Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of preconditions and consequences of authenticity in leadership for healthcare educational SMEs, through the 5th wave theory and meta-analysis

Background: Orchestrating personnel with different roles in healthcare sector using a proper leadership style is one of the main challenges in Iran. The coronavirus disease 2019 (COVID-19) pandemic, as a major threat to global public health, makes the leadership challenge in Iran healthcare sector bolder due the complexity of health industry as a service industry with twisted processes and aim of satisfying patients with proper service and managing healthcare personnel with increased workload and stress. As a result, to strengthen healthcare systems for unprecedented threats, a cohesive leadership is required. Authentic leadership is a method in which leaders are genuine, self-aware, and transparent. The aim of this paper is to clarify preconditions and consequences of authenticity in leadership of healthcare educational Small & Medium Enterprises (SMEs). Methods: The research is done in documentary research method, developmental research and descriptive correlation. Data collection in Meta-analysis phase is by library research method. Statistical society of the research is more than 38 articles written in the period of 2015 to 2022, from which collected data is codified and then analyzed by SPSS in order to describe research’s specifications and CMA2 for statistical calculations. Results: Preconditions of authentic leadership, including organizational structure, EQ, relationship oriented organizational culture, rules and regulations, ethical reasons of the leader and Industry attributes as well as consequences of authentic leadership, including employees’ bullying, extra-role behavior, work ethics, burnout, work productivity, product innovation, self-control, employees’ mindfulness, optimism and personnel health, have been identified through the 5th wave theory based on future planning and Meta-analysis and the Doost Research Methodology (DRM). The results suggest that authentic leadership considering attributes of healthcare industry has positive effect on performance of healthcare companies. Conclusions: As SMEs are formed more than 95% of businesses all around the World, they play indispensable role within developing economic and technical sustainability as well as seven pillars of sustainability (7PS) model. Identifying necessities and consequences of authentic leadership in healthcare companies can be used as a road map for modern managers. Investigating the effects of training, welfare, and health in improving the productivity of SMEs’ HR simultaneously through the 5th wave theory is the main conclusion of this paper.
Information Technology Governance Analysis Using the COBIT 2019 Framework at PT. Daya Adicipta Wisesa

Information Technology can determine competitiveness and the ability to improve performace, as well as enhance effectiveness and efficienc for businesses, IT Governance can be considered successful when it aligns with organizational goals and the implementation for IT itself. Therefore, it is necessary to evaluate its maturity level. COBIT 2019 is one of the audit frameworks that can be used to control the Governance of information technology. PT. Daya Adicipta Wisesa is the subject of this research, with the aim of determining whether the implemented IT Governance has the desired Capability Level and to indentify the level of gaps in IT Governance based on the COBIT 2019 framework. Ater analyzing the 11 design factors of COBIT 2019, seven priority onjectives were found to have a target capability level 4, which is 80% namely APO13, BAI04, DSS01, DSS02, DSS03, DSS04, and DSS05. Then, the capability level calculation was conducted, and the priority onbjectives were determined as follows: APO13 achieved capability level 5, BAI03 achieved capability level 4, DSS01 achieved capability level 5, DSS02 achieved capability level 5, DSS03 achieved capability level 4, DSS04 achieved capability level 5, and DSS05 acvieved capability level 4, with a rating of fully achieved for the process activity category.
What corpus data reveal about the Position of Antecedent Strategy: anaphora resolution in Spanish monolinguals and L1 English-L2 Spanish bilinguals

This study investigates the acquisition of anaphora resolution (AR) in Spanish as a second language (L2). According to the Position of Antecedent Strategy (PAS), in native Spanish null pronominal subjects are biased toward subject antecedents, whereas overt pronominal subjects show a “flexible” bias (typically toward non-subject but also toward subject antecedents). The PAS has been extensively investigated in experimental studies, though little is known about real production. We show how naturalistic production (corpus methods) can uncover crucial factors in the PAS that have not been explored in the experimental literature. We analyzed written samples from the CEDEL2 corpus: L1 English-L2 Spanish adult late-bilingual learners (intermediate, lower-advanced and upper-advanced proficiency levels) and a control group of adult Spanish monolinguals (N = 75 texts). Anaphors were manually annotated via a fine-grained, linguistically-motivated tagset in UAM Corpus Tool. Against traditional assumptions, our results reveal that (i) the PAS is not a privileged mechanism for resolving anaphora; (ii) it is more complex than assumed (in terms of the division of labor of anaphoric forms, their antecedents and the syntactic configuration in which they appear); (iii) the much-debated “flexible” bias of overt pronouns is apparent since they are hardly produced and are replaced by repeated NPs, which show a clear non-subject antecedent bias; (iv) at the syntax-discourse interface, the PAS is constrained by information structure in more complex ways than assumed: null pronouns mark topic continuity, whereas overtly realized referential expressions (overt REs: overt pronouns and NPs) mark topic shift. Learners show more difficulties with topic continuity (where they redundantly use overt pronouns) than with topic shift (where they normally disambiguate by using overtly realized REs), thus being more redundant than ambiguous, in line with the Pragmatic Principles Violation Hypothesis (PPVH) (Lozano, 2016). We finally argue that the insights from corpora should be implemented into experiments. The triangulation of corpus and experimental methods in bilingualism ultimately provides a clearer understanding of the phenomenon under investigation.
Seroprevalence of Toxoplasmosis in Blood Donors: An Investigation from the Endemicity. Non-systematic Review

Toxoplasma gondii is an intracellular protozoan that causes toxoplasmosis, the most common parasitic disease in the world, which infects humans and animals. It is transmitted through cat feces through the process of excretion and ingestion of oocysts. Humans become infected by eating improperly washed vegetables, raw or undercooked meat, contaminated water, the soil or area where the cat defecates, or by transfusing infected blood. The study aims to identify, through scientific evidence, the seroprevalence of T. gondii in blood donors, considering its global endemicity. A non-systematic documentary review was used for searching articles worldwide on the topic of T. gondii seroprevalence in blood donors, in different databases such as SciELO, Google Academics, etc. A total of 27 articles that had the required information; grouping the seroprevalence results by region, obtained positive IgG and IgG + IgM. Calculating the prevalence, it was identified in Asia at 7.8%, America 32.8%, Africa 40.7%, Oceania 34%, and Europe 38.1%. In conclusion, it is convenient to carry out this type of study in each country to verify the prevalence and, in addition, to determine the incidence of infection in blood donors to calculate the risk of transmission. Keywords: Toxoplasma, seroepidemiologic studies, antibodies, immunoglobulin G, immunoglobulin M, blood donors. Resumen Introducción: Toxoplasma gondii en un protozoo intracelular causante de la toxoplasmosis, la enfermedad parasitaria más frecuente del mundo la cual infecta a humanos y animales, se trasmite por medio de las heces de los gatos mediante el proceso de excreción e ingestión de los ooquistes. Los humanos se infectan al ingerir vegetales mal lavados, carne cruda o poco cocida, agua contaminada, el suelo o área donde defeca el gato, o al transfundir sangre infectada. Objetivo: identificar mediante evidencias científicas la seroprevalencia de T. gondii en donantes de sangre, considerando su endemicidad global. Metodología: se empleó una revisión no sistemática, documental realizando la búsqueda de artículos a nivel mundial del tema seroprevalencia de T. gondii en donantes de sangre, en diferentes bases como SciELO, Google Academics, etc. Resultados: un total de 27 artículos que contaron con la información requerida; agrupando los resultados de seroprevalencia por región, anticuerpos positivos IgG e IgG + IgM. Calculando la prevalencia se identificó, en Asia 7,8%; América 32,8%; África 40,7%; Oceanía 34% y Europa 38,1%. Conclusión: es conveniente realizar este tipo de estudio en cada país para comprobar la prevalencia y, además, determinar la incidencia de la infección en donantes de sangre para calcular el riesgo de transmisión. Palabras Clave: Toxoplasma, estudios seroepidemiológicos, anticuerpos, inmunoglobulina G, inmunoglobulina M, donantes de sangre.
Enrichment and remediation of uranium by microorganisms: A review

Uranium is a key raw material for the nuclear energy industry, the generation of nuclear energy is projected to double by 2040 to address the growing energy demands, which makes the uranium supply a matter of energy security. In addition, uranium is a heavy metal with both chemotoxicity and radiotoxicity, which seriously endangers human health and environmental safety. The growth in the utilization of uranium resources boosts the release of uranium into the environment. Therefore, the remediation of uranium contamination and recovery of uranium from a non-conventional approach is highly needed. Microorganism exhibits a high potential for immobilization of uranium. This review summarizes the ability of microorganisms to immobilize uranium from aqueous solutions and wastewater in terms of microbial species, performance, mechanism of enrichment and remediation, and applicable environment.
The Rate and Mechanism of Deep-Sea Glauconite Formation at the Ivory Coast-Ghana Marginal Ridge

The environmental conditions and reaction paths of shallow-water glauconitization (<500 m water depth, ~15°C) close to the sediment-seawater interface are generally considered to be well understood. In contrast, the key factors controlling deep-sea glauconite formation are still poorly constrained. In the present study, green grains formed in the recent deep-sea environment of the ODP Site 959, Ivory Coast-Ghana Marginal Ridge, (~2100 m water depth, 3-6°C) were investigated by X-ray diffraction and electron microscopic methods in order to determine the rate and mechanism of glauconitization.Green clay authigenesis at Hole 959C occurred mainly in the tests of calcareous foraminifera which provided post-depositional conditions ideal for glauconitization. Within this organic-rich microenvironment, Fe-smectite developed <10 ky after deposition of the sediments by precipitation from precursor gels containing Fe, Mg, Al, and silica. This gel formation was supported by microbial activity and cation supply from the interstitial solution by diffusion. At a later stage of early marine diagenesis (900 ky), the Fe-smectites reacted to form mixed-layer glauconite-smectite. Further down (~2500 ky), almost pure glauconite with no compositional gaps between the Fe-smectite and glauconite end members formed. This burial-related Fe-smectite-to-glauconite reaction indicates that the glauconitization process was controlled mainly by the chemistry of the interstitial solutions. The composition of the interstitial solution depends heavily on micro-environmental changes related to early diagenetic oxidation of biodegradable (marine) organic matter, microbial sulfate reduction, silicate mineral alteration, carbonate dissolution, and Fe redox reactions. The availability of Fe is suggested as the probable limiting factor for glauconitization, explaining the various states of green-grain maturity within the samples, and this cation may be the most important rate-determining element.The rate of glauconite formation at ODP Site 959 is given by %GlSed = 22.6·log(ageSed) + 1.6 (R2 = 0.97) where %GlSed is the state of glauconitization in the sediment and ageSed is the sediment age (in ky). This glauconitization rate depends mainly on continuous cation supply (in particular Fe) and is about five times less than that in shallow-shelf regions, suggesting significantly slower reaction at the lower temperature of deep-sea environments.
Enhanced negation handling for sentiment analysis on Twitter using deep neural networks

Sentiment analysis is a tool to identify and measure the emotion in a piece of text. Negation handling is an important aspect of natural language processing (NLP) for Twitter data. It is a process of correctly interpreting the sentences containing negation words, such as, "never", "no", "neither" and so on. Negation words are used in machine learning to express negative sentiment or indicate the absence of something. In this article, a negation handling technique using deep learning models. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) for classification is proposed. The system is evaluated on SemEval-2017 dataset. The classification performance is improved by using ANN and CNN on the negative tweets. The study aims to improve the classification accuracy by considering negation words in the text. The paper compares the performance of ANNs and CNNs in handling negation words and evaluates them on the tweets data. This study provides insights into the effectiveness of using deep learning techniques for negation handling in sentiment analysis and highlights the importance of considering negation words in the text for improved sentiment analysis performance. The proposed negation strategy attains a superior performance accuracy over machine learning models by preventing misclassified tweets.
A Sigma-Delta Modulator with Single-Pole Double-Throw Analog Switch

In this paper, a high precision and high energy efficiency discrete-time switching capacitive 3-order Sigma-Delta modulator (SDM) is proposed. The SDM is applied to portable electroencephalograms (EEG) because of its little energy and good performance. The energy efficiency levels of the current mainstream modulator systems are analyzed and compared, and the CIFF modulator architecture which is most conducive to realizing high energy efficiency is selected. The single-loop CIFF structure is selected to give consideration to the accuracy and stability of the circuit. The circuit is implemented in a hierarchical structure, and a single-pole double-throw (SPDT) analog switch is adopted to overcome the difficulty of opening conventional CMOS analog switches at low supply voltages and the increase in threshold voltage Vth of NMOS devices due to the base bias effect. The proposed discrete-time Sigma delta ADC modulator is designed with SMIC 0.18um CMOS technology and achieves an SNDR of 101.6dB under a 1.8V power supply voltage and a signal bandwidth of 2kHz. The power consumption is 520uW and the significant bit (ENOB) is 16.58 bits.
Model Ensemble With Dropout for Uncertainty Estimation in Sea Ice Segmentation Using Sentinel-1 SAR

Despite the growing use of deep learning in sea ice mapping with synthetic aperture radar (SAR) imagery, the study of model uncertainty and segmentation results remains limited. Deep learning models often produce overconfident predictions, posing challenges for domain experts to assess prediction quality and identify potential errors. This is especially important in sea ice mapping, where misclassifying ice and water can increase risk to marine navigation. In this article, we incorporate and compare dropout and model ensemble within a convolutional neural network (CNN) segmentation architecture to highlight regions with prediction uncertainty and investigate the effects of loss function choice on the results. We evaluate model generalization and uncertainty characterization by training models on a primary dataset and testing on a secondary benchmark dataset. We obtain test F1 results higher than 0.97 for the primary dataset. Although the F1 performance for the secondary dataset is reduced to 0.93, the generated sea ice maps are reasonable across several Sentinel-1 scenes, and our proposed strategy helps in identification of misclassified and uncertain regions for human quality control. Our models seem to be robust against banding noise in Sentinel-1 SAR, and the prediction uncertainty frequently highlights ice regions misclassified as water, indicating its potential for real-world applications. Our study advances the field of machine-learning-based sea ice mapping and highlights the importance of uncertainty estimation and cross-dataset evaluation for model development and deployment. Our approach can be adopted for other remote sensing applications as well.
EU27 regional trade networks for medical products in fight against Covid-19 pandemic: Quantifying vulnerability and self sufficiency in critical inputs

The Covid-19 pandemic spread fast due to a lack of vaccines and a severe shortage of medical products to treat and combat the disease. Many studies have focused on the characteristics of extant global supply chains and trade networks that are determined by globalization drives for production to low-cost countries and the technological complexity of products with many components distributed globally. This, along with the lockdown of many sectors and national policies that divert exports for domestic use, are reasons for lack of access, especially, in Western countries to these products. Governments adopted policies that aim to mitigate vulnerability to imports of critical medical products that include self-sufficiency measures such as increased domestic production, stockpiling and reduction of exports. However, there is as yet no quantitative way to assess if a country’s vulnerability to critical imports has been reduced by such drives for self-sufficiency, when other countries in the trade network follow similar policies. For this we develop a Google PageRank style centrality measure based on the Markose-Giansante eigen-pair method for a specially constructed global bilateral trade network to assess the vulnerability of net importers of critical medical products when they attempt to mitigate it by regional or domestic buffers. We use the network vulnerability centrality measure to quantify the regional self-sufficiency for EU27 countries over 2019–2021 for four Covid-19 critical medical products, viz. facemasks, personal protective equipment, ventilators and hand sanitizers. Despite, growth in value and share of intra-EU regional trade in most of these products by 2021, some countries did not achieve the reduction of vulnerability centrality.
Multi-Channel PA, LNA, and Switch MMICs for Beam-Switching Applications at 160 GHz, Based on an InGaAs mHEMT Technology

In this paper, we report 1:8 switch as well as 4-channel low-noise-amplifier (LNA) and power amplifier (PA) circuits, that have been developed for the implementation of Rotman lens-based beam-switching front ends at 160 GHz. The circuits are implemented in an InGaAs-channel metamorphic high-electron mobility transistor (mHEMT) technology with 35-nm gate length. A passive and an active 160-GHz switch variant with 8 dB of insertion loss and 15 dB of gain, respectively, are described. The 4-channel LNA circuit has a noise figure below 3.0 dB, achieving state-of-the-art performance at the lower G-band. Furthermore, more than 14 dBm of linear output power are measured for each channel of the PA circuit, which is state of the art for InGaAs-channel HEMT PA circuits at 160 GHz, featuring more than 18.5 dBm of saturated output power per channel.
Potentielle marginaljorder bestemt ud fra naturgivne faktorer

Madsen, Henrik Breuning: Potentielle marginaljorder besternt ud fra naturgivne faktorer. Geografisk Tidsskrift 89: 25–30. Kobenhavn 1989. Initiated and funded by the Ministry of Environment a number of investigations were carried out in 1986 to elucidate different aspects about marginal land. One of these projects was a nation-wide mapping of soil conditions that might influence the marginalization of farmland. The mapping was based upon soil data bases covering the whole country, established by the Ministry of Agriculture, Bureau of Land Data.
Design Trade-offs between Series-Peaking Inductor and High $f_T$ SiGe HBTs in Transimpedance Amplifiers

Series-peaking inductor is a crucial block in a shunt-feedback Transimpedance amplifier (TIA) used for bandwidth enhancement. Achieving high-quality factor (Q) and high self-resonant frequency (SRF) of on-chip inductors is challenging for large bandwidth TIAs. A higher $f_T$ heterojunction bipolar transistor (HBT) can relax the requirement of series-peaking inductance. Due to the lower inductance value, a high $Q$ and high SRF can be achieved. This paper presents design trade-offs between the series-peaking inductor and HBT device parameters using two different GlobalFoundries SiGe BiCMOS processes 9HP+ (90nm Bulk) and 45nm SiGe (45nm RFSOI). The simulation and measurement results are shown to validate the trade-offs. Measurements show that 45nm SiGe TIAs have 15-25% more bandwidth than 9HP+ TIAs, with a 14-30% reduction in inductance across various device sizes.
Development of a 3-DOF Interactive Modular Robot with Human-like Head Motions

When introducing robotic systems to home environments, there are several aspects to consider such as accessibility to robotic systems and interaction capability with human subjects. Thus, in this paper, a 3-DOF robotic sensor module is suggested to embrace the concerns. It includes a handy plug-and-play feature to use one module in different spots at home. In addition, it delivers non-verbal, non-display communicative cues through human-like head motions while it is plugged into docking mounts with different states, including angled or moving. It also can track human subjects to detect and focus on users who try to interact with the robot. The performance of the robotic module was evaluated, and its compatibility with different systems was demonstrated.
The mid-transition in the electricity sector: impacts of growing wind and solar electricity on generation costs and natural gas generation in Alberta

In response to climate change, electricity grids are decreasing their carbon intensity with the addition of wind and solar variable renewable energy generation (VREN). This leads to a mid-transition period, where renewable energy is unable to satisfy electricity demand without contributions from other fossil sources such as natural gas, but also generates sufficiently to constrain conventional generation—changing their operating and market conditions. We use a simplified copper plate model, which scales up and down historical wind and solar generation, to examine how and when the patterns and generation costs for fossil fuel power could change by the increasing capacities of VREN on the relatively isolated Alberta electricity grid. We find that beginning at 20% VREN an increasingly diverse range and reduced hours of dispatched capacity is necessitated from the existing generation. However, even as capacity factors for fossil fuel generation decrease their costs remain reasonable and we found this to be a low-cost pathway for achieving moderate to deep emission reduction goals. A full 86% of demand could be met with VREN before generation costs exceeded 100$/MWh, allowing for an emissions reduction of 28.4–9 million tonnes yr−1 of CO2eq, on a lifecycle basis. In order to integrate the renewable generation, new and existing fossil fuel units will require market rules that incentivise flexibility and ensure they remain in place throughout the transitionary period as they are crucial to balance variable renewable generation.