bluuebunny/crossref_metadata_2025_split huggingface dataset:
Triple-band transparency effect by multiple couplings based on toroidal dipole resonance
Authors: Li Jie , Chang Shiqi , Wang Yinghua , Wang Yongjin 
Published: April 2024

Abstract
We explored multiple couplings properties in composite metastructure. One part is the asymmetric double rings, supporting the narrow toroidal dipole resonance, and the other component is an upright rod that excites the broad electric dipole resonance. When these two resonant modes coincide in the spectrum, dual-band plasmon induced transparency (PIT) behavior can be obtained, which is attributed to in-phase and out-of-phase couplings between the toroidal dipole and electric dipole modes. Meanwhile, the dual-band features will become a single PIT band by varying the rotation offset angle between the upper- and lower-rings. Moreover, by introducing lateral displacement of the rod with respect to the toroidal component, a triple-band PIT effect can be achieved. In particular, under a large lateral displacement, a broadband transparency window appears across a wavelength range greater than 120 nm, where the transmission exceeds 0.9. It is derived from the hybrid coupling between toroidal dipole, electric dipole and induced high-order resonance modes. The toroidal-based PIT metamaterials not only promote the understanding of toroidal dipole moment but also provide a positive reference for toroidal-based meta-devices.
The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)
Authors: Senthilnathan S , Shenbaga Devi S , Sasikala M , Satheesh Santhosh , Selvaraj Raja J 
Published: April 2024

Abstract
Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.
Analysis of the effect of landscape component classification on landscape diversity index
Authors: Ma Biao , Li Quanxi , Mao Zixuan , Liu Xuelu 
Published: April 2024

Abstract
The landscape diversity index (LDI) is an important level in biodiversity conservation, and its scale dependence has an important role in regional landscape planning and biological conservation. The aim of this study is to analyze in depth the effects of spatial scale changes in the classification of different landscape components on LDI and to explore the thresholds of LDI. The classification of landscape components was accomplished in the ArcMap environment using fusion and merging tools, and the LDI thresholds and scale changes were quantitatively assessed by LDI values. The results show that there are differences in LDI values for different classifications, and the threshold for LDI without considering scale changes can be interpreted as: 0.4215 ≤ LDI ≤ 1.9754. The grain sizes suitable for landscape diversity analysis are 160m and 1280 m, and the effective amplitude range of the I, II and III land type is 9~31 km, while the effective amplitude of three land use types is 20∼31 km, relatively lagging behind. However, when considering amplitude changes, the LDI threshold can be interpreted as 0.3027 ≤ LDI ≤ 2.0947, which is suitable for large-scale regional landscape diversity studies when the grain size is large. In conclusion, the essence of landscape diversity change with scale is caused by changes in the number and area of landscape components, and the threshold analysis should not only take into account the grain size and amplitude, but also consider the landscape background of the study area.
Filamentary-based organic memristors for wearable neuromorphic computing systems
Authors: Beak Chang-Jae , Lee Jihwan , Kim Junseok , Park Jiwoo , Lee Sin-Hyung 
Published: April 2024

Abstract
A filamentary-based organic memristor is a promising synaptic component for the development of neuromorphic systems for wearable electronics. In the organic memristors, metallic conductive filaments (CF) are formed via electrochemical metallization under electric stimuli, and it results in the resistive switching characteristics. To realize the bio-inspired computing systems utilizing the organic memristors, it is essential to effectively engineer the CF growth for emulating the complete synaptic functions in the device. Here, the fundamental principles underlying the operation of organic memristors and parameters related to CF growth are discussed. Additionally, recent studies that focused on controlling CF growth to replicate synaptic functions, including reproducible resistive switching, continuous conductance levels, and synaptic plasticity, are reviewed. Finally, upcoming research directions in the field of organic memristors for wearable smart computing systems are suggested.
Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation
Authors: Bin Thaneya Ahmad , Gursel Aysegul Petek , Kane Seth , Miller Sabbie A , Horvath Arpad 
Published: April 2024

Abstract
Global greenhouse gas emissions from the built environment remain high, driving innovative approaches to develop and adopt building materials that can mitigate some of those emissions. However, life-cycle assessment (LCA) practices still lack standardized quantitative uncertainty assessment frameworks, which are urgently needed to robustly assess mitigation efforts. Previous works emphasize the importance of accounting for the three types of uncertainties that may exist within any quantitative assessment: parameter, scenario, and model uncertainty. Herein, we develop a quantitative uncertainty assessment framework that distinguishes between different types of uncertainties and suggest how these uncertainties could be handled systematically through a scenario-aware Monte Carlo simulation (MCS). We demonstrate the framework’s decision-informing power through a case study of two multilevel ordinary Portland cement (OPC) manufacturing scenarios. The MCS utilizes a first-principles-based OPC life-cycle inventory, which mitigates some of the model uncertainty that may exist in other empirical-based cement models. Remaining uncertainties are handled by scenario specification or sampling from developed probability distribution functions. We also suggest a standardized method for fitting distributions to parameter data by enumerating through and implementing distributions based on the Kolmogorov–Smirnov test. The level of detail brought by the high-resolution parameter breakdown of the model allows for developing emission distributions for each process of OPC manufacturing. This approach highlights how specific parameters, along with scenario framing, can impact overall OPC emissions. Another key takeaway includes relating the uncertainty of each process to its contributions to total OPC emissions, which can guide LCA modelers in allocating data collection and refinement efforts to processes with the highest contribution to cumulative uncertainty. Ultimately, the aim of this work is to provide a standardized framework that can provide robust estimates of building material emissions and be readily integrated within any uncertainty assessment.
Cortical activations associated with spatial remapping of finger touch using EEG
Authors: Alouit Anaëlle , Gavaret Martine , Ramdani Céline , Lindberg Påvel G , Dupin Lucile 
Published: April 2024

Abstract
The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100–125 ms in the parietal cortex.
Prediction of incident chronic kidney disease in community-based electronic health records: a systematic review and meta-analysis
Authors: Haris Mohammad , Raveendra Keerthenan , Travlos Christoforos K , Lewington Andrew , Wu Jianhua , Shuweidhi Farag , Nadarajah Ramesh , Gale Chris P 
Published: April 2024

ABSTRACT

Background
Chronic kidney disease (CKD) is a major global health problem and its early identification would allow timely intervention to reduce complications. We performed a systematic review and meta-analysis of multivariable prediction models derived and/or validated in community-based electronic health records (EHRs) for the prediction of incident CKD in the community.

Methods
Ovid Medline and Ovid Embase were searched for records from 1947 to 31 January 2024. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST) and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation (GRADE).

Results
Seven studies met inclusion criteria, describing 12 prediction models, with two eligible for meta-analysis including 2 173 202 patients. The Chronic Kidney Disease Prognosis Consortium (CKD-PC) (summary c-statistic 0.847; 95% CI 0.827–0.867; 95% PI 0.780–0.905) and SCreening for Occult REnal Disease (SCORED) (summary c-statistic 0.811; 95% CI 0.691–0.926; 95% PI 0.514–0.992) models had good model discrimination performance. Risk of bias was high in 64% of models, and driven by the analysis domain. No model met eligibility for meta-analysis if studies at high risk of bias were excluded, and certainty of effect estimates was ‘low’. No clinical utility analyses or clinical impact studies were found for any of the models.

Conclusions
Models derived and/or externally validated for prediction of incident CKD in community-based EHRs demonstrate good prediction performance, but assessment of clinical usefulness is limited by high risk of bias, low certainty of evidence and a lack of impact studies.
Diversity of fishing strategies and high spatial adaptivity in the Alaskan snow crab fishery
Authors: Wang Terrance , Szuwalski Cody S , Punt André E , Hilborn Ray 
Published: April 2024

Abstract
Fishing behavior is an important link between management decisions and bioeconomic outcomes. The diversity in fishers’ behavior, motivations, and abilities should inform incentive structures if management aims to consider heterogeneous impacts on and by communities. Modeling human behavior in resource management is important for successful fisheries, especially for fisheries undergoing rapid transformation, such as the Alaskan snow crab fishery. This study modeled how snow crabbers choose where to fish in the eastern Bering Sea and examined the diversity of strategies among individuals by fitting a random utility model to data on fishing locations using a variety of potential drivers of behavior as covariates. The overall fishing strategy of the snow crab fleet prioritizes revenue and shared information, while avoiding risk, poor weather, and cost. Diversity of fishing strategies was driven by differences in spatial footprint, vessel size, and ports of landing. Larger vessels ventured farther north, where weather conditions are more extreme. Despite differences in vessel size, crabbers were spatially adaptive and switched fishing regions depending on the abundance and distribution of crab. These findings on the patterns of fishing strategies can inform better management of the Alaskan snow crab fishery as it undergoes rebuilding.
The Extremal Number of Cycles with All Diagonals
Authors: Bradač Domagoj , Methuku Abhishek , Sudakov Benny 
Published: April 2024

Abstract
In 1975, Erd̋s asked the following question: What is the maximum number of edges that an $n$-vertex graph can have without containing a cycle with all diagonals? Erd̋s observed that the upper bound $O(n^{5/3})$ holds since the complete bipartite graph $K_{3,3}$ can be viewed as a cycle of length six with all diagonals. In this paper, we resolve this old problem. We prove that there exists a constant $C$ such that every $n$-vertex graph with at least $Cn^{3/2}$ edges contains a cycle with all diagonals. Since any cycle with all diagonals contains cycles of length four, this bound is best possible using well-known constructions of graphs without four-cycles based on finite geometry. Among other ideas, our proof involves a novel lemma about finding an almost-spanning robust expander, which might be of independent interest.
Combining compositional data sets introduces error in covariance network reconstruction
Authors: Brunner James D , Robinson Aaron J , Chain Patrick S G 
Published: April 2024

Abstract
Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.
Real-time automated deep learning based railroad trespassing violation detection and tracking at highway-rail grade crossing
Authors: Yang Xue , Li Joshua Qiang , Zhan You (Jason) , Yu Wenying 
Published: April 2024

Abstract
Trespassing is one of the utmost safety concerns at highway-rail grade crossings (HRGCs) but many trespassing incidents have not been recorded and deeply studied since no collision or otherwise injured or killed, which might possibly contribute to crashes if they repeatedly occur. Detection and prevention of such events are critical for railroad safety improvements, while this task is challenging due to the immense labor costs required for processing streamed video files. This study developed an advanced You Look Only Once (YOLO) deep learning architecture and the Deep Simple Online and Real-time Tracking (Deep SORT) algorithm with a low confidence tracking filter for real-time trespassing violation detection. Different types of trespassing violation were detected at a gated HRGC in Folkston, Georgia. 436 trespassing violations were identified in the selected 104-hour video data. The automated trespassing violation detection speed ranged from 43.2 to 654.5 frames per second (FPS), exceeding the field video data recording rate at 30 FPS. The developed methodology resulted in 32 false negatives and 20 false positive detections, with the precision, recall, and F1 values scoring above 92.0%. This work could assist railroad agencies in reducing trespassing violations based on real-time detection and tracking.
Lessons learned from Jordan’s response to the Syria refugee crisis
Authors: Popescu Christian , Abu-Siam Ibraheem , Khalifa Adam , Ababneh Huda , Al Raiby Jamela , Bartsch Dominik , Bellizzi S 
Published: April 2024

The number of refugees across the world has jumped from 15.7 million to 34.6 million in the past 10 years, and their forced displacement situation has remain for an average period of 20 years. To ensure sustainable benefits, health system strengthening through development cooperation should be a priority in parallel to humanitarian aid early on in refugee crises.
Assessing the quality of data on international migration flows in Europe: The case of undercounting
Authors: Dańko Maciej J , Wiśniowski Arkadiusz , Jasilionis Domantas , Jdanov Dmitri A , Zagheni Emilio 
Published: April 2024

Abstract
Undercounting is a critical issue in migration statistics, resulting in bias. This typically arises from insufficient reporting requirements and problems with enforcing such requirements. The main sources of information on undercounting are the metadata accompanying official statistics and expert opinions. However, metadata and arbitrary expert opinions may be limited by overlooking important details in migration data shared by various countries. This includes potential oversight of changes in methodologies, definitions, or retrospective updates to the data following censuses. This work presents a methodological solution with three objectives to address undercounting in international migration data. First, we provide an overview of available metadata and expert opinions on undercounting in European migration flows. Secondly, we propose a novel data-driven approach that incorporates year-specific and duration-of-stay-adjusted classifications. The proposed methodological solution relies on comparisons of flows in the same direction reported by a given country with high-quality data reported by another set of countries. We use bilateral migration data provided by Eurostat, United Nations, and selected national statistical institutes. Duration-of-stay correction coefficients are derived through an optimization model or borrowed from the literature. Metadata and expert opinion scores can also be integrated to classify undercounting. Finally, we provide a dynamic classification of undercounting for thirty-two European countries (2002–21), accessible through an online Shiny application, offering flexibility and adaptability. The findings highlight significant undercounting in new European Union Member States, particularly Bulgaria, Latvia, and Romania. Interestingly, other European countries, including those presumed to maintain reliable population statistics, also exhibit notable periods of undercounting.
What molecular allergy teaches us about genetics and epidemiology of allergies
Authors: Biagioni Benedetta , Scala Enrico , Cecchi Lorenzo 
Published: April 2024

Purpose of review
To delineate pertinent information regarding the application of molecular allergology within the realm of both genetic and epidemiological facets of allergic diseases.

Recent findings
The emergence of molecular allergy has facilitated the comprehension of the biochemical characteristics of allergens originating from diverse sources. It has allowed for the exploration of sensitization trajectories and provided novel insights into the influence of genetics and environmental exposure on the initiation and development of allergic diseases. This review delves into the primary discoveries related to the genetics and epidemiology of allergies, facilitated by the application of molecular allergy. It also scrutinizes the impact of environmental exposure across varied geoclimatic, socioeconomic, and lifestyle contexts. Additionally, the review introduces specific models of molecular allergy within the realms of plants and animals.

Summary
The utilization of molecular allergy in clinical practice holds crucially acknowledged diagnostic and therapeutic implications. From a research standpoint, there is a growing need for the widespread adoption of molecular diagnostic tools to achieve a more profound understanding of the epidemiology and natural progression of allergic diseases.
Improving Osteoporosis Prediction Using Vertebral Bone Quality Score and Paravertebral Muscle Measurements From Lumbar MRI Scans
Authors: Özmen Emre , Biçer Ozancan , Bariş Alican , Circi Esra , Yüksel Serdar , Beytemür Ozan , Kesiktaş Fatma Nur 
Published: April 2024

Study Design:
Retrospective cohort.

Objective:
This study aims to use a novel method of combining vertebral bone quality score with paravertebral cross-sectional area measurements to improve the accuracy of predicting individuals with total hip T-scores <−2.5.

Summary of Background Data:
Osteoporosis is a prevalent skeletal condition associated with decreased bone density and increased fracture risk. Dual-energy x-ray absorptiometry (DXA) is the conventional method for diagnosing osteoporosis, but it has limitations. Opportunistic osteoporosis screening techniques using lumbar magnetic resonance imaging (MRI), particularly the vertebral bone quality (VBQ) score, have shown promise. This study aims to improve the accuracy of predicting individuals with low total hip T-scores using a novel method that combines VBQ scores with paravertebral cross-sectional area (CSA) measurements.

Methods:
A retrospective cohort of 98 patients with DXA and lumbar MRI scans was analyzed. VBQ scores were calculated based on lumbar MRI images, and CSA measurements of paravertebral and psoas muscles were obtained. Threshold-based logistic regression was used to identify optimal thresholds for predicting total hip T-scores <−2.5.

Results:
The combined model incorporating the VBQ score and paravertebral muscle percent achieved an accuracy of 96.9% for predicting total hip T-scores <−2.5, compared to 81.6% when using the VBQ score alone. Incorporating paravertebral muscle measurements significantly improved the accuracy of identifying osteoporotic individuals.

Conclusions:
The combination of VBQ score and paravertebral muscle measurements enhances the accuracy of predicting individuals with low total hip T-scores. Lumbar MRI scans provide valuable information beyond opportunistic osteoporosis screening, and the inclusion of paravertebral muscle measurements could aid in identifying at-risk individuals more accurately.