bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility of Multiple Advanced Machine Learning Techniques for Synthetic Finite-fault Earthquake Ground Motion Data
Authors: Titos Luzon Manuel , Kowsari Milad , Halldorsson Benedikt
Published: March 2023

The overwhelming success of data-driven models to solve complex predictive real-world problems has made them an effective alternative to the simulation-driven models. In addition to their computational cost, simulation-driven approaches need to be calibrated by actual data that links both to the physical theory and thereby improves both our knowledge and existing models. Similarly, data-driven methods allow deepening the knowledge by analyzing existing data. Therefore, to develop improved predictive models, one needs to pursue a balance between both data-driven and simulation-driven approaches, keeping data as a common pivot. This can be done by using Machine Learning (ML) which is a powerful tool to extract knowledge directly from the data and provide complementary information to the previously developed physics-based models. The main advantage of ML methods is their ability to process massive and complex structure data sets that are difficult to be processed by traditional data-processing methods. Therein lies also the main disadvantage of ML methods i.e., they need massive amounts of data that often are not available. In this study however we take advantage of a new physics-based model of the earthquake fault system of the Southwest Iceland transform zone and generate synthetic, but physically realistic, finite-fault earthquake catalogues. For each earthquake in the catalogues we simulate seismicground motion parameters at a large hypothetical station network  thus generating a massive parametric dataset of synthetic seismic data from earthquakes of magnitude 5 to 7. We will apply multiple types of new generation machine learning techniques such as deep neural network (DNN), deep Bayesian neural networks (DBNN) and deep Gaussian processes (DGP) to investigate the ability and efficiency of the methods in capturing the characteristics of the synthetic dataset in terms of key parameters e.g., ground motion amplitudes, ground motion attenuation versus source-to-site distance and site effects and independent parameters such as earthquake magnitude, fault extend and depth, etc. The ML methods will be trained using a procedure known as greedy layer-wise pretraining where each layer is initialized via the unsupervised pretraining, and the output of the previous layer can be used as the input for the next one. A typical advantage of these pre-trained networks compared to the other deep learning models is that the weights initialization renders the optimization process more effective, providing faster convergence by initializing the network parameters near a convergence region. This can help to avoid underfitting/overfitting problems when the training samples are highly correlated. The results will provide a new insight into the efficiency and usefulness of ML methods on synthetic seismic datasets with implications for their use on actual and more sparser datasets.
Monitoring the sediment dynamics of Maltese beaches. The SIPOBED project and its future challenges.
Authors: Galone Luciano , Colica Emanuele , Iregbeyen Peter , Piroddi Luca , Alan Deidun , Valentino Gianluca , Gauci Adam , D’Amico Sebastiano
Published: March 2023

Pocket beaches are small beaches bounded by natural promontories, free from direct sedimentary inputs other than those coming from the erosion of their cliffs.Malta's pocket beaches are one of the most significant geomorphological features of the archipelago. They play an important role for a variety of ecological and economic reasons. Sediment dynamics (mainly sand) is one of the most relevant factors to be considered in those beach system. As the pocket beach system behaves as an integrated unit, periodic bathymetric monitoring is essential - and challenging - from an environmental management perspective.The SIPOBED project (Satellite Investigation to study POcket BEach Dynamics) develops an integrated tool capable of monitoring sediment dynamics using SAR and digital photogrammetry to monitor beach topographic variations and multispectral UAV and satellite images to derive bathymetry.Obtaining updated in situ bathymetric measurements is essential to calibrate and re-calibrate the model over time and conduct more actualized and accurate multispectral-derived bathymetry.In this context, the collection of data by citizens, for instance, bathymetric data collected by private boats abundant in the archipelago, in conjunction with the processing power of modern computing, represents the new challenge of Maltese pocket beach monitoring.The SIPOBED project is financed by the Malta Council for Science and Technology (MCST, https://mcst.gov.mt/) through the Space Research Fund (Building capacity in the downstream Earth Observation Sector), a programme supported by the European Space Agency.
The European High Performance Computing Joint Undertaking (EuroHPC JU) – Leading the way in European Supercomputing
Authors: Gesenhues Linda
Published: March 2023

The European High Performance Computing Joint Undertaking (EuroHPC JU) is a joint initiative pooling together the resources of the EU, 32 European countries & private partners, it has the objective of making Europe a world leader in supercomputing.The EuroHPC JU is procuring and installing supercomputers across Europe. Wherever in Europe they are located, European scientists & users from the public sector and industry can benefit from these supercomputers. Free access is already being provided to European research organisations, with wider access planned for the future.In parallel, the EuroHPC JU is funding an ambitious R&I programme to develop a full European supercomputing supply chain: from processors and software to applications to be run on these supercomputers & know-how to develop strong European expertise.
Accelerating Time-To-Science in Geophysical Simulations
Authors: Sarasua Ignacio , Spiga Filippo
Published: March 2023

Accelerated computing is nowadays, de-facto, accepted as the path forward to deploy large-scale energy-efficient scientific and technical computing (including Exascale). The positive side effect has been a tremendous opportunity for domain scientists to accelerate the pace of discovery and innovation, as well being capable to quickly respond and adapt to unforeseen natural scenarios by quickly deploy computational tools in support of coordinated mitigation strategies and on-the-ground responses (so called ‘urgent computing’, work pioneered by the ChEESE Eu Centre of Excellence). The purpose of this talk is to briefly introduce the NVIDIA platform, hardware and software, showcasing few examples of geophysical applications that have been successfully accelerated using NVIDIA GPU and set the stage for the future in computing which involve classic HPC simulations coupled or argument by AI methods.
A computationally efficient numerical model to understand potential CO2 leakage risk within gigatonne scale geologic storage
Authors: Kivi Iman R. , Makhnenko Roman Y. , Oldenburg Curtis M. , Rutqvist Jonny , Vilarrasa Victor
Published: March 2023

The majority of available climate change mitigation pathways, targeting net-zero CO2 emissions by 2050, rely heavily on the permanent storage of CO2 in deep geologic formations at the gigatonne scale. The spatial and temporal scales of interest to geologic carbon storage (GCS) raise concerns about CO2 leakage to shallow sediments or back into the atmosphere. The assessment of CO2 storage performance is subject to huge computational costs of numerically simulating CO2 migration across geologic layers at the basin scale and is therefore restricted in practice to multi-century periods. Here, we present a computationally affordable and yet physically sound model to understand the likelihood of CO2 leakage over geologic time scales (millions of years) (Kivi et al., 2022). The model accounts for vertical two-phase flow and transport of CO2 and brine in a multi-layered system, comprising a sequence of aquifers and sealing rocks from the crystalline basement up to the surface (a total thickness of 1600 m), representative of a sedimentary basin. We argue that the model is capable of capturing the dynamics of CO2 leakage during basin-wide storage because the lateral advancement of CO2 plume injected from a dense grid of wellbores transforms into buoyant vertical rise within a short period after shut-in. A critical step in the proposed model is its initialization, which should reproduce the average CO2 saturation column and pressure profiles. We initialize the model by injecting CO2 at a constant overpressure into an upper lateral portion of the target aquifer while the bottom boundary is permeable to brine, resembling brine displacement by CO2 plume or leakage at basin margins. The optimum model setting can be achieved by adjusting the brine leakage parameter through calibration. We solve the governing equations using the finite element code CODE_BRIGHT. Discretizing the model with 7,100 quadrilateral elements and using an adaptive time-stepping scheme, the CPU time for the simulation of CO2 containment in the subsurface for 1 million years is around 140 hours on a Xeon CPU of speed 2.5 GHz. The obtained results suggest that the upward CO2 flow in free phase is strongly hindered by the sequence of caprocks even if they are pervasively fractured. CO2 leakage towards the surface is governed by the intrinsically slow molecular diffusion process, featuring aqueous CO2 transport rates as low as 1 meter per several thousand years. The model shows that GCS in multi-layered geologic settings is extremely unlikely to be associated with leakage, implying that GCS is a secure carbon removal technology.  ReferenceKivi, I. R., Makhnenko, R. Y., Oldenburg, C. M., Rutqvist, J., & Vilarrasa, V. (2022). Multi-layered systems for permanent geologic storage of CO2 at the gigatonne scale. Geophysical Research Letters, 49, e2022GL100443. https://doi.org/10.1029/2022GL100443
Enabling End-to-End Accelerated Multiphysics Simulations in the Exascale Era Using PETSc
Authors: Tran Mills Richard , Adams Mark , Balay Satish , Brown Jed , Faibussowitsch Jacob , G. Knepley Matthew , Kruger Scott , Morgan Hannah , Munson Todd , Rupp Karl , Smith Barry , Zampini Stefano , Zhang Hong , Zhang Junchao
Published: March 2023

The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization; it is used in dozens of scientific fields, and has been an important building block for many computational geoscience applications. Starting from the terascale era in the 1990s and continuing into the present dawn of the exascale era, a major goal of PETSc development has been achieving the scalability required to fully utilize leadership-class supercomputers. We will describe some of the algorithmic developments made during the era in which achieving inter-node scalability was the primary challenge to enabling extreme-scale computation, and then survey the challenges posed by the current era in which harnessing the abundant fine-scale parallelism within compute nodes -- primarily in the form of GPU-based accelerators -- has assumed at least equal importance. We will discuss how the PETSc design for performance portability addresses these challenges while stressing flexibility and extensibility by separating the programming model used by application code from that used by the library. Additionally, we will discuss recent developments in PETSc's communication module, PetscSF, that enable flexibility and scalable performance across large GPU-based systems while overcoming some of the difficulties posed by working directly with the Message Passing Interface (MPI) on such systems. A particular goal of this talk will be to go beyond simply describing the work performed to prepare PETSc and simulation codes that rely on it to run on exascale-class systems, but to enumerate the challenges we encountered and to share the essential lessons learned that can help other developers to prepare and optimize their high-performance scientific computing codes for the exascale era.
The Collaborative Seismic Earth Model: Generation 2
Authors: Noe Sebastian , van Herwaarden Dirk-Philip , Thrastarson Sölvi , Fichtner Andreas
Published: March 2023

We present the second generation of the Collaborative Seismic Earth Model (CSEM), a multi-scale global tomographic Earth model that continuously evolves via successive regional and global-scale refinements. Given finite computational resources, a systematic community effort enables the Earth model construction within the CSEM-architecture. It thereby takes advantage of the distributed human and computing power within the seismological community. The basic update methodology utilizes the current version of the CSEM as the initial model for regional tomographies. This setup allows to consistently incorporate previously accumulated knowledge into each new iteration of the CSEM. The latest generation of the CSEM includes 21 regional refinements from full seismic waveform inversion, ranging from several tens of kilometers to the entire globe. Some noticeable changes since the first generation include detailed local waveform inversions for the Central Andes, Iran, South-east Asia and the Western United States, continental-scale refinements for Africa and Asia and a global long-period tomography in areas that are not included in any of the submodels. Across all regional refinements in the current CSEM, three-component waveform data from 1,637 events and over 700,000 unique source-receiver pairs are utilized to resolve subsurface structure. Minimum periods of models range between 8 and 55 seconds. Using this model as a starting point, a global full-waveform inversion over multiple period bands down to periods of 50 seconds is deployed to ensure that the regional updates predict waveforms and that whole-Earth structure is honored. In this contribution, we will present the CSEM updating scheme and its parameterization, as well as the current state of the model. We show that the model predicts seismic waveforms on global and regional scales. Active participation in the project is encouraged.
A geodatabase of historical landslide events occurring in the highly urbanized volcanic area of Campi Flegrei, Italy
Authors: Esposito Giuseppe , Matano Fabio
Published: March 2023

Abstract. The analysis of geological processes threatening people
and properties in a populated region should be based on a comprehensive
knowledge of historical events and related characteristics. This type of
information is essential for predisposing event scenarios, validating
prediction models, and planning risk mitigation measures. Such activities
may be more complex in some geological settings, where urban settlements are
exposed to multi-hazard conditions. This is the case of the densely
populated Campi Flegrei volcanic area located in the Campania region,
southern Italy. Here, volcanic and seismic hazards are associated with
landslides, floods, and coastal erosion, which are playing a relevant role
in the landscape modification. The CAmpi Flegrei LAndslide Geodatabase
(CAFLAG), here presented, provides information related to 2302 landslides
that occurred in the continental, coastal, and insular sectors of the study
area during the 1828–2017 time interval. Data associated with the collected
landslide events highlight the characteristics of both landslides and of the
affected sites. Most of the cataloged mass movements consist of rock falls
affecting rocky slopes formed by lithified volcanic rocks, such as tuff or
ignimbrite. In addition, rainfall-induced earth and debris slides
translating into debris avalanches or rapid flows are widespread along steep
slopes mantled by weakly welded pyroclastic airfall deposits, similarly to
other areas of the region. The highest density of landslides is concentrated
along the coastline, where mass movements are contributing to the retreat of
coastal cliffs, and along inland slopes exposed towards the western
directions, where most of the storm systems come from. Temporal
information shows peaks of landslides in the years 1986, 1997, and 2005. A total
of 127 people lost their lives as a consequence of 53 cataloged landslides,
with a frequency of deadly events observed however to have decreased since the
early 1980s. This information will be useful to analyze mortality and risk
conditions still affecting the population of the Campi Flegrei caldera, which should be fully addressed with advanced knowledge and accurate
scenarios. The full database is freely available online at
https://doi.org/10.4121/14440757.v2 (Esposito and Matano, 2021).
A global database on holdover time of lightning-ignited wildfires
Authors: Moris Jose V. , Álvarez-Álvarez Pedro , Conedera Marco , Dorph Annalie , Hessilt Thomas D. , Hunt Hugh G. P. , Libonati Renata , Menezes Lucas S. , Müller Mortimer M. , Pérez-Invernón Francisco J. , Pezzatti Gianni B. , Pineda Nicolau , Scholten Rebecca C. , Veraverbeke Sander , Wotton B. Mike , Ascoli Davide
Published: March 2023

Abstract. Holdover fires are usually associated with
lightning-ignited wildfires (LIWs), which can experience a smoldering phase
or go undetected for several hours, days or even weeks before being reported.
Since the existence and duration of the smoldering combustion in LIWs is
usually unknown, holdover time is conventionally defined as the time between
the lightning event that ignited the fire and the time the fire is detected.
Therefore, all LIWs have an associated holdover time, which may range from a
few minutes to several days. However, we lack a comprehensive understanding
of holdover times. Here, we introduce a global database on holdover times of
LIWs. We have collected holdover time data from 29 different studies across
the world through a literature review and datasets assembled by authors of
the original studies. The database is composed of three data files (censored
data, non-censored data, ancillary data) and three metadata files
(description of database variables, list of references, reproducible
examples). Censored data are the core of the database and consist of
different frequency distributions reporting the number or relative frequency
of LIWs per interval of holdover time. In addition, ancillary data provide
further information to understand the methods and contexts in which the data
were generated in the original studies. The first version of the database
contains 42 frequency distributions of holdover time built with data on more
than 152 375 LIWs from 13 countries in five continents covering a time span
from 1921 to 2020. This database is the first freely available, harmonized
and ready-to-use global source of holdover time data, which may be used in
different ways to investigate LIWs and model the holdover phenomenon. The
complete database can be downloaded at
https://doi.org/10.5281/zenodo.7352172 (Moris et al., 2022).
New Hydrometeorological Observations from Inglefield Land and Thule, NW Greenland
Authors: Esenther Sarah E. , Smith Laurence C. , LeWinter Adam , Pitcher Lincoln H. , Overstreet Brandon T. , Kehl Aaron , Onclin Cuyler , Goldstein Seth , Ryan Jonathan C.
Published: March 2023

Abstract. Meltwater runoff from the Greenland Ice Sheet (GrIS) is an important contributor to global sea level rise, but substantial uncertainty exists in its measurement and prediction. Common approaches for estimating ice sheet runoff are in situ gauging of proglacial rivers draining the ice sheet, and surface mass balance (SMB) modeling. To obtain hydrological and meteorological datasets suitable for both runoff characterization and SMB model validation, we established an automated weather station (AWS) and cluster of traditional and experimental river stage sensors on the Minturn River, the largest proglacial river draining Inglefield Land, NW Greenland. Secondary installations measuring river stage were installed in the Fox Canyon River and North River at Thule Air Base, NW Greenland. Proglacial runoff at these sites is dominated by supraglacial processes only, uniquely advantaging them for SMB studies. The three installations provide rare hydrological time-series and an opportunity to evaluate experimental measurements of river stage from a harsh, little-studied polar region. The installed instruments include submerged vented and non-vented pressure transducers, a bubbler sensor, experimental bank-mounted laser rangefinders, and time-lapse cameras. The first three years of observations (2019 to 2021) from these stations indicate a) a meltwater runoff season from late June to late August/early September, roughly synchronous throughout the region; b) early onset (~ June 23 to July 8) of a strong diurnal runoff signal in 2019 and 2020, suggesting minimal meltwater storage in snow/firn; c) one-day lagged air temperature displays the strongest correlation with river stage; d) river stage correlates more strongly with ablation zone albedo than with net radiation; and e) late-summer rain-on-ice events appear to trigger the region’s sharpest and largest floods. The new gauging stations provide valuable in situ hydrological observations from a little-studied, rapidly changing area and are freely available through the PROMICE network (https://promice.org/weather-stations/).
Strategies for conservative and non-conservative monotone remapping on the sphere
Authors: Marsico David H. , Ullrich Paul A.
Published: March 2023

Abstract. Monotonicity is an important property of remapping operators for coupled weather and climate models. However, it is often challenging to design highly accurate operators that avoid the generation of new extrema or keep a remapped field between physically prescribed bounds. To that end, this paper explores several traditional and novel approaches for both conservative and non-conservative monotone remapping on the sphere. The accuracy and effectiveness of these algorithms are evaluated in the context of several different real and idealized fields and meshes.
Identifying the drivers of private flood precautionary measures in Ho Chi Minh City, Vietnam
Authors: Vishwanath Harish Thulasi , Sairam Nivedita , Yang Liang Emlyn , Garschagen Matthias , Kreibich Heidi
Published: March 2023

Abstract. Private flood precautionary measures have proven to
reduce flood damage effectively. Integration of these measures into flood
response systems can improve flood risk management in high-risk areas such
as Ho Chi Minh City (HCMC). Since uptake of such measures is voluntary, it
is important to know what drives householders to implement precautionary
measures. In this study, we developed a framework representing the uptake of private precautionary measures based on protection motivation theory and
the transtheoretical model. Using empirical survey data collected from 1000
flood-prone households in HCMC, we implemented lasso and elastic-net
regression to identify the drivers of private precaution. The measures were
classified into structural measures and non-structural measures based on
whether structural changes to the building were required. The households
were classified into proactive and reactive households based on whether
their decision to reduce risk (i.e. uptake of precautionary measures) was
preceded by experiencing a flood. The data-driven model revealed that the
household's level of education, the degree of belief in the government to
implement regional flood protection measures and the degree of belief that
in case of flooding one has to deal with the consequences of flooding by
themselves positively influence the proactive uptake of non-structural
measures. Among the households that experienced flooding before implementing
the measures, the uptake was found to be driven by the severity of the
experienced damage. For the same group of households, perceiving a high
severity of future flood impacts was found to negatively influence the
uptake of structural flood precautionary measures. These results highlight
that efforts to improve the implementation of private precautionary measures
should consider the socio-economic characteristics of the members of the household, their
past flood experience and their perception of flood risk management for
communicating flood risk and incentivizing private precautionary measures.
Abstracts of significant cases bearing on the regulation of insurance 2015
Authors: Cole Cassandra , McAdam Jennifer
Published: March 2023

The article presents abstracts on court cases about insurance-related topics in the U.S. including the federal Affordable Care Act (ACA), the denial of benefits under a plan governed by ERISA, Missouri's Health Insurance Marketplace Innovation Act (HIMIA), and health care service plans (HCSPs) in California.
Local Wisdom of Ponorogo Regency as a Learning Resource for the Kingdom in the Archipelago
Authors: Ayu Fitriyani Lintang , Suharini Erni , Utomo Udi
Published: March 2023

Material on teaching materials from the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, on science subjects, especially royal material in the Archipelago, has very little material exposure. So, it is necessary to have additional learning resources to support students' understanding of the material and strengthen students' awareness of the surrounding environment, in accordance with their respective local wisdom. This study uses descriptive qualitative research methods to determine the local wisdom of Ponorogo Regency which can be used as a source of learning on kingdom material in the Archipelago in class IV of elementary school. The results of the study show that some of the local wisdom of Ponorogo Regency can be used as a source of learning about kingdoms in the archipelago in grade IV elementary schools in the form of simple historical stories, and evidence of their legacy. This research can be followed up to motivate students in learning, and can instill local values in students.

Keywords: Learning resources, Local Wisdom, Elementary School, Kingdom, History
Oral Health Status and Oral Health Related Quality of Life Among Prisoners in Central Prison of Nellore District - A Cross Sectional Study
Authors: Durga Desu Ravali , Kumar R V S Krishna , Srinivasulu G
Published: March 2023

INTRODUCTION: Prisoners are unique population with many oral health problems. Prisoners are psychologically, socially, morally and economically affected group, which makes them to neglect their oral health. Oral Health Impact Profile (OHIP-14) was the most widely used instrument to assess the Oral health-related quality of life (OHRQoL) in an individual. Few studies have observed that oral hygiene status of the prisoners is poor and there is a higher prevalence of dental caries and periodontal diseases.
AIM & OBJECTIVE: The aim was to evaluate the dental caries status, periodontal status, and the OHRQoL among prisoners of Nellore District. In addition, it is an attempt to find the effect of dental caries and periodontal disease on OHRQoL.
MATERIALS AND METHODS: A cross sectional study was conducted to assess dental caries, periodontal health status, and oral health-related quality of life (OHRQoL) in prisoners of Nellore district. The Oral Health Impact Profile -14 questionnaires was given to the participants followed by Oral health status was assessed using the WHO Oral Health Assessment Form (2013).
RESULTS: A total of 115 prisoners were included in the study. The prevalence of dental caries was 53.1% with mean DMFT of 2.6±3.5. Pockets measuring 4-5mm was observed among 10.4% and >6 mm was observed among 21.7% of prisoners. LOA was seen among 27.8% of prisoners. A significant correlation was found between OHIP-14 with Dental caries and periodontal diseases.
CONCLUSION: This study concluded that periodontal health and dentition status was poor among prisoners due to lack of accessibility to dental care and dental health education.

Keywords: Oral health, oral health-related quality of life (OHRQoL), prison inmates, dental caries, periodontal disease, loss of attachment.
Effect of Online Simulation-Based Education on Nurses' Knowledge and Skills Toward Electrocardiogram Interpretation
Authors: Abdel Rahman Asmaa Abdel Rahman
Published: March 2023

Background: Osteoarthritis (OA); a whole joint disorder is a most common and progressive disease that worsens with age. Synovitis and synovial effusion can play important role in early OA diagnosis and progression. Limited pharmacological interventions affecting synovial effusion are studied clinically in Indian population.
Aim: This was a pilot, observational, single arm, open label study in real world set-up designed to evaluate the efficacy and safety of Mobileye® capsules containing sodium hyaluronate along with collagen and polysaccharides derived from rooster comb extract in subjects suffering from synovial effusion and knee pain of osteoarthritis.
Materials and Method: The study was conducted on total 30 subjects at Outpatient Department (OPD) at three different sites under the supervision of medical doctor. All subjects consumed Mobileye® capsule orally twice daily for 3 months. Synovial effusion, VAS score, WOMAC score and safety parameters were assessed as scheduled.
Results: Treatment with Mobileye® capsule showed significant and progressive improvement in all study related parameters. Baseline score vs. day 90 score (mean + SD) for synovial effusion, WOMAC pain score, WOMAC stiffness score, WOMAC physical function score, WOMAC total score and VAS score were 8.52+2.960 mm vs. 2.58+0.903 mm (p<0.0001), 15.13+3.10 vs. 4.13+2.4 (p<0.0001), 6.63+1.098 vs. 1.60+1.192 (p<0.0001), 54.33+8.39 vs. 14.23+8.67 (p<0.0001), 76.13+ 11 vs. 20.33+ 10.86 and 8.13 + 1.28 vs. 1.93+1.17 (p<0.0001) respectively. No significant adverse events were reported.
Conclusion: Oral intake of Mobileye® capsule is safe and effective intervention in reducing synovial effusion, pain, stiffness and in improving joint mobility.

Keywords: Electrocardiogram interpretation, nurses' knowledge and skills, online simulation education.
Terahertz networks for future Industrial Internet of Things
Authors: Cavallero Sara , Decarli Nicolò , Cuozzo Giampaolo , Buratti Chiara , Dardari Davide , Verdone Roberto
Published: March 2023

Wireless technology is expected to become a fundamental enabler to improve the efficiency, safety, and revenues of advanced manufacturing processes, as well as to realize new paradigms such as digital twins. The extremely challenging industrial scenario requires some technological shifts such as the adoption of the so far unexplored THz band. The purpose of this paper is to provide an overview of THz networks applied to the Industrial Internet of Things (IIoT). First, the main requirements of future industrial THz-based networks, challenges, and state-of-the-art are described. Subsequently, the key enabling technologies are introduced and discussed. Finally, we present some research directions for THz-based industrial networks.
Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview
Authors: Eze Elias , Eze Joy
Published: March 2023

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.
Why and how edge cloud computing can address performance and economic sustainability issues for telco domestic networks
Authors: Ciccarella Gianfranco , Giuliano Romeo , Mazzenga Franco , Vatalaro Francesco , Vizzarri Alessandro
Published: March 2023

This paper analyzes some of the telecommunication companies' (telcos) domestic networks' main objectives and issues related to the application services Quality of Experience (QoE), i.e., "technical QoE," and the economic sustainability of the Very High Capacity (VHC) networks. Telco domestic networks and Over The Top (OTT) networks are the Internet segments connecting the end-user equipment to the servers/clouds that provide application services. With the advent of VHC networks, telcos afford difficulties in managing effectively and efficiently the application services and their business sustainability. This paper affords some issues related to traditional telcos' domestic network architectures and the approach to managing the application services' performance improvement. Some telcos started changing their architectures, although a massive transformation had not yet started in the industry. Traditionally, telcos focus on network services, i.e., the transport of IP packets. Performance improvement is obtained by QoS-based traffic management techniques, such as bandwidth reservation and packet prioritization. To manage application performance improvement, the use of layer 4 techniques and Edge Cloud Computing (ECC) is effective, as demonstrated by the multiyear experience of OTTs which already use these technologies. In telco domestic networks, layer 2 tunnels increase the complexity of deploying ECC. However, some vendors provide non-standard solutions to make the IP layer 3 user plane visible and to deploy ECC. ECC is the key factor for the transformation. It is a mini/micro-data center that distributes some applications and content closer to the end users. The distribution can provide a paradigm shift in a telco's business. In the paper, we first highlight the need for regulators to appreciate the need to encourage this industry transformation fully.
Federated learning for performance prediction in multi-operator environments
Authors: Lan Xiaoyu , Taghia Jalil , Moradi Farnaz , Khoshkholghi Mohammad Ali , Zec Edvin Listo , Mogren Olof , Mahmoodi Toktam , Johnsson Andreas
Published: March 2023

Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an operator network. Federated learning is compared against local and central learning strategies for multi-operator performance prediction, and it is shown to balance the requirements on data privacy, model performance, and the network overhead. Further, the paper provides insights on how data heterogeneity affects model performance, where the conclusion is that standard federated learning has certain robustness to data heterogeneity. Finally, we discuss the challenges related to training a federated learning model with a limited budget on the communication rounds. The evaluation is performed using a set of realistic publicly available data traces, that are adapted specifically for the purpose of studying multi-operator service performance prediction.
RIS-aided mmWave beam-forming for two-way communications of multiple pairs
Authors: Torkzaban Nariman , Khojastepour Mohammad A. (Amir) , Farajzadeh-Tehrani Mohammad , Baras John S.
Published: March 2023

Millimeter-wave (mmWave) communications is a key enabler towards realizing enhanced Mobile Broadband (eMBB) as a key promise of 5G and beyond, due to the abundance of bandwidth available at mmWave bands. An mmWave coverage map consists of blind spots due to shadowing and fading especially in dense urban environments. Beam-forming employing massive MIMO is primarily used to address high attenuation in the mmWave channel. Due to their ability in manipulating the impinging electromagnetic waves in an energy-efficient fashion, Reconfigurable Intelligent Surfaces (RISs) are considered a great match to complement the massive MIMO systems in realizing the beam-forming task and therefore effectively filling in the mmWave coverage gap. In this paper, we propose a novel RIS architecture, namely RIS-UPA where the RIS elements are arranged in a Uniform Planar Array (UPA). We show how RIS-UPA can be used in an RIS-aided MIMO system to fill the coverage gap in mmWave by forming beams of a custom footprint, with optimized main lobe gain, minimum leakage, and fairly sharp edges. Further, we propose a configuration for RIS-UPA that can support multiple two-way communication pairs, simultaneously. We theoretically obtain closed-form low-complexity solutions for our design and validate our theoretical findings by extensive numerical experiments.
On the use of intelligent metasurfaces in data centers
Authors: Aziz Waqar Ali , Vassiliou Vasos , Saeed Taqwa , Pitsillides Andreas , Lestas Marios
Published: March 2023

On-demand wireless links in hybrid data centers can augment the existing network capacity to improve the performance. In the absence of line of sight between the rack transceivers, reflectors have been considered in literature to support high bandwidth, on-demand wireless links within the data centers. Metasurfaces, as a candidate smart reflector technology can be harnessed to bring the intelligence burden to the reflector side which, coupled with the expected low power expenditure, can render the technology a useful alternative. Given the fact that this has not been considered in the literature before, in this paper we highlight the design challenges which emanate from such a consideration and present tools and methodologies which can be utilized to address these challenges, focusing on two problems: wireless link selection to be served by the metasurface, and workload characterization within the metasurface. The former is formulated as an optimization problem with demonstrated effectiveness in minimizing the job completion time. Moreover, the workload analysis reveals a number of interesting attributes, as for example, uneven spatial distribution of traffic on the metasurface's controller network and how this is affected by the metasurface size and location, while the use of beam splitting on the metasurface to support parallel processing and storage functionalities does not significantly affect the incurred workload. Unlike previous work on metasurfaces, which consider mobility-driven reconfigurations, in this paper for the first time we consider data-driven reconfigurations.
Connectivity and racial equity in responding to COVID-19 impacts in the Chicago regional food system
Authors: Obach Rowan , Schusler Tania , Vaca Paulina , Durkin Sydney , Sheikh Ma'raj
Published: March 2023

The COVID-19 outbreak led to major disruptions in food systems across the globe. In the United States’ Chicago region, the outbreak created immediate concerns around increased hunger, food insecurity, supply chain disruptions, and loss of local liveli­hoods. This was especially evident in communities of color, which faced disproportionate impacts from the pandemic. In March 2020, the Chicago Food Policy Action Council (CFPAC) coordinated a Rapid Response Effort that convened people in working groups related to emergency food assis­tance, local food producers, small businesses, and food system workers to address urgent needs that arose due to the pandemic. Each working group met regularly through virtual calls. This effort has persisted throughout the pandemic in various forms. For this study, we interviewed CFPAC staff members and participants in these calls to create narratives that document respondents’ perceptions of the Rapid Response Effort’s evolution, benefits, challenges, and potential for long-term impacts. Thematic analysis conducted across these narra­tives revealed the importance of network connec­tions to overcoming food system disruptions caused by the COVID-19 pandemic. Our analysis also underscored challenges associated with racism in efforts to strengthen local and regional food sys­tems. These findings indicate a need for research and practice that intentionally attend to power dis­parities related to race within collaborative net­works in order to structure local and regional food systems to achieve greater racial equity and resili­ence to future shocks.
Students as co-researchers: Using participatory action research to address college food insecurity
Authors: Brand Rachel
Published: March 2023

Studies indicate that college students experience high rates of food insecurity. Growing awareness of food insecurity on college campuses has resulted in efforts by many institutions to address the prob­lem through innovative programs such as food pantries, campus gardens, and educational work­shops. While these initiatives play an important role in facilitating food access, they fall short of meeting students’ needs. There is little research on how students’ experiences or knowledge can inform strategies to address food insecurity, nor is there extensive research on how students view this issue for themselves and their peers. This study looks at the benefits of engaging students in participatory action research (PAR) to address college food insecurity. PAR is particularly well suited to address campus food insecurity given its tenets of research, reflection, and action. This paper examines how a PAR project, conducted throughout a semester-long community-engaged learning course at the University of San Francisco (USF), resulted in innovative strategies to address college food insecurity. This justice-based research approach deepened students’ understanding of the issue and inspired them to want to change their campus food systems. Students worked to shift the narrative of food insecurity on campus away from an individual experience that carries stigma toward one of community, relationships, and collective action. This study shows the opportunities to address food insecurity not only through immedi­ate needs-based solutions but also through a justice-based research methodology that centers student experiences and knowledge.
Food insecurity and utilization of campus food resources differ by demographic and academic group
Authors: Tanner Zoee , Loofbourrow Brittany , Chodur Gwen , Kemp Leslie , Scherr Rachel
Published: March 2023

Food insecurity is a major challenge for many college students, negatively affecting their well-being and academic success. To address the challenge, universities are implementing food resources to provide free access to food; however, little is known about how students’ identities affect their utilization of these resources. This study analyzed the relationships among food insecurity, campus food resource participation, and student demographic and academic identity. Survey data were collected from a representative sample (n=1,190) of undergraduate students at the University of California (UC), Davis. Analyses were conducted using chi-square tests of independence and logistic regression to assess factors related to food insecurity and campus food resource participation. The results indicate that transfer students are 84% more likely to experience food insecurity, but 39% less likely to use campus food resources. Both first-generation and fourth- year students disproportionately experience food insecurity and utilize campus food resources more. Latino(a)/Chicano(a)/Hispanic students are twice as likely to experience food insecurity and 49% more likely to use food resources than white/European American students. These results demonstrate that student identity intersects with food insecurity and access in the college environment. These findings can guide recommendations for improving and expanding campus food resources by utilizing equitable outreach strategies that build a support network of food access while reflecting the diverse needs of student populations.
2022年國家醫療品質獎特優機構─高雄長庚紀念醫院追求創新卓越的醫療品質：注重品質管理與病人安全零錯誤
Authors: 王慧卿 王慧卿 , Hui-Ching Wang 郭美玲 , Mei-Ling Kao 吳承誌 , Cheng-Chih Wu 莊柏羣 , Po-Chun Chuang 張永達 , Yung-Ta Chang 林新景 , Hsin-Ying Lin 江明珠 , 林偕益 林偕益 , 林志宏 林志宏 , 龔嘉德 龔嘉德 , 李建德 李建德 , 王植熙 王植熙
Published: March 2023

高雄長庚紀念醫院創立於1986年，深耕南臺灣37年，從專業走向卓越，重視醫療品質；以王創辦人「要做就做最好的」為共同目標，自2003年開始參與財團法人醫院評鑑暨醫療品質策進會（以下簡稱醫策會）主辦「NHQA國家醫療品質獎」，19年期間屢獲佳績，2016年榮獲醫策會國家醫療品質獎頒發智慧醫療（Smart Hospital）全機構標章，近年連續獲得「醫品標竿獎」、「熱心分享獎」、「防疫貢獻獎」、「實證醫學類持續參與特別獎」、「主題類深耕品質獎」，更獲得3項「卓越中心」獎項，於2022年榮獲「特優機構」獎項。

本院在院長、副院長、執行長等領導下，全員投入持續改善，整合教學、研究、辯論與知識轉譯等多元面向的發展，體現實證醫學於提升醫療品質之價值；透過高擬真情境訓練增強臨床知識、專業精神、決策能力和信心，確保病人的品質和安全；超前部署精進創新，打造屬於高雄長庚之專業品牌形象。追求品質永無止盡，需要實事求是、追根究柢，以展現卓越之價值與提供卓越的醫療服務。

 

Founded in 1986, the Kaohsiung Chang Gung Memorial Hospital has rooted its operation in Taiwan to pursue professionalism and excellence while focusing on healthcare quality over the past 37 years. The common goal set by Yung-Ching Wang, the founder of the Hospital, for all staff members is to complete every task to the best of their ability. The Hospital began participating in the National Healthcare Quality Award launched by the Joint Commission of Taiwan (JCT) in 2003 and has received multiple awards. Notably, the Hospital received the Smart Hospital Award - Whole Institution from JCT in 2016. It proceeded to win the Healthcare Benchmark Award, Experience Sharing Award, Pandemic Prevention Contribution Award, Special Award for Continual Participation in Evidencebased Medicine, and Special Theme Quality Awards consecutively in recent years, in addition to receiving three Excellent Center awards. In 2022, the Hospital won the Distinguish Institution Award. Under the leadership of the superintendent, deputy superintendent, and chief executive officer, all members of the Hospital continue to improve every aspect of its operation and ensure multifaceted development in the areas of medical education, research, debates, and knowledge translation, thereby highlighting the value of evidence-based medicine in improving healthcare quality. Hospital staff also participate in high-fidelity simulation to ensure patient care quality and safety by enhancing their clinical knowledge, professionalism, decision-making ability, and confidence. Relevant strategies have been deployed in advance to create a professional brand image for the Hospital, which continues to pursue outstanding healthcare quality, seek truth from facts, foster adequate values, and provide excellent healthcare services.

 