bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compressed sensing: a discrete optimization approach
Authors: Bertsimas Dimitris , Johnson Nicholas A. G. 
Published: July 2024

AbstractWe study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine Learning which arises in applications such as signal processing, data compression, image reconstruction, and multi-label learning. We introduce an $$\ell _2$$

ℓ
2

regularized formulation of CS which we reformulate as a mixed integer second order cone program. We derive a second order cone relaxation of this problem and show that under mild conditions on the regularization parameter, the resulting relaxation is equivalent to the well studied basis pursuit denoising problem. We present a semidefinite relaxation that strengthens the second order cone relaxation and develop a custom branch-and-bound algorithm that leverages our second order cone relaxation to solve small-scale instances of CS to certifiable optimality. When compared against solutions produced by three state of the art benchmark methods on synthetic data, our numerical results show that our approach produces solutions that are on average $$6.22\%$$

6.22
%

more sparse. When compared only against the experiment-wise best performing benchmark method on synthetic data, our approach produces solutions that are on average $$3.10\%$$

3.10
%

more sparse. On real world ECG data, for a given $$\ell _2$$

ℓ
2

reconstruction error our approach produces solutions that are on average $$9.95\%$$

9.95
%

more sparse than benchmark methods ($$3.88\%$$

3.88
%

more sparse if only compared against the best performing benchmark), while for a given sparsity level our approach produces solutions that have on average $$10.77\%$$

10.77
%

lower reconstruction error than benchmark methods ($$1.42\%$$

1.42
%

lower error if only compared against the best performing benchmark). When used as a component of a multi-label classification algorithm, our approach achieves greater classification accuracy than benchmark compressed sensing methods. This improved accuracy comes at the cost of an increase in computation time by several orders of magnitude. Thus, for applications where runtime is not of critical importance, leveraging integer optimization can yield sparser and lower error solutions to CS than existing benchmarks.
On Stage-Wise Backpropagation for Improving Cheng’s Method for Fully Connected Cascade Networks
Authors: Mizutani Eiji , Kubota Naoyuki , Truong Tam Chi 
Published: July 2024

AbstractIn this journal, Cheng has proposed a backpropagation (BP) procedure called BPFCC for deep fully connected cascaded (FCC) neural network learning in comparison with a neuron-by-neuron (NBN) algorithm of Wilamowski and Yu. Both BPFCC and NBN are designed to implement the Levenberg-Marquardt method, which requires an efficient evaluation of the Gauss-Newton (approximate Hessian) matrix $$\nabla \textbf{r}^\textsf{T} \nabla \textbf{r}$$

∇

r
T

∇
r

, the cross product of the Jacobian matrix $$\nabla \textbf{r}$$

∇
r

of the residual vector $$\textbf{r}$$
r
in nonlinear least squares sense. Here, the dominant cost is to form $$\nabla \textbf{r}^\textsf{T} \nabla \textbf{r}$$

∇

r
T

∇
r

by rank updates on each data pattern. Notably, NBN is better than BPFCC for the multiple $$q~\!(>\!1)$$

q

(
>

1
)

-output FCC-learning when q rows (per pattern) of the Jacobian matrix $$\nabla \textbf{r}$$

∇
r

are evaluated; however, the dominant cost (for rank updates) is common to both BPFCC and NBN. The purpose of this paper is to present a new more efficient stage-wise BP procedure (for q-output FCC-learning) that reduces the dominant cost with no rows of $$\nabla \textbf{r}$$

∇
r

explicitly evaluated, just as standard BP evaluates the gradient vector $$\nabla \textbf{r}^\textsf{T} \textbf{r}$$

∇

r
T

r

with no explicit evaluation of any rows of the Jacobian matrix $$\nabla \textbf{r}$$

∇
r

.
Hydrodynamic modelling for simulating nearshore waves and sea levels: classification of extreme events from the English Channel to the Normandy coasts
Authors: López Solano C. , Turki E. I. , Mendoza E. T. , Gutiérrez Barceló A. D. , Migaud A. , Hamdi Y. , Laignel B. , Lafite R. 
Published: July 2024

AbstractAssessing the vulnerability of coastal systems to storms often rely on an accurate modelling of extreme events and the identification of their impacts that depend on their physical characteristics. This requires the development of an exhaustive numerical downscaling of extreme hydrodynamics from deep to shallow areas to enhance our ability to predict the risks induced by these events, which is of fundamental importance for coastal managers. This work aims at investigating the nearshore dynamics of extreme events and their evolution from the shelf seas of the English Channel to the Normandy Coasts by developing a new numerical field of wave and sea level simulations during a period of 40 years. This dataset has been explored for the classification of extreme events considering their severity, direction, and duration. The overall results highlighted that more than 90% of the storms are coming from the Atlantic Ocean with a substantial change in their amplitude and their duration along the Channel: storms with Northern component are more severe while those coming from the South last longer with higher energy. A detailed monitoring of three different storm events exhibited that their impact depends on their travelling direction, being more significant for hydrodynamic events propagating following the orientation of the Channel, of about 70 degrees to the North, with lower diffraction. Extreme events coming from South Atlantic experience a stronger wave modulation when they arrive nearshore.
Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees
Authors: Akay Hüseyin 
Published: July 2024

AbstractAccurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.
Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye
Authors: Demir Yetiş Ayşegül , İlhan Nagehan , Kara Hatice 
Published: July 2024

AbstractGroundwater resources in Bitlis province and its surroundings in Türkiye’s Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region’s volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the “all” and “rainy season” datasets, while the convolutional neural network (CNN) outperformed in the “dry season” dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.
To work or to care? Herausforderungen der Herstellung des Alltags von Mehrkindfamilien
Authors: Simon Romy 
Published: July 2024

ZusammenfassungIn Mehrkindfamilien, d. h. Familien mit drei und mehr Kindern, sind die sozialen Beziehungen aufgrund der größeren Zahl der Mitglieder komplexer, so dass die Gestaltung des Familienalltags herausfordernder sein kann. Aus einer Doing Family Perspektive ist Familie keine selbstverständlich vorhandene Ressource, sondern muss tagtäglich durch die Herstellungsleistungen der Mitglieder aktiv erbracht werden. Der Artikel geht der leitenden Frage nach, welche Unterschiede in den Herstellungsleistungen des Alltags von Mehrkindfamilien, die das Ernährer- oder Doppelverdienermodell ausüben, herausgearbeitet werden können. Die empirische Grundlage dieses Beitrags bildet eine qualitative Studie mit zwölf Mehrkindfamilien, in welcher Eltern- und Kinderinterviews sowie teilnehmende Beobachtungen durchgeführt wurden. Es zeigt sich, dass das Arrangement der Erwerbstätigkeit die verfügbaren Zeiten und die Gestaltung des Familienlebens stark beeinflusst. Dabei nimmt die mütterliche Einbindung in die Erwerbsarbeit einen zentralen Einflussfaktor für die geschlechtsspezifische Arbeitsteilung ein. Eine mütterliche Nichteinbindung oder Teilzeiterwerbstätigkeit kann die Organisation der Care-Arbeit entzerren, führt jedoch einerseits zu der primären Hauptverantwortung der Mutter sowie andererseits häufig zu einer angespannteren finanziellen Situation der Familie. Dahingehend zeigen sich bei den Doppelverdienerpaaren knappere Zeitressourcen für die Care-Arbeit bei gleichzeitiger finanzieller Sicherheit, die wiederum dieses Erwerbstätigkeitsarrangement neben dem individuellen Wunsch nach Selbstverwirklichung der Elternteile legitimiert. Insbesondere Mütter, die die Hausfrauenrolle übernehmen oder voll erwerbstätig sind, sehen sich mit den von Außenstehenden an sie herangetragenen Normen und Wertevorstellungen konfrontiert.
Impact and Adhesion Mechanics of Block Copolymers in Cold Spray: Effects of Rubbery Domain Content
Authors: Duran Salih , Kim Ara , Lee Jae-Hwang , Müftü Sinan 
Published: July 2024

AbstractThe impact and adhesion mechanics of two-phase block copolymers during high-velocity impacts are studied experimentally and computationally to understand the effect of the rubbery phase on bonding behavior in cold spray additive manufacturing. Micron-scale (10-20 μm) spherical particles of polystyrene-block-polydimethylsiloxane with varying rubbery phases are impacted on a silicon substrate by using a laser-induced projectile impact test setup with impact velocities in the range of 50-600 m/s. Experiments indicate that the minimum impact velocity for polymer particles adhering to the substrate decreases with increasing rubbery phase content. A strain rate- and temperature-dependent constitutive model and cohesive zone model are calibrated for each material by comparing the deformed and computed deformed particle shapes and coefficient of restitution values of the rebounding particles. Computational results show that increasing the rubbery phase content in block copolymers increases plastic energy dissipation from 89 to 96% and the critical strain energy release rate from 1.87 to 9.3 J/m2 at 140 m/s, and thus contributes to the observed decrease in the minimum impact velocity required for block copolymers to adhere to substrates. The discovered direct relationship between soft phase content and critical strain energy release rate implies that increased soft-rubbery PDMS content in block copolymers enhances adhesion through improved chain mobility, better surface asperities coverage, and enhanced wetting, due to its lower surface energy and greater adiabatic heating.
Decentralized traffic management of autonomous drones
Authors: Balázs Boldizsár , Vicsek Tamás , Somorjai Gergő , Nepusz Tamás , Vásárhelyi Gábor 
Published: July 2024

Abstract
Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task—filled with conflicts—is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area.
Association between metabolic dysfunction-associated steatotic liver disease and risk of urolithiasis: an updated systematic review and meta-analysis
Authors: Mantovani Alessandro , Morandin Riccardo , Fiorio Veronica , Lando Maria Giovanna , Petta Salvatore , Ferraro Pietro Manuel , Targher Giovanni 
Published: July 2024

AbstractEpidemiological studies have reported an association between metabolic dysfunction-associated steatotic liver disease (MASLD) and the risk of urolithiasis. However, the magnitude of the risk and whether this risk varies with the severity of MASLD remains uncertain. We performed a meta-analysis of observational studies to quantify the magnitude of the association between MASLD and urolithiasis. We systematically searched PubMed, Scopus, and Web of Science from database inception to March 31, 2024, using predefined keywords to identify relevant observational studies in which imaging methods or survey questionnaires diagnosed MASLD and urolithiasis. Meta-analysis was performed using random-effects modelling. We identified seven cross-sectional studies and one prospective cohort study with aggregate data on 248,936 adults from different countries. MASLD was significantly associated with an increased risk of prevalent urolithiasis (pooled random-effects odds ratio 1.87, 95% CI 1.34–2.60; I2 = 91%). This association remained significant in those studies whose results were adjusted for age, sex, ethnicity, obesity, diabetes, and other potential confounders. There was a positive graded association between the ultrasonographic severity of MASLD and urolithiasis. Meta-analysis of the single prospective cohort study showed that MAFLD was not associated with risk of developing incident urolithiasis (pooled random-effects hazard ratio 1.08, 95% CI 0.90–1.30), although a significant association was reported in men. Sensitivity analyses did not modify these findings. The funnel plot did not reveal any significant publication bias. This updated meta-analysis provides evidence for a significant association between MASLD and the presence of urolithiasis. Whether MASLD is associated with a higher risk of developing incident urolithiasis remains to be established.
Biogenic synthesis and characterization of gold nanoparticles using transformed mesophilic Escherichia coli BL21 and thermophilic Thermus thermophilus HB27
Authors: Erasmus Mariana , Idris Oladayo Amed , Adetunji Adegoke Isiaka , Cason Errol Duncan 
Published: July 2024

AbstractGold nanoparticles have numerous applications, many of which are notable in industries. The biosynthesis of gold nanoparticles offers an easy, effective, green, and eco-friendly approach. In organisms capable of synthesizing nanoparticles, enzymes and proteins are responsible for the structural and functional modifications that lead to their formation. These include ABC transporter, peptide-binding proteins, which are dependent on abiotic parameters. This study uses the purified ABC transporter, peptide-binding protein transformed from Thermus scotoductus SA-01 and expressed in mesophilic Escherichia coli BL21 and thermophilic Thermus thermophilus HB27 hosts for the biosynthesis of gold nanoparticles at different concentrations, temperatures, and pH values. Gold nanoparticle formation was evaluated with a range of gold (III) concentrations (0–10 mM), incubated at temperatures ranging from 30–85 ºC and pH levels from 3.6–9.0. Transmission electron microscopy (TEM), energy dispersive X-ray spectrometry (EDX), and UV–Vis absorption spectroscopy were used to characterise the formation of nanoparticles. In all of the protein reactions, UV–Vis absorbance peaks at approximately 520–560 nm confirmed the formation of gold nanoparticles. Optimum nanoparticle synthesis was observed at pH values ranging from 5.5 to 9.0, gold (III) solution (HAuCl4) concentrations from 0.5–2.0 mM, and a maximum temperature of 65ºC in the mesophilic host and 85ºC in the thermophilic host, indicating the significance of temperature in both hosts for the expression and bioactivity of the purified ABC transporter protein. However, the biogenic formation of gold nanoparticles using E. coli and T. thermophilus hosts was not monodispersed, suggesting a necessity for further development of the procedure.
Unraveling high-quality mathematics teaching in algebra: insights from overlaying domain-specific and content-specific observation instruments
Authors: Litke Erica , Candela Amber , Boston Melissa , Dietiker Leslie 
Published: July 2024

AbstractWhen analyzing instructional quality in mathematics, observational measures can consider domain-specific instructional approaches (e.g., effective teaching of mathematics) and content-focused practices (e.g., effective teaching of a specific mathematical topic). In this study, we analyze lesson videos from secondary algebra classrooms using two different instruments—one focused on ambitious mathematics teaching practices and one focused on instructional features that support students’ learning opportunities in algebra specifically. We consider the information provided by overlaying these instruments to raise implications for instructional improvement efforts, particularly related to how information from the instruments might inform formative feedback to teachers. We consider how alignment between instrument and instructional emphasis can influence the strengths and areas of instructional improvement identified in a given lesson. We further model a collaborative process for looking across observation instruments to deepen understanding of the information provided.
Gastrointestinal Manifestation of MPox
Authors: Trestrail Timothy , Kodia Karishma , Hui Vanessa W. 
Published: July 2024

Abstract
Purpose of Review
Mpox, formerly known as monkeypox, is a zoonotic illness caused by a virus that is part of the Orthopoxvirus family. Originally identified in humans in the Democratic Republic of Congo in 1970, the disease has been endemic in central African nations. In 2022, an outbreak of Monkeypox warranted a declaration by the World Health Organization (WHO) that the virus was a Public Health Emergency of International Concern. Prior literature documented the dermatological manifestations of the disease, but fewer papers have described and navigated the complexities of mpox gastrointestinal manifestations. We aim to update the current literature on the gastrointestinal (GI) manifestations of mpox, through a review of the literature via PubMed search for English language papers reporting GI manifestations of the virus.

Recent Findings
Individual reports of symptomatic manifestations of mpox have been reported. Upper and lower GI symptoms have been described and, in multiple cases, required multidisciplinary team care to successfully treat the patients.

Summary
GI manifestations of mpox disease are reported in a variety of severities and, in some instances, may require multidisciplinary management.