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Commonly comprised of cyanobacteria, algae, bacteria and fungi, hypolithic communities inhabit the underside of cobblestones and pebbles in diverse desert biomes. Notwithstanding their abundance and widespread geographic distribution and their growth in the driest regions on Earth, the source of water supporting these communities remains puzzling. Adding to the puzzle is the presence of cyanobacteria that require liquid water for net photosynthesis. Here we report results from six-year monitoring in the Negev Desert (with average annual precipitation of ~ 90 mm) during which periodical measurements of the water content of cobblestone undersides were carried out. We show that while no effective wetting took place following direct rain, dew or fog, high vapor flux, induced by a sharp temperature gradient, took place from the wet subsurface soil after rain, resulting in wet-dry cycles and wetting of the cobblestone undersides. Up to 12 wet-dry cycles were recorded following a single rain event, which resulted in vapor condensation on the undersides of the cobblestones, with the daily wet phase lasting for several hours during daylight. This 'concealed mechanism' expands the distribution of photoautotrophic organisms into hostile regions where the abiotic conditions limit their growth, and provides the driving force for important evolutionary processes not yet fully explored. [ABSTRACT FROM AUTHOR]

Introduction: Though the effects of fog and friction on decision-making are frequently simulated in military settings, they are less frequently extended to non-military and non-governmental MENA spaces. This article provides a description and evaluation of a disaster simulation rooted in the real-world event of the August 4, 2020 Lebanese port explosion. The aim of the simulation is to prompt student learning of the decision-making dynamics of CSOs amidst fog and friction in democratizing MENA spaces. Background: A brief overview of the Lebanon-CSO landscape is provided. As part of their ruling strategy, sectarian elites engineer fog and friction to co-opt and fragment CSOs. This complex power sharing milieu creates dilemmas for CSOs. The Intervention: The simulation was designed for an entire classroom of students to experience the fog and friction in decision-making that CSOs encounter in MENA conditions of limited resources and urgent humanitarian need. Methods and Limitations: Student learning, framed by Bloom's taxonomy of levels of intellectual behavior, was assessed using qualitative entry and exit classroom surveys based on three iterations of the simulation. Qualitative interpretive content analysis was used to categorize survey responses. The sample is not statistically significant given the small class sizes at the College. The study serves as an initial exploration for assessing the promising pedagogical benefits of the Lebanon port explosion simulation. Results: Before the simulation, students had knowledge and understanding of CSOs, democratization, and the Lebanese context. After the simulation, student surveys yielded results that evidenced higher- ordered learning. Students discovered the challenge of applying their knowledge of Lebanese CSOs amidst fog. Next, the panel stress- testing and critical reflection essay exercises prompted an analysis and evaluation of CSOs as weak or fraught vehicles for democratization in the region. In sum, fog and friction prompted students to experience and learn how CSOs may subvert democratization in the MENA despite intentions to "do good." Discussion: The Lebanon port explosion survey data indicate the value of simulations as "experience vehicles" that may push students to higher orders of abstracted thinking about CSOs in the MENA. Conclusion: Coupled with historical knowledge of co-optation and fragmentation strategies of elites vis-à-vis CSOs in Lebanon, the classroom simulation experience provided a low-cost and high-reward learning opportunity for students to grapple with the constraints and consequences of fog and friction on CSO decision-making. [ABSTRACT FROM AUTHOR]

Right now, Contemporary technologies have become imperative in many domains to achieve societal safety. As is practiced in transportation systems through merging information and communication technologies (ICTs) in transportation to be an intelligent sector. As well Internet of Vehicles (IoVs) for facilitating communication between vehicles for safe driving. Similarly, fog computing in Vehicular Ad Hoc Networking (VANET) contributes significantly to addressing timing and latency issues by enabling cloud services for nearby vehicles. Nonetheless, there are hazards of cyber-attacks on vehicles in VFN, which makes it uneasy to disclose personal information to unidentified fog devices. Consequently, an online criminal might target vehicles with counterfeit attacks. Herein, blockchain technology (BCT) is another technology of ICTs and is provided in this study as a handler for the problem of cyber-attacks. Due to BCT's characteristics of permanent, or immutable, peer-to-peer, decentralized, and distributed ledger technology. Thereby, this study contributes to constructing an appraiser model for appraising BCT as the secured methodology in VFN. Multi-criteria decision-making (MCDM) techniques such as entropy and weighted sum method (WSM) have been harnessed in the appraising process motivated by the uncertainty theory of Type-2 neutrosophic sets (T2NSs). The appraiser model's findings indicated that BCT 5(A5) was the optimal candidate based on its ranking. In contrast, BCT 4 (A4) is the worst one. [ABSTRACT FROM AUTHOR]

Traditional dehazing algorithms have long been limited in their ability to remove fog effectively, especially in preserving details. This study proposes an improved dark channel prior dehazing algorithm designed to overcome the limitations of traditional algorithms by refining the transmission map and estimating atmospheric light intensity. The algorithm fully exploits the anti‐haze characteristics of the Gabor filter to extract multi‐directional texture features. By fusing these features to form a guidance map for guided filtering, it effectively reduces the blurring caused by guided filtering on the image edges, thereby producing a more accurate transmission map. Simultaneously, an enhanced atmospheric light successfully reduces interference from white objects in the image. The experiment phase utilized the publicly available RESIDE dataset for validation. The algorithm achieved a Peak Signal‐to‐Noise Ratio (PSNR) of 22.4 and a Structural Similarity Index (SSIM) of 0.88. These metrics indicate the algorithm's superior dehazing capabilities. [ABSTRACT FROM AUTHOR]

For the current article, forty-two fogwater samples are collected at four sites in Alsace (Strasbourg, Geispolsheim, Erstein, and Cronenbourg) between 2015 and 2021, except 2019 and 2020. Spatio-temporal evolution is studied for their inorganic fraction (ions and heavy metals), and physico-chemical properties (pH, conductivity (K), liquid water content (LWC), and dissolved organic carbon (DOC)). The analyses show a remarkable shifting in pH from acidic to basic mainly due to the significant decrease in sulfate and nitrate levels. The calculated median LWC is somehow low (37.8–69.5 g m3) in fog samples, preventing the collection of large fog volumes. The median DOC varies between 14.3 and 24.4 ppm, whereas the median conductivity varies from 97.8 to 169.8 µS cm−1. Total ionic concentration (TIC) varies from 1338.3 to 1952.4 µEq L−1, whereas the total concentration of metals varies in the range of 1547.2 and 2860.3 µg L−1. The marine contribution is found to be negligible at all sites for the investigated elements. N H 4 + , in most samples, is capable alone to neutralize the acidity. On one hand, N H 4 + , C a 2 + , N O 3 − , and S O 4 2 − are the dominant ions found in all samples, accounting for more than 80% of the TIC. On the other hand, Zn and Ni are the dominant metals accounting for more than 78% of the total elemental concentration. Heavy metals are found to primarily originate from crust as well as human-made activities. The median concentrations of individual elements either decrease or increase over the sampling period due to the wet deposition phenomenon or weather conditions. A Pearson analysis proves some of the suggested pollutant sources due to the presence of strong and significant correlations between elements. [ABSTRACT FROM AUTHOR]

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In this study, an efficient fog collector—type 1 and 2 cylindrical brush—has been introduced and its harvesting performance compared to other conventional collectors under field and laboratory conditions. Under field conditions, type 1 cylindrical brush with Raschel and aluminum mesh was installed in the inlet of the humidity front of the Caspian Sea to Ardabil plain in Abi-beyglu in the northwest of Iran. The results of the analysis of the collected water revealed that type 1 cylindrical brush collected 9.3% and aluminum mesh collected 4.3% more water than Raschel mesh. Under laboratory conditions, by applying different fog speeds, the performance of three conventional collectors and two proposed cylindrical brush were determined. The results indicated that type 2 cylindrical brush had the highest efficiency compared to others, and due to the lack of clogging, its efficiency did not diminish over time. Upon increasing the speed of the output fog, the amount of collected water and accordingly the efficiency of the collectors increased. Type 2 cylindrical brush showed the highest increase in efficiency at high fog speeds. Its maximum value reached about 50%, while the maximum efficiency of Raschel collector was equal to 28%. In general, it can be stated that cylindrical brush introduced in this study, in addition to the high performance of fog harvesting and the high efficiency of water collection, has a high resistance against strong winds. Therefore, it can be a suitable collector for water harvesting in foggy areas without any implementation problems. [ABSTRACT FROM AUTHOR]

The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems' deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management. [ABSTRACT FROM AUTHOR]

Patient care, research, and decision-making are all aided by real-time medical data analysis in today's rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study's findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare. [ABSTRACT FROM AUTHOR]

Climate refugia, areas where climate is expected to remain relatively stable, can offer a near‐term safe haven for species sensitive to warming temperatures and drought. Understanding the influence of temperature, moisture, and disturbance on sensitive species is critical during this time of rapid climate change. Coastal habitats can serve as important refugia. Many of these areas consist of working forestlands, and there is a growing recognition that conservation efforts worldwide must consider the habitat value of working lands, in addition to protected areas, to effectively manage large landscapes that support biodiversity. The sensitivity of forest bats to climate and habitat disturbance makes them a useful indicator taxon. We tested how microclimate and forest management influence habitat use for 13 species of insectivorous bats in a large climate refugium in a global biodiversity hotspot. We examined whether bat activity during the summer dry season is greater in forests where coastal fog provides moisture and more stable temperatures across both protected mature stands and those regularly logged. Acoustic monitoring was conducted at a landscape scale with 20 study sites, and generalized linear mixed models were used to examine the influence of habitat variables. Six species were positively associated with warmer nighttime temperature, and 5 species had a negative relationship with humidity or a positive relationship with climatic moisture deficit. Our results suggest that these mammals may have greater climate adaptive capacity than expected, and, for now, that habitat use may be more related to optimal foraging conditions than to avoidance of warming temperatures and drought. We also determined that 12 of the 13 regionally present bat species were regularly detected in commercial timberland stands. Because forest bats are highly mobile, forage over long distances, and frequently change roosts, the stewardship of working forests must be addressed to protect these species. [ABSTRACT FROM AUTHOR]

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security. [ABSTRACT FROM AUTHOR]

Summary: The technique to overcome the challenge while implementing the fiber optical link is investigated using an alternative optical link called a free‐space optical (FSO) link with advanced modulation and signal processing techniques. A dual‐polarized 16‐quadrature amplitude‐modulated signal is transmitted over single and multi‐FSO channels under various atmospheric attenuation conditions. High bandwidth and data rates (up to 10 Gbps), unlicensed spectrum (above 300 GHz), and simplicity of deployment are significant benefits of the FSO system. However, the weather conditions of haze, rain, and fog cause signal attenuation when the optical carrier wave is transmitted over free space, and atmospheric turbulence is a challenge to FSO implementation, which degrades the transmission performance of optical signals for long‐distance communication. For 120 Gbps data transmission, the performance of a multi‐input and multi‐output (MIMO) FSO system is evaluated by observing the bit error rate (BER) and received signal constellation diagram. Optical signal‐to‐noise ratio (OSNR) parameters were iterated to determine the optimal value for back‐to‐back, single‐input, single‐output (SISO), and MIMO systems. Moreover, we examined the proposed system's output with recent articles, including MIMO, multiplexing, and various modulation techniques. The comparison of results shows that the suggested MIMO‐FSO model has enhanced the performance with the lower BER as less than 3.8 × 10−3 (log of BER is −2.42) forward error correction (FEC) limit for 18.7 km link range in clear air with 0.54 dB/km attenuation, 4.8, 4.5, 1.35, and 0.57 km under haze, light fog, medium, and heavy fog with 4.6, 6.9, 28.9, and 75 dB/km attenuations. [ABSTRACT FROM AUTHOR]

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments. [ABSTRACT FROM AUTHOR]

Fog, a form of cloud in contact with the Earth's surface, is one of the high-impact weather phenomena in northern India during the winter months. A new day-time fog detection scheme using the normalized difference snow index (NDSI) has been developed. The present analysis focuses on the detection of fog at high spatial resolution using data from the Resourcesat-2 AWiFS. The fog area detected is cross-validated with that detected using INSAT-3DR data at 1 km resolution using the same technique. The NDSI-based technique discussed here has shown a strong potential for fog detection during day-time. This study is also significant as a pre-launch sensitivity study for future GISAT with MX-VNIR, HyS-VNIR, HyS-SWIR, or similar other kinds of present-or-future sensors. Even though GISAT does not have a MX-SWIR channel, a combination of both MX-VNIR and HyS-SWIR with resampled spatial resolution may be useful for day-time fog detection using this technique. [ABSTRACT FROM AUTHOR]

Abstract Commonly comprised of cyanobacteria, algae, bacteria and fungi, hypolithic communities inhabit the underside of cobblestones and pebbles in diverse desert biomes. Notwithstanding their abundance and widespread geographic distribution and their growth in the driest regions on Earth, the source of water supporting these communities remains puzzling. Adding to the puzzle is the presence of cyanobacteria that require liquid water for net photosynthesis. Here we report results from six-year monitoring in the Negev Desert (with average annual precipitation of ~ 90 mm) during which periodical measurements of the water content of cobblestone undersides were carried out. We show that while no effective wetting took place following direct rain, dew or fog, high vapor flux, induced by a sharp temperature gradient, took place from the wet subsurface soil after rain, resulting in wet-dry cycles and wetting of the cobblestone undersides. Up to 12 wet-dry cycles were recorded following a single rain event, which resulted in vapor condensation on the undersides of the cobblestones, with the daily wet phase lasting for several hours during daylight. This ‘concealed mechanism’ expands the distribution of photoautotrophic organisms into hostile regions where the abiotic conditions limit their growth, and provides the driving force for important evolutionary processes not yet fully explored.

Abstract Traditional dehazing algorithms have long been limited in their ability to remove fog effectively, especially in preserving details. This study proposes an improved dark channel prior dehazing algorithm designed to overcome the limitations of traditional algorithms by refining the transmission map and estimating atmospheric light intensity. The algorithm fully exploits the anti‐haze characteristics of the Gabor filter to extract multi‐directional texture features. By fusing these features to form a guidance map for guided filtering, it effectively reduces the blurring caused by guided filtering on the image edges, thereby producing a more accurate transmission map. Simultaneously, an enhanced atmospheric light successfully reduces interference from white objects in the image. The experiment phase utilized the publicly available RESIDE dataset for validation. The algorithm achieved a Peak Signal‐to‐Noise Ratio (PSNR) of 22.4 and a Structural Similarity Index (SSIM) of 0.88. These metrics indicate the algorithm's superior dehazing capabilities.

Abstract Patient care, research, and decision-making are all aided by real-time medical data analysis in today’s rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study’s findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare.

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) refers to a network of physical devices that are embedded with sensors, software, and network connectivity, allowing them to collect and share data. The devices are the core of any IoT ecosystem. Browsing the extant literature, it emerges that the meaning of the term device depends on the reference context. It follows that, it is an important topic to investigate the reasons behind such a degree of indeterminacy. This paper elaborates on the evolution of the concept of device moving from IoT to artificial intelligence of things (AIoT). The finding that comes from this study is that this evolution is a direct consequence of the evolution of the IoT computing paradigms. [ABSTRACT FROM AUTHOR]

First-Person Shooter (FPS) games are among the most popular latency-sensitive multiplayer online games. A separate game server manages each multiplayer group hosted appropriately to meet the given Quality of Service (QoS) and Quality of Experience (QoE) requirements of the game and the players. Cloud computing is typically used to host these game servers to cope with changing workload requirements, given its elasticity, scalability, and on-demand provisioning characteristics [1]. However, the Cloud still suffers from known latency limitations, especially when dealing with latency-sensitive applications such as FPS games. Hence, Fog computing can be a good alternative for hosting FPS game servers, given that Fog nodes hosting the servers can be placed close to the players, thereby reducing latency. However, a careful resource management is necessary given the Fog's capacity unpredictability. This paper introduces an optimization-based game server placement approach that minimizes server costs while optimizing latency delays and, eventually, QoE for the players. A static model for the server placement problem is formulated as an Integer Linear Program (ILP) with valid inequalities. Given the complexity of the problem, a resolution strategy based on a non-linear, double penalty relaxation technique in conjunction with a proven Genetic Algorithm (GA) is developed. A dynamic version of the placement problem is also formulated to cope with the realistic scenarios dealing with changing multiplayer groups and Fog servers. A two-step dynamic scheme, based on the developed GA, is applied. Extensive simulations are conducted showing a good performance of the proposed approach both in terms of solution quality and execution time. [ABSTRACT FROM AUTHOR]

The escalating global demand for dairy products due to population growth has led to increased production in the dairy industry, resulting in a significant rise in wastewater generation. This wastewater, laden with contaminants such as fats, oils, and greases (FOGs), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrogen, and phosphorus, poses a threat to freshwater sources. Anaerobic digestion (AD) is considered the optimal treatment method for dairy wastewater, but the high-fat content poses challenges like reactor clogging. To overcome this, various authors propose and implement an enzymatic pre-treatment strategy that improves FOG and organic content removal, increases biogas production, and addresses economic and environmental concerns. Despite the proven efficacy of enzymatic pre-treatment, a significant drawback is the associated cost. However, it remains a promising strategy for enhancing the biodegradability of complex organic compounds in dairy effluents. This review delves into the crucial role of enzyme-producing microorganisms in enhancing AD efficiency for dairy wastewater treatment, emphasizing their potential benefits and addressing the economic and environmental considerations associated with this approach. HIGHLIGHTS Enzymatic pre-treatment coupled with an AD produces a high-quality effluent.; Enzymatic pre-treatment is effective in the reduction of SS, FOGs in DWW but the cost implication makes it less appealing.; Recommendations have been offered for promising research areas in enzymatic pre-treatment with the use of new enzymes.;

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase and a simple neural network for the deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to improve tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and costs. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, a speed increase by 37.56%, and identity switches decreased by 46.81%. [ABSTRACT FROM AUTHOR]

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments. [ABSTRACT FROM AUTHOR]

In this study, 10 min and 2 km high-resolution blended fog data (HRBFD) were generated using grid visibility data (GVD) and data from a GK2A (GEO-KOMPSAT-2A) fog product (GKFP) in Korea. As the blending method, the decision tree method (DTM) was used to consider the different characteristics of the two-input data (categorical data and continuity data). The blending of the two datasets was performed according to the presence or absence of the input data and considered the spatial representation of the GVD and the accuracy of the GKFP. The quality of the HRBFD was evaluated through visual comparison using GVD, GKFP, and visible images of the GK2A. The HRBFD seems to have partly solved the problem of fog detection in areas where visibility meters are rare or absent through the detection of fog occurring in the sea or mountain areas. In addition, the critical problem of the GKFP, which has limitations in detecting fog occurring under clouds, has been mostly overcome. Using the DTM, we generated 23 fog cases of 10 min and 2 km HRBFD. The results confirmed that detailed spatiotemporal characteristics of fog in Korea can be analyzed if such HRBFD is generated for a long time. [ABSTRACT FROM AUTHOR]

Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and variability. This study proposes an advanced ocean fog prediction model for the Yellow Sea region, leveraging satellite-based detection and high-performance data-driven methods. We used Himawari-8 satellite data to obtain a lot of spatiotemporal ocean fog references and employed AutoML to integrate numerical weather prediction (NWP) outputs and sea surface temperature (SST)-related variables. The model demonstrated superior performance compared to traditional NWP-based methods, achieving high performance in both quantitative—probability of detection of 81.6%, false alarm ratio of 24.4%, f1 score of 75%, and proportion correct of 79.8%—and qualitative evaluations for 1 to 6 h lead times. Key contributing variables included relative humidity, accumulated shortwave radiation, and atmospheric pressure, indicating the importance of integrating diverse data sources. The study emphasizes the potential of using satellite-derived data to improve ocean fog prediction, while also addressing the challenges of overfitting and the need for more comprehensive reference data. [ABSTRACT FROM AUTHOR]

Sea fog is a societally relevant phenomenon that occurs under the influence of specific oceanic and atmospheric conditions including aerosol conditions. The Yellow Sea region in China regularly experiences sea fog events, of varying intensity, that impact coastal regions and maritime activities. The occurrence and structure of fog are impacted by the concentration of aerosols in the air where the fog forms. Along with industrial development, air pollution has become a serious environmental problem in Northeastern China. These higher pollution levels are confirmed by various satellite remote sensing instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite that observes aerosol and cloud properties. These observations show a clear influence of aerosol loading over the Yellow Sea region, which can impact regional sea fog. In this study, high-resolution data sets from MODIS Aqua L2 are used to investigate the relationships between cloud properties and aerosol features. Using a bi-variate comparison method, we find that, for most cases, larger values of COT (cloud optical thickness) are related to both a smaller DER (droplet effective radius) and higher CTH (cloud top height). However, in the cases where fog is thinner with many zero values in CTH, the larger COT is related to both a smaller DER and CTH. For fog cases where the aerosol type is dominated by smoke (e.g., confirmed fire activities in the East China Plain), the semi-direct effect is indicated and may play a role in determining fog structure such that a smaller DER corresponds with thinner fog and smaller COT values. [ABSTRACT FROM AUTHOR]

Efforts to develop innovative water harvesting strategies offer powerful solutions to alleviate the water crisis, especially in remote and arid areas. Inspired by the hydrophobic/hydrophilic pattern of desert beetles and water self-propulsion property of spider silks, a double-strand hydrophobic PVDF-HFP/hydrophilic PAN nanofibers yarn is proposed by electrospinning and twisting techniques. The double-strand cooperation approach allows for water deposition on hydrophobic PVDF-HFP segment and transport under the asymmetric capillary driving force of hydrophilic PAN segment, thus speeded up the aggregation and growth of droplets. The effects of the composition and the diameter ratio of the two primary yarns were studied and optimized for boosting fog collection performance. The double-strand anisotropic yarn not only provide an effective method for water harvesting, but also hold the potential to inspire innovative design concepts for fog collection materials in challenging environments. [ABSTRACT FROM AUTHOR]

This study examines the effect of surface moisture flux on fog formation, as it is an essential factor of water vapor distribution that supports fog formation. A one‐way nested large‐eddy simulation embedded in the mesoscale community Weather Research and Forecasting model is used to examine the effect of surface moisture flux on a cold fog event over the Heber Valley on January 16, 2015. Results indicate that large‐eddy simulation successfully reproduces the fog over the mountainous valley, with turbulent mixing of the fog aloft in the valley downward. However, the simulated fog is too dense and has higher humidity, a larger mean surface moisture flux, more extensive liquid water content, and longer duration relative to the observations. The sensitivity of fog simulations to surface moisture flux is then examined. Results indicate that reduction of surface moisture flux leads to fog with a shorter duration and a lower height extension than the original simulation, as the decrease in surface moisture flux impairs water vapor transport from the surface. Consequently, the lower humidity combined with the cold air helps the model reproduce a realistic thin fog close to the observations. The outcomes of this study illustrate that a minor change in moisture flux can have a significant impact on the formation and evolution of fog events over complex terrain, even during the winter when moisture flux is typically very weak. [ABSTRACT FROM AUTHOR]

With the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius reff$$ {r}_{\mathrm{eff}} $$ change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity κ$$ \kappa $$, which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species. [ABSTRACT FROM AUTHOR]

Transitional changes in the atmospheric boundary layer (ABL) are known to facilitate the onset of terrestrial fog, which is defined as a condition with near‐surface visibility <1 km due to airborne water droplets. In particular, the evening transition from a daytime convective ABL to a night‐time stable ABL provides favorable conditions for fog. This article describes a local fog event observed during the evening transition at a Canadian islet in the north Atlantic known as Sable Island during the "Fog and Turbulence Interactions in the Marine Atmosphere (Fatima)" field campaign. The comprehensive set of data collected using a myriad of instruments covering a wide range of scales allowed identification of a novel mechanism underlying this fog event. Therein an ocean–land discontinuity created a flow regime consisting of several stacked boundary layers, interplay of which produced a thin low‐level cloud that then diffused downward to the surface, causing visibility reduction. This mechanism offers useful insights on the role of boundary layers, stratification, and turbulence in fog genesis over oceanic islands. [ABSTRACT FROM AUTHOR]

Community mask use is an efficacious non-pharmacologic way to minimize viral infection spread. It is a recommendation that individuals wear face masks as protective gear. Under ideal weather conditions, machine and artificial intelligence techniques can typically determine if a person is wearing a mask properly. Identification becomes more difficult under inclement weather such as fog, clouds, haze or rain. In this work, we propose a technique that can detect a human face wearing a mask even in adverse weather. For this, homogeneous foggy images have been considered. The main challenge with this problem is that video quality degrades because of fog. Here, diverse Deep learning models train regular datasets containing digital pictures of persons with facial and non-facial masks. The training and validation parameters ensure 97% accuracy in classifying faces wearing a mask. [ABSTRACT FROM AUTHOR]

This paper introduces a 400 Gbps free space optics (FSO) system to meet the expected surge in the demand for data traffic. The proposed system is based on using 4-level pulse amplitude modulation (PAM-4) signals in addition to polarization division multiplexing and orbital angular momentum (OAM) multiplexing schemes. To reduce spectrum usage, only a single wavelength of 1550 nm is used. A 400 Gbps is transmitted over two polarization states and eight OAM beams. The signal on each polarization carries the data of four different OAM beams ( LG 0 0 , LG 15 0 , LG 40 0 , and LG 80 0 ). The performance of the proposed model is examined under the influence of light haze, medium haze, heavy haze, low fog, medium fog, heavy fog, wet Snowfall, dry snowfall, light dust storms, medium dust storms, and heavy dust storms. The maximum transmission range and the Bit Error Rate (BER) are used for evaluating the system performance. The simulation results reported the longest FSO link with ranges from 800 to 1475 m under haze conditions. A slight decrease in these ranges is achieved under fog conditions (ranges from 450 to 859 m). The FSO highest attenuation is experienced for HDS case, which results in the shortest range of 73 m. These ranges are calculated at BER Forward Error Correction limit of 3.8 × 10–3. Subsequently, the proposed system can be used for the next 6G applications due to its simplicity in design and high transmission capacity. [ABSTRACT FROM AUTHOR]

Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security. [ABSTRACT FROM AUTHOR]

In the context of workflows, scheduling involves the systematic arrangement of tasks, allocation of resources, and coordination of dependencies to achieve desired outcomes. Effective scheduling is paramount in cloud–fog computing environments to harness the full potential of distributed resources while meeting application requirements. However, finding the most efficient solution often involves the quest for an optimal algorithm. An optimal algorithm is one that produces the best possible outcome in terms of a specified objective. While metaheuristic algorithms have been proposed by several researchers to address this issue, the solutions often converge at the local level, limiting their effectiveness. This study proposes the hybrid particle whale optimization algorithm as a solution for workflow scheduling problems. It is a combination of particle swarm optimization (PSO) and the whale optimization algorithm (WOA). By integrating the features of both algorithms, this algorithm tries to avoid becoming trapped in local optima. This algorithm aims to minimize the total execution cost, makespan, and energy consumption of tasks in a cloud–fog computing environment. The performance of the proposed algorithm is compared with that of the round robin, ant colony optimization, PSO, and WOA algorithms using five different scientific workflows, namely, the Inspiral, Montage, Epigenomics, Cybershake, and Sipht algorithms, which deploy various numbers of tasks and VMs. The proposed algorithm outperformed the other existing scheduling strategies. Moreover, the approach provides users with the flexibility to customize the scheduling strategy based on their preferences. Overall, this paper presents a novel approach for workflow scheduling in cloud–fog computing environments that improves the performance and efficiency of applications while also providing users with greater control over the scheduling process. [ABSTRACT FROM AUTHOR]

Fog processing has a significant impact on sulfur chemistry in the atmosphere. This study analyzed three winter fog events in Hong Kong using single‐particle aerosol mass spectrometry (SPAMS) and a Monitor for AeRosols and GAses in ambient air (MARGA). Black carbon (BC)‐related carbonaceous particles with substantial sulfate amounts comprised the largest particle number fraction (56.7%). Sulfate mass concentration decreased during fog due to the cloud's effective scavenging, but fog processing notably promoted sulfate formation at the single‐particle level (average peak area increases of 31.2%). Hydroxymethanesulfonate (HMS), an important S(IV) compound and fog tracer, was identified accounting for up to 12% by particle number fraction. Although pH showed a positive correlation (r = 0.53–0.69) with HMS particles in each fog scenario, a negative overall correlation (r = −0.51) was observed. Further analysis revealed that the higher aerosol acidity (pH 0.65–3.11), promoted Fe dissolution, leading to 49% of HMS particles being mixed with Fe, which potentially facilitated sulfate formation via the Fenton reaction. Additionally, around 40% of HMS‐Fe particles are mixed with oxalate, thereby warranting further attention for their potential to cause more intricate sulfur oxidation processes. This study reveals the initial identification of a high mixed‐state of HMS‐Fe, which could potentially serve as a crucial avenue for the formation of sulfate on individual particulate matter. Considering the persistent augmentation of aerosol acidity in the Asian region, this phenomenon necessitates further investigation and attention. Plain Language Summary: Cloud and fog processes are crucial for atmospheric chemistry and important pathways in sulfate formation. However, current models often underestimate sulfate production in the atmosphere. While several potential pathways have been proposed, the influence of organic compounds on hydroxymethanesulfonate (HMS) formation and its subsequent sulfate generation is yet to be fully explored. This study, utilizing a single‐particle perspective, reveals that higher aerosol acidity in coastal areas can lead to the dissolution of iron (Fe) in coastal aerosols, thereby promoting the formation of HMS‐Fe. Additionally, as a transition metal, Fe has the potential to catalyze the conversion of HMS to sulfates. This finding challenges the prevailing notion that HMS hinders sulfate production by being less susceptible to oxidation by hydrogen peroxide (H2O2) and other radicals. The significance of this process is particularly pronounced in coastal or remote marine atmospheres, where cloud and fog processes exhibit higher aerosol acidity and limited ammonia sources. Furthermore, considering the current global scenario of increased aerosol acidity, the impact of transition metals on sulfate formation in HMS is likely to be more prominent. Consequently, this phenomenon necessitates further investigation and attention. [ABSTRACT FROM AUTHOR]

Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks. [ABSTRACT FROM AUTHOR]

Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as daytime scenes, and cannot be applied to low-light scenes. Due to the insufficient illumination at night and the differences in polarization characteristics between it and sunlight, polarization images captured in a low-light environment can suffer from loss of polarization and intensity information. Therefore, this paper proposes a two-stage low-light image dehazing method based on polarization. We firstly construct a polarization-based low-light enhancement module to remove noise interference in polarization images and improve image brightness. Then, we design a low-light polarization dehazing module, which combines the polarization characteristics of the scene and objects to remove fog, thereby restoring the intensity and polarization information of the scene and improving image contrast. For network training, we generate a simulation dataset for low-light polarization dehazing. We also collect a low-light polarization hazy dataset to test the performance of our method. Experimental results indicate that our proposed method can achieve the best dehazing effect. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement: The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes. [ABSTRACT FROM AUTHOR]

This study aimed to improve the daytime fog detection algorithm GK2A_HR_FDA using the GEO-KOMPSAT-2A (GK2A) satellite by increasing the resolution (2 km to 500 m), improving predicted surface temperature by the numerical model, and optimizing some threshold values. GK2A_HR_FDA uses numerical model prediction temperature to distinguish between fog and low clouds and evaluates the fog detection level using ground observation visibility data. To correct the errors of the numerical model prediction temperature, a dynamic bias correction (DBC) technique was developed that reflects the geographic location, time, and altitude in real time. As the numerical model prediction temperature was significantly improved after DBC application, the fog detection level improved (FAR: −0.02–−0.06; bias: −0.07–−0.23) regardless of the training and validation cases and validation method. In most cases, the fog detection level was improved due to DBC and threshold adjustment. Still, the detection level was abnormally low in some cases due to background reflectance problems caused by cloud shadow effects and navigation errors. As a result of removing navigation errors and cloud shadow effects, the fog detection level was greatly improved. Therefore, it is necessary to improve navigation accuracy and develop removal techniques for cloud shadows to improve fog detection levels. [ABSTRACT FROM AUTHOR]

Cloud forests are unique biomes that thrive in foggy environments for a substantial part of the season. Fog in cloud forests plays two critical roles: it reduces incoming radiation and creates a humid environment, leading to the wetting of the canopy. This paper aims to investigate the combined effect of both radiation and wetness on Myrica faya Wilbur—a cloud forest species present in subtropical regions—both directly in plants and through simulations. Experiments consisted of a controlled environment with two levels of radiation and leaf wetness: low radiation/wet conditions, and high radiation/no-wetness; and three treatments: continuous low radiation and wetness, continuous high radiation and no wetness and alternate high low radiation and alternate wetness. The results revealed that a combination of low radiation and leaf wetness significantly improves leaf stomata conductance and increases the specific leaf area (SLA). Changes in SLA were driven by leaf size changes. However, the minimum leaf conductance (gmin) did not respond to any of the treatments. The simulations focused on exploring the impact of radiation and canopy wetness on transpiration efficiency (TE), i.e. the ratio between photosynthesis (An) and transpiration (Tc). The simulations demonstrated that TE increased exponentially as the canopy was gradually wetted, regardless of the radiation environment. This increase in TE results from Tc approaching zero while An maintains positive values. Overall, this study provides an integrated understanding of how fog alters M. faya functioning and, potentially, other cloud forest tree species. [ABSTRACT FROM AUTHOR]

This article has three intentions. It starts with the name "Mudfog" as this appears in Dickens, and as the combination of mud and fog – why these? – runs throughout Dickens's prose and his settings, and his interests. It gives particular attention here to Bleak House and Little Dorrit and Great Expectations. Second, it is interested in thinking of mud and fog not as "symbols," but as modes of thinking (definable as allegorical), and here it pursues Dickens's reading of Shakespeare by looking at the uses of mud and fog as they accumulate from Shakespeare's plays. It attempts to read Dickens not as merely drawing on Shakespeare but as enabling a reading of Shakespeare in turn – here discussion of Hamlet becomes crucial for a reading of Bleak House. [ABSTRACT FROM AUTHOR]

This study examines Free Space Optical (FSO) communication's performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC's fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas. [ABSTRACT FROM AUTHOR]

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann–Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)—impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality. [ABSTRACT FROM AUTHOR]

The present research aimed to measure the degree of connectivity and create a map of the ecological connectivity that highlights the real or potential presence of green, ecological, or ecotourism circuits integrating the green infrastructure of San Juan de Lurigancho and the Mangomarca hills using graph theory applications implemented in the Graphab 2.8 software. Mangomarca and Huiracocha Park were selected for this study. In terms of the methodology, a simple approach based on landscape metrics, which are easy to interpret, was proposed to measure the connectivity of the mosaic of patches in the designated area. The IndiFrag software was used to obtain landscape metrics for the structural connectivity analysis. The Graphab software was employed for the functional connectivity analysis. Both tools proved effective in identifying vegetation gaps or the intensity of the greenery. Landsat 8 images from 8 July 2021 and 4 October 2021 were selected for this research due to the lower amount of cloud cover. Concerning the structural connectivity, the TMCl (patch size), NobCl (number of patches), and PerimCl (perimeter) metrics were effective in distinguishing the mosaic of urban landscape patches from the hill landscape. These indices confirm that the urban landscape patches have a higher number of fragments but are smaller in size compared to the hill landscape. Regarding the functional connectivity, it is evident that the patches are connected at lower-cost distances, averaging 7 cost units (210 m) during the wet season and 23 cost units (410 m) during the less humid season. However, these distances are too extensive and do not form ecological corridors. A survey of the population's perception of the maximum separation distances between patches of vegetation cover that could still be considered a green corridor was included. The results indicate that a third of the sample (36%) prefer to walk down a hallway with a maximum separation distance of 10 m, while almost two-thirds (68%) would prefer a maximum separation distance of 50 m. Therefore, city planning should consider actions to reduce these distances and enable ecological connectivity in the area. It is recommended to continue researching the functional connectivity and determining the green corridors in the city to establish monitoring guidelines for the ecological connectivity of the city. [ABSTRACT FROM AUTHOR]

During boreal summer, prevailing southerlies traverse the sharp sea surface temperature (SST) front in the northwest Pacific (NWP) Ocean, creating a stable air–sea interface characterized by surface air temperature (SAT) higher than SST, which promotes the frequent occurrence of advection fog. However, long-term shipborne observations reveal that during episodes of advection fog, SAT usually decreases below SST, with a peak relative frequency (∼ 34.5 %) in all fog observations before sunrise and a minimum relative frequency (∼ 18.8 %) before sunset. From a Lagrangian perspective, this study employs a turbulence-closure large-eddy simulation (LES) model to trace a fog column across the SST front and investigates how SAT drops below SST during an advection fog event. The LES model, incorporating constant solar radiation, successfully simulates the evolution of advection fog and the negative difference between SAT and SST. Simulation results show that once the near-surface air condenses, thermal turbulence is generated by strong longwave radiation cooling (LWC) at the fog top. The influence of LWC on the fog layer surpasses the cooling effect of the near-surface mechanical turbulence ∼ 2 h after the fog formation while the fog column is still positioned over the SST front. When the fog column arrives at the cold flank of the SST front, the top-down-developing mixed layer induced by the LWC reaches the surface, causing SAT to drop below SST. The LES model with diurnal solar radiation successfully simulates the observational diurnal variation in SAT and SST (SAT-SST) during the fog event, suggesting that the model captures the essential processes responsible for negative SAT-SST. This study highlights the significance of fog-top cooling and its associated thermal turbulence in the evolution of advection fog. Given the challenges faced by numerical weather prediction models in forecasting sea fog, our findings suggest that observations of negative SAT-SST during advection fog episodes present an opportunity to enhance the performance of these models in simulating the thermal turbulence induced by the LWC at the fog top. [ABSTRACT FROM AUTHOR]

Winter fog and severe aerosol loading in the boundary layer over northern India, particularly in the Indo-Gangetic Plain (IGP), disrupt the daily lives of millions of people in the region. To better understand the role of aerosol–radiation (AR) feedback on the occurrence, spatial extent, and persistence of winter fog, as well as the associated aqueous chemistry in fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). While WRF-Chem was able to represent the fog formation for the 23–24 December 2017 fog event over the central IGP in comparison to station and satellite observations, the model underestimated PM 2.5 concentrations compared to the Central Pollution Control Board (CPCB) of India monitoring network. While evaluating aerosol composition for fog events in the IGP, we found that the WRF-Chem aerosol composition was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with secondary aerosols, particularly the chloride aerosol fraction, being strongly underpredicted (∼ 66.6 %). Missing emission sources (e.g., industry and residential burning of cow dung and trash) and aerosol and chemistry processes need to be investigated to improve model–observation agreement. By investigating a fog event on 23–24 December 2017 over the central IGP, we found that the aerosol–radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM 2.5 concentrations and relative humidity (RH) within the boundary layer. Factors affecting the feedback include loss of aerosols through deposition of cloud droplets and internal mixing of absorbing and scattering aerosols. Aqueous-phase chemistry increases the PM 2.5 concentrations, which subsequently affect the aerosol–radiation feedback by both increased mass concentrations and aerosol sizes. With aerosol–radiation interaction and aqueous-phase chemistry, fog formation began 1–2 h earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. The increase in RH in both experiments was found to be important for fog formation as it promoted the growth of aerosol size through water uptake, increasing the fog water content over the IGP. The results from this study suggest that the aerosol–radiation feedback and secondary aerosol formation play an important role in the air quality and the intensity and lifetime of fog over the IGP, yet other feedbacks, such as aerosol–cloud interactions, need to be quantified. [ABSTRACT FROM AUTHOR]

Due to the characteristics of high resolution and rich texture information, visible light images are widely used for maritime ship detection. However, these images are susceptible to sea fog and ships of different sizes, which can result in missed detections and false alarms, ultimately resulting in lower detection accuracy. To address these issues, a novel multi‐granularity feature enhancement network, MFENet, which includes a three‐way dehazing module (3WDM) and a multi‐granularity feature enhancement module (MFEM) is proposed. The 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three‐way decisions and FFA‐Net to obtain clear image samples. Additionally, the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super‐resolution reconstruction convolutional neural network to enhance the resolution and semantic representation capability of the feature maps from YOLOv7. Experimental results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Precision metric on two benchmark datasets, achieving 96.28% on the McShips dataset and 97.71% on the SeaShips dataset. [ABSTRACT FROM AUTHOR]

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage. [ABSTRACT FROM AUTHOR]

A micrometeorological fog experiment was carried out in Budapest, Hungary during the winter half year of 2020–2021. The field observation involved (i) standard meteorological and radiosonde measurements; (ii) surface radiation balance and energy budget components, and (iii) ceilometer measurements. 23 fog events occurred during the whole campaign. Foggy events were categorized based on two different methods suggested by Tardif and Rasmussen (2007) and Lin et al. (2022). Using the Present Weather Detector and Visibility sensor (PWD12), duration of foggy periods are approximately shorter (~ 9%) compared to ceilometer measurements. The categorization of fog based on two different methods suggests that duration of radiation fogs is lower compared to that of cloud base lowering (CBL) fogs. The results of analysis of observed data about the longest fog event suggest that (i) it was a radiation fog that developed from the surface upwards with condition of a near neutral temperature profile. Near the surface the turbulent kinetic energy and turbulent momentum fluxes remained smaller than 0.4 m2 s–2 and 0.06 kg m–1 s–2, respectively. In the surface layer the vertical profile of the sensible heat flux was near constant (it changes with height ~ 10%), and during the evolution of the fog, its maximum value was smaller than 25 W m–2, (ii) the dissipation of the fog occurred due to increase of turbulence, (iii) longwave energy budget was close to zero during fog, and a significant increase of virtual potential temperature with height was observed before fog onset. The complete dataset gives an opportunity to quantify local effects, such as tracking the effect of strengthening of wind for modification of stability, surface layer profiles and visibility. Fog formation, development and dissipation are quantified based on the micrometeorological observations performed in suburb area of Budapest, providing a processing algorithm for investigating various fog events for synoptic analysis and for optimization of numerical model parameterizations. [ABSTRACT FROM AUTHOR]