bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy
Authors: Azzolina Danila , Lanera Corrado , Comoretto Rosanna , Francavilla Andrea , Rosi Paolo , Casotto Veronica , Navalesi Paolo , Gregori Dario 
Published: August 2023

AbstractThe experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization
Authors: Elsedimy E. I. , AboHashish Sara M. M. , Algarni Fahad 
Published: August 2023

AbstractCardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).
Fast elitist ABC for makespan optimisation in interval JSP
Authors: Díaz Hernán , Palacios Juan José , González-Rodríguez Inés , Vela Camino R. 
Published: August 2023

AbstractThis paper addresses a variant of the Job Shop Scheduling Problem with makespan minimisation where uncertainty in task durations is taken into account and modelled with intervals. A novel Artificial Bee Colony algorithm is proposed where the classical layout is simplified, increasing the algorithm’s speed and reducing the number of parameters to set up. We also take into account the fundamental principles of exploration around a local solution and attraction to a global solution to improve diversity in the hive. The increase on speed and diversity allows to include a Local Search phase to better exploit promising areas of the search space. A parametric analysis is conducted and the contribution of the new strategies is analysed. The results of the new approach are competitive with those obtained with previous methods in the literature, but taking less runtime. The addition of Local Search improves the results even further, outperforming the best-known ones from the literature. An additional sensitivity study is conducted to assess the advantages of considering uncertainty and how increasing it affects the solution’s robustness.
Temporal and spatial evolution of the 2021 eruption in the Tajogaite volcano (Cumbre Vieja rift zone, La Palma, Canary Islands) from geophysical and geodetic parameter analyses
Authors: Benito M. B. , Alvarado G. E. , Marchamalo M. , Rejas J. G. , Murphy P. , Franco R. , Castro D. , Garcia-Lanchares C. , Sanchez J. 
Published: August 2023

AbstractThe eruption of the Tajogaite volcano in 2021, in the Cumbre Vieja rift zone (La Palma, Canary Islands), is the most recent but also the most damaging and longest-lasting eruption on the island since historical records began. We analyzed geophysical parameters (seismicity, surface deformation, thermal anomalies, and volcanic activity) with the aim of reconstructing the temporal development of the magma plumbing system. The seismic sequence (> 9000 events, mbLg ≤ 5.0) can be grouped into nine distinct phases, which correspond to well-separated spatial clusters and distinct earthquake regimes. A strong premonitory thermal anomaly was calculated on July 2021 in the South of La Palma. The source of the magma was within the upper mantle (40–20 km depth). A second magma budget (< 15 km depth) indicated a short-term stagnation of alkaline magma within the lower crust, near the Moho. The outline of these earthquakes delineates the magma pathway. Just before and immediately after the eruption (phases 2 and 3), the high-frequency seismicity in the mantle disappeared completely, reappearing ten days after the eruption started. The pattern of deformation began before the eruption with a breakpoint around September 11, after which, deformation accelerated sharply, followed by stabilization in phase 3 and a decrease in the following phases. The level of deformation suggested that a shallow magma intrusion was established. Based on the historical eruptive pattern, it is possible to infer two possible hazard-forecast scenarios: (a) the volcano could enter a prolonged phase of tranquility for more than one century or, (b) a new eruption could occur between ca. 2045 and 2065, with a slightly shorter duration than this recent one. Therefore, if the more pessimistic scenario is assumed, appropriate territorial planning is required.
Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery
Authors: Khalili Mohammad Amin , Guerriero Luigi , Pouralizadeh Mostafa , Calcaterra Domenico , Di Martire Diego 
Published: August 2023

AbstractA key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm.
Global stability of road vehicle motion with driver control
Authors: Mastinu Gianpiero , Della Rossa Fabio , Previati Giorgio , Gobbi Massimiliano , Fainello Marco 
Published: August 2023

AbstractThe paper contributes to unveil how drivers—either human or not—may lose control of road vehicles after a disturbance. First, a simple vehicle-and-driver model is considered: Its motion is characterized by the existence of limit cycles whose amplitude depend on vehicle forward velocity (both oversteering and understeering vehicles may exibit this property). Such limit cycles are originated by a Hopf bifurcation occurring at a relatively high vehicle forward velocity. A mathematical proof of the existence of Hopf bifurcations is given. The existence of Hopf bifurcations and saddle limit cycles is confirmed by experimental tests performed by a dynamic driving simulator with a complex vehicle model and human in the loop. By a Zubov method, a Lyapunov function is derived to compute the region of asymptotic stability for the simple vehicle-and-driver model. A necessary and sufficient condition is derived for global asymptotic stability. Such a condition refers to the variation of the kinetic energy which must vanish at the end of the disturbed motion. This occurrence has been detected at the driving simulator too. Just a single stable equilibrium has been found inside the domain of attraction in all of the examined cases.
Initial Study on Metal Dusting Behavior of Fe–Cr and Fe–Ni-Cr Alloys Under a Simulated Blast Furnace Operating Condition
Authors: Murakami Tatsuya , Nguyen Thuan Dinh , Xi Xuteng , Zhang Jianqiang 
Published: August 2023

AbstractBlast furnace ironmaking produces a reducing and carburizing atmosphere in the blast furnace which may lead to metal dusting. However, there is limited research on metal dusting under this particular atmosphere. This paper investigated metal dusting behaviors of ferritic Fe-(10, 20, 25)Cr and austenitic Fe-20Cr-(20, 33)Ni, Fe-25Cr-20Ni (wt.%) alloys at 550 and 650 °C in an Ar-33CO-7H2-7CO2-2H2O (vol.%) gas simulating a blast furnace operating condition. A relatively short reaction time, up to 100 h, was used to evaluate the initial stage of metal dusting of these alloys in the blast furnace gas condition. Severe metal dusting and coke deposition were observed at 550 °C, while no significant dusting but internal oxidation and carburization were observed at 650 °C. Higher Cr and Ni contents in the alloy lowered kinetics of metal dusting at 550 °C and of carburization at both temperatures. Austenitic alloys were more susceptible to metal dusting and coke deposition than ferritic alloys with the same Cr content at 550 °C. At 650 °C, however, ferritic alloys experienced higher extents of carburization and oxidation than austenitic alloys with the same Cr concentration. The effects of alloy composition and reaction temperature on metal dusting in this simulated blast furnace operating condition were discussed.
Will AI avoid exploitation? Artificial general intelligence and expected utility theory
Authors: Bales Adam 
Published: August 2023

AbstractA simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive: in exploring the argument, we gain insight into how to model advanced AI systems.
Do research performances of universities and disciplines in England converge or diverge? An assessment of the progress between research excellence frameworks in 2014 and 2021
Authors: Pinar Mehmet 
Published: August 2023

AbstractPerformance-based research funding systems (PBRFSs) have been used in selectively distributing research funding, increasing public money accountability and efficiency. Two recent such evaluations in England were called the Research Excellence Framework (REF), which took place in 2014 and 2021, and the research environment, outputs and impact of the research were evaluated. Even though various aspects of the REF were examined, there has been limited research on how the performance of the universities and disciplines changed between the two evaluation periods. This paper assesses whether there has been convergence or divergence in research quality across universities and subject areas between 2014 and 2021 and found that there was an absolute convergence between universities in all three research elements evaluated, and universities that performed relatively worse in REF in 2014 experienced higher growth in their performance between 2014 and 2021. There was also an absolute convergence in the research environment and impact across different subject areas, but there is no significant convergence in the quality of research outputs across disciplines. Our findings also highlight that there has been an absolute convergence in research quality within the universities (between different disciplines in a given university) and within disciplines (between universities in a given subject).
The ability of phase angle and body composition to predict risk of death in maintenance hemodialysis patients
Authors: Xu Yuanzhao , Ling Shuyi , Liu Zheyan , Luo Denggui , Qi Airong , Zeng Youjia 
Published: August 2023

Abstract
Objective
The objective of this study was to investigate the ability of phase angle and body composition to identify risk factors for mortality among patients receiving maintenance hemodialysis (MHD) treatment.

Methods
In this retrospective study, we examined the causes of death in 43 MHD patients who were treated at our hemodialysis center between January 2016 and December 2021 and compared the patients to 71 patients who survived during the same period. Body composition was measured using direct segmental multi-frequency bioelectrical impedance to obtain phase angle, fat-free mass (FFM), extracellular water/total body water (ECW/TBW), and waist circumference (WC). Laboratory data were also collected. Phase angle cut-off value-associated variables were identified using ROC analysis. The ability of body composition variables to identify risk factors for death in MHD patients was evaluated.

Results
We found that cardiovascular disease was the most common cause of death among MHD patients. ROC curve analysis revealed that the optimal cut-off value for phase angle as a predictor of death risk in MHD patients was 4.50°. Additionally, lower phase angle, increased age, longer dialysis vintage, lower KT/V, and hypoproteinemia were identified as significant risk factors for death in MHD patients.

Conclusion
In conclusion, our findings suggest that cardiovascular disease is the leading cause of death among MHD patients and that lower phase angle, increased age, longer dialysis duration, and hypoproteinemia can be used to predict the risk of mortality in this patient population. The underlying mechanism by which lower phase angle can be used to predict the prognosis of MHD patients warrants further investigation.
An Expanded IrO2/Ti Durable Electrode for Electrochemical Degradation of Basic Red 46 Textile Dye in Both Single and Binary Mixture Solutions
Authors: Belal Raghda M. , Zayed Mohamed A. , Ghany Nabil A. Abdel 
Published: August 2023

AbstractThe use of dimensionally stable anode (DSA) for degrading organic pollutants is of concern recently. The electrocatalytic technique can efficiently treat biodegradable organic matter like dye-containing effluent discharge. This study focuses on applying modified DSA meshed IrO2/Ti electrode for the electrochemical degradation of wastewater effluents of Basic Red 46 (BR46) textile dye using different supporting electrolytes; sodium sulfate (Na2SO4) or/and sodium chloride (NaCl). The parameters affecting the proposed electrochemical degradation process were evaluated to select the optimal operating conditions. The results revealed that using a mixture of supporting electrolyte Na2SO4 and NaCl enhanced the removal of dye color %, chemical oxygen demand (COD) %, and total organic carbon (TOC) % by 94.9%, 65.7%, and 76.0%, respectively. The UV-Vis and FT-IR spectroscopic techniques followed the degradation efficiency. FT-IR data were in good agreement with the COD, TOC, and UV-Vis measurements, confirming the high refractory performance of the BR46 dye. The redox process was performed on the surface of the modified electrode and explained via cyclic voltammetry (CV), and it showed a diffusion mass transport mechanism followed by product adsorption. The identification of the degradation products was assessed by gas chromatography–mass spectrometry (GC-MS) analysis. The electrochemical degradation and mineralization of a binary mixture of Basic Yellow (BY28) and BR46 textile dyes were also studied. It gave similar degradation and mineralization behavior to the same extent as the single dye in solutions.
Acmella oleracea extracts as green pesticides against eight arthropods attacking stored products
Authors: Kavallieratos Nickolas G. , Spinozzi Eleonora , Filintas Constantin S. , Nika Erifili P. , Skourti Anna , Panariti Anna Maria E. , Ferrati Marta , Petrelli Riccardo , Ricciutelli Massimo , Angeloni Simone , Drenaggi Ettore , Sensini Alessia , Maggi Filippo , Canale Angelo , Benelli Giovanni 
Published: August 2023

Abstract
Developing sustainable control tools for managing noxious pests attacking stored foodstuffs is a timely research challenge. Acmella oleracea (L.) R. K. Jansen is a crop widely cultivated for its multiple usages on an industrial level. In this study, the extracts prepared with A. oleracea aerial parts were applied on wheat kernels for the management of eight important arthropod pests attacking stored products, i.e., Cryptolestes ferrugineus, Tenebrio molitor, Oryzaephilus surinamensis, Trogoderma granarium, Tribolium castaneum, Tribolium confusum, Alphitobius diaperinus (adults/larvae), and Acarus siro (adults/nymphs). Extraction of A. oleracea was optimized on the base of the yield and content of spilanthol and other N-alkylamides which were analysed by HPLC–DAD-MS. Two concentrations of n-hexane or methanol extracts (500 ppm and 1000 ppm), obtained through Soxhlet extraction, were tested to acquire mortality data on the above-mentioned pests after 4, 8, and 16 h and 1 to 7 days of exposure. Both extracts achieved complete mortality (100.0%) of C. ferrugineus adults. In the case of A. diaperinus adults, mortalities were very low at any concentrations of both extracts. In general, the n-hexane extract was more efficient than methanol extract against almost all species and stages. Considering both extracts, the susceptibility order, from most to least susceptible species/stage, was C. ferrugineus adults > A. diaperinus larvae > C. ferrugineus larvae > T. granarium adults > T. molitor larvae > O. surinamensis adults > O. surinamensis larvae > T. confusum larvae > T. castaneum larvae > A. siro adults > T. molitor adults > A. siro nymphs > T. granarium larvae > T. castaneum adults > T. confusum adults > A. diaperinus adults. Our research provides useful knowledge on the efficacy of N-alkylamides-rich A. oleracea extracts as grain protectants, pointing out the importance of targeting the most susceptible species/ developmental stages.
Validation of biomarkers for neonicotinoid exposure in Folsomia candida under mutual exposure to diethyl maleate
Authors: Bakker Ruben , Xie Liyan , Vooijs Riet , Roelofs Dick , Hoedjes Katja M. , van Gestel Cornelis A. M. 
Published: August 2023

AbstractNeonicotinoid insecticides are harmful to non-target soil invertebrates, which are crucial for sustainable agriculture. Gene expression biomarkers could provide economic and high-throughput metrics of neonicotinoid exposure and toxicity to non-target invertebrates. Thereby, biomarkers can help guide remediation efforts or policy enforcement. Gene expression of Glutathione S-Transferase 3 (GST3) has previously been proposed as a biomarker for the neonicotinoid imidacloprid in the soil ecotoxicological model species Folsomia candida (Collembola). However, it remains unclear how reliably gene expression of neonicotinoid biomarkers, such as GST3, can indicate the exposure to the broader neonicotinoid family under putative GST enzymatic inhibition. In this work, we exposed springtails to two neonicotinoids, thiacloprid and imidacloprid, alongside diethyl maleate (DEM), a known GST metabolic inhibitor that imposes oxidative stress. First, we determined the influence of DEM on neonicotinoid toxicity to springtail fecundity. Second, we surveyed the gene expression of four biomarkers, including GST3, under mutual exposure to neonicotinoids and DEM. We observed no effect of DEM on springtail fecundity. Moreover, the expression of GST3 was only influenced by DEM under mutual exposure with thiacloprid but not with imidacloprid. The results indicate that GST3 is not a robust indicator of neonicotinoid exposure and that probable GST enzymatic inhibition mediates the toxicity of imidacloprid and thiacloprid differentially. Future research should investigate biomarker reliability under shifting metabolic conditions such as provided by DEM exposure.
Analysis of Premature Failure of a PVC Water Pipe
Authors: Espejo Edgar 
Published: August 2023

AbstractA case study of the failure analysis of a PVC drinking water pipeline is presented. One week after installation in a construction zone of a building, a longitudinal cracking, and pressure drop occurred in an underground pipeline, and an investigation was carried out to determine the causes of the event. Based on the results of visual inspection, dimensional metrology, plastography, microhardness, fractography, FTIR analysis, and stress analysis, the failure mechanisms of the pipe were identified as creep and slow crack growth. The cause of the development of these failure mechanisms was the exposure of the pipe, to a mixture of paint thinner and polyurethane paint, which generated plasticization, with the corresponding deterioration of the mechanical properties. The source of the plasticizer could have been an accidental spill or an incorrect final disposition of this chemical residue during the construction process of the building.