Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone-based Identification of Critical Levels of Glycated Hemoglobin A1c using Transdermal Optical Imaging

Abstract: Worldwide, the prevalence of diabetes has continued to increase rapidly. This gives rise to concerns regarding appropriate diabetes management to ensure optimal glycemic control. Untreated or uncontrolled diabetes can lead to a host of complications, such as cardiovascular diseases, an increased likelihood of morbidity and mortality (Deshpande, Harris-Hayes, & Schootman, 2008). A challenging problem which arises in diabetes management is the limitations of current blood glucose monitoring techniques. Electronic medical devices can potentially overcome the persistent problems in the healthcare industry. Thus, for this study, it was of interest to investigate whether advanced machine learning methods and Anura, a smartphone-based transdermal optical imaging technology (TOI) that assess health markers, can be a viable solution for diabetes management. Objectives: To examine the validity of TOI and a novel machine algorithm for diabetes prediction (i.e diabetes and non-diabetes). We compared the diabetes classification from TOI’s obtained glycated hemoglobin A1c (HbA1c) concentrations against data obtained from FDA approved blood immunoassay. Methods: In the present study, we used a kitchen sink random forest machine algorithm for diabetes prediction. The data set was obtained from 513 participants recruited during their annual physical examination at the Health Management Center of The Affiliated Hospital of Hangzhou Normal University, China. This included participant’s TOI and blood immunoassay determined HbA1c concentrations. To validate the model, pristine testing was done on 400 pristine participants pseudo randomly selected during 20 trials of training/testing. Results: The confusion matrix found TOI to have a classification accuracy of 66%, and the ROC curve of the RF classifier found TOI to have a ROC AUC of 0.69. Conclusions: The present study provides evidence for the potential use of the TOI technology, Anura, for contactless, non-invasive, and inexpensive assessments of diabetes.
Preferences and Prejudice: Does Empathy Affect Preferences for Microaggression Training?

The present study examines the relationships between a participants' empathy levels and training preferences. It is hypothesized that participants with higher empathy levels will prefer video and group discussion activity, while those lower in empathy will prefer a lecture format. Also, it is hypothesized that the video will be most favored, followed by the group discussion and lecture. The sample (N = 183) consists of individuals from a large southwestern university. Participants attended a training workshop on microaggressions in which they were tasked to watch a video, listen to a lecture, and engage in group discussions. After completing these tasks, participants completed a packet that assessed their preferred training modality, how much they learned from certain aspects of the training, and how relatable they found the scenarios presented in the training. Results revealed that empathy was not related to training preference, but that participants overall 1 Berk’s (2017) article is an example of microaggression training literature as the researcher discusses what steps to take when conducting a microaggression workshop. His steps range from instructing about the topic of microaggressions to having participants take an IAT. Douglas and colleagues (2018) state in their article that lecture does not seem to captivate students as much. Therefore, more contemporary teaching methods are being utilized such as group discussions. Another example of a favored the video (64%), followed by the group discussions (26.1%), and the lecture (9.3%). Overall, while there were training preferences, the participant’s preference did not affect how much they learned about the subject. This study suggests that it is not the modality of training that makes the difference. What is important is that more training should be conducted.
Vehicular crowd-sensing: a parametric routing algorithm to increase spatio-temporal road network coverage

ABSTRACT Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data. In this paper, we present an evolution of the standard A routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles.
Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization

Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it’s also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal–Wallis test. The results showed that the p values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.
Flexible circularly polarized antenna with axial ratio bandwidth enhancement for off‐body communications

National Natural Science Foundation of China, Grant/Award Number: 61372008; Science and Technology Planning Project of Guangdong Province, Grant/Award Number: 2014A010103014, 2015B010101006; Foundation of the State Key Laboratory of Subtropical Building Science, Grant/ Award Number: 2020ZA05 Abstract A flexible circularly polarized (CP) wearable antenna is proposed for a wireless body area network at the 5.8‐GHz industrial, scientific, and medical band. The proposed antenna is made of flexible polyimide and foam material. The CP radiation is enabled by loading two pairs of shorting pins on a rectangular patch antenna. The 3‐dB axial ratio (AR) bandwidth is doubled by adding two inverted L‐shaped parasitic stubs on both sides of the antenna. For validation, the antennawith the dimensions of 0. 68λ0� 0. 68λ0� 0. 044λ0 was fabricated and measuredinvariousscenarioswithan impedancebandwidthof6.6%(5.67–6.05GHz),anAR bandwidthof3.85%(5.730–5.955GHz),andapeakgainof7.2dBi. Themeasuredradiationin the off‐body direction is achieved with a half‐power beamwidth of 65° and a 3‐dB AR beamwidthof84°. With thespecified inputpowerof100mW,thespecificabsorptionratesare 0.613 and 0.294W/kg in 1‐g and 10‐g average tissues. Moreover, through analysing the link budget a reliable communication can be guaranteed within 20 m in indoor line‐of‐sight environments.
Bearing fault detection using an alternative analytic energy operator: a fast and non-filtering method

Teager energy operator (TEO), as a classic energy demodulation method, is specially employed to detect both the amplitude and frequency modulations of the vibration signals recorded from damaged elements. The fault characteristic frequency is identified in the spectrum of energy-transformed signal through the inherent amplitude demodulation. However, the TEO is easily sensitive to intensive noise and vibration interferences. To conquer this disadvantage, an alternative energy operator method, which is realized using a multiresolution symmetric difference and analytic energy operator, is proposed to cope with the vibration signals corrupted by strong noise and multiple vibration interference components. It is simple to implement bearing fault detection and provides excellent time resolution. So, the proposed energy detector is a fast and non-filtering method. The results of simulated tests and real experiments show that the enhanced energy operator can detect fault characteristics effectively in harsh working conditions and certify its superiority according to compare with previous energy operator techniques as well as some popular fault detection methods.
WHAT CHANGES IN IMPLEMENTING COMPETENCE-BASED MEDICAL EDUCATION IN INDONESIA: A QUALITATIVE STUDY

Background: Competence-based Medical Education (CBME) is the latest curriculum model adopted by many countries since 1970s. Reforming medical curriculum to adopt CBME implicates major changes in all aspects and research on this is still lacking. This study aims at identifying changes in implementing CBME from the aspects of organization and structural changes, curricular design, implementation and evaluation and cultural changes. Methods: Retrospective qualitative method is applied using purposive sampling. Selected documents are used as the data and analysed using thematic analysis. Results: Four themes are identified for the first aspect, i.e. the role of central authority, multidepartment committees, committed change agents, and decision-making procedures. Whereas for the second aspect, five themes are pinpointed, consisting of curriculum design at macro level, meso and micro level, faculty development program, learning resources, implementation, and monitoring and qualitative evaluation. For the third aspect, four themes emerge, namely enabling factor, inhibiting factor, the paradox of the new curriculum and quality assurance. Conclusion: Major changes taking place in the design and implementation of CBME have been identified from three aspects inductively. Awareness of the kinds of changes and using them for curriculum planning could improve the success in shifting towards CBME.
Univariate water consumption time series prediction using deep learning in neural network (DLNN)

The ability to predict water consumption benefits in terms of future temporal water patterns, operational reasons in distribution and decision making related to the development of water supply systems. Although, there have been numerous research work on time series forecasting, the methodology may still need further development in the context of non-linear modeling and analysis. There is many unpredictability that results in complex temporal dependence with traditional time series approaches. But now, artificial neural networks are available to help resolve these issues can improve model prediction. Therefore, this paper presents an experimental study to perform univariate water consumption for time series prediction processes using Deep Learning in Neural Network (DLNN). DLNN has abilities to learn the algorithm from previous data. It also able to process an explosion of data with a nonlinear approach, which the univariate water consumption time series prediction problem was modelled into artificial neural networks. The vast majority of learning algorithms along with DLNN are used in this study called Multilayer Perceptron (DLNN-MLP), Convolutional Neural Networks (DLNN-CNN) and Long Short-Term Memory (DLNN-LSTM) for the model prediction to generate prediction with the lowest error. Dataset from the residential water usage in SIBU Division was chosen for the experiments of this study. The performance of the DLNNs’ models is measured using RMSE and state-of-the-art algorithms. The value of RMSE for a problem relates to the calculation of the lowest error. Based on the experiments result indicates that the DLNN-LSTM (RMSE:0.051) can make decent predictions for water consumption time series despite being inferior to SARIMA (RMSE:0.183). Hence, this finding implies that DLNN-LSTM can be implemented on a univariate water consumption time series prediction problem given that the problem is modelled as supervised learning.
Analyzing the Factors Affecting the COVID-19 Risk Level in the US Counties

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.
Endoscopic Endonasal Surgery of a Large Vidian Nerve Schwannoma With Preparation for Avoiding Major Vascular Injury

Vidian nerve schwannomas are extremely rare, and their surgical management requires an awareness of the surrounding vascular and nervous systems, including the internal carotid artery. Herein, we report a case of a vidian nerve schwannoma that was successfully removed using an endoscopic endonasal approach in a 21-year-old patient who presented with lacrimal hyposecretion. Imaging revealed a large mass extending to the middle cranial fossa posteriorly, to the pterygopalatine fossa laterally, and to the sphenoid sinus medially. The paraclival and petrosal portions of the internal carotid artery were displaced posteriorly. Endoscopic observation of the right nose demonstrated anterior displacement of the inferior portion of the middle turbinate. Based on the above, we suspected a vidian nerve schwannoma, and endoscopic endonasal surgery was performed with particular attention to avoid vascular injuries. An endoscopic transmaxillary approach was used to expose the anterior surface of the tumor. After confirming the pathological diagnosis intraoperatively, intracapsular resection of the tumor was completed using an ultrasonic surgical aspirator with Doppler monitoring of the location of the internal carotid artery. Endoscopic management of the surgical field and preparation to avoid vascular injury are essential for safe and efficient tumor resection.
Application of exchange transfusion in the treatment of hyperleukocytosis:a retrospective analysis

Background Few reports have described the application of exchange transfusion in patients with hyperleukocytosis. Methods A retrospective clinical analysis of infants with exchange transfusion in the treatment of hyperleukocytosis was performed. Results A total of 13 patients were enrolled in the study.7 patients survived and 6 died after exchange transfusion.The incidence of pulmonary hypertension, heart failure, pertussis encephalopathy, Increased C-reactive protein, and increased PCT in the survival group were lower than those in the death group,The incidence of received Gamma globulin and received steroids were higher than those in the death group.The highest WBC count in the survival group was 74.15 ± 19.68*10^9/L, and in the death group patients was 86.29 ± 23.51*10^9/L.There were not significant differences two groups(P = 0.332). Decrease rate of WBC after the first ET in the survival group was significantly higher than that of the death group (P༜0.05). In the survival group, there were 5 patients (71.42%) which decrease rate of WBC after the first ET more than 50%, and the remaining 2 patients (28.58%) all exceeded 45%. In contrast, there was 1 patient which decrease rate of WBC exceeded 45% ,no patient exceed 50%. All patients with Decrease rate of WBC after the first ET exceed 50% survived. In the death group, there were 4 patients (66.67%) which N/L exceeded 1, 3patients (50%) which N/L exceeded 2. In the survival group, only 3 patients (42.85%) which N/L exceeded 1, but there was no patient which N/L exceed 2. Conclusion Exchange transfusion is an effective method to reduce WBC count. Patient with Decrease rate of WBC after the first ET exceed 50% were easier to survive. Patients with hyperleukocytoemia which N/L exceeding 2 predict death.
Frequent Pattern Mining in Big Social Graphs

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a significant role in many domains, such as social networks and bioinformatics. However, due to the exponential time complexity of FPM, it is a challenge for most existing techniques in big dense graphs, such as social graphs. In this paper, with the defined concept of social pattern, a corresponding linear time computable support calculation measurement, called Minimum Independent Individual based support, is proposed. Then, we adopt the concept of pathgraph to store the appearance of social patterns and propose a novel approach (SocMi) to solve the problem of frequent social pattern mining. Additionally, in order to reduce the exponential time consumption to explore big graphs, an approximate approach (ASocMi) with a quick exploration strategy is proposed. Moreover, the proposed approaches have been further optimized by using cache during processing. Finally, an extensive empirical study in real-world social graphs has demonstrated the effectiveness and efficiency of the proposed approaches compared with the state-of-the-art approaches.
Electroless deposition and investigation of composite coatings based on nickel or cobalt matrix, including boron nitrides as dispersoids, on polyethylene terephthalate substrate

Composite coatings, based on nickel or cobalt matrix with incorporated boron nitride particles (BN) as dispersoid, have been chemically deposited and investigated. The mass, and relative thicknesses, of the obtained composite coatings, as well as the number of co-deposited particles, depending on the type (alpha boron nitride – BN(h) or cubic boron nitride – BN(c)) and particle size of the dispersoid, as a function of dispersoid concentration in the working electrolyte and the concentration of the reducing agent in the plating electrolyte have been determined. The morphology, structure and chemical composition of the deposited coatings have been characterised by means of SEM and EDS analysis.
Vegetable and Fruit Waste Production Related to Consumption in Turkey and Certain Middle East Countries

Vegetable and fruit wastes (VFW) comprise a large portion of wastes that contribute to pollution in different ways. This is because there is a large demand for such perishable products by all countries. The Turkish economy was mostly depending on agriculture before 40 years ago but due to globalization this has been changed into industrialization. Even though the industrialization dominates, the consumption of fruits and vegetables has been in an increasing trend in Turkey and its environs. The usage of such resources can be regulated by governments and organizations to avoid overusing them. Also, there are many ways for treating such inevitable wastes. To mention a few, extracting pigment, antioxidants, pesticides, producing single-celled proteins, enzymes, biofertilizers, green energy, and many others from the wastes can be done.
The Impact of Policy Shift from In-kind Transfers to Direct Cash Transfers on Paddy Production: Evidence from Mahaweli H System in Sri Lanka

In 2016, the Sri Lankan government introduced a policy change related to fertilizer subsidy by converting the in-kind transfer into a direct cash transfer. This research article analyses the consequences of this policy change on the paddy production from economics perspective. The analysis uses national-level data from 1961 to 2013 and farm-level data collected in 2016. Macro-level findings manifest that the use of fertilizer significantly increases the paddy production in Sri Lanka. It was also identified that the cash amount granted under the direct cash transfer policy is not equivalent to the in-kind transfer programme. As a result, paddy production is expected to decline under the direct cash transfer programme when compared to the material subsidy scheme. However, this is against the preference of economists on direct cash transfers over in-kind transfers. The findings reveal that direct cash transfers increase the paddy production under two conditions: (a) when rational farmers effectively utilize the cash grants to optimize their production inputs; and (b) an equivalent amount of in-kind transfer is provided as direct cash transfer. Hence, direct cash transfers are not always better than in-kind transfers; it is better when in-kind transfer is compensated with an equivalent amount of cash transfer. JEL: A1, B1, B2, C1, C5, D6, N5
Antioxidant enzymes and lipid peroxidation of human blood platelets in essential hypertension

The present work was conducted to evaluate the oxygen free radical system and antioxidants in platelets of patients suffering from essential hypertension (EH). Fifteen age and sex matched, healthy persons were taken as controls and 45 untreated essential hypertensive patients (Group I, mild - 15; Group II, moderate - 15 Group III, Severe 15) were studied. The levels of malonyldialdehyde (MDA), superoxide dismutase (SOD) and catalase were estimated. MDA was increased significantly (p<0.001), while SOD and catalase were reduced significantly (p<0.001) in all EH subjects as compared to controls.
A Corner-Highlighting Method for Ambient Occlusion

Graphical user experiences are now ubiquitous features, and therefore widespread. Specifically, the computer graphics field and the game industry have been continually favoring the ambient occlusion post-processing method for its superb indirect light approximation and its effectiveness. Nonetheless of its canonical performance, its operation on non-occluded surfaces is often seen redundant and unfavorable. In this paper, we propose a new perspective to handle such issues by highlighting the corners where ambient occlusion is likely to occur. Potential illumination occlusions are highlighted by checking the corners of the surfaces in the screen-space. Our algorithm showed feasibility for renderers to avoid unwanted computations by achieving performance improvements of 15% to 28% acceleration, in comparison to the previous works.