Domain Of Interest Research Articles

Stochastic modelling of symmetric positive definite material tensors

Spatial symmetries and invariances play an important role in the behaviour of materials and should be respected in the description and modelling of material properties. The focus here is the class of physically symmetric and positive definite tensors, as they appear often in the description of materials, and one wants to be able to prescribe certain classes of spatial symmetries and invariances for each member of the whole ensemble, while at the same time demanding that the mean or expected value of the ensemble be subject to a possibly ‘higher’ spatial invariance class. We formulate a modelling framework which not only respects these two requirements—positive definiteness and invariance—but also allows a fine control over orientation on one hand, and strength / size on the other. As the set of positive definite tensors is not a linear space, but rather an open convex cone in the linear space of physically symmetric tensors, we consider it advantageous to widen the notion of mean to the so-called Fréchet mean on a metric space, which is based on distance measures or metrics between positive definite tensors other than the usual Euclidean one. It is shown how the random ensemble can be modelled and generated, independently in its scaling and orientational or directional aspects, with a Lie algebra representation via a memoryless transformation. The parameters which describe the elements in this Lie algebra are then to be considered as random fields on the domain of interest. As an example, a 2D and a 3D model of steady-state heat conduction in a human proximal femur, a bone with high material anisotropy, is modelled with a random thermal conductivity tensor, and the numerical results show the distinct impact of incorporating into the constitutive model different material uncertainties—scaling, orientation, and prescribed material symmetry—on the desired quantities of interest.

Achieving Higher Levels of Crack Simulation with the Improved Adaptive Static Condensation Method

Accurately simulating concrete cracking in large-dimensional structures is a challenging task. To address this issue, an adaptive static condensation (ASC) method has been developed that has demonstrated effectiveness in localized nonlinearities. The ASC method aims to concentrate computational efforts solely on the damaged area, which may evolve due to crack initiation or propagation. However, the efficiency of the ASC method may be limited as it is based on a non-evolving mesh. To overcome this limitation, a novel approach is proposed in this study, which utilizes an evolutionary mesh with mesh refinement. The proposed approach employs a fine mesh solely in the activated and evolving domain of interest. The ASC method with mesh refinement is demonstrated on a notched bending beam, indicating that the accuracy of the ASC is maintained while providing an additional gain in computational time. Furthermore, a reinforced concrete vessel subjected to internal pressure is considered, and it is shown that this new approach results in a significant improvement in computational time, with a 14-fold improvement compared to a 5-fold improvement without mesh refinement. This study demonstrates that the proposed improvement on the ASC method allows for finer discretization in the zones of interest that were previously inaccessible with the nominal ASC method or a direct numerical simulation strategy.

Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments

Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms.

Health-related quality of life in patients with triple-class exposed relapsed and refractory multiple myeloma treated with idecabtagene vicleucel or standard regimens: patient-reported outcomes from the phase 3, randomised, open-label KarMMa-3 clinical trial

Chimeric antigen receptor T-cell therapy idecabtagene vicleucel (ide-cel) showed significantly improved progression-free survival compared with standard regimens in adults with relapsed and refractory multiple myeloma who had received two to four previous regimens in the ongoing phase 3 KarMMa-3 trial (NCT03651128). This study analysed patient-reported outcomes (PROs), a KarMMa-3 secondary endpoint. In the randomised, open-label, phase 3 KarMMa-3 trial, 386 patients in hospitals (≥18 years of age, with measurable disease and an Eastern Cooperative Oncology Group performance status score of 0 or 1, who had received two to four previous regimens-including an immunomodulatory agent, a proteasome inhibitor, and daratumumab-and had documented disease progression after receiving their last dose of the last therapy) were randomly assigned to ide-cel (n=254) or standard regimens (daratumumab, pomalidomide, and dexamethasone; daratumumab, bortezomib, and dexamethasone; ixazomib, lenalidomide, and dexamethasone; carfilzomib and dexamethasone; or elotuzumab, pomalidomide, and dexamethasone; n=132). Patients were expected to complete the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life C30 Questionnaire (QLQ-C30), Multiple Myeloma Module (QLQ-MY20), EQ 5 dimensions (EQ-5D), and EQ-5D visual analogue scale (VAS) at baseline and follow-up timepoints (data cutoff April 18, 2022). PROs included nine prespecified primary domains: EORTC QLQ-C30 GHS-quality of life (QoL), physical functioning, cognitive functioning, fatigue, and pain; QLQ-MY20 disease symptoms and side effects of treatment; and five-level EQ-5D (EQ-5D-5L) index score and EQ-5D visual VAS. Differences in overall least-squares mean changes from baseline to month 20 were analysed using post-hoc constrained longitudinal data analysis. Time to confirmed improvement or deterioration from baseline was analysed using Cox proportional hazard models. Patients were randomly assigned between May 6, 2019, and April 8, 2022. Overall, the median age was 63 years (IQR 55-68); 151 (39%) patients were female; and 250 (65%) patients were White, 36 (9%) Black or African American, 19 (5%) Hispanic or Latino, 12 (3%) Asian, and seven (2%) of other race. The median follow-up was 18·6 months (IQR 14·0-26·4). PRO compliance was higher than 75% throughout. Overall least-squares mean changes from baseline favoured ide-cel with Hedges' g effect sizes from 0·3 to 0·7 for most domains. Patients in the ide-cel group showed statistically significant and clinically meaningful improvements across the primary PRO domains of interest, with the exception of QLQ-MY20 disease symptoms, side effects of treatment, and EQ-5D-5L index score, which showed improvement across assessment visits but did not exceed the within-group minimally important difference thresholds. The ide-cel group had shorter times to clinically meaningful improvement than the standard regimens group in QLQ-C30 domains except in role functioning, diarrhoea, and financial difficulties; in QLQ-MY20 domains except body image; and in EQ-5D-VAS. Ide-cel offers improved health-related quality of life compared with standard regimens for patients with relapsed and refractory multiple myeloma after previous lines of therapy. The PRO data highlight the extended QoL benefits of a one-time infusion with ide-cel compared with continuous treatment with standard regimens in the treatment of triple-class exposed patients with relapsed and refractory multiple myeloma. 2seventy bio and Celgene, a Bristol Myers Squibb Company.

An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications

Ontologies have the potential to play an important role in the cybersecurity landscape as they are able to provide a structured and standardized way to semantically represent and organize knowledge about a domain of interest. They help in unambiguously modeling the complex relationships between various cybersecurity concepts and properties. Leveraging this knowledge, they provide a foundation for designing more intelligent and adaptive cybersecurity systems. In this work, we propose an ontology-based cybersecurity framework that extends well-known cybersecurity ontologies to specifically model and manage threats imposed on applications, systems, and services that rely on artificial intelligence (AI). More specifically, our efforts focus on documenting prevalent machine learning (ML) threats and countermeasures, including the mechanisms by which emerging attacks circumvent existing defenses as well as the arms race between them. In the ever-expanding AI threat landscape, the goal of this work is to systematically formalize a body of knowledge intended to complement existing taxonomies and threat-modeling approaches of applications empowered by AI and to facilitate their automated assessment by leveraging enhanced reasoning capabilities.

Path integration method for stochastic responses of differential equations under Lévy white noise.

A path integration (PI) approach that is progressive for studying the stochastic response driven by Lévy white noise is presented. First, a probability mapping is constructed, which decouples the domain of interest for the system state and the probability space derived from the randomness of Lévy white noise within a short time interval. Then, solving the probability mapping yields the short-time response of the system. Finally, the stochastic evolution of the system can be grasped in a stepwise manner based on the fundamental concept of the PI method. The applicability and effectiveness of our approach in addressing the transient and stationary responses under Lévy white noises are verified by Monte Carlo simulation results. Moreover, the advances in utilization of this method are that it eliminates the restriction of the previous PI method on the controlling parameter of Lévy white noises, and it is highly efficient for solving responses of systems under Lévy white noises.

Patients' Experiences of Dental Diagnostic Failures: A Qualitative Study Using Social Media.

Despite the many advancements made in patient safety over the past decade, combating diagnostic errors (DEs) remains a crucial, yet understudied initiative toward improvement. This study sought to understand the perception of dental patients who have experienced a dental diagnostic failure (DDF) and to identify patient-centered strategies to help reduce future occurrences of DDF. Through social media recruitment, we conducted a screening survey, initial assessment, and 67 individual patient interviews to capture the effects of misdiagnosis, missed diagnosis, or delayed diagnosis on patient lives. Audio recordings of patient interviews were transcribed, and a hybrid thematic analysis approach was used to capture details about 4 main domains of interest: the patient's DDF experience, contributing factors, impact, and strategies to mitigate future occurrences. Dental patients endured prolonged suffering, disease progression, unnecessary treatments, and the development of new symptoms as a result of experiencing DE. Poor provider communication, inadequate time with provider, and lack of patient self-advocacy and health literacy were among the top attributes patients believed contributed to the development of a DE. Patients suggested that improvements in provider chairside manners, more detailed patient diagnostic workups, and improving personal self-advocacy; along with enhanced reporting systems, could help mitigate future DE. This study demonstrates the valuable insight the patient perspective provides in understanding DEs, therefore aiding the development of strategies to help reduce the occurrences of future DDF events. Given the challenges patients expressed, there is a significant need to create an accessible reporting system that fosters constructive clinician learning.

Managing Provenance Data in Knowledge Graph Management Platforms

Knowledge Graphs (KGs) present factual information about domains of interest. They are used in a wide variety of applications and in different domains, serving as powerful backbones for organizing and extracting knowledge from complex data. In both industry and academia, a variety of platforms have been proposed for managing Knowledge Graphs. To use the full potential of KGs within these platforms, it is essential to have proper provenance management to understand where certain information in a KG stems from. This plays an important role in increasing trust and supporting open science principles. It enables reproducibility and updatability of KGs. In this paper, we propose a framework for provenance management of KG generation within a web portal. We present how our framework captures, stores, and retrieves provenance information. Our provenance representation is aligned with the standardized W3C Provenance Ontology. Through our framework, we can rerun the KG generation process over the same or different source data. With this, we support four applications: reproducibility, altered rerun, undo operation, and provenance retrieval. In summary, our framework aligns with the core principles of open science. By promoting transparency and reproducibility, it enhances the reliability and trustworthiness of research outcomes.

Assessing the publicness of three streets in ethnically-diverse neighbourhoods

ABSTRACT Public space is the domain of interest for urban designers, and streets represent one of its most significant forms. In multicultural societies, public space users come from diverse social and ethnic backgrounds with varying interests and needs. Therefore, streets should serve as inclusive spaces designed for use by a wide-ranging public. While streets are inherently public, the questions regarding the extent of their publicness and their role in promoting multiculturalism remain significant. This paper aims to evaluate and compare the degree of “publicness” exhibited by streets situated within ethnically diverse neighbourhoods. Drawing upon dimensions of publicness established by previous models and informed by observations and interviews, a set of criteria has been selected. The core dimensions of publicness encompass accessibility, management, and inclusiveness. The study assesses and compares three streets located in diverse neighbourhoods across New Zealand based on these criteria. The findings illuminate varying levels of publicness within the studied streets, providing insights into the key factors that impact the promotion of multiculturalism in urban settings. The results indicate that the publicness of a multicultural street is significantly influenced by the types of businesses clustered in the area, their associated characteristics, and the overall design of the built environment.

The feasibility and impact of a brief behavioural intervention for anxiety in later life: perceptions of general practice nurses

Background Anxiety is prevalent in older people and is associated with adverse health outcomes. Early detection and intervention are imperative, yet clinically significant anxiety in older people is often under-diagnosed. When diagnosed, pharmacotherapy is often the initial treatment, but resistance due to stigma, polypharmacy concerns, and side effects are common among the elderly. Non-pharmacological interventions such as deep breathing and progressive muscle relaxation may offer more acceptable and cost-effective options to managing anxiety. While primary care is ideal for addressing anxiety in older adults, it is important to assess the feasibility and acceptability of nurse-led interventions in general practice. Objective To explore the perspectives of General Practice Nurses (GPNs) on the feasibility and acceptability of implementing a guided relaxation intervention for older people living with symptoms of anxiety, in Australian metropolitan and regional settings. Methods GPNs were trained and then administered a guided relaxation intervention to older people at three metropolitan general practices and one large rural practice encompassing four sites. Subsequently, five GPNs participated in semi-structured interviews exploring the acceptability and feasibility of implementing the intervention. Interviews were recorded, transcribed and findings were mapped to four key domains of interest: “Screening”, “Training and Delivery”, “Perceived impact of the Intervention” and “Barriers to the intervention”. Results Overall, GPNs reported that the intervention was easy to implement, was well received, helped build relationships and enabled conversations about mental health and well-being. They noted broader applicability of acquired knowledge and skills with other clients. Conclusion The intervention shows promise in promoting and supporting healthcare capabilities in primary care. GPNs are ideally placed to detect clinical symptoms of anxiety among older people and deliver brief well-being initiatives. However, to sustain and scale well-being interventions structured skill development, commitment from general practices, and policy shifts such as government rebates for GPNs are required.

Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp.

Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.

Kırılgan Piyasa Ekonomileri Arasında Döviz Kurları Yakınsıyor mu? LM ve RALS-LM Birim Kök Testlerinden Yeni Kanıtlar

Global financial integration causes the economic consequences of economic crises, wars, or pandemics to be felt more in developing countries and triggers high exchange rate volatilities in these economies. In such an integrated financial environment, it is an interesting research domain how and why the exchange rate volatilities of countries are not affected similarly but tend to diverge from each other. This study investigates whether the exchange rate volatilities of fragile market economies converge in the stochastic convergence framework. To answer this question, we analyzed the stochastic behavior of the series using the traditional and structural break unit root tests besides RALS unit root tests, which consider the information of non-normal errors. The discussions regarding the size and power properties of test procedures in the unit root testing literature have formed a crucial part of the implications of the test results. In light of these discussions, we conclude that the stochastic convergence assumption is valid for Brazil, South Africa, India, and Hungary, whereas it is not valid for Argentina, Mexico, and Türkiye. The policy implications of our findings are that fragile market economies have different fragility levels among themselves and countries with high fragility levels show higher volatility than others.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Enhancing tuberculosis treatment adherence and motivation through gamified real-time mobile app utilization: a single-arm intervention study

BackgroundFinding innovative methods to enhance Tuberculosis treatment adherence in Malaysia is imperative, given the rising trend of non-adhere TB patients. Direct Observed Therapy (DOTS) has been used to ensure Tuberculosis (TB) drug compliance worldwide. However, due to its inconvenience, digitalizing this system into a virtual monitoring system via a mobile app can help deliver a more efficient tuberculosis management system. A gamified video-observed therapy is developed that connects three users the patient, supervisor, and administrator, allowing drug monitoring and patient loss to follow up with the patient tracking system. Thus, the objective of this study is to determine the impact of Gamified Real-time Video Observed Therapy (GRVOTS) mobile apps on patient medication adherence rates and motivation.Methods71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.ResultsThis study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p < 0.001). The participants’ MyMAAT and IMI scores significantly increased over 3-time intervals with the IMI Interest domain showing the highest mean difference 19.76 (95% CI: 16.37, 21.152: p < 0.001).ConclusionsBy utilizing GRVOTS, a mobile application based on gamification and real-time features, we can enhance motivation and medication adherence among TB patients, while also addressing the limitations of physical DOTS.Trial registrationIRCT20230308057657N1, Registered on (15/03/23).

Coupling numerical models of deltaic wetlands with AirSWOT, UAVSAR, and AVIRIS-NG remote sensing data

Abstract. Coastal marsh survival relies on the ability to increase elevation and offset sea level rise. It is therefore important to realistically model sediment fluxes between marshes, tidal channels, and bays as sediment availability controls accretion. Traditionally, numerical models have been calibrated and validated using in situ measurements at a few locations within the domain of interest. These datasets typically provide temporal information but lack spatial variability. This paper explores the potential of coupling numerical models with high-resolution remote sensing imagery. Products from three sensors from the NASA Delta-X airborne mission are used. Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides vertical water level change on the marshland and was used to adjust the bathymetry and calibrate water fluxes over the marsh. AirSWOT yields water surface elevation within bays, lakes, and channels, and was used to calibrate the Chezy bottom friction coefficient. Finally, imagery from AVIRIS-NG provides maps of total suspended solids (TSS) concentration that were used to calibrate sediment parameters of settling velocity and critical shear stress for erosion. Three numerical models were developed at different locations along coastal Louisiana using Delft3D. The coupling enabled a spatial evaluation of model performance that was not possible using simple point measurements. Overall, the study shows that calibration of numerical models and their general performance will greatly benefit from remote sensing.

Local Identifiability Analysis, Parameter Subset Selection and Verification for a Minimal Brain PBPK Model.

Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.

Attenuation Tomography Using Low-frequency Ultrasound with Variational Autoencoder for Thorax Imaging: Experimental Study.

In this paper, we introduce a novel inversion methodology employing the variational autoencoder (VAE) for human thorax attenuation tomography using low-frequency ultrasound. The VAE is trained to assimilate the structural priors of the human thorax, utilizing training samples generated from computed tomography (CT) scans. This approach enables the compression of high-dimensional attenuation distributions into a lowerdimensional latent space. During the inversion process, the latent code is optimized, and then the reconstructed model is generated by the decoder of the VAE. This process can effectively integrate prior information of the domain of interest (DOI) into the inversion through coding and decoding, which would mitigate the ill-posedness of the inverse problem and facilitate better outcomes. Our method demonstrates robust generalization capabilities and noise resilience in numerical simulations, outperforming the conventional pixel-based Gauss-Newton method. Human subject experiment further corroborates the effectiveness of our approach. This is also the first experimental validation of the feasibility of low-frequency ultrasound functional imaging of the human thorax. Although the current study presents certain limitations, it underscores the potential of low-frequency ultrasound in the continuous monitoring of the human respiratory system.

Relationship between implementation of systematic advance care planning and the quality of death among nursing home residents: a survey.

Advance care planning (ACP) is beneficial for the quality of death (QOD). However, the effects of ACP on the QOD may vary across cultures. This study aimed to explore the relationship between the 15-step ACP program and the QOD among Japanese nursing home residents. A cross-sectional survey. A cross-sectional survey was conducted among the family members of 39 nursing home residents who died between April 2017 and March 2019 by distributing the survey questionnaire by post. The survey included questions about the QOD of residents, and responses were evaluated using the Good Death Inventory (GDI) scale. Responses were obtained from 30 of the 39 bereaved families (76.9%). Data were analyzed using hierarchical clustering to determine five groups and conduct multiple comparisons. The following three domains of interest were identified: 'Dying in a favorite place', 'Good relationship with the medical staff', and 'Independence'. GDI scores were significantly higher for residents with higher ACP completion rates than for those with lower rates (p < 0.01). Residents who had taken ACP interviews had significantly higher GDI scores (p < 0.01) than those who had not taken interviews. Overall, these findings suggest that systematic ACP might be related to the QOD among Japanese nursing home residents in the above mentioned three domains. Limitations of the present study were small sample size, cross-sectional survey design as opposed to a cohort survey design, and multiple biases, including the emotional instability of bereaved family members, the length of stay of the residents, the degree of dementia of the residents, and their tendency to talk about the place of death and to develop good relationships with the medical staff.

A Review of Oil Spills on Marine Life and Associated Wildlife

Oil is an ancient fossil fuel used to power sectors like heat and electricity production. A wide range of fuels in automobiles and lubricants for mechanical machines have a significant role in our economy. Oil spills occur due to accidents, such as vessels crashing or being damaged, problems with oil platforms and drilling, and people making mistakes or equipment breaking down. Other causes include natural disasters like earthquakes, hurricanes, and volcanic activity underwater. Volcanic eruptions can release oil and gas stored beneath the seafloor. The intense heat and pressure can cause hydrocarbons to escape, forming oil slicks on the water's surface. We provide a summary literature review and overview framework to help communities systematically consider the factors and linkages that would influence the consequences of a potential oil spill. The focus is on spills from oil tanker accidents. Drawing primarily on empirical studies of previous oil spill disasters, we focused on several domains of interest: the oil spill, the physical marine environment, marine biology, human health, and policy

Big data vehicle density management in vehicular ad-hoc network

&lt;span&gt;Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment. &lt;br /&gt; We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy.&lt;/span&gt;