Search Results (4,126)

The 1.9 billion metric tons of steel globally manufactured in 2023 justify the steel industry’s pivotal role in modern society’s growth. Considering the rapid development of countries that have not fully taken part in the global market, such as Africa, steel production is expected to increase in the next decade. However, the environmental burden associated with steel manufacturing must be mitigated to achieve sustainable production, which would align with the European Green Deal pathway. Such a burden is associated both with the GHG emissions and with the solid residues arising from steel manufacturing, considering both the integrated and electrical routes. The valorisation of the main steel residues from the electrical steelmaking is the central theme of this work, referring to the steel electric manufacturing in the Dalmine case study. The investigation was carried out from two different points of view, comprising the action of a plasma electric reactor and a RecoDust unit to optimize the recovery of iron and zinc, respectively, being the two main technologies envisioned in the EU-funded research project ReMFra. This work focuses on those preliminary steps required to detect the optimal recipes to consider for such industrial units, such as thermodynamic modelling, testing the mechanical properties of the briquettes produced, and the smelting trials carried out at pilot scale. However, tests for the usability of the dusty feedstock for RecoDust are carried out, and, with the results, some recommendations for pretreatment can be made. The outcomes show the high potential of these streams for metal and mineral recovery. Full article

Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. Histopathological analysis is a widely used diagnostic method, but it is a time-consuming process that heavily depends on the expertise of highly trained specialists. Recent advances in Artificial Intelligence have shown promising results in image classification, highlighting its potential as a supportive tool for medical diagnosis. In this study, we explore the application of hybrid Artificial Intelligence models for melanoma diagnosis using histopathological images. The dataset used consisted of 506 histopathological images, from which 313 curated images were selected after quality control and preprocessing. We propose a two-step framework that employs an Autoencoder for dimensionality reduction and feature extraction of the images, followed by a classification algorithm to distinguish between melanoma and nevus, trained on the extracted feature vectors from the bottleneck of the Autoencoder. We evaluated Support Vector Machines, Random Forest, Multilayer Perceptron, and K-Nearest Neighbours as classifiers. Among these, the combinations of Autoencoder with K-Nearest Neighbours achieved the best performance and inference time, reaching an average accuracy of approximately 97.95% on the test set and requiring 3.44 min per diagnosis. The baseline comparison results were consistent, demonstrating strong generalisation and outperforming the other models by 2 to 13 percentage points. Full article

Background/Objectives: Vertical jump is considered a reliable and valid method of assessing the level of muscular power and coordination across one’s lifespan. The main aim of the present study was to establish sex- and age-normative data for vertical jump outcomes in pre-school children. Methods: We recruited 411 boys and girls aged 3−6 years from four major cities in Croatia and Slovenia. Vertical jump was assessed with two tests: countermovement jump (CMJ) without and with arm swing using a reliable and valid Optojump measuring platform. Data were presented for the 5th, 15th, 25th, 50th (median), 75th, 90th, and 95th percentile. Results: No significant differences were observed in multiple vertical jump outcomes between boys and girls. The mean values for CMJ without and with arm swing between boys and girls were as follows: contact time (1.4 vs. 1.4 s/1.8 vs. 1.7 s), flight time (0.32 vs. 0.31 s/0.33 vs. 0.32), height (12.3 vs. 12.2 cm/13.0 vs. 12.5 cm), power (9.4 vs. 9.5 W/kg/9.3 vs. 9.1 W/kg), pace (0.7 vs. 0.7 steps/s/0.6 vs. 0.6 steps/s), reactive strength index (RSI; 0.10 vs. 0.09 m/s/0.08 vs. 0.08 m/s), and verticality (2.5 vs. 2.3/1.9 vs. 1.9). A gradual increase in all measures according to ‘age’ was observed (p for trend < 0.05). No significant ‘sex*age’ interaction was observed (p > 0.05). Conclusions: This is one of the first studies to provide sex- and age-normative data for complete vertical jump outcomes in pre-school children. These data will serve as an avenue for monitoring and tracking motor development in this sensitive period. Full article

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers. Full article

Background/Objectives: The gene pool of the Apennine wolf is affected by admixture with domestic variants due to anthropogenic hybridisation with dogs. Genetic monitoring at the population level involves assessing the extent of admixture in single individuals, ranging from pure wolves to recent hybrids or wolf backcrosses, through the analysis of nuclear and mitochondrial DNA (mtDNA) markers. Although individually non-diagnostic, mtDNA is nevertheless essential for completing the final diagnosis of genetic admixture. Typically, the identification of wolf mtDNA haplotypes is carried out via sequencing of coding genes and non-coding DNA stretches. Our objective was to develop a fast real-time PCR assay to detect the mtDNA haplotypes that occur exclusively in the Apennine wolf population, as a valuable alternative to the demanding sequence-based typing. Methods: We validated a qualitative duplex real-time PCR that exploits the combined presence of diagnostic point mutations in two mtDNA segments, the NDH-4 gene and the control region, and is performed in a single-tube step through TaqMan-MGB chemistry. The aim was to detect mtDNA multi-fragment haplotypes that are exclusive to the Apennine wolf, bypassing sequencing. Results: Basic validation of 149 field samples, consisting of pure Apennine wolves, dogs, wolf × dog hybrids, and Dinaric wolves, showed that the assay is highly specific and sensitive, with genomic DNA amounts as low as 10⁻⁵ ng still producing positive results. It also proved high repeatability and reproducibility, thereby enabling reliable high-throughput testing. Conclusions: The results indicate that the assay presented here provides a valuable alternative method to the time- and cost-consuming sequencing procedure to reliably diagnose the maternal lineage of the still-threatened Apennine wolf, and it covers a wide range of applications, from scientific research to conservation, diagnostics, and forensics. Full article

Background/Objectives: Maternal exposures are known to influence the risk of isolated cleft lip with or without cleft palate (CL/P)—a common and highly heritable birth defect with a multifactorial etiology. Methods: To identify new risk loci, we conducted a genome-wide gene–environment interaction (GEI) analysis of CL/P with maternal smoking and vitamin use in Filipinos (N_cases = 540, N_controls = 260). Since GEI analyses are typically low in power and the results can be difficult to interpret, we applied multiple testing frameworks to evaluate potential GEI effects: a one degree-of-freedom (1df) GxE test, the 3df joint test, and the two-step EDGE approach. Results: While no genome-wide significant interactions were detected, we identified 11 suggestive GEIs with smoking and 24 with vitamin use. Several implicated loci contain biologically plausible genes. Notable interactions with smoking include loci near FEZF1, TWIST2, and NET1. While FEZF1 is involved in early neuronal development, TWIST2 and NET1 regulate epithelial–mesenchymal transition, which is required for proper lip and palate fusion. Interactions with vitamins encompass CECR2—a chromatin remodeling protein required for neural tube closure—and FURIN, a critical protease during early embryogenesis that activates various growth factors and extracellular matrix proteins. The activity of both proteins is influenced by folic acid. Conclusions: Our findings highlight the critical role of maternal exposures in identifying genes associated with structural birth defects such as CL/P and provide new paths to explore for CL/P genetics. Full article

In the energy sector and in other types of industries (cement, iron/steel, chemical and petrochemical), highly roasted coffee ground residue can be used as a source material for producing bioadsorbents suitable for CO₂ capture. In this study, a bioadsorbent was produced in a two-step process involving biowaste carbonization and biocarbon activation within a KOH solution. The physicochemical properties of the bioadsorbent were assessed using LECO, TG, SEM, BET and FT-IR methods. Investigating the CO₂, O₂ and N₂ equilibrium adsorption capacity using an IGA analyzer allowed us to calculate CO₂ selectivity factors. We assessed the influence of exhaust gas carbon dioxide concentration (16%, 30%, 81.5% and 100% vol.) and adsorption step temperature (25 °C, 50 °C and 75 °C) on the CO₂ adsorption capacity of the bioadsorbent. We also investigated its stability and regenerability in multi-step adsorption–desorption using a TG-Vacuum system, simulating the VSA process and applying different pressures in the regeneration step (30, 60 and 100 mbar_abs). The tests conducted assessed the possibility of using a produced bioadsorbent for capturing CO₂ using the VSA technique. Full article

Background: Diabetes and cancer are two of the most life-threatening disorders affecting individuals of all ages worldwide. This study aimed to develop a novel Acrocomia aculeata (bocaiuva) fruit pulp oil-loaded nanoemulsion and evaluate its inhibitory effects on α-glucosidase and pancreatic lipase, as well as its antiglycant activity and cytotoxicity against cancer cells. Additionally, this study assessed the impact of both the oil and the nanoemulsion on blood cells. Methods: The pulp oil was extracted by cold pressing. The oil’s physicochemical properties were determined according to the AOAC and the Brazilian Pharmacopeia. The lipid profile was performed by GC-MS. The nanoemulsion was prepared by the phase inversion method using ultrasonic stirring for particle size reduction and for homogenization. Response Surface Methodology was used for optimizing nanoemulsion preparation. Enzyme inhibition tests were conducted using assay kits. Cytotoxicity in cancer cells was evaluated using the Sulforhodamine B assay. Results: Comprehensive physicochemical and chemical characterization of bocaiuva oil was performed, identifying oleic acid (71.25%) as the main component. The oil contains 23.04% saturated fatty acids, 73.79% monounsaturated acids, and 3.0% polyunsaturated fatty acids. The nanoemulsion (particle size 173.6 nm; zeta potential −14.10 mV) inhibited α-glucosidase (IC₅₀: 43.21 µg/mL) and pancreatic lipase (IC₅₀: 41.99 µg/mL), and revealed a potent antiglycation effect (oxidative IC₅0: 18.36 µg/mL; non-oxidative pathway IC₅₀: 16.33 µg/mL). The nanoemulsion demonstrated good cytotoxicity and selectivity against prostate cancer cells (IC₅₀: 19.13 µg/mL) and breast cancer cells (IC₅₀: 27.22 µg/mL), without inducing hemolysis, platelet aggregation, or anticoagulant effects. Conclusions: In this study, a comprehensive physical and chemical characterization of bocaiuva fruit pulp oil was conducted for the first time as a preliminary step toward its future standardization as an active ingredient in cosmetic and pharmaceutical formulations. The resulting nanoemulsion represents a novel alternative for managing diabetes and cancer. Although the nanoemulsion exhibited lower cytotoxicity compared to doxorubicin, it remains promising due to its composition of essential fatty acids, phenols, and carotenoids, which offer multiple health benefits. Further studies are needed to validate its efficacy and safety in clinical applications. Full article

Polymethyl methacrylate nanoparticles functionalized with three different compounds, acrylic acid (AA), curcumin (CUR), and fumaramide (FA), were tested in a two-step solid–liquid extraction process (extraction and stripping) for yttrium recovery. In both stages, the best conditions were determined: pH, solid–liquid ratio and the compound with the highest affinity for yttrium recovery, obtaining 90% of efficiency for both stages in a single work cycle. The results obtained by SEM ruled out the growing of nanoparticles by swelling and confirmed the formation of structural arrangements by the addition of the metal to the system. In addition, there is evidence that the recovery process can be selective considering the mixing of rare earth elements through changes in pH. Using isothermal titration calorimetry (ITC), the thermodynamic properties of the extraction process were calculated, understanding the system as the union of a macromolecule and a ligand. The results showed that the extraction process was spontaneous and highly entropic. Full article

Against the backdrop of global energy transition and strict environmental regulations, supercritical carbon dioxide (scCO₂) fracturing and oil displacement technologies have emerged as pivotal green approaches in shale gas exploitation, offering the dual advantages of zero water consumption and carbon sequestration. However, the inherent low viscosity of scCO₂ severely restricts its sand-carrying capacity, fracture propagation efficiency, and oil recovery rate, necessitating the urgent development of high-performance thickeners. The current research on scCO₂ thickeners faces a critical trade-off: traditional fluorinated polymers exhibit excellent philicity CO₂, but suffer from high costs and environmental hazards, while non-fluorinated systems often struggle to balance solubility and thickening performance. The development of new thickeners primarily involves two directions. On one hand, efforts focus on modifying non-fluorinated polymers, driven by environmental protection needs—traditional fluorinated thickeners may cause environmental pollution, and improving non-fluorinated polymers can maintain good thickening performance while reducing environmental impacts. On the other hand, there is a commitment to developing non-noble metal-catalyzed siloxane modification and synthesis processes, aiming to enhance the technical and economic feasibility of scCO₂ thickeners. Compared with noble metal catalysts like platinum, non-noble metal catalysts can reduce production costs, making the synthesis process more economically viable for large-scale industrial applications. These studies are crucial for promoting the practical application of scCO₂ technology in unconventional oil and gas development, including improving fracturing efficiency and oil displacement efficiency, and providing new technical support for the sustainable development of the energy industry. This study innovatively designed an amphiphilic modified amino silicone oil polymer (MA-co-MPEGA-AS) by combining maleic anhydride (MA), methoxy polyethylene glycol acrylate (MPEGA), and amino silicone oil (AS) through a molecular bridge strategy. The synthesis process involved three key steps: radical polymerization of MA and MPEGA, amidation with AS, and in situ network formation. Fourier transform infrared spectroscopy (FT-IR) confirmed the successful introduction of ether-based CO₂-philic groups. Rheological tests conducted under scCO₂ conditions demonstrated a 114-fold increase in viscosity for MA-co-MPEGA-AS. Mechanistic studies revealed that the ether oxygen atoms (Lewis base) in MPEGA formed dipole–quadrupole interactions with CO₂ (Lewis acid), enhancing solubility by 47%. Simultaneously, the self-assembly of siloxane chains into a three-dimensional network suppressed interlayer sliding in scCO₂ and maintained over 90% viscosity retention at 80 °C. This fluorine-free design eliminates the need for platinum-based catalysts and reduces production costs compared to fluorinated polymers. The hierarchical interactions (coordination bonds and hydrogen bonds) within the system provide a novel synthetic paradigm for scCO₂ thickeners. This research lays the foundation for green CO₂-based energy extraction technologies. Full article

This study assessed the host plant selection behavior of female stalk-eyed flies (SEFs) or Diopsis apicalis, where a Y-tube olfactometer was used to compare SEF attraction to the odor of leaves from four rice varieties (ITA306, WAB56-104, CG14, and RAM55). Another step of the evaluation consisted of pairing leaf odors from two rice varieties. Also, potted plants of the tested varieties were displayed in a screened cage and submitted to female SEF selection. The results indicated that the odor produced by leaves from rice varieties CG14, WAB56-104, and ITA306 significantly attracted SEFs, at rates of 81%, 70%, and 97%, respectively, while SEF females were rarely attracted by the odor of leaves from the resistant rice variety RAM55, at a rate of 35%. The results suggested that the use of a Y-tube olfactometer was similar to the use of a screened cage. The resistance exhibited by rice variety CG14 against SEFs is related to an antibiosis interaction acting as bait, while that in RAM55 is an antixenosis one. Farmers can plant the traditional CG14 variety on the edge of rice fields to draw SEFs and poison their larvae. However, RAM55 can be inserted in an intercropping system to repel SEFs from laying eggs. The authors recommend CG14 and RAM55 as candidates for breeding to create resistant lines against SEF. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

One of the goals of Agenda 2030 is to increase the share of renewable energy in the global energy mix. In this context, photovoltaic systems play a key role in the transition to clean energy. According to the International Energy Agency, in 2023, solar photovoltaic alone accounted for three-quarters of renewable capacity additions worldwide. Designing a performing photovoltaic system requires careful planning that takes into account various factors, both internal and external, in order to maximize energy production and optimize costs. In addition to the technical characteristics of the system (internal factors), the positions and the shapes of external buildings and surrounding obstacles (external factors) have a significant impact on the output of photovoltaic systems. However, given the complexity of these environmental factors, they cannot be treated accurately in manual design practice. For this reason, this paper proposes a Building Information Modeling-based workflow for the design of a photovoltaic system that can guide the professional step-by-step throughout the design process, starting from the embryonic phase to the definitive, and therefore more detailed, one. The developed methodology allows for an in-depth analysis of the shading, the photovoltaic potential of the building, the performance of the photovoltaic system, and the costs for its construction in order to evaluate the appropriateness of the investment. The main aim of the paper is to create a standardized procedure applicable on a large scale for photovoltaic integration within Building Information Modeling workflows. The methodology is tested on two case studies, characterized by different architectural features and geographical positions. Full article

Given that student entrepreneurship contributes to the intensification of economic activities and the improvement of the social well-being of the parties involved, evaluating and fostering students’ entrepreneurial intentions can be a step in moving from intention to action in the entrepreneurial process. From this perspective, the present study assesses students’ entrepreneurial intentions and measures the impact of the most important determinants based on online questionnaires addressed to students from two different fields of study: economics and engineering. Using the collected data (N = 392 students) and analysis methods based on correlation and stratified multiple regression as well as non-parametric tests (Mann–Whitney U), the study reveals that students’ entrepreneurial intentions are influenced by mindset and resilience. The study indicates that the influences can vary significantly when the analyses include control variables, such as gender, field of study, year of study, professional experience, age, and country of origin. It is also important to note that the statistical significance of the results regarding the impact of resilience varies depending on the specifics of the control variables. This study considered both analyses of resilience (as a synthetic indicator) and its subcomponents. The results of this study have both theoretical and practical utility. Full article