Miracle-dz/UniFAR-FTUs huggingface dataset:
Osteoarthritis Progression after ACL Reconstruction Was Significantly Higher Than That of the Healthy Contralateral Knees: Long-Term Follow Up Study of Mean 16.4 Years

Background: This study aimed to assess long-term progression of osteoarthritis (OA) after isolated anterior cruciate ligament (ACL) reconstruction in athletes compared to the healthy contralateral side. Methods: The study included 15 patients and 30 knees with a mean age of 40 years (range, 35–46) years, none of whom had had revision surgery or an injury to the contralateral side. The mean follow-up period was 16.4 years (range, 13–22). Clinical and radiographic assessment included the Tegner activity scale (TAS), International Knee Documentation Committee (IKDC) score, Knee injury and Osteoarthritis Outcome Score (KOOS), and Kellgren and Lawrence (KL) grade. The long-term results of the injured knees were compared with the status of the healthy contralateral side and compared with previously published mid-term results of the same cohort of patients. Results: Patients generally remained clinically asymptomatic or mildly symptomatic at final follow-up, which is reflected by a KOOS pain score of 33 points (maximum 36 points) and an IKDC total subjective score of 87% (maximum 100%). There was a significant difference between mid-term and final follow-up in terms of the function score of the IKDC subjective questionnaire (p = 0.031), compartment findings and donor site morbidity of the IKDC functional examination (both p = 0.034), and the total KOOS score (p = 0.047). The KL score indicated significant progression of OA from mid-term to final follow-up in the injured knees (p = 0.004) and healthy contralateral knees (p = 0.014). Mean OA grades of the injured knees were significantly higher compared with the healthy contralateral side (p = 0.006) at final follow-up, and two patients showed moderate to severe signs of OA in the injured knee. Conclusions: Although most patients remained clinically asymptomatic or mildly symptomatic, long-term progression of OA after isolated ACL reconstruction in athletes was significantly higher compared with the healthy contralateral knee.
Effect of EDMed rake face grooves on the chip breaking capability of twist drills during deep hole drilling of Al 6061 aluminum alloy

ABSTRACT Deep hole drilling of ductile materials like Al6061 with conventional twist drills is challenging due to premature tool failure caused by chip clogging. Chip breakers, in the form of grooves of varying angles (0°, 5° & 10°) and depth (0.4 & 0.5 mm), created using EDM on the rake face, are proposed to overcome chip clogging. Tool wear, hole roughness, and diameter deviation were measured and analyzed along with chip morphology study for the six grooved tools and compared with plain drill. The grooved tools were successful in breaking the chips which allows for easy evacuation without any clogging. Grooved drills produced desired spiral chips compared to string chips produced by plain drill thus resulting in lesser tool wear rate. While the grooved tools showed stable but slightly higher roughness values, the conventional tools exhibited a linear increasing trend as the drilling progressed. The machined hole surfaces had defects such as feed marks, pitting, grooves, and burnished surfaces. The grooved tools produced comparatively more diameter deviation probably because the well broken chips entered into the drill land/hole surface space. From the results it is clear that groove angle and depth have significant effect on the drill machinability characteristics.
A Support Vector Machine-based Approach to Chance Constrained Problems using Huge Data Sets

In this paper, a new approach to solve data-driven Chance Constrained Problems (CCPs) is proposed. First of all, a large data set is used to formulate CCP because such a large data set is available nowadays due to advanced information technologies. However, since the size of the data set is too large, a Support Vector Machine (SVM) is used to estimate the probability of meeting all constraints of CCP for the large data set. In order to generate a training data set for the SVM, a sampling technique called Space Stratiﬁed Sampling (SSS) is proposed in this paper. According to the ﬁrst principal component obtained by Principal Component Analysis (PCA), SSS divides the large data into several strata and selects some data from each stratum. The SVM trained by SSS is called S SVM. In order to solve CCPs based on large data sets eﬃciently, a new optimization method called Adaptive Diﬀerential Evolution with Pruning technique (ADEP) is also proposed.
Experimental Study of Onset of Nucleate Boiling in Vertical Rectangular Channels with Different Flow Path Heights

A study on ONB (onset of nucleate boiling) in two vertical rectangular channels are experimentally conducted in a range of mass flux varying from 100 to 300 kg/(m2·s), inlet water temperature from 70 to 100°C, heat flux from 10 to 70 kW/m2, and local pressure of 0.145 MPa. The cross-section sizes are 1.8 mm ∗ 60 mm and 2.8 mm ∗ 60 mm, respectively. Three boiling incipience judgment methods have been used to locate ONB sites and found that Δ T ONB (the wall superheat at ONB site) increases with the decrease of inlet temperature and increases as mass flux increases. The results also indicate that although the bubble size and behaviors in the narrow channel are different from that in the nonnarrow channel at the ONB site, the heat transfer has not been influenced evidently. In addition, Δ T ONB in both channels can be predicted by the correlation proposed by Thom within the error range of ±30%.
Repurposing Based Identification of Novel Inhibitors against MmpS5-MmpL5 Efflux Pump of Mycobacterium smegmatis: A Combined In Silico and In Vitro Study

In the current era of a pandemic, infections of COVID-19 and Tuberculosis (TB) enhance the detrimental effects of both diseases in suffering individuals. The resistance mechanisms evolving in Mycobacterium tuberculosis are limiting the efficiency of current therapeutic measures and pressurizing the stressed medical infrastructures. The bacterial efflux pumps enable the development of resistance against recently approved drugs such as bedaquiline and clofazimine. Consequently, the MmpS5-MmpL5 protein system was selected because of its role in efflux pumping of anti-TB drugs. The MmpS5-MmpL5 systems of Mycobacterium smegmatis were modelled and the virtual screening was performed using an ASINEX library of 5968 anti-bacterial compounds. The inhibitors with the highest binding affinities and QSAR based highest predicted inhibitory concentration were selected. The MmpS5-MmpL5 associated systems with BDE_26593610 and BDD_27860195 showed highest inhibitory parameters. These were subjected to 100 ns Molecular Dynamics simulations and provided the validation regarding the interaction studies. The in vitro studies demonstrated that the BDE_26593610 and BDD_27860195 can be considered as active inhibitors for M. smegmatis MmpS5-MmpL5. The outcomes of this study can be utilized in other experimentation aimed at drug design and discovery against the drug resistance strains of M. tuberculosis.
Experimental and Theoretical Study of an Autowave Process in a Magnetic Fluid

Magnetic fluid (MF) is a colloidal system consisting of ferromagnetic particles (magnetite) with a diameter of ~10 nm suspended in a dispersion medium of a carrier fluid (for example, kerosene). A distinctive feature of magnetic fluid is the fact that when an electric field is applied to it using two electrodes, thin layers consisting of close-packed particles of the dispersed phase are formed in the regions near the surface of both electrodes. These layers significantly affect the macroscopic properties of the colloidal system. In this work, the interpretation of the near-electrode layer is for the first time given as a new type of liquid membrane, in which the particles of the dispersed phase become charged with the opposite sign. On the basis of experimental studies, we propose a physicochemical mechanism of the autowave process in a cell with a magnetic fluid. It is based on the idea of oppositely recharging colloidal particles of magnetite in a liquid membrane. A mathematical model of an autowave process, which is described by a system of coupled partial differential equations of Nernst–Planck–Poisson and Navier–Stokes with appropriate boundary conditions, is proposed for the first time. One-dimensional, two-dimensional, and three-dimensional versions of the model are considered. The dependence of the frequency of concentration fluctuations on the stationary voltage between the electrodes was obtained, and the time of formation of a liquid membrane was estimated. Qualitative agreement between theoretical and experimental results has been established.
Evaluating the Dynamics of Knowledge-Based Network ThroughSimulation: The Case of Canadian Nanotechnology Industry

Collaboration is a major factor in the knowledge and innovation creation in emerging science-driven industries, where the technology is rapidly changing and constantly evolving, such as nanotechnology. The scientific collaborations among individuals and organizations form knowledge co-creation network within which information is shared, innovative ideas are exchanged and new knowledge is generated. Although various simulation attempts have been carried out recently to analyze the performance of such networks at the firm level, the individual level has not been much explored in the literature yet. The objective of this thesis is to investigate the role of individual scientists and their collaborations in enhancing the knowledge flows, and consequently the scientific production within the Canadian nanotechnology scientists. The methodology involves two main phases. First, in order to understand the collaborative behavior of scientists in the real world, the data on all the nanotechnology journal publications in Canada was extracted from the SCOPUS database and the scientists' research performance and partnership history was analyzed using social network analysis. Moreover, the predominant properties that make a scientist sufficiently attractive to be selected as a research partner were determined using data mining and through a questionnaire sent directly to the researchers selected from our database. In the second phase, an agent-based model using Netlogo has been developed to simulate the knowledge-based network where several factors regarding the ratio, existence and absence of various categories of scientists could be controlled. It was found that scientists in centralized positions in such network have a considerable positive impact on the knowledge flows, while loyalty and cliquishness negatively affected the knowledge transmission. Star scientists appear to play a substitutive role in the network as most famous and trustable partners to be selected when usual collaborators are scarce or missing. Besides, the changes in the performance of some categories in case of the absence of others have been also observed. The major contribution of this work stems from the fact that the developed simulation model is the first one, which is fully based on the real data and on the observed behavior of the scientists in knowledge-based network.
Secreted Neutrophil Gelatinase-Associated Lipocalin Shows Stronger Ability to Inhibit Cyst Enlargement of ADPKD Cells Compared with Nonsecreted Form

Polycystic kidney disease (PKD) is one of the most common inherited diseases and is characterized by the development of fluid-filled cysts along multiple segments of the nephron. Autosomal dominant polycystic kidney disease (ADPKD) is the most common form of PKD, which is caused by mutations in either PKD1 or PKD2 genes that encode polycystin-1 (PC1) and polycystin-2 (PC2), respectively. As ADPKD progresses, cysts enlarge and disrupt normal kidney architecture, eventually leading to kidney failure. Our previous study showed that overexpression of exogenous kidney-specific neutrophil gelatinase-associated lipocalin (NGAL) reduced cyst progression and prolonged the lifespan of ADPKD mice (Pkd1L3/L3, 2L3 for short). In this study, we attempted to explore the underlying mechanism of reduced cyst progression in the presence of NGAL using immortalized 2L3 cells. The results of MTT and BrdU incorporation assays showed that recombinant mouse NGAL (mNGAL) protein significantly decreased the viability and proliferation of 2L3 cells. Flow cytometry and western blot analyses showed that mNGAL inhibited activation of the ERK and AKT pathways and induced apoptosis and autophagy in 2L3 cells. In addition, a 3D cell culture platform was established to identify cyst progression in 2L3 cells and showed that mNGAL significantly inhibited cyst enlargement in 2L3 cells. Overexpression of secreted mNGAL (pN + LS) and nonsecreted mNGAL (pN − LS) repressed cell proliferation and cyst enlargement in 2L3 cells and had effects on markers involved in proliferation, apoptosis, and autophagy. However, secreted mNGAL had a more pronounced and consistent effect than that of nonsecreted form. These results reveal that secreted mNGAL has stronger ability to inhibit cyst enlargement of ADPKD cells than that of nonsecreted form. These findings could help to identify strategies for the future clinical treatment of ADPKD.
A Non-local Enhanced Network for Image Restoration

Non-local modules have been widely studied in image restoration (IR) tasks since they can learn long-range dependencies to enhance local features. However, most existing non-local modules still focus on extracting long-range dependencies within a single image or feature map. On the other hand, most IR methods simply employ a single type of non-local module in the network. A combination of various types of non-local modules to enhance local features can be more effective. In this paper, we propose a batch-wise non-local module to explore richer non-local dependencies within images. Furthermore, we combine various non-local extractors (different attention modules) with the proposed batch-wise non-local module as the Enhanced Batch-wise Non-local Attentive module (EBNA). Besides exploring richer non-local information, we build the Non-local and Local Information extracting Block (NLIB), in which we combine the EBNA with DEformable-Convolution Block (DECB) to utilize richer non-local and adaptive local information. Finally, We embed the NLIB within a U-net-like structure and build the Non-local Enhanced Network (NLENet). Extensive experiments on synthetic image denoising, real image denoising, JPEG artifacts removal, and real image super resolution tasks demonstrate that our proposed network achieves state-of-the-art performance on several IR benchmark datasets.
Supplementary material to "Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4"

Abstract. The modeling of ship emissions in port areas involves several uncertainties and approximations. In Eulerian grid models, the vertical distribution of emissions plays a decisive role for the ground-level pollutant concentration. In this study, model results of a microscale model, which takes thermal plume rise and turbulence into account, are derived for the parameterization of vertical ship exhaust plume distributions. This is done considering various meteorological and ship-technical conditions. The influence of three different approximated parameterizations (Gaussian distribution, single cell emission and exponential Gaussian distribution) on the ground-level concentration are then evaluated in a city-scale model. Choosing a Gaussian distribution is particularly suitable for high wind speeds (> 5 m s−1) and a stable atmosphere, while at low wind speeds or unstable atmospheric conditions the plume rise can be more closely approximated by an exponential Gaussian distribution. While Gaussian and exponential Gaussian distributions lead to ground-level concentration maxima close to the source, with single cell emission assumptions the maxima ground-level concentration occurs at a distance of about 1500 m from the source. Particularly high-resolution city-scale studies should therefore consider ship emissions with a suitable Gaussian or exponential Gaussian distribution. From a distance of around 4 km, the selected initial distribution no longer shows significant differences for the pollutant concentration near the ground, therefore model studies with lower resolution can reasonably approximate ship plumes with a single cell emission.
An Efficient Robot Precision Assembly Skill Learning Framework Based on Several Demonstrations

This paper proposes an efficient robot assembly skill learning framework based on only a few demonstrations. The assembly skill learning process consists of two phases, e.g., the pre-training phase and the self-learning phase. In pre-training phase, the assembly networks are initialized from demonstration data. A novel data augmentation model based on state transition model is designed, which only requires several human demonstrations to learn the parameters of the model and reduces the cost of demonstration data collection in pre-training. In self-learning phase, the pre-trained assembly networks are further optimized by a hybrid exploration strategy in assembly environment, which explores both parameter space and action space to increase exploration efficiency. On par with the learning framework, a fuzzy reward function balancing the efficiency and compliance of assembly is elaborately designed to evaluate action’s performance in assembly skill self-learning process. Series of physical experiments were well conducted on a sophisticated assembly platform to verify the effectiveness of the proposed efficient robot assembly skill learning framework. Experimental results demonstrate that the learning efficiency of the proposed framework is at least three times as efficient as the state-of-art methods, while the skill performance outperforms the state-of-art by more than 50%. Note to Practitioners—The motivation of this paper is to develop a skill learning framework to achieve efficient assembly skill learning for robot in real-world system. The proposed framework is only based on several demonstrations, which shows high practicability for robot skill learning. The self-learning method is dexterously used to optimize the learned assembly skill from demonstrations since the limited space of demonstrations. Besides, the hybrid exploration strategy and fuzzy reward strategy are well designed to improve the behavior of self-learning. The proposed framework can improve the efficiency and performance of robot assembly learning skill. It is extremely important for robot working in practical applications.
Effect of Traditional Ritual ‘Garbhotsav Sanskar’ on Infant’s Mental Health in Karnal District Haryana: A Case Study

Garbhotsav Sanskar is an Indian traditional ritual performed by pregnant women for an upcoming child. Garbhotsav Sanskar is performed throughout India by All World Gayatri Pariwar (AWGP) under Aao Gadhe Sanskarwan Pidhi (Building a cultured generation) movement. The present study aimed to evaluate the strength and difficulties in the children whose mother performed Garbhotsav Sanskar and followed the holistic lifestyle which compare with the children whose mother did not perform the same. The study aimed to the psychological effect of the ritual and given instructions, a comparative study was done on the children of district Karnal in Haryana, India where 20 children of age 2-4 years (experimental group) were selected whose mother performed the Garbhotsav Sanskar and followed the holistic lifestyle taught by AWGP volunteers, while 20 children of the same age (control group) were selected whose mothers were unaware of such rituals and lifestyle. Children from both groups were evaluated using the ‘Strength and Difficulty Questionnaire’. The findings showed that children of mothers who went through the Sanskar and followed the guidelines given by the team had significantly higher strength (p<0.05) than the children of mothers who didn’t go through the process of Sanskar. Though the numbers of samples were less, the case study indicated the potential of the philosophy and the message of this traditional ritual Garbhotsav Sanskar for the mother in nurturing and developing a child from its beginning.
Eating Habits during the COVID-19 Pandemic and the Level of Antibodies IgG and FRAP—Experiences of Polish School Staff: A Pilot Study

The coronavirus 19 (COVID-19) pandemic has brought many changes in terms of lifestyle, education, stress levels, and social contacts. The aim of our research was to evaluate changes in eating habits, physical activity, and selected lifestyle elements in a group of school staff, as well as their immune response to vaccination against COVID-19, and FRAP (ferric reducing antioxidant power) level. In total, 108 primary school teachers and other school staff with integration departments were included in the study. An original survey was conducted with the school staff. Of the study group, 45.4% chose to be vaccinated against COVID-19. In this group, the level of IgG antibodies was assessed, as well as the level of FRAP before vaccination, and after the first and second dose. An original questionnaire was also carried out. A decrease in physical activity and an increase in the time spent in front of the computer have been demonstrated, but a positive observation was a favorable change in most eating habits. After the second dose of vaccination, all subjects achieved the appropriate level of IgG antibodies (above 22 U/mL), with the maximum level recorded in 51%. There was also a significant increase in FRAP levels in the group after the first and second dose of the vaccine compared to the baseline level; an issue that requires further observation.
Trends, Correlates, and Contexts of Robbery-Homicide in South Africa

South Africa has amongst the highest rates of homicide in the world, yet little is known about the contexts that shape fatal violence. One frequently feared context is robbery. We examine 68,801 robberies reported between 2003 and 2014 to predict risk factors for cases resulting in victim death. Robbery-homicide is rare in South Africa and its risk factors differ from the country’s overall homicide profile. Significant correlates include day of the week, time of the day and the victim’s race. These findings demonstrate how context-sensitive understandings of violence are crucial to advancing research on homicide in low- and middle-income countries.
Application of Double-cross-limiting Ratio of Closed-loop Control in Boiler Control System

Throughout the burning process of industrial boiler,the proportion between the burning media and air must be controlled well to avoid producing white or black smoke,thus not only can save energy but can keep the maximum burning efficiency.A double-crossing-limiting ratio control method was proposed which can get the best proportion.The best proportion was transferred to a limited safe value of a valve,which feedbacks across the control loop and compares with the setting value.This method realizes the combination of temperature in series double-crossing-limiting ratio control system by PLC of Siemens S7-300,which meets the requirements of quantificational burning and temperature control.