bluuebunny/crossref_metadata_2025_split huggingface dataset:
A review of deep learning and Generative Adversarial Networks applications in medical image analysis
Authors: Sindhura D. N. , Pai Radhika M. , Bhat Shyamasunder N. , Pai Manohara M. M. 
Published: May 2024

AbstractNowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.
Impacts of a municipal solid waste classification policy on carbon emissions: case study of Beijing, China
Authors: Liu Tingting , Cao Jing , Miatto Alessio 
Published: May 2024

AbstractNational and local governments implement municipal solid waste classification policies to reduce waste disposal and minimize environmental pollution. Beijing started implementing its classification of municipal solid waste policy in May 2020. This study evaluates the impact of Beijing’s household waste classification policy on carbon emissions during collection, transportation, and treatment. The policy’s introduction reduced the number of trash bins, influenced transportation modes, and altered waste treatment emissions. We found a notable reduction in average net carbon emissions post-policy implementation (2403 kg CO2eq per metric ton of waste) compared with pre-policy (3584 kg CO2eq per metric ton of waste). If Beijing reaches its 2025 waste target, these emissions will decrease further to 1760 kg CO2eq per metric ton of waste. Translating these findings into monetary terms confirms this policy’s environmental and economic efficacy.
Invisible to Machines: Designing AI that Supports Vision Work in Radiology
Authors: Anichini Giulia , Natali Chiara , Cabitza Federico 
Published: May 2024

Abstract
In this article we provide an analysis focusing on clinical use of two deep learning-based automatic detection tools in the field of radiology. The value of these technologies conceived to assist the physicians in the reading of imaging data (like X-rays) is generally assessed by the human-machine performance comparison, which does not take into account the complexity of the interpretation process of radiologists in its social, tacit and emotional dimensions. In this radiological vision work, data which informs the physician about the context surrounding a visible anomaly are essential to the definition of its pathological nature. Likewise, experiential data resulting from the contextual tacit knowledge that regulates professional conduct allows for the assessment of an anomaly according to the radiologist’s, and patient’s, experience. These data, which remain excluded from artificial intelligence processing, question the gap between the norms incorporated by the machine and those leveraged in the daily work of radiologists. The possibility that automated detection may modify the incorporation or the exercise of tacit knowledge raises questions about the impact of AI technologies on medical work. This article aims to highlight how the standards that emerge from the observation practices of radiologists challenge the automation of their vision work, but also under what conditions AI technologies are considered “objective” and trustworthy by professionals.
“Missing the Trees for the Forest?” An Analysis of the Harms to European Eels Caused by Their Trafficking and Trade
Authors: Pons-Hernandez Monica 
Published: May 2024

AbstractWildlife trafficking has generally been studied for its harm to humans, to species of nonhuman animals, and to ecosystems. Such approaches fail to recognize the intrinsic value of nonhuman animal life. As discussed by various authors, even biodiversity loss discourses tend to adopt a human-centered perspective which overlooks the suffering of those individual nonhuman animals who are trafficked and traded. Through a case study of the European eel based on data collected through interviews, observation, and document analysis, this paper exposes the demand for European eels and the harms caused to them throughout the supply chain. Moreover, using a visual approach to green criminology, the research employs photographs as evidence of harm. By doing so, the paper makes visible the impact of wildlife trafficking and trade on European eels. As the research shows, European eels suffer along the supply chain not only when they are trafficked, but when they are subjected to a legal trade.
Climate, Technology and Value: Insights from the First Decade with Mass-Consumption of Electric Vehicles
Authors: Andreassen Gøril L. , Lind Jo Thori 
Published: May 2024

AbstractAdoption of low-carbon technology is key to mitigating climate change. A possible unwanted consequence of fast technological progress is that products get outdated before their technical lifetime is over. We investigate whether the market value of electric vehicles, characterized by rapid technological progress, declines faster over their lifetime than gasoline vehicles, which represent a mature technology. We use novel data from Norway, the market with the highest market shares for electric vehicles in the world. The data are from the largest web platform for secondhand vehicles over the period 2011–2021. Prices of electric vehicles decline faster than gasoline vehicles. This seems to be driven by the electric vehicles with below median driving range. We hypothesize that the large price drop is mainly due to the fast technological improvement of electric vehicles.
A comparative biochemical characterization of Allium genetic resources of Türkiye
Authors: Paşazade Ebubekir , Hanci Fatih 
Published: May 2024

Abstract
The present study aimed to determine the variation in onion and garlic genetic resources collected from the Kelkit Valley, a unique biodiversity concerved region in Türkiye. For this purpose, 17 onion genotypes and 12 garlic genotypes were collected from local growers. A total of 9 biochemical analyses (total phenolic matter, total soluble protein, total dry matter, total flavonoid matter, ferric reducing antioxidant power, cupric reducing antioxidant capacity, 2,2-diphenyl-1-picrylhydrazylradical scavenging activity, hydrogen peroxide scavenging capacity and ascorbic acid) were performed on bulb and leaf extracts of all genotypes. According to variance analysis of the biochemical results from the samples, the differences between genotypes for all parameters were found to be significant (p < 0.05). A post-hoc test (Tukey) and multiple means comparison were performed to analyze the results between groups in cases of significant differences. The results indicated that there is a large variation between different genotypes of both species and between both species and organs.
From experimentation to prediction: comprehensive study of dielectric properties through experimental research and theoretical modeling
Authors: Lebda H. I. , Atyia H. E. , Habashy D. M. 
Published: May 2024

AbstractThis study discusses the experimental findings on the frequency & temperature influences on the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials based on Se83Bi17 composition performed in the temperature range 303 K–393 K and frequency range (100–1000000 Hz). As the frequency increases, multiple polarization mechanisms contribute to the reduction of the dielectric constant. The addition of germanium (Ge) to a composition increases ε1 more than tellurium (Te). The dielectric loss decreases with frequency while increasing with temperature and AC conductivity. Understanding these behaviors is important for material characterization and applications in fields like electronics and solar cells. The theoretical section introduces adaptive neuro-fuzzy inference systems (ANFIS), which are utilized in the estimation of the dielectric characteristics of Se83Bi17 (SB), Se83Bi17Te5 (SB-T), and Se83Bi17Ge5 (SB-G). Experimentation-related data are a source of input. ANFIS model of the Takagi–Sugeno type has been trained. With MATLAB, the most effective networks are created. The outcomes of the ANFIS modeling are exceptional. The accuracy of the modeling process is due to the error values. This study demonstrates that the ANFIS technique can accurately anticipate the dielectric properties of the compositions under consideration when they are formed into thin films. The ANFIS can describe the experimental data of the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials for all the mentioned temperatures and frequencies. This leads to using the ANFIS model to produce the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials for various temperatures and frequencies which there are no experimental data yet to compare with.
Investigation of photosensitive and photodetector characteristics of n-TPA-IFA/p-Si heterojunction structure
Authors: Çavdar Şükrü , Oruç Pınar , Eymur Serkan , Tuğluoğlu Nihat 
Published: May 2024

Abstractn-TPA-IFA organic material was synthesized and deposited on p-Si by spin coating method to produce n-TPA-IFA/p-Si heterojunction diode. We determined that the dielectric constant and energy band gap of TPA-IFA organic material were 3.91 and 3.37 eV by DFT/B3LYP/6-311G(d,p) method using on Gaussian 09 W, respectively, and the carrier type of TPA-IFA organic semiconductor material was also n-type at room temperature from temperature-dependent hall effect measurements. Using forward and reverse bias I–V measurements in the dark and under various light intensities, we examined the key electrical characteristics of the n-TPA-IFA/p-Si heterojunction diode, including $${\Phi }_{b}$$

Φ
b

and $$n$$
n
. It was determined that the rectification ratio (RR) was approximately 104. The reverse current's observed increasing behavior with increasing light indicates that the produced heterojunction diode can be utilized as a photodiode, detector, or sensor. The values of n, $${\Phi }_{b}$$

Φ
b

, and Io were determined using I–V data in dark as 1.34, 0.91 eV, and 7.25 9 10–12 A, respectively. On the other hand, when the n-TPA-IFA/p-Si heterojunction diode is exposed to 100 mW/cm2 illumination, these parameters were obtained as 1.85, 0.80 eV, and 5.11 9 10–10 A, respectively. Photovoltaic parameters such as short circuit current (Isc) and open circuit voltage (Voc) were determined as 0.018 mV, 0.08 A and 0.050 mV, 0.29 A under 20 mW/cm2 light and 100 mW/cm2 light intensity, respectively. The photodetector properties of n-TPA-IFA/p-Si heterostructure were explored at different light intensities, and the photoresponsivity (R), photosensitivity (PS), specific detectivity ($${D}^{*}$$

D

∗

), and linear dynamic range (LDR) of the heterojunction changed with reverse voltage and light intensity. It was found that as light intensity increased, the linear dynamic range (LDR), a crucial characteristic for image sensors, increased as well (10.15 dB and 18.84 dB for 20 and 100 mW/cm2).
Optimality and Duality for Robust Optimization Problems Involving Intersection of Closed Sets
Authors: Hung Nguyen Canh , Chuong Thai Doan , Anh Nguyen Le Hoang 
Published: May 2024

AbstractIn this paper, we study a robust optimization problem whose constraints include nonsmooth and nonconvex functions and the intersection of closed sets. Using advanced variational analysis tools, we first provide necessary conditions for the optimality of the robust optimization problem. We then establish sufficient conditions for the optimality of the considered problem under the assumption of generalized convexity. In addition, we present a dual problem to the primal robust optimization problem and examine duality relations.
An extended Monte Carlo simulation code for modeling gas chromatography experiments with superheavy elements and their homologs
Authors: Dietzel Dominik , Yakushev Alexander , Düllmann Christoph E. 
Published: May 2024

AbstractMonte Carlo simulations are commonly used to model the behavior of chemical species of the heaviest elements and their homologs in gas chromatography experiments. In this paper, we present an extension of the fundamental Monte Carlo simulation proposed by Zvara in 1985. While preserving the core functionality, our code features two enhancements: first, it allows simulating experiments in which a primary radioisotope decays into a daughter isotope belonging to a different element, hence exhibiting different chemical properties. Second, it allows modeling scenarios where conversion of an initial chemical species to a different one can occur at temperatures high enough to overcome an activation barrier, facilitating simulations of related physisorption and chemisorption processes. This Monte Carlo code is applicable to open tubular and rectangular chromatography columns.
Addressing and resolving issues with hybrid flexible/dual mode teaching and technology in learning spaces: the 2 × n matrix model
Authors: Detyna Michael , Dommett Eleanor J. 
Published: May 2024

AbstractHybrid flexible learning is a mechanism for providing flexible learning to both online and on-campus students. Synchronous HyFlex teaching uses both technology and pedagogy to connect both groups within the same cohort. It is gaining popularity in higher educational institutions, but it also can create challenges associated with pedagogy, including technology, acoustics, and logistics. Finding solutions to these challenges is an organisational imperative for institutions aiming to address them. This article provides an overview of a mechanism for reviewing and problem solving as it relates to hybrid flexible teaching. A conceptual overview is detailed, building on previous literature; and experience from implementation, and rooted in a learning environments context, is described. The unique contribution of the study is demonstrated through the framework for resolving and understanding problems to provide solutions within the specific example of hybrid learning, which has not been analysed or considered in this way before. Example case studies are provided and considered, with an emphasis on practical real-world solutions (rooted in literature and evidence) that are likely to be useful to fellow practitioners.
Geometric Dirac operator on noncommutative torus and $$M_2({\mathbb {C}})$$
Authors: Lira-Torres E. , Majid S. 
Published: May 2024

AbstractWe solve for quantum geometrically realised pre-spectral triples or ‘Dirac operators’ on the noncommutative torus $${\mathbb {C}}_\theta [T^2]$$

C
θ

[

T
2

]

and on the algebra $$M_2({\mathbb {C}})$$

M
2

(
C
)

of $$2\times 2$$

2
×
2

matrices with their standard quantum metrics and associated quantum Riemannian geometry. For $${\mathbb {C}}_\theta [T^2]$$

C
θ

[

T
2

]

, we obtain a standard even spectral triple but now uniquely determined by full geometric realisability. For $$M_2({\mathbb {C}})$$

M
2

(
C
)

, we are forced to a particular flat quantum Levi-Civita connection and again obtain a natural fully geometrically realised even spectral triple. In both cases there is an odd spectral triple for a different choice of a sign parameter. We also consider an alternate quantum metric on $$M_2({\mathbb {C}})$$

M
2

(
C
)

with curved quantum Levi-Civita connection and find a natural 2-parameter family of Dirac operators which are almost spectral triples, where "Equation missing" fails to be antihermitian. In all cases, we split the construction into a local tensorial level related to the quantum Riemannian geometry, where we classify the results more broadly, and the further requirements relating to the pre-Hilbert space structure. We also illustrate the Lichnerowicz formula for "Equation missing" which applies in the case of a full geometric realisation.
Volume singularities in general relativity
Authors: García-Heveling Leonardo 
Published: May 2024

AbstractWe propose a new notion of singularity in general relativity which complements the usual notions of geodesic incompleteness and curvature singularities. Concretely, we say that a spacetime has a volume singularity if there exist points whose future or past has arbitrarily small spacetime volume: in particular, smaller than a Planck volume. From a cosmological perspective, we show that the (geodesic) singularities predicted by Hawking’s theorem are also volume singularities. In the black hole setting, we show that volume singularities are always hidden by an event horizon, prompting a discussion of Penrose’s cosmic censorship conjecture.
Quantum particle localization observables on Cauchy surfaces of Minkowski spacetime and their causal properties
Authors: De Rosa Carmine , Moretti Valter 
Published: May 2024

AbstractWe introduce and study a general notion of spatial localization on spacelike smooth Cauchy surfaces of quantum systems in Minkowski spacetime. The notion is constructed in terms of a coherent family of normalized POVMs, one for each said Cauchy surface. We prove that a family of POVMs of this type automatically satisfies a causality condition which generalizes Castrigiano’s one and implies it when restricting to flat spacelike Cauchy surfaces. As a consequence, no conflict with Hegerfeldt’s theorem arises. We furthermore prove that such families of POVMs do exist for massive Klein–Gordon particles, since some of them are extensions of already known spatial localization observables. These are constructed out of positive definite kernels or are defined in terms of the stress–energy tensor operator. Some further features of these structures are investigated, in particular the relation with the triple of Newton–Wigner selfadjoint operators and a modified form of Heisenberg inequality in the rest 3-spaces of Minkowski reference frames.
Convergence in Financial Development and Growth
Authors: He Zhiheng , You Yang 
Published: May 2024

AbstractWe evaluate the cross-country convergence of financial development and its relationship with GDP growth. Financial inclusion variables have been widely converged across countries, and the catch-up effect of countries with poor financial coverage mainly drives the convergence. In contrast, financial development measures — including domestic credit, liability, mutual fund size, and stock market capitalization — have diverged since 1985 despite the absolute convergence in GDP and financial inclusion. The GDP growth rates strongly correlate with the change in financial development but not the improvement in financial inclusion.
Limiting environmental reporting flexibility: investor judgment based on the EU taxonomy
Authors: Chrzan Sandra , Pott Christiane 
Published: May 2024

AbstractThis experimental research investigates the effect of different types of environmental information on investor judgment. By examining three experimental cases varying the level of environmental disclosure, we evaluate the investment judgments of professional (Study 1) and private German investors (Study 2). Primarily, we investigate whether traditional, commonly disclosed environmental information affects investor judgments. Furthermore, we explore the effects of linking non-financial reporting elements to quantitative financial measures through the EU taxonomy by adding taxonomy indicators. Specifically, we operationalized the case where companies fall into a category of poor environmental performance by taxonomy classification. We find that only traditional environmental disclosure in combination with standardized taxonomy-aligned information (below average), influences the investment judgment. However, professional investors exhibit a significantly negative response, while private investors show a significantly positive reaction when constraining reporting flexibility through the inclusion of standardized taxonomy measures with poor performance. Consequently, we conclude that the connection between non-financial reporting elements and quantitative standardized financial measures enhances transparency for professional investors. Private investors, on the other hand, reward additional taxonomy-aligned environmental information irrespective of its content. This implies that environmental information generally conveys positive signals to private investors, but uncertainty in investment judgment can be assumed.
The Ionosphere of Mars After 20 Years of Mars Express Contributions
Authors: Peter Kerstin , Sánchez-Cano Beatriz , Němec František , González-Galindo Francisco , Kopf Andrew J. , Lester Mark , Pätzold Martin , Regan Catherine E. , Holmström Mats 
Published: May 2024

AbstractThe Martian ionosphere originates from the ionization of the planetary neutral atmosphere by solar radiation. This conductive layer is embedded within the thermosphere and exosphere of Mars where it forms a highly variable interaction region with the solar wind. The Martian ionosphere has been continuously observed by the three plasma instruments MaRS, MARSIS and ASPERA-3 on Mars Express for the last 20 years ( >10 Martian years). Those long-term observations laid a solid foundation for what we know today about the Martian ionosphere, and provided numerous opportunities for collaboration and coordinated observations with other missions. This review describes the most significant achievements of Mars Express for the ionosphere, such as the dynamics and structures of both day and nightside, its variability and couplings with the lower atmosphere, as well as the improvement of atmospheric and ionosphere modelling. Mars Express has also provided a better characterization of the role of several external and internal drivers in controlling the ionosphere, such as the Martian crustal magnetic fields, solar activity, seasons, dust lifting from the surface, and even the direct interaction of the Martian ionosphere with the coma of an Oort-cloud comet (C/2013 A1, Siding Spring).