Miracle-dz/UniFAR-FTUs huggingface dataset:
Shadow Removal in Indoor Scenes

In this paper, we propose a shadow removal algorithm for indoor scenes. This algorithm uses three types of constraints: chromaticity consistency, texture consistency and range of shadow intensity. The chromaticity consistency is verified in both HSV and RGB color spaces. The texture verification is based on the local coherency (over a pixel neighbourhood) of intensity reduction ratio between shadows and background. Finally, for the range of shadow intensity, we define a localized lower bound of the intensity reduction ratio so that dark mobile objects are not classified as shadows. Because the chromaticity constraint is only correct if the chromaticity of ambient light is the same as that of diffuse light, our algorithms only works in the indoor scenes.
Linking ENSO to Synoptic Weather Systems in Eastern Australia

El Niño‐Southern Oscillation (ENSO) is the main driver of interannual east Australian rainfall variability, but its link with rain‐producing synoptic weather systems is unclear. By tracking low pressure systems in ERA5 over 1979 to 2021, we find that springtime cyclones are linked to variations in the large‐scale atmospheric circulation during ENSO events. On spring days with a cyclone during La Niña, a pressure dipole occurs with a strong anticyclonic anomaly southeast of Australia and a cyclonic anomaly over eastern Australia. The northeasterly circulation directs tropical moisture toward eastern Australia, and coupled with induced ascent, promotes rainfall in this region. Both dynamical and thermodynamical changes are important for the rainfall response. An almost opposite circulation response occurs on cyclone days during El Niño events: high‐pressure over the Australian continent reduces rainfall in eastern Australia. These synoptic setups resemble the seasonal‐mean Rossby wave teleconnections, indicating a link between weather systems and ENSO.
Gratitude to God and its psychological benefits in Islamic contexts: a systematic review of the literature

ABSTRACT Over the last two decades, the positive psychology movement has inspired interdisciplinary inquiries into the nature of religious gratitude and its beneficial health effects. However, the majority of this research has been conducted with Christian populations and informed by assumptions about the theological significance of gratitude that are grounded in Judeo-Christian religious traditions. This systematic review identified 125 scholarly articles available in Arabic, English, Farsi, and Turkish related to the topic of religious gratitude to provide a synthesis of the theological significance of gratitude to God within Islamic doctrine and everyday practices, according to the presently available scholarly literature. Empirical findings on the relationship between religious gratitude and mental health among Muslims are analyzed and reviewed, with particular attention paid to the metrics and methodological approaches employed by authors in diverse cultural and linguistic contexts. From this qualitative synthesis, the authors propose promising areas for future research on this topic.
Medication Adherence in Elderly Diabetic Patients: A Cross-Sectional Study From Dakshina Kannada, India

Diabetes Mellitus (DM) has emerged as a major global healthcare problem. The risk of diabetes can be reduced by maintaining blood glycaemic levels, which can be achieved by stringent adherence to the treatment regime. Therefore, there is a continuing need to assess the level of adherence to medication/self-care activities and the factors that are related to non-adherence to medication and self-care. This would facilitate healthcare professionals to identify subjects with low medication adherence and thereby aid them in planning interventions to improve medication and self-care adherence. In this study, we aimed to estimate the proportion of medication adherence among diabetic patients above 60 years of age attending a tertiary care hospital in Southern India. We found that 72% of type 2 diabetes patients were adherent to the medications prescribed to them and there was a discernible effect of gender and literacy on medication adherence. However, more such regional studies need to be conducted with a larger sample size from diverse hospital setups to obtain a clear and unbiased picture of the drug adherence scenario in India.
Tell Me More, Tell Me More: Getting Writing Teachers Excited About the Framework

Page 8 Can I let you in on a little secret? I love the ACRL Framework. Love it. I think it’s gutsy, ambitious, thoughtful, and highly relevant in today’s information landscape. It’s especially applicable to college students as they confront their ideas, practices, and habits related to information usage whether it be for personal use or for scholastic study. And though it’s a bit hard to really get at such lofty concepts in one-shot sessions, I think that if we effectively communicate the framework’s main tenants to the faculty members who do spend extended amounts of time with these students, the framework just might creep up in other class contexts that can connect information literacy skills to mainstream curriculum in a stronger way.
Stochastic centring of BARRA rainfall space-time patterns in a hydrological model

: When using hydrological models to estimate flood characteristics for infrastructure design, a key consideration is how the potential variability in rainfall over the catchment is represented. The most recent iteration of Australian Rainfall and Runoff (Nathan and Ball 2019) encourages representing this rainfall variability in a Monte Carlo framework. For most applications, these design storms are constructed through combination of design depths with temporal patterns and spatial patterns. However, for larger catchments it can be more appropriate to use space-time patterns rather than distinct spatial and temporal patterns, to ensure that the combined spatiotemporal characteristics are physically reasonable. In both approaches, it is often assumed that the storm is centred over the catchment. However, floods can occur even when storms only partially cover a catchment. Representing variability in storm position can result in a greater variety of modelled flood hydrographs. In Scorah et al. (2021), a technique was developed to incorporate stochastic centring of storm space-time patterns within a catchment. This method involved development of rainfall frequency curves through transposition of regional storm space-time patterns. From this transposition process the probability of a particular storm at a particular location resulting in a given catchment average depth being exceeded could be estimated. A
DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82–0.89, p = 1.22 × 10−29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66–0.76, p = 9.07 × 10−17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.
iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks

Identification of disease-associated long non-coding RNAs (lncRNAs) is crucial for unveiling the underlying genetic mechanisms of complex diseases. Multiple types of similarity networks of lncRNAs (or diseases) can complementary and comprehensively characterize their similarities. Hence, in this study, we presented a computational model iLncDA-RSN based on reliable similarity networks for identifying potential lncRNA-disease associations (LDAs). Specifically, for constructing reliable similarity networks of lncRNAs and diseases, miRNA heuristic information with lncRNAs and diseases is firstly introduced to construct their respective Jaccard similarity networks; then Gaussian interaction profile (GIP) kernel similarity networks and Jaccard similarity networks of lncRNAs and diseases are provided based on the lncRNA-disease association network; a random walk with restart strategy is finally applied on Jaccard similarity networks, GIP kernel similarity networks, as well as lncRNA functional similarity network and disease semantic similarity network to construct reliable similarity networks. Depending on the lncRNA-disease association network and the reliable similarity networks, feature vectors of lncRNA-disease pairs are integrated from lncRNA and disease perspectives respectively, and then dimensionality reduced by the elastic net. Two random forests are at last used together on different lncRNA-disease association feature sets to identify potential LDAs. The iLncDA-RSN is evaluated by five-fold cross-validation to analyse its prediction performance, results of which show that the iLncDA-RSN outperforms the compared models. Furthermore, case studies of different complex diseases demonstrate the effectiveness of the iLncDA-RSN in identifying potential LDAs.
Detection and Attribution of Regional Climate Change

We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and ocean circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale onmore » climate time scales. « less
Assessing the Efficacy of a Brief Universal Family Skills Programme on Violence and Substance-Use Indicators in Youth in Trentino and Parma, Italy: Study Protocol for a Multi-Centre, Non-Blinded, Cluster-Randomised Controlled Trial (cRCT) of Family UNited

Homes in which families are experiencing stressful and challenging circumstances can foster a social space that engenders violent behaviours in parents, inadequate childcare, and the exposure of children to criminal and antisocial behaviours at an early age in addition to many other negative social and health consequences throughout their development. Family Skills Training offers a combination of parenting knowledge, skill building, competency enhancement, and support to strengthen family protective factors, such as communication, trust, problem-solving skills, and conflict resolution. Through over a decade-long experience piloting evidence-based family skills packages globally, we developed a universal open-source family skills package, “Family UNited” (FU), designed for families with children aged 8 to 15 years living in low- and middle-income countries (LMIC). The current study aims to explore the efficacy, fidelity, and acceptability of FU in Trentino and Parma, Italy. We plan to conduct a multi-site, non-blinded, two-armed, cluster-randomised controlled trial to assess efficacy in 160 families: the intervention group receiving FU and the waitlist/control group only receiving FU after the completion of all data collection points. We will prospectively collect outcome data, assessing changes in parenting skills and family adjustment in caregivers, children’s behaviour, resilience capacities, and attitudes towards peer violence. To assess programme delivery, fidelity, feasibility, and acceptability we will include an embedded process evaluation. This study aims to evaluate the improvement in parenting skills, child well-being, and family mental health after participation in FU, compared to no intervention. Even though this trial is to be conducted in a high-income country, such results complement the existing piloting experience in LMIC. with impact-related measures encouraging the adoption of such approaches globally and beyond the EU borders.
A framework for implementing surface water treatment trains for large developments

Nicolas Bastien (corresponding author) Scott Arthur Stephen Wallis School of the Built Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK E-mail: nrb5@hw.ac. uk Miklas Scholz School of Engineering, Institute for Infrastructure and Environment, The University of Edinburgh, Edinburgh EH9 3JL, UK The use of Sustainable Drainage Systems (SuDS) or Best Management Practice (BMP) is becoming increasingly common. However, rather than adopting the preferred “treatment train” implementation, many developments opt for end of pipe control ponds. This paper discusses the use of SuDS in series to form treatment trains and compares their potential performance and effectiveness with end of pipe solutions. Land-use, site and catchment characteristics have been used alongside up-to-date guidance, Infoworks CS and MUSIC to determine whole-life-costs, land-take, water quality and water quantity for different SuDS combinations. The results presented show that the use of a treatment train allows approaches differing from the traditional use of single SuDS, either source or “end of pipe”, to be proposed to treat and attenuate runoff. The outcome is a more flexible solution where the footprint allocated to SuDS, costs and water quality can be managed differently to satisfy more efficiently the holistically stakeholders’ objectives.
Changes in Food Consumption in Postmenopausal Women during the COVID-19 Pandemic: A Longitudinal Study

Studying the dietary habits and symptoms of postmenopausal women during situations such as the COVID-19 pandemic is important to mitigate long-term post-pandemic health problems. We compared the menopausal symptoms and food consumption in postmenopausal women before and during the COVID-19 pandemic. A longitudinal survey was conducted on postmenopausal Brazilian women between 2018 and 2021. The Kupperman–Blatt Menopausal Index, Women’s Health Questionnaire, and 24 h food recall were used. Of 274 women, 78 (28.5%) participated in the study during the COVID-19 pandemic. The intensity of the symptoms was lower during the pandemic than during the previous period (p < 0.05). Energy and processed food consumption were lower during the pandemic than before (p = 0.003 and p = 0.003, respectively). Milk and plain yogurt consumption were also lower (p = 0.043), while the consumption of sugar-sweetened beverages and sweet foods was higher (p = 0.007 and p = 0.001, respectively) during the pandemic. There was also a decrease in the consumption of proteins and lipids (p = 0.001 and p = 0.004, respectively). In conclusion, we found that postmenopausal women consumed sweet foods and sugar-sweetened beverages in higher quantities and had a lower consumption of milk and plain yogurt and processed foods during the pandemic than during the pre-pandemic period. Furthermore, decreases in energy and macronutrient consumption were observed.
Production and N-glycan engineering of Varlilumab in Nicotiana benthamiana

N-glycan engineering has dramatically evolved for the development and quality control of recombinant antibodies. Fc region of IgG contains two N-glycans whose galactose terminals on Fc-glycan have been shown to increase the stability of CH2 domain and improve effector functions. Nicotiana benthamiana has become one of the most attractive production systems for therapeutic antibodies. In this study, Varlilumab, a CD27-targeting monoclonal antibody, was transiently produced in fresh leaves of soil-grown and hydroponic-grown N. benthamiana, resulted in the yield of 174 and 618 µg/gram, respectively. However, the IgG produced in wild-type N. benthamiana lacked the terminal galactose residues in its N-glycan. Therefore, N-glycan engineering was applied to fine-tune recombinant antibodies produced in plant platforms. We further co-expressed IgG together with murine β1,4-galactosyltransferase (β1,4-GALT) to modify plant N-glycan with β1,4-linked Gal residue(s) and Arabidopsis thaliana β1,3-galactosylatransferase (β1,3-GALT) to improve galactosylation. The co-expression of IgG with each of GALTs successfully resulted in modification of N-glycan structures on the plant-produced IgG. Notably, IgG co-expressed with murine β1,4-GALT in soil-grown N. benthamiana had 42.5% of N-glycans variants having galactose (Gal) residues at the non-reducing terminus and 55.3% of that in hydroponic-grown N. benthamiana plants. Concomitantly, N-glycan profile analysis of IgG co-expressed with β1,3-GALT demonstrated that there was an increased efficiency of galactosylation and an enhancement in the formation of Lewis a structure in plant-derived antibodies. Taken together, our findings show that the first plant-derived Varlilumab was successfully produced with biantennary β1,4-galactosylated N-glycan structures.
Comparative Analysis of Hotspot Stress Endurance in Pristine and Thermal Cycled Prestressed Glass–Glass Photovoltaic Modules

Hotspots pose a significant long-term reliability challenge in photovoltaic (PV) modules that can have a detrimental impact on the efficiency, safety, and financial viability of a PV system. This paper aims to evaluate the endurance of hotspot stress in pristine and prestressed glass–glass (GG) modules. The accelerated prestressing was conducted for 600 thermal cycles (TC600) to represent decades of field exposure. GG modules are quickly becoming an alternative to the traditional glass–backsheet (GB) modules that have been the industry standard. Unlike other conventional studies that subject only pristine modules to hotspot stress, this paper evaluates the performance of an accelerated/simulated field-aged GG module (using TC600) and a pristine GG module. Pre- and post-characterizations were performed before and after each test to determine changes in electrical performance and observe any defects in GG modules. During the hotspot test, an approximately 200 °C maximum cell temperature was observed with a cell shading of 25% (the worst-case shading ratio). After the hotspot test, electroluminescence imaging indicated that most cells in the prestressed GG module exhibited severe damage whereas no significant defects were evident in the pristine GG module where the prestressed GG module degraded 8.2% and the pristine GG module degraded 1.5% in maximum power. These findings are critical for the industry, considering that GG bifacial modules will dominate the market.
A Convolutional Neural Network Image Classiﬁcation Based on Extreme Learning Machine

—To improve the classiﬁcation accuracy of the massive amount of image data, we propose a novel extreme learning machine classiﬁcation model for image classiﬁcation which based on convolutional neural networks. Based on the framework of Alex Net network, the hash function is constructed as the hidden layer between image representation and classiﬁcation output in convolutional neural network. At the same time, the extreme learning machine is introduced at the end of the network layer, which saves the classiﬁcation time, improves the classiﬁcation efﬁciency and further improves the feature expression ability of the network. Through comparative experiments in the standard databases MNIST and CIFAR-10, the effects of various improved methods under different situations are analyzed. The experimental results show that the proposed convolutional neural network image classiﬁcation method based on the extreme learning machine improves the average precision by 3 % -31 % compared with other image classiﬁcation methods in this paper.
changes in background electroencephalography and regional cerebral glucose metabolism in focal epilepsy patients after 1-month administration of levetiracetam

: The antiseizure efficacy and safety of levetiracetam (LEV) is well documented; however, few clinical studies have investigated the predictability of patient responsiveness to LEV, especially when the drug is first administered. The aim of this study was to ascertain the utility of clinical, electrophysiological, and neuroimaging parameters for assessing the early response to LEV treatment in focal epilepsy patients. Twelve confirmed focal epilepsy patients were included who had never taken LEV before. At baseline and 1 month after LEV administration, all subjects underwent 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and electroencephalography (EEG), and completed the Quality of Life in Epilepsy questionnaire (QOLIE-31). Participants were divided by drug response: good versus poor. The good response group (seven subjects) was defined by a  50% decrease in seizure frequency compared to baseline (3 months before LEV intake). The other five participants with a  50% decrease in seizure frequency were placed into the poor response group. We compared the differential changes in brain glucose metabolism on FDG-PET, power spectrum on the EEG, and QOLIE-31 results between the two groups after a 1-month LEV trial. In the good response group, it was possible to identify brain regions with increased glucose metabolism, including the bilateral caudate nuclei and both frontal and left parietal regions (uncorrected P  0.005). In the poor response group, FDG-PET did not reveal any areas with significantly increased glucose metabolism. In the good response group, spectral EEG analysis revealed decreased delta power (1–3 Hz, P  0.05) in the parietal region and increased beta1 power (13–19 Hz, P  0.05) in the frontal region, whereas no significant changes were observed in the poor response group. There were no significant changes on the QOLIE-31 in either group after a 1-month LEV trial. Our results suggest that LEV-induced glucose metabolism and EEG spectral changes may be indicative of initial drug responsiveness as early as 1 month following treatment initiation. These parameters may be useful prognostic markers of antiseizure effects caused by LEV medication or may indicate an epiphenomenon of LEV-induced changes in glucose metabolism and EEG frequency. Further studies with
Empowering Metropolitan Regions Through New Forms of Cooperation

Contents: Preface Introduction Brainport Eindhoven Centrope: a cross-border alliance in the Central European region New forms of regional cooperation in the Helsinki region The networks of wonderful Copenhagen Planning for sustainable development in the Munich region Integrated public transport policies in the Budapest metropolitan area Noordrand project: cooperation in the Rotterdam region The better districts programme of Catalonia: Serra d'en Mena Porto's experience with the Serrasalves Foundation Synthesis Index.