Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. Full article

To improve the clinical and microbiological outcomes of non-surgical mechanical periodontal therapy, the adjunctive use of antimicrobials has been utilized in treating moderate-to-severe periodontitis. In our study, the retrospective design included previously collected health-related patient data, obtained from the printed and digital charts of patients who received systemic or local antibiotic adjuncts to SI (subgingival instrumentation). A total of 34 patients (diagnosed with generalized Stage III/IV periodontitis) met the inclusion and exclusion criteria and were evaluated. The samples were tested for the following bacterial strains: Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans), Porphyromonas gingivalis (P. gingivalis), Prevotella intermedia (P. intermedia), Tanererella forsythia (T. forsythia), and Treponema denticola (T. denticola). The inter-group comparisons of the bacterial species did not show statistically significant differences between groups. The present study aimed to evaluate the clinical effects after SI and the adjunctive use of systemically administered (SA) AMX (amoxicillin) + MET (metronidazole) (administered for 7 days), with locally delivered (LDD) piperacillin + tazobactam in step 2 of periodontal therapy. Results: Overall, all parameters were improved in the groups, with a significant difference in inter-group comparison regarding the full-mouth bleeding score (FMBS) (p < 0.05) in favor of the SA group, and the p-value < 0.05 was considered to be statistically significant. Statistically significant PPD (probing pocket depth) reductions and CAL (clinical attachment level) gains were observed in both groups at the 3-month follow-up. In conclusion, within the limitations, the outcomes of this study suggest that SI, with adjunctive local or systemic antibiotic therapy, provided comparable clinical improvements. Systemic AMX + MET protocols were more efficacious with regard to the reduction in FMBS. Follow-up studies with larger patient numbers are needed to further investigate this effect. Full article

Shallow-water deltas are a subject of sedimentary research and represent a significant target for oil and gas exploration. The Yanchang Formation of the Triassic in the Ordos Basin comprises numerous shallow-water delta blocks. This paper addresses the core issues pertaining to the sedimentary facies, sedimentary characteristics and sand body distribution of the Chang 9 oil layer formation of the Upper Triassic in the Ordos Basin. Guided by the relevant theories and methods of contemporary sedimentology and sedimentary geology, the reservoir characteristics are described and studied in detail through ordinary thin sections, cast thin sections, graphical representations of particle size and scanning electron microscopy experiments. The experimental results indicate that the porosity in the study area ranges from 3% to 12% and that the permeability is between 0 and 1.5 × 10⁻³ μm², which is consistent with classification as an ultra-low-porosity and ultra-low-permeability reservoir. The Chang 9 sandstone is composed of feldspar sandstone and lithic feldspar sandstone. The average content of quartz is low, at less than 31%, while the average content of feldspar is high, at more than 34%. The average content of rock debris is between 10% and 20%. Therefore, the compositional maturity of the Chang 9 sand body is generally low. The particle size distribution exhibits a positive deviation, indicating that the sediments in the sand body are primarily coarse-grained components. The kurtosis of the particle size–frequency curve is observed to vary from flat to very sharp. The Chang 91 lake is classified as a shore shallow lake with basin subsidence and lake transgression. The Chang 9 period saw the development of the Chang 91 sedimentary facies into a semi-deep lake–deep lake environment. The vertical structural style of the Chang 9 oil layer formation in the basin can be roughly summarized into three basic structural types: the sedimentary structures observed in the area include box-shaped upward thinning, bell-shaped upward thinning and funnel-shaped upward thickening. The delta front area in Chang 9 is notable for its size and the prevalence of underwater distributary channel microfacies. The sand body distribution is stable, with sand layer thicknesses ranging from 15 to 30 m. The evaluation and summary of the sedimentary characteristics of the Chang 9 oil layer formation provide a geological basis for future exploration and development in the study area. Full article

With the rapid development of deep learning, its powerful capabilities make it possible to perform mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). Among deep learning approaches, the convolutional neural network is widely used. However, while it can extract features effectively, it also has some limitations. Specifically, it depends on a large number of training data and only takes data information into account without considering structural information. These shortcomings lead to unused information and unsatisfactory model results. To address these shortcomings, this paper proposes AKNN-DMGCN, a novel dynamic multi-attention graph convolutional network based on an adaptively constructed graph, which can achieve high accuracy and robust mechanical fault diagnosis of HVCBs. First, a novel adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct informative graphs. The AKNN method can mine the relationship between the original data samples and utilize the data and label information. Thus, it has high fault tolerance to noise signals and can construct a structure graph with rich and accurate information, which can improve the overall model performance. Then, a dynamic multi-attention graph convolutional network (DMGCN) is applied for mechanical fault diagnosis of HVCBs. DMGCN fully utilizes structural and numerical information representing HVCB signals to perform classification. DMGCN has a dynamic multi-attention mechanism with strong expressive ability, which allows it to achieve high diagnostic accuracy. The experimental results indicate that the accuracy of AKNN-DMGCN reaches 97.22% on a balanced dataset and 95.01% on an imbalanced dataset, which demonstrates that the proposed method is effective for both balanced and imbalanced samples. Full article

Pulses—comprising the dry, edible seeds of leguminous plants—have long been lauded for their culinary flexibility and substantial nutritional advantages. This scoping review aimed to map the evidence on how pulses contribute to overall human health. Four electronic databases were searched for clinical and observational studies in English. We identified 30 articles (3 cross-sectional studies, 1 federated meta-analysis, 8 prospective cohort studies, 1 before-and-after study, and 17 randomized controlled trials) that met our inclusion criteria. Predominant among the pulses studied were lentils, chickpeas, common bean varieties (e.g., pinto, black, navy, red, kidney), black-eyed peas, cowpeas, and split peas. Consumption modalities varied; most studies examined mixed pulses, while five isolated individual types. In intervention studies, pulses were incorporated into diets by allotting a fixed pulse serving on top of a regular diet or by substituting red meat with pulses, offering a comparative analysis of dietary effects. The health outcomes evaluated were multifaceted, ranging from lipid profiles to blood pressure, cardiovascular disease risk and mortality, type 2 diabetes and glycemic control, metabolic syndrome indicators, inflammatory markers, oxidative stress biomarkers, and hormonal profiles. The most frequently assessed study outcomes included changes in low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, fasting blood sugar, hemoglobin A1c, waist circumference, and C-reactive protein or high-sensitivity C-reactive protein. This review should serve as a call to action for the scientific community to build upon the existing evidence, enriching our understanding of the nutritional and health-promoting attributes of pulses. Full article

When processing the carrier phase, the global navigation satellite system (GNSS) grants the highest precision for geodetic measurements. The analysis centers (ACs) from the International GNSS Service (IGS) provide different data such as precise clock data, precise orbits, reference frame, ionosphere and troposphere data, as well as other geodetic products. Each individual AC has its own strategy for delivering the abovementioned products, with one of the key elements being the cutoff elevation angle. Typically, this angle is arbitrarily chosen using generic values without studying the impact of this choice on the obtained results, in particular when very precise positions are considered. This article addresses this issue. To this end, the article has two key sections, and the first is to evaluate the impact of using the two different cutoff elevation angles that are most widely used: (a) 3 degrees cutoff and (b) 10 degrees cutoff elevation angle. This analysis is completed in two major parts: (i) the analysis of the root mean square (RMS) for the carrier phase and (ii) the analysis of the station position in terms of repeatability. The second key section of the paper is a comprehensive carrier phase analysis conducted by adopting a new approach using a mean of the 25-point average RMS (A-RMS) and the single-point RMS and using an ionosphere-free linear combination. By using the ratio between the 25-point average RMS and the single-point RMS we can define the type of scatter that dominates the phase solution. The analyzed data span a one-year period. The tested GNSS stations belong to the EUREF Permanent Network (EPN) and the International GNSS Service (IGS). These comprise 55 GNSS stations, of which only 23 GNSS stations had more than 95% data availability for the entire year. The RMS and A-RMS are analyzed in conjunction with the precipitable water vapor (PWV), which shows clear signs of temporal correlation. Of the 23 GNSS stations, three stations show an increase of around 50% of the phase RMS when using a 3° cutoff elevation angle, and only four stations have a difference of 5% between the phase RMS when using both cutoff elevation angles. When using the A-RMS, there is an average improvement of 37% of the phase scatter for the 10° cutoff elevation angle, whereas for the 3° cutoff elevation angle, the improvement is around 33%. Based on studying this ratio, four stations indicate that the scatter is dominated by the stronger-than-usual dominance of long-period variations, whereas the others show short-term noise. In terms of station position repeatability, the weighted root mean square (WRMS) is used as an indicator, and the results between the differences of using a 3° and 10° cutoff elevation angle strategy show a difference of −0.16 mm for the North component, −0.21 mm for the East component and a value of −0.75 mm for the Up component, indicating the importance of using optimal cutoff angles. Full article

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10⁻³ m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10⁻⁴ m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory. Full article

Beef is an excellent source of nutrients important for maternal health and fetal development. It is also true that the Mediterranean diet is beneficial for the health of both the mother and offspring; however, the relative value of fresh beef intake within Mediterranean diet patterns during pregnancy is unknown. The objective of this project was two-fold: (1) assess the relationship between beef intake and nutrient intake in a pregnant population; (2) assess the relationship between maternal beef consumption among varying degrees of Mediterranean diet adherence with maternal risk of anemia and infant health outcomes. This is a secondary analysis of an existing cohort of pregnant women (n = 1076) who participated in one of two completed clinical trials examining the effect of a docosahexaenoic acid supplementation on birth and offspring outcomes. Women were enrolled between 12 and 20 weeks of gestation and were followed throughout their pregnancies to collect maternal and infant characteristics, food frequency questionnaires [providing beef intake and Mediterranean diet (MedD) adherence], and supplement intake. Women with the highest fresh beef intake had the highest intake of many micronutrients that are commonly deficient among pregnant women. Fresh beef intake alone was not related to any maternal or infant outcomes. There was a reduced risk of anemia among women with medium to high MedD quality and higher fresh beef intake. Women in the medium MedD group had 31% lower odds of anemia, and women in the high MedD group had 38% lower odds of anemia with every one-ounce increase in fresh beef intake, suggesting that diet quality indices may be misrepresenting the role of fresh beef within a healthy diet. These findings show that beef intake increases micronutrient intake and may be protective against maternal anemia when consumed within a healthy Mediterranean diet pattern. Full article

The novelty of the paper is the analysis of the possibilities of reducing the operating costs of a mine water pumping station in an abandoned coal mine. To meet the energy needs of the pumping station and reduce the carbon footprint, “green” energy from a photovoltaic farm was used. Surplus green energy generated during peak production is stored in the form of green hydrogen from the water electrolysis process. Rainwater and process water are still underutilized sources for increasing water resources and reducing water stress in the European Union. The article presents the possibilities of using these waters, after purification, in the production of green hydrogen by electrolysis. The article also presents three variants that ensure the energy self-sufficiency of the proposed concepts of operation of the pumping station. Full article

This study offers, by an empirical analysis, another perspective on post-socialist development, highlighting the role of the urban–rural interface in regional dynamics. The current literature on the relationships between both issues is not too rich and our paper analyzes the relationships between core cities, their peri-urban areas, and their regions, through a comparative overview of their growth over the last three decades. Romania, as a special case study for a contradictory transition, due to the great step from a drastic dictatorial regime to a democracy and a market economy, is a good example to test these complex relationships. Considering the new development trend at the urban–rural interfaces, our key idea was to depict their contribution to regional development (NUTS 3) compared to city cores. The second question was how this differentiated contribution can be measured, using the simplest tool. The starting point was the fact that population dynamics reflect all changes in the city core and at the urban–rural interface, and less so at a regional level. Consequently, we selected the dynamics of the number of inhabitants for the first two, as well as the dynamics of GDP per capita at the regional level. We found higher and significant correlations between GDP per capita and urban–rural interfaces, but no significant correlations in the case of city cores. Our conclusion is that, in the transition period, the dynamics of urban–rural interfaces influenced more regional development dynamics, than those of city cores. This means that urban–rural interfaces amplify the development coming from cities, adding their own contribution and then dissipating it regionally. Future research should identify what the urban–rural interface offers to regions, in addition to the city core. Full article

Previous studies on the clap–fling mechanism have predominantly focused on the initial downward and forward phases of flight in miniature insects, either during hovering or forward flight. However, this study presents the first comprehensive kinematic data of Coccinella septempunctata during climbing flight. It reveals, for the first time, that a clap-and-fling mechanism occurs during the initial upward and backward phase of the hind wings’ motion. This discovery addresses the previously limited understanding of the clap-and-fling mechanism by demonstrating that, during the clap motion, the leading edges of beetle’s wings come into proximity to form a figure-eight shape before rotating around their trailing edge to open into a “V” shape. By employing numerical solutions to solve Navier–Stokes (N-S) equations, we simulated both single hind wings’ and double hind wings’ aerodynamic conditions. Our findings demonstrate that this fling mechanism not only significantly enhances the lift coefficient by approximately 9.65% but also reduces the drag coefficient by about 1.7%, indicating an extension of the applicability range of this clap-and-fling mechanism beyond minute insect flight. Consequently, these insights into insect flight mechanics deepen our understanding of their biological characteristics and inspire advancements in robotics and biomimetics. Full article

This paper presents a new electric drive-reconfigured on-board charger and initial electromagnetic torque suppression method. This proposed reconfigured on-board charger does not need many components added to the original electric drive system: only a connector is needed, which is easy to add. Specifically, the inverter for propulsion is reconfigured as a buck chopper and a conduction path to match the reconfigured windings. Two of the machine phase windings serve as inductors, while the third phase winding is reutilized as a common-mode inductor. In addition, the initial charging torque is generated at the outset of the charging process, which may cause an instant shock or even rotational movement. In order to prevent vehicle movement, the reason for the charging torque and suppression method were analyzed. Further, predictive control of the model based on mutual inductance analysis was adopted, where the charging torque was directly used as a control object in the cost function. Finally, experimental performances were applied to verify the proposed reconfigured on-board charger under constant current and constant voltage charging. Full article

UAV remote sensing (RS) image object detection is a very valuable and challenging technology. This article discusses the importance of key features and proposes an object detection network (URSNet) based on a bidirectional multi-span feature pyramid and key feature capture mechanism. Firstly, a bidirectional multi-span feature pyramid (BMSFPN) is constructed. In the process of bidirectional sampling, bicubic interpolation and cross layer fusion are used to filter out image noise and enhance the details of object features. Secondly, the designed feature polarization module (FPM) uses the internal polarization attention mechanism to build a powerful feature representation for classification and regression tasks, making it easier for the network to capture the key object features with more semantic discrimination. In addition, the anchor rotation alignment module (ARAM) further refines the preset anchor frame based on the key regression features extracted by FPM to obtain high-quality rotation anchors with a high matching degree and rich positioning visual information. Finally, the dynamic anchor optimization module (DAOM) is used to improve the ability of feature alignment and positive and negative sample discrimination of the model so that the model can dynamically select the candidate anchor to capture the key regression features so as to further eliminate the deviation between the classification and regression. URSNet has conducted comprehensive ablation and SOTA comparative experiments on challenging RS datasets such as DOTA-V2.0, DIOR and RSOD. The optimal experimental results (87.19% mAP, 108.2 FPS) show that URSNet has efficient and reliable detection performance. Full article

Computational thinking (CT) has garnered significant interest in both computer science and education sciences as it delineates a set of skills that emerge during the problem-solving process. Consequently, numerous assessment instruments aimed at measuring CT have been developed in the recent years. However, a scarce part of the existing CT measurement instruments has been dedicated to early school ages, and few have undergone rigorous validation or reliability testing. Therefore, this work introduces a new instrument for measuring CT in the early grades of elementary education: the Computational Thinking Test for Children (CTTC). To this end, in this work, we provide the design and validation of the CTTC, which is constructed around spatial, sequential, and logical thinking and encompasses abstraction, decomposition, pattern recognition, and coding items organized in five question blocks. The validation and standardization process employs the Kuder–Richardson statistic (KR-20) and expert judgment using V-Aiken for consistency. Additionally, item difficulty indices were utilized to gauge the difficulty level of each question in the CTTC. The study concludes that the CTTC demonstrates consistency and suitability for children in the first cycle of primary education (encompassing the first to third grades). Full article

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features. Full article

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas. Full article

In rotating detonation engines the turbine inlet conditions may be transonic with unprecedented unsteady fluctuations. To ensure an acceptable engine performance, the turbine passages must be suited to these conditions. This article focuses on designing and characterizing highly diffusive turbine vanes to operate at any inlet Mach number up to Mach 1. First, the effect of pressure loss on the starting limit is presented. Afterward, a multi-objective optimization with steady RANS simulations, including the endwall and 3D vane design is performed. Compared to previous research, significant reductions in pressure loss and stator-induced rotor forcing are obtained, with an extended operating range and preserving high flow turning. Finally, the influence of the inlet boundary layer thickness on the vane performance is evaluated, inducing remarkable increases in pressure loss and downstream pressure distortion. Employing an optimization with a thicker inlet boundary layer, specific endwall design recommendations are found, providing a notable improvement in both objective functions. Full article

This paper integrates intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAV) to enhance the transmission performance of the Internet of Vehicles (IoV) through non-orthogonal multiple access (NOMA). It focuses on strengthening the signals from cell edge vehicles (CEVs) to the base station by optimizing the wireless propagation environment via an IRS-equipped UAV. The primary goal is to maximize the sum data rate of CEVs while satisfying the constraint of the successive interference cancellation (SIC) decoding threshold. The challenge lies in the non-convex nature of jointly considering the power control, subcarrier allocation, and phase shift design, making the problem difficult to optimally solve. To address this, the problem is decomposed into two independent subproblems, which are then solved iteratively. Specifically, the optimal phase shift design is achieved using the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the graph theory is applied to determine the subcarrier allocation policy and derive a closed-form solution for optimal power control. Finally, the simulation results show that the proposed joint phase shift and resource management scheme significantly enhances the sum data rate compared to the state-of-the-art schemes, thereby demonstrating the benefits of integrating the IRS-equipped UAV into NOMA-enhanced IoV. Full article

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification. Full article

Mannosylerythritol lipids (MELs), one of the most promising biosurfactants (BS), are glycolipids produced by yeasts or fungi, which have great environmental performance and high compatibility with the human body. MELs, besides working as typical surfactants, can form diverse structures when at or above the critical aggregation concentration (CAC), reduce the surface tension of water and other solutions, and be stable over a wide range of conditions. Among others, MELs present antimicrobial, antitumor, antioxidant and anti-inflammatory activities and skin and hair repair capacity, which opens possibilities for their use in applications from cosmetics and pharmaceutics to bioremediation and agriculture. However, their market share is still low when compared to other glycolipids, due to their less developed production process and higher production cost. This review gathers information on the potential applications of MELs mentioned in the literature since 1993. Furthermore, it also explores the current strategies being developed to enhance the market presence of MELs, in parallel with the ones developed for rhamnolipids and sophorolipids. Full article

Rootstocks serve as a strategic tool for grapevine adaptation to specific biotic and abiotic conditions and for managing vine growth, grape yield, and berry composition in commercial vineyards. This study investigates the influences of four different rootstock varieties (101-14 MGt, 3309 C, 110 R, and 140 Ru) on the viticultural performance of ‘Xinomavro’ vines, a prominent Greek red winegrape varietal. By conducting a two-year field experiment using various rootstocks, we assessed parameters related to water status, vegetative growth, yield, and berry composition. Our results revealed that rootstock selection has a significant impact on vine development, especially in terms of berry size and the concentrations of secondary metabolites. Principal component analysis confirmed the complex interaction between rootstock vigor and vine productivity. This study underscores the importance of rootstock variety in manipulating grapevine characteristics, particularly for the ‘Xinomavro’ variety, in response to regional climatic conditions. Full article

Heart failure (HF) presents a significant global health challenge recognised by frequent hospitalisation and high mortality rates. The assessment of left ventricular (LV) ejection fraction (EF) plays a crucial role in diagnosing and predicting outcomes in HF, leading to its classification into preserved (HFpEF), reduced (HFrEF), and mildly reduced (HFmrEF) EF. HFmrEF shares features of both HFrEF and HFpEF but also exhibits distinct characteristics. Despite advancements, managing HFmrEF remains challenging due to its diverse presentation. Large-scale studies are needed to identify the predictors of clinical outcomes and treatment responses. Utilising biomarkers for phenotyping holds the potential for discovering new treatment targets. Given the uncertainty surrounding optimal management, individualised approaches are imperative for HFmrEF patients. This chapter examines HFmrEF, discusses the rationale for its re-classification, and elucidates HFmrEF’s key attributes. Furthermore, it provides a comprehensive review of current treatment strategies for HFmrEF patients. Full article