bluuebunny/crossref_metadata_2025 huggingface dataset:
Positive psychotic symptoms as a marker of clinical severity in a transdiagnostic sample of help-seeking adolescents
Authors: Kaeser Janko M. , Lerch Stefan , Sele Silvano , Reichl Corinna , Koenig Julian , Mürner-Lavanchy Ines , Berger Thomas , Kaess Michael , Cavelti Marialuisa
Published: March 2024

The present study aimed to examine the association between the presence, number, and type of positive psychotic symptoms (PPS) and clinical severity in adolescent patients. Five hundred-six patients aged 11–17 years were assigned to either the noPPS (n = 341), the delusional beliefs only (del; n = 32), the hallucinations only (hall; n = 80), or the delusional beliefs and hallucinations (del&hall; n = 53) group. Generalized Structural Equation Modeling was applied to identify the best-fitting model representing clinical severity indicated by psychiatric diagnoses, depressivity, personality pathology, non-suicidal self-injury, suicide attempts, perceived stress, and psychosocial impairments, assessed by interviews and questionnaires. The groups were compared concerning the final model’s factors. The final model consisted of three factors representing psychopathology and functional impairments, self-harming behavior, and perceived stress (BIC difference to reference model: 103.99). Participants with any PPS scored higher on all factors than the noPPS group (differences in SD: 0.49–1.48). Additionally, the del&hall group scored 1.31 SD higher on psychopathology and functional impairments than the hall group, and 1.16 SD higher on self-harming behavior compared to the del group. Finally, the hall group scored 0.84 SD higher on self-harming behavior than the del group, with no group differences in the other factors. In adolescent patients, the presence of PPS may represent a marker for a more severe form of mental disorder, with hallucinations being indicative of self-harming behavior. Early transdiagnostic assessment of PPS seems indicated as it may inform treatment in the context of clinical staging.
Scale-up of dry impregnation processes for porous spherical catalyst particles in a rotating drum: experiments and simulations
Authors: Xu Pengfei , Shen Yangyang , Avila Bryant , Makse Hernán A. , Tomassone Maria S.
Published: March 2024

Catalyst impregnation is the first step and one of the most crucial steps for preparing industrial catalysts. The process is typically performed in rotating vessels with a spray-nozzle to distribute the liquid onto porous catalyst supports until the pore volume is reached. The inter-particle variability of the impregnated liquid inside the particles significantly affects the activity and selectivity of the resulting catalyst. Current scale-up practices lead to poor fluid distribution and inhomogeneity in the liquid content. The aim of this work is to understand the dynamic behavior of the particles under the spray nozzle, which is essential for desired content uniformity, and to develop a scale-up model for the dry impregnation process. In this work, we considered four dimensionless numbers in the scaling analysis. The scale-up rules require that the dimensionless numbers are kept constant for different scales. Both DEM simulations and matching experiments of dry impregnation inside the porous particles were performed for different vessel sizes. The water content of the particles was compared for different times and locations, and the relative standard deviation is calculated from the axial water content. Simulation and experimental results show that particles achieve similar content uniformity at the end of impregnation, confirming that the scale-up rules are applicable to all vessel sizes. The dimensionless numbers give very good scale-up performance since curves collapse indicating similarity in the processes. In addition, the scale-up method is validated for different particle sizes in simulations.

Graphical abstract
Do dogs preferentially encode the identity of the target object or the location of others’ actions?
Authors: Lonardo Lucrezia , Völter Christoph J. , Hepach Robert , Lamm Claus , Huber Ludwig
Published: March 2024

The ability to make sense of and predict others’ actions is foundational for many socio-cognitive abilities. Dogs (Canis familiaris) constitute interesting comparative models for the study of action perception due to their marked sensitivity to human actions. We tested companion dogs (N = 21) in two screen-based eye-tracking experiments, adopting a task previously used with human infants and apes, to assess which aspects of an agent’s action dogs consider relevant to the agent’s underlying intentions. An agent was shown repeatedly acting upon the same one of two objects, positioned in the same location. We then presented the objects in swapped locations and the agent approached the objects centrally (Experiment 1) or the old object in the new location or the new object in the old location (Experiment 2). Dogs’ anticipatory fixations and looking times did not reflect an expectation that agents should have continued approaching the same object nor the same location as witnessed during the brief familiarization phase; this contrasts with some findings with infants and apes, but aligns with findings in younger infants before they have sufficient motor experience with the observed action. However, dogs’ pupil dilation and latency to make an anticipatory fixation suggested that, if anything, dogs expected the agents to keep approaching the same location rather than the same object, and their looking times showed sensitivity to the animacy of the agents. We conclude that dogs, lacking motor experience with the observed actions of grasping or kicking performed by a human or inanimate agent, might interpret such actions as directed toward a specific location rather than a specific object. Future research will need to further probe the suitability of anticipatory looking as measure of dogs’ socio-cognitive abilities given differences between the visual systems of dogs and primates.
Investigating the role of circulating tumor cells in gastric cancer: a comprehensive systematic review and meta-analysis
Authors: Eskandarion Mohammad Reza , Eskandarieh Sharareh , Tutunchi Sara , Shakoori Farahani Abbas , Shirkoohi Reza
Published: March 2024

Investigating the role of circulating tumor cells (CTCs) and their characteristics is still controversial in patients with gastric cancer (GC). Therefore, in this study, to provide a comprehensive review and meta-analyses of the literature on association of CTCs with gastric cancer, Scopus, Web of Science, Embase, and Medline were searched for systematic reviews and meta-analyses conducted during February 2022 using the keywords. Risk of bias, hazard ratios (HRs), and risk differences (RD) were assessed. Forty-five studies containing 3,342 GC patients from nine countries were assessed. The overall prevalence of CTC in GC was 69.37% (60.27, 77.78). The pooled result showed that increased mortality in GC patients was significantly associated with positive CTCs, poor overall survival (HR = 2.73, 95%CI 2.34–3.24, p < 0.001), and progression-free survival rate (HR = 2.78, 95%CI 2.01–3.85, p < 0.001). Subgroup analyses regarding markers, detection methods, treatment type, presence of distance metastasis, presence of lymph node metastasis, and overall risk of bias showed significant associations between the groups in terms of the incidence rates of CTCs, OS, and PFS. In addition, the results of risk differences based on sampling time showed that the use of the cell search method (RD: − 0.19, 95%CI (− 0.28, − 0.10), p < 0.001), epithelial marker (RD: − 0.12, 95%CI (− 0.25, 0.00), p 0.05) and mesenchymal markers (RD: − 0.35, 95%CI (− 0.57, − 0.13), p 0.002) before the treatment might have a higher diagnostic power to identify CTCs and also chemotherapy treatment (RD: − 0.17, 95%CI (− 0.31, − 0.03), p 0.016) could significantly reduce the number of CTCs after the treatment. We also found that the risk differences between the clinical early and advanced stages were not statistically significant (RD: − 0.10, 95%CI (− 0.23, 0.02), P 0.105). Also, in the Lauren classification, the incidence of CTC in the diffuse type (RD: − 0.19, 95%CI (− 0.37, − 0.01), P0.045) was higher than that in the intestinal type. Meta-regression analysis showed that baseline characteristics were not associated with the detection of CTCs in GC patients. According to our systematic review and meta-analysis, CTCs identification may be suggested as a diagnostic technique for gastric cancer screening, and the outcomes of CTC detection may also be utilized in the future to create personalized medicine programs.
Environmental migration? A systematic review and meta-analysis of the literature
Authors: Cipollina Maria , De Benedictis Luca , Scibè Elisa
Published: March 2024

This article provides a comprehensive quantitative overview of the literature on the relationship between environmental changes and human migration. It begins with a systematic approach to bibliographic research and offers a bibliometric analysis of the empirical contributions. Specifically, we map the literature and conduct systematic research using main bibliographic databases, reviews, and bibliometric analysis of all resulting papers. By constructing a citation-based network, we identify four separate clusters of papers grouped according to certain characteristics of the analysis and resulting outcomes. Finally, we apply a meta-analysis to a sample of 96 published and unpublished studies between 2003 and 2020, providing 3904 point estimates of the effect of slow-onset events and 2065 point estimates of the effect of fast-onset events. Overall, the meta-analytic average effect on migration is small for both slow- and rapid-onset events; however, it is positive and significant. Accounting for the clustering of the literature, which highlights how specific common features of the collected studies influence the magnitude of the estimated effect, reveals a significant heterogeneity among the four clusters of papers. This heterogeneity gives rise to new evidence on the formation of club-like convergence of literature outcomes.
Video shot-boundary detection: issues, challenges and solutions
Authors: Kar T. , Kanungo P. , Mohanty Sachi Nandan , Groppe Sven , Groppe Jinghua
Published: March 2024

The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the internet, in seconds. Additionally, uploading videos to different websites is no more confined to expert software professionals resulting in duplication of video data which led to exorbitant growth of multimedia information in cyberspace in a short span of time. This necessitates the development of efficient data management techniques including storage, searching and annotation mechanism. Automatic shot boundary detection is considered to be the first and foremost step towards such management. It is a booming area of research gaining attention in the domain of image processing, computer vision and pattern recognition. In this review paper, we present a detailed description of the methods and algorithms of shot boundary detection, reported in the last two decades. This review shows that using multiple features performs well in comparison to using only a single feature in the shot boundary detection problem although it leads to higher complexity. The major sources of disturbance in the boundary detection are the sudden illumination variation and presence of high motion in the video. An adaptive threshold outperforms a single global threshold in the boundary detection problem and the threshold requirement can be avoided through learning based strategies at the cost of larger training data and higher computation time. Moreover the present review includes a critical analysis of relative merits and demerits of existing algorithms and finally opens promising research directions in the area.
The finance-growth nexus and public-private ownership of banks in Brazil since 1870
Authors: Campos Nauro , Karanasos Menelaos , Koutroumpis Panagiotis , Glebkina Ekaterina
Published: March 2024

How does finance affect economic growth? And does ownership matter? This paper investigates whether and how deposits in public vis-a-vis in private banks affect economic growth. It uses the power-ARCH framework with annual time series for Brazil from 1870 to 2018. There are three main findings: (a) the indirect impact of domestic financial development on economic growth is negative, whereas that of international financial development is positive, (b) the direct short-run effect of public and private banks is negative, while only for the latter does the positive direct long-run effect dominate, and (c) the indirect and direct short-run effect of public ownership banks is greater in size than that of private ownership banks.
Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data
Authors: Vecchio Giuseppe , Palazzo Simone , Guastella Dario C. , Giordano Daniela , Muscato Giovanni , Spampinato Concetto
Published: March 2024

Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation of a large number of images for training. To address this limitation, we present a method for traversability estimation on unlabeled videos that combines dataset synthesis, self-supervision and unsupervised domain adaptation. We pose the traversability estimation as a vector regression task over vertical bands of the observed frame. The model is pre-trained through self-supervision to reduce the distribution shift between synthetic and real data and encourage shared feature learning. Then, supervised training on synthetic videos is carried out, while employing an unsupervised domain adaptation loss to improve its generalization capabilities on real scenes. Experimental results show that our approach is on par with standard supervised training, and effectively supports robot navigation without the need of manual annotations. Training code and synthetic dataset will be publicly released at: https://github.com/perceivelab/traversability-synth.
Personality Discrimination and the Wrongness of Hiring Based on Extraversion
Authors: Räsänen Joona , Lippert-Rasmussen Kasper
Published: March 2024

Employers sometimes use personality tests in hiring or specifically look for candidates with certain personality traits such as being social, outgoing, active, and extraverted. Therefore, they hire based on personality, specifically extraversion in part at least. The question arises whether this practice is morally permissible. We argue that, in a range of cases, it is not. The common belief is that, generally, it is not permissible to hire based on sex or race, and the wrongness of such hiring practices is based on two widely accepted principles: the Relevance Principle and the Fairness Principle. The Relevance Principle states that hiring should be based on what is relevant to job performance, while the Fairness Principle states that hiring should be based on features that individuals can control. Since hiring based on sex or race violates these principles, it is wrong. However, we argue that, in a range of cases, hiring based on extraversion also violates these principles, and assuming the validity of the Relevance and the Fairness Principles, it follows that personality discrimination is morally wrong in those cases.
Interactions of NaCl with cellulose I$$\beta$$ crystal surfaces and the effect on cellulose hydration: a molecular dynamics study
Authors: Kou Zhennan , Tolmachev Dmitry , Vuorte Maisa , Sammalkorpi Maria
Published: March 2024

Crystalline nanocellulose is widely used for example, in the paper-making and food industries, as support matrix material or reinforcement of polymer materials, but also in drug carrier and nanomedicine applications. Interestingly, aqueous solutions of cellulose are extremely sensitive to small amounts of added salt yet mere considerations of charge screening leave open questions regarding the mechanisms, especially for unmodified cellulose in aqueous solutions. Here, we map NaCl ion distributions and the effect of added NaCl salt on the hydration of I$$\beta$$
β
cellulose nanocrystal (CNC) surfaces by atomistic detail molecular dynamics simulations with explicit water solvent. The simulations reveal the dependency of the hydration layers of the six surfaces of CNCs on the ions, as well as NaCl ion binding sites, and preferences in terms of binding free energy for the ions near CNC surfaces at different NaCl concentrations. We discuss the modelling results against our prior rheology characterization of cellulose solutions. Together, the results indicate that the high sensitivity of cellulose aqueous solutions to added salt rises from the ions near the surface changing locally the ordering and structure of the hydration layers of the CNC surfaces. The revealed mechanism of salt-induced viscosity changes in cellulose aqueous solutions allows advanced design of gelling CNC systems for various end uses and may also guide tuning cellulose interactions by different solvent environments.
Meta-inductive Justification of Inductive Generalizations
Authors: Schurz Gerhard
Published: March 2024

The account of meta-induction (G. Schurz, Hume’s problem solved: the optimality of meta-induction, MIT Press, Cambridge, 2019) proposes a two-step solution to the problem of induction. Step 1 consists in a mathematical a priori justification of the predictive optimality of meta-induction, upon which step 2 builds a meta-inductive a posteriori justification of object-induction based on its superior track record (Sect. 1). Sterkenburg (Br J Philos Sci, forthcoming. 10.1086/717068/) challenged this account by arguing that meta-induction can only provide a (non-circular) justification of inductive predictions for now and for the next future, but not a justification of inductive generalizations (Sect. 2). This paper develops a meta-inductive method that does provide an a posteriori justification of inductive generalizations, in the form of exchangeability conditions (Sect. 3). In Sect. 4, a limitation of the proposed method is worked out: while the method can justify weakly lawlike generalizations, the justification of strongly lawlike generalizations (claimed to hold for all eternity) requires epistemic principles going beyond meta-induction based on predictive success.
Integrating ecological niche modeling and rates of evolution to model geographic regions of mimetic color pattern selection
Authors: Muell Morgan R. , Brown Jason L.
Published: March 2024

Geographic variation in natural selection derived from biotic sources is an important driver of trait evolution. The evolution of Müllerian mimicry is governed by dual biotic forces of frequency-dependent predator selection and densities of prey populations consisting of conspecifics or congeners. Difficulties in quantifying these biotic forces can lead to difficulties in delimiting and studying phenomena such as mimicry evolution. We explore the spatial distribution of morphotypes and identify areas of high mimetic selection using a novel combination of methods to generate maps of mimetic phenotype prevalence in Ranitomeya poison frogs, a group of frogs characterized by great phenotypic variation and multiple putative Müllerian mimic pairs. We categorized representative populations of all species into four major recurring color patterns observed in Ranitomeya: striped, spotted, redhead, and banded morphs. We calculated rates of phenotypic evolution for each of the 4 morphs separately and generated ecological niche models (ENMs) for all species. We then split our species-level ENMs on the basis of intraspecific variation in color pattern categorization, and weighted ENM layers by relative evolutionary rate to produce mimicry maps. Our phenotypic evolutionary rate analyses identified multiple significant shifts in rates of evolution for the spotted, redhead, and banded phenotypes. Our mimicry maps successfully identify all suspected and known areas of Müllerian mimicry selection in Ranitomeya from the literature and show geographic areas with a gradient of suitability for Müllerian mimicry surrounding mimic hotspots. This approach offers an effective hypothesis generation method for studying traits that are tied to geography by explicitly connecting evolutionary patterns of traits to trends in their geographic distribution, particularly in situations where there are unknowns about drivers of trait evolution.
The never-ending book: the role of new material and peer feedback in user-generated content production
Authors: Marchenko Maria , Sonnabend Hendrik
Published: March 2024

This paper studies the determinants of the voluntary provision of user-generated (online) content. Using data from the largest fanfiction website, we find that writers respond differently to new original material: writing times increase for the average writer and even more for the elite of prolific writers. We explain this finding with quality concerns. In addition, we find supportive evidence that community feedback encourages first-time contributors to continue publishing. However, for more established writers, community feedback has a rather dampening effect on text lengths and writing times. These effects are more pronounced for more informative community feedback (‘reviews’) than less informative community feedback (‘following’, ‘favoriting’).
Online car-sharing problem with variable booking times
Authors: Liu Haodong , Luo Kelin , Xu Yinfeng , Zhang Huili
Published: March 2024

In this paper, we address the problem of online car-sharing with variable booking times (CSV for short). In this scenario, customers submit ride requests, each specifying two important time parameters: the booking time and the pick-up time (start time), as well as two location parameters—the pick-up location and the drop-off location within a graph. For each request, it’s important to note that it must be booked before its scheduled start time. The booking time can fall within a specific interval prior to the request’s starting time. Additionally, each car is capable of serving only one request at any given time. The primary objective of the scheduler is to optimize the utilization of k cars to serve as many requests as possible. As requests arrive at their booking times, the scheduler faces an immediate decision: whether to accept or decline the request. This decision must be made promptly upon request submission, precisely at the booking time. We prove that no deterministic online algorithm can achieve a competitive ratio smaller than $$L+1$$

L
+
1

even on a special case of a path (where L denotes the ratio between the largest and the smallest request travel time). For general graphs, we give a Greedy Algorithm that achieves $$(3L+1)$$

(
3
L
+
1
)

-competitive ratio for CSV. We also give a Parted Greedy Algorithm with competitive ratio $$(\frac{5}{2}L+10)$$

(

5
2

L
+
10
)

when the number of cars k is no less than $$\frac{5}{4}L+20$$

5
4

L
+
20

; for CSV on a special case of a path, the competitive ratio of Parted Greedy Algorithm is $$(2L+10)$$

(
2
L
+
10
)

when $$k\ge L+20$$

k
≥
L
+
20

.
How and When Resilience can Boost Student Academic Performance: A Weekly Diary Study on the Roles of Self-Regulation Behaviors, Grit, and Social Support
Authors: Li Kongqi , Wang Huatian , Siu Oi-Ling , Yu Hong
Published: March 2024

Resilience has been found to have positive impacts on college students’ well-being and mental health. However, we still lack knowledge on how and under what conditions resilience can help to facilitate college students’ academic performance. Based on the conservation of resources theory, this study investigated how resilience could be positively linked to college students’ academic performance through increasing self-regulation behaviors, and to what extent this indirect effect could be strengthened by individual grit and social support. Using 74 Chinese college students with a four-week longitudinal survey (296 observations in total), we confirmed our hypotheses. The results of multilevel moderated mediation showed that weekly self-regulation behaviors mediated the positive relationship between weekly resilience and college students’ weekly academic performance and that this positive indirect effect became stronger for those who had a higher level of grit and perceived a higher level of social support. Further, we found that weekly resilience was also positively related to the next week’s self-regulation behaviors, which in turn, increased next week’s academic performance. Social support can strengthen such a carry-over impact of resilience on next week’s self-regulation behaviors (but not for grit). To conclude, our study uncovered the short-term fluctuations of resilience and its impacts on students’ study outcomes. We highlight the important roles of personal resources (grit) and social resources (social support) that can leverage the positive effects of resilience on students’ weekly basis.
Parenting, Coparenting, and Adolescents’ Sense of Autonomy and Belonging After Divorce
Authors: Rejaän Zoë , van der Valk Inge , Schrama Wendy , Branje Susan
Published: March 2024

Although there is ample evidence on the importance of experiencing autonomy and belonging for positive adolescent development and the supporting role of parents in this regard, most knowledge stems from intact families. As many youth grow up with divorced parents, this study tested longitudinal links between warm and autonomy supportive parenting and coparental cooperation and conflict on the one hand, and adolescents’ post-divorce autonomy and belonging on the other. Data consisted of three-wave self-report data of 191 Dutch adolescents (Mage = 14.36, 61.3% girls) and 227 divorced parents (Mage = 46.08, 74% mothers). Random-intercept cross-lagged panel models showed stable between-family differences, with autonomy relating positively to coparenting and parental autonomy support, and belongingness associating positively solely with parenting. No significant effects were found within families, meaning that changes in (co)parental behaviors did not predict adolescents’ experiences of autonomy and belonging or vice versa.
Deep Embedding Clustering Based on
Residual Autoencoder
Authors: Li Mengli , Cao Chao , Li Chungui , Yang Shuhong
Published: March 2024

Clustering algorithm is
one of the most widely used and influential analysis techniques.
With the advent of deep learning, deep embedding clustering
algorithms have rapidly evolved and yield promising results. Much of
the success of these algorithms depends on the potential expression
captured by the autoencoder network. Therefore, the quality of the
potential expression directly determines the algorithm’s
performance. In view of this, researchers have proposed many
improvements. Although the performance has been slightly improved,
they all have one shortcoming, that is, too much emphasis is placed
on the original data reconstruction ability during the process of
feature expression, which greatly limits the further expression of
potential features according to specific clustering tasks. Moreover,
there is a large amount of noise in the original data, so blindly
emphasizing reconstruction will only backfire. Hence, we
innovatively propose a deep embedding clustering algorithm based on
residual autoencoder (DECRA) after in-depth research. Specifically,
a novel autoencoder network with residual structure is proposed and
introduced into deep embedded clustering tasks. The network
introduces an adaptive weight layer in feature representation z,
which can make it have good robustness, generalization for specific
tasks, and adaptive learning of better feature embeddings according
to category classification. In this paper, the reasons for the
validity of this structure are explained theoretically, and
comprehensive experiments on six benchmark datasets including
various types show that the clustering performance of the DECRA is
very competitive and significantly superior to the most advanced
methods.
Freedom of choice impact on country-specific liquidity commonality
Authors: Jain Pawan , Mekhaimer Mohamed , Spahr Ronald W. , Sunderman Mark A.
Published: March 2024

We use three facets of freedom of choice (opportunity, decisional autonomy, and immunity from interference) to develop a freedom of choice index for 46 countries and test its impact on commonality in liquidity. We find that the three aspects of individual freedom of choice are negatively related to country-specific market liquidity commonality. Building on Sen’s (1993) theoretical framework, we document that in the presence of opportunities for making independent choices, immunity from encroaching activities, and decisional autonomy, investors tend to show less correlated trading decisions, measured by the levels of liquidity co-movement. To address possible endogeneity bias, we use 9/11 and country-level terrorist attacks as an exogenous source of variations to personal freedom of choice. We find that higher levels of perceived threats in a country restrain personal freedoms and tend to reduce freedom of choice effects on liquidity commonality. Our results are robust to alternative explanations, freedom of choice definitions, and model specifications.
Randomized controlled trial of the CMR immersive virtual reality (IVR) headset training compared to e-learning for operating room configuration of the CMR versius robot
Authors: Eley Catherine L. , Palaniappan Varun , Carter Abbie , Sogaolu Opeyemi , Horwood James , Davies Michael , Torkington Jared , Ansell James
Published: March 2024

Robotic surgery offers potential advantages over laparoscopic procedures, but the training for configuring robotic systems in the operating room remains underexplored. This study seeks to validate immersive virtual reality (IVR) headset training for setting up the CMR Versius in the operating room. This single-blinded randomized control trial randomised medical students with no prior robotic experience using an online randomiser. The intervention group received IVR headset training, and the control group, e-learning modules. Assessors were blinded to participant group. Primary endpoint was overall score (OS): Likert-scale 1–5: 1 reflecting independent performance, with increasing verbal prompts to a maximum score of 5, requiring physical assistance to complete the task. Secondary endpoints included task scores, time, inter-rater reliability, and concordance with participant confidence scores. Statistical analysis was performed using IBM SPSS Version 27. Of 23 participants analysed, 11 received IVR and 12 received e-learning. The median OS was lower in the IVR group than the e-learning group 53.5 vs 84.5 (p < 0.001). VR recipients performed tasks independently more frequently and required less physical assistance than e-learning participants (p < 0.001). There was no significant difference in time to completion (p = 0.880). Self-assessed confidence scores and assessor scores differed for e-learning participants (p = 0.008), though not IVR participants (p = 0.607). IVR learning is more effective than e-learning for preparing robot-naïve individuals in operating room set-up of the CMR Versius. It offers a feasible, realistic, and accessible option in resource-limited settings and changing dynamics of operating theatre teams. Ongoing deliberate practice, however, is still necessary for achieving optimal performance. ISCRTN Number 10064213.
Prospects for liquid biopsy using microRNA and extracellular vesicles in breast cancer
Authors: Ochiya Takahiro , Hashimoto Kazuki , Shimomura Akihiko
Published: March 2024

Among the analytes circulating in body fluids, microRNAs, a type of non-coding RNA and known to exist 2655 in primates, have attracted attention as a novel biomarker for cancer screening. MicroRNAs are signaling molecules with important gene expression regulatory functions that can simultaneously control many gene functions and multiple different pathways in living organisms. These microRNAs are transported in extracellular vesicles (EVs), which are lipid bilayers with 50–150 nm in diameter, and are used as communication tools between cells. Furthermore, the EVs that carry these microRNAs circulate in the bloodstream and have other important implications for understanding the pathogenesis and diagnosis of breast cancer. The greatest benefit from cancer screening is the reduction in breast cancer mortality rate through early detection. Other benefits include reduced incidence of breast cancer, improved quality of life, prognosis prediction, contribution to personalized medicine, and relative healthcare cost containment. This paper outlines the latest developments in liquid biopsy for breast cancer, especially focusing on microRNA and EV diagnostics.
A memetic algorithm for a real-world dynamic pickup and delivery problem
Authors: Zhou Ying , Kong Lingjing , Yan Lijun , Liu Yunxia , Wang Hui
Published: March 2024

The dynamic pickup and delivery problem (DPDP) is essential in supply chain management and logistics. In this study, we consider a real-world DPDP from daily delivery scenarios of a company. In the problem, orders are generated randomly and released periodically. The orders should be completed as soon as possible to minimize the cost. We propose a novel memetic algorithm (MA) to address this problem. The proposed MA consists of a genetic algorithm and a local search strategy that periodically solves a static pickup and delivery problem when new orders are released. We have conducted extensive experiments on 64 real-world instances to assess the performance of our method. Three state-of-the-art algorithms are chosen as the baseline algorithms. Experimental results demonstrate the effectiveness of the MA in solving the real-world DPDP.
The study of mineral distribution using hyperspectral Hyperion data along the shores of Lake Salda/Türkiye
Authors: Akgül Mehmet Ali , Ural Suphi
Published: March 2024

Lake Salda is considered the most similar region on Earth to search for evidence of life on Mars, and its shoreline is home to microbialites containing some of the oldest known fossilized records of life on our planet. Understanding the historical process of Mars is also crucial for predicting the future of our planet, given its transition from a watery world to an arid one. In mineralogical studies to be carried out in sensitive areas such as private protected areas, it is a priority not to damage the area, and mineralogical research using remote sensing methods is frequently used in such studies. In this study, the mineralogical characteristics of the geological formations in and around Kocaadalar Burnu peninsula, located in the southwest of Lake Salda, were examined using remote sensing techniques. Mineral studies of Lake Salda were conducted using data from the Hyperion data of the Earth Observation-1 (EO-1) satellite, which can detect in 220 bands between 357 and 2576 nm wavelengths which is capable of hyperspectral sensing. The EO-1 satellite, the first spacecraft of NASA’s New Millennium Programme, which operated successfully from its launch in 2000 until the end of its mission in 2017, carried the multispectral Advanced Land Imager and LEISA Atmospheric Corrector sensors, in addition to the Hyperion hyperspectral sensor. The Hyperspectral Material Identification tool in the Tactical Hyperspectral Operations Resource module of the ENVI software, the United States Geological Survey spectral library, and the Adaptive Coherence Estimator algorithm were used in the study. According to the results of this study conducted on the shores of Lake Salda, in addition to the hydromagnesite mineral, the existence of which was previously detected by various researchers, trona, vermiculite, rivadavite and borate minerals such as ulexite, tincalconite and colemanite were also detected. The results of this study have shown that remote sensing methods can make significant contributions to research in special protected areas such as Lake Salda. It is thought that the presence of trona and boron minerals in the region will contribute positively to the understanding of the historical process of Salda Lake.
Experimental data-driven model development for ESP failure diagnosis based on the principal component analysis
Authors: Song Youngsoo , Jun Sungjun , Nguyen Tan C. , Wang Jihoon
Published: March 2024

The reliable diagnosis of electrical submersible pump (ESP) failure is a vital process for establishing of the optimal production strategies and achieving minimum development costs. Although traditional ammeter charts and nodal analysis are commonly used for ESP failure diagnosis, the techniques have limitations, as it requires manpower and is difficult to diagnose the failure in real-time. Therefore, in this study, ESP failure diagnosis was performed using the principal component analysis (PCA). First, 11 types of 9,955 pieces of data were acquired from a newly constructed ESP experimental system for 300 days. During the experimental period, ESP failure occurred twice with a significant drop in performance: first on day 112 and second on day 271. The PCA model was constructed with the 8,928 pieces of normal status data and tested with the 1,027 pieces of normal and failure status data. Three principal components were extracted from the measured data to identify the patterns of the normal and failure status. Based on the logistic regression method to analyze the efficiency of the PCA model, it was found out that the developed PCA model showed an accuracy of 93.3%. Therefore, the PCA model was found to be reliable and effective for the ESP failure diagnosis and performance analysis.
Tyre heat of multi-wheel bogie undercarriage at touchdown
Authors: Li Yu , Wang Weiji
Published: March 2024

Almost all studies on tyre heat during aircraft touchdown are based on the assumption that the tyres touch the ground simultaneously, and the aircraft’s load is evenly distributed among each tyre. However, the unique design of multi-wheel bogie undercarriage on wide-body aircraft gives the tyres a specific touchdown sequence. The friction, heat, and thermal wear they face are entirely different. Therefore, this study establishes a model to simulate the dynamics of the multi-wheel bogie undercarriage and relies on Laplace’s equation to calculate the treads’ heat generation and temperature rise. The innovative three-dimensional tread temperature display method can further determine the tyre’s thermal wear. It is found that faster landing increases tyre heat by extending the friction time and distance for all tyres. Softer landing increases the independent rotation time of the early-touched tyres, thus significantly raising the tread temperature due to high frictional power density around the tread centreline areas.
Outcomes of severely injured pregnant trauma patients: a multicenter analysis
Authors: Awad Kyrillos G. , Nahmias Jeffry , Aryan Negaar , Lucas Alexa N. , Fierro Nicole , Dhillon Navpreet K. , Ley Eric J. , Smith Jennifer , Burruss Sigrid , Dahan Alden , Johnson Arianne , Ganske William , Biffl Walter L. , Bayat Dunya , Castelo Matthew , Wintz Diane , Schaffer Kathryn B. , Zheng Dennis J. , Tillou Areti , Coimbra Raul , Tuli Rahul , Santorelli Jarrett E. , Emigh Brent , Schellenberg Morgan , Inaba Kenji , Duncan Thomas K. , Diaz Graal , Tay-Lasso Erika , Zezoff Danielle C. , Grigorian Areg
Published: March 2024

Nearly 10% of pregnant women suffer traumatic injury. Clinical outcomes for pregnant trauma patients (PTPs) with severe injuries have not been well studied. We sought to describe outcomes for PTPs presenting with severe injuries, hypothesizing that PTPs with severe injuries will have higher rates of complications and mortality compared to less injured PTPs. A post-hoc analysis of a multi-institutional retrospective study at 12 Level-I/II trauma centers was performed. Patients were stratified into severely injured (injury severity score [ISS] > 15) and not severely injured (ISS < 15) and compared with bivariate analyses. From 950 patients, 32 (3.4%) had severe injuries. Compared to non-severely injured PTPs, severely injured PTPs were of similar maternal age but had younger gestational age (21 vs 26 weeks, p = 0.009). Penetrating trauma was more common in the severely injured cohort (15.6% vs 1.4%, p < 0.001). The severely injured cohort more often underwent an operation (68.8% vs 3.8%, p < 0.001), including a hysterectomy (6.3% vs 0.3%, p < 0.001). The severely injured group had higher rates of complications (34.4% vs 0.9%, p < 0.001), mortality (15.6% vs 0.1%, p < 0.001), a higher rate of fetal delivery (37.5% vs. 6.0%, p < 0.001) and resuscitative hysterotomy (9.4% vs. 0%, p < 0.001). Only approximately 3% of PTPs were severely injured. However, severely injured PTPs had a nearly 40% rate of fetal delivery as well as increased complications and mortality. This included a resuscitative hysterotomy rate of nearly 10%. Significant vigilance must remain when caring for this population.
Static Response of Functionally Graded Porous Circular Plates via Finite Element Method
Authors: Doori Silda Ghazi Mohammed , Noori Ahmad Reshad , Etemadi Ali
Published: March 2024

The main purpose of this paper is to investigate the axisymmetric bending response of functionally graded porous (FGP) circular plates. The material properties are changed continuously in the thickness direction of the plate. Three distinct porosity distributions uniform, symmetric and monolithic are employed. The effect of porosity on the axisymmetric bending analysis of circular plates is examined parametrically. In this study, clamped and roller supports which commonly serve to achieve ideal boundary conditions in numerous engineering applications are used. The finite element method is employed for numerical analysis. The principal of the potential energy is used to obtain the governing equations. To generate the model of the FGP circular plates, an eight-node quadratic quadrilateral element with two degrees of freedom on each node is utilized. The results of this study are confirmed by the existing published literature. A good agreement between the results of the presented model and the previous literature has been observed. The results of the present study show that plate deflection increases with the increase of the porosity coefficient and the ratio of radius to thickness of circular plates. By increasing the porosity coefficient, the displacement values of the plates made of uniform porosity distribution is effected more than those of other porosity distributions.
SARS-CoV-2 journey: from alpha variant to omicron and its sub-variants
Authors: Hattab Dima , Amer Mumen F. A. , Al-Alami Zina M. , Bakhtiar Athirah
Published: March 2024

The COVID-19 pandemic has affected hundreds of millions of individuals and caused more than six million deaths. The prolonged pandemic duration and the continual inter-individual transmissibility have contributed to the emergence of a wide variety of SARS-CoV-2 variants. Genomic surveillance and phylogenetic studies have shown that substantial mutations in crucial supersites of spike glycoprotein modulate the binding affinity of the evolved SARS-COV-2 lineages to ACE2 receptors and modify the binding of spike protein with neutralizing antibodies. The immunological spike mutations have been associated with differential transmissibility, infectivity, and therapeutic efficacy of the vaccines and the immunological therapies among the new variants. This review highlights the diverse genetic mutations assimilated in various SARS-CoV-2 variants. The implications of the acquired mutations related to viral transmission, infectivity, and COVID-19 severity are discussed. This review also addresses the effectiveness of human neutralizing antibodies induced by SARS-CoV-2 infection or immunization and the therapeutic antibodies against the ascended variants.
An optimization method of human skeleton keyframes selection for action recognition
Authors: Chen Hao , Pan Yuekai , Wang Chenwu
Published: March 2024

In the action recognition field based on the characteristics of human skeleton joint points, the selection of keyframes in the skeleton sequence is a significant issue, which directly affects the action recognition accuracy. In order to improve the effectiveness of keyframes selection, this paper proposes inflection point frames, and transforms keyframes selection into a multi-objective optimization problem based on it. First, the pose features are extracted from the input skeleton joint point data, which used to construct the pose feature vector of each frame in time sequence; then, the inflection point frames in the sequence are determined according to the flow of momentum of each body part. Next, the pose feature vectors are input into the keyframes multi-objective optimization model, with the fusion of domain information and the number of keyframes; finally, the output keyframes are input to the action classifier. To verify the effectiveness of the method, the MSR-Action3D, the UTKinect-Action and Florence3D-Action, and the 3 public datasets, are chosen for simulation experiments and the results show that the keyframes sequence obtained by this method can significantly improve the accuracy of multiple action classifiers, and the average recognition accuracy of the three data sets can reach 94.6%, 97.6% and 94.2% respectively. Besides, combining the optimized keyframes with deep learning classifier on the NTU RGB + D dataset can make the accuracies reaching 83.2% and 93.7%.
E pluribus, quaedam. Gross Domestic Product out of a Dashboard of Indicators
Authors: Guerini Mattia , Vanni Fabio , Napoletano Mauro
Published: March 2024

Is aggregate income enough to summarize well-being? We address this long-standing question by exploiting a quantitative approach that studies the relationship between gross domestic product (GDP) and a set of economic, social and environmental indicators for nine developed economies. We introduce a mathematical approach to the analysis of economic indicators. By employing dimensionality reduction and time series reconstruction techniques, we quantify the share of variability stemming from a large set of different indicators that can be compressed into a univariate index. We also evaluate how well this variability can be explained if the univariate index is assumed to be respectively the gross domestic product, national income, household income, or household spending. Our results indicate that all the four univariate measures are doomed to fail in accounting for the variability of all the domains. Even if GDP emerges as the best option among the four economic variables, its quality in synthesizing the variability of indicators belonging to other domains is poor (about 35%). Our approach provides additional support for policy makers interested in measuring the trade offs between income and other relevant social, health and ecological dimensions. Finally, our work adds new quantitative evidence to the vast literature criticizing the usage of GDP as a measure of well-being.
Inverse problem solver for epidemiological geographic profiling
Authors: Maeno Yoshiharu
Published: March 2024

This work studies machine-learning-based inverse problem solvers for a reaction–diffusion process. The study focus is on the performance of a state-of-the-art convolutional neural network in discovering the source of disease spreading. This problem is called epidemiological geographic profiling. The performance is investigated with synthetic datasets for SIR epidemiological compartments on a square grid geo-space. The convolutional neural network works effectively in discovering a single source and achieves the largest time average of accuracy for growing infection in a heterogeneous geo-space. The hit score remains near the lower bound over time. Discovering multiple sources is feasible potentially as well by learning the dataset for a single source.