Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical study of InAs/GaAs quantum dot solar cells

Solar energy conversion is a promising way to provide future energy demand since it is a clean energy. Unfortunately, the photovoltaic (PV) conversion of the solar energy is expensive, therefore, making attempts to increase the efficiency of PV is essential. A conventional single junction solar cell presents an efficiency limit that is determined by the Shockley-Queisser detailed balance principle (i.e. 40.7% under full sun concentration). The limit comes from the fact that only photons with energy close to the energy bandgap are efficiently converted. Below energy gap, photons are not absorbed since the cell is transparent to them and high energy photons only contribute part of their energy that is equal to the energy bandgap. Many concepts have been developed in order to increase the efficiency limit of solar cells. Among them the intermediate band solar cell (IBSC) has gained considerable attention. In principle, IBSCs have the potential to overcome Shockley-Queisser (SQ) limit of single junction solar cells by providing high current while preserving large voltage. The theoretical limit calculated for an ideal IBSC under full sun concentration is 63.1%. One of the most promising ways to realize the IBSC is to incorporate a QD superlattice in the active region of p-i-n single junction solar cells. The nano-size QDs behave like 3D potential well for the carriers and create discrete energy levels within the forbidden bandgap that allows sub-bandgap photon absorption. Stranski-Krastanov (S-K) growth mode (also called ’layer-plus-island growth’) is one of the most common methods to fabricate QDs. This method has been used in many experimental studies for InAs/GaAs heteroepitaxial system which has lattice mismatch of 7.2%. Although InAs/GaAs is not an optimal material system for the IBSC performance, its properties and parameters are well reported in literature compared to other material systems. The drift-diffusion model is the most widely used mathematical approach to describe semiconductor devices. However, in case of quantum dot solar cells, the physics
The Ross procedure versus repair for treatment of a unicuspid aortic valve in adults

Abstract OBJECTIVES Aortic stenosis or regurgitation in patients with a unicuspid valve morphology requires interventions early in life. We have performed either primary valve repair or the Ross procedure. The goal of this study was to compare the midterm results of repair and pulmonary autograft replacement. METHODS Between December 1998 and April 2022, a total of 345 patients (77% male; mean age 34 ± 9.7 years) underwent treatment of a unicuspid aortic valve. Patients were excluded if they were <18 years (n = 84) or >54 years (n = 3) at the time of the operation. The remaining cohort was divided into 2 groups: 167 (64%) patients underwent valve repair; 91 (36%) patients underwent pulmonary autograft replacement. The indications for surgery were aortic regurgitation (n = 104), aortic stenosis (n = 45), combined disease (n = 103) and endocarditis (n = 6). Fifty-one patients had root dilatation (>43 mm) with aortic regurgitation (repair n = 23; Ross n = 28). Mean follow-up was 5.9 years (SD: 5 years) [range 0.1–22.3 years]. RESULTS There were 1 early and 3 late deaths; 47 patients required reintervention. Survival at 10 years was 95% in the Ross group and 97% after valve repair (P = 0.769). Freedom from reintervention at 10 years was 98% in the Ross group and 80% after valve repair (P = 0.012). A receiver operating characteristics curve analysis showed a trend towards better durability in patients < 26 years. CONCLUSIONS The ideal treatment of the unicuspid aortic valve remains debatable. Repair of a unicuspid valve can be considered a bridge to pulmonary autograft replacement, at least in younger patients. The appropriate times to replace and to repair require further investigation.
Investigating the Vulnerability of Motorcyclists to Crashes and Injury

Motorcyclists represent a segment of vulnerable road users that have high levels of risk, mostly because they lack protection. There are about 5,000 motorcyclist fatalities every year in the US. In response, the U.S. Congress has passed legislation initiating comprehensive research that identifies the causes of motorcycle crashes. This in turn resulted in a new federally collected Motorcycle Crash Causation Study (MCCS) data. This project uses MCCS to conduct research and generate new knowledge about motorcycle safety. The key objectives of this project are to investigate motorcycle crash risk factors, e
Size Effect on the Seismic Performance of High-Strength Reinforced Concrete Columns with Different Shear Span-to-Depth Ratios

The size effect on the seismic performance of conventional reinforced concrete columns has been observed in terms of flexural failure and shear failure. Under earthquake loading, slender columns experience flexural failure, and short columns experience flexure-shear failure and shear failure. However, the effect of section size on the seismic performance of high-strength reinforced concrete columns under the conditions of different shear span-to-depth ratios requires further confirmation. For this purpose, six high-strength reinforced concrete columns with shear span-to-depth ratios of 2 and 4 were subjected to cyclic loading in this study. The experimental results indicated that relative nominal flexural strength, energy dissipation coefficient, factor of safety, and local factor of safety all exhibited a strong size effect by decreasing with increasing column size. Furthermore, the size effect became stronger as the shear span-to-depth ratio was increased, except for average energy dissipation coefficient. The observed changes in the factor of safety were in good agreement with the Type 2 size effect model proposed by Bažant. Thus, based on the local factor of safety and Bažant’s Type 2 model, the code equation for moment capacity of different shear span-to-depth ratios was modified to provide a consistent factor of safety regardless of column size.
Optimization of Markov Random Fields in Computer Vision

A large variety of computer vision tasks can be formulated using Markov Random Fields (MRF). Except in certain special cases, optimizing an MRF is intractable, due to a large number of variables and complex dependencies between them. In this thesis, we present new algorithms to perform inference in MRFs, that are either more efficient (in terms of running time and/or memory usage) or more effective (in terms of solution quality), than the state-of-the-art methods. First, we introduce a memory efficient max-flow algorithm for multi-label submodular MRFs. In fact, such MRFs have been shown to be optimally solvable using maxflow based on an encoding of the labels proposed by Ishikawa, in which each variable Xi is represented by ` nodes (where ` is the number of labels) arranged in a column. However, this method in general requires 2 `2 edges for each pair of neighbouring variables. This makes it inapplicable to realistic problems with many variables and labels, due to excessive memory requirement. By contrast, our max-flow algorithm stores 2 ` values per variable pair, requiring much less storage. Consequently, our algorithm makes it possible to optimally solve multi-label submodular problems involving large numbers of variables and labels on a standard computer. Next, we present a move-making style algorithm for multi-label MRFs with robust non-convex priors. In particular, our algorithm iteratively approximates the original MRF energy with an appropriately weighted surrogate energy that is easier to minimize. Furthermore, it guarantees that the original energy decreases at each iteration. To this end, we consider the scenario where the weighted surrogate energy is multi-label submodular (i.e., it can be optimally minimized by max-flow), and show that our algorithm then lets us handle of a large variety of non-convex priors. Finally, we consider the fully connected Conditional Random Field (dense CRF) with Gaussian pairwise potentials that has proven popular and effective for multiclass semantic segmentation. While the energy of a dense CRF can be minimized accurately using a Linear Programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block-coordinate descent. We show that each block of variables can be optimized in a time linear in the number of pixels and labels. Consequently, our algorithm enables efficient and effective optimization of dense CRFs with Gaussian pairwise potentials. We evaluated all our algorithms on standard energy minimization datasets consisting of computer vision problems, such as stereo, inpainting and semantic segmentation. The experiments at the end of each chapter provide compelling evidence that all our approaches are either more efficient or more effective than all existing baselines.
BLOOD GROUP PROBABILITIES BY NEXT OF KIN

For rare blood groups the recruitment of donor relatives, for example siblings, is expected to be effective, since the probability of a similar rare blood group is likely. However, the likelihood differs between blood groups and is not commonly available. This paper provides a unified mathematical formulation to calculate such likelihoods. From a mathematical and probabilistic point of view, it is shown that these likelihoods can be obtained from the computation of a stationary genotype distribution. This, in turn, can be brought down to a system of quadratic stochastic operators. A generic mathematical approach is presented which directly leads to a stationary genotype distribution for arbitrary blood groups. The approach enables an exact computation for the effectiveness of recruiting next of kin for blood donorship. Next to an illustration of computations for ‘standard’ ABO and Rhesus-D blood groups, it is particularly illustrated for the extended Rhesus blood group system. Also other applications requiring next of kin blood group associations can be solved directly by using the unified mathematical formulation.
The Effect of Sound Localization on Auditory-Only and Audiovisual Speech Recognition in a Simulated Multitalker Environment

Information regarding sound-source spatial location provides several speech-perception benefits, including auditory spatial cues for perceptual talker separation and localization cues to face the talker to obtain visual speech information. These benefits have typically been examined separately. A real-time processing algorithm for sound-localization degradation (LocDeg) was used to investigate how spatial-hearing benefits interact in a multitalker environment. Normal-hearing adults performed auditory-only and auditory-visual sentence recognition with target speech and maskers presented from loudspeakers at −90°, −36°, 36°, or 90° azimuths. For auditory-visual conditions, one target and three masking talker videos (always spatially separated) were rendered virtually in rectangular windows at these locations on a head-mounted display. Auditory-only conditions presented blank windows at these locations. Auditory target speech (always spatially aligned with the target video) was presented in co-located speech-shaped noise (experiment 1) or with three co-located or spatially separated auditory interfering talkers corresponding to the masker videos (experiment 2). In the co-located conditions, the LocDeg algorithm did not affect auditory-only performance but reduced target orientation accuracy, reducing auditory-visual benefit. In the multitalker environment, two spatial-hearing benefits were observed: perceptually separating competing speech based on auditory spatial differences and orienting to the target talker to obtain visual speech cues. These two benefits were additive, and both were diminished by the LocDeg algorithm. Although visual cues always improved performance when the target was accurately localized, there was no strong evidence that they provided additional assistance in perceptually separating co-located competing speech. These results highlight the importance of sound localization in everyday communication.
Associations between COVID-19 mental impact and distress, resilience, burnout and well-being in Hong Kong community adults: A structural equation model

ABSTRACT The COVID-19 pandemic poses substantial risks to individuals’ physical and mental health and prolonged psychological responses to the pandemic could lead to emotional exhaustion. The present study aimed to examine the mediating role of COVID-19 related mental impact and distress in the relationship among resilience, burnout, and well-being. The present study recruited 500 community adults (mean age = 38.8 years, SD = 13.9; 76% females) in Hong Kong via an online survey in autumn 2021. The participants completed the Mental Impact and Distress Scale: COVID-19 (MIDc) and validated measures on resilience, burnout, and well-being. Confirmatory factor analysis was conducted to evaluate the psychometric properties of the MIDc. Direct and indirect effects of resilience on burnout and well-being via MIDc were examined via structural equation modeling. Confirmatory factor analysis supported factorial validity for the three factors of MIDc (situational impact, anticipation, and modulation). Resilience showed negative effects on the MIDc (β = −0.69, SE = 0.04, p < 0.01) and burnout (β = 0.23, SE = 0.06, p < 0.01). Burnout was positively associated with MIDc (β = 0.63, SE = 0.06, p < 0.01) and negatively associated with well-being (β = −0.47, SE = 0.07, p < 0.01). Resilience showed a significant and positive indirect effect (αβγ = 0.203, 95% CI = 0.131 to 0.285) on well-being via MIDc and burnout. The results support a potential mediating role for MIDc as psychological responses in the relationship among resilience and burnout and well-being.
A portable primary-standard level graphite calorimeter for absolute dosimetry in clinical pencil beam scanning proton beams

Objective. To report the use of a portable primary standard level graphite calorimeter for direct dose determination in clinical pencil beam scanning proton beams, which forms part of the recommendations of the proposed Institute of Physics and Engineering in Medicine (IPEM) Code of Practice (CoP) for proton therapy dosimetry. Approach. The primary standard proton calorimeter (PSPC) was developed at the National Physical Laboratory (NPL) and measurements were performed at four clinical proton therapy facilities that use pencil beam scanning for beam delivery. Correction factors for the presence of impurities and vacuum gaps were calculated and applied, as well as dose conversion factors to obtain dose to water. Measurements were performed in the middle of 10 × 10 × 10 cm3 homogeneous dose volumes, centred at 10.0, 15.0 and 25.0 g·cm−2 depth in water. The absorbed dose to water determined with the calorimeter was compared to the dose obtained using PTW Roos-type ionisation chambers calibrated in terms of absorbed dose to water in 60Co applying the recommendations in the IAEA TRS-398 CoP. Main results. The relative dose difference between the two protocols varied between 0.4% and 2.1% depending on the facility. The reported overall uncertainty in the determination of absorbed dose to water using the calorimeter is 0.9% (k = 1), which corresponds to a significant reduction of uncertainty in comparison with the TRS-398 CoP (currently with an uncertainty equal or larger than 2.0% (k = 1) for proton beams). Significance. The establishment of a purpose-built primary standard and associated CoP will considerably reduce the uncertainty of the absorbed dose to water determination and ensure improved accuracy and consistency in the dose delivered to patients treated with proton therapy and bring proton reference dosimetry uncertainty in line with megavoltage photon radiotherapy.
Efficient acidic hydrogen evolution in proton exchange membrane electrolyzers over a sulfur-doped marcasite-type electrocatalyst

Large-scale deployment of proton exchange membrane (PEM) water electrolyzers has to overcome a cost barrier resulting from the exclusive adoption of platinum group metal (PGM) catalysts. Ideally, carbon-supported platinum used at cathode should be replaced with PGM-free catalysts, but they often undergo insufficient activity and stability subjecting to corrosive acidic conditions. Inspired by marcasite existed under acidic environments in nature, we report a sulfur doping–driven structural transformation from pyrite-type cobalt diselenide to pure marcasite counterpart. The resultant catalyst drives hydrogen evolution reaction with low overpotential of 67 millivolts at 10 milliamperes per square centimeter and exhibits no degradation after 1000 hours of testing in acid. Moreover, a PEM electrolyzer with this catalyst as cathode runs stably over 410 hours at 1 ampere per square centimeter and 60°C. The marked properties arise from sulfur doping that not only triggers formation of acid-resistant marcasite structure but also tailors electronic states (e.g., work function) for improved hydrogen diffusion and electrocatalysis.
Improving Tumor Classification by Reusing Self-Predicted Segmentation of Medical Images as Guiding Knowledge

Differential diagnosis of tumors is important for computer-aided diagnosis. In computer-aided diagnosis systems, expert knowledge of lesion segmentation masks is limited as it is only used during preprocessing or as supervision to guide feature extraction. To improve the utilization of lesion segmentation masks, this study proposes a simple and effective multitask learning network that improves medical image classification using self-predicted segmentation as guiding knowledge; we call this network RS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-net. In RS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-net, the predicted segmentation probability map obtained from the initial segmentation inference is added to the original image to form a new input, which is then reinput to the network for the final classification inference. We validated the proposed RS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-net using three datasets: the pNENs-Grade dataset, which tested the prediction of pancreatic neuroendocrine neoplasm grading, and the HCC-MVI dataset, which tested the prediction of microvascular invasion of hepatocellular carcinoma, and ISIC 2017 public skin lesion dataset. The experimental results indicate that the proposed strategy of reusing self-predicted segmentation is effective, and RS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-net outperforms other popular networks and existing state-of-the-art studies. Interpretive analytics based on feature visualization demonstrates that the improved classification performance of our reuse strategy is due to the semantic information that can be acquired in advance in a shallow network.
The Heliosphere Is Not Round

Observations from the Ion and Neutral Camera (INCA) on the Cassini mission of energetic neutral atoms (ENAs) at ∼10 keV and ∼45 keV showed significant correlated time variations over relatively fast 2–3 yr timescales. These observed ENA variations have been interpreted as indicating limited scale lengths of ∼80–120 au along the line of sight for the size of the heliosphere. We show here, however, that rather than a heliosphere with a quasi-spherical shape, the INCA line-of-sight observations vary in response to episodic cooling and heating of the inner heliosheath plasma during periods of large-scale expansion and compression.
Longitudinal effects of elexacaftor/tezacaftor/ivacaftor on sputum viscoelastic properties, airway infection and inflammation in patients with cystic fibrosis

Background Recent studies demonstrated that the triple combination cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) improves lung function and reduces pulmonary exacerbations in cystic fibrosis (CF) patients with at least one F508del allele. However, effects of ETI on downstream consequences of CFTR dysfunction, i.e. abnormal viscoelastic properties of airway mucus, chronic airway infection and inflammation have not been studied. The aim of this study was to determine the longitudinal effects of ETI on airway mucus rheology, microbiome and inflammation in CF patients with one or two F508del alleles aged ≥12 years throughout the first 12 months of therapy. Methods In this prospective observational study, we assessed sputum rheology, the microbiome, inflammation markers and proteome before and 1, 3 and 12 months after initiation of ETI. Results In total, 79 patients with CF and at least one F508del allele and 10 healthy controls were enrolled in this study. ETI improved the elastic modulus and viscous modulus of CF sputum at 3 and 12 months after initiation (all p<0.01). Furthermore, ETI decreased the relative abundance of Pseudomonas aeruginosa in CF sputum at 3 months and increased the microbiome α-diversity at all time points. In addition, ETI reduced interleukin-8 at 3 months (p<0.05) and free neutrophil elastase activity at all time points (all p<0.001), and shifted the CF sputum proteome towards healthy. Conclusions Our data demonstrate that restoration of CFTR function by ETI improves sputum viscoelastic properties, chronic airway infection and inflammation in CF patients with at least one F508del allele over the first 12 months of therapy; however, levels close to healthy were not reached. Tweetable abstract The triple combination CFTR modulator therapy ETI improves viscoelastic properties of airway mucus, chronic airway infection and inflammation in CF patients with at least one F508del allele, but without reaching levels close to healthy https://bit.ly/3OGBp3m
Childhood attention-deficit hyperactivity disorder problems and mid-life cardiovascular risk: prospective population cohort study

Background It is well-known that childhood attention-deficit hyperactivity disorder (ADHD) is associated with later adverse mental health and social outcomes. Patient-based studies suggest that ADHD may be associated with later cardiovascular disease (CVD) but the focus of preventive interventions is unclear. It is unknown whether ADHD leads to established cardiovascular risk factors because so few cohort studies measure ADHD and also follow up to an age where CVD risk is evident. Aims To examine associations between childhood ADHD problems and directly measured CVD risk factors at ages 44/45 years in a UK population-based cohort study (National Child Development Study) of individuals born in 1958. Method Childhood ADHD problems were defined by elevated ratings on both the parent Rutter A scale and a teacher-rated questionnaire at age 7 years. Outcomes were known cardiovascular risk factors (blood pressure, lipid measurements, body mass index and smoking) at the age 44/45 biomedical assessment. Results Of the 8016 individuals assessed both during childhood and at the biomedical assessment 3.0% were categorised as having childhood ADHD problems. ADHD problems were associated with higher body mass index (B = 0.92 kg/m2, s.d. = 0.27–1.56), systolic (3.5 mmHg, s.d. = 1.4–5.6) and diastolic (2.2 mmHg, s.d. = 0.8–3.6) blood pressure, triglyceride levels (0.24 mol/l, s.d. = 0.02–0.46) and being a current smoker (odds ratio OR = 1.6, s.d. = 1.2–2.1) but not with LDL cholesterol. Conclusions Childhood ADHD problems predicted multiple cardiovascular risk factors by mid-life. These findings, when taken together with previously observed associations with cardiovascular disease in registries, suggest that individuals with ADHD could benefit from cardiovascular risk monitoring, given these risk factors are modifiable with timely intervention.