Search Results (36,542)

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article

Osteoporosis is a common yet underdiagnosed condition that increases the risk of fractures, contributing to substantial morbidity, mortality, and healthcare costs. Distal radius fractures (DRFs) are some of the most common fractures associated with osteoporosis and often precede more severe fractures. Managing DRFs in patients with osteoporosis can be a challenge due to altered bone quality, which can affect healing and surgical fixation. This review examines both operative and nonoperative management strategies for DRFs in osteoporotic patients, emphasizing the importance of individualized treatment. Surgical interventions, like open reduction and internal fixation (ORIF) with plating, can facilitate early mobilization and improved alignment, especially in more active patients. However, osteoporosis poses risks such as hardware failure, infection, and malunion, calling for careful patient selection. Conversely, nonoperative management may be more suitable for patients with lower functional demands or higher surgical risks, despite the increased risk of malunion. By adapting treatment strategies to individual patient characteristics, orthopedic surgeons can optimize outcomes, minimize complications, and potentially prevent future fractures. Both operative and nonoperative treatments can yield positive outcomes when personalized to the patient’s needs. Full article

The present study investigates the real-time estimation of CO₂ absorption capacity (CAC) based on the electrical conductivity (EC) of low-concentration di-methyl-ethanolamine (DMEA) solutions (0.1–0.5 M). CO₂ absorption experiments are conducted to measure the variation in CAC and EC during CO₂ absorption, revealing a strong correlation between the two properties. The ionic conductivity of DMEAH⁺ formed during absorption is calculated to be 53.1 S·cm²/(mol·z), which is found to be larger than that of TEAH⁺ and MDEAH⁺. This can be attributed to the smaller molar mass and higher ionic mobility of DMEAH⁺. A significant finding is that the measured EC (EC_M) of the DMEA solutions consistently demonstrates a lower value than the theoretically predicted value. This discrepancy is due to the larger ionic size of DMEAH⁺, which results in a reduction in the real mean ionic activity coefficient. This effect becomes more pronounced with increasing DMEA concentration. Consequently, a higher CAC is required to produce the same change in EC at higher amine concentrations. Based on these findings, an empirical equation is devised to estimate CAC from EC_M in solutions of constant DMEA concentration. This equation will be employed as a practical approach for the in situ monitoring of CO₂ absorption using DMEA aqueous solution. Full article

This article describes the leadership and organizational elements that have made the Seven Mountains Mandate (7MM), one of the main doctrines of the Apostolic Restoration Movement (ARM), a very adaptable, simple to explain, and feasible to implement message and portable practice, which can be started at the local level and expand progressively to acquire regional and national dimensions. A new classification of apostolic networks is proposed in this article and a partial map of some of the networks that participated in the expansion of the 7MM is presented. Additionally, using definitions from network leadership theory, it is shown how different types of leaders, who function as horizontal connectors in apostolic networks, such as conveners, catalysts, mobilizers, facilitators, weavers, provocateurs, illuminators, hosts, and curators, contribute to the diffusion of the 7MM by promoting its global Widening, Deepening, and Lengthening (WDL). The article first describes the evolution of the church from denominations to networks and how the ARM adopted the network organizational structure that serves as a channel for the diffusion of the 7MM. Cases from the USA, Guatemala, Zambia, and Venezuela are used as examples to demonstrate how the 7MM’s expansion, or Widening (W); its contextualization and adaption, or Deepening (D); and its sustainability, reproduction, and evolution, or Lengthening (L), occur globally based on the network nature of the ARM and the network leadership enacted in the process. Full article

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. Full article

Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section’s condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data. Full article

The future charging requirements of electric trucks will lead to new demands on the power grid. In order not to slow the expansion of the charging infrastructure for electric trucks, the power grid must be strengthened for this purpose. However, due to the limited penetration of electric trucks in fleets to date, grid planners lack information on their time- and location-dependent charging demand. The question arises as to how the charging demand of electric trucks can be realistically taken into account in power grid simulations. This paper therefore presents a methodology that makes it possible to quantify the charging demand of electric trucks at typical charging locations and derives initial parameters for power system planning with electric trucks. For location-based charging demand modeling, the arrival and departure behavior of trucks at representative logistics centers is combined with mobility data and vehicle parameters. This allows the determination of time series-based charging demand. A charging demand analysis at five different logistics center types shows that that energy demand, peak load, and temporal behavior vary greatly depending on the center type. It is therefore advisable to take these different charging location types into account when designing the electricity grids. Full article

As the adoption of Electric Vehicles (EVs) accelerates, driven by increasing urbanization and the push for sustainable infrastructure, the need for innovative solutions to support this growth has become more pressing. Vehicle-to-Grid (V2G) technology presents a promising solution by enabling EVs to engage in bidirectional interactions with the electrical grid. Through V2G, EVs can supply energy back to the grid during peak demand periods and draw power during off-peak times, offering a valuable tool for enhancing grid stability, improving energy management, and supporting environmental sustainability. Despite its potential, the large-scale implementation of V2G faces significant challenges, particularly from a technological and regulatory standpoint. The success of V2G requires coordinated efforts among various stakeholders, including vehicle manufacturers, infrastructure providers, grid operators, and policymakers. In addition to the technical barriers, such as battery degradation due to frequent charging cycles and the need for advanced bidirectional charging systems, regulatory frameworks must evolve to accommodate this new energy paradigm. This review aims to provide a comprehensive analysis of V2G technology, focusing on different perspectives—such as those of users, vehicles, infrastructures, and the electricity grid. This study will also explore ex ante, ex post, and ongoing assessment studies, alongside the experiences of pioneer cities in implementing V2G. Full article

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces. Full article

CO₂ emissions from urban daily mobility play a major role in both environmental degradation, rising economic costs, and sustainability. Reducing these emissions not only advances environmental sustainability but also fosters economic development by enhancing public health, lowering energy consumption, and alleviating the financial strain caused by climate change. Understanding the dynamics of CO₂ emissions from urban daily mobility provides valuable historical insights into environmental impacts and economic costs tied to urban development. This study takes a historical perspective, examining changes in CO₂ emissions associated with daily mobility in the Lyon agglomeration across two decades, drawing on data from the 1995 and 2006 household travel surveys. Our findings reveal that individual factors such as gender, age, employment status, and income significantly influence CO₂ emissions, with males and full-time workers exhibiting higher emissions. Furthermore, household characteristics, including size and vehicle ownership, are critical in shaping emission levels, while urban form variables such as population density and mixed land use demonstrate a negative correlation with emissions, highlighting the importance of urban planning in mitigating CO₂ output. The analysis emphasizes that greater accessibility and proximity to essential services are vital in reducing individual emissions. Based on these insights, we discuss the implications for policy design, suggesting targeted strategies to enhance urban mobility, improve public transport accessibility, and promote sustainable urban development. Finally, we outline directions for future research to further explore the intricate relationship between urban characteristics and emissions, ultimately aiming to contribute to the development of effective climate policies. Full article

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species. Full article

Infection by retroviruses and the mobilization of transposable elements cause DNA damage that can be catastrophic for a cell. If the cell survives, the mutations generated by retrotransposition may confer a selective advantage, although, more commonly, the effect of new integrants is neutral or detrimental. If retrotransposition occurs in gametes or in the early embryo, it introduces genetic modifications that can be transmitted to the progeny and may become fixed in the germline of that species. PIWI-interacting RNAs (piRNAs) are single-stranded, 21–35 nucleotide RNAs generated by the PIWI clade of Argonaute proteins that maintain the integrity of the animal germline by silencing transposons. The sequence specific manner by which piRNAs and germline-encoded PIWI proteins repress transposons is reminiscent of CRISPR, which retains memory for invading pathogen sequences. piRNAs are processed preferentially from the unspliced transcripts of piRNA clusters. Via complementary base pairing, mature antisense piRNAs guide the PIWI clade of Argonaute proteins to transposon RNAs for degradation. Moreover, these piRNA-loaded PIWI proteins are imported into the nucleus to modulate the co-transcriptional repression of transposons by initiating histone and DNA methylation. How retroviruses that invade germ cells are first recognized as foreign by the piRNA machinery, as well as how endogenous piRNA clusters targeting the sequences of invasive genetic elements are acquired, is not known. Currently, koalas (Phascolarctos cinereus) are going through an epidemic due to the horizontal and vertical transmission of the KoRV-A gammaretrovirus. This provides an unprecedented opportunity to study how an exogenous retrovirus becomes fixed in the genome of its host, and how piRNAs targeting this retrovirus are generated in germ cells of the infected animal. Initial experiments have shown that the unspliced transcript from KoRV-A proviruses in koala testes, but not the spliced KoRV-A transcript, is directly processed into sense-strand piRNAs. The cleavage of unspliced sense-strand transcripts is thought to serve as an initial innate defense until antisense piRNAs are generated and an adaptive KoRV-A-specific genome immune response is established. Further research is expected to determine how the piRNA machinery recognizes a new foreign genetic invader, how it distinguishes between spliced and unspliced transcripts, and how a mature genome immune response is established, with both sense and antisense piRNAs and the methylation of histones and DNA at the provirus promoter. Full article

As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In₂O₃) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In₂O₃ assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO₂). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices. Full article

(1) Background: As cycling gains popularity as a mode of transportation, the frequency of accidents involving cyclists also rises. This has become a major concern for traffic safety, sustainability, and city planning. Identifying the risk factors that contribute to bicycle road accidents remains a significant challenge. This study aims to figure out which risk factors make some road segments more dangerous for cyclists than others. (2) Methods: This study introduces the use of a bicycle simulator to test different road segments involving thirty-nine participants. The impact of demographics and some risk factors related to infrastructure were analyzed in terms of their influence on the perceived level of risk through pre- and post-surveys. (3) Results: The findings showed that the bicycle facility type affects the perceived level of risk. Shared-use roads were ranked as riskiest, while separated bike lanes were least risky. Bicycle roads with no separated safety barriers had higher risks. Heavy traffic jams increased danger among cyclists. Women gave higher risk ratings than men. The perceived levels of risk were then compared with the previously developed risk index and they correlated well. (4) Conclusions: This confirms that the risk index can reliably evaluate the degree of risk of each road segment. Full article

Under the influence of the sedimentation process, the phenomenon of intraformational non-homogeneity is widely observed in low-permeability reservoirs. In the development process of water and gas replacement (WAG), the transport law of water and gas and the distribution of residual oil are seriously affected by the non-homogeneity of reservoir properties. In this paper, a study on two types of reservoirs with certain lengths and thicknesses is carried out, and a reasonable development method is proposed according to the characteristics of each reservoir. Firstly, through indoor physical simulation experiments combined with low-field nuclear magnetic resonance scanning (NMR), this study investigates the influence of injection rate and core length on the double-layer low-permeability inhomogeneous core replacement and pore throat mobilization characteristics. Then, a two-layer inhomogeneous low-permeability microscopic model is designed to investigate the model’s replacement and pore throat mobilization characteristics under the combined influence of rhythmites, gravity, the injection rate, etc. Finally, based on the results of the core replacement and numerical simulation, a more reasonable development method is proposed for each type of reservoir. The results show that for inhomogeneous cores of a certain length, the WAG process can significantly increase the injection pressure and effectively seal the high-permeability layer through the Jamin effect to improve the degree of recovery. Moreover, for positive and reverse rhythm reservoirs of a certain thickness, the injection rate can be reduced according to the physical properties of the reservoir, and the gravity overburden phenomenon of the gas is used to achieve the effective development of the upper layers. The effect of the development of a positive rhythm reservoir therefore improved significantly. These findings provide data support for improving the development effectiveness of CO₂ in low-permeability inhomogeneous reservoirs and emphasize the importance of the influence of multiple factors, such as injection flow rate, gravity, and rhythm, in CO₂ replacement. Full article