Miracle-dz/UniFAR-FTUs huggingface dataset:
Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province

Career success and its evaluation in university graduates generate growing interest in the academy when evaluating the university according to its mission and social mandate. Therefore, monitoring university graduates is essential in measuring career success in the State Technical University of Quevedo (UTEQ, acronym in Spanish). In this sense, this article aims to identify the predictive career success factors through survey application, development of two mathematical functions, and Weka’s classification learning algorithms application for objective career success levels determination in UTEQ university graduates. Researchers established a methodology that considers: (i) sample and data analysis, (ii) career success variables, (iii) variables selection, (iv) mathematical functions construction, and (v) classification models. The methodology shows the integration of the objective and subjective factors by approximating linear functions, which experts validated. Therefore, career success can classify university graduates into three levels: (1) not successful, (2) moderately successful, and (3) successful. Results showed that from 548 university graduates sample, 307 are men and 241 women. In addition, Pearson correlation coefficient between Objective Career Success (OCS) and Subjective Career Success (SCS) was 0.297, reason why construction models were separately using Weka’s classification learning algorithms, which allow OCS and SCS levels classification. Between these algorithms are the following: Logistic Model Tree (LMT), J48 pruned tree, Random Forest Tree (RF), and Random Tree (RT). LMT algorithm is the best suited to the predictive objective career success factors, because it presented 76.09% of instances correctly classified, which means 417 of the 548 UTEQ university graduates correctly classified according to OCS levels. In SCS model, RF algorithm shows the best results, with 94.59% of instances correctly classified (518 university graduates). Finally, 67.1% of UTEQ university graduates are considered successful, showing compliance with the university’s mission.
Impact of Drought and Changing Water Sources on Water Use and Soil Salinity of Almond and Pistachio Orchards: 1. Observations

Soil salinity increases when growers are forced to use higher salinity irrigation waters due to water shortages. It is necessary to estimate the impact of irrigation water on soil properties and conditions for crop growth to manage the effects of salinity on perennial crops. Therefore, in this study, we monitored root zone salinity in five almond and pistachio orchards in eastern and western San Joaquin Valley (SJV), California (CA). Volumetric soil water contents and bulk electrical conductivities were measured at four root-zone depths. Evapotranspiration was measured by eddy covariance along with three other types of data. The first is seasonal precipitation and irrigation patterns, including the temporal distribution of rains, irrigation events, and irrigation water salinity. The second is soil chemistry, including the initial sodium adsorption ratio (SAR) and soil solute electrical conductivity (ECe). The third type is the physical properties, including soil type, hydraulic conductivity, and bulk density. As expected, we found low salinity at the eastern sites and higher salinity at the western sites. The western sites have finer textured soils and lower quality irrigation water; measured actual ET was about 90% of modeled crop ET. Across the three western sites, the annual average apparent leaching fraction ranged from 11 to 28%. At the eastern sites, measured ET almost exactly matched modeled crop ET each year. Apparent leaching fractions in the eastern sites were approximately 20%.
Better off with you: Exploring congruity between caregivers’ and Veterans’ experience of efforts to cope with suicide

ABSTRACT The interpersonal theory of suicide posits people are more likely to consider suicide when they perceive themselves as alone and as a burden. However, there is limited research on whether these self-perceptions reflect caregiver experiences. As part of a larger study of collaborative safety planning, 43 Veteran/caregiver dyads (N = 86 individuals) completed measures of belongingness and burdensomeness, caregiver burden, family problem solving, and suicide-related coping. We conducted dyad-level actor interdependence models allowing two types of social coping (i.e., general problem solving and suicide-specific coping) to predict Veteran’s self-views and caregiver interpersonal perceptions. Results suggested that Veteran social coping predicted lower Veteran thwarted belonginess and burdensomeness and caregiver involvement in problem solving was similarly associated with their own lower caregiver emotional burden. But examination of cross-partner effects demonstrated that greater Veteran coping was associated with greater time burden for caregivers. Findings suggest that social coping is associated with positive perceptions at the individual level (i.e., Veterans and caregivers to themselves) but does not indicate positive effects at the partner level. Clinicians working with Veterans may wish to involve supports in care to encourage effective collaboration that meets both caregiver/recipient needs.
Close-range Hyperspectral Spectroscopy Reveals Leaf Water Content Dynamics

Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350-2450 nm spectral range in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m2) and up to 90% of the variation in relative water content (RWC). These laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in good agreement (R2=0.79) with a time-lagged change of tree xylem diameter. Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. We conclude that close-range hyperspectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.
Photocatalytic Properties of Amorphous N-Doped TiO2 Photocatalyst under Visible Light Irradiation

Amorphous TiO2 doped with N was characterized by its photocatalytic activity under visible light irradiation. The amorphous N-doped TiO2 was prepared by the sol-gel method through heat treatment at a low temperature. The photocatalyst showing activity in visible light despite heat treatment at low temperature can be applied to plastics and has excellent utility. The N-doped TiO2 appeared amorphous when heat-treated at 130 °C. It was converted into an anatase-type N-doped TiO2 when this was calcined at 500 °C. The photocatalyst showed photocatalytic activities in the photocatalytic decomposition of formaldehyde and methylene blue under visible light irradiation. The photocatalyst exhibited a higher rate of hydrogen production than that of TiO2 in photocatalytic decomposition of water under liquid-phase plasma irradiation. The bandgap of the amorphous N-doped TiO2 measured by investigation of optical properties was 2.4 eV. The lower bandgap induced the photocatalytic activities under visible light irradiation.
When Technologies are Not Enough: The Challenges of Digital Interventions to Address Loneliness in Later Life

This article discusses sociotechnical challenges of technology-based interventions to address loneliness in later life. We bring together participatory and multidisciplinary research conducted in Canada and Australia to explore the limits of digital technologies to help tackle loneliness among frail older people (aged 65+). Drawing on three case studies, we focus on instances when technology-based interventions, such as communication apps, were limiting or failed, seeming to enhance rather than lessen loneliness. We also unpack instances where the technologies being considered did not match participants’ social needs and expectations, preventing adoption, use, and the intended outcomes. To better grasp the negative unintended consequences of these technological interventions, we combine a relational sociological approach to loneliness with the Strong Structuration Theory developed by sociologist Rob Stones. This combined lens highlights the connection between sociotechnical factors and their agentic and structural contexts, facilitating a rich understanding of why and when technologies fail and limit.
Who is the culprit: Is pest infestation responsible for crop yield losses close to semi‐natural habitats?

Abstract Semi‐natural habitats (SNHs) are becoming increasingly scarce in modern agricultural landscapes. This may reduce natural ecosystem services such as pest control with its putatively positive effect on crop production. In agreement with other studies, we recently reported wheat yield reductions at field borders which were linked to the type of SNH and the distance to the border. In this experimental landscape‐wide study, we asked whether these yield losses have a biotic origin while analyzing fungal seed and fungal leaf pathogens, herbivory of cereal leaf beetles, and weed cover as hypothesized mediators between SNHs and yield. We established experimental winter wheat plots of a single variety within conventionally managed wheat fields at fixed distances either to a hedgerow or to an in‐field kettle hole. For each plot, we recorded the fungal infection rate on seeds, fungal infection and herbivory rates on leaves, and weed cover. Using several generalized linear mixed‐effects models as well as a structural equation model, we tested the effects of SNHs at a field scale (SNH type and distance to SNH) and at a landscape scale (percentage and diversity of SNHs within a 1000‐m radius). In the dry year of 2016, we detected one putative biotic culprit: Weed cover was negatively associated with yield values at a 1‐m and 5‐m distance from the field border with a SNH. None of the fungal and insect pests, however, significantly affected yield, neither solely nor depending on type of or distance to a SNH. However, the pest groups themselves responded differently to SNH at the field scale and at the landscape scale. Our findings highlight that crop losses at field borders may be caused by biotic culprits; however, their negative impact seems weak and is putatively reduced by conventional farming practices.
Hybrid Deep Learning Model For Diagnosis Of Covid-19 Using Ct Scans And Clinical/Demographic Data

The unprecedented COVID-19 pandemic has been remarkably impacting the world and influencing a broad aspect of people’s lives since its first emergence in late 2019. The highly contagious nature of the COVID-19 has raised the necessity of developing deep learning-based diagnostic tools to identify the infected cases in the early stages. Recently, we proposed a fully-automated framework based on Capsule Networks, referred to as the CT-CAPS, to distinguish COVID-19 infection from normal and Community Acquired Pneumonia (CAP) cases using chest Computed Tomography (CT) scans. Although CT scans can provide a comprehensive illustration of the lung abnormalities, COVID-19 lung manifestations highly overlap with the CAP findings making their identification challenging even for experienced radiologists. Here, the CT-CAPS is augmented with a wide range of clinical/demographic data, including patients’ gender, age, weight and symptoms. More specifically, we propose a hybrid deep learning model that utilizes both clinical/demographic data and CT scans to classify COVID-19 and non-COVID cases using a Random Forest Classifier. The proposed hybrid model specifies the most important predictive factors increasing the explainability of the model. The experimental results show that the proposed hybrid model improves the CT-CAPS performance, achieving accuracy of 90.8%, sensitivity of 94.5% and specificity of 86.0%.
Everyday Knowledge and Apothecary Craft: Pharmacopoeias of Ancient Northwestern Honduras

Medicinal practices were critical in ancient societies, yet we have limited insight into these practices outside references found in ancient texts. Meanwhile, historic and ethnographic resources have documented how a number of plants, from across the landscape, are assembled into pharmacopoeias and transformed into materia medica. These documentary resources attest to diverse healthcare practices that incorporate botanical elements, while residues in the archaeological record (seeds, phytoliths and starch grains) point to a variety of activities, some of them therapeutic in nature. Focusing on four pre-Hispanic communities in northwestern Honduras, I draw upon ethnobotanical and ethnobiological knowledge to infer medical practices potentially represented by ancient plant residues. Comparing these findings with prior investigations, I address the limits of dividing taxa into mutually exclusive categories such as ‘food’, ‘fuel’ and ‘medicine’. I consider the importance of apothecary craft in past lifeways, as well as the persistence of many traditions in contemporary medical practice.
Optimization of Agrobacterium tumefaciens-Mediated Transformation of Xylaria grammica EL000614, an Endolichenic Fungus Producing Grammicin

Abstract An endolichenic fungus Xylaria grammica EL000614 produces grammicin, a potent nematicidal pyrone derivative that can serve as a new control option for root-knot nematodes. We optimized an Agrobacterium tumefaciens-mediated transformation (ATMT) protocol for X. grammica to support genetic studies. Transformants were successfully generated after co-cultivation of homogenized young mycelia of X. grammica with A. tumefaciens strain AGL-1 carrying a binary vector that contains the bacterial hygromycin B phosphotransferase (hph) gene and the eGFP gene in T-DNA. The resulting transformants were mitotically stable, and PCR analysis showed the integratin of both genes in the genome of transformants. Expression of eGFP was confirmed via fluorescence microscopy. Southern analysis showed that 131 (78.9%) out of 166 transformants contained a single T-DNA insertion. Crucial factors for producing predominantly single T-DNA transformants include 48 h of co-cultivation, pre-treatment of A. tumefaciens cells with acetosyringone before co-cultivation, and using freshly prepared mycelia. The established ATMT protocol offers an efficient tool for random insertional mutagenesis and gene transfer in studying the biology and ecology of X. grammica.
Automatic Lab on PCB System for Agarose Gel Preparation and Electrophoresis for Biomedical Applications

In this paper, a prototype of an automatic lab on PCB for agarose preparation and electrophoresis is developed. The dimensions of the device are 38×34 mm2 and includes a conductivity sensor for detecting the TAE buffer (Tris-Acetate-EDTA buffer), a microheater for mixing, a NTC thermistor for controlling the temperature, a LDR sensor for measuring the transparency of the mixture, and two electrodes for performing the electrophoresis. The agarose preparation functions are governed by a microcontroller. The device requires a PMMA structure to define the wells of the agarose gel, and to release the electrodes from the agarose. The maximum voltage and current that the system requires are 40 V to perform the electrophoresis, and 1 A for activating the microheater. The chosen temperature for mixing is 80ºC, with a mixing time of 10 min. In addition, the curing time is about 30 min. This device is intended to be integrated as a part of a larger lab on PCB system for DNA amplification and detection. However, it can be used to migrate DNA amplified in conventional thermocyclers. Moreover, the device can be modified for preparing larger agarose gels and performing electrophoresis in an automatic manner.
Repeatability of the searching process in reviews of restoration outcomes

In the recently declared United Nations Decade on Ecosystem Restoration (2021–2030), evidence‐based research has an essential role in summarizing restoration outcomes toward broad generalizations to advance restoration practice and science globally. However, to present trustworthy, high‐quality recommendations, evidence reviews should be based on rigorous methods that minimize bias and enhance systematicity, transparency, objectivity, comprehensiveness, and repeatability. To assess the current value of restoration reviews in terms of methodological repeatability at the searching stage, a fundamental review stage, we evaluated a sample of meta‐analyses and narrative syntheses (n = 79) and critically appraised how searches were conducted based on the information reported within each study. By assessing whether review methods were reported in sufficient detail to be repeated by an independent party, we found that repeatability varied substantially by the standard we used. Overall, reviews performed relatively poorly, with a median score of 3 out of 9 points available, and a mean score of 3.6. Most reviews (n = 76/79) failed in effectively reporting all necessary information to allow repeatability at this stage. We found no statistically significant differences considering review types, suggesting that the type of synthesis alone does not solely reflect the repeatability of a review. Toward increasingly repeatable, reliable reviews in restoration ecology, we recommend that authors incorporate and apply principles of systematic reviews and maps in their review process, and suggest that environmental journals should broadly emphasize ways of performing rigorous reviews, as well as increase efforts for the publication of open access review protocols.
Phytolith Content In Larix Gmelinii Forest Soils And Its Relations With Soil Properties

Aims Phytolith occluded carbon (PhytOC) has become one of the important mechanisms of long-term carbon sequestration in soil. Ecosystems with higher latitude in the northern hemisphere are expected to face the largest loss of soil carbon due to global warming. The contributions of phytoliths storage in soil layers of cold temperate forest need to be studied in depth. Methods We examined soil phytolith contents and other soil physicochemical properties of Larix gmelinii forest soils in Daxing’anling. ANOVA was employed to analyze differences in the quantitative characteristics of phytoliths. Bivariate correlation, regression analysis, principal component analysis and redundancy analysis were employed to analyze the relations between soil phytolith and soil properties. Results The phytolith in Larix gmelinii forest soil was mainly in elongate shape with a mean content of 19.1 g kg− 1. The phytolith had a stronger correlation with phosphorus rather than potassium and nitrogen and had no correlation with soil water content, and the dominant factors were Na, Mg and K content. Soil phytolith storage in the cold temperate zone (41.0 t ha− 1) was significantly higher than that in the tropical and subtropical zone. Conclusions Phytolith is an important way for long-term sequestration of soil organic carbon. Soil phytolith storage is affected by soil physicochemical properties and climate. However, the acid-base level of soil is more important for phytolith preservation. The effects of hydrothermal conditions on the yield and stability of phytoliths on a large spatial scale need to be studied in future.
A Conspiracy of Data: QAnon, Social Media, and Information Visualization

Seeing is believing, so goes the cliché. In our extremely online world, the particular nexus between visual information and political belief has become one of the thorniest challenges to truth. We live in an extremely visual world in which we navigate social media, search engines, platforms, interfaces, icons, memes, and smartphones. Despite the fact that we navigate visual information at an astounding rate, we have not nationally developed literacies to debunk bad information. I argue that we are witnessing a confluence between extremely online, crowd-sourced conspiracies, whose adherents possess a high capacity for online information gathering, and visualization, meant to communicate data about our world effectively and accurately through optical means which has been co-opted for information warfare. Deploying such informatics further legitimates bizarre, unhinged theories about political reality. QAnon, the extremely online conspiracy theory that has cast its shadow over the Internet, relies exclusively on information visualization to communicate its message and is symptomatic of our inability to combat misinformation that mimics the methods of data analysis and information literacy. I argue that QAnon’s success—indeed, its very existence—relies on (at least) two principal factors: (1) QAnon relies, intentionally or no, on a slippage between data and information that obscures the interventions by Q and Q’s anons in leveraging information warfare, and (2) QAnon supports such a slippage with complex and interactive visualizations of bad information, thereby accelerating apophenia, the tendency to see linkages between random events and data points.
Predictors of object naming in aphasia: does cognitive control mediate the effects of psycholinguistic variables?

ABSTRACT Background and Aims Previous studies have shown that age of acquisition affects language production in persons with aphasia (PWA), specifically, earlier-acquired words are better preserved compared to later-learned ones (for review, see Brysbaert & Ellis, 2016). Also, it has been argued that naming objects with lower name agreement requires inhibition of alternative names (Alario et al., 2004), and therefore puts higher demands on cognitive control. Bose and Schafer (2017) showed that although both PWA and healthy controls performed better at naming words with high naming agreement, the difference between the naming conditions was significantly greater for PWA. This could be due to reduced ability to inhibit irrelevant information in PWA. The current study aims to investigate whether cognitive control mediates the effects of psycholinguistic variables on object naming accuracy in aphasia. Methods and Procedures Participants (N = 31, 32% female) were right-handed, native Russian speakers with preserved visual and hearing abilities diagnosed with mild to moderate post-stroke aphasia. They were aged 40–70 (mean = 59.5, SD = 8.6). The participants were tested on a picture-naming task including 247 items and 2 subtests from the Russian Birmingham Cognitive Screen, namely Auditory attention, and Rule Finding tasks. Outcomes and Results To define whether cognitive control mediates the effect of psycholinguistic variables on naming response accuracy, multiple linear regression was used. Significant main effects of log-transformed word frequency, AoA, and cognitive control were found, as well as a significant interaction between log-transformed word frequency and cognitive control. Conclusions Our findings suggest that cognitive control mediated the effect of word frequency in naming in aphasia. PWA with weaker cognitive control name pictures depicting less-frequent words less accurately than more frequent words. This points to the fact that PWA have difficulty in lexical access when producing language. The implications of the study are that the focus of aphasia rehabilitation should be on very frequent structures and words as these are usually more preserved in PWA with weaker cognitive control.