bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frailty: the perioperative and anesthesia challenges of an emerging pandemic
Authors: Jin Zhaosheng , Rismany Joshua , Gidicsin Christopher , Bergese Sergio D. 
Published: June 2023

AbstractFrailty is a complex and multisystem biological process characterized by reductions in physiological reserve. It is an increasingly common phenomena in the surgical population, and significantly impacts postoperative recovery. In this review, we will discuss the pathophysiology of frailty, as well as preoperative, intraoperative, and postoperative considerations for frailty care. We will also discuss the different models of postoperative care, including enhanced recovery pathways, as well as elective critical care admission. With discoveries of new effective interventions, and advances in healthcare information technology, optimized pathways could be developed to provide the best care possible that meets the challenges of perioperative frailty.
Digitale Maßnahmen der Kundenkommunikation im inhabergeführten Einzelhandel zwischen kurzfristiger Reaktion auf die Coronapandemie und zukunftsfähiger Strategie
Authors: Gliesner Elisa , Schiller Daniel 
Published: June 2023

ZusammenfassungDer inhabergeführte Einzelhandel steht vor besonderen Herausforderungen bei der Einführung und Etablierung digitaler Kundenkommunikationsmaßnahmen (KKM). Im vorliegenden Beitrag wird untersucht, welche Maßnahmen im Zuge der Coronapandemie eingeführt worden sind und welche dieser Maßnahmen besonders zukunftsfähig sein können. Darüber hinaus wird die Bedeutung von regionalen Netzwerken beim Wissenstransfer über digitale Maßnahmen betrachtet. Für die Untersuchung wurde in den drei Bundesländern Nordrhein-Westfalen, Sachsen und Sachsen-Anhalt im März 2022 eine Online-Befragung von Inhabern durchgeführt und durch fünf Experteninterviews im April und Mai 2022 ergänzt. Für die Vergleichbarkeit der gewonnenen Ergebnisse wird eine großflächige Handelsstudie der IHK Köln und dem ibi Research Institut Regensburg aus dem Jahr 2020 herangezogen. Dabei stellte sich heraus, dass insbesondere die digitale Sichtbarkeit für den Erhalt der inhabergeführten Einzelhandelsgeschäfte in Zukunft von zentraler Bedeutung sein wird. Ferner präsentieren sich die sozialen Medien mit ihrer perspektivisch ausgestatteten Verkaufsfunktion als geeignetes Instrument. Außerdem konnten durch eine differenzierte Betrachtung von Branchen und Zielgruppen sowie unterschiedlichen Ansätzen für den Einstieg in die digitale Kundenkommunikation zukunftsfähige und individuelle Maßnahmen identifiziert werden.
Montagne Pelée volcano (Martinique, in the French Lesser Antilles) hydrogeological system revealed by high-resolution helicopter-borne electromagnetic imagery
Authors: Vittecoq Benoit , Reninger Pierre-Alexandre , Bellier Vincent , Taïlamé Anne-Lise , Nacimento Laureen , Gros Emile , Violette Sophie 
Published: June 2023

AbstractMontagne Pelée, on the French island of Martinique, eastern Caribbean Sea, has been one of the deadliest volcanoes in the world, with 30,000 victims following the 1902 eruption. Thousands of people still live nearby, and this volcano is a strategic “water tank” for Martinique Island, providing 40% of the island’s water supply. This research aimed to better understand its hydrogeological functioning and the relationship with its complex volcanological evolution, taking advantage of a high-resolution helicopter-borne geophysical survey correlated with hydrogeological data from the boreholes and springs databases. Electromagnetic data, correlated with hydrogeological data, allowed for the identification of unsaturated zones, aquifers, and seawater intrusions, as well as the main geological units. In addition, data synthesised from pumping tests revealed that the older the unconsolidated pyroclastic deposits, the lower their hydraulic conductivity. The structural asymmetry between the northeastern and southwestern volcano flanks impacts its hydrogeological functioning. Consequently, the Montagne Pelée hydrogeological conceptual model is marked by several distinguishable aquifers. The upper perched aquifer within recent lava domes is directly involved in, and impacted by, phreatic eruptions, and it supports low flowrate springs. The remaining effective rainfall infiltrates to depth and recharges the hydrothermal system through vertical fractures. The other aquifers are categorized into three groups: northeastern, southeastern and southwestern flank aquifers. This research is a new step toward a better understanding of the Lesser Antilles volcanoes and more broadly of the central and proximal parts of the andesitic active volcanoes.
Fürther Gespräche: Expertenforum für den Rettungsdienst mit Forderungen an Politik und Gesellschaft
Authors: Meyer Klaus , Dormann Harald , Prescher Thomas 
Published: June 2023

ZusammenfassungDie rettungsdienstliche Versorgung, die Rettungsfachkräfteausbildung und die rechtlichen Vorraussetzungen in Deutschland bieten aktuell keine guten Rahmenbedingungen für die Zukunft. Ein Expertenforum mit deutschlandweiten Akteuren aus Wissenschaft, Bildung und Rettungsdienst diskutierten im Rahmen der Fürther Gespräche die Herausforderungen der Professionalisierung und der Kompetenzentwicklung des Rettungsdienstes und der präklinischen Versorgung in Deutschland. Die zentralen Ergebnisse und Schwerpunktthemen wurden in 7 Thesen zusammengefasst und konkrete Lösungsansätze entwickelt. Das Spannungsfeld umfasst hierbei die Anforderungen und Versorgungsbedarfe, die Prävention und erweiterte Versorgungskomponenten, die Digitalisierungsstrategie, integrierte Leitstellen und vernetzte Gesundheitsdienstleistungen, Personalentwicklung und -bindung sowie Qualifikationsniveaus und Notarztqualifikation.
Trigeminocervical pain sensitivity during the migraine cycle depends on headache frequency
Authors: Di Antonio Stefano , Arendt-Nielsen Lars , Ponzano Marta , Bovis Francesca , Torelli Paola , Finocchi Cinzia , Castaldo Matteo 
Published: June 2023

Abstract
Objective
This experimental study aimed to assess pain sensitivity in low-frequency episodic migraine (LFEM), high-frequency episodic migraine (HFEM), and chronic migraine (CM) patients across the different phases of the migraine cycle.

Method
In this observational, experimental study, clinical characteristics (diary and time from the last/next headache attack), and quantitative sensory testing (QST) (wind-up pain ratio (WUR) and pressure pain threshold (PPT) from the trigeminal area and PPT from the cervical spine) was performed. LFEM, HFEM, and CM were assessed in each of the 4 migraine phases (HFEM and LFEM: interictal, preictal, ictal, and postictal; CM: interictal and ictal) and compared vs. each other’s (matched for the phase) and controls.

Results
A total of 56 controls, 105 LFEM, 74 HFEM, and 32 CM were included. No differences in QST parameters were observed between LFEM, HFEM, and CM in any of the phases.
During the interictal phase and when comparing with controls the following were found: 1) LFEM had lower trigeminal PPT (p = 0.001) and 2) lower cervical PPT (p = 0.001). No differences were observed between HFEM or CM and healthy controls. During the ictal phase and when comparing with controls the following were found: HFEM and CM had 1) lower trigeminal PPTs (HFEM p = 0.001; CM = p < 0.001), 2) lower cervical PPT s (HFEM p = 0.007; CM p < 0.001), and 3) higher trigeminal WUR (HFEM p = 0.001, CM p = 0.006). No differences were observed between LFEM and healthy controls. During the preictal phase and when comparing with controls the following were found: 1) LFEM had lower cervical PPT (p = 0.007), 2) HFEM had lower trigeminal (p = 0.013) and 3) HFEM had lower cervical (p = .006) PPTs. During the postictal phase and when comparing with controls the following were found: 1) LFEM had lower cervical PPT (p = 0.003), 2) HFEM had lower trigeminal PPT (p = 0.005), and 3) and HFEM had lower cervical (p = 0.007) PPTs.

Conclusion
This study suggested that HFEM patients have a sensory profile matching CM better than LFEM. When assessing pain sensitivity in migraine populations, the phase with respects to headache attacks is of utmost importance and can explain the inconsistency in pain sensitivity data reported in the literature.
Sequential adaptive strategies for sampling rare clustered populations
Authors: Mecatti Fulvia , Sismanidis Charalambos , Furfaro Emanuela , Conti Pier Luigi 
Published: June 2023

AbstractA new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and proved more efficient. The selection of primary sampling units is also illustrated for tuberculosis prevalence surveys, which are recommended in many countries and supported by the World Health Organisation as an emblematic example of the need for an improved sampling design. Simulation results are given in the tuberculosis application to illustrate the strengths and weaknesses of the proposed sequential adaptive sampling strategies with respect to traditional cross-sectional non-informative sampling as currently suggested by World Health Organisation guidelines.
The newcomer takes it all: the invader Texas citrus mite, Eutetranychus banksi (Acari: Tetranychidae), displaces the resident relatives in citrus agrosystems
Authors: López-Olmos Sergio , Ferragut Francisco 
Published: June 2023

AbstractMany studies have emphasized the importance of interspecific competition in shaping natural ecosystem communities. In contrast, few investigations have explored the role of competition in agricultural environments after the arrival of an invasive pest species. We evaluated the ecological impact produced by the invasive Texas citrus mite, Eutetranychus banksi (McGregor), on the resident spider mites Panonychus citri (McGregor) and Eutetranychus orientalis (Klein) on the main citrus crop area in Eastern Spain. Since its arrival in 2013, E. banksi has become the most common and abundant spider mite on citrus, apparently leading to a reduction in the presence and geographic range of the other related species. Competitive relationships were detected between E. banksi–E. orientalis and E. banksi–P. citri pairs using co-occurrence analysis. Furthermore, generalized linear model analysis showed that the probability of finding E. orientalis or P. citri decreases with increasing E. banksi density and vice versa. Principal component analysis and permutational multivariate analysis of variance found competition between these two pairs, and also between the E. orientalis–P. citri pair. Redundancy and variation partitioning analysis revealed how the geographic distribution of the three spider mites is not caused by the environmental conditions, but it is strongly influenced by their colonization history and competitive relationships, since the areas with the highest density of the three species are related to their place of first detection, and do not coincide geographically. Finally, the mechanisms that may be involved in the competitive displacement and the possible future scenarios are discussed.
Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
Authors: Ibarra-Vazquez Gerardo , Ramí­rez-Montoya María Soledad , Terashima Hugo 
Published: June 2023

AbstractThis article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students’ gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models’ performance during training and testing stages, and 3) study the models’ prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students’ gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender.
Sharp bounds on the least eigenvalue of a graph determined from edge clique partitions
Authors: Cardoso Domingos M. , Costa Inês Serôdio , Duarte Rui 
Published: June 2023

AbstractSharp bounds on the least eigenvalue of an arbitrary graph are presented. Necessary and sufficient (just sufficient) conditions for the lower (upper) bound to be attained are deduced using edge clique partitions. As an application, we prove that the least eigenvalue of the n-Queens graph $${\mathcal {Q}}(n)$$

Q
(
n
)

is equal to $$-4$$

-
4

for every $$n \ge 4$$

n
≥
4

and it is also proven that the multiplicity of this eigenvalue is $$(n-3)^2$$

(
n
-
3
)

2

. Finally, edge clique partitions of additional infinite families of connected graphs and their relations with the least eigenvalues are presented.
Learning system parameters from turing patterns
Authors: Schnörr David , Schnörr Christoph 
Published: June 2023

AbstractThe Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction–diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a challenging problem. This paper introduces an approach to the prediction of Turing parameter values from observed Turing patterns. The parameter values correspond to a parametrized system of reaction–diffusion equations that generate Turing patterns as steady state. The Gierer–Meinhardt model with four parameters is chosen as a case study. A novel invariant pattern representation based on resistance distance histograms is employed, along with Wasserstein kernels, in order to cope with the highly variable arrangement of local pattern structure that depends on the initial conditions which are assumed to be unknown. This enables us to compute physically plausible distances between patterns, to compute clusters of patterns and, above all, model parameter prediction based on training data that can be generated by numerical model evaluation with random initial data: for small training sets, classical state-of-the-art methods including operator-valued kernels outperform neural networks that are applied to raw pattern data, whereas for large training sets the latter are more accurate. A prominent property of our approach is that only a single pattern is required as input data for model parameter predicion. Excellent predictions are obtained for single parameter values and reasonably accurate results for jointly predicting all four parameter values.
Explaining image enhancement black-box methods through a path planning based algorithm
Authors: Cotogni Marco , Cusano Claudio 
Published: June 2023

AbstractNowadays, image-to-image translation methods, are the state of the art for the enhancement of natural images. Even if they usually show high performance in terms of accuracy, they often suffer from several limitations such as the generation of artifacts and the scalability to high resolutions. Moreover, their main drawback is the completely black-box approach that does not allow to provide the final user with any insight about the enhancement processes applied. In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods. This algorithm, called eXIE, uses a variant of A$$^*$$

∗

to emulate the enhancement process of another method through the application of an equivalent sequence of enhancing operators. We applied eXIE to explain the output of several state-of-the-art models trained on the Five-K dataset, obtaining sequences of enhancing operators able to produce very similar results in terms of performance and overcoming the huge limitation of poor interpretability of the best performing algorithms.
Machine learning in detection and classification of leukemia using C-NMC_Leukemia
Authors: Talaat Fatma M. , Gamel Samah A. 
Published: June 2023

AbstractA significant issue in the field of illness diagnostics is the early detection and diagnosis of leukemia, that is, the accurate distinction of malignant leukocytes with minimal costs in the early stages of the disease. Flow cytometer equipment is few, and the methods used at laboratory diagnostic centers are laborious despite the high prevalence of leukemia. The present systematic review was carried out to review the works intending to identify and categories leukemia by utilizing machine learning. It was motivated by the potential of machine learning (machine learning (ML)) in disease diagnosis. Leukemia is a blood-forming tissues cancer that affects the bone marrow and lymphatic system. It can be treated more effectively if it is detected early. This work developed a new classification model for blood microscopic pictures that distinguishes between leukemia-free and leukemia-affected images. The general proposed method in this paper consists of three main steps which are: (i) Image_Preprocessing, (ii) Feature Extraction, and (iii) Classification. An optimized CNN (OCNN) is used for classification. OCNN is utilized to detect and classify the photo as "normal" or "abnormal". Fuzzy optimization is used to optimize the hyperparameters of CNN. It is a quite beneficial to use fuzzy logic in the optimization of CNN. As illustrated from results it is shown that, with the using of OCNN classifier and after the optimization of the hyperparameters of the CNN, it achieved the best results due to the enhancement of the performance of the CNN. The OCNN has achieved 99.99% accuracy with C-NMC_Leukemia dataset.
Influence of Varying Operational Parameters on the Defect Detection Performance of a High-Speed Ultrasonic Rail Inspection System During Field Tests
Authors: Datta D. , Lanza di Scalea F. 
Published: June 2023

Abstract
Background
Continuous monitoring is essential for detecting internal defects in rails and prevent derailment related accidents. Existing techniques do not facilitate continuous monitoring because they require specialized test cars and can only operate at speeds of up to 30 mph.

Objective
The objective of this study is to evaluate the performance of a high-speed rail inspection system using a non-contact ultrasonic technique with the potential of operating at train revenue speeds.

Methods
The technique utilizes air-coupled transducers that record the ultrasonic guided waves generated by the rail-wheel contact and does not require a controlled acoustic source of excitation. A modified version of the traditional Welch’s periodogram technique is utilized to extract the Green’s function between two points on the rail. The passively extracted Green’s function is then analysed statistically to detect structural discontinuities (e.g., defects) in the rail.

Results
Results from fields tests performed at the Transportation Technology Centre (TTC) in Pueblo, CO, USA, demonstrate possible test speeds as high as 80 mph. From these field tests, the performance of the system is evaluated using Receiver Operating Characteristic (ROC) curves for a range of different operational parameters including test speed, location of the sensors relative to the locomotive (source), signal-to-noise ratio (SNR) of the raw signals, SNR of the reconstructed transfer function, baseline distribution length in the statistical analysis, wheel-rail interactions, and redundancies introduced from multiple runs over the same track.

Conclusions
This study presents the current stage of development and performance of the passive rail inspection system with full-scale experiments under field conditions. The results indicate the potential of the system to operate at high speeds as well as possible avenues of future improvement to the system.
Prediction of cognitive performance differences in older age from multimodal neuroimaging data
Authors: Krämer Camilla , Stumme Johanna , da Costa Campos Lucas , Dellani Paulo , Rubbert Christian , Caspers Julian , Caspers Svenja , Jockwitz Christiane 
Published: June 2023

Abstract

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55–85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.