Search Results (3,009)

This paper analyzes the macroeconomic evolution of the European Union member states that have adopted the Euro, compared to those that continue to use national currencies, with a specific focus on the Central and Eastern European countries during the period 2018–2024. Using a quantitative and exploratory approach and data provided by Eurostat, the European Central Bank, and the International Monetary Fund, we examined a series of key indicators: interest rates, inflation, GDP per capita, public debt, and foreign direct investment. The results highlight several macroeconomic advantages for Eurozone countries, including lower interest rate volatility and a quicker recovery from inflation, largely due to access to monetary tools such as PEPP and TPI. Non-Euro countries have experienced more severe inflationary episodes and higher financing costs, which have negatively impacted FDI inflows. Although some of these countries, such as Romania and Poland, have recorded solid GDP growth, they remain exposed to structural vulnerabilities and political and economic uncertainties. Correlation analyses confirm significant negative relationships between interest rates, inflation, and FDI levels. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

C. humilis is a small shrub belonging to the Rosaceae family, and grafting is one of the main ways for propagation. However, the influence of different rootstocks on volatile aroma is still unclear. In this study, an untargeted metabolomics approach based on gas chromatography–mass spectrometry (GC-MS) was utilized to analyze the volatile differential metabolites between the rootstock–scion combinations and self-rooted seedlings. Furthermore, metabolic pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In total, 191,162 and 150 volatile differential metabolites were identified in different rootstock–scion combinations. The rootstock–scion combinations of ZG/MYT and ZG/BT could improve the volatile aroma in the fruit of C. humilis and made significant contributions to the rose and fruity flavors. KEGG pathway analysis indicated that the differential metabolites were mainly enriched in the butanoate metabolism and glycolysis/gluconeogenesis pathways, showing an increasing trend. Prunus tomentosa and Amygdalus communis can serve as preferred rootstocks for enhancing the aroma quality of C. humilis fruits. These results provide new insight into rootstock-based propagation and breeding and also offer some guidance for graft-based fruit production. Full article

Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with multiple clinical and inflammatory phenotypes. The coexistence of bronchiectasis, known as bronchiectasis–COPD overlap (BCO), identifies a subgroup with increased morbidity and mortality. Non-invasive breath analysis using electronic noses (e-noses) has shown promise in identifying disease-specific volatile organic compound (VOC) patterns (“breathprints”). Our aim was to evaluate the ability of an e-nose to differentiate between COPD and BCO patients, and to assess its utility in detecting inflammatory endotypes (neutrophilic vs. eosinophilic). In a monocentric, prospective, real-life study, 98 patients were enrolled over nine months. Forty-two patients had radiologically confirmed BCO, while fifty-six had COPD without bronchiectasis. Exhaled breath samples were analyzed using the Cyranose 320 e-nose. Principal component analysis (PCA) and discriminant analysis were used to identify group-specific breathprints and inflammatory profiles. PCA revealed significant breathprint differences between BCO and COPD (p = 0.021). Discriminant analysis yielded an overall accuracy of 69.6% (AUC 0.768, p = 0.037). The highest classification performance (76.8%) was achieved when distinguishing eosinophilic COPD from neutrophilic BCO. These findings suggest distinct inflammatory profiles that may be captured non-invasively. E-nose technology holds potential for the non-invasive endotyping of COPD, especially in identifying neutrophilic BCO as a unique inflammatory entity. Breathomics may support early, personalized treatment strategies. Full article

In this study, the effects of non-thermal pretreatment such as corona discharge plasma (CDP-21 kV), dielectric barrier discharge plasma (DBDP-32 kV), and ultrasonic waves of different powers (US-180 W, 210 W, 240 W) on hot-air drying of ferruginous yam were compared. The regulatory effects of ultrasonic and cold plasma pretreatment on the drying characteristics and quality of yam were systematically evaluated by determining the drying kinetic parameters, physicochemical indexes, volatile components, and energy consumption. The results showed that ultrasonic pretreatment significantly improved the drying performance of yam compared with different cold plasma treatments, with the highest drying rate and effective moisture diffusion coefficient in the US-180 W group. In terms of quality, this treatment group exhibited better color retention, higher total phenol content (366 mg/100 g) and antioxidant activity, and optimal rehydration performance. Low-field nuclear magnetic resonance (NMR) analyses showed a more homogeneous water distribution, and gas chromatography–mass spectrometry (GC-MS) identified 55 volatile components. This study confirms that the US-180 W ultrasonic pretreatment technology can effectively improve the drying efficiency and product quality of yam and at the same time reduce the energy consumption. The results of this study provide a practical solution for the optimization of a process that can be replicated in the food drying industry. Full article

Understanding the resistive switching (RS) mechanisms in memristive devices is crucial for developing non-volatile memory technologies. Here, we investigate the memristor effect in hydrothermally grown Au-nanoseeded CuO films. Based on I-V measurements, conductive-AFM, S/TEM, and EDS analyses, we examine the changes within the switching layer associated with RS. Our results reveal a filamentary mechanism of RS. Notably, EDS mapping shows directional Au redistribution between the bottom nanoseeds and the top electrode, while Cu and O remain uniformly distributed. These findings support an electrochemical metallization (ECM)-like filamentary mechanism driven by Au species migration. The use of Au-nanoseeds, required by the solution-based growth method, critically affects filament formation and RS behavior. Our results emphasize the importance of microstructure and electrode–oxide interfaces in determining the switching mechanism in oxide-based memristors. Full article

Integrating two-dimensional transition-metal dichalcogenides with graphene is attractive for low-power memory and neuromorphic hardware, yet sequential wet transfer leaves polymer residues and high contact resistance. We demonstrate a complementary metal–oxide–semiconductor (CMOS)-compatible, transfer-free route in which an atomically thin amorphous MoS₂ precursor is RF-sputtered directly onto chemical vapor-deposited few-layer graphene and crystallized by confined-space sulfurization at 800 °C. Grazing-incidence X-ray reflectivity, Raman spectroscopy, and X-ray photoelectron spectroscopy confirm the formation of residue-free, three-to-four-layer 2H-MoS₂ (roughness: 0.8–0.9 nm) over 1.5 cm × 2 cm coupons. Lateral MoS₂/graphene devices exhibit reproducible non-volatile resistive switching with a set transition (SET) near +6 V and an analogue ON/OFF ≈2.1, attributable to vacancy-induced Schottky-barrier modulation. The single-furnace magnetron sputtering + sulfurization sequence avoids toxic H₂S, polymer transfer steps, and high-resistance contacts, offering a cost-effective pathway toward wafer-scale 2D memristors compatible with back-end CMOS temperatures. Full article

We have recently described a murine model of vaginal secretion that allows the measurement of minute changes in vaginal secretion. Using this model, we determined that female mice experience a vaginal secretory response to the scent of males, a response regulated by circadian and estrous factors since females did not respond during their sleep phase, nor when in metestrus. Female mice can distinguish the social status of a male by scent cues and show a preference for the scent of dominant males. We therefore tested whether or not vaginal responses to male scent differ by the social status of that male. Vaginal secretory responses were measured using a recently described method employing a colorimetric thread. In addition, while we have shown that the proposed female attractant α/β farnesenes evoked a strong vaginal response in female mice, a second volatile preputial gland-derived messenger, 1-hexadecanol, has also been proposed to serve as a female attractant. Here we test whether or not 1-hexadecanol similarly stimulates a vaginal secretory response. We now report that the female vaginal secretory response differs according to the social status of the male: the urine-borne scent of dominant males elicited a vaginal response, while samples from non-dominant males did not. In related odor-preference tests we confirmed that female mice spend more time investigating the urine scent of dominant males. We additionally tested whether or not a second putative female attractant 1-hexadecanol would elicit a vaginal secretory response. Like the α/β farnesenes, 1-hexadecanol is volatile, derived from preputial glands, and induces an investigatory response in females. However female mice did not experience a vaginal secretory response to the scent of 1-hexadecanol. We did confirm that females spent more time investigating hexadecanol over vehicle, indicating that there can be a disconnect between behavioral measures of interest and a vaginal preparatory response. In summary, we find that subordinate male mice do not elicit a vaginal secretory response, indicating that male social status impacts the physiological responses of females to the prospect of coitus. We additionally find that in contrast to farnesenes, the putative female attractant 1-hexadecanol does not elicit a vaginal response. These findings underscore the potential value of this murine model and indicate that even in mice, vaginal responses are under complex regulation. Full article

By proportionally blending fresh latex from PR107, Reyan 72059, and Reyan 73397, and employing both acid- and enzyme-assisted microbial coagulation methods, this study analyzed the effects of the specific latex formulation on the following: physicochemical properties, non-rubber components, molecular weight and distribution, vulcanization characteristics of compounded rubber, and physical–mechanical properties of vulcanized natural rubber. The results indicate that, compared to acid-coagulated natural rubber, enzyme-assisted microbial coagulated natural rubber exhibits slightly lower levels of volatile matter, impurities, plasticity retention index (PRI), nitrogen content, calcium ions (Ca²⁺), iron ions (Fe³⁺), and fatty acid content. Conversely, it demonstrates higher values in ash content, initial plasticity (P₀), Mooney viscosity (M_L(1+4)), acetone extract, magnesium ions (Mg²⁺), copper ions (Cu²⁺), manganese ions (Mn²⁺), gel content, molecular weight and distribution, and glass transition temperature (Tg). With the increase in the proportion of PR107 and Reyan 72059 fresh latex, the ash content, volatile matter content, fatty acid content, gel content, and dispersion coefficient (PDI) of natural rubber gradually decrease, while the impurity content, PRI, nitrogen content, weight-average molecular weight (Mw), and number-average molecular weight (Mn) gradually increase. Compared to acid-coagulated natural rubber compounds, enzyme-assisted microbial-coagulated natural rubber compounds exhibit higher minimum torque (M_L) and maximum torque (M_H), but shorter scorch time (t₁₀) and optimum cure time (t₉₀). Furthermore, as the proportion of PR107 and Reyan 72059 fresh latex increases, the ML of the compounds gradually decreases. In pure rubber formulations, enzyme-assisted microbial-coagulated natural rubber vulcanizates demonstrate higher tensile strength, tear strength, modulus at 300%, and Shore A hardness compared to acid-coagulated natural rubber vulcanizates. When the fresh latex ratio of PR107, Reyan 72059, and Reyan 73397 is 1:1:3, the tensile strength and 300% modulus of the natural rubber vulcanizates reach their maximum values. In carbon black formulations, the tensile strength and tear strength of enzyme-assisted microbial-coagulated natural rubber vulcanizates are significantly higher than those of acid-coagulated natural rubber vulcanizates in pure rubber formulations, with the increase exceeding that of other samples. Full article

Thermogravimetric analysis (TGA) is a key technique for evaluating the kinetics and thermodynamics of thermal degradation, providing essential data for material assessment and system design. When coupled with Fourier-transform infrared (FT-IR) spectroscopy or mass spectroscopy (MS), it enables the identification of evolved gases and correlates mass loss with specific chemical species, offering detailed insight into decomposition mechanisms. In this study, TGA was coupled with FT-IR and MS to investigate the thermal degradation behavior of Bakelite, with the aim of evaluating its kinetic and thermodynamic parameters under non-isothermal conditions, identifying evolved volatile compounds, and elucidating the degradation process. The results showed that higher heating rates led to increased decomposition temperatures and broader dTG peaks due to thermal lag effects. The degradation proceeded in multiple stages between 220 °C and 860 °C, ultimately yielding a carbonaceous residue. The activation energy increased with conversion, particularly beyond 0.5, indicating a greater energy requirement as degradation progressed. Peak values at conversion degrees of 0.8–0.9 suggested enhanced thermal stability or changes in the dominant reaction mechanism. Detailed kinetic analysis revealed complex decomposition pathways with variable activation energies and a pronounced kinetic compensation effect. Thermodynamic analysis confirmed the endothermic nature of the process, with increasing energy demand and non-spontaneous degradation of the resulting char. TGA/FT-IR and TGA/MS analyses identified the release of several compounds, including CO₂, water, formaldehyde, and phenolic derivatives, at distinct stages. This comprehensive understanding of Bakelite’s thermal behavior supports its optimization for high-temperature applications, enhances material reliability and safety, and contributes to sustainable processing and recycling strategies. Full article

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K. Full article

This study evaluated the effects of replacing SBM with CWYWEP on in vitro rumen fermentation, nutrient degradability, and gas production kinetics. Citric waste was co-fermented with yeast waste and a multi-enzyme complex for 14 days, then sun-dried and pelleted. The final CWYWEP product contained 50.4% crude protein (DM basis). A completely randomized design tested seven diets in which SBM was replaced by CWYWEP or non-enzymatic citric waste–yeast waste pellets (CWYWP) at 0%, 33%, 66%, or 100% inclusion. Replacing SBM with CWYWEP significantly increased cumulative gas production at 96 h, with the 100% CWYWEP group achieving 93.7 mL/0.5 g DM—a 14% increase over the control (p < 0.01). Microbial lag time was reduced to 0.17 h vs. 0.28 h in the control (p < 0.05), suggesting faster microbial colonization. The highest in vitro DM degradability (IVDMD) at 48 h was observed in the 100% CWYWEP group (64.5%), outperforming both the SBM control and all CWYWP treatments (p < 0.01). Notably, CWYWEP increased total volatile fatty acids by 5% at 4 h and propionate by 9% at 2 h, while reducing methane production by 5% (p < 0.05). Other parameters, including pH, ammonia nitrogen, organic matter digestibility, and protozoal counts, were unaffected (p > 0.05). In contrast, CWYWP without enzymes showed minimal improvement. These findings indicate that CWYWEP is a promising high-protein alternative to SBM, enhancing fermentation efficiency and reducing methane under in vitro conditions. Further in vivo studies are warranted to validate these effects. Full article

Fermentation plays a pivotal role in shaping the flavor and overall quality of Pu-erh tea, a microbially fermented dark tea. Here, we monitored physicochemical properties, chemical constituents, and microbial succession at 15 fermentation time points. Amplicon sequencing identified Staphylococcus, Bacillus, Kocuria, Aspergillus, Blastobotrys, Thermomyces, and Rasamsonia as dominant genera, with prokaryotic communities showing greater richness and diversity than eukaryotic ones. Beta diversity and clustering analyses revealed stable microbial structures during late fermentation stages. Non-targeted metabolomics detected 347 metabolites, including 56 significantly differential compounds enriched in caffeine metabolism and unsaturated fatty acid biosynthesis. Fermentation phases exhibited distinct metabolic patterns, with volatile aroma compounds (2-acetyl-1-pyrroline, 2,5-dimethylpyrazine) and health-beneficial fatty acids (linoleic acid, arachidonic acid) accumulating in later stages. OPLS-DA and KEGG PATHWAY analyses confirmed significant shifts in metabolite profiles relevant to flavor and biofunctionality. RDA revealed strong correlations between microbial taxa, environmental parameters, and representative metabolites. To functionally verify microbial contributions, 17 bacterial and 10 fungal strains were isolated. Six representative strains, mainly Bacillus and Aspergillus, exhibited high enzymatic activity on macromolecules, confirming their roles in polysaccharide and protein degradation. This integrative multi-omics investigation provides mechanistic insights into Pu-erh tea fermentation and offers a scientific basis for microbial community optimization in tea processing. Full article

Human cognitive performance is influenced by external factors, including indoor environmental quality (IEQ). Understanding how these factors affect stress, attention, and relaxation is essential in environments such as workplaces and educational institutions, where cognitive function directly impacts performance. This study examines the effects of IEQ on students’ attention and relaxation levels during various lecture periods, focusing on design major students. Three key IEQ parameters (air temperature, relative humidity, and natural lighting) were evaluated for their effects on cognitive states using electroencephalogram (EEG) measurements in a controlled setting. Participants wore non-invasive, portable EEG devices to monitor neurophysiological activity across two sessions, each involving four scenarios: (i) baseline, (ii) increased natural light exposure, (iii) elevated relative humidity, and (iv) increased air temperature. EEG-derived metrics of attention and relaxation were analyzed alongside environmental data, including temperature, humidity, lighting conditions, carbon dioxide (CO₂) concentration, total volatile organic compounds (TVOC), and particulate matter (PM), to identify potential correlations. Results showed that natural light exposure improved relaxation but reduced attention, suggesting a restorative effect on stress that may also introduce distractions. Attention peaked under moderately warm, dry conditions (25–26 °C and 16–19% relative humidity), correlating positively with temperature (Pearson correlation coefficient, $r$ = 0.32) and negatively with humidity ($r$ = −0.50). Conversely, relaxation was highest under cooler, more humid conditions (23–24 °C and 24–26% relative humidity). Attention was negatively correlated with CO₂ ($r$ = −0.47) and PM_2.5 ($r$ = −0.46), suggesting that poor air quality impairs alertness. Relaxation showed weaker but positive correlations with PM_2.5 ($r$ = 0.38), PM_1.0 ($r$ = 0.35), and CO₂ ($r$ = 0.32). Ultrafine particles (PM_0.3, PM_0.5) and TVOC had minimal association with cognitive states. Overall, this study underscores the importance of optimizing indoor environments in educational settings to enhance academic performance and supports the development of evidence-based design standards to foster healthy, effective learning environments. Full article

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article