Search Results (29,479)

IgG Fc binding protein (FcGBP) is a mucin-like protein that binds strongly to IgG and IgG–antigen complexes in intestinal mucus. FcGBP presence and its altered expression levels in meconium accumulating in the fetal intestine and amniotic fluid flowing in the intestine may provide new knowledge of the mechanisms responsible for the immune adaptation of the fetus to extrauterine life. FcGBP concentrations were measured by ELISA in the first-pass meconium and amniotic fluid samples collected from 120 healthy neonates delivered by either vaginal birth (n = 35) or cesarean section (n = 85) at 36 to 41 weeks gestation. The meconium FcGBP concentrations (405.78 ± 145.22 ng/g) decreased (r = −0.241, p = 0.007) over the course of 36 to 41 weeks gestation, but there were no significant changes (p > 0.05) in the amniotic fluid FcGBP (135.70 ± 35.83 ng/mL) in the same period. Both meconium and amniotic fluid FcGBP concentrations were higher (p < 0.05) in neonates delivered by cesarean section. Decreases in the meconium FcGBP concentrations correlated (r = −0.37, p = 0.027) with the gestational age in neonates delivered by vaginal birth but not in those delivered by cesarean section (p > 0.05). No association was found between the FcGBP concentrations in meconium and amniotic fluid and the birth weight (p > 0.05). With the development of the mucosal immune system in the fetal intestine over the course of the third trimester of gestation, the meconium FcGBP concentrations decrease. Increased FcGBP concentrations measured in the meconium and amniotic fluid of neonates delivered by cesarean section may possibly indicate altered intestinal mucosal function. Intrauterine growth is not associated with the intestinal mucosal barrier maturation involving FcGBP. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule). Full article

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Background: Transfection is vital for gene therapy, mRNA treatments, CAR-T cell therapy, and regenerative medicine. While viral vectors are effective, non-viral systems like lipid nanoparticles (LNPs) offer safer, more flexible alternatives. This work explores emerging non-viral transfection technologies to improve delivery efficiency and therapeutic outcomes. Methods: This review synthesizes the current literature and recent advancements in non-viral transfection technologies. It focuses on the mechanisms, advantages, and limitations of various delivery systems, including lipid nanoparticles, biodegradable polymers, electroporation, peptide-based carriers, and microfluidic platforms. Comparative analysis was conducted to evaluate their performance in terms of transfection efficiency, cellular uptake, biocompatibility, and potential for clinical translation. Several academic search engines and online resources were utilized for data collection, including Science Direct, PubMed, Google Scholar Scopus, the National Cancer Institute’s online portal, and other reputable online databases. Results: Non-viral systems demonstrated superior performance in delivering mRNA, siRNA, and antisense oligonucleotides, particularly in clinical applications. Biodegradable polymers and peptide-based systems showed promise in enhancing biocompatibility and targeted delivery. Electroporation and microfluidic systems offered precise control over transfection parameters, improving reproducibility and scalability. Collectively, these innovations address key challenges in gene delivery, such as stability, immune response, and cell-type specificity. Conclusions: The continuous evolution of transfection technologies is pivotal for advancing gene and cell-based therapies. Non-viral delivery systems, particularly LNPs and emerging platforms like microfluidics and biodegradable polymers, offer safer and more adaptable alternatives to viral vectors. These innovations are critical for optimizing therapeutic efficacy and enabling personalized medicine, immunotherapy, and regenerative treatments. Future research should focus on integrating these technologies to develop next-generation transfection platforms with enhanced precision and clinical applicability. Full article

Background/Objectives: This study examines how seasonality, pollution, and sample type (water and sediment) influence the presence and distribution of antibiotic resistance genes (ARGs), with a focus on antibiotic resistance genes (ARGs) located on plasmids (the complete set of plasmid-derived sequences, including ARGs) in a tropical urban river. Methods: Samples were collected from three sites along a pollution gradient in the Virilla River, Costa Rica, during three seasonal campaigns (wet 2021, dry 2022, and wet 2022). ARGs in water and sediment were quantified by qPCR, and metagenomic sequencing was applied to analyze chromosomal and plasmid-associated resistance profiles in sediments. Tobit and linear regression models, along with multivariate ordination, were used to assess spatial and seasonal trends. Results: During the wet season of 2021, the abundance of antibiotic resistance genes (ARGs) such as sul-1, intI-1, and tetA in water samples decreased significantly, likely due to dilution, while intI-1 and tetQ increased in sediments, suggesting particle-bound accumulation. In the wet season 2022, intI-1 remained low in water, qnrS increased, and sediments showed significant increases in tetQ, tetA, and qnrS, along with decreases in sul-1 and sul-2. Metagenomic analysis revealed spatial differences in plasmid-associated ARGs, with the highest abundance at the most polluted site (Site 3). Bacterial taxa also showed spatial differences, with greater plasmidome diversity and a higher representation of potential pathogens in the most contaminated site. Conclusions: Seasonality and pollution gradients jointly shape ARG dynamics in this tropical river. Plasmid-mediated resistance responds rapidly to environmental change and is enriched at polluted sites, while sediments serve as long-term reservoirs. These findings support the use of plasmid-based monitoring for antimicrobial resistance surveillance in aquatic systems. Full article

The extensive use of pesticides has significant implications for public health and the environment. Breeding crop plants is the most effective and environmentally friendly approach to improve the plants’ resistance. However, it is time-consuming and costly, and it is sometimes difficult to achieve satisfactory results. Plants induce defense responses to natural elicitors by interpreting multiple genes that encode proteins, including enzymes, secondary metabolites, and pathogenesis-related (PR) proteins. These responses characterize systemic acquired resistance. Humic substances trigger positive local and systemic physiological responses through a complex network of hormone-like signaling pathways and can be used to induce biotic and abiotic stress resistance. This study aimed to assess the effect of humic substances on the activity of phenylalanine ammonia-lyase (PAL), peroxidase (POX), and β-1,3-glucanase (GLU) used as a resistance marker in various plant species, including orange, coffee, sugarcane, soybeans, maize, and tomato. Seedlings were treated with a dilute aqueous suspension of humic substances (4 mM C L⁻¹) as a foliar spray or left untreated (control). Leaf tissues were collected for enzyme assessment two days later. Humic substances significantly promoted the systemic acquired resistance marker activities compared to the control in all independent assays. Overall, all enzymes studied in this work, PAL, GLUC, and POX, showed an increase in activity by 133%, 181%, and 149%, respectively. Among the crops studied, citrus and coffee achieved the highest activity increase in all enzymes, except for POX in coffee, which showed a decrease of 29% compared to the control. GLUC exhibited the highest response to HS treatment, the enzyme most prominently involved in increasing enzymatic activity in all crops. Plants can improve their resistance to pathogens through the exogenous application of HSs as this promotes the activity of enzymes related to plant resistance. Finally, we consider the potential use of humic substances as a natural chemical priming agent to boost plant resistance in agriculture Full article

Soil microbial communities are crucial for ecosystem services, soil fertility, and the resilience of agroecosystems. This study investigated how long-term (31 years) agronomic practices—tillage, NPK fertilization, and cropping system—along with measured environmental variables influence the microbial biomass and its community composition in Chernozem soil under corn cultivation. The polyfactorial field experiment included three tillage treatments ((moldboard (MT), ripped (RT), strip (ST)), two fertilization regimes (NPK (N: 160; P: 26; K: 74 kg/ha), and unfertilized control) and two cropping systems (corn monoculture and corn–wheat biculture). The soil samples (0–30 cm) were collected in June and September 2023. Microbial biomass and community structure were quantified using phospholipid fatty acid (PLFA) analysis, which allowed the estimation of total microbial biomass and community composition (arbuscular mycorrhizal (AM) fungi, fungi, Gram-negative (GN) and Gram-positive (GP) bacteria, actinomycetes). Our results showed that microbial biomass increased from June to September, rising by 270% in unfertilized plots and by 135% in NPK-fertilized plots, due to higher soil moisture. Reduced tillage, especially ST, promoted significantly higher microbial biomass, with biomass reaching 290% and 182% of that in MT plots in June and September, respectively. MT had a higher ratio of bacteria-to-fungi compared to RT and ST, indicating a greater sensitivity of fungi to disturbance. NPK fertilization lowered soil pH by about one unit (to 4.1–4.8) and reduced microbial biomass—by 2% in June and 48% in September—compared to the control, with the particular suppression of AM fungi. The cropping system had a smaller overall effect on microbial biomass. Full article

This paper aims to explore the potentialities and systemic relationships between the ‘regenerative’ process and ‘circular economy’ concept within the conservation and reuse of a built cultural heritage framework through contextualizing the concept of ‘process programming’ of the Preventive and Planned Conservation methodology. As a case study, it depicts a decommissioned industrial agricultural silo in Corbetta—a small historic city with its hinterland located in the protected Southern Milan Regional Agricultural Park. The context includes the industrial agricultural lands of the 20th century, together with historical water infrastructure, farmhouses, and the typical flora of the Lombardy region, all evidences of Corbetta’s rural archaeological values and the sophisticated material culture of its past collective production/economy system—the locus in which the silo was once one of the main productive symbols of Corbetta’s agricultural identity. Within such a complex and challenging context, this paper argues in favor of the constructive role of such a methodology in upholding circular economy principles within the process of conservation and reuse of the silo, highlighting its broader application of the ‘coevolution’ concept from a multidisciplinary long-term perspective. Full article

The objective of this study was to evaluate the efficacy of selected herbicides permitted for use in sustainable agriculture, specifically targeting spring rye and spring barley in a no-till farming system. The application of chemical herbicide protection in the cultivation of spring rye and barley significantly increased the yield and improved the quality parameters of the harvested grain, with the most pronounced effect observed in spring barley. The effectiveness of the herbicide treatment in reducing the number of weeds was 99.4% for spring rye and 82.39% for spring barley. The study demonstrated that the application of chemical herbicide protection had a positive impact on the quality parameters of spring barley grain. Both the thousand-grain weight and protein content were significantly higher in the grain collected from protected plots compared to the control plots. By utilizing herbicides permitted for use in integrated production (IP) in a sustainable manner, we protect the environment while minimizing the impact on crop yields and maintaining the quality of the harvested produce. Full article

Background: School-based tobacco control policies are critical for preventing youth tobacco use. While many districts adopt formal policies to create smoke- and vape-free environments, the degree to which these policies are enforced at the school level may vary, influencing their effectiveness. Little is known about how consistently such policies are implemented across schools within urban school districts. Objectives: This study aimed to examine the existence and enforcement of school-level tobacco control policies in an urban public school system, using Baltimore City schools as a case example. Methods: We conducted a survey of school personnel from 20 high schools in Baltimore City in 2024. The survey instrument assessed the presence and enforcement of policies related to tobacco use prevention, communication, signage, disciplinary actions, and institutional support. Descriptive statistics (frequencies and percentages) were used to summarize responses. Spearman correlations were also used for bivariate correlations. Additional school-level and neighborhood-level contextual data were collected from the internet (neighborhood socioeconomic status and school performance). Results: While many policies existed across the 20 participating schools, their enforcement was widely inconsistent. Most schools reported the existence of policies prohibiting tobacco use in school buildings (60%) and vehicles (55%). However, few schools had visible tobacco-free signage (35%) or offered cessation programs (15%). Communication of policies to students (70%) and staff (65%) was the most commonly enforced aspect of tobacco control policies. Conclusions: Findings suggest that while tobacco control policies may be adopted across urban school systems, their enforcement at the school level remains uneven. Greater attention may be needed to support policy implementation and to reduce variability in school-level practices. Baltimore City serves as a useful case study to understand these challenges and identify opportunities for strengthening school-based tobacco prevention efforts. Full article

This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article

The safe and reliable operation of autonomous vehicles is contingent on comprehensive testing. However, the operational scenarios are inexhaustible. Corner cases, which critically influence autonomous vehicle safety, occur at an extremely low probability and follow a long-tail distribution. Corner cases can be defined as combinations of driving task and scenario elements. These scenarios are characterized by low probability, high risk, and a tendency to reveal functional limitations inherent to autonomous driving systems, triggering anomalous behavior. This study constructs a novel corner case dataset using naturalistic driving data, specifically tailored for autonomous vehicle testing. A scenario marginality quantification method is designed to analyze multi-source naturalistic driving data, enabling efficient extraction of corner cases. Heterogeneous scenarios are systematically transformed, resulting in a dataset characterized by diverse interaction behaviors and standardized formatting. The results indicate that the scenario marginality of the dataset constructed in this study is 2.78 times that of mainstream naturalistic driving datasets, and the scenarios exhibit considerable diversity. The trajectory and velocity fluctuations, quantified at 0.013 m and 0.021 m/s, respectively, are consistent with the kinematic characteristics of real-world driving scenarios. These results collectively demonstrate the dataset’s high marginality, diversity, and applicability. Full article

GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article

This paper introduces a novel system that utilizes OpenPose for skeleton estimation to enable a tabletop robot to interact with humans proactively. By accurately recognizing upper-body poses based on the skeleton information, the robot autonomously approaches individuals and initiates conversations. The contributions of this paper can be summarized into three main features. Firstly, we conducted a comprehensive data collection process, capturing five different table-front poses: looking down, looking at the screen, looking at the robot, resting the head on hands, and stretching both hands. These poses were selected to represent common interaction scenarios. Secondly, we designed the robot’s dialog content and movement patterns to correspond with the identified table-front poses. By aligning the robot’s responses with the specific pose, we aimed to create a more engaging and intuitive interaction experience for users. Finally, we performed an extensive evaluation by exploring the performance of three classification models—non-linear Support Vector Machine (SVM), Artificial Neural Network (ANN), and convolutional neural network (CNN)—for accurately recognizing table-front poses. We used an Asus Zenbo Junior robot to acquire images and leveraged OpenPose to extract 12 upper-body skeleton points as input for training the classification models. The experimental results indicate that the ANN model outperformed the other models, demonstrating its effectiveness in pose recognition. Overall, the proposed system not only showcases the potential of utilizing OpenPose for proactive human–robot interaction but also demonstrates its real-world applicability. By combining advanced pose recognition techniques with carefully designed dialog and movement patterns, the tabletop robot successfully engages with humans in a proactive manner. Full article