bluuebunny/crossref_metadata_2025 huggingface dataset:
The use of the second thoracic vertebral landmark for skeletal muscle assessment and computed tomography‐defined sarcopenia evaluation in patients with head and neck cancer
Authors: Vangelov Belinda , Bauer Judith , Moses Daniel , Smee Robert
Published: February 2023

Background

The cross‐sectional area (CSA) of skeletal muscle (SM) at the third lumbar vertebra (L3) is used to determine computed tomography (CT)‐defined sarcopenia. We investigated the feasibility of SM assessment at the second thoracic vertebra (T2) in patients with head and neck cancer (HNC).

Methods

Diagnostic PET‐CT scans were used to develop a prediction model for L3‐CSA using T2‐CSA. Effectiveness of the model and cancer‐specific survival (CSS) were investigated.

Results

Scans of 111 patients (85% male) were evaluated. The predictive formula: L3‐CSA (cm2) = 174.15 + [0.212 × T2‐CSA (cm2)] – [40.032 × sex] – [0.928 × age (years)] + [0.285 × weight (kg)] had good correlation r = 0.796, ICC = 0.882 (p < 0.001). SM index (SMI) mean difference (bias) was −3.6% (SD 10.2, 95% CI −8.7% to 1.3%). Sensitivity (82.8%), specificity (78.2%), with moderate agreement (ƙ = 0.540, p < 0.001). Worse 5‐year CSS with lower quartile T2‐SMI (51%, p = 0.003).

Conclusions

SM at T2 can be effectively used for CT‐defined sarcopenia evaluation in HNC.
A data‐driven approach for flood prediction using grid‐based meteorological data
Authors: Wang Yizhi , Liu Jia , Li Chuanzhe , Liu Yuchen , Xu Lin , Yu Fuliang
Published: February 2023

Establishing a physically‐based hydrological model for flood prediction in ungauged or data‐limited catchments has always been a difficult problem. In this study, a data‐driven approach based on the NASA Global Land Data Assimilation System (GLDAS) data is proposed for flood prediction with the assistance of the Gamma Test. A runoff generation model together with a routing model based on the most advanced deep learning model, i.e. Long Short‐Term Memory (LSTM) network is established. By calculating the noise of the input data, Gamma Test can effectively avoid the overfitting phenomenon with the LSTM network. Taking the a small‐scale mountainous catchment from northern China as an example, Gamma Test in this study is used to help select the optimal combination of the GLDAS inputs for the runoff generation model, and meanwhile the input grids involved in the routing model. The established models are then verified based on the Nash‐Sutcliffe Efficiency coefficient (NSE), and it is found that the surface runoff generation model shows a good performance, with an average NSE value of 0.8708, while the baseflow‐groundwater runoff generation model also results in an acceptable performance with an average NSE value of 0.6320. After the involvement of the observed rainfall data, the model performance (NSE) of the routing model has increased from 0.5738 to 0.7144. Finally, the runoff generation models and the routing model are integrated, and the grid‐based GLDAS meteorological data are used directly to simulate the streamflow at the catchment outlet. The integrated model performs well with an NSE value of 0.7909, which indicates the feasibility of this data‐driven approach for flood prediction using the gird‐based meteorological data. The methodology adopted in this study provides a reference for flood prediction using data‐driven models in ungauged or data‐limited catchments.
A joint segmentation and classification framework for COVID‐19 infection segmentation and detection from chest CT images
Authors: Jeevitha S. , Valarmathi K.
Published: February 2023

In COVID19 management, CT images are used as noninvasive diagnostic tools for screening and disease monitoring. Segmentation of infections provides valuable visual interpretations in the process of prognosis and decision making. Segmentation of COVID19 infection from chest CT images is challenging due to the presence of multiple infection types and complex morphological patterns. This paper presents a novel multi task learning framework for COVID19 infection segmentation and detection. The proposed model called DB‐YNet, is built on YNet architecture with a dense bottleneck and attention based UNet backbone. This model is trained and tested with standard datasets, demonstrating superior segmentation and classification metrics. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. 0.9875 and 0.9961 under binary and multi class classifications respectively. In COVID19 triage, DB‐YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions.
Automated diagnosis of COVID stages using texture‐based Gabor features in variational mode decomposition from CT images
Authors: Patel Rajneesh Kumar , Kashyap Manish
Published: February 2023

COVID‐19 is a deadly and fast‐spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time‐inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed; however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two‐dimensional variational mode decomposition in this work. This proposed approach decomposes pre‐processed CT scan pictures into sub‐bands. The texture‐based Gabor filter bank extracts the relevant features, and the student's t‐value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square‐ support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non‐COVID CT lung images. The results of the trial showed that our model outperformed cutting‐edge methods for COVID classification. Using tenfold cross‐validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning‐based models like random forest, K‐nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily.
An updated review of the association of host genetic factors with susceptibility and resistance to COVID‐19
Authors: Mohammadpour Somayeh , Torshizi Esfahani Amir , Halaji Mehrdad , Lak Mina , Ranjbar Reza
Published: February 2023

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in human populations sparked a global pandemic of the coronavirus disease 2019 (COVID‐19). According to preliminary data, about 14% of cases are considered severe and 5% of cases result in critical illness and, reported case fatality rates vary from 1% to more than 7%. However, the symptoms of the disease and the clinical outcome are very different in infected people. In view of these differences, it is clearly apparent that to gain insight into the biology of the SARS‐CoV‐2, it is important to study not just the infectious particle in itself but also to investigate the virus‐host cell interactions that occur during infection. This review seeks to consider the various aspects of genetic factors in determining the susceptibility and host resistance to SARS‐CoV‐2 throughout the recently published literature.
The vaginal microbiota among the different status of human papillomavirus infection and bacterial vaginosis
Authors: Liu Yujing , Li Tingyuan , Guo Ruochun , Chen Tingting , Wang Sumeng , Wu Dingkun , Li Jing , Liu Zhihua , Zhao Yuqian , Yin Jian , Qin Junjie , Sun Li , Chen Wen
Published: February 2023

Although human papillomavirus (HPV) infection plays a decisive role in causing tumors, its infection is insufficient for independently promoting cancer development and other co‐factors facilitate the carcinogenic process. The objective of this study was to demonstrate the association between vaginal microbiota and high‐risk human papillomavirus (HR‐HPV) infection in women with and without bacterial vaginosis (BV). The study included 1015 women aged 21–64 who participated in cervical cancer screening in two areas of China from 2018 to 2019. Women were collected cervical exfoliated cell specimens and reproductive tract secretions samples for HR‐HPV, BV and microbial composition testing. From the non‐BV & HPV− group (414 HPV‐negative women without BV) to the non‐BV & HPV+ group (108 HPV‐positive women without BV), to the BV & HPV−group (330 HPV‐negative women with BV) and then to the BV & HPV+ group (163 HPV positive‐women with BV), microbial diversity increased. The relative abundance of 12 genera, including Gardnerella, Prevotella, and Sneathia increased, while Lactobacillus declined. Correlation networks of these genera and host characteristics were disrupted in the non‐BV & HPV+ group, and the network trended more disordered in the BV & HPV+ group. Besides, multiple HPV infection, certain HPV genotype infection and cervical intraepithelial neoplasia (CIN) status were associated with some microbes and higher microbial diversity. HPV shifted the composition and diversity of vaginal microbiota, and BV further reinforced the trend. The relative abundance of 12 genera increased and 1 genus decreased on account of BV and HPV infection, and some genera including Lactobacillus, Prevotella, and Sneathia were associated with some specific HPV genotypes infection and CIN.
Biomechanical evaluation of a novel biomimetic artificial intervertebral disc in canine cervical cadaveric spines
Authors: Jacobs Celien A. M. , Doodkorte Remco J. P. , Kamali S. Amir , Abdelgawad Abdelrahman M. , Ghazanfari Samaneh , Jockenhoevel Stefan , Arts J. J. Chris , Tryfonidou Marianna A. , Meij Björn P. , Ito Keita
Published: February 2023

Background Context

Cervical disc replacement (CDR) aims to restore motion of the treated level to reduce the risk of adjacent segment disease (ASD) compared with spinal fusion. However, first‐generation articulating devices are unable to mimic the complex deformation kinematics of a natural disc. Thus, a biomimetic artificial intervertebral CDR (bioAID), containing a hydroxyethylmethacrylate (HEMA)—sodium methacrylate (NaMA) hydrogel core representing the nucleus pulposus, an ultra‐high‐molecular‐weight‐polyethylene fiber jacket as annulus fibrosus, and titanium endplates with pins for primary mechanical fixation, was developed.

Purpose

To assess the initial biomechanical effect of the bioAID on the kinematic behavior of the canine spine, an ex vivo biomechanical study in 6‐degrees‐of‐freedom was performed.

Study Design

A canine cadaveric biomechanical study.

Methods

Six cadaveric canine specimens (C3‐C6) were tested in flexion‐extension (FE), lateral bending (LB) axial rotation (AR) using a spine tester in three conditions: intact, after C4‐C5 disc replacement with bioAID, and after C4‐C5 interbody fusion. A hybrid protocol was used where first the intact spines were subjected to a pure moment of ±1 Nm, whereafter the treated spines were subjected to the full range of motion (ROM) of the intact condition. 3D segmental motions at all levels were measured while recording the reaction torsion. Biomechanical parameters studied included ROM, neutral zone (NZ), and intradiscal pressure (IDP) at the adjacent cranial level (C3‐C4).

Results

The bioAID retained the sigmoid shape of the moment‐rotation curves with a NZ similar to the intact condition in LB and FE. Additionally, the normalized ROMs at the bioAID‐treated level were statistically equivalent to intact during FE and AR while slightly decreased in LB. At the two adjacent levels, ROMs showed similar values for the intact compared to the bioAID for FE and AR and an increase in LB. In contrast, levels adjacent to the fused segment showed an increased motion in FE and LB as compensation for the loss of motion at the treated level. The IDP at the adjacent C3‐C4 level after implantation of bioAID was close to intact values. After fusion, increased IDP was found compared with intact but did not reach statistical significance.

Conclusion

This study indicates that the bioAID can mimic the kinematic behavior of the replaced intervertebral disc and preserves that for the adjacent levels better than fusion. As a result, CDR using the novel bioAID is a promising alternative treatment for replacing severely degenerated intervertebral discs.
Effect of skull porosity on ultrasound transmission and wave mode conversion at large incidence angles
Authors: Jing Bowen , Strassle Rojas Stephan , Lindsey Brooks D.
Published: February 2023

Background

Transcranial ultrasound imaging and therapy depend on the efficient transmission of acoustic energy through the skull. Multiple previous studies have concluded that a large incidence angle should be avoided during transcranial‐focused ultrasound therapy to ensure transmission through the skull. Alternatively, some other studies have shown that longitudinal‐to‐shear wave mode conversion might improve transmission through the skull when the incidence angle is increased above the critical angle (i.e., 25° to 30°).

Purpose

The effect of skull porosity on the transmission of ultrasound through the skull at varying incidence angles was investigated for the first time to elucidate why transmission through the skull at large angles of incidence is decreased in some cases but improved in other cases.

Methods

Transcranial ultrasound transmission at varying incidence angles (0°–50°) was investigated in phantoms and ex vivo skull samples with varying bone porosity (0% to 28.54% ± 3.36%) using both numerical and experimental methods. First, the elastic acoustic wave transmission through the skull was simulated using micro‐computed tomography data of ex vivo skull samples. The trans‐skull pressure was compared between skull segments having three levels of porosity, that is, low porosity (2.65% ± 0.03%), medium porosity (13.41% ± 0.12%), and high porosity (26.9%). Next, transmission through two 3D‐printed resin skull phantoms (compact vs. porous phantoms) was experimentally measured to test the effect of porous microstructure alone on ultrasound transmission through flat plates. Finally, the effect of skull porosity on ultrasound transmission was investigated experimentally by comparing transmission through two ex vivo human skull segments having similar thicknesses but different porosities (13.78% ± 2.05% vs. 28.54% ± 3.36%).

Results

Numerical simulations indicated that an increase in transmission pressure occurs at large incidence angles for skull segments having low porosities but not for those with high porosity. In experimental studies, a similar phenomenon was observed. Specifically, for the low porosity skull sample (13.78% ± 2.05%), the normalized pressure was 0.25 when the incidence angle increased to 35°. However, for the high porosity sample (28.54% ± 3.36%), the pressure was no more than 0.1 at large incidence angles.

Conclusions

These results indicate that the skull porosity has an evident effect on the transmission of ultrasound at large incidence angles. The wave mode conversion at large, oblique incidence angles could enhance the transmission of ultrasound through parts of the skull having lower porosity in the trabecular layer. However, for transcranial ultrasound therapy in the presence of highly porous trabecular bone, transmission at a normal incidence angle is preferable relative to oblique incidence angles due to the higher transmission efficiency.
Validation of AND/ASPEN pediatric malnutrition diagnosis in children admitted to medical wards in two tertiary hospitals in Malaysia
Authors: Ong Shu Hwa , Chen Seong Ting , Chee Winnie Siew Swee
Published: February 2023

Background

Identification and management of malnutrition among pediatric hospitalized patients is critical for improved clinical outcomes and recovery. This study investigated the use of the Academy of Nutrition and Dietetics and the American Society for Parenteral and Enteral Nutrition (AND/ASPEN) pediatric malnutrition diagnosis in comparison with the Subjective Global Nutritional Assessment (SGNA) tool and single anthropometric indicators (weight, height, body mass index, and mid‐upper arm circumference) among hospitalized children.

Methods

A cross‐sectional study was conducted among 260 children admitted to general medical wards. SGNA and anthropometric measurements were used as references. Kappa agreement, diagnostic values, and area under the curve (AUC) were analyzed to evaluate the diagnostic ability of the AND/ASPEN malnutrition diagnosis tool. Logistic binary regression was performed to determine the predictive ability of each malnutrition diagnosis tool on the length of hospital stay.

Results

The AND/ASPEN diagnosis tool detected the highest malnutrition rate (41%) among the hospitalized children in comparison with the reference methods. This tool demonstrated fair specificity of 74% and sensitivity of 70% compared with the SGNA. It obtained a weak agreement in determining the presence of malnutrition by kappa (0.06–0.42) and receiver operating characteristic curve analysis (AUC = 0.54–0.72). The use of the AND/ASPEN tool obtained an odds ratio of 0.84 (95% CI, 0.44–1.61; P = 0.59) in predicting the length of hospital stay.

Conclusions

The AND/ASPEN malnutrition tool is an acceptable nutrition assessment tool for hospitalized children in general medical wards.
Life satisfaction and difficulties experienced by the family members of individuals with thalassemia
Authors: Sevinç Sibel
Published: February 2023

Aim

This study aimed to examine the life satisfaction and difficulties experienced by the family members of individuals with thalassemia.

Design

This study design is mix‐method research. This research adheres to the COREQ guidelines and checklist.

Methods

The research was conducted in the Blood Diseases Polyclinic of a state hospital in a Mediterranean city in Turkey between February 2022 and April 2022.

Results

The mean life satisfaction scale score was 11.18 ± 5.13, and a negative correlation was found between the mother's age and life satisfaction score (r = −0.438; p = 0.042, p < 0.05). Qualitative analysis of the experiences of the family members of individuals with thalassemia yielded 10 themes.
Genomics in neglected and underutilized fruit crops: A chromosome‐scale genome sequence of cherimoya (Annona cherimola)
Authors: Talavera Alicia , Fernandez‐Pozo Noe , Matas Antonio J. , Hormaza Jose I. , Bombarely Aureliano
Published: February 2023

Societal Impact Statement

Cherimoya has been an important food source since Pre‐Columbian times in the Americas. Although it is currently considered an underutilized fruit crop, it is still important at the local level in several regions of Central and South America, and has a clear niche for expansion in regions with subtropical climates. However, the availability of genomic resources to facilitate breeding programs and improve the understanding of Annonaceae genetic diversity is limited. In this work, we provide an important resource to fill this knowledge gap, with the hope that it will ultimately increase this crop's resilience under different climate change scenarios, which will increase food security in regions with subtropical climates.

Cherimoya (Annona cherimola Mill.) is a perennial fruit tree crop native to the Neotropics valued since pre‐Columbian times by different native American civilizations. It belongs to the Annonaceae, the largest family of the Magnoliid clade, sister to the eudicot and monocot clades of angiosperms. Despite its excellent organoleptic and nutritive qualities, this crop remains underutilized, although it has a clear niche for expansion in regions with subtropical climates. To date, no previous significant genomic information is available for this species, which would be a key tool to optimize breeding programs and advance in the study and conservation of its extant genetic diversity.

A combination of different sequencing technologies (Illumina, Pacific Biosciences) has been implemented in order to assemble a chromosome‐level reference genome of A. cherimola.

The final reference genome resulted in an assembly of 1.13 Gb and N50 of 170.86 Mb, anchored into seven pseudomolecules and with a completeness of 95.6%. A total of 41,413 protein‐coding genes were identified, many of which were related to secondary metabolism, defense mechanisms, stress response, and development.

The results of this study provide novel significant genomic resources not only for cherimoya and other species of the Annonaceae but also for understanding the evolution of the earlier divergent angiosperms.
Managing patients with tuberculosis in primary care
Authors: Chavda Aneeka , Capstick Toby GD , Kon Onn Min
Published: February 2023

Tuberculosis is treated mainly in secondary care, but patients commonly present to primary care for investigation of initial symptoms and treatment monitoring. This article discusses the most frequently used anti‐tuberculosis drugs, their monitoring requirements and how to manage drug interactions and adverse reactions.
A prototype autonomous robot for underwater crime scene investigation and emergency response
Authors: Rymansaib Zuhayr , Thomas Benjamin , Treloar Alfred Anthony , Metcalfe Benjamin , Wilson Peter , Hunter Alan
Published: February 2023

Underwater crime scene investigation and emergency response are tasks typically carried out by divers constituting part of a specialist team. Operating in such dynamic environments, often with poor visibility and risk of concealed hazards, can be time consuming and dangerous. Autonomous uncrewed vessels with underwater acoustic imaging sensors have been used for similar purposes in other fields (e.g., hydrography, naval mine countermeasures [MCMS], etc.) but have not been adopted in this specific application domain. The Police Robot for Inspection and Mapping of underwater Evidence (PRIME) is an autonomous uncrewed surface vessel that is being developed for this purpose. It is a novel application of existing robotic technology that is intended to be used within an end‐to‐end police and emergency underwater search process. It aims to enhance the effectiveness, efficiency, and safety of divers by autonomously locating and highlighting target objects or regions of interest, as well as benign regions, thereby reducing their time spent underwater. Side‐scan imaging sonars are used to sense the underwater environment using techniques leveraged from the similar application domain of naval MCMs. The system autonomously generates actionable intelligence in the form of simplified coverage and anomaly maps for easy interpretation by the dive team. These are communicated to shore in real‐time and georeferenced on satellite maps. This paper details the PRIME system prototype and presents results from initial field experimentation. The prototype has been operated in various urban, shallow‐water environments. The experimental results shown here were collected in Bristol Harbour (the UK) with a water depth of approximately 5 m. In the experiment, a clothed mannequin resembling a human body was deployed on the muddy floor. Autonomous searches were executed and the body was detected successfully as an anomaly against the background, illustrating the feasibility and viability of the system as an autonomous robotic aid for locating missing persons in a representative, unstructured, and dynamic real‐world environment.
Interdisciplinarity, Transdisciplinarity, and Health Humanities: Eye Tracking, Ableism, Disability, and Art Creation
Authors: Riegel Christian , Robinson Katherine M.
Published: February 2023

This chapter examines a transdisciplinary research project that develops eye tracking hardware and software for the purpose of art creation. Interdisciplinarity and transdisciplinarity are defined in relation to the development of the health humanities as a field that inherently draws from multiple disciplines. Transdisciplinary research is seen to transcend disciplinary boundaries and to integrate community collaboration as a mode that is geared to addressing social challenges. Eye tracking art creation relies solely on eye movements to create art on digital screens and thus has implications for individuals with limited mobility. Disability is defined in relation to ableism, which is the discriminatory practice of enforcing a corporeal norm. We discuss how technology development that has implications for individuals with disabilities, such as ours, must resist ableist tendencies to attempt to solve disability as a problem that requires a cure. Thus, we frame our research project that has as its goal the development of tools that provide the enjoyment of art creation above all.
Correction to: Imagining Gender in Biographical Fiction: Introduction
Authors: Novak Julia , Ní Dhúill Caitríona
Published: February 2023

The original version of this chapter was revised: this chapter was previously published non-open access. It has been changed to Open Access.
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Authors: Elkelany Amany , Ross Robert , Mckeever Susan
Published: February 2023

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments.
Challenges Associated with the Adoption of Artificial Intelligence in Medical Device Software
Authors: Cepeda Zapata Karla Aniela , Ward Tomás , Loughran Róisín , McCaffery Fergal
Published: February 2023

The utilization of Artificial Intelligence (AI) has changed and enhanced several industries across the world, such as education, research, manufacturing and healthcare. The potential of AI to create new and enhanced applications that can benefit patients and physicians has created interest and enthusiasm, especially in a Medical Device Software (MDS) context. Although, the adoption of AI in MDS has also brought concerns for regulatory agencies and policymakers. The complexity of AI has challenged the standard requirements set by regulatory agencies, especially in the context of the differences between traditional MDS and AI. Additionally, the unique capacity of AI to continuous learning for optimal performance in real-world settings may also bring potential harm and risk to patients and physicians. The challenges discussed in this paper are in relation to: (1) Software Development Life Cycle (SDLC) frameworks; (2) learning processes and adaptability of AI algorithms; (3) explainability and traceability; and (4) conflictive terminology. At the end of this paper, conclusions and future work are presented to contribute to the safety and methodical implementation of AI in health care settings.
An Intelligent Empowering Agent (IEA) to Provide Easily Understood and Trusted Health Information Appropriate to the User Needs
Authors: Alfano Marco , Kellett John , Lenzitti Biagio , Helfert Markus
Published: February 2023

Most members of the public, including patients, usually obtain health information from Web searches using generic search engines, which is often overwhelming, too generic, and of poor quality. Although patients may be better informed, they are often none the wiser and not empowered to communicate with medical professionals so that their care is compatible with their needs, values, and best interests. Intelligent Empowering Agents (IEA) use AI to filter medical information and assist the user in the understanding of health information about specific complaints or health in general. We have designed and developed a prototype of an IEA that dialogues with the user in simple language, collects health information from the Web, and provides tailored, easily understood, and trusted information. It empowers users to create their own comprehensive and objective opinion on health matters that concern them. This paper describes the IEA main characteristics and presents the results of subjective and objective tests carried out to assess the effectiveness of the IEA.
Comparison and Analysis of 3 Key AI Documents: EU’s Proposed AI Act, Assessment List for Trustworthy AI (ALTAI), and ISO/IEC 42001 AI Management System
Authors: Golpayegani Delaram , Pandit Harshvardhan J. , Lewis Dave
Published: February 2023

Conforming to multiple and sometimes conflicting guidelines, standards, and legislations regarding development, deployment, and governance of AI is a serious challenge for organisations. While the AI standards and regulations are both in early stages of development, it is prudent to avoid a highly-fragmented landscape and market confusion by finding out the gaps and resolving the potential conflicts. This paper provides an initial comparison of ISO/IEC 42001 AI management system standard with the EU trustworthy AI assessment list (ALTAI) and the proposed AI Act using an upper-level ontology for semantic interoperability between trustworthy AI documents with a focus on activities. The comparison is provided as an RDF resource graph to enable further enhancement and reuse in an extensible and interoperable manner.
AI and ML in School Level Computing Education: Who, What and Where?
Authors: Mahon Joyce , Becker Brett A. , Namee Brian Mac
Published: February 2023

This paper presents the results of a systematic review of the literature relating to artificial intelligence (AI) and machine learning (ML) education at school level. We conducted a search of the ACM Full-text Collection and 33 papers from the 197 search results were selected for analysis. In this context, we considered the research questions: 1) Who has been the focus of the research?, 2) What course content appears in the research?, and 3) Where has the research taken place? We find that there has been a recent marked increase in research on AI/ML for school level education, although most of this has been based in the United States. The majority of this research focuses on students, with very little specifically addressing teachers, experts, parents, or the wider school community. There is also a lack of attention paid to research focused on women or those from historically underrepresented groups and equity of access to AI/ML courses for school-level students. Finally, the content covered in the courses described in this research varies widely, possibly because there is so little alignment to computer science (CS) frameworks or curricula.
Intelligent Image Compression Using Traffic Scene Analysis
Authors: Bowden David , Grimes Diarmuid
Published: February 2023

The quantity of images generated at the edge of the Cloud is growing year-on-year, which puts an increasing strain on existing telecommunications infrastructure. There is also an associated increased cost for transmission bandwidth and storage of video images in the Cloud. In our modern society we tend to accumulate data, and are reluctant to throw it away, without asking “what is the value of this data?” and “do we need it?”. One of the major sources of video streams are the increasing number of traffic cameras, used to maintain the efficient flow of vehicles on our roads. In this work we focus on images taken from road traffic cameras, and show how their transmission bandwidth and storage requirements can be reduced. By analysing video feeds on a simulated edge device, we have shown that it is possible to extract objects of interest from the image, and discard or dramatically reduce irrelevant information in the content. Our technique also generates associated metadata, in the form of JSON-LD, which annotates the original image and maintains its semantic fidelity and provenance after compression. Our technique is compatible with conventional compression techniques, and thus the potential bandwidth savings would be incremental. We present the potential savings that can be made in the transmission and storage of unstructured data, as well as some of the challenges still to be overcome.
Cryptocurrency Volatility Index: An Efficient Way to Predict the Future CVI
Authors: Nguyen An Pham Ngoc , Crane Martin , Bezbradica Marija
Published: February 2023

The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods like Simple Moving Average, Random Forest and Support Vector Regression. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations.
Unimodal and Multimodal Representation Training for Relation Extraction
Authors: Cooney Ciaran , Heyburn Rachel , Madigan Liam , O’Cuinn Mairead , Thompson Chloe , Cavadas Joana
Published: February 2023

Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1 = 0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.
Run-Time Norms Synthesis in Dynamic Environments with Changing Objectives
Authors: Riad Maha , Ghanadbashi Saeedeh , Golpayegani Fatemeh
Published: February 2023

Normative Multi-Agent Systems (NorMAS) can model real-world applications as multi-agent systems and facilitate the coordination of the social behaviour of various entities (agents) interacting in an environment using norms. Aligning such norms with the objectives of the agents is crucially important to ensure that applying the norms would not affect the achievement of their objectives. However, when the environment is dynamic, agents can face unseen situations and might need to change their objectives accordingly. Therefore, it becomes more challenging to understand the change, synthesise norms, and align them with such dynamic objectives. This paper introduces a Dynamic Objectives and Norms Synthesizer and Reasoner (DONSR) model to align objectives and norms using a utility-based approach. An ontology-based schema, forward reasoning, and backward reasoning are used to identify the change in the environment and synthesise new objectives. Case-based reasoning enables the dynamic changing and reasoning of previously created objectives and synthesising norms. DONSR is evaluated using multiple simulated traffic scenarios, including different unseen situations (emergency events).

Results show that norms can be synthesised and maintained efficiently while the objectives are being created and changed. Further, DONSR showed its efficacy in handling unseen situations, creating new objectives, and aligning them with the created norms.
Modellskizze eines anderen, zukunftsorientierten Marketing
Authors: Rieck Louise Ina Hannah
Published: February 2023

Zum vorläufigen Ende meiner Erkundungsreise ist ein Modell eines zukunftsorientierten Marketing entstanden, wie ich es mir zu Beginn meines Forschungsweges nicht hätte ausmalen können: Die Zukunftsfähigkeit der Profession liegt nicht in einer Fortsetzung des herkömmlichen Mindset, wie es in Komposita wie „Nachhaltigkeitsmarketing“ respektive „Green Marketing“ semantisch zum Ausdruck kommt. Notwendig scheint vielmehr, Marketing radikal anders zu denken.
Theoretische Bezugsrahmen
Authors: Rieck Louise Ina Hannah
Published: February 2023

Theoretische Bezugsrahmen sorgen in der Forschung mit Aufstellungen für die notwendige theoretische Sensibilität. Die von mir herangezogenen Theorien beschäftigen sich mit der Bewältigung von Komplexität: Niklas Luhmanns Blick ist auf die System-Umwelt-Grenze ausgerichtet. Die jüngeren Gesellschafts- und Systemtheorien gehen von einer systemisch-integralen bzw. holonischen Denkweise aus.
Zusammenfassung: Methodologische Grundlagen
Authors: Rieck Louise Ina Hannah
Published: February 2023

Das Forschungsdesign dieser Arbeit gründet in genauem Beobachten und geordnetem Denken. Es lädt dazu ein, Irritationen zuzulassen, die bisherige Sicht der Dinge (zumindest für den Moment) loszulassen und auf diesem Wege – ganz spielerisch und leicht – neue Perspektiven und Möglichkeiten zu entdecken.
Zusammenfassung: Systemwissen
Authors: Rieck Louise Ina Hannah
Published: February 2023

Im analytischen, ersten Teil meiner Arbeit habe ich eine teils völlig neue Sicht auf meinen Forschungsgegenstand gewonnen. Vor diesem Hintergrund konkretisiere und – wo nötig – revidiere ich meine initialen forschungsleitenden Fragen und bestimme für den weiteren Untersuchungsverlauf Schwerpunkte, die mir besonders geeignet erscheinen, neues konzeptrelevantes Material hervorzubringen.
Digitale Lernumgebungen zur Vor- und Nachbereitung realer Experimentiereinheiten
Authors: Neff Sascha , Engl Alexander , Risch Björn
Published: February 2023

Im Projekt Open MINT Labs werden virtuelle Labore für die naturwissenschaftlichen Fächer an den weiterführenden Schulen entwickelt und getestet. Ziel der Konzeption ist es, eine digitale Vor- und Nachbereitung realer Experimentiereinheiten anbieten zu können. Dies ermöglicht den Teilnehmenden unter anderem Einblicke in chemische Prozesse, welche in der realen experimentellen Durchführung nicht sichtbar gemacht werden können. Beispielhaft sei hier die Verdeutlichung submikroskopischer Vorgänge auf Teilchenebene genannt. Die projektbegleitende empirische Studie fokussiert auf die Nutzung und Wirksamkeit der virtuellen Labore sowie auf deren Transfer in den schulischen Unterricht. Im Rahmen des vorliegenden Beitrags wird zum einen die Konzeption des virtuellen Labors zum Sauerstoffgehalt eines Gewässers vorgestellt und zum anderen eine Teilstudie präsentiert, in der die Nutzungsmuster der virtuellen Labore („digitale Lernpfade“) anhand der Logfiles der Schülerinnen und Schüler analysiert werden.
Unterstützung von Experimenten zu Linsensystemen mit Simulationen, Augmented und Virtual Reality: Ein Praxisbericht
Authors: Mukhametov Sergey , Wörner Salome , Hoyer Christoph , Becker Sebastian , Kuhn Jochen
Published: February 2023

Beim Lernen mit Experimenten spielt insbesondere die Verbindung der experimentellen Realität mit Modellvorstellungen und theoriebasierter Hypothesenbildung eine wichtige Rolle. Im Kontext der geometrischen Optik werden zur Vorhersage und Erklärung von optischen Abbildungen in Linsensystemen die sogenannten Konstruktionsstrahlen genutzt. Traditionell erfolgt die Darstellung der Konstruktionsstrahlen als zweidimensionale schematische Repräsentation jedoch getrennt vom Experiment. Eine Integration oder Ergänzung multimediabasierter Visualisierungen in das Experiment kann es Lernenden bei angemessener Unterstützung erleichtern, eine Verbindung der theoretischen Strahlengänge mit dem realen experimentellen Aufbau herzustellen. Dieser Praxisbericht beschreibt und vergleicht die Erweiterung realer Experimente durch verschiedene Arten virtueller Visualisierungen, sogenannte multiple externale Repräsentationen. Die Gestaltung der Repräsentationen sowie die Beurteilung ihrer Funktionen und wechselseitigen Beeinflussungen erfolgen dabei nach dem DeFT-Framework (Design, Function, Tasks). Zusätzlich berücksichtigt die Einbettung der Visualisierungen die Prinzipien der räumlichen und zeitlichen Kontiguität zur Reduktion lernirrelevanter kognitiver Belastung basierend auf Designprinzipien multimedialen Lernens. Ausgehend von Simulationen über Augmented-Reality-Experimente bis hin zu Virtual-Reality-Experimenten wird das naturwissenschaftliche Phänomen optischer Abbildungen durch Sammellinsen entlang des virtuellen Kontinuums exemplarisch an drei multimedialbasierten Erweiterungen des realen Experiments beschrieben. Die Möglichkeiten zur Einbindung von multiplen externen Repräsentationen werden abschließend vergleichend beschrieben und Vorteile der einzelnen Umsetzungen werden genannt.
Amplitude’s positivity vs. subluminality: causality and unitarity constraints on dimension 6 & 8 gluonic operators in the SMEFT
Authors: Ghosh Diptimoy , Sharma Rajat , Ullah Farman
Published: February 2023

Abstract

We derive the causality and unitarity constraints on dimension 6 and dimension 8 Gluon field strength operators in the Standard Model Effective Field Theory (SMEFT). In the first part of the paper, we use the ‘amplitude analysis’ i.e. dispersion relation for 2 → 2 scattering in the forward limit, to put bounds on the Wilson coefficients. We show that the dimension 6 operators can exist only in the presence of certain dimension 8 operators. It is interesting that the square of the dimension 6 Wilson coefficients can be constrained in this case even at the tree level. In the second part of this work, we successfully rederive all these bounds using the classical causality argument that demands that the speed of fluctuations about any non-trivial background should not exceed the speed of light. We also point out some subtleties in the superluminality analysis regarding whether the low-frequency phase velocity can always be used as the relevant quantity for Causality violation: as an example, we show that, due to these subtleties, if a small pion mass is added in the chiral Lagrangian, it is unclear if any strict positivity bound can be derived on the dimension 8 Wilson coefficient. Finally, we mention an interesting non-relativistic example where the subluminality requirement produces a stronger bound than the ‘amplitude analysis’.
A topological completeness theorem for transfinite provability logic
Authors: Aguilera Juan P.
Published: February 2023

We prove a topological completeness theorem for the modal logic $$\textsf{GLP}$$
GLP
containing operators $$\{\langle \xi \rangle :\xi \in \textsf{Ord}\}$$

{
⟨
ξ
⟩
:
ξ
∈
Ord
}

intended to capture a wellordered sequence of consistency operators increasing in strength. More specifically, we prove that, given a tall-enough scattered space X, any sentence $$\phi $$
ϕ
consistent with $$\textsf{GLP}$$
GLP
can be satisfied on a polytopological space based on finitely many Icard topologies constructed over X and corresponding to the finitely many modalities that occur in $$\phi $$
ϕ
.
Addition of the FTD Module to the Neuropsychiatric Inventory improves classification of frontotemporal dementia spectrum disorders
Authors: Jiskoot Lize C. , Russell Lucy L. , Greaves Caroline V. , van Schaik Esther , van den Berg Esther , Poos Jackie M. , de Boer Liset , Donker Kaat Laura , Seelaar Harro , Pijnenburg Yolande A. L. , van Swieten John C. , Rohrer Jonathan D.
Published: February 2023

Most neuropsychiatric symptoms (NPS) common in frontotemporal dementia (FTD) are currently not part of the Neuropsychiatric Inventory (NPI). We piloted an FTD Module that included eight extra items to be used in conjunction with the NPI. Caregivers of patients with behavioural variant FTD (n = 49), primary progressive aphasia (PPA; n = 52), Alzheimer’s dementia (AD; n = 41), psychiatric disorders (n = 18), presymptomatic mutation carriers (n = 58) and controls (n = 58) completed the NPI and FTD Module. We investigated (concurrent and construct) validity, factor structure and internal consistency of the NPI and FTD Module. We performed group comparisons on item prevalence, mean item and total NPI and NPI with FTD Module scores, and multinomial logistic regression to determine its classification abilities. We extracted four components, together explaining 64.1% of the total variance, of which the largest indicated the underlying dimension ‘frontal-behavioural symptoms’. Whilst apathy (original NPI) occurred most frequently in AD, logopenic and non-fluent variant PPA, the most common NPS in behavioural variant FTD and semantic variant PPA were loss of sympathy/empathy and poor response to social/emotional cues (part of FTD Module). Patients with primary psychiatric disorders and behavioural variant FTD showed the most severe behavioural problems on both the NPI as well as the NPI with FTD Module. The NPI with FTD Module correctly classified more FTD patients than the NPI alone. By quantifying common NPS in FTD the NPI with FTD Module has large diagnostic potential. Future studies should investigate whether it can also prove a useful addition to the NPI in therapeutic trials.
Effect of convalescent plasma transfusion on outcomes of coronavirus disease 2019: a meta-analysis with trial sequential analysis
Authors: Hakim Sameh M. , Chikhouni Ghosoun M. A. , Ammar Mona A. , Amer Akram M.
Published: February 2023

The aim of this review was to update evidence for benefit of convalescent plasma transfusion (CPT) in patients with coronavirus disease 2019 (COVID-19). Databases were searched for randomized controlled trials (RCT) comparing CPT plus standard treatment versus standard treatment only in adults with COVID-19. Primary outcome measures were mortality and need for invasive mechanical ventilation (IMV). Twenty-Six RCT involving 19,816 patients were included in meta-analysis for mortality. Quantitative synthesis showed no statistically significant benefit of adding CPT to standard treatment (RR = 0.97, 95% CI = 0.92 to 1.02) with unimportant heterogeneity (Q(25) = 26.48, p = .38, I2 = 0.00%). Trim-and-fill-adjusted effect size was unimportantly changed and level of evidence was graded as high. Trial sequential analysis (TSA) indicated information size was adequate and CPT was futile. Seventeen trials involving 16,083 patients were included in meta-analysis for need of IMV. There was no statistically significant effect of CPT (RR = 1.02, 95% CI = 0.95 to 1.10) with unimportant heterogeneity (Q(16) = 9.43, p = .89, I2 = 3.30%). Trim-and-fill-adjusted effect size was trivially changed and level of evidence was graded as high. TSA showed information size was adequate and indicated futility of CPT. It is concluded with high level of certainty that CPT added to standard treatment of COVID-19 is not associated with reduced mortality or need of IMV compared with standard treatment alone. In view of these findings, further trials on efficacy of CPT in COVID-19 patients are probably not needed.