Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer The growth of social commerce

: Over recent years, social commerce has evolved into a powerful segment of e-commerce, creating new opportunities for brands of all types and sizes. However, if social commerce is to continue to grow and deliver the many beneﬁts it promises, it must address a number of key challenges, including privacy, trust, and ethical concerns. This paper explores the extent to which privacy issues affect the attitudes and behaviours of social media platform (SMP) users towards social commerce, and investigates whether these attitudes and behaviours are a function of cultural context. The approach adopted for the research is a two-stage method, which initially uses semi-structured interviews of social-commerce users to identify their key privacy concerns. These concerns are then used to develop, using the theory of reasoned action (TRA), a structural model that facilitates the formation of hypotheses which relate users’ attitudes to privacy to subsequent behaviour. This model is assessed by analysing the responses to a questionnaire from a large sample of participants. This allows us to evaluate the general accuracy of the model and to compare culturally distinct subgroups (Saudi vs. Chinese) using partial least-squares analysis. Results show good support for all of our hypotheses and indicate that there are clear cultural effects. One of these effects is the inadequacy of privacy policies implemented by SMP providers, regarding culturally speciﬁc ethical concerns.
M ANUEL D E L EÓN P AULO R. R ODRIGUES Dynamical connections and non-autonomous lagrangian systems

RÉSUMÉ. - On montre que si 03BE est une equation differentielle du deuxieme ordre (semigerbe) sur le fibre des jets M) telle que les courbes integrales sont des solutions de 1’equation de Lagrange non-autonome alors il existe une connexion F sur J~ (R, M) dont les courbes integrales sont aussi des solutions de la même equation. En plus, F est une connexion ayant comme semigerbe ~. L’etude est une extension a la dynamique Lagrangienne non-autonome de quelques résultats de Grifone pour le cas autonome. ABSTRACT. - We show that if $ is a second-order differential equation (semispray) on the jet bundle Jl (R, M) whose paths are solutions of the non-autonomous Lagrange equations then there is a connection r on Jl (R, M) whose paths are also solutions of the same equations. Moreover, r is a connection whose associated semispray is precisely ~. This is an extension to non-autonomous Lagrangian dynamics of a previous result due to Grifone for autonomous Lagrangians.
The Analysis of Unintended Open Reading Frame ORF-130 Expression in Maize Event MZIR098 by LC-MS/MS as Part of the Allergenicity Risk Assessment of Genetically Modified Crops

Rigorous safety assessments are required for genetically modified (GM) crops to support their use as food or feed. These assessments include bioinformatics analysis of the amino acid sequences of all potential open reading frames (ORFs) encoded in the exogenous DNA introduced into the plant through genetic modification. Sequence similarities of potential DNA products to known or potential toxins and allergens trigger subsequent assessments of potential hazards for the consumer or the environment. The bioinformatics analysis of Syngenta maize event MZIR098 insert DNA revealed a total of 415 ORFs. One putative ORF ( orf-130 2 ) triggered further risk assessment based on its sequence similarities with known allergens, including peanut allergen Ara h 1, wheat glutenin, and others. We examined the potential expression of orf-130 at a protein level to exclude the possibility of exposure during consumption of MZIR098 maize. Specifically, we developed and validated a liquid chromatography coupled to a tandem mass spectrometry (LC-MS/MS) method, called parallel reaction monitoring (PRM). This measure of expression was quantitative and targeted against the peptides unique to ORF-130 amino acid sequence. The linear range of the method was 25-5000 amol ORF-130/µl. Both, limit of detection (LOD) and lower limit of quantitation (LLOQ) of the method, were 12 μg ORF-130 per gram of dry MZIR098 maize leaf. The method was accurate and specific to ORF-130. PRM analysis of MZIR098 extracts did not detect putative ORF-130 above the limits of detection/quantitation. Furthermore, the broad proteomic data dependent acquisition (DDA) analysis of the MZIR098 extracts demonstrated no evidence of ORF-130 corroborating the PRM analysis. Our results confirm that orf-130 is not characterized as a functional gene in Syngenta maize event MZIR098 insert DNA and does not express gene products at detectable levels.
Cyber Security Challenges and Opportunities in a Multi-Agent Environment: The case of Swedish Transport Administration

Cyber risks are considered as one of the main challenges that harm technology, data, and privacy of individuals and organizations. While cyber criminals tend to use improved methods and tools to steal data or hack into systems, the ability of organizations to mitigate such risks tend to become more difficult. Especially when the organization and various other agents operate in a multi-agent environment that is often connected to the internet. This report highlights some of the most common and fundamental cyber threats and cyber security gaps or vulnerabilities which might be found in risk mitigation tools that are used for cyber defense in a multi-agent environment. The Swedish Transport Administration for example operates in such an environment. The study exhibits latest challenges and opportunities in the transport arena while focusing on unique and specific disciplines related to the Swedish Transport Sector. It suggests future applicative research studies that will yield recommendations to cyber defense managers in the transport sector whose one of the major tasks is to perform mitigation of cyber risks. The study employed a structured literature review methodology drawing on existing scientific scholarship to prepare the report.
Orthogonal Dictionary Guided Shape Completion Network for Point Cloud

Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications. Prior approaches achieve this goal by employing a two-stage completion framework, generating a coarse yet complete seed point cloud through an encoder-decoder network, followed by refinement and upsampling. However, the encoded features suffer from information loss of the missing portion, leading to an inability of the decoder to reconstruct seed points with detailed geometric clues. To tackle this issue, we propose a novel Orthogonal Dictionary Guided Shape Completion Network (ODGNet). The proposed ODGNet consists of a Seed Generation U-Net, which leverages multi-level feature extraction and concatenation to significantly enhance the representation capability of seed points, and Orthogonal Dictionaries that can learn shape priors from training samples and thus compensate for the information loss of the missing portions during inference. Our design is simple but to the point, extensive experiment results indicate that the proposed method can reconstruct point clouds with more details and outperform previous state-of-the-art counterparts. The implementation code is available at https://github.com/corecai163/ODGNet.
Insights into the life strategy of the common marine diatom Chaetoceros peruvianus

Chaetoceros peruvianus is a marine diatom species with circumglobal distribution. While frequently observed, it appears never to dominate the marine phytoplankton community hence it can be characterized as a rather opportunistic, generalistic species. Here we present ecological interpretations from a long-term data set on marine microphytoplankton in the northern Adriatic Sea, where the abundancies and relative contributions of C . peruvianus were observed along a set of steep ecological gradients. Limited supply of dissolved inorganic phosphate was identified as the driving ecological factor for this ecosystem. In parallel C . peruvianus was cultivated in monoclonal cultures and its morphological and physiological reaction to replete and phosphorus depleted medium was analysed. C . peruvianus reacted to phosphorus depletion by an increase in cell height and length as well as thickness and length of setae. This morphological reaction included an increase in cellular volume and calculated carbon content. Additionally, it represents the transition between two described morphological varieties, C . peruvianus and C . peruvianus var. robusta . C . peruvianus showed a significant induction of extracellular alkaline phosphatase activity if grown in phosphate depleted medium. Microscopical analysis demonstrated this activity to be located exclusively on the setae of the cells.
PhD Studies Hurt Mental Health, but Less than Previously Feared *

We study the mental health of PhD students in Sweden using comprehensive administrative data on prescriptions, specialist care visits, hospitalizations, and causes of death. We find about 7% (5%) of PhD students receive medication or diagnosis for depression (anxiety) in a given year. These prevalence rates are less than one-third of the earlier reported survey-based estimates, and even after adjusting for difference in methodology, 43% (72%) of the rates in the literature. Nevertheless, PhD students still fare worse than their peers not pursuing graduate studies. Our difference-in-differences research design can attribute at least 80% of this health disadvantage to the time in the PhD program. This deterioration suggests doctoral studies causally affect mental health.
Comparison of artificial neural networks and adaptive neuro-fuzzy inference system for regional flood estimation in Australia

Regional flood frequency analysis (RFFA) involves transfer of flood characteristics from gauged to ungauged catchments. In Australia, RFFA methods generally focus on the application of empirical methods based on linear forms of model such as the Probabilistic Rational Method, the Index Flood Method and the Quantile Regression Technique. There have been successful applications of nonlinear models in RFFA in some other countries such as Artificial neural networks (ANN) and Adaptive neuro-fuzzy inference system (ANFIS). The application of these non-linear RFFA methods in Australia is limited. This study focuses on the application of ANN and ANFIS-based RFFA models to Australian data. Using data from 452 catchments in eastern Australia (a part of Australian Rainfall and Runoff Revision Project 5 Regional flood methods database), it has been found that the ANN-based RFFA performs better than the ANFIS and provides quite accurate regional flood quantile estimates. However, the Bayesian generalised least squares based QRT couples with the region of influence approach outperforms both the ANN and ANFIS based RFFA models.
Durability Enhancement of Sustainable Concrete Composites Comprising Waste Metalized Film Food Packaging Fibers and Palm Oil Fuel Ash

The utilization of waste materials in sustainable and green concrete manufacturing is particularly appealing because of the low cost of waste resources, the saving of landfill space, and the development and enhancement of concrete qualities. This paper investigates the strength and durability of green concrete composites made of waste metalized film food packaging (MFP) fibers and palm oil fuel ash (POFA). Compressive and tensile strengths, carbonation, drying shrinkage, electrical resistivity, and rapid chloride penetration tests in concrete mixtures are among the properties explored. With ordinary Portland cement (OPC), MFP fibers of 20 mm in length and six-volume fractions ranging from 0 to 1.25% were employed. Another six concrete mixes were made with 20% POFA in place of OPC. The results showed that adding MFP fibers to concrete mixes reduced their compressive strength. Despite a minor reduction in compressive strength, the inclusion of MFP fibers significantly increased tensile strength. The findings show that the combination of MFP fibers with POFA substantially impacts concrete durability. The addition of MFP fibers to concrete mixes resulted in a reduction in carbonation and drying shrinkage. The chloride penetration of specimens was also reduced, whereas the electrical resistivity of reinforced samples rose by nearly 80% compared to ordinary concrete.
Application of Augmented Reality (AR) In Vocational Education: A Systematic Literature Review

In education, Augmented Reality has been widely used to complement the standard curriculum. This article aims to examine how the application, and impact of the use of AR in Vocational Education. The Systematic Literature Review method is used to identify, study, evaluate, and interpret Augmented Reality in vocational education. Research data were identified by help of the PRISMA 2020 form. Article was collected from Science Direct database. There were 454 articles. After being selected using inclusion and exclusion criteria, the data used were 14 articles. The results of the study reveal that AR is applied in learning as an innovative learning medium, a new approach to learning to improve students' understanding of knowledge, and skills and provide a concrete learning experience. The integration of AR in education has the impact of increasing students' understanding and learning experience, promoting positive attitudes, making the learning process fun and interesting and learning efficiency.
POTENTIAL RISK FACTORS TO PREVENT THE RISE IN ELECTRIC CIGARETTE CONSUMPTION AMONG INDONESIAN ADOLESCENTS AGED 13 TO 15 YEARS OLD

Data showing a significant increase trend in sales of electronic cigarettes in Indonesia and users aged ≥ 15 years have increased tenfold to 6.6 million in 2021, and there has been no e-cigarette regulation. The purpose of the study is to provide evidence to support policies intended for increasing the number of electric smokers. Using data from the 2019 Global Youth Tobacco Survey with national representation. Simple and multiple logistic regression analyses (p < 0.05) were performed. The analysis states: 15% of youth 13-15 years old use e-cigarettes and most users are boys 13 years old, with a weekly disposable income below IDR 50,000. Multivariate analysis shows; friends or shop workers who provide e-cigarettes or attend events promoting tobacco products have a 15-fold greater influence on adolescents than the reference group. When compared to not seeing the advertisements, e-cigarette advertisements on the streets/stores/TV/ internet have the potential to increase adolescent motivation by 8 times. Nonsmokers who have tried tobacco products are potentially three times more likely to be interested in e-cigarettes than those who have never tried them. Adolescent smokers are twice as likely as nonsmokers to use e-cigarettes, and close friends who smoke are twice as likely as nonsmokers to influence e-cigarette use. Information shows that the previous Tobacco Advertising Promotion and Sponsorship Policy was ineffective, it needed to be strengthened with a total ban. It is critical to strengthen smoking cessation efforts, as well as raise parental and community awareness of the dangers of consuming tobacco products.
A Comparative Study of Virtual Education Challenges Across Semesters for Laboratory Science Students

Background: Virtual education is planned learning in which learning and teaching usually take place in separate environments. In this research, the challenges of using virtual education in different academic years for laboratory science students are investigated during the days of COVID-19. Methods: The research population consisted of 143 laboratory science students, who were selected based on the entry criteria using a stratified random method. The data collection tool was a researcher-made questionnaire. Exploratory factor analysis was used to determine the dimensions of the questionnaire, and Cronbach’s alpha coefficient was utilized to check the internal consistency of the questions. The results were analyzed using SPSS 26. Results: Most of the students participating in this study were girls, between the ages of 18 and 20, and in the 4th semester. The mean standard deviation of the overall score of the virtual education problem questionnaire was 53.57 (17.41). In addition, the highest score in the aspect of academic conditions and motivation was 64.86 (19.73), and the lowest score in universal access to the Internet was 25.45 (54). Conclusion: The technical factor and the overall score of the virtual education problem questionnaire were significantly higher in male students than in female students. Further, the average scores of the questionnaire of virtual education problems and all the dimensions of the questionnaire, including conditions and overall motivation, were significantly higher in younger students than in older students.
Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods

This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source’s wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.
Health Benefits of Electrolyzed Hydrogen Water: Antioxidant and Anti-Inflammatory Effects in Living Organisms

Molecular hydrogen, the smallest and lightest molecule, serves as an intense reducing agent. Its distinct characteristics, including minimal size and neutral charge, enhance bioavailability and facilitate significant biological effects. Previously considered physiologically inert, hydrogen has gained recognition as a powerful therapeutic agent, known for its antioxidative and anti-inflammatory properties. Electrolyzed hydrogen water (EHW), enriched with molecular hydrogen, demonstrates remarkable antioxidative capabilities, indicating potential benefits for various diseases. Inflammation-induced reactive oxygen species (ROS) amplify inflammation, leading to secondary oxidative stress and creating a crosstalk between ROS and inflammatory responses. This crosstalk contributes to the pathogenesis and progression of chronic diseases. EHW interrupts this crosstalk, reducing inflammatory cytokines and oxidative stress across various disease models, suggesting therapeutic potential. EHW is also known for its anti-inflammatory effects, extending to pain management, as evidenced in models like sciatic nerve ligation and inflammatory pain. In an inflammatory bowel disease (IBD) model, EHW effectively alleviates abdominal pain, mitigating 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced inflammation and oxidative stress, offering insights for clinical applications. Additionally, hydrogen selectively targets harmful radicals, and EHW intake helps balance stress-induced hormonal dysregulation, potentially easing disorders associated with chronic stress.
Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH4) with anomalies driven by the influence of the El Niño-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH4 emissions during El Niño versus La Niña events is from wetlands and (ii) how large the contribution of wetland CH4 emissions is to the interannual variability of atmospheric CH4. We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH4 emissions from 1980–2016. The simulations show that while wetland CH4 responds with negative annual anomalies during the El Niño events, the instantaneous growth rate of wetland CH4 emissions exhibits complex phase dynamics. We find that wetland CH4 instantaneous growth rates were declined at the onset of the 2015–2016 El Niño event but then increased to a record-high at later stages of the El Niño event (January through May 2016). We also find evidence for a step increase of CH4 emissions by 7.8±1.6 Tg CH4 yr−1 during 2007–2014 compared to the average of 2000–2006 from simulations using meteorological reanalyses, which is equivalent to a ~3.5 ppb yr−1 rise in CH4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30°S–30°N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH4 fluxes.
Improved Reservoir Characterization from Integration of Towed Streamer EM and Dual-sensor Broadband Seismic Data

We present a data integration study for reservoir characterization, resulting in estimates of hydrocarbon saturation in place within a reservoir. The limitations of using only seismic data for detailed reservoir characterization are well recognized. Seismic data are well suited to resolving structure and some rock properties, notably porosity, but attempts to map fluids from seismic data may be misleading due to the ambiguity between fluid and lithology effects. It is well known that CSEM data are sensitive to the presence of hydrocarbons, but the diffusive energy propagation results in low resolution images. Thus, a careful combination of both types of data helps to overcome their individual weaknesses. In this paper, we outline with a dataset example how the CSEM and seismic data can be integrated together with limited rock physical knowledge in the prospect area to estimate the total volume of hydrocarbon in place.
Thin-Walled Aircraft Panel Edge Extraction From 3-D Measurement Surfaces via Feature-Aware Displacement Learning

To enable the generation of reliable tool paths for precision machining along the edges of thin-walled aircraft assembly panels, we introduce a feature-aware displacement learning framework for accurately extracting the edges of aircraft panels. Specifically, we design a dual-task neural network, named aircraft panel edge extraction network (APEE-Net). This network serves the dual purpose of identifying points located near the edges of aircraft panels and predicting displacement vectors pointing toward local edge features. The detected edge points are subsequently repositioned using these displacement vectors, resulting in the extraction of precise edge points. Our proposed method is fortified with feature-aware displacement optimization loss during the training phase, significantly enhancing its robustness and accuracy when dealing with noisy sharp geometric features. Extensive experiments demonstrate that our approach outperforms existing extraction methods in terms of accuracy. Furthermore, practical machine applications further validate its feasibility and real-world applicability.