Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Thinking Through the Engineering Design Process in Chemistry Education

This study investigated the influence of CThink4CS2 Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS2 Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.
Application Of Data Mining Technologies In The Management Of Educational Content On Social Networks In E-Learning

Currently, social networks are confidently integrated into the lives of modern people. These networks are a rich source of data. In the context of a large amount of information presented here, the task of intellectual analysis of the content of a social network arises. This article highlights the possibility of using social media information for educational purposes, as well as reviewing relevant research in the field of applying Data Mining methods for intellectual analysis of educational data. The article also shows the possibility of using data clustering methods using k-means to identify the content of educational materials distributed by students in a social network. This data can be used by the teachers of the educational institution to improve educational content and improve its quality.
Dynamics of Housing Affordability and Effects of Regional Migration: Evidence From England and Japan

This study aims to identify the primary driving forces behind the recent high appreciation in housing prices, discover how regional mobility influences housing affordability, and determine what policies are effective to address the problem. To do this, we estimate the structural model of housing prices and regional mobility using 2012/2013 to 2020/2021 panel data for England and Japan. The results reveal that the housing market in England is quite responsive to changes in expected housing price appreciation and that regional mobility alleviates the housing affordability problem; however, the Japanese housing market lacks mobility and is responsive to the supply of new houses. Regional mobility in England was driven expected wage and house prices, which alleviated the negative effect of increased house prices and contributed to convergence of regional differences in affordability. In both countries, increased share of parttime workers would invite workers’ inflow and worsen the affordability. To address these issues, each country has implemented different policies, with varying degrees of effectiveness. The British government has targeted a substantial increase in housing supply, but it should be associated with measures for curving the expectation of house price appreciation in urban regions. The Japanese government, on the contrary, should increase housing stock by renovating vacant houses and liquidate the labour market.
In Silico and In Vitro Search for Dual Inhibitors of the Trypanosoma brucei and Leishmania major Pteridine Reductase 1 and Dihydrofolate Reductase

The parasites Trypanosoma brucei (Tb) and Leishmania major (Lm) cause the tropical diseases sleeping sickness, nagana, and cutaneous leishmaniasis. Every year, millions of humans, as well as animals, living in tropical to subtropical climates fall victim to these illnesses’ health threats. The parasites’ frequent drug resistance and widely spread natural reservoirs heavily impede disease prevention and treatment. Due to pteridine auxotrophy, trypanosomatid parasites have developed a peculiar enzyme system consisting of dihydrofolate reductase-thymidylate synthase (DHFR-TS) and pteridine reductase 1 (PTR1) to support cell survival. Extending our previous studies, we conducted a comparative study of the T. brucei (TbDHFR, TbPTR1) and L. major (LmDHFR, LmPTR1) enzymes to identify lead structures with a dual inhibitory effect. A pharmacophore-based in silico screening of three natural product databases (approximately 4880 compounds) was performed to preselect possible inhibitors. Building on the in silico results, the inhibitory potential of promising compounds was verified in vitro against the recombinant DHFR and PTR1 of both parasites using spectrophotometric enzyme assays. Twelve compounds were identified as dual inhibitors against the Tb enzymes (0.2 μM < IC50 < 85.1 μM) and ten against the respective Lm enzymes (0.6 μM < IC50 < 84.5 μM). These highly promising results may represent the starting point for the future development of new leads and drugs utilizing the trypanosomatid pteridine metabolism as a target.
Load Modulation Feedback in Adaptive Matching Networks for Low-Coupling Wireless Power Transfer Systems

This paper explores the use of load modulation feedback (LMF) in adaptive matching networks (MN) for low-coupling inductive wireless power transfer systems, with an emphasis on its use in implantable medical devices. After deriving the handy expressions of link efficiency and modulation depth in the case of LMF in the case of loose coupling, a brief overview of the most common capacitive resonance networks is presented. In particular, the MN employing two capacitors in Series–Parallel and in Parallel–Series configurations allow adaptivity with a wide range of load conditions. Then, the authors describe an effective design procedure of an adaptive matching network with LMF for an inductive wireless power transfer system, exploring the trade-off between power efficiency and modulation depth. Analytical and electrical simulations show that the proposed simple modulation strategy can successfully achieve high power transfer efficiency while maintaining steady back telemetry under varying loading conditions.
Implications of digital risks on teachers' motivation and intention to use digital tools: a PLS-POS perspective in Romanian preuniversity education system

PurposeThe study aims to analyze the different types of risks related to the use of technology and determine their positive or negative influence on teachers' motivation and behavioral intention to use digital tools.Design/methodology/approachThe research is based on survey data from 200 teachers in the Romanian preuniversity education system. The data analysis followed a four-step approach, using a partial least squares structural equation modeling (PLS-SEM) model for hypothesized relationships among research concepts and a PLS prediction-oriented segmentation (POS) procedure.FindingsThis study showed that increased risk awareness influences both motivation and, consequently, the intention to adopt digital tools in the preuniversity education system.Research limitations/implicationsThe scope of research remains constrained with regard to the examined population, considering the substantial number of teachers within the preuniversity education system. Another limit lies in the basic classification of identified risk types.Practical implicationsSchool managers should design a strategy to increase the level of motivation for integrating digital tools in the educational process.Originality/valueLittle scholarly attention has been devoted to investigating the risks associated with digitalization in the preuniversity education system. In addition, no prior research has been conducted to assess the influence of risk perception on people's motivation and intention to use digital tools in preuniversity education.
Stack-Layer Dual-Frequency Ultrasound Array With Ground Shielding for Super-Harmonic Imaging

Contrast-enhanced harmonic imaging has shown unique advantages in identifying micro-vasculature for tumor characterization. Recent studies indicate that the third to fourth harmonics are the optimal ranges to detect microbubble contrast agents in microvessels. However, it is challenging for conventional piezoelectrical transducers to detect high-order harmonics due to their limited bandwidth. This article describes a stacked dual-frequency array for microvessel detection, which comprises 32 transmitting (TX) elements at 2 MHz and 96 receiving (RX) elements at 7 MHz. Design with ground shielding at the front surface ensures the safety and low noise of the transducer. Real-time contrast imaging was performed with vessel phantom inside water or porcine tissue. The contrast to noise ratio in water was 23 dB, whereas the contrast to tissue ratio (CTR) in porcine was 16 dB in the two experiments. These results suggest that the array has a potential for detecting microvessels with high fidelity.
Adult-Onset Transcriptomic Effects of Developmental Exposure to Benzene in Zebrafish (Danio rerio): Evaluating a Volatile Organic Compound of Concern

Urban environments are afflicted by mixtures of anthropogenic volatile organic compounds (VOCs). VOC sources that drive human exposure include vehicle exhaust, industrial emissions, and oil spillage. The highly volatile VOC benzene has been linked to adverse health outcomes. However, few studies have focused on the later-in-life effects of low-level benzene exposure during the susceptible window of early development. Transcriptomic responses during embryogenesis have potential long-term consequences at levels equal to or lower than 1 ppm, therefore justifying the analysis of adult zebrafish that were exposed during early development. Previously, we identified transcriptomic alteration following controlled VOC exposures to 0.1 or 1 ppm benzene during the first five days of embryogenesis using a zebrafish model. In this study, we evaluated the adult-onset transcriptomic responses to this low-level benzene embryogenesis exposure (n = 20/treatment). We identified key genes, including col1a2 and evi5b, that were differentially expressed in adult zebrafish in both concentrations. Some DEGs overlapped at the larval and adult stages, specifically nfkbiaa, mecr, and reep1. The observed transcriptomic results suggest dose- and sex-dependent changes, with the highest impact of benzene exposure to be on cancer outcomes, endocrine system disorders, reproductive success, neurodevelopment, neurological disease, and associated pathways. Due to molecular pathways being highly conserved between zebrafish and mammals, developmentally exposed adult zebrafish transcriptomics is an important endpoint for providing insight into the long term-effects of VOCs on human health and disease.
Thick-skin-dominated orogens; from initial inversion to full accretion: an introduction

Abstract Fifty per cent of orogens have a thick-skin character, and have evolved from passive margin and intra-cratonic rift systems. One group of thick-skin provinces can be found at both pro- and retro-wedges of orogens associated with advancing subduction zones, that is, orogenic wedges whose advance vectors oppose the mantle flow. A second group can be found at pro-wedges of orogens associated with retreating subduction zones, that is, orogens whose advance vectors have the same direction as mantle flow. A third group is formed in intra-plate settings where mechanical strengthening is produced by internal shortening. Thick-skin province development is controlled by driving factors such as individual plate movement rates, overall convergence rates, orogen movement sense with respect to mantle flow, and pro-wedge v. retro-wedge location. These driving factors are themselves constrained by numerous internal and external factors. This introductory chapter focusses primarily on least-deformed case areas in order to understand the role of different factors in controlling the evolution of thick-skin tectonic provinces from the initial inversion stage to full accretion stage.
Frailty and its associates in community-dwelling older adults

SUMMARY OBJECTIVE: While the literature contains several studies on the frailty assessed during hospitalization and/or outpatient settings and nursing homes, few studies have assessed frailty in community-dwelling older adults. We investigated the prevalence of frailty and associated factors among older adults in a sample of community-dwelling older adults. METHODS: We included community-dwelling older adults >60 years living in the Fatih District of the Istanbul Province. We conducted the study between November 2014 and May 2015. We collected the data such as age, sex, number of diseases and drugs, functional status, frailty, the presence of geriatric syndromes, common diseases, and quality-of-life assessment. Frailty was evaluated by the FRAIL scale. RESULTS: A total of 204 adults (mean age: 75.4±7.3 years) were included, of whom 30.4% were robust, 42.6% were pre-frail, and 27% were frail. In multivariate analyses, associated factors of frailty were the number of drugs [odds ratio (OR)=1.240, p=0.036], the presence of cognitive impairment (OR=0.300, p=0.016), and falls (OR=1.984, p=0.048). CONCLUSION: The present study established the prevalence of frailty in a large district in the largest metropolis in the country through a valid screening method. Our results suggest that clinicians should consider frailty evaluation in patients with multiple drug usage, cognitive impairment, and falls.
A Compact and Novel Photoacoustic Sensing Technique for In Vitro Characterization of Blood Viscosity: A Phantom-Based Study

The feasibility of the continuous wave laser diode-based photoacoustic (CWPA)sensing technique in estimating blood viscosity and red blood cell concentration, as well as measuring the blood vessel diameter, is demonstrated here. The laser-enabled swept frequency acoustic interferometry (LE-SFAI) principle is applied here to obtain the high signal-to-noise ratio CWPA signal. The experiments are performed using the liquid dye–glycerol mixture and further with the tissue-mimicking phantoms. Certain distinct features from the time domain CWPA signal are used to estimate the aforementioned parameters. The results are verified by the results obtained from the standard instruments. The usage of a continuous wave laser diode enables a compact and economical photoacoustic (PA) sensing system for blood viscosity characterization and field deployment.
Nice and Nasty Theory of Mind for Social and Antisocial Robots

The objective of this work is to develop computational cognitive models embedded in a humanoid robot. We focus on Dark Triad constructs and the so-called “Nice and Nasty” Theory of Mind that have never been investigated through a robotic approach. To this end, DT and ToM conceptual models in psychology have been taken as a reference for developing a framework based on the popular PDDL planning language. Next, a cognitive architecture has been implemented on a humanoid robot, with the final objective of making adverse personalities emerge. The motivations of the present work are both theoretical and practical. On the one side, we aim to provide researchers with new insights into DT constructs through simulated and robotic setups. On the other side, we aim to provide a tool to train psychologists to deal with social and antisocial behaviour in a controlled setup. The article includes all the details about the model and the experiments performed.
Experimental Investigation into the Proportion of Cemented Aeolian Sand-Coal Gangue-Fly Ash Backfill on Mechanical and Rheological Properties

Aiming at the problems of large water secretion, poor suspensibility and low strength of cemented aeolian sand (AS)-fly ash (FA) backfill (CAFB) mixtures, CAFB was doped with fine coal gangue (CG) particles crushed to less than 4 mm and configured as cemented aeolian sand-coal gangue-fly ash backfill (CACFB) mixtures, in which coal gangue accounted for 8% of the mass ratio of the slurry. Through UCS and rheological experiments, using the response surface methodology and an orthogonal design, the following conclusions were drawn: (1) With the increase in ordinary Portland cement (PO) and slurry concentration, the UCS of the CACFB increased. (2) With the increase in the FA dosage, the UCS of the CACFB decreased first and then increased due to the gradual increase in FA dosage, destroying the reasonable ratio of the material and leading to the reduction in the material’s UCS, and with the growth in time, the volcanic ash effect of the FA caused the UCS of the material to increase. (3) With the increases in slurry concentration, the yield stress and viscosity coefficient of the slurry increased. (4) Reasonable proportions for CACFB should ensure the strength characteristics and rheological properties of the material. Through theoretical and experimental research, the final reasonable proportions were as follows: the concentrations of slurry, AS, CG, FA and PO were 77.5%, 42%, 8%, 17.5% and 10%, respectively. This ensured that the UCSs of the CACFB at 3 d, 7 d and 28 d were 1.2 MPa, 2.5 MPa and 4.3 MPa, respectively; the yield stress of the CACFB was 495 Pa, and the viscosity coefficient was 3.97 Pa·s. These reasonable proportions of the CACFB can meet the strength index and flow property of material industrial experiments.
Topic Modeling for Text Structure Assessment: The case of Russian Academic Texts

Background: Automatic assessment of text complexity levels is viewed as an important task, primarily in education. The existing methods of computing text complexity employ simple surface text properties neglecting complexity of text content and structure. The current paradigm of complexity studies can no longer keep up with the challenges of automatic evaluation of text structure. Purpose: The aim of the paper is twofold: (1) it introduces a new notion, i.e. complexity of a text topical structure which we define as a quantifiable measure and combination of four parameters, i.e. number of topics, topic coherence, topic distribution, and topic weight. We hypothesize that these parameters are dependent variables of text complexity and aligned with the grade level; (2) the paper is also aimed at justifying applicability of the recently developed methods of topic modeling to measuring complexity of a text topical structure. Method: To test this hypothesis, we use Russian Academic Corpus comprising school textbooks, texts of Russian as a foreign language and fiction texts recommended for reading in different grades, and employ it in three versions: (i) Full Texts Corpus, (ii) Corpus of Segments, (iii) Corpus of Paragraphs. The software tools we implement include LDA (Latent Dirichlet Allocation), OnlineLDA and Additive Regularization Of Topic Models with Word2vec-based metric and Normalized Pairwise Mutual Information. Results: Our findings include the following: the optimal number of topics in educational texts varies around 20; topic coherence and topic distribution are identified to be functions of grade level complexity; text complexity is suggested to be estimated with structural organization parameters and viewed as a new algorithm complementing the classical approach of text complexity assessment based on linguistic features. Conclusion: The results reported and discussed in the article strongly suggest that the theoretical framework and the analytic algorithms used in the study might be fruitfully applied in education and provide a basis for assessing complexity of academic texts.
Practical Approaches to Industrializing Near-THz Communication Systems

In the past few years we have seen the proliferation of mm-Wave & sub-6GHz communication radios due to the global effort to make ultra-high capacity 5G a reality. Such systems benefit not only from RFIC innovations, but also from packaging, material engineering and co-design of various elements to produce scalable and manufacturable products. While near-THz communication systems may not be initially considered for mobility applications, they are poised to offer unprecedented capacity for backhaul links reaching 100Gb/s. Furthermore, applications such as imaging, high-resolution radar & instrumentation continue to benefit from advancements in near-THz systems. This plenary talk focuses on practical approaches to enable manufacturing of near-THz modules at scale, and at a price-point suitable for mass deployment. This plenary talk presents various feature-rich RFICs, packaging and interposer technology operating at E-Band (71-86GHz) & D-Band (110GHz - 170GHz) which are capable of achieving tens of gigabit per second at high spectral efficiency and output power.
LongCGDroid: Android malware detection through longitudinal study for machine learning and deep learning

This study aims to compare the longitudinal performance between machine learning and deep learning classifiers for Android malware detection, employing different levels of feature abstraction. Using a dataset of 200k Android apps labeled by date within a 10-year range (2013-2022), we propose the LongCGDroid, an image-based effective approach for Android malware detection. We use the semantic Call Graph API representation that is derived from the Control Flow Graph and Data Flow Graph to extract abstracted API calls. Thus, we evaluate the longitudinal performance of LongCGDroid against API changes. Different models are used, machine learning models (LR, RF, KNN, SVM) and deep learning models (CNN, RNN). Empirical experiments demonstrate a progressive decline in performance for all classifiers when evaluated on samples from later periods. Whereas, the deep learning CNN model under the class abstraction maintains a certain stability over time. In comparison with eight state-of-the-art approaches, LongCGDroid achieves higher accuracy.
The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

—This paper aims to provide a high-level overview of practical approaches to machine-learning respecting the privacy and confidentiality of customer information, which is called Privacy-Preserving Machine Learning . First, the security approaches in offline-learning privacy methods are assessed. Those focused on modern cryptographic methods, such as Homomor-phic Encryption and Secure Multi-Party Computation , as well as on dedicated combined hardware and software platforms like Trusted Execution Environment - Intel ® Software Guard Extensions (Intel ® SGX) . Combining the security approaches with different machine learning architectures leads to our Proof of Concept in which the accuracy and speed of the security solutions will be examined. The next step was exploring and comparing the Open-Source Python -based solutions for PPML . Four solutions were selected from almost 40 separate, state-of-the-art systems: SyMPC , TF-Encrypted , TenSEAL , and Gramine . Three different Neural Network architectures were designed to show different libraries’ capabilities. The POC solves the image classification problem based on the MNIST dataset. As the computational results show, the accuracy of all considered secure approaches is similar. The maximum difference between non-secure and secure flow does not exceed 1.2%. In terms of secure computations, the most effective Privacy-Preserving Machine Learning library is based on Trusted Execution Environment , followed by Secure Multi-Party Computation and Homomorphic Encryption . However, most of those are at least 1000 times slower than the non-secure evaluation. Unfortunately, it is not acceptable for a real-world scenario. Future work could combine different security approaches, explore other new and existing state-of-the-art libraries or implement support for hardware-accelerated secure computation.