Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of an Early Detection and Quantification of Osteoarthritis Severity in Knee using Machine Learning Techniques

The major cause of frailty in older and overweight people is Osteoarthritis. It is a joint illness that generally influences the ligament that could be principally affects the cartilage. Cartilage is a smooth elastic tissue which makes the bones move easily, stabilizes the joint and prevents them from abrading one another. The protective cartilage in Osteoarthritis is ruptured and makes the bones rub one another, inflicting joint stiffness and excessive pain. The current system for the assessment of Osteoarthritis incorporates clinical examination and restorative imaging methods. In this project, by utilizing profound features and medical images we need to recognize and classify OA affected in knee. This can impact on the detection and classification of target area in images and cause of it the irrelevant features could be selected from the medical images. This project will moreover center on dealing the colossal quantity of image information by using high speed computing. This paper also discovers the Magnetic Resonance imaging (MRI) techniques for detection and classification of Osteoarthritis in descriptive and comparative manner. Hence a coordinating discourse of different location strategies, highlight extraction procedures and classification methods regarding Osteoarthritis is worn out in a logical way.
The influence of Nd3+ concentration and alkali ions on the sensitivity of non-contact temperature measurements in ALaP4O12:Nd3+ (A = Li, K, Na, Rb) nanocrystalline luminescent thermometers

Luminescent properties of ALaP4O12:Nd (A = Li, Na, K, Rb) nanocrystals were measured as a function of temperature. It was found that three spectroscopic parameters were temperature dependent, namely: the position of the emission band (λM), its full width at half maximum (FWHM) and the luminescence intensity ratio (LIR) between the R1 → Z1 and R2 → Z1 Stark components. The influence of the dopant concentration and the type of tetraphosphate host on the sensitivity of luminescent thermometers was analyzed. It was found that the sensitivity of Nd3+ based luminescent thermometers (LIR) decreases with increasing dopant concentration.The band position was found to have the highest temperature sensitivity, while the lowest was that of the luminescence intensity ratio figure of merit. The highest sensitivity of LIR based thermometers was found for LiLaP4O12:1%Nd nanocrystals, reaching 0.31% K−1. The influence of dopant concentration on the sensitivity of luminescent thermometers was discussed in terms of different electron–phonon interaction strengths.
Structural Identification and Damage Detection in Bridges using Wave Method and Uniform Shear Beam Models: A Feasibility Study

This report presents a wave method to be used for the structural identification and damage detection of structural components in bridges, e.g., bridge piers. This method has proven to be promising when applied to real structures and large amplitude responses in buildings (e.g., mid-rise and high-rise buildings). This study is the first application of the method to damaged bridge structures. The bridge identification was performed using wave propagation in a simple uniform shear beam model. The method identifies a wave velocity for the structure by fitting an equivalent uniform shear beam model to the impulse response functions of the recorded earthquake response. The structural damage is detected by measuring changes in the identified velocities from one damaging event to another. The method uses the acceleration response recorded in the structure to detect damage. In this study, the acceleration response from a shake-table four-span bridge tested to failure was used. Pairs of sensors were identified to represent a specific wave passage in the bridge. Wave velocities were identified for several sensor pairs and various shaking intensities are reported; further, actual observed damage in the bridge was compared with the detected reductions in the identified velocities. The results show that the identified shear wave velocities presented a decreasing trend as the shaking intensity was increased, and the average percentage reduction in the velocities was consistent with the overall observed damage in the bridge. However, there was no clear correlation between a specific wave passage and the observed reduction in the velocities. This indicates that the uniform shear beam model was too simple to localize the damage in the bridge. Instead, it provides a proxy for the overall extent of change in the response due to damage.
Working efficiency analysis of space mirror antenna reflector made of composite materials

The study is devoted to the possibility of using composite materials, composite sandwich panels and composite mesh structures as constructions for space mirror antennas using at space satellites. The modelling of the thermal and stress-strain states of the structure, which are characteristic of radiation heat transfer in open space operating conditions, has been carried out. Based on the results obtained, a design with the best indicators of mass-dimensional stability under operating conditions was selected.
Upping the internal quantum efficiency of green light‐emitting diodes by employing a graded AlGaN barrier and an electron blocking layer

Two different device structures are numerically studied, and their optoelectronic characteristics with standard structure are compared. The authors minimized the uneven carrier distribution across the active region. By replacing the first quantum barrier with graded AlGaN and using a graded AlGaN electron blocking layer, in the structure proposed by the authors, the injection of holes into the active region is improved. By using the graded AlGaN barrier, the concentration of injected electrons is also controlled because of the additional barrier height. As a result, the contribution of radiative recombination is boosted. The droop ratio of the proposed structure is reduced to ∼ 34% at 100 A cm (cid:0) 2 in comparison to other devices.
Image Processing Identifacation for Indonesian Cake Cuisine using CNN Classification Technique

Indonesia is famous for its traditional food that is popular both domestically and abroad. A number of cakes are among the favorite traditional foods. There are types of cakes that can be processed in Indonesia, such as kue dadar gulung, kastangel, klepon, lapis, lumpur, putri salju, risoles and serabi. The most of types of cakes available, visually the human recognize are easy, however computer visiion requires a special technicality in identifying the object of the image to the type of cakes. Therefore, to recognize objects in the form of images from cakes as one of Indonesia’s traditional foods, deep learning algorithm techniques can be used, namely Convolutional Neural Network (CNN). In this paper, the CNN Algorithm technique will be applied to 1676 datasets consisting of 80% training data and 20% testing data in which there are images of traditional cakes from Indonesia. The stages are carried out through preprocessing, operational datasets, visualization datasets, modeling techniques, performance evaluations, errors analysis which finally result in the conclusion that performance evaluation reaches 65.00%.
HMGA2 rs968697 T > C polymorphism is associated with the risk of colorectal cancer

Abstract A genetic polymorphism (rs968697 T > C) in the HMGA2 gene has recently been linked to an increased risk of hepatoblastoma. However, no studies have been conducted to investigate the effect of the polymorphism on the risk of colorectal cancer (CRC). The study aimed to explore whether the rs968697 polymorphism had a significant impact on CRC risk. A total of 500 CRC patients and 500 age and gender matched healthy individuals were genotyped by using the SNaPshot method. Quantitative real-time PCR technology was used to detect the relative expression of the HMGA2 gene in 30 pairs of primary CRC and adjacent non-cancerous tissues. Results: HMGA2 rs968697 polymorphism was significantly associated with CRC risk [CC vs. TT: OR = 0.20, 95%CI = 0.06–0.70, P = 0.01; (CC + CT) vs. TT: OR = 0.71, 95%CI = 0.53–0.96, P = 0.02; CC vs. (CT + TT): OR = 0.21, 95%CI = 0.06–0.73, P = 0.01; C vs. T: OR = 0.67, 95%CI = 0.51–0.89, P < 0.01]. The analysis based on tumor stage indicated that the CRC patients with HMGA2 rs968697 C allele were less likely to have high-stage tumors. Furthermore, the genotype-tissue expression analysis revealed that the rs968697 CC genotype was linked to the low expression of HMGA2 gene. The in silico analysis revealed that the rs968697 polymorphism in the promoter region of the HMGA2 gene could influence transcription factor binding, including ATF6, DBP, CDPCR3, DR3, NRSF, PAX8, PPARA, SZF11, TAXCREB and POLR2A. In conclusion, our findings suggested that the HMGA2 rs968697 polymorphism was linked to CRC risk and could be used as a biomarker to detect CRC risk.
The effects of UV-C and HNO2 mutagen, pH and the use of commercial fertilizers on the growth of microalgae Botryococcus braunii

Botryococcus braunii microalgae is a potential producer of biodiesel as an alternative fossil fuel because it has a high lipid content. This paper was conducted to determine the optimal mutation agent, microalgae resistance to changes in pH and, optimal nutrients from commercial fertilizers for the growth of Botryococcus Braunii. To find the best mutation agent, culture was planted in seawater media with Walne nutrient with UV-C and HNO2 exposure. The results showed that the optimal mutation agent was a mutation with UV-C exposure expressed by lipid productivity values. Furthermore, to see microalgae resistance to changes in pH, B. braunii was cultured in seawater media with Walne nutrients at various pHs, namely 3-8 with mutations of UV-C rays and without mutation agents. The results show that natural and mutated B. braunii microalgae grow optimally at pH 8. This also shows that mutated microalgae have better adaptability compared to natural microalgae, as evidenced by the number of natural microalgae cells that continues to decrease with a decrease in pH. The final goal is to find the optimal nutrient for growth in B. Braunii from commercial fertilizer. Culture was planted in seawater media with various concentrations of commercial fertilizer, for 7 days of culture, the results showed that each nitrogen source had a different effect on the growth of microalgae
Modified Speech Separation Deep Learning Network Based on Hamming window

Speech separation is attracting widespread interest due to the sound mixing in real environments in and out door applications. Although the researchers have used many algorithms, the separation rate in the real environment is still poor. This paper presents speech separation using a modified Deep learning neural (DLN) algorithm. Interestingly, the modification has reduced the complexity of the original DLN algorithm, while, high value of separation rate has been gained caused by using Hamming instead of Hanning windows against the other algorithms. The separation rate reaches 98.6%, while, the advancement over the nearest algorithm is 2.8%.
An analysis of heavy metals contamination and estimating the daily intakes of vegetables from Uganda

Background: Environmental contamination with elevated levels of copper (Cu), cobalt (Co), iron (Fe), zinc (Zn), lead (Pb), chromium (Cr6+), cadmium (Cd), and nickel (Ni)—all states of which are found in Uganda—raises health risk to the public. Pb, Cr6+, Cd, and Ni for instance are generally considered nonessential to cellular functions, notwithstanding the importance of the oxidative state of the metals in bioavailability. As such, we aimed in this study (i) to evaluate heavy metal concentrations in four vegetables from a typical open-air market in Uganda, (ii) to assess the safety of consuming these vegetables against the World Health Organization (WHO) recommended limits of heavy metals consumption, and (iii) to formulate a model of estimated daily intake (EDI) among consumers in the country. Methods: This was a cross-sectional study conducted in five georeferenced markets of Bushenyi district in January 2020. Amaranthus, cabbages, scarlet eggplants, and tomatoes were collected from open markets, processed, and analyzed by atomic absorption spectrometry. Modeled EDI, principal component (PCA) and cluster analysis (CA) were conducted to identify relationships in the samples. Results: The levels of essential elements in the four vegetables were found to fall from Co > Cu > Fe > Zn. Those of non-essential metals were significantly higher and followed the pattern Cd > Cr > Pb > Ni. The highest EDI values were those of Cu in scarlet eggplants, Zn in amaranthus, Fe in amaranthus, Co in amaranthus, Pb in cabbages, total Cr in scarlet eggplant, Cd in cabbages and tomatoes, and Ni in cabbages. In comparison to international limits, EDIs for Zn, Cu, Co and Fe were low while Ni in cabbages were high. PCA showed high variations in scarlet eggplant and amaranthus. The study vegetables were found to be related with each other, not according to the location of the markets from where they were obtained, but according to their species by CA. Conclusion: The presence of non-essential elements above WHO limits raises policy challenges for the consumption and marketing of vegetables in the study area. Furthermore, low EDIs of essential elements in the vegetables create demand for nutritious foods to promote healthy communities.
Dynamics of Ecotourism Benefits Distribution

ABSTRACT The purpose of this study was to explore the dynamics of ecotourism benefits distribution at Boabeng, which is found within the Boabeng-Fiema Monkey Sanctuary (BFMS) in Ghana. Data collection instruments included schedules, in-depth interview (IDI) and focus group discussion (FGD) guides. Findings from the study showed that there was disparity in the distribution of ecotourism benefits to the local households at Boabeng. Adverse impacts of ecotourism are making it difficult for households whose livelihood activities are located in the fringes of BFMS to engage in normal activities. The study has brought to the fore the complete personified livelihood experience within the individual households and ecotourism livelihoods in the Boabeng community. Again, this study argues that there is no significant relationship between socio-demographic characteristics of households and the distribution of ecotourism benefits. The study recommends that managers of the project in the community should consider the provision of social amenities using revenue from the project.
Photoluminescence enhancement of CsPbBr3 quantum dots on Au island fiber film surface for optical communication and quantum information communication

Compared with the glass surface, the photoluminescence intensity of CsPbBr3 quantum dots excited by 1200 nm femtosecond laser on the surface of Au island fiber film (AIF) is significantly enhanced. On the other hand, the photoluminescence intensity decay of CsPbBr3 quantum dots on the AIF surface shows a fast decay of the average photoluminescence lifetime. The Photoluminescence enhancement effect of CsPbBr3 quantum dots was analysed by photoluminescence spectrum, transmission electron microscope, absorption spectrum and photoluminescence decay. The spectral changes of CsPbBr3 quantum dots on the AIF surface are related to surface plasmon resonance. The enhanced photoluminescence of CsPbBr3 quantum dots on the AIF surface can be used in optical communication and quantum information communication.
LncRNA GAS8-AS1 is a Novel Prognostic and Diagnostic Biomarker for Pancreatic Cancer.

BACKGROUND LncRNA GAS8-AS1 inhibits thyroid carcinoma, but its function in other malignancies is unknown. The present study aimed to investigate the involvement of GAS8-AS1 in pancreatic cancer (PC). METHODS The present study included 68 PC patients (38 males and 30 females, 42-66 years, 52.1 ± 4.5) and 62 healthy volunteers (28 males and 24 females, 43-67 years, 52.3 ± 4.9). Real-time quantitative PCR, transient cell transfection, and in vitro cell migration and invasion assays were applied for the research. RESULTS The study showed that plasma GAS8-AS1 was lower in PC patients than in healthy controls. Downregulation of plasma GAS8-AS1 distinguished early-stage PC patients from healthy controls. Patients with low GAS8-AS1 plasma levels showed a significantly lower 5-year overall survival rate. Plasma miR-1179 levels were also significantly lower in PC patients than in healthy controls and were positively correlated with plasma GAS8-AS1 levels in PC patients but not healthy controls. GAS8-AS1 overexpression upregulated miR-1179, and MiR-1179 overexpression increased GAS8-AS1 level. Overexpression of both GAS8-AS1 and miR-1179 inhibited PC cell migration and invasion. CONCLUSION GAS8-AS1 may promote PC by positively interacting with miR-1179.
Multifunctional Electronic Skin With a Stack of Temperature and Pressure Sensor Arrays

This paper presents multifunctional electronic skin (e-Skin) with a stack of pressure and temperature sensors arrays. The pressure sensor layer comprises of an 8x 8 array of capacitive sensors using soft elastomers as the dielectric medium and the temperature sensing layer comprises of 4 x4 array of conductive polymers based resistive sensors. Three variants of capacitive pressure sensors were developed using two different dielectric materials (PDMS and Ecoflex) to find the best combination of performance and softness. The Ecoflex-based pressure sensor showed high sensitivity (~4.11 kPa−1) at a low-pressure regime (< 1 kPa) and the 7.5:1 PDMS based pressure sensor showed high sensitivity (~2.32 kPa−1) in the high-pressure regime (>1 kPa). Two variants of temperature sensors were fabricated using CNT and CNT & PEDOT:PSS conducting polymer composite and their performance compared. Finally, a highly sensitive CNT+PEDOT:PSS based resistive temperature sensors layer was integrated on top of 7.5:1 PDMS based capacitive pressure sensors layer to realize the e-Skin prototype. The developed e-Skin is capable of sensing pressures greater than 10 kPa with a high sensitivity of ~2.32 kPa−1 at 1 kPa and temperatures with the sensitivity of ~0.64 (%)/(°C) up to 80°C, thus demonstrating high potential for use in robotics and touch based interactive systems.
Applying the Deep Learning Model on an IoT Board for Breast Cancer Detection based on Histopathological Images

In breast cancer diagnosis, pathologists evaluate microscopic images of tissue samples to determine if it is benign or malignant. The manual examination process could result in delayed diagnosis, which leads to late cancer treatment and can risk lives. In this paper, we proposed an automated, low-cost, and portable breast cancer detection based on histopathological images by using deep learning. The deep learning models were designed by using the Convolutional Neural Network (CNN). This paper compares the performance of the CNN model by using transfer learning utilizing a pre-trained model (VGG16) and the performance of a CNN model without transfer learning. The result shows that transfer learning provides a good base for classification of histopathological images. The model was successfully deployed on a Raspberry Pi, which demonstrates the model efficiency to run on a lightweight and portable processor.
Exploring Domestic Precycling Behavior: A Social Identity Perspective

Increasing amounts of disposable food packaging waste are contributing towards a global environmental crisis, and approaches to successfully preventing such waste—called precycling—are urgently needed. The human ability to define oneself as a member of a group (social identity) may represent a powerful source for realizing environmental endeavors. Therefore, in this article we conceptualize precycling behavior in households as pro-environmental behavior embedded in social identity processes. To explore precycling, we combined food diaries and qualitative virtual interviews with 26 households in Berlin, Germany. We analyzed our data based on the Social Identity Model of Pro-Environmental Action (SIMPEA). Starting from the behavioral element of the model (response), we substantiate the concept of precycling suggesting that it can be distinguished into six types of behavior. Furthermore, we propose that the enactment of these precycling behaviors is shaped by social identity processes and social influence in different groups, including: the household itself, neighbors, family and friends, or food collectives. We conclude that these processes are important to realizing precycling in small and private groups as well as in larger collectives. Implications are derived for empirical research and theoretical development as well as for public programs and intervention studies.
Integrated Planar LTE MIMO Dual-antenna System on Laptop Computer’s Hinge

ABSTRACT This paper presents an LTE MIMO dual antenna system for laptops. The LTE antenna is composed of a fed strip and a shorted strip. The antenna structure is very simple and can cover LTE octa-band operation (LET700/GSM850/900/GSM1800/1900/UMTS /LTE2300/2500) with the antenna size only 50 × 5 mm2 printed on the FR4 substrate. The LTE antenna is a design concept that integrates the metal hinges and ground plane into one of the antenna’s resonant paths, which can effectively reduce the size of the antenna and can be tightly configured with the surrounding metal, while at the same time the antenna has good radiation characteristics. To effectively utilize the existing space of laptops, two LTE antennas of the same structure and size are symmetrically arranged in the clearance area on the left and right sides of the hinge of the laptop to form a MIMO dual antenna system. The measured isolation in the operating frequency band is better than 24 dB, while the measured antenna efficiency is better than 40%, which exhibits good antenna characteristics.
Analysis and Research of Blockchain Technology in Audit System

Blockchain technology has attracted widespread attention from all walks of life due to its decentralization, openness and transparency, and data that cannot be tampered with. At the same time, it has a good development prospect in the accounting and auditing industries. This article is based on the consensus algorithm research experiment. In the network environment, computers will be attacked in many situations, which may cause unexpected behaviors of the computer and the network. The consensus algorithm can deal with these unpredictable behaviors. The experiment uses a questionnaire survey method, and the survey objects rank the importance of accounting informatization, the necessity and feasibility of applying blockchain technology and other related issues, and different scores form different results. The experimental data shows that it is necessary to analyze and build a credibility guarantee mechanism for audit information systems based on blockchain technology. Each aspect includes its own specific content and details, and an average score is obtained for each category of options. Let’s compare the importance levels of the four factors. The experimental results show that, overall, the average scores of the four factors are between 3.9-4.4 points, with little difference in importance levels. Audit information quality issues scored the highest at 4.37 points, followed by management control issues and security issues at 4.24 and 4.21 points respectively. Although blockchain technology in the audit system is still in the research and development stage, the application of blockchain in the audit industry is a major trend. Researchers are working hard to solve the problems of blockchain storage and confidentiality.