Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Bioprinting Using a Coaxial Needle with Viscous Inks in Bone Tissue Engineering - An In vitro Study

Introduction: Vascularized autologous tissue grafts are considered “gold standard” for the management of larger bony defects in the craniomaxillofacial area. This modality does however carry limitations, such as the absolute requirement for healthy donor tissues and recipient vessels. In addition, the significant morbidity of large bone graft is deterrent to fibula bone flap use. Therefore, less morbid strategies would be beneficial. The purpose of this study was to develop a printing method to manufacture scaffold structure with viable stem cells. Materials and Methods: In total, three different combinations of ground beta tri-calcium phosphate and CELLINK (bioinks) were printed with a nozzle to identify a suitable bioink for three-dimensional printing. Subsequently, a coaxial needle, with three different nozzle gauge combinations, was evaluated for printing of the bioinks. Scaffold structures (grids) were then printed alone and with additional adipose stem cells before being transferred into an active medium and incubated overnight. Following incubation, grid stability was evaluated by assessing the degree of maintained grid outline, and cell viability was determined using the live/dead cell assay. Results: Among the three evaluated combinations of bioinks, two resulted in good printability for bioprinting. Adequate printing was obtained with two out of the three nozzle gauge combinations tested. However, due to the smaller total opening, one combination revealed a better stability. Intact grids with maintained stability were obtained using Ink B23 and Ink B42, and approximately 80% of the printed stem cells were viable following 24 hours. Discussion: Using a coaxial needle enables printing of a stable scaffold with viable stem cells. Furthermore, cell viability is maintained after the bioprinting process.
Management of Mild Brain Trauma in the Elderly: Literature Review

Purpose: The world population is aging. As direct consequence, geriatric trauma is increasing both in absolute number and in the proportion of annual admissions causing a challenge for the health-care system worldwide. The aim of this review is to delineate the specific and practice rules for the management of mild brain trauma in the elderly. Methods: Systematic review of the last 15 years literature on mild traumatic brain injury (nTBI) in elderly patients. Results: A total of 68 articles met all eligibility criteria and were selected for the systematic review. We collected 29% high-quality studies and 71% low-quality studies. Conclusion: Clinical advices for a comprehensive management are provided. Current outcome data from mTBIs in the elderly show a condition that cannot be sustained in the future by families, society, and health-care systems. There is a strong need for more research on geriatric mild brain trauma addressed to prevent falls, to reduce the impact of polypharmacy, and to define specific management strategies.
Strategic Regulation of Empathy

Empathy is an integral part of socioemotional well-being, but recent research has highlighted some of its downsides. Here we examine literature that establishes when, how much, and what aspects of empathy promote specific outcomes. After reviewing a theoretical framework that characterizes empathy as a suite of separable components, we examine evidence showing how dissociations of these components affect important socioemotional outcomes and describe emerging evidence suggesting that these components can be independently and deliberately regulated. Finally, we advocate for an approach to a multicomponent view of empathy that accounts for the interrelations among components. This perspective advances scientific conceptualization of empathy and offers suggestions for tailoring empathy to help people realize their social, emotional, and occupational goals.
Symbolic Protein Data Base

The report describes the process of selecting and transforming protein data with known 3D structures into Symbolic Protein Data Base (SPDB). SPDB is a relational database, used to analyze properties of protein structures and as a source of examples to be used by machine learning. The report gives a detailed structure of SPDB, and some preliminary analysis results
Paper title : Key characteristics for designing a supply chain performance measurement system

Purpose: The paper aims to conduct a review of the literature that gives insight into design elements for constructing a supply chain performance measurement (SCPM) system. Design/methodology/approach: A systematic review of published research on SCPM systems and frameworks over the last two decades was conducted with the purpose of categorising key functions of SCPM systems by providing an insight into the design, functionality, implementation and practical implications of SCPM systems. Findings: The review revealed a set of functions governing the SCPM system that have not been fully explored in previous research: the process focus, prioritisation, integration and causality functions of a SCPM system. A relationship between two or more functions can be created to include more functionality based on the needs of the company to create a comprehensive performance measurement system. Research limitations/implications: The paper concludes with a conceptual framework to guide future research in the area of designing a SCPM system and define the main aspects that should be considered when developing a SCPM system. Practical implications: The paper brings a new dimension to the SCPM research by identifying the main functions of SCPM systems that could benefit practitioners to set up a SCPM system relevant to its intended function. The paper presents multiple potential stages of merging different functions in one SCPM system. Based on the company's needs and context, the functionality of the SCPM system can be designed at four levels creating ten possible scenarios when designing a company's SCPM system. Originality/value: The paper integrated the literature and findings of 269 research papers of the last two decades, up on which a conceptual framework was developed as a guide for constructing an effective SCPM system.
Mating-Defective ste Mutations Are Suppressed by Cell Division Cycle Start Mutations in Saccharomyces cerevisiae

Temperature-sensitive mutants which arrest in the Gl phase of the cell cycle have been described for the yeast Saccharomyces cerevisiae. One class of these mutants (carrying cdc28, cdc36, cdc37, or cdc39) forms a shmoo morphology at restrictive temperature, characteristic of mating pheromone-arrested wild-type cells. Therefore, one hypothesis to explain the control of cell division by mating factors states that mating pheromones arrest wild-type cells by inactivating one or more of these CDC gene products. A class of mutants (carrying ste4, steS, ste7, stell, or stel2) which is insensitive to mating pheromone and sterile has also been described. One possible function of the STE gene products is the inactivation of the CDC gene products in the presence of a mating pheromone. A model incorporating these two hypotheses predicts that such STE gene products will not be required for mating in strains carrying an appropriate cdc lesion. This prediction was tested by assaying the mating abilities of double mutants for all of the pairwise combinations of cdc and ste mutations. Lesions in either cdc36 or cdc39 suppressed the mating defect due to ste4 and ste5. Allele specificity was observed in the suppression of both ste4 and ste5. The results indicate that the CDC36, CDC39, STE4, and STE5 gene products interact functionally or physically or both in the regulation of cell division mediated by the presence or absence of mating pheromones. The cdc36 and cdc39 mutations did not suppress ste7, stell, or stel2. Lesions in cdc28 or cdc37 did not suppress any of the ste mutations. Other models of CDC and STE gene action which predicted that some of the cdc and ste mutations would be alleles of the same locus were tested. None of the cdc mutations was allelic to the ste mutations and, therefore, these models were eliminated.
Magnetron sputter coating of microspherical substrates

The fabrication of laser fusion targets requires the deposition of various metal and dielectric layers on microspherical substrates (100–500 μm diameter). The continuous motion for uniformly coating these spherical substrates has been accomplished for rf sputtering applications. In one approach, the spheres being coated are bounced and laterally swept across a table as the frequency of vibration of the table is changed continously. In another approach, the sputtering gas is also a levitation gas. The argon is fed through a collimated hole structure and keeps the microsphere both levitated and rotating during the sputter deposition. The uniformity and surface quality of the deposited films obtained by these two techniques are compared.
EEG-based Authentication With 1D-Convolutional Long Short-Term Memory Neural Networks

—Electroencephalographic signals (EEG) have widely used in medical applications, yet the use of the signals as biometric identiﬁer has only gained interests in the last few years. The advantage of EEG-based biometric recognition lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. A novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based biometric identiﬁcation is proposed in this paper. The complementarity of CNNs and LSTMs largely improves the performance of the identiﬁcation by removing variation of the input, facilitating the temporal modeling and transforming the features into the high dimensional space which makes the classiﬁcation more effective respectively. We investigate the architecture to determine the appropriate topology to improve the efﬁcacy of the 1D-Convolutional LSTMs for the authentication. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high recognition rate of 99.58%, when using only 16 channel EEG signals, which outperforms the state-of-the-art EEG-based biometric identiﬁcation methods.
Influence of seed priming with organic bio- fertilizers and botanicals on seed quality of foxtail millet

A laboratory experiment was carried out at Seed Quality and Research Laboratory, Seed Unit, National Seed Project (Crops), University of Agricultural Sciences, Dharwad during kharif -2018 to study the influence of seed priming with organic bio-fertilizers and botanicals on seed quality of foxtail millet. Seed quality of foxtail millet was significantly influenced by organic seed priming treatments. Seed priming with Cow urine at 3% (T3) was found to be significant compared to control (T9). However, Cow urine at 3% (T3) was on par with Panchagavya at 3% (T5), Jeevamrutha (T7) and Vermiwash at 3% (T4) for all seed quality parameters. Hence, either of the treatments can be used for enhancing seed quality in foxtail millet.
Learning to Rank Sentences for Explaining Statutory Terms

We explore using classical feature engineering-based learning-to-rank approaches (LTR) to discover sentences for explaining the meaning of statutory terms. We compiled a list of 129 descriptive features that model retrieved sentences, their relationships to statutory terms, and their statutory provisions of origin. Using a statutory interpretation data set (26,959 sentences) we showed how the proposed feature set could be utilized in learning-to-rank settings with reasonable effectiveness. We showed that off-the-shelf machine learning algorithms perform signiﬁ-cantly better than BM25 baselines (NDCG@100 of 0.77 vs 0.68).
Baby Monitoring System using Object Detection

- In practice, there are two types of mainstream object detection algorithms. Algorithms like R-CNN and Fast(er) R-CNN use a two-step approach - first to identify regions where objects are expected to be found and then detect objects only in those regions using convnet. On the other hand, algorithms like YOLO (You Only Look Once) and SSD (Single-Shot Detector) use a fully convolutional approach in which the network is able to find all objects within an image in one pass (hence ‘single-shot’ or ‘look once’) through the convolutional network. The region proposal algorithms usually have slightly better accuracy but slower to run, while single-shot algorithms are more efficient and have as good accuracy.
A Review on Graphical Symbols Identification on Food Packages using Machine Learning

– In the domain of product marketing and labelling, packaging symbols have become a significant part of increasing consumer awareness. When considering food items, several important factors that should be known by consumers. Having good knowledge of these symbols leads to choose the most protective, healthier and suitable food items for themselves. Anyhow with the development of computer technology, automated image classification systems are very popular, especially in symbol recognition. Therefore currently, there are so many research works that are carried on for identifying different kinds of symbols. Artificial Neural Network is one of the most accurate data mining technique and it is widely used in this research works. There are various kinds of neural network techniques that have been used in various existing systems. Here, 15 different proposed systems have been reviewed with analysis of feature extraction techniques and other technologies that have used into a more accurate and efficient symbol recognition system
Algebraic Foundations for Non-Monotonic Practical Reasoning

Practical reasoning is a hallmark of human intelligence. We are confronted everyday with situations that require us to meticulously choose among our possibly conﬂicting desires, and we usually do so with ease guided by beliefs which may be uncertain or even contradictory. The desires we end up choosing to pursue make up intentions which we seamlessly revise whenever our beliefs change. Modelling the intricate process of practical reasoning has attracted a lot of attention in the KR community giving rise to a wide array of logics coming in diverse ﬂavours. However, a robust logic of practical reasoning with adequate semantics representing the preferences among the agent’s different mental attitudes, while capturing the intertwined revision of beliefs and intentions, remains missing. In this paper, we aspire to ﬁll this gap by introducing general algebraic foundations for practical reasoning. We present an algebraic logic we refer to as Log A PR capable of modelling preferences among the agent’s different mental attitudes and capturing their joint revision.
Quantitative Analysis of Tunneling Current through Ultrathin Gate Oxides

The tunneling current through ultrathin (3.0-6.0 nm) gate oxides has been measured as a function of the voltage across SiO2, V OX, and compared with results of existing theories utilizing the WKB approximation. The electron effective mass in the Fowler-Nordheim tunneling region (V OX≥3.25 V) is obtained to be (0.34±0.04)m0 and that in the direct tunneling region (V OX<3.25 V) is (0.29±0.02)m0. It is also shown that the charge-to-breakdown for electron injection from n+poly-Si gates is not significantly degraded by decreasing the oxide thickness and is even dramatically improved for the case of a 3.0 nm-thick gate oxide.
A Non-destructive Ultrasonic Testing Approach for Measurement and Modelling of Tensile Strength in Rubbers

Currently, non-destructive testing is widely used to investigate various mechanical and structural properties of materials. In the present study, non-destructive ultrasonic testing was applied to study the relationship between the tensile strength value and the velocity of longitudinal ultrasonic waves. For this purpose, fourteen specimens of composites with different formulations were prepared. The tensile strength of the composites and the velocity of longitudinal ultrasonic waves inside them was measured. The relevance vector machine regression analysis, as a new methodology in supervised machine learning, was used to define a mathematical expression for the functional relationship between the tensile strength and the velocity of longitudinal ultrasonic waves. The accuracy of the mathematical expression was tested based on standard statistical indices, which proved the expression to be an efficient model. Based on these results, the developed model has the capability of being used for the online measurement of the tensile strength of rubber with the proposed formulation in the rubber industry. doi: 10.5829/ije.2020. 33.12c. 16
Phase relations in the Fe-rich part of the system Fe2O3(-Fe3O4)-CaO-SiO2 at 1240-1300°C and oxygen partial pressure of 5 × 10-3 atm: implications for iron ore sinter

Abstract Equilibrium phase relations in the Fe-rich portion of the system Fe2O3(-Fe3O4)-CaO-SiO2 (FCS) have been determined at 1240-1300°C and under an oxygen partial pressure of 5 × 10-3 atm, typical for iron ore sintering. Experiments were performed with the use of a rapid-quench technique and the chemical compositions of the melts and coexisting condensed phases were determined by combined optical microscopy and electron-probe microanalysis. At temperatures greater than ca 1250°C the major phase fields comprise Mt + Liq, C 2S + Liq, C 2 S + C 2 F + Liq and C 2 F + Liq (where Mt represents magnetite, C = CaO, F = Fe2O3, S = SiO2 and Liq denotes a quench liquid phase) plus an extensive, continuous, Liq-only phase field that extends from Fe-rich to SiO2-rich compositions. At less than about 1250°C the Liq-only melt region segregates into two distinct melt fields—one at high Fe contents and basicities greater than 1.8-2.0 and a second melt at low basicities (<1.8) and high SiO2 contents. At all temperatures examined the reduced oxygen conditions significantly enlarged the field of silicate melt present relative to that of Ca-ferrite melt. Magnetite (predominantly) or hematite may be the stable Fe-oxide phase; however, the occurrence of any particular Fe-oxide phase is strongly linked to the overall basicity (CaO/SiO2) of the charge. Experiments show that SFCss (silico-ferrite of calcia solid solution), which is the major ferrite bonding phase in low-Al sinter, cannot be produced as a single, unique, crystalline phase within the FCS system at 5 × 10-3 atm O2. At this oxygen partial pressure the bulk composition lies within the partially reduced Fe2O3(-Fe3O4)-CaO-SiO2 pseudo-ternary with the three-phase assemblage Mt + SFCss + Liq α stabilized at low temperatures (1240°C). Combined with data for the FCS ternary in air the results indicate that to maximize the formation of SFCss bonding phase a low-temperature (<1260°C), semi-reduced heating environment followed by a relatively slow, oxidizing, cooling stage is desirable.
Do Young Children Modulate Their Cognitive Control? Sequential Congruency Effects Across Three Conflict Tasks in 5-to-6 Year-Olds

Dynamic, trial-by-trial modulations of inhibitory control are well documented in adults but rarely investigated in children. Here, we examined whether 5-to-7 year-old children, an age range when inhibitory control is still partially immature, achieve such modulations. Fifty three children took flanker, Simon, and Stroop tasks. Above and beyond classic congruency effects, the present results showed two crucial findings. First, we found evidence for sequential modulations of congruency effects in these young children in the three conflict tasks. Second, our results showed both task specificities and task commonalities. These findings in young children have important implications as they suggest that, to be modulated, inhibitory control does not require full maturation and that the precise pattern of trial-by-trial modulations may depend on the nature of conflict.
Analysis of Speech Parameters as Indicators of Engagement in Conversation

In human conversations, the words being said may not always contain the whole of the information intended to be conveyed; instead, such information may be conveyed through the way those words are being said. Thus understanding the information beyond the words is vital in understanding the behaviours of the speaker in a conversation. The acoustic-prosodic features in speech are excellent sources of information which can indicate the internal states of a speaker. Humans have the capability of doing this processing of extra-linguistic information to understand their conversational partner — computers, on the other hand, lack this ability. The analysis of speech signal can reveal a lot of the information which may not be captured by analysing the linguistic content alone. In this study, the information encapsulated by the features of the speech signal is analysed to model conversational engagement of the participants. Casual conversations are analysed as part of this study to identify features of the speech signal, which can be used as cues for detecting engagement in conversations. Voice quality features (including glottal parameters) are mainly analysed in this study to model engagement in conversation. The proposed methods attempt to understand the conversational engagement of participants in casual conversations.
SENTIMENT ANALYSIS FOR AFAAN OROMOO USING COMBINED CONVOLUTIONAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

Sentiment analysis has become the most popular research topic due to its various application in business, politics, entertainment, however analyzing opinion of people from short text such as Twitter message and single sentence is quite a challenging task due to their informality, misspell and semantic error. In this study, we propose character level multiscale sentiment analysis for Afaan Oromoo using combined Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) approach. Since there is no standardized and sufficient corpus prepared for Afaan Oromoo Natural Language Processing (NLP) task including sentiment analysis so far, we have collected data from two domain, Facebook and Twitter for the experiment. After collecting data
DYNAMIC RELATIONSHIP BETWEEN EXCHANGE RATE AND STOCK PRICE: EVIDENCE FROM KOREA

This paper investigates the price returns and volatility linkages between the foreign exchange (KRW) and stock (KOSPI) markets in Korea, using the cointegration test, Granger causality test, and bivariate GARCH-BEKK model. In addition, we examine how the financial crisis affected the dynamic relationship between these two markets. Our findings from empirical analysis are summarized as follows. First, there is no longterm equilibrium relationship between the KRW and KOSPI markets. Second, there is a strong causal relationship from the KOSPI returns to KRW returns. Third, a strong bidirectional volatility spillover exists between the KRW and KOSPI markets. Fourth, our results provide strong evidence of a volatility spillover effect between the KRW and KOSPI markets in the post-crisis period, implying that financial crisis leads to linkages between the two markets. Thus, investors in the Korean stock market and/or the foreign exchange market need to consider the relationship between these two markets as part of their investment decisions.