Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Betweenness-Based Measure for Prediction of Traffic Flow on Urban Roads

The conventional network structure measure, betweenness centrality (BC), has been used to predict roadway traffic flow; however, without consideration of variable travel demand on the roadway network, the measure’s prediction capability is limited by its static nature. With the objective of explicitly addressing the effects of travel pattern [e.g., origin–destination (O-D) distribution] on the prediction of urban roadway traffic flow, this paper proposes a modified measure that integrates the BC index and the traditional travel demand metrics (i.e., the O-D demand and the ratios of total demand). In the case study, roadway networks of two cities—San Francisco, California, and Nanjing, China—were selected to demonstrate the effectiveness of the proposed method. Taxi GPS trace data in the two cities were retrieved and applied to calibrate and validate the proposed measure. Correlation analyses were conducted to compare the predictions of the conventional and modified betweenness measures against the observed taxi traffic flows. The results show that, compared with the traditional approach, the modified betweenness measure produces predictions that have better correlations with the observed taxi traffic flows in both case studies. More accurate predictions for the future year traffic flow can be expected with the application of the new modified measure.
Evaluation of the Relationship between Occupational Accidents and Obstructive Sleep Apnea Risk among Employees of a University Hospital

Context: Occupational accidents are still one of the important causes of morbidity and mortality. Sleep apnea is a significant risk factor for occupational accidents in hospitals. Aims: To determine the frequency of occupational accidents and their relationship with the risk of obstructive sleep apnea (OSAS). Settings and Design: Cross-sectional study in a hospital setting. Methods and Material: This study was conducted on 331 hospital workers between 2019-2020. Accidents from the Social Security Institution records were used. The risk of OSAS was evaluated with Berlin Survey, daytime sleepiness with Epworth Sleepiness Scale, sleep quality with Pittsburg Sleep Quality Scale. Statistical Analysis: The Chi-square test was used for analysis; P < 0.05 was accepted as a statistical significance value. Results: Of the participants, 231 (69.8%) were female. Their ages ranged between 19 and 55; the mean age was 33.8 ± 8.0 years. The frequency of occupational accidents was 8.8%, 51.4% had a high risk of OSAS, 80.4% had high daytime sleepiness, and 53.5% had poor sleep quality. Conclusion: The study group had a high risk of sleep disorders. No relationship was found between sleep disorders and occupational accidents. More comprehensive studies are needed to clarify this relationship.
Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU

To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.
Beyond the call of duty: A Qualitative study into the experiences of family members acting as a Nearest Relative in Mental Health Act assessments

Research shows that tensions between family carers and professionals become acute where the issue of compulsory admission to hospital is at stake. In England and Wales, a specific family member is appointed to safeguard the interests of a person assessed under the Mental Health Act 1983. This currently occurs through the Nearest Relative (NR) role. The Government is proposing to replace this with a Nominated Person role, chosen by the service user. Drawing on the concept of carer burden, this study reports on the views of nineteen NRs in England to discover their experiences of being involved in a Mental Health Act assessment. Participants identified that they undertook the role due to a sense of duty. Their experiences were mixed with participants highlighting both feelings of distress during the assessment and feelings of relief once their relative had been detained. Participants reported feeling conflicted when their relative was detained and feelings of frustration towards mental health services. The findings have implications for proposals to reform the Mental Health Act 1983. They show that education and support programmes should be created for NRs/Nominated Persons and that research is needed to assess whether such support is effective at reducing carer burden.
Opleiding Informatica Bomb-r-cover Expanding the diﬃculties of Bomberman

Bomberman can be considered a classic in the gaming community. The main character, Bomberman, armed with bombs is tasked to eliminate the enemy, be that an AI or another player. This thesis looks into a smaller problem where we are only interested in the crates used in the game. Given a Bomberman board and a number of bombs with limited range, are you able to destroy all the crates on the board? In the work of R. van de Vlasakker
Histomorphometry of humeral primary bone: Evaluating the endosteal lamellar pocket as an indicator of modeling drift in archaeological and modern skeletal samples

ii Dedication.v Acknowledgments. vi Vita . ix List of Tables . xvi List of Figures . xviii List of Common Abbreviations . xxvii Chapter 1: Introduction . 1 1.1 Background and Research Aims . 1 1.2 Questions and Hypotheses . 9 1.3 Context for Research and Adopted Conventions . 12 1.4 Broader Implications . 17 1.5 Topics Addressed . 21 Chapter 2: Bone Cell Histology . 24 2.
Mediator of Breast Cancer Progression Thymic Stromal Lymphopoietin Is a Key

Inﬂammation is a double-edged sword that can promote or suppress cancer progression. In this study, we report that thymic stromal lymphopoietin (TSLP), an IL-7–like type 1 inﬂammatory cytokine that is often associated with the induction of Th2-type allergic responses in the lungs, is also expressed in human and murine cancers. Our studies with murine cancer cells indicate that TSLP plays an essential role in cancer escape, as its inactivation in cancer cells alone was sufﬁcient to almost completely abrogate cancer progression and lung metastasis. The cancer-promoting activity of TSLP primarily required signaling through the TSLP receptor on CD4 + T cells, promoting Th2-skewed immune responses and production of immunosuppressive factors such as IL-10 and IL-13. Expression of TSLP therefore may be a useful prognostic marker, and its targeting could have therapeutic potential. The Journal of Immunology , 2011, 186: 000–000.
Photoprotection Is Achieved by Photorespiration and Modification of the Leaf Incident Light, and Their Extent Is Modulated by the Stomatal Sensitivity to Water Deficit in Grapevines

Absorbed energy in excess of that used by photosynthesis induces photoinhibition, which is common in water deficit conditions, resulting in reductions in stomatal conductance. In grapevines, controlled water deficit is a common field practice, but little is known about the impact of a given water shortage on the energy transduction processes at the leaf level in relation to contrasting stomatal sensitivities to drought. Here, we assessed the effect of a nearly similar water deficit condition on four grapevine varieties: Cabernet Sauvignon (CS) and Sauvignon Blanc (SB), which are stomatal sensitive, and Chardonnay (CH) and Carménère (CM), which are less stomatal sensitive, grown in 20 L pots outdoors. Plants were maintained to nearly 94% of field capacity (WW) and 83% field capacity (WD). We have assessed plant water status, photosynthesis (AN), photorespiration, AN vs. PAR, ACi curves, photochemical (qP) and non-photochemical (qN) fluorescence quenching vs. PAR, the photoprotective effectiveness of NPQ (qPd) and light interception by leaves. Photorespiration is important under WD, but to a different extent between varieties. This is related to stomatal sensitivity, maintaining a safe proportion of PSII reaction centres in an open state. Additionally, the capacity for carboxylation is affected by WD, but to a greater extent in more sensitive varieties. As for qN, in WD it saturates at 750 μmol PAR m−2s−1, irrespective of the variety, which coincides with PAR, from which qN photoprotective effectiveness declines, and qP is reduced to risky thresholds. Additionally, that same PAR intensity is intercepted by WD leaves from highly stomatal-sensitive varieties, likely due to a modification of the leaf angle in those plants. Pigments associated with qN, as well as chlorophylls, do not seem to be a relevant physiological target for acclimation.
Dentist Role in Increasing Quality of Life in Mucositis Patient with Acute Lymphoblastic Leukemia

Background: Mucositis, a painful condition common in patients undergoing chemotherapy, can decrease the patient's quality of life. Treatment of mucositis by dentists supports the improvement of general health conditions and the patient's quality of life. This case report aims to discuss a case of a patient with mucositis after undergoing chemotherapy treatment for acute lymphoblastic leukemia. Case: 21 years old female diagnosed with acute lymphoblastic leukemia, referred to the oral medicine department from internal medicine, complained of pain in oral mucosa and lockjaw in the last three days after chemotherapy treatment with methotrexate. Internal medicine prescribes ceftriaxone intravenous (IV), dexamethasone IV, chlorhexidine gargle, and nystatin. Intraoral examination showed erosive lesions in the tongue, labial, and buccal mucosa and was diagnosed as oral mucositis grade 3 according to the World Health Organization scale. Her quality-of-life score is 46 according to The Functional Assessment of Cancer Therapy-General (FACT-G) and 58 according to Oropharyngeal Mucositis Quality of Life (OMQoL). Case management: She was treated with magic mouthwash containing diphenhydramine HCL, sucralfate, and aluminum hydroxide-magnesium hydroxide. Chlorhexidine and nystatin were advised to stop. The patient was instructed to keep her oral hygiene by gargling with sodium chloride and povidone-iodine after eating. Five days after the therapy, her condition improved (grade 1), and there was an increase in the FACT-G and OMQoL scores to 54 and 93. Conclusion: Mucositis can cause pain in the patient. A dentist can help diagnose and provide appropriate treatment to reduce the patient's complaints and improve the patient's quality of life.
Fault Detection of Propeller of an Overactuated Unmanned Surface Vehicle based on Convolutional Neural Network

This paper proposes a fault detection method for a Unmanned Surface Vehicle (USV) with overactuated system. Current status information for fault detection is expressed as a scalogram image. The scalogram image is obtained by wavelet-transforming the USV's control input and sensor information. The fault detection scheme is based on Convolutional Neural Network (CNN) algorithm. The previously generated scalogram data was transferred learning to GoogLeNet algorithm. The data are generated as scalogram images in real time, and fault is detected through a learning model. The result of fault detection is very robust and highly accurate.
Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

: Lately, traffic panel detection has been engrossed by academia and industry. This study proposes a new categorization method for traffic panels. The traffic panels are classified into three classes: symbol-based, text-based, and supplementary/additional traffic panels. Although few types of research have investigated text-based traffic panels, this type is considered in detail in this study. However, there are many challenges in this type of traffic panel, such as having different languages in different countries, their similarity with other text panels, and the lack of suitable quality datasets. The panels need to be detected first to obtain a reasonable accuracy in recognizing the text. Since there are few public text-based traffic panels datasets, this study gathered a novel dataset for the Persian text-based traffic panels all over the streets of Tehran-Iran. This dataset includes two collections of images. The first collection has 9294 images, and the latter has 3305 images. The latter dataset is more monotonous than the first one. Thus, the latter is utilized as the main dataset, and the first is used as an additional dataset. To this end, the algorithm uses the additional dataset for pre-training and the main datasets for training the network. The tiny YOLOv3 algorithm that is fast and has low complexity compared to the YOLOv3 is used for pre-training, training, and testing the data to examine the utility and advantages of the data. The K-fold cross-validation procedure is used to estimate the model's skill on the new data. It achieves 0.973 for Precision, 0.945 for Recall, and 0.955 for F measure .
Verapamil, a possible repurposed therapeutic candidate for stroke under hyperglycemia

Our study concludes that verapamil can be used as an adjunctive therapy to mitigate the damaging effect of tPA in hyperglycemic stroke. The current study only addressed the benefit of verapamil in intraluminal filament model of middle cerebral artery occlusion to study the reperfusion independent toxicity of tPA. Further investigations are needed to confirm the beneficial effect of TXNIP inhibition on long term functional outcome in clinically relevant embolic model of middle cerebral artery occlusion with tPA recanalization.
Robot and FES technology for augmenting standing-up and sitting-down capabilities

In this paper, a technology for facilitating the standing-up and sitting-down maneuvers in paraplegia is presented. A novel closed loop FES control approach is proposed and implemented in commercially available Stanmore stimulator. Furthermore, a robot assistive device is presented aimed for providing posture stability and additional support in FES training. The robot device is driven by an electrohydraulic servosystem capable to operate in a position or in force control mode. The experimental results proved that the device can be efficiently used for training and evaluation of standing-up and sitting-down maneuvers. Moreover, an algorithm is proposed which incorporates the subject's voluntary activity into the robot control scheme. The simulation results proved an adequate and robust performance. In this manner, the robot device can be utilized in the "robot driven" or the "patient driven" training.
THE NEXUS BETWEEN ENTREPENEURIAL EDUCATION, THE ROLE OF PARENTS AND ENTREPRENEURIAL ATTITUDE AMONG UNIVERSITY STUDENTS: THE MEDIATING ROLE OF SELF-EFFICACY

: This study aims to investigate the relationship between the role of parents, entrepreneurial education and attitude, as well as the mediating role of self-efficacy. This research used a quantitative approach through a structural equation modelling to comprehensively understand the relationship between the variables. The data for this study were gathered from 290 economics students in various state universities in Indonesia. The results showed that entrepreneurial self-efficacy plays a crucial role in affecting entrepreneurial attitude. Besides, the role of parents is a variable that needs to be considered in the relationship between entrepreneurial attitude and self-efficacy. These findings provide insights into the collaboration of various parties, such as universities, the government and community to stimulate young entrepreneurs.
Sensorized T-Shirt for Cardiological Patients in Telemonitoring

: Technological innovations in the development of wearable sensors have led to advance-ments in smart wearable devices targeted at health monitoring. In this work, a multisensor T-shirt for remote telemonitoring of vital signs related to cardiovascular diseases was developed. The prototype includes a single-lead electrocardiogram (ECG), a pulse oximeter, a temperature sensor, and a three-axis accelerometer. Data collected are sent by a Bluetooth module, then ﬁltered and visualized thanks to a MATLAB script to provide information about heart rhythm, average temperature and oxygen saturation, and respiratory rate. The result is a simple, low-cost, and low-power system; an easily applicable solution within everyone’s reach.
Understanding the inelastic response of collagen fibrils: a viscoelastic-plastic constitutive model.

Collagen fibrils, which are the lowest level fibrillar unit of organization of collagen, are thus of primary interest towards understanding the mechanical behavior of load-bearing soft tissues. The deformation of collagen fibrils shows unique mechanical features; namely, their high energy dissipation is even superior compared to most engineering materials. Additionally, there are indications that cyclic loading can further improve the toughness of collagen fibrils. Recent experiments from Liu at al. (2018) focused on the response of type I collagen fibrils to uniaxial cyclic loading, revealing some interesting results regarding their rate-dependent and inelastic response. In this work, we aim to develop a model that allows interpreting the complex nonlinear and inelastic response of collagen fibrils under cyclic loading. We propose a constitutive model that accounts for viscoelastic deformations through a decoupled strain-energy density function (into an elastic and a viscous parts), and for plastic deformations through plastic evolution laws. The stress-stretch response results obtained using this constitutive law showed good agreement with experimental data over complex loading paths. Ultimately we use the model to gain more insights on how cyclic loading and rate effects control the interplay between viscoelastic and plastic deformation in collagen fibrils, and to extrapolate the results from experimental data, analyzing how complex cyclic load influences energy dissipation and deformation mechanisms.
Proactive Radar Protection System in Shared Spectrum via Forecasting Secondary User Power Levels

Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.
Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning

Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.
Electrochemical Self-Assembled Gold Nanoparticle SERS Substrate Coupled with Diazotization for Sensitive Detection of Nitrite

The accurate determination of nitrite in food samples is of great significance for ensuring people’s health and safety. Herein, a rapid and low-cost detection method was developed for highly sensitive and selective detection of nitrite based on a surface-enhanced Raman scattering (SERS) sensor combined with electrochemical technology and diazo reaction. In this work, a gold nanoparticle (AuNP)/indium tin oxide (ITO) chip as a superior SERS substrate was obtained by electrochemical self-assembled AuNPs on ITO with the advantages of good uniformity, high reproducibility, and long-time stability. The azo compounds generated from the diazotization-coupling reaction between nitrite, 4-aminothiophenol (4-ATP), and N-(1-naphthyl) ethylenediamine dihydrochloride (NED) in acid condition were further assembled on the surface of AuNP/ITO. The detection of nitrite was realized using a portable Raman spectrometer based on the significant SERS enhancement of azo compounds assembled on the AuNP/ITO chip. Many experimental conditions were optimized such as the time of electrochemical self-assembly and the concentration of HAuCl4. Under the optimal conditions, the designed SERS sensor could detect nitride in a large linear range from 1.0 × 10−6 to 1.0 × 10−3 mol L−1 with a low limit of detection of 0.33 μmol L−1. Additionally, nitrite in real samples was further analyzed with a recovery of 95.1−109.7%. Therefore, the proposed SERS method has shown potential application in the detection of nitrite in complex food samples.