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In this paper, we focus on ultra-dense network modelingwhere both the Base Stations (BSs) and Mobile Terminals(MTs) are UAVs. In this case, two communication nodes canbe very close to each other. However, existing cellular networkanalyses typically use the standard unbounded path loss modelwhere received power decays like r^beta over a distance r. Thisstandard model is a good approximation for the

Future Internet-of-Things (IoT) systems need to combine heterogeneous IoT components and support weak connectivity. This paper introduces ComPOS, a domain-specific language for composing IoT services into systems. ComPOS is a small language but supports powerful message mediation, using stateful reactions with nested and parallel message sequences and anonymous futures. To deal with weak connectiv

Amyloid fibrils are highly insoluble in neutral aqueous media of regular ionic strengths making solubilization a difficult task that normally calls for extremely harsh treatment. This is among the reasons for the routine employment of synthetic proteins in amyloid research, where the amylogenic components are needed. Here we describe a process for solubilizing amyloid in pure water that we adopted

Many ecclesiologists assume that pluralisation is a problem for churches. By drawing on Dietrich Bonhoeffer’s reception of Ernst Troeltsch, however, the author argues that pluralisation is instead a promise. Portraits which paint Bonhoeffer as ‘alternative’ to Troeltsch (and Troeltsch as ‘alternative’ to Bonhoeffer) have been proposed persistently. But in the ecclesiological explorations which Bon

Under global warming, flooding has become one of the most destructive natural disasters along the China–Pakistan Economic Corridor (CPEC), which significantly jeopardizes the construction and ongoing stability of the CPEC. The assessment of regional flood potential is, therefore, crucial for effective flood prevention and relief measures. In light of this, our study applied MIKE 11 hydrodynamic mo

Bayesian Optimization has emerged as a crucial technique for optimizing costly, black-box functions where each evaluation comes at a high cost, such as in scientific experiments, and machine learning hyperparameter optimization. By combining probabilistic modeling with sequential decision-making, Bayesian Optimization achieves efficient exploration, guiding the search toward optimal parameters wit

This thesis explores the challenges and advancements in high-dimensional Bayesian optimization (HDBO), focusing on understanding, quantifying, and improving optimization techniques in high-dimensional spaces.Bayesian optimization (BO) is a powerful method for optimizing expensive black-box functions, but its effectiveness diminishes as the dimensionality of the search space increases due to the cu

Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniqu

In North and East Syria—also known as Rojava—the convergence of war, politics, and environmental challenges presents a complex and pressing reality. This blog is dedicated to exploring these interlinked issues, highlighting how conflict and ecological degradation are deeply connected, and how these dynamics affect communities in varied and unequal ways.

Background: Desmoplastic melanoma (DM) is a rare subtype, accounting for less than 5% of primary cutaneous invasive melanomas. DM often arises in chronically sun-exposed skin, in older individuals. While the incidence of cutaneous melanoma has increased globally, trends specific to DM are less documented and studies on survival outcomes for DM are inconsistent. Objectives: To study patient and tum

This article examines the emergence of idealism in evolutionary cognitive science by analyzing the conscious realism of the cognitive theorist Donald D. Hoffman and the mathematician Chetan Prakash. Perception is for them not about veridical experience, but rather a mechanism for survival. It helps us survive but does not give us access to reality as such. By relating Hoffman and Prakash's conscio

This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XR/VR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadl

BACKGROUND: The widespread use of intramedullary nails (IMNs) compared with sliding hip screws (SHSs) in extracapsular hip fractures (AO/OTA 31-A1, 31-A2, 31-A3) has been questioned because of a higher complication rate, although the outcome might have improved through more recent implant designs and the learning curve. This study aimed to investigate if there is a difference with regard to the cu