Search Results (842)

Carbapenem resistance is considered one of the greatest current threats to public health, particularly in the management of infections in clinical settings. Carbapenem resistance in bacteria is mainly due to mechanisms such as the production of carbapenemases (such as the imipenemase IMP, or other enzymes like VIM, NDM, and KPC), that can be detected by several laboratory tests, including immunochromatography and automated real-time PCR (qPCR). Methods: As part of local studies to monitor carbapenem-resistant bacteria in Costa Rica, two cases were initially identified with inconsistent IMP detection results. A possible gene drop-out in the automated qPCR test was suggested based on the negative result, contrasting with the positive result by immunochromatography and whole-genome sequencing. We hypothesized that molecular testing could be optimized through the development of tailored assays to improve the detection of IMP genes. Thus, using IMP gene sequences from the local isolates and regional sequences in databases, primers were redesigned to extend the detection of IMP alleles of regional relevance. Results: The tailored qPCR was applied to a local collection of 119 carbapenem-resistant isolates. The genomes of all 14 positive cases were sequenced, verifying the results of the custom qPCR, despite the negative results of the automated testing. Conclusions: Guided by whole-genome sequencing, it was possible to extend the molecular detection of IMP alleles circulating in Latin America using a tailored qPCR to overcome IMP gene drop-out and false-negative results in an automated qPCR. Full article

In the era of climate change, the strategic importance of forestry products for sustainable development is increasingly recognized. Amid a global resurgence of industrial policy aimed at addressing environmental challenges, this study investigates the impact of China’s central and provincial green industrial policies on the export competitiveness of wood-processing enterprises. Utilizing firm-level data from the China Industrial Enterprise Database and China Customs Export Database (2000–2013), we apply a double machine learning (DML) approach and construct a heterogeneous competitiveness model to evaluate policy effects along two dimensions: export quantity (volume and intensity) and export quality (product complexity and consumer-perceived quality). Our findings reveal a clear dichotomy in policy outcomes. While industrial policies have significantly improved export product complexity—reflecting China’s comparative advantage in labor-intensive production—they have had limited or even negative effects on export volume, intensity, and product quality. This suggests that current policy frameworks disproportionately reward horizontal innovation (product diversification) while neglecting vertical upgrading (quality enhancement), thereby hindering comprehensive export performance gains. Those results highlight the need for more balanced and targeted policy design. By aligning industrial policy instruments with both complexity and quality objectives, policymakers can better support the sustainable transformation of China’s forestry sector and enhance its competitiveness in global value chains. Full article

Background: Energy drinks (EDs) are non-alcoholic, functional beverages sold worldwide in more than 165 countries. These products are very popular and often consumed by children, teenagers, and young adults to improve physical performance, reduce drowsiness, and improve memory and concentration with increased intellectual effort. However, their consumption is associated with an increased risk of various health consequences. Objectives: The purpose of this non-systematic review was to discuss the components of EDs and their effects, summarize the AEs reported in the literature associated with the consumption of EDs, and briefly characterize the possible ED-related drug interactions. Methods: Scientific evidence was extracted by searching the databases PubMed and Google Scholar. In addition, the reference lists of the retrieved papers were reviewed and cross-referenced to reveal additional relevant scientific evidence. Results: The most common ingredients in EDs are caffeine, taurine, glucuronolactone, B vitamins, the vitamin-like compound inositol, and sweeteners (sugar, fructose, glucose–fructose syrup or artificial sweeteners). Although it is difficult to conclusively prove a cause-and-effect relationship between the consumption of EDs and the observed pathophysiological abnormalities, most scientific evidence (mostly clinical case reports) indicates that both occasional and especially chronic use of EDs is associated with the occurrence of numerous adverse effects (AEs). Among these, the best documented AEs are those on the cardiovascular system. It should also be noted that the components of EDs (primarily caffeine) may have drug interactions; therefore, EDs may be an important factor influencing the safety of pharmacotherapy in patients consuming EDs. Conclusions: Consuming energy drinks lead to various health problems and may interfere with pharmacotherapy due to the potential development of drug interactions. Due to the widespread availability of EDs, their suggestive advertising aimed at the youngest customers, and ambiguous regulations, new legislative policies are required to limit the widespread consumption of such products and their negative health effects. Full article

Customer sentiment analysis plays a pivotal role in the digital economy by offering comprehensive insights that inform strategic business decisions, optimize digital marketing initiatives, and improve overall customer satisfaction. We propose a large language model-based topic-level sentiment analysis framework. We employ a BERT-based model to generate contextualized vector representations of the documents, and then clustering algorithms are automatically applied to group documents into topics. Once the topics are formed, a GPT model is used to perform sentiment classification on the content related to each topic. The simulations show the effectiveness of this approach, where selecting appropriate clustering techniques yields more semantically coherent topics. Furthermore, topic-level sentiment polarization shows that 31.7% of all negative sentiment concentrates on the shopping experience, despite an overall positive sentiment trend. Full article

Background: Pathological complete response (pCR) serves as a prognostic surrogate endpoint for long-term clinical outcomes in breast cancer patients receiving neoadjuvant systemic therapy (NAST). This study aims to develop and evaluate machine learning-based biomarkers for predicting pCR and recurrence-free survival (RFS). Methods: This retrospective monocentric study included 235 women (mean age 46 ± 11 years) with non-metastatic breast cancer treated with NAST. We developed various machine learning models using clinical features (age, genetic mutations, TNM stage, hormonal receptor expression, HER2 status, and histological grade), along with morphological features (size, T2 signal, and surrounding edema) and radiomics data extracted from pre-treatment MRI. Patients were divided into training and test groups with different MRI models. A customized machine learning pipeline was implemented to handle these diverse data types, consisting of feature selection and classification components. Results: The models demonstrated superior prediction ability using radiomics features, with the best model achieving an AUC of 0.72. Subgroup analysis revealed optimal performance in triple-negative breast cancer (AUC of 0.80) and HER2-positive subgroups (AUC of 0.65). Conclusion: Machine learning models incorporating clinical, qualitative, and radiomics data from pre-treatment MRI can effectively predict pCR in breast cancer patients receiving NAST, particularly among triple-negative and HER2-positive breast cancer subgroups. Full article

Spores of filamentous fungi are common biological particles in indoor air that can negatively impact human health, particularly among immunocompromised individuals and patients with chronic respiratory conditions. Airborne viruses represent an equally pervasive threat, with some carrying the potential for pandemic spread, affecting both healthy individuals and the immunosuppressed alike. This study investigated the abundance and diversity of airborne fungal spores in both hospital and residential environments, using custom designed air samplers with or without the presence of negative air ions (NAIs) inside the sampler. The main purpose of investigation was the assessment of biological effects of NAIs on fungal spore viability, deposition, mycelial growth, and sporulation, as well as airborne viral load. The precise assessment of mentioned biological effects is otherwise difficult to carry out due to low concentrations of studied specimens; therefore, specially devised and designed, ion-bioaerosol interaction air samplers were used for prolonged collection of specimens of interest. The total fungal spore concentrations were quantified, and fungal isolates were identified using cultural and microscopic methods, complemented by MALDI-TOF mass spectrometry. Results indicated no significant difference in overall spore concentration between environments or treatments; however, presence of NAIs induced a delay in the sporulation process of Cladosporium herbarum, Aspergillus flavus, and Aspergillus niger within 72 h. These effects of NAIs are for the first time demonstrated in this work; most likely, they are mediated by oxidative stress mechanisms. A parallel experiment demonstrated a substantially reduced concentration of aerosolized equine herpesvirus 1 (EHV-1) DNA within 10–30 min of exposure to NAIs, with more than 98% genomic load reduction beyond natural decay. These new results on the NAIs interaction with a virus, as well as new findings regarding the fungal sporulation, resulted in part from a novel interaction setup designed for experiments with the bioaerosols. Our findings highlight the potential of NAIs as a possible approach for controlling fungal sporulation and reducing airborne viral particle quantities in indoor environments. Full article

Out of 2495 samples, L. monocytogenes was isolated from 262 (10.5%). Among these, 30 isolates (11.5% of the 262) exhibited unique phenotypic and genetic characteristics compared to reference strains. Hemolysin-negative L. monocytogenes isolates have been increasingly reported in recent years and are challenging to identify due to their altered phenotypic traits and limitations of standard microbiological methods. This study aimed to evaluate the performance of MALDI-TOF MS in identifying and differentiating 30 hemolysin-negative and hemolysin-positive L. monocytogenes isolates and 12 reference strains, using both a commercial Bruker database and a proprietary in-house database developed from newly characterized isolates. The Bruker database correctly identified only 21% of the environmental isolates, misclassifying most as L. innocua, and showed 83.3% accuracy for reference strains. In contrast, the in-house database achieved 96.6% and 100% accuracy for the environmental and reference strains, respectively. Statistical methods, including hierarchical clustering, heatmaps, PCA, and Pearson correlation, revealed grouping based on phenotypic traits and origin, with key peptides influencing classification. Biomarkers linked to hemolysis and antibiotic resistance differentiated the environmental isolates from reference strains. These findings highlight the need for the development of customized spectral databases to improve the detection of L. monocytogenes in food safety monitoring. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

This study explores the relationship between customer satisfaction and loyalty in mobile banking, emphasizing the moderating role of Technology Readiness. As mobile banking becomes increasingly central to financial service delivery, understanding the nuanced drivers of customer loyalty is essential for strategic growth. Drawing from the Technology Readiness Index, this study examines how four dimensions, optimism, innovativeness, discomfort, and insecurity, moderate the satisfaction–loyalty linkage. Data were collected via a structured survey from 258 mobile banking users in the United States, analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that optimism and innovativeness positively moderate this relationship, while discomfort and insecurity act as negative moderators. Practically, this research introduces a segmented approach to mobile banking service design, underscoring the need for differentiated strategies that address varying levels of user readiness. Theoretically, this study addresses a gap in mobile banking literature by shifting the focus from adoption to sustained usage and satisfaction-based loyalty, enriching the discourse on customer behavior in digital finance. Full article

In today’s competitive environment of online service industries, particularly e-commerce, meeting consumer expectations is essential for service providers to ensure service quality. However, service failures are unavoidable, leading to unfavorable consequences for businesses. Understanding the mechanisms for customer recovery after negative service experiences is crucial. Using cognitive–emotional personality systems theory and benign violation theory, this study constructed a theoretical model. A total of 351 samples were collected through a situational simulation experiment for a linear regression analysis. A self-mocking response strategy positively influenced brand trust through perceived brand authenticity regarding the dimensions of credibility, integrity, and symbolism. Simultaneously, brand trust was identified as a key driver of post-recovery satisfaction. This study proposes a chain mediation model, which incorporates perceived authenticity and brand trust, to fully comprehend the mechanisms underlying consumers’ satisfaction after service recovery. Our findings provide empirical evidence for the effects of self-mockery on post-recovery satisfaction, as well as suggestions for marketers seeking efficient means to meet consumers’ emotional and cognitive demands during service recovery situations. Full article

Emotion recognition by wearable devices is essential for advancing emotion-aware human–computer interaction in real life. Earphones have the potential to naturally capture brain activity and its lateralization, which is associated with emotion. In this study, we newly introduced tympanic membrane temperature (TMT), previously used as an index of lateralized brain activation, for earphone-based emotion recognition. We developed custom earphones to measure bilateral TMT and conducted two experiments consisting of emotion induction by autobiographical recall and scenario imagination. Using features derived from the right–left TMT difference, we trained classifiers for both four-class discrete emotion and valence (positive vs. negative) classification tasks. The classifiers achieved 36.2% and 42.5% accuracy for four-class classification and 72.5% and 68.8% accuracy for binary classification, respectively, in the two experiments, confirmed by leave-one-participant-out cross-validation. Notably, consistent improvement in accuracy was specific to models utilizing right–left TMT and not observed in models utilizing the right–left wrist skin temperature. These findings suggest that lateralization in TMT provides unique information about emotional state, making it valuable for emotion recognition. With the ease of measurement by earphones, TMT has significant potential for real-world application of emotion recognition. Full article

Increased market demand for plant herbs has prompted growers to ensure a continuous and assured supply of superior nutritional quality over the years. Apart from the nutritional value, culinary herbs contain phytochemical benefits that can improve human health. However, a significant amount of research has focused on enhancing yield, frequently overlooking the impact of production practices on the antioxidant and phytonutritional content of the produce. Thus, the study aimed to evaluate the yield, phytonutrients, and essential mineral profiling in selected aromatic herbs and their intricate role in nutritional quality when grown under different production systems. Five selected aromatic herbs (coriander, rocket, fennel, basil, and moss-curled parsley) were evaluated at harvest when grown under three production systems: in a gravel-film technique (GFT) hydroponic system and in soil, both under the 40% white shade-net structure, as well as in a soilless medium using sawdust under a non-temperature-controlled plastic tunnel (NTC). The phytonutritional quality properties (total phenolic, flavonoids, β-carotene-linoleic acid, and condensed tannins contents) as well as 1,1-diphenyl-2-picrylhydrazyl (DPPH) were assessed using spectrophotometry, while vitamin C and β-carotene were analyzed using HPLC-PDA, and leaf mineral content was evaluated using ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometry). The results show that the health benefits vary greatly owing to the particular culinary herb. The fresh leaf mass (yield) of coriander, parsley, and rocket was not significantly affected by the production system, whereas basil was high in soil cultivation, followed by GFT. Fennel had a high yield in the GFT system compared to in-soil and in-soilless cultivation. The highest levels of vitamin C were found in basil leaves grown in GFT and in soil compared to the soilless medium. The amount of total phenolic and flavonoid compounds, β-carotene, β-carotene-linoleic acid, and DPPH, were considerably high in soil cultivation, except on condensed tannins compared to the GFT and soilless medium, which could be a result of Photosynthetic Active Radiation (PAR) values (683 μmol/m²/s) and not favoring the accumulation of tannins. Overall, the mineral content was greatly influenced by the production system. Leaf calcium and magnesium contents were highly accumulated in rockets grown in the soilless medium and the GFT hydroponic system. The results have highlighted that growing environmental conditions significantly impact the accumulation of health-promoting phytonutrients in aromatic herbs. Some have positive ramifications, while others have negative ramifications. As a result, growers should prioritize in-soil production systems over GFT (under the shade-net) and soilless cultivation (under NTC) to produce aromatic herbs to improve the functional benefits and customer health. Full article

Due to consumer needs and the prevalence of gluten-related disorders such as celiac disease, the gluten-free food market is expanding rapidly and is expected to surpass USD 2.4 billion by 2036. The objective of this study was to substitute wheat flour with oat, black rice, brown rice, buckwheat, and rice flours in the production of gluten-free scones, to assess consumer acceptability, and to identify factors contributing to consumer acceptability using check-all-that-apply questions. The 10 attributes of appearance, color, texture, grainy flavor, sweetness, familiar flavor, novelty, familiarity, moistness, and consistency exhibited statistically significant differences among the samples (p < 0.001). One hundred consumers evaluated 18 attributes using a nine-point hedonic scale, and all attributes demonstrated statistically significant differences across six samples (p < 0.001). The samples from buckwheat and wheat scored the highest in consumer acceptability. The results indicate a strong positive correlation between overall liking and purchase intention, with sensory attributes such as nutty flavor, cohesiveness, appearance, moistness, color, texture, and inner softness positively influencing consumer acceptability. The attributes affecting negatively were thick throat sensation, unique flavor, and stuffiness. This study is expected to provide data to aid in the development of better-tasting gluten-free products that meet customer and market needs. Full article

This study examines the performance characteristics of four commonly used short-path algorithms, including Dijkstra, Bellman–Ford, Floyd–Warshall, and Dantzig, on randomly generated directed graphs. We analyze theoretical computational complexity and empirical execution time using a custom-built testing framework. The experimental results demonstrate significant performance differences across varying graph densities and sizes, with Dijkstra’s algorithm showing superior performance for sparse graphs while Floyd–Warshall and Dantzig provide more consistent performance for dense graphs. Time complexity analysis confirms the theoretical expectations: Dijkstra’s algorithm performs best on sparse graphs with O (E + V log V) complexity, Bellman–Ford shows O (V · E) complexity suitable for graphs with negative edges, while Floyd–Warshall and Dantzig both demonstrate O(V³) complexity that becomes efficient for dense graphs. This research provides practical insights for algorithm selection based on specific graph properties, guiding developers and researchers in choosing the most efficient algorithm for their particular graph structure requirements. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article