Exploring the Impact and Value of Collaborative Care Model in Diabetes Care at a Primary Healthcare Setting In Qatar

Background: Diabetes mellitus (DM) is one of the top health priorities in Qatar due to its high prevalence and negative health consequences. The current prevalence of DM in Qatar is 15.5%, which is projected to increase to 29.7% by 2035. DM management is still challenging despite healthcare advancement, warranting the need for a comprehensive Collaborative Care Model (CCM). In an effort to deliver comprehensive and integrated patient-centered healthcare services in the community, the government of Qatar focuses on primary care. Therefore, we aim to evaluate the impact and value of CCM in DM care at a primary healthcare (PHC) setting in Qatar. Methodology: Phase I of this project was a multiple-time series, retrospective, observational study with a control group among patients with DM who received care at Qatar Petroleum Diabetes Clinic (QPDC) in Dukhan. The impact of CCM on glycemic control, blood pressure, lipid profile, and anthropometric parameters was evaluated at baseline and up to 17 months of follow-up. Patients were retrospectively categorized as intervention group if they received CCM and appropriate follow-up (n = 168) or usual care if they did not receive CCM and appropriate follow-up (n = 86). Quantitative data were analyzed descriptively and inferentially using the Statistical Package for the Social Sciences software. Phase II was a qualitative exploration of healthcare professionals’ (HCPs’) and patients’ perspectives on the value of CCM provided at the center. Twelve patients and twelve HCPs participated in semi-structured one-to-one interviews. Qualitative data were analyzed and interpreted using a deductive coding thematic analysis process. Results: Patients in the intervention and control groups had similar baseline sociodemographic and clinical characteristics. The provision of CCM resulted in statistically significant improvements (p<0.05) in mean values (baseline vs. 17 months) of glycated hemoglobin A1c (6.9% vs. 6.5%), random blood glucose (194 mg/dL vs. 141 mg/dL), low-density lipoprotein cholesterol (3.7 mmol/L vs. 2.8 mmol/L), total cholesterol (5.4 mmol/L vs. 4.3 mmol/L), weight (78.5 Kg vs. 77.9 Kg), and body mass index (30.4 Kg/m2 vs. 30.2 Kg/m2) over 17-months within the intervention group; whereas, no significant changes occurred within the control group. Similarly, the between group comparisons demonstrated the superiority of CCM over usual care in improving several clinical outcomes. The qualitative phase resulted in 14 different themes under the predefined domains: components of CCM (five themes), the impact of CCM (three themes), facilitators of CCM provision (three themes), and barriers of CCM provision (three themes). The majority of the participants indicated easy access to and communication with HCPs at QPDC. Participants appreciated the extra time spent with HCPs, frequent follow-up visits, and health education, which empowered them to self-manage DM. Generally, participants identified barriers and facilitators related to patients, HCPs, and healthcare system.Conclusion: The implementation of CCM in a PHC setting improved several DM-related clinical outcomes over a 17-month period. The providers and users of CCM had an overall positive perception and appreciation of this model in PHC settings. Barriers to CCM such as unpleasant attitude and undesirable attributes of HCPs and patients, unsupportive hospital system, and high workload must be addressed before implementing the model in other PHC settings

AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds

Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against via simple statistical methods. To this extent, we develop a new point cloud attack (dubbed AdvPC) that exploits the input data distribution by adding an adversarial loss, after Auto-Encoder reconstruction, to the objective it optimizes. AdvPC leads to perturbations that are resilient against current defenses, while remaining highly transferable compared to state-of-the-art attacks. We test AdvPC using four popular point cloud networks: PointNet, PointNet++ (MSG and SSG), and DGCNN. Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network. AdvPC also increases the ability to break defenses by up to 38% as compared to other baselines on the ModelNet40 dataset.Comment: Presented at European conference on computer vision (ECCV), 2020. The code is available at https://github.com/ajhamdi/AdvP

Climate sensitivity to land use changes over the City of Brussels

Prompted with the ongoing and projected climate change, a wide range of cities have committed, not only to mitigate greenhouse gas emissions but also to implement different climate change adaptation measures. These measures serve to ensure the wellbeing of the urban population. In practice, however, the planning of realistic adaptation measures is a complex process. Prior to starting such endeavor, it may therefore be useful to explore the maximum potential benefit that can be gained through adaptation measures. In this work, simple, extreme yet realistic adaptation measures are proposed in terms of changes in albedo and vegetation fraction. The impact of these land-use scenarios is explored by use of the land surface model SURFEX on the summer climate in terms of heat waves and the urban heat island for the city of Brussels. This is done for different periods in the future using the greenhouse gas scenario RCP8.5

Integrating SystemC-AMS Power Modeling with a RISC-V ISS for Virtual Prototyping of Battery-operated Embedded Devices

RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive simulation frameworks for RISC-V that cover both the functional and extra-functional aspects. This gap hinders progress in the field, as fast yet accurate system-level simulation is crucial for Design Space Exploration (DSE). This work presents an open-source framework designed to tackle this challenge, integrating functional RISC-V simulation (achieved with GVSoC) with SystemC-AMS (used to model extra-functional aspects, in detail power storage and distribution). The combination of GVSoC and SystemC-AMS in a single simulation framework allows to perform a DSE that is dependent on the mutual impact between functional and extra-functional aspects. In our experiments, we validate the framework's effectiveness by creating a virtual prototype of a compact, battery-powered embedded system.Comment: 4 pages, 4 figures, to be published in Computing Frontiers Workshops and Special Sessions (CF '24 Companion), May 7--9, 2024, Ischia, Ital

Micromorphology and leaf ecological anatomy of Bassia halophyte species (Amaranthaceae) from Iran

Bassia belongs to the family Chenopodiaceae, which is widely distributed in the world, especially in Irano-Turanian Region. According to the morphological similarities among the species of the genus, ecological implications of structural features were studied. In fact, understanding these relationships is of great importance in natural classification. We have studied the relationships of Bassia species using morphological, anatomical, and micro-morphological methods. The current results indicated that phenotypic plasticity and repetitive patterns were probably due to ecological adaptations, especially in decreasing the leaf surface by changing the inner structure. All species have a Kranz anatomy structure (Kochioid subtype), related to C4 photosynthesis. The changes in cell size increasing the cell membrane thickness, the density of two-vascular systems, the increase of palisade to water storage parenchyma ratio and photosynthetic system. The leaf surface is covered with long highly dense hairs and microechinate ornamentation. Though the adaptation caused some morphological similarities, the variation was seen in other descriptive characteristics such as morphological and anatomical features especially in two synonym species of B. turkestanica and B. pilosa. Information about the similarity species is provided

The urban climate of Ghent, Belgium : a case study combining a high-accuracy monitoring network with numerical simulations

As urban environments have a specific climate that poses extra challenges (e.g. increased heat stress during heat waves), gaining detailed insight into the urban climate is important. This paper presents the high-accuracy MOCCA (MOnitoring the City's Climate and Atmosphere) network, which is monitoring the urban climate of the city of Ghent since July 2016. The study illustrates the complementarity between modelling and observing the urban climate. Two different modelling approaches are used: 1 km resolution runs of the SURFEX land surface model and 100 m resolution runs of the computationally cheaper UrbClim boundary layer model. On the one hand, urban models are able to simulate the spatial variability of the urban climate. As such, these models serve as a tool to help deciding on the locations of the measurement stations. On the other hand, the MOCCA observations are used to validate the high-resolution urban model experiments for the summer (July-August-September) of 2016. Our results demonstrate that the models capture the nighttime intra-urban temperature differences, but they are not able to reproduce the observed daytime temperature differences which are determined by the micro-scale environment

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22 degrees resolution over the CORDEX Central Asia domain

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22 degrees (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980-2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temper-ature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified

Wheat yield estimation from NDVI and regional climate models in Latvia

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics