The Internet of Things (IoT) has been an area of growing research interest for decades. Task allocation is an important problem for the optimized operation of Internet-of-Things networks. This paper ...provides an overview of recent research in the field of Internet-of-Things task allocation optimization. First, the task allocation problem for the IoT itself is analyzed and divided into distinct sub-problem categories, such as deployment optimization, static or dynamic optimization as well as single- or multi-objective optimization. Following that, the commonly used optimization objectives are explained. Various recent works in the field of task allocation optimization are then summarized and catalogued according to the problem categories. Finally, the paper concludes with a qualitative analysis of the categorized approaches and a description of open problems and highlights promising directions for future research.
This paper presents a single-copter localization system as a first step towards a scalable multihop drone swarm localization system. The drone was equipped with ultrawideband (UWB) transceiver ...modules, which can be used for communication, as well as distance measurement. The location of the drone was detected based on fixed anchor points using a single type of UWB transceiver. Our aim is to create a swarm localization system that enables drones to switch their role between an active swarm member and an anchor node to enhance the localization of the whole swarm. To this end, this paper presents our current baseline localization system and its performance regarding single-drone localization with fixed anchors and its integration into our current modular quadcopters, which was designed to be easily extendable to a swarm localization system. The distance between each drone and the anchors was measured periodically, and a specially tailored gradient descent algorithm was used to solve the resulting nonlinear optimization problem. Additional copter and wireless-specific adaptations were performed to enhance the robustness. The system was tested with a Vicon system as a position reference and showed a high precision of 0.2 m with an update rate of <10 Hz. Additionally, the system was integrated into the FINken copters of the SwarmLab and evaluated in multiple outdoor scenarios. These scenarios showed the generic usability of the approach, even though no accurate precision measurement was possible.
The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and ...take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.
Non-anastomotic biliary strictures (NAS) are a common cause of morbidity and mortality after liver transplantation.
All patients with NAS from 2008 to 2016 were retrospectively analyzed. The success ...rate and overall mortality of an ERCP-based stent program (EBSP) were the primary outcomes.
A total of 40 (13.9%) patients with NAS were identified, of which 35 patients were further treated in an EBSP. Furthermore, 16 (46%) patients terminated EBSP successfully, and nine (26%) patients died during the program. All deaths were caused by cholangitis. Of those, one (11%) patient had an extrahepatic stricture, while the other eight patients had either intrahepatic (3, 33%) or combined extra- and intrahepatic strictures (5, 56%). Risk factors of overall mortality were age (
= 0.03), bilirubin (
< 0.0001), alanine transaminase (
= 0.006), and aspartate transaminase (
= 0.0003). The median duration of the stent program was 34 months (ITBL: 36 months; IBL: 10 months), and procedural complications were rare.
EBSP is safe, but lengthy and successful in only about half the patients. Intrahepatic strictures were associated with an increased risk of cholangitis.
Introduction: The impact of etiology on response to immunotherapy in advanced hepatocellular carcinoma (HCC) is being debated, with contrasting findings between early and recent post hoc analyses of ...IMbrave-150 and metanalyses of clinical trials of PD-1/PD-L1 blockers. As a results, it is not clear whether the first-line systemic treatment atezolizumab plus bevacizumab (A + B) is equally effective in viral and nonviral patients. Methods: We retrospectively analyzed 885 HCC patients treated with the first-line A + B from multiple centers from Eastern and Western countries, 53.9% having viral and 46.1% nonviral etiology. Baseline clinical and laboratory characteristics were analyzed with uni- and multivariate models to explore potential differences on overall survival (OS), time-to-progression (TTP), disease control rates (DCRs) based on etiology and to identify putative prognostic factors in etiology subgroups. Treatment toxicities and access to the second-line treatments and outcomes were also reported and compared between etiologies. Results: Overall, no statistically significant differences were found in median OS (mOS: viral 15.9 months; nonviral 16.3 months), TTP (mTTP: viral 8.3 months; nonviral 7.2 months), and DCRs (viral 78.1%; nonviral 80.8%) based on etiology. Prognostic factors of survival and progression were mainly shared between viral and nonviral etiologies, including alpha-fetoprotein, aspartate transaminase, neutrophil-to-lymphocyte ratio (NLR) and ALBI score. Exploratory analyses highlighted a possible stronger association of immunological factors, i.e., NLR and eosinophil count, to treatment outcomes in viral patients. The toxicity profile, the access to and type of the second-line treatments and their outcome in terms of OS almost overlap in the two etiology subgroups. Conclusion: Atezolizumab plus bevacizumab efficacy does not vary according to underlying etiology of HCC in a multicenter, real-world population, matching recent post hoc findings from the IMbrave-150 trial. Preliminary analyses suggest that some prognostic factors differ between viral and nonviral patients, potentially due to biological and immunological differences. Prospective and comparative trials stratifying by etiology are warranted to validate these findings and guide clinical practice.
Node failures are known to be among the generic problems in Internet of Things (IoT) networks. These failures can be caused by communication disturbances, battery depletion, or even hardware faults. ...The larger the IoT network and the larger the task to be executed in the network, the higher is the probability of a node failure in the relevant part of the network. This article studies the node failures and proposes a new task allocation algorithm based on multiobjective optimization to address this issue. This article proposes a specialized archive-selection mechanism to enhance diversity in the search space of the so-called multiobjective task allocation algorithm (MOTA). High diversity in the archive allows a reliable selection of alternative task assignments in the case of node failures in the IoT network. We evaluate the performance of the proposed approach regarding the network lifetime, its latency, and its availability, using a network simulation model and compare the results with the baseline MOTA and the dynamic task allocation scheduler (DTAS). The results show that the proposed approach provides significant performance improvements over the existing algorithms, especially in scenarios with high task-to-node ratios.
Pancreatic tumor cells release small extracellular vesicles (sEVs, exosomes) that contain lipids and proteins, RNA, and DNA molecules that might promote formation of metastases. It is not clear what ...cargo these vesicles contain and how they are released. Protein kinase D1 (PRKD1) inhibits cell motility and is believed to be dysregulated in pancreatic ductal adenocarcinomas. We investigated whether it regulates production of sEVs in pancreatic cancer cells and their ability to form premetastatic niches for pancreatic cancer cells in mice.
We analyzed data from UALCAN and human pancreatic tissue microarrays to compare levels of PRKD1 between tumor and nontumor tissues. We studied mice with pancreas-specific disruption of Prkd1 (PRKD1KO mice), mice that express oncogenic KRAS (KC mice), and KC mice with disruption of Prkd1 (PRKD1KO-KC mice). Subcutaneous xenograft tumors were grown in NSG mice from Panc1 cells; some mice were then given injections of sEVs. Pancreata and lung tissues from mice were analyzed by histology, immunohistochemistry, and/or quantitative polymerase chain reaction; we performed nanoparticle tracking analysis of plasma sEVs. The Prkd1 gene was disrupted in Panc1 cells using CRISPR-Cas9 or knocked down with small hairpin RNAs, or PRKD1 activity was inhibited with the selective inhibitor CRT0066101. Pancreatic cancer cell lines were analyzed by gene-expression microarray, quantitative polymerase chain reaction, immunoblot, and immunofluorescence analyses. sEVs secreted by Panc1 cell lines were analyzed by flow cytometry, transmission electron microscopy, and mass spectrometry.
Levels of PRKD1 were reduced in human pancreatic ductal adenocarcinoma tissues compared with nontumor tissues. PRKD1KO-KC mice developed more pancreatic intraepithelial neoplasia, at a faster rate, than KC mice, and had more lung metastases and significantly shorter average survival time. Serum from PRKD1KO-KC mice had increased levels of sEVs compared with KC mice. Pancreatic cancer cells with loss or inhibition of PRKD1 increased secretion of sEVs; loss of PRKD1 reduced phosphorylation of its substrate, cortactin, resulting in increased F-actin levels at the plasma membrane. sEVs from cells with loss or reduced expression of PRKD1 had altered content, and injection of these sEVs into mice increased metastasis of xenograft tumors to lung, compared with sEVs from pancreatic cells that expressed PRKD1. PRKD1-deficient pancreatic cancer cells showed increased loading of integrin α6β4 into sEVs—a process that required CD82.
Human pancreatic ductal adenocarcinoma has reduced levels of PRKD1 compared with nontumor pancreatic tissues. Loss of PRKD1 results in reduced phosphorylation of cortactin in pancreatic cancer cell lines, resulting in increased in F-actin at the plasma membrane and increased release of sEVs, with altered content. These sEVs promote metastasis of xenograft and pancreatic tumors to lung in mice.
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Atezolizumab plus bevacizumab and lenvatinib have not been compared in a randomised controlled trial. We conducted a retrospective multi-centre study to compare the clinical efficacy and safety of ...lenvatinib and atezolizumab with bevacizumab as a first-line treatment for patients with unresectable HCC in the real-world scenario.
Clinical features of lenvatinib and atezolizumab plus bevacizumab patients were balanced through inverse probability of treatment weighting (IPTW) methodology, which weights patients' characteristics and measured outcomes of each patient in both treatment arms. Overall survival (OS) was the primary end-point.
The analysis included 1341 patients who received lenvatinib, and 864 patients who received atezolizumab plus bevacizumab. After IPTW adjustment, atezolizumab plus bevacizumab did not show a survival advantage over lenvatinib HR 0.97 (p = 0.739). OS was prolonged by atezolizumab plus bevacizumab over lenvatinib in viral patients (HR: 0.76; p = 0.024). Conversely, OS was prolonged by lenvatinib in patients with non-alcoholic steatohepatitis/non-alcoholic fatty liver disease (HR: 1.88; p = 0.014).
In the IPTW-adjusted population, atezolizumab plus bevacizumab provided better safety profile for most of the recorded adverse events.
Our study did not identify any meaningful difference in OS between atezolizumab plus bevacizumab and lenvatinib. Although some hints are provided suggesting that patients with non-alcoholic steatohepatitis/non-alcoholic fatty liver disease might benefit more from lenvatinib therapy and patients with viral aetiology more from atezolizumab plus bevacizumab.
•No randomised trial has been conducted to compare atezolizumab plus bevacizumab to lenvatinib.•Atezolizumab plus bevacizumab did not show a survival advantage over lenvatinib.•Lenvatinib provided longer in patients with NASH/NAFLD.•Atezolizumab plus bevacizumab provided longer in patients with viral aetiology.•Atezolizumab plus bevacizumab consistently reduced any toxicity and those graded as 3–4.
This article describes the Distance Minimisation Problem (DMP) from a metaheuristic optimisation point of view. The problem is motivated by real applications and can be used to test the performance ...of optimisation methods like Evolutionary Algorithms. After formally describing the problem and its extensions using different metrics or dynamics, we perform experiments with well-known metaheuristic methods to demonstrate the performance on various DMP instances. The results show that modern algorithms like NSGA-II and SMPSO can struggle with this kind of problem under certain conditions, especially when Manhattan distances are used. On the other hand, specialised methods like GRA lack diversity of solutions in some cases. This indicates that even modern and powerful metaheuristic algorithms need to be chosen with care and with the respective optimisation task in mind.
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