Search Results (7,352)

As the amygdala is associated with fear and anxiety, it is important to determine the potential effects of gestational stressors on behavior and stress responses in offspring. The objective of this study was to investigate the effects of prenatal transportation stress on amygdala gene expression in 25-day-old Brahman calves, focusing on sex-specific differences. Amygdala tissue samples from prenatally stressed (PNS) and control bull and heifer calves were analyzed using RNA sequencing. A thorough outlier detection process, utilizing visual inspection of multidimensional scaling plots, robust principal component analysis, and PCAGrid methods, led to the exclusion of 5 of 32 samples from subsequent analyses. Differential expression analysis revealed no significant treatment differences between the control and PNS groups within either sex. However, sex-specific differences in gene expression were identified in both the control and PNS groups. The control group showed seven differentially expressed genes between sexes, while ten were identified between PNS males and females, with seven located on the X chromosome. Among these was the ubiquitin-specific peptidase 9 X-linked gene, which plays a role in neurodevelopmental pathways. When comparing males to females, regardless of treatment, a total of 58 genes were differentially expressed, with 45 showing increased expression in females. Gene enrichment analysis indicated that many differentially expressed genes are associated with infectious disease-related pathways. Future research should explore amygdala size and functional responses to various postnatal stimuli. Full article

This work introduces a boost converter with quadratic gain. Its main advantage compared to well-known similar quadratic boost converters is that it requires capacitors with a relatively small capacitance and inductors with small inductance, leading to a reduction in the size or stored energy while performing a power conversion of similar power rating and the same switching ripples in both the input current and the output voltage. It is inspired by the recently introduced ISB converter and uses a specific PWM method. This results in achieving switching ripple constraints while using smaller energy storage elements (capacitors and inductors). The updated converter offers the same voltage gain compared to the conventional quadratic boost topology with the benefit of compact component sizes. While it has more passive elements, they are of reduced size. An analysis of energy storage revealed that this new converter uses only half the energy in inductors and 14% in capacitors when compared to specific design parameters. Full article

The study is the first to examine the combined use of blast-furnace sludge as a source of microelements and converter slag as a soil-deoxidizing agent in oat (Avena sativa L.) cultivation in sod-podzolic soils. It has been established that blast-furnace sludge is a highly dispersed waste, which contains about 50% iron, 7% zinc, and a small amount of calcium, silicon, magnesium, aluminum, and sulfur. Hazardous components such as lead, arsenic, etc., are not detected. Converter slag comprises porous granules up to 3 mm in size, consisting mainly of calcium compounds (CaO, Ca(CO)₃, CaSiO₃, CaFe₂O₄) and a small amount of Mn, Al, and Mg trace elements. In a laboratory experiment, blast-furnace sludge increased the germination of oats by 5–10%, regardless of the addition of a deoxidizer (slag), but at the same time suppressed the growth of stem length by a maximum of 18% at 1 g∙kg⁻¹. The addition of slag raised substrate pH and increased the index by 8% at a sludge concentration of 0.1 g∙kg⁻¹. Root length in deoxidizer-free variants increased by 50–60% and with the addition of slag by 27–47%. Root dry mass also increased under the addition of sludge by 85–98%; however, the addition of slag reduced the indicator to the control level. In a field experiment with the combined application of waste, an increase in yield by more than 30% was shown. When soil was treated with slag and sludge, the height of plants increased by an average of 18%. It should be noted that the introduction of waste did not affect the quality of the grain. The use of slag increased the lead content in the soil, which is probably due to the sorption properties of calcium compounds in the slag, since lead was not found in the analyzed waste. Presumably, lead is sorbed by slag from the lower soil horizons, concentrating and immobilizing it in the upper layer. This version is supported by the absence of lead accumulation in straw and oat grain. The zinc-containing sludge increased the content of this element by 33% in the soil, as well as by 6% in straw and by 14% in grain. Thus, we found that the studied metallurgical wastes can be used as nutrients for agriculture, both individually and jointly. Overall, the proposed approach will contribute both to reducing the amount of accumulated waste and to improving the efficiency and sustainability of agricultural production and CO₂ sequestration. However, the features of the accumulation of heavy metals in soil and plants under the influence of the analyzed types of waste require more in-depth study, including within the framework of long-term field experiments. Full article

As the integration of renewable energy sources into the grid increases, the insulation systems of grid components such as transformers and switchgear encounter significant challenges due to the transients and harmonics generated by power-electronic-based converters. A test generator capable of replicating these component stresses is essential to accurately evaluate these insulation systems under real-grid conditions. This paper proposes a modular cascaded H-bridge-based high-voltage arbitrary waveform generator, prototyped with three stages to generate customized waveforms (triangular, sawtooth, pulse, and complex) up to 8 kV. The H-bridge modules are designed using Si MOSFETs with a maximum blocking voltage of 4.5 kV. The input to the HV H-bridge module is provided by a 10 kV medium-frequency transformer, whose design is described with a focus on the insulation system and winding configuration. This transformer is driven by a zero-voltage switching driver. This arbitrary waveform generator excels in several aspects, including a straightforward design procedure, compact size, high voltage capability, ease of integration, and cost. Full article

The aim of the present study is to evaluate the stability of DMPC:Pluronic F-127 and DPPC:Pluronic F-127 liposomes, both with and without incorporated quercetin. Quercetin belongs to the class of flavonoids and has shown antioxidant, antiviral, anti-inflammatory, anti-cancer, and antimicrobial activities. Dynamic light scattering, electrophoretic light scattering, and differential scanning calorimetry (DSC) were utilized to investigate the cooperative behavior between liposomal components and its effect on stability. All formulations were stored at 4 °C and 25 °C and studied over 42 days. Furthermore, the interaction of the final formulations with serum proteins was assessed to evaluate the potential of Pluronic F-127 as a stabilizer in these liposomal nanosystems. This study highlights the impact of DSC in preformulation evaluations by correlating thermal behavior with quercetin incorporation and variations in size and the polydispersity index. According to the results, quercetin increased the fluidity and stability of liposomal nanosystems, while Pluronic F-127 was not sufficient for effective steric stabilization. Additionally, DSC thermograms revealed the integration of Pluronic F-127 into lipid membranes and showed phase separation in the DMPC nanosystem. In conclusion, the results indicate that the DPPC:Pluronic F-127:quercetin nanosystem exhibited the desired physicochemical and thermotropic properties for the effective delivery of quercetin for pharmaceutical purposes. Full article

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated. Full article

Background: Cardiovascular diseases (CVDs) are a major public health concern. The impact of dietary components on CVD risk has been recognized, but their interactions require further investigation. This study aimed to examine the associations between major nutrient intake and CVD risk and to assess potential causal relationships via Mendelian randomization. Methods: We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) 2017–2020, with a sample size of 5464 adult participants. Nutrient intake was derived from two 24 h dietary recalls. Associations between four principal nutrients and CVD risk were evaluated via Mendelian randomization analysis. Additionally, weighted multivariable logistic regression analyses were performed to adjust for potential confounders, including age, sex, BMI, and other lifestyle factors. Results: An observational analysis revealed that increased log-transformed dietary fat intake was associated with reduced heart failure risk (OR = 0.722, 95% CI: 0.549–0.954). Log-transformed protein intake was protective against heart failure (OR = 0.645, 95% CI: 0.471–0.889), coronary artery disease (OR = 0.684, 95% CI: 0.504–0.931), and stroke (OR = 0.747, 95% CI: 0.568–0.988). IVW-MR analyses confirmed causal relationships between relative fat intake and heart failure risk (OR = 0.766, 95% CI: 0.598–0.982, p = 0.035) and between protein intake and stroke risk (OR = 0.993, 95% CI: 0.988–0.998, p = 0.010). MR analysis also revealed causal relationships between relative fat intake and coronary artery disease risk and between relative protein intake and hypertension risk. Conclusions: Both the observational and Mendelian randomization studies indicated that dietary fat is inversely associated with heart failure risk and that protein intake is correlated with reduced stroke risk. Future studies should investigate the optimal balance of macronutrients for CVD prevention, explore potential mechanisms underlying these associations, and consider long-term dietary interventions to validate these findings. Full article

Copper’s high thermal and electrical conductivity enables its application in heat exchangers and high-frequency components. For those applications, additive manufacturing has advantages with respect to functional integration, miniaturization, and reduced waste. However, the processing of copper is a challenge for established laser-based processes since copper’s high reflectivity impedes energy input. Sinter-based additive manufacturing processes do not exhibit this limitation since the energy for the fusion of material is applied by thermal energy during sintering. This makes them an ideal candidate for copper manufacturing. In the following work, Lithography-based Metal Manufacturing (LMM) of copper is demonstrated. Curing behavior is investigated by single-layer exposure (SLE) tests measuring curing thickness for different loading factors, particle sizes, and exposure times. Bending strength is investigated as a function of exposure time, loading factor, and orientation in the building space. A higher exposure time and lower loading factors increase bending strength. Furthermore, samples with different loading factors are produced to measure the impact of the loading factor on sintered density. For these parameters, no clear trend is demonstrated. Full article

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability. Full article

The complex materials comprised of both micron and nanometer-sized particles (MNPs) present special properties different from conventional single-size particles due to their special size effect. In this study, the MNPs could be simultaneously synthesized in a one-pot medium by soap-free emulsion polymerization, without harsh preparation conditions and material waste. In the whole process, the amphipathic siloxane oligomers would migrate to the mixed monomer droplet surface to reduce the surface energy of the system and further complete hydrolysis–condensation to obtain the SiO₂ shell at the water–oil interface. On the one hand, the mixed monomers inside the above shell would migrate outward driven by the capillary force generated at the shell mesopore and be further initiated by the water-soluble initiator potassium persulfate (KPS), resulting in the formation of bowl-shaped micron particles with “lunar surface” structure. On the other hand, the residual mixed monomers dissolve in water and could be polymerized by initiating free radicals in the water phase to obtain popcorn-like nano-sized particles. The above two particles are clearly displayed in the SEM photos, and the DLS characterization further shows that the sizes of two particles are concentrated at 1.4 μm and 130 nm, respectively. Interestingly, the uniformity of obtained particles has a great relationship with the added amount of BA, and the perfect MNPs would appear when the St/BA feed mass ratio is 1:2. Moreover, the MNPs exhibit film-forming property, and the SiO₂ component is evenly distributed in the formed coating. Thus, this study is not only beneficial to the theoretical research of soap-free emulsion polymerization but also to the application of multifunctional coatings. Full article

Cold spray additive manufacturing (CSAM) is a cutting-edge high-speed additive manufacturing process enabling the production of high-strength components without relying on traditional high-temperature methods. Unlike other techniques, CSAM produces oxide-free deposits and preserves the feedstock’s original characteristics without adversely affecting the substrate. This makes it ideal for industries requiring materials that maintain structural integrity. This paper explores strategies for improving material quality, focusing on nozzle design, particle size distribution, and fine-tuning of process parameters such as gas pressure, temperature, and spray distance. These factors are key to achieving efficient deposition and optimal bonding, which enhance the mechanical properties of the final products. Challenges in CSAM, including porosity control and achieving uniform coating thickness, are discussed, with solutions offered through the advancements in machine learning (ML). ML algorithms analyze extensive data to predict optimal process parameters, allowing for more precise control, reduced trial-and-error, and improved material usage. Advances in material strength, such as enhanced tensile strength and corrosion resistance, are also highlighted, making CSAM applicable to sectors like aerospace, defense, and automotive. The ability to produce high-performance, durable components positions CSAM as a promising additive-manufacturing technology. By addressing these innovations, this study offers insights into optimizing CSAM processes, guiding future research and industrial applications toward more efficient and high-performing manufacturing systems. Full article

Forests are an important component of the Earth’s ecosystems. Forest canopy height is an important fundamental indicator for quantifying forest ecosystems. The current spaceborne photon-counting Light Detection and Ranging (LiDAR) technique has photon cloud characteristic parameters to estimate forest canopy height, and factors such as the sampling window size have not been quantitatively studied. To better understand the precision for estimating canopy height using spaceborne photon-counting LiDAR ICESat-2/ATLAS (Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System), this study quantified the impact of photon-counting characteristic parameters, sampling window size, and forest cover. Estimation accuracy was evaluated across nine study areas in North America. The findings revealed that when the photon-counting characteristic parameter was set to H70 (70% of canopy height) and the sampling window length was 20 m, the estimation results aligned more closely with the airborne validation data, yielding superior accuracy evaluation indicators with a root mean square error (RMSE) of 4.13 m. Under forest cover of 81%–100%, our algorithms exhibited high estimation accuracy. These study results offer novel perspectives for the application of spaceborne photon-counting LiDAR ICESat-2/ATLAS in forestry. Full article

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies. Full article

The current study has four main objectives. First, it aims to investigate the effect of the relationship between information technology (IT) dimensions (customer relationship management, knowledge management, and human resource management) and innovative practices on organizational agility in small and medium-size companies (SMEs). Second, it seeks to measure the relationship between IT components and innovative performance. Third, it examines the impact of innovative performance on organizational agility. Fourth it explores the mediating role of innovative performance in the relationship between IT and organizational agility. These objectives provide a clear roadmap for the research and guide the analysis and interpretation of the findings. This paper’s statistical population was composed of senior managers in SMEs in Khorsaran Razavi, Iran. The data were collected using standard questionnaires, 172 which were received in 2023 and analyzed using SPSS version 25 and SmartPLS version 4 software. The results demonstrate that using customer relationships, human resources, and knowledge management as three dimensions of IT and innovative performance can enhance organizational agility. Moreover, innovative performance plays a crucial role as a mediator, strengthening the impact of information IT dimensions on organizational agility. These findings underscore the practical relevance for companies operating in a dynamic economic environment. Special attention to organizational agility and practical factors will increase flexibility, speed of response, etc., and, ultimately, companies’ success in this tense economic environment. The innovation of this research is that the three dimensions of IT, including evaluating customer relationship management, human resource management, and knowledge management, is a growing research field in organizational agility. Therefore, this research is vital in empowering SMEs to increase agility. By evaluating the effect of the four variables of knowledge management, customer relationship management, human resource management, and innovative performance on organizational agility in SMEs, on the one hand, this research expands the theoretical literature and, on the other hand, helps such companies. Full article

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal component analysis reduced dimensionality to eight factors with a 74% explained variance. Factor 1 was positively related to pre-incubation variables; factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3, or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16% of the total), incubators with strong deficits in incubation phases 1, 2, and 3. They are small-sized business incubators, often located in rural areas or cities, with a low graduation rate. Group 2 (30%), business incubators with a very high graduation rate and strongly positive values in factors 1 and 2. Factor 3, although positive, is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54%) is the majority, with values close to clusters 2 and 3. Full article