Search Results (1,754)

User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common method. It is hard to fully render nuances, emotions, and complexities depending only on text by superficial emotional tendencies alone. Numerical representations may lead to misleading conclusions and undermine public participation. In addition, the use of physiological test data does not reflect the subjective reasons for the comments made. Therefore, it is essential to deeply parse the text and distinguish between segments with different semantic differences. In this study, we propose a perceptual psychology-based workflow to extract and visualize multifaceted views from user-generated data. The analysis methods of FCN, LDA, and LSTM were incorporated into the workflow. Six areas in Fuzhou City, China, with 12 city parks, were selected as the study object. Firstly, 9987 review data and 1747 pictures with corresponding visitor trajectories were crawled separately on the Dianping and Liangbulu websites. For in-depth analysis of comment texts and making relevant heat maps. Secondly, the process of clauses was added to get a more accurate representation of the sentiment of things based on the LSTM sentiment analysis model. Thirdly, various factors affecting the perception of landscapes were explored. Based on such, the overall people’s perception of urban parks in Fuzhou was finally obtained. The study results show that (1) the texts in terms of ‘wind’, ‘temperature’, ‘structures’, ‘edge space (spatial boundaries)’, and ‘passed space’ are the five most representative factors of the urban parks in Fuzhou; (2) the textual analyses further confirmed the influence of spatial factors on perception in the temporal dimension; and (3) environmental factors influence people’s sense of urban parks concerning specificity, clocking behavior, and comfort feelings. These research results provide indispensable references for optimizing and transforming urban environments using user-generated data. Full article

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed. Full article

Coatings can be created using various technologies and serve different roles, including protection, functionality, and decorative purposes. Among these technologies, electrodeposition has emerged as a low-cost, versatile, and straightforward process with remarkable scalability and manufacturability. Nickel, extensively studied in the context of electrodeposition, has many applications ranging from decorative to functional. The main objective of the present work is the electrodeposition of double-layer nickel coatings, consisting of a bright nickel pre-coating followed by a black nickel layer with enhanced properties, onto steel substrates. The influence of deposition parameters on colour, morphology, adhesion, roughness, and coefficient of friction was studied. The effects of cetyltrimethylammonium bromide (CTAB) and WS₂ nanoparticles on the coatings’ properties and performance were also investigated. Additionally, the influence of the steel substrate’s pre-treatment, consisting of immersion in an HCl solution, prior to the electrodeposition, to etch the surface and activate it, was evaluated and optimized. The characterization of the pre-coating revealed a homogeneous surface with a medium superficial feature of 2.56 μm. Energy dispersive X-ray spectroscopy (EDS) results showed a high content of Ni, and X-ray diffraction (XRD) confirmed its crystallinity. In contrast, the black films’ characterization revealed their amorphous nature. The BN10 sample, which corresponds to a black nickel layer with a deposition time of 10 min, showed the best results for colour and roughness, presenting the lowest brightness (L*) value (closest to absolute black) and the most homogeneous roughness. EDS analysis confirmed the incorporation of WS₂, but all samples with CTAB exhibited signs of corrosion and cracks, along with higher coefficient of friction (COF) values. Full article

The left displacement of the abomasum (LDA) is a common condition in dairy cows that can significantly impact their welfare, productivity, and fertility. This study was carried out in Greek dairy farms over a period of 3 years. To ensure early detection, the farmers were trained to accurately identify the disease. The reproductive performance and milk production of 306 cows was assessed by considering the time to the first estrus, the calving-to-conception interval, and the number of artificial inseminations required for the establishment of pregnancy. Uterine health status, the timing of disease diagnosis, and the season of the year were also evaluated. In a separate study, the outcomes of 26 cases where cows suffered LDA and underwent surgical treatment with a delay of at least one week from disease onset, were compared to those of cases promptly treated. The results indicate that even early identification and treatment of LDA affects fertility and milk yield; these impacts worsen with the co-existence of uterine infections of affected. However, in late-treated cases, all reproductive and production indices show significant deterioration. Our findings suggest that timely diagnosis of the disease, preferably by the farmer, ensures minimal losses in cows affected by LDA. Full article

Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data, including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection. Full article

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice. Full article

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm⁻¹, 1013 cm⁻¹, 1002 cm⁻¹, 1004 cm⁻¹, and 1011 cm⁻¹ in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool. Full article

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom. Full article

The objectives of the current study were to evaluate the fluctuations in production performance, rumen fermentation, and microbial community in lactating dairy cows fed a high-grain diet (HG). In this study, 16 healthy Holstein lactating dairy cattle with similar milk yields of 16.80 ± 4.30 kg/d, days in milk 171.44 ± 23.25 days, and parity 2.2 ± 1.5 times were selected and randomly allocated into two groups. One group was fed a low-grain diet (LG; 40% concentrate, DM basis; n = 8), and the other group was fed a high-grain diet (HG; 60% concentrate, DM basis; n = 8). The experiment lasted 6 weeks, including 1 week for adaptation. The experimental results showed that the milk fat content in the milk of lactating cows in the HG group was significantly reduced (p < 0.05), and the milk urea nitrogen (MUN) content showed an increasing trend (0.05 < p < 0.10) compared with the LG group. Compared with the LG group, rumen fluid pH was significantly decreased after feeding a high-grain diet, and contents of total volatile fatty acids (TVFA), acetate, propionate, and butyrate were significantly increased (p < 0.05). The acetate/propionate significantly decreased (p < 0.05). HG group significantly increased the abundance of Prevotella and Bacteroides in rumen fluid while significantly reducing the abundance of Methanobrevibacter and Lachnospiraceae ND3007_group (p < 0.05). Microorganisms with LDA scores > 2 were defined as unique, with the bacterial genus Anaerorhabdus_furcosa_group identified as a biomarker for the LG group, and the unique bacterial genus in the HG group were Prevotella, Stenotrophomonas, and Xanthomonadaceae. The prediction results of microbial function showed that a total of 18 KEGG differential pathways were generated between the two treatment groups, mainly manifested in metabolic pathways, signal transduction, and the immune system. In conclusion, the HG group promoted rumen fermentation by altering the microbial composition of lactating cows. Our findings provide a theoretical basis for the rational use of high-grain diets to achieve high yields in intensive dairy farming. Full article

Climate change has attracted global attention, highlighting the critical role of low-carbon technologies in addressing environmental challenges. Due to the multidisciplinary nature, complexity, and diversity of research content on low-carbon technologies, a comprehensive overview is still limited. This paper uses bibliometrics analysis to discuss the research status and hotspots of low-carbon technology from a macro-perspective. The LDA2Vec topic recognition model is adopted to identify key technical terms, and CiteSpace software 6.3.1 Advanced Edition is used to conduct in-depth analysis of the development trajectory of low-carbon technology. After checking the frequency of the relevant keywords, four key techniques were identified. In order to further analyze the research results, the learning curve theory is used to predict the cost development trend of key low-carbon technologies. The results show that: (i) low-carbon technologies play a key role in the energy sector and have a potential impact on policy making, and the cost of related technologies will be significantly reduced in the next few years. (ii) Global low-carbon technologies have entered an important period of development, but remaining challenges need to be addressed by optimizing technological performance. (iii) It is very important to strengthen the research on hydrogen production technology and photovoltaic power generation technology; the cost reduction in hydrogen production technology is still significant and there is room for further optimization. (iv) To effectively address the high costs and technical barriers associated with emerging low-carbon technologies, increased funding for research and development is critical. Full article

The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec^® and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (>98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation. Full article

Innovative Industrial Clusters (IIC), characterized by geographical aggregation and technological collaboration among technology enterprises and institutions, serve as pivotal drivers of regional economic competitiveness and technological advancements. Prior research on cluster identification, crucial for IIC analysis, has predominantly emphasized geographical dimensions while overlooking technological proximity. Addressing these limitations, this study introduces a comprehensive framework incorporating multiple indices and methods for accurately identifying IIC using patent data. To unearth latent technological insights within patent documents, Latent Dirichlet Allocation (LDA) is employed to generate topics from a collection of terms. Utilizing the applicants’ names and addresses recorded in patents, an Application Programming Interface (API) map systems facilitates the extraction of geographic locations. Subsequently, a Multivariate Density-Based Spatial Clustering of Applications with Noise (MDBSCAN) algorithm, which accounts for both technological and spatial distances, is deployed to delineate IIC. Moreover, a bipartite network model based on patent geographic information collected from the patent is constructed to analyze the technological distribution on the geography and development mode of IIC. The utilization of the model and methodologies is demonstrated through a case study on the China flexible electronics industry (FEI). The findings reveal that the clusters identified via this novel approach are significantly correlated with both technological innovation and geographical factors. Moreover, the MDBSCAN algorithm demonstrates notable superiority over other algorithms in terms of computational precision and efficiency, as evidenced by the case analysis. Full article

One of the many unresolved obstacles in the field of cardiovascular research is an uncompromising in vitro cardiac model. While primary cell sources from animal models offer both advantages and disadvantages, efforts over the past half-century have aimed to reduce their use. Additionally, obtaining a sufficient quantity of human primary cardiomyocytes faces ethical and legal challenges. As the practically unlimited source of human cardiomyocytes from induced pluripotent stem cells (hiPSC-CM) is now mostly resolved, there are great efforts to improve their quality and applicability by overcoming their intrinsic limitations. The greatest bottleneck in the field is the in vitro ageing of hiPSC-CMs to reach a maturity status that closely resembles that of the adult heart, thereby allowing for more appropriate drug developmental procedures as there is a clear correlation between ageing and developing cardiovascular diseases. Here, we review the current state-of-the-art techniques in the most realistic heart models used in disease modelling and toxicity evaluations from hiPSC-CM maturation through heart-on-a-chip platforms and in silico models to the in vitro models of certain cardiovascular diseases. Full article

This study aimed to evaluate consumer perception and acceptance of non-alcoholic cocktails compared to their traditional alcoholic counterparts in a restaurant setting. Three popular cocktails—gintonic, mojito, and mule—and their non-alcoholic versions (NoLo) were assessed following a three × two experimental design. A total of 600 participants (approximately 100 per cocktail) participated at the Basque Culinary Center’s restaurant. Participants rated their liking of the cocktails using a nine-point hedonic scale and provided open-ended responses about the sensory characteristics and the consumption contexts or emotions evoked by the different cocktails. The results showed differences in the acceptance of the six cocktails, but no significant differences between the alcoholic and non-alcoholic versions, suggesting that NoLo alternatives were similarly well-received. Open-ended responses were analyzed using latent dirichlet allocation (LDA) to uncover latent topics, and Fisher’s exact test and correspondence analysis were used to identify differences in the mentioned topics per cocktail. Specific sensory attributes, emotions, and contexts were associated with each type of cocktail, but no differences were found between the alcoholic and non-alcoholic versions. These findings demonstrate the viability of non-alcoholic cocktails in real consumption settings, eliciting similar liking scores, sensory attributes, contexts, and emotions in consumers. This study also highlighted the potential of natural language processing techniques for analyzing open-ended questions. Full article

The combination of cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) with endocrine therapy (ET) is the standard-of-care for estrogen receptor (ER)-positive, HER2-negative (ER+/HER2− advanced/metastatic breast cancer (mBC). However, the impact of CDK4/6i on circulating immune cells and circulating tumor cells (CTCs) in patients receiving CDK4/6i and ET (CDK4/6i+ET) remains poorly understood. This was a prospective cohort study including 44 patients with ER+/HER2− mBC treated with CDK4/6i+ET in either first or second line. Peripheral blood samples were collected before (baseline) and 3 months (t2) after therapy. Immune cell’s subsets were quantified by flow cytometry, and microfluidic-captured CTCs were counted and classified according to the expression of cytokeratin and/or vimentin. Patients were categorized according to response as responders (progression-free survival [PFS] ≥ 6.0 months; 79.1%) and non-responders (PFS < 6.0 months; 20.9%). CDK4/6i+ET resulted in significant changes in the hematological parameters, including decreased hemoglobin levels and increased mean corpuscular volume, as well as reductions in neutrophil, eosinophil, and basophil counts. Specific immune cell subsets, such as early-stage myeloid-derived suppressor cells, central memory CD4+ T cells, and Vδ2+ T cells expressing NKG2D, decreased 3 months after CDK4/6i+ET. Additionally, correlations between the presence of CTCs and immune cell populations were observed, highlighting the interplay between immune dysfunction and tumor dissemination. This study provides insights into the immunomodulatory effects of CDK4/6i+ET, underscoring the importance of considering immune dynamics in the management of ER+/HER2− mBC. Full article