Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery

Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN’s prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the proposed method is as an effective and efficient approach to solve the challenging problem of crop classification using FSR imagery (including from different remotely sensed platforms). More importantly, the OSVM-OCNN method is readily generalisable to other landscape classes and, thus, should provide a general solution to solve the complex FSR image classification problem.
The study on RBC deformability of iron deficiency anemia

RBC deformability of rats for iron deficiency anemia (IDA) with and without sports was measured with Ektacytometry, and it was' found that, compared with the normal group, the RBC deformability of IDA group decreased markably and the drop of RBC deformability for IDA with sports even further. A regressive equation between the hemoglobin concentration and RBC deformability was obtained. Results were discussed from a medical sports hemorheological point of view.
Root defects following air polishing: An in vitro study on the effects of working parameters

AIM Air-polishing devices (APDs) are highly effective in removing plaque and extrinsic staining. Their application on root surfaces, however, may result in clinically relevant substance removal, limiting the use in patients with periodontitis, where denuded root surfaces are frequently found. Therefore, the purpose of the study was to assess the influence of different working parameters on root damage and to identify those minimizing root damage. MATERIAL AND METHODS Defect depth and defect volume after instrumentation of roots with an APD (Dentsply Prophy-Jet) using conventional NaHCO3 powder at instrumentation times of 5, 10 and 20 s, combinations of low, medium and high powder and water settings, distances of 2, 4 and 6 mm, and angulations of 45 degrees and 90 degrees were quantified laseroptically. A total of 297 roots were instrumented and parameter combinations were performed in triplicate. The influence of each working parameter on substance loss was determined by multiple regression analysis. RESULTS Time had the greatest influence on defect volume and depth (beta-weights 0.6 and 0.57, respectively), when compared with powder setting (beta-weights 0.49 and 0.3) and water setting (beta-weights 0.28 and 0.3). Variations in distance affected defect depth (beta-weight 0.44), but not volume (beta-weight 0.04). No major differences were found at 45 degrees and 90 degrees. Various parameter combinations led to maximal defect depths of 473.5 +/- 26.2 micro m within 20 s. CONCLUSION Root damage varies among combinations of working parameters. Using the APD with the assessed NaHCO3 powder, all parameter combinations led to substantial root damage. Thus, APDs using NaHCO3 may not be safely utilized on exposed root surfaces.
A Prescriptive Analytics Method for Cost Reduction in Clinical Decision Making

Containing skyrocketing health care costs is imperative. Toward that end, prescriptive analytics that analyzes health care data to recommend optimal decisions is both relevant and crucial. We develop a novel prescriptive analytics method to improve the cost effectiveness in clinical decision making (CDM), a critical health care dimension that can greatly benefit from analytics. Effective prescriptive analytics for CDM has to address its probabilistic, cost-sensitive, and investment-related characteristics simultaneously. Unlike existing methods that often overlook the investment-related characteristic, the proposed method accounts for all of these characteristics. Specifically, our method considers two sets of costs associated with clinical decisions — before and after an investment — in combination with the probabilities of cost changes due to the investment. In contrast, prevalent methods only emphasize one set of costs, before an investment. Furthermore, the proposed method involves both clinical and investment decisions, whereas existing methods ignore investment decisions. Empirical evaluations with two real-world clinical data sets indicate that the proposed method consistently and significantly outperforms several salient methods from previous research, thereby demonstrating the value of addressing the investment-related characteristic in efforts to improve CDM.
High Gain Amplifier Design for Switched-Capacitor Circuit Applications

In early decades, CMOS technology made its way into analog circuit design through discrete systems comprised of Switched-Capacitor circuits. Robust amplifier designs made it suitable for multiple applications. Few of these amplifier designs are implemented in this paper to meet the requirements of switched-capacitor integrator circuitries. One of the designs is a Two-Stage OP Amp and another one is Folded Cascode OP amp. Both the designed OP Amps are analyzed and tested for providing optimum Switched-Capacitor performance requirements. In accordance with this design strategy, both the proposed OP amps are optimized with design rules of 0. 18μm CMOS technology. Small signal analysis is done in order to achieve the required design parameters.
A Novel System for Deep Analysis of Large-Scale Hand Pose Datasets

This degree project proposes the design and the implementation of a novel system for deep analysis on large-scale datasets of hand poses. The system consists of a set of modules for automatic redundancy removal, classification, statistical analysis and visualization of large-scale datasets based on their content characteristics. In this project, work is performed on the specific use case of images of hand movements in front of smartphone cameras. The characteristics of the images are investigated, and the images are pre-processed to reduce repetitive content and noise in the data. Two different design paradigms for content analysis and image classification are employed, a computer vision pipeline and a deep learning pipeline. The computer vision pipeline incorporates several stages of image processing including image segmentation, hand detection as well as feature extraction followed by a classification stage. The deep learning pipeline utilizes a convolutional neural network for classification. For industrial applications with high diversity on data content, deep learning is suggested for image classification and computer vision is recommended for feature analysis. Finally, statistical analysis is performed to visually extract required information about hand features and diversity of the classified data. The main contribution of this work lies in the customization of computer vision and deep learning tools for the design and the implementation of a hybrid system for deep data analysis.
Educational Interface of Load Flow Methods Using Matlab Graphic User’s Interface Software

this paper introduces a new created program application built based on MATLAB programing language to solve power system networks at steady state problems, which include power flow studies with significant approach. The load flow solution uses iterative techniques that are namely, Newton-Raphson, Gauss-Seidel and Fast Decouple. After obtaining their results, line changes like line switching, tap changer and bus changing such as bus deletion or any specific information at the same bus can be easily achieved. Moreover, the users can enter their own system, and save it to apply the pervious analysis. This friendly interactive program designed to be simple for the users with accurate results and it can be applied for any network.
Taste Disorders and Zinc Deficiency

There are many causes of taste disorders, and, accordingly, a variety of clinical manifestations. Among these disorders, the most frequent are secondary taste disorders caused by zinc deficiency or by medications used to treat other diseases. The mechanisms of taste disorders have been investigated in association with zinc, a trace element. In this article, the causes of taste disorders are described mainly in relation to zinc. In addition, zinc therapy, which has been examined extensively, is evaluated.
Industrialization Drive and Economic Growth in Africa

This paper empirically examines the impact of industrialization on economic growth in Africa, and also how trade openness augments this effect. We consider the study of industrialization imperative following recent commitments of African governments and the African Development Bank to it, and also it been a core part of the Sustainable Development Goals. Industrialization is argued to be the way forward for economic transformation of developing countries. Employing data for the period 1980-2014 from 37 African countries and the generalized method of moments (System GMM), we show two main results; first, industrialization has on its own boosted economic growth in Africa. Second, trade openness augments the effect of industrialization on economic growth. Our results are robust to alternative measures of industrialization and subsampling analyses. Our results therefore indicate that the commitments to industrialization are calls in the right direction. They serve as a major signal for African governments to initiate and intensify policies of export promotion, providing requisite infrastructure (such as power, transportation and telecommunication) and also promoting entrepreneurship in the industrial sector especially among the youth.
Endurance training in adults with diabetes mellitus type 2

OBJECTIVES Test the effects of an endurance training exercise on glycosylated hemoglobin (HbA1c) levels, muscle strength and perceived muscle strength. Explore the influence of diet, exercise, medication and manifestations of hypo or hyperglycemia in the blood glucose control of adults with diabetes mellitus type 2, who attended endocrinology clinics at two public hospitals in Monterrey, Mexico. BASIC PROCEDURES A 12-week non equivalent control group design was used. At baseline, the experimental group consisted of 14 participants and the control group 11. The exercise sessions were held twice a week for one hour. All participants had received a verbal recommendation from their physician. RESULTS The experimental group displayed a significant decrease in HbA1c levels and increases in muscle strength and perceived muscle strength (p < .001). Eight participants out of eleven achieved normal HbA1c values after the intervention. No effect of the co-variables was found. CONCLUSIONS This type of intervention can be of help to control blood glucose levels in adults with diabetes mellitus type 2.
Revisão do stent coronário com eluição de sirolimus

The sirolimus-eluting coronary stent received CE Mark approval in Europe in April 2002. In the US, FDA approval followed in April 2003. Since the preliminary results from the First-in-Man feasibility study were presented, several randomized, controlled trials have documented the profound antiproliferative effects of sirolimus, a macrolide antibiotic and potent cytostatic inhibitor of smooth muscle cell proliferation. Subsequently, the body of clinical evidence was increased by the second wave of evidence from trials in more complex lesions (such as in-stent reestenosis, small vessels, chronic total occlusions) and "high-risk" patients such as those with diabetes. More recently we have had the opportunity to compare the two commercially available drug-eluting stents following the presentation of data from five head-to-head trials. As a result of numerous single and multi-center, national and international studies in which the safety and efficacy of sirolimus-eluting coronary stents have been subjected to close scrutiny, the global intervention cardiology community now has evidence in support of the use of this technology resulting in dramatically improved patient outcomes after percutaneous intervention.
Multiomics analysis of microbiota-metabolites relations and meta-analysis for the effects of probiotics on children with atopic dermatitis(本文)

The gastrointestinal microbiota colonize the digestive tract and are composed of approximately 1014 microbes and more than 400 species. These microbes are responsible for maintaining the gastrointestinal environment by facilitating digestion and interacting with the host immune system. In this study, the relationships among microbiota, their host and metabolites are analyzed from the viewpoint of molecular biology and public health. First, the effect of microbiota on atopic dermatitis is reviewed and analyzed. The results of a meta-analysis examining the effects of probiotics on children with atopic dermatitis make it difficult to conclude that probiotics have a suppressing effect on atomic dermatitis (AD) symptoms based on some qualified papers. Among these studies, the differences of patient age and country region produced the strongest effects on AD. On the other hand, it is difficult to discuss the effect of probiotics considering the differences in probiotics, as well as the differing dynamics of microbial profiles, which proved to be a major limitation of this study. Second, metabolic analysis and microbial population analysis of the murine intestinal tract components are conducted to understand the ecosystem among the host, metabolites and microbiota. Metabolic profiles have differences among each part of the intestine. Particularly, sugar shows the largest differences, and sugars are clustered into seven groups.
Discourse-Aware Neural Extractive Model for Text Summarization

Recently BERT has been adopted in state-of-the-art text summarization models for document encoding. However, such BERT-based extractive models use the sentence as the minimal selection unit, which often results in redundant or uninformative phrases in the generated summaries. As BERT is pre-trained on sentence pairs, not documents, the long-range dependencies between sentences are not well captured. To address these issues, we present a graph-based discourse-aware neural summarization model - DiscoBert. By utilizing discourse segmentation to extract discourse units (instead of sentences) as candidates, DiscoBert provides a fine-grained granularity for extractive selection, which helps reduce redundancy in extracted summaries. Based on this, two discourse graphs are further proposed: ($i$) RST Graph based on RST discourse trees; and ($ii$) Coreference Graph based on coreference mentions in the document. DiscoBert first encodes the extracted discourse units with BERT, and then uses a graph convolutional network to capture the long-range dependencies among discourse units through the constructed graphs. Experimental results on two popular summarization datasets demonstrate that DiscoBert outperforms state-of-the-art methods by a significant margin.
The effect of fast trombone slide glissandi on the mechanics of artificial lips

The lips of a brass player can be modelled as a mechanical oscillator which couples with the acoustic resonances of the instrument to generate and sustain a musical note. On instruments like the modern orchestral trumpet, operation of a valve changes the length of the resonator without significantly altering the mean pressure of the air column. On a slide trombone, however, a glissando involves a telescopic change in the length of the resonator, resulting in localised changes in the mean pressure in the air column. The consequent low frequency fluctuations in mouthpiece pressure can disturb the equilibrium position of the lips, affecting their dynamic behaviour. This paper describes experimental studies in which an artificial mouth is used to investigate the infrasound mouthpiece pressure gradients generated by fast glissandi on a trombone, and their influence on the mechanics of the oscillating lips.
Outcomes with Combination Neo-adjuvant Chemotherapy for HER2Neu Over-Expressed Breast Cancer

Certain breast cancers that over-express HER2Neu (H2N) are now considered for neo-adjuvant chemotherapy using dual anti-H2N targeted drugs. Since January of 2013, 14 women with H2N over-expressed breast cancer were treated with a regimen including trastuzumab (Herceptin) and pertuzumab (Perjeta) prior to definitive surgery in our institution. 7 of 14 (50%) were estrogen receptor negative (ER-/H2N+) while the other 50% were ER+/H2N+. 9 of 14 (64%) had six cycles of chemotherapy prior to surgery while the remaining had four or five cycles prior to surgery. 9 of 14 (64%) had a mastectomy while 5 of 14 (36%) had breast conserving therapy. Final pathology showed that 50% had a complete pathologic response (pCR), while the remaining patients all had a significant partial response. Patients who received all six neo-adjuvant cycles had a higher rate of pCR. 6 of 14 (43%) patients had full axillary lymph node dissections (ALND); only 2 of the 6 (33%) contained residual tumor. 7 of 14 (50%) patients had sentinel lymph node biopsies (SLNB) done during definitive surgery. No SLNB specimens contained tumor. We observed a very low rate of residual axillary nodal disease suggesting that ALND may not always be necessary in these cases. based therapy which can be used to augment chemotherapy treatment [4,5]. Advances in translational science have led to the development of a large spectrum of H2N directed therapies. Studies have shown that targeting H2N receptors combined with chemotherapy offers benefits to patients with metastatic H2N+ breast cancer compared with chemotherapy alone [4,6]. This finding led to the use of chemotherapy and anti-H2N targeted therapies in the neo-adjuvant setting with promising outcomes. Over the last several years, studies have explored using dual anti-H2N targeted therapy paired with cytotoxic agents to augment treatment effects. Several large clinical trials have shown outstanding results with the use of dual anti-H2N receptor targeting combined with cytotoxic chemotherapy in the neo-adjuvant setting [4,7,8]. Complete pathological response (pCR) has been used as the primary endpoint in most studies as this is directly correlated with improved survival [4]. The preferred definition of pCR is the absence of residual invasive disease in both the breast and lymph nodes after pathological analysis. Some studies count residual in-situ disease as pCR while others do not. Using pCR as a primary endpoint allows for accelerated studies in neo-adjuvant ABBREVIATIONS ER: Estrogen Receptor; H2N: HER2Neu; ALND: Axillary Lymph Node Dissection; SLNB: Sentinel Lymph Node Biopsy; pCR: Complete Pathologic Response; IHC: Immno-Histochemistry; FISH: Fluorescence in situ Hybridization
Analisis Sistem Cloud Computing IAAS Penyedia Server Cloud dengan Standar NIST Special Publication 800-145

The National Standards and Technology Institute (NIST) is a science laboratory, and a nonregulatory institution in the United States Department of Commerce. NIST is responsible for developing standards and policies with minimum requirements, to provide adequate information for all operations and agency asset. One recommendation issued by NIST is Special Publication (SP) 800-145 concerning definition of cloud computing. SP 800-145 recommended 5 facts cloud computing On-demand self-service, Broad network access, Resource pooling, Rapid elasticity, and measured services. The system that can be categorized as cloud according to NIST already has recommended features. The problem is whether an IAAS cloud service provider system in providing a server is a cloud computing system based on NIST standards, in this paper will analyze a cloud computing system with the Infrastructure AS A Service (IAAS) Cloud server provider with the NIST SP 800-145. The method used is to analyze whether the system created has fulfilled the characteristics of the NIST with some evidence. This paper produces an analysis of the IAAS cloud system provider cloud server with the NIST SP 800-145 approach. Result of this research is server provider application from previous research studies that have fulfilled NIST Cloud computing
Activity Recognition Using Wearable Sensors for Healthcare

Wireless Sensor Networks (WSNs) formed by wireless sensors can be designed to interact continuously with the real world. WSNs help in communicating accurate real time information and have proliferated from the areas of industrial processing and environmental monitoring to science and healthcare. Today’s healthcare systems are facing two serious challenges: rapid growth in adult population and severe nursing shortage. As per United Nations (UN) statistics, the number of people in the world aged 60 years and above are expected to increase from 605 million to 2 billion by 2050. Nursing shortage has been identified as a global crisis since 2002 and India has a nurse density per thousand population of 0.80. These statistics suggest a urgent need for automated monitoring systems to aid the healthcare industry. In this paper, we describe the implementation of a patient activity monitoring system using wearable flex sensors. Patient activities are classified as standing, sitting, or walking using a lightweight, low latency algorithm. Our approach features a unique sensing technique and facilitates a cost effective and energy efficient solution for healthcare. Keywords—Healthcare; activity recognition; flex sensor; wireless sensor networks.
Empirical Evidence of Market Reaction to Stock Splits

The study has analyzed the Market’s reaction to stock split announcements by Companies during the period of study. The period of study selected is 2014-2015.Using event study methodology, the study finds that there is no reaction of the market to stock split announcements. Average Abnormal Returns (AAR) and Cumulative Abnormal Returns (CAR) are negative on the split date and around the split date. To compare the AAR and CAR before and after the split date, paired t test was employed. It was found that AAR has increased considerably in the postsplit period thereby indicating a slow momentum in the reaction to stock split announcements. Average Security Return Valuation (ASRV) shows that there is no association between the stock splits as a news and valuation of stocks. Liquidity and Trading Range Hypotheses holds good for the period of study. KeywordsLiquidity, Trading Range, Stock Splits, AAR, CAR, ASRV.