Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Cluster Detection of Air Pollution Exposure Inequities across the United States

Air quality is known to be a key factor in affecting the wellbeing and quality of life of the general populous and there is a large body of knowledge indicating that certain underrepresented groups may be overexposed to air pollution. Therefore, a more precise understanding of air pollution exposure as a driving cause of health disparities between and among ethnic and racial groups is necessary. Utilizing 52,613 urban census tracts across the United States, this study investigates age, racial, educational attainment and income differences in exposure to benzene pollution in 1999 as a case. The study examines spatial clustering patterns of these inequities using logistic regression modeling and spatial autocorrelation methods such as the Global Moran's I index and the Anselin Local Moran's I index. Results show that the age groups of 0 to 14 and those over 60 years old, individuals with less than 12 years of education, racial minorities including Blacks, American Indians, Asians, some other races, and those with low income were exposed to higher levels of benzene pollution in some census tracts. Clustering analyses stratified by age, education, and race revealed a clear case of disparities in spatial distribution of exposure to benzene pollution across the entire United States. For example, people aged less than 4 years from the western south and the Pacific coastal areas exhibit statistically significant clusters. The findings confirmed that there are geographical-location based disproportionate pattern of exposures to benzene air pollution by various socio-demographic factors across the United States and this type of disproportionate exposure pattern can be effectively detected by a spatial autocorrelation based cluster analysis method. It is suggested that there is a clear and present need for programs and services that will reduce inequities and ultimately improve environmental conditions for all underrepresented groups in the United States.
Integrity protection in a smart grid environment for wireless access of smart meters

To meet future challenges of energy grids, secure communication between involved control systems is necessary. Therefore the German Federal Office for Information Security (BSI) has published security standards concerning a central communication unit for energy grids called Smart Meter Gateway (SMGW). The present security concept of the SPIDER project takes these standards into consideration but extends their level of information security by integrating elements from the Trusted Computing approach. Additionally, a tamper resistant grid is integrated with chosen hardware modules and a trustworthy boot process is applied. To continually measure the SMGW and smart meter (SM) integrity the approach Trusted Network Connect (TNC) from the Trusted Computing Group (TCG) is used. Hereby a Trusted Core Network (TCN) can be established to protect the smart grid components against IT based attacks. That is necessary, especially by the use of wireless connections between the SMGW and smart meter components.
A Classification of certain finite Double Coset Collections in the Classical Groups

Let G be a classical algebraic group, X a maximal rank reductive subgroup and P a parabolic subgroup. This paper classifies when X∖G/P is finite. Finiteness is proven using geometric arguments about the action of X on subspaces of the natural module for G. Infiniteness is proven using a dimension criterion that involves root systems. 2000 Mathematics Subject Classification 14L30, 14L35, 20G15.
‘Give us a break!’: using a solution focused programme to help young people cope with loss and negative change

Abstract This research evaluated the impact of Give us a break! (GUAB! ) — a programme for children and young people who have experienced bereavement or negative change including the impact of divorce/separation; experiences around ill health and circumstances leading to individuals being ‘looked after’. A repeated measures design was utilised to measure the pre-post difference in the factors which contribute to young people’s resilience following intervention. Additionally, as the programme is based on a model of Solution Focused Brief Therapy — which aims to develop a positive orientation towards the future — optimistic thinking was closely evaluated. Young people completed the Resiliency Scales for Children and Adolescents (Prince-Embury, 2006) and, to increase the rigour of data collection, parents/carers and teachers completed the Devereux Student Strengths Assessment (LeBuffe, Shapiro & Naglieri, 2009). The findings provide evidence that the programme positively impacted on young people in terms of their Sense of Relatedness to others, Sense of Mastery, Social and Emotional Competence and Optimistic Thinking. Furthermore, these findings were not contingent upon the type of loss experienced (bereaved/non-bereaved). Some methodological limitations are highlighted and directions for future research considered.
Study on high voltage ride through control strategy of PMSG‐based wind turbine generation system with SCESU

Regarding PMSG-based wind turbine generation system, this study proposes a super-capacitor energy storage unit (SCECU) connected parallel to the DC-link of the back-to-back converter to enhance its high voltage ride through performance. The analysis of the operation and control for the grid-side converter and SCECU are conducted. Based on real-time digital simulators (RTDS), a model of PMSG-based wind turbine with SCECU is developed, and the simulation results show that the SCECU absorbs the imbalanced energy and the grid-side converter absorbs inductive reactive power during the period of voltage swell. The simulation results based on RTDS verify the correctness and feasibility of the high voltage ride through control strategy.
Cloud condensation nucleus activity of secondary organic aerosol particles mixed with sulfate

The cloud condensation nucleus (CCN) activity of organic‐sulfate particles was investigated using a steady‐state environmental chamber. The organic component consisted of secondary organic aerosol (SOA) generated in the dark from 24 ± 2 ppb α‐pinene for conditions of 300 ± 5 ppb ozone, 40 ± 2% relative humidity, and 25 ± 1°C, with the organic mass loading in the chamber ranging from 23 to 37 μg m−3. CCN analysis was performed for 80‐ to 150‐nm particles having variable organic‐sulfate volume fractions, which were estimated from the diameter of the organic‐sulfate particle relative to that of the seed as well as independently from mass spectra. Critical supersaturation, which increased for greater SOA volume fraction and smaller particle diameter, was well predicted by a Köhler model having two components, one for ammonium sulfate and another for SOA. The entire data set could be successfully modeled by a single suite of effective chemical parameters for SOA. The results suggest that the effects of limited organic solubility in mixed SOA‐sulfate particles may be reliably omitted in the treatment of cloud droplet formation.
Review of Optimization Methods for Cancer Chemotherapy Treatment Planning

Tumors in humans are believed to be caused by a sequence of genetic abnormality. Understanding these sequences is important for improving cancer treatments. Biologists have uncovered some of the most basic mechanisms by which normal stem cells develop into cancerous tumors. These biological theories can then be transformed into mathematical models. In this paper, we review the mathematical models applied to the optimal design of cancer chemotherapy. However, chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumors with the adverse toxic side effects caused by the anti-cancer drugs. Some methods of computational optimization have proven useful in helping to strike the right balance. The purpose of this paper is to discuss the limitations of the existing theoretical research and provide several directions to improve research in optimizing chemotherapy treatment planning using real protocol treatments defined by the oncologist.
Differential Geometry Boosts Convolutional Neural Networks for Object Detection

Convolutional neural networks (CNNs) have had dramatic success in appearance based object recognition tasks such as the ImageNet visual recognition challenge [8]. However, their application to object recognition and detection thus far has focused largely on intensity or color images as inputs. Motivated by demonstrations that depth can enhance the performance of CNN-based approaches [2][5], in this paper we consider the benefits of also including differential geometric shape features. This elementary idea of using zeroth order (depth), first-order (surface normal) and second-order (surface curvature) features in a principled manner boosts the performance of a CNN that has been pretrained on a color image database. Notably, in an object detection task involving 19 categories we achieve 39.30% accuracy on the NYUv2 dataset, which is a 10.4% improvement over the current state-of-the-art accuracy of 35.6% using the method in [5]. In the simpler scenario of turntable style object recognition, our experiments on the University of Washington (UW) RGB-D dataset yield an accuracy of 88.7% correct recognition over 51 object categories, where the best competing result is 87.5% [2]. Taken together, our results provide strong evidence that the abstraction of surface shape benefits object detection and recognition.
Enabling Indirect Mutual Trust for Cloud Storage Systems

Cloud computing is a forthcoming revolution in information technology (IT) industry because of its performance, accessibility, low cost and many other luxuries. It provides gigantic storage for data and faster computing to customers over the internet. It essentially shifts the database and application software to the large data centers, i.e., Cloud, where management of data and services may not be completely trustworthy. That is why companies are reluctant to deploy their business in the cloud even cloud computing offers a wide range of luxuries. Security of data in the cloud is one of the major issues which acts as an obstacle in the implementation of cloud computing. In the current era of digital world, the amount of sensitive data produced by many organizations is outpacing their storage ability. The management of such huge amount of data is quite expensive due to the requirements of high storage capacity and qualified personnel. Storage-as-a-Service (SaaS) offered by cloud service providers (CSPs) is a paid facility that enables organizations to outsource their data to be stored on remote servers. Thus, SaaS reduces the maintenance cost and mitigates the burden of large local data storage at the organization's end. A data owner pays for a desired level of security and must get some compensation in case of any misbehaviour committed by the CSP. On the other hand, the CSP needs a protection from any false accusations that may be claimed by the owner to get illegal compensations. In this paper, a cloud-based storage scheme is proposed that allows the data owner to benefit from the facilities offered by the CSP and enables indirect mutual trust between them. The proposed scheme has two important features: (i) It allows the owner to outsource sensitive data to a CSP, and it ensures that only authorized users (i.e., Those who have the right to access the owner’s file) receive the outsourced data i.e. It enforces the access control of the outsourced data. (ii) It enables indirect mutual trust between the owner and the CSP.
HOG based multi-object detection for urban navigation

A necessary condition to perform a fully autonomous driving system in urban environment is to detect object types in real scenes. Visual object recognition is a key solution, but multi-object detection still remain unsolved. In this paper, we present a fast and efficient multi-object detection system built to recognize, at the same time, pedestrians cars and bicycles. For each target type, we construct a holistic detector in a cascade manner, using a dense overlapping grid based on histograms of oriented gradients (HOG). The selection of HOG features is obtained through a learning process using AdaBoost algorithm. Experiments have been conducted on the car-like robot Robucar, where the single detectors are combined and implemented on its embedded computer, which is endowed with a modular software platform. Results are promising as the system can process up to 20 fps with VGA images.
A transactional approach to predicting stress experienced when writing dissertations

Although substantial attention has been given to doctoral students, the attention given to master’s students writing dissertations is limited. This article outlines a transactional model of stress that conceptualises dissertation writing as a stressor consisting of a series of phases that tend to increase the risk of negative outcomes. A mixed-method design was used to address the aim of the study. An adapted version of the Stress and Support Questionnaire for University Students was used to measure the stress that master’s students experienced. Open-ended questions were employed to measure students’ evaluations of the stress experienced when writing a dissertation. The model proposed that the phases of dissertation writing moderate the relationship between stressor factors and the stress experienced. In an empirical study making use of the model, the moderation analyses revealed that Phase 1 of dissertation writing significantly moderated the relationship between the stress experienced, stressor factors of relationships, financial and transport problems and health related problems. The qualitative data suggested that stress is evaluated either positively or negatively depending on time-management styles and support systems. The results obtained underscore the importance of support structures in alleviating the stress experienced when writing dissertations in order to improve students’ retention and graduation rates.
Parameters Optimization for Urban Vegetation Classification Using KPCA

It is important that urban vegetation can be extracted automatically and precisely from high spatial resolution satellite imagery. However, the data is subject to spectral shortage and has strong nonlinear characteristics in the original spectral space. In this paper, the kernel principal component analysis (KPCA) is used to analyze the separability of the vegetation features and to select features using IKONOS data. The parameter of the kernel function is determined and the sample number is optimized with mean B-distance (MBD) using our test dataset. Experiments have shown that the maximum MBD is found to be 6.8 and 2.5 after and before KPCA respectively for six vegetation types in 50 dimensions with the power of 0.01. The KPCA-based spectral angle mapping (SAM) is also compared with both the original SAM and classic classifiers. It is demonstrated that the proposed approach is able to achieve more accurate classifications thus produce better urban vegetation map than the conventional methods
Prevalence and correlates of face recognition impairments after acquired brain injury

Impairments of face recognition after acquired brain injury (ABI) are not restricted to prosopagnosia but commonly arise in association with other cognitive deficits and can be psychosocially debilitating. Despite this, the prevalence and cognitive concomitants of such impairments after ABI have not been systematically investigated. We tested 91 adults with ABI on a range of cognitive measures including several indices of face recognition and learning. The proportion of patients who show impaired performance varied across face learning/recognition tests between 21% and 80%. Principal components analyses indicated orthogonality between impairments of “directed facial processing”, associated with memory and visuoperceptual deficits and manifest in slow learning and matching of previously unfamiliar faces, and of “face identification”, associated with deficits on verbal tests and manifest in difficulty in naming famous faces. Theoretical and rehabilitative implications are considered. This research was supported by a grant from the James S. McDonnell Foundation to whom we are most grateful.
Labetalol Prevents Intestinal Dysfunction Induced by Traumatic Brain Injury

Background Beta-adrenergic blockade has been hypothesized to have a protective effect on intestinal dysfunction and increased intestinal permeability associated with the epinephrine surge after traumatic brain injury (TBI). Methods Wister rats were subjected to either a weight drop TBI, and intraperitoneally injected or not with labetalol, or a sham procedure (18 rats per group). After 3, 6, or 12h (6 rats per subgroup), intestinal permeability to 4.4 kDa FITC-Dextran and plasma epinephrine levels were measured as was intestinal tight junction protein ZO-1 expression at 12h. Terminal ileum was harvested to measure levels of intestinal tumor necrosis factor (TNF)-α and to evaluate histopathology. Results In TBI group vs. sham group, intestinal permeability (P<0.01) was significantly higher at all time-points, and intestinal ZO-1 expression was lower at 12h. In TBI with vs. without labetalol group, 1) intestinal permeability was significantly lower at 6 and 12h (94.31±7.64 vs. 102.16±6.40 μg/mL; 110.21±7.52 vs. 118.95±7.11 μg/mL, respectively); 2) levels of plasma epinephrine and intestinal TNF-α were significantly lower at 3, 6 and 12h; and 3) intestinal ZO-1 expression was higher at 3, 6 and 12h (p=0.018). Histopathological evaluation showed that labetalol use preserved intestinal architecture throughout. Conclusion In a rat model of TBI, labetalol reduced TBI-induced sympathetic hyperactivity, and prevented histopathological intestinal injury accompanied by changes in gut permeability and gut TNF-α expression.
Aberrant Hypomethylation of Solute Carrier Family 6 Member 12 Promoter Induces Metastasis of Ovarian Cancer

Purpose Ovarian cancer (OC) is the most fatal of gynecological malignancies with a high rate of recurrence. We aimed to evaluate the expression of solute carrier family 6, member 12 (SLC6A12) and methylation of its promoter CpG sites in a xenograft mouse model of metastatic OC, and to investigate the regulatory mechanisms that promote aggressive properties during OC progression. Materials and Methods Expression of SLC6A12 mRNA was determined by reverse-transcription quantitative polymerase chain reaction (RT-qPCR), and DNA methylation status of its promoter CpGs was detected by quantitative methylation-specific PCR. The metastatic potential of SLC6A12 was evaluated by in vitro migration/invasion transwell assays. Gene expression and DNA methylation of SLC6A12 and clinical outcomes were further investigated from publicly available databases from curatedOvarianData and The Cancer Genome Atlas. Results SLC6A12 expression was 8.1–14.0-fold upregulated and its DNA methylation of promoter CpG sites was 41–62% decreased in tumor metastases. After treatment with DNA methyltransferase inhibitor and/or histone deacetylase inhibitor, the expression of SLC6A12 was profoundly enhanced (~8.0-fold), strongly supporting DNA methylation-dependent epigenetic regulation of SLC6A12. Overexpression of SLC6A12 led to increased migration and invasion of ovarian carcinoma cells in vitro, approximately 2.0-fold and 3.3-fold, respectively. The meta-analysis showed that high expression of SLC6A12 was significantly associated with poor overall survival [hazard ratio (HR)=1.07, p value=0.016] and that low DNA methylation levels of SLC6A12 at specific promoter CpG site negatively affected patient survival. Conclusion Our findings provide novel evidence for the biological and clinical significance of SLC6A12 as a metastasis-promoting gene.
The organization and anatomy of narrative comprehension and expression in Lewy body spectrum disorders.

OBJECTIVE Patients with Lewy body spectrum disorders (LBSD) such as Parkinson's disease, Parkinson's disease with dementia, and dementia with Lewy bodies exhibit deficits in both narrative comprehension and narrative expression. The present research examines the hypothesis that these impairments are due to a material-neutral deficit in organizational executive resources rather than to impairments of language per se. We predicted that comprehension and expression of narrative would be similarly affected and that deficits in both expression and comprehension of narrative would be related to the same anatomic distribution of prefrontal disease. METHOD We examined 29 LBSD patients and 26 healthy seniors on their comprehension and expression of narrative discourse. For comprehension, we measured accuracy and latency in judging events with high and low associativity from familiar scripts such as "going fishing. " The expression task involved maintaining the connectedness of events while narrating a story from a wordless picture book. RESULTS LBSD patients were impaired on measures of narrative organization during both comprehension and expression relative to healthy seniors. Measures of organization during narrative expression and comprehension were significantly correlated with each other. These measures both correlated with executive measures but not with neuropsychological measures of lexical semantics or grammar. Voxel-based morphometry revealed overlapping regressions relating frontal atrophy to narrative comprehension, narrative expression, and measures of executive control. CONCLUSIONS Difficulty with narrative discourse in LBSD stems in part from a deficit of organization common to comprehension and expression. This deficit is related to prefrontal cortical atrophy in LBSD.
The Effect of Palm Oil Fly Ash Reinforcement on Recycle Aluminium via Sand Casting

Recycle aluminium has been mixed with palm oil fly ash based on fraction weight of overall weight of palm oil fly ashes to aluminium recycle. The ratios discussed on this paper are 0%, 3% 6%, 9%, 12% and 15%. The samples have been developed via sand casting method. The result shows that the best composition has been achieved when we added 3% of palm oil fly ashes, with the new composite aluminum have a highest hardness with the value of 119.7HV, with the Modulus of raptured of 7.71Mkg/m2