Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of computer games for teaching and learning cybersecurity in higher education institutions

In higher education, teaching cybersecurity concepts to students such as encryption-based security protocols is a challenging task, but it is fundamental for personal and national security. One of the reasons for this is related to the inadequate mathematical knowledge of students, which limits their understanding of the cryptographic algorithms underlying the protocols. Therefore, higher education institutions are seeking out engaging and effective strategies for developing students’ skills in this area. The aim of this research is to explore the use and potential effectiveness of game-based learning to assist in the teaching and learning of cybersecurity concepts in higher education. It contributes to the literature by raising public interest in cybersecurity and helping learners to understand suitable and safe behaviors online. It also offers a systematic overview of game-based learning tools that have been used in previous studies to improve students’ understanding of cryptographic algorithms. This research also presents a framework for the effective teaching of cryptography in higher education, relying on animation and gamification.
A Novel Recombinant Chimeric Proteinous Bio-adhesive Consisting of Mussel Foot Protein 3, 5 and Curli Proteins GvpA and CsgA Expressed in Pichia Pastoris

Despite various efforts to produce strong recombinant bio-adhesive proteins for medical purposes, efficient production of a safe and feasible bio-glue is not yet a commercial reality due to the weak properties or low expression level. Here, a feasible expression system has been developed to produce strong recombinant fusion protein using Mfp3 and Mfp5 along with two curli proteins, GvpA and CsgA expressed under the control of alcohol oxidase (AOX1) promoter for high-level production in P. pastoris using pPICZα vector. Purified chimeric proteins were first evaluated using western blotting and its remaining dopa were measured in the modified proteins by NBT assay. We further elucidated mechanistic properties of obtained adhesive protein assembly in various pH based on its different subunits using atomic force microscopy (AFM) when adsorbed onto the mica surface. We found that combinational structural features of subunits and post-translational changes during expression in yeast host have led to potent adherence due to higher dopa residues majorly in acidic condition and tetrad complex that is higher than that of earlier reports in prokaryotic systems. We believe that our obtained chimeric protein resulted through fusion of GvpA and CsgA proteins with dopa-containing Mfp proteins expressed in the methylotrophic yeast, P. pastoris, not only presents a candidate for future biomedical applications, but also provides novel biological clues using for high-performance bioinspired biomaterials designation.
Carbon emissions, diverse energy usage and economic growth in south africa: Investigating existence of the environmental kuznets curve (EKC)

This article examined the relationships involving carbon emissions, economic growth and energy consumption by employing the environmental Kuznets curve (EKC) in South Africa from 1980 to 2014. The auto regressive distributed lag approach and Johansen cointegration tests proved that the variables are cointegrated. The article findings show that combined (total energy consumption) and hydrocarbon gas and petroleum consumption demonstrates evidence of EKC in the long‐run. Other separated data (primary coal, secondary coal, and electricity consumption) show no evidence of the EKC in the long‐run. Primary coal, secondary coal, electricity and hydrocarbon gas consumption develop positive and statistically significant relationships with carbon emissions in the long‐run but the case of total energy and petroleum consumption was negative and statistically significant. The short‐run results illustrate that combined (total energy consumption) and hydrocarbon gas consumption indicate evidence of EKC. Other separated data (primary coal, secondary coal, electricity, and petroleum consumption) show no evidence of the EKC in the short‐run. Short‐run results also indicated that total energy, primary coal, secondary coal, and electricity consumption report positive and statistically significant relationship with carbon emissions but hydrocarbon gas and petroleum consumption indicate positive but insignificant associations. Granger causality test based on vector error correction method (VECM) are also presented to ascertain causality. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 30–46, 2019
A Psychology-Driven Computational Analysis of Political Interviews

Can an interviewer inﬂuence the cooperativeness of an intervie-wee? The role of an interviewer in actualising a successful inter-view is an active ﬁeld of social psychological research. A large-scale analysis of interviews, however, typically involves time-exorbitant manual tasks and considerable human effort. Despite recent advances in computational ﬁelds, many automated meth-ods continue to rely on manually labelled training data to establish ground-truth. This reliance obscures explainability and hinders the mobility of analysis between applications. In this work, we introduce a cross-disciplinary approach to analysing interviewer efﬁcacy. We suggest computational success measures as a transparent, automated, and reproducible alternative for pre-labelled data. We validate these measures with a small-scale study with human-responders. To study the interviewer’s inﬂuence on the interviewee we utilise features informed by social psychological theory to predict interview quality based on the interviewer’s linguistic behaviour. Our psychologically informed model signiﬁcantly outperforms a bag-of-words model, demonstrating the strength of a cross-disciplinary approach toward the analysis of conversational data at scale.
Chitosan/Graphene Oxide Composite Films and Their Biomedical and Drug Delivery Applications: A Review

The healing of wounds is still a challenging clinical problem for which an efficient and fast treatment is needed. Therefore, recent studies have created a new generation of wound dressings that can accelerate the wound healing process with minimal side effects. Chitosan, a natural biopolymer, is an attractive candidate for preparing biocompatible dressings. The biodegradability, non-toxicity, and antibacterial activities of chitosan have made it a promising biopolymer for treating wounds. Graphene oxide has also been considered by researchers as a non-toxic, inexpensive, and biocompatible material for wound healing applications. This review paper discusses the potential use of chitosan/graphene oxide composite films and their application in wound dressing and drug delivery systems.
Educational Software based on Augmented Reality: A Systematic Literature Review

Augmented Reality (AR) is a technology that has been seen to have potential in the educational sector for some years due to the tremendous positive impact that it could generate when used in teaching and learning processes. However, to achieve this, it is essential to consider educational and technological aspects to develop appropriate applications that provide the student and the teacher with innovative teaching methods. For this reason, this study presents a Systematic Review of Literature (SRL) under the protocol proposed by Kitchenham & Stuart (2007) to collect relevant information that provides new research opportunities. This review focuses on augmented reality's technological aspect since the authors previously conducted an initial study considering the educational aspect. As part of this SRL, four sources of information were consulted: IEEE, ACM, Springer, and Elsevier, from which forty-two primary studies were analyzed that describe educational initiatives that incorporate Augmented Reality technology as part of their instructional design in the last decade. Finally, for this study's purposes, information is reported about the devices and mechanisms of interaction, tools and programming languages, recognition models, and the scarcity of methodologies used to develop educational applications with Augmented Reality is identified. In this way, an opportunity arises for research and innovation in the field of Software Engineering Methods and Models.
Fabrication and characterization of an acellular annulus fibrosus scaffold with aligned porous construct for tissue engineering

Scaffolds mimicking the native annulus fibrosus (AF) extracellular matrix (ECM) structure are crucial to guide the seeding cells to regenerate aligned tissue, while fabricating such a scaffold by synthetic material is challengeable. Native acellular scaffolds derived from AF tissue certainly possess the advantages of natural structure and composition. Based on previous studies, we modified decellularization procedure and especially compared two drying methods, including gradient dehydration and freeze-drying. The decellularization process can effectively remove the host cells and antigens such as α-Gal, while maintaining the original ECM including GAG and collagen I. Compared with gradient dehydration, freeze-drying not only rendered the decellularized scaffold in dry state for storage but also gave the scaffold more aligned porous structure and hydrophilicity. And, the acellular porous scaffold manifested better capacity of supporting cell ingrowth when seeded human bone marrow mesenchymal stem cells (hBMSCs) or implanted in vivo. Furthermore, this optimized freeze-dried scaffold showed similar mechanical elastic modulus as native AF and demonstrated rare inflammatory granuloma and immune rejection as observed in HE staining and immunohistochemistry staining (IHC) of CD8 and MAC387 epitopes when implanted subcutaneously in vivo. To sum up, through our decellularization and freeze-drying procedure, an aligned porous three-dimensional scaffold derived from the natural AF ECM was successfully fabricated with good retention of ECM components and benign biocompatibility. It will be a promising scaffold for AF tissue engineering.
Reducing Construction Logistics Costs and Embodied Carbon With Ccc and Kitting: A Case Study

Supply chain management was originally proposed to improve construction sites performances, nonetheless this simultaneously presents a potential solution for reducing the carbon footprint of the construction sector. Therefore, both environmental and cost impacts must be considered in order to raise the sector’s awareness and foster change towards more sustainable practices. The purpose of this research is to evaluate the applicability of such a model for the supply chain by implementing Just-in-Time deliveries using kitting and a Construction Consolidation Centre managed by a Third-Party Logistics operator on a real-life construction project. Data was collected on actual tasks durations, time losses for site’s workers and deliveries, and used as input to estimate the corresponding values with a traditional logistics and to model impact on both direct and indirect costs for comparison and discussion. Findings indicate that this new logistics paradigm can lead to productivity improvements and overall reduction in transportation needs. These have an implicit positive impact on both the environment and cost savings, which are calculated and discussed. Based on these results, it is argued that the adoption of this model contributes to a lean-green deal by demonstrating the positive impact of Lean Construction techniques towards better supply chain integration.
Unsupervised Learning for Salient Object Detection via Minimization of Bilinear Factor Matrix Norm

Saliency detection is an important but challenging task in the study of computer vision. In this article, we develop a new unsupervised learning approach for the saliency detection by an intrinsic regularization model, in which the Schatten-2/3 norm is integrated with the nonconvex sparse <inline-formula> <tex-math notation="LaTeX">${l_{2/3}}$ </tex-math></inline-formula> norm. The <inline-formula> <tex-math notation="LaTeX">${l_{2/3}}$ </tex-math></inline-formula>-norm is shown to be capable of detecting consistent values among sparse foreground by using image geometrical structure and feature similarity, while the Schatten-2/3 norm can capture the lower rank of background by matrix factorization. To improve effective performance of separation for Schatten-2/3-norm and <inline-formula> <tex-math notation="LaTeX">${l_{2/3}}$ </tex-math></inline-formula>-norm, a Laplacian regularization is adopted to the foreground for the smoothness. The proposed model essentially converts the required nonconvex optimization problem into the convex one, conducted by splitting the objective function based on singular value decomposition on one much smaller factor matrix and then optimized by using the alternating direction method of the multiplier. The convergence of the proposed algorithm is discussed in detail. Extensive experiments on three benchmark datasets demonstrate that our unsupervised learning approach is very competitive and appears to be more consistent across various salient objects than the current existing approaches.
A Thousand Words are Worth More Than One Recording: Word-Embedding Based Speaker Change Detection

Speaker Change Detection (SCD) is the task of segmenting an input audio-recording according to speaker interchanges. This task is essential for many applications, such as automatic voice transcription or Speaker Diarization (SD). This paper focuses on the essential task of audio segmentation and suggests a word-embedding-based solution for the SCD problem. More-over, we show how to use our approach in order to outperform voice-based solutions for the SD problem. We empirically show that our method can accurately identify the speaker-turns in an audio-recording with 82.12% and 89.02% success in the Recall and F1-score measures.
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-Pooling Strategy and Window-Based Attention

In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
Mechanisms of chemotherapy‐induced muscle wasting in mice with cancer cachexia

Cachexia is a debilitating complication of cancer characterized by progressive wasting and weakness of skeletal muscles that reduces quality of life and can compromise survival. Many anticancer treatments, such as chemotherapy, also cause muscle wasting, which impairs the response to treatment. Given that many cancer patients present with cachexia at the initiation of treatment, we investigated whether cachectic mice were susceptible to chemotherapy‐induced muscle wasting and to investigate contributing mechanisms, including the dysregulation of microRNAs (miRs).
Charge transport in the assemblies of magnetic, non-magnetic and spin-cross over nano-structures

La comprehension des proprietes de transport de charge des nanostructures metalliques et magnetiques est tres importante pour le developpement et la miniaturisation des dispositifs fonctionnels modernes. En particulier, les nanostructures synthetisees chimiquement sont interessant car elles permettent de mieux controler leur forme et leur taille, ce qui peut etre utilise pour ajuster leurs proprietes de transport de charge. L'objectif de cette these est d'etudier les aspects differents des proprietes de transport de charge qui resultent de la petite taille et de la nature magnetique de differents types de nanostructures comprenant des nanoparticules de Pt (1,3-3 nm), des particules magnetiques FeCo (⁓10 nm), et complexe de coordination a base de triazole Fe (II). Pour preciser davantage, des phenomenes tels que le blocage de Coulomb, la magnetoresistance tunnel et la transition de spin seront mis en evidence. En fonction de la propriete souhaitee, ces nanostructures peuvent etre exploitees pour leurs applications dans divers capteurs, actionneurs et dispositifs spintroniques, etc.
Techniques for in vivo assessment of myopathies in broiler chicken breasts using a biopsy as a support tool

ABSTRACT An experiment was conducted to validate non-invasive evaluation techniques as in vivo evaluation tools for the myopathies wooden breast and white striping that affect broilers, using biopsy as a support tool. It evaluated 30 Cobb 500® broiler chickens in a completely randomized design consisting of two sexes (males and females) and 15 replications per treatment. At 14, 21, 35, 42 and 49 days, the surface temperature of the birds’ breasts was recorded using infrared thermography, analysis of fillet depth, and echogenicity by ultrasound imaging. All broiler chickens were sent for biopsy at 21, 35 and 42 days to collect a fragment of the pectoral muscle for descriptive morphological analyses of histological lesions and muscle fibre morphometry. Males had higher echogenicity values at 14, 21 and 42 days. There was no influence of bird sex on the occurrence of the wooden breast and white striping myopathies at 49 days of age in broilers selected and submitted to the biopsy protocol. There was a general trend of decreasing surface temperature for both sexes according to their age. Muscles with less area occupied with fibres had a lower surface temperature. It was concluded that ultrasonography with a 3.5 MHz transducer detects muscle changes after 28 days of age, which is consistent with the myopathic lesions studied. Infrared thermography is a potential method for detecting changes in breast temperature indicating myopathic lesions. A biopsy can be used as an auxiliary tool in the study of myopathies in broiler chicken breasts. RESEARCH HIGHLIGHTS Detection of changes in the breast muscle after 28 days of age with ultrasonography. Changes in the surface temperature range of pectoral muscle with infrared thermography. Use of biopsy as a tool for early diagnostic evaluation in broiler myopathies.
Merging of ozone profiles from SCIAMACHY, OMPS and SAGE II
observations to study stratospheric ozone changes

Abstract. This paper presents vertically and zonally resolved merged ozone time series from limb measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and the Ozone Mapping and Profiler Suite (OMPS). In addition, we present the merging of the latter two data sets with zonally averaged profiles from the Stratospheric Aerosol and Gas Experiment (SAGE) II. The retrieval of ozone profiles from SCIAMACHY and OMPS is performed at the University of Bremen. Within the merging procedure of these two time series we use data from the Microwave Limb Sounder (MLS) as a transfer function and we follow two approaches: (1) a standard method involving the calculation of deseasonalized anomalies and (2) a plain-debiasing approach, generally not considered in previous similar studies, which preserves the seasonal cycles of each instrument. We find a good correlation and no significant drifts between the merged and MLS time series. Using the merged data set, we apply a multivariate regression analysis to study ozone changes over the 2003–2018 period in the 20–50 km vertical range. Exploiting the high horizontal sampling of the instruments, we investigate not only the zonally averaged field but also the longitudinally resolved long-term ozone variations, finding a remarkable variability, especially at mid- and high-latitudes. Significant positive linear trends of about 2–4 % per decade were identified in the upper stratosphere between 38 and 45 km at mid-latitudes. This is in agreement with the predicted recovery of upper stratospheric ozone, which is attributed both to the adoption of measures to limit the release of halogen-containing ozone-depleting substances included in the Montreal protocol and to the increasing concentration of greenhouse gases. In the tropical stratosphere below 25 km negative but non-significant trends were found. We compare our results with similar previous studies and with short-term trends calculated over the SCIAMACHY period: while a general agreement is found, some discrepancies are seen in the tropical mid-stratosphere. Regarding the merging of SAGE II with SCIAMACHY and OMPS, zonal mean anomalies are taken into consideration and ozone trends after and before 1997 are shown. Negative trends above 30 km are found for the 1985–1997 period, with a peak of −6 % per decade at mid-latitudes, in agreement with previous studies. The increase of ozone concentration in the upper stratosphere is confirmed considering the 1998–2018 period. Trends in the middle and lower tropical stratosphere are found to be non-significant.
Genetic Diversity and Population Structure of Japanese Plum-Type (Hybrids of P. salicina) Accessions Assessed by SSR Markers

Japanese plum (Prunus salicina Lindl.) is widely distributed in temperate zones across the world. Since its introduction to USA in the late 19th century, this species has been hybridized with up to 15 different diploid Prunus species. This high level of introgression has resulted in a wide range of traits and agronomic behaviors among currently grown cultivars. In this work, 161 Japanese plum-type accessions were genotyped using a set of eight Simple Sequence Repeats (SSR) markers to assess the current genetic diversity and population structure. A total of 104 alleles were detected, with an average of 13 alleles per locus. The overall Polymorphic Informative Content (PIC) value of SSR markers was 0.75, which indicates that these SSR markers are highly polymorphic. The Unweighted Pair Group Method with Arithmetic (UPGMA) dendrogram and the seven groups inferred by Discriminant Analysis of Principal Components (DAPC) revealed a strong correlation of the population structure to the parentage background of the accessions, supported by a moderate but highly significant genetic differentiation. The results reported herein provide useful information for breeders and for the preservation of germplasm resources.
Deep Spectral-Cepstral Fusion for Shouted and Normal Speech Classification

Discrimination between shouted and normal speech is crucial in audio surveillance and monitoring. Although deep neural networks are used in recent methods, traditional low-level speech features are applied, such as mel-frequency cepstral coefﬁcients and the mel spectrum. This paper presents a deep spectral– cepstral fusion approach that learns descriptive features for target classiﬁcation from high-dimensional spectrograms and cepstrograms. We compare the following three types of architectures as base networks: convolutional neural networks (CNNs), gated recurrent unit (GRU) networks, and their combination (CNN–GRU). Using a corpus comprising real shouts and speech, we present a comprehensive comparison with conventional methods to verify the effectiveness of the proposed feature learning method. The results of experiments conducted in various noisy environments demonstrate that the CNN–GRU based on our spectral–cepstral features achieves better classiﬁ-cation performance than single feature-based networks. This ﬁnding suggests the effectiveness of using high-dimensional sources for speech-type recognition in sound event detection.