Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prognostic Value of Long Non-Coding RNA SNHG7 in Human Cancer: A Meta-Analysis.

BACKGROUND The long non-coding RNA SNHG7 is upregulated in many types of cancer and plays a role as an oncogene. However, its overall predictive ability in human cancer prognosis has not been assessed using existing databases. Therefore, further study of its prognostic value and clinical significance in human malignancies is warranted. METHODS We systematically collected relevant literature from multiple electronic document databases about the relationship between SNHG7 expression level and prognosis in patients with solid cancers. We further screened them for eligibility. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to assess the prognostic value. Odds ratios (ORs) and their 95% CIs were collected to evaluate the relationship between the expression of SNHG7 and clinicopathological features, including lymph node metastasis (LNM), tumour size, tumour node metastasis (TNM) stage and histological grade. RESULTS Fourteen original studies involving 971 patients were enrolled strictly following the inclusion and exclusion criteria. The meta-analysis showed that SNHG7 expression was significantly correlated with poor overall survival (HR = 1.93, 95% CI: 1.64-2.26, p<0.001) in human cancer patients. In addition, the pooled OR indicated that overexpression of SNHG7 was associated with earlier LNM (OR = 1.83, 95% CI: 1.44-2.32; P <0.001), and advanced TNM stage (OR = 1.82, 95% CI: 1.44-2.30; P <0.001). Meanwhile, there was no significant heterogeneity between the selected studies, proving the reliability of the meta-analysis results. CONCLUSIONS High SNHG7 expression may predict poor oncological outcomes in patients with multiple human cancers, which could be a novel prognostic biomarker of unfulfilled clinicopathological features. However, further high-quality studies are needed to verify and strengthen the clinical value of SNHG7 in different types of cancer.
Role of privacy/safety risk and trust on the development of prosumption and value co-creation under the sharing economy: a moderated mediation model

ABSTRACT Information technologies (ITs), including digital platforms and the Internet of things have dramatically supported prosumption behavior in value co-creation (VCC) development under the sharing economy perspective. However, privacy-safety risk (PSR) and prosumers’ trust may significantly impact this transaction through digital platforms under the sharing economy. Therefore, this study examined the prosumption role as a mediator between PSR and VCC through a digital platform under the sharing economy. Moreover, the moderating role of prosumers’ trust between prosumption and VCC has been explored. This study used quantitative methodology and data collected from Uber customers in Pakistan and DiDi customers in China. This study found that prosumption mediates the relationship between PSR and VCC and trust moderates the relationship between prosumption and VCC. This study portrays the influence of PSR and prosumers’ trust factors to understand the prosumption and VCC relationship through a digital platform that provides a win-win outcome for customers and the company under the sharing economy perspective.
Isolation Of Compounds Of Steroids Teripang Gamat (Stichopus variegatus) With Various Types Of Solvents

Abstract Sea cucumber is one of the fisheries commodity that has an important economic value. Generally is traded in dried form (beche-de-mer). One of thebioactive substances contained in sea cucumber is steroid compounds that serves as an aphrodisiac and sex reversal. The purpose of this study was to extract the steroid of the gamma sea cucumber by using three types of solvents (methanol, ethyl acetate and hexane) and get the best solvent in producing the highest yield of the steroids. The study revealed that steroid of gamma sea cucumber (Stichopus variegatus) dissolved completely ethyl acetate (semi-polar solvent) during the first phase, second phase and the third phase of extraction. In the methanol (polar solvent) steroids only dissolved in the first extraction phase, while using the hexane (non polar solvent) steroid was undetectable. Fractionation by thin layer chromatography was obtained two fractions that identified as cholesterol (Rf = 0.96) and testosterone (Rf = 0.91).
On-Chip Nanoscale Light Source Based on Quantum Tunneling: Enabling Ultrafast Quantum Device and Sensing Applications

Light sources are ubiquitous in our lives. Common light sources include sunlight and incandescent lamps, which emit visible spectrum to help us to see surrounding objects via blackbody radiation. Further understanding and control on the quanta of light, called photons, have led to numerous advances in nanophotonics and related research fields, but recently their impact has been most notable in quantum computing, com- munications, biosensing, and imaging technology. Most of these applications have been made possible through the development of ultrasmall and ultrafast light sources based on advanced nanotechnology. In this review, we aim to give a clear picture of the historical achievements regarding light sources and how recent studies have developed the field of ultrasmall light sources, especially in relation to quantum electron tunneling mechanisms. Finally, we discuss the potential applications for emerging quantum devices and sensing technologies.
A double‐edged sword: Realities of artisanal and small‐scale mining for rural people in the Alaotra region of Madagascar

A growing number of people are entering the artisanal and small‐scale mining (ASM) sector worldwide. In Madagascar, millions of individuals depend on this informal activity. Through a case study in the Alaotra‐Mangoro region of Madagascar, our research aimed to understand the “bottom‐up” dynamics and ripple effects of the sector, by looking at the realities for rural communities where inhabitants are both directly and indirectly affected by ASM. We were interested in community members' and miners' perceptions of the socio‐economic and environmental impacts of ASM, and in identifying the factors attracting people living in one of the country's agricultural hubs to this activity. Our results show a wide diversity of push and pull factors leading people to enter the sector. Although many positive impacts of ASM exist for miners and communities within the vicinity of mines, most miner participants considered themselves worse off since starting to mine, highlighting the high risk and low probability of return of ASM. ASM's potential for local and national development will remain squandered if its negative impacts continue to go unmanaged. Accounting for local contexts and the ripple effects of ASM will be crucial in achieving safety and security for miners, and to tap into the benefits it may offer communities while minimising environmental damage.
Contribution of organic amendments to soil properties and survival of Stenocarpella on maize stalk

Com a implementacao do sistalka de plantio direto, principalmente, em monocultivo de milho, houve aumento da incidencia de doencas causadas por patogenos necrotroficos como Stenocarpella spp. Uma das estrategias para reduzir o tempo de sobrevivencia destes patogenos e estimular a degradacao dos restos de cultura. O presente estudo avaliou o efeito da aplicacao de residuos orgânicos e nutrientes no solo na sobrevivencia de Stenocarpella spp. em colmos de milho. Nos ensaios foram conduzidos nas fazendas experimentais da UFLA/Lavras e da Embrapa Milho e Sorgo/Sete Lagoas, MG num delineamento de blocos casualizados. Os colmos de milho, previamente inoculados com o patogeno, foram mantidos no campo por um periodo de tres meses apos a aplicacao dos residuos orgânicos (cama de frango, esterco suino, hidrolisado de peixe e lodo de esgoto) e ureia. Como controles, foram mantidos os colmos infestados com o patogeno, porem nao tratados na superficie ou incorporados ao solo. As avaliacoes da atividade microbiana indireta (β-glicosidase, hidrolise de diacetato de fluoresceina (FDA) e urease), analise quimica do solo e a sobrevivencia do patogeno, por meio de qRT-PCR, foram realizadas no final do periodo. Em Lavras e Sete Lagoas verificou-se aumento no teor de calcio com a aplicacao de cama de frango. A aplicacao de cama de frango e lodo de esgoto aumentou a atividade de β–glicosidase e a hidrolise de FDA nas duas localidades A dinâmica da populacao do patogeno por qPCR, quando comparado com a porcentagem de Ct do tratamento com colmo na superficie, nos dois locais, os colmos enterrados apresentaram a maior reducao do patogeno, seguido por lodo de esgoto em Lavras e, hidrolisado de peixe e lodo de esgoto em Sete Lagoas. Hidrolisado de peixe e lodo de esgoto sao promissores na inducao de supressividade, pois elevaram a atividade enzimatica do solo e reduziram a quantidade de patogeno.
Abdominal Obesity Phenotype Predicts COVID-19 Chest X-Ray Severity Score better than General Obesity

PurposeChest x-ray (CXR) severity score and general obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored.MethodsThis retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19.ResultsPatients with abdominal obesity had significantly higher CXR severity scores and higher rates of these scores than those without abdominal obesity (P<0.001; P=0.001, respectively). While, there were no significant differences between BMI classes (P=0.104; P=0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity score than BMI (r=0.43, P<0.001; r=0.41, P<0.001; r=0.17, P=0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than those for BMI for distinguishing a high CXR severity score (≥8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P=0.001). Multivariable analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P<0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P=0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P<0.001) as the only independent predictors of a high CXR severity score. ConclusionAbdominal obesity might predict a high CXR severity score better than general obesity in hospitalized patients with COVID-19. Therefore, when performing clinical hospital practices, waist circumference should be assessed, and patients with abdominal obesity should be monitored closely when hospitalized.
A mathematical assessment of the isolation random forest method for anomaly detection in big data

We present the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection, proposed by Liu F.T. , Ting K.M. and Zhou Z. H. in their seminal work as a heuristic method for anomaly detection in Big Data. We prove that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved, using the Law of Large Numbers. A couple of counterexamples are presented to show that the method is inconclusive and no certificate of quality can be given, when using it as a means to detect anomalies. Hence, an alternative version of the method is proposed whose mathematical foundation is fully justified. Furthermore, a criterion for choosing the number of sampled trees needed to guarantee confidence intervals of the numerical results is presented. Finally, numerical experiments are presented to compare the performance of the classic method with the proposed one.
A Novel SAR Image Ship Small Targets Detection Method

To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection method based on the improved You Only Look Once Version 3 (YOLOv3). The main contributions of this study are threefold. First, the feature extraction network of the original YOLOV3 algorithm is replaced with the VGG16 network convolution layer. Second, general convolution is transformed into depthwise separable convolution, thereby reducing the computational cost of the algorithm. Third, a residual network structure is introduced into the feature extraction network to reuse the shallow target feature information, which enhances the detailed features of the target and ensures the improvement in accuracy of small target detection performance. To evaluate the performance of the proposed method, many experiments are conducted on public SAR image datasets. For ship targets with complex backgrounds and small ship targets in the SAR image, the effectiveness of the proposed algorithm is verified. Results show that the accuracy and recall rate improved by 5.31% and 2.77%, respectively, compared with the original YOLOV3. Furthermore, the proposed model not only significantly reduces the computational effort, but also improves the detection accuracy of ship small target.
Recent Development of Methods for Iodine Analysis

Methods for iodine analysis were reviewed in this paper,mainly including titrimetry,spectrophotometry,electro-chemistry,neutron activation,chromatography,atomic spectrometry and mass spectrometry,etc.The development in iodine speciation analysis was also briefly introduced.113 literatures were cited.
An Intrusion Detection Model Based on SMOTE and Convolutional Neural Network Ensemble

Massive, multi-dimensional and imbalanced network traffic data has brought new challenges to traditional intrusion detection systems (IDSs). The detection performance of traditional algorithms is closely related to feature extractions, which are not effective in the massive and imbalanced data environments. In this paper, we propose an intrusion detection model based on synthetic minority oversampling technology (SMOTE) and convolutional neural network (CNN) ensemble. It converts original traffic vectors into images, designs a CNN structure, and combines SMOTE and CNN ensemble to solve the problem of imbalanced datasets. Using the standard KDD CUP 99 dataset to evaluate the performance of the proposed model and analysing the contribution of features to model decision-making show that the model’s F1 score are better than traditional algorithms in the classes with few samples and the model improves the efficiency of network intrusion detection.
Convolutional Neural Network for Roadside Barriers Detection: Transfer Learning versus Non-Transfer Learning

Increasingly more governmental organizations in the U.S. have started to implement artificial intelligence to enhance the asset management process with an objective of controlling the costs of data collection. To help the Wyoming Department of Transportation (WYDOT) to automate the data collections process, related to various assets in the state, an automated assets management data collection was proposed. As an example, the automated traffic barriers asset dataset would collect geometric characteristics, and barriers’ materials’ conditions, e.g., being rusty or not. The information would be stored and accessed for asset-management-decision-making and optimization process to fulfill various objectives such as traffic safety improvement, or assets’ enhancement. For instance, the State of Wyoming has more than a million feet of roadside barriers, worth more than 100 million dollars. One-time collection of various characteristics of those barriers has cost the state more than half a million dollars. Thus, this study, as a first step for comprehensive data collection, proposed a novel approach in identification of roadside barrier types. Pre-trained inception v3, denseNet 121, and VGG 19 were implemented in this study. Transfer learning was used as there were only 250 images for training of the dataset for each category. For that method, the topmost layers were removed, along with adding two more new layers while freezing the remaining layers. This study achieved an accuracy of 97% by the VGG 19 network, training only the few last layers of the model along with adding two dense layers for top layers. The results indicated that although there are not enough observations related to traffic barrier images, a transfer learning application could be considered in data collection. A simple architecture non-transfer model was also implemented. This model achieved an accuracy of 85%, being better that the two other transfer learning techniques. It should be reiterated that although non-transfer learning technique outperformed inception and denseNet networks, it comes short significantly when it come to the VGG network.
Indonesia Infrastructure Development Topic Discovery on Online News with Latent Dirichlet Allocation

In the period of 2014-2019, the Indonesian government has defined infrastructure development as a priority. Related to this, various media, in both online and offline, routinely report the infrastructure development news to the public. However, it is quite difficult for people to obtain a summary of information from the internet about the infrastructure development that have been carried out by the government. This study aims to provide a brief summary about infrastructure development in Indonesia by performing topic modeling approach using Latent Dirichlet Allocation. We found that the use of bigram language model could help identify phrase from the corpus. Therefore, the keywords contained in the topics are more interpretable and acceptable. Moreover, a coherence score measurement was applied to find the best number of topics from our dataset. Based on the experiments, we obtained 40 topic models from our dataset. From those 40 topics, we inferred several topic labels, such as, oil and gas infrastructure; power plant infrastructure; information technology infrastructure and internet networks; road infrastructure; reservoir infrastructure, irrigation networks and water resources; railway infrastructure; and airport infrastructure.
Automotive Brake Part Inspection and Fault Localization using Deep Learning

Inspection of brake components is very essential to detect the damaged manufactured parts before it is assembled in any vehicle. Manual inspection of brakes is extremely difficult since most of defects are very minute and cannot be identified by human eyes. Therefore, automatic inspection of manufactured brakes is indispensible to prevent failure of brakes and accidents. Previously, various research articles perform inspection of brake through conventional image processing and traditional image processing algorithms. However, these techniques are capable of identifying a single fault only and are less robust to detecting numerous faults. Further, the existing techniques hardly localize the exact location of faults in the surface of brake. In order to over these drawbacks, in this research we utilize deep learning object detection algorithms namely Single Shot Detector and Faster RCNN to identify and localize the exact location of fault on the brake surface. Furthermore, the proposed system is capable to detect different types of faults in a single algorithm and is robust to brake’s material surface, environmental and lightening factors. The deep learning algorithms are trained using transfer learning on custom collected dataset. The proposed algorithms deliver an accuracy of 95.64% and mAP of 73.2% on cylindrical grey shade brakes.
In-situ analyzing of air pollution by laser induced breakdown spectroscopy in laser paint removal

Laser paint removal is widely used in industry in recent years. Compared with traditional paint removal methods, laser paint removal is considered clean and environmentally-friendly. However, particles generated in the cleaning process may cause significant air pollution and harm to the operator’s health. In this work, we demonstrated that particles with different sizes, especially those smaller than 2.5 μm, are generated in the process of laser paint removal. The chemical composition of these particles is analyzed in-situ by using laser induced breakdown spectroscopy. The results show that heavy metal elements (such as Ba, Pb and Ti) can be found in the particles. These heavy metal elements containing, small size particles pollute the air to an unacceptable level. We suggest that great attention should be taken to deal with this particulate air pollution to reduce the health risk for laser paint removal workers.
The Impact of Social Networking Sites on Luxury Vehicles Purchase Decision Process in Gulf Cooperation Council Countries

Abstract Social networking sites are becoming an essential part of consumer daily activities, with profound changes in purchasing behavior compared to traditional buying formats. This study investigates the possible impact of social networking sites on the purchasing decision process concerning luxury vehicles in one of the most important markets for such products, the countries of the Gulf Cooperation Council (GCC). The study also investigates the possible impact of relative demographics on the direct relationship between social networking sites (independent variable) and the purchase decision process (dependent variable) of luxury vehicles. In order to achieve this aim, a descriptive analytical methodology is utilized with quantitative survey strategy, with a five-point Likert-type questionnaire. Of 1,500 personally submitted questionnaires distributed over a non-probability quota sample to the main showrooms and dealerships in the GCC, 1,257 were retrieved (a response rate of 83.8%); after the filtration and screening process, 1,224 valid questionnaire forms were statistically analyzed. The results indicate that social networking sites have a significant positive direct impact on the purchasing decision process of luxury vehicles in the examined population, with the highest impact being for the first two stages of the process (need recognition and information search). As for the moderation effect of relative demographics, only income and time daily time spent on social media had positive significant moderating effects on the direct relationship between social networking sites and purchasing decision process, while education had a negative significant effect. The results also indicated particular roles of certain social networking sites, like YouTube, and the fact that females prefer Snapchat over other digital platforms. Discussion, implications, and future research recommendation are provided based on the results of the analyses.
Optimization of Near‐Surface Quantum Well Processing

Herein, an optimized process flow of near‐surface quantum well metal–oxide–semiconductor field‐effect transistors (MOSFETs) based on planar layers of metalorganic vapor‐phase epitaxy (MOVPE) grown InxGa1−xAs is presented. It is found that by an optimized pre‐growth cleaning and post‐metal anneal, the quality of the MOS structure can be greatly enhanced. This optimization is a first step toward realization of a scalable platform for topological qubits based on a well‐defined network of lateral InxGa1−xAs nanowires grown by selective area growth.