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(1) Background: A significant proportion of cancer survivors report experiencing a cognitive fog that affects their ability to think coherently and quickly, and reason with clarity. This has been referred to as cancer-related cognitive impairment (CRCI). CRCI has extensive impacts on the daily lives of people living with or beyond cancer, including occupational, social, and psychological functioning. Oncology health professionals report feeling under-resourced to effectively assess the needs of an individual with CRCI and then provide optimal care and referral. (2) Methods: The objective of this project is to develop and provide an initial validation of the first purpose-built unmet needs assessment for CRCI: the Unmet Needs Assessment of Cancer-Related Cognitive Impairment Impact (COG-IMPACT). We will use a multiple-stage, co-design, mixed-methods approach to develop and provide an initial validation of the COG-IMPACT. (3) Results: The primary anticipated result of this research is the production of the COG-IMPACT, the first purpose-built unmet needs assessment for CRCI. The assessment could be used by health professionals to understand the unmet needs and facilitate optimal care and referral for cancer survivors, by survivors to elucidate their supportive needs and advocate for their care, and by researchers to examine the correlates of unmet needs relating to CRCI, as well as how best to support people with CRCI.

The Internet of Things (IoT) facilitates the connectivity of billions of physical devices for exchanging information and enabling a wide range of applications. These applications can be presented in the form of dependent tasks, as outlined in a workflow. These workflows face limitations due to constraints in IoT sensors. To address these limitations, cloud computing has emerged to offer a large capacity of computing and storing with a great capability to adjust resources according to the need. However, cloud computing might not adequately meet the low‐latency of IoT workflow requirements when scheduling a workflow composed of IoT tasks due to its centralized nature. Moreover, cloud computing is not ideal for delay‐sensitive workflows and may increase communication costs. In response to these challenges, the use of fog computing as an extension to cloud computing scheme is recommended. Fog computing aims to process workflow tasks close to IoT devices. While fog computing offers various advantages, integrating these systems into workflow scheduling remains one of the most formidable challenges in distributed environments. Indeed, significant issues arise due to the timely execution and the resource limitations. In this survey paper, we present a Systematic Literature Review (SLR) on the current state of the art in this domain. We propose a taxonomy to compare and evaluate the existing studies on workflow scheduling approaches in cloud–fog computing environments. This taxonomy encompasses various criteria, including scheduling techniques, performance metrics, workflow dependencies, scheduling policies, and evaluation tools. We highlight certain recommendations for open issues which require more investigations. Our aim is to provide valuable insights for researchers and developers interested in understanding the contributions and challenges of current workflow scheduling approaches in cloud–fog computing environments. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic has resulted in 762 million infections worldwide from 2020 to date, of which approximately ten percent are suffering from the effects after infection in 2019 (COVID-19) [1, 40]. In Germany, it is now assumed that at least one million people suffer from post-COVID condition with long-term consequences. These have been previously reported in diseases like Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS). Symptoms show a changing variability and recent surveys in the COVID context indicate that 10–30 % of outpatients, 50 to 70% of hospitalised patients suffer from sequelae. Recent data suggest that only 13% of all ill people were completely free of symptoms after recovery [3, 9]. Current hypotheses consider chronic inflammation, mitochondrial dysfunction, latent viral persistence, autoimmunity, changes of the human microbiome or multilocular sequelae in various organ system after infection. Hyperbaric oxygen therapy (HBOT) is applied since 1957 for heart surgery, scuba dive accidents, CO intoxication, air embolisms and infections with anaerobic pathogens. Under hyperbaric pressure, oxygen is physically dissolved in the blood in higher concentrations and reaches levels four times higher than under normobaric oxygen application. Moreover, the alternation of hyperoxia and normoxia induces a variety of processes at the cellular level, which improves oxygen supply in areas of locoregional hypoxia. Numerous target gene effects on new vessel formation, anti-inflammatory and anti-oedematous effects have been demonstrated [74]. The provision of intermittently high, local oxygen concentrations increases repair and regeneration processes and normalises the predominance of hyperinflammation. At present time only one prospective, randomized and placebo-controlled study exists with positive effects on global cognitive function, attention and executive function, psychiatric symptoms and pain interference. In conclusion, up to this date HBO is the only scientifically proven treatment in a prospective randomized controlled trial to be effective for cognitive improvement, regeneration of brain network and improvement of cardiac function. HBOT may have not only theoretical but also potential impact on targets of current pathophysiology of Post COVID condition, which warrants further scientific studies in patients. [ABSTRACT FROM AUTHOR]

Cognitive symptoms (CS) belong to the most common manifestations of the Post COVID-19 (PC) condition. We sought to objectify CS in PC patients using routine diagnostic assessments: neurocognitive testing (NCT) and brain imaging (BI). Further, we investigated possible associations of CS with patient reported outcomes (PROs), and risk factors for developing CS. Clinical data and PROs of 315 PC patients were assessed at a mean of 6 months after SARS-CoV-2 infection. 231 (73.3%) patients reported any sort of CS. Among them, 78 underwent NCT and 55 received BI. In NCT, the cognitive domains most affected were the working memory, attention, and concentration. Nonetheless, pathological thresholds were exceeded only in few cases. Neurocognitive performance did not differ significantly between patients complaining of severe (n = 26) versus non-severe (n = 52) CS. BI findings were abnormal in 8 (14.5%) cases with CS but were most likely not related to PC. Patients reporting high severity of CS scored worse in the PHQ-9, FSS, WHOQOL-BREF, were more likely to report impaired sleep, and had a higher prevalence of psychiatric diagnoses. Overall, NCT could confirm mild impairment in some but not all PC patients with CS, while BI studies were abnormal in only few cases. CS severity did not affect NCT results, but severe CS were associated with symptoms of depression (PHQ-9), fatigue (FSS), reduced quality of life (WHOQOL-BREF) and higher prevalence of psychiatric illnesses. These findings support the importance of NCT, BI, and neuro-psychological assessment in the work-up of PC patients reporting CS. Trial registration: Trial registration number and date of registration: DRKS00030974, 22 Dec 2022, retrospectively registered. [ABSTRACT FROM AUTHOR]

Patients suffering from post-acute sequelae of COVID-19 (PASC) have a higher prevalence of anxiety and depression than the general population. The long-term trajectory of these sequelae is still unfolding. To assess the burden of anxiety and depression among patients presenting to the University of Iowa Hospitals and Clinics (UIHC) post-COVID-19 clinic, we analyzed how patient factors influenced Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scores. In this retrospective cohort study, the GAD-7 and PHQ-9 questionnaire scores of patients presenting to the UIHC post-COVID clinic between March 2021–February 2022 (N = 455) were compared to the scores of a sample of patients presenting to the general internal medicine (GIM) clinic during the same period (N = 94). Our analysis showed that patients with an absent history of depression on their electronic medical record (EMR) problem list scored significantly higher on the GAD-7 (mean difference –1.62, 95% CI –3.12 to –0.12, p = 0.034) and PHQ-9 (mean difference –4.45, 95% CI –5.53 to –3.37, p < 0.001) questionnaires compared to their similar counterparts in the GIM clinic. On the other hand, patients with an absent history of anxiety on their EMR problem list scored significantly higher on the GAD-7 (mean difference –2.90, 95% CI –4.0 to –1.80, p < 0.001) but not on the PHQ-9 questionnaire (p = 0.196). Overall, patients with PASC may have experienced a heavier burden of newly manifest anxiety and depression symptoms compared to patients seen in the GIM clinic. This suggests that the mental health impacts of PASC may be more pronounced in patients with no prior history of anxiety or depression. [ABSTRACT FROM AUTHOR]

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]

It has been more than three years since COVID-19 impacted the lives of millions of people, many of whom suffer from long-term effects known as long-haulers. Notwithstanding multiorgan complaints in long-haulers, signs and symptoms associated with cognitive characteristics commonly known as "brain fog" occur in COVID patients over 50, women, obesity, and asthma at excessive. Brain fog is a set of symptoms that include cognitive impairment, inability to concentrate and multitask, and short-term and long-term memory loss. Of course, brain fog contributes to high levels of anxiety and stress, necessitating an empathetic response to this group of COVID patients. Although the etiology of brain fog in COVID-19 is currently unknown, regarding the mechanisms of pathogenesis, the following hypotheses exist: activation of astrocytes and microglia to release pro-inflammatory cytokines, aggregation of tau protein, and COVID-19 entry in the brain can trigger an autoimmune reaction. There are currently no specific tests to detect brain fog or any specific cognitive rehabilitation methods. However, a healthy lifestyle can help reduce symptoms to some extent, and symptom-based clinical management is also well suited to minimize brain fog side effects in COVID-19 patients. Therefore, this review discusses mechanisms of SARS-CoV-2 pathogenesis that may contribute to brain fog, as well as some approaches to providing therapies that may help COVID-19 patients avoid annoying brain fog symptoms. [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]

The exploitation of mineral resources in Brazil did not have any formal regulation until 1934, when the mining code was established, which is one of the main reasons for the lack of data on Brazilian quarrying, including about Rio de Janeiro city, worldwide recognized for its landscape. After facing environmental issues for decades, most of the city’s quarries have been closed or relocated to neighbouring cities. Many of these quarried rocks were used as cladding and/or crushed stone. One of them is the nepheline syenite, commercially known as Ás de Paus Granite, object of this study, which aimed to determine its main technological and petrographical characteristics and to investigate the stone’s behaviour under the action of sea spray and/or polluted areas by artificial weathering tests. These tests consisted of exposure of the rock to salt fog and sulphur dioxide in climatic test chambers with changes monitored by density, porosity, water absorption, ultrasonic pulse velocity test and spectrophotometry. The results indicated that Ás de Paus Granite is a durable stone as it was not significantly affected by the selected weathering tests, showing nearly imperceptible visual effects, a result of its physical and technological properties. [ABSTRACT FROM AUTHOR]

Coastal fog occurs along many of the world's west coast continental environments. It is particularly consequential during summer when an increased frequency of fog co-occurs with the seasonal dryness characteristic of most west coast climate systems, for example, in the Pacific coast of North and South America, the southwestern African coast, and southern coastal Europe. Understanding coastal fog formation and effects has consequences for many disciplines, including the physical (e.g., atmospheric science, oceanography), biological (e.g., biogeography, ecophysiology), and socio-ecological realms (e.g., Indigenous cultural knowledge, public safety, economics). Although research practices differ across disciplines, they share many of the challenges needed to advance fog science. For example, coastal fog remains difficult to reliably monitor when, where, and why it occurs, which adds difficulty to understanding fog's effects on all facets of the integrated coastal system. These shared challenges provide ripe opportunities for interdisciplinary collaboration, a template with past success in advancing fog-related science that can continue to have success in the future. In this perspectives review, we summarize the current status and frontiers of fog-related science from multiple disciplines, leveraging examples primarily drawn from the Pacific Northwest coastal region of the United States to show how interdisciplinary collaboration is needed to continue to advance our collective understanding of coastal fog formation and effects on west coast environments. [ABSTRACT FROM AUTHOR]

AbstractThe term “brain fog” has emerged from the observations of neuropsychiatric conditions present in post-COVID-19 infections. This is characterized by concentration and memory problems, selective attention disorders and difficulties in executive functions, yet it is unclear how long these deficits may persist and which cognitive functions are most vulnerable. Therefore, there is a need to properly evaluate these cognitive complaints using an assessment tool that specifies their intensity and nature. Our primary objective was to explore subjective perceptions of cognitive functioning in COVID-19-associated with brain fog using a tool that was previously validated for assessing subjective cognitive complaints. A total of 68 participants were recruited and the Subjective Scale to Investigate Cognition (SSTICS) was used to assess cognitive complaints. This was the first time that the SSTICS was used for this purpose in subjects with COVID-19. In addition, participants were administered a questionnaire assessing for the presence of various symptoms, as well as COVID-19 clinical parameters. The neuropsychological basis for the construct of the SSTICS was related to the cognitive complaints expressed by participants. A reliability analysis of our sample indicated a high degree of internal consistency (Cronbach’s alpha= 0.951). Associations between various SSTICS scores and COVID-related symptomatology and the differences between group of participants who reported cognitive complaints (“complainers”) and those who did not were assessed. We performed an exploratory factorial analysis based on Principal Component Analysis (PCA). Based on their distribution, participants were grouped into: “good functioning” - scores 0-9 (35.3%); “medium functioning” - scores 14-23 (25%); and “poor functioning” - scores 26-71 (39.7%). The mean SSTICS score was 20.59 (SD 16.61) and correlated with the quarantine duration and loss of smell. Complainers differed significantly from non-complainers in the total number of symptoms, the quarantine duration and the presence/absence of specific symptoms, such as loss of smell, tiredness and aches/pains. Our study showed that >10% of patients reported subjective cognitive complaints following COVID-19, with most reporting mild or serious cognitive complaints, mostly within the domains of memory, attention, language, executive functioning or praxis. [ABSTRACT FROM AUTHOR]

Workflow scheduling poses a significant challenge due to the large scale of workflows and heterogeneity of algorithms in cloud-fog environment. Also, these nature-inspired algorithms have limitations, such as being trapped in local optima, leading to failure in achieving a global optimal solution. Since, workflow scheduling is used to enhance the optimization of task allocation and execution within cloud-fog computing. Therefore, various metaheuristic algorithms have been recommended by researchers for addressing scheduling problems. Our paper used a hybrid algorithm "particle swarm optimization-whale optimization algorithm" (PSO-WOA) to overcome the abovementioned issues. When applied individually, the PSO becomes trapped in the global optimum due to the presence of a high-dimensional search space, while the WOA, which lacks flexibility in search spacing, becomes trapped in local optima. The proposed PSO-WOA algorithm overcomes the constrained exploration of search spaces observed in the population-based WOA algorithm and efficiently mitigates the challenge of becoming trapped in local optima. The simulation was performed using the WorkflowSim toolkit, and the results were tested for several test cases on five scientific workflows with varying numbers of tasks and iterations. The results illustrate that the hybrid PSO-WOA approach produced superior outcomes when compared with state-of-the-art algorithms (ACO, PSO, WOA, GA and BH-FWA) for both total execution cost (TEC) and total execution time (TET). After analysis of results, two workflows i.e., montage and epigenomics stood apart from others in terms of time and cost. Statistical analysis was done with Wilcoxon and Friedman tests and were found to be significant. In summary, this study provides a comparative analysis of enhancing both TET and TEC in a hybrid algorithm along with its impact on performance metrics. [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]

Fog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley. Plain Language Summary: Fog and low stratus clouds (FLS) are influenced by various drivers in the atmosphere and near the ground. Here, the impact of the large‐scale weather situation and aerosols is analyzed over the Po valley in northern Italy. Using reanalysis and satellite data we find that FLS events driven by nighttime cooling under stable conditions can persist longer than FLS events formed as a result of moisture transport. Investigating ground‐based observations of aerosols, particles on which moisture can condensate and fog and cloud droplets form, we find that FLS event duration is higher when a higher amount of aerosols is present. These results can help when predicting the duration of FLS events, which is particularly important for traffic safety. Key Points: Fog and low stratus (FLS) cloud formation in the Po valley is primarily controlled by radiative processes or moisture advectionFLS persistence from the satellite perspective is highest for radiatively formed FLS events likely due to a stable boundary layerFLS persistence is significantly higher under high aerosol loading [ABSTRACT FROM AUTHOR]

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas. Plain Language Summary: In Delhi, accurately predicting fog in urban areas is difficult due to complex factors like city layout and infrastructure. This study employed the recently developed WRF‐UACM with detailed UT‐GLOBUS urban morphological parameters for fog simulation. Compared to existing models, WRF‐UACM predicted wind, temperature, and humidity better under both foggy days and clear skies. The model accurately reproduced urban warming and cooling patterns crucial for fog prediction and urban meteorology. WRF‐UACM improves the diurnal variation of winds and reduces temperature‐cold bias after sunset, thus improving fog onset by ∼3 hr. This work highlights the potential of WRF‐UACM for fog prediction and offers invaluable insights for urban meteorology modeling. Key Points: The new multilayer WRF‐UACM, explicitly incorporating urban physics and morphology, is implemented to simulate fog over the Delhi regionImprovements in predicting urban wind, temperature, and relative humidity are demonstrated using UACM during fog events and clear skiesThe abilities of the novel UACM in capturing urban fog phenomena and its operational mode capabilities over the Delhi region are examined [ABSTRACT FROM AUTHOR]

This paper presents the results of an evaluation of corrosion properties of PEO pretreated AlMg3 aluminum alloy samples with polymer coatings obtained by dip-coating in a suspension of superdispersed polytetrafluoroethylene (SPTFE) in a solution of polyvinylidene fluoride (PVDF) in N-methyl-2-pyrrolidone at different PVDF:SPTFE ratios (1:1, 1:3, 1:5, and 1:10). The electrochemical tests showed that samples with a coating formed at a ratio of PVDF to SPTFE of 1:5 possessed the best corrosion properties. The corrosion current density of these samples was more than five orders of magnitude lower than this parameter for bare aluminum alloy. During the 40-day salt spray test (SST) for samples prepared in a suspension at a PVDF:SPTFE ratio of 1:1–1:5, the formation of any pittings or defects was not detected. The PVDF:SPTFE 1:5 sample demonstrated, as a result of the 40-day SST, an increase in corrosion current density of less than an order of magnitude. The evolution of the protective properties of the studied samples was assessed by a two-year field atmospheric corrosion test on the coast of the Sea of Japan. It was revealed that the samples with the PVDF:SPTFE 1:5 coating had electrochemical parameters that remained consistently high throughout the one year of exposure. After this period, the polymer layer was destroyed, which led to a deterioration in the protective characteristics of the coatings. [ABSTRACT FROM AUTHOR]

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]

Cloud microphysics fulfills a fundamental role in the formation and evolution of marine fog, but it is not fully understood. Numerous studies have addressed this by means of direct observations and modeling efforts. However, collision–coalescence of aerosols and fog droplets is a process often neglected. In this study we perform an analysis of the role of particle collections on the formation, development, and microphysical structure of marine fog. It was found that collisions open a path for aerosol activation by means of collisional activation. In addition, collisions contribute to the diffusional activation of fog particles by adding water mass to the growing aerosols, making them reach the required critical radius faster. Furthermore, collisions have a homogenizing effect on hygroscopicity, facilitating the activation of accumulation‐mode aerosols by increasing their diffusional growth. [ABSTRACT FROM AUTHOR]

In this work, we explore the intricacies of the potential‐temperature variance budget in coastal fog. We propose an improvement to the theoretical framework of the budget, whereby we include the heat exchange due to water‐phase changes. We then show this framework's consistency with a real‐world case study from the Coastal Fog (C‐FOG) Research Program. Results show that the presence of intermittent energy bursts is driven by the sudden turbulent injection of heat into the environment caused by the condensation of water vapour, and the improved theoretical framework proves satisfactory in detailing the observed process. The heat excess is transported vertically, creating a two‐term balance of high‐order moments. A bulk parametrization of this balance is also proposed to provide a simplified representation of the phase‐change process and suggest that it could be used for operational purposes. Finally, the length‐scales of the processes are evaluated from the parametrizations. The analysis indicates that the scales of the phase change of water vapour are consistent with the buoyancy production and Taylor scales. [ABSTRACT FROM AUTHOR]

Cold fog refers to a type of fog that forms when the temperature is below 0°C. It can be composed of liquid, ice, and mixed‐phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small‐scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near‐surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above‐freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m2·s−2), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high‐resolution (˜400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold‐fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold‐fog events. At the same time, they are also critical fields for the NWP model validation. [ABSTRACT FROM AUTHOR]

The work presented in this article studies the impact of cloud condensation nuclei (CCN) activation for fog forecasting and improves its parameterization in the LIMA (Liquid, Ice, Multiple Aerosols) two‐moment microphysical scheme, building upon the Local And Non‐local Fog EXperiment (LANFEX) field campaign observations, specifically the intensive observation period (IOP) 1 and the DEMISTIFY intercomparison. Large‐eddy simulations were performed with the Meso‐NH model, first using a prognostic supersaturation allowing us to compute the number of activated CCN at each time step, and then with the usual saturation adjustment hypothesis and a diagnostic maximum supersaturation. The prognostic supersaturation method provided very good results, similar to those from earlier simulations of this case using a bin scheme, and was thus used as a reference. In contrast, the diagnostic maximum supersaturation method strongly overestimated droplet numbers and produced a too‐thick fog. Thus, improvements to the maximum supersaturation diagnostic were proposed, by (1) revising the temperature tendency and (2) accounting for pre‐existing cloud droplets in the activation parameterization. These improvements resulted in a simulation in good agreement with observations and the reference simulation, and are promising for use in numerical weather prediction systems with a lower resolution and/or a longer time step. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the ongoing scientific debate regarding the existence, causes, characteristics and reversibility of cognitive sequelae associated with COVID-19 infection. The aim of the study is to describe the neuropsychological profile of individuals diagnosed with COVID-19, distinguishing between younger (<65 years) and older (≥65 years) adults and evaluating them at baseline and at 3 and 6 months after infection. Significant differences in cognitive performance between younger and older adults are observed, consistent with their different physiological conditions. However, distinct memory recall patterns not attributable to physiological differences are also observed. The results contribute to the understanding of COVID-19 cognitive sequelae, suggesting that cognitive deficits in COVID-19 survivors may primarily reflect difficulties in attention and concentration impacting retrieval processes, aligning with the concept of "brain fog" associated with post-COVID-19 syndrome. The literature on COVID-19 continues to increase daily. Cognitive sequelae associated with COVID-19 infection still draw the attention of the scientific community given the lack of consensus about their existence, etiology, characterization and reversibility. The aim of this study is to provide a neuropsychological identikit for younger (<65 years) and older (≥65 years) individuals diagnosed with COVID-19 infection, at baseline and after 3 and 6 months. In total, 226 individuals took part in a retrospective observational study and their cognitive performance was compared across groups (younger adults vs. older adults) and time (T0, T1, T2). The results highlighted differences between younger and older adults in the Montreal Cognitive Assessment (MoCA) global score, as expected in consideration of the different physiological conditions of the two populations. However, memory performance highlighted the two groups as characterized by a difference in patterns of recall that may move beyond a physiological explanation and provide information about COVID-19 cognitive sequelae. This study suggests that cognitive deficits observed in COVID-19 survivors may reflect a difficulty in attention and concentration that interferes mainly with retrieval processes. This result fits well with the concept of "brain fog" typical of post-COVID-19 syndrome and may also reflect the stress experienced while facing the pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Many patients report neuropsychiatric symptoms after SARS‐CoV‐2 infection. Data on prevalence of post‐COVID‐19 condition (PCC) vary due to the lack of specific diagnostic criteria, the report of unspecific symptoms, and reliable biomarkers. Methods: PCC patients seen in a neurological outpatient department were followed for up to 18 months. Neurological examination, SARS‐CoV‐2 antibodies, Epstein–Barr virus antibodies, and cortisol levels as possible biomarkers, questionnaires to evaluate neuropsychiatric symptoms and somatization (Patient Health Questionnaires D [PHQ‐D]), cognition deficits (Montreal Cognitive Assessment [MoCA]), sleep disorders (ISS, Epworth Sleepiness Scale [ESS]), and fatigue (FSS) were included. Results: A total of 175 consecutive patients (78% females, median age 42 years) were seen between May 2021 and February 2023. Fatigue, subjective stress intolerance, and subjective cognitive deficits were the most common symptoms. Specific scores were positive for fatigue, insomnia, and sleepiness and were present in 95%, 62.1%, and 44.0%, respectively. Cognitive deficits were found in 2.3%. Signs of somatization were identified in 61%, who also had an average of two symptoms more than patients without somatization. Overall, 28% had a psychiatric disorder, including depression and anxiety. At the second visit (n = 92), fatigue (67.3%) and insomnia (45.5%) had decreased. At visit three (n = 43), symptom load had decreased in 76.8%; overall, 51.2% of patients were symptom‐free. Biomarker testing did not confirm an anti‐EBV response. SARS‐CoV‐2‐specific immune reactions increased over time, and cortisol levels were within the physiological range. Conclusion: Despite high initial symptom load, 76.8% improved over time. The prevalence of somatization and psychiatric disorders was high. Our data do not confirm the role of previously suggested biomarkers in PCC patients. [ABSTRACT FROM AUTHOR]

Embryologically, the left brachiocephalic vein (LBV) originates as an anastomotic channel between the right and left anterior cardinal veins. This positions the LBV between the manubrium sterni anteriorly and the innominate artery posteriorly. This pattern of adjacency of the aorta to the LBV is unique to mammals and results from a quirk of evolution. With age, the ascending aorta unfolds, elongates and dilates. Simultaneously, there is a change in the thoracic geometry that reduces the thoracic volume primarily from disc height loss and kyphosis. These transitions progressively compress the LBV. Normally, this compression is circumvented via collateral pathways and "Blood finds a way". However, traversing these circuitous pathways comes at a cost and can result in delayed transit times and venous congestion. While it is possible that compression of the LBV in the setting of adequate collateral channels may fail to provoke any pathologic sequelae, we propose a phenomenon in which such compression in the setting of inadequate collateral circulation may lead to a state of pathologic venous congestion. This anatomic anomaly and its associated clinical features, if identified, can offer a new avenue for treatment options for some of the hitherto unexplained neurologic disorders. [ABSTRACT FROM AUTHOR]

As the COVID‐19 pandemic persists, SARS‐CoV‐2 infection is increasingly associated with long‐term neurological side effects including cognitive impairment, fatigue, depression, and anxiety, colloquially known as "long‐COVID." While the full extent of long‐COVID neuropathology across years or even decades is not yet known, we can perhaps take direction from long‐standing research into other respiratory diseases, such as influenza, that can present with similar long‐term neurological consequences. In this review, we highlight commonalities in the neurological impacts of influenza and COVID‐19. We first focus on the common potential mechanisms underlying neurological sequelae of long‐COVID and influenza, namely (1) viral neurotropism and (2) dysregulated peripheral inflammation. The latter, namely heightened peripheral inflammation leading to central nervous system dysfunction, is emerging as a shared mechanism in various peripheral inflammatory or inflammation‐associated diseases and conditions. We then discuss historical and modern examples of influenza‐ and COVID‐19‐associated cognitive impairment, depression, anxiety, and fatigue, revealing key similarities in their neurological sequelae. Although we are learning that the effects of influenza and COVID differ somewhat in terms of their influence on the brain, as the impacts of long‐COVID grow, such comparisons will likely prove valuable in guiding ongoing research into long‐COVID, and perhaps foreshadow what could be in store for individuals with COVID‐19 and their brain health. [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]

Many coronavirus disease 2019 (COVID‐19) positive individuals exhibit abnormal electroencephalographic (EEG) activity reflecting "brain fog" and mild cognitive impairments even months after the acute phase of infection. Resting‐state EEG abnormalities include EEG slowing (reduced alpha rhythm; increased slow waves) and epileptiform activity. An expert panel conducted a systematic review to present compelling evidence that cognitive deficits due to COVID‐19 and to Alzheimer's disease and related dementia (ADRD) are driven by overlapping pathologies and neurophysiological abnormalities. EEG abnormalities seen in COVID‐19 patients resemble those observed in early stages of neurodegenerative diseases, particularly ADRD. It is proposed that similar EEG abnormalities in Long COVID and ADRD are due to parallel neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. These neurophysiological abnormalities underpinning cognitive decline in COVID‐19 can be detected by routine EEG exams. Future research will explore the value of EEG monitoring of COVID‐19 patients for predicting long‐term outcomes and monitoring efficacy of therapeutic interventions. Highlights: Abnormal intrinsic electrophysiological brain activity, such as slowing of EEG, reduced alpha wave, and epileptiform are characteristic findings in COVID‐19 patients. EEG abnormalities have the potential as neural biomarkers to identify neurological complications at the early stage of the disease, to assist clinical assessment, and to assess cognitive decline risk in Long COVID patients.Similar slowing of intrinsic brain activity to that of COVID‐19 patients is typically seen in patients with mild cognitive impairments, ADRD. Evidence presented supports the idea that cognitive deficits in Long COVID and ADRD are driven by overlapping neurophysiological abnormalities resulting, at least in part, from neuroinflammatory mechanisms and astrocyte reactivity.Identifying common biological mechanisms in Long COVID‐19 and ADRD can highlight critical pathologies underlying brain disorders and cognitive decline. It elucidates research questions regarding cognitive EEG and mild cognitive impairment in Long COVID that have not yet been adequately investigated. [ABSTRACT FROM AUTHOR]

To guarantee the reliability of Tactile Internet (TI) applications such as telesurgery, which demand extremely high reliability and are experiencing rapid expansion, we propose a novel smart resilience mechanism for Next-Generation Ethernet Passive Optical Networks (NG-EPONs). Our architecture integrates Artificial Intelligence (AI) and Software-Defined Networking (SDN)-Enabled Broadband Access (SEBA) platform to proactively enhance network reliability and performance. By harnessing the AI's capabilities, our system automatically detects and localizes fiber faults, establishing backup communication links using Radio Frequency over Glass (RFoG) to prevent service disruptions. This empowers NG-EPONs to maintain uninterrupted, high-quality network service even in the face of unexpected failures, meeting the stringent Quality-of-Service (QoS) requirements of critical TI applications. Our AI model, rigorously validated through 5-fold cross-validation, boasts an average accuracy of 81.49%, with a precision of 84.33%, recall of 78.18%, and F1-score of 81.00%, demonstrating its robust performance in fault detection and prediction. The AI model triggers immediate corrective actions through the SDN controller. Simulation results confirm the efficacy of our proposed mechanism in terms of delay, system throughputs and packet drop rate, and bandwidth waste, ultimately ensuring the delivery of high-quality network services. [ABSTRACT FROM AUTHOR]

Abstract With the ability of servers to remotely control and manage a mobile robot, mobile robots are becoming more widespread as a form of remote communication and human-robot interaction. Controlling these robots, however, can be challenging because of their power consumption, delays, or the challenge of selecting the right robot for a certain task. This paper introduces a novel methodology for enhancing the efficacy of a mobile robotic network. The key two contributions of our suggested methodology are: I: A recommended strategy that eliminates the unwieldy robots before selecting the ideal robot to satisfy the task. II: A suggested procedure that uses a fuzzy algorithm to schedule the robots that need to be recharged. Since multiple robots may need to be recharged at once, this process aims to manage and control the recharging of robots in order to avoid conflicts or crowding. The suggested approach aims to preserve the charging capacity, physical resources (e.g. Hardware components), and battery life of the robots by loading the application onto a remote server node instead of individual robots. Furthermore, our solution makes use of fog servers to speed up data transfers between smart devices and the cloud, it is also used to move processing from remote cloud servers closer to the robots, improving on-site access to location-based services and real-time interaction. Simulation results showed that, our method achieved a 2.4% improvement in average accuracy and a 2.2% enhancement in average power usage over the most recent methods in the same comparable settings.

NWP (numerical weather prediction) and statistical methods still have limitations in forecasting low visibility. Therefore, enhancing nowcasting techniques is crucial for ensuring the safety of daily life and industrial activities. A short-term visibility forecast model in Jiangsu Province PhyDNet-ATT-VIS is established based on PhyDNet-ATT (physical dynamics network with attention) deep learning method, using high spatial and temporal resolution ground observations and NWP data. Ground observations contain 1 min average horizontal visibility from automatic weather stations in Jiangsu Province, as well as its upstream regions of Anhui Province, Henan Province, and Shandong Province. NWP data are derived from PWAFS (Precision Weather Analysis and Forecast System) gridded meteorological forecast data. Temporal resolution and spatial resolutions of PWAFS forecast products are 1 h and 3 km, respectively. Due to PhyDNet-ATT-VIS's outstanding ability to handle nonlinear problems, visibility forecast from 6 h to 18 h with a spatial resolution of 3 km and a temporal resolution of 1 h is achieved, and model results are tested and evaluated. The visibility forecasting product of ECMWF (European Centre for Medium-Range Weather Forecasts) is selected to evaluate and compare the forecasting skills with that of PhyDNet-ATT-VIS. The initial forecast time of ECMWF is 0800 BT and 2000 BT every day, with the time interval of 3 h and the spatial resolution of 0.125°×0.125°. Compared to ECMWF visibility product, the rootmean square error (RMSE) and mean absolute error (MAE) of PhyDNet-ATT-VIS are reduced by 201% and 310%, respectively. Across different visibility levels, the probability of detection (POD) significantly improves, while the false alarm ratio (FAR) substantially decreases. Threat score (TS) of the forecast demonstrates a clear advantage, although the model's ability to predict low visibility (defined as visibility less than 0.2 km) in 15 h to 18 h range still requires further improvement. In terms of spatial distribution, the forecast error for visibility in lake and coastal areas is significantly lower than that in other regions for NWP. For PhyDNet-ATT-VIS, the error in areas with dense observation sites is significantly lower than that in regions with sparse observation sites. Compared to ECMWF model, PhyDNet-ATT-VIS can more accurately predict key characteristic parameters, such as affected areas, intensity, onset, and dissipation, in both regional and local fog processes. This study can provide a reliable and operationally replicable reference for improving short-term forecasting skills in visibility.

Based on the observation data from the coastal island stations in Beibu Gulf and the fifth generation ECMWF reanalysis (ERA5) data, the probability of occurrence of mixed-type (that is, high-pressure rear type to southwest inverted trough type) sea fog processes in Beibu Gulf is analyzed. Furthermore, the typical mixed-type sea fog process from 15 March to 23 March 2016 is selected for in-depth analysis. The results show the following: (1) Compared with other types of sea fog processes, the probability of occurrence of the mixed-type sea fog processes was the highest during 2015-2020, with a cumulative number of 15 times (58 days), accounting for 34.9% (39.5%). (2) The typical mixed-type sea fog process from 15 March to 23 March 2016 was characterised by long duration and wide range. In the early stage, the Beibu Gulf was influenced by the easterly flow behind the high pressure entering the sea. In the later period, the plateau trough developed and moved eastward, which guided the warm and humid south-western airflow to expand inland, and the easterly airflow gradually turned to the southerly airflow at the edge of the south-west inverted trough in Beibu Gulf. (3) In terms of thermal conditions, the strong warm and humid transport of the low-level jet stream increased the specific humidity over the Beibu Gulf, and then the water vapour saturated layer extended from the near-surface layer to 800 hPa on the one hand. It also contributed to the establishment of the temperature inversion layer on the other hand. And the inversion layer cooperated with the high humidity region below to reach a stable state. As for the dynamical conditions, the wind shear in the vertical direction maintained a certain intensity, which was favourable to the turbulent mixing in the boundary layer. The cooperation of the wind shear with the baroclinic atmosphere was the configuration that favoured the maintenance of a stable layer in the low-layer. In addition, the weak upward motion below 800 hPa was conducive to water vapour uplift and condensation in the low-layer, which promoted the development and maintenance of the sea fog. (4) During the occurrence of the typical mixed-type sea fog, the air temperature was mostly higher than the sea surface temperature in each sea area. And the difference between the air temperature and sea surface temperature in the early stage (late stage) was between 0 and 1.5 ℃ (1.5 and 2.5 ℃).

Embryologically, the left brachiocephalic vein (LBV) originates as an anastomotic channel between the right and left anterior cardinal veins. This positions the LBV between the manubrium sterni anteriorly and the innominate artery posteriorly. This pattern of adjacency of the aorta to the LBV is unique to mammals and results from a quirk of evolution. With age, the ascending aorta unfolds, elongates and dilates. Simultaneously, there is a change in the thoracic geometry that reduces the thoracic volume primarily from disc height loss and kyphosis. These transitions progressively compress the LBV. Normally, this compression is circumvented via collateral pathways and “Blood finds a way”. However, traversing these circuitous pathways comes at a cost and can result in delayed transit times and venous congestion. While it is possible that compression of the LBV in the setting of adequate collateral channels may fail to provoke any pathologic sequelae, we propose a phenomenon in which such compression in the setting of inadequate collateral circulation may lead to a state of pathologic venous congestion. This anatomic anomaly and its associated clinical features, if identified, can offer a new avenue for treatment options for some of the hitherto unexplained neurologic disorders.

The presence of fog in offshore regions poses significant hazards to navigation and aviation, making fog nowcasting indispensable for various industries, including oil and gas. This study presented a novel approach utilizing Recurrent Neural Networks (RNN) within a deep learning framework to address this need. Leveraging geostationary GOES-16 satellite data from the summers of 2018 and 2019, fog maps were generated as input. The model incorporated Convolutional Long Short-Term Memory (ConvLSTM) layers and was trained with a unique loss function combining Minimum Squared Error (MSE) and structural DISSIMilarity (DSSIM) metrics. Validation results demonstrated an approximate 60% accuracy for both two-hour and three-hour nowcasting. Furthermore, evaluation against in-situ data from an offshore platform revealed a Probability of Detection (PoD) of 0.75 and False Alarm Rate (FAR) of 0.14 for two-hour nowcasting, PoD of 0.75 and FAR of 0.20 for three-hour nowcasting, and PoD of 0.70 and FAR of 0.20 for six-hour nowcasting. These findings suggested the operational viability of the proposed method for short-term fog forecasting in offshore environments.

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.

In Industry 4.0 (I4.0), IoRT-aware Business Processes aim to automate classic Business Processes (BP) by integrating IoT and robotics capacities. However, executing these processes inside the enterprise may be costly due to the required software and hardware components. To overcome this deficiency, the Business Process Outsourcing (BPO) strategy can be used to execute an IoRT-aware BP using external environments such as Fog and Cloud. In these environments, heterogeneous resources may have different specifications, which makes allocating Fog and Cloud resources challenging. Therefore, this work addresses the resource allocation (RA) issue for outsourcing an IoRT-aware BP. Toward this objective, we propose an optimal context-aware RA approach based on the decentralized multi-agent reinforcement learning (MARL) technique. The effectiveness and feasibility of the proposed context-aware RA-based decentralized MARL are demonstrated through a set of experiments. The preliminary experimental evaluation of the proposed approach demonstrates a high precision value and an encouraging recall value regarding other RA approaches. [ABSTRACT FROM AUTHOR]

Although neuropsychiatric manifestations are common in survivors of coronavirus disease 2019 (COVID-19), the pathophysiology is not yet elucidated. Here we describe the case of a geriatric inpatient who developed post-COVID depression with psychomotor retardation, anxiety, hopelessness, executive function problems, and suicidal ideations. The language problems and cognitive impairments coemerged with the motor problems. We propose a mechanism associated with problems in energy prediction and regulation in which the coronavirus infection, which causes neuroinflammation and viral activity in the nervous system, interferes with the reward pathway and sensory prediction process. Sigma-1 receptor agonists such as sertraline may regulate energy expenditure and, thus, be beneficial to the process. The treatment improvements in our patient included those in the autonomic nervous system, activity, and circadian rhythm.

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.

Systemic Lupus Erythematosus (SLE) is a versatile autoimmune disorder with multisystem organ involvement, most commonly involving the skin, joints, vasculature, and associated immunological abnormalities. The hallmark of SLE is the presence of circulating non organ specific humoral autoantibodies synthesised by so-called T cell-dependent B cells, which are germline encoded. Patients with SLE can exhibit symptoms of Central Nervous System (CNS) involvement, termed CNS Lupus, which may also have associated psychiatric manifestations, where the entity gets the designation of Neuropsychiatric SLE (NPSLE). CNS Lupus is often a diagnosis of exclusion, as there are many other aetiological factors that could result in seizures and encephalitis picture. A detailed examination of patients presenting with isolated late-onset seizures is very helpful in uncovering Connective Tissue Disorder (CTD) as the cause of seizures. The manifestations of NPSLE (19 Neuropsychiatric syndromes) can be focal and diffuse, ranging from non specific symptoms such as headache and cognitive impairment to severe manifestations such as memory loss, seizures, stroke, and suicidal intentions. Neuropsychiatric symptoms often occur during the first year of SLE but can rarely also be the presenting symptom of the disease. Antiphospholipid antibodies (Anticardiolipin, Lupus anticoagulant, Anti β2 glycoprotein) and Anti Ribosomal P antibodies are among the frequently associated antibodies in NPSLE, which, in turn, increase the risk of both focal and diffuse neurological complications. A single episode of abortion or miscarriage is a vital clue for a detailed work-up of female patients for the possibility of an underlying occult potential of CNS Lupus in the absence of seizures. The present case series focuses on five female patients who presented with NP symptoms, and finally, the cause was attributed to the involvement of CNS in SLE after excluding other possible organic causes.

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.

In the process of coal mining, a large amount of respirable dust is generated, which seriously endangers the miners’ health. As the most widely used dust reduction technology, the spray technology has the advantages of high efficiency and cleanliness, but the existing spray technology does not have a strong ability to capture respirable dust, and the atomization efficiency is low. In order to solve those problems, the supersonic coaxial aerodynamic atomization technology was developed. The atomization characteristics of the technology were studied by experimental and numerical simulations, and the dust reduction characteristics of supersonic water drawing siphon aerodynamic atomization and supersonic coaxial atomization were compared based on the self-designed dust reduction experimental platform. At the same time, the dust separation experiment of the two technologies revealed the sedimentation mechanism of dust under the action of supersonic dynamic micro-fog curtain. The results show that under different aerodynamic pressures, the coaxial probe water injection method adopted by the supersonic coaxial atomization dust reduction device greatly reduces the energy loss of the probe structure on the supersonic flow field, significantly improves the atomization efficiency, and produces a large number of high-speed droplets below 11 μm with an uniform spatial distribution, and the particle size is reduced by 12%−50% compared with the siphon atomization device, forming a large-scale high-speed fine fog area in the spray flow field. The coupling effect of the droplet field and the dust field can be characterized by the instantaneous dispersion of the dust and determined by the distribution characteristics of the droplet field. At different times, the variation trend of the graded dust reduction efficiency in each particle size interval is different, and the contribution to the total dust reduction efficiency under different pressures is also different. The large-scale high-speed fine mist generated by the supersonic coaxial atomization technology is easy to capture respirable dust, and the classification efficiency of PM0−PM2.5 is more than 75%, and the maximum is 90%. The increase of pressure enlarges the range of high-speed fine mist, which is conducive to the capture of fine particles. During the confined space migration of dust-containing airflow, the sedimentation process of dust under the action of supersonic dynamic micro-fog curtain can be divided into droplet dust capture area, condensation and sedimentation area, and evaporation escape area. The different behaviors and concentration distributions of fog droplets and dust in different regions are the results of the drag migration of spray airflow and airborne wind flow, the capture of high-speed micro-fog collision, the condensation and settlement of fog droplets, and the weight loss of fog droplet evaporation.

Summary: The rise of the Internet of Things (IoT) has given rise to an era marked by interconnected devices and substantial data generation. This has led to an increased reliance on cloud computing for data processing and storage, primarily due to its cost‐effective pay‐for‐use model. However, this dependence has prompted critical inquiries into the optimal replication of data: what data to replicate, when to replicate it, and where to place new replicas strategically. Conventional cloud data replication often results in resource overutilization, performance bottlenecks, increased workloads, energy consumption, prolonged user wait times, and suboptimal response times. In response to these challenges, this paper introduces a novel approach named Multiobjective Optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA). This approach employs multiobjective optimization (MOO) alongside Harris Hawks Optimization (HHO) and IoT‐based Salp Swarm Algorithm (SSA) for cloud computing environments. MOHHOSSA efficiently identifies data replication opportunities and strategically allocates them across nodes in cloud computing infrastructures. The algorithm aims to enhance key performance metrics, including energy consumption, carbon dioxide emission rate, and mean service time. Extensive experimental validation demonstrates MOHHOSSA's superior performance compared to alternative algorithms. It excels in optimizing energy efficiency, load distribution, mean service time, and the establishment of cost‐effective communication paths between nodes. This research represents a significant advancement in addressing challenges related to IoT data replication in cloud computing, ultimately promoting more sustainable, efficient, and responsive cloud‐based services. [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]

Introduction: Freezing of gait (FOG) is a disabling and heterogeneous symptom in patients with Parkinson's disease (PD). Among them, dopamine-induced FOG is rare and difficult to identify. The treatment of dopamine-induced FOG is complex. Case presentation: We herein presented a case of PD patient who complicated with refractory FOG. It was identified as dopamine-induced FOG during levodopa challenge test. Her symptoms were alleviated after we reduced the total equivalent dosage of levodopa. Conclusion: Our report emphasizes the importance of levodopa challenge test in identifying different types of FOG, which is very important for further adjusting treatment. [ABSTRACT FROM AUTHOR]

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ( T a − T d ), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. [ABSTRACT FROM AUTHOR]

The density of fog is directly related to visibility and is one of the decision-making criteria for airport flight management and highway traffic management. Estimating fog density based on images and videos has been a popular research topic in recent years. However, the fog density estimated results based on images should be further evaluated and analyzed by combining weather information from other sensors. The data obtained by different sensors often need to be aligned in terms of time because of the difference in acquisition methods. In this paper, we propose a video and a visibility data alignment method based on temporal consistency for data alignment. After data alignment, the fog density estimation results based on images and videos can be analyzed, and the incorrect estimation results can be efficiently detected and corrected. The experimental results show that the new method effectively combines videos and visibility for fog density estimation. [ABSTRACT FROM AUTHOR]

This study presents an approach for fabricating super hydrophilic self-cleaning anti-fog micro-nano structures on silicon-coated glass utilizing a laser marking machine. The surface features a periodic microstructure interspersed with irregular nanoparticles, which contribute to its durable superhydrophilicity, maintaining a 0° contact angle for up to one year—the longest duration recorded to date. Moreover, it sustained its anti-fogging and self-cleaning abilities for 467 days, establishing the lengthiest duration for maintaining a 0° contact angle and retaining anti-fogging performance without visible deterioration. The sample also retained excellent anti-fogging and self-cleaning properties even after 43 days of outdoor exposure. Such remarkable performance was attributed not only to surface chemistry and roughness but also to the presence of dense, uniform, and minimally varying microstructures. The unique structure also enhanced the solar spectrum (AM 1.5) weighted average transmission by 3.84 % over control glass in the wavelength range of 400–1100 nm. It holds promising potential for use in automotive rearview mirrors, windows, and solar cells. [ABSTRACT FROM AUTHOR]

Although neuropsychiatric manifestations are common in survivors of coronavirus disease 2019 (COVID-19), the pathophysiology is not yet elucidated. Here we describe the case of a geriatric inpatient who developed post-COVID depression with psychomotor retardation, anxiety, hopelessness, executive function problems, and suicidal ideations. The language problems and cognitive impairments coemerged with the motor problems. We propose a mechanism associated with problems in energy prediction and regulation in which the coronavirus infection, which causes neuroinflammation and viral activity in the nervous system, interferes with the reward pathway and sensory prediction process. Sigma-1 receptor agonists such as sertraline may regulate energy expenditure and, thus, be beneficial to the process. The treatment improvements in our patient included those in the autonomic nervous system, activity, and circadian rhythm. [ABSTRACT FROM AUTHOR]

Long-lasting COVID-19 (long COVID) diseases constitute a real life-changing burden for many patients around the globe and, overall, can be considered societal and economic issues. They include a variety of symptoms, such as fatigue, loss of smell (anosmia), and neurological–cognitive sequelae, such as memory loss, anxiety, brain fog, acute encephalitis, and stroke, collectively called long neuro-COVID-19 (long neuro-COVID). They also include cardiopulmonary sequelae, such as myocardial infarction, pulmonary damage, fibrosis, gastrointestinal dysregulation, renal failure, and vascular endothelial dysregulation, and the onset of new diabetes, with each symptom usually being treated individually. The main unmet challenge is to understand the mechanisms of the pathophysiologic sequelae, in particular the neurological symptoms. This mini-review presents the main mechanistic hypotheses considered to explain the multiple long neuro-COVID symptoms, namely immune dysregulation and prolonged inflammation, persistent viral reservoirs, vascular and endothelial dysfunction, and the disruption of the neurotransmitter signaling along various paths. We suggest that the nucleoprotein N of SARS-CoV-2 constitutes a "hub" between the virus and the host inflammation, immunity, and neurotransmission. [ABSTRACT FROM AUTHOR]

In this investigation, the laser marker ablation technique was employed on Cu-coated glass to fabricate micro-nanostructured antifog glass. The resulting surfaces exhibited a quasi-periodic micron hillock-hollow structure with dispersed nanoparticles distributed throughout, which played a role in the antifog property and superhydrophilicity. However, airborne organic pollutant deposition degraded the superhydrophilicity of ablated glass surfaces and, therefore, their antifog performance, which cannot be circumvented. Conventionally, furnace annealing for at least 1 h was used to decompose the organic pollutants and restore the superhydrophilicity, limiting the throughput and application scenario. Remarkably, the rapid regeneration of this property was achieved through either a 5 min rapid thermal treatment at 400 °C or a 1 s flame treatment. These are interventions that are hitherto unreported. Such short and simple treatment methods underscore the potential of laser-ablated glass for diverse practical applications. [ABSTRACT FROM AUTHOR]