Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION OF NEOSPORA CANINUM AND COXIELLA BURNETII ANTIBODIES IN MILK AND SERUM OF INFECTED DAIRY CATTLE

Coxiella burnetii and Neospora caninum are frequent infectious causes of decreased reproductive outcomes in cattle around the world. So our study aimed to assess the performance of IgG-ELISA for diagnosis of N. caninum and C. burnetii and compare the sensitivity of ELISA and IFA in detection IgG to C.burnetii phase II ,to achieve these, a total of 200 samples including milk and serum samples (100 for each) were collected from 100 dairy cattle suffering from infertility and abortion during the period from January to December 2017 in four governorates of upper Egypt, these samples were transferred to laboratory for serological examination. The results revealed that N.caninum antibodies were detected in 29% of serum samples and 10% of milk samples while C.burnetii antibodies were detected in 34% and 17% of serum and milk samples respectively. The IgGELISA of both N.caninum and C.burnetii were detected in the same serum and milk samples with percentage 14% and 4% respectively, amid dairy cattle in 4 governorates of upper Egypt. IgM-IFA indicated the acute infection of C.burnetii in 11% and 4% of serum and milk samples while chronic infection (IgG-IFA) was detected in 19% of serum samples and 8% of milk samples. The comparison between sensitivity of ELISA and IFA in detection of IGg for C.burnetii phase II showed that ELISA was more sensitive and excellent diagnostic method for C.burnetii infection.
Generating Datasets from 3D CAD Models for Object Detection

It is time-consuming to prepare training and testing data for object detection by using deep learning. In this paper, we proposed a method to generate the training data using CAD models instead of using real objects. To achieve this, we needed to convert the CAD models into point clouds. Then, the objects to be detected also needed to be converted into the point-cloud format. The key to obtaining the point clouds of the objects was to find their masks using depth images captured by a depth camera. After the mask of all the objects were available, we separated the mask into each object's mask. The separated mask was then used on the depth image to obtain the object's point cloud for object detection. Using the proposed method, it was possible to use 3D CAD data to quickly train a deep learning model to detect objects. Our preliminary results showed that the accuracy of object detection reached about 89%.
The Influence of Oxidative Stress on Neurological Outcomes in Spontaneous Intracerebral Hemorrhage

Spontaneous intracerebral hemorrhage (ICH) causes, besides the primary brain injury, a secondary brain injury (SBI), which is induced, amongst other things, by oxidative stress (OS) and inflammation, determining the patient’s outcome. This study aims to assess the impact of OS in plasma and cerebrospinal fluid (CSF) on clinical outcomes in patients with ICH. A total of 19 ICH (volume > 30 cc) patients and 29 control patients were included. From day one until seven, blood and CSF samples were obtained, and ICH volume was calculated. OS markers, like malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), glutathione-sulfhydryl (GSH), and the total antioxidant status (TAS) were measured. Clinical data on treatment and outcome were determined. Patients with mRS ≤ 4 showed significantly elevated SOD and GSH-Px levels in plasma compared to patients with poor CO (p = 0.004; p = 0.002). Initial increased TAS in plasma and increased MDA in CSF were linked to an unfavorable outcome after six months (p = 0.06, r = 0.45; p = 0.05, r = 0.44). A higher ICH volume was associated with a worse outcome at week six (p = 0.04, r = 0.47). OS plays a significant role in SBI. Larger ICHs, elevated MDA in CSF, and TAS in plasma were associated with a detrimental outcome, whereas higher plasma-SOD and -GSH-Px were associated with a favorable outcome.
Phenotypic and Functional Responses of Human Decidua Basalis Mesenchymal Stem/Stromal Cells to Lipopolysaccharide of Gram-Negative Bacteria

Introduction Human decidua basalis mesenchymal stem cells (DBMSCs) are potential therapeutics for the medication to cure inflammatory diseases, like atherosclerosis. The current study investigates the capacity of DBMSCs to stay alive and function in a harmful inflammatory environment induced by high levels of lipopolysaccharide (LPS). Methods DBMSCs were exposed to different levels of LPS, and their viability and functional responses (proliferation, adhesion, and migration) were examined. Furthermore, DBMSCs’ expression of 84 genes associated with their functional activities in the presence of LPS was investigated. Results Results indicated that LPS had no significant effect on DBMSCs’ adhesion, migration, and proliferation (24 h and 72 h) (p > 0.05). However, DBMSCs’ proliferation was significantly reduced at 10 µg/mL of LPS at 48 h (p < 0.05). In addition, inflammatory cytokines and receptors related to adhesion, proliferation, migration, and differentiation were significantly overexpressed when DBMSCs were treated with 10 µg/mL of LPS (p < 0.05). Conclusion These results indicated that DBMSCs maintained their functional activities (proliferation, adhesion, and migration) in the presence of LPS as there was no variation between the treated DBMSCs and the control group. This study will lay the foundation for future preclinical and clinical studies to confirm the appropriateness of DBMSCs as a potential medication to cure inflammatory diseases, like atherosclerosis.
Metode Improved Gaussian Sampling pada Algoritma Rapidly Exploring Random Tree*

Penelitian ini bertujuan merancang metode improved gaussian sampling dan mengimplementasikan pada algoritma Rapidly Exploring Random Tree* (RRT*). Perancangan yang dilakukan menggunakan bahasa pemograman Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). Improved gaussian sampling merupakan pengembangan dari metode gaussian sampling dengan menambahkan jumlah sampling. Jumlah sampling yang digunakan pada metode ini yaitu sejumlah 10 sampling. Untuk mengukur performansi dari metode sampling yang diusulkan, maka kami melakukan perbandingan performansi metode improved gaussian sampling, gaussian sampling dan random sampling. Berdasarkan hasil pengujian improved gaussian sampling didapatkan rata-rata nilai path cost dan waktu komputasi senilai: clutter sepanjang 8,89 dengan waktu 40,05; narrow sepanjang 12,58 dengan waktu 40,03 dan trap sepanjang 9,93 dengan waktu 40,04. Sedangkan hasil pengujian gaussian sampling didapatkan: clutter sepanjang 10 dengan waktu 40,09; narrow sepanjang 13,53 dengan waktu 40,03 dan trap sepanjang 10,95 dengan waktu 40,12. Hasil pengujian random sampling didapatkan: clutter sepanjang 10,86 dengan waktu 0,03; narrow sepanjang 14,82 dengan waktu 0,25 dan trap sepanjang 11,71 dengan waktu 0,21. Disimpulkan bahwa pada algoritma RRT* dengan menggunakan metode improved gaussian sampling menghasilkan performansi yang lebih baik dibandingkan algoritma RRT* yang menggunakan metode sampling lainnya. Hasil perbandingan pengukuran berdasarkan nilai sampling didapatkan rata-rata nilai path cost rata-rata 10,12 dengan jumlah sampling hanya 1 dan nilai path cost terpendek 8,9 dengan jumlah sampling 10. Berdasarkan pengukuran tersebut didapatkan semakin banyak jumlah sampling yang diberikan maka nilai path cost yang dihasilkan optimal.
A probabilistic seabed-ice keel interaction model

Abstract. In some coastal regions of the Arctic Ocean, as well as in shallow seasonally ice-covered seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. Recently, a grounding scheme representing this effect on sea ice dynamics was introduced and tested in a coupled ice-ocean model. This grounding scheme, based on a parameterization of ridged keel thickness linearly correlated to the mean thickness, improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea and along the coast of Alaska. Nevertheless, this parameterization is based solely on the mean properties of sea ice. Here, we extend the parameterization by taking into account subgridscale ice thickness distribution and bathymetry distributions, which are generally non-normal, and by computing the maximum seabed stress as a joint probability interaction between the ice and the seabed. The probabilistic approach shows a reasonably good agreement with observations and with the previously proposed grounding scheme while potentially offering more physical insights in the formation of landfast ice.
Second-hand house price index forecasting with neural networks

ABSTRACT The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neural networks for second-hand house price index forecasting from 10 major cities for March 2012–May 2020. Our purpose is to build simple and accurate neural networks to contribute to pure technical house price forecasting of the Chinese market. We explore various model settings across the algorithm, delay, hidden neuron, and data spitting ratio, and arrive at a rather simple neural network with three delays and eight hidden neurons, which leads to stable performance of 0.8% average relative root-mean-square error across the 10 cities for the training, validation, and testing phases. Results here can be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis.
Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.
On Pulsed Power Generation Using Hybrid Energy Storage

Pulsed power has been generated by using either capacitive energy storage (CES) or inductive energy storage (IES). In this article, the combination of CES and IES, which is called hybrid energy storage (HES), is studied. Both the capacitor and the inductor can be charged with initial energy and they can release their stored energy together either in series or in parallel with each other. Circuit equations have been solved and, based on the analytical solutions, the circuit behavior has been investigated. It is found that, compared with CES or IES, HES can largely improve the circuit operation flexibility and the output waveform variation. This conclusion has been confirmed by circuit simulation and experiment.
Model matching fault detection filter design for a linear discrete-time system with mixed uncertainties

The paper discusses the design of a robust fault detection system for linear discrete-time invariant uncertain systems. The intended system consists of norm-bounded uncertainty, stochastic uncertainty, and external unknown input. Due to the mixed uncertainties, H ∞ based model matching technique is used to develop a robust system that offers maximum sensitivity to faults and minimum sensitivity to all other unknown inputs. Reference residual model is designed using co-inner-outer factorization and the fault detection system is designed so that the error between reference residual and robust residual generated by fault detection filter (FDF) is minimized in H ∞ sense. The existing condition of FDF is exploited in terms of linear matrix inequalities. A numerical example and a benchmark three-tank system are simulated to illustrate the performance of the proposed fault detection system. Results confirm the effectiveness of the proposed approach by timely detecting the occurrence of the fault.
Revisiting the Wasson fractional crystallization model for IIIAB iron meteorites with implications for the interpretation of their Fe isotope ratios

The trapped melt fractional crystallization model for the IIIAB iron meteorites put forward by J. T. Wasson two decades prior is revisited. The basic precepts upon which the model was based remain true, and the model can be implemented using Ir and Au solid/liquid distribution coefficients that are broadly consistent with experimental data. For this reason, the difference between the Wasson model and some more recent trapped melt models lies mainly with inferences about the S concentrations of the core of the IIIAB iron meteorite parent body. For the Wasson model, S bulk concentrations of about 2 wt% are implied. For the more recent model, much greater concentrations of between about 12–15 wt% are indicated. The two different trapped melt models profoundly influence the interpretation of high δ57Fe values relative to chondrites in the IIIAB irons. The Wasson model suggests that there should be more variations in δ57Fe than are observed among these meteorites, while the more recent trapped melt model relies on the crystallization of FeS from the trapped melt to raise the δ57Fe of the latter, thus minimizing the variability. The interpretation of Fe isotope ratios in the IIIAB meteorites therefore depends critically on the S concentration of the parent body core.
A 3D-Printed Microfluidic Device for qPCR Detection of Macrolide-Resistant Mutations of Mycoplasma pneumoniae

Mycoplasma pneumonia (MP) is a common respiratory infection generally treated with macrolides, but resistance mutations against macrolides are often detected in mycoplasma pneumoniae in China. Rapid and accurate identification of mycoplasma pneumoniae and its mutant type is necessary for precise medication. This paper presents a 3D-printed microfluidic device to achieve this. By 3D printing, the stereoscopic structures such as microvalves, reservoirs, drainage tubes, and connectors were fabricated in one step. The device integrated commercial polymerase chain reaction (PCR) tubes as PCR chambers. The detection was a sample-to-answer procedure. First, the sample, a PCR mix, and mineral oil were respectively added to the reservoirs on the device. Next, the device automatically mixed the sample with the PCR mix and evenly dispensed the mixed solution and mineral oil into the PCR chambers, which were preloaded with the specified primers and probes. Subsequently, quantitative real-time PCR (qPCR) was carried out with the homemade instrument. Within 80 min, mycoplasma pneumoniae and its mutation type in the clinical samples were determined, which was verified by DNA sequencing. The easy-to-make and easy-to-use device provides a rapid and integrated detection approach for pathogens and antibiotic resistance mutations, which is urgently needed on the infection scene and in hospital emergency departments.
A simplified OFDR system with an integrated in-fiber auxiliary interferometer

The traditional optical frequency domain reflectometry usually requires a separate auxiliary interferometer to compensate for laser nonlinear tuning effect. In order to break through this limitation, this paper proposes a simplified optical frequency domain reflectometry system by integrating an in-fiber auxiliary interferometer before the sensing fiber. We experimentally characterized the laser nonlinear tuning effect, and then verified the feasibility of the proposed system in compensating the laser nonlinear tuning effect by both simulation and experiment. Finally, the capabilities of the distributed sensing of our proposed system was confirmed by the temperature and strain sensing experiments.
Clinical awareness of therapeutic drug monitoring among medical students—A descriptive cross-sectional study

“A doctor without effective clinical skills is a dodo knowledge. ” Today’s budding medical student is tomorrow’s prolific doctor and the pillar of the health care system. The objective was to determine the level of medical students’ knowledge on therapeutic drug monitoring (TDM) and create awareness. This study was an institutional-based, descriptive cross-sectional study among the medical students and interns using self-designed, experts validated TDM questionnaire. The questionnaire was divided into Section A—Importance of TDM, Section B—Questions on the core concept, and Section C—On analysis, interpretation, measuring, monitoring, and limitations of TDM. We scored the outcomes for the framed positive and negative questions. On analyzing the grading of scores of students and interns, 11.3% (44/390) scored excellently, 59.5% (232/390) showed a good response, and 29.2% (114/390) reciprocated with a poor grade. We performed an analysis of responses of all three categories related to “most impactful and significant clinical concepts of TDM” requiring in-depth knowledge. The knowledge in medical students, including interns' stands average. The only way to improve this knowledge deficit by conducting well-designed training programs, implementing the concept of TDM so the students are made aware at the undergraduate level and become productive clinically.
A Flexible Demand Response Dispatch Strategy Considering Multiple Response Modes and Wind Power Uncertainty

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.
"Mercury (II) Binding Activity of Vegetable and Fruit Juices: Identifying Potential Detoxifying Juices for the Citizens of Portovelo-Zaruma, Ecuador"

Artisanal gold mining, especially in developing countries, is defined by the use of rudimentary techniques to extract ore and process the gold, which mainly involves amalgamation. The use of mercury in gold processing, especially when involving the whole ore, as opposed to simply amalgamating the concentrates, is highly inefficient, culminating in losses of up to 50% of the initial mercury introduced [1,2]. Although the largest environmental problem in gold mining is the release of mercury and other heavy metals in tailings to fluvial drainages, the most serious health problem is the burning of gold amalgams. Although the use of mercury is prohibited by international accords like the Minamata Convention (Mercury Convention, 2013), artisanal miners in developing countries like Ecuador routinely use open pans to burn the amalgams without any protective safety equipment or mercury capture systems, exposing themselves, family members and the rest of the community to the noxious gases. It has been estimated that inhaled mercury vapour gets absorbed in the lungs at a rate of approximately 80%, where it is rapidly oxidized to the mercuric form (Hg2+) in the red blood cells and distributed to all of the tissues and organs, including the brain [3]. The objective of this study was to evaluate selected vegetable and fruit juices for their mercury (II) binding capabilities and their potential use as Hg detoxifying agents.
Temperature Dependent Relaxation Behavior of Ag-Sintered Copper Joints

In the field of power electronics device function leads to high operating temperatures. High stresses and strains are induced as a result of mismatch in Coefficient of Thermal Expansion (CTE) leading to plastic deformation in constrained joints. The thermomechanical stresses stimulate mechanisms such as dislocation glide or creep. Especially when the homologous temperature increases, time dependent creep failure becomes a dominant issue in the joint reliability. This work is devoted to the development of a temperature dependent material model for pressure assisted silver sintered joints. This model is validated on the basis of stress relaxation experiments aside from measurements of shear strength and mechanical fatigue. For this purpose, Ag-sintered copper joints were manufactured (0-hour) and heat treated at 250 °C for 250 hours in air and in protective atmosphere. Investigations of microstructure were made by scanning electron microscopy (SEM). Shear and stress relaxation tests were conducted in a tensile machine with thermal chamber at temperatures of 25 °C, 130 °C and 200 °C. For lifetime estimation of the samples Weibull probability plots for low cycle fatigue were determined. Further, Norton power law was employed to determine material parameters such as stress exponent n and creep activation energy Q. In conclusion, a unified model of plasticity and creep was established.