Miracle-dz/UniFAR-FTUs huggingface dataset:
An Umbrella Review of Text Message Programs for Adults With Type 2 Diabetes

Purpose The purpose of this umbrella review was to synthesize the evidence from published systematic reviews on the effectiveness of text message programs for adults with type 2 diabetes (T2DM) on glycemic management (A1C), self-management, and other clinical outcomes. The effect of directionality of the program was also explored. Methods A systematic search was conducted using multiple databases. Inclusion criteria were systematic review of text message programs for adults with T2DM, evaluated A1C, and English language. Quality assessment was completed using AMSTAR-2 guidelines. Data were extracted by multiple coders, and results were synthesized. Results The final sample included 9 systematic reviews published between 2011 and 2019, with 72 unique international studies. Text message programs focused on diabetes self-management and reducing health risks through educational and motivational content with some providing personalized feedback. A meta-analysis of program effect on A1C was conducted in 5 reviews with a pooled difference in A1C from −0.38% to −0.8%. Adults with T2DM of shorter duration and lower A1C had better treatment effects. Evidence on unidirectional versus bidirectional programs is conflicting; however, both improve outcomes. Evidence of text message programs targeting medication engagement was inconclusive. Some programs improved blood pressure, lipids, self-management, self-efficacy, and health behaviors. High satisfaction and an average of 9.6% to 18.7% attrition was reported. Conclusions Text messaging programs can improve T2DM outcomes, are a highly accessible mode of communication, are relatively inexpensive, and are an underutilized adjunct to clinical care.
A retrospective cohort study of early mortality among patients with HIV/TB co‐infection in Shanghai municipality

Tuberculosis (TB) is the most common and fatal opportunistic co‐infection among HIV‐infected individuals. While TB‐associated mortality predominantly occurs in the first 90 days after admission, such a correlation remains unclear in HIV/TB co‐infected patients. Thus, we aimed to investigate the 90‐day mortality and associated risk factors among HIV/TB co‐infected patients in China.
Differentiation between Resins Boswellia carterii and Boswellia frereana (Frankincense) of Somali Origin

ABSTRACT The chemical composition of a methanolic extract of the resins of Boswellia frereana and B. carterii were examined by GC and GC/MS. Although only a limited number of constituents were identified, it is possible to use the data presented to differentiate between resins of each species.
Recreational benefits from the Dartmoor National Park

The travel cost method (TCM) is commonly used by Government agencies to evaluate the benefits users derive from access to parks and other recreational sites. The results of such studies can provide useful input into policy-design, in informing park designation decisions and in helping guide management on issues such as visitor access. The authors investigate various aspects of the application of the methodology in the context of a case study of the Dartmoor National Park in England.
Effect of fining treatments on the aromatic potential of white wines from Muscat Ottonel and Gewurztraminer cultivars

Des vins de Muscat Ottonel et de Gewurztraminer sont soumis a des traitements de collage par addition de differents adjuvants: bentonite, ichthyocolle, caseine et polyvinylpolypirrolidone (PVPP). Apres collage, les composes d'aromes libres et glycosyles sont extraits des vins par passage sur colonne de resine d'amberlite XAD2, et analyses par chromatographie en phase gazeuse couplee a un spectrometre de masse (GC-MS). Les traitements de collage entrainent une diminution des teneurs de differents composes. Les composes glycosyles, constituant un potentiel important de l'arome des vins, sont les plus affectes par les traitements, les pourcentages de leurs pertes varient entre 14 et 33 % de la valeur des vins temoins non traites. Pour certains composes, les pertes sont differentes selon le vin traite et le produit de collage utilise. Par analyse sensorielle, seul le vin de Gewurztraminer traite a l'ichthyocolle presente une difference significative par rapport au vin temoin.
The validity of international instruments for assessing South African engineering students

This study examines the validity of three international instruments for assessing South African first-year engineering students. The Grit-S, Dweck's Implicit Theories of Intelligence Scale (ITIS), and the Revised Purdue Spatial Visualization Test: Visualisation of Rotations (PSVT:R) were assessed for internal functioning and usefulness in predicting engineering drawing subject marks. Grit-S and ITIS were chosen as they may offer potential areas for intervention to enhance psycho-social adjustment to university. The PSVT: R was administered as it was expected to have a relationship with engineering drawing, and spatial reasoning is a skill that can be taught, which will assist first-year engineering students. The study found that Grit-S was not internally reliable and valid for assessing our engineering students. Both Grit-S and ITIS had low discrimination, with most students strongly agreeing with the statements. The overly positive responses to the instruments led to no predictive power. The PSVT:R had a small but significant relationship to the engineering drawing semester mark as well as evidence for internal reliability and validity. Constructs such as Grit and mindset may need to be recontextualised for the African setting, or the instruments would need to be redesigned to offer greater discrimination power. Only the PSVT:R showed some potential for predicting first-year engineering achievement in the graphics module.
Analysis of Bit-Flipping Algorithm of Irregular Low-Density Parity-Check Codes

In this work, we analytically derive the evolution of single-bit messages in the bit-flipping algorithm (BFA) for irregular low-density parity-check (LDPC) codes on the binary symmetric channel (BSC). In particular, the analysis tracks the probability distributions of the flipping functions and the bit-decision error at each iteration, which is possible by introducing a state variable at each variable node. The probability distributions are indirectly obtained by taking advantage of the state and state transition probabilities. The analysis will be confirmed by comparing analytic evaluations with simulation results. For the comparison, we implement LDPC codes at a finite length.
Indoor Path Loss Statistical Characterization and Modeling for the Broad VHF–UHF Band

In this letter, an investigation of the very high frequency–ultrahigh frequency (VHF–UHF) wireless broadband indoor channel is carried out. In particular, a wireless channel measurement campaign in an indoor environment has been made, in order to characterize the path loss in a broad set of frequencies. The wireless channel is sounded by using a set of discone wideband antennas and a vector network analyzer set to sweep the transmitted signal in the considered band. The measurement results lead to the derivation of a well specified indoor path loss exponent statistical model that takes into account the frequency dependence as well as the distance between the antennas. Three different statistical model refinements are presented, and each of them shows an enhanced ability to predict the path loss.
Smart Plant Disorder Identification using Computer Vision Technology

The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.
Short-term Unit Commitment Using Advanced Direct Load Control

In recent years, there is a tendency to reduce greenhouse gas emissions internationally in consideration of environmental problems. In the field of electric power systems, the introduction of renewable energy power generation facilities centered on photovoltaic power generation is being promoted in order to reduce greenhouse gas emissions. However, if these power generation facilities are introduced excessively, a duck curve phenomenon occurs in which the operating efficiency of the thermal power generator is poor. Currently, we are reducing the output of photovoltaic power generation equipment to avoid the duck curve phenomenon. In order to suppress the output, it is not possible to further install renewable energy power generation equipment, and the limit of introduction comes with a fixed amount. Therefore, this paper proposes a new load demand control method that improves the duck curve phenomenon without hindering the introduction of renewable energy power generation equipment. In addition, in order to make the proposed method more active, the usual unit commitment has a time interval of 1 hour, but at the same time, we also devised a time interval of 30 minutes. We show that by performing a short-term unit commitment using the proposed method, the optimal operation can be performed even when a large amount of renewable energy power generation is introduced.
ADSORPTION‐DESORPTION OF 2, 4, 6‐TRINITROTOLUENE AND HEXAHYDRO‐1, 3, 5‐TRINITRO‐1, 3, 5‐TRIAZINE IN SOILS

We studied the adsorption-desorption behavior of TNT (2, 4, 6-trinitrotoluene) and RDX (hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine) in a bentonite/sand reference material (Swy-1 montmorillonite clay mixed with acidwashed sand) and two selected soils (Norwood and Kolin). Release of TNT, RDX, and other compounds from a contaminated soil obtained from the Louisiana Army Ammunition Plant (AAP) site was also investigated. The kinetics of TNT and RDX retention were measured using batch methods for a range of input concentrations. For RDX, the adsorption isotherms were distinctly linear. The TNT adsorption isotherm for bentonite/sand mixture appeared linear and was described equally well using linear, Freundlich, Langmuir, and a modified Langmuir model. For the Norwood and Kolin soils, TNT adsorption isotherms exhibited distinct nonlinearity and the Freundlich model provided the best fit. As indicated by the Kd values, TNT exhibited stronger retention or affinity to all soils and the bentonite/sand mixture than for RDX. The RDX retention data indicated little time-dependent behavior. The TNT retention data indicated a continued decrease in TNT concentration with time in the Norwood and Kolin soils. This was possibly caused by the formation and subsequent adsorption of transformation products because transformation products, such as amino nitro toluene compounds, were identified during batch experiments. For the bentonite/sand mixture, TNT retention was rapid initially and reached apparent equilibrium within 1 day. Unlike Kolin and Norwood soils, there was no hysteretic behavior of TNT adsorption-desorption by the bentonite/sand mixture and a mass balance suggested fully reversible retention mechanisms.
Delay-Sensitive Task Offloading for Internet of Things in Nonorthogonal Multiple Access MEC Networks

With the rapid development of the Internet of Things (IoTs), the fifth-generation (5G) networks need to serve massive connection and accommodate ultra-low delay. In response to these challenges, mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) have been considered as the promising solutions. In this paper, we investigate the joint optimization problem of computation offloading and resource allocation in NOMA MEC networks to minimize the delay to complete tasks of all users. Different from the conventional optimization approach, we propose and develop an online solution based on deep reinforcement learning (DRL) algorithm, which can fit with dynamic networks with time-varying channels. In particular, we employ deep neural networks (DNNs) to process the raw state inputs and then output the computation offloading decision and resource allocation at different times. The weights of DNNs are continuously trained with the observed data via interactions with the environment. Simulation results reveal that our proposed algorithm achieves higher delay reduction compared to the existing strategies.
Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach

In Industrial internet of things(IIoT), the infrastructure and the technology has been improved a lot throughout times. With those improvements, the threats and attacks are also growing rapidly to become more various and advanced attacks. One of the weakest parts in IIoT infrastructure is in the cloud layer that can cause the system failure, but it can reduce the possibility by controlling and maximizing the ability in the fog layer as its near to the edge of devices. In this paper, attack detection in fog computing framework with several machine learning algorithms to efficiently detecting malicious activities is proposed. The evaluation performed by using KDD Cup’99 dataset and compared by using Decision Tree, K-Means, and Random Forest algorithms.
A Reference Flux Calculation Method for Realizing Maximum Efficiency Operation in Direct Torque Controlled PMSM Drives

This paper proposes a maximum efficiency operation strategy for direct-torque-controlled interior permanent magnet synchronous motor drives. The proposed method is based on the solutions of a quartic equation, a motor model in the d-q rotating frame often used for the synchronous motor controllers, and a well-known equation for maximum efficiency operation. Therefore, the proposed method does not use a look-up table that depends on parameters of the motor and that has to be created in advance. In addition, the stator flux observer considering iron losses is proposed and analyzed the effectiveness with the consideration of iron losses. The simulation results validate that the proposed method can achieve the maximum efficiency operation by the iron losses considered in the stator flux observer.
German Sign Language Translation using 3D Hand Pose Estimation and Deep Learning

Sign language is the primary medium of communication for the majority of the world’s population suffering from disabling hearing loss that creates a barrier between the hearing and the hearing-impaired people. In this paper, sign language translation is undertaken for German Sign Language (GSL) characters from a single image by leveraging the technique of 3D object detection. We make use of a three-network architecture that performs segmentation, keypoint localization, and elevation from a two-dimensional plane to the three-dimensional space, from a single RGB image containing the signed gesture. Thirty gestures have been used and the best results were obtained using a combination of pose representation coordinates, joint angles, and pool layer features of AlexNet for classification. The system gives a character error rate of 0.29, a reduction of error rate by 12.12% when compared to the state-of-the-art approach.
Whole Grains and Health, Evidence from Observational and Intervention Studies

ABSTRACT Nutritionists, dietitians and food manufacturers have advocated an increased consumption of whole grain foods. In some countries, this message is supported by governmental guidelines and health claims that can be used on whole grain foods. The bulk of evidence to support these messages comes from observational studies; in some cases, cohort studies with follow-up. Although these data are powerful indicators of the relationship between whole grain intake and improved health, they do not demonstrate causality. Nevertheless, it is difficult to argue against the evidence, particularly for the benefit of whole grains in reducing the risk of cardiovascular diseases. Repeated meta-analyses show that CVD risk is reduced by ≈30% when comparing the lowest whole grain consumers with the highest whole grain consumers. It is argued that to help explain the benefits of whole grain and, in particular, to inform the development of health claims for whole grain foods, intervention studies are needed to link obser.
Image Search System Based on Feature Vectors of Convolutional Neural Network

Edge computing offers real-time applications because the edge device closes with the data source such as the end device. This condition gives the challenge to implement deep learning in the edge device. Unfortunately, deep learning requires high computing resources, but often edge-side devices have limitations. In this study, we built an image search system based on CNN (Convolutional Neural Network)'s feature vectors to address the challenges by enlarging the implementation of CNN in the edge device such as Raspberry Pi 3. The image search system applied these informative features vector to get similar images in the image searching task by using cosine similarity. We used a 102-flower categories dataset and we prepared a light database to run the system as an off-line system in the edge device. The MobileNetV2 as CNN's model reached 70.02% of the top 1 accuracy and 92.84 % for the top 5 accuracy. As a result, the image search system showed five images result with the most similar image from the same image category. Image resolution, model complexity, and hardware capability give the significant time in this image search system. The framework of this system can be simply used for other deep learning models and applications by updating the model, dataset, database, and hardware.
Exploration of an Independent Training Framework for Speech Emotion Recognition

Speech emotion recognition (SER) plays an indispensable role in human-computer interaction tasks, where the ultimate performance is determined by features, such as empirically learned features (ELFs) and automatically learned features (ALFs). Although the fusion of both ELFs and ALFs can complement some new features for SER, the fused training within one softmax layer is inappropriate due to the different performance of using either ELFs or ALFs for emotion recognition. Based on this consideration, this paper proposes an independent training framework that can fully enjoy the complementary advantages of human knowledge and powerful learning ability of deep learning models. Specifically, we first feed Mel frequency cepstral coefficient and openSMILE features respectively into a pair of independent models, which are composed of an attention-based convolution long short-term memory neural network and a fully connected neural network. We then design a feedback mechanism for each model to extract ALFs and ELFs independently, where hard example mining and re-training with a hard example loss are applied to focus the feature extraction on hard examples during training. Finally, a classifier is adopted to distinguish emotion by using both the independent features of ALFs and ELFs. Based on extensive experiments on three public speech emotion datasets (IEMOCAP, EMODB, and CASIA), we show that the proposed independent training framework outperforms the conventional feature fusion methods.
How Can Sellers React to Consumers’ Anticipated Regret in an Online Markdown Policy?

The purpose of this study is to understand consumers’ anticipated regret in online purchase decisions and provide a realistic method for sellers to determine their markdown policy to optimize their revenue. This paper presented a mathematical model of a seller’s markdown policy under consumers’ anticipated regret and conducted a laboratory experiment to justify the existence of consumers’ anticipated regret in the markdown context. Consumers’ behavioral data were collected and used to estimate the values of the anticipated regret parameters in the markdown policy. Two interesting findings are achieved. First, the behavioral experiment justifies that consumers’ online purchase decisions will be affected by their anticipated regret behavior. Second, the seller’s optimal markdown policy that considers consumers’ anticipated regret performs better than the general markdown policy at maximizing revenue. This study can help sellers to better understand consumers’ anticipated regret in online purchase decisions and protect them from the negative consequences of miscalibrating the behavioral parameters in practice. It establishes a bridge between the mathematical modeling and behavioral laboratory experiment used to determine a seller’s optimal markdown policy. It also incorporates the impact of the product category and characteristics on consumers’ anticipated regret, which is a novel perspective. Finally, some valuable managerial insights are discussed.
CMOS CDBA Based 6th Order Inverse Filter Realization for Low-Power Applications

This article describes a realization of higher-order Inverse Band Pass Filter (SO-IBPF) configuration for low-power applications based on Current Differencing Buffered Amplifier (CDBA) as active elements and a few passive components. The proposed structure is proficient to operate with 0.6V supply voltage, 0.45V bias voltage and dissipates 0.918mW power. To justify the practicability of the proposed design, frequency and phase response outputs are produced through PSPICE simulation using 0. 18µm TSMC CMOS process parameters. The other performance investigations such as sensitivity, non-ideality, input-output noise analysis, temperature variations, Monte-Carlo analysis and percentage of total harmonic distortion (%THD) are also examined. Moreover, the proposed configuration is experimentally tested using commercially available IC AD844AN to verify the theoretical propositions and simulation results. The layout of the proposed structure is also realized with Analog Design Environment tool (ADE) of Cadence/ spectre environment.
A Pluvial Flood Nowcasting Approach using Knowledge Distillation

Accurate flood simulations based on hydraulic models have been performed for nowcasting pluvial flood prediction. However, real-time simulations require high-performance computers. We apply knowledge distillation (KD), an auto model compression approach for large deep neural networks, to pluvial flood prediction based on hydraulic models. We consider a lightweight student neural network architecture and a loss function in KD for applying distillation techniques to a flood simulation. We evaluate real precipitation data obtained using rainfall radars. Results show that the KD approach can reduce the prediction time by up to 0.32% ($313.34\times$ faster) while maintaining the prediction accuracy. The mean squared error is 1.67 cm.