Month: July 2024

Abstract

Pakistan is exposed to variety of hazards. Drought is one of the hydro-meteorological hazards causing loss of life and soil moisture, reduction in agricultural production and decline in groundwater level. Balochistan is the largest province of Pakistan which has been severely affected by drought. This study is an effort to analyze and forecast the spatial pattern of drought in Balochistan using Markov Model. Both primary and secondary data were utilized in Geographical Information System (GIS) environment to model the current and predict the future pattern of drought in the region. The main input spatial layers included land cover, natural difference vegetation index (NDVI), natural difference water index (NDWI), groundwater level, precipitation and current drought. The spatial pattern of drought is delineated into extreme, moderate and low/no drought zones. The analysis reveals that the currently, the extreme, moderate and low/no drought zones are spatially extended over 9.9% (37,053 km²), 83.11% (288,553 km²) and 6.21% (21,584 km²), respectively. While the predicted drought presents shocking results, where the prevalence of extreme, moderate and low/no drought zones may spatially extend over to 51.14% (191,364 km²),28.78% (99,945 km²) and 16.09 (55,881 km²). The extreme drought may encroach current moderate drought into predicted extreme drought. Additionally, the occurrence of drought in Chaghi district may be more in future, where chances of extreme drought occurrence is 20.15%. Second most prone region is Kharan, where chances of drought are 11.35% and finally, third region is Khuzdar, where the probability of extreme drought is 8.90%. However, Awaran, Panjgur, Kech, Zhob and Sherani districts are also prone to moderate drought in future. There is dire need to build mini and micro dams to conserve rain water and farmers should plant less water dependent crops. The results and findings of this study have potential to assist disaster management authorities and decision makers to formulate zone specific drought risk reduction strategies.

Abstract

Discrete gradients (DG) or more exactly discrete gradient methods are time integration schemes that are custom-built to preserve first integrals or Lyapunov functions of a given ordinary differential equation (ODE). In conservative molecular dynamics (MD) simulations, the energy of the system is constant and therefore a first integral of motion. Hence, discrete gradient methods seem to be a natural choice as an integration scheme in conservative molecular dynamics simulations.

Abstract

Purpose

Vessel-encoded arterial spin labeling (VE-ASL) is able to provide noninvasive information about the contribution of individual arteries to the cerebral perfusion. The aim of this study was to compare VE-ASL to the diagnostic standard digital subtraction angiography (DSA) with respect to its ability to visualize vascular territories.

Methods

In total, 20 VE-ASL and DSA data sets of 17 patients with Moyamoya angiopathy with and without revascularization surgery were retrospectively analyzed. Two neuroradiologists independently assessed the agreement between VE-ASL and DSA using a 4-point Likert scale (no- very high agreement). Additionally, grading of the vascular supply of subterritories (A1-A2, M1-M6) on the VE-ASL images and angiograms was performed. The intermodal agreement was calculated for all subterritories in total and for the subdivision into without and after revascularization (direct or indirect bypass).

Results

There was a very high agreement between the VE-ASL and the DSA data sets (median = 1, modus = 1) with a substantial inter-rater agreement (k_w = 0.762 (95% CI 0.561–0.963)). The inter-modality agreement between VE-ASL and DSA in vascular subterritories was almost perfect for all subterritories (k = 0.899 (0.865–0.945)), in the subgroup of direct revascularized subterritories (k = 0.827 (0.738–0.915)), in the subgroup of indirect revascularized subterritories (k = 0.843 (0.683–1.003)), and in the subgroup of never revascularized subterritories (k = 0.958 (0.899–1.017)).

Conclusion

Vessel-encoded ASL seems to be a promising non-invasive method to depict the contributions of individual arteries to the cerebral perfusion before and after revascularization surgery.

Abstract

Purpose

Intracranial hypertension (IH) frequently complicates cerebral venous thrombosis (CVT). Distinct neuroimaging findings are associated with IH, yet their discriminative power, reversibility and factors favoring normalization in prospective CVT patients are unknown. We determined test performance measures of neuroimaging signs in acute CVT patients, their longitudinal change under anticoagulation, association with IH at baseline and with recanalization at follow-up.

Methods

We included 26 consecutive acute CVT patients and 26 healthy controls. Patients were classified as having IH based on CSF pressure > 25 cmH₂O and/or papilledema on ophthalmological examination or ocular MRI. We assessed optic nerve sheath diameter (ONSD), optic nerve tortuousity, bulbar flattening, lateral and IV^th ventricle size, pituitary configuration at baseline and follow-up, and their association with IH and venous recanalization.

Results

46% of CVT patients had IH. ONSD enlargement > 5.8 mm, optic nerve tortuousity and pituitary grade ≥ III had highest sensitivity, ocular bulb flattening and pituitary grade ≥ III highest specificity for IH. Only ONSD reliably discriminated IH at baseline. Recanalization was significantly associated with regressive ONSD and pituitary grade. Other neuroimaging signs tended to regress with recanalization. After treatment, 184.9 ± 44.7 days after diagnosis, bulbar flattening resolved, whereas compared with controls ONSD enlargement (p < 0.001) and partially empty sella (p = 0.017), among other indicators, persisted.

Conclusion

ONSD and pituitary grading have a high diagnostic value in diagnosing and monitoring CVT-associated IH. Given their limited sensitivity during early CVT and potentially persistent alterations following IH, neuroimaging indicators can neither replace CSF pressure measurement in diagnosing IH, nor determine the duration of anticoagulation.

Abstract

Free Open-Access Medical Education (FOAMed) has transformed medical education in the past decade by complementing and substituting for traditional medical education when needed. The attractiveness of FOAMed resources is due to their inexpensive nature, wide availability, and user ability to access on demand across a variety of devices, making it easy to create, share, and participate. The subject of nephrology is complex, fascinating, and challenging. Traditional didactic lectures can be passive and ineffective in uncovering these difficult concepts and may need frequent revisions. Active teaching methods like flipped classrooms have shown some benefits, and these benefits can only be multifold with current social media tools. Social media will inspire the involvement of students and allow them to create and share educational content in a “trendy way,” encouraging the participation of their peers and thus building an educational environment more conducive to them while promoting revision and retainment. FOAMed also promotes asynchronous learning, spaced learning, microlearning, and multimodal presentation with a meaningful variation. This article discusses the evolution of digital education, social media platforms, tools for creating and developing FOAMed resources, and digital scholarship.

Abstract

Investigations into the behaviour of pedestrians and autonomous driving both frequently employ object detection. It has always been a popular area for research in computer vision and artificial intelligence. Because of the advancement of deep learning, object detectors are improving in accuracy and speed. However, the majority of them struggle to reconcile speed and precision. Because of this, the object detection model employed in this work, which is based on a better You Only Look Once Version 4 (YOLOv4) algorithm, considers both detection accuracy and efficiency. Several different sensor types, including cameras and mmWave (millimetre Wave) radar, are employed to observe the environment. This study employs a YOLOv4-based hybrid feature enhancement network for robust object detection under adverse weather conditions (AWC), utilizing the CRUW dataset. A camera–radar-fused approach that detects things that are cross-supervised by a deep radar object detection network, such as the YOLOv4 algorithm, reduces processing complexity. To correct category imbalance and further boost the robustness of the suggested model, the class-balanced sampling strategy (CBSS) is employed. Python software was employed to analyse the suggested method. In terms of mAP and accuracy, the suggested method is contrasted with the current OD ensemble framework and faster R-CNN methodologies. The suggested method’s accuracy and mAP are higher than those of the other existing methods; as a result, the proposed method’s mAP produces 40% and its accuracy produces 98.7%, respectively. Additionally, the suggested method performs better when classifying the object and detecting it, but it also strengthens the system’s stability and reduces category imbalance.