Ethylene Oxide (EtO) is a colorless, flammable gas at room temperature, produced by the catalytic oxidation of ethylene. EtO is widely used by medical sterilization facilities to clean medical supplies and equipment. Recent epidemiological studies showed that EtO is a more potent carcinogen than previously documented, leading the Environmental Protection Agency (EPA) to update, in December 2016, the inhalation unit risk estimate for EtO. This resulted in the identification of EtO as a potential health concern in several areas across the US, including the state of Utah. The geography surrounding Salt Lake Valley creates a bowl; ideal for collecting air pollution emissions. The region often experiences inversion episodes which inhibit vertical mixing and cause an accumulation of air pollutants, leading to unhealthy pollution levels. Using the EPA's dispersion modeling software, AERMOD, this study estimated EtO concentrations through facility stack and fugitive emissions modeling results. These values were compared with those of canister-based concentrations from ambient air samples taken near a medical device sterilization facility in the Salt Lake Valley. Stainless steel, whole-air passivated canisters were used to collect 24-hour ambient concentration samples of EtO. Eight locations surrounding a Salt Lake Valley medical device sterilization facility and four background sites were chosen to measure the ambient concentrations. Accounting for potential atmospheric impacts on EtO, measurements were sampled in winter 2022 (January-March) and summer 2022 (July-September). The modeled EtO concentrations were adjusted to account for background values associated with the winter or summer data. Then the two methodologies were compared using a Wilcoxon Sign-ranked, paired test. The statistical analysis resulted in six of the eight sample locations surrounding the sterilization facility being significantly different when comparing canister-based measurements of ambient EtO to modeled estimates. Canister-based measurements taken at sites one, three, and four were statistically greater than the modeled estimates, while sites two, five, and seven were statistically less than the modeled estimates. Also, the summer background value calculated was almost 2.5 times greater than the winter. The results do not suggest whether one method is more or less conservative than the other. In conclusion, the five of the closest sites and site seven were statistically different when comparing measured and modeled ambient concentrations of EtO. The comparison results do not clearly indicate if a correction factor could be derived for future human exposure to cancer risk assessment modeling. However, it is reasonable that the closer to the sterilization facility, the more total EtO exposure will be realized.

The ambient air quality data for particulate matter as well as criteria of gaseous pollutants were assembled during December 2013 to December 2015 from the Continuous Air Quality Monitoring Station (CAMS) located at Agrabad, Chittagong. The observation showed that during April- October, 24 hour average concentration of PM10 and PM2.5 were within the National Ambient Air Quality Standard (NAAQS) level but it increased occasionally by more than two and a half times during the whole non-monsoon period (November-March). The highest values found of PM2.5 were 321.1 µg/m³ in January, 2013 and 220.34 µg/m³ in December 2015. Whether, the highest alarming concentration of PM10 was reported as 474 µg/m3 in January 2007. The other gaseous pollutants such as SO₂, NO₂, O₃, CO and Hydrocarbons remain well within the permissible limit except dry non-monsoon period. The yearly average increase of Air Quality Index (AQI) value indicates the growth rate of air pollution in Chittagong city. The main responsible pollutant for air pollution is found PM_2.5.

In this study, a basic study was performed to analyze the seasonal temperature status of a research room in the Global Environment Research Building where ceiling-embedded indoor units are installed to study the room temperature status of the building as well as to improve its thermal environment. In addition, a direction for improvement of the indoor thermal environment in the winter was proposed through a CFD (computational fluid dynamics) simulation and was proven by an additional experiment. Through the results of this study, it appeared that if the ceiling-embedded indoor unit was installed in the small indoor space without considering the thermal vulnerability of its perimeter boundary, the air temperature of the upper part was greatly different from that of the bottom part in the winter. Hence, in this study, as a means to improve it, convectors were installed to minimize the effect of the external thermal environment and angle-controllable air flowing fans were installed to mitigate the stratification distribution. With such result, it was intended to present the essential data for improvement of the thermal environment as well as conservation of heating energy in the winter for buildings by reviewing the use of the ceiling-embedded indoor unit in the future.

Nowadays a recognized need for accurate observations of atmospheric aerosols (AEs) and reactive gases (RGs) exists in the framework of regional, national and global near-surface networks based on permanent or mobile measurement stations. In this context, a paramount and not-trivial issue is related to the correct execution of continuous sampling of ambient air and its subsequent distribution to measurement analyzers hosted inside the stations. Sampling artifacts must be minimized for obtaining reliable pictures of ambient air composition. To respond to this need, a suite of novel “smart” and relatively low-cost systems for the continuous sampling of ambient air was developed in the framework of the Project I-AMICA (2012 – 2015, www.i-amica.eu). These systems were designed to execute AE and RG measurements according with WMO/GAW and ACTRIS recommendations and standard operation procedures. A particular attention was dedicated to the stabilization and control of the sampling flow rates and temperatures. The analysis of one full year of operations at the WMO/GAW regional station of Capo Granitola (GAW ID: CGR, Italy), allowed to conclude that these systems are effective in meeting the technical requirements for correct execution of AE and RG measurements

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K and type M pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant.

In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) to achieve the attack. These methods do not discover the common vulnerabilities of the ODs and thus the transferability is weak. Based on this, this paper proposes a foreground feature approximation (FFA) method to generate adversarial examples (AEs) that discover the common vulnerabilities of the ODs by changing the feature information carried by the image itself to implement the attack. Specifically, firstly, the high-quality predictions are filtered as attacked objects using the detector, after which a hybrid image without any target is made, and the hybrid foreground is created based on the attacked targets. The images' shallow features are extracted using the backbone network, and the features of the input foreground are approximated towards the hybrid foreground to implement the attack. In contrast, the model predictions are used to assist in realizing the attack. In addition, we have found the effectiveness of FFA for targeted attacks, and replacing the hybrid foreground with the targeted foreground can realize targeted attacks. Extensive experiments with seven rotating ODs on the RSOD datasets DOTA and UCAS-AOD show that FFA achieves both targetless and targeted attacks with a high success rate and transferability.

This paper represents the first investigation into the consensus problem of linear time-varying multi-agent systems utilizing an event-triggered communication scheme. First, a general event-triggered consensus control scheme is proposed for a general category of linear time-varying multi-agent systems. Under some suitable assumptions, it is demonstrated that all agents’ states will converge exponentially, with Zeno behaviour being ruled out. Second, the consensus problem in a network of linear time-varying multi-agent systems with a spanning tree is investigated using the proposed control strategy. It demonstrates that the consensus issue for the specified system can be reformulated as a stabilization problem for an error system through a time-varying linear transformation. Then, the event-triggered consensus problem is just a special instance of the general event-triggered consensus problem mentioned above. Finally, to illustrate the efficacy of the event-triggered method proposed in this study, simulation results are shown.

The Double Diaphragm Forming (DDF) process uses vacuum pressure and heat to pre-shape composite reinforcements between flexible diaphragms. Accurate modelling of heat transfer within DDF requires knowledge of the thermophysical properties of the constituent materials. This study investigates the specific heat and thermal conductivity of silicone diaphragms, carbon fibers (chopped and powdered), thermoplastic veils, and their combinations. Experiments measured the specific heat of each material using differential scanning calorimetry and the thermal conductivity of silicone and carbon fabric preforms using the transient plane source method and modified transient plane source method respectively. The influence of reheating cycles on specific heat of carbon fiber-veil samples and the effect of compaction on thermal conductivity of silicone and carbon fabrics were explored. While silicone exhibited linear specific heat behavior, the thermoplastic veil showed non-linearity due to phase change. The combined carbon fiber-veil samples demonstrated slight non-linear specific heat variations depending on reheating cycles. Thermal conductivity of the fabric preforms decreased with the addition of thermoplastic veil and under vacuum-compaction conditions. The established temperature-dependent property relationships provide valuable data for optimizing DDF preforming parameters and enhancing energy efficiency in composite manufacturing.

This study determined the temporal variation of PM2.5 in ambient air in Thohoyandou and further assessed the associated health risks. The levels of PM2.5 were quantified for a period of 1 year (April 2017-April 2018) using the gravimetric method. There was no significant difference (P-value = 0.18) in concentrations of both PM2.5 samples collected during weekdays (11.29 µg.m-3) and weekends (9.86 µg.m-3). However, higher concentrations of PM2.5 were measured in spring and the lowest was measured in summer. The cancer risk obtained for PM2.5 (2.21 × 10−5, 3 × 10-4, and 5 × 10-4 for infants, children, and adults respectively) in the outdoor air of Thohoyandou has exceeded the limit values by the USEPA and WHO, implying a significant risk for the whole population. For non-carcinogenic risks, the HQ values were 2.60, 4.81, and 2.60 for infants, children, and adults respectively. The HQ value >1 indicates a non-carcinogenic risk to the residents in Thohoyandou and a higher risk to children. Moreover, PM2.5 in Thohoyandou is responsible for 0.15% and 0.13% of deaths resulting from cardiovascular disease and lung cancer respectively for adults above 30 years. PM2.5 is causing adverse health effects in Thohoyandou as deduced from the health risk assessment. Therefore, it is recommended that further epidemiological studies be conducted in Thohoyandou to estimate the burden of disease due to exposure to particulate matter and suitable controlling policies be arranged to reduce particulate matter.

(1) Background: Environmental and public health research has given considerable attention to the impact of air quality on brain health, with systematic reviews widespread. No literature review has been done for cognitive frailty – a multidimensional syndrome combining physical frailty and cognitive impairment and their apparent co-dependence, linked to increased vulnerability and adverse health outcomes, including dementia. Instead, cognitive decline and frailty is implicitly explored through research on air quality and comorbid cognitive and physical decline in elderly populations. (2) Methods: A scoping review was conducted to explore the need for a systematic review. Combining Arksey and O’Malley [1] and PRISMA-ScR checklist [2], a scoping review of SCOPUS using ‘cogniti*’ + ‘resilience’ + ‘air quality’ or ‘cogniti*’ + ‘ageing’ + ‘air quality’ resulted in N=2503 articles, screened and reduced using inclusion and exclusion criteria, to N=16 articles. (3) Results: Air quality appears to be a critical risk factor for cognitive decline, even at air quality levels below WHO targets. Moderate long-term ambient air pollution appears linked to increased risk of cognitive frailty, suggesting earlier and more active interventions to protect older people. There are varied effects on cognition across the life course, with both emotional and functional impacts. Effects may be more detrimental to elderly people with existing conditions, including economic and health inequalities. Generalisation of results is limited due to the absence of a dose-response, variations in methods, controlling for comorbid effects, and variance across studies. (4) Conclusions: The findings support the need for more research and a more extensive summary of the literature.

Background: Lung cancer is one of the leading causes of morbidity and mortality worldwide with 25% of deaths due to lung cancer occurring in Europe. This study therefore sought to assess the burden of lung cancer by country and to evaluate the magnitude of fine Particulate Matter (PM2.5) and cigarette smoking by country in Europe. Methods: An ecological study nested on the World Health Organization air pollution database 2016 was conducted. We sampled 30 European Countries, with a total of 1625 mean annual samples of Particulate Matter (PM2.5) collected from 1625 designated sites (n = 1625). We further used the ‘World Health Disease Rankings’ database to extract Lung Cancer Morbidity and Mortality Rate by country. We used SAS version 9.4 to indicate the distribution of PM2.5 and Lung Cancer Mortality Rate. Results: Lung cancer Relative Risk (RR) was 1.0 in all never- smokers. RR for Ex-smokers for Adeno carcinoma was 3.5 in males and 1.1 in females, small cell carcinoma was 16.2 in males and 3.8 in females. RR for current smokers for Adeno carcinoma was 8.0 in males and 4.1 in females, small cell carcinoma was 57.9 in males and 18.2 in females. Mean annual PM2.5 by country ranged from 6.01 to 37.28µg/m3 whereas lung cancer mortality rate by country ranged from 19.67 to 54.26 deaths per 100,000 population. Conclusion: Cigarette smoking and exposure to both second hand smoke and high concentration of PM2.5 resulted into increased burden of lung cancer in Europe. Countries should re-strategize to reduce the burden of lung cancer in Europe.

Abstract: Metal-air batteries provide a most promising battery technology given their outstanding potential energy densities, which are desirable for both stationary and mobile applications in a ‘beyond lithium-ion’ battery market. Silicon- and iron-air batteries underwent less research and development compared to lithium- and zinc-air batteries. Nevertheless, in the recent past, the two also-ran battery systems made considerable progress and attracted rising research interest due to the excellent resource-efficiency of silicon and iron. Silicon and iron are among the top five of the most abundant elements in the earth’s crust, which ensures almost infinite material supply of the anode materials, even for large scale applications. Furthermore, primary silicon-air batteries are set to provide one of the highest energy densities among all batteries, while iron-air batteries are frequently considered as a highly rechargeable system with decent performance characteristics. Considering fundamental aspects for the anode materials, i.e., the metal electrodes, in this review, we will first outline the challenges, which explicitly apply to silicon- and iron-air batteries and prevented them from a broad implementation so far. Afterwards, we provide an extensive literature survey regarding state-of-the-art experimental approaches, which are set to resolve the aforementioned challenges and might enable the introduction of silicon- and iron-air batteries into the battery market in the future.

Mechanical equipment is composed of several parts, the interaction between the parts exists throughout whole life cycle leads to the widespread phenomenon of fault coupling and the diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the topological structure of graph neural network is used to describe the correlation of coupling faults and multiple fault types are regarded as network nodes respectively. The fault features are fused and extracted by graph convolution to achieve the classification of coupling faults. In this paper, dynamic vertexes are defined in data topology and the parameters of DIGNN classification model are optimized by changing the fault type of dynamic vertexes during training stage. The data is loaded into dynamic vertexes for classification and analysis during testing stage. The one-dimensional vibration data is converted into two-dimensional time-frequency domain data by wavelet transform making the input features of the model interpretable to reduce the uncertainty of model training. The data topology is then fed into DIGNN for fault classification, and GCN's node aggregation gives interpretability to each layer of network data processing. The method proposed in this paper can realize accurate diagnosis of independent faults on the data set, and can effectively judge the coupling mode of coupling faults, which is suitable for coupling fault analysis of mechanical equipment.

To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model based on a parallel architecture based on the concept of intelligent aerodynamics, named PA-TLA (Parallel Architecture combining TCN, LSTM, and Attention mechanism). This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) by 6.80%, mean absolute error (MAE) by 7.70%, and root mean square error (RMSE) by 4.47%, with an average increase in the coefficient of determination (R²) by 0.36%, and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulation and the PA-TLA deep learning architecture to analyze the characteristics of wake evolution during the near-ground phase, providing theoretical value for further reducing aircraft wake intervals and enhancing airport operational efficiency.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

In the paper, a study of the performance of unmanned ground vehicle (UGV), establishes numerical simulation model of the chassis system by three-dimensional software and finite element analysis software. Based on the numerical modelling and analysis, the work focuses on study of the strength and stiffness of the vehicle's chassis and researched the distribution of stress and deformation under the extremal condition. The paper also presents an autopilot design with new cascade control system for an autonomous motion of an unmanned ground vehicle based on Proportional-Integral-Derivative (PID) and Feed-Forward (FF) control. The PID-FF controller is a part of dedicated for presented UGV used in a hybrid control system for precise control and stabilization, necessary to increase the vehicle motion stability and maneuvers precision. The hybrid PID-FF control system proposed for the ground vehicle model gives a satisfactory quality of control, while maintaining the simplicity of the control system. Presented tests performed in area of mechanical design and control analysis gives good results and proves usefulness of designed unmanned device.

Introduction: Ambient air pollution is major global health problem and commercial drivers are particularly exposed to it. No systematic assessment of the health risks associated with occupational exposure to ambient air pollution in this population has been carried out. Methods: We conducted a systematic review using a protocol-driven strategy. Papers published from inception to 20th April 2018 in MEDLINE, EMBASE, CINAHL, African journals online, Cochrane library, ISRCTN and WHO ICTRP databases were screened for inclusion by two independent reviewers. Original articles with at least an available abstract in English or French were included. Results: The initial search retrieved 1454 published articles of which 20 articles were included. 3 Studies reported a significant difference in white blood cells (106/L) among commercial motorcyclists compared to rural inhabitants (5.041±1.209 vs 5.900±1.213,p=0.001), an increased risk of lung cancer (RR=1.6, 95%CI 1.5-1.8) in bus drivers and an increased standardized mortality ratio (SMR) in bus drivers from Hodgkin’s lymphoma (SMR 2.17, 95%CI 1.19-3.87) compared to white collar workers. Other studies also found that drivers had more oxidative DNA damage and chromosome breaks. 4 papers failed to demonstrate that the drivers were more exposed to air pollution than the controls. 3 other studies also reported no significant difference in lung function parameters and respiratory symptoms. The genetic polymorphisms of detoxifying enzymes were not also homogeneously distributed compared to the controls. Conclusion: There is some evidence that occupational exposure to ambient air pollution among commercial drivers is associated with adverse health outcomes but the existing literature is limited with few studies of small sample size, methodological weaknesses and contradictory findings. Further research is recommended.

The Automatic Dependent Surveillance-Broadcast (ADS-B) protocol stands as a cornerstone in modern aviation surveillance and Aircraft Traffic Control systems, integral to the NextGen project initiated by the US Department of Federal Aviation Administration (FAA) in 2005. ADS-B utilizes data links to autonomously broadcast Aircraft navigational and identification information to augment air transportation system capacity and safety. However, concerns regarding the protocol's security persist, particularly as its adoption expands. The unencrypted nature of transmitted aircraft data and the absence of robust authentication mechanisms render the protocol susceptible to exploitation by malicious actors. In this study, we comprehensively reviewed existing research in the field, identifying a crucial gap necessitating a holistic survey. Prior surveys have predominantly focused on specific aspects, such as vulnerabilities, attacks, or critiques of existing solutions. Our survey addresses this gap by thoroughly exploring the aviation system, providing readers with a nuanced understanding of ADS-B security. Utilizing a detailed SWOT analysis diagram, our paper delves into the vulnerabilities inherent in the ADS-B protocol outlines potential threats and scrutinizes various attack scenarios. We systematically categorize and analyze existing security solutions, considering cost-effectiveness, scalability, implementation complexity, and coverage against diverse attack vectors. Furthermore, we critically evaluate these solutions, elucidate ADS-B security requirements, discuss current challenges, and propose future research directions. This survey serves as a comprehensive resource for researchers and practitioners alike, shedding light on the multifaceted landscape of ADS-B security and paving the way for enhanced aviation system resilience in the face of evolving cybersecurity threats.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

In this study, it was analyzed that the characteristics of ammonia and their effects on secondary particulate matter (PM) formation by region in Korea during 2020. The NH3 concentration was high in GJ (11.4 ppb) neighboring agricultural area followed by DJ (9.0 ppb) and SE (8.6 ppb) locating in the urban area. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, were lower than other area. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that ammonia in the atmosphere changes depending on not only local emissions but also temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, the effect of ammonia on secondary PM formation was conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with an ammonia or PM2.5 concentration increasing. It can be assumed that PM2.5 concentration increases as nitrates are formed under the ammonia sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that reduction of ammonia would not effective to restrain the nitrate formation.

Urban cities in sub-Saharan Africa (SSA) are faced with ambient air pollution. This is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM_2.5). Going through most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM_2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24-hour concentration measured across all sites was 38 µg/m³ with the highest PM_2.5 period average concentration of 91 µg/m³ measured in Kampala, Uganda and lowest concentrations of 15 µg/m³ measured in Faraja, The Gambia. Kampala-Uganda and Nnewi-Nigeria recorded the longest periods with concentrations>250µg/m³. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM_2.5 concentrations in SSA countries. Such data are crucially needed to raise public-, societal and policymaker awareness about air pollution across SSA.

Air pollution is a leading cause of death in the United States, and is associated with adverse health outcomes, including increased vulnerability to coronavirus disease 2019 (COVID-19). The AirBeam2 was used to measure particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5), to investigate differences between indoor and ambient levels at seven private homes in New York during and after the COVID-19 lockdown. Measurements taken in 2020 winter, spring and fall and in fall 2022 showed that 90% of the time, indoor PM2.5 levels exceeded outdoor levels both during and after the COVID-19 lockdown, p = 0.03, and exceeded safety levels. Higher indoor PM2.5 levels attributed to little or no ventilation from cooking and smoke in the kitchen and basements, were significantly greater in fall than in winter. Higher ambient PM2.5 levels were attributed to vehicular traffic at a street-facing sampling site. PM2.5 sources identified in this study may help in devising control strategies to improve indoor air quality (IAQ), and consequently alleviate respiratory health effects. These findings may be used as a basis for in-house modifications including natural ventilation and use of air purifiers to reduce exposures, mitigate future risks, and prevent potential harm to vulnerable residents.

We introduce the Symplectic Structure of Information Geometry based on Souriau’s Lie Group Thermodynamics model, with a covariant definition of Gibbs equilibrium via invariances through co-adjoint action of a group on its moment space, defining physical observables like energy, heat, and moment as pure geometrical objects. Using Geometric (Planck) Temperature of Souriau model and Symplectic cocycle notion, the Fisher metric is identified as a Souriau Geometric Heat Capacity. Souriau model is based on affine representation of Lie Group and Lie algebra that we compare with Koszul works on G/K homogeneous space and bijective correspondence between the set of G-invariant flat connections on G/K and the set of affine representations of the Lie algebra of G. In the framework of Lie Group Thermodynamics, an Euler-Poincaré equation is elaborated with respect to thermodynamic variables, and a new variational principal for thermodynamics is built through an invariant Poincaré-Cartan-Souriau integral. The Souriau-Fisher metric is linked to KKS (Kostant-Kirillov-Souriau) 2-form that associates a canonical homogeneous symplectic manifold to the co-adjoint orbits. We apply this model in the framework of Information Geometry for the action of an affine Group for exponentiel families, and provide some illustrations of use cases for multivariate Gaussian densities. Information Geometry is presented in the context of seminal work of Fréchet and his Clairaut-Legendre equation. Souriau model of Statistical Physics is validated as compatible with Balian gauge model of thermodynamics. We recall the precursor work of Casalis on affine group invariance for natural exponential families.

The concentration of total suspended particles (TSP) and nanoparticles (PM0.1) over Hat Yai city, Songkhla province, southern Thailand was measured in 2019. Organic carbon (OC) and elemental carbon (EC) were evaluated by carbon aerosol analyzer (IMPROVE-TOR) method. Thirteen trace elements including Al, Ba, K, Cu, Cr, Fe, Mg, Mn, Na, Ni, Ti, Pb, and Zn were evaluated by ICP-OES. Annual average TSP and PM0.1 mass concentrations were determined to be 58.3 ± 7.8 and 10.4 ± 1.2 µg/m3, respectively. The highest levels of PM occurred in the wet season with the corresponding values for the dry seasons being lower. The annual average OC/EC ratio ranged from 3.8 - 4.2 (TSP) and 2.5 - 2.7 (PM0.1). The char to soot ratios were constantly less than 1.0 for both TSP and PM0.1, indicating that land transportation is the main emission source. A principal component analysis (PCA) revealed that road transportation, industry, and biomass burning are the key sources of these particles. However, PM arising from Indonesian peatland fires causes an increase in the carbon and trace element concentrations in southern Thailand. The findings make useful information for air quality management and strategies for controlling this problem, based on a source apportionment analysis.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditional manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large numbers of drones expected to operate at low altitudes in complex urban environments. To address this critical issue, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include: i) Developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; ii) Designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; iii) Improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and iv) Investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

As of now, anthropogenic habitat injury via dissemination of manufactured chemicals is injuring the world’s ecosystems, but doctors have yet to recognize its consequences to humans. Recognition of overt toxicity due to chronic low-dose exposure will open the door to diagnosis, sequential elimination studies, and prescriptions for preventive cure that represent a first step in preventing escalating chronic diseases. This paper offers a diagnostic construct for such use: chronic ambient poisoning. Incorporating it into clinical practice can reduce misdiagnosis of gastro- and near-toxicity that doctors and patients presently suppress rather than remove, and reduce patient co-dependence with proton-pump inhibitors, anxiolytics, and antidepressants—and the thriving “alternative” health industry. This diagnostic elephant-in-the-room is presently missed for reasons of habit (when an ailment is not recognized, it cannot be included in the differential diagnosis) and of poor conduct and interpretation of non-clinical research studies that are taken to mean an absence of evidence is evidence of absence. Moreover, modern methods fail when: the exposure comprises a “daily cocktail” of poisons acting additively, interactively, and cumulatively over a lifetime; when there is no normal, unexposed comparison group; and when absence of a robust etiopathogenetic model disallows inapt statistics. Only patient-led, consultant-informed sequential elimination studies can “peel the onion” of toxicity and its recognition and “preventive cure”. Clinical use of the chronic ambient poisoning diagnosis can thus open the door to stemming and reversing anthropogenic epidemics.

The purpose of this study is to assess the concentration-response relations between conjunctivitis and exposure to ambient ozone. This retrospective study includes emergency department (ED) visits for conjunctivitis in Edmonton, Canada, for the period April 1992–March 2002. Daily average levels of ozone (range: 1.2–50.9, ppb), of temperature, and of relative humidity were estimated and used for the period of the study. For each of the considered exposure lags, (from 0 to 9 days), six different models were fitted to estimate the concentration-response function. The goodness of fit was assessed using the Akaike information criterion. During the period of the study, 17,211 ED visits for conjunctivitis were recorded and used. For all subjects together, a positive statistically significant association was obtained for the exposure lagged by 5 days. For female subjects, lags 1, 3, and 9 had positive statistically significant associations (lag 2 had negative associations). For male subjects, only lag 5 had a positive statistically significant association. The estimated non-linear concentration-response functions for the considered groups (all, males, females) and lags, revealed the associations along the exposure levels. The fitted shapes are described by algebraic functions and may have various forms. The estimated functions are useful to determine the risk associated with exposure to ground-level ozone.

Ecuador is an important center of biodiversity of the plant family Amaryllidaceae, specially the subfamily Amaryllidoideae, known as a source of important bioactive molecules. The aim of this study was to assess the chemical and biological potential of four different species of Amaryllidoideae collected in Ecuador: Urceolina formosa, Urceolina ruthiana, Clinanthus incarnatus, and Stenomesson aurantiacum. Sixteen alkaloids were identified in the bulb extract of these species by GC-MS, which the extract of S. aurantiacum exhibiting the greatest structural diversity. The potential of these three genera against Alzheimer’s disease was evaluated by measuring their AChE and BuChE inhibitory activity, revealing that C. incarnatus and U. formosa (from Sucumbíos province) showed the best results with IC50 values of 1.73 ± 0.25 and 30.56 ± 1.56 g·ml-1, respectively. Molecular dynamic assays were conducted to elucidate the possible interactions that occurs among 2-hydroxyanhydrolycorine and AChE enzyme, suggesting some similarity with those observed for galanthamine. This study enhances our understanding of the biodiversity of Amaryllidoideae species from Ecuador, highlighting their potential as a source of chemical compounds with pharmaceutical potential.

An important aspect of the new sustainable development goals (SDGs) is a greater emphasis on reducing the health impacts of urban ambient air pollution (AAP) in developing countries. Meanwhile, the burden of disease attributable to AAP in sub-Saharan Africa (SSA) is growing, yet estimates of its impact in the region are likely underestimated due to a lack of air quality monitoring, the paucity of epidemiological studies, and important population vulnerabilities in the region. The lack of studies in the SSA region also represents an important global health disparity and environmental justice issue because thousands of air pollution health effects studies have been conducted in Europe and North America rather than in some of the most polluted regions of the world, such as SSA. In this review, we synthesize all of the ambient air pollution epidemiological studies that have been conducted in SSA to date. We highlight the gaps in AAP epidemiological studies conducted in different sub-regions of SSA and provide methodological recommendations for future environmental epidemiology studies addressing AAP in the SSA region.

Photonic sensors are innovative devices that leverage the principles of light-matter interaction to enable precise and efficient detection of various physical parameters. These sensors operate by exploiting the interaction between photons, the fundamental particles of light, and matter. The light propagates through an optical waveguide, and a portion of the electromagnetic field extends beyond the waveguide surface, forming the evanescent field. This evanescent field interacts with the surrounding medium, allowing for highly sensitive detection of changes in the refractive index or properties of nearby substances. The underlying concept involves the modulation of light properties, such as intensity, wavelength, or phase, when it interacts with a target substance or undergoes changes in the surrounding environment. The advancements in photonic sensor technology have paved the way for enhanced sensitivity, selectivity, and miniaturization, making them invaluable tools in diverse industries. The ability to harness light-matter interactions for sensing purposes not only facilitates highly sensitive measurements but also allows for non-intrusive and remote monitoring, contributing to the evolution of smart and connected systems. In this overview, the material platforms and waveguide structures are explored that can make highly sensitive photonic devices applied to gas and biosensing applications.

To analyze the combined response of ocular cyclotorsion and head tilt during body tilt and to interpret the physiologic role of those movements, each of the three subjects completed six different experiments of body tilt not fixating their head movements using a flight simulator, which consisted of combinations of three tilt angles (30, 45, and 60) and two visual conditions. Head tilt and ocular cyclotorsion of the right eye before, during, and after the tilt maneuver were recorded and analyzed by time sequence. In the active tilting phase, the right eye showed a stepladder pattern of multiple repeats of fast and slow cyclotorsion along with head tilt to obtain maximum visual information. During the stationary tilting phase, there occurred a small amount (mean, 2.3) of ocular cyclotorsion which was reciprocal to that of head tilt. The visual cue of body tilt through the central visual field induced a larger amount of head tilt but affected little ocular cyclotorsion. The maximum degree of ocular cyclotorsion during each experiment was 10.2 on average. In conclusion, the combined response of ocular cyclotorsion and head tilt develops during body tilt and it compromises the conflicting needs between binocular vision and gravitational orientation.

The author demonstrates a stable Lagrangian solid modeling method, tracking the interactions of solid mass particles rather than using a meshed grid. This numerical method avoids the problem of tensile instability often seen with smooth particle applied mechanics by having the solid particles apply stresses expected with Hooke's law, as opposed to using a smoothing function for neighboring solid particles. This method has been tested successfully with a bar in tension, compression, and shear, as well as a disk compressed into a flat plate, and the numerical model consistently matched the analytical Hooke's law as well as Hertz contact theory for all examples. The solid modeling numerical method was then built into a 2-D model of a pressure vessel, which was tested with liquid water particles under pressure and simulated with smoothed particle hydrodynamics. This simulation was stable, and demonstrated the feasibility of Lagrangian specification modeling for fluid–solid interactions.

This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. The Ukraine War has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (>100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” x 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability.

Particulate matter (PM) is one of the problems faced in environmental science. It has health effects on man and animals in both developed and developing countries. Research and efforts have been on it several years back. Policy statements and efforts have been published. This review paper is an added information on air pollution. In it, efforts were made in discussing these: classification, effects, methodology, case studies and source apportionment. It is hoped that this paper would contribute to existing knowledge on PM.

Nanoparticles (NP), Nanoaerosols (NA), ultrafine particles (UFP), and PM0.1 (particles with a diameter ≤ 0.1 µm or 100 nm) are interchangeably used in the field of atmospheric studies. PM0.1 are emitted from both natural and anthropogenic sources. The main emission sources of PM0.1 are combustion processes, including biomass and fossil fuel. Moreover, secondary formation via atmospheric photochemical reactions can also occur depending on meteorological conditions and the suspended pollutant species. Identifying the physical and chemical characteristics and spatial and temporal variations is vital in terms of understanding the effect of NPs on the environment, the global climate, and human health risks. This review article summarizes recent research on PM0.1 in Thailand. The review involves peer-reviewed papers from Scopus and the Web of Science databases, and includes the most recently published articles in the past ten years (2013-2022). UFPs mainly come from the combustion processes such as motor vehicles. The high mass concentration of PM0.1 that occurs during the dry season in which open fires depend on the specific region of Thailand. Particulate pollution from local and cross-border countries also needs to be considered in terms of the concentrations of ambient nanoparticles. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Acute and chronic respiratory diseases impose a significant economic and health burden. Distal small airway assessment requires invasive procedures such as a bronchoscopy and bronchoalveolar lavage. The Particles in Exhaled Air (PExA) device provides a non-invasive means of assessing small airways; it captures distal airway particles sized around 0.5-7m and contains particles of respiratory tract lining fluid (RTLF) that originate during airway closure and opening. The PExA device can measure particle count and mass according to particle size. Subsequent capture of particles in a thin membrane enables application of various analytical methods to assess a broad range of biomolecules including phospholipids, proteins, inflammatory profile, and other metabolites to characterise inflammation and diseases processes such as asthma, chronic obstructive pulmonary disease, acute respiratory distress syndrome, lung cancer, and more recently acute infections such as COVID-19. PExA analysis provides a promising alternative to traditional investigative methods in the assessment, diagnosis, and management of respiratory conditions. By evaluating specific biomarkers in the RTLF associated with airway inflammation, PExA may offer a more accurate and comprehensive understanding of the molecular-level changes in the small airways of patients with acute and chronic lung diseases.

Background: Bioaerosol can represent a danger to the health. During SARS-CoV-2 pandemic, portable devices were used in different environments and considered a valuable prevention tool. This study has evaluated the effectiveness of the air treatment device “AEROK 1.0” in reducing microbial, particulate and pollen airborne contamination indoors, during normal activity. Methods: In an administrative room, airborne microbial contamination was measured using active (DUOSAS 360 and MD8) and passive sampling; a particle counter was used to evaluate particle concentrations; a Hirst-type pollen trap was used to assess airborne pollen and Alternaria spores. Statistical analysis was performed using SPSS 26.0; p values <0.05 were considered statistically significant. Results: The airborne bacterial contamination assessed by the two different samplers decreased by 55.54% and 69.13%, respectively. The airborne bacterial contamination assessed by passive sampling decreased by 44.16%. For fungi, the reduction was 38.7% by active sampling. Airborne particles (diameters ≥ 1.0, 2.0 μm) and the ratio of indoor/outdoor concentrations of total pollen and Alternaria spp spores significantly decreased. Conclusions: The results highlight the effectiveness of AEROK 1.0 in reducing airborne contamination. The approach carried out represents a contribution to the definition of a standardized model for evaluating the effectiveness of this type of devices.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, the review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies up to 96.34%, PM reductions by 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. The review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. The study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation.

It is concerning that Cameroon's urban centers are experiencing an alarming rise in air pollution as a result of urbanization, population growth, industrial activity, and transportation emissions. We examined the causes, health impacts, research methodology, case studies, and government measures to address the urgent issue of air pollution in Cameroon's urban regions. We have emphasized how Cameroon's air pollution levels have significantly increased as a result of causes like population expansion, urbanization, industrial emissions, and automobile exhaust A thorough discussion of the negative health impacts of extended exposure to air pollution is included, with special attention to the sensitivity of the old and young. These effects include respiratory disorders, cardiovascular ailments, and early mortality. We emphasized the pressing need for all-encompassing strategies to address air pollution, such as increased public awareness campaigns, stronger emissions regulations, the development of renewable energy sources, and better waste management. The techniques employed in studies on air pollution, like as epidemiological research and air quality monitoring, are also explained to provide light on the unique health dangers that Cameroonian urban dwellers must contend with. We emphasized in our conclusion the significance of joint efforts by legislators, medical professionals, and the general people to reduce air pollution and protect public health in Cameroon in order to create a cleaner and healthier future. In order to protect public health in the future, it is imperative that Cameroon's air pollution be addressed.

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

Agaricus bisporus is one of the most popular mushroom species in the world; however, mushrooms are highly susceptible to browning due to the absence of a protective cuticle layer and high respiration rate. The molecular mechanism underlying the process of mushroom browningneeds to be explored. Here, we analyzed the transcriptomic and metabolomic data from A. bisporusat ambient temperature. Specifically, a total of 263 significantly changed metabolites and 4492 differentially expressed genes were identified. Lipid metabolites associated with cell membrane degradation were predominantly up-regulated during ambient storage. Transcriptomic data further revealed the alterations of the expression of membrane lipid metabolism-related enzymes. Additionally, energy metabolic processes and products such as glycolysis and linoleic acid changed significantly during ambient storage, indicating their potential roles in the quality deterioration of A. bisporus. These findings provide new insights into the underlyingl ipidmetabolic mechanism of A. bisporus during postharvest ambient storage and will provide values for mushroom preservation techniques.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

Abstract: Mass spectrometry research laboratories reported multiple probes for ambient ionization in the last years. Combining them with a mechanical moving stage enables automated sampling and imaging applications. We developed a robotic platform, which is based on RepRap 3D-printer components, and therefore easy to reproduce and to adopt for custom prototypes. The minimal step width of the Open LabBot is 12.5 μm, and the sampling dimensions (x, y, z) are 18 × 15 × 20 cm. Adjustable rails in an aluminium frame construction facilitate the mounting of additional parts such as sensors, probes, or optical components. The Open LabBot uses industry-standard G-code for its control. The simple syntax facilitates the programming of the movement. We developed two programs: 1) LABI-Imaging, for direct control via a USB connection and the synchronization with MS data acquisition. 2) RmsiGUI, which integrates all steps of mass spectrometry imaging: The creation of G-code for robot control, the assembly of imzML files from raw data and the analysis of imzML files. We proved the functionality of the system by the automated sampling and classification of essential oils with a PlasmaChip probe. Further, we performed an ambient ionization mass spectrometry imaging (AIMSI) experiment of a lime slice with laser desorption low-temperature plasma (LD-LTP) ionization, demonstrating the integration of the complete workflow in RmsiGUI. The design of the Open LabBot and the software are released under open licenses to promote their use and adoption in the instrument developers’ community.

Nicotiana benthamiana plants infected with a potato virus Y isolate (PVYO) under ambient conditions of 25 °C and ~405 parts-per-million (ppm) ambient CO2 display symptoms of chlorosis, leaf curling, and stunting. However, under elevated temperature and CO2 levels (30 °C, 970 ppm), systemic viral titers decrease several-fold, and plants appear asymptomatic. Under the latter conditions we have studied how infection, drought, or both combined affect infection, drought-driven loss of leaf turgor, plant water content, stomatal conductance, metabolome, and hormone (ABA, SA, and JA) levels. We found that drought did not affect viral titers. As expected, drought decreased plant water content and stomatal conductance. Under drought conditions, infection did not alter significantly final water content or loss of leaf turgor progression, but it increased the levels of some sugars and other osmo-protectants. Infection and drought altered the levels of many components of the metabolome. Some components altered by infection were not altered by drought, but most of those altered by drought were also altered by infection. Drought increased the levels of ABA, infection increased the levels of ABA, SA, and JA, and altered their relative balance. The effects of drought and of infection on hormone levels were cumulative when combined. Thus, under ambient conditions that turn a severe infection into an asymptomatic one both, plant physiology parameters and the interplay with drought are affected.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system adjustments and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs. generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO₂ emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and the Rest of PJM region leading to scenario-varying changes in the plant-level release of several air pollutants. Due to air pollution chemistry and transport and socio-demographics, these changes in turn give rise to a reduction of 10 to 182 PM_2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Scenarios that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, vulnerable populations—in both PA and Rest of PJM—benefit most in scenarios that prioritize plant closures in PA near Environmental Justice Areas. These results demonstrate the importance of considering cross-regional linkages and socio-demographics in designing equitable retirement strategies.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Warsaw is among European cities with the worst atmospheric air quality, mainly due to very high pollution emitted by the residential sector and road traffic. This results in high concentrations of particulate matter and nitrogen oxides, often exceeding WHO standards. The paper discusses the current and expected effects of actions taken by the Warsaw authorities, to significantly improve air quality in the city. The Clean Air Program currently implemented mainly involves the reduction, and ultimately the total elimination, of coal combustion in all heat sources of the municipal sector, which is identified in numerous publications as the main source of air quality degradation in cities. The program's goal, which timeframe covers the years 2018-2029, is a definite reduction in particulate pollution. The second part of the remedial activities, covering the years 2024-2034, mainly concerns the reduction of nitrogen oxide pollution, the main source of which is urban road traffic. In the latter case, the expected effects of introducing Low-Emission Zone (launched July 2024) in the city center are considered. According to the project, the zone's implementation will consist of five two-year stages, in which the car emission limits will gradually tighten. The paper compares the effectiveness of two being considered versions, differing in size zones.

The use of artificial intelligence technologies may help companies obtain the optimal use of resources and accomplish more activities with an increase in productivity by 40%. With access to real-time data and the ability to learn from patterns and behaviors, AI can perform these routine tasks more efficiently. Ambient intelligence, Ami, is one of the applications of artificial intelligence, which is called artificial environmental intelligence, which is a concept that focuses on using technology to create an environment that is intelligent and responsive to the needs of its users. This technology has the potential to revolutionize disaster response and management, which in turn facilitates access to necessary information and resources. necessary vitality in the event of a crisis. It may use ambient intelligence to create a network of sensors that can detect and respond to changes in the environment. So that this network can be used to monitor the environment for potential disasters, such as floods, fires and earthquakes. Sensors can also be used to detect changes in temperature, humidity, and other environmental factors that may indicate a potential disaster. This information can then be used to alert emergency responders and provide them with the information needed to respond quickly and effectively. Ambient intelligence can also be used to create a comprehensive database of resources and information that can be accessed in times of crisis. This database may include information about the location of shelters, medical facilities, and other resources that can be used to assist those affected by a disaster. It may also provide information about the condition of roads, bridges, and other infrastructure that may be affected by a disaster. This information can be used to inform response and management decisions, helping to ensure that resources are used in the most efficient way possible. Ambient intelligence can be used to create a communication platform between emergency responders and those affected by a disaster. This platform can also be used to provide updates on the emergency situation, in addition to providing information about available resources to those affected. This can help ensure that those affected by a disaster can access the resources they need in a timely manner.

We examined whether the ambient illuminance during extended wakefulness modulates the homeostatic increase in human deep sleep [i.e. slow wave sleep (SWS) and electroencephalographic (EEG) slow-wave activity (SWA)] in healthy young and older volunteers. Thirty-eight young and older participants underwent 40 hours of extended wakefulness [i.e. sleep deprivation (SD)] once under dim light (DL: 8 lux, 2800K), and once under either white light (WL: 250 lux, 2800K) or blue-enriched white light (BL: 250 lux, 9000K) exposure. Subjective sleepiness was assessed hourly and polysomnography was quantified during the baseline night prior to the 40-h SD and during the subsequent recovery night. Both the young and older participants responded with a higher homeostatic sleep response to 40-h SD after WL and BL than after DL. This was indexed by a significantly faster intra-night accumulation of SWS and a significantly higher response in relative EEG SWA during the recovery night after WL and BL than after DL for both age groups. No significant differences were observed between the WL and BL condition for these two particular SWS and SWA measures. Subjective sleepiness ratings during the 40-h SD were significantly reduced under both WL and BL compared to DL, but were not significantly associated with markers of sleep homeostasis in both age groups. Our data indicate that not only the duration of prior wakefulness, but also the experienced illuminance during wakefulness affects homeostatic sleep regulation in humans. Thus, working extended hours under low illuminance may negatively impact subsequent sleep intensity in humans.

As the rapid development of communication industry, especially the 5G communication and IOT, there will be plenty of electromagnetic (EM) waves in the free space, which carry lots of energies. However, some of these energies are wasted in free space. To reuse these wasted energies, significantly growing interests are towards to the energy harvesting antennas. This paper is aimed to investigate a wearable antenna which can transfer RF energy from ambient sources to direct current by a soft and portable textile antenna. Among the numerous signals in mobile network, the GSM 1800, 3G, WiFi and 4G/5G will be chosen due to the city signal intensity distribution. Hence, a corresponding triple bands antenna has been designed to cover those frequency bands. The CST STUDIO SUITE is used in whole process of antenna design and simulation. The proposed antenna is a hooked dipole antenna with tuning bar (HATB) whose ends have small folded part in each side for better bandwidth performance. The presented antenna provides a wide operating band from 1.8 GHz to 2.5 GHz below -10 dB in return loss. And methods to overcome interference between antennas is found. Furthermore, using the corresponding rectifier to achieve the RF/DC conversion. The overall efficiency of whole rectifier is about 56.8 %, and output power level of the antenna system is 45.92nW. The experimental results could indicate that my textile hooked antenna harvester is a good choice for the charging system of personal wearable attachment, which could achieve low power absorbing for long distance anytime and anywhere.

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r&gt;0.90) and peri-urban (HWM, HWA, r&gt;0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

Malignant lymphoma is an unusual form of gallbladder neoplasm. Almost all of these tumors are diffuse large B-cell lymphomas or mucosa-associated lymphoid tissue-type lymphomas. Herein, we report a rare case of a gallbladder Burkitt’s lymphoma (BL) in a child living with HIV. As far as we are concerned, this is the first report in the pediatric population and in an individual living with HIV. The patient attended the Federal Hospital of Lagoa, Rio de Janeiro, Brazil, underwent cholecystectomy and the postoperative pathological analysis of the gallbladder revealed a diagnosis of BL. Also, HIV serology was performed and returned positive. She was transferred to the Martagão Gesteira Institute of Pediatrics and Childcare for oncological treatment, dying from sepsis and disease progression about 18 months later. Patient did not undergo ART/cART. Previous cases of gallbladder BL were analyzed to characterize the clinicopathological features. BL can occur in the gallbladder both in the context of HIV infection and in the pediatric population. Biopsy is mandatory in cases with suggestive findings of lymphoma, and an early diagnosis can change the course of the disease. Furthermore, the case highlights the importance of an early initiation of ART/cART in people living with HIV (PLWH).

Urban air pollution is a critical global environmental issue, necessitating an understanding of the spatiotemporal characteristics and driving factors for effective environmental protection and urban planning. This study introduced an advanced air quality assessment system that integrates environmental, socio-economic, and urban layout factors, addressing gaps in traditional models that often overlook the impact of urban spatial structures. Analyzing air quality data from Beijing's main urban area (2016-2020) alongside multi-source geographic data, the research develops a comprehensive evaluation system, incorporating 14 key driving factors. Employing Geographically Weighted Regression (GWR) and Multi-scale Geographically Weighted Regression (MGWR), the study quantitatively assessed the influence and spatial heterogeneity of these factors. Findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model, in particular, provided a superior fit over GWR, effectively capturing the spatial variability of factors. Variables such as NDVI), economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These insights enhance the understanding of air pollution dynamics and aid in refining urban planning strategies.

The proliferation of Unmanned Aerial Vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within UAV networks’ highly dynamic and adaptable environments. Decentralized reputation systems emerge as a promising solution to enhance the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. It highlights the crucial role of reputation systems in strengthening trust, security, and privacy throughout these networks and discusses various strategies to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

This work presents the results regarding the synthesis of tungsten nanoparticles (W NPs) by magnetron sputtering combined with gas aggregation (MSGA) when H2 mixed with Ar is used as working gas, the amount of H2 being increased up to 82%. The results show that up to 40% H2 in discharge the synthesis rate increases over 60 times, rapidly decreasing for over 50% H2 in discharge. The W dust is still produced in H2 dominated discharge (82%), the deposition rate being small but not negligible (20 mg/hour). The obtained WNPs are isolated, with the diameter decreasing from 50 nm to 15 nm when the amount of H2 in discharge is smaller than 41%. Over this value, the particles tend to agglomerate, forming structures similar to film-like deposits. Also, the diameter of the deposited spots on substrates is dependent on the H2 content in discharge. This allows the efficient coating of substrates with dimensions up to 26 mm by translating them in front of the MSGA cluster source exit aperture. Supplementary, for 41% H2 in discharge, the influence of synthetic air leaks (0-5%) in discharge was investigated. The deposition rate decreases rapidly (ceasing for around 4% air in discharge) and the obtained nanoparticles tend to agglomerate (at 2% air content, the dust deposit has the aspect of a near continuous film). Chemical composition investigations show a pronounced tendency of oxidation, nitridation and oxynitride formation in the presence of air leaks.

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations have instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well designed efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist- media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in petroleum refining industry, and a method to manage flare emissions. Moreover, this work will discuss flare Combustion Efficiency (CE) and Distraction and Removal Efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory is based on a standard LES methodology to conduct transient flare analysis is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analysed. This new flare design reduces the emission rate that demonstrates the design effectiveness. The analysis considers a flare 1m high and (6") diameter in the centre of a 4m*4m*4m domain. Boundary conditions considering no cross wind and an ambient temperature of 300K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air assist simulation, stoichiometric ratio will be a variable and therefore more than one case was considered.

This study investigates the effects of ambient illumination and negatively polarized text color on visual fatigue, exploring the issue of visual fatigue when using visual display terminals in low-illumination environments. The research methodology utilized an experimental design to collect data on changes in pupil accommodation and blink rate through an eye tracker. Partici-pants completed a reading task while exposed to various text colors and ambient light conditions to evaluate visual fatigue and cognitive performance. The study's findings suggest that text color significantly affects visual fatigue, with red text causing the highest level of visual fatigue and yellow text causing the lowest level of visual fatigue. Improvements in ambient lighting reduce visual fatigue, but the degree of improvement varies depending on the text color. Additionally, cognitive performance is better when using yellow and white text but worse when using red text. Yellow text is the most effective choice for reducing visual fatigue under negative polarity. In-creasing ambient lighting can also improve visual fatigue in low-illumination conditions. These findings will offer valuable guidance for designing visual terminal device interfaces, especially for low-illumination or night environments, to minimize visual fatigue and improve the user ex-perience.

Elaborate sound design, including background music, ambient sounds (sounds describing the game world), and character sounds (sounds generated by the character’s actions), plays a pivotal role in modern videogames. However, the influence of these different types of sound on the player experience has not been extensively researched. This study examines the influence of these sound types on immersion, avatar identification, fun and perceived competence. In two experiments, participants played League of Legends in four different sound conditions. The first experiment revealed a non-significant trend in the effect of character sound on avatar identification. Ambient sounds, however, were limited in this experiment because the task restricted participants’ movement across the game map. Consequently, we adapted the task to allow for a wider variety of ambient sounds in the second experiment. Here, a significant impact of character sounds on immersion, avatar identification, and fun, was observed, as well as an interaction effect of character sounds and ambient sounds on fun. Furthermore, we observed a trend, though not statistically significant, suggesting that ambient sounds may influence the player’s sense of flow. These findings underline the distinct effects of different sound types and we discuss implications for the design of sound in video games.

Findings from studies of prenatal exposure to pesticides and adverse birth outcomes have been equivocal so far. We examined prenatal exposure to agricultural pesticides in relation to preterm birth and term low birthweight, respectively, in children born between 1998 and 2010 randomly selected from California birth records. We estimated residential exposures to agriculturally applied pesticides within 2 km of residential addresses at birth by pregnancy trimester for 17 individual pesticides and 3 chemical classes (organophosphates, pyrethroids, and carbamates). Among maternal addresses located within 2 km of any agricultural pesticide application, we identified 24,693 preterm and 220,297 term births, and 4,412 term low birthweight and 194,732 term normal birthweight infants. First or second trimester exposures to individual pesticides (e.g., glyphosates, paraquat, imidacloprid) or exposures to 2+ pesticides in the three chemical classes were associated with small increases (3-7%) in risk for preterm birth; associations were stronger for female offspring. We did not find associations between term low birthweight and exposures to pesticides other than for myclobutanil (OR: 1.11; 95% CI: 1.04-1.20) and possibly pyrethroids as a class. Our improved exposure assessment revealed that first and second trimester exposures to pesticides were associated with preterm delivery but few affected term low birthweight.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban-rural transition affects PM2.5 are warranted within the framework of urban-rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban-rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban-rural transformation and PM2.5. The results are as follows: (1) Over time, there was a discernible upward tendency in the change in urban-rural areas. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban-rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative-positive-negative changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. Currently, an inverted "U-shaped" curve forms the left side of the impact of population transition (PT) on PM2.5. Land development (LT) has a "U-shaped" curve for its effect on PM2.5. This research provides a new perspective on the topic of PM2.5 and its connection to urban-rural integration, which is crucial to understanding the dynamics of this shift. To achieve its goal of high-quality development, it supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, it can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.

The Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are used to assess the indoor environment in terms of human thermal comfort-discomfort. In this study, an experimental combined objective and subjective investigation of thermal comfort perception has been performed in students between 16-18 years old, in a non-air-conditioned school building. The objective approach included instrumentation measurements and data processing according to ISO 7730, whereas, the subjective one was based on answers collection following ISO 10551. The study is mainly devoted to the verification of Fanger’s approach in a building, in free running conditions, under a mild (moderate) climate.The comparison between instrumentation data and questionnaire results presented an underestimation of the mean vote, predicting a cooler sensation than the actual one.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

Selection is a fundamental interaction element in Virtual Reality (VR) and 3D user interfaces (UIs). Raycasting, one of the most common object selection techniques, has been known to have difficulties in selecting small or distant objects. Meanwhile, recent advancements in computer vision technology have enabled seamless vision-based hand tracking in consumer VR headsets, enhancing accessibility to freehand mid-air interaction and highlighting the need for further research in this area. This study proposed a new technique called TouchView which utilizes a virtual panel with a modern adaptation of the Through-the-lens metaphor to improve freehand selection for VR UI. TouchView enables faster and less demanding target selection by allowing direct touch interaction with the magnified object proxies reflected on the panel view. Repeated-measures ANOVA on the results of a follow-up user experiment on multitarget selection with 23 participants showed that TouchView outperformed the current market-dominating freehand raycasting technique, Hybrid Ray in terms of task performance, perceived workload, and preference. The user behavior was also analyzed to understand the underlying reasons for these improvements. The proposed technique can be used in VR UI applications to enhance the selection of distant objects, especially for cases with frequent view shifts.

This study presents a comparative analysis of indoor air quality (IAQ) across health centers in Arakale, Iju, Owode, FECA, and Oba Ile, Ondo State, Nigeria. Particle count data were analyzed to identify common trends, similarities, and differences in IAQ parameters, with the aim of informing targeted interventions for improving indoor air quality in healthcare settings. The findings reveal consistent positive skewness in particle count distributions, indicating occasional spikes in pollution levels amidst generally lower concentrations. Fine particles in the size range of PC 2.5 were consistently dominant across all locations, emphasizing their significance in IAQ assessment. Similar variability in particle count data suggests comparable levels of fluctuations in IAQ among the health centers. However, variations in PCA patterns highlight unique factors influencing IAQ in specific locations, such as combustion sources and control strategies. Targeted interventions, including enhancing ventilation systems, controlling indoor pollution sources, and promoting smoking cessation programs, are proposed to mitigate health risks associated with indoor air pollution exposure. Ongoing monitoring and collaboration with environmental experts are crucial for assessing IAQ trends and implementing evidence-based interventions to safeguard the health of patients, staff, and visitors in healthcare facilities.

Temperature is a critical factor that influences the proliferation of pathogens in hosts. The impact of temperature on pathogens is commonly explored in controlled and constant temperatures. Experiments under varying environmental temperature are becoming more frequent, however, testing every temperature scenario à la carte is unachievable. One example of this is the human pathogen Vibrio parahaemolyticus (Vp) in oysters. Here, a predictive model was developed for predicting the growth of Vp in oysters under varying ambient temperature. The model was fitted and evaluated against data from experiments studying growth and inactivation of Vp in oysters at eight constant temperatures. Once evaluated, Vp dynamics in oysters were estimated at different post-harvest varying temperature scenarios affected by water and air temperature, and different ice treatment timing. The model performed adequately under varying temperature, reflecting that (i) increasing temperature, particularly in hot summers, favors a rapid Vp growth in oysters, resulting in a very high risk of gastroenteritis in humans after consumption of a serving of raw oysters, (ii) pathogen inactivation due to day/night oscillations, and more evidently, due to ice treatments, (iii) ice treatment is much more effective limiting risk of illness when applied immediately onboard compared to dockside. The model results to be a promising tool for improving the understanding of the Vp-oyster system and support studies on public health impact of pathogenic Vp associated with raw oyster consumption. Although robust validation of the model predictions is needed, initial results and evaluation show the potential of the model to be easily modified to match similar systems where the temperature is a critical factor shaping the proliferation of pathogens in hosts.

The impact of cyclic heat stress (CHS) and lighting events on pig feeding behavior (FB) was investigated. The FB of 90 gilts was recorded in real time under two ambient temperatures (AT): thermoneutrality (TN, 22 °C) or CHS (22°/35 °C). The day was divided into four periods: PI (06-08 h); PII (08-18 h); PIII (18-20 h); and PIV (20-06 h). Automatic and Intelligent Precision Feeders recorded each feed event for each pig. An estimated meal criterion (49 minutes) was used to calculate the FB variables. Feed behavior in both ATs followed a circadian pattern. The CHS reduced the feed intake by 6.9%. Pigs prioritized feed intake during the coolest hours of the day; however, nocturnal cooling did not allow the pigs to compensate for the reduced meal size due to CHS. The highest meal size and most of the meals were observed during the lighting-on period. Pigs reduced their interval between meals during periods II and III. The lighting program increased the meal size when the lights were switched on and reduced the meal size when the lights were switched off. Thus, the dynamics of the FB were largely influenced by AT, while the meal size was affected by the lighting program.

Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers' take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task-Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW.

In CCS projects, the transportation of CO₂ by ship can be an attractive alternative to transportation using a pipeline, particularly when the distance between source and disposal location is large. However, the energy consumption of the liquefaction process can be significant, making the selection of an energy-efficient design an important factor in the minimization of operating costs. Since the liquefaction process operates at low temperature, its energy consumption will vary with ambient temperature, which could be a factor that influences the trade-off point between pipelines and shipping in different geographic locations. A consistent set of data showing the relationship between energy consumption and cooling temperature is therefore potentially useful to CCS system modelling. This study compares the performance of a wide range of CO₂ liquefaction schemes. It applies a methodical approach to the optimization of process operating parameters and studies performance across a range of operating temperatures. A set of data for the minimum energy consumption cases is presented. The main findings are that open-cycle CO₂ processes often offer minimum energy consumption; NH₃ based schemes often offer better performance at higher ambient temperatures; and that for the cooling temperature range 15 to 50 °C, the energy consumption for the best performing liquefaction process rises by around 40%.

In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real-time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 seconds, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.

Traffic congestion is one of the foremost problems confronted by the urban and suburban tenants of today. Traffic congestion increases vehicle emissions and degrades air quality. Urban planners and policy makers have consequently been always investigating choices to alleviate traffic congestion and to enhance air quality. Teleworking is one option that has received significant consideration and has been studied in the recent past. The aim of the study is to explore the relationship between teleworking, air quality and traffic in Switzerland. The analysis relies on panel individual and household level data over the period 2002-2013. We examine five main air pollutants; the sulphur dioxide (SO₂), the ground-level ozone (O₃) the nitrogen dioxide (NO₂), the carbon monoxide (CO) and the particulate matter less than 10 microns (PM₁₀). Based on the fixed effects estimates, teleworking reduces traffic volume by 1.9 per cent. Furthermore, the reduction observed on air pollution is higher for NO₂, CO and PM₁₀ ranging between 3.3-3.7 per cent, followed by O₃ at 2.3 per cent and SO₂ at 2.1 per cent. According to instrumental variable (IV) approach and the two stage least squares (2SLS) method, the effect is higher ranging between 2.6-4.1 per cent. The respective reduction on traffic becomes 2.7 per cent. Overall, the main concluding remark of the study is that teleworking can be a promising tool for urban planning and development, focusing at the traffic volume reduction, and the air quality improvement. Additional policy implications of teleworking and its beneficial effects for the society are further discussed.

PM_0.1 (particles diameter ≤ 0.1 µm), nanoparticles (NPs), and ultrafine particles (UFPs), were interchangeably used in the scientific communities. PM_0.1 originated from both natural and human sources. However, investigations of PM_0.1 and its effects on the environment, visibility, and human health risk to understand the levels of air pollution, sources, and impacts in South East Asia (SEA) countries continue to be lacking. The concentration of PM_0.1 in most SEA countries are much worse than those in western countries environment. A further motivation of this reviewed article is to provide a critical synthesis of the current knowledge and study of ambient PM_0.1 in SEA cities. The main influence of characteristics of PM_0.1 appears to be local sources including biomass burning and motor vehicles. Continuous monitoring of PM_0.1 in terms of both mass and number concentration should be further understood. A critical review is of great importance to facilitating air pollution control policies and predicting the behavior of PM_0.1 in SEA.

As an active device, air source heat pumps (ASHP) are widely used for heating and cooling in buildings. However, in cold regions, their efficiency decreases, and frost formation becomes a significant issue during winter. This paper proposes a new scheme that couples air source heat pumps with passive heating. By using software simulations and mathematical models, the new scheme is compared to traditional ASHP systems to analyze its heating performance in rural homes during winter in China’s cold regions. According to the simulation and calculation analysis, on the coldest day of winter, the coupling scheme can provide approximately 99.41kWh of heat to the interior, surpassing the 86.67kWh required to maintain an indoor temperature of 20℃. The system’s power consumption is 36.96 kWh, which is 66.88% lower than that of traditional heat pump heating. The study shows that a coupled system of air source heat pumps and passive heating has better heating performance in cold regions. By incorporating phase change materials and heat storage media to address the shortage of solar energy at night, the system can meet the heating needs throughout the entire day.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

The previous studies on the diversion pipe of pumped storage power stations (PSPS) primarily focused on numerical simulations of pulsating pressure propagation within the pipe and its inducing factors. However, there was a lack of field monitoring and analysis of measured data to support surface ambient vibration, resulting in insufficient credibility and practicality of the numerical results. Based on ambient monitoring and data analysis, this paper examines the factors contributing to the ambient vibration of the water diversion pipeline in Changlongshan (CLS) PSPS. It is concluded that: (1) The primary source of vibration originates from units operation-induced ambient vibrations; (2) Vibration during pumping conditions exceeds that during power generation conditions; (3) The ground measuring point's vibration acceleration primarily depends on the distance from the vibration source; (4) Different types of units have varying effects on water diversion pipeline vibrations.

For the first time, it is shown that inhaled ambient air-dust particles settled in the human lower respiratory tract induce lung calcification. Chemical- and mineral compositions of pulmonary calcium precipitates in the lung right lower-lobe (RLL) tissues of 12 individuals who lived in Upper Silesia Conurbation in Poland and who had died from causes not related to lung disorder were determined by transmission- and scanning electron microscopy. Whereas calcium salts in lungs are usually reported as phosphates, calcium salts precipitated in RLL are almost exclusively carbonates, i.e. Mg-calcite and calcite. These constitute 37% of 1652 mineral particles examined. Mg-calcite predominates in the submicron size range with the MgCO3 content up to 50 mol%. Magnesium plays a significant role in the lung mineralization, a fact so far overlooked. The calcium phosphate (hydroxyapatite) content in RLL is negligible. The predominance of carbonates is explained by increased CO2 fugacity in RLL. Carbonates enveloped inhaled mineral-dust particles, including uranium-bearing oxides, quartz, aluminosilicates, and metal sulfides. Three possible pathways for the carbonates precipitation on the dust particles are postulated: (1) precipitation of amorphous calcium carbonate (ACC) followed by its transformation to calcite; (2) precipitation of Mg-ACC followed by its transformation to Mg-calcite; (3) precipitation of Mg-free ACC causing a localized relative enrichment in Mg ions and subsequent heterogeneous nucleation and crystal growth of Mg-calcite. The actual number of inhaled dust particles may be significantly greater than observed because of the masking effect of the carbonate coatings. There is no simple correlation between smoking habit and lung calcification.

This study aimed to develop a laboratory-scale direct air capture unit for evaluating and comparing amine-based adsorbents under temperature vacuum swing adsorption conditions. The experimental campaign conducted with the direct air capture unit allowed for the determination of equilibrium loading, CO2 uptake capacity, and other main performance parameters of the investigated adsorbent Lewatit VP OC 1065®. The investigations also helped to understand the co-adsorption of both CO2 and H2O on the tested material, which is crucial for improving temperature vacuum swing adsorption processes. This was achieved by obtaining pure component isotherms for CO2 and H2O as well as three different co-adsorption isotherm models from literature. It was found that the weighted average dual-site Toth model emerged as the most accurate and reliable model for simulating this co-adsorption behavior. Its predictions closely align with experimental data, particularly in capturing the adsorption equilibrium at various temperatures. It was also observed that this lab-scale unit offers advantages over thermogravimetric analysis when conducting adsorption experiments on the chosen amine. The final aim of this study is to provide a pathway to develop devices for testing and developing efficient and cost-effective adsorbents for direct air capture.

Pulmonary hypertension (PH) has a high mortality, and few treatment options. Adaptive immune mediators of PH in mice challenged with antigen/particulate matter (antigen/PM) has been the focus of our prior work. We identified key roles of type 2 and type 17 responses in C57BL/6 mice. Here, we focused on type 2 response related cytokines, specifically Resistin-like-molecule (RELM)α, a critical mediator of hypoxia-induced PH. Because of strain differences in the immune responses to type 2 stimuli, we compared C57BL/6J and BALB/c mice. A model of intraperitoneal antigen sensitization with subsequent, intranasal challenges with antigen/PM (ovalbumin and urban ambient PM2.5) or saline was used in C57BL/6 and BALB/c wild type or RELMα-/- mice. Vascular remodeling was assessed by histology, right ventricular (RV) pressure, RV weights and cytokines were quantified. Upon challenge with antigen/PM2.5, both C57BL/6 and BALB/c mice developed pulmonary vascular remodeling; these changes were more prominent in the C57BL/6 strain. Compared to wild type mice, RELMα-/- had significantly reduced pulmonary vascular remodeling in BALB/c, but not in C57BL/6 mice. RV weights, RV IL-33 and RV ST2-IL-33-receptor were significantly increased in BALB/c wild type mice, but not in BALB/c-RELMα-/- or in C57BL/6-wild type or C57BL/6-RELMα-/- mice. RV systolic pressures (RVSP) were higher in BALB/c compared to C57BL/6J mice, and RELMα-/- were not different from their respective wild type controls. In BALB/c mice, RELMα was a key contributor to antigen/PM2.5 induced pulmonary vascular remodeling, RV thickening and RV cytokine responses, highlighting the significance of the genetic background. The RELMα-/- animals demonstrated significantly decreased expression of RELMβ and RELM which makes these mice comparable to a situation where human RELMα levels would be significantly modified, as humans only have one RELM molecule.

Based on a constant volume combustion chamber, the initial ambient pressure effect on combustion performance and flame propagation of the active and passive pre-chamber ignition systems were studied. Compared with the passive pre-chamber, the active pre-chamber can significantly expand the lean combustion limit, enhance the ignition and combustion performance, and speed up the combustion of the main combustion chamber. At slightly lean combustion conditions, the heat release performance has a positive correlation with the initial ambient pressure. As the premixed λ increases, the heat release performance and the initial ambient pressure begin to become negatively correlated. Ignition delay can be minimized by controlling the excess air coefficient in the pre-chamber close to the stoichiometric ratio. The high ambient pressure in the constant volume chamber greatly reduces the mixture entrainment ability of the jet flame at lean combustion conditions, thereby reducing the combustion speed and peak heat release rate.

This study investigates the mean annual water temperature trends at the confluence of the Sava and Danube Rivers, along with air temperature trends at the Belgrade meteorological station, spanning from 1956 to 2020. Results reveal a consistent increase in temperature across all three measuring stations, with the Danube experiencing a rise of 0.34°C/10 years, the Sava at 0.44°C/10 years, and Belgrade's air temperature increasing by 0.39°C/10 years. Employing the RAPS method, sharp rises in water temperature were pinpointed in 1989 for the Sava and 1990 for the Danube, while Belgrade's air temperature surge began in 1998. The highest intensity of air temperature increase within the recent period (1998-2020) was observed at the Belgrade observatory, reaching 0.76°C/10 years. Notably, the Sava exhibited a faster increase in water temperature over the last thirty years compared to the Danube. August marked the peak average water temperature for both rivers, while July recorded the highest average air temperature in Belgrade. Despite differing flow rates, both rivers exhibit similar hydrological regimes, with maximum flows occurring in April and minimum flows in August for the Sava, and October for the Danube. Seasonal temperature increases were most pronounced in summer, notably in August, with the smallest rises occurring during cold periods. Additionally, an inverse proportional relationship between mean annual water temperatures and discharges was observed at both river stations.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

In this paper, we investigate the so-called electronic dressed states, a unified quasiparticle resulting from the interaction between electrons in a two-dimensional material with an off-resonance optical dressing field. If the frequency of this field is much larger than all characteristic energies in the system, such as the Fermi energy or bandgap(s), the electronic band structure is affected by radiation so that some important properties of the electron dispersions could be modified in a way desirable for practical applications. For example, a circularly polarized light can be used to vary the bandgap of Dirac materials: it opens a gap in graphene and other metallic and semimetallic lattices, or modifies the magnitude of an existing gap. This will either enhance or reduce a gap, depending on its initial value as well as properties of a host material. Here, we consider gaped dice and Lieb lattices as samples, and put forward a full theoretical model to reveal how these electronic states are deformed by an elliptically-polarized irradiation with a focus on generation and modification of a bandgap.

Air pollution is a man-made environmental-related problem and has importance globally among all environmental issues. It is described as an atmospheric situation in which several components appear at high attention above their ambient level and generate a quantifiable effect on vegetation, animals, and people. Air pollution and its control are a universal problem and have become an issue of great trouble for us in current years. However, monitoring and measurement of the concentration of air pollutants are most difficult in assessment to monitoring other components in the environment. Air pollutants are transported to polar regions by different mechanisms such as long-range atmospheric transport or grasshopper effects and via anthropogenic activities. After reaching the polar regions, they settle down and remain as such for a long-time due to their persistent nature. They can distress the environment and wildlife of the polar regions. Generally, dangerous chemicals may cause a risk of well-being effects, whereas lethal pollutants focus on one physiological reaction. We reviewed the available information on air pollution (occurrence, sources, and harmful effects) in the polar regions.

This paper focuses on environmental policies for mitigating the impacts of climate change on the Taj Mahal, an iconic structure built on the banks of the Yamuna River from 1632 to 1653 with a blend of Persian and Indian architectural styles. The Taj Mahal is threatened by damage caused by climate change, including air pollution resulting in acid rain and floods that lead to erosion and discoloration of the main marble material of the Taj Mahal. This study employs a Systematic Literature Review (SLR) of peer-reviewed journal articles with a primary focus on environmental policy, the impacts of climate change, and the preventive measures undertaken by the Indian government. Based on the analysis of 270 papers, it is concluded that out of the total, 30 papers specifically address objectives relevant to this research. The study period spans from 2014 to 2024. The research findings reveal that air pollution has a significantly adverse impact on the Taj Mahal. Furthermore, nine policies are generally identified from the journal literature review aimed at preserving and protecting the Taj Mahal from the negative effects caused by air pollution.

This study investigates the characteristics of formaldehyde (HCHO) concentration and its reduction in newly constructed apartment buildings in Dubai. It addresses the significant health risks of high HCHO levels due to poor ventilation and extensive use of VOC-emitting materials. The research explores the effectiveness of various mitigation strategies, including air purifiers, photocatalyst applications, and window ventilation, in the living room, master bedroom (MBR), Room 1, and Room 2. HCHO concentrations were measured under different conditions: sealed windows, operation of an air purifier with a VOCs filter, and the application of a photocatalyst. The study followed a quantitative approach, recording real-time HCHO levels over 24 hours. Results indicated that sealing windows led to HCHO concentrations surpassing the domestic standard of 0.1 ppm, with the MBR recording the highest concentration at 0.73 ppm. The air purifier showed minimal impact within the first 8 hours but achieved an 18% reduction after 24 hours of operation. Applying a photocatalyst to room surfaces resulted in a 50% reduction in HCHO levels. Ventilation through window openings significantly improved air exchange rates, with the air exchange per hour (ACH) increasing proportionally with window opening size. Smaller rooms with larger window-to-volume ratios, such as Room 1 and Room 2, maintained HCHO concentrations below the WHO standard of 0.1 ppm with partial window openings. The study concludes that window ventilation is the most effective strategy for reducing HCHO concentrations, highlighting the importance of integrating comprehensive ventilation designs in modern residential buildings to ensure healthier indoor environments in Dubai.

Shenzhen is China’s top ten clean air city and the cleanest air megacity. Even so, epidemiologic studies have shown ambient air pollution had significant adverse impacts on human health in this less polluted city. In this study, the concentrations of six criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) from 2014 to 2017 were analyzed and compared to thresholds of both national and international air quality standards. The results showed concentrations of all air pollutants were below target values of current national air quality standard, but levels of particulate matter (PM) and O3 were still much higher than the recommended levels by the World Health Organization. Within national air quality standards, the number of over-limit days was rare with few variations between highly polluted and low pollution areas. The air quality improvement was slowing down recently. Our results suggest annual and daily thresholds for PM are too loose for air quality improvement in Shenzhen. Hence, we call for evaluation and establishment of tougher air quality standard.

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

Grazing incidence X-Ray Diffraction spectroscopy was employed to characterize crystallinity of Iron, Nickel, Copper and Tungsten, prepared by sparking discharge process in presence of 0.4 T magnetic field at ambient temperature 25 °C. Iron thin film preserved crystallinity even after one year of ageing. Nickel exhibit higher crystallinity when sparked in nitrogen gas flow from the one sparked in oxygen. Tungsten was successfully crystalized after just 40 minutes of sparking inside of magnetic field.

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

Worldwide emission standards are now required to cover engine operation under extreme ambient conditions, which affect the raw emissions and the efficiency of the exhaust aftertreatment systems. These regulations also target new combustion technologies for decarbonization, such as neat hydrogen (H2) combustion or dual-fuel strategies, which involve a challenge to the analysis of exhaust aftertreatment systems requirements and performance. This work addresses the impact of high altitude and low ambient temperature conditions on the reactivity of an oxidation catalyst in the presence of H2. A reaction mechanism is proposed to cover the main conversion paths of CO, HC, and H2, including the formation and consumption of high-energy surface reaction intermediates. The mechanism has been implemented into a faster-than-real-time reduced-order model for multi-layer washcoat honeycomb catalytic converters. The model was utilized to investigate the effect of H2 concentration on the reactivity of CO and HC within the catalyst under various operating and ambient conditions. By applying the model and examining the selectivity towards different reaction pathways in the presence H2, insights into surface intermediates and reactivity across different cross-sections of the monolith were obtained. This analysis discusses the underlying causes of reactivity changes promoted by H2 and its relative importance as a function of driving boundary conditions.

Chronic heat stress (CHS) has been exerting great pressure on the poultry industry due to the global warming. CHS-induced negative impacts deeply change the physiology, metabolism, egg production and meat quality of ducks. To reduce the increasing economic loss, different environmental strategies have been adopted. Here, we comprehensively compared three rearing modes for Cherry-Valley ducks (CVds) mainly by changing the environmental temperature. Plus, the transcriptome analysis and physiological anatomy were implied to investigate the underlying molecular biology and pneumonocyte dynamics. It turns out that the CVds affected by CHS have poor growth performance and bad carcass traits. In addition, the irreversible damage caused by CHS in the lung tissue of CVds was observed. Furthermore, several potential genes related to CHS have been found, such as HSPA8, IGF1, FGFR1, ACSL3, ACSL6, NR1H3 and GAPDH. This study strongly indicates the severe injury of CHS from the high-temperature environment and points out one of the adopting directions for duck rearing.

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity and climatic conditions, and has not been assessed at a cross-national level to date. The current study examines the association between the prevalence of depression in each country, based on the most recent Global Burden of Disease Study data, and the average national level of PM2.5 based on the World Health Organization’s database. The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that there was a modest but significant positive correlation between PM2.5 level and the prevalence of depression even after adjusting for the above confounders. This association was more marked above a certain threshold and applied chiefly to major depressive episodes. These findings are of significant public health importance in terms of preventive strategies aimed at reducing the population-level burden of depression.