Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge and Spin Transport in Parallel-Coupled Quantum Dots in Nanowires

This thesis explores crystal-phase engineering of nanowires to fabricate advanced quantum structures for charge and spin transport studies. Quantum dots formed by crystal-phase tuning during epitaxial growth of InAs nanowires were used as a starting point to realize and electrically characterize two different types of parallel-coupled quantum dots; electron-hole quantum dots and electron-electron quantum dots. In the InAs nanowire, two thin segments of wurtzite in an otherwise zinc blende crystal structure acted as tunnel barriers for electron transport and defined the quantum dot in the axial dimension. We estimated the offset in the conduction-band alignment at the wurtzite-zinc blende interface to be ~100 meV. The axial extension of the quantum dot could be tuned to less than 10 nm, which led to a strong quantum confinement and enabled the quantum dot to become fully depleted of electrons.In few-electron InAs quantum dots, pairs of local side gates and a global back gate were used to reproducibly tune the system from one quantum dot into parallel double quantum dots, for which we can control the populations down to the last electrons. Here, the interdot tunnel coupling of the two first orbitals could be tuned by one order of magnitude, owing to the combination of hard-wall barriers to the source and drain, shallow interdot tunnel barriers, and very high single-particle excitation energies (up to ~30 meV). In addition, the large |g*|-factors (~10) facilitated detailed studies of the magnetic-field dependency of the one- and two-electron states. In particular, we investigated the magnetic field-induced transition between singlet and triplet two-electron ground states. Here, the strong spin-orbit coupling in the system hybridized the single and triplet states. By controlling the interdot tunneling coupling we demonstrated a widely tunable anticrossing of the ground and excited states. Parallel electron-hole core-shell quantum dots were realized by using the InAs nanowire quantum dot as a template for selective radial growth of GaSb on the zinc blende crystal phase. As a heterostructure in bulk, InAs and GaSb form a broken band-gap alignment with spatially separated electrons and holes. In quantum dots, the overlap of the InAs conduction band and GaSb valence band can be tuned, which is of interest in studies of electron-hole interactions and transport via hybridized states. The electrical measurements of devices in the many-electron/hole regime showed clear evidence of transport via parallel quantum dots in the form of a beating pattern of small and much larger diamonds. We attributed the small-diamond pattern to electron transport in the core and the larger-diamond pattern to hole transport via the shell. From shifts in the conduction lines at the degeneracy point, we extracted an upper estimation of the electron-hole interaction strength of 4.5 meV.The work presented in this thesis demonstrate the great potential of using atomically precise crystal-phase design of nanowires to access and probe fundamental quantum physics.
Evaluation of some functional properties of purple corn (Zea mays L.) dye, during its processing at pilot scale.

The present research aimed the evaluation of changes in functional properties during processing of purple corn dye extracted of its cob by solid-liquid extraction, then concentration and spray-dried in order to obtain a dye powder. The test performed were determination of Total Anthocynins (TA), Total Phenolic Content (TPC), and Antioxidant Capacity (AC) by ABTS method. Results obtained were TA content of 125,76 ± 1,23 mg TA/g DM, TPC of 127.47 ± 0.59 mg GAE/g DM, ABTS EC50 of 72.15 ± 1.16 μg TE/g (water-methanol extract) in purple corn cob characterization; in purple corn cob aqueous extract were TA content of 0.53 mg TA/ml, 1.81 mg GAE/ml, 787.17 μg TE/ml; and after concentration TA content of 0.62 mg TA/ml, 2.26 mg GAE/ml, 2471.77 μg TE/ml; and for purple corn dye powder were TA content of 0.37mg TA/ml, 0.81 mg GAE/ml, 1961.20 μg TE/ml, where degradation is observable due to exposure to high temperatures, but keeping a high ratios of these functional compounds which is very desirable in order to use the natural dyes as substitutes of artificial ones, and is a promissory source of functional compounds for increasing nutritional values into food products. Keywords—Zea mays L. , natural dyes, functional properties,
Lambert-Eaton Myasthenic Syndrome Caused by Atezolizumab in a Patient with Small-cell Lung Cancer

We herein report a 74-year-old man who developed Lambert-Eaton myasthenic syndrome (LEMS) during atezolizumab treatment for extensive-stage small-cell lung cancer. He was started on maintenance immunotherapy with atezolizumab every three weeks after four cycles of atezolizumab plus carboplatin plus etoposide combination therapy. After 13 cycles of maintenance atezolizumab therapy, he complained of muscular weakness and fatigue. Findings from a nerve conduction study and positive findings for anti-P/Q-type voltage-gated calcium channel antibody resulted in a diagnosis of LEMS. This was a rare case of LEMS as a neurological immune-related adverse event induced by atezolizumab therapy.
Duckiefloat: a Collision-Tolerant Resource-Constrained Blimp for Long-Term Autonomy in Subterranean Environments

There are several challenges for search and rescue robots: mobility, perception, autonomy, and communication. Inspired by the DARPA Subterranean (SubT) Challenge, we propose an autonomous blimp robot, which has the advantages of low power consumption and collision-tolerance compared to other aerial vehicles like drones. This is important for search and rescue tasks that usually last for one or more hours. However, the underground constrained passages limit the size of blimp envelope and its payload, making the proposed system resource-constrained. Therefore, a careful design consideration is needed to build a blimp system with on-board artifact search and SLAM. In order to reach long-term operation, a failure-aware algorithm with minimal communication to human supervisor to have situational awareness and send control signals to the blimp when needed.
A geostationary lightning pseudo-observation generator utilizing low frequency ground-based lightning observations

Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine learning (ML) models, that predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flashes. In the perspective of lightning data assimilation, Flash Extent Density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation, while cumulated absolute differences remain of about 22 %. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED>0 flashes per 5 km×5 km pixel simulated by some pseudo-GLM generators differs by only 7% to 8% from the observed values. The recommended generator uses a linear Support Vector Regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area.
Synthesis of novel high-affinity FimH antagonists with improved pharmacokinetic properties

Urinary tract infections (UTIs) belong to the most common bacterial infections worldwide. Uropathogenic Escherichia coli (UPEC) are held accountable for majority of the cases. Millions of people suffer from UTI each year, with the highest incidence rate reported among women. Moreover, woman once affected will most likely experience a recurrent infection. High prevalence and recurrence of UTI lead to considerable medical costs. The current treatment generally involves antibiotics. However, choosing a proper antibiotic therapy becomes more difficult as resistant strains rapidly proliferate. Therefore, the need to develop alternative, non-antibiotic strategies is more pressing than ever. UPEC express filamentous organelles called type 1 pili (fimbriae), which protrude from the bacterial surface and mediate the adhesion to the bladder-epithelial cells. The mannose-specific adhesin FimH is located on the distal end of the pili. It contains the mannose-specific carbohydrate recognition domain (CRD), which binds to highly mannosylated uroplakin 1a (UP1a) expressed by urothelial cells leading to the infection. The compounds capable of blocking the interaction between FimH and surface-exposed glycans pave the way for a novel anti-adhesive strategy to treat and prevent UTI. The first part of the thesis addresses the problem of poor oral bioavailability arising from high polarity of the FimH antagonists. The strategy involves a prodrug approach, in which lipophilic esters are introduced to the parent compounds either on the aglycone’s carboxylic acid or the mannose moiety. The absorption potential as well as propensity to hydrolysis by esterases of liver or plasma was evaluated. The second part of the thesis emphasizes the optimization of the pharmacodynamic properties of FimH antagonists. In the first approach, the mannose moiety was modified in order to explore a cavity located at the entrance to binding pocket. The obtained antagonists were evaluated in competitive binding assay and by isothermal titration calorimetry (ITC) to reveal their thermodynamic binding profile. The second approach involved an elongation of the aglycone to allow additional interactions with the guanidinium side chain of Arg98. For the evaluation of these antagonists, competitive binding assays with the wild type FimH and the R98A mutant was established.
A prospective six-month audit of inpatient hypoglycemia in step-down general medical and geriatric wards

This study aimed to assess the incidence and associates of hypoglycemia in patients transferred after stabilization on an Acute Medical Unit to two general medical or two geriatric wards at an urban Australian hospital. In a six-month audit representing 20,284 patient-days of observation, 59 inpatients experienced hypoglycaemia (blood glucose ≤3.9 mmol/L) during 65 hospitalizations. Inpatients experiencing hypoglycemia accounted for 7.2% of all inpatient bed-days, a figure that was greater for general medical (9.2% of bed-days) compared with geriatric (6.0% of bed-days) wards (P<0.001). Inpatient hypoglycemia often had no precipitant such as a missed/delayed meal, occurred disproportionately at night (41% of episodes), was severe (blood glucose ≤3.0 mmol/L) in one-third of cases, and appeared more frequent in patients with psychiatric/cognitive issues. These data highlight the ongoing issue of hypoglycemia in relatively stable inpatients in an era of blood glucose-lowering therapies associated with a low rate of this acute metabolic complication.
Cross-Cultural Adaptation, Reliability, and Psychophysical Validation of the Pain and Sleep Questionnaire Three-Item Index in Finnish

Reciprocal relationships between chronic musculoskeletal pain and various sleep disturbances are well established. The Pain and Sleep Questionnaire three-item index (PSQ-3) is a concise, valid, and reliable patient-reported outcome measure (PROM) that directly evaluates how sleep is affected by chronic low back pain (CLBP). Translation and cross-cultural validation of The Pain and Sleep Questionnaire three-item index Finnish version (PSQ-3-FI) were conducted according to established guidelines. The validation sample was 229 subjects, including 42 pain-free controls and 187 subjects with chronic musculoskeletal pain. Our aims were to evaluate internal consistency, test–retest reliability, measurement error, structural validity, convergent validity, and discriminative validity and, furthermore, to study the relationships between dizziness, postural control on a force plate, and objective sleep quality metrics and total PSQ-3-FI score. The PSQ-3-FI demonstrated good internal consistency, excellent test–retest reliability, and small measurement error. Confirmatory factor analysis confirmed acceptable fit indices to a one-factor model. Convergent validity indicated fair to good correlation with pain history and well-established pain-related PROMs. The PSQ-3-FI total score successfully distinguished between the groups with no pain, single-site pain, and multisite pain. A higher prevalence of dizziness, more impaired postural control, and a general trend towards poorer sleep quality were observed among subjects with higher PSQ-3-FI scores. Postural control instability was more evident in eyes-open tests. The Finnish PSQ-3 translation was successfully cross-culturally adapted and validated. The PSQ-3-FI appears to be a valid and reliable PROM for the Finnish-speaking CLBP population. More widespread implementation of PSQ-3 would lead to better understanding of the direct effects of pain on sleep.
Learning Geometry and Semantics for Deep Image Restoration

This thesis investigates possible deep learning-based solutions for three challenging computer vision problems. We first tackle reconstructing depth from focus cue and devise a suitable network architecture for the problem of depth from focus. To this end, we generate a large dataset composed of light-field images and co-registered depth maps to train a deep network on real-world scenarios. Our approach outperforms classical methods by a large margin and is generalizable for other datasets and cameras with a minimal effort. Following, we present a fusion-based CNN architecture to incorporate depth into semantic segmentation. Our network simultaneously extract features on color and depth images independently which are then fused into color features as the network goes deeper. Our comprehensive experiments show that the proposed method is the most effective way of fusing different input modalities into one network. Furthermore, we propose a multimodal CNN architecture that exploits pixelwise semantic labels in addition to color information and thus improves image restoration tasks. Our method processes semantically homogeneous image regions independently and learns to combine them for a refined output. This novel architecture requires semantic annotations only during the training phase and therefore, can perform image restoration on images without the necessity of semantic labeling. Consequently, we discuss the limitations of the proposed approaches and provide potential directions for future research.
Community-acquired Respiratory Virus Cases Mimic COVID-19 on Lung Computed Tomography

We herein report four patients with community-acquired respiratory virus (CRV) infection. Although they had no history of contact with any individual with coronavirus disease 2019 (COVID-19), they were suspected of having COVID-19 based on findings of high-resolution computed tomography (CT) of the lungs. Among the four patients, two were infected with rhinovirus, one with metapneumovirus, and one with influenza A. Their chest CT findings were similar to those of COVID-19 patients reported in previous studies. Both CRV infection and COVID-19 can show various patterns on chest CT. CRV infection is thus indistinguishable from COVID-19 based on CT findings alone.
Chasing Accuracy and Privacy, and Catching Both: A Literature Survey on Differentially Private Histogram Publication

Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations. Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature for understanding the trade off and addressing it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we provide an understanding of the problem by crystallizing the categories of accuracy improving techniques, the core problems they solve, as well as to investigate how composable the techniques are, and 2) we pave the way for future work. In order to provide an understanding, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of noise reduction each technique is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy.
Robust Precoding Techniques for Multibeam Mobile Satellite Systems

This paper presents designing precoding technique at the gateway of a multibeam mobile satellite systems, enabling full frequency reuse pattern among the beams. Such a system brings in two critical challenges to overcome. The inter-beam interference makes applying interference mitigation techniques necessary. Further, when the user terminals are mobile the Channel State Information (CSI) becomes time-varying which is another challenge to overcome. Therefore, the gateway has only access to an outdated CSI, which can eventually limit the precoding gains. In this way, employing a proper CSI estimation mechanism at the gateway can improve the performance of the precoding scheme. In this context, the objectives of this paper are two folds. First, we present different CSI feedback mechanisms which aim at preserving a lower CSI variations at the gateway. Then, we develop the corresponding precoding schemes which are adapted with the proposed CSI feedback mechanisms. To keep the complexity of the proposed precoding schemes affordable, we consider a maritime communication scenario so that the signals received by mobile user terminals suffer from a lower pathloss compared to the Land Mobile communication. Finally, we provide several simulations results in order to evaluate the performance of the proposed precoding techniques.
Screening the Physicochemical Properties of Thermosonically Treated Pomelo Juice

Pomelo (Citrus grandis L. Osbeck) tastes sweet, slightly acidic with a hint of bitterness. It has many beneficial health effects. The aim of this study was to evaluate the effect of thermosonication treatment on physicochemical properties of pomelo juice by subjecting the juice to different times and temperatures. Thermosonication is a treatment where ultrasound is conducted at moderate temperature ranging between 37 and 75°C. Pomelo juice was treated with thermosonication for 2, 46 and 90 minutes with initial temperature ranging from 20°C, 35°C and 50°C. The treated juice were analysed for its physicochemical properties, such as colour values (L*, a* and b*), total soluble solids (TSS) content, pH, titratable acidity and electrical conductivity. Results showed that the lightness (L*), pH, titratable acidity and electrical conductivity of the pomelo juice does not changed during treatment. However, redness (a*) and yellowness (b*) and TSS showed highest reading at 50°C at 90 minutes.
The benefits of mindfulness in mental healthcare professionals

Background: Burnout is a widely reported syndrome consisting of emotional exhaustion, depersonalization, and a lowered sense of accomplishment. Mindfulness practices have been shown to be useful in lowering distress and burnout in clinical and non-clinical cohorts. Our aim was to explore the potential personal and occupational benefits of a structured mindfulness intervention on a cohort of mental health professionals. A mixed-methods approach was utilised in order to enhance the exploratory power of the study. Methods: We conducted a pilot study involving healthcare practitioners employed at a community outpatient mental health clinic. As a pilot, we relied on a single group and implemented a quasi-experimental, simultaneous mixed methods design by incorporating both quantitative pre- and post- testing alongside written qualitative post-test responses. Results: Analysis of the data demonstrated a significant difference between overall mindfulness when comparing post-test (mean=140.8, standard deviation=18.9) with pre-test data (mean=128.3, standard deviation=28.6). Participants also showed a statistically significant difference in three of the subscales: observation, describing, and non-reactivity. A moderate effect size was seen for each of the above differences. Analysis of the qualitative data revealed a range of potential themes which may be used to explain the differences exhibited across participants’ personal and professional lives, which can be grouped into two thematic overarching groups: emotional reactivity and listening/communicating. Conclusions: The results of this pilot study indicate that a structured, six-week mindfulness program has the potential to benefit clinicians, personally by reducing emotional reactivity and professionally by promoting deep listening and communication.
Research on Students' Mental Health Based on Data Mining Algorithms

With the diversification and rapid development of society, people's living conditions, learning and friendship conditions, and employment conditions are facing increasing pressure, which greatly challenges people's psychological endurance. Therefore, strengthening the mental health education of students has become an urgent need of society and a hot issue of common concern. In order to solve the problems of high misjudgment rate and low work efficiency in the current mental health intelligence evaluation process, a mental health intelligence evaluation system based on a joint optimization algorithm is proposed. The joint optimization algorithm consists of an improved decision tree algorithm and an improved ANN algorithm. First, analyze the current research status of mental health intelligence evaluation, and construct the framework of mental health intelligence evaluation system; then collect mental health intelligence evaluation data based on data mining, use joint learning algorithm to analyze and classify mental health intelligence evaluation data, and obtain mental health intelligence evaluation results. Finally, through specific simulation experiments, the feasibility and superiority of the mental health intelligent evaluation system are analyzed. The results show that the system in the article overcomes the shortcomings of the existing mental health intelligence evaluation system, improves the accuracy of mental health intelligence evaluation, and improves the efficiency of mental health intelligence evaluation. It has good system stability and can meet the actual current situation, which are requirements for mental health intelligence evaluation.