bluuebunny/crossref_metadata_2025_split huggingface dataset:
The asymmetric role of temperature deviations in economic growth: Fresh evidence from global countries and panel quantile estimates
Authors: Apergis Nicholas , Rehman Mobeen Ur 
Published: February 2024

AbstractThis work explores the role of weather shocks, measured as temperature deviations from their normal, in affecting GDP growth through a panel of 148 countries, spanning the period 1960–2019 and a panel quantile approach. The findings show that GDP growth is negatively affected by such deviations at higher quantiles, with the results receiving robust support from three alternative methodologies. The analysis also identifies two channels through which the findings receive further support, such as, the ‘heat‐exposed’ sectors effect on labour productivity, and the impact of temperature deviations on the capital accumulation capacity. The results also survive the role of the country development status. Finally, the analysis identifies the role of the countries' geographical location. The results can have substantial merit for policymakers in terms of the role of climate conditions in the growth process, especially in poor countries.
PRICELESS: Privacy enhanced AI‐driven scalable framework for IoT applications in serverless edge computing environments
Authors: Golec Muhammed , Golec Mustafa , Xu Minxian , Wu Huaming , Gill Sukhpal Singh , Uhlig Steve 
Published: February 2024

AbstractServerless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time‐sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain‐based AI‐driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk‐based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state‐of‐the‐art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.
Statistically identified structural VAR model with potentially skewed and fat‐tailed errors
Authors: Anttonen Jetro , Lanne Markku , Luoto Jani 
Published: February 2024

AbstractWe introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t‐distributions, whose flexibility compared with commonly considered Student's t‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical application, the data support narrative sign restrictions in identifying the US monetary policy shock. In contrast to some of the previous literature, we find a strong negative response of real activity to contractionary monetary policy after a few months' delay.
Identifying factors via automatic debiased machine learning
Authors: Maasoumi Esfandiar , Wang Jianqiu , Wang Zhuo , Wu Ke 
Published: February 2024

SummaryIdentifying risk factors that have significant explanatory power for the cross‐sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear stochastic discount factor (SDF) assumption. The ADML resolves biased estimation, non‐robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama–French sparse factors and factors identified via the double‐selection LASSO method under a linear factor model assumption. Out of a high‐dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross‐section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods.
Two‐step reconstruction framework of fluorescence molecular tomography based on energy statistical probability
Authors: Zhao Yizhe , Li Shuangchen , Zhang Lizhi , Tang Zijian , Wei De , Zhang Heng , Xie Qiong , Yi Huangjian , He Xiaowei 
Published: February 2024

AbstractFluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non‐invasively visualize the distribution of fluorescent marker probe three‐dimensionally. However, FMT reconstruction is a severely ill‐posed problem, which remains an obstacle to wider application of FMT. In this paper, a two‐step reconstruction framework was proposed for FMT based on the energy statistical probability. First, the tissue structural information obtained from computed tomography (CT) is employed to associate the tissue optical parameters for rough solution in the global region. Then, according to the global‐region reconstruction results, the probability that the target belongs to each region can be calculated. The region with the highest probability is delineated as region of interest to realize accurate and fast source reconstruction. Numerical simulations and in vivo experiments were carried out to evaluate the effectiveness of the proposed framework. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.
“On‐the‐fly” Crystal: How to reliably and automatically characterize and construct potential energy surfaces
Authors: Aarabi Mahdi , Pandey Ankit , Poirier Bill 
Published: February 2024

AbstractIn this work, the code, developed previously by the authors to find “holes” as well as legitimate transition states in existing potential energy surface (PES) functions [JPC Lett. 11, 6468 (2020)], is retooled to perform on‐the‐fly “direct dynamics”‐type PES explorations, as well as automatic construction of new PES functions. In all of these contexts, the chief advantage of over other methods is its ability to globally map the PES, thereby determining the most relevant regions of configuration space quickly and reliably—even when the dimensionality is rather large. Here, is used to generate a uniformly spaced grid of density functional theory (DFT) or ab initio points, truncated over the relevant regions, which can then be used to either: (a) hone in precisely on PES features such as minima and transition states, or; (b) create a new PES function automatically, via interpolation. Proof of concept is demonstrated via application to three molecular systems: water (HO), (reduced‐dimensional) methane (CH), and methylene imine (CHNH).
A review of touch research in consumer psychology
Authors: Krishna Aradhna , Luangrath Andrea Webb , Peck Joann 
Published: February 2024

AbstractThis article details the theoretical foundation of haptics in consumer research. We structure the review using a continuum from proximal touch‐based interactions to increasingly distal interactions through devices, imagery, or language use. We begin with a focus on product/object touch in marketing highlighting touch for haptic information, touch for haptic pleasure, and touch for nonhaptic functional reasons. We then elaborate on research considering work compensating for actual touch through various mechanisms including device‐mediated touch and imagery processing. Next, we examine interpersonal touch followed by a discussion on touch in sensory words and textual paralanguage. The authors note various avenues for future research in haptics with the aim to encourage research in consumer psychology and marketing.
Lipid profile of circulating placental extracellular vesicles during pregnancy identifies foetal growth restriction risk
Authors: Klemetti Miira M. , Pettersson Ante B. V. , Ahmad Khan Aafaque , Ermini Leonardo , Porter Tyler R. , Litvack Michael L. , Alahari Sruthi , Zamudio Stacy , Illsley Nicholas P. , Röst Hannes , Post Martin , Caniggia Isabella 
Published: February 2024

AbstractSmall‐for‐gestational age (SGA) neonates exhibit increased perinatal morbidity and mortality, and a greater risk of developing chronic diseases in adulthood. Currently, no effective maternal blood‐based screening methods for determining SGA risk are available. We used a high‐resolution MS/MSALL shotgun lipidomic approach to explore the lipid profiles of small extracellular vesicles (sEV) released from the placenta into the circulation of pregnant individuals. Samples were acquired from 195 normal and 41 SGA pregnancies. Lipid profiles were determined serially across pregnancy. We identified specific lipid signatures of placental sEVs that define the trajectory of a normal pregnancy and their changes occurring in relation to maternal characteristics (parity and ethnicity) and birthweight centile. We constructed a multivariate model demonstrating that specific lipid features of circulating placental sEVs, particularly during early gestation, are highly predictive of SGA infants. Lipidomic‐based biomarker development promises to improve the early detection of pregnancies at risk of developing SGA, an unmet clinical need in obstetrics.
Degree criteria and stability for independent transversals
Authors: Haxell Penny , Wdowinski Ronen 
Published: February 2024

AbstractAnindependent transversal(IT) in a graph with a given vertex partition is an independent set of vertices of (i.e., it induces no edges), that consists of one vertex from each part (block) of . Over the years, various criteria have been established that guarantee the existence of an IT, often given in terms of being ‐thick, meaning all blocks have size at least . One such result, obtained recently by Wanless and Wood, is based on themaximum average block degree. They proved that if then an IT exists. Resolving a problem posed by Groenland, Kaiser, Treffers and Wales (who showed that the ratio 1/4 is best possible), here we give a full characterization of pairs such that the following holds for every : whenever is a graph with maximum degree , and is a ‐thick vertex partition of such that , there exists an IT of with respect to . Our proof makes use of another previously known criterion for the existence of ITs that involve the topological connectedness of the independence complex of graphs, and establishes a general technical theorem on the structure of graphs for which this parameter is bounded above by a known quantity. Our result interpolates between the criterion and the old and frequently applied theorem that if then an IT exists. Using the same approach, we also extend a theorem of Aharoni, Holzman, Howard and Sprüssel, by giving a stability version of the latter result.
Adherence to treatment in dermatology: Literature review
Authors: Lo Angela , Lovell Katie K. , Greenzaid Jonathan D. , Oscherwitz Max E. , Feldman Steven R. 
Published: February 2024

AbstractPoor adherence to therapies in dermatology remains a prevalent issue associated with treatment failure, poor clinical outcomes, and reduced quality of life. The aim of this review is to summarize the current literature on medical adherence specifically in dermatology, including ways to measure treatment adherence, factors contributing to nonadherence, interventions to increase adherence, and the impact of adherence on patient outcomes. We conducted a MEDLINE search between the years 2006 and 2023 using the following term: [adherence AND dermatology AND treatment]. The search was limited to English article types: clinical study, clinical trial, observational study, and randomized controlled trials in human subjects. The literature search yielded 323 articles. 52 of these articles met the inclusion criteria and were reviewed to define, measure, understand, and suggest means to increase adherence in dermatology. Additional articles were added from in text citations. Adherence can be measured using subjective and objective methods. Patient‐, treatment‐, and disease‐centered factors are important to consider when prescribing medications and implementing interventions to increase adherence. Reducing treatment adverse reactions, simplifying treatment regimens, and eHealth, education, communication, and psychological interventions are associated with improved adherence and disease outcomes. Understanding and enhancing adherence is crucial because of its impact on costs, treatment efficacy, and healthcare outcomes.
To collaborate or not? Analysis of competitive manufacturers' green innovation strategy toward sustainability
Authors: Liu Wen , Han Hongshuai , Hou Pengwen , Li Huimei 
Published: February 2024

AbstractIn response to the demand for carbon emission reduction, enterprises engage in green technology innovation. Technical collaboration between rival companies has emerged as a strategy to lower the costs of green innovation, reduce uncertainties and risks, and meet market demands by accelerating the introduction of new products. This paper establishes three game models based on the different cooperation contracts of the heterogeneous brand firms: the non‐cooperative model, the innovation cost‐sharing contract model, and the technology licensing contract model. Comprehensive analysis shows that competing firms intend to cooperate based on reducing green innovation costs and producing products with higher green levels to meet the market. Although cooperation between competing firms is beneficial for the profit gaining of both firms, the cooperation does not necessarily lead to an increase in the green level of products. Comparing with the cost‐sharing contract, although technology licensing may highlight the brand disadvantage of the emerging enterprise under significant disparity in brand differentiation, the emerging enterprise can still benefit from increased sales generated by the dominant enterprise and achieve higher gains. Furthermore, competing firms can achieve a win‐win‐win situation in terms of profit, environment, and social welfare through both cost‐sharing contract and technology licensing contract under certain circumstances.
Assessing the Gut Microbiota's Ability to Metabolize Oligomeric and Polymeric Flavan‐3‐ols from Aronia and Cranberry
Authors: Lessard‐Lord Jacob , Roussel Charlène , Guay Valérie , Desjardins Yves 
Published: February 2024

AbstractClinical trials investigating the health effects of flavan‐3‐ols yield heterogeneous results due to interindividual variability in the gut microbiota metabolism. In fact, different groups in the population have similar metabolic profiles following (−)‐epicatechin and (+)‐catechin gut microbial metabolism and can be regrouped into so‐called metabotypes. In this study, the capacity of 34 donors to metabolize polymeric B‐type flavan‐3‐ols from aronia and oligomeric A‐type flavan‐3‐ols from cranberry is investigated by in vitro fecal batch fermentations. Less than 1% of the flavan‐3‐ols from both sources are converted into microbial metabolites, such as phenyl‐γ‐valerolactones (PVLs). To further confirm this result, gut microbial metabolites from flavan‐3‐ols are quantified in urine samples collected from participants, before and after a 4‐day supplementation of cranberry extract providing 82.3 mg of flavan‐3‐ols per day. No significant difference is observed in the urinary excretion of flavan‐3‐ols microbial metabolites. Hence, it demonstrates by both in vitro and in vivo approaches that flavan‐3‐ols from aronia and cranberry are poorly degraded by the gut microbiota. The beneficial health impacts of these molecules likely stem from their capacity to affect gut microbiota and their interactions with the gut epithelium, rather than from their breakdown into smaller metabolites.
Training of a deep learning based digital subtraction angiography method using synthetic data
Authors: Duan Lizhen , Eulig Elias , Knaup Michael , Adamus Ralf , Lell Michael , Kachelrieß Marc 
Published: February 2024

AbstractBackgroundDigital subtraction angiography (DSA) is a fluoroscopy method primarily used for the diagnosis of cardiovascular diseases (CVDs). Deep learning‐based DSA (DDSA) is developed to extract DSA‐like images directly from fluoroscopic images, which helps in saving dose while improving image quality. It can also be applied where C‐arm or patient motion is present and conventional DSA cannot be applied. However, due to the lack of clinical training data and unavoidable artifacts in DSA targets, current DDSA models still cannot satisfactorily display specific structures, nor can they predict noise‐free images.PurposeIn this study, we propose a strategy for producing abundant synthetic DSA image pairs in which synthetic DSA targets are free of typical artifacts and noise commonly found in conventional DSA targets for DDSA model training.MethodsMore than 7,000 forward‐projected computed tomography (CT) images and more than 25,000 synthetic vascular projection images were employed to create contrast‐enhanced fluoroscopic images and corresponding DSA images, which were utilized as DSA image pairs for training of the DDSA networks. The CT projection images and vascular projection images were generated from eight whole‐body CT scans and 1,584 3D vascular skeletons, respectively. All vessel skeletons were generated with stochastic Lindenmayer systems. We trained DDSA models on this synthetic dataset and compared them to the trainings on a clinical DSA dataset, which contains nearly 4,000 fluoroscopic x‐ray images obtained from different models of C‐arms.ResultsWe evaluated DDSA models on clinical fluoroscopic data of different anatomies, including the leg, abdomen, and heart. The results on leg data showed for different methods that training on synthetic data performed similarly and sometimes outperformed training on clinical data. The results on abdomen and cardiac data demonstrated that models trained on synthetic data were able to extract clearer DSA‐like images than conventional DSA and models trained on clinical data. The models trained on synthetic data consistently outperformed their clinical data counterparts, achieving higher scores in the quantitative evaluation of peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) metrics for DDSA images, as well as accuracy, precision, and Dice scores for segmentation of the DDSA images.ConclusionsWe proposed an approach to train DDSA networks with synthetic DSA image pairs and extract DSA‐like images from contrast‐enhanced x‐ray images directly. This is a potential tool to aid in diagnosis.
Forensic geochemistry identifies the illegal introduction of Walleye into Lake Cascade, Idaho
Authors: Whitman George , Messner Jordan , Johnson Rachel C. , Willmes Malte , Kennedy Brian P. , Jeffres Carson 
Published: February 2024

Abstract

Objective
The illegal introduction of fish species can disrupt ecosystems, collapse food webs, and undermine recreational fishing opportunities. Determining whether introduced fish are locally reproducing is important for resource managers. Here, we used the geochemical analysis of otoliths to investigate the potential illegal introduction of a Walleye Sander vitreus caught in 2022, in Lake Cascade, Idaho. Lake Cascade is known for its recreational Yellow Perch Perca flavescens fishery and has no documented resident population of Walleye.

Methods
To determine if the Walleye was spawned in Lake Cascade or introduced from elsewhere, we analyzed otoliths for strontium isotopes (87Sr/86Sr) and compared them to local water samples and three locally caught Yellow Perch.

Result
The Walleye otolith revealed a shift from higher 87Sr/86Sr values (0.70878) in its early life to a period of intermediate value (0.70842) equal to Payette Lake water, then finally to a lower value (0.70807) comparable to the Lake Cascade water and resident Yellow Perch otoliths.

Conclusion
These results suggest that the Walleye was initially transplanted to the Payette Lake area from a currently unknown source in 2020, 2 years before its capture. It resided there briefly before migrating south into Lake Cascade. This study further highlights the benefit of geochemical analyses to identify the illegal introduction of fish and to provide resource managers with a powerful tool for early detection and prevention of the establishment of illegally introduced fish species.
Cochlear Implant Qualification in Noise Versus Quiet: Do Patients Demonstrate Similar Postoperative Benefits?
Authors: Schauwecker Natalie , Patro Ankita , Holder Jourdan T. , Bennett Marc L. , Perkins Elizabeth , Moberly Aaron C. 
Published: February 2024

AbstractObjectiveTo assess patient factors, audiometric performance, and patient‐reported outcomes in cochlear implant (CI) patients who would not have qualified with in‐quiet testing alone.Study DesignRetrospective chart review.SettingTertiary referral center.MethodsAdult CI recipients implanted between 2012 and 2022 were identified. Patients with preoperative AzBio Quiet > 60% in the implanted ear, requiring multitalker babble to qualify, comprised the in‐noise qualifying (NQ) group. NQ postoperative performance was compared with the in‐quiet qualifying (QQ) group using CNC, AzBio Quiet, and AzBio +5 dB signal‐to‐noise ratio. Speech, Spatial and Qualities of Hearing Scale (SSQ), Cochlear Implant Quality of Life scale (CIQOL‐10), and daily device usage were also compared between the groups.ResultsThe QQ group (n = 771) and NQ group (n = 67) were similar in age and hearing loss duration. NQ had higher average preoperative and postoperative speech recognition scores. A larger proportion of QQ saw significant improvement in CNC and AzBio Quiet scores in the CI‐only listening condition (eg, CI‐only AzBio Quiet: 88% QQ vs 51% NQ, P < .001). Improvement in CI‐only AzBio +5 dB and in all open set testing in the best‐aided binaural listening condition was similar between groups (eg, Binaural AzBio Quiet 73% QQ vs 59% NQ, P = .345). Postoperative SSQ ratings, CIQOL scores, and device usage were also equivalent between both groups.ConclusionPatients who require in‐noise testing to meet CI candidacy demonstrate similar improvements in best‐aided speech perception and patient‐reported outcomes as in‐QQ, supporting the use of in‐noise testing to determine CI qualification for borderline CI candidates.