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Vicia sativa L., commonly known as the common vetch, is an annual, self-pollinating legume used primarily as fodder both by livestock and wildlife. Additionally, it contributes to environmental balance through nitrogen fixation and the improvement of soil properties. The phenotypic, genetic, and metabolite variability among four advanced lines (BK45, BK29, BK23, BK27) and two commercial varieties (M-6900, BI-65) of V. sativa were evaluated in order to be used for future breeding programs aimed at producing genetically improved varieties. BK45 was the most promising line due to its high genetic polymorphism, but also because it exhibited a significant amount of seed production and high seed quality based on its metabolomics profile. A stepwise multiple regression analysis (MRA) revealed a relationship between SCoT alleles, seed, and biomass yield. Additionally, several statistically significant marker bands linked to metabolites were found using the SCoT marker analysis. Hence, data assessed via MRA may be helpful in marker-assisted breeding programs. Finally, the two commercial varieties can be further exploited in breeding programs due to their high genetic diversity.

In recent years, we have witnessed a massive increase in the number of startups, which are also producing significant amounts of digital data. This poses a new challenge for expert analysts due to their limited attention spans and knowledge, also considering the low success rate of empirical startup evaluation. However, this new era also presents a great opportunity for the application of artificial intelligence (AI) towards intelligent startup investments. There are only a few works that have considered the potential of AI for startup recommendation, and they have not paid attention to the actual requirements of investors, also neglecting to investigate the desirability, feasibility, and value proposition of this venture. In this paper, we answer these questions by conducting a survey in collaboration with three major organizations of the Greek startup ecosystem. Furthermore, this paper also presents the design specifications for an AI-based decision support system for forecasting startup sustainability that is aligned with the requirements of expert analysts. Preliminary experiments with 44 Greek startups demonstrate Random Forest’s strong ability to predict sustainability scores.

The fundamental period is one of the most important parameters for the design of new structures as well as for estimating the capacity of existing ones. Thus, to estimate it, various design codes and researchers have adopted several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for deriving the analytical equations for the fundamental period of reinforced concrete structures. The methodology is based on machine learning explainability techniques, specifically the so-called SHapley Additive exPlanations values. These values are commonly employed as an explainability tool. However, in the proposed novel approach they are employed as a basis to fit analytical curves, which allows the resulting equations to be constructed sequentially and in an informed manner while controlling the balance between accuracy and complexity. An extended dataset consisting of 4026 data points is employed, on which a Gradient Boosting Machine model is fitted. The model achieves excellent accuracy, with a coefficient of determination R2≈0.99, while the equations derived from the proposed formulation achieve an R2≈0.95 and Mean Absolute Error ≈0.12. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.

The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.

Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi.

Molecular biology is a complex, abstract, subject that can be challenging for higher education students to comprehend. The current manuscript describes the design, implementation, and evaluation of two immersive VR simulations of a DNA lab and a crime scene investigation (CSI) for a forensic molecular biology course in the context of the “TESLA” Erasmus+ project. It illustrates the instructional design and technical aspects of the VR simulations’ development. The experimental study employed a comparative quantitative research design. The guiding research questions examined how instructional modalities (online vs. face-to-face) affect learners’ perceptions of VR-based training in higher education and the key factors influencing learners’ intention for their adoption. Forty-six (n = 46) undergraduate students completed a 17-item questionnaire, which served as the main data collection instrument. Results demonstrate that both online and face-to-face VR-based instruction can effectively convey core concepts, thus challenging the traditional notion that face-to-face interaction is inherently superior. Its implications underscore the potential of VR simulations to supplement or even substitute traditional teaching methods, particularly for complex science subjects.

Sweet whey (SW) and yogurt acid whey (YAW) are dairy by-products of the cheese-making process and Greek-style yogurt production, respectively. Both of them are considered pollutants with huge volumes of SW and YAW produced due to the growing demand for dairy products worldwide. Moreover, whey-derived peptides, resulting from fermentation as well as from further hydrolysis during digestion, have been associated with various biological activities. In the present study, the angiotensin-converting enzyme (ACE)-inhibitory activity of 48 SW samples and 33 YAW samples from bovine, ovine, caprine, and ovine/caprine milk obtained were evaluated. Additionally, the SW and YAW digestates and two of their fractions (smaller than 10 kDa, SW-D-P10 and YAW-D-P10, and smaller than 3 kDa, SW-D-P3 and YAW-D-P3), which were obtained after in vitro digestion and subsequent ultrafiltration, were also subjected to evaluation. Our data indicated that the D-P10 and D-P3 fractions exhibited higher ACE-inhibitory activity compared to the corresponding values before digestion. The ACE-inhibitory capacity after in vitro digestion was higher for the ovine SW samples compared to their bovine and caprine counterparts. The effect of the D-P3 fraction on the inhibition of nitric oxide (NO) production and the expression of a selected panel of immune-response-related genes in LPS-stimulated RAW 264.7 macrophages was also evaluated. Fractions from both dairy by-products inhibited NO production in LPS-stimulated RAW 264.7 cells. Especially, ovine SW-D-P3 showed a strong NO inhibitory activity and suppressed inducible nitric oxide synthase (Nos2) mRNA levels. However, YAW-D-P3 could not trigger neither the gene expression of inflammatory macrophage mediators Nos2 and cyclooxygenase-2 (Ptgs2) nor tumor necrosis factor-α (Tnf) and interleukin 6 (Il6) in LPS-stimulated murine macrophages regardless of animal origin. These findings suggest that in vitro digestion could enhance the production of ACE-inhibitory peptides in both dairy by-products, while SW from ovine origin displays higher potential as an anti-inflammatory agent, effectively preventing excessive NO production.

The purpose of this research is to investigate the differences in specific GPS variables related to the running performance of amateur football players, comparing data over 15 min periods of time throughout the match, as well as examining differences between players of different positions. The participants are adult male amateur football players from the same team. The analysis of the players’ performance was carried out using GPS transmitters over twelve league matches (Amateur Division A) for all starting players (except the goalkeeper) who completed the match without being substituted. The formation was 1-4-3-3, and the players were divided into five categories: Central Defenders (CDs), Full Backs (FBs), Central Midfielders (CMs), Wingers (Ws), and Center Forwards (FWs). Each match was divided into two halves and into periods of fifteen minutes. The analysis concerns the total distance covered within these time periods and in different speed zones. The results showed that CDs covered the shortest distance (p < 0.05), while CMs and Ws covered the longest (p < 0.05) in the most of the 15 min time periods. In both halves, it was observed that players covered greater distances at low intensity in the last 15-min periods. Finally, the total distance covered at low intensity was greater in the last 15-min period of the 2nd half compared to that of the 1st half. As footballers’ loads vary based on their playing position, the data from this study warrant extensive consideration for better training planning.

There is an increased demand for healthy foods by the consumer nowadays, while at the same time, circular bio-economy and sustainability in food production represent top priority issues for the food industry. In this context, purslane, a highly nutritious annual plant that grows abundant during the hottest months of the year but is considered a by-product of the agricultural process, was utilized for the development of an innovative, ready-to-eat food product suitable for a vegetarian diet in the form of a bread spread. Following an initial small-scale experimentation for the stabilization of apparent quality attributes, the product’s recipe was finalized on an industrial scale, and the hazard analysis and critical control point (HACCP) for the manufacturing process, the physicochemical (pH, water activity) and sensorial analysis of the end product, together with its nutritional value, shelf-life, and antioxidant capacity, were determined. The results suggested that the acidic (pH 4.3) product had a shelf-life of a minimum of six months to one year and, according to EU legislation, it comprised a source of (dietary) fibers and protein, while being high in poly-unsaturated (e.g., omega-3, omega-6), and mono-unsaturated (e.g., oleic acid) fatty acids. Total phenolic content (TPC) with the Folin–Ciocalteau assay and total antioxidant capacity (TAC) using the Ferric-Reducing Antioxidant Power assay of the product, presented concentrations of 0.95 mg of gallic acid equivalent/g and 0.016 mmol of Fe2+/g of dry weight of sample, respectively. However, the predicted bioavailability for TPC and TAC was 28% and 31%, respectively. The product was evaluated positively by a panel of potential consumers without significant differences compared to conventional familiar products. The potential of using agro-food chain by-products, such as purslane, for the development of novel foods representing a healthy and tasty food choice at any time of the day is a promising opportunity for the food industry to meet growing consumer demands for more sustainable, nutritious, and healthy food products with a reduced environmental footprint compared to the traditional plant-originated products of intensive agriculture.

This paper proposes a novel ensemble approach, integrating Artificial Neural Networks (ANNs), Symbolic Regression (SR), and Decision Trees (DTs), to predict fish mortality caused by infectious diseases. The intensifying global burden of fish diseases threatens the sustainability of aquatic ecosystems and the aquaculture industry, necessitating sophisticated modelling strategies for effective disease management and control. The proposed approach capitalizes on the non-linear data modelling strength of ANNs, the explanatory power of SR, and the decision-making efficiency of DTs, offering both predictive accuracy and interpretable insights. The architecture of the proposed ensemble method is developed in two stages. In the intermediate stage, an ANN is employed to learn the complex, non-linear interactions between various biological and environmental factors impacting fish health. Additionally, SR is applied to produce a symbolic equation that effectively maps the input variables to fish mortality rates. In the final stage, a DT model is included to enhance prediction performance by capturing decision rules from the data. This hybrid approach offers superior prediction performance while also revealing meaningful biological/environmental relationships that can guide preventive and reactive interventions in the management of fish health. We evaluate the developed models using extensive real-world datasets acquired from two large Greek fish-farming units, which encompass representative disease types. The results demonstrate that our ensemble approach significantly outperforms traditional standalone models developed in our recent previous work, achieving enhanced predictive accuracy, robustness, and interpretability. Overall, this research has far-reaching implications for improving disease predictions, facilitating optimal decision-making in aquaculture management, and contributing to the sustainability of global fish stocks.

Medium-fluence fast electron irradiation (1019 e/cm2 to 1020 e/cm2) or the changes in the praseodymium concentration in the range of 0.0 ≤ z ≤ 0.5 on the excess conductivity of YBa2Cu3O7–δ single crystals ware investigated. These can lead to a wider range of the temperature interval of excess conductivity which narrows the interval of linearity in the ab plane. At fluences 0 ≤ Φ ≤ 6.5 × 1019 e/cm2, there was a threefold increase in the transverse coherence length ξc(0) with an increase in Φ of more than four times as the praseodymium concentration increased to z ≈ 0.42. The two-dimensional–three-dimensional (2D–3D) crossover point shifted upward in temperature. Conversely, to irradiation with low fluences (Φ ≤ 1019 e/cm2) or low praseodymium doping (z ≤ 0.39), irradiation with medium fluences or high praseodymium doping led to a non-monotonic dependence of ξc(0) on the irradiation fluence, with characteristic maxima at Φ~(7–8) × 1019 e/cm2 and z ≈ 0.42, likely due to the suppression of the superconducting characteristics.

The purpose of this study was to compare the step parameters, the Body Center of Mass (BCM) kinematics, as well as the angular and linear kinematics of the lower extremities’ joints of prepubescent and adult sprinters of both sexes. A total of forty-two athletes were examined, including adult men (AM) and women (AF) as well as preadolescent boys (PPB) and girls (PPG). A 2D-DLT analysis was conducted on video recordings (sampling frequency: 100 fps) of the participants’ maximum effort trial while in their maximum sprinting velocity phase. A 2 (age) × 2 (sex) ANOVA revealed significant (p < 0.05) effects of the factors age and sex, and an interaction of age × sex in the sprint running velocity. It was also found that the spatiotemporal structure of the step parameters was significantly (p < 0.05) different between AM and AF but not between PPB and PPG. Also, a significant (p < 0.05) main effect of age was evident mainly for the ankle joint of both legs, as well as for the angular kinematics of the swing leg’s knee joint. In conclusion, apart from the possible disparities in training experience, differences due to age and sex exist in the step parameters and the kinematics of the sprinting technique due to differences in the technical elements related to coordination, strength application capability, and stiffness.

Low dietary intake of calcium, vitamin D, vitamin C and magnesium has been associated with increased risk of osteoporosis. The purpose of the study was to evaluate changes in several bone health indicators and metabolic biomarkers in postmenopausal women with high osteopenia or osteoporosis, followed by a nutritional intervention program. In total, 115 apparently healthy postmenopausal women (52 ± 8 years old) were randomized into four groups: group I (n = 40) received daily 1000 mg vitamin C, 100 mcg vitamin D3, 500 mg calcium(Ca) and 300 mg magnesium (Mg); group II (n = 42) received daily 100 mcg vitamin D3, 500 mg Ca and 300 mg Mg; group III (n = 18) received daily 5 mg bisphosphonates, 100 mcg vitamin D3, 500 mg Ca and 300 mg Mg; and group IV (n = 15) received daily about 364 mg polyphenols via an innovative functional food (50 g olive paste enriched with mountain tea extract) along with 100 mcg vitamin D3, 500 mg Ca and 300 mg Mg. Groups I–III received supplementation for a year whereas group IV received supplementation for 5 months. Changes in bone health indicators and metabolic biomarkers were assessed at the beginning and at the end of the study. Anthropometric indices and whole-body bone mineral density (BMD) were also evaluated at the beginning and at the end of the intervention period. The results revealed improved elevated levels of 25(OH)D3 in groups II, III and IV (+3.71% and +1.45% and +5.62%, respectively). Significant positive changes were recorded for whole-body BMD in all four study groups. Significant beneficial changes for total cholesterol were observed in group IV (−2.07%, p < 0.05) and positive changes in group I for HDL biomarkers (+61.62%, p < 0.05). Additional larger-scale clinical trials and intervention studies are considered essential to fully investigate and elucidate associations between dietary components and biochemical indices of bone health.

This article sums up and compares the most important techniques for magnetic sensing of ferromagnetic nanoparticles. In addition, the most well-known magnetic sensing instruments are presented, while the advantages and disadvantages of each instrument category are summarized. Finally, a measurement system based on fluxgate magnetometers is proposed for the magnetic characterization of a cobalt-based material applicable in the catalysis process. The authors conclude that this arrangement can provide ferromagnetic material sensing with the most advantages for this catalysis application. Indeed, as nanoparticle materials can be used in many applications, like catalysis, their properties and the phase of the catalyst should be known at any time. Moreover, as the industrial processes operate at a rapid pace, the need for simple, fast, and low-cost measurement systems that will also enable in vivo material characterization is rising. Consequently, this article aims to propose the best candidate magnetic sensing method as well as the best candidate instrument for every application based on the advantages and disadvantages of each sensor.

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have developed Rapid Visual Screening (RVS) tools aiming to efficiently estimate vulnerability indices. In general, RVS tools employ a set of structural features and their associated weights to obtain a vulnerability index, which can be used for ranking. In this paper, Machine Learning (ML) models are implemented within this framework. The proposed formulation is used to train binary classifiers in conjunction with ad hoc rules, employing the features of various Codes (e.g., the Federal Emergency Management Agency, New Zealand, and Canada). The efficiency of this modeling effort is evaluated for each Code separately and it is clearly demonstrated that ML-based models are capable of outperforming currently established engineering practices. Furthermore, in the spirit of the aforementioned Codes, a linearization of the fully trained ML model is proposed. ML feature attribution techniques, namely SHapley Additive exPlanations (SHAP) are employed to introduce weights similar to engineering practices. The promising results motivate the potential applicability of this methodology towards the recalibration of the RVS procedures for various types of cases.

In recent years, along with the remarkable development of women’s soccer, significant attention has been given to the study of asymmetry in lower limbs. However, there is uncertainty about whether and to what extent jumping asymmetries affect the performance of female soccer players. The aims of this study were to examine (a) possible asymmetries in jumping ability and (b) the correlations between asymmetries and performance of female soccer players in 10 m and 30 m speed tests, as well as in change-of-direction speed tests. The study involved 12 adolescent (age: 15.8 ± 0.8 years, body mass: 59.4 ± 7 kg and height: 160.5 ± 5.1 cm) and 10 adult female soccer players (age: 22.3 ± 4.4 years, body mass: 62.2 ± 7.5 kg and height: 165.7 ± 6.1 cm). Pearson’s correlations showed no statistically significant relationships between vertical and horizontal asymmetries and time in 10 m, 30 m and 505 change-of-direction speed tests for adolescent players. In adult players, a significantly high correlation was found between asymmetries in single-leg hop tests (for distance) and time in 505 change-of-direction speed tests (r = 0.68, p < 0.05). Adult players showed higher asymmetry values in vertical and horizontal jump tests, but these asymmetries were not significant (p > 0.05). Practitioners are recommended to implement strength and power training programs, as well as injury prevention protocols, aiming to reduce asymmetries, in order to minimize the risk of injuries, and potentially improve performance of female soccer players in certain fitness tests.

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.

Hiking offers both recreational enjoyment and physical challenges, requiring speed adjustments when traversing uphill and downhill slopes. These adjustments prompt compensatory responses in kinematics and kinetics to mitigate fatigue and musculoskeletal strains. The study aimed to explore the impact of slope-specific walking speeds on spatiotemporal gait parameters, vertical ground reaction forces (vGRFs), and position of the center of pressure (COP) during uphill and downhill walking. Thirty-two healthy individuals completed five 4-min walks on an instrumented treadmill set to 0% (level), +10%, and +20% (uphill), and −10% and −20% (downhill), slopes, at 5.0, 3.5, 2.5, 5.0 and 3.5 km h−1, respectively. Uphill walking led to reduced stride length and cadence, increased foot rotation, step time, and durations of stance, swing, and double-stance phases. Conversely, downhill walking exhibited decreased step length, step time, and durations of stance, swing, and double-stance phases but increased step width and cadence compared to level walking. Speed adjustments to accommodate slope led to reduced vGRFs for uphill and downhill walking. Additionally, the COP shifted forward during uphill and backward during downhill walking and displaced laterally as walking became more demanding. The observed responses indicate adaptations aimed at maintaining postural control, reducing excessive load application, and optimizing energy expenditure on sloping terrain.

The aim of this study was twofold: (a) to examine the differences on physical indexes between players of different playing positions and (b) to reveal the correlations between their physical parameters and the running profile within the weekly microcycles. The subjects (n = 44) were separated into five positions: central defenders (n = 7), wide defenders (n = 9), midfielders (n = 13), wingers (n = 8) and strikers (n = 7), and all of them belonged to a professional 1st division European team during the 2020–21 and 2021–22 seasons. An incremental treadmill protocol was used to assess the VO2max, vVO2max and the vRER in the last week before the regular season. Then, during the first semesters, external load data were collected for two consecutive seasons to analyze the running profiles of the different positions. There were no differences on physical parameters between the players of various positions, but interestingly there were different correlations with the external load during the microcycles. Central defenders did not show any correlation. For the other positions, correlations were found between respiratory exchange ratio (vRER) and metabolic power efforts (MPWEF) (p = 0.028, r = 0.721) for wide defenders, between vRER and medium intensity accelerations (ACC2) (p = 0.004, r = 0.741) for midfielders, medium intensity decelerations (DEC2) (p = 0.01, r = 0.685), metabolic power zone 5 (MPW5) (p = 0.016, r = 0.652), MPWEF (p = 0.005, r = 0.725), and also between maximal rate of oxygen consumption (VO2max) and high intensity decelerations (DEC3) (p = 0.045, r = 0.564), metabolic power zone 4 (MPW4) (p = 0.026, r = 0.614), MPW5 (p = 0.017, r = 0.646), MPWEF (p = 0.01, r = 0.685). A correlation between velocity on VO2max (vVO2max) and ACC2 (p = 0.038, r = 0.733), MPW5 (p = 0.005, r = 0.873), and MPWEF (p = 0.009, r = 0.84) was found for the wingers. Finally, there was a correlation between vRER and MPWEF (p = 0.026, r = 0.866), and between vVO2max and high intensity accelerations (ACC3) (p = 0.03, r = 0.801) for the strikers. Our study supports that the external load of players in different positions during the training sessions is correlated with their physical indexes. These correlations are determined mostly by their tactical role, but not from their physical level, as there were no differences.

Being able to express broad families of equivariant or invariant attributed graph functions is a popular measuring stick of whether graph neural networks should be employed in practical applications. However, it is equally important to find deep local minima of losses (i.e., produce outputs with much smaller loss values compared to other minima), even when architectures cannot express global minima. In this work we introduce the architectural property of attracting optimization trajectories to local minima as a means of achieving smaller loss values. We take first steps in satisfying this property for losses defined over attributed undirected unweighted graphs with an architecture called universal local attractor (ULA). This refines each dimension of end-to-end-trained node feature embeddings based on graph structure to track the optimization trajectories of losses satisfying some mild conditions. The refined dimensions are then linearly pooled to create predictions. We experiment on 11 tasks, from node classification to clique detection, on which ULA is comparable with or outperforms popular alternatives of similar or greater theoretical expressive power.

European islands have been leading the charge in renewable energy innovation. Yet, the intermittent nature of sources like solar and wind poses challenges such as grid saturation and frequency variations. Limited interconnection with mainland grids exacerbates these issues, necessitating backup from conventional power sources during low-production periods. Until 2021, Crete operated independently, but new infrastructure now integrates it with the Greek mainland grid, facilitating swift energy transfers. This integration enables surplus power from Crete’s solar and wind systems to be transmitted to the mainland and vice versa. However, reliance on remote power production exposes the island to market fluctuations and distant disruptions, impacting electricity production. Storage technologies offer a solution, enhancing renewable energy penetration while reducing carbon emissions. Green hydrogen, a rising storage method, shows promise in offsetting carbon emissions. Its clean-burning nature minimizes environmental impact and reduces reliance on costly and harmful conventional sources. This study aims to evaluate the feasibility of achieving carbon-neutral electricity production in Crete, Greece, using hydrogen storage to offset annual carbon emissions in a financially viable and sustainable manner. Hydrogen’s clean-burning properties reduce environmental impact and lessen dependence on expensive and environmentally harmful conventional sources. The methodology prioritizes the independence of the Cretan electricity system, utilizing electrolysis to produce green hydrogen and proton-exchange membrane (PEM) fuel cells for energy generation. It investigates the optimal expansion of renewable energy systems, including photovoltaic (PV) and wind turbine (WT) parks, alongside the installation of hydrogen storage, under specific assumptions. This proposed installation aims to achieve both island independence and profitability, requiring an additional expansion in PV capacity of 2.13 GW, WT capacity of 3.55 GW, and a hydrogen system with electrolyzer and fuel cell capacities totaling 278.83 MW each, along with a hydrogen tank capacity of 69.20 MWh. The investment entails a capital expenditure (CAPEX) of 6,516,043,003.31 EUR for a nearly zero net present value (NPV) over 20 years. However, carbon neutrality cannot be attained through this optimal solution alone, as relying solely on carbon sequestration from olive groves, the primary crop cultivated on the island, is insufficient as a carbon sink method. The annual net carbon emissions from electricity production, island transport, residential heating, and carbon sequestration are estimated at 94,772.22 tCO2.

The aim of this study was to investigate alternative methods to control longhorn beetle (Oberea linearis) infestations in walnut orchards. Wild isolates of entomopathogenic fungi obtained from soil samples from Greece and Cyprus were tested for their efficacy against adults and larvae of O. linearis. Insect populations were acquired from a heavily infested walnut orchard and individuals were placed in Petri dishes provided with ground walnut wood for larvae and fresh leaves for adults. The tested insects were subjected to 16 different wild isolates from the genera Beauveria, Cordyceps, Metarhizium, and Purpureocillium, where 108 conidia/mL were applied by spraying, and insects were monitored daily for 16 days. The results showed that all the tested fungi resulted in a mortality rate of 66–100%, with Cordyceps fumosorosea exhibiting the highest virulence, causing complete mortality to both larvae and adults. These findings suggest that the management of O. linearis, which has traditionally relied on chemical applications, could transition to an organic approach by utilizing entomopathogenic fungi.

Gold nanoparticles (AuNPs) radiolabeled with therapeutic and diagnostic radioisotopes have been broadly studied as a promising platform for early diagnosis and treatment of many diseases including cancer. Our main goal for this study was the comparison of the biodistribution profiles of four different concentrations of gold nanoconjugates radiolabeled with Technetium-99m (99mTc). More specifically, AuNPs with an average diameter of 2 nm were functionalized with a tridentate thiol ligand. Four different concentrations were radiolabeled with 99mTc-tricarbonyls with high radiolabeling yields (>85%) and were further purified, leading to radiochemical purity of >95%. In vitro stability of the radiolabeled nanoconstructs was examined in cysteine and histidine solutions as well as in human serum, exhibiting robust radiolabeling up to 24 h post-preparation. Moreover, in vitro cytotoxicity studies were carried out in 4T1 murine mammary cancer cells. In vivo tracking of the radiolabeled nanoconjugates at both concentrations was examined in normal mice in order to examine the effect of AuNPs’ concentration on their in vivo kinetics. Our work demonstrates that varying concentrations of radiolabeled AuNPs lead to notably different biodistribution profiles.

Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure.

Air quality directly affects the health of humans. The health implications of poor air quality are recognized by professionals and the public alike and these concerns have driven both the proliferation of formal sensor networks, but also low-cost sensors which can be used in the home. The advancement of technology in recent years has also led to the rapid development of low-cost sensors. Given that citizens are concerned about the air quality of the environment in which they live, they are turning to the supply of low-cost sensors, as they are affordable. The question of the reliability of measurements from low-cost sensors remains an area of research. In this research work, the optimization of ozone (O3) and nitrogen dioxide (NO2) measurements of low-cost electrochemical air quality sensors is investigated by applying nonlinear regression, using a second-order polynomial equation as a correction factor. The proposed correction method is implementable in IoT devices, as it does not require high computational resources. The results show that the measurements are susceptible to correction, with the effect that the corrected values are close to the actual values obtained by the reference instruments of the Department of Environmental Pollution Control Project of Athens (PERPA), a service of the Greek Ministry of the Environment and Energy.

Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism.

In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own service description languages to describe deployment and adaptation processes, whereas application developers opt for cloud-agnostic open standards capable of modeling applications. However, not all open standards are able to capture concepts that relate to the adaptation of the underlying computing environment to changes in the application lifecycle. In our quest for a formal approach to encapsulate these concepts, this study presents various Cloud Modeling Languages (CMLs). In this study, when referring to CMLs, we are discussing service description languages, domain-specific languages, and open standards. The output of this study is a review that performs a classification on CMLs based on their effectiveness in describing deployment and adaptation of applications in both cloud and edge environments. According to our findings, approximately 90.9% of the examined languages offer support for deployment descriptions overall. In contrast, only around 27.2% of examined languages allow developers the choice to specify whether their application components should be deployed on the edge or in a cloud environment. Regarding runtime adaptation descriptions, approximately 54.5% of the languages provide support in general.

The aim of this study was to examine the effects of concurrent dryland and sprint swimming interval training (SIT), and of SIT only, on swimmers’ performance and biomechanical variables before, during, and following 6 weeks of training. Twenty-four swimmers (age: 16.5 ± 2.9 years) were assigned to three groups of equal performance level and applied concurrent dryland and SIT three times per week, as follows: (i) maximum strength (three sets × four repetitions, load 90% of one-repetition maximum) [1RM]) prior to SΙT (group: G-MS); (ii) muscular endurance (2 sets × 20 repetitions, load 55% of 1RM) prior to SΙT (group: G-ME); and (iii) SΙT only (consisting of 2 series of 4 × 50 m sprints (group: G-CON)). Performance time, stroke rate (SR), stroke length (SL), and stroke index (SI) were measured during 4 × 50 m sprints. For pre- vs. post-performance time, SR, SL, and SI were similar between groups (p > 0.05). SR increased in G-MS and G-ME in week 6 vs. week 1 (p = 0.02), while SL and SI were similar between groups (p > 0.05). Concurrent dryland compared with sprint interval swimming training on the same day may progressively increase SR within a 6-week period, and all types of training improved front crawl efficiency following a mesocycle of training.

The present study deals with the discrimination of various European cheese products based on spectroscopic data and chemometric analysis. It is the first study that includes cheese products from Cyprus along with cheese samples from abroad and several different cheese types. Therefore, forty-nine samples were collected, freeze-dried, and measured by using spectroscopic techniques, such as FTIR (Fourier-Transform Infrared Spectroscopy) and 1H-NMR (proton nuclear magnetic resonance). Discriminant analysis was applied, particularly OPLS-DA. All data obtained from 1H-NMR were included, whereas, regarding the FTIR data, only the spectral subregion between 1900 and 400 cm−1 was used in the extracted model. The cheese samples were classified according to the milk species’ origin. In the future, the samples of this study will be enriched for further testing with spectroscopic techniques and chemometrics.

Structures inevitably suffer damage after an earthquake, with severity ranging from minimal damage of nonstructural elements to partial or even total collapse, possibly with loss of human lives. Thus, it is essential for engineers to understand the crucial factors that drive a structure towards suffering higher degrees of damage in order for preventative measures to be taken. In the present study, we focus on three well-known damage thresholds: the Collapse Limit State, Ultimate Limit State, and Serviceability Limit State. We analyze the features obtained via Rapid Visual Screening to determine whether or not a given structure crosses these thresholds. To this end, we use machine learning to perform binary classification for each damage threshold, and use explainability to quantify the effect of each parameter via SHAP values (SHapley Additive exPlanations). The quantitative results that we obtain demonstrate the potential applicability of ML methods for recalibrating the computation of structural vulnerability indices using data from recent earthquakes.

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity.

The purpose of the present study was to evaluate the antioxidant, anti-inflammatory activity and peptide levels of Feta cheese and other brined cheeses, and Metsovone cheese and other smoked cheeses. Feta, goat and cow cheeses are classified as brined. Feta cheese is made exclusively from ewe milk or ewe milk with the addition of a small amount of goat milk. Metsovone and other smoked cheeses are made from cow milk with a possible addition of small amounts of ewe and goat milk. The antioxidant activity was determined using Folin and FRAP assays, while the peptide content was determined using Bradford and Lowry assays. The anti-inflammatory activity was assessed using the lipoxygenase inhibition assay. The assays were applied in the water-soluble extract of cheeses. The results showed that Feta cheese and brined cow cheese differed in antioxidant activity. Feta cheese and brined goat cheese also differed in both antioxidant activity and peptide levels. Specifically, Feta cheese had higher antioxidant activity in comparison with both goat and cow cheeses. As for peptide content, Feta cheese had a higher peptide level compared to goat cheese. The results also showed that Metsovone cheese and other smoked cheeses exhibited significant antioxidant activity and peptide levels. Moreover, the water-soluble extracts of all cheeses showed some anti-inflammatory activity.

A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of rocking behavior, employing machine learning algorithms to predict rocking response presents a notable challenge. In the present study, the performance of supervised ML algorithms in predicting the maximum seismic response of free-standing rigid blocks subjected to ground motion excitations is evaluated. As such, both regression and classification algorithms were developed and tested, aiming to model the finite rocking response and rocking overturn. From this point of view, it is essential to estimate the maximum rocking rotation and to efficiently classify its magnitude by successfully assigning respective labels. To this end, a dataset containing the response data of 1100 rigid blocks subjected to 15,000 ground motion excitations, was employed. The results showed high accuracy in both the classification (95% accuracy) and regression (coefficient of determination R2=0.89) tasks.

The response characteristics of carbyne-enriched surface-acoustic-wave (SAW)-based gas sensors utilizing meander and rectangular microheater topologies were investigated to assess their desorption and recovery properties. Comparative analysis of contact resistance and interface capacitance before and after heating revealed minimal deviation in contact resistance, signifying strong thermal stability in the carbyne-enriched layer. However, the interface capacitance varied with the microheater size. Our analysis reveals that a small meander microheater configuration (line width: 300 µm) facilitates efficient sensor recovery at ethanol concentration measurements in the range of 180–680 ppm, maintaining a low deviation in time delay across different concentrations (~2.3%), resulting in a narrow hysteresis and linear sensor response. Conversely, the large meander microheater (line width: 450 µm) and rectangular dense microheater induce irreversible changes in the sensing structure, leading to a widened hysteresis at higher concentrations and increased power consumption. Recovery patterns display substantial deviations from initial values at different concentration levels. Higher concentrations exhibit broader hysteresis, while lower concentrations show narrower hysteresis loops, compared to the small meander microheater. The study offers insights into desorption rates, power consumption variations, and recovery behaviors related to different microheater configurations. It demonstrates the importance of microheater topology selection in tailoring recovery properties and response characteristics, contributing to the advancement of carbyne-based sensor technology.

Edible blossoms and extracted oils from various parts of plants have gained the interest of researchers in recent years due to their strong antioxidant activity and their high content of vitamins. In addition, they contain a plethora of polyphenols, and they do not have high caloric content. The blossoms of Prunus dulcis (i.e., almond tree) are edible; however, they have not been examined in terms of nutritional value. The present study aimed to examine the nutritional value of almond blossoms, as well as their extracted oil. The fat content of the blossoms was 1.75 g/100 g dry weight (dw), while the defatted blossoms were found to contain 1.34 g/100 g dw of crude protein and 29.97 g/100 g dw of carbohydrates. In addition, the blossom oil was tested for its composition of fatty acids, polyphenols, and total carotenoids. According to the results, several important fatty acids for human health were identified, such as oleic (25.17%), linoleic (15.64%), and linolenic (10.15%). Simultaneously, a low oxidation index (COX), i.e., 4.05, and many monounsaturated (25.17%) and unsaturated (67.56%) fats were detected, while both polyphenols (51.86 mg GAE/kg) and carotenoids were in abundance. Finally, the combination of simple stirring with ultrasound (a green extraction method) was found to be the most appropriate method to ensure maximum amounts of various antioxidant compounds in the blossom extracts (i.e., polyphenols and L-ascorbic acid). After optimization, the total polyphenol content increased by 23.98% and L-ascorbic acid content by 6.96%. In addition, antioxidant activity was tested by different antioxidant assays and specifically FRAP, DPPH, and H2O2, which showed a corresponding increase (14.46, 17.23, and 8.79%, respectively). Therefore, it can be concluded that Prunus dulcis blossoms, besides being edible, are also highly nutritious, and their oil has nutritional value and deserves further exploration.

Six wild-type non-Saccharomyces strains, belonging to the species Zygosaccharomyces bailii, Priceomyces carsonii, Trigonopsis californica, and Pichia manshurica, were evaluated for white wine production using Assyrtiko grapes from Santorini in Greece. Fermentation kinetics, in terms of glucose and fructose consumption and sensory analysis, was first employed to test the enological potential of the yeast strains. Based on their performance, two strains of Z. bailii (Zb-A19Y5 and Zb-K29Y2) and one strain of T. californica (Tc-A9Y1) selected for further analysis. The selected strains were tested in larger fermentation volumes for sugar consumption, while the produced wines were assessed for classical enological parameters, volatile compounds (GC/MS), and sensory analysis. Tc-A9Y1 could lead to dry wine (1 g/L of residual sugars) with 1.6 vol (12%) less alcohol than the other experimental wines. The wines that were fermented with the strain Zb-K29Y2 exhibited very high concentrations of isoamyl alcohol (604.33 ± 76.8 mg/L), but at the same time, they were characterized by low fruity notes. None of the produced wines presented any off-flavor aromas. Exploiting non-Saccharomyces strains with great fermentation capacity, which are able to produce high-quality wines and adapted to global warming conditions, is a new challenge for the wine industry.

The research on entomopathogenic viruses is of major significance as they could serve as alternatives to chemical pesticides. There are various types of entomopathogenic viruses; among them, Baculoviruses (BVs) are a potential option because they are eco-friendly and target specific. The experiment in question aimed to evaluate the effect of three insect-specific commercial viruses, Cydia pomonella Granulovirus (CpGV), Helicoverpa armigera Nucleopoyhedrovirus (HearNPV), and Phthorimaea operculella Granulovirus (PoG), on the third-instar larvae of Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) and Thaumetopoea pityocampa Schiff (Lepidoptera: Notodontidae). The viruses’ concentrations when tested were 500 ppm, 1000 ppm, 1500 ppm, 3000 ppm, and 6000 ppm, and were applied on the eating medium. Both mortality and larval weight were monitored for 6 days. All three viruses had significant mortality rates on both moths (23.3–83.3% in the highest dose) and larval weights had considerable decreases (70–80% in the highest dose). Generally, noteworthy insecticidal action was recorded after 4 days and in doses higher than 1500 ppm. These results highlight that entomopathogenic viruses may infect species other than their natural host and can be implemented in terms of Integrated Pest Management.

Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.

Solid oxide fuel cells (SOFCs) are a key component of the future energy landscape. Although there is considerable research on the physical properties and technology of classic oxide materials for electrode and electrolytes in SOFCs, the field is very active as new experimental and theoretical techniques are now available that can improve these systems. In the present review, we consider key systems such as perovskite-related materials, the impact of strain and interfaces and advanced concepts that can improve the properties of SOFC materials. In particular, we consider the oxygen diffusion properties of perovskite-related materials and focus on La2NiO4+δ and the double perovskites such as GdBaCo2O5.5. Then, we review the importance of interfaces and strain as a way to engineer defect processes. Finally, we consider advanced concepts to form designed structures that explore the effect of local high entropy on lattice stabilization.

The purpose of this study was to investigate the internal load of amateur soccer players during official championship matches when playing with the 1-4-3-3 formation. Additionally, the possible relationship between internal load variables and external load indices was explored. The study involved 18 amateur soccer players, and the Polar Team Pro GPS system was used to record external load and heart rate (HR) during official championship matches. Internal load was assessed using %HRmax and Edward’s training impulse (TRIMPEdw). External load indices included total distance (TD), pace (distance/min), number of sprints, distance covered in different speed zones, accelerations (>2 m/s2), and decelerations (2). Players were categorized as central defenders (CD), side defenders (SD), central midfielders (CM), side midfielders (SM), and forwards (F). The results indicated no differences in internal load indices among playing positions. During matches, SM performed the most sprints, while CD performed the fewest (p < 0.05). CD covered the shortest distances in speed zones >7.20 km/h, whereas CM and SM covered the longest distances (p < 0.05). Additionally, CD had fewer accelerations and decelerations compared to SM (p < 0.05). In the first half, differences were observed in pace (distance/min) (p < 0.05), but not in the second half. Between halves, differences in %HRmax were observed for all positions, while TRIMPEdw differed in CM and F. CM showed the most differences between halves. Throughout the matches, a high correlation was found between %HRmax and pace (distance/min), while TRIMPEdw correlated with TD, as well as distances in high-speed zones (14.40–25.19 km/h). This suggests that TRIMPEdw is a comprehensive indicator dependent on volume (TD) and on high-intensity actions, which are crucial in soccer, influencing players’ and teams’ performances. Therefore, for assessing the internal load of amateur soccer players, TRIMPEdw may be a more useful indicator. This study provides valuable information for coaches of amateur soccer teams using the 1-4-3-3 formation regarding running profiles, accelerations/decelerations, and the profile of internal load in each playing position. This information can be used to individualize training based on players’ playing positions.

Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.

Myocardial Perfusion Imaging (MPI) has played a central role in the non-invasive identification of patients with Coronary Artery Disease (CAD). Clinical factors, such as recurrent diseases, predisposing factors, and diagnostic tests, also play a vital role. However, none of these factors offer a straightforward and reliable indication, making the diagnosis of CAD a non-trivial task for nuclear medicine experts. While Machine Learning (ML) and Deep Learning (DL) techniques have shown promise in this domain, their “black-box” nature remains a significant barrier to clinical adoption, a challenge that the existing literature has not yet fully addressed. This study introduces the Deep Fuzzy Cognitive Map (DeepFCM), a novel, transparent, and explainable model designed to diagnose CAD using imaging and clinical data. DeepFCM employs an inner Convolutional Neural Network (CNN) to classify MPI polar map images. The CNN’s prediction is combined with clinical data by the FCM-based classifier to reach an outcome regarding the presence of CAD. For the initialization of interconnections among DeepFCM concepts, expert knowledge is provided. Particle Swarm Optimization (PSO) is utilized to adjust the weight values to the correlated dataset and expert knowledge. The model’s key advantage lies in its explainability, provided through three main functionalities. First, DeepFCM integrates a Gradient Class Activation Mapping (Grad-CAM) algorithm to highlight significant regions on the polar maps. Second, DeepFCM discloses its internal weights and their impact on the diagnostic outcome. Third, the model employs the Generative Pre-trained Transformer (GPT) version 3.5 model to generate meaningful explanations for medical staff. Our dataset comprises 594 patients, who underwent invasive coronary angiography (ICA) at the department of Nuclear Medicine of the University Hospital of Patras in Greece. As far as the classification results are concerned, DeepFCM achieved an accuracy of 83.07%, a sensitivity of 86.21%, and a specificity of 79.99%. The explainability-enhancing methods were assessed by the medical experts on the authors’ team and are presented within. The proposed framework can have immediate application in daily routines and can also serve educational purposes.

The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.

During the Ottoman period, wood had replaced marble in Orthodox churches. Between the 16th and 19th century, common, unknown craftsmen created wonderful wood-carved ecclesiastical works by hand, though many of them have been partially or completely destroyed, usually without any of their images being saved. Advances in technology now make it possible to develop a detailed digital copy of an object of our cultural heritage, creating an unaltered prototype material to be studied by all, while offering in parallel the ease of reproducing precise copies, ensuring even museum quality standards. In the present study, a corresponding attempt to record, scan and then reconstruct a 17th century “Bema Door” (located in the Holy Monastery of Hilandar in Mount Athos) is implemented, presented step-by-step and discussed, in order to highlight the challenges and potential of the applied methods. The process included on-site recording of a 3D digital scan, and then the creation of the Bema door copy, first applying Computerized Numerical Control (CNC) machining and then, with elaborate handwork by experienced wood-carvers who successfully completed the process, imparting the necessary and unique human artistic character. The current work transfers the experience on this reproduction technique applied in a Bema door element, shares significant information and details on tools, materials, process steps, etc., in order to be an inspiration and stimulus for the preservation or reproduction of other cultural heritage monuments of high importance.

Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between E-field and the ensuing cortical activation, validate in a clinically relevant manner the results of E-field modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.

The aim of the present study was to investigate if the use of Fe (II) and H2O2 (Fenton reaction) along with heating can be implicated for the rapid evaluation of the browning susceptibility of white wine. In this context, increasing concentrations of Fe (II) and H2O2 were added to two white wines (Roditis–Malagouzia and Debina), the samples were stored at 45 °C or 18 °C, and the absorbance at 420 nm (browning index) was measured. Moreover, total sulfur dioxide, total phenolics, flavanols, hydroxycinnamates and total free sulfhydryls were assessed during storage of Roditis–Malagouzia wine samples. The results showed that the addition of Fe (II) + H2O2 mixture in white wines increases rapidly the browning index at 45 °C, indicating the ability to develop a rapid test for the estimation of the browning susceptibility of white wines. Results also showed that the level of flavanols is a suitable index to follow the forced oxidative browning of white wine by adding Fe II + H2O2 at 45 °C. Moreover, results indicated that the levels of total free sulfhydryls may be a suitable index to follow the forced oxidative browning of white wine and in general, white wine oxidation.

We investigate an earthquake sequence involving an Mw = 4.6 mainshock on 2 December 2020, followed by a seismic swarm in July–October 2021 near Thiva, Central Greece, to identify the activated structures and understand its triggering mechanisms. For this purpose, we employ double-difference relocation to construct a high-resolution earthquake catalogue and examine in detail the distribution of hypocenters and the spatiotemporal evolution of the sequence. Furthermore, we apply instrumental and imaging geodesy to map the local deformation and identify long-term trends or anomalies that could have contributed to stress loading. The 2021 seismic swarm was hosted on a system of conjugate normal faults, including the eastward extension of the Yliki fault, with the main activated structures trending WNW–ESE and dipping south. No pre- or coseismic deformation could be associated with the 2021 swarm, while Coulomb stress transfer due to the Mw = 4.6 mainshock of December 2020 was found to be insufficient to trigger its nucleation. However, the evolution of the swarm is related to stress triggering by its major events and facilitated by pore-fluid pressure diffusion. The re-evaluated seismic history of the area reveals its potential to generate destructive Mw = 6.0 earthquakes; therefore, the continued monitoring of its microseismicity is considered important.

This study focuses on the airflow and pollutant dispersion around an isolated cubical building located in a warm Mediterranean climate, taking into account the local microclimate conditions (of airflow, albedo of building and soil, and air humidity) using a large-eddy simulation (LES) numerical approach. To test the reliability of computations, comparisons are made against the SILSOE cube experimental data. Three different scenarios are examined: (a) Scenario A with adiabatic walls, (b) Scenario B with the same constant temperature on all the surfaces of the building, and (c) Scenario C using convective and radiative conditions imposed by the local microclimate. For the first two cases the velocity and temperature fields resulting are almost identical. In the third case, the resulting temperature on the surfaces of the building is increased by 19.5%, the center (eye) of the wake zone is raised from the ground and the maximum pollutant concentration is drastically reduced (89%).