Miracle-dz/UniFAR-FTUs huggingface dataset:
Why the forcing from carbon dioxide scales as the logarithm of its concentration

The radiative forcing from carbon dioxide is approximately logarithmic in its concentration, producing about four watts per square meter of global-mean forcing for each doubling. Although these are basic facts of climate science, competing explanations for them have been given in the literature. Here, the reasons for the logarithmic forcing of carbon dioxide are explored in detail and a simplified model for the forcing is constructed. An essential component is the particular distribution of absorption coefficients within the 15-micron band of carbon dioxide. An alternative explanation, which does not depend on the spectrum of carbon dioxide, but hinges on the tropospheric lapse rate instead, is shown to be neither necessary nor sufficient to explain the logarithmic forcing of carbon dioxide and to be generally inapplicable to well-mixed greenhouse gases in Earth’s atmosphere.
A Comparative Study of Birdcage RF Coil Configurations for Ultra-High Field Magnetic Resonance Imaging

Improvements in transmission and reception sensitivities of radiofrequency (RF) coils used in ultra-high field (UHF) magnetic resonance imaging (MRI) are needed to reduce specific absorption rates (SAR) and RF power deposition, albeit without applying high-power RF. Here, we propose a method to simultaneously improve transmission efficiency and reception sensitivity of a band-pass birdcage RF coil (BP-BC RF coil) by combining a multi-channel wireless RF element (MCWE) with a high permittivity material (HPM) in a 7.0 T MRI. Electromagnetic field (EM-field) simulations, performed using two types of phantoms, viz., a cylindrical phantom filled with oil and a human head model, were used to compare the effects of MCWE and HPM on BP-BC RF coils. EM-fields were calculated using the finite difference time-domain (FDTD) method and analyzed using Matlab software. Next, to improve RF transmission efficiency, we compared two HPM structures, namely, a hollow cylinder shape HPM (hcHPM) and segmented cylinder shape HPM (scHPM). The scHPM and MCWE model comprised 16 elements (16-rad BP-BC RF coil) and this coil configuration demonstrated superior RF transmission efficiency and reception sensitivity along with an acceptable SAR. We expect wider clinical application of this combination in 7.0 T MRIs, which were recently approved by the United States Food and Drug Administration.
Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband

Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea’s government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability.
Identification of Women’s Participation in the Household Economy in the Agricultural Sector

The role of agricultural development is increasingly visible from the ability of the agricultural sector to contribute to Indonesia's income, including in the absorption of labor. The development of human resource aspects in the agricultural sector is key in the long-term development of the agricultural sector. Increasing the capacity of women as an important part of human resources in the agricultural sector has not yet been implemented optimally. Various efforts made to improve access to information as a means of acquiring knowledge for farmers still do not involve many women in it. The role of women in the agricultural sector is undeniable. The push for gender equality itself provides space for women to be more involved in the economy, both in terms of quantity and quality. This research aimed to determine the characteristics of women farmers and to identify women's participation in household economic activities in the agricultural sector, especially Kemuning Lor Village, Jember Regency. Based on the results, it was known that the participation rate of women in playing a role in supporting the family economy was 64%. As many as 25% of the female population who worked in agriculture were landowners/farmers. Then as many as 6% of the female population worked as odd farm laborers, 12% as coffee farmers, 18% as sugarcane workers, 14% as workers in the non-formal sector such as tailors, selling food, stalls. The partial test showed that the most influential variable for working women was income with Exp(B) of 629,876 meaning that the higher the wife's income, the higher the tendency of women to work.
Think Aloud for Teaching Extensive Reading

This research applies the Think Aloud Method for teaching Extensive Reading. This method has a purpose for not only improving comprehension, but also for assessing and monitoring students’ performance in presenting their ability on understanding passages. The students are trained to read, think, and expose their understanding. The monitoring process is being done when the students expose their understanding on the passages they read through speaking or writing activity. The research aims at finding out the students’ reading absility before and after taught by using Think Alouud, whether there is any different effect on teaching Extensive Reading before and after taught by using Think Aloud, to design teaching Extensive Reading instruction by using Think Aloud. In designing this research, the researcher utilizes both qualitative and quantitative method. The third-semester students of English Literature in academic year 2018/2019 are the subject of the research which consists of 25 students. To find the data of significant effect on teaching Extensive Reading before and after taught by using Think Aloud Method, the measurement is conducted by using dependent ttest. Meanwhile, Flow Model by Miles and Huberman is used to analyze the qualitative data. The findings reveal that the student’s reading ability before being taught by using Think Aloud Method shows the average score of 76.32, and after being taught by using Think Aloud Method shows an average score of 77.92. Meanwhile,significant value of 2 tailed with 5 % level of significance is 0.048 and the value of sig < 0.05. In other words, Ho (null hypothesis) is rejected and Ha (alternative hypothesis) is accepted, meaning that there is adifferent effect on teaching extensive reading before and after taught by using Think Aloud method. The model of teaching extensive reading instruction by using Think Aloud consists of the descriptions on the competence of the course to be achieved, the activities conducted in the classroom, the Think Aloud Method and its analysis to make a proof that the model presented is suitable with the characteristics of Think Aloud Method. In brief, the activities contain the process of thinking while the students reading the passages and the process of thinking aloud when students are assigned to write some reports and to speakabout the passages they have read.
Phase experiments in multi-microphone recordings: a practical exploration

Ever since the advent of multi-microphone recording, sound engineers have wrestled with the colouration of sound by phasing issues. For some this was an anathema; for others this colouration was a crucial ingredient of the finished product. Traditionally, delicate microphone placement was essential, with subtle movements and tilts allowing the producer/engineer to determine when a sound was “in phase” based on perception alone. More recently, DAW’s have allowed us to view multiple waveforms and manually nudge them into coherence with visual feedback now supporting the aural, although still a manual process. Building upon technology first developed for the author’s paper, “Killing Spillage”, presented at ARP2006 in Edinburgh, this paper will present an algorithm that allows automatic correction of phase via a unique Max/MSP patch operating on multiple audio components simultaneously. With a single button push, the producer can now hear a stereo recording with maximum coherence and thus make an artistic judgement as to whether the “ideal” is ideal, or better to pursue naturally occurring phase colouration in preference. The patch allows zoning in to spatially separated sound sources, eg tuning drum kit overheads to phase lock with the snare drum or hi-hat microphone and subsequently optimising other components of the array to concur. In addition, the patch allows retrospective “manual re-positioning” of microphones through a number of user controlled sample-accurate delay lines that emulate the proximity of a given microphone to its target sound source to afford the producer the desired degree of non-ideal phase colouration if preferred. Audio examples will be played to demonstrate the patch’s effect on professionally recorded drum kits, orchestras with spot mics and others. Limiting factors, contexts, applications and future developments will also be discussed.
Comparison of Immune Response Assessment in Colon Cancer by Immunoscore (Automated Digital Pathology) and Pathologist Visual Scoring

Simple Summary The immune response to colon cancer (CC) is highly variable among patients and is clinically relevant. In this study, we compared the immune response assessment for early-stage CC, as measured by Immunoscore (IS), to pathologist visual scoring of the CD3+ and CD8+ T-cell densities at the tumor site (T-score). The objectives were to determine the inter-observer agreement between pathologists and the concordance between the two methods. Agreement between pathologists was minimal to weak. Moreover, a weak concordance between the two methods was observed, leading to misclassification of 48% of cases by pathologist scoring. Due to the high level of immune infiltrate heterogeneity resulting in disagreement of interpretation among pathologists, IS is unlikely to be reproduced via non-standardized methods. Abstract Adjunction of immune response into the TNM classification system improves the prediction of colon cancer (CC) prognosis. However, immune response measurements have not been used as robust biomarkers of pathology in clinical practice until the introduction of Immunoscore (IS), a standardized assay based on automated artificial intelligence assisted digital pathology. The strong prognostic impact of the immune response, as assessed by IS, has been widely validated and IS can help to refine treatment decision making in early CC. In this study, we compared pathologist visual scoring to IS. Four pathologists evaluated tumor specimens from 50 early-stage CC patients and classified the CD3+ and CD8+ T-cell densities at the tumor site (T-score) into 2 (High/Low) categories. Individual and overall pathologist scoring of immune response (before and after training for immune response assessment) were compared to the reference IS (High/Low). Pathologists’ disagreement with the reference IS was observed in almost half of the cases (48%) and training only slightly improved the accuracy of pathologists’ classification. Agreement among pathologists was minimal with a Kappa of 0.34 and 0.57 before and after training, respectively. The standardized IS assay outperformed expert pathologist assessment in the clinical setting.
OVERLOADING OF HIGHWAY BRIDGES--INITIATION OF DECK DAMAGE

Various surveys have indicated that highway bridges are subjected to vehicular load levels and combinations far in excess of those for which they were designed. The prediction of the effects of the overloading as well as the over-load permit operations do not reflect the sophistication scientific or technical fields have attained. The paper presents the findings of a parametric investigation employing Program BOVA (Bridge Overload Analysis), that encompasses 45 case studies. Five different commonly encountered overload vehicles and nine different bridge configurations are considered. The bridge superstructures are simple span beam-slab bridges with prestressed concrete I-beams and reinforced concrete deck, and having no skew. The results of the findings are presented in tabular form for the bridges and the overload vehicles. For given bridges and vehicles the gross vehicular weights, axle weights and "equivalent area loads" are defined that will induce cracking in the concrete that will reach the reinforcing bars. It has been found that if a small amount of damage to the bridge deck is permitted, the load levels which the superstructure can carry are usually in excess of the commonly reported permissible overload weights. Recommendations have been made for the use of the tabulated results in overload permit operations. It has been suggested that the findings can be applied to overload permit operations in the absence of reliable methods of predicting the overload response of bridges. It is also indicated that the suggested guidelines are useful for infrequent traverses of vehicles until the completion of research programs that will delineate the permissible and impermissible overloadings that will be based on limited damage criteria. /Author/
Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin

The world is transforming into a predominantly urban space, and it entails that cities have to be ready to provide services, for instance, to ensure availability and sustainable management of water and sanitation for all. In this scenario, the water quality evaluation has a crucial role and often needs multiple sources segregated. Our purpose is to build bridges between these data silos to provide an integrated and interoperable view, where different datasets can be provided and combined through knowledge graphs in order to characterize water quality. This work shows the quality of the Bogota river basin's water bodies by analyzing physicochemical and biological properties and using spatio-temporal and legal elements. So, our knowledge graphs allow us to discover what, when, and where infractions happened on water quality in a river basin of the most populated cities of Latin America during a critical period (2007–2013), highlighting the presence of high values of suspended solids and nitrites, lower amounts of dissolved oxygen, and the worst water quality during the driest periods (appearing until a maximum of 63 infractions in a year).
The use of animal models in disease research

Animal experimentation has always been an important issue in scientific practice. However, in recent years the use of animals in biomedicine and toxicology has become even more relevant not only for the scientific community, but for the public of non-specialists as well. New considerations in relation to animal welfare and ethics of research has brought to fundamental legislative changes, reflected in the publication of the recent Directive 2010/63/EU on the protection of animals used in scientific procedures. This legislation has now been implemented by the 28 Member States (European Commission, 2010; see also Petrini, 2014). It is not our intention here to discuss the pros and the cons of animal experimentation in the eyes of the different stakeholders taking part in this discussion, but rather point out some important elements regarding the use of animals in research. We believe that this can be of some interest for the study of rare diseases because, as we will see at the end of this contribution, animal models can be of great help in this particular branch a biomedical research. We apologize with our readers, because some of the concepts are very well known and embedded in experimental biomedicine, but we will mention them as useful for our reasoning.
On the Cybernetics of Crowdsourcing Innovation: A Process Model

For small and medium-sized enterprises and large enterprises alike, crowdsourcing innovation has become an important element of a product’s whole life cycle. It is the open call process of soliciting consumers to harvest and evaluate ideas or other intellectual assets. The previous proposed taxonomic framework for charactering this process is mainly for general crowdsourcing process and summarized by empirical study. The purpose of this paper is to propose a conceptual model for crowdsourcing innovation from a cybernetic and knowledge management perspective by normative research. The authors performed a normative study and deduced five systemic characteristics from the general laws of control system that guarantee ongoing efficiency for the innovation process. The normative research results provide two key contributions. Firstly, general control laws deduce five indispensable characteristics, and they reveal the intrinsic mechanism of crowdsourcing innovation: the knowledge flow controls, which are also the connotation of open innovation. Secondly, the authors have analyzed a five-characteristics system model and tested the model in several classical cases to show the design tricks of cases. This study provides a new conceptual framework that integrates the theory of open innovation and cybernetics to provide a new view of crowdsourcing innovation process design. In practice, this framework guides managers through the design criteria needed to implement a success crowdsourcing process.
Telework and Mental Health during COVID-19

COVID-19 has come to change societal organization. Due to lockdowns, work typologies have been rethought and telework has gained strength. However, the impact of the constant use of information and communication technologies on the mental health of workers needs to be considered. We aimed to investigate the impact of different work conditions on mental health, to which end we disseminated an online questionnaire during lockdowns to assess imagined surveillance, mobile maintenance expectation, communication overload, feelings of entrapment, depression, anxiety, stress, and flourishing in four groups (employed in telework, employed on-site, employed in layoff, and unemployed). We computed mean comparisons and serial mediations. We show that depression and anxiety were more prevalent in women; parents flourished more than people without children; and people with a higher level of education feel more entrapment. Crucially, we show that telework was associated with imagined surveillance and communication overload, which mediated the association with mobile maintenance expectations and entrapment (which was exacerbated by parenthood), impacting mental health and the quality of life. However, this was also partially observed in the remaining work conditions. Finally, flourishing worked as a protector against mental health issues in all work conditions. We discuss this given the massification of digital migration.
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis

Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
A Modelling Study of Seawater Intrusion in the Liao Dong Bay Coastal Plain, China

To investigate the extent of seawater intrusion in the Liao Dong Bay coastal plain, China, a numerical model for variable-density groundwater flow and miscible salt transport was developed. The SEAWAT code was used to solve the density-dependent groundwater flow and solute transport governing equations. The simulation was conducted for 55 months from October, 2004 to April, 2009. The numerical model was calibrated by the hydraulic heads measured in April, 2009. Using the calibrated model and the same hydrogeological conditions in 2004, the extent of seawater intrusion prediction was conducted for the next 40 years. The results show that the extent of seawater intrusion area will increase in all geologic layers with nearly 6.2 km in the upper Quaternary aquifer and 4.3 km in lower Quaternary aquifer for 40 years. In the Minhuazhen group aquifer, the maximum speed of seawater intrusion is 62.2 m/yr. Therefore, some protection of the freshwater aquifer from seawater intrusion in the Liao Dong Bay coastal plain is imperative.