Search Results (167)

The study examined the relations and Granger causality among environmental pollution, air quality, life expectancy, religious tourism, petroleum consumption and economic growth in three countries, Italy, Saudi Arabia and Türkiye, three countries with a prominent role of religious tourism, given the high shares of religious tourism revenues in their economies and due to pilgrimage-type religious tourism activities in total tourism activities. The study employed a yearly sample of 1975–2019 and novel Fourier-augmented vector autoregressive and Fourier Granger causality tests, under the structural breaks in the data. The findings indicate negative effects on environmental pollution and air quality from tourism in addition to such effects on life expectancy in all countries analyzed, and in this relation, fossil fuel consumption in these nations and its acceleration with tourism play crucial roles. These effects are amplified by economic growth coupled with tourism revenues that go in hand with high fossil fuel consumption, which further worsen the impacts on the environment. In the causality testing stage, the results determined unidirectional causality from tourism, fossil fuel energy consumption, and economic growth to both carbon dioxide emissions and to particulate matter 2.5. These effects are also reinforced by feedback effects between air pollution and life expectancy, which enhance the effects on both environment and air quality. These findings are used to suggest important policy recommendations, among which, the reduction in high dependency on fossil fuel in the energy mix is most central. Equally, policies are suggested to encourage sustainable tourism to reverse the adverse effects on health, environmental degradation and worsened air quality in these nations. Full article

Naturally reinforced polymer composites have emerged as a promising sustainable alternative to conventional polymers due to their biodegradability. This study aimed to develop a composite by incorporating charcoal particulate into a recycled high-density polyethylene (HDPE) matrix and evaluating its mechanical properties. Two manufacturing methods (compression molding and extrusion) and four charcoal concentrations (0, 5, 10, and 15%) were investigated. Characterization involved tensile tests and non-destructive evaluation using wave propagation and ultrasound techniques. The experiment followed a completely randomized design with a 4 × 2 factorial arrangement, comprising eight treatments. Statistical analysis was conducted using Tukey’s test for multiple comparisons. The tensile test results indicated that the manufacturing methods of compression molding and extrusion led to significant differences in the elastic modulus (MOE) variable, in contrast to the results observed for the maximum stress variable. However, the addition of charcoal particulate caused a notable reduction in maximum tensile strength (approximately 50%), from 20.17 to 11.19 MPa, and a 22% decrease in the MOE, from 310.93 to 242.88 MPa, compared to unreinforced HDPE. Non-destructive testing confirmed the tensile test findings, also indicating a reduction in MOE. Despite the decline in mechanical properties, these composites remain viable for applications prioritizing lightweight structures, thermal insulation, or chemical resistance. Furthermore, their use enhances the valorization of waste and increases sustainability by reducing environmental impact. Full article

This study aimed to evaluate the nanomechanical surface properties and water uptake of a flowable short-fiber-reinforced composite (SFRC) using various restorative techniques in order to assess its potential as a standalone restorative material. Nanoindentation and compressive creep testing were employed to characterize material performance. Three resin composites were examined: a flowable SFRC (everX Flow), a bulk-fill particulate filler composite (PFC), and a conventional PFC. Five experimental groups were established based on the restorative technique: layered PFC, layered SFRC, bulk SFRC, bulk PFC, and a bi-structure combining SFRC and PFC. Ninety standardized specimens (n = 18/group) were fabricated. Static and creep nanoindentation tests were conducted to assess surface properties, and water uptake was measured over a 30-day period. Data were analyzed using one-way ANOVA and Bonferroni post hoc tests. Nanoindentation revealed significant differences in hardness, with bulk PFC exhibiting the lowest values (p < 0.001). Creep testing indicated changes in modulus and viscosity following water storage. Notably, bulk SFRC showed the lowest water absorption (p < 0.001). Overall, bulk-applied SFRC demonstrated favorable nanomechanical properties and reduced water uptake, demonstrating its suitability as a standalone restorative material. Further clinical investigations are recommended to validate its long-term performance. Full article

The objective of this study was to explore the potential of creating advanced insulating biocomposites using waste almond and hazelnut shells as particulate fillers, combined with a geopolymer binder, to develop sustainable materials with minimal environmental impact. Optimal conditions for the preparation of biocomposites were determined by measuring the compressive strengths. The aforementioned optimal conditions included a geopolymer to waste nutshell mass ratio of 2, room-temperature curing, and the use of metakaolin geopolymers activated with potassium solutions. Notably, the highest compressive strengths of 4.1 MPa for hazelnut shells biocomposite and 6.4 MPa for almond shells biocomposite were obtained with milk of lime pretreatment at 80 °C for 1 h. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) and Fourier transform infrared spectroscopy (FTIR) analyses revealed better adhesion, as well as improved geopolymer gel polymerization. Furthermore, thermal conductivity and diffusivity measurements demonstrated values characteristic of insulating materials, reinforcing their potential for eco-friendly construction applications. Full article

A novel approach to the calculation and optimization methodology of repairable elements of unmanned aerial vehicle (UAV) structures using pre-cured composite patches is proposed. These patches are glued to the damaged structure with adhesives filled with short fibers or particulate fillers. Compared with conventional repair procedures (in which composite prepregs or wet lay-up are used), the suggested method allows damaged UAV structures to be repaired relatively quickly in field conditions without the need for a vacuum or special equipment. In most scientific studies on this problem, significant attention is devoted to the investigation of rectangular patches used for reinforcing plates that have defects such as cracks and damage. This study focuses on the potential application of circular patches for reinforcing plates with defects or damage and includes further parametric optimization of the geometric parameters of the patch. A fundamental approach to the topological and structural optimization of adhesive bonding, along with an experimental study of adhesive properties, has been combined into a single model. This model includes the optimization of the shape and structure of patches for bonded repair, allowing for changes in adhesive thickness to restore the load-carrying capacity of the structure. The simulation and analysis of the results of the renovation of damaged structures for double-sided and single-sided repaired elements of the UAV structure were performed. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. Full article

This paper provides deeper insights into the performance of diamond particulate reinforced refractory composites used for cutting tools in the oil and gas industries. In particular, 25C_diamond–70.5WC–4.5Co composites were enhanced with zirconia additives in proportions of 4 wt.% and 10 wt.% via the spark plasma sintering method. Wear tests were performed, and the analyses of elemental composition, morphology, and microstructure were completed. It was found that the addition of yttria-stabilized zirconia increased the plasticity of the matrix and thus introduced the ductile fracture mechanism, reducing the role of abrasive wear. As a result, the specific wear rate was reduced by 44% after the addition of 4 wt.% of zirconia and by 80% with 10 wt.% of ZrO₂. The presence of zirconia contributed to the increase in the retention force between the matrix and diamond grits, which further reduced the intensity of the abrasive mechanism. Full article

Hybrid composite materials reinforced with both fibers and particulate fillers are increasingly used in engineering due to their favorable balance of mechanical strength, reduced weight, and enhanced tribological performance. This study investigated the effect of CuSn10 bronze powder additions (5%, 10%, and 15% by weight) on the mechanical and tribological properties of novel basalt fiber-reinforced polymer (BFRP) composites. The composites were fabricated via vacuum-assisted processing and tested under dry sliding conditions with varying loads (10, 20, and 30 N) and sliding speeds (0.1, 0.25, and 0.36 m/s). The results show that the optimal tensile strength (440.6 MPa) was achieved at 10 wt% CuSn10, while the best tribological performance was observed at 15 wt% CuSn10, under a 10 N load and 0.25 m/s sliding speed, where the coefficient of friction decreased by up to 38% and the specific wear rate was reduced by more than 50% compared to the unreinforced BFRP composite. These enhancements are attributed to the formation of a stable oxide-based tribolayer (CuO, SnO₂) and improved load transfer at the fiber–matrix interface. Statistical analysis (GLM) confirmed that CuSn10 content had the most significant influence on tribological parameters. The findings provide valuable insight into the design of high-performance hybrid composites for structural and tribological applications. Full article

COPD induced by biomass-burning smoke is a public health problem in developing countries. Biomass-based fuels are ineffective and deliver elevated levels of carbon monoxide, polycyclic aromatic hydrocarbons, and fine particulate matter. PRSS23 participates in extracellular matrix remodeling processes in COPD patients. Our objective was to estimate the DNA methylation levels of cg23771366 (PRSS23) and their clinical relevance in COPD caused by chronic exposure to biomass-burning smoke (BBS). We included 80 women with COPD (COPD-BBS) (≥200 h per year), 180 women with exposure to BBS (≥200 h per year) but without COPD (BBES), and 79 lung-healthy women (HW) without exposure to biomass-burning smoke. The DNA methylation analysis shows significant differences between the three groups included in this study (p < 0.001). HW had high methylation levels (100%) in cg23771366 (PRSS23). In comparison, COPD-BBS and BBES had low levels [0.91% vs. 9.17%, respectively], showing statistically significant differences (p = 0.011) between both groups, with the COPD-BBS presenting the lowest levels in the methylation of cg23771366. In conclusion, chronic biomass-burning smoke exposure is associated with decreased levels of DNA methylation at the CpG cg23771366 site in PRSS23, reinforcing the relationship between PRSS23 and particulate matter. Full article

This review explores advanced sensing technologies and deep learning (DL) methodologies for monitoring airborne particulate matter (PM), which is critical for environmental health assessments. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. This review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. This review highlights the data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. This review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications. Full article

Although dental resin composite restoratives offer a widely used direct-placement treatment option aimed at replacing the form and function of a natural tooth, there are several clinically relevant performance aspects of these materials that can be improved. The formulation of the resin matrix phase of dental composites for high-efficiency photopolymerization leading to polymers with excellent mechanical properties has always been a challenge that is addressed here through the use of structurally new and more reactive monomers as well as the formation of polymer networks that incorporate non-covalent reinforcing interactions. The purpose of this study was to validate that a set of tetraurethane diacrylates (TUDAs) with a novel configuration of their urethane linkages in coordination with acidic comonomers could be devised to obtain highly robust new composite materials. Due to the novel molecular design, this exploratory approach was conducted using reaction kinetics and three-point bend testing to assess the performance. Conversion and mechanical properties were measured to refine these formulations prior to the addition of filler. The initial formulations demonstrated outstanding dry mechanical test results that subsequently showed a major intolerance to water storage, which led to a model study using urethane diacrylate (UDA) followed by the addition of hydrophobic TUDA monomers. Once the resin formulations were optimized, silane-treated particulate filler was added to determine the effectiveness as composite materials. The final formulation used a hydrophobic, aromatic TUDA along with 4-methacryloxyethyl trimellitic anhydride (4-META) as a latent acidic comonomer and a mixture of acrylic acid (AA) and methacrylic acid (MAA). This formulation achieves a very high level of both reactivity and mechanical properties relative to current dental composite restoratives. Full article

Background/Objectives: It is estimated that gestational diabetes mellitus (GDM) affects approximately 14% of pregnant women. This is due to the inability of the body to produce enough insulin for gestation. With greater appearance during the second and third trimesters, GDM has a multifactorial cause including hypertension, cardiovascular issues (CVD), family history both or either type two diabetes mellitus (T2DM) or GDM, obesity, advanced maternal age, and polycystic ovarian syndrome (PCOS). However, it has been suggested that except for genetic predisposition, environmental factors can increase the risk of GDM development to a great extent. The aim of this systematic review and meta-analysis is the examination of different environmental contributors that play a significant role in the development of GDM. Methods: The databases used were PubMed and ScienceDirect. The inclusion criteria were a 10-year duration (2014–2024), English language, research articles, and only humans included. Afterwards, tables were created to summarize the most important information from each article. Forest and funnel plots were created to assess the possibility of a greatly significant difference between each environmental contributor. Results: Initially, 9361 articles were found. After the automation tools were applied, 706 were left. The total number of articles used in the study after the screening process was 26. Through the systematic review analysis, the following risk factors were stated to play a contributing role with GDM: extreme temperatures (both high and low), organophosphorus flame retardants (OFRs), bisphenol A (BPA), selenium (Se), metallic elements, urinary antimony (Sb), trace elements, thiamine and riboflavin, and fine particulate matter PM_2.5. Conclusions: Through this meta-analysis, it can be concluded that there is statistical significance for fine particulate matter PM_2.5, especially in the first (p < 0.001) and second (p < 0.001) trimesters, proving the acknowledged connection between PM_2.5 and GDM pathogenesis during pregnancy. Apart from that, fetal sex can play an important role in the development of GDM, as there is the greatest risk in males (p < 0.001), whereas no correlation between maternal smoking habit and bisphenol A with GDM was found. In conclusion, it can be stated that environmental factors can have a great impact on the development of GDM during the gestational period, but more studies must be carried out to reinforce our outcomes. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article

The recent progress in the acoustic fractionation of particulate suspensions within microfluidic devices emphasizes the utility of the acoustic fractionation process also for gas-suspended particles as a significant advancement in the field of mechanical process engineering. In the literature, analytical and numerical studies have found the gas-based acoustic particle fractionation process to be suitable for particles in a size range below 10 µm. The viability remains experimentally unassessed. In this article, we present particle fractionation experiments conducted on gas-born particles suspended in high-intensity acoustic fields. A particle-size-dependent accumulation of particles in the acoustic sound velocity lobes and nodes could be observed, indicative of an acoustic fractionation process. Additionally, evidence of acoustic streaming and acoustic focusing has been found, both of which have the potential to impede the fractionation process. The experimental results align with the conclusions of numeric simulations. The in-process particle behavior is discussed in the context of the relevant literature and reinforces the notion of selective entrainment. Full article

This study explores how the environmental conditions of sustainable classrooms influence the concentration and academic performance of students in university environments, integrating the use of sustainable design strategies. Within the framework of education for sustainability, the importance of sustainable urban spaces, buildings, interiors, and green infrastructure products in communicating and promoting scientific and environmental knowledge is recognized. Using a systematic mapping methodology, the research examines how real-time monitoring of environmental variables such as air quality, humidity, temperature, CO₂, particulate matter, and lighting, through the Internet of Things (IoT), can enhance learning. The study focuses on examining the impact of environmental factors on students’ academic performance, as well as exploring how sustainable educational spaces can promote greater awareness and favorable attitudes towards the environment. Based on an analysis of 454 articles and success stories on green educational infrastructure projects, the results reveal a significant correlation between optimal environmental conditions such as good ventilation and temperature control and increased student concentration and performance. This study also highlights the role of educational interventions, both formal and informal, that integrate sustainably built environments to reinforce occupants’ environmental engagement. The conclusion is clear: improving classroom environmental conditions, especially in terms of ventilation and temperature control, not only optimizes learning, but also acts as a powerful environmental education tool, fostering education for sustainable development and strengthening ecological attitudes among students. Full article