Miracle-dz/UniFAR-FTUs huggingface dataset:
Early mobilisation of patients with community-acquired pneumonia reduce length of hospitalisation—a pilot study

[Purpose] To examine if length of stay was reduced following an early mobilisation programme in patients with community-acquired pneumonia, and secondary, if such a program influenced short-term rehospitalisation and mortality rates. [Participants and Methods] Ninety seven consecutive patients (51% men; over all mean ± SD age 71.9 ± 16.5 years) with community-acquired pneumonia were included in the intervention group, and compared with a historical control group of 97 patients, matching at case level. Early mobilisation was defined as more than 20 minutes out of bed within 24 hours of hospitalisation. [Results] Eighty out of 97 patients in the intervention group were mobilised within 24 hours and length of stay for all patients was reduced with an average of 1.5 (95%CI: −0.2; 3.2) days compared to the control group. There was no significant difference between the two groups according to 30-day rehospitalisation and mortality. [Conclusion] An early mobilisation program seem to reduce the length of stay for patients with community-acquired pneumonia, and without an increase in short-term mortality and re-hospitalisation rates.
A Hierarchical Demand Prediction Method with Station Clustering for Bike Sharing System

Bike sharing system is widely used in many cities. However, the imbalanced usage pattern of bicycles causes over-demand issue which affects user experience. Bike demand prediction is necessary as it is the basis of bike pre-allocation which can satisfy people's demand in advance. In this paper, we propose a hierarchical traffic prediction model to predict bike check-out/in number of each station cluster. Firstly, we conduct station clustering with iterative spectral clustering algorithm, since the pattern of bike usage of several stations close to each other is more regular compared with that of a single station. Then, we adopt gradient boosting regression tree to predict the total check-out number of the whole bike sharing system. Thirdly, each cluster's check-out number is inferred based on its predicted proportion in the total check-out number. Fourthly, we propose inter-cluster transition proportion model which can describe the bike rent-return relationships between cluster pairs and predict check-in numbers of clusters. Finally, we evaluate our prediction model with data from Citi Bike System in New York City. Experiment results show that our prediction method can achieve more accurate and reasonable result compared with existing work.
Sentiment Analysis Based on Deep Learning and Its Application in Screening for Perinatal Depression

In the specific field of sentiment analysis tasks, traditional machine learning models lack effective attention to certain features. To address this issue, we extracted and classified user emotion features based on the LSTM network model and applied them to perinatal depression screening scenarios. We use emoticons as feature extraction and modeling for document-level sentiment analysis in specific regions and achieved good results. The results were basically consistent with the findings of the Edinburgh Postnatal Depression Scale. This method greatly shortens the screening time and reduces the doctor-patient communication costs. It has positive significance for specific areas of sentiment classification tasks and provides references for document-level sentiment analysis.
Toward improving photosynthesis in cassava: Characterizing photosynthetic limitations in four current African cultivars

Abstract Despite the vast importance of cassava (Manihot esculenta Crantz) for smallholder farmers in Africa, yields per unit land area have not increased over the past 55 years. Genetic engineering or breeding for increased photosynthetic efficiency may represent a new approach. This requires the understanding of limitations to photosynthesis within existing germplasm. Here, leaf photosynthetic gas exchange, leaf carbon and nitrogen content, and nonstructural carbohydrates content and growth were analyzed in four high‐yielding and farm‐preferred African cultivars: two landraces (TME 7, TME 419) and two improved lines (TMS 98/0581 and TMS 30572). Surprisingly, the two landraces had, on average, 18% higher light‐saturating leaf CO 2 uptake (A sat) than the improved lines due to higher maximum apparent carboxylation rates of Rubisco carboxylation (V cmax) and regeneration of ribulose‐1,5‐biphosphate expressed as electron transport rate (J max). TME 419 also showed a greater intrinsic water use efficiency. Except for the cultivar TMS 30572, photosynthesis in cassava showed a triose phosphate utilization (TPU) limitation at high intercellular [CO 2]. The capacity for TPU in the leaf would not limit photosynthesis rates under current conditions, but without modification would be a barrier to increasing photosynthetic efficiency to levels predicted possible in this crop. The lower capacity of the lines improved through breeding, may perhaps reflect the predominant need, until now, in cassava breeding for improved disease and pest resistance. However, the availability today of equipment for high‐throughput screening of photosynthetic capacity provides a means to select for maintenance or improvement of photosynthetic capacity while also selecting for pest and disease resistance.
JMS: Joint Bandwidth Allocation and Flow Assignment for Transfers with Multiple Sources

The increasing prevalence of data-intensive applications has made large-scale data transfers more important in datacenter networks. Excessive traffic demand in oversubscribed networks has caused serious performance bottlenecks. Data replicas, with the advantage of source diversity, can potentially improve the transmission performance, but current work focuses heavily on best replica selection rather than multi-source transmission. In this paper, we present JMS, a novel traffic management system that optimizes bulk multi-source transfers in software-defined datacenter networks. With a global network view and consistent data access, JMS conveys data in parallel from multiple distributed sources and dynamically adjusts the flow volumes to maximize network utilization. The joint bandwidth allocation and flow assignment optimization problem poses a major challenge with respect to nonlinearity and multiple objectives. To cope with this, we design a fair allocation algorithm that derives a novel transformation with simple equivalent canonical linear programming to efficiently achieve global optimality. Simulation results demonstrate that JMS outperforms other transmission approaches with substantial gains, where JMS improves the network throughput by up to 52% and reduces the transfer completion time by up to 44%.
Species adulteration in raw milk samples using polymerase chain reaction-restriction fragment length polymorphism

Background and Aim: Milk adulteration is pivotal because it leads to worse effects in public health as human adverse reactions with clinical signs ranged from gastrointestinal signs to anaphylactic shock. This study was carried out to estimate the prevalence of adulteration in buffalo’s milk sold in Assiut City, Egypt. Materials and Methods: A total of 50 raw buffalo’s milk samples were collected and examined for adulteration by addition of cow’s milk. The examination carried out by applying polymerase chain reaction-restriction fragment length polymorphism technique using cytochrome b (cyt b) gene primers and Hinf I enzymes. The size of target gene was 360 bp in both animal species and amplicon can be digested using Hinf I enzyme, this restriction enzyme divided the essential band to clear three bands at 360, 210, and 150 bp in cows’ milk, while, the enzyme could not be cleaved the amplicon in buffalo’s samples. Results: The obtained results cleared that the incidence of adulteration of buffalo’s milk very high percentage reaches 90%. Conclusion: It could be concluded that the raw buffalo’s milk sold in Assiut City subject to fraudulent practice and thus can lead to public health hazards.
Tensor Decomposition of Gait Dynamics in Parkinson's Disease

Objective: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multichannel or multisensor data analysis of gait dynamics in Parkinson's disease. Method: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multisensor time series of gait force between Parkinson's disease and healthy control cohorts. Results: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross validations. Conclusion: Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease. Significance: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.
Fast Computation of Sample Entropy Based on the Iteration of Neighborhood Matrix

In order to reduce the computational complexity of sample entropy, a fast method is presented based on the proposed neighborhood matrix. The neighborhood matrix of highdimensional signal vector can be efficiently deduced from that of the lower dimensional signal vector. The efficiency of the fast algorithm is analyzed theoretically, and its validity is proved by experiments. Compared with the traditional algorithm, the computation time is significantly reduced. The experimental results show that the proposed method is effective, which facilitates real-time applications of the sample entropy.
Topic Analysis Model for Online Social Network

With the rapid development of social network platform and the widespread use of mobile terminals, the topic analysis technology based on data of social network gets more and more attention. Due to the characteristics as short length of data and the seriousness of content fragmentation, the topic analysis technology on social network has evolved into three main parts: information recovery, topic detection and topic evolution. Among them, information recovery is the basis of topic detection and topic evolution relies on topic detection. Based on the characteristics summary of related works of the three parts, a generalized model of topic analysis on social networks is proposed with the consideration of factors such as community, influence and user. It expounds the inner process of topic analysis technology and is an effective summary of topic analysis technology on social networks.
Opposite Aerosol Index‐Cloud Droplet Effective Radius Correlations Over Major Industrial Regions and Their Adjacent Oceans

The Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 and the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis data from 2003 to 2016 are employed to study aerosol‐cloud correlations over three industrial regions and their adjacent oceans, as well as explore the impact of meteorological conditions on the correlations. The analysis focusing on liquid and single‐layer clouds indicates an opposite aerosol‐cloud correlation between land and ocean; namely, cloud effective radius is positively correlated with aerosol index over industrial regions (positive slopes), but negatively correlated over their adjacent oceans (negative slopes), for a quasi‐constant liquid water path. The positive slopes are relatively large under low lower‐tropospheric stability (LTS; weakly stable condition), but much weaker or even become negative under high LTS (stable conditions) and high liquid water path. The occurrence frequency of cloud top height (CTH) and LTS suggests that positive correlations are more likely corresponding to relatively high CTH and low LTS, while negative to low CTH and high LTS.
What Matters Most at the End-of-Life for Chinese Americans?

Background: To provide optimal end-of-life (EOL) care in the Chinese American population, we need to have a better understanding of what matters most at EOL from their perspective. Experiencing a “good death” at the EOL is the optimal goal of palliative care. Studies show that the meaning and description of good death varies across cultures in different populations. In the United States, Chinese Americans comprise the largest Asian demographic. Aim: To describe EOL wishes, which define a good death for Chinese Americans. Method: Qualitative study using focus groups. A convenience sample of 60 Chinese Americans was recruited from a community organization in Maryland. Ten focus group discussion sessions were conducted. Results: Wishes at the EOL that defined a good death for the participants in this study included being pain-free, not being a burden to family, being with family, having a trusted physician, maintaining dignity, and prayer. Conclusion: A good death is a complex concept. What matters most to patients at the EOL differs depending on their cultural background. When caring for Chinese Americans, a comprehensive EOL care plan should include cultural considerations in addition to physical, psychosocial, and spiritual needs.
Chinese herbal medicine for functional dyspepsia: systematic review of systematic reviews

Background: Pharmacotherapy, including prokinetics and proton pump inhibitors for functional dyspepsia (FD) have limited effectiveness, and their safety has been recently questioned. Chinese herbal medicine (CHM) could be considered as an alternative. A systematic review (SR) of SRs was performed to evaluate the potential effectiveness and safety of CHM. Method: We conducted a comprehensive literature search for SRs with meta-analyses in eight international and Chinese databases. Pooled effect estimation from each meta-analysis was extracted. The AMSTAR instrument was used to assess the methodological quality of the included SRs. Results: A total of 14 SRs of mediocre quality assessing various CHMs, alone or in combination with conventional pharmacotherapy, were included. Meta-analyses showed that CHM was more effective than prokinetic agents for the alleviation of global dyspeptic symptoms. Three specific CHM formulae appeared to show superior results in the alleviation of global dyspeptic symptoms, including Si Ni San, modified Xiao Yao San and Xiang Sha Liu Jun Zi decoction. No significant difference in the occurrence of adverse events in using CHM or pharmacotherapy was reported. Conclusion: CHM can be considered as an alternative for the treatment of FD symptoms when prokinetic agents and proton pump inhibitors are contraindicated. Future trial design should focus on measuring changes in individual dyspeptic symptoms and differentiate the effectiveness of different CHM for postprandial distress syndrome and epigastric pain syndrome. A network meta-analysis approach should be used to explore the most promising CHM formula for FD treatment in the future.
DLV: Exploiting Device Level Latency Variations for Performance Improvement on Flash Memory Storage Systems

NAND flash has been widely adopted in storage systems due to its better read and write performance and lower power consumption over traditional mechanical hard drives. To meet the increasing performance demand of modern applications, recent studies speed up flash accesses by exploiting access latency variations at the device level. Unfortunately, existing flash access schedulers are still oblivious to such variations, leading to suboptimal I/O performance improvements. In this paper, we propose DLV, a novel flash access scheduler for exploring scheduling opportunities due to device level access latency variations. DLV improves flash access speeds based on process variations and data retention time difference across flash blocks. More importantly, DLV integrates access speed optimization with access scheduling such that the average access response time can be effectively reduced on flash memory storage systems. Our experimental results show that DLV achieves an average of 41.5% performance improvement over the state-of-the-art.
Neural Monitoring With CMOS Image Sensors

Implantable image sensors have several biomedical applications due to their miniature size, light weight, and low power consumption achieved through sub-micron standard CMOS (Complementary Metal Oxide Semiconductor) technologies. The main applications are in specific cell labeling, neural activity detection, and biomedical imaging. In this paper the recent research studies on implantable CMOS image sensors for neural activity monitoring of brain are being quantified and reviewed. Based on the results, the suitable implantable image sensors for brain neural monitoring should have high signal to noise ratio of above 60 dB, high dynamic range of near 88 dB and low power consumption than the safety threshold of 4W/cm2. Moreover, it is found out that the next generation of implantable imaging device trend should reduce the pixel size and power consumption of CMOS image sensors to increase spatial resolution of sample images.
A Power Distributed Control Method for Proportional Load Power Sharing and Bus Voltage Restoration in a DC Microgrid

This paper proposes a new power distributed control method for a dc microgrid. This method was developed to share the load power proportionally to the distributed power source ratings and restore the dc bus voltage when the load changes. To achieve this effectively, a shifted voltage technique was developed based on the power rating and the instantaneous power of distributed generators. By adding the shifted voltage, the voltage drop caused by the droop controller is effectively compensated such that the dc bus voltage is constantly regulated regardless of the load change. To realize the method, all the required information to determine the reference voltage is transmitted through low-bandwidth communication. The controller design process is presented in detail along with a system stability analysis. The effectiveness of the method was verified by simulation and experiments, which were carried out using a 2.8-kW prototype of a dc microgrid.
The Research about Public Sentiment Differences between the Belt and Road Countries on China's Hot Issues

With the gradually advancing of the Belt and Road Initiative, studying the public sentiment differences between the Belt and Road countries on China's hot issues has become a task for practitioners in journalism, diplomacy and other areas. First, the paper gives an outline of the search's analysis framework and emphatically introduces its methods of data acquisition and data mining. Then the paper presents a case to show the analysis method and result. At last it analyzes the similarity and differences between the Belt and Road countries on latest China's hot issues.
The miR-200 family: multiple effects on gliomas

Gliomas are the most common type of primary brain tumors. MicroRNAs (miRNAs) are small noncoding RNAs that can epigenetically regulate target gene expression. The microRNA 200 family includes miR-200a, 200b, 200c, 141 and 429. Numerous studies have indicated that members of the miR-200 family play an important role in glioma development and metastasis. In this review, we summarize the data from various studies and highlight the effects of miR-200 on glioma metastasis, therapeutic response and prognosis.
Post-Pareto Optimality Analysis With Sum of Ranking Differences

In multiobjective optimization, objectives are typically conflicting, i.e., they cannot reach their individual optima simultaneously. Identifying the best one among Pareto-optimal solutions is not a simple task for the decision maker (DM), since the Pareto-optimal set can potentially contain a very large number of solutions. To ease this task, it is possible to resort to aggregate objective functions that should take into consideration the DM’s preferences and objectives; however, accurately specifying meaningful weights can be a challenge for many practitioners. Moreover, for the same DM’s preferences, different criteria give different results. A new post-Pareto analysis methodology, based on sum of ranking differences, is proposed to rank and detect the possible groupings of similar solutions of the Pareto front that match the DM’s preferences. This way the proposed technique provides the DM a smaller set of optimal solutions. The proposed method was tested in two practical benchmark problems, in the design of a brushless dc motor and in the optimization of a die press model (TEAM Workshop Problem 25).
A Comprehensive Study on Shaft Voltages and Bearing Currents in Rotating Machines

This paper puts into perspective the state-of-the art knowledge on bearing currents and diagnosis tools, which rely on shaft voltages and bearing currents phenomena. Today, shaft signals measurements are progressively included in predictive health monitoring of large turbo and hydro generators. In addition, bearing fault diagnosis through shaft signals have recently gained some attention to improve reliability and estimation of the remaining useful life in electrical drives. The main objective of this paper is to provide diagnosticians with a broad overview of the current trends in fault diagnosis through shaft signals. Therefore, the study presents a critical review of the measurements methods, advantages, and disadvantages of fault diagnosis methods, and suggests future possible enhancements for bearing health monitoring through shaft signals.
Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers

Objective: The objectives of this paper are to develop and test the ability of a wearable physiological sensors system, based on ECG, EDA, and EEG, to capture human stress and to assess whether the detected changes in physiological signals correlate with changes in salivary cortisol level, which is a reliable, objective biomarker of stress. Methods: 15 healthy participants, eight males and seven females, mean age 40.8 ± 9.5 years, wore a set of three commercial sensors to record physiological signals during the Maastricht Acute Stress Test, an experimental protocol known to elicit robust physical and mental stress in humans. Salivary samples were collected throughout the different phases of the test. Statistical analysis was performed using a support vector machine (SVM) classification algorithm. A correlation analysis between extracted physiological features and salivary cortisol levels was also performed. Results: 15 features extracted from heart rate variability, electrodermal, and electroencephalography signals showed a high degree of significance in disentangling stress from a relaxed state. The classification algorithm, based on significant features, provided satisfactory outcomes with 86% accuracy. Furthermore, correlation analysis showed that the observed changes in physiological features were consistent with the trend of salivary cortisol levels (R2 = 0.714). Conclusion: The tested set of wearable sensors was able to successfully capture human stress and quantify stress level. Significance: The results of this pilot study may be useful in designing portable and remote control systems, such as medical devices used to turn on interventions and prevent stress consequences.