Miracle-dz/UniFAR-FTUs huggingface dataset:
Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography/Angiography and Machine Learning Diagnostic Models

Purpose To establish optical coherence tomography (OCT)/angiography (OCTA) parameter ranges for healthy eyes (HE) and glaucomatous eyes (GE) for a North Texas based population; to develop a machine learning (ML) tool and to identify the most accurate diagnostic parameters for clinical glaucoma diagnosis. Patients and Methods In this retrospective cross-sectional study, we included 1371 eligible eyes, 462 HE and 909 GE (377 ocular hypertension, 160 mild, 156 moderate, 216 severe), from 735 subjects. Demographic data and full OCTA parameters were collected. A Kruskal–Wallis test was used to produce the normative database. Models were trained to solve a two-class problem (HE vs GE) and four-class problem (HE vs mild vs moderate vs severe GE). A rigorous nested, stratified, group, 5×10 fold cross-validation strategy was applied to partition the data. Six ML algorithms were compared using classical and deep learning approaches. Over 2500 ML models were optimized using random search, with performance compared using mean validation accuracy. Final performance was reported on held-out test data using accuracy and F1 score. Decision trees and feature importance were produced for the final model. Results We found differences across glaucoma severities for age, gender, hypertension, Black and Asian race, and all OCTA parameters, except foveal avascular zone area and perimeter (p<0.05). The XGBoost algorithm achieved the highest test performance for both the two-class (F1 score 83.8%; accuracy 83.9%; standard deviation 0.03%) and four-class (F1 score 62.4%; accuracy 71.3%; standard deviation 0.013%) problem. A set of interpretable decision trees provided the most important predictors of the final model; inferior temporal and inferior hemisphere vessel density and peripapillary retinal nerve fiber layer thickness were identified as key diagnostic parameters. Conclusion This study established a normative database for our North Texas based population and created ML tools utilizing OCT/A that may aid clinicians in glaucoma management.
Wavelet neural network sliding mode control of two rigid joint robot manipulator

To solve the problems of low accuracy and poor stability due to uncertainties, external disturbances and unknown load, which exist in the position control of rigid joint robot manipulator, this article is to propose Non-Singular Fast Terminal Sliding Mode Control strategy with Wavelet neural networks observer (NSFTSMCW). The wavelet observer is designed using the online approximation capability of the neural network, which is used to online estimate the modeling error, external disturbances and uncertainties generated by the dynamic surface control of the joint robot online. Combining the above strategies, the robot manipulators position controller is designed. The stability of this control strategy is demonstrated by stability analysis using the Lyapunov criterion. Simulations on the 2-Link Rigid Joint (2LRJ) robot show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.
Appropriate data normalization range for daily river flow forecasting using an artificial neural network.

Advance time-step river flow forecasting is of paramount importance in controlling flood damage. For the last few years, artificial neural networks (ANN) have been used extensively in river flow forecasting and have proven to be an efficient technique. Various statistical techniques have been used to pre-process the input data sets before using them in ANN model training to reduce the forecasting error. Normalizing the input―output data sets between certain ranges is one of the methods of pre-processing the raw data; this improves input―output generalization to yield accurate forecasts. In the present study, 1-day, 2-day and 3-day advance flow forecasts were carried out at a site on the Brahmani River, on the east coast of India, using the ANN approach, utilizing the data sets normalized within four different ranges (―1.0 to +1.0; 0.0―1; 0.1―0.9 and 0.2―0.8). The results obtained using the normalized data of three different ranges were compared in terms of root mean square error (RMSE) and modelling efficiency (E) to determine the best range of data normalization. The study revealed that normalizing the data sets within any of the three ranges (0.0―1; 0.1―0.9 and 0.2―0.8), yielded significantly improved results compared to the range ―1 to +1. In the case of the 1-day ahead forecast, all three data ranges produced comparable and satisfactory results, whereas for 2-day and 3-day ahead forecasts, the ANN model trained using the normalized data of the range 0.2―0.8 yielded the best result.
Employee stock ownership plans and firm productivity in China

A growing number of Chinese firms motivate their employees through employee stock ownership plans (ESOPs). Using a sample of listed firms in China, this paper examines the impact of ESOPs on firms’ total factor productivity (TFP), as well as the mechanisms of ESOPs. The empirical results show that ESOPs have a positive impact on firm TFP. The mechanism tests convey that ESOPs increase firm TFP by promoting research and development (R&D) investment and mitigating agency costs. These results are robust after accounting for endogeneity and using alternative metrics of TFP. In addition, we find that the positive effect of ESOPs on firm TFP is more pronounced in non-state-owned firms and firms with a less severe free-riding problem. Furthermore, the effect on firm TFP is positively associated with the subscription proportion of non-executive employees in ESOPs. Overall, the results of this study underscore the important role of employee ownership in firms’ productivity improvement. JEL Codes: D24, G30, J33
Investigating the Impact of Staff' Digital Literacy on Socialization with the Mediating Role of Organizational Transformability (Case Study: Staff of Bojnurd University)

The increasing development of information and communication technology tools and the speed of its adaptation to human needs has led to the beginning of a new period in societies. In new century, most jobs require new knowledge and skills, including digital literacy. This study aimed to investigate the effect of staff’s digital literacy on socialization with the mediating role of organizational transformability through structural equation. The statistical population of the study was all staff of Bojnurd University (118 employees) who were selected using the Morgan table as a sample and answered the questionnaire. SPSS and 3Smart PLS software were used to analyze the data. Findings show that the relationship between digital literacy and transformability was positive and significant. Also, the relationship between transformability and socialization was positive and significant, and finally, transformability mediated the relationship between digital literacy and socialization.
Investigation of principal reactive pathways in CO2 plasmas by numerical techniques

A resource-efficient Europe is the flagship initiative of the Europe 2020 strategy, with the CO2 emissions control as its backbone activity. Within this framework, the main idea of this study is to investigate a novel approach that aims to convert biogas into chemicals and fuels. Central in the approach, is the use of a plasma to store energy in chemical bonds. This thesis presents a study on principal pathways in a CO2 plasma and a first application of numerical reduction techniques to reduce the chemistry.
Verification of Dam Spatial Location in Open Datasets Based on Geographic Knowledge and Deep Learning

Dams are important water conservation hubs; however, dam locations provided by open datasets are often unreliable. The aim of this article is to provide a single, geographic location-reliable dataset of dams for the scientific community by fusing existing dam datasets and verifying these locations. Using Southeast Asia as the case study, we propose an efficient and automatic method to verify dam locations and developed a process framework. First, the possible location of a dam was obtained by analyzing its geographic location characteristics. Then, the deep learning method was used to detect dams. Finally, a variety of geographic knowledge was applied to comprehensively verify the detection results to obtain accurate and reliable dam location information. The fused dam dataset we produced includes the locations of 4493 dams in Southeast Asia, which were verified using the proposed framework. The verification results were then evaluated via manual visual inspection. The verification accuracy of the framework was 86.7%. The experimental results show that the proposed framework can quickly and reliably verify dam spatial locations and provide solutions for the spatial location verification of other remote sensing objects.
Design Criteria of Flux-Barriers in Synchronous Reluctance Machines

A criteria to design the rotor of multi flux-barrier synchronous reluctance (REL) machines is proposed. In particular, the focus will be on the proper design of flux-barriers geometry. An analytical model is adopted to compute the impact of the rotor flux-barriers on the torque, focusing on the torque ripple. Thanks to proper assumptions, the analytical model is simplified so as to highlight the main causes of torque ripple. Such an analytical model allows to derive the angles of the flux-barrier ends corresponding to the minimum torque ripple in a closed form. This result has never been presented in the past literature and it represents a useful tool as far as the motor design is concerned. Rotors with one, two, and three flux-barriers per rotor pole are taken into account. However, the advantageous analytical model can be extended to any number of flux-barriers per pole. Some examples referring to four-pole synchronous REL machines with different numbers of stator slots are investigated and illustrated. The results are compared with a full-featured analytical model and validated through finite element simulations.
Mathematical Isothermal Modeling of Remazol Black B Biosorption by Aspergillus flavus

Azo dyes, like Remazol Black B, form covalent bonds with textile fibers like cotton, setting them apart from traditional dyes. Due to their advantageous qualities of vivid color, water resistance, straightforward application processes, and low energy consumption, they are widely used in the textile industry. Major environmental effects of their discharge into receiving streams include reduced photosynthesis in aquatic life as a result of decreased light penetration. Seven isotherm models—Henry, Langmuir, Freundlich, BET, Toth, Fritz-Schlunder IV, and Fritz-Schlunder V—were used to analyze the biosorption isotherm data of Remazol Black B dye biosorption by Aspergillus flavus and were fitted using non-linear regression. Based on statistical analysis, the Fritz-Schlunder IV was determined to be the best model using root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC), and Hannan-Quinn information criterion (HQC). The calculated Fritz-Schlunder IV parameter, bFS value was found to be 3.812 mg/g (95% confidence interval of 0.312 to 7.311) and qmFS value of 0.0224 (95% confidence interval of -21725.002 to 21725.047).
Popularity Goals and Bullying Behaviors Among Chinese Adolescents: The Moderating Roles of Popularity Status and Cognitive Empathy

Bullying is a goal-directed behavior that has long been in the spotlight of worldwide school mental health programs and research. However, the role of popularity goals in bullying and its potential mechanisms are unclear for adolescents in a non-Western cultural context. Based on 333 Chinese adolescents (52% female), the current study was the first to explore the potential moderating roles of popularity status and cognitive empathy in the association between popularity goals and early Chinese adolescents’ bullying behaviors. Moderated moderation analyses indicated that popularity status and cognitive empathy significantly moderated the positive relationship between popularity goals and Chinese adolescents’ bullying behaviors. Specifically, the statistically significant three-way interaction revealed that low popularity status and low cognitive empathy strengthen the positive links between popularity goals and bullying. The significant two-way interaction showed that the positive relations between popularity goals and bullying were stronger for low popularity among Chinese adolescents.
Transient Heat Conduction in the Orthotropic Model with Rectangular Heat Source

Epidermal electronic systems (EESs) are a representative achievement for utilizing the full advantages of ultra-thin, stretchable and conformal attachment of flexible electronics, and are extremely suitable for integration with human physiological systems, especially in medical hyperthermia. The stretchable heater with stable electrical characteristics and a uniform temperature field is an irreplaceable core component. The inorganic stretchable heater has the advantage of maintaining stable electrical characteristics under tensile deformation. However, the space between the patterned electrodes that provides tensile properties causes uneven distribution of the temperature field. Aiming at improving the temperature distribution uniformity of stretchable thermotherapy electrodes, an orthotropic heat transfer substrate for stretchable heaters is proposed in this paper. An analytical model for transient heat conduction of stretchable rectangular heaters based on orthotropic transfer characteristics is established, which is validated by finite element analysis (FEA). The homogenization effect of orthotropic heat transfer characteristics on temperature distribution and its evolutionary relationship with time are investigated based on this model. This study will provide beneficial help for the temperature distribution homogenization design of stretchable heaters and the exploration of its transient heat transfer mechanism.
How Do Teaching Assistants Teach? Characterizing the Interactions Between Students and TAs in a Computer Science Course

—Teaching assistants (TAs) play a crucial role in Computer Science courses. When a student is stuck or confused, they often rely on a TA to help them understand a concept or debug their program. At the same time, TAs in Computer Science courses are often very new at teaching, and somewhat new at programming. They may lack the knowledge and resources necessary to help students learn effectively. This work seeks to better understand the nature of TA-student interactions and identify potential opportunities for improvement. We conducted an observational study of one-on-one TA-Student interactions during ofﬁce hours of a Computer Science course, and analyzed these interactions through the lens of known practices of effective one-on-one tutors. We found that TA-Student interactions focus on code over concepts, and this focus may be detrimental to TAs’ use of good tutoring practices.
Preneoplastic Lesions in Surgical Specimens Do Not Worsen the Prognosis of Patients Who Underwent Surgery for Pancreatic Adenocarcinoma: Post-Hoc Analysis of the PRODIGE 24-CCTG PA 6 Trial

Simple Summary Pancreatic cancer patients who undergo curative surgery are still likely to recur. We therefore analyzed the data of the 493 patients from the PRODIGE 24-CCTG PA 6 trial, which validated the benefit of adjuvant mFOLFIRINOX regimen over gemcitabine after pancreatic adenocarcinoma resection. We investigated whether the presence of dysplasia (noninvasive intraductal papillary mucinous neoplasm, mucinous cystic neoplasm or pancreatic intraepithelial neoplasia) might decrease in disease-free survival. A preneoplastic lesion was identified in 226 patients (45.8%). In a multivariate analysis, the presence of dysplasia is not an independent predictor of diminished disease-free survival. This finding should be useful for future prospective trials and for surgeons’ decision making, as the pre-existence of a preneoplastic lesion should not preclude a plan for curative surgery. Abstract Objective: The prognosis of pancreatic cancer after curative surgery is burdened by frequent recurrence. The aim of this study was to evaluate the impact of dysplasia in the surgical specimen on disease-free survival (DFS). Methods: A post-hoc analysis of the phase III PRODIGE 24-CCTG PA 6 trial was performed. From April 2012 to October 2016, 493 patients were included in the primary study. Assessment for dysplasia in the surgical specimens was secondarily performed. Dysplasia was defined based on presence and grade of three most common pre-malignant lesions (intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN) and pancreatic intraepithelial neoplasia (PanIN). The primary endpoint was DFS validated through multivariate analysis. Results: Two hundred twenty-six patients (45.9%) had a preneoplastic lesion. PanIN lesions were found in 193 patients (39.2%), including 100 high-grade lesions (20.6%); 43 patients had IPMN lesions (8.7%), including high-grade lesions in 32 (6.5%). Three MCN were described (0.6%). In bivariate analysis, the presence of dysplasia was not associated with poorer DFS (HR = 0.82, 95% CI [0.66; 1.03]). In multivariate analysis, risk factors for poorer DFS were poorly differentiated/undifferentiated tumor, N1 status, R1 surgical margins and perineural invasion. Conclusions: The presence of dysplasia in the surgical specimen after pancreatic cancer surgery does not worsen DFS.
A novel computerized cognitive test for the detection of MCI and its association with neurodegeneration in Alzheimer’s disease prone brain regions

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identi�ed by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments sensitive enough to measure the early brain-behavior manifestations of AD and correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been one major limitation of traditionally used assessment tools. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Sematic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone regions. Data was gathered from 127 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU; 36 aMCI) underwent MRI imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory indices correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of . 876 (76.1% sensitivity, 82.7% speci�city). Further, LASSI-BC measures of frPSI were associated with volumetric reductions in AD prone regions including the hippocampus, amygdala, inferior temporal lobes, precuneus and posterior cingulate.
Coupled-Region Visual Tracking Formulation Based on a Discriminative Correlation Filter Bank

The visual tracking algorithm based on discriminative correlation filter (DCF) has shown excellent performance in recent years, especially as the higher tracking speed meets the real-time requirement of object tracking. However, when the target is partially occluded, the traditional single discriminative correlation filter will not be able to effectively learn information reliability, resulting in tracker drift and even failure. To address this issue, this paper proposes a novel tracking-by-detection framework, which uses multiple discriminative correlation filters called discriminative correlation filter bank (DCFB), corresponding to different target sub-regions and global region patches to combine and optimize the final correlation output in the frequency domain. In tracking, the sub-region patches are zero-padded to the same size as the global target region, which can effectively avoid noise aliasing during correlation operation, thereby improving the robustness of the discriminative correlation filter. Considering that the sub-region target motion model is constrained by the global target region, adding the global region appearance model to our framework will completely preserve the intrinsic structure of the target, thus effectively utilizing the discriminative information of the visible sub-region to mitigate tracker drift when partial occlusion occurs. In addition, an adaptive scale estimation scheme is incorporated into our algorithm to make the tracker more robust against potential challenging attributes. The experimental results from the OTB-2015 and VOT-2015 datasets demonstrate that our method performs favorably compared with several state-of-the-art trackers.
Bus-Privacy-Preserving Distributed Economic Dispatch Method for Power Systems Using Iteration Functions

The sufficiency of electricity market competition and the economy of power system operation require that the economic dispatch should not only preserve the bus privacy but consider network losses. The existing economic dispatch methods, however, can’t preserve the privacy of each bus from leakage and even complicate the solution when they reasonably consider network losses, due to the coupling complexity of network losses. To address these issues, this paper presents a novel distributed economic dispatch (DED) method based on KKT optimality conditions and iteration functions. It focuses on an economic dispatch model that contains bus active power balance equations and generator power limit inequalities. First, the model’s KKT optimality conditions with inequalities are simplified to equivalent pure equality-type optimality conditions. Then a set of iteration functions is created using the Taylor series, which overcomes the difficulty caused by the fact that unknown bus voltage phases are completely contained in the sine and cosine functions of optimality conditions. Finally, a fast fixed-point iteration procedure is proposed for bus agents to solve the economic dispatch model. The proposed method reasonably considers network losses by using the set of bus active power balance equations. It not only preserves the privacy of each bus from leakage but is straightforward in solution (does not complicate the solution). In addition, it is fully bus-level distributed. Numerical simulation results of systems with different sizes verified the effectiveness and advantages of the proposed method.