Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser direct write techniques for the fabrication of paper-based diagnostic devices

We report on the use of laser direct-write techniques for the fabrication of point-of-care paper-based diagnostic sensors. These include laser-based deposition, laser ablation and laser-induced photo-polymerisation. Firstly, Laser Induced Forward Transfer (LIFT) was employed to deposit biomolecules from a donor film onto paper receivers. Paper was chosen as the ideal receiver because of its inherent properties which make it an efficient and suitable platform for point-of-care diagnostic sensors. Both enzyme-tagged and untagged antibodies were LIFT-printed and their viability was confirmed via a colorimetric enzyme-linked immunosorbent assay (ELISA). Secondly, we report on the laser-based structuring of paper-based fluidic devices. Laser-scanning the paper defines the areas that will be polymerised, thus creating barriers that keep the liquid solutions contained. Complicated devices are easy to fabricate and the flexibility of this technique allows for unique patterns, making it appropriate for rapid prototyping but also for large-scale production. Furthermore, the laser patterning technique allows control of the depth or degree of polymerisation, thereby allowing the liquid to wick through but also imposition of flow delays. Finally, the use of lasers for the fabrication of a 'master' which can be used for casting a PDMS mould for applications in micro-contact printing. The combination of the above mentioned techniques represent the platform technology for the rapid, precise and versatile laser-based fabrication of diagnostic point-of-care sensors.
Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.
Methods of frequency power allocation

In telecommunication history, frequencies are always a crucial resource and factor. Thus, frequency reusing methods appear, which aim to reduce the costs and increase frequency using efficiency. However, they also cause interference. Therefore, optimizing the methods of the allocation remains critical. This paper shows different methods of frequency reusing. These methods include IFRN, FFR, water-filling, tx-ITL and FLG. From comparing the net-work capacities of different frequency allocation methods and analyzing the real situation, we get the conclusion that when making the frequency allocation plans, not only should we consider the capacity, but we also need to consider the real situation. Only in this way, can we find the best solution.
Eco-marathon Car Driving Pattern and Miles Per Gallon

Annually, young students participate in the Shell Eco-marathon, a competition organised by Shell, in which teams are to present their design of cars that can drive extreme distances on 1 gallon of fuel. The first category in the competition is for the teams to design very aerodynamic cars and the second category is to design cars similar in body structure to the regular passenger car. Based on the allowed driving pattern and innovation of the teams in design of the vehicles and the engines, over 4,800 km/litre is the 2011 record compared with about 60 km/litre in the modern efficient regular cars. In this paper, we model the driving pattern around the Honda GX series engines for team using the internal combustion engine, a possible performance of 1,360 km/litre could be reached in the mixed mode driving using an engine whose brake specific fuel consumption is 23% lower and that can provide 33% more torque than the regular GX series.
Effects of Intravenous Antimicrobial Drugs on the Equine Fecal Microbiome

Simple Summary The objectives of this study were to determine the effect of intravenous administration of antimicrobials (ceftiofur, enrofloxacin, oxytetracycline) on equine fecal bacterial communities over time. Healthy horses were treated for 5 days with enrofloxacin, ceftiofur sodium, oxytetracycline and saline solution, and fecal samples were collected over 30 days. Microbiome analysis was carried out via 16S rRNA gene sequencing. Microbial diversity and abundance were altered using ceftiofur and enrofloxacin. This study showed that antimicrobials alter gut bacterial communities, which could predispose horses to gastrointestinal inflammation, diarrhea and possibly systemic disorders. Abstract Alterations in the gastrointestinal microbiota after antimicrobial therapy in horses can result in loss of colonization resistance and changes in bacterial metabolic function. It is hypothesized that these changes facilitate gastrointestinal inflammation, pathogen expansion and the development of diarrhea. The objectives of this study were to determine the effect of intravenous administration of antimicrobial drugs (ceftiofur, enrofloxacin, oxytetracycline) on equine fecal bacterial communities over time, to investigate whether those changes are detectable after 5 days of treatment and whether they persist over time (30 days). Sixteen horses were randomly assigned into 4 treatment groups: group 1 (enrofloxacin, n = 4); group 2 (ceftiofur sodium, n = 4); group 3 (oxytetracycline, n = 4); group 4 (0.9% saline solution, placebo, n = 4). Antimicrobial therapy was administered for 5 days. Fecal samples were obtained before (day 0) and at 3, 5 and 30 days of the study period. Bacterial DNA was amplified using specific primers to the hypervariable region V1–V3 of the 16S rRNA gene using a 454 FLX-Titanium pyrosequencer. Antimicrobial therapy failed to cause any changes in physical examination parameters, behavior, appetite or fecal output or consistency throughout the study in any horse. There was a significant effect of treatment on alpha diversity indices (richness) over the treatment interval for ceftiofur on days 0 vs. 3 (p < 0.05), but not for other antimicrobials (p > 0.05). Microbial composition was significantly different (p < 0.05) across treatment group and day, but not for interactions between treatment and day, regardless of taxonomic level and beta-diversity distance metric. The most significant antimicrobial effects on relative abundance were noted after intravenous administration of ceftiofur and enrofloxacin. The relative abundance of Fibrobacteres was markedly lower on day 3 compared to other days in the ceftiofur and enrofloxacin treatment groups. There was an increase in Clostridia and Lachnospiraceae from day 0 to days 3 and 5 in ceftiofur and enrofloxacin treated groups. These findings showed the negative effect of antimicrobial drugs on bacterial communities associated with gut health (Fibrobacteres and Lachnospiraceae) and indicate that changes in specific taxa could predispose horses to gastrointestinal inflammation and the development of diarrhea.
The Pedagogy of Blended Learning and Intellectual Capital Disclosure in Higher Education post COVID19

: Purpose - This frame aims to develop a framework for measuring intellectual capital disclosure in higher education institutions after Covid19. In addition, this paper uses the proposed framework to measure the level of intellectual capital disclosure in a wide range of faculties of higher education institutions. This paper reviews prior frameworks for measuring intellectual capital disclosure developed in public sectors (universities) to develop the proposed framework, which includes dimensions of online learning to account for the effects of the Covid19 pandemic on learning pedagogy. In addition, content analysis of official websites is used to assess the level of intellectual capital disclosure for higher education institutions (10 faculties of social sciences and 10 faculties of applied sciences). - The empirical results demonstrate that: i) the prior frameworks which are introduced to measure intellectual capital disclosure average of face-to-face learning theories, frameworks, and models. identified blended learning student, lecturer, content, technology, student support institution). pedagogy online learning. online several empirical Recent evidence suggests that In this paper, I develop a three-dimensional and three-role matrix for measuring intellectual capital disclosure in universities, to reflect the shift to online learning in higher education institutions following COVID-19. The matrix includes the three dimensions of the intellectual capital of human capital, structural capital and relational capital. In addition, to reflect the triple role of these institutions, this matrix subcategorizes the main mission of higher education institutions into three aspects: education, research, and administration. The proposed framework provides a thorough overview of how to measure intellectual capital disclosure. The human capital dimension of intellectual capital is the first dimension in the matrix.
How Do Determiners of Job Performance Matter During COVID-19? The Conservation of Resource Theory

During the COVID-19 pandemic, business managers are facing many challenges from a severe challenge. Many organizations have changed their original management mode and organizational behavior to improve employees’ organizational citizenship behavior, thus reducing their sense of anxiety and incapability. Thereinto, job performance of the employees also affects the growth and development of the organization. To explore how to fragment employees’ positive psychology and job performance, this study discusses the influence on employees’ subjective wellbeing and job performance from relevant factors at the organizational and individual levels. Also, to explore the influence of organizational support and occupation self-efficacy on job performance and the mediating role of subjective wellbeing during COVID-19, a total of 618 valid questionnaires were collected from all walks of life in 2020. Hypotheses were tested by structural equation modeling and Bootstrap technology. The results show that: (1) Professional self-efficacy and subjective wellbeing have a significant positive impact on job performance; (2) Subjective wellbeing plays a complete mediating role between organizational support and job performance, and subjective wellbeing plays a partial mediating role between professional self-efficacy and job performance; (3) Compared with the sense of organizational support, the positive effect of self-efficacy on job performance is more significant.
Evaluating the microbial growth kinetics and artificial gastric digestion survival of a novel Pichia kudriavzevii FOL-04 isolated from shalgam

Present study aims to explore Pichia kudriavzevii FOL-04 ( FOL-04 )’s: i) survival against artificial gastric juice ( AGJ ) and artificial bile juice ( ABJ ), ii) growth kinetics in shake flask ( SF ) and fed-batch trials ( FBT ). Survival of FOL-04 as measured by relative cell density ( RCD ) against AGJ and ABJ was screened at four different pH-levels (control, 3, 2, 1.5) and ox-bile concentrations (control, 0.2%, 1%, 2%), respectively. Growth kinetics was calculated by periodic measurement of OD 600 in SF (225 rpm, 30°C) or in FBT using exponential feeding regimen where pH, dissolved-oxygen and temperature were controlled at 5.5, 21%, and 30°C, respectively. The doubling-time, maximum specific growth rate, and final cell densities achieved for SF and FBT were 81.7min, 1.67, 11.79 and 170.4 min, 4.75, 37.95, respectively. RCDs calculated were similar for pH=3 and control vs both were significantly higher(p<0.05) than pH=1.5 and 2 with the latter two pH-levels were not significantly different(p>0.05). RCDs were similar across control, 0.2%, and 1% ox-bile levels(p>0.05). However, 2% ox-bile yielded significantly lower RCD (p<0.05) compared to all except 1%. FOL-04 is a potential probiotic candidate showing robustness against AGJ and ABJ and remarkable biomass increase was achieved when grown under FBT which could pave the way for developing a yeast-based probiotic using this strain.
A Taxonomy for Threat Actors’ Delivery Techniques

The main contribution of this paper is to provide an accurate taxonomy for delivery techniques, which allows the detection of novel techniques and the identification of appropriate countermeasures. Delivery is a key stage for offensive cyber operations. During delivery, a threat actor tries to gain an initial foothold into the targeted infrastructure. It is the first step of an offensive cyber operation, where the threat actor interacts with its victim in a hostile way; thus, its success is mandatory for the global achievement of the operation. However, delivery techniques are not well structured among the literature, being in many cases a simple list of techniques with which, if one of them is slightly modified by the threat actor, its detection becomes very difficult. This situation hinders the modeling of hostile actors, a fact that makes it difficult to identify countermeasures to detect and neutralize their malicious activities. In this work, we analyze the current delivery techniques’ classification approaches and the problems linked to them. From this analysis, we propose a novel taxonomy that allows the accurate classification of techniques, overcoming the identified problems and allowing both the discovery of new techniques and the detection of gaps in deployed countermeasures. Our proposal significantly reduces the amount of effort needed to identify, analyze, and neutralize hostile activities from advanced threat actors, in particular their initial access stage. It follows a logical structure that can be easy to expand and adapt, and it can be directly used in the industry’s commonly accepted standards, such as MITRE ATT&CK.
Does the value of human life in Russia increase with age and higher levels of education?

Estimates of the value of life, reflecting society’s preferences regarding the choice between safety and money, are key indicators for state management in areas such as healthcare, transport, demographic policy, and environmental protection. This article is a logical continuation of the previous research presenting the initial estimates of the value of life in Russia based on analysis of the revealed preferences about employment in industries associated with high fatality risks. In addition to the previous results, this study provides a new theoretical model explaining the logic of choosing employment considering fatality risks and offers estimates of the value of life across educational and age groups. The empirical part of the paper is based on the RLMS HSE data for the period from 2010 to 2020; the author uses panel regression with random effects. The analysis shows that the average value of life in Russia is 287 million rubles, varying from 241 to 450 million rubles depending on levels of education achieved, and considering age value of life ranges from 329 to 349 million rubles (in those groups for which estimates are significant). Possible explanations for this variability are related to the human capital factor, which changes with age and education level. At the same time, the impact of human capital on the value of life can be both positive and negative.
COST AND CO2 EMISSION OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING ENHANCED COLLIDING BODIES ALGORITHM

This paper investigates discrete design optimization of reinforcement concrete frames using the recently developed meta-heuristic called Enhanced Colliding Bodies Optimization (ECBO) and the Non-dominated Sorting Enhanced Colliding Bodies Optimization (NSECBO) algorithm. The objective function of algorithms consists of construction material costs of reinforced concrete structural elements and carbon dioxide (CO2) emissions through different phases of a building life cycle that meets the standards and requirements of the American Concrete Institute’s Building Code. The proposed method uses predetermined section database (DB) for design variables that are taken as the area of steel and the geometry of cross-sections of beams and columns. A number of benchmark test problems are optimized to verify the good performance of this methodology. The use of ECBO algorithm for designing reinforced concrete frames indicates an improvement in the computational efficiency over the designs performed by Big Bang-Big Crunch (BB-BC) algorithm. The analysis also reveals that the two objective functions are quite relevant and designs focused on mitigating CO2 emissions could be achieved at an acceptable cost increment in practice. Pareto results of the NSECBO algorithm indicate that both objective yield similar solutions.
Injected Current Sensitivity Based Load Flow Algorithm for Multi-Phase Distribution System in the Presence of Distributed Energy Resources

This paper presents an injected current sensitivity (ICS) based multi-phase load flow (LF) methodology with minimal Jacobian computation for the three-phase unbalanced distribution system with multiple Distributed Energy Resources (DERs). In the proposed approach, first, a generalized bus admittance and Jacobian matrix have been formulated that include various distribution system components such as lines, shunt capacitors, voltage control devices such as regulators, different transformer connections, and load models. Second, DERs are modeled as a PV bus and incorporated into the proposed LF algorithm by formulating a reactive power injection-based sensitivity matrix that iteratively computes its net power injections. Unlike the traditional Jacobian matrix-based LF methods where an update of Jacobian (size and element) is required for each PV bus, this method does not require any update, hence, making it faster and more flexible to include a large number of DERs. From the various tests performed on the standard IEEE three-phase distribution network and their derivatives, it has been observed that the proposed approach is accurate, efficient, computationally less complex, and scalable.
A bibliometric analysis of clinical research on fracture-related infection

Background Infection following orthopaedic trauma surgery is increasingly recognized as one of the major research priorities with as primary goal, improving patient care. This increased interest has been anecdotally recognized through published research, research grants, and, finally, with the development of the fracture-related infection (FRI) consensus group. In 2017, the accepted consensus definition of FRI was published, which has been followed by consensus recommendations from both a surgical and medical perspective. A bibliometric analysis was performed to objectively describe the trends in published clinical research related to FRI. Methods The terms related to FRI were searched in the Web of Science database between 2000 and 2020. The characteristics of clinical research on FRI regarding the author, country, journal, institution, scientific output, top 100 most cited articles, and trend topics were analyzed using Bibliometrix and WPS Office. Results A total of 2597 records were eligible for inclusion in this bibliometric approach, with studies originating from 89 countries, including eight languages. The United States of America (USA) published the highest number of articles and citations. International collaborations were present between 72 countries, with the most active country being the USA. The most contributive institution was the University of California. The highest number of papers and citations were from the Injury-International Journal of the Care of the Injured and the Journal of Orthopaedic Trauma. The top 100 most cited articles were published in 27 different journals, with the number of citations ranging between 97 and 1004. The latest trend topics were related to the diagnosis of FRI. Conclusion The present bibliometric analysis shows the research characteristics and trends of FRI from multiple perspectives. The fact that there is an increasing number of studies being published on FRI shows the agreement among scientists and clinicians that standardization with respect to this topic is very important.
Hidden Markov Model Based Control Augmentation Design for a Class of Human-in-the-Loop Systems

Control augmentation can significantly boost the performance of systems with human-in-the-loop. However, the benefit of such designs has yet been fully realized because many parameters of human internal vehicle models are inaccurate. Here, a control augmentation framework is studied to assist human operators in controlling a system to precisely follow desired commands. There are two steps involved in this framework: (1) a Hidden Markov Model based estimator for unknown parameters in a human internal vehicle model; and (2) a regulator based on the identified human internal vehicle model to reduce tracking errors. A general form of the human internal vehicle model is applied to describe the operator’s understanding about the system dynamics. A recursive, closed-form solution is derived for a class of dynamical systems so that the computational cost can be significantly reduced. The algorithm is validated in a simulated, pilot-in-the-loop quadrotor scenario.