Miracle-dz/UniFAR-FTUs huggingface dataset:
The WHO-ITU MyopiaEd Programme: A Digital Message Programme Targeting Education on Myopia and Its Prevention

The objective of this paper is to provide an overview of the World Health Organization - International Telecommunication Union MyopiaEd programme - a digital message programme targeting education on myopia and its prevention. The development of the MyopiaEd programme included 4 key steps: (1) Conceptualization and consultation with experts in the field of myopia, mHealth and health behavior change; (2) Creation of SMS message libraries and programme algorithm; (3) Review of the message libraries to ensure relevance to the target audience; and (4) Pre-testing amongst end-user groups to ensure that the design of the programme and the message content were understandable. After reviewing the available evidence and considering input of the experts, the aims, end users and key themes of the programme were finalized. Separate SMS-adapted message libraries were developed, reviewed and pre-tested for four target end-user groups; (1) general population involved in the care of children (2) parents or caregivers of children with myopia; (3) adolescents with myopia; and (4) adults with myopia. The message libraries are part of a comprehensive toolkit, developed through a consultative process with experts in digital health, to support implementation within countries. The development of the MyopiaEd programme aims to provide a basis for Member States and other stakeholders to develop, implement and monitor large-scale mHealth programmes. It is aimed at raising awareness of good eye care behaviors and addressing common reasons for non-compliance to spectacle wear. The next steps will involve adapting and evaluating the MyopiaEd programme in selected settings.
Whole-Brain Network Models: From Physics to Bedside

Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.
Signaling Downstream of Focal Adhesions Regulates Stiffness-Dependent Differences in the TGF-β1-Mediated Myofibroblast Differentiation of Corneal Keratocytes

Following injury and refractive surgery, corneal wound healing can initiate a protracted fibrotic response that interferes with ocular function. This fibrosis is related, in part, to the myofibroblast differentiation of corneal keratocytes in response to transforming growth factor beta 1 (TGF-β1). Previous studies have shown that changes in the mechanical properties of the extracellular matrix (ECM) can regulate this process, but the mechanotransductive pathways that govern stiffness-dependent changes in keratocyte differentiation remain unclear. Here, we used a polyacrylamide (PA) gel system to investigate how mechanosensing via focal adhesions (FAs) regulates the stiffness-dependent myofibroblast differentiation of primary corneal keratocytes treated with TGF-β1. Soft (1 kPa) and stiff (10 kPa) PA substrata were fabricated on glass coverslips, plated with corneal keratocytes, and cultured in defined serum free media with or without exogenous TGF-β1. In some experiments, an inhibitor of focal adhesion kinase (FAK) activation was also added to the media. Cells were fixed and stained for F-actin, as well as markers for myofibroblast differentiation (α-SMA), actomyosin contractility phosphorylated myosin light chain (pMLC), focal adhesions (vinculin), or Smad activity (pSmad3). We also used traction force microscopy (TFM) to quantify cellular traction stresses. Treatment with TGF-β1 elicited stiffness-dependent differences in the number, size, and subcellular distribution of FAs, but not in the nuclear localization of pSmad3. On stiff substrata, cells exhibited large FAs distributed throughout the entire cell body, while on soft gels, the FAs were smaller, fewer in number, and localized primarily to the distal tips of thin cellular extensions. Larger and increased numbers of FAs correlated with elevated traction stresses, increased levels of α-SMA immunofluorescence, and more prominent and broadly distributed pMLC staining. Inhibition of FAK disrupted stiffness-dependent differences in keratocyte contractility, FA patterning, and myofibroblast differentiation in the presence of TGF-β1. Taken together, these data suggest that signaling downstream of FAs has important implications for the stiffness-dependent myofibroblast differentiation of corneal keratocytes.
HA/CD44 Regulates the T Helper 1 Cells Differentiation by Activating Annexin A1/Akt/mTOR Signaling to Drive the Pathogenesis of EAP

CD44 partcipates in multiple inflammatory reactions. Here, we aimed to investigate the role of CD44 and the ligand, hyaluronan (HA), on chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) pathogenesis. We found that CD44 was universally expressed in CD4+ lymphocytes in the peripheral blood of CP/CPPS patients. After silencing CD44 expression or delivering 4-methylumbelliferone (4-MU), the pain severity and prostatic inflammation were significantly relieved. In vitro assay found that HA/CD44 was able to regulate T helper 1 (Th1) cells differentiation, the deficiency of which diminished experimental autoimmune prostatitis (EAP) susceptibility. Bioinformatic analysis suggested that after HA or 4-MU treatment, mTOR signaling was significantly altered, and these results were confirmed by subsequent Western blotting assay. Besides, mass spectrometry and co-immunoprecipitation assays found that CD44 was able to interact with Annexin A1 (ANX A1), and this kind of interaction stabilized ANX A1 protein and maintained the activation of Akt/mTOR pathway. Meanwhile, HA-treatment-enhanced prostatic inflammation, Th1 cell differentiation, and Akt/mTOR pathway activation were reversed after silencing the expression of ANX A1 using shANX A1-lentivirus. The present study systematically investigates the functional role of HA/CD44 in CP/CPPS and identifies novel mechanisms for HA/CD44 promoting Th1 cell differentiation. Targeting the HA/CD44/ANX A1/Akt/mTOR signaling represents novel potential therapeutic strategies for patients with CP/CPPS.
Passively Q-Switched and Mode-Locked Fiber Laser Based on a Zeolitic Imidazolate Framework-67 Saturable Absorber

Exploring new saturable absorber (SA) materials with excellent performance to achieve Q-switching and mode-locking operations is a hot topic in ultrafast laser research. The impressive specific surface areas, structural tunability, and high thermal and chemical stability of the zeolitic imidazolate framework (ZIF) materials make them promising candidates as high-performance SAs. In this work, we demonstrated the potential of the ZIF-67 SAs for ultrafast fiber laser applications. The nonlinear optical absorption characteristics of the ZIF-67 SAs at the telecommunication band were investigated by using the balanced twin-detector method. By incorporating the ZIF-67 SAs into the Er3+-doped fiber laser cavity, stable Q-switching and mode-locking operations were achieved. The research results are essential for promoting the application of novel hybrid nanomaterials in ultrafast lasers.
Automated Resume Screener using Natural Language Processing(NLP)

Resume Screening is the process of evaluating the resume of the job seekers based on a specific requirement. It is used to identify the candidate eligibility for a job by matching all the requirements needed for the offered role with their resume information such as education qualification, skill sets, technical stuff etc. Resume Screening is a crucial stage in candidate's selection for a job role, it is the stage where the decision making is done whether to move the candidate to the next level of hiring process or not. Traditionally, this process is performed manually, but companies often receive thousands of resumes for job applications. In order to reduce the human involvement and errors, many new ways were introduced in this process. This paper discusses about one such process which is very efficient in performing Resume screening. It includes Natural Language Processing (NLP), an automated Machine Learning Algorithm for screening the resumes. This paper explains the end to end working of a python application which efficiently screens the resumes of the candidates based on the organization's requirement.
An Enhanced Finite-Settling-Step Direct Torque and Flux Control (FSS-DTFC) for IPMSM Drives

This paper presents a discrete-time version of voltage and current limited operation using an enhanced direct torque and flux control method for interior permanent magnet synchronous motor (IPMSM) drives. A command voltage vector for airgap torque and stator flux regulation can be uniquely determined by the finite-settling-step direct torque and flux control (FSS-DTFC) algorithm under physical constraints. The proposed command voltage vector trajectories can be developed to achieve the maximum inverter voltage utilization for the discrete-time current limit (DTCL)-based FSS-DTFC. The algorithm can produce adequate results over a number of the potential secondary upsets found in the steady-state current limit (SSCL)-based DTFC. The fast changes in the torque and stator flux linkage improve the dynamic responses significantly over a wide constant-power operating region. The control strategy was evaluated on a 900W IPMSM in both simulations and experiments.
Multi-target Super-resolution Technology of Remote Sensing based on L Regularization

Based on regularization theory, L regularizer is used as a constraint term for regularized deconvolution for super-resolution imaging of remote sensing targets. In this paper, a super-resolution imaging model is established with L regulars as constraints, and the super-resolution imaging problem is transformed into a constrained optimization problem. Then, a half - threshold iterative algorithm for L - regular deconvolution convolution model is derived. Finally, point target simulation shows that the super-resolution imaging method based on L-regular deconvolution can achieve lower relative error than truncated singular value decomposition (TSVD) results, and has higher azimuth resolution and better robustness.
Adaptive PLL-Based Sensorless Control for Improved Dynamics of High-Speed PMSM

This article presents an adaptive quadrature phase-locked loop (AQPLL) for improving the dynamic performance of sensorless high-speed permanent magnet synchronous motors (PMSMs). A conventional quadrature phase-locked loop (QPLL) operating with a fixed bandwidth tends to have a tradeoff between antidisturbance performance and phase delay. The AQPLL can start to operate with a low bandwidth, while the bandwidth is adapted during motor acceleration in real time. Thus, the position estimation error is quickly minimized, leading to correctly decoupled rotor currents and higher available torque. Key parameters of the adaptation mechanism are examined. Sensorless field-oriented control (FOC) with a full-order observer and an AQPLL has been implemented on a field-programmable gate array and verified using a 100 kr/min PMSM. When motor dynamics is unknown or the QPLL cannot be tuned properly, the AQPLL can auto-tune the bandwidth, facilitating up to 25% faster acceleration from 5 to 100 kr/min. The steady-state position estimation error is less than 3${}^{\circ }$ at 100 kr/min. Experimental results confirm the effectiveness of the method for achieving a high-performance sensorless FOC of high-speed PMSMs. Furthermore, the AQPLL algorithm has the potential to replace the ordinary QPLL, helping to avoid time-consuming online phase-locked loop tuning in standard drives. This article is accompanied by a MATLAB script demonstrating proposed AQPLL fundamentals.
The Analysis of Optimization Algorithms: A Dissipativity Approach

Optimization problems in engineering and applied mathematics are typically solved in an iterative fashion, by systematically adjusting the variables of interest until an adequate solution is found. The iterative algorithms that govern these systematic adjustments can be viewed as a control system. In control systems, the output is measured and the input is adjusted using feedback to drive the error to zero. Similarly, in iterative algorithms, the optimization objective is evaluated and the candidate solution is adjusted to drive it toward the optimal point. Choosing an algorithm that works well for a variety of optimization problems is akin to robust controller design. Just as dissipativity theory can be used to analyze the stability properties of control systems, it can also be used to analyze the convergence properties of iterative algorithms. By defining an appropriate notion of “energy” that dissipates with every iteration of the algorithm, the convergence properties of the algorithm can be characterized. This article formalizes the connection between iterative algorithms and control systems and shows through examples how dissipativity theory can be used to analyze the performance of many classes of optimization algorithms. This control-theoretic viewpoint enables the selection and tuning of optimization algorithms to be performed in an automated and systematic way.
Safety helmet detection system of smart construction site based on YOLOv5S

Because of the high risk of construction work, the safety of construction personnel have been widely paid attention from all walks of life. Some construction personnel does not obey the rules and regulations of the construction site, such as often do not wear safety helmets, which greatly increases the potential safety hazard (on the construction site). However, since the construction site environment is relatively complex and usually of the small target, with relatively backward hardware facilities and low economic investment, the model real-time detection performance is not ideal and has low robustness. To settle these problems, this paper proposes a YOLOv5 model to judge the real-time proportion of work-site construction workers wearing safety helmets and build the helmet detection for the complex environment based on YOLOv5S. Experimental evaluation shows that the verification of the given safety helmet wear test data set reached the average accuracy of 93.8%. Compared with the previous YOLOv5 algorithm, the accuracy of helmet-wearing detection is improved by 1.1%, which meets the helmet detection requirements in complex construction scenes.
Design and analysis of intelligent cases push system for drilling accident based on cloud platform

With the increasing complexity of oil drilling operations, the probability of accidents in drilling operations has increased dramatically, which seriously affects the quality and efficiency of drilling operations. This paper studies the drilling accident case retrieval method by monitoring the well leak and well kick risks in real time during the drilling process, which can assist engineers in making decisions to reduce the losses due to the occurrence of well leak and well kick accidents. Based on the cloud platform environment, this paper investigates the method of building an intelligent cases push system for drilling accident with the architecture of microservices. By analyzing and designing the system, it provides users with the functions of real-time monitoring of drilling operations and accident case recommendation, and lays the foundation for the intelligent construction of drilling and completion operations.
Dam deformation monitoring data prediction using Bayesian optimization Gaussian process regression

In order to ensure the safe operation of dams, it is necessary to carry out safety monitoring, among which deformation monitoring is the most important.A large number of deformation monitoring data should be analyzed to determine the safety state of the dam. Aiming at constructing a high accuracy dam deformation prediction model, Gaussian process regression(GPR) model which is a widely used data prediction model has the high adaptability to non-stationary data.The selection of hyper-parameter is the key to the accuracy of GPR.In order to improve the prediction accuracy of GPR, Bayesian optimization(BO) is used to optimize its hyper-parameter.Compared the precition results of BP ,GPR and BO-GPR, the result shows that the Bayesian optimization Gaussian process regression model has higher prediction accuracy and is an effective method for dam deformation analysis and prediction.
Behavioral Segmentation with Product Estimation using K-Means Clustering and Seasonal ARIMA

Definite purchasing patterns, usage, interests, engagement level, and responses emphasize the consumer's needs and want. Relating to these behavioral patterns, supermarkets can generate the right experience for their buyers, thus accounting for their interaction with the companies in real-time. The proposed methodology will allow the supermarket and associated brands to have an even better qualitative customer-centric approach, a key outcome of which is an increase in their CLV (or Customer Lifetime Value). The framework consists of an unsupervised Machine Learning technique of K-means clustering applied on a supermarket sales dataset. The resultant clusters will depict distinct segments of customers grouped on behavioral patterns extending the conventional RFM analysis approach. Further, the Seasonal ARIMA method models the time-series data to estimate the sales of top-selling products. It is a suggested approach to maintain and manage the supermarket-base more effectively along with a satisfied customer base.
MFENet: Multi-Feature Extraction Net for Remote Sensing Semantic Segmentation

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net’s ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel’s information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+" remote sensing image dataset. Experiments show that our network is sufficient against several advanced models.
COVID-19 detection based on Computer Vision and Big Data

For detecting COVID-19 and checking the severity of the patient's condition, CT examination of the lungs is significant. However, the current manual viewing of CT images requires professionalism. In order to improve the inspection efficiency of the huge number of CT images, it is necessary to develop an intelligent detection algorithm to perform CT inspections. This paper proposes a COVID-19 detection algorithm based on EfficientDet. EfficientDet leverages a faster and easier multi-scale fusion approach, which is more suitable for COVID-19 detection tasks with finer feature granularity. In addition, data augmentation is also significant in COVID-19 detection tasks. This paper verifies the effectiveness of EfficientDet on the SIIMFISABIO-RSNA COVID-19 Detection dataset provided by Kaggle platform. Experimental results show that EfficientDet has achieved better performance than other detection algorithms. Taking MAP@0.5 as an indicator, EfficientDet reaches 0.545, which is 7.9% and 3.3% higher than the Faster RCNN algorithm and YOLO-V5.
An image defogging algorithm of a designed-FCMSPCNN for natural image

In order to address the problem of defogging images of foggy scenes, this paper proposes a parameter-tunable image defogging algorithm based on the FC-MSPCNN model (DFC-MSPCNN). Firstly, the proposed model is used to defogger color fogging images. Secondly, we simple four key parameters and one synaptic weight matrix in the model, including α, β, V, Q and Wijkl, and how they are set. Thirdly, our proposed method for image defogging is derived from dark channel theory and retinex theory. Finally, our proposed model in image defogging aspect brings a good image defogging performance than other popular algorithms.
Web APIs Structures and Data Models Analysis

Microservice architectures emphasize keeping components small, to foster autonomy, low coupling and independent evolution. In this large-scale empirical study we measure the size of Web API specifications mined from open source repositories. These APIs are modeled using the OpenAPI Specification (OAS), which, in addition to documenting the offered operations, also contain schemas definitions for the data exchanged with the API request and response message payloads. This study has as a goal to build empirical knowledge about: (1) How big and diverse are real-world web APIs both in terms of their operations and data, (2) How different API structures use and reuse schema definitions. By mining public software repositories on Github, we gathered 42,194 valid OAS specifications published between 2014-2021. These specifications include descriptions of Web APIs of well-known services providers such as Google, VMware (Avi Networks), Twilio, Amazon. After measuring the size of API structures and their data model schemas, we found that most APIs are rather small. Also there is a medium correlation between the size of the APIs’ functional structures and their data models. API developers do reuse schema definitions within the same API model.
An Analysis of the Brake Performance of Radar-based Adaptive Cruise Control During Ramp Merging on Simulation Software

Adaptive Cruise Control System (ACC) is an essential ADAS system for the autonomous vehicle for preventing all kinds of collision in the complex traffic and road situations. Ramp merging has been considered as one of the special traffic situations that can be used for testing the performance of ACC and sensor fusion requirements. To realise the project outcomes, the parametres of Long-Range Radar and Short-Range Radar has been implemented onto the IPG CarMaker with several scenarios of ramp merging been generated for simulating the ACC performance under different sensors and sensor fusion strategies. According to the simulation result, the stability of ACC performance under ramp merging needs to be permitted through adding sensor fusion between Long and Short Range Radar in order to prevent all kinds of collision between different dynamic models of ego and front target vehicle.