Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Association between Canned Fish Consumption and Colorectal Cancer Risk: Analysis of Two Large Case–Control Studies

Fish is among the foods exerting favourable effects on colorectal cancer (CRC), but the possible role of canned fish has been insufficiently investigated. We aimed to investigate the relationship between canned fish consumption and CRC risk. We analysed data from two case–control studies conducted between 1992 and 2010 in several Italian areas, comprising a total of 2419 incident cases and 4723 hospital controls. Canned fish consumption was analysed according to the weekly frequency of consumption as <1 serving per week (s/w) (reference category), 1 < 2 s/w, and ≥2 s/w. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using unconditional logistic regression models, adjusting for several recognised confounding factors. Overall, canned fish consumption was lower among cases than among controls (23.8% vs. 28.6%). An inverse association was found between canned fish consumption and CRC risk with a significant trend in risk (OR = 0.81, 95% CI: 0.71–0.92 for intermediate consumption and OR = 0.66, 95% CI: 0.51–0.85 for the highest one), which was consistent across strata of several covariates. This study is the first to offer a basis of support for canned fish consumption as a component of a healthy diet, and it has relevant public health implications given the high ranking of CRC in incidence and mortality worldwide.
Is There an Optimal Design for

While there is an increasing body of literature on the effectiveness of on-line courses, studies on the effects of factors predicting Web-based course success over time are limited. I consider the effects of technological characteristics, the pedagogical structure of courses, and their relationship to student learning and satisfaction with the Internet as a course delivery medium in hopes of identifying a framework for Web-based course design that on-line MBA programs and individual faculty members might apply to their courses and be reasonably effective even if they are new to teaching on-line. My results suggest that a framework of selecting an on-line course software platform that students perceive to be both useful and easy to use, using a variety of media on the course Website, promoting the "any time, anywhere" aspects of the learning environment, and encouraging participant interaction is positively associated with effective course outcomes for Web-based courses.
Changes of landscape structure and soil production function since the 18th century in north-west saxony

The objectives of this paper are (1) to reconstruct time series of the historical and current landscape structures based on historical documents and serial cadastral maps, (2) to analyse the changes of agricultural production function by the application of historical soil assessments and (3) to analyse the connections between landscape structure and production function in reference to the social and economic driving forces. The case study area is today an intensively-used agricultural landscape located nearby Taucha-Eilenburg (NW-Saxony), Germany. Arable landscapes in Germany are changing with increasing dynamics: valuable structures and landscape functions of the traditional and multifunctional landscape were lost. New landscape structures replaced the traditional ones slowly or sometimes also in short time steps. Therefore, this paper focuses on the changes of landscape structures and that of the soil production function induced by land use since the 18th century. The changes are analysed on the basis of historical and serial cadastral maps and documents by covering four time steps from 1750 to 2005. The historical maps were scanned, geo-referenced and digitalised in GIS. Thus, quantitative analysis of landscape structure changes on parcel level is enabled. The production function is explicitly reconstructed on the basis of the Prussian Taxation of the real estate of 1864 (Preußische Grundsteuerbonitierung) and The German Soil Taxation (Reichsbodenschätzung) of 1937. Changes observed on the serial cadastral maps were linked with the social and economical driving forces and the soil production function. Moreover, there is a high demand for the development of methodologies to analyse and to assess time series of landscape structures, land use and landscape functions in the historical context of landscape development.
Short-Time Adaline Based Fault Feature Extraction for Inter-Turn Short Circuit Diagnosis of PMSM via Residual Insulation Monitoring

Inter-turn short circuit (ITSC) is a common fault in electrical machines, which may result in further devastation to the whole winding and magnets. In this article, a method for real-time residual insulation capacity monitoring of ITSC fault in permanent magnet synchronous machines (PMSMs) is proposed, which is robust to speed/torque variation and independent of machine parameters and extra sensors. It is based on the theory that the ITSC fault initiates as an insulation deterioration among adjacent turns and the fault severity can be described by the residual insulation capacity being relevant to the 2nd harmonic component in the dq-axis currents. Thus, a short-time Adaline-based harmonic extraction method is proposed for the separation of the needed 2nd harmonic component from time-varying current signals, being used as the fault indicator afterward. Finally, the performance of the proposed method is experimentally evaluated on a PMSM and shows quite good performance in fast-tracking and detection of faults in various stages.
Research and Challenges of Reinforcement Learning in Cyber Defense Decision-Making for Intranet Security

In recent years, cyber attacks have shown diversified, purposeful, and organized characteristics, which pose significant challenges to cyber defense decision-making on internal networks. Due to the continuous confrontation between attackers and defenders, only using data-based statistical or supervised learning methods cannot cope with increasingly severe security threats. It is urgent to rethink network defense from the perspective of decision-making, and prepare for every possible situation. Reinforcement learning has made great breakthroughs in addressing complicated decision-making problems. We propose a framework that defines four modules based on the life cycle of threats: pentest, design, response, recovery. Our aims are to clarify the problem boundary of network defense decision-making problems, to study the problem characteristics in different contexts, to compare the strengths and weaknesses of existing research, and to identify promising challenges for future work. Our work provides a systematic view for understanding and solving decision-making problems in the application of reinforcement learning to cyber defense.
Single-Element and MIMO Circularly Polarized Microstrip Antennas with Negligible Back Radiation for 5G Mid-Band Handsets

In this paper, single-element and MIMO microstrip antenna with two pairs of unequal slits is proposed as a circularly polarized antenna with negligible back radiation for 5G mid-band handsets. The unequal pairs of slits are engraved on the antenna patch to guarantee the presence of the circular polarization (CP). The proximity-coupled feeding technique is used to excite the proposed microstrip antenna in order to provide larger antenna −10 dB bandwidth which approaches 10.8% (3.48–3.87 GHz). A novel analysis technique is proposed in this paper that demonstrates the 3D axial ratio pattern in order to generate CP in the broadside direction without affecting the structure of the ground plane which ensures weak back radiation. The 3 dB axial ratio bandwidth (ARBW) is found to be equal to 4.1% extended along the range (3.58–3.73 GHz). To make the design more compatible with the 5G mid-band handsets, the 2 × 2 MIMO structure of the proposed antenna with reduced mutual coupling (less than −20 dB) is also presented in this work. The simulation and measured results are in good agreement, and both verify the CP characteristics and the weak back radiation of the proposed antenna.
Comparison between the Severity Scoring Systems A-DROP and CURB-65 for Predicting Safe Discharge from the Emergency Department in Patients with Community-Acquired Pneumonia

Background In most community-acquired pneumonia (CAP) treatment guidelines, the Pneumonia Severity Index (PSI) and CURB-65 are used as prognostic tools. Recently, simpler and more effective predictive tools for CAP treatment, such as the A-DROP scoring system, have been developed. However, no study has performed a comparative evaluation to identify the superior tool for predicting when patients can be discharged safely. Objectives To compare the performances of A-DROP and CURB-65, simple predictive tools for CAP, based on 30-day death rates and 72-hour revisit rates for CAP following discharge from the emergency department (ED). Method This single-center retrospective observational study enrolled patients who were at least 18 years old and diagnosed with CAP at the Songklanagarind Hospital ED from January 2015 to April 2021. Following a severity assessment using the A-DROP and CURB-65 scoring systems, the 30-day mortality rates and 72-hour revisit rates after discharge from the ED were compared. Results A total of 408 patients were enrolled in this study. Six (1.47%) died within 30 days after presentation, whereas 29 (7.1%) returned to the ED within 72 hours after discharge. Most patients (72%) who revisited the ED were over the age of 65 years. The areas under the receiver operating characteristic curves for the prediction of 30-day mortality were 0.756 (95% confidence interval [CI]: 0.526–0.987) and 0.808 (95% CI: 0.647–0.970) for A-DROP and CURB-65, respectively. The areas under the receiver operating characteristic curves for the prediction of 72-hour revisit were 0.617 (95% confidence interval [CI]: 0.507–0.728) and 0.639 (95% CI: 0.536–0.743) for A-DROP and CURB-65, respectively. Conclusion A-DROP and CURB-65 yield similar results and can be used to assess low-risk patients with CAP for discharge from the ED. Older patients, even those with low-risk scores, should be particularly considered for admission to a short-term observation unit or ward.
Validation of the Chinese Version Community’s Self-Efficacy Scale in Community-Dwelling Older Adults

Background The original study confirmed that the Japanese version of the community’s self-efficacy scale (CSES) may help to promote health policies, practices and interventions in the community. In China, research on the self-efficacy of community’s life is in its infancy. The aim of this study was to assess the validity, the reliability and the predictors of the Chinese version CSES in the aging population. Methods (1) Translation of the original Japanese version CSES into Chinese; (2) validation of the Chinese version in the aging population. Instrument measurement included reliability testing, item generation, construct validity and test–retest reliability. Confirmatory factor analysis was applied to determine construct validity and internal consistency. Meanwhile, we built the Bayesian network model of the Chinese version of CSES and determined target variables. Results Finally, 143 sample individuals have been included in this research. By confirmatory factor analysis, we confirmed that the Chinese version CSES fits a two-dimensional model. Additionally, this scale showed good internal consistency (Cronbach’s α coefficient 0.900) and test–retest reliability (kappa coefficient 0.754). The results of the Bayesian network model showed that the education (0.3278) and the perceived efficacy patient–physician interactions scale (0.2055) are important predictors of the CSES. Conclusion This is the first study to validate the Chinese version of CSES in older people. Our research confirmed that the Chinese version CSES has good internal consistency, construct validity and test–retest reliability. Meanwhile, patient's confidence in communication with a physician and the patient's educational level were the important predictors of community self-efficacy.
A Differentially Private Federated Learning Model Against Poisoning Attacks in Edge Computing

Federated learning is increasingly popular, as it allows us to circumvent challenges due to data islands (e.g., challenges in getting access to raw datasets, particularly when data are owned by multiple data owners) by training a global model using data from one or more data owners / sources. However, in an edge computing deployment one cannot assume that resource-constrained end devices are sufficiently secure. In other words, we have to consider the possibility of these devices being compromised and can be abused to facilitate poisoning attacks. Privacy-preserving is another important property to consider when dealing with sensitive user data on end devices. Most existing approaches only consider either defending against poisoning attacks or supporting privacy, but not both properties simultaneously. In this paper, we propose a differentially private federated learning model against poisoning attacks, designed for edge computing deployment. First, we design a weight-based algorithm to perform anomaly detection on the parameters uploaded by end devices in edge nodes, which improves detection rate using only small-size validation datasets and minimizes the communication cost. Then, differential privacy technology is leveraged to protect the privacy of both data and model in an edge computing setting. We also evaluate the detection performance in the presence of random and customized malicious end devices, and compare its performance with those of two other competing approaches published in IEEE Transactions on Dependable and Secure Computing, in terms of attack resiliency, and communication and computation costs. Experimental results demonstrate that our scheme can achieve an optimal tradeoff between security, efficiency and accuracy.
EyeGlove for Inspection and Measurement in Confined Environments

A novel wearable vision system is proposed in this article. The wearable system contains a vision glove (EyeGlove) and a wearable display unit, which assists operators with inspection and measurement in confined environments. Three microcameras and an artificial fiducial marker are integrated on the index and middle fingers of the EyeGlove with disjointed camera configurations. Operators can achieve complex camera configurations and movements by using their dexterous hands while wearing the EyeGlove. The wearable vision system distinguishes itself by having two different functions, of which the inspection function uses one or two cameras to inspect in confined environments. For the measurement function, all three cameras and the artificial marker are used to identify object dimensions. Two of the three cameras form a stereovision system with disjointed camera configurations and variable baselines. The third camera and the artificial marker are used to determine the variable baseline in real-time. Inspection and measurement results are presented by the wearable display unit onto a virtual display. To make the wearable vision system adapt to dark environments, three auxiliary LED lights are integrated adjoining the three cameras. Experiments are conducted in both bright and dark environments to verify the measurement accuracy with an average relative error ≤ 5.0%. Furthermore, experiments are also conducted in the confined turbine engine combustor to verify its potential application in industrial environments.
Multiview Clustering of Adaptive Sparse Representation Based on Coupled P Systems

A multiview clustering (MVC) has been a significant technique to dispose data mining issues. Most of the existing studies on this topic adopt a fixed number of neighbors when constructing the similarity matrix of each view, like single-view clustering. However, this may reduce the clustering effect due to the diversity of multiview data sources. Moreover, most MVC utilizes iterative optimization to obtain clustering results, which consumes a significant amount of time. Therefore, this paper proposes a multiview clustering of adaptive sparse representation based on coupled P system (MVCS-CP) without iteration. The whole algorithm flow runs in the coupled P system. Firstly, the natural neighbor search algorithm without parameters automatically determines the number of neighbors of each view. In turn, manifold learning and sparse representation are employed to construct the similarity matrix, which preserves the internal geometry of the views. Next, a soft thresholding operator is introduced to form the unified graph to gain the clustering results. The experimental results on nine real datasets indicate that the MVCS-CP outperforms other state-of-the-art comparison algorithms.
Text recall and use of advance organisers in first and second language

Abstract Previous research identified that studying texts in a second language (L2) as opposed to the first (L1) results in substantially weaker recall. We hypothesized that use of advance organizers (AOs) might attenuate this L2 recall cost by supporting L2 users in the construction of more solid memory representations. One hundred Dutch-English bilinguals studied two texts in either L1 or L2, and with or without the help of a mind map. The previously reported L2 cost was replicated, with lower recall scores in L2 relative to L1. Whereas L1 and L2 students were equally aided by AO use, the initial cost dissipated when comparing L2 test scores in the AO condition with those of L1 in the non-AO condition. We therefore conclude that employing AOs does not entirely diminish L2 disadvantages but brings L2 students up to the initial level of L1 students.
Developing Global Connections through Computer-Mediated Communication

Myra Shulman mshul [at] american.edu American University (Washington DC, USA) The author shares her experiences in developing and implementing a collaborative Internet project connecting American University in Washington, D.C., and the Federal University of Parana in Curitiba, Brazil. Topics discussed include identifying an appropriate partner, designing a viable online environment, setting realistic goals, and encouraging interactive and authentic communication among students.
A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of 0.05° against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.
Adoption intention of photovoltaic solar systems

Purpose – The research objective is to analyze the influence of key factors contributing to consumers’ purchase intention grid-connected photovoltaic systems among residential energy consumers. Design/methodology/approach – A survey based on Korcaj et al. (2014) was conducted in a major Brazilian city; 209 valid responses were obtained directly. Data was analyzed using structural equation modeling. Findings – Among significant decision influences are environmental, financial, and autarchy benefits onto perceived global benefit and perceived behavioral control construct onto purchase intention; perceived social benefits, however, were not a relevant influence as opposed to previous studies. Research limitations/implications - Weakness of the model's reliability leading to the exclusion of the perceived total cost construct, which in turn could reduce sample bias and increase the reliability of the model and regarding clarity regarding the product “solar photovoltaic energy system”, as no text was used; this could have left questions unanswered due to the lack of knowledge of respondents about solar technology. Practical implications - The high purchasing power and high education level, along with favorable weather and geography, may contribute to promising perspectives for the product. Furthermore, to promote adhesion of the technology in the city there is a need to increase benefits, to reduce perceived technology costs, and to value the importance of solar energy generation among reference groups. Social implications - The analysis of factors influencing the city’s residents’ intentions of to adopt photovoltaic systems favors further promotion of the technology in the city. Originality/value - It contributes to the development of consumer behavior studies regarding the adoption intention of ecologically sustainable technologies, i.e., GCPSs, thus, filling a gap in the literature on consumer behavior for this product.
Further Developments in Geometrical Algorithms for Ear Biometrics

The paper presents new geometrical methods of feature ex- traction from ear images in order to perform human identification. Geo- metrical approach is motivated by the actual procedures used by police and forensic experts. In the article novel algorithms of ear feature extrac- tion from contour images are described in detail. Moreover, identification results obtained for each of the methods, based on the distance of feature vectors in the feature space, are presented.