Future Perspectives in HLA Typing Technologies

A wide range of malignant and nonmalignant diseases require hematopoietic stem cell transplantation (HSCT) as last resort therapeutic approach. Graft versus host disease (GVHD), which is one of the major causes of transplant-related mortality, is minimized whenever increased matching of human leukocyte antigens (HLAs) between donor and recipient is present. Suitable donor selection is determined with the utilization of HLA typing. HLAs are highly polymorphic glycoproteins encoded by a region of genes known as the major histocompatibility complex (MHC). Their biological function is to present antigenic peptides to T lymphocytes. However, they also play important role in HSCT acceptance/rejection. During the previous years, various techniques have been acquired in order to better characterize the HLA profile of transplant donors and recipients. This effort is particularly challenging due to MHC size, but most importantly due to high sequence variability in specific regions of the respective genetic loci, between individuals. Initially, HLA typing was performed using serological typing, hybridization techniques, and restriction fragment length polymorphism (RFLP) approaches. Later on, polymerase chain reaction (PCR) based techniques and direct sequencing (dideoxy-based Sanger sequencing) capillary electrophoretic analyses arose. Nowadays, 2nd and 3rd generation sequencing (NGS) technologies show great potential in effectively identifying these polymorphic regions

Analysis of the effect of perforation on the permeability of biodegradable non-barrier films

AbstractPerforated plastic films are used in Equilibrium Modified Atmosphere Packaging (EMAP) of fresh produce such as fruits and vegetables. The most common material used in such applications is the oriented polypropylene (OPP), which has low permeability with respect to relevant gases, namely water vapor (WV), CO2, and O2. Therefore, the synthesis of the in-package atmosphere is regulated only by the size and number of perforated holes. The replacement of the OPP films with biodegradable ones made of polylactic acid (PLA) or starch based polymers for environmental reasons results into difficulties with respect to designing the EMAP system, since these films are more permeable to the relevant gases and in particular to WV. As a result, the effect of micro-perforation is influenced by the permeability of the film. In the present work, the dependence of the gas flux through perforation on the permeability of the film for the same gas was investigated by experimental and numerical methods. It was shown that the effect of perforation decreases as the permeability of the film increases. The diffusive gas flux through perforation becomes independent of the film permeability, if it is about 100 times smaller than the diffusivity of the studied gas in air

Uplink NOMA for UAV-Aided Maritime Internet-of-Things

Maritime activities are vital for economic growth, being further accelerated by various emerging maritime Internet of Things (IoT) use cases, including smart ports, autonomous navigation, and ocean monitoring systems. However, broadband, low-delay, and reliable wireless connectivity to the ever-increasing number of vessels, buoys, platforms and sensors in maritime communication networks (MCNs) has not yet been achieved. Towards this end, the integration of unmanned aerial vehicles (UAVs) in MCNs provides an aerial dimension to current deployments, relying on shore-based base stations (BSs) with limited coverage and satellite links with high latency. In this work, a maritime IoT topology is examined where direct uplink communication with a shore BS cannot be established due to excessive pathloss. In this context, we employ multiple UAVs for end-to-end connectivity, simultaneously receiving data from the maritime IoT nodes, following the non-orthogonal multiple access (NOMA) paradigm. In contrast to other UAV-aided NOMA schemes in maritime settings, dynamic decoding ordering at the UAVs is used to improve the performance of successive interference cancellation (SIC), considering the rate requirements and the channel state information (CSI) of each maritime node towards the UAVs. Moreover, the UAVs are equipped with buffers to store data and provide increased degrees of freedom in opportunistic UAV selection. Simulations reveal that the proposed opportunistic UAV-aided NOMA improves the average sum-rate of NOMA-based maritime IoT communication, leveraging the dynamic decoding ordering and caching capabilities of the UAVs

Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios. Considering a realistic network configuration, the performance of the RL algorithm is tested under strict user demands. The results suggest that the proposed modeling of the RL parameters, namely the state space and the rewarding system, is promising when the network controller attempts to fulfill the user requests by regulating the power of base stations. Based on comparative simulations, even for strict demands requested by multiple users (2.5 – 5 Mbps), the proposed scheme achieves a performance rate of about 96%

A Stacking Ensemble Learning Model for Waste Prediction in Offset Printing

The production of quality printing products requires a highly complex and uncertain process, which leads to the unavoidable generation of printing defects. This common phenomenon has severe impacts on many levels for Offset Printing manufacturers, ranging from a direct economic loss to the environmental impact of wasted resources. Therefore, the accurate estimation of the amount of paper waste expected during each press run, will minimize the paper consumption while promoting environmentally sustainable principles. This work proposed a Machine Leaning (ML) framework for proactively predicting paper waste for each printing order. Based on a historical dataset extracted by an Offset Printing manufacturer, a two-level stacking ensemble learning model combining Support Vector Machine (SVM), Kernel Ridge Regression (KRR) and Extreme Gradient Boosting (XGBoost) as base learners, and Elastic Net as a meta-learner, was trained and evaluated using cross-validation. The evaluation outcomes demonstrated the ability of the proposed framework to accurately estimate the amount of waste expected to be generated for each printing run, by significantly outperforming the rest of the benchmarking models

A reference architecture for cloud-edge meta-operating systems enabling cross-domain, data-intensive, ML-assisted applications: architectural overview and key concepts

Future data-intensive intelligent applications are required to traverse across the cloudto-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.For UPC authors: this research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100.Peer ReviewedPostprint (published version

Review of solar energetic particle models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.</p