Miracle-dz/UniFAR-FTUs huggingface dataset:
Attribution of feature magnitudes is influenced by trained reading-writing direction

ABSTRACT Spatial configurations amongst stimuli can influence magnitude attributions. Someone’s acquired reading and writing direction (RWD) can provide a spatial schema of primacy extending from left (maximum) to right (minimum) for Westerners and opposite for leftward RWD languages. Primacy information can be transformed into a magnitude attribution regarding a feature quality, perceiving an object as having “more” of a certain quality for Westerners when positioned left amongst two similar objects, likewise when positioned right for people with a right-to-left RWD. Results showed that native English speakers tended to attribute greater magnitude of a given feature in fictitious products displayed left within a pair, indicating which of two products was “most” representative of a certain quality (Experiment 1a) but they would randomly choose when asked which product represented “least” of the quality (Experiment 1b). A similar, but reversed pattern of effects was obtained for Farsi participants only familiar with Farsi (Experiment 2).
Transferable Convolutional Neural Network for Weed Mapping With Multisensor Imagery

Automatic weed monitoring and classification are critical for effective site-specific weed management. With the increasing availability of different sensors, it is possible for weed management to be achieved by processing a wide range of images captured from various remote sensing platforms. A deep learning-based convolutional neural network (CNN) can learn the sophisticated spectral, spatial, and structural features to discriminate weed species. The challenge is to train a CNN architecture for each dataset with limited training samples. In this study, we develop a partial transferable CNN to cope with a new dataset with a different spatial resolution, a different number of bands, and variation in the signal-to-noise ratio. The goal is to make the training for each new dataset less demanding. We conducted a series of experiments on simulated image datasets from two sensors. This study reveals that the dropout layers between the convolutional layers have a significant impact for partial transferable CNN. Even-numbered subset layers from source CNN has a stronger impact on dealing with a task of different spatial resolution. For a different number of bands in source and target datasets, except for the first convolutional layer, the remaining layers are used for the analysis. Results show that network transfer is possible when the numbers of bands of the two datasets are not very different. For the variation in signal-to-noise ratio, it is found that the performance of transfer learning is acceptable when the noise level is not high. Based on these findings, experiments were conducted on two real datasets from two sensors, which includes all the variations. The comparison results using different state-of-the-art models show that partial CNN transfer with even-numbered layers provides better mapping accuracy for the target dataset with a limited number of training samples.
High-throughput analysis of lncRNA in cows with naturally infected Staphylococcus aureus mammary gland

Abstract LncRNA (long non-coding RNA) is an RNA molecule with a length between 200 and 100,000 nt. It does not encode proteins and is involved in a variety of intracellular processes, becoming a research hotspot of genetics. To identify key lncRNAs associated with dairy mastitis, we collected mammary epithelial tissue samples of Normal disease-free Holstein cows (HCN) and unhealthy Holstein cows with Staphylococcus aureus (HCU) and performed RNA sequencing (RNA-seq) on the samples. A total of 270 differentially expressed lncRNAs and 500 differentially expressed mRNAs were identified by high-throughput sequencing and bioinformatics analysis. Furthermore, Hydrolase activity is the most enriched in GO, and ErbB signaling pathway is significantly enriched in KEGG. In addition, through qPCR validation of 5 candidate lncRNAs in HCN and HCU, four differentially expressed lncRNAs MSTRG.498, MSTRG57.1, MSTRG.41. 1 and MSTRG 124.1 were confirmed to have significant differentially expressed in cow mastitis. Also, lncRNA MSTRG.498 and its target gene, SMC4, might directly or indirectly play a role in cow mastitis. The regulatory network of lncRNA-miRNA-mRNA has been inferred from a bioinformatics perspective, which may assist understand the underlying molecular mechanism of lncRNAs involved in regulating mastitis in cows. Our findings will provide meaningful resources for further research on the regulatory function of lncRNAs in cow mastitis.
Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.
Current-Sharing Worst-Case Analysis of Three-Phase CLLC Resonant Converters

Three-phase CLLC resonant converters provide higher power conversion capability as compared to half-bridge and full-bridge structures. In addition to the unique features of CLLC converters for bidirectional applications, the three-phase structure provides significantly reduced output current ripple (smaller output capacitor), parallel power processing (reduced components size and current peak stress), and better thermal distribution (smaller heatsinks). However, with practical, i.e., nonzero, resonant component tolerances, these benefits are normally less, and sometimes significantly less than expected in the ideal case. In this article, the unbalanced behavior of the converter with 15 unknown resonant components is identified and analyzed. A new analysis methodology is proposed to investigate the worst-cases of current-sharing among above 32 000 possible scenarios in three-phase CLLC resonant converters. In addition, this article shows that phase-shifting techniques can be effective to mitigate the unbalanced behavior of the converter. The proposed analysis in this article helps to determine the highest admissible tolerance in the components to keep the converter working within a certain range of unbalanced behavior without requiring any balancing techniques. The proposed analytical framework is verified with experimental and simulation results of a 3-kW bidirectional three-phase CLLC experimental prototype.
Flint sources and mobility at the Chalcolithic (3500–2200 BCE) settlement of Zambujal (Portugal)

Provenance studies of the siliceous sedimentary raw material used by ancient communities are essential to understanding the mobility and exchange networks of these communities. The establishment of the direct procurement territory is the first step to understanding the role of the ‘imported’ artefacts. In this study, flint cores, flakes, bladelets and debitage from the Chalcolithic settlement of Zambujal (Estremadura, Portugal), recovered in excavations between 1964 and 1979, are analysed. We attempt to identify the local and regional source areas of these stone materials and compare their petrography (facies and microfacies textures). This study demonstrates the importance of employing three scales of analysis in petrographic studies (the macro‐, meso‐ and micro‐) to structure and determine flint provenance. Analysis of cortical material shows the relevance of secondary detrital outcrops during the Chalcolithic of the Portuguese Estremadura. Small cobbles were directly collected in both local and regional formations dated to the Palaeogene and Cenomanian, in a large (approx. 40 km) exploration territory, to produce expedient and specialized artefacts (i.e., bladelets). However, a large portion of stone tools from Zambujal made from Cenomanian flint cannot be assigned to local or regional procurement. The unclear provenance of these materials suggests directions for future research.
Predicting Beliefs in Psychological Misconceptions with Psychology Knowledge and the Critical Reflection Test: A Replication and Extension

Background: Psychological misconceptions permeate our culture, with many people endorsing these erroneous beliefs. Objective: The purpose of this study was to replicate and extend previous findings that reported psychology knowledge and cognitive thinking style predict undergraduates’ beliefs in psychological misconceptions. Method: Two hundred and ninety-six undergraduates participated in an online survey in which they completed a psychological misconceptions questionnaire and the Critical Reflection Test (CRT). Participants’ academic information (i.e., number of psychology and non-psychology courses taken and overall psychology GPA) was retrieved via institutional data. Results: Number of psychology courses, psychology GPA, and CRT score were all positive correlates of rejecting psychological misconceptions. Conclusion: Students who took more and attained higher grades in psychology courses and engaged in more reflective and analytical thinking are less likely to affirm psychological misconceptions. Teaching Implications: Psychology courses, particularly those that include activities and assessments to bolster reflective and analytical thinking skills, could be effective means to reduce students’ beliefs in psychological misconceptions.
Quantifying the precipitation–temperature relationship in China during 1961–2018

As a direct manifestation of global warming, the air temperature has been reported to rise significantly during the last several decades. Meanwhile, precipitation around the world has also changed profoundly. Therefore, it is important to unravel the long‐term changes in temperature and precipitation as well as their relationship. This study investigates the characteristics of temperature and precipitation in China from 1961 to 2018. Based on theoretical simulations, the effect of cloud droplet growth and precipitation formation on cloud temperature change has been determined. It has been found that for a specific cloud, the change in surface tension with temperature can increase the supersaturation rate in the cloud, which promotes precipitation by about 10%·K−1. A real dataset was then used to analyse the state change in temperature and precipitation under climate change. Precipitation showed an increasing trend by 13.9%·K−1 throughout China with increasing temperature, which reflects the combined effect of the decrease in rainfall frequency and the increase in rainfall intensity. This indicates that the theoretical simulations could provide a reference model to analyse the actual precipitation–temperature relationship (PTR). In addition, the PTR showed large regional variations: 5.1%·K−1 in North China, 5.6%·K−1 in the Northeast region, 19.6%·K−1 in the Yangtze River valley, 20.4%·K−1 in the Southeast, 14.1%·K−1 in the Southwest, and 9.2%·K−1 in the Northwest, which may be limited by the relative humidity.
Roles of Iron Limitation in Phytoplankton Dynamics in the Western and Eastern Subarctic Pacific

The subarctic Pacific is one of the major high-nitrate, low-chlorophyll (HNLC) regions where marine productivity is greatly limited by the supply of iron (Fe) in the region. There is a distinct seasonal difference in the chlorophyll concentrations of the east and west sides of the subarctic Pacific because of the differences in their driving mechanisms. In the western subarctic Pacific, two chlorophyll concentration peaks occur: the peak in spring and early summer is dominated by diatoms, while the peak in late summer and autumn is dominated by small phytoplankton. In the eastern subarctic Pacific, a single chlorophyll concentration peak occurs in late summer, while small phytoplankton dominate throughout the year. In this study, two one-dimensional (1D) physical–biological models with Fe cycles were applied to Ocean Station K2 (Stn. K2) in the western subarctic Pacific and Ocean Station Papa (Stn. Papa) in the eastern subarctic Pacific. These models were used to study the role of Fe limitation in regulating the seasonal differences in phytoplankton populations by reproducing the seasonal variability in ocean properties in each region. The results were reasonably comparable with observational data, i.e., cruise and Biogeochemical-Argo data, showing that the difference in bioavailable Fe (BFe) between Stn. K2 and Stn. Papa played a dominant role in controlling the respective seasonal variabilities of diatom and small phytoplankton growth. At Stn. Papa, there was less BFe, and the Fe limitation of diatom growth was two times as strong as that at Stn. K2; however, the difference in the Fe limitation of small phytoplankton growth between these two regions was relatively small. At Stn. K2, the decrease in BFe during summer reduced the growth rate of diatoms, which led to a rapid reduction in diatom biomass. Simultaneously, the decrease in BFe had little impact on small phytoplankton growth, which helped maintain the relatively high small phytoplankton biomass until autumn. The experiments that stimulated a further increase in atmospheric Fe deposition also showed that the responses of phytoplankton primary production in the eastern subarctic Pacific were stronger than those in the western subarctic Pacific but contributed little to primary production, as the Fe limitation of phytoplankton growth was replaced by macronutrient limitation.
A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer.

Background Triple-negative breast cancer (TNBC) patients have a high 2-year post-operative incidence of brain metastasis (BM). Currently, there is no early prediction tool to predict the risk of BM in TNBC patients. Methods Data of breast cancer patients, who had been scanned, resected, and pathologically diagnosed at a local hospital from May 2012 to June 2018 were collected. Primary and radiological secondary exclusion criteria were used to determine patients' eligibility for inclusion in the study. Data for the TNBC cohort included qualified 2-year post-operative follow-up information, BM status, and pre-operative MRI data. Age-based propensity score matching (PSM) was used to build a comparable study cohort. The tumor regions of interest were segmented and used for lattice radiomics feature extraction. The filtered and normalized lattice radiomics features were then trained with BM status using the random forest (RF), support vector machine (SVM), k-nearest neighbor, least absolute shrinkage and selection operator regression, naïve Bayesian, and neural network algorithms. The generated prediction models were evaluated using 10-fold cross verification, and the areas under the curve (AUCs), accuracy, sensitivity, and specificity were reported. Results Data from 643 breast cancer patients were collected. Among these, 84 TNBC cases (comprising 42 pairs) were included in this study after primary exclusion, radiological secondary exclusion, and PSM. We extracted 3,854 lattice radiomics features from the pre-operative MRI. Of these, 2,480 were used for model training after filtration. The 10-fold verification results showed that the BM risk-prediction model, which was based on the normalized and filtered lattice radiomics features of collected cases trained by naïve Bayesian algorithm, had a high AUC (0.878), accuracy (0.786), specificity (81.0%), and sensitivity (76.2%). Conclusions The pre-operative MRI data of TNBC patients can be used to predict 2-year BM risk. This application could help to achieve better early stratification, BM screening, and the overall prognosis.
The potential for expansion of whale shark (Rhincodon typus) tourism in St. Helena: stakeholder engagement and willingness to pay

ABSTRACT Whale shark (Rhincodon typus) tourism is increasingly popular wherever the species occur, including a fledgling industry on the South Atlantic island of St. Helena. We interviewed 154 people to elicit the social and economic value of whale shark tourism on the island. Additionally, 77 survey participants were queried about their willingness to pay, 32% of those responding indicated that they would be willing to pay at least £10 more, a 20% increase from existing charges. Tourists also supported the concept that the local population should pay less for whale shark snorkel tours. Perceptions among visitors and locals were that the marine environment was well managed on St. Helena and that protecting it was important. Results from this study could help inform St. Helena Government policy and support MWT operators on the island to further develop a sustainable and equitable whale shark tourism industry.
Decentralize Your Dollars: Incorporating Participatory Grant Making into Resource Allocation Decisions

Participatory grant making is a concept that can support decentralized decision making, shared power, and racial equity in Extension programming. The underlying philosophy of participatory grant making is to empower individuals and communities with flexibility and support to make collective decisions about financial resources. In this article, we draw on our experiences and provide concrete examples to demonstrate the power of this model for improving Extension programming and internal procedures. We conclude that the concept could be adapted for an array of financial decision-making settings to help Extension stay relevant and rooted in the communities we serve.
Centering Community-Led Indigenous Gender Sovereignty

Kinship Rising is an Indigenous community partnership project that offers urgently needed action research led by Indigenous youth and communities impacted by colonial histories of gendered and sexualized violence. Working with hundreds of Indigenous youth, practitioners, Elders, and knowledge keepers across diverse rural and urban Indigenous communities in western Canada, our project engages land- and arts-based practices, intergenerational mentoring, and community leadership to recenter Indigenous knowledges of gender and sexual wellbeing and sovereignty.
Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.
Chronological salp swarm algorithm based deep belief network for intrusion detection in cloud using fuzzy entropy

Cloud computing is susceptible to the existing information technology attacks, as it extends and uses the traditional operating system, information technology infrastructure, and applications. However, in addition to the existing threats, the cloud computing environment faces various security issues in detecting anomalous network behaviors. In order to resolve the security issues, an effective intrusion detection system named Chronological Salp Swarm Algorithm‐based Deep Belief Network is proposed for detecting the suspicious intrusions in a cloud environment. Accordingly, the proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network is developed by integrating the Chronological concept with the Salp Swarm Algorithm. The optimal solution for detecting the intrusion is revealed using the fitness function, which accepts the minimal error vale as the optimum solution. Here, the weights are optimally tuned by the proposed algorithm to generate an effective and optimal solution for detecting the intruders. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance through the facility of exploitation and the exploration in search space. The performance of the proposed method is analyzed using two datasets, namely KDD cup dataset and BoT‐IoT dataset, the comparative analysis is performed with the existing methods, such as Host based intrusion detection system, Deep learning, and Deep Neural Network + Genetic Algorithm. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance in terms of accuracy, sensitivity, and specificity, with the values of 0.9618%, 0.9702%, and 0.9307% using KDD cup dataset, and 0.9764%, 0.9824%, and 0.9309% using BoT‐IoT dataset.
Cross-generational Phonetic Alignment between Mothers and Their Children

ABSTRACT Over time, people who spend a lot of time together (e.g., roommates) begin sounding alike. Even over the course of short conversations, interlocutors often become more acoustically similar to one another. This phenomenon – known as phonetic alignment – has been well studied in adult interactions, but much less is known about alignment patterns in intergenerational, adult-child dyads. In the current study, we investigated alignment between mothers and their children in a picture-naming task, as assessed using a perceptual similarity task and acoustic measures. Experiments 1 and 2 examined alignment in 2.5- and 4-year-old children and their mothers, both when mothers shadowed their children (Experiment 1), and when children shadowed their mothers (Experiment 2). Experiments 3 and 4 investigated long-term similarity between mothers and children when they were recorded separately. Results show that children and mothers aligned to one another in the shadowing task, regardless of who shadowed whom, and while there was no evidence for long-term alignment in younger children, there was some evidence of long-term alignment with 8-year-old children and their moms, but only for male children.