bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA)
Authors: Koehler Alessandra , Scroferneker Maria Lúcia , de Souza Nikolas Mateus Pereira , de Moraes Paulo Cezar , Pereira Beatriz Aparecida Soares , de Souza Cavalcante Ricardo , Mendes Rinaldo Pôncio , Corbellini Valeriano Antonio
Published: February 2024

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551–1095 cm−1. The wavenumber that had the highest AUC value was 1264 cm−1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551–1095 cm−1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.
Association between Bell’s Palsy and Cardiometabolic Risks: An Age- and Sex-Matched Case–Control Study
Authors: Mueanchoo Panitta , Tepparak Nualsakol , Chongphattararot Pensri , Pruphetkaew Nannapat , Setthawatcharawanich Suwanna , Korathanakhun Pat , Amornpojnimman Thanyalak , Sathirapanya Chutarat , Sathirapanya Pornchai
Published: February 2024

Background: Bell’s palsy is possibly an ischemic cranial neuropathy, although reactivation of herpes virus infection has been proposed. Methods: This was an age-and sex-matched and 1:2 case–control study enrolling Bell’s palsy patients during 2011–2021 in a university hospital to investigate the significant associations of cardiometabolic risks (CMRs) with Bell’s palsy. We analyzed the differences in waist circumference (WC), body mass index (BMI), systolic and diastolic blood pressures (SBP and DBP), fasting blood sugar (FBS), and lipid levels at 12 weeks post-Bell’s palsy with those of the controls by descriptive statistics (p < 0.05). The differences in means or medians of individual CMR values across the consecutive 10-year age intervals were analyzed by ANOVA F-tests and Kruskal–Wallis tests (p < 0.05). Results: A total of 140 cases and 280 controls were enrolled. Bell’s palsy patients had significantly higher WC, BMI, SBP, DBP, FBS, and triglyceride but lower high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). Moreover, high WC, elevated FBS (≥100 mg/dL), SBP (≥130 mmHg), and total cholesterol were significantly associated with Bell’s palsy cases by multivariable analysis. Only FBS in Bell’s palsy patients significantly elevated across consecutive 10-year age intervals. Conclusion: Screening and monitoring for CMRs, especially hyperglycemia, in every patient presenting with Bell’s palsy is essential despite initial normoglycemia, particularly in older-onset cases.
Decrease in Sperm Parameters in the 21st Century: Obesity, Lifestyle, or Environmental Factors? An Updated Narrative Review
Authors: Sciorio Romualdo , Tramontano Luca , Adel Mohammed , Fleming Steven
Published: February 2024

Semen quality represents a compelling factor for fertility, and delineating the normal values has proven difficult. In the last four decades, several authors have reported a noticeable decline in sperm parameters. Also, studies investigating ‘time to pregnancy’ have shown that fecundity begins to be reduced when sperm numbers decrease below 30 million, even though according to the 6th edition of the WHO manual, the normal value is currently 16 million/mL or 39 million per ejaculate. There exists sufficient data to suggest a decline in sperm counts over time, even though the clear reason for this adverse trend is not well established, but some associations have been hypothesised, such as maternal smoking during pregnancy. Additional potential factors have yet to be fully illustrated but involve poor diet, increased obesity, and exposure to environmental toxins. Moreover, the change in environmental conditions and more common exposure to endocrine-disrupting chemicals (EDCs), such as pesticides and herbicides, as well as bisphenol A, phthalates, polychlorinated biphenyls, and heavy metals, starting from prenatal life and continuing into adulthood, may exhibit probable features explaining the reduction in sperm parameters. Therefore, the main goal of this narrative review is to furnish an overview of the possible effects of exposure to EDCs on testicular function and spermatogenesis and, also, to summarise the evidence regarding a decrease in sperm quality and examine its potential consequences.
Mind–Body Training: A Plausible Strategy against Osteomuscular Chronic Pain—A Systematic Review with Meta-Analysis
Authors: Gámez-Iruela Julia , Aibar-Almazán Agustín , Afanador-Restrepo Diego Fernando , Castellote-Caballero Yolanda , Hita-Contreras Fidel , Carcelén-Fraile María del Carmen , González-Martín Ana María
Published: February 2024

(1) Background: Chronic pain, which affects more than one in five adults worldwide, has a negative impact on the quality of life, limiting daily activities and generating absences from work. The aim of the present review is to analyze the efficacy of mind–body therapies as therapeutic strategies for patients with chronic pain. (2) Methods: A systematic review with a meta-analysis was carried out, searching PubMed, Scopus, and Web of Science databases using specific keywords. We selected studies that included mind–body therapies as the primary intervention for older adults with chronic pain. The methodological quality of the articles was assessed using the PEDro scale. (3) Results: Of the 861 studies identified, 11 were included in this review, all of which employed different mind–body therapies as an intervention. The selected studies measured chronic pain as the main variable. (4) Conclusions: This review highlights the value of mind–body exercises in reducing chronic pain in older adults, suggesting their integration as a non-pharmacological therapeutic alternative that improves the quality of life, promoting a holistic approach to pain management.
Option Pricing with the Logistic Return Distribution
Authors: Levy Haim , Levy Moshe
Published: February 2024

The Black–Scholes model and many of its extensions imply a log-normal distribution of stock total returns over any finite holding period. However, for a holding period of up to one year, empirical stock return distributions (both conditional and unconditional) are not log-normal, but rather much closer to the logistic distribution. This paper derives analytic option pricing formulas for an underlying asset with a logistic return distribution. These formulas are simple and elegant and employ exactly the same parameters as B&S. The logistic option pricing formula fits empirical option prices much better than B&S, providing explanatory power comparable to much more complex models with a larger number of parameters.
Almost Perfect Shadow Prices
Authors: Mayerhofer Eberhard
Published: February 2024

Shadow prices simplify the derivation of optimal trading strategies in markets with transaction costs by transferring optimization into a more tractable, frictionless market. This paper establishes that a naïve shadow price ansatz for maximizing long-term returns, given average volatility yields a strategy that is, for small bid–ask spreads, asymptotically optimal at the third order. Considering the second-order impact of transaction costs, such a strategy is essentially optimal. However, for risk aversion different from one, we devise alternative strategies that outperform the shadow market at the fourth order. Finally, it is shown that the risk-neutral objective rules out the existence of shadow prices.
Do Consumption-Based Asset Pricing Models Explain the Dynamics of Stock Market Returns?
Authors: Ashby Michael William , Linton Oliver Bruce
Published: February 2024

We show that three prominent consumption-based asset pricing models—the Bansal–Yaron, Campbell–Cochrane and Cecchetti–Lam–Mark models—cannot explain the dynamic properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised and expected returns is a martingale difference sequence, which it is not. Mincer–Zarnowitz regressions show that the models’ out-of-sample expected returns are systematically biased. Furthermore, semi-parametric tests of whether the models’ state variables are consistent with the degree of own-history predictability in stock returns suggest that only the Campbell–Cochrane habit variable may be able to explain return predictability, although the evidence on this is mixed.
Integrated Land-Use Systems Contribute to Restoring Water Cycles in the Brazilian Cerrado Biome
Authors: Glatzle Sarah , de Almeida Roberto Giolo , Pereira Barsotti Mariana , Bungenstab Davi José , Giese Marcus , Macedo Manuel Claudio M. , Stuerz Sabine , Asch Folkard
Published: February 2024

Cerrado, constituting native Brazilian vegetation in the tropical and subtropical grasslands, savannas, and shrublands biome, has been extensively replaced by crop and pastureland, resulting in reduced water recycling to the atmosphere via evapotranspiration (ET). Re-introducing trees via integrated land-use systems potentially restores soil health and water-related processes; however, field data are scarce. During two years, we monitored soil moisture dynamics of natural Cerrado (CER), continuous pasture (COP), integrated crop-livestock (ICL), and integrated crop-livestock-forestry (ICLF) systems across 100 cm soil depth. Across years, mean soil moisture was highest for ICL, followed by COP and lowest in systems with trees (ICLF and CER). However, seasonal and spatial analyses revealed pronounced differences between soil layers and systems. COP and ICL mainly lost water from upper soil layers, whereas in ICLF, the strongest water depletion was observed at 40–100 cm depth, almost reaching a permanent wilting point during the dry season. CER was driest in the upper 40 cm, but water storage was highest below 60 cm depth. Our results suggest that compared to conventional land-use practices, integrated systems, including trees, increase water recycling to the atmosphere via ET and potentially compensate for the loss of key ecological functions of degraded or replaced Cerrado.
Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)
Authors: Li Wenbo , Samat Alim , Abuduwaili Jilili , Wang Wei
Published: February 2024

Considering climate change and increasing human impact, ecological quality and its assessment have also received increasing attention. Taking the Irtysh River Basin as an example, we utilize multi-period MODIS composite imagery to obtain five factors (greenness, humidity, heat, dryness, and salinity) to construct the model for the amended RSEI (ARSEI) based on the Google Earth Engine platform. We used the Otsu algorithm to generate dynamic thresholds to improve the accuracy of ARSEI results, performed spatiotemporal pattern and evolutionary trend analysis on the results, and explored the influencing factors of ecological quality. Results indicate that: (1) The ARSEI demonstrates a correlation exceeding 0.88 with each indicator, offering an efficient approach to characterizing ecological quality. The ecological quality of the Irtysh River Basin exhibits significant spatial heterogeneity, demonstrating a gradual enhancement from south to north. (2) To evaluate the ecological quality of the Irtysh River Basin, the ARSEI was utilized, exposing a stable condition with slight fluctuations. In the current research context, the ecological quality of the Irtysh River Basin watershed area is projected to continuously enhance in the future. This is due to the constant ecological protection and management initiatives carried out by countries within the basin. (3) Precipitation, soil pH, elevation, and human population are the main factors influencing ecological quality. Due to the spatial heterogeneity, the driving factors for different ecological quality classes vary. Overall, the ARSEI is an effective method for ecological quality assessment, and the research findings can provide references for watershed ecological environment protection, management, and sustainable development.
Toxic Prisons? Local Environmental Quality and the Wellbeing of Incarcerated Populations
Authors: Moran Dominique , Jordaan Jacob A. , Jones Phil I.
Published: February 2024

A growing body of scholarship draws attention to prisons and environmental justice, pointing out the propensity for prisons to be located on contaminated sites and to be in close proximity to polluting industries, as well as for prisons themselves to contribute to local environmental degradation. Prisoners’ immobility renders them unable to relocate away from harmful environments, and there are now numerous suggestions that their wellbeing suffers as a result of the poor quality of many local environments. However, since a relationship between environmental quality and prisoner wellbeing is yet to be robustly demonstrated, there is currently no firm evidence base from which to argue for positive change. This paper therefore examines the effect of the environmental quality of the locations of prisons, approximated as the presence of greenspace in the immediate vicinity of prisons, local species biodiversity and local levels of air and noise pollution, on wellbeing outcomes in a set of prisons in England and Wales. It finds that good environmental quality, in the form of high biodiversity and/or low air pollution, enhances the already-recognized positive effects of greenspace on the wellbeing of incarcerated populations. On the basis of these findings, this paper makes evidence-based policy recommendations intended to enhance the wellbeing of incarcerated populations.
Urban Disparities in Energy Performance Premium Prices: Towards an Unjust Transition?
Authors: Micelli Ezio , Giliberto Giulia , Righetto Eleonora , Tafuri Greta
Published: February 2024

In recent years, numerous studies have explored how energy and environmental performance impact property values. Superior energy efficiency is the basis for value disparities in real estate markets. However, measurements of these variations vary significantly. This research aims to investigate the relationship between market size and vitality and market value differences. This has significant implications for the nature of the energy transition, potentially determining fairness or inequality. The study considers the real estate market in six Italian cities: three metropolitan (Milan, Turin, and Florence) and three medium-sized cities (Padua, Mestre, and Bergamo). The sample includes 2935 properties. In metropolitan cities, hedonic pricing models confirm the relevance of energy performance in market value formation, highlighting a potential depreciation in property values by up to 30% between properties belonging to the highest energy class (A) compared to the lowest (G), and 14% between class D and G. Such premium gaps are halved in medium-sized cities. Conclusions foresee a scenario of socially and economically unjust transition that must be considered in policies aimed at improving the energy efficiency of existing buildings, with a specific concern for the nature and characteristics of the real estate markets involved.
Can Green Infrastructure Investment Reduce Urban Carbon Emissions:Empirical Evidence from China
Authors: Ai Kunpeng , Yan Xiangwu
Published: February 2024

Green infrastructure (GI) plays a pivotal role in contemporary urban infrastructure. Green infrastructure investment (GII) provides a fresh perspective for controlling urban carbon emissions in the context of global climate change. Based on theoretical analysis, we employed panel data from Chinese cities to examine the effects and operating mechanisms of GII on urban carbon emissions. The research reveals that the incremental GII can notably decrease urban carbon emissions, and various robustness tests and endogeneity checks corroborate this finding. However, when considering the cumulative effect, the GII stocks do not appear to influence urban carbon emissions; GII mitigates urban carbon emissions by drawing in pollution control talents, improving the efficiency of household waste treatment, increasing urban green spaces, and heightening public attention to the environment. Relative to cities in the central-western region, northern cities, smaller cities, resource-based cities, smart pilot cities, and cities with a lesser environmental emphasis, GII is more effective in curbing carbon emissions in eastern cities, southern cities, larger cities, non-resource-intensive cities, cities not in the smart pilot initiative, and cities with a stronger environmental focus. This research enhances the understanding of GI’s environmental outcomes and the determinants of urban carbon emissions from an investment viewpoint. It also dissects the four operative mechanisms through which GII lowers urban carbon emissions, offering a novel interpretation of GII for the variance in carbon emission levels across cities with diverse traits.
Decision Support Systems in Forestry and Tree-Planting Practices and the Prioritization of Ecosystem Services: A Review
Authors: Yadav Neelesh , Rakholia Shrey , Yosef Reuven
Published: February 2024

In this study, tree-selection/plantation decision support systems (DSSs) were reviewed and evaluated against essential objectives in the available literature. We verified whether existing DSSs leverage multiple data sources and available online resources such as web interfaces. We compared the existing DSSs, and in this study mainly focused on five main objectives that DSSs can consider in tree selection, including (a) climate resilience, (b) infrastructure/space optimization, (c) agroforestry, (d) ecosystem services, and (e) urban sustainability. The climate resilience of tree species and urban sustainability are relatively rarely taken into account in existing systems, which can be integrated holistically in future DSS tools. Based on this review, deep neural networks (DNNs) are recommended to achieve trade-offs between complex objectives such as maximizing ecosystem services, the climate resilience of tree species, agroforestry conservation, and other benefits.
N-(2,2-Diphenylethyl)-4-nitrobenzamide
Authors: Dimitrova Diyana , Manolov Stanimir , Ivanov Iliyan , Bojilov Dimitar , Kasamova Lyusi , Nedialkov Paraskev
Published: February 2024

In this study, we outline the eco-friendly mechanosynthesis of N-(2,2-diphenylethyl)-4-nitrobenzamide by reacting 2,2-diphenylethan-1-amine with 4-nitrobenzoyl chloride. The resulting bio-functional hybrid compound was meticulously characterized through the analysis of 1H-, 13C-NMR, UV, and detailed mass spectral analysis.
Nanocrystals Incorporated with Mordenite Zeolite Composites with Enhanced Upconversion Emission for Cu2+ Detection
Authors: Lin Peixuan , Ye Song , Pan Ling , Huang Ruihao , Zhang Haoran , Wang Deping
Published: February 2024

In this research, upconversion nanocrystals incorporated with MOR zeolite composites were synthesized using the desilicated MOR zeolite as a host for the in situ growth of NaREF4 (RE = Y, Gd) Yb/Er nanocrystals. The structure and morphology of the composites were studied with XRD, XPS, and TEM measurements, and the spectral studies indicated that the subsequent thermal treatment can effectively improve the upconversion emission intensity of Er3+. By using the NaYF4:Yb/Er@DSi1.0MOR-HT composite that holds the strongest upconversion emission, a probe of UCNC@DSiMOR/BPEI was constructed with the modification of branched poly ethylenimine for the detection of Cu2+. It was indicated that the integrated emission intensity of Er3+ shows a linear dependence with the logarithm value of the Cu2+ concentration ranging from 0.1 to 10 μM. This study offered a feasible method for the construction of UCNC@zeolite composites with enhanced upconversion emission, which may have a potential application as fluorescent probes for the detection of various metal ions by adjusting the doping luminescent center.
Gearbox Condition Monitoring and Diagnosis of Unlabeled Vibration Signals Using a Supervised Learning Classifier
Authors: Seo Myung-Kyo , Yun Won-Young
Published: February 2024

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics.
Analytical Sensitivity Analysis of Dynamic Problems with Direct Differentiation of Generalized-α Time Integration
Authors: Wehrle Erich , Gufler Veit
Published: February 2024

In this paper, the direct differentiation of generalized-α time integration is derived, equations are introduced and results are shown. Although generalized-α time integration has found usage, the derivation and the resulting equations for the analytical sensitivity analysis via direct differentiation are missing. Thus, here, the sensitivity equations of generalized-α time integration via direct differentiation are provided. Results with generalized-α are compared with Newmark-β time integration and their sensitivities with numerical sensitivities via forward finite differencing in terms of accuracy and performance. An example is shown for each linear structural dynamics and flexible multibody dynamics.
Digital Twin Data Management: Framework and Performance Metrics of Cloud-Based ETL System
Authors: Dapkute Austeja , Siozinys Vytautas , Jonaitis Martynas , Kaminickas Mantas , Siozinys Milvydas
Published: February 2024

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.
Explicit Physics-Informed Deep Learning for Computer-Aided Diagnostic Tasks in Medical Imaging
Authors: Nemirovsky-Rotman Shira , Bercovich Eyal
Published: February 2024

DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and generalizable networks is a major challenge, especially when considering critical applications such as medical computer-aided diagnostics (CAD) and other medical imaging tasks. Within this context, a line of approaches incorporating prior knowledge domain information into deep learning methods has recently emerged. In particular, many of these approaches utilize known physics-based forward imaging models, aimed at improving the stability and generalization ability of DNNs for medical imaging applications. In this paper, we review recent work focused on such physics-based or physics-prior-based learning for a variety of imaging modalities and medical applications. We discuss how the inclusion of such physics priors to the training process and/or network architecture supports their stability and generalization ability. Moreover, we propose a new physics-based approach, in which an explicit physics prior, which describes the relation between the input and output of the forward imaging model, is included as an additional input into the network architecture. Furthermore, we propose a tailored training process for this extended architecture, for which training data are generated with perturbed physical priors that are also integrated into the network. Within the scope of this approach, we offer a problem formulation for a regression task with a highly nonlinear forward model and highlight possible useful applications for this task. Finally, we briefly discuss future challenges for physics-informed deep learning in the context of medical imaging.
Autonomous Ride-Sharing Service Using Graph Embedding and Dial-a-Ride Problem: Application to the Last-Mile Transit in Lyon City
Authors: Rifki Omar
Published: February 2024

Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features. In this study, we propose a holistic solving approach to this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering the exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of “Vallée de la Chimie” in Lyon city, France. Instances involving more than 50 transport requests and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with lower vehicle kilometers traveled compared to the constrained K-means-based reduction. Reductions in terms of GHG emissions are estimated to be around 75% less than the private vehicle mode in our applied service. A sensitivity analysis is also provided.
Generalized Almost Periodicity in Measure
Authors: Kostić Marko , Du Wei-Shih , Koyuncuoğlu Halis Can , Velinov Daniel
Published: February 2024

This paper investigates diverse classes of multidimensional Weyl and Doss ρ-almost periodic functions in a general measure setting. This study establishes the fundamental structural properties of these generalized ρ-almost periodic functions, extending previous classes such as m-almost periodic and (equi-)Weyl-p-almost periodic functions. Notably, a new class of (equi-)Weyl-p-almost periodic functions is introduced, where the exponent p>0 is general. This paper delves into the abstract Volterra integro-differential inclusions, showcasing the practical implications of the derived results. This work builds upon the extensions made in the realm of Levitan N-almost periodic functions, contributing to the broader understanding of mathematical functions in diverse measure spaces.
Finite-Time Robust Path-Following Control of Perturbed Autonomous Ground Vehicles Using a Novel Self-Tuning Nonsingular Fast Terminal Sliding Manifold
Authors: Vo Cong Phat , Hoang Quoc Hung , Kim Tae-Hyun , Jeon Jeong hwan
Published: February 2024

This work presents a finite-time robust path-following control scheme for perturbed autonomous ground vehicles. Specifically, a novel self-tuning nonsingular fast-terminal sliding manifold that further enhances the convergence rate and tracking accuracy is proposed. Then, uncertain dynamics and external disturbances are estimated by a high-gain disturbance observer to compensate for the designed control input. Successively, a super-twisting algorithm is incorporated into the final control law, significantly mitigating the chattering phenomenon of both the input control signal and the output trajectory. Furthermore, the global finite-time convergence and stability of the whole proposed control algorithm are proven by the Lyapunov theory. Finally, the efficacy of the proposed method is validated with comparisons in a numerical example. It obtains high control performance, reduced chattering, fast convergence rate, singularity avoidance, and robustness against uncertainties.
Branching Random Walks in a Random Killing Environment with a Single Reproduction Source
Authors: Kutsenko Vladimir , Molchanov Stanislav , Yarovaya Elena
Published: February 2024

We consider a continuous-time branching random walk on Z in a random non-homogeneous environment. The process starts with a single particle at initial time t=0. This particle can walk on the lattice points or disappear with a random intensity until it reaches the certain point, which we call the reproduction source. At the source, the particle can split into two offspring or jump out of the source. The offspring of the initial particle evolves according to the same law, independently of each other and the entire prehistory. The aim of this paper is to study the conditions for the presence of exponential growth of the average number of particles at every lattice point. For this purpose, we investigate the spectrum of the random evolution operator of the average particle numbers. We derive the condition under which there is exponential growth with probability one. We also study the process under the violation of this condition and present the lower and upper estimates for the probability of exponential growth.
Pooled Steganalysis via Model Discrepancy
Authors: Yu Jiang , Zhang Jing , Li Fengyong
Published: February 2024

Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity.
Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision
Authors: Ekhlakov Roman , Andriyanov Nikita
Published: February 2024

Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card.
Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network
Authors: Yang Jing , Liao Tianzheng , Zhao Jingjing , Yan Yan , Huang Yichun , Zhao Zhijia , Xiong Jing , Liu Changhong
Published: February 2024

Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.
Effects of Ultrasonic Power on the Structure and Rheological Properties of Skin Collagen from Albacore (Thunnus alalunga)
Authors: Pan Hao , Zhang Xuehua , Ni Jianbo , Liang Qianqian , Jiang Xin , Zhou Zihui , Shi Wenzheng
Published: February 2024

The effects of ultrasonic power (0, 150, 300, 450, and 600 W) on the extraction yield and the structure and rheological properties of pepsin-soluble collagen (PSC) from albacore skin were investigated. Compared with the conventional pepsin extraction method, ultrasonic treatment (UPSC) significantly increased the extraction yield of collagen from albacore skin, with a maximum increase of 8.56%. The sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis revealed that peptides of low molecular weight were produced when the ultrasonic power exceeded 300 W. Meanwhile, secondary structure, tertiary structure, and X-ray diffraction analyses showed that the original triple helix structure of collagen was intact after the ultrasonic treatment. The collagen solutions extracted under different ultrasonic powers had significant effects on the dynamic frequency sweep, but a steady shear test suggested that the collagen extracted at 150 W had the best viscosity. These results indicate that an ultrasonic power between 150 and 300 W can improve not only the extraction yield of natural collagen, but also the rheological properties of the collagen solution without compromising the triple helix structure.
Case Study of a Complex Neurovascular Disorder: Choroidal Arteriovenous Malformation
Authors: Toader Corneliu , Covache-Busuioc Razvan-Adrian , Bratu Bogdan-Gabriel , Glavan Luca Andrei , Corlatescu Antonio Daniel , Ciurea Alexandru Vlad
Published: February 2024

This study conducts an in-depth analysis of the management of a complex arteriovenous malformation (AVM) in a 44-year-old individual, who initially manifested with acute left hemiparesis and progressively declined into a comatose state. Diagnostic neuroimaging identified a substantial right fronto-temporal intraparenchymal hematoma via a CT scan. Cerebral angiography further elucidated a choroidal AVM originating from the anterior choroidal artery, accompanied by intranidal aneurysms. The elected treatment strategy was the surgical excision of the AVM. The procedure achieved complete removal of the intracranial AVM, situated in a neurologically sensitive region, leading to notable neurological recovery. This study thoroughly explores and critically evaluates a wide spectrum of treatment approaches for intracranial arteriovenous malformations, including novel endovascular therapies. Despite extensive discourse on AVM in contemporary literature, this report is among the few documenting the treatment of a choroidal AVM via a microsurgical technique, and highlights various therapeutic options.
Delirium and Its Associations with Critical Care Utilizations and Outcomes at the Time of Hospital Discharge in Patients with Acute Brain Injury
Authors: Raquer Alex P. , Fong Christine T. , Walters Andrew M. , Souter Michael J. , Lele Abhijit V.
Published: February 2024

Background and Objectives: We analyzed delirium testing, delirium prevalence, critical care associations outcomes at the time of hospital discharge in patients with acute brain injury (ABI) due to acute ischemic stroke (AIS), non-traumatic subarachnoid hemorrhage (SAH), non-traumatic intraparenchymal hemorrhage (IPH), and traumatic brain injury (TBI) admitted to an intensive care unit. Materials and Methods: We examined the frequency of assessment for delirium using the Confusion Assessment Method for the intensive care unit. We assessed delirium testing frequency, associated factors, positive test outcomes, and their correlations with clinical care, including nonpharmacological interventions and pain, agitation, and distress management. Results: Amongst 11,322 patients with ABI, delirium was tested in 8220 (726%). Compared to patients 18–44 years of age, patients 65–79 years (aOR 0.79 [0.69, 0.90]), and those 80 years and older (aOR 0.58 [0.50, 0.68]) were less likely to undergo delirium testing. Compared to English-speaking patients, non-English-speaking patients (aOR 0.73 [0.64, 0.84]) were less likely to undergo delirium testing. Amongst 8220, 2217 (27.2%) tested positive for delirium. For every day in the ICU, the odds of testing positive for delirium increased by 1.11 [0.10, 0.12]. Delirium was highest in those 80 years and older (aOR 3.18 [2.59, 3.90]). Delirium was associated with critical care resource utilization and with significant odds of mortality (aOR 7.26 [6.07, 8.70] at the time of hospital discharge. Conclusions: In conclusion, we find that seven out of ten patients in the neurocritical care unit are tested for delirium, and approximately two out of every five patients test positive for delirium. We demonstrate disparities in delirium testing by age and preferred language, identified high-risk subgroups, and the association between delirium, critical care resource use, complications, discharge GCS, and disposition. Prioritizing equitable testing and diagnosis, especially for elderly and non-English-speaking patients, is crucial for delivering quality care to this vulnerable group.
Field-Scale Testing of a High-Efficiency Membrane Reactor (MR)—Adsorptive Reactor (AR) Process for H2 Generation and Pre-Combustion CO2 Capture
Authors: Margull Nicholas , Parsley Doug , Somiari Ibubeleye , Zhao Linghao , Cao Mingyuan , Koumoulis Dimitrios , Liu Paul K. T. , Manousiouthakis Vasilios I. , Tsotsis Theodore T.
Published: February 2024

The study objective was to field-validate the technical feasibility of a membrane- and adsorption-enhanced water gas shift reaction process employing a carbon molecular sieve membrane (CMSM)-based membrane reactor (MR) followed by an adsorptive reactor (AR) for pre-combustion CO2 capture. The project was carried out in two different phases. In Phase I, the field-scale experimental MR-AR system was designed and constructed, the membranes, and adsorbents were prepared, and the unit was tested with simulated syngas to validate functionality. In Phase II, the unit was installed at the test site, field-tested using real syngas, and a technoeconomic analysis (TEA) of the technology was completed. All project milestones were met. Specifically, (i) high-performance CMSMs were prepared meeting the target H2 permeance (>1 m3/(m2.hbar) and H2/CO selectivity of >80 at temperatures of up to 300 °C and pressures of up to 25 bar with a <10% performance decline over the testing period; (ii) pelletized adsorbents were prepared for use in relevant conditions (250 °C < T < 450 °C, pressures up to 25 bar) with a working capacity of >2.5 wt.% and an attrition rate of <0.2; (iii) TEA showed that the MR-AR technology met the CO2 capture goals of 95% CO2 purity at a cost of electricity (COE) 30% less than baseline approaches.
Screen-Printed Carbon Electrode Modified with Carbon Nanotubes and Copper Film as a Simple Tool for Determination of Trace Concentrations of Lead Ions
Authors: Grabarczyk Malgorzata , Wawruch Agnieszka
Published: February 2024

A copper film-modified, carboxyl-functionalized, and multi-walled carbon nanotube (MWCNT-COOH)-modified screen-printed carbon electrode (CuF/MWCNTs/SPCE) was used for lead determination using anodic stripping voltammetry. The main parameters were investigated and optimized during the development of the research procedure. The most optimal electrolyte concentrations were determined to be 0.4 M HCl and 6.3 × 10−5 M Cu(II). The optimal parameters for voltammetric stripping measurements are as follows: an accumulation potential of −0.7 V; an accumulation time of 120 s; and a pulse amplitude and pulse time of 120 mV and 2 ms, respectively. The effect of surface active substances and humic substances as potential interferents present in aqueous environmental samples was investigated. The validation of the procedure was carried out using certified reference materials, like waste water SPS-WW1 and environmental matrix TM-25.5. In addition, the developed procedure was applied to investigate lead recovery from natural environmental water, such as rivers and lakes.
The Effect of Deformation and Isothermal Heat Treatment of a 5005 Aluminum Alloy
Authors: Holmestad Jon , Marioara Calin Daniel , Myrold Benedikte Jørgensen , Jensrud Ola
Published: February 2024

In the aluminum industry, forming is an important process step that introduces dislocations in the material. To investigate the effect of dislocation retention after ageing on 6xxx-series alloys, a non-heat-treatable 5005 alloy was selected to measure the change in mechanical properties due to dislocation annihilation during dynamic recovery. However, the isothermal ageing treatment led to an unexpected and significant increase in mechanical properties after deformation. Increases in yield strength of 120% and tensile strength of 50% compared with the as-received material were achieved. However, this caused a significant decrease in elongation properties. The deformation start temperature did not have any impact on the final mechanical properties. TEM analysis attributed the increase in mechanical properties to an increased precipitation and dislocation density compared with the undeformed reference material. The precipitates are located along dislocation lines, showing that the solute elements are preferentially segregating to dislocations and precipitating. The precipitates were typical for the Al–Mg–Si(–Cu) system; therefore, the low amounts of Si and, to a lesser extent, Cu were responsible for the precipitation hardening in the 5005 alloy.