Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance Learning Effects Among Italian Children and Parents During COVID-19 Related School Lockdown

Background: During the COVID-19 pandemic, both children and their parents experienced consequences related to distance learning (DL). However, positive and negative effects have varied greatly among families, and the specific factors explaining these differences in experiences are still underexplored. In this study, we examined children's executive functions (EF) and parents' psychological well-being in relation to negative and positive effects of DL on both children and their parents. Method: Participants were 637 Italian parents (92% mothers) with a child (48% male) aged between 6 and 19 years involved in DL due to school closures during the first wave of the COVID-19 pandemic. Data were collected using an online survey. We performed three fixed-order hierarchical multiple regression analyses with child age and sex, children's EF deficits, and parents' psychological well-being as independent variables, and DL-related negative effects (on the child and on the parent) and DL-related positive effects as dependent variables. Results: The results of the regression analyses showed that for negative effects of DL, younger age and greater EF deficits explained most part of the variance. Specifically, regarding negative effects on children, the most important factor was EF deficits, whereas regarding negative effects on parents, child age was the most important factor. For positive effects of DL, all variables explained only a small part of the variance. Child age was the most important factor, but EF deficits and parents' psychological well-being also had a significant impact. Conclusions: The effects of DL during school closures vary widely across families. Our findings indicate that intervention efforts need to consider background variables, child factors, as well as parent factors when supporting families with homeschooling in times of pandemic.
A Comparative Analysis of Different Approach for Basic Emotions Recognition from Speech

Human-Computer Interaction is one of the most progressive technology of recent times, and speech emotion recognition is one of the factors working behind this technology. The communication between two humans via verbal expressions is a tough process when this process is about to be implemented on robots or any system. If the robot or system cannot recognize the emotion that it receives from the speech, it will be a disastrous situation. This article shows some Machine Learning and Deep Learning approaches for recognizing emotions from speech. In this paper, many algorithms were compared to bring out a better model to perform the task more precisely. Algorithms like Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) were used here. Among these algorithms, the CNN model has performed well by giving an accuracy of more than 85%. Moreover, this customized CNN model has also outperformed all the models that were applied to the Emo-DB dataset.
Feedback perceptions: preliminary analysis of semistructured group interviews with first-year bachelor students of Computer Science

This full paper reports on research into feedback perceptions. In recent years enrolment in Engineering Education went up and the number of international students increased, leading to larger classes and greater variety in prior education within the classroom. Literature review reveals large classes lead to fewer opportunities for students to interact with their teachers and receive high quality formative feedback. Feedback helps students to reflect on the quality of their work. Engineering Education is characterized by overloaded, heavily scaffolded curricula, and learning activities; it is paramount to provide students with feedback they perceive as useful and meaningful. The larger diversity in the classroom means there is a larger diversity in feedback perceptions that needs to be taken into account when providing feedback. In this work we present the preliminary outcomes of a study that was informed by the following research question: How do first-year bachelor students perceive feedback? We performed multiple semistructured interviews with 17 first-year students in computer science during their first semester, and aimed to collect data on the students' lived experiences and the spread of student realities, using a phenomenological approach. The preliminary results show that there are many essential aspects that influence students' feedback perceptions. Study shows that such aspects as large class and relationships with teacher play very important role in developing certain feedback perceptions amongst students.
Understanding and Optimizing Hybrid SSD with High-Density and Low-Cost Flash Memory

With the development of NAND flash technology, hybrid SSDs with high-density and low-cost flash memory have become the mainstream of the existing SSD architecture. In this architecture, two flash modes can be dynamically switched, such as single-level cell (SLC) mode and quad-level cell (QLC) mode. Based on evaluations and analysis of multiple real devices, this paper presents two interesting findings. They demonstrate that the coordination between the two flash-modes is not well-designed in existing architectures. This paper proposes HyFlex, which redesigns the strategies of data placement and flash-mode management of hybrid SSDs in a flexible approach. Specifically, two novel optimization strategies are proposed: velocity-based I/O scheduling (VIS) and garbage collection (GC)-aware capacity tuning (GCT). Experimental results show that HyFlex achieves encouraging performance and endurance improvement.
Machining Forces: Some Effects of Tool Vibration:

When tool vibration occurs during machining both the undeformed chip thickness and the cutting forces have oscillating components. An examination of previous work reveals that both the relative phases and amplitudes of the oscillating forces and the oscillating undeformed chip thickness can be affected appreciably by changes in frequency. The explanations for this behaviour which have been put forward are not entirely consistent with previous experimental evidence. In the present work an analysis of the thrust forces occurring during tool vibration is proposed. The analysis is based on the assumption that there are two components to the oscillating thrust force: (1) a component proportional to, and in phase with, the oscillations in undeformed chip thickness and (2) a component, caused by contact between a small area of the tool flank and the freshly cut work surface, which leads the oscillation in undeformed chip thickness by 90°. Experimental results are presented which validate the assumptions made in .
Six-port reflectometer: influence of Q-points position in Gamma -plane on sidearm power detector error propagation

The high-frequency linear junction used in the network analyzers based on multiport measurement technique has a great number of equivalent implementations, at least theoretically. Some restrictions must be set to minimize the propagation of the error due to power detectors. The effort to look for an optimum circuit configuration set leads to a general conclusion, namely, that every time the q-points position in the Gamma -plane has a high degree of symmetry, the related circuit configuration gives an error minimization. >
Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

This Research To Practice Full Paper presents how learning experience influences students' capability to learn and their motivation for further learning. Although each student is different, standard instruction methods do not adapt to individual students. Adaptive learning reverses this practice and attempts to improve the student experience. While adaptive learning is well-established in programming, it is rarely used in cybersecurity education. This paper is one of the first works investigating adaptive learning in cybersecurity training. First, we analyze the performance of 95 students in 12 training sessions to understand the limitations of the current training practice. Less than half of the students (45 out of 95) completed the training without displaying any solution, and only in two sessions, all students completed all phases. Then, we simulate how students would proceed in one of the past training sessions if it would offer more paths of various difficulty. Based on this simulation, we propose a novel tutor model for adaptive training, which considers students' proficiency before and during an ongoing training session. The proficiency is assessed using a pre-training questionnaire and various in-training metrics. Finally, we conduct a case study with 24 students and new training using the proposed tutor model and adaptive training format. The results show that the adaptive training does not overwhelm students as the original static training format. In particular, adaptive training enables students to enter several alternative training phases with lower difficulty than the phases in the original training. The proposed adaptive format is not restricted to particular training used in our case study. Therefore, it can be applied to practicing any cybersecurity topic or even in other related computing fields, such as networking or operating systems. Our study indicates that adaptive learning is a promising approach for improving the student experience in cybersecurity education. We also highlight diverse implications for educational practice that improve students' experience.
Exploring the Association between Self-Regulation of Learning and Programming Learning: A Multinational Investigation

This Research Full Paper presents a collection of evidences about the association between self-regulation variables and programming learning. Researchers have been investigating this thematic and despite the apparent benefits, it is necessary to summarize the published evidence and provide a new collection of them, which this study seeks to contribute. An observational investigation was performed in two countries with fifty-nine students, who had their SRL and programming learning metrics collected and correlated. Moreover, a systematic literature review was also conducted, and the existing evidence summarized. The results support an association between metacognitive and motivational regulatory strategies with programming learning but do not support for cognitive strategies. Our analysis shows a need for more studies to provide a solid body of knowledge on this thematic.
Research Trends of Mechanism and Machine Theory Using Bibliometrics

Technology has evolved through the dynamic process of knowledge transfer. The exploration of technological evolution from a quantitative perspective has often been mentioned in the intellectual pursuit. The study applies bibliometric method to the articles published in the field of mechanism and machine theory (MMT). Afterward we analyzed the trends of MMT articles during the 30 years. We found that the ratios of the MMT papers in each journal have a trend of growth and have been centered on the certain two magazines. The articles of MMT exhibit a trend of growth. The USA, China and Canada are the major countries which frequently published the papers in the field of MMT and have significant performance in citations. China has rapidly grown in the quantity and the ratios of papers. In addition, the top 10 researchers with the highest h-index in the field of MMT are primarily affiliated with institutions in English-spoken countries, trumping the other countries.
On the time compression (test acceleration) of broadband random vibration tests

Many broadband random vibration tests are time compressed. This is done by increasing test intensity according to the Basquin model of cyclic fatigue. Conventionally, the test level is accelerated from the root mean acceleration and an assumed power constant (k = 2) is applied. Using conventional analysis the potential error in test severity can be very large if k is incorrect. The Miner–Palmgren hypothesis of accumulated fatigue is used to re‐assess the potential error in test severity accounting for the non‐stationarity found in road distribution. This shows a substantially reduced sensitivity to the value of k depending on the distribution of actual vibration intensities around the time‐compressed test intensity. Using an example of a leaf‐sprung vehicle, the conventional level of time compression is shown to have low sensitivity to errors in k, whereas for an example of an air‐ride vehicle a lower level of time compression is needed to reduce error sensitivity. Copyright © 2010 John Wiley & Sons, Ltd.
Multiobjective molecular design for integrated process‐solvent systems synthesis

The limitations are addressed that emerge from the computer aided molecular design (CAMD) applications in the optimal synthesis of solvents through a novel approach of designing solvents built upon multiple objective optimization technology. This technology has certain advantages over single objective optimization approaches that have been used for the design of optimal solvent molecules in the past. The different synthesis drives, described by the various objective functions, are treated independently without being submitted to unnecessary fixed assumptions that bias the designs. As a result, all the underlying trends and trade-offs among the properties of the candidate optimal molecules, as well as the structure – property relations are allowed to be revealed. The ability of the presented synthesis framework to embed highly inclusive solutions is elaborated, and the trends are revealed that dominate the relations between the utilized objective functions. It is also shown that solvent molecules, designed in the event that process performance feedback is accounted for, are existent in the Pareto optimal front. The proposed methodology is illustrated with two comprehensive examples in the design of solvents for liquid-liquid extraction and gas-absorption processes. © 2005 American Institute of Chemical Engineers AIChE J, 2006
Intelligent Prediction of Flash Lifetime via Online Domain Adaptation

To resolve the low generalization ability of the flash lifetime model caused by a small training sample, we propose a multiple source ensemble online domain adaptation scheme, called MSE. MSE uses multiple offline source blocks to assist in establishing a lifetime prediction model for the online target block. MSE migrates information from these blocks to the target block, effectively solving the pain point of insufficient samples for the target block. We simulate the actual use scenarios of NAND flash on the FPGA-based test platform. Experimental results show that prediction accuracy of MSE exceeds 0.91 using only a small number of samples of the target block. Therefore, MSE can be used to improve the space utilization of the flash with low overhead.
Evaluation of Neem Seed Water Extract, Biobit and Karate on Insect Abundance and Yield of Local Garden Egg (Solanvm Integrifolium L.)

The effect of Neem Seed Water Extract (NSWE) on insects and growth performance of eggplant (local garden egg) was evaluated in the field from June to October 1998 at Ekoso in the Eastern region, Ghana. The extract was applied at the rate of 75 g of seed per litre of water and compared with a standard insecticide (Karate), a registered Bacillus thuringiensis Berl. (Biobit) and water only as control. The effect of each treatment on leaf, shoot and fruit damage caused by defoliators, shoot, bud, and fruit borers were determined. The influence of the treatments on plant biomass and other vegetative growth indices such as plant height, plant girth, number of functional leaves and branches per plant, flower production as well as fruit yield were also assessed. Water traps were used to monitor the effects of the various treatments on the abundance of insects associated with the crop. Economic benefits of each pesticide-use programme based on crop value, cost of pest control and net profits were determined. Karate and NSWE treatments significantly (P<0.05) reduced percentage fruit damage, number of borers per fruit and defoliation. Although NSWE could not effectively control shoot University of Ghana http://ugspace.ug. edu.gh
Untargeted Metabolomics of Breast Cancer Cells MCF-7 and SkBr3 Treated With Tamoxifen/Trastuzumab

Background/Aim: Trastuzumab and tamoxifen are two of the most widely prescribed anti-cancer drugs for breast cancer (BC). To date, few studies have explored the impact of anticancer drugs on metabolic pathways in BC. Metabolomics is an emerging technology that can identify new biomarkers for tracking therapy response and novel therapeutic targets. Materials and Methods: We employed ultra-high-performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS) to investigate changes in MCF-7 and SkBr3 cell lines treated with either tamoxifen, trastuzumab or a combination of both. The Bruker Human Metabolome Database (HMDB) metabolite library was used to match spectra and the MetaboScape software to assign each feature with a putative metabolite name or molecular formula for metabolite annotation. Results: A total of 98 metabolites were found to significantly differ in abundance in MCF-7 and SkBr3 treated cells. Moreover, the metabolic profile of the combination medication is similar to that of tamoxifen alone, according to functional enrichment analysis. Conclusion: Tamoxifen/trastuzumab treatment had a significant effect on pathways essential for the control of energy-production, which have previously been linked to cancer progression, and aggressiveness
Design Features Impacting Mobile Phone Upgrading Frequency

This study examines the impact of design on customer upgrading frequency, and differentiates between functional and aesthetic aspects of design. For the empirical study, we conducted an intercept survey in Beijing and Shanghai with Chinese consumers (n=170) in a retail mall setting. The survey’s results suggest that the functional design aspects of mobile phones (screen size, keyboard, and width of body) affect perceived ease of use, and that design aesthetics (color and thickness of body) moderate the relationship between satisfaction and upgrading frequency. However, we did not gain significant results for the relationship between functional design and perceived usefulness. The results provide initial evidence of the importance of design for consumers when upgrading their mobile phones. The findings highlight the need to further investigate the impact of design.
Grid-Forming Control for Solar PV Systems with Real-Time MPP Estimation

Grid-forming control (GFC) for photovoltaic (PV) systems is seen lately as a promising solution for enhanced grid support and improved power system stability. However, various factors, such as the non-monotonic nature of the power-voltage (P-V) characteristic and the maximum power point (MPP) estimation accuracy, affect the controller performance and reliability. This paper presents a GFC scheme with real-time MPP estimation that overcomes these challenges. The adopted model-based MPP estimation algorithm performs most satisfactorily, even during fast irradiance changes. The proposed control scheme considers the converter power losses when translating the available reserves from the PV side to the grid side, usually neglected in the literature. Furthermore, the GFC method is designed in such a way that it can afford reasonable MPP miscalculations with no stability issues, resulting in guaranteed reliable operation. Simulations in Matlab/Simulink on a modified WSCC 9-bus test system at load and irradiance disturbances validate the favorable operation of the proposed method.
The “Muddiest Point” in Undergraduate Research: A Survey on Students and Faculty about Existing Challenges

This work-in-progress (WIP) research paper presents a plan and discusses some preliminary efforts to study the existing challenges of STEM undergraduate research (UR) from both faculty mentors' and students' perspectives. The benefits for undergraduate students to conduct research activities are well recognized by engineering educators. However, challenges to engage more novice researchers and to create values still exist, especially in primarily undergraduate institutes (PUI) where research resources may be limited. Other possible difficulties include misconception of research, time commitment, disengagement with mentors and lack of technical preparation. The goal of this project is to further investigate and identify these issues by a direct interaction with participants at multiple PUIs. Through one-on-one interviews, we will hear the voices from student participants regarding their experience in UR, such as common misunderstandings, motivation, typical learning curves and impact on the careers. For faculty mentors, the focus is their reflections on the recruitment process and mentoring experiences. Using snowball recruitment that starts from the authors' own institution, we will reach out to participants at other PUIs based on our professional contacts, as well as local and national research conferences (e.g., Undergraduate Research Symposium and National Conference on Undergraduate Research). The firsthand information will ultimately help us address these potential challenges by developing a more personalized training program that aims to enhance students' preparedness and performance in research. We also envision the project itself will facilitate more productive conversations between faculty mentors and students.
Transformer based Multi-output Regression Learning for Wastewater Treatment

Wastewater treatment is an important link to solve the pollution of water resources and realize the recycling of water resources. Predicting effluent water quality by influent water quality is an important part of the actual production process. The existing forecasting methods have the problem of low accuracy and efficiency, and cannot be used well in the actual production process. To tackle the above issues, we propose a novel method to use the transformer model to fit the wastewater treatment process which improves the prediction of effluent water quality. Experiments on various datasets validate the effectiveness of our proposed method. In the worst case, the sample prediction accuracy rate of our model achieves as high as 96.8%. At the same time, our model training with thousands of real production data, the training time is not more than two minutes. As far as we know, we are the first to use the transformer model in wastewater treatment tasks and achieve fast and accurate predictions. In the future, we will apply our model in actual production to achieve accurate predictions in dynamic data update scenarios.