Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Benign Joint Hypermobility on Pain, Physical Function and Quality of Life in Patients with Knee Osteoarthritis

Objective: This study investigated the effect of hypermobility on pain, joint stiffness, physical function, and quality of life in patients with knee osteoarthritis. Design: Sixty-four patients diagnosed with bilateral knee osteoarthritis were included; 42 patients in Group 1 were those with hypermobility and 22 patients in Group 2. There were 40 women and 2 men in Group 1, and 16 women and six men in Group 2 were those without hypermobility. The age, sex, and body mass index of all patients recorded. The WOMAC and SF-36 tests performed on the patients.Results: The average ages were 51.40±5.42 and 53.36±4.31, respectively, and there was no statistically significant difference. The body mass index was high in the hypermobility group, and the difference was statistically significant (p=0.028). Pain and stiffness were higher in the hypermobility group than in the non-hypermobility group in the WOMAC subgroups and total scores (p=0.030, p=0.002, p=0.047). The non-hypermobility group had better SF-36 social function and pain scale scores. The difference was statistically significant (p=0.016, p=0.004). Conclusions: Hypermobility aggravates the symptoms of knee osteoarthritis. Hypermobility evaluated in all patients diagnosed with knee OA. A more intense and long-term rehabilitation program should be determined for these patients to prevent injuries and improve proprioception.
BANKING DIGITALLY TO MICRO-BUSINESS: EXPLORING VALUE CO-CREATION STRATEGY IN NEW PRODUCT DEVELOPMENT

Manuscript type: Research Article Research Aims: To further academic knowledge around the topic of value co-creation and to provide a practical strategy for Digital Bank in Indonesia on conducting better co-creation activities. Design/methodology/approach: This research relies on a qualitative method for collecting the primary data through in-depth interviews. Research Findings: Indonesian Bank called Jenius currently practice community-based value co-creation, and it is recommended for the Banks in general to also explore other forms of co-creation initiatives, namely customization, reputation and sensing. These initiatives should be practiced across different stages of co-ideation, co-evaluation, co-design, co-test and co-launch. To better engage consumers and other external parties in value co-creation activities, Banks can consider driving the involvement with financial driver; learning driver; hedonic driver; and personal, social and psychological driver. Theoretical Contribution/Originality: This research has sucessfuly show different theoretical framework of value co-creation strategy to be implemented in Digital Banking industry, especially in the micro-business segment and explore what motivates Digital Banking consumers to collaborate in value co-creation. Research limitation/Implications: The scope of this research is limited only to the micro-business segment in Banking and not exploring other segments. This research will also focus on the area of value co-creation only and will not cover other business functional areas such as sales or branding.
An in vivo Evaluation of Antihyperlipidaemic Activity of Ethanolic Extract of Amaranthus spinosus Leaves on Dexamethasone Induced Hyperlipidaemic Rats

Natural products derived from plants play a vital part in preventing or treating various diseases or disorders in humans. Hyperlipidemia is one of the main pathological features of diseases affecting the circulatory system and diabetes mellitus. Presently available lipid-lowering drugs have been linked with some side effects. Herbal treatment for hyperlipidaemia has significantly fewer or no side effects and is reasonably inexpensive and locally accessible. Amaranthus spinosus belongs to the family Amaranthaceae is well known by many researchers for its various medicinal properties and is also known as "pigweed. " The present study sought to assess the antihyperlipidaemic activity of the leaf extract by in vivo animal models. Here, acute hyperlipidemia was induced by intraperitoneal administration of dexamethasone (10 mgkg-1). The ethanol extract of A. spinosus leaf (EEASL) was administered daily at single doses of 250 and 500 mgkg-1, to dexamethasone-induced hyperlipidaemic rats for 8 days. The effect of EEASL on serum lipid profiles (total cholesterol, triglycerides low-density, very low-density, and high-density lipoprotein) were determined. EEASL was established to significantly decrease total serum cholesterol, triglycerides, low-density lipoprotein, Very low-density lipoprotein, and increased serum high-density lipoprotein compared to the hyperlipidaemic and vehicle only control models. The activities were also paralleled to the outcome exhibited by a standard antihyperlipidaemic agent, atorvastatin. The present investigation established pharmacological evidence to support the claim that EEASL contains active antihyperlipidaemic agents.
Three-body QED test and fifth-force constraint from vibrations and rotations of 
HD+

We present a parts-per-million test of quantum electrodynamics (QED) in the HD + molecular hydrogen ion, improving on previous tests based on vibrational and rotational transitions by factors of 76 and 1.4, respectively. The test is performed following a unified statistical approach that also produces improved constraints on physics beyond the standard model. We furthermore show how individual constraints derived from the various degrees of freedom in HD + and antiprotonic helium can be combined to enhance the sensitivity, thus ruling out “fifth forces” on the Angstrom scale that are 10 11 times weaker than the electromagnetic interaction.
Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals

Background: The study of regressive evolution has yielded a wealth of examples where the underlying genes bear molecular signatures of trait degradation, such as pseudogenization or deletion. Typically, it appears that such disrupted genes are limited to the function of the regressed trait, whereas pleiotropic genes tend to be maintained by natural selection to support their myriad purposes. One such set of pleiotropic genes is involved in the synthesis ( AANAT, ASMT) and signaling ( MTNR1A, MTNR1B) of melatonin, a hormone secreted by the vertebrate pineal gland. Melatonin provides a signal of environmental darkness, thereby influencing the circadian and circannual rhythmicity of numerous physiological traits. Therefore, the complete loss of a pineal gland and the underlying melatonin pathway genes seems likely to be maladaptive, unless compensated by extrapineal sources of melatonin. Methods: We examined AANAT, ASMT, MTNR1A and MTNR1B in 123 vertebrate species, including pineal-less placental mammals and crocodylians. We searched for inactivating mutations and modelled selective pressures (dN/dS) to test whether the genes remain functionally intact. Results: We report that crocodylians retain intact melatonin genes and express AANAT and ASMT in their eyes, whereas all four genes have been repeatedly inactivated in the pineal-less xenarthrans, pangolins, sirenians, and whales. Furthermore, colugos have lost these genes, and several lineages of subterranean mammals have partial melatonin pathway dysfunction. These results are supported by the presence of shared inactivating mutations across clades and analyses of selection pressure based on the ratio of non-synonymous to synonymous substitutions (dN/dS), suggesting extended periods of relaxed selection on these genes. Conclusions: The losses of melatonin synthesis and signaling date to tens of millions of years ago in several lineages of placental mammals, raising questions about the evolutionary resilience of pleiotropic genes, and the causes and consequences of losing melatonin pathways in these species.
Investigation of the Radical Nitration of Isooctane Fuel via Nitromethane Propellant

The possible radical nitration reactions of isooctane fuel with nitromethane propellant, which is generally used as an additive in fuel formulations, were thermodynamically investigated both at room temperature and at a higher temperature of 691.15 K. The temperature of 691.15 K was chosen because it is the auto-ignition temperature of isooctane and nitromethane and has the potential to mimic better engine conditions. The computational calculations were performed at the theoretical level of DFT UB3LYP/cc-pVDZ. Four different nitration reactions and nitrated products were considered and interpreted in detail. The most and the least favorable nitrations were observed at the primary and secondary carbons of isooctane at 691.15 K, respectively. Four of the designated reactions were endothermic at this temperature. The other outcome of this study was that there was a direct relationship between the thermodynamic tendencies of the considered reactions and the ballistic performances (detonation velocities, detonation pressures, and specific impulses) of their nitrated products. The thermodynamic properties of heats of combustion and deflagration temperatures were calculated via empirical formulations based on the stoichiometry and some other structural parameters of the energetic materials. The results for nitromethane and the nitroisooctane products were examined.
Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.
Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. For the day-ahead scheduling, we propose a data-based distributionally robust chance-constrained (DRCC) energy dispatch model for grid-connected microgrids, to trade off the economic efficiency and operational risk. This model gains a robust and low conservative day-ahead scheduling solution against uncertainties by formulating the chance-constraint based on Wasserstein ambiguity set into a tractable convex constraint with conditional value-at-risk (CVaR) approximation. For the intraday scheduling, we blend the shorter-time scale prediction with a robust day-ahead scheduling plan as well as the model predictive control (MPC) rolling optimization method. This ensures accurate intraday dispatch solution and balanced supply-demand. Finally, the effectiveness and performance of the proposed method are verified via case studies.
Synthesis and Acivity of Chiral Amino Alcohols and Amides as New Cholinesterase Inhibitors for Alzheimer's Disease

A series of biological active compounds 1–14 have been synthesized and used as potential inhibitors for AChE and BuChE. Potential inhibitor efficacy of these molecules to the target enzymes have been searched in vitro and theoretical by dock and molecular dynamic calculations. The results show that chiral amino alcohol compounds 6, 7 and 9 exhibited good value for medication scores. Among the tested compounds the best inhibition activities have been obtained with compounds 6 for AChE and BuChE by 87.68 and 92.46 % values, respectively at 50µg/mL concentration. The anticipated value of 6 also justified superb correlation with invitro statistics and it could be taken into consideration as drug candidate molecule for designing of novel drug. Potential inhibitory outcomes of those molecules on the right track proteins were investigated the use of Docking and Molecular Dynamics calculations. Dock score evaluation and Lipinski parameters have been proven those ligands are ability inhibitors against applicable enzymes. Our findings endorse that related compounds can be applied as a capacity supply of anti-alzheimer active molecules for designing novel products.
An Adaptive Deep Learning Framework for Fast Recognition of Integrated Circuit Markings

Fast recognition of integrated circuit (IC) markings is an essential but challenging task in electronic device manufacturing lines. This article develops an adaptive deep learning framework to facilitate the fast marking recognition of IC chips. The proposed framework contains four deep learning components, namely, chip segmentation, orientation correction, character extraction, and character recognition. The four components utilize different convolutional neural network structures to guarantee excellent adaptivity to a wide range of IC types and mitigate the influence of the low-quality chip images. In particular, the character extraction model is comprised of two improved label generation strategies and a proposed border correction method, so as to accommodate tiny scale chips and compactly printed markings. Experiments from the chip image dataset of a real laptop manufacturing line reached a recognition Precision of 91.73% and the Recall of 92.93%. The results demonstrate the superiority of the proposed framework to the state-of-the-art models and the effectiveness of handling a great diversity of chips with different scales, shapes, text fonts, marking colors, and layouts.
Predicting mechanical properties of crystalline materials through topological analysis

With the aim to develop simple, programmatically generated, topology-based descriptors of crystal structures for application to mechanical properties prediction methods, we have developed a new geometric analysis protocol using the CSD Python API. By scanning a crystal structure for Miller planes with the least physical impediment to translational slipping, we are able to predict plausible slip planes for a crystal structure – a feature shown to correlate strongly with tabletability. A simple, automatic hydrogen-bond network dimensionality analysis method has also been developed which, when used in conjunction with the slip plane analysis, can detect whether the proposed slip plane is bridged by hydrogen bonding interactions. These methods are combined with other calculated topological features into a set of crystal structure descriptors, which provide a fast, qualitative way to describe the aspect of crystal plasticity that results from packing geometry. While intended for use in conjunction with other methods, these descriptors alone are shown to correctly predict the relative tabletability measured for multiple drug systems.
Performance evaluation of wind power industry chain based on three-stage DEA

With the development of the economy and society, the problems of resources and the environment are becoming more and more prominent. As an environmentally friendly and clean energy, wind energy is developing rapidly with the support of relevant policies and technologies. At the same time, the development of the wind power industry is also faced with some problems, as the “abandoning wind” phenomenon of wind farms is still relatively common; the continuous decline of wind turbine prices and the sharp rise in operation and maintenance costs have further reduced the profit space of wind power manufacturers. The fundamental reason for the series of difficulties in the development of the wind power industry is the imbalance between the various links in the wind power industry chain. There is an urgent need to evaluate the performance of the wind power industry chain to improve the weak links. In this paper, by using the three-stage data envelopment analysis (DEA) model, the input-oriented BCC model is used in the first stage, and the stochastic Frontier analysis method is used to analyze the input relaxation variables in the second stage to eliminate two exogenous factors. In the third stage, the adjusted input variables are recalculated in the first stage, and the chain performance value of the wind power industry excluding environmental factors and random errors is obtained in the performance evaluation. This paper takes China's wind power industry as an example and makes an overall analysis of the upstream, middle, and downstream of the wind power industry chain from three aspects: technology performance, scale performance, and pure technology performance. The results show that under the support of policy and economy, the technical performance of the upper and lower reaches of China's wind power industry chain is higher, while the performance of the wind power industry chain is underestimated by environmental factors. The three-stage DEA model can eliminate the influence of environmental and random errors, which makes the evaluation of the wind power industry chain more objective. In addition, according to the evaluation results, the optimization direction of each structure of the industrial chain is put forward.
Comprehensive Analysis of Nanocrystalline Ribbon Cores in High-Power-Density Wireless Power Transfer Pads for Electric Vehicles

The nanocrystalline alloys are gradually used as shielding materials in wireless power transfer (WPT) systems with the development of high power density. In this article, the system properties of WPT using ferrite and four types of nanocrystalline cores are simulated by 3-D finite element analysis (FEA). Based on this, a novel shielding pad composed of nanocrystalline alloy and ferrite is designed to improve the WPT system properties. The WPT systems are preliminarily compared with 3-D FEA when the different nanocrystalline alloys are used in the novel shielding pad. The experimental WPT system for 85 kHz and 1.5 kW with a 24 cm air gap between the primary and secondary coils is built to validate the simulation. The results show that significantly greater shielding can be realized for the novel pad at the price of a slight decrease of transmission efficiency than conventional ferrite core.
Research on the River Channel-adaptive Inspection Technology of Sewage Outlets Based on DJI M300 Flight Platform Equipped with Orthogonal Laser Radar

Water pollution control for black and odorous water bodies becomes increasingly concerned in China. As the final checkpoint for pollutants entering the water body, the management of sewage outlets is an important task of water pollution control and the black and odorous water bodies' remediation work. This research fully considers the urban river inspection scene to develop a river channel-adaptive inspection system based on the DJI M300 flight platform equipped with a circular orthogonal laser radar as the sensor, which provides automatic obstacle avoidance and route planning functions. The UAV inspection system overcomes shortcomings in traditional artificial inspection work such as incomprehensive, time-consuming, and high labor cost, and helps management departments to build up a long-term control mechanism of water quality management.
Analysis of Factors Affecting the Use of the 64QAM Modulation on the Long-Term Evolution Network by Using Random Forest Method

Nowadays internet traffic using cellular telecommunication network is increasing very rapidly. Good LTE (Long-Term Evolution) cellular network performance is very important for any telecommunication operator to maintain customer satisfaction. Poor network performance can also cause customers to switch to other operators. One of the indicator variables in observing the radio quality of the LTE cellular network is Penetration using 64QAM Modulation. 64QAM modulation can transmit higher bitrates with lower power usage. 64QAM modulation will be used if the Channel Quality Index (CQI) condition is very good. Network quality improvement can be done by adding new BTS or optimizing existing BTS. The addition of new BTS will increase coverage, quality, and capacity but cost is high, and the time required to build BTS is also long, while improving network quality by optimizing BTS can be done by purchasing LTE features and costs incurred still relatively low. In increasing the penetration of using 64QAM modulation, it is necessary to analyze the other variables. The traditional method to improve this Key Performance Indicator (KPI) requires an expert and professional but is often inaccurate and spends a lot of time finding the factors that cause it. To solve this problem, Random forest method is proposed. By knowing the variables that have a significant effect on network quality, the capital costs incurred by cellular operators for improving network quality will be more effective and efficient because the capital costs invested only focus on influencing variables such as purchasing LTE network features only done for those related to these variables. The results of this study, we make CQI improvement flow based on the classification of the random forest method that produces feature/variable importance.
Fruit micromorphology of Siberian Apiaceae and its value for taxonomy of the family

Using a scanning electron microscope, the fruit micromorphology of the family Apiaceae of Siberia was studied. In total, the study covered 97 species of wild, adventive, and the most important cultivated plants. Within one fruit, micromorphology is heterogeneous; the most informative is the surface of the grooves and the rib bases. Exocarp cells are described (relative position, shape, size, nature of the boundaries between cells, outer walls, and fine relief of the cell wall). Characters most consistent within the species, for example, outgrowths on cell surface and a thin relief of cell wall, are noted. The characteristics of the ridges of the ribs, stomata (their presence, position on the surface of the fruit, and cuticle on the cells adjacent to the stomata), and epicuticular wax are also given. 25 species have hairs, scales, spines or warts on the surface; the shape of these structures and their fine relief are described. The importance of micromorphology for species identification and taxonomy is shown. The wide distribution of parallel variability in the family is noted.