Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and Compliance Management System Using Computer Vision and Deep Learning

With the advancement in the Deep Learning and Computer Vision-based methodologies, the safety and productivity analysis of the construction entities (workers, equipment, and structures) on-site can potentially be improved. Construction industry is majorly the most dangerous sectors with respect to occupational safety and health because of the chaotic and dynamic environment at construction sites. The study of safety and compliance monitoring measures at construction by using the computer vision and deep learning-based approaches is the key focus of this paper. Safety and compliance are acclamatory to each other and are very essential for managing the safe conditions at construction. Manual monitoring of the compliant and unsafe conditions at the site is a challenging job and thus, a smart framework for automated monitoring is necessary for the assurance of a safe and healthy environment. A framework for safety and compliance management system is presented in this paper for enhancing the safer work culture along with the measures. This paper examines the prior literature on computer vision as per the safety and compliance constraints for (I) Understanding the existing SOTA (state-of-the-art) methods and their respective outcomes (II) Finding the challenges and limitations in the currently employed approaches and (III) Providing the potential directions for a future line of work. The purpose is to build a culture of safety using Computer Vision and Deep Learning.
3D optimal light distribution in brain tumors for photodynamic therapy

Photodynamic therapy is a treatment technique that takes advantage of the effects induced by the body itself, together with a photosensitizer, to destroy unwanted tumor volumes with high accuracy and low invasiveness. This study analyzes treatment volume by 3D optical distributions in a realistic way from MRI images. First of all a volumetric model of a real head is built from MRI images. Optical distributions generated by the source over the tissue are considered at different brain tumor stages, and with multitude of processes that occur within the volume to be treated. By means of Monte Carlo we can estimate the photonic density that is absorbed by the tissues, whose optical properties are previously collected. This application considers that a reasonable time has passed for the photosensitizer to have reached the area under study, and that there is a minimum concentration in adjoining areas during radiation exposure. With this approach it is possible to estimate the level of radiation exposure and the affected volume. This is very relevant due to the fact that, as the radiation increases, different areas with different energy densities appear. This makes it much more complicated to apply a certain known optimal radiation on the treatment volume. A non-optimal high radiation density would damage healthy tissue, while, on the contrary, a non-optimal low radiation would not bring unwanted tissue to necrosis or apoptosis for tumor destruction, generating recurrence. This tool could be of great interest in treatment planning.
Evaporation of aqueous solutions of organic acids through spread films of poly(diallyldimethylammonium chloride)/sodium dodecylsulfate complex

ABSTRACT The films with the extreme concentration of poly (diallyldimethylammonium chloride)/sodium dodecylsulfate complex spread to the surface of aqueous solutions of formic and acetic acids reduce the evaporation rate of liquids by 3–6% and demonstrate selective properties increasing water content in the vapor by 1–3 abs. %. The concentrations of solutions were up to 30 vol.%. The formation conditions of such films were determined by means of the Wilhelmy plate technique. Surface tension isotherms of aqueous solutions of acids were also obtained.
The polarization-dependent anisotropic Raman response of few-layer and bulk WTe2 under different excitation wavelengths

WTe2, an orthorhombic semimetal crystallized in Td phase, is a new transition metal dichalcogenide (TMD) that recently arouses great interest because of its properties of unsaturated giant magnetoresistance, Lifshitz transition and novel type II Weyl semimetal. Its covalently bonded W atoms form a zigzag W–W chain, which leads to the distinct in-plane anisotropy. In this paper, we investigate the polarization-dependent anisotropic Raman response of WTe2 with the thickness from monolayer to bulk under different excitation wavelengths, by rotating the incident laser polarization while fixing the sample and scattered polarization. The polar plots of the intensities for all detected modes exhibit two-lobed shape, while the main-axis orientations for different symmetric modes are different. These anisotropic results are first roughly fitted based on the Placzek approximated intensity, and then analyzed by the full quantum model considering the anisotropic electron–photon and anisotropic electron–phonon interactions. This work not only demonstrates the thickness and excitation wavelength dependence of the anisotropic Raman modes in WTe2, but also reveals the origin of the Raman anisotropy, which will be helpful for the research about anisotropic optical, electrical, and mechanical properties of TMDs as well as their device application.
Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.
Numerical Simulation of Double Diffusive Laminar Mixed Convection in a Horizontal Rotating Annulus Part (II): Effect of Prandtl number and buoyancy ratio

A numerical study of double-diffusive mixed convection within a horizontal rotating annulus has been investigated. The outer cylinder is fixed but the inner cylinder is considered to rotate in clockwise and anti-clockwise directions to introduce the forced convection effect. In addition, the solutal and thermal buoyancy forces are sustained by maintaining the inner and outer cylinder at uniform temperatures and concentrations but their values for the inner are higher than the outer. The flow is considered laminar regime under steady state conditions. The transport equations for the continuity, momentum, energy and mass transfer are solved using the finite volume technique. The considered domains in this investigation are: -15 ≤ N ≤ 15, 0.01 ≤ Ri ≤ 100 and 0.01 ≤ Pr ≤ 100. While the thermal Grashof number, Lewis number and the radius ratio are kept constant at values equal to104, 1 and 2 respectively. The effect of the selected parameters on the local and average Nusselt and Sherwood numbers are presented and studied. Finally, this investigation concerned with selection the best direction of the inner cylinder rotation to enhance both heat and mass transfer. A comparison was made with the published results and a good agreement was found.
Content Based Image Retrieval Using Deep Learning Convolutional Neural Network

Content-based image retrieval (CBIR) is a widely used method for image retrieval from large and unlabeled image collections. However, users are not satisfied with the traditional methods of retrieving information. Moreover the abundance of online networks for production and distribution, as well as the quantity of images accessible to consumers, continues to expand. Therefore, in many areas, permanent as well as widespread digital image processing takes place. Therefore, the rapid access to these large image databases as well as the extraction of identical images from this large set of images from a given image (Query) pose significant challenges as well as involves efficient techniques. A CBIR system’s efficiency depends fundamentally on the calculation of feature representation as well as similarity. For this purpose, they present a basic but powerful deep learning system focused on Convolutional Neural Networks (CNN) and composed of feature extraction and classification for fast image retrieval. We get some promising findings from many detailed observational studies for a number of CBIR tasks using image database, which reveals some valuable lessons for improving the efficiency of CBIR. CBIR systems allow another image dataset to locate related images to such a query image. The search per picture function of Google search has to be the most popular CBIR method.
All-Fiber Hollow Bessel-Like Beam for Large-Size Particle Trap

A 32 μm large-size microparticle is trapped by an all-fiber hollow Bessel-like beam. In order to realize the proposed high-order Bessel-like beam, we fabricate the optical fiber probe by splicing an annular core fiber with a step-index multimode fiber. We grind the fiber tip into a truncated cone shape to converge the hollow Bessel-like beam to obtain a large focal area. Simulation and experimental results show that the trapping field introduced by the Bessel-like beam provides a relatively strong optical force and has the capability of 3-D capture of a large particle (whose diameter is 32 μm). These highly integrated fiber tweezers provide hollow Bessel-like beams with strong optical force and large trapping area, which can be promisingly applied in biomedical and integrated optical research.
L
 1
 -Multiscale Galerkin’s Scheme with Multilevel Augmentation Algorithm for Solving Time Fractional Burgers’ Equation

<jats:p>In this paper, we consider the initial boundary value problem of the time fractional Burgers equation. A fully discrete scheme is proposed for the time fractional nonlinear Burgers equation with time discretized by <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M2"> <mi>L</mi> <mn>1</mn> </math> </jats:inline-formula>-type formula and space discretized by the multiscale Galerkin method. The optimal convergence orders reach <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M3"> <mi mathvariant="script">O</mi> <mfenced open="(" close=")"> <mrow> <msup> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>2</mn> <mo>−</mo> <mi>α</mi> </mrow> </msup> <mo>+</mo> <msup> <mrow> <mi>h</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msup> </mrow> </mfenced> </math> </jats:inline-formula> in the <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M4"> <msup> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> </jats:inline-formula> norm and <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M5"> <mi mathvariant="script">O</mi> <mfenced open="(" close=")"> <mrow> <msup> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>2</mn> <mo>−</mo> <mi>α</mi> </mrow> </msup> <mo>+</mo> <msup> <mrow> <mi>h</mi> </mrow> <mrow> <mi>r</mi> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </mfenced> </math> </jats:inline-formula> in the <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M6"> <msup> <mrow> <mi>H</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msup> </math> </jats:inline-formula> norm, respectively, in which <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M7"> <mi>τ</mi> </math> </jats:inline-formula> is the time step size, <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M8"> <mi>h</mi> </math> </jats:inline-formula> is the space step size, and <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M9"> <mi>r</mi> </math> </jats:inline-formula> is the order of piecewise polynomial space. Then, a fast multilevel augmentation method (MAM) is developed for solving the nonlinear algebraic equations resulting from the fully discrete scheme at each time step. We show that the MAM preserves the optimal convergence orders, and the computational cost is greatly reduced. Numerical experiments are presented to verify the theoretical analysis, and comparisons between MAM and Newton’s method show the efficiency of our algorithm. </jats:p>
Cloud-Based Sentiment Analysis for Measuring Customer Satisfaction in the Moroccan Banking Sector Using Naïve Bayes and Stanford NLP

In a world where every day we produce 2.5 quintillion bytes of data, sentiment analysis has been a key for making sense of that data. However, to process huge text data in real-time requires building a data processing pipeline in order to minimize the latency to process data streams. In this paper, we explain and evaluate our proposed real-time customer’ sentiment analysis pipeline on the Moroccan banking sector through data from the web and social network using open-source big data tools such as data ingestion using Apache Kafka, In-memory data processing using Apache Spark, Apache HBase for storing tweets and the satisfaction indicator, and ElasticSearch and Kibana for visualization then NodeJS for building a web application. The performance evaluation of Naïve Bayesian model show that for French Tweets the accuracy has reached 76.19% while for English Tweets the result was unsatisfactory and the resulting accuracy is 56%. To remedy this problem, we used the Stanford core NLP which, for English Tweets, reaches a precision of 80.7%.
Wheat Yield in a Tolerant Winter Wheat Line Infested by Hessian Fly (Mayetiola destructor)

The Hessian fly, Mayetiola destructor, is a harmful pest of wheat (Triticum aestivum). Pioneer variety 25R78 is putatively tolerant, meaning that the plant can survive successful Hessian fly infestation with reduced growth effects. To understand if Hessian fly-tolerance in wheat results in reduced yield effects and to analyze the economic feasibility of tolerant wheat as a Hessian fly control method, this study focused on analyzing the effect of infestation on tolerant wheat yield. This study analyzed tolerant Pioneer variety 25R78, resistant Pioneer brand variety 25R32, and susceptible Pioneer brand variety 25R47 through harvest. Treated plants were infested using a plastic cover and allowing 1 - 2 female flies to lay eggs for two hours. We measured head, fertile head number and tiller number. Seeds were analyzed by measuring total seed number and weight, as well as average seed number and weight. Tolerant and resistant plants showed no significant effects on yield in comparison to susceptible wheat. The infested tolerant plants were comparable in yield to infested resistant plants. Therefore, we propose that tolerance incorporated into wheat varieties will lower selection pressure on Hessian fly populations and increase the durability of these wheat lines.
Automated Classification of Radiographic Positioning of Hand X-Rays Using a Deep Neural Network

Background: Hand X-rays are ordered in outpatient, inpatient, and emergency settings, the results of which are often initially interpreted by non-radiology trained health care providers. There may be utility in automating upper extremity X-ray analysis to aid with rapid initial analysis. Deep neural networks have been effective in several medical imaging analysis applications. The purpose of this work was to apply a deep learning framework to automatically classify the radiographic positioning of hand X-rays. Methods: A 152-layer deep neural network was trained using the musculoskeletal radiographs data set. This data set contains 6003 hand X-rays. The data set was filtered to remove pediatric X-rays and atypical views. The X-rays were all labeled as either posteroanterior (PA), lateral, or oblique views. A subset of images was set aside for model validation and testing. Data set augmentation was performed, including horizontal and vertical flips, rotations, as well as modifications in image brightness and contrast. The model was evaluated, and performance was reported as a confusion matrix from which accuracy, precision, sensitivity, and specificity were calculated. Results: The augmented training data set consisted of 80 672 images. Their distribution was 38% PA, 35% lateral, and 27% oblique projections. When evaluated on the test data set, the model performed with overall 96.0% accuracy, 93.6% precision, 93.6% sensitivity, and 97.1% specificity. Conclusions: Radiographic positioning of hand X-rays can be effectively classified by a deep neural network. Further work will be performed on localization of abnormalities, automated assessment of standard radiographic measures and eventually on computer-aided diagnosis and management guidance of skeletal pathology.
Effects of External Public Debt Cancellation on the Economic Growth of the Republic Of Congo

This study, through temporal data and the ARDL process, examined the effects of the cancellation of external public debt on economic growth in the Republic of Congo over the 1990 to 2016 period. From this review, it appears that debt cancellation has a positive impact on economic growth over the short-term and a negative impact over the long term. Beyond these results, we believe that additional research is needed to examine the channels through which external public debt cancellation influence economic growth in the Republic of Congo.
RF and Crosstalk Analysis of Copper and MLGNR Interconnects Using Different Repeaters in Sub-10 nm Regime

This paper presents RF and crosstalk analysis of Copper (Cu) and multi-layer Graphene nanoribbon (MLGNR) based interconnects using Fin field-effect transistor (FinFET) and virtual-source carbon nanotube field effect transistor (CNFET) based repeater insertions in sub-10 nm regime. The SPICE based analysis utilizes an accurate π-type equivalent single conductor (ESC) model for mutually coupled interconnects at 7 nm technology node. The transfer function and 3-dB bandwidth results of lithium-doped MLGNRs offer many fold improved RF performance than Cu. The out-of-phase crosstalk induced (OPXT) delay results with FinFET repeaters demonstrate 27.54 and 67.6 % reductions for pristine and lithium-doped MLGNRs as compared to Cu, whereas CNFET repeaters demonstrate 20.48 and 81.88 % reductions at interconnect length of 1000 µm. The peak far-end crosstalk (FEXT) noise voltage results demonstrate 86.03 and 62.5 % using FinFET repeaters and 88.14 and 69.9 % reductions using CNFET repeaters for pristine and Li-doped MLGNRs than Cu at 1000 µm length. Further, the energy-delay-product (EDP) results demonstrate 59.7 and 97 % reductions using FinFET repeaters for pristine and Li-doped MLGNRs than Cu at length of 1000 µm. The EDP results using CNFET repeaters exhibit 34 % degradations for pristine-MLGNRs than Cu while Li-MLGNR exhibit 98.61% reductions than Cu at length of 1000 µm.
Genome Mining and Heterologous Expression of Lipoamides and Amicoumacins From Compost-Associated Bacillus subtilis fmb60

Bacillus subtilis fmb60, which has broad-spectrum antimicrobial activities was isolated from plant straw compost. A hybrid NRPS/PKS cluster was screened from the genome. Sixteen secondary metabolites produced by the gene cluster were isolated and identified using LC-HRMS and NMR. Three lipoamides D–F (1-3) and two amicoumacin derivatives, amicoumacins D, E (4, 5), were identified, and are reported here for the first time. Lipoamides D–F exhibited strong antibacterial activities against harmful foodborne bacteria, with the MIC ranging from 6.25 to 25 μg/mL. Amicoumacin E scavenged 38.8% of ABTS radicals at 1 mg/mL. Direct cloning and heterologous expression of the NRPS/PKS and ace gene cluster identified its importance for the biosynthesis of amicoumacins. This study demonstrated that there is a high potential for biocontrol utilization of B. subtilis fmb60, and genome mining for clusters of secondary metabolites of B. subtilis fmb60 has revealed a greater biosynthetic potential for the production of novel natural products than previously anticipated.
Preparing I-Kiribati for the future: probing the English language research-policy nexus in Kiribati education in the South Pacific

ABSTRACT Histories of colonization conjointly with the current economic global climate have enabled English to become a dominant global language. The desire for citizens to attain English language proficiency on a nation-wide level is a common pursuit in developing countries, including the island nations of the South Pacific where English competency is viewed as invaluable to education and associated with promising futures. Despite the threat to their own indigenous languages, in Education, a strong emphasis upon English permeates the South Pacific islands through government agendas, policies, and into teaching practice. Confronted with preparing for imminent migration due to rising sea levels, the Kiribati government is striving to prepare its citizens for impending diaspora. This article reports a study of the research-policy nexus in English language learning in schools, in the context of the Micronesian nation of Kiribati. Critical analysis of two government English teaching policies that shape curriculum, identified that the research-policy nexus lacked sufficient clarity; characterized with undefined terms, some contradictory information and an unclear research base. Implications for improving Education policies, Teacher Education, and classroom English teaching practices are provided and the argument made that further research is warranted to strengthen English language learning policies in the South Pacific.
Changes of Köppen–Trewartha climate types in the Tibetan Plateau during the mid-Holocene, present-day, and the future based on high-resolution datasets

Tibetan Plateau (TP) is strongly impacted by accelerated anthropogenic warming that already exceeds natural variability. However, the spatial differences in climate and ecosystem response in TP between anthropogenic and natural forcings are unclear. Here, the Köppen–Trewartha climate classification is applied to examine the shifts in terrestrial climate regimes and vegetation distribution in TP by analyzing the WorldClim high-resolution downscaled climate dataset for the mid-Holocene (MH, ∼6 cal ka BP), the present-day (PD, 1960–1990), and in the future (2050s) under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios. The results show that the polar climate and the dry climate occupied more than 70% of TP area in the three periods. The MH, dominated by the natural forcing, is featured as having less distribution of dry climate than the PD. The anthropogenic change will promote a transition from a cold climate type to a warmer climate type in the 2050s. The precipitation change is responsible for the differences in climate types between the MH and PD, while warming dominates the evolution of climate types across TP in the 2050s. Moreover, vegetation types can be represented by major climate types in a large proportion of TP. In particular, polar climate regions correspond well to the areas of meadows, and the dry climate regions correspond to the steppes and deserts areas. The large changes in the climate regimes of TP suggest an extensive (shrinking) area of meadows (steppes) under natural forcing and a shrinking area of meadows under anthropogenic warming.