Miracle-dz/UniFAR-FTUs huggingface dataset:
Explaining purchase intent via expressed reasons to follow an influencer, perceived homophily, and perceived authenticity

Abstract The influencer trend, where people are often paid by brands to use, talk about and otherwise promote their products on social media has become increasingly popular. This study examines the potential interplay of perceived homophily with, perceived authenticity of an influencer and the reasons people may follow an influencer on subsequent purchase intentions of items the influencer has showcased on their Instagram account. Key findings suggest that the type of perceived homophily one shares with their favorite influencer is linked to the reasons they follow that influencer, and that following an influencer because they are trendy when a person also indicated perceived status homophily (e.g., similarities in size) did lead to increased purchase intent. However, influencers that are followed because they are unique and share morals and values with a follower (i.e., ‘value homophily’) do not encourage purchase intent. We find that an influencer who is perceived as more ‘authentic’ will mediate the relationship between perceptions of homophily and subsequent purchase intent.
A Distributed Multimode Control Strategy for the Cascaded DC–DC Converter Applied to MVAC Grid-Tied PV System

The cascaded dc–dc converter is a popular topology for large-scale medium-voltage photovoltaic (PV) system with the advantages of high voltage conversion ratio, high efficiency, great flexibility, and independent control capability. However, the absence of optimal string maximum power point tracking (MPPT) operation under power mismatch is a big issue in common centralized control. Furthermore, the cost and time delay are unacceptable resulting from the distance among PV strings in the large-scale PV station and the cascaded dc–dc converter needs to adopt the distributed control method without real-time communication. To this end, by introducing other necessary constraints including voltage constraints, power constraints, and gain constraints on the basis of the distributed MPPT control strategy, a distributed multimode control scheme is proposed in this article. Each submodule (SM) can be switched flexibly between multiple operation modes to maximize the extracted solar energy. Individual string MPPT and autonomous distribution of output voltage are achieved without communication under the proposed distributed control strategy. In this way, optimal operation range is wider, mode switching is simpler, and applicability and robustness are improved. A downscaled prototype and a 35-kV/6-MW simulation system are constructed to verify the effectiveness of the proposed control strategy.
Real-World Learning Control for Autonomous Exploration of a Biomimetic Robotic Shark

With the development of learning-based autonomous underwater exploration method of robotic fish, how to improve data quality and sampling efficiency, so as to achieve better control performance becomes a challenging research subject. To address this issue, a feasible real-world deep reinforcement learning framework for autonomous underwater exploration of robotic fish is proposed, which ably avoids model discrepancy generated in virtual training. The designed framework consists of three phases: teaching initialization, regular update of reinforcement learning, and phased consolidation training. Specially, reasonable teaching initialization improves the data sampling efficiency and stabilizes the real-world early training. The consolidation training ensures the reproducibility of good controllers by interim imitation learning in the middle training phase. Extensive underwater experiments on a novel self-developed biomimetic robotic shark show that the proposed real-world learning method significantly improves the safety and efficiency of autonomous exploration based on local sensor information, providing a promising solution for exploring in uncharted wild waters.
An Investigation of Chinese People’s Attitudes Toward Black People

The present study investigated implicitracial bias among Chinese people toward Black people. To address this question, we used implicit associatin test (IAT) among 44 Chinese adults. The results suggested that Chinese people do not have racial bias against Black people. The investigation contributed to the study of implicit method and racial bias and point to the future work to further assess individual difference in racial bias.
Outage Performance of Satellite Terrestrial Full-Duplex Relaying Networks With co-Channel Interference

This letter investigates the performance of the satellite-terrestrial networks (STN), where a satellite tries to transmit information to a ground user through the help of multiple decode-and-forward relays and the existence of co-channel interference sources. In particular, the full-duplex technique and partial relay selection are applied at the relay to increase the total throughput at the destination, enhance the system reliability, and reduce the complexity. In this context, the outage probability (OP) is computed in a closed-form expression. Numerical results are provided to confirm the accuracy of the proposed mathematical framework. Our findings illustrate that the outage performance can be effectively enhanced by increasing either number of relays or transmit power.
A Conceptual Model of Consumer Intention to Continue Buying Eco-Labeled Products

The study of eco-labeled product purchasing behavior usually starts from a multidisciplinary set of fundamental theories and models depending on the approach towards the ecological behavior of the consumer. The proposed conceptual model is based on quantitative research on the intention of consumers to continue to buy eco-labeled goods for personal use. The model is analyzed using the structural equation modeling (SEM) technique, based on variance analysis, on a sample of 587 respondents. The analysis process consisted of two distinct stages, evaluating the accuracy of measurements, and testing the hypotheses. The conformity of the model was validated by studying its specific indicators. The 17 study hypotheses were tested by path coefficient analysis within the structural model, as well as the postulated moderating effect. Based on the conformity indices obtained, the model has good predictive and explanatory capacity. The only hypothesis refuted is the relationship between consumers’ perception of personal effectiveness and their intention to continue buying eco-labeled products. This relationship was not verified at the chosen significance threshold of . 01. By analyzing the indirect and total effects, we demonstrated the existence of certain beliefs that directly influence consumers’ intention to continue buying eco-labeled products. The influence is also indirect through other variables such as perceived trust and perceived usefulness. The existence of a statistically significant effect from variables on an indirect path (expectations confirmation, ease of use of the ecolabel in purchasing eco-products) was also demonstrated. The explanatory and predictive capacity of the intention to continue buying eco-labeled products was increased by validating the proposed model.
The 1975 Ride Quality Symposium

A compilation is presented of papers reported at the 1975 Ride Quality Symposium held in Williamsburg, Virginia, August 11-12, 1975. The symposium, jointly sponsored by NASA and the United States Department of Transportation, was held to provide a forum for determining the current state of the art relative to the technology base of ride quality information applicable to current and proposed transportation systems. Emphasis focused on passenger reactions to ride environment and on implications of these reactions to the design and operation of air, land, and water transportation systems acceptable to the traveling public. Papers are grouped in the following five categories: needs and uses for ride quality technology, vehicle environments and dynamics, investigative approaches and testing procedures, experimental ride quality studies, and ride quality modeling and criteria.
A Novel Approach to the Extraction of Key Points From 3-D Rigid Point Cloud Using 2-D Images Transformation

Most traditional methods for extracting key points from the 3-D point cloud are based on the geometric features of points, and they pose problems such as low accuracy. In order to solve these problems, this article proposes a novel approach based on 2-D image mapping, making it able to achieve highly accurate localization of key points. Specifically, it works as follows: input images are first selected for Harris corner detection; the three pairs of marker points of the images and the point cloud are then selected to calculate the transformation matrix <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> between them; next, the image key points are mapped onto the 3-D points through the transformation matrix <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, for which the extraction of key points is achieved. Experimental results show that the proposed algorithm is able to greatly improve the extraction accuracy of key points in comparison with traditional algorithms.
Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Over the years, researchers have been studying the effects of weather and context data on transport mode choices. Existing research studies are predominantly designed around travel surveys, but the accuracy of their findings relies on how travelers give accurate and honest answers. The proliferation of smartphones, however, now offers the possibility of utilizing GPS positioning data as an alternative information source, opening the potential to accurately model and better understand factors which influence transport mode choices, compared to travel surveys. The objective of this work is to develop a model to predict the transport mode choices based on GPS trajectories, weather and context data. We use 2671 GPS trajectories from the Geolife GPS trajectories dataset, weather data, such as temperature and air quality, and context data, such as rush hour, day/night time and onetime events, such as the Olympics. In the statistical analysis, we apply both descriptive and statistical models, such as the multinomial logit and probit models. We find that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27%, and the bike share reduces by 21%, which is line with the results from several survey-based studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous, as people are encouraged to use environmentally friendly transport mode choices, such as the bike instead of the bus or car, which are known CO2 emitters. Our conclusion is that GPS trajectories can be used as a means to model passenger behavior, e.g. the choice of transport mode, in a quantitative way, which will support transport mode operators and policy makers in their efforts to design and plan the transport mode infrastructure to best suit the passengers’ needs.
Government subsidies, ownership structure and operating performance of state-owned enterprises: evidence from China

ABSTRACT Government subsidies have been used as a policy tool by many countries. Given the importance of government subsidies in the context of Chinese economy and state-owned enterprises (SOEs), this study seeks to understand the role played by government subsidies in the operating performance of Chinese SOEs. Using panel data on Chinese SOEs to construct the fixed-effects regression models, this study examines the effects of government subsidies and explores the moderating role of ownership structure in the correlation between government subsidies and operating performance of SOEs. Government subsidies have improved the operating performance of SOEs through easing financial constraints and stimulating research investment. However, high proportion of state-owned shares is not conducive to the positive effect of subsidies. The heterogeneous analyzes show, for SOEs located in eastern China, at the local level or with a higher R&D level, an increase in state-owned shares is more detrimental to the positive effect of subsidies on their performance. Tax-based-subsidies have significantly positive effect on the operating performance of SOEs, with the state-owned shares exerting a negative moderating effect on this positive correlation. Based on the empirical findings, we propose some policy suggestions for the mixed ownership reform of Chinese SOEs and reasonable allocation of government subsidies.
FOCUSeR: A Fog Online Context-Aware Up-to-Date Sensor Ranking Method

Data obtained from sensors connected to wireless sensor networks must be stored and processed to enable environments such as smart cities. However, with the exponential growth in the number of devices at the edge of the network, it is necessary to implement robust techniques, capable of selecting reliable data sources and meeting low latency requirements, in order to serve critical applications. Thus, to overcome these challenges, this research work presents FOCUSeR, a method for ranking sensors. The method uses the evaluation of data as a criterion for the ranking, allowing us to identify occurrences of failures in sensors and anomalies in environments. In order to meet the requirements inherent to WSNs, the proposed method was developed to run in a fog computing environment, using online learning and constant updating over time to avoid effects such as time drift. The generated ranking lists are managed through distributed hash tables. To provide reliability to the experimental results, a real experimental environment was developed. Moreover, using this developed testbed, a dataset with labels was created, to support the evaluation of the method. In addition, four other real datasets were used, three of which were labeled through artificial fault injection. These datasets were labeled in a related work that focused on injecting artificial faults. The experimental results obtained demonstrate that the proposed approach can provide reliability in the use of sensor data, using low computational resources and reducing latency in the sensor selection process. Precision rates are approximately 98% and Accuracy rates are greater than 94% across all datasets. In addition, the analyses carried out show that the Accuracy has an increasing rate as the number of samples also increases. Results obtained in the failure data recovery also demonstrate the feasibility of the proposal in this resource.
Machine learning applied to BOTDR optical fibre distributed sensing in a controlled environment

We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system, to extract temperature information from single-mode optical fibre over a 40-km distance. The application is for power line monitoring of underground cables that are collocated with optical fibres that form part of the Electricity Authority of Cyprus’ island wide power distribution networks. The existing optical fibre infrastructure acts as the sensing element, monitoring temperature changes when in close proximity to the power lines. The initial training measurements for the machine learning algorithm were recorded in a laboratory setting using temperature and humidity-controlled elements, with sections of fibre spliced to underground fibre cables subjected to temperature excursions. A machine learning approach was implemented for the prediction task of finding points that are likely to get damaged, mimicking the behavior of power cable joints that are prone to failure, along with general monitoring for unusual behavior and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as the collocated fibre’s temperature rising to dangerously high values, and “0” to the rest. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The training was undertaken on the laboratory data and re-training is done periodically with new field measurements. The completion of the training phase shows the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre; this will be extended to BOTDR data taken in the field.
Human photoreceptors switch from autonomous axon extension to cell-mediated process pulling during synaptic marker redistribution

Photoreceptors (PRs) are the primary visual sensory cells, and their loss leads to blindness that is currently incurable. Cell replacement therapy holds promise as a therapeutic approach to restore vision to those who have lost PRs through damage or disease. While PR transplant research is ongoing in animal models, success is hindered by our limited understanding of PR axon growth during development and regeneration. Using a human pluripotent stem cell (hPSC) reporter line that labels PRs (WA09 CRX+/tdTomato), we generated retinal organoids in order to study mechanisms of PR process extension. We found that the earliest born PRs exhibit autonomous axon extension from dynamic terminals that appear similar to projection neuron growth cones. However, as hPSC-derived PRs age from 40 to 80 days of differentiation, they lose dynamic terminals in 2D plated cultures and within 3D retinal organoids, which does not correlate with cell birth date. Using a rod-specific hPSC reporter line (WA09 NRL+/eGFP), we further determined that rod PRs never form motile growth cones. Interestingly, PRs without motile terminals are still capable of extending axons, but neurites are generated from process stretching via their attachment to motile non-PR cells, which underlies the observed differences in PR neurite lengths on different substrata. While immobile PR terminals express actin, it is less polymerized and less organized than actin present in motile terminals. However, immobile PRs do localize synaptic proteins to their terminals, suggesting a normal developmental progression. These findings help inform the development of PR transplant therapies to treat blinding diseases and provide a platform to test treatments that restore autonomous PR axon extension. Significance Statement Loss of photoreceptors (PRs) in the retina through damage or disease causes irreversible vision loss and blindness. One treatment approach is to replace lost cells with transplanted human stem cell-derived PRs, but this requires PR axons to integrate into the host retina to restore the required neural connections. For this strategy to succeed, we need to understand how PRs extend processes to their targets during development in situ, and whether dissociated human stem cell (hPSC)-derived PRs behave in a similar fashion. In this paper, we show that hPSC-PRs have only a short window during which they are capable of autonomous axon extension, which has implications for PR transplant efforts and for our basic understanding of human retinal development.
Nutritional and functional properties of Hyphaene thebaica L. flour: a critical treatise and review

ABSTRACT The importance of plant secondary metabolites in food and their possible impacts on human health are the subjects of a steadily developing number of studies. Furthermore, as consumers become more conscious of diet-related issues, they desire natural ingredients that are anticipated to be safe and health-promoting; as a result, doum flour might be regarded as an excellent source of functional compounds. Hyphaene thebaica L. is a palm plant species with eatable ovoid fruits and a woody texture that is endemic to upper Egypt. It is one of the world’s most significant and valuable plants. Doum flour is considered to contain higher moisture content, crude fiber, ash content, protein, fat, and vitamins, especially niacin, folic acid, pyridoxine, riboflavin, and thiamin. Previous findings showed that doum flour has high functional and nutritional qualities and may be used for a variety of key applications in the food sector. Doum nuts contain antioxidants as well as metabolites like tannins, saponins, steroids, glycosides, terpenes, and terpenoids. The results of different studies have indicated that doum flour is effective in various health maladies. This review highlights the anti-inflammatory, antioxidant, antibacterial, anticancer, nutritional content, fertility activity, the rheological, and pharmacological ability of Hyphaene thebaica L. flour extracts and their significant phyto-constituents such as polyphenol, essential-oil, and flavonoid components.
A Cyanobacteria Enriched Layer of Shark Bay Stromatolites Reveals a New Acaryochloris Strain Living in Near Infrared Light

The genus Acaryochloris is unique among phototrophic organisms due to the dominance of chlorophyll d in its photosynthetic reaction centres and light-harvesting proteins. This allows Acaryochloris to capture light energy for photosynthesis over an extended spectrum of up to ~760 nm in the near infra-red (NIR) spectrum. Acaryochloris sp. has been reported in a variety of ecological niches, ranging from polar to tropical shallow aquatic sites. Here, we report a new Acarychloris strain isolated from an NIR-enriched stratified microbial layer 4–6 mm under the surface of stromatolite mats located in the Hamelin Pool of Shark Bay, Western Australia. Pigment analysis by spectrometry/fluorometry, flow cytometry and spectral confocal microscopy identifies unique patterns in pigment content that likely reflect niche adaption. For example, unlike the original A. marina species (type strain MBIC11017), this new strain, Acarychloris LARK001, shows little change in the chlorophyll d/a ratio in response to changes in light wavelength, displays a different Fv/Fm response and lacks detectable levels of phycocyanin. Indeed, 16S rRNA analysis supports the identity of the A. marina LARK001 strain as close to but distinct from from the A. marina HICR111A strain first isolated from Heron Island and previously found on the Great Barrier Reef under coral rubble on the reef flat. Taken together, A. marina LARK001 is a new cyanobacterial strain adapted to the stromatolite mats in Shark Bay.
Orchestrating Global Climate Governance Through Data: The UNFCCC Secretariat and the Global Climate Action Platform

Abstract Since the adoption of the Paris Agreement, the focus of the United Nations climate regime has shifted from forging consensus among national governments toward animating implementation activity across multiple levels. Based on a case study of the Global Climate Action Portal—an online database designed to document nonstate actor climate commitments and implementation efforts—we trace, conceptualize, and assess how the roles of data, data infrastructures, and actor constellations have changed as a result of this shift. We argue that in the pre-COP21 negotiation phase, the United Nations Climate Secretariat strategically used the database to orchestrate and leverage nonstate actor commitments to exert pressure on intergovernmental negotiations. By contrast, in the post-COP21 implementation phase, the Secretariat, in collaboration with climate data specialists, is seeking to develop the portal to track and animate implementation activity. Given these developments, we discuss the potential and limitations of data-driven climate governance and set out avenues for future research.