Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment considerations during lockdown in Norway: An exploratory case study with focus on misconducts in university mathematics

Abstract The present paper raises a discussion about assessment formats in mathematics courses at Norwegian universities during the Covid lockdown. This proved to be challenging since the European GDPR regulations are strictly interpreted in Norway, making proctoring at home difficult. Based on analyses of nine university teachers’ feedback on how exams were carried out at their university during lockdown, a discussion is raised about assessment modes and misconducts. The result shows how a framework from another research field can be adjusted to analyze data about the assessment situations. Next, by utilizing the different components (themes) of the adjusted framework, we shed light on perspectives on misconducts in un-proctored home exams. In doing so, the paper informs the discussion on challenges related to assessing students in mathematics at home. Results are relevant for future educational settings since change in the demographic profile of students increase topicality of online assessment.
Impaired visuo-spatial statistical learning with mathematical learning difficulties

ABSTRACT Rapid extraction of temporal and spatial patterns from repeated experience is known as statistical learning (SL). Studies on SL show that after few minutes of exposure, observers exhibit knowledge of regularities hidden in a sequence or array of objects. Previous findings suggest that visuo-spatial statistical learning might relate to numerical processing mechanisms. Hence, the current study examines for the first time visuo-spatial SL in a population with a deficiency in the numerical system: individuals with mathematical learning difficulties (MLD). Thirty-two female participants (16 with MLD and 16 matched controls) were tested on a visuo-spatial statistical learning task. The results revealed that visuo-spatial SL was significantly worse in the MLD group than in a control group, although MLD performed as well as controls in a visual discrimination task. In addition, whereas the control group showed reliable visuo-spatial SL above chance, the MLD group did not. Because learned regularities can broadly facilitate cognitive processing, individuals with MLD may thus suffer from additional behavioural challenges beyond their numerical difficulties.
DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network

A novel wearable consumer electronics device for detecting Major Depressive Disorder (MDD) has been developed using deep learning techniques for smart healthcare. Accurate identification of MDD through individual interviews or perceiving Electroencephalogram (EEG) signals is challenging. This study presents the concept of a novel wearable smart cap named DepCap for real-time detection of depression using EEG signals. First, spectrogram images are generated from the EEG signals of depressed and healthy patients using Short-Time Fourier Transform (STFT) to extract valuable features. Then, these spectrogram images obtained from STFT are used as input to the classification model. A deep analysis is done using various neural networks consisting of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). RNNs are used to extract temporal data from the EEG, while CNNs are used to retrieve spatial information. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are the two different kinds of RNNs evaluated in this work. The implemented combination models are (STFT+CNN), (STFT+CNN-LSTM) and (STFT+CNN-GRU). Four pre-trained models, Inception, AlexNet, VGG16, and ResNet50 are also implemented along with the combination models. The dataset for this work is a publicly accessible dataset with 33 major depressive disorders and 30 healthy subjects. The evaluation results show that the STFT+CNN-LSTM has much better performance in terms of accuracy, sensitivity, specificity, and precision of 99.9%, 100%, 99.8%, and 99.4%, respectively, than other implemented models. The proposed wearable device DepCap is based on the STFT+CNN+LSTM model and is also integrated with the Internet of Medical Things (IoMT) framework for real-time depression detection.
Silicon avalanche photodiode linear-array detector with multichannel scaling system for pulsed synchrotron X-ray experiments

We developed an X-ray detector system using a 64-pixel silicon avalanche photodiode (Si-APD) linear array and fast pulse-counting electronics for multichannel scaling. The Si-APD linear array consists of 64 pixels each 100 × 200 μ m2 in size, with a pixel pitch of 150 μ m and depletion depth of 10 μ m. The fast electronics, consisting of an ultrafast application-specific integrated circuit and field-programmable gate arrays, can record both the position and timing of X-rays arriving at each pixel of the linear array with 1 ns pulse-pair resolution. The count distribution was measured using 14.4 keV synchrotron X-rays for nuclear resonant forward scattering experiments on 57Fe, and the spatial resolution was improved by inclining the detector. We also successfully measured amorphous alloy Fe78Si9B13 during heating.
Invariant Quadratic Estimators in the Random, One-Way ANOVA Model

There are no uniformly best invariant quadratic estimators of the variance components in the random, one-way ANOVA model. In the balanced model the ANOVA estimators are best among unbiased estimators, but are inadmissible among all invariant quadratic estimators. Two classes of admissible invariant quadratic estimators are presented in this paper: unbiased estimators which have minimum average variance over a prior distribution on the variance components and biased estimators which have minimum average mean squared-error.
The Italian Framework of Bipolar Disorders in the Elderly: Old and Current Issues and New Suggestions for the Geriatric Psycho-Oncology Research

Background: Older adults with mood disorders constitute a heterogeneous group in a complex spectrum interlinked with physical comorbidities. Worldwide, Bipolar disorders in older people (OABD) remain underestimated and underdiagnosed. OABD is challenging in the clinical setting and is associated with adverse outcomes (increased risk of anti-social behaviour triggered by inappropriate drugs and increased incidence of health deficits, including cancer). This article aims to describe the state of the art of OABD in the Italian framework and provide a new field of research. Methods: We performed an overview of the literature, selecting our target population (over 65 years) and synthesising the main challenging issues. By exploiting the Italian database from the Minister of Health in 2021, we analysed epidemiological data in the age range 65–74 years and 75–84 years old. Results: Females showed the highest prevalence and incidence in both groups, with a regional difference across the country but more evident in the Autonomous Provinces of Bolzano and Trento for the 65–74 years range. Several projects recently focused on this topic, and the urgency to define better the epidemiological framework is mandatory. Conclusions: This study represented the first attempt to report the comprehensive Italian framework on OABD aimed at fostering research activities and knowledge.
Multivariate time-frequency analysis

Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.
Negotiating Toilet Access: A Qualitative Exploration of Women's Incarceration

Norms and behaviors about toileting in prison can expand understanding of women's lived experience of incarceration. Knowledge about this subject is significant because access to clean, safe toilets is a human rights issue, and toilet habits can impact social, mental, and physical outcomes. Three focus groups were conducted with 15 incarcerated women about their quotidian prison toilet experiences. While the toilet was physically available, institutional regulations, social norms, and women's individual psychologies limited their access and utilization. The ways in which toilet use was negotiated with self, peers, and are described, and the implications of these findings are discussed.
ID-based threshold ring signcryption scheme

An ID-based threshold ring signcryption scheme is proposed.It is based on the concepts of ID-based ring signcryption scheme and threshold signature scheme.The scheme not only has the properties of signcryption:confidentiality and authentication,but also has the properties of ring signcryption and threshold signature.The existential unforgeability against adaptive chosen message attacks is proved to depend on the hardness of CDHP.The indistinguishability against adaptive chosen ciphertext attacks is proved to depend on the hardness of DBDHP.
Dysfunction of Foxp3+ Regulatory T Cells Induces Dysbiosis of Gut Microbiota via Aberrant Binding of Immunoglobulins to Microbes in the Intestinal Lumen

Foxp3+ regulatory T (Treg) cells prevent excessive immune responses against dietary antigens and commensal bacteria in the intestine. Moreover, Treg cells contribute to the establishment of a symbiotic relationship between the host and gut microbes, partly through immunoglobulin A. However, the mechanism by which Treg cell dysfunction disturbs the balanced intestinal microbiota remains unclear. In this study, we used Foxp3 conditional knockout mice to conditionally ablate the Foxp3 gene in adult mice and examine the relationship between Treg cells and intestinal bacterial communities. Deletion of Foxp3 reduced the relative abundance of Clostridia, suggesting that Treg cells have a role in maintaining Treg-inducing microbes. Additionally, the knockout increased the levels of fecal immunoglobulins and immunoglobulin-coated bacteria. This increase was due to immunoglobulin leakage into the gut lumen as a result of loss of mucosal integrity, which is dependent on the gut microbiota. Our findings suggest that Treg cell dysfunction leads to gut dysbiosis via aberrant antibody binding to the intestinal microbes.
Monitoring of Water Freeze–Thaw Cycle by Means of an Etched Single- Mode–Multimode–Single-Mode Fiber-Optic Refractometer

As an alternative to the different technologies that permit the detection of in situ ice formation on different surfaces, this contribution proposes the design of an etched single-mode–multimode–single-mode (E-SMS) fiber-optic-based structure as a multimode interference refractometer. This sensor provides enhanced properties with respect to a basic SMS structure, including a higher sensitivity and periodical interferometry bands that can measure surrounding refractive indices with repeatability and robustness. Since ice and water refractive indices are sufficiently different, this structure has been used to detect the freezing-thawing process of water taking place inside a freezer between −20 °C and +20 °C. Also, this work intends to show a proof of concept of a simple technology that can be applied in different situations, such as in smart cities, avionics, structural health monitoring (SHM) or even to avoid a cold chain breakage. Inside, novel developments to better understand the working operation of the E-SMS structure are shown, together with a study on how to correlate optical and thermal measurements from a refractive index point of view.
Infectious SARS-CoV-2 Particles from Rectal Swab Samples from COVID-19 Patients in Brazil

The main objective of this study was to investigate the dynamic of SARS-CoV-2 viral excretion in rectal swab (RS), saliva, and nasopharyngeal swab (NS) samples from symptomatic patients and asymptomatic contacts. In addition, in order to evaluate the replication potential of SARS-CoV-2 in the gastrointestinal (GI) tract and the excretion of infectious SARS-CoV-2 from feces, we investigated the presence of subgenomic nucleoprotein gene (N) mRNA (sgN) in RS samples and cytopathic effects in Vero cell culture. A prospective cohort study was performed to collect samples from symptomatic patients and contacts in Rio de Janeiro, Brazil, from May to October 2020. One hundred and seventy-six patients had samples collected at home visits and/or during the follow up, resulting in a total of 1633 RS, saliva, or NS samples. SARS-CoV-2 RNA was detected in 130 (73.9%) patients who had at least one sample that tested positive for SARS-CoV-2. The presence of replicating SARS-CoV-2 in RS samples, measured by the detection of sgN mRNA, was successfully achieved in 19.4% (6/31) of samples, whilst infectious SARS-CoV-2, measured by the generation of cytopathic effects in cell culture, was identified in only one RS sample. Although rare, our results demonstrated the replication capacity of SARS-CoV-2 in the GI tract, and infectious viruses in one RS sample. There is still a gap in the knowledge regarding SARS-CoV-2 fecal–oral transmission. Additional studies are warranted to investigate fecal or wastewater exposure as a risk factor for transmission in human populations.
Human Sleep Posture Recognition Method Based on Interactive Learning of Ultra-Long Short-Term Information

The sleep posture recognition and tracking using radar sensor is one of the most important techniques because of its advantages of stable performance, privacy protection, and so on. However, radar echo signals are highly sensitive to different humans, resulting in a week generalization ability. To solve this problem, we propose a human sleep posture recognition method based on interactive learning of ultra-long short-term information using millimeter-wave radar. The motivation of the proposed method using the interactive learning strategy lies that it is easy to obtain stable information with an ultra-long-term observation. Specifically, we first conduct short-term human state separation sequentially by body movement index estimation, big movement detection, range–Doppler map calculation, and lateral and nonlateral rough identification. This could separate the three human states of big movement, lateral posture, and other recumbency, and prepare for finer posture recognition. Second, we sort the three sleep postures of supine, side, and prone and extract personalized features with an ultra-long-term observation to enhance the robustness. Finally, combined with the extracted personalized ultra-long-term features, we achieve recognition of three sleep postures and seven sleep postural conversions (SPCs) with a short-term observation for real-time judgment. The experimental results show that the proposed method has low computational complexity and can achieve an average accuracy of 91% in three sleep postures’ recognition and 83.7% in seven SPCs’ classification.
Design and Development of Sublingual Printlets Containing Domperidone Nanocrystals Using 3D Melting Solidification Printing Process (MESO-PP)

Domperidone (DOM) is a drug commonly used to treat nausea and vomiting, as well as gastrointestinal disorders. However, its low solubility and extensive metabolism pose significant administration challenges. In this study, we aimed to improve DOM solubility and avoid its metabolism by developing nanocrystals (NC) of DOM through a 3D printing technology—melting solidification printing process (MESO-PP)—to be delivered via a solid dosage form (SDF) that can be administered sublingually. We obtained DOM-NCs using the wet milling process and designed an ultra-rapid release ink (composed of PEG 1500, propylene glycol, sodium starch glycolate, croscarmellose sodium, and sodium citrate) for the 3D printing process. The results demonstrated an increase in the saturation solubility of DOM in both water and simulated saliva without any physicochemical changes in the ink as observed by DSC, TGA, DRX, and FT-IR. The combination of nanotechnology and 3D printing technology enabled us to produce a rapidly disintegrating SDF with an improved drug-release profile. This study demonstrates the potential of developing sublingual dosage forms for drugs with low aqueous solubility using nanotechnology and 3D printing technology, providing a feasible solution to the challenges associated with the administration of drugs with low solubility and extensive metabolism in pharmacology.
Standard Deviation of Fiber-Coupling Efficiency for Free-Space Optical Communication Through Atmospheric Turbulence

In fiber-based optical receivers such as coherent receiver, the optical signal reaching the receiver's aperture is coupled to the optical fiber through a set of lenses. Due to atmospheric turbulence, however, the coupling efficiency, which is defined by the ratio of the fiber-coupled optical power to the optical power reaching the aperture, fluctuates over time, and thus should be treated as a random variable. Previous theoretical works predict the average coupling efficiency accurately, but fail to estimate the standard deviation, especially when the turbulence is strong. In this study, we propose new theoretical formulas for the standard deviation of coupling efficiency applicable to a wide range of turbulence conditions. For this purpose, we derive a new approximated formula for the second moment of coupling efficiency using the second-order Taylor expansion. Next, a new formula for the second moment of coupled optical power is developed by introducing the spatial filter to the fourth-order coherence function. The computer simulation is carried out to evaluate the accuracy of the formulas. The results show that our formulas agree with the simulation results over a wide range of turbulence strength. Also, the new formulas provide better accuracy than the previously reported ones.
Reflections on problems of educational practice in a project course design for professional authenticity, cultural relevance, and sociotechnical integration

ABSTRACT This paper examines specific problems of educational practice that I, an undergraduate engineering educator and education researcher, have encountered in creating an interdisciplinary engineering project course. I reveal my process of theorising about problems of practice to position engineering educators’ pedagogical theorising as worthy of scholarly focus. In a reflective scholarship of teaching and learning paradigm, I take the iterative design of this course as the object of inquiry, across semesters and institutional contexts. I discuss the iteration of five key dimensions of the course: (1) authenticity to engineering professional work, (2) promoting learning of teamwork skills, (3) complexities and equity dimensions of situated project-based learning environments, (4) valuing social sciences and social context reasoning for engineers, and (5) creating a responsive curriculum for students. I distil insights related to each of these dimensions. I conclude with a consideration of the interplay between these dimensions and a reflection on the form of scholarship for engineering education.
Optimal Siting and Sizing of EV Charging Station Using Stochastic Power Flow Analysis for Voltage Stability

Existing literature on planning for electric vehicle charging station (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users’ average wait time and distance to charge costs, and minimize the DSVS index. To simplify the problem, we use the super efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multiobjective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.
Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network, and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of ‘rapid increase—fluctuating decrease—continuous increase’ from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of renewable energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.