Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetics of Saccharomyces cerevisiae Fermentation under Metal Ions Stress during Ethanol Production

This research investigated the effects of inorganic compounds or metal ions (calcium ion, Ca2+; potassium ion, K+; magnesium ion, Mg2+) on ethanol production efficiency invertase, an enzyme produced by Saccharomyces cerevisiae, in sucrose solution, which was the substrate for yeast fermentation. The results showed that all metal ions (concentration 0.20 and 0.60 % (w/v)) acted as inhibitors on invertase activity in the order Ca2+ > K+ > Mg2+. Subsequently, these ions inhibited sugar conversion, reducing sucrose utilization and less glucose and fructose consumption based on the high content of remaining sugars in the culture medium. The reduction of the substrate was due to the consumption and an increased growth rate of S. cerevisiae, which all resulted in low efficiency of ethanol production and an increase in glycerol content. The glycerol content was increased due to yeast cells' developed mechanism or adaptation to enhance cell survival following metal ion contamination, especially from Ca2+ and K+; furthermore, the glycerol content significantly increased during the changed conditions, such as when the sugars were nearly all consumed. The kinetic parameters such as specific growth rate (µ-1), substrate consumption rate (Qs), and ethanol production of the research work were also undertaken. In conclusion, metal ion contamination in the sucrose substrate of yeast fermentation resulted in low efficiency of ethanol production, specific growth rate, and substrate consumption rate decrease with the Ca2+ ion (concentration 0.20 - 0.60 % (w/v)) acting more harshly as an inhibitor of ethanol production than the other ions, particularly where there was a high concentration of contamination.
A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.
Effect of additives on emissions and ash-related problems from small-scale combustion of reed canary grass

Agricultural producers are interested in using biomass available on farms to substitute fossil fuels for heat production. However, agricultural residues and energy crops contain high nitrogen (N), sulfur (S), chlorine (Cl), potassium (K), silicon (Si) and other ash-forming elements which lead to increased emissions of particulate matter (PM), nitrogen oxides (NOx), sulfur oxides (SOx) and hydrogen chloride (HCl) and ash-related problems (sintering, slagging, fouling and corrosion). One possibility to address these problematic behaviours is the application of fuel additives such as aluminium-silicates based additives (e.g. kaolin), calcium based additives (e.g. limestone) and sulfur based additives (e.g. magnesium lignosulfonate) with strong chemical adsorption and reaction capacities. Another option is to mix agricultural products with woody materials to dilute the effects of the problematic elements. Therefore, the objective of the present work was to compare emissions levels (PM, CO, CO2, NOx, SOx and HCl) and sintering/slagging propensities from reed canary grass (a purpose-grown energy with great development potential in the province of Quebec, Canada) combustion to co-combustion of reed canary grass with kaolin, limestone, magnesium lignosulfonate and wood. All blends resulted in a decrease of sintering, slagging, fouling and PM emissions because of formation of high melting compounds such as K-Al and K-Ca silicates in the bottom ash. Nevertheless, SOx and HCl emissions were increased because S and Cl were less bound as alkali chlorides and sulfates.
Novel model selection criteria for LMARS: MARS designed for biological networks

ABSTRACT In higher dimensions, the loop-based multivariate adaptive regression splines (LMARS) model is used to build sparse and complex gene structure nonparametrically by correctly defining its interactions in the network. Also, it prefers to apply the generalized cross-validation (GCV) value as its original model selection criterion in order to select the best model, in turn, represent the true network structure. In this study, we suggest to modify the model selection procedure of LMARS by changing GCV with our Kullback–Leibler information-based criteria, namely, consistent Akaike information criterion (CAIC), CAIC with Fisher information matrix and information complexity. Thereby, we aim to compare the performance of our proposal model selection criteria together with the state-of-art model selection criteria, namely, AIC and Bayesian information criterion by comparing their accuracy with GCV while modelling different dimensional and topological biological networks under both simulated and real datasets.
Teacher efficacy for online teaching during the COVID-19 pandemic

The purpose of this study was to examine secondary teachers’ efficacy for teaching in a fully online teaching environment during the sudden transition to online teaching that happened due to the COVID-19 pandemic. This study was aimed at understanding how specific variables, teaching experience, professional development (PD) experience, and teaching supports might correlate with self-efficacy perceptions of teachers transitioning to online teaching during a pandemic in the domains of student engagement, instructional strategies, classroom management and computer skills. The instrument used to measure teacher efficacy for online teaching was a web based 32-item survey that was given to Ontario secondary teachers in a greater Toronto district school board. We argued that prior experience with online learning such as Additional Qualification (AQ) courses or online professional development would build greater self-efficacy amongst teachers as they transition to online learning. The results indicated that higher online teaching efficacy scores correlated with having taken online Additional Qualification (AQ) courses[1] and online professional development sessions. The highest online teaching efficacy scores correlated with having previously used the board provided learning management system (LMS) and using virtual technology supports. These indicators are correlated with higher scores of online teaching efficacy but require further investigation as to how they can better provide support for teachers in online learning environments.
Seismic Performance of Deeply Embedded Conceptual Advanced Reactors: Three-Dimensional Nonlinear Soil-Structure Interaction Analyses

Abstract The U.S. Nuclear Regulatory Commission’s functional containment concept provides advanced nuclear power plant designers with more flexibility in terms of the civil/structural design if the appropriate set of barriers for prevention of radioactive material release exist. Some of the conceptual advanced reactor structures, without the traditional pressure boundaries of large containment structures, are proposed to be deeply embedded or buried into soil. This approach is expected to provide (1) lesser seismic demands on the structures and safety-critical structures, (2) eased regulatory efforts and overall design against other external hazards such as aircraft impact, and (3) overall cost savings. One of the important aspects of assessing the technical and economic viability of deeply embedding advanced reactor buildings is to assess the seismic performance with the understanding of effects with material and geometric nonlinearities. This study investigates the seismic response of deeply embedded or buried advanced reactors by conducting three-dimensional nonlinear soil-structure interaction analyses. Although the results indicate that there is a general trend of decreased seismic response with increased embedment depths, the change in the dynamic environment with different embedment depths and the nonlinear environment under high-intensity seismic inputs may result in increased peak response at increased embedment depths.
The Role of Obesity Centers in the Fight Against Obesity: The Case of Antalya Province

Amac: Bu arastirmada Saglik Bilimleri Universitesi Antalya Egitim ve Arastirma Hastanesi bunyesinde yer alan obezite merkezimizde aldigimiz sonuclarla ilgili verilerimizi paylasmayi hedefledik. Yontem: Retrospektif, tanimlayici, kesitsel arastirmaya 1 Kasim 2018 ve 20 Mart 2020 tarihleri arasinda Saglik Bilimleri Universitesi Antalya Egitim ve Arastirma Hastanesi Obezite Merkezinde takipli 281 obez hasta dahil edildi. Saglikli beslenme ve duzenli fiziksel aktiviteyi yasam tarzi haline getirmenin hedeflendigi merkezimizden hizmet alan 281 hastanin ilk basvuru ve son kontrol antropometrik olcumleri ile kontrol kan tetkikleri yapilan 106 hastanin baslangic ve 6 veya 12. ay kontrollerinde (en son yapilan test ve es zamanli bakilan olcumleri degerlendirmeye alinmistir) bakilan aclik kan sekeri, glikozillenmis hemoglobin, aclik insulini, insulin direnci, total kolesterol, dusuk dansiteli lipoprotein kolesterol, yuksek dansiteli lipoprotein kolesterol ve trigliserit duzeyleri karsilastirildi. Bulgular: Calismaya dahil edilen hastalarin 260’i (%92,5) kadin, 21’i erkekti (%7,5); yas ortalamasi 52,05±9,52 (21-73) yil idi. Ortalama takip suresi 161,1±82,97 (11-365) gun olarak hesaplandi. Calismaya dahil edilen tum hastalarin kilo ve vucut kitle indeksi ortancalarinin sirasiyla 93,4 (68,5-152,6) kg’dan 83,7 (59,3-138) kg’a ve 36,9 (30-65,2) kg/m2’den 32,6 (24,68-58,9) kg/m2’ye dustugu gozlendi (p<0,001). Benzer sekilde, hastalarin bel ve kalca cevrelerinde de istatistiksel olarak anlamli bir dusus tespit edildi (p<0,001). Kontrol kan tetkikleri yapilmis olan 106 hastanin aclik kan sekeri, glikozillenmis hemoglobin, aclik insulini, insulin direnci (p<0,001), sistolik kan basinci (p=0,014) ve trigliserit (p=0,007) duzeylerinde anlamli dusus, yuksek dansiteli lipoprotein kolesterol duzeyinde ise anlamli yukselme goruldu (p<0,001). Hastalarin verdikleri kilo oranina gore vucut kitle indeksi (p<0,001), bel cevresi (p<0,001), kalca cevresi (p<0,001), bel/kalca oraninin (p<0,001) yanisira glikozillenmis hemoglobin (p=0,043) dususunde de istatistiksel olarak anlamli bir degisiklik oldugu gozlendi. Sonuc: Sadece dogru yasam tarzi degisikligi ile saglanan kilo kaybinin kan basinci, kan sekeri ve lipid parametreleri gibi endokrin ve metabolik belirtecler uzerine olumlu etkisi gozlenmektedir. Ozellikle programa tam uyum saglayabilen hastalarda obezite merkezleri saglikli, guvenilir, maliyet etkin ve surdurulebilir bir tedavi olanagi saglamaktadir.
Permutation-Based TCP and UDP Transmissions to Improve Goodput and Latency in the Internet of Things

To circumvent the degraded goodput, high latency and low resource utilization efficiency of conventional transport-layer protocols in the Internet of Things, a novel permutation-based encapsulation scheme is proposed for seamless delivery of packets to achieve high goodput and low latency. In this scheme, extra application-layer data is delivered by our permutation-based data unit (PBDU), which is mapped onto the permutation associated with a legitimate tuple of various packet lengths in a group. Therefore, the network interface throughput is substantially improved for a given number of resource units in the physical channel. The optimal design of this encapsulation is expressed in closed form to maximize the PBDU size, allowing us to quantify the attainable improvements of goodput and latency. In addition, the proposed scheme achieves higher physical-layer throughput and secrecy rate than conventional encapsulation.
In Situ SEM Study of the Micro-Mechanical Behaviour of 3D-Printed Aluminium Alloy

Currently, 3D-printed aluminium alloy fabrications made by selective laser melting (SLM) offer a promising route for the production of small series of custom-designed support brackets and heat exchangers with complex geometry and shape and miniature size. Alloy composition and printing parameters need to be optimised to mitigate fabrication defects (pores and microcracks) and enhance the parts’ performance. The deformation response needs to be studied with adequate characterisation techniques at relevant dimensional scale, capturing the peculiarities of micro-mechanical behaviour relevant to the particular article and specimen dimensions. Purposefully designed Al-Si-Mg 3D-printable RS-333 alloy was investigated with a number of microscopy techniques, including in situ mechanical testing with a Deben Microtest 1-kN stage integrated and synchronised with Tescan Vega3 SEM to acquire high-resolution image datasets for digital image correlation (DIC) analysis. Dog bone specimens were 3D-printed in different orientations of gauge zone cross-section with respect to the fast laser beam scanning and growth directions. This corresponded to the varying local conditions of metal solidification and cooling. Specimens showed variation in mechanical properties, namely Young’s modulus (65–78 GPa), yield stress (80–150 MPa), ultimate tensile strength (115–225 MPa) and elongation at break (0.75–1.4%). Furthermore, the failure localisation and character were altered with the change in gauge cross-section orientation. DIC analysis allowed correct strain evaluation that overcame the load frame compliance effect and helped to identify the unevenness of deformation distribution (plasticity waves), which ultimately resulted in exceptionally high strain localisation near the ultimate failure crack position.
Developing a socio-psychological model explaining farmers’ income diversification in response to groundwater scarcity in Iran

ABSTRACT Income diversification is an essential livelihood strategy for farmers facing unsustainable groundwater withdrawals. This paper develops a general structural equation model that analyses socio-psychological factors that affect the intentions to adopt and the actual adoption of income diversification in response to groundwater scarcity. The developed model includes affective attitudes, instrumental attitudes and self-efficacy. This model explains 55% and 36% of the variance in intentions to pursue and the actual pursuit of income diversification among farmers in the Rafsanjan Plain, Iran, respectively. These results can inform policies for promoting income diversification, and have implications for sustaining farmers’ livelihoods and groundwater resources.
Fortification Against Cascade Propagation Under Uncertainty

Network cascades represent a number of real-life applications: social influence, electrical grid failures, viral spread, and so on. The commonality between these phenomena is that they begin from a set of seed nodes and spread to other regions of the network. We consider a variant of a critical node detection problem dubbed the robust critical node fortification problem, wherein the decision maker wishes to fortify nodes (within a budget) to limit the spread of cascading behavior under uncertain conditions. In particular, the arc weights—how much influence one node has on another in the cascade process—are uncertain but are known to lie in some range bounded by a worst-case budget uncertainty. This problem is shown to be [Formula: see text]-hard even in the deterministic case. We formulate a mixed-integer program (MIP) to solve the deterministic problem and improve its continuous relaxation via nonlinear constraints and convexification. The robust problem is computationally more difficult, and we present an MIP-based expand-and-cut exact solution algorithm, in which the expansion is enhanced by cutting planes, which are themselves tied to the expansion process. Insights from these exact solutions motivate two novel (interrelated) centrality measures, and a centrality-based heuristic that obtains high-quality solutions within a few seconds. Finally, extensive computational results are given to validate our theoretical developments as well as provide insights into structural properties of the robust problem and its solution.
An application of a non-homogeneous Poisson model to study PM2.5 exceedances in Mexico City and Bogota

It is very important to study the occurrence of high levels of particulate matter due to the potential harm to people's health and to the environment. In the present work we use a non-homogeneous Poisson model to analyse the rate of exceedances of particulate matter with diameter smaller that 2.5 microns (PM ). Models with and without change-points are considered and they are applied to data from Bogota, Colombia, and Mexico City, Mexico. Results show that whereas in Bogota larger particles pose a more serious problem, in Mexico City, even though nowadays levels are more controlled, in the recent past PM were the ones causing serious problems.
The Proportion of Shares Held by the Largest Shareholder and the Proportion of Independent Directors

In this paper, the relevant data of the proportions of major shareholders and independent directors from 2008 to 2017 are examined. Firstly, the functions of the board of directors and the role of independent directors are introduced. Then, the impact of the institutional changes of the board of directors on the proportion of independent directors is investigated. Next, the determinants of the structure of the board of directors are reviewed. Thus, two hypotheses are proposed. According to the regression results of the model, hypothesis 1B presents positive significance. It can be concluded through the influence of the largest shareholder’s shareholding ratio on the proportion of independent directors in the board of directors that the higher the shareholding ratio of the largest shareholder, the higher the proportion of independent directors. By increasing the proportion of independent directors, the voting space of non-independent directors will be squeezed, so as to increase their control. This study reveals that the ownership structure is an essential factor influencing the structure of the board of directors, constituting a crucial supplement to the literature on the determinants of the structure of the board of directors.
Configuration of the Earth’s Magnetotail Current Sheet

The spatial scale and intensity of Earth’s magnetotail current sheet determine the magnetotail configuration, which is critical to one of the most energetically powerful phenomena in the Earth’s magnetosphere, substorms. In the absence of statistical information about plasma currents, theories of the magnetotail current sheets were mostly based on the isotropic stress balance. Such models suggest that thin current sheets cannot be long and should have strong plasma pressure gradients along the magnetotail. Using Magnetospheric Multiscale and THEMIS observations and global simulations, we explore realistic configuration of the magnetotail current sheet. We find that the magnetotail current sheet is thinner than expected from theories that assume isotropic stress balance. Observed plasma pressure gradients in thin current sheets are insufficiently strong (i.e., current sheets are too long) to balance the magnetic field line tension force. Therefore, pressure anisotropy is essential in the configuration of thin current sheets where instability precedes substorm onset.
Expanding the property profile of Bayfol HX films towards NIR recording and ultra-high index modulation

Bayfol® HX photopolymer films prove themselves as easy-to-process recording materials for volume holographic optical elements (vHOEs) and are available in customized grades at industrial scale. Their full-color (RGB) recording and replay capabilities are two of their major advantages. Moreover, the adjustable diffraction efficiency, tunable angular and spectral selectivity of vHOEs recorded into Bayfol® HX as well as their unmatched optical clarity enables superior invisible "off Bragg" optical functionality. As a film product, the replication of vHOEs in Bayfol® HX can be carried out in a highly cost-efficient and purely photonic roll-to-roll (R2R) process. Utilizing thermoplastic substrates, Bayfol® HX was demonstrated to be compatible to state-of-the-art plastic processing techniques like thermoforming, film insert molding and casting, all enabled using a variety of industry-proven integration technologies for vHOEs. Therefore, Bayfol® HX made its way in applications in the field of augmented reality such as Head-up-Displays (HUD) and Headmounted- Displays (HMD), in free-space combiners, in plastic optical waveguides, and in transparent screens. Also, vHOEs made from Bayfol® HX are utilized in highly sophisticated spectrometers in astronomy as well as in narrow band notch filters for eyeglasses against laser strikes. Based on a well-established toolbox, Bayfol® HX can be adopted for a variety of applications. To further offer access to more applications in sensing and continuously improve the performance in existing applications, we recently extended our chemical toolbox to address both the sensitization beyond RGB into the Near Infrared Region (NIR) and increase the achievable index modulation ▵n1 beyond 0.06. In this paper, we will report on our latest developments in these fields.
Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking

Online support groups offer social support and an outlet for expressing emotions when dealing with health-related challenges. This study examines whether automated text analysis of emotional expressions using Linguistic Inquiry and Word Count (LIWC) can identify emotions related to abstinence expressed in online support groups for quitting smoking, suggesting promise for offering targeted mood management to members. The emotional expressions in 1 month of posts by members of 36 online support groups were related to abstinence at month end. Using the available LIWC dictionary, posts were scored for overall positive emotions, overall negative emotions, anxiety, anger, sadness, and an upbeat emotional tone. Greater expressions of negative emotions, and specifically anxiety, related to nonabstinence, while a more upbeat emotional tone related to abstinence. The results indicate that automated text analysis can identify emotions expressed in online support groups for quitting smoking and enable targeted delivery of mood management to group members.