and ηc )o f a rectangular microstrip patch antenna is introduced in order to observe its effects on the complex resonant frequency, half-power bandwidth and input impedance. Numerical calculations and analysis based on the dominant mode are carried out to show that the latter is directly related to the former. This paper is based on the Moment Method as full-wave spectral domain approach using sinusoidal basis functions. Two new results, namely the appearance of the difference (ξc − ηc )a nd sum (ξc + ηc )o f the two magneto-electric elements are obtained in the electric transverse components and Green tensor expressions, respectively. These new results can be considered as a generalisation form of the previously published work.
Anti-ulcer activity of Pistacia Atlantica and Punica Granatum hydroalcoholic extract in comparison with omeprazole in male wister rats

The current study was conducted to investigate the protective effects of Pistacia Atlantica (mastic gum) hydro-alcoholic extract and Punica Granatum (pomegranate) peel hydro-alcoholic extract treatment against alcohol-induced peptic ulcer in Wister rat and to compare this protective effects between each other and with omeprazole as the most common drug in treatment of peptic ulcer. Thirty five male Wister rats were assigned into five groups (seven rats each group). C- group rats were received normal saline for one week. C+ group rats were received normal saline for one week before receiving one dose of absolute ethanol. OME group rats were received normal saline for one week and one dose of omeprazole one hour before receiving one dose of absolute ethanol. MG group rats were received Pistacia Atlantica hydro-alcoholic extract for one week before receiving one dose of absolute ethanol. PG group rats were received Punica Granatum hydro-alcoholic extract daily for one week before receiving one dose of absolute ethanol. The gross and histopathology lesion of gastric ulcer were assessed. Ulcer index results showed a significant (p<0.05) differences between C+ group and other groups, However, the ulcer index of PG group was significant (p<0.05) compared with OME group.
DFT-Net: Deep Feature Transformation Based Network for Object Categorization and Part Segmentation in 3-Dimensional Point Clouds

Unlike 2-dimensional (2D) images, direct 3-dimensional (3D) point cloud processing using deep neural network architectures is challenging, mainly due to the lack of explicit neighbor relationships. Many researchers attempt to remedy this by performing an additional voxelization preprocessing step. However, this adds additional computational overhead and introduces quantization error issues, limiting an accurate estimate of the underlying structure of objects that appear in the scene. To this end, in this article, we propose a deep network that can directly consume raw unstructured point clouds to perform object classification and part segmentation. In particular, a Deep Feature Transformation Network (DFT-Net) has been proposed, consisting of a cascading combination of edge convolutions and a feature transformation layer that captures the local geometric features by preserving neighborhood relationships among the points. The proposed network builds a graph in which the edges are dynamically and independently calculated on each layer. To achieve object classification and part segmentation, we ensure point order invariance while conducting network training simultaneously—the evaluation of the proposed network has been carried out on two standard benchmark datasets for object classification and part segmentation. The results were comparable to or better than existing state-of-the-art methodologies. The overall score obtained using the proposed DFT-Net is significantly improved compared to the state-of-the-art methods with the ModelNet40 dataset for object categorization.
Predicting Gene-Drug-Disease Interactions by integrating Heterogeneous Biological Data Through a Network Model

Abstract Prediction of gene-drug-disease interactions have talented new insights in biology. Discovering unknown interactions will provide new therapeutic approaches to explore gene expressions. Recent improvements in machine learning techniques have gotten considerable interest due to higher efficiency, accurate results, and their lower cost. However, most of the studies were ignoring relevant associations, by representing only drug-disease interactions on a network while public available data offers a large variety of interactions. Additionally, some computational techniques used in this domain are faced with new challenges, related to the organization of heterogeneous data which suffer from a high imbalance rate since there are extensively more non-interacting gene-drug-disease triplets than interacting ones. In this paper we present integration of heterogeneous biological data about genes, drugs, and diseases to build a model, and building a new graph representation relating genedrug-disease interactions. Using extreme gradient boosting (XGBoost) algorithm, we have been able to extract a list of valid interactions about gene-drug-disease triplets, and a list of gene-drug pairs related to lung cancer. Keywords: Biological heterogeneous data, Data integration, Gene-DrugDisease interactions, Machine learning.
Forward error correction in 5G heterogeneous network

In this research, the feasibility of developing a complete polar FEC chain of 5 generation cellular mobile communication standard in software. Specifically, on general purpose processors. Paperwork attempts to achieve stringent latency requirements through software, algorithmic and platform specific optimizations. Many algorithms in FEC chain are optimized for hardware implementations. Direct implementation of these algorithms in software results in poor performance. To obtain best performance in terms of latency on general purpose processors, these algorithms are modified or reformulated to suit processor architecture and software implementation. Initially both encoding and decoding FEC chains are implemented naively without any optimization. Code profiling is performed on this naive implementation to identify the significant latency contributors. The research split algorithms of significant latency contributing components into primitive operations. These primitive operations are optimized either with software optimizations or mapped to specialized functional units of a general-purpose processor to achieve best performance using CRC calculation in 5G cellular networks. Optimizations reduced the worst-case latency of the encoding FEC chain from 158μs which is more than 10x reduction in latency with communication rate.
Robotic Visual and Electrical-guided Whole-Cell Patch Clamp

Whole-cell patch clamp recording is a powerful technique for studying electrophysiological activities of single cells. Due to the lack of position information of tilt-mounted electrode pipettes, cells and focal planes, manual whole-cell patch clamp is time-consuming and inefficient. In this paper, automated electrode pipette detections based on the image corner detection and motion history images (MHI) were realized respectively under the field of different objectives lens for a fast and precise electrode pipette localization, and the target cells were positioned by mouse clicking. Based on the above work, a novel robotic visual and electrical-guided whole-cell patch clamp process was established to accelerate cell operation time by reducing the time consumption before cell sealing. The results on neurons in the V1 pyramidal cell layer in mouse brain slices show that this method doubled the operation speed before cell sealing (3 min 35 s Vs 8 min), reduced the whole-cell operation time by 44% and without reducing the success rate (60% Vs 60%). Our method is expected to improve the level of automation and operation speed of patch clamp in the future.
Application of Patient-Specific Instrumentation in a Dog Model with Antebrachial Growth Deformity Using a 3-D Phantom Bone Model

One of the most frequent bone deformities in dogs is antebrachial growth deformity (AGD), which results from malunion of the distal growth plates. The objective of the present study was to re-align the limbs, which can correct the length mismatch and reset the coherence of the joint with the aid of a 3-D phantom model for surgical preplanning. A 14-month-old, intact female Golden Retriever with an angular deformity of the left radius and ulna was selected for the study. The diagnosis was confirmed by orthogonal radiographs. Moreover, computed tomography (CT) scans revealed a multiplane deformity with valgus, procurator, and external rotation of the left radius. The pre-surgical planning started with the quantification of the angular deformity, followed by a simulated virtual osteotomy, and concluded with an in vitro rehearsal surgery on 3-D printed phantom bone models. In the operating room, prefabricated patient-specific instrumentation (PSI) was attached at the planned site of the radial bone surface for a precise closing wedge osteotomy. Then two locking plates were fixed routinely. Post-operative radiographs showed accurate correction of the deformity as we had planned. At 12 weeks post-operatively, the follow-up surveys revealed improved gait, weight-bearing, and progression of bone healing. Our PSI design, based on novel surgical planning, was steady yet straightforward during the osteotomy. The osteotomy was performed without difficulty since the PSI that pre-determined the sites and angles let the surgeon perform the antebrachial malformation surgery. This method of operation reduces stress on the operator and helps to improve accuracy, repeatability, and surgery time.
The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG

The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.
Morphogen Signals Shaping the Gastric Glands in Health and Disease

The adult gastric mucosa is characterised by deep invaginations of the epithelium called glands. These tissue architectural elements are maintained with the contribution of morphogen signals. Morphogens are expressed in specific areas of the tissue, and their diffusion generates gradients in the microenvironment. Cells at different positions in the gland sense a specific combination of signals that instruct them to differentiate, proliferate, regenerate, or migrate. Differentiated cells perform specific functions involved in digestion, such as the production of protective mucus and the secretion of digestive enzymes or gastric acid. Biopsies from gastric precancerous conditions usually display tissue aberrations and change the shape of the glands. Alteration of the morphogen signalling microenvironment is likely to underlie those conditions. Furthermore, genes involved in morphogen signalling pathways are found to be frequently mutated in gastric cancer. We summarise the most recent findings regarding alterations of morphogen signalling during gastric carcinogenesis, and we highlight the new stem cell technologies that are improving our understanding of the regulation of human tissue shape.
Integrating field-based tests into weekly soccer training sessions: a comparison of physiological demands of three progressive multistage fitness tests and regular training demands

Purpose. This study compares the physiological demands of 30-15 Intermittent Fitness Test (30-15IFT), Yo-Yo Intermittent Recovery Test level 1 (YYIRT), and VAMEVAL test as related to training and match demands among youth soccer players. Methods. Overall, 12 under-19 male soccer players (18.21 ± 0.34 years old) were monitored over 3 consecutive weeks for heart rate (HR) responses during training sessions and matches. During this period, they were assessed with 30-15IFT, YYIRT, and VAMEVAL. The measures were taken on the same day of the week to ensure replicable conditions between the tests. Results. The Friedman test revealed significant differences in the average HR ( p = 0.006), time in HR zone 3 ( p < 0.001), and time in HR zone 4 ( p = 0.039) between the tests. Considering the representativeness of the tests in comparison with training sessions and matches, both 30-15IFT and VAMEVAL corresponded to 48% and 46% of the training session load 3 days before the match day (936 ± 447 s) and on the match day (831 ± 533 s), respectively. Conclusions. YYIRT imposed greater time exposure to high HR intensities (in the zones of 80–100% of HR maximum) than VAMEVAL and 30-15IFT. Moreover, the multistage fitness tests corresponded to slightly more than half of the time in which a player was exposed to 80% and 100% of HR maximum during a regular training session and match day. These findings may assist coaches in understanding how to incorporate intermittent fitness tests within training in order to accurately replicate HR responses of match conditions.
Application of Multi-agent System in Simulation of Urban Development:A Review

The multi-agent system(MAS) is a loose network composed of mass Agents,each agent communicates and cooperates with each other based on its own properties and behavior rules.MAS studies the macro regularity of the whole system through simulating and observing a large number of agents’micro behaviors.The evolution of complex system mechanism has developed to a more reasonable and more accurate extent,therefore,MAS has a large application prospect in urban development simulation.This paper introduced the basic framework of multi-agent system,reviewed present research progress of the application of MAS theory,and analyze the five key topics of MAS in urban development simulation: acquisition of micro individual data,selection of agents,extraction and set of behavior rules,expression of interactions between agents,extraction and import of environment variables.Research shows that the prediction and evaluation of urban development policy is the key breakthrough in the application of MAS.
Multimode delta-E effect magnetic field sensors with adapted electrodes

We present an analytical and experimental study on low-noise piezoelectric thin film resonators that utilize the delta-E effect of a magnetostrictive layer to measure magnetic fields at low frequencies. Calculations from a physical model of the electromechanical resonator enable electrode designs to efficiently operate in the first and second transversal bending modes. As predicted by our calculations, the adapted electrode design improves the sensitivity by a factor of 6 and reduces the dynamic range of the sensor output by 16 dB, which significantly eases the requirements on readout electronics. Magnetic measurements show a bandwidth of 100 Hz at a noise level of about 100 pTHz−0.5.
A review on metal-doped chalcogenide films and their effect on various optoelectronic properties for different applications

Chalcogenide thin films have been investigated and explored in the last several decades to widen their use in optical, electronic, and optoelectronic device sectors. The phenomenon corresponding to different induced stimuli effects, doping foreign elements is the most productive and efficient way to improve their structural ability, optical characteristics, and electronic approaches. Based on that, metal doping has an enormous impact on the aspects and understanding of the mechanism inside the matrix. This review is mainly based on metal-doped chalcogenide thin films, their effect on various properties of the host materials, and several applications based on that. Thin films doped primarily with bismuth (Bi), antimony (Sb), silver (Ag), tin (Sn), and copper (Cu) were analyzed and discussed. Progress in understanding their structure, bonding, and properties within the matrix was also discussed. This paper also describes the importance and developments of these metal-doped thin films, their physicochemical aspects, and their applications in optoelectronic devices. Different potential applications of these metal-doped chalcogenide thin films in manufacturing technology-based optoelectronic devices, namely sensors, waveguides, switching devices, batteries, optical memories, etc., are also highlighted.
Study of Economic and Environmental Impact of Photovoltaic Installation Located in Morocco

Many countries have committed to reducing their emissions of gases responsible for the increase in the greenhouse effect. This decision could decrease the consumption of fossil fuels in favour of renewable energies, which have the advantage of being economically profitable and ecologically clean since their use does not generate harmful waste or polluting gas emissions. As a developing country, Morocco has adopted an energy policy focused on developing renewable energy through the launch of the Moroccan Solar Project, which aims to reduce greenhouse gas emissions and dependence on fossil fuels.The purpose of this article is therefore to study the economic and environmental impact of a photovoltaic installation in a shopping centre in Sale city, Morocco, while analyzing its investment costs and studying its profitability in time. This study shows that the project can generate sufficient profits to recover the investment capital with a payback period of 5 years, a meaningful reduction of CO2, and a huge annual saving from the production of solar electricity.
Scheduling Optimization of the Fermentation Process with Resource-Constrained

The fermentation process with air supply constraint,a kind of resource constraints,was studied.An optimal scheduling model which adopted total production time as objective function was established for this process.A heuristic separated-constricted strategy to calculate the total production time of certain production sequence was proposed by using the line-up competition algorithm to determine the optimal production sequence.This method has been applied to the production scheduling optimization of a bio-pesticide plant,which can meet the air-demand and minimize the total production time.