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Serous epithelial ovarian cancer, classified as either high-grade (90%) or low-grade (10%), varies in molecular, histological, and clinicopathological presentation. Low-grade serous ovarian cancer (LGSOC) is a rare histologic subtype that lacks disease-specific evidence-based treatment regimens. However, LGSOC is relatively chemo-resistant and has a poor response to traditional treatments. Alternative treatments, including biologic therapies such as bevacizumab, have shown some activity in LGSOC. Thus, the objective of this systematic review is to determine the effect and safety of bevacizumab in the treatment of LGSOC. Following PRISMA guidelines, Medline ALL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase all from the OvidSP platform, ClinicalTrials.gov, International Clinical Trials Registry Platform, International Standard Randomised Controlled Trial Number Registry were searched from inception to February 2022. Articles describing bevacizumab use in patients with LGSOC were included. Article screening, data extraction, and critical appraisal of included studies were completed by two independent reviewers. The effect of bevacizumab on the overall response rate, progression-free survival, overall survival, and adverse effects were summarized. The literature search identified 3064 articles, 6 of which were included in this study. A total of 153 patients were analyzed; the majority had stage IIIC cancer (56.2%). The overall median response rate reported in the studies was 47.5%. Overall, bevacizumab is a promising treatment for LGSOC, with response rates higher than traditional treatment modalities such as conventional chemotherapy, and is often overlooked as a treatment tool. A prospective clinical trial evaluating the use of bevacizumab in LGSOC is necessary to provide greater evidence and support these findings.

The success of CO2 storage projects largely depends on addressing formation damage, such as salt precipitation, hydrate formation, and fines migration. While analytical models for reservoir behaviour during CO2 storage in aquifers and depleted gas fields are widely available, models addressing formation damage and injectivity decline are scarce. This work aims to develop an analytical model for CO2 injection in a layer-cake reservoir, considering permeability damage. We extend Dietz’s model for gravity-dominant flows by incorporating an abrupt permeability decrease upon the gas-water interface arrival in each layer. The exact Buckley-Leverett solution of the averaged quasi-2D (x, z) problem provides explicit formulae for sweep efficiency, well impedance, and skin factor of the injection well. Our findings reveal that despite the induced permeability decline and subsequent well impedance increase, reservoir sweep efficiency improves, enhancing storage capacity by involving a larger rock volume in CO2 sequestration. The formation damage factor d, representing the ratio between damaged and initial permeabilities, varies from 0.016 in highly damaged rock to 1 in undamaged rock, resulting in a sweep efficiency enhancement from 1–3% to 50–53%. The developed analytical model was applied to predict CO2 injection into a depleted gas field.

The primary goal of this study is to develop and test a small-scale horizontal-axis underwater Ocean Current Turbine (OCT) by creating a mathematical model for coupled dynamics aided by a Blade Element Momentum (BEM) simulation-integrated experimental approach. This research is motivated by the urgent need for sustainable energy sources and the vast potential of ocean currents. By integrating mathematical modeling with the experimental testing of scaled model OCTs, this study aims to evaluate performance accurately. The experimental setup involves encapsulating a 3D-printed turbine model within a watertight nacelle which is equipped with sensors for comprehensive data recording during towing tank tests. Through these experiments, we seek to establish correlations between the generated power, force, and rotational speed of the turbine’s Permanent Magnet DC (PMDC) motor, which determines the turbine’s capability to extract dynamic energy inflow. Moreover, this research aims to provide valuable insights into the accuracy and applicability of theoretical predictions in real-world scenarios by comparing the experimental results with BEM simulations. This combined approach not only advances our understanding of hydrokinetic energy conversion, but also contributes to the development of reliable and efficient renewable energy technologies that address global energy challenges while mitigating environmental impacts.

Ischemic heart disease, manifesting as myocardial infarction (MI), remains the leading cause of death in the western world. Both ischemia and reperfusion (I/R) cause myocardial injury and result in cardiac inflammatory responses. This sterile inflammation in the myocardium consists of multiple phases, involving cell death, tissue remodeling, healing, and scar formation, modulated by various cytokines, including the macrophage migration inhibitory factor (MIF). Meanwhile, different immune cells participate in these phases, with myeloid cells acting as first responders. They migrate to the injured myocardium and regulate the initial phase of inflammation. The MIF modulates the acute inflammatory response by affecting the metabolic profile and activity of myeloid cells. This review summarizes the role of the MIF in regulating myeloid cell subsets in MI and I/R injury and discusses emerging evidence of metabolism-directed cellular inflammatory responses. Based on the multifaceted role of the MIF affecting myeloid cells in MI or I/R, the MIF can be a therapeutic target to achieve metabolic balance under pathology and alleviate inflammation in the heart.

Community care encompasses inherent risks for both clients and healthcare providers. Maintaining a safe environment for the delivery of care services ensures that any risk of unintentional or intentional personal harm is minimised. The aim of this scoping review is to (a) provide an overview of existing knowledge by summarising the current literature and (b) identify gaps pertaining to understanding and managing environmental risk in community care settings. Guided by the Population/Concept/Context approach and PRISMA guidelines, this paper used two questions to answer how a score-based tool for assessing client suitability in community care is developed and how an environmental screening tool assists with reducing risk to community care workers. Literature searches of CINAHL, PubMed (Medline), Web of Science and PsychINFO databases were conducted between September 2023 and November 2023. We included full text articles published from 2018 to 2023. The following four broad areas were identified as key components in the structure of an environmental screening tool: environmental factors, health factors, socioeconomic factors and cultural factors. The results of this review provide valuable information which can be utilised by care organisations to develop and/or refine tools to ensure the safety and wellbeing of workers within the community care sector.

In recent years, there has been growing interest in bioactive plant compounds for their beneficial effects on health and for their potential in reducing the risk of developing certain diseases such as cancer, cardiovascular diseases, and neurodegenerative disorders. The extraction techniques conventionally used to obtain these phytocompounds, however, due to the use of toxic solvents and high temperatures, tend to be supplanted by innovative and unconventional techniques, in line with the demand for environmental and economic sustainability of new chemical processes. Among non-thermal technologies, cold plasma (CP), which has been successfully used for some years in the food industry as a treatment to improve food shelf life, seems to be one of the most promising solutions in green extraction processes. CP is characterized by its low environmental impact, low cost, and better extraction yield of phytochemicals, saving time, energy, and solvents compared with other classical extraction processes. In light of these considerations, this review aims to provide an overview of the potential and critical issues related to the use of CP in the extraction of phytochemicals, particularly polyphenols and essential oils. To review the current knowledge status and future insights of CP in this sector, a bibliometric study, providing quantitative information on the research activity based on the available published scientific literature, was carried out by the VOSviewer software (v. 1.6.18). Scientometric analysis has seen an increase in scientific studies over the past two years, underlining the growing interest of the scientific community in this natural substance extraction technique. The literature studies analyzed have shown that, in general, the use of CP was able to increase the yield of essential oil and polyphenols. Furthermore, the composition of the phytoextract obtained with CP would appear to be influenced by process parameters such as intensity (power and voltage), treatment time, and the working gas used. In general, the studies analyzed showed that the best yields in terms of total polyphenols and the antioxidant and antimicrobial properties of the phytoextracts were obtained using mild process conditions and nitrogen as the working gas. The use of CP as a non-conventional extraction technique is very recent, and further studies are needed to better understand the optimal process conditions to be adopted, and above all, in-depth studies are needed to better understand the mechanisms of plasma–plant matrix interaction to verify the possibility of any side reactions that could generate, in a highly oxidative environment, potentially hazardous substances, which would limit the exploitation of this technique at the industrial level.

The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading. In this research, we proposed to analyze such data by introducing modern and up-to-date techniques such as support vector regression, symmetry functional regression, ridge, and lasso regression methods. In this study, we developed the support vector regression approach called generalized support vector regression to provide more efficient shrinkage estimation and variable selection in high-dimensional datasets. The generalized support vector regression can improve the performance of the support vector regression by employing an accurate algorithm for obtaining the optimum value of the penalty parameter using a cross-validation score, which is an asymptotically unbiased feasible estimator of the risk function. In this regard, using the proposed methods to analyze two real high-dimensional datasets (yeast gene data and riboflavin data) and a simulated dataset, the most efficient model is determined based on three criteria (correlation squared, mean squared error, and mean absolute error percentage deviation) according to the type of datasets. On the basis of the above criteria, the efficiency of the proposed estimators is evaluated.

Given that a significant fraction of buildings and architectural heritage in Europe’s historical centers are masonry structures, the selection of proper diagnosis, technological surveys, non-destructive testing, and interpretations of crack and decay patterns is paramount for a risk assessment of possible damage. Identifying the possible crack patterns, discontinuities, and associated brittle failure mechanisms within unreinforced masonry under seismic and gravity actions allows for reliable retrofitting interventions. Traditional and modern materials and strengthening techniques create a wide range of compatible, removable, and sustainable conservation strategies. Steel/timber tie-rods are mainly used to support the horizontal thrust of arches, vaults, and roofs and are particularly suitable for better connecting structural elements, e.g., masonry walls and floors. Composite reinforcing systems using carbon, glass fibers, and thin mortar layers can improve tensile resistance, ultimate strength, and displacement capacity to avoid brittle shear failures. This study overviews masonry structural diagnostics and compares traditional and advanced strengthening techniques of masonry walls, arches, vaults, and columns. Several research results in automatic surface crack detection for unreinforced masonry (URM) walls are presented considering crack detection based on machine learning and deep learning algorithms. In addition, the kinematic and static principles of Limit Analysis within the rigid no-tension model framework are presented. The manuscript sets a practical perspective, providing an inclusive list of papers describing the essential latest research in this field; thus, this paper is useful for researchers and practitioners in masonry structures.

Active power filters (APFs) are used to mitigate the harmonics generated by nonlinear loads in distribution networks. Therefore, due to the increase of nonlinear loads in power systems, it is necessary to reduce current harmonics. One typical method is utilizing Shunt Active Power Filters (SAPFs). This paper proposes an outstanding controller to improve the performance of the three-phase 25-kVA SAPF. This controller can reduce the current total harmonic distortion (THD), and is called fractional order PI-fractional order PD (FOPI-FOPD) cascade controller. In this study, another qualified controller was applied, called multistage fractional order PID controller, to show the superiority of the FOPI-FOPD cascade controller to the multistage FOPID controller. Both controllers were designed based on a non-dominated sorting genetic algorithm (NSGA-II). The obtained results demonstrate that the steady-state response and transient characteristics achieved by the FO (PI + PD) cascade controller are superior to the ones obtained by the multistage FOPID controller. The proposed controller was able to significantly reduce the source current THD to less than 2%, which is about a 52% reduction compared to the previous work in the introduction. Finally, the studied SAPF system with the proposed cascade controller was developed in the hardware-In-the Loop (HiL) simulation for real-time examinations.

Anthocyanins are excellent antioxidant/antimicrobial agents as well as pH-sensitive indicators that provide new prospects to foster innovative smart packaging systems due to their ability to improve food shelf life and detect physicochemical and biological changes in packaged food. Compared with anthocyanins from other natural sources, red cabbage anthocyanins (RCAs) are of great interest in food packaging because they represent an acceptable color spectrum over a broad range of pH values. The current review addressed the recent advances in the application of RCAs in smart bio-based food packaging systems and sensors. This review was prepared based on the scientific reports found on Web of Science, Scopus, and Google Scholar from February 2000 to February 2022. The studies showed that the incorporation of RCAs in different biopolymeric films could affect their physical, mechanical, thermal, and structural properties. Moreover, the use of RCAs as colorimetric pH-responsive agents can reliably monitor the qualitative properties of the packaged food products in a real-time assessment. Therefore, the development of smart biodegradable films using RCAs is a promising approach to the prospect of food packaging.

Different soil cover saturation has a significant effect in influencing slope stability conditions of weathered covers under earthquake-induced shaking. Here we analyze the Montecilfone, Italy (2018), case history, an Mw 5.1 earthquake that revealed an exceptionality in the spatial distribution of the surveyed earthquake-induced shallow landslides. This feature can be justified as intense rainfall occurred in the epicentral area before the seismic event, contributing to increasing the saturation and the weight of the soil covers. To verify the effective influence of antecedent rainfall as a preparatory factor in the earthquake triggering of soil covers, stability conditions for both static and dynamic scenarios were validated by reconstructing different saturation conditions related to a rainfall event that occurred before the earthquake. Soil cover surveying was performed within a 150 km2 area to output its spatial distribution in terms of their compositional features and thickness, whose variability was constrained through empirical models. Based on laboratory test results, 1D infiltration numerical models were performed through the Hydrus-1D free domain software to estimate the saturation degree of the soil cover and the water infiltration depth, taking as a reference the intensity of the rainfall event. Soil cover sequential charts of water content were obtained at different depths and times up to those recorded at the time of earthquake occurrence by the performed numerical modelling. Safety factors (SFs) of the slope covers were quantified assuming an unsaturated condition in the slope stability equation. The outputs reveal that pore pressure spatial distribution in the unsaturated medium infers on the earthquake-induced scenario of shallow landsliding, demonstrating its role as a preparatory factor for earthquake-induced shallow landslides.

The present manuscript deals with the seismic vulnerability assessment of existing masonry churches, which is a fundamental process for risk and consequent prioritization analyses, as well as application of effective retrofitting strategies. In the past, different approaches with various levels of accuracy and application ranges have been developed to assess the vulnerability to damage of such structures in case of seismic events. Based on the classification provided in the Italian Guidelines for the Cultural Heritage, in this paper a review of seismic vulnerability assessment methodologies for existing masonry churches is presented. The main goal of the current study is to provide a critical comparative overview about these procedures, highlighting the main issues related to the application of each detail level. Moreover, particular attention is focused on the applications present in literature, allowing for the definition of a potential systematic procedure for smart management policy aimed at preserving cultural, architectural and historical heritage.

Mixing at the microscale is of great importance for various applications ranging from biological and chemical synthesis to drug delivery. Among the numerous types of micromixers that have been developed, planar passive spiral micromixers have gained considerable interest due to their ease of fabrication and integration into complex miniaturized systems. However, less attention has been paid to non-planar spiral micromixers with various cross-sections and the effects of these cross-sections on the total performance of the micromixer. Here, mixing performance in a spiral micromixer with different channel cross-sections is evaluated experimentally and numerically in the Re range of 0.001 to 50. The accuracy of the 3D-finite element model was first verified at different flow rates by tracking the mixing index across the loops, which were directly proportional to the spiral radius and were hence also proportional to the Dean flow. It is shown that higher flow rates induce stronger vortices compared to lower flow rates; thus, fewer loops are required for efficient mixing. The numerical study revealed that a large-angle outward trapezoidal cross-section provides the highest mixing performance, reaching efficiencies of up to 95%. Moreover, the velocity/vorticity along the channel length was analyzed and discussed to evaluate channel mixing performance. A relatively low pressure drop (

The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.

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The goal of this study is to assess CFD capability for the prediction of shock wave laminar boundary layer interactions at hypersonic velocities. More specifically, the flow field over a double-cone configuration is simulated using both perfect gas and non-equilibrium Navier–Stokes models. Computations are compared with recent experimental data obtained from measurements conducted in the LENS XX (Large Energy National Shock Expansion Tunnel Version 2) at the Calspan University of Buffalo Research Center (CUBRC). Four separate cases of freestream conditions are simulated to examine the models for a range of stagnation enthalpies from 5.44 MJ/kg to 21.77 MJ/kg and Mach numbers from 10.9 to 12.82.