Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing a Method for Electrical Immobilization of Whitefish on Board Fishing Vessels

ABSTRACT A downscaled version of an electrical dry stunner used in aquaculture has been adapted for use on fishing vessels to immobilize the catch (cod, haddock and saithe) shortly after capture to facilitate immediate and easy catch handling, and to minimize downgrading due to poor bleed-out. Possible effects of electrical stimulation of white muscle (blood lactate, initial pH and twitch ability) were determined. Eye roll, respiration, equilibrium, swim ability and noxious stimuli were assessed after stunning and fillets were examined with respect to color shade, possible blood spots and discolorations. Suitable voltages, exposure times to electricity, and rows of electrodes were 40 and 70 V, 4 to 6 s, and 3 to 5 rows of electrodes, respectively. Cod and haddock can be immobilized without skeletal damages and occurrence of blood spots and discolorations. In contrast, 11% of the saithe had fractured spine usually accompanied by a single large blood spot.
Caractérisation du transport fluidique et thermique dans les mousses métalliques capillaires en cuivre

Thermal management o f electronics has become a major challenge in manufacturing and production of high performance electronic chips. Constant rise o f computation power requires higher amount of energy and subsequently this energy (in the form of heat) should be transferred out o f the computer. Among other solutions, heat pipes are proposed as a means to transfer and eventually remove this excess heat. The main part of a typical heat pipe is the wick which provides a medium for transport o f capillary driven flow and evaporation at the vapor-liquid interface. Different materials are proposed as wick for a heat pipe and among them, recently invented Bi-porous metal foams exhibit a very significant performance improve, i.e. high transport limit in comparison with competing materials. By a mainly experimental approach, capillary, wetting and evaporation properties o f copper metal foams with different porosities have been investigated. An in depth surface characterization study is done on the foams to identify the role o f surface wettability on the capillary performance. It is found for the first time that the hydrophilicity loss o f the copper based porous materials when exposed to air is caused by the adsorption o f volatile organic compounds and not by copper oxidation. It is also inferred that the reason for high transport limit o f the foams compared with other materials is their unique microstructure which has two levels o f porosity. This biporous microstructure provides paths for liquid transport with low pressure drop while the smaller pores provide for thin film evaporation and produce high capillary pressure. Permeability and effective pore radius, as two key parameters defining the pumping capacity, are measured experimentally by the rate o f rise method. It is also found that the evaporation rate o f a rising liquid in a porous material is lower compared with that of the same material while saturated with stationary liquid. This will allow ignoring natural evaporation in the rate of rise method and using simplified models to capture permeability and effective pore radius. The role o f meniscus recession in capillary pumping and evaporation rate is characterized for the first time and a model is proposed to measure the effective pore radius o f porous materials in operating conditions. It is shown that the effective pore radius can decrease up to 50% due to forced evaporation. In a more general perspective, through different experiments, it is shown that there is a coupling between capillarity and evaporation. This coupling is established through variation in meniscus shape which will affect both capillarity and evaporation. The findings of this thesis will shed light on the capillarity, evaporation and their interconnected nature in the capillary wicks in two phase thermal management devices.
Environmental Product Declarations as Data Source for the Environmental Assessment of Buildings in the Context of Level(s) and DGNB: How Feasible Is Their Adoption?

This study aims to examine the feasibility of using environmental product declarations (EPDs) as a data source for life-cycle assessment (LCA) in two sustainable building assessment schemes–the pilot version of the European framework Level(s) and the German system DGNB (Deutsche Gesellschaft für Nachhaltiges Bauen). An EPD is a standardized and third-party certified label to communicate product-specific environmental data based on LCA. Some green building rating systems consider it a robust LCA data source and encourage its use over generic data. This work evaluates the environmental profile of the envelope of an office building in the context of level(s) and DGNB adopting EPD as a data source. The results indicate that the EPDs did not cover the mandatory scope of the schemes. Furthermore, there was a lack of EPDs appropriate to the geographical context of the case study, leading to the adoption of EPDs of products from places other than the building site and an overestimation of the environmental impacts of transportation. Moreover, the need for EPDs considering suitable and comprehensive scenarios as well as life-cycle stages beyond the product stage is highlighted. This gap, in fact, hinders the performance of a complete LCA within the analyzed building assessment schemes when relying solely on EPDs as a data source. With this paper, we wish to encourage the further development of EPDs related to the integration of more life-cycle modules and more comprehensive scenarios, considering the direction of the latest amendment of the ISO 15804 for EPDs of construction products.
A Sound-based Machine Learning to Predict Traffic Vehicle Density

Traffic flow mismanagement is a significant challenge in all countries especially in crowded cities. An alternative solution is to utilize smart technologies to predict traffic flow. In this study, frequency spectrum describing traffic sound characteristics is used as an indicator to predict the next five-minute vehicle density. Sound frequency and vehicle intensity are collected during a thirteen-hour data gathering. The collected sound intensity and frequency are then used to learn three machine-learning models - support vector machine, artificial neural network, and random forest and to predict vehicle intensity. It was found out that the performances of the three models based on root-mean-square-error values are 12.97, 16.01, and 10.67, respectively. These initial and satisfactory results pave a new way to predict traffic flow based on traffic sound characteristics which may serve as a better alternative to conventional features.
The biology of bilirubin production: detection and inhibition

Newborn jaundice is a benign condition commonly seen in the first postnatal week of life (or the transitional period). It is primarily due to an imbalance between the rate of production of the yellow-orange pigment bilirubin and its elimination by the liver. Infants with high bilirubin production rates (such as those who are undergoing hemolysis) or with insufficient hepatic bilirubin conjugating ability [such as those with uridine 5'-diphosho-glucuronosyltransferase (UGT1A1) deficiencies] can subsequently develop excessive circulating total serum/plasma bilirubin (TB) levels or hyperbilirubinemia. Bilirubin is formed during the degradation of heme, derived from the turnover of red blood cells (RBCs). In this reaction, which is catalyzed by the rate-limiting enzyme heme oxygenase (HO), carbon monoxide (CO), iron (Fe), and bilirubin are produced in equimolar quantities. As a result, measurements of total body CO production rates can be used as indices of bilirubin production. Standard treatment strategies for hyperbilirubinemia involves the use of phototherapy (specifically narrow-band blue wavelength light) and/or exchange transfusion. However, if infants with excessive hyperbilirubinemia are not identified or treated in a timely manner, they are at risk for developing bilirubin neurotoxicity, which can manifest as bilirubin-induced neurologic dysfunction (BIND) and result in neurologic sequelae (such as acute or chronic bilirubin encephalopathy. Here, we review the biology of bilirubin production and current technologies and approaches to identify and treat these high-risk
Design of novel multi filter union feature selection framework for breast cancer dataset

Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.
Examining Individual Differences in Language Learning: A Neurocognitive Model of Language Aptitude

Abstract A common practice in the cognitive neurosciences is to investigate population-typical phenomena, treating individuals as equal except for a few outliers that are usually discarded from analyses or that disappear on group-level patterns. Only a few studies to date have captured the heterogeneity of language processing across individuals as so-called “individual differences”; fewer have explicitly researched language aptitude, which designates an individual’s ability for acquiring foreign languages. Existing studies show that, relative to average learners, very gifted language learners display different task-related patterns of functional activation and connectivity during linguistic tasks, and structural differences in white and grey matter morphology, and in white matter connectivity. Despite growing interest in language aptitude, there is no recent comprehensive review, nor a theoretical model to date that includes the neural level. To fill this gap, we review neuroscientific research on individual differences in language learning and language aptitude and present a first, preliminary neurocognitive model of language aptitude. We suggest that language aptitude could arise from an advantageous neurocognitive profile, which leads to high intrinsic motivation and proactive engagement in language learning activities. On the neural level, interindividual differences in the morphology of the bilateral auditory cortex constrain individual neural plasticity, as is evident in the speed and efficiency of language learning. We suggest that language learning success is further dependent upon highly efficient auditory-motor connections (speech-motor networks) and the structural characteristics of dorsal and ventral fibre tracts during language learning.
Climate Risk Management

Accelerating global climate change drives new climate risks. People around the world are researching, designing, and implementing strategies to manage these risks. Identifying and implementing sound climate risk management strategies poses nontrivial challenges including ( a) linking the required disciplines, ( b) identifying relevant values and objectives, ( c) identifying and quantifying important uncertainties, ( d) resolving interactions between decision levers and the system dynamics, ( e) quantifying the trade-offs between diverse values under deep and dynamic uncertainties, ( f) communicating to inform decisions, and ( g) learning from the decision-making needs to inform research design. Here we review these challenges and avenues to overcome them. ▪ People and institutions are confronted with emerging and dynamic climate risks. ▪ Stakeholder values are central to defining the decision problem. ▪ Mission-oriented basic research helps to improve the design of climate risk management strategies.
Determinants of Rice Farmers’ Willingness to Pay for Conservation and Sustainable Use of Swampy Wetlands in Ghana’s Northern and Ashanti Regions

In light of the increased importance of sustainability issues in the world economy, this study calculates the economic value of the swampy wetlands in Ghana’s Northern and Ashanti region, based on rice farmers’ and community members’ willingness to pay (WTP) for the sustainable use of the wetlands. It also examines the factors influencing this WTP. Data from 160 rice farmers and community members in four districts were collected via a questionnaire survey. A logistic regression model was employed to determine the significance of the influencing factors, while the respondents’ WTP was estimated by the contingent valuation method. The results revealed that 89.4% of the respondents were willing to contribute about 54.90–213.11 USD/household/ha/year and an aggregated 0.298–1.158 million USD/year to ensure swampy wetland sustainability. On an average, they were willing to pay 180.17 USD/household/ha/year and an aggregated 0.979 million USD/year. Gender, household size, household disposable income, and knowledge of the importance of wetlands were found to be the determinants of WTP. We recommend that farmers and the public be educated on wetland ecosystems and family planning. In addition, reducing household size and providing rice farmers with adequate irrigation and postharvest facilities are worthwhile conservation measures.
Prognostic Factors of Complications After Thoracoscopic Lobectomy for Non-Small Cell Lung Cancer-A Retrospective Study

Background: Video-assisted thoracoscopic surgery, safe and minimally invasive, is the first strategy recommended for non-small cell lung cancer. The purpose of this study was to determine the risk factors for postoperative cardiopulmonary complications (including cardiac and pulmonary complications) in patients with NSCLC who underwent video-assisted thoracoscopic surgery (VATS). Methods: We retrospectively collected information of 3142 lung cancer patients undergoing VATS tumor resection at Jiangsu Provincial People's Hospital from January 2017 to June 2018, and established a clinical prediction model using the factors selected by univariate analysis.Results: A total of 305 in 3142 patients developed postoperative cardiopulmonary complications. In univariate analysis, age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, albumin before surgery, pre-albumin, Δalbumin and pleura adhesion were all associated with cardiac and pulmonary complications. Multivariate analysis showed that age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, pre-albumin, Δalbumin were important independent risk factors for complications. Finally, age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, Δalbumin variables were included in the model (AUC=0.743). Conclusion: Nutritional status (PNI, pre-albumin), CCI, age, long-term smoking history before surgery, conversion to thoracotomy were all independent risk factors for postoperative complications and poor prognosis. Experienced surgeons should instruct the operation of high-risk patients to avoid long-term hospitalization and possible poor prognosis after surgery.
SFDroid: Android Malware Detection using Ranked Static Features

Over the past few years, malware attacks have risen in huge numbers on the Android platform. Significant threats are posed by these attacks which may cause financial loss, information leakage, and damage to the system. Around 25 million smartphones were infected with malware within the first half of 2019 that depicts the seriousness of these attacks. Taking into account the danger posed by the Android malware to the users' community, we aim to develop a static Android malware detector named SFDroid that analyzes manifest file components for malware detection. In this work, first, the proposed model ranks the manifest features according to their frequency in normal and malicious apps. This helps us to identify the significant features present in normal and malware datasets. Additionally, we apply support thresholds to remove the unnecessary and redundant features from the rankings. Further, we propose a novel algorithm that uses the ranked features, and several machine learning classifiers to detect Android malware. The experimental results demonstrate that by using the Random Forest classifier at 10% support threshold, the proposed model gives a detection accuracy of 95.90% with 36 manifest components.
Novel principal component analysis‐based feature selection mechanism for classroom sound classification

Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the computational resources requested, so the number of features has to be carefully selected. In this work, we propose a methodology for feature selection based on the principal component analysis. The case study has been the classification of classroom sounds during face‐to‐face module delivery and six sound types have been defined. The proposed method is applied upon a set of 143 sound features to produce feature ranking. The ranking results are compared with those provided by the Relief‐F. Then the selected features are used by five classification algorithms, Linear Discriminant Analysis (LDA), Quadratic Support Vector Machine (QSVM), k Nearest Neighbors, Boosted Trees, and Random Forest. The algorithms are executed with increasing number of features, from 1 to 143, considering both feature rankings, creating 1430 models. The performance of the classification algorithms increases rapidly with the number of features with LDA, QSVM, and Boosted Trees outperforming other methods and surpassing the accuracy ratio of 90% with 25 features.
The effect of temperature and digested sewage sludge cover over tailings on the leaching of contaminants from Ballangen tailings deposit

Dry covers can be applied above tailings to reduce and prevent formation of acid mine drainage and leaching of contaminants. Efficiency of covers is affected by different parameters, of which temperature change under climate change context is one. Here, a laboratory column leaching experiment was performed under four temperatures, 5, 10, 14, and 18 °C on unoxidized tailings from Ballangen, Norway. 600 mL of water was added to each column every second week and leachate was collected and analyzed for pH, salinity, alkalinity, concentrations of sulfate, Co, Fe, Mn, Ni, and Zn. A thin layer of digested sewage sludge was added to columns after the 16th leaching cycle. In total, 21 leaching cycles were performed. Results showed low oxidation of tailings and therefore high pH and low salinity, SO42−, Fe, Ni, and Co in the leachates at leaching temperature of 5 °C. Addition of sludge cover slowed down oxidation of underlying tailings and decreased leaching of SO42−, Fe, Mn, Co, Ni, and Zn from the tailings deposit, especially at relatively high temperatures. 10 °C is a threshold temperature, below which leaching is not affected by the cover addition so much. At a leaching temperature higher than 10 °C, the sludge cover addition can reduce the leaching of elements significantly.
Advances in the application of QD-based intracellular sensing systems

ABSTRACT In recent years, many quantum dot (QD)-based sensing systems have been developed and expanded into intracellular analytical applications. In this review, recent reports on the intracellular sensing of QDs are reviewed, with special emphasis on the relation between QDs' intrinsic properties and their analytical activities in various cells. First, the general synthetic routes and the representative surface modification methods of different types of QDs are discussed. The effects of QDs' structural and component characteristics on the cytotoxicity and the cellar uptake ways are then described in detail. We highlight recent advances in the sensing strategies and illustrate analytical applications in living cells. Finally, we discuss some critical challenges and future trends of the intracellular sensor development.
Comparison of Inorganic Chlorine in the Southern Hemispheric lowermost stratosphere during Late Winter 2019

Abstract. Inorganic chlorine (Cly) is the sum of the degradation products of long-lived chlorinated source gases. These include the reservoir species (HCl and ClONO2) and active chlorine species (i.e. ClOx). The active chlorine species drive catalytic cycles that deplete ozone in the polar winter stratosphere. This work presents calculations of inorganic chlorine (Cly) derived from chlorinated source gas measurements on board the High Altitude and Long Range Research Aircraft (HALO) during the Southern hemisphere Transport, Dynamic and Chemistry (SouthTRAC) campaign in late winter and early spring 2019. Results are compared to Cly of the Northern Hemisphere derived from measurements of the POLSTRACC-GW-LCYCLE-SALSA (PGS) campaign in the Arctic winter of 2015/2016. A scaled correlation was used for PGS data, since not all source gases were measured. Cly from a scaled correlation was compared to directly determined Cly and agreed well. An air mass classification based on in situ N2O measurements allocates the measurements to the vortex, the vortex boundary region, and mid-latitudes. Although the Antarctic vortex was weakened in 2019 compared to previous years, Cly reached 1687 ± 20 ppt at 385 K, therefore up to around 50 % of total chlorine could be found in inorganic form inside the Antarctic vortex, whereas only 15 % of total chlorine could be found in inorganic form in the southern mid-latitudes. In contrast, only 40 % of total chlorine could be found in inorganic form in the Arctic vortex during PGS and roughly 20 % in the northern mid-latitudes. Differences inside the respective vortex reaches up to 565 ppt more Cly in the Antarctic vortex 2019 than in the Arctic vortex 2016 (at comparable distance to the local tropopause). As far as is known, this is the first comparison of inorganic chlorine within the respective polar vortex. Based on the results of these two campaigns, the difference of Cly inside the respective vortex is significant and larger than reported inter annual variations.