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The use of cloud computing as a cost-effective and flexible method has currently drawn the attention of cloud service providers. When offering various cloud services to users, a certain level of Service Level Agreement (SLA) must be guaranteed by the cloud operator, based on the service received at the user level. Considering the non-linear dependency of network users’ Quality of Experience (QoE) on cloud resources (RAM, disk memory, network bandwidth, and CPU core usage) as well as the non-linear and time-varying dependency of cloud operator efficiency on the level of resources consumed by users, joint optimization of user experience and cloud operator efficiency poses significant computational challenges. This paper focuses on the green optimization of both user experience and the benefits of cloud data centre operators, employing a metaheuristic algorithm, which essentially combines a genetic algorithm with an innovative approach. In this approach, the efficiency of the cloud data centre operator, energy consumption of data centres, and user experience quality are jointly optimized. Simulation results demonstrate the improvement of the combined user/operator utility function compared to traditional methods, considering energy consumption constraints.

Abstract Deep learning-based computer vision systems have become powerful tools for automated and cost-effective pavement distress detection, essential for efficient road maintenance. Current methods focus primarily on developing supervised learning architectures, which are limited by the scarcity of annotated image datasets. The use of data augmentation with synthetic images created by generative models to improve these supervised systems is not widely explored. The few studies that do focus on generative architectures are mostly non-conditional, requiring extra labeling, and typically address only road crack defects while aiming to improve classification models rather than object detection. This study introduces AsphaltGAN, a novel class-conditional Generative Adversarial Network with attention mechanisms, designed to augment datasets with various rare road defects to enhance object detection. An in-depth analysis evaluates the impact of different loss functions and hyperparameter tuning. The optimized AsphaltGAN outperforms state-of-the-art generative architectures on public datasets. Additionally, a new workflow is proposed to improve object detection models using synthetic road images. The augmented datasets significantly improve the object detection metrics of You Only Look Once version 8 by 33.0 $$\%$$ , 3.8 $$\%$$ , 46.3 $$\%$$ , and 51.8 $$\%$$ on the Road Damage Detection 2022 dataset, Crack Dataset, Asphalt Pavement Detection Dataset, and Crack Surface Dataset, respectively.

The design and implementation of boundary conditions for the robust generation and simulation of periodic finite-amplitude internal waves is examined in a quasi two-layer continuous stratification using a spectral-element-method-based incompressible flow solver. The commonly used Eulerian approach develops spurious, and potentially catastrophic small-scale numerical features near the wave-generating boundary in a non-linear stratification when the parameter A/(δc) is sufficiently larger than unity; A and δ are measures of the maximum wave-induced vertical velocity and pycnocline thickness, respectively, and c is the linear wave propagation speed. To this end, an Euler–Lagrange approach is developed and implemented to generate robust high-amplitude periodic deep-water internal waves. Central to this approach is to take into account the wave-induced (isopycnal) displacement of the pycnocline in both the vertical and (effectively) upstream directions. With amplitudes not restricted by the limits of linear theory, the Euler–Lagrange-generated waves maintain their structural integrity as they propagate away from the source. The advantages of the high-accuracy numerical method, whose minimal numerical dissipation cannot damp the above near-source spurious numerical features of the purely Eulerian case, can still be preserved and leveraged further along the wave propagation path through the robust reproduction of the non-linear adjustments of the waveform. The near- and far-source robustness of the optimized Euler–Lagrange approach is demonstrated for finite-amplitude waves in a sharp quasi two-layer continuous stratification representative of seasonally stratified lakes. The findings of this study provide an enabling framework for two-dimensional simulations of internal swash zones driven by well-developed non-linear internal waves and, ultimately, the accompanying turbulence-resolving three-dimensional simulations.

Abstract We report that the eukaryotic replicative helicase CMG (Cdc45-MCM-GINS) is required for differential gene expression in cells produced by asymmetric cell divisions in C. elegans. We found that the C. elegans CMG component, PSF-2 GINS2, is necessary for transcriptional upregulation of the pro-apoptotic gene egl-1 BH3-only that occurs in cells programmed to die after they are produced through asymmetric cell divisions. We propose that CMG’s histone chaperone activity causes epigenetic changes at the egl-1 locus during replication in mother cells, and that these changes are required for egl-1 upregulation in cells programmed to die. We find that PSF-2 is also required for the divergence of other cell fates during C. elegans development, suggesting that this function is not unique to egl-1 expression. Our work uncovers an unexpected role of CMG in cell fate decisions and an intrinsic mechanism for gene expression plasticity in the context of asymmetric cell division.

Purpose: Higher education institutions (HEIs) offer courses and programmes focusing on sustainability in economics, as courses on sustainable development (SD), which examine the economic, social and environmental dimensions of SD. This paper aims to examine sustainability integration in economics degree programmes. Design/methodology/approach: Through an extensive literature review in Web of Science (WoS) and information search in Google, conducting to 28 relevant case studies, this paper elucidates the emphasis given to sustainability as part of economics degree programmes in HEIs. Findings: The results suggest that, whereas the inclusion of sustainability components in this field is a growing trend, much still needs to be done to ensure that matters related to SD are part of the routine of university students studying economics. Research limitations/implications: It is worth noting that the literature review conducted in WoS was primarily aimed at assisting in the selection of university case studies. The 28 university case studies scrutinised in this study may lack sufficient representation from numerous developing countries. Practical implications: This study highlights challenges in integrating the SD into economics degree programmes, suggesting the need for curriculum adjustments as underscoring operational issues, acting as barriers. The inclusion of sustainability in economics programmes must navigate operational issues stemming from packed timetables and busy schedules, requiring innovative solutions. Social implications: As far as the authors are aware, this study holds substantial importance in its emphasis on implementing sustainability within HEIs' economics programmes, assisting in pursuing SD. Originality/value: The novelty of this study lies in addressing sustainability with the specific economics focus programmes within the HEIs context.

Abstract Background Alzheimer's disease (AD) is the most prevalent dementia, showing higher incidence in women. Besides, lipids play an essential role in brain, and they could be dysregulated in neurodegeneration. Specifically, impaired plasma lipid levels could predict early AD diagnosis. This work aims to identify the main plasma lipids altered in early AD female mouse model and evaluate their relationship with brain lipidome. Also, the possible involvement of the estrous cycle in lipid metabolism has been evaluated. Methods Plasma samples of wild-type (n = 10) and APP/PS1 (n = 10) female mice of 5 months of age were collected, processed, and analysed using a lipidomic mass spectrometry-based method. A statistical analysis involving univariate and multivariate approaches was performed to identify significant lipid differences related to AD between groups. Also, cytology tests were conducted to confirm estrous cycle phases. Results Three hundred thirty lipids were detected in plasma, 18 of them showed significant differences between groups; specifically, some triacylglycerols, cholesteryl esters, lysophosphatidylcholines, phosphatidylcholines, and ether-linked phosphatidylcholines, increased in early AD; while other phosphatidylcholines, phosphatidylethanolamines, ceramides, and ether-linked phosphatidylethanolamines decreased in early AD. A multivariate approach was developed from some lipid variables, showing high diagnostic indexes (70% sensitivity, 90% specificity, 80% accuracy). From brain and plasma lipidome, some significant correlations were observed, mainly in the glycerophospholipid family. Also, some differences were found in both plasma and brain lipids, according to the estrous cycle phase. Conclusions Therefore, lipid alterations can be identified in plasma at early AD stages in mice females, with a relationship with brain lipid metabolism for most of the lipid subfamilies, suggesting some lipids as potential AD biomarkers. In addition, the estrous cycle monitoring could be relevant in female studies.

Resumen: Antecedentes y objetivos: El desarrollo de lesión renal aguda (FRA) durante la hospitalización por SARS-Cov-2 se ha asociado a una elevada morbimortalidad. El Registro FRA-COVID SEN ha recogido datos de pacientes con este perfil durante los meses de la pandemia. El objetivo de este trabajo fue caracterizar la población española ingresada por COVID-19 que desarrolló FRA, con/sin necesidades de tratamiento renal sustitutivo (TRS), modalidades terapéuticas utilizadas y resultados en términos de mortalidad. Material y método: Estudio retrospectivo de datos procedentes de 30 hospitales españoles, recopilados en el Registro español FRA COVID desde mayo de 2020 hasta noviembre 2021. Se registraron variables clínicas y demográficas, datos relacionados con la gravedad de la COVID-19, con el FRA y de supervivencia. Mediante estudio de regresión multivariante se analizan los factores relacionados con la necesidad de TRS y con la mortalidad. Resultados: Se registraron datos de 730 pacientes. El 71,9% eran hombres, con edad media de 70 años (60-78). El 70,1% eran hipertensos, el 32,9% diabéticos, el 33,3% con enfermedad cardiovascular y el 23,9% presentaban algún grado de enfermedad renal crónica. Desarrollaron neumonía el 94,6%, con necesidad de soporte ventilatorio en el 54,2% e ingreso en UCI en un 44,1% de los casos.La mediana de tiempo desde el inicio de los síntomas de COVID hasta la aparición de FRA fue de 6 días (4-10). En cuanto a la gravedad: 37,1% KDIGO I, 18,3% KDIGO II y 44,6% KDIGO III; requirieron TRS 235 pacientes (33,9%). Las técnicas continuas (TCRR) fueron las más empleadas (155 pacientes), seguidas de hemodiálisis (HD) intermitente (89 pacientes), HD diaria (36 pacientes), hemodiafiltración (HDF) 17 pacientes y HD expandida (HDexp) en 24 casos. El hábito tabáquico (OR 3,41), la necesidad de soporte ventilatorio (OR 20,2), la cifra de creatinina máxima (OR 2,41) y el tiempo transcurrido desde el inicio de los síntomas hasta la aparición del FRA (OR 1,13) se identificaron como predictores de la necesidad de TRS en nuestra cohorte, mientras la edad se comporta como protector (OR: 0,95). El grupo que no requirió TRS se caracterizó por presentar mayor edad, menor gravedad de FRA y un tiempo de aparición y recuperación de la lesión renal más corto (p

Abstract The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

Background/purpose: The dental adhesive market is constantly evolving to meet the demands of dentists and patients, but new products and upgrades should be rigorously evaluated before being used in clinical practice. This study investigated the physicomechanical properties and dentin bonding efficacy of a newly upgraded universal adhesive compared to its predecessor. Materials and methods: Twenty-four molars were divided into four groups (n = 6/group) based on adhesive (new vs. predecessor) and application mode [self-etch (SE) vs. etch-and-rinse (ER)] for evaluating their dentin microtensile bond strength (μTBS), failure pattern, and bonding interface. Additional thirty-six molars’ crowns were perpendicularly sectioned to obtain flat mid-coronal dentin discs. The opposing dentin surfaces of each disc received contrasting treatments (new/predecessor adhesive applied in SE/ER mode), resulting in six interventions. The bonded discs (n = 6/intervention) were used to assess the adhesives’ survival probability employing a double-sided μTBS test. The other physicomechanical properties examined were adhesives’ oxygen inhibition layer (OIL), viscosity, hardness, elastic modulus, degree of conversion (DC), and in-situ DC. Results: Both adhesive versions showed similar μTBS (P > 0.05), failure pattern (P > 0.05), and survival probability (P > 0.008). ER mode promoted resin tag formation and exhibited a slender adhesive layer for both adhesives. The newer adhesive version showed a thinner adhesive layer in general with narrower OIL (P

Abstract Population loss in rural areas is rapidly increasing in high‐income countries, raising concerns and debate, given its socio‐economic consequences. Despite the evident environmental dimension of the phenomenon, ecological knowledge has been neglected in the analysis of actions aiming to reverse rural depopulation. Particularly, cultural landscapes reflect memories of ecological processes that have configured current patterns of biodiversity and ecosystem services. Based on ecological principles, we present a conceptual procedure to assess the behaviour of social–ecological systems subjected to depopulation, projecting their expected trajectories through time within a framework defined jointly by demographic and environmental–ecological dimensions (in our case, biodiversity, carbon storage, pollution control, water resources and soil conservation). We applied this procedure to various alternative interventions designed to confront depopulation in Spain: (1) non‐intervention, (2) maintenance of the historical landscape configuration, (3) active conservation, (4) extensive, sustainable land use and (5) intensified land use. We conclude that extensive, sustainable land use better optimizes criteria of demographic consolidation, environmental impact, resilience and implementation of actions confronting depopulation. We highlight the need to incorporate ecological knowledge into the assessment and application of actions confronting rural depopulation. Read the free Plain Language Summary for this article on the Journal blog.