Fires constitute a significant threat due to the pollutants emitted and the destruction they cause. People who take part in fire-fighting operations must be equipped with appropriate tools, including special clothing that will allow them to work and guarantee safety. One of the threats are compounds from the PAH group, which are characterized by high toxicity and carcinogenicity. Therefore, it is important that the materials used constitute a barrier to contamination. Various materials from which individual elements of special firefighter's clothing are made were tested. Additionally, the importance of height on the possibility of sorption of PAH compounds on a given type of material was analyzed. Based on the results obtained, it was found that the type of material and the zone in which the clothing items are used are important in the sorption processes of pollutants. Data in this area may be important for taking further actions regarding the modification of materials used in special fire brigade clothing and in their cleaning processes.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

Continuous technological progress places significant demands on the materials used in increasingly modern devices. An important parameter is often the long-term thermal resistance of the material. The use of heat-resistant polymer materials worked well in technologically advanced products. An economically justified direction in searching for new materials is the area of polymer nanocomposite materials. It is necessary to appropriately select both the polymer matrix and the nanofillers best able to demonstrate the synergistic effect. A promising area of exploration for such nanocomposites is the use of organosilicon polymers, which results from the unique properties of these polymers related to their structure. This review presents the results of the analysis of the most important literature reports regarding organosilicon polymer nanocomposites with increased thermal resistance. Particular attention was paid to modification methods of silicone nanocomposites focused on the increase of their thermal resistance related to the modification of siloxane molecular structure and by making nanocomposites using inorganic additives, carbon nanomaterials. Attention was also paid to such important issues as the influence of the dispersion of additives in the polymer matrix on the thermal resistance of silicone nanocomposites and the possibility of modifying the polymer matrix and permanently introducing nanofillers thanks to the presence of various reactive groups. The thermal stability mechanism of these nanocomposites was also analysed.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming an emerging fire restoration/resilience paradigm shift replaces it, the paper engages in speculative explorations of the process and product of this paradigm shift on future collective identities for a workforce doing ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory's concept of hegemony, and New-Social-Movement theory's concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required--the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the "Greenfire revolution," is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce.

Warming-induced drought stress and El Nino associated summer precipitation failure are responsible for increased forest fire intensities of tropical and temperate forests in Asia and Australia. However, both effects are unclear for boreal forests, the largest biome and carbon stock over land. Here we combined fire frequency, burned area and climate data in the Altai boreal forests, the southmost extension of Siberia boreal forest into China, and explored their link with ENSO (El Nino and South Oscillation). Surprisingly, both summer drought severity and fire occurrence have shown significant (P<0.05) teleconnections with La Nina events of the previous year, and therefore provide an important reference for forest fire prediction and prevention in Altai. Despite a significant warming trend, the increased moisture over Altai has largely offset the effect of warming-induced drought stress, and lead to an insignificant fire frequency trend in the last decades, and largely reduced burned area since the 1980s. The reduced burned area could also benefit from the fire suppression efforts and greatly increased investment in fire prevention since 1987.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

As Li-ion batteries use is spreading, incidents in large energy storage systems (stationary storage containers, …) or in large-scale cell and battery storages (warehouse, recyclers, …), often leading to fire, are occurring on regular basis. Water remains one of the most efficient fire extinguishing agents for tackling those battery incidents and large quantities are usually necessary. Since bat-teries contains various potentially harmful components (metals and their oxides or salts, sol-vents…) and that thermal runway induced battery incidents are accompanied with complex and potentially multistage fume emissions (containing both gas and particles) the potential impact of fire run-off water on the environment should be considered and assessed carefully. The tests presented in this paper focus on the analysis of the composition of run-off waters used to spray NMC Li-ion modules under thermal runaway. It highlights that waters used for fire-fighting are susceptible to contain many metals, including Ni, Mn, Co, Li and Al mixed with other carbonaceous species (soot, tarballs) and sometimes undecomposed solvents used in the elec-trolyte. Extra-polation of pollutant concentrations compared with PNEC values showed that, for large scale incidents, run-off water could be potentially hazardous to the environment.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

Previous research on consumer perceptions of salmon has often neglected Indigenous rights within the Canadian salmon industry. This study acknowledges the decline in salmon farm licenses in British Columbia and aims to achieve three main objectives: (1) gain a comprehensive understanding of four consumer perceptions—environmental sustainability, economic considerations, Indigenous rights, and price increase—across diverse consumer profiles through a cross-national online survey; (2) identify factors influencing these four consumer perceptions; and (3) assess the impact of these perceptions, along with socio-demographic variables and consumer motivations, on seven purchasing behaviors related to Canadian salmon products. Data analysis employs the graded response model and cumulative link models. The results illustrate how consumer profiles influence the four perceptions and their key determinants. Additionally, the study quantifies the impact of environmental sustainability, economic considerations, Indigenous rights, and a price increase on consumer purchasing behaviors.

The European Union's recent decision to renew the authorization for the use of glyphosate until 15 December 2033 stimulates the scientific discussion all around the world regarding its toxicity or otherwise for humans. Glyphosate is a chemical used by millions of tons in the last 50 years worldwide to drying weeds in cultivated fields, greenhouses, roadsides. Concern has been raised in many quarters about the possible presence in the food chain and the consequent adverse effects on health: both aspects that argue in favor of toxicity and those that instead may indicate limited toxicity of glyphosate are here discussed. The great debate that has been generated requires further investigations and field measurements to understand its fate once dispersed in the environment and its concentration in the food chain. Hence the need for validated analytical methods that are available to analysts in the field. In the present review, methods for the analytical determination of glyphosate and its main metabolite, AMPA, are discussed, with a specific focus on chromatographic techniques applied to cereal products. The experimental procedures are explained in any detail, including cleanup, derivatization, and instrumental conditions to give the laboratories involved enough information to proceed with the implementation of this line of analysis. The prevalent chromatographic methods used are LC-MS/MS, GC-MS/SIM, and GC-MS/MS but sufficient indications are also given to those laboratories that wish to use the simpler HPLC-FLD, HPLC-UV, GC-NPD or GC-FPD techniques for screening purposes. Concentrations from literature measured in wheat, corn, barley, rye, oats, soybean, and cereal-based foods are reported together with the regulatory status in various parts of the world and the accumulation mechanism. As for the accumulation in cereals, available data show that glyphosate tends to accumulate more in wholemeal flours than in refined ones, that its concentration in the product strictly depends on the treatment period (the closer it is to the time of harvesting, the higher the concentration) and that in cold climates the herbicide tends to persist in the soil for a long time.

Wildfires have always been a part of the history of Mediterranean forests. However, forest regeneration after a wildfire is not certain. It depends on many factors, some of which may be influenced by land management activities. Failure of regeneration will cause a regime shift in the ecosystem, reducing the provision of ecosystem services and ultimately leading to desertification. How can we increase Mediterranean forests’ resilience to fire? To answer this question, we did a literature review, investigating chains of processes that allow forests to regenerate (which we label “regeneration mechanisms”), and assessed the impact of selected management practices documented in the WOCAT database on the regeneration mechanisms. We identified three distinct regeneration mechanisms that enable Mediterranean forests to recover, as well as the time frame before and after a fire in which they are at work, and factors that can hinder or support resilience. The three regeneration mechanisms enabling a forest to regenerate after a fire consist of regeneration (1) from a seed bank; (2) from resprouting individuals; and (3) from unburned plants that escaped the fire. Management practices were grouped into four categories: (1) fuel breaks, (2) fuel management, (3) afforestation, and (4) mulching. We assessed how and under what conditions land management modifies the ecosystem’s resilience. The results show that land management influences resilience by interacting with resilience mechanisms before and after the fire, and not just by modifying the fire regime. Our analysis demonstrates a need for adaptive – i.e. context- and time-specific – management strategies.

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

Introduction: Cleft lip with or without cleft palate (CL/P) is the most common congenital face anomaly in the world of multifactorial etiology. Objective: To determine the prevalence and characteristics of cleft palate. To identify associated risk factors to guide prevention and/or prenatal detection for early intervention and better health outcomes. To assess the rehabilitation protocols after operations in cleft palate. Design: Retrospective descriptive and clinical study. Patients: One hundred and three children with cleft palate treated in the Department of Head and Neck Surgery Clinic for Children and Young Adults, Department of Clinical Pediatrics, University of Warmia and Mazury, were included in the study. Methods: Records evaluation was conducted regarding sex, cleft type, mother occupation, parents education, histories of cleft in any of parents. The data were analyzed using R (R core team 2021) and the ordinal logistic regression analyses. Results: Children of stressed mothers during pregnancy had 9.4 times higher odds of having bilateral cleft. While there was no evidence that children sex, order of birth, and body mass as well as risk of maternal toxicity at work or reporting infections or drugs toxicity had a significant effect on the dependent variable. Conclusions: Children of parents with cleft history and having low educated mothers had higher probability of bilateral cleft palate. Children of stressed mothers during pregnancy had higher odds of having bilateral cleft. In literature, there is a lack of reports about rehabilitation in different operations methods in children with cleft palates.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

The Yixian Formation (Lower Cretaceous) of China is famous worldwide for its fossils of early angio-sperms, but there is only one record of flower bud hitherto. A previous fossil record of flower bud just showed the appearance and lacked any internal details. Such a lacuna in knowledge of flower buds hin-ders our understanding of the evolution of flowers. Our new specimen was collected from an outcrop of the Yixian Formation (the Barremian-Aptian, Lower Cretaceous) near Dawangzhangzi, Lingyuan, Liao-ning, China. The specimen was observed and photographed using a Sony ILCE-7 digital camera and a Nikon SMZ1500 stereomicroscope, and details were observed using a TESCAN MAIA3 SEM. Our ob-servations document a fossil flower bud, Archaebuda cretaca sp. nov, from the Lower Cretaceous of China. The occurrence of two species of Archaebuda in the Yixian Formation not only confirms the ex-istence of the expected gynoecium (and possible androecium) in a flower bud, but also underscores the occurrence of typical flowers in the Early Cretaceous.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Background: Malignant pleural mesothelioma (MPM) is an atypical, belligerent tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Pleural mesothelioma is a common type of mesothelioma that accounts for about 75 percent of all mesothelioma diagnosed yearly in the United States. Diagnosis of mesothelioma takes several months and is expensive. Given the difficulty of diagnosing MPM, early identification is crucial for patient survival. Our study implements artificial intelligence and recommends the best fit model for early diagnosis and prognosis of MPM. Method: We retrospectively retrieved patient’s medical reports generated by Dicle University, Turkey and implemented multi-layered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1 SGD, AdaBoost.M1, KLR, MLP, VFDT generates optimal results with the highest possible performance measures. In phase-2, AdaBoost with a classification accuracy of 71.29% outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate mesothelioma. Conclusion: This study confirms that data obtained from biopsy and imagining tests are strong predictors of mesothelioma but are associated with high cost, however, can identify mesothelioma with optimal accuracy. Predictive analytics without using biopsy results can diagnose mesothelioma with acceptable accuracy. Implementation of phase-2 followed by phase-1 can address diagnosis expenses and maximize disease prognosis. Additionally, results indicate improved MPM diagnosis using AI methods dependent upon the specific application.

In the context of an increasingly globalized economy and rapid technological advancements, the agri-food sector faces unprecedented challenges and opportunities. Digital traceability systems have emerged as pivotal in enhancing operational efficiencies, ensuring food safety, and fostering supply chain transparency. This study conducts a comparative analysis of the adoption and impact of digital traceability technologies across member countries of the Organisation for Economic Co-operation and Development (OECD). Utilizing a multidimensional analytical framework, this research investigates the spectrum of national regulations, legal frameworks, and specific food commodities most influenced by digital traceability implementations. It systematically evaluates the efficacy of these systems in addressing consumer transparency demands, regulatory compliance, and the overarching goal of sustainable agri-food supply chains. Through a meticulous examination of case studies and empirical data, the paper elucidates the dynamic interplay between technological innovation and regulatory environments, offering insights into best practices and potential barriers to digital traceability integration. This comprehensive inquiry aims to contribute to the scholarly discourse on digital traceability, providing actionable recommendations for policymakers, industry stakeholders, and academia to navigate the complexities of modern agri-food systems.

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

Although migraine belongs to the main causes of disability worldwide the mechanisms of its pathogenesis are poorly known, which is among the reasons that this disease is underdiagnosed and undertreated. As migraine diagnosis is based on the subjective assessment of symptoms, there is a need to establish objective markers of its occurrence and severity. Tryptophan (TRP) metabolism has been associated with the pathogenesis of neurological and psychiatric disorders. In the present work, we investigated an association between migraine and the urine concentration of TRP and its metabolites 5-hydroxyindoleacetic acid (5-HIAA), kynurenine (KYN), kynurenic acid (KYNA) and quinolinic acid (QA) in 21 low-frequency episodic migraine patients and 32 controls. Migraine patients displayed lower urinary levels of 5-HIAA and KYNA, but KYN and QA were enhanced as compared with the controls. Consequently, the patients were characterized by different values of the 5-HIAA/TRP, KYN/TRP, KYNA/KYN, and KYNA/QA ratios. Furthermore, urinary concentration of 5-HIAA was negatively correlated with Migraine Disability Assessment score, monthly migraine, and monthly headache days. A negative correlation between Patient Health Questionnaire 9 scores assessing depression. In conclusion, the urinary 5-HIAA level may be an easy-to-determine marker of migraine occurrence and severity and may contribute to the identification of migraine patients with depression.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km² despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices, avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to two learning analytics problems: dropout prediction and unsupervised student classification. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding centralizing the data for training the models.

Over the years, there has been a significant rise in the world's scientific knowledge. However, most of it lacks structure and is often termed as Dark Data. Both humans and expert systems have continually faced difficulty in analyzing and comprehending such overwhelming amounts of information which is crucial in solving several real-world problems. Information and data visualization techniques proffer a promising solution to explore such data by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns, etc. In this tutorial, we utilize the rich corpus of PubMed comprising of more than 30 million citations from biomedical literature to visually explore and understand the underlying key-insights using various information visualization techniques. With this study, we aim to diminish the limitation of human cognition and perception in handling and examining such large volumes of data by speeding up the process of decision making and pattern recognition and enabling decision-makers to fully understand data insights and make informed decisions.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

Using lightweight fire-rated board (LFRB) presents cost-effective opportunities for various passive fire protection measures. The aim of the project is to develop a LFRB with enhanced fire resistance, acoustic properties, and mechanical properties. These properties were determined using the Bunsen burner, furnace, energy dispersive X-Ray, impedance tube instrument, and Instron universal testing machine. To fabricate the LFRBs, vermiculite and perlite were blended with flame-retardant bind-ers, and four types of LFRBs were produced. A fire test was conducted to compare the fire-resistance performance of the LFRBs with a commercially available flame retardant board. The B2 prototype showed exceptional fire-resistant properties, with a temperature reduction of up to 73.0 °C as compared to the commercially available fire-rated magnesium board. Incorporating nano chicken eggshell into the specially formulated flame-retardant binder preserved the LFRBs’ structural integrity, enabling them to withstand fire for up to 120 minutes with an equilibrium temperature of 92.6 °C. This approach also provided an absorption coefficient of α = 2.0, a high flexural strength of 3.54 MPa, and effective flame retardancy properties with a low oxygen/carbon ratio of 2.60. These results make the LFRBs valuable for passive fire protection applications in the construction and building materials industry.

The growing requirements regarding the safety of using polymers and their composites are related to the emergence of more effective, sustainable, and hazardous-limited fire retardants (FR). Significant amounts of FR are usually required to effectively affect the polymer burning behavior, while the knowledge of their recycling potential is still insufficient. At the same time, concerns are related not only to the reduced effectiveness of flame retardancy but, above all, to the potential deterioration of mechanical properties caused by the degradation of temperature-affected additives under processing conditions. This study describes the impact of four-time reprocessing of bio-based polyamide 11 (PA11) modified with an intumescent flame retardant (IFR) system composed of ammonium polyphosphate (APP), melamine cyanurate (MC) and pentaerythritol (PER) and its composites containing additionally short basalt fibers (BF). Composites manufactured by twin-screw extrusion were subjected to four reprocessing cycles by injection molding, comprehensively analyzing structural, mechanical, and fire behavior changes in each cycle. The obtained results confirmed the safety of using the proposed flame-retardant system for polyamide, processed under the recommended process parameters, without the risk of significant changes in the structure despite the partial increase in flammability caused mainly by the degradation of the polymer.

In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.

Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

The role of Social Media Marketing (SMM) in marketing strategies is rapidly growing. Because the use of social media is growing, the industry of SMM will grow bigger in the coming years; the pace of this growth is faster than ever. To survive in the modern competitive world, effective use of SMM for a firm is a must; for that, every SMM channel needs to be used to its full potential. For a marketing campaign to be effective, there is a need for some metrics to measure the success of the SMM campaign. These metrics measure if the campaign is successfully implemented or not. This would help firms understand the market, gain a competitive advantage, and ultimately get a positive impact on the overall business. This study categorizes SMM strategy into 4 dimensions and associates 10 broad categories of SMM metrics to these dimensions. The proposed model of this study suggests the application of Social Media Analytics (SMA) ineffective use of metrics to measure SMM campaigns. There are so many SMA Tools available for free and time-efficient data analysis that can lead to faster and better results than manual analysis. Following this model, the importance of SMA tools in devising an effective SMM strategy is highlighted. The implication of this research is towards a better understanding of the application of SMA for any firm to have a solid SMM Strategy, especially small and medium-sized enterprises that have limited resources.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Coronavirus Disease-19 (COVID-19) is presently wreaking havoc on public health and socio-economic development. Besides the upper and lower respiratory tract involvement, gastrointestinal symptoms are also reported in COVID-19 patients through gut-lung axis. Finding its way through the feces of infected individuals and other sources, the genetic material of SARS-CoV-2 (ssRNA) is reported widely in wastewater and is being used as a fingerprint for its detection. With millions of cases arriving every day, there is a need to level up the testing speed efficiency. Due to the restricted sampling potential of testing laboratories, clinical testing is unable to track all the symptomatic and asymptomatic cases. Wastewater-based epidemiology (WBE) bestows an auxiliary monitoring tool that will contribute in community level screening. Sample collection, concentration, RNA extraction, quantification and data analysis are the main steps involved in implementation of WBE that can be relied upon as an alarm call for an upcoming wave, emergence of a new variant or any future pandemic. WBE can be a cheaper and more practical alternative to high end and sophisticated clinical testing for community transmission detection. Worldwide, there are more than 300 reports entailing the occurrence of SARS-CoV-2 in wastewater exhibiting unique temporal trends with five of them in India. This review aims to address the present knowledge on surveillance of SARS-CoV-2 in wastewater and its implications.

In the realm of educational technology, Data Analytics (DA) is reshaping teaching and learning. This paper investigates the impact of DA on education, specifically comparing Student Information Systems (SIS) and Learning Management Systems (LMS). Data-driven insights from SIS and LMS platforms enable educators to tailor instructional strategies to individual student needs effectively. While SIS manages student data for administrative tasks, LMS offers tools for content delivery and collaboration. Using the strengths of SIS and LMS, educators can optimise teaching and learning experiences, tracking student progress, and fostering collaboration. The comparison emphasises their distinct roles: SIS manages administrative tasks, while LMS focusses on content delivery and collaboration. Using both systems, educators improve the teaching and learning experience, leveraging DA for continuous improvement. In summary, this paper underscores the transformative role of DA in education, framed within the context of comparing the SIS and LMS functionalities. By examining the intersection of data-driven decision making and educational technology, educators are equipped with the tools and knowledge to harness the power of data for meaningful and impactful teaching and learning.

This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan's rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Using advanced technologies such as smart sensing and predictive analytics through kriging and ensemble learning, the study aims to improve the accuracy of geotechnical investigations and urban planning. By analyzing data from 433 geotechnical surveys in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers, with significant implications for smart city development and the sustainability of urban environments. This interdisciplinary approach not only seeks to mitigate risks associated with geological disturbances, but also promotes sustainable urban growth that prioritizes safety and prosperity. Through advanced technology and comprehensive research, the study underscores the potential of smart cities to address urban complexities and ensure a resilient, sustainable future.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. Cricket, the mainstream and widely played sport across India which has the most noteworthy fan base. Indian Premier League follows 20-20 format which is very unpredictable. IPL match predictor is a ML based prediction approach where the data sets and previous stats are trained in all dimensions covering all important factors such as: Toss, Home Ground, Captains, Favorite Players, Opposition Battle, Previous Stats etc, with each factor having different strength with the help of KNIME Tool and with the added intelligence of Naive Bayes network and Eulers strength calculation formula.

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved prediction of readmission would also be of great help hospital, allowing them to better manage their treatment programs and budgets. This systematic review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure and readmission. Some of these models use a statistical approach and others a machine learning approach. Electronic patient health records, including demography, physical and clinical values, and laboratory findings are used to design and build predictive models. Predictive analytics with good performance have become essential for clinicians and specialists. Choosing a suitable machine learning algorithm and data preprocessing technique will improve predictive model performance, including data imputation and class imbalance.

Although learning analytics (LA) holds great potential to improve teaching and learning, LA research and practice are currently riddled with limitations that impact every stage of the LA life cycle. The present paper offers an overview of these challenges before proposing strategies to overcome them and exploring how the recent innovations brought forth by language models can improve LA research and practice. In particular, we encourage the empowerment of teachers during LA development, as this would strengthen the theoretical foundation of LA solutions and increase their interpretability and usability. Furthermore, we provide examples of how process data can be used to understand learning processes and generate more interpretable LA insights. Furthermore, we explore how LLMs could come into play in LA to generate interpretable insights, timely and actionable feedback, increase personalization, and support teachers’ tasks more broadly.

This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consists of five integrated system components: 1) data preparation system, 2) data exploration system, 3) data visualization system, 4) data analysis system, and 5) knowledge extraction system. The functional requirements of the integrated systems are elucidated. In addition, JAVA- and spreadsheet-based systems are developed to realize the proposed integrated system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry electrical discharge machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In synopsis, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

In this review article, the occurrence of nor-lignans and their biological activities are explored and described. Nor-lignans have proven to be present in several different families also belonging to chemosystematically distant orders as well as to have many different beneficial pharmacological activities. This review article represents the first one on this argument and is thought to give a first overview on these compounds with the hope that their study may continue if not raise after this.

Polyurethane is used extensively in the furniture industry, primarily in beds and sofas. There is a significant volume of research pertaining to the flammability of polyurethane materials, but very little regarding smoke toxicity. Given toxic fire effluents are a primary cause of injury and fatalities, more work needs to be done to test the toxicity of polyurethane materials and the fire retardants they are coated/bound with. The aim of the review is to provide a comprehensive analysis of the decomposition of polyurethane and discuss the development and application of modern fire retardants and their effect on smoke toxicity. This work then provides discussion on the challenges faced when attempting to quantify the toxicity of the effluent produced during combustion. Future work should aim to reduce the toxicity and flammability of polyurethane simultaneously by; reducing the pyrolysis of the polymer, preventing the escape of volatile products, reducing radicals within the system and lowering the temperature of the system. Before this can be achieved, a better understanding of smoke toxicity and the ability to test materials is needed.

Fires pose a significant threat to human life and property. An autonomous fire detection and fighting robot has the potential to mitigate the challenges associated with firefighting. This paper presents the implementation of an autonomous fire detection and fighting robot equipped with sensors, algorithms, and actuators to detect, locate, and suppress fires autonomously. The robot's mechanical and electrical components were designed, prototyped, and tested in a controlled environment to evaluate its ability to navigate through obstacles, detect fires, and suppress them autonomously. The results show that the robot effectively detects and suppresses fires, making it a promising solution for firefighting applications. The study contributes to the development of autonomous robots for firefighting, enhancing the safety of firefighters and the effectiveness of firefighting operations.

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.

Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.

This paper presents work done as part of a transformation effort towards a greener and more sustainable Aluminium manufacturing plant. The effort includes reducing the carbon footprint by minimising waste and increasing operational efficiency. The contribution of this work includes the reduction of waste through the implementation of autonomous, real-time quality measurement and classification at an Aluminium casthouse. Data is collected from the MV20/20 which uses ultrasound pulses to detect molten Aluminium inclusions, which degrade the quality of the metal and cause subsequent metal waste. The sensor measures cleanliness, inclusion counts and distributions from 20 - 160 microns. The contribution of this work is in the development of business analytics to implement condition-based monitoring through anomaly detection, and to classify inclusion types for samples that failed. For anomaly detection, multivariate K-Means and DBSCAN algorithms are compared as they have been proven to work in a wide range of datasets. For classification, a two-stage classifier is implemented. The first stage classifies the success or failure of the sample, while the second stage classifies the inclusion responsible for the failed sample. The algorithms considered include logistic regression, support vector machine, multi-layer perceptron and radial basis function network. The multi-layer perceptron offers the best performance using k-fold cross-validation, and is further tuned using grid search to explore the possibility of an even better performance. The results reveal that the model has achieved a global maximum in performance. Recommendations include the integration of additional sensor systems and the improvements in quality assurance practices.

The commercial popularity of Virtual Reality attracted educators’ interest and brought new opportunities to the educational landscape. At the same time, Learning Analytics emerged with the promise to revolutionise the traditional practices by introducing ways to systematically assess and improve the effectiveness of instruction. However, the collection of ‘big’ educational data is mostly associated with web-based platforms as they offer direct access to learners’ activities with minimal effort. On the antipode, the nature of VR limits the opportunities for such data collection. Hence, in the context of this work, we present a four-dimensional theoretical framework, that accounts the information that can be gathered from VR-supported instruction, and propose a set of structural elements which can be utilised for the development of a Learning Analytics prototype system. The outcomes of this work are expected to support practitioners to maximise the potential of their interventions and provide inspiration for new ones.

While the role of safe riding behavior as a safety contributor for cyclists has been increasingly studied in recent years, there have been few studies analysing cycling behavior in relation to crash-related outcomes in Baltic countries. Indeed, to the best of our knowledge, this is the first time this issue has been addressed in the case of Latvia. Aim: The objective of this study was to assess the relationships among self-repotted cyclists’ behavior, traffic safety literacy, and their cycling crash involvement rates. Method: A total of 299 cyclists aged M=32.8 from across Latvia (42% females) participated in an online survey, which comprised questions regarding respondents’ demographics, frequency of riding, and the number of crashes in the past five years. The Cycling Behavior Questionnaire (CBQ) and the Cyclist Risk Perception and Regulation Scale (RPRS) were applied to assess cyclists' behavior patterns and traffic safety literacy. Results: According to the findings, it can be inferred that cyclists frequently engage in riding errors and traffic violations while cycling. Those who exhibit more antisocial behavior patterns are also more likely to be involved in road crashes. Conversely, cyclists with greater positive behavior rates more often also tend to possess better knowledge of traffic rules and exhibit a heightened risk perception, indicating a greater awareness of road traffic safety. Conclusion: All in sum, this study underscores key age differences, with older individuals significantly less involved in riding crashes, exhibiting fewer driving errors and a higher level of risk perception, which serves as a crucial factor in road safety. At the practical level, these results stress the need to address both traffic safety literacy and protective cycling factors of cyclists, in a manner to improve overall road safety and promote active transport modes in Latvia.

In recent years, there has been an intensification of weather variability worldwide as a result of climate change. Some regions have been affected by drought, while others have experienced more intense rainfall. The incidence and severity of mouldy grain and mycotoxin contamination during the growing and harvesting seasons have increased as a result of these weather conditions. Addi-tionally, torrential rains and wet conditions may cause delays in grain drying, leading to mold growth in the field. In July 2023, a wheat field in Lecco (Lombardy, Italy) was affected by torren-tial rains that led to the development of the Claviceps fungi. In the field, dark sclerotia were identi-fied on some ears. Wheat ears, kernels and sclerotia were collected and analyzed by LC-MS/MS at IZSLER, Food Chemical Department, in Bologna. The wheat ears, kernels and sclerotia were ana-lyzed for 12 ergot alkaloids (EAs) according to (EU) Regulation 2023/915 (er-gocornine/ergocorninine; ergocristine/ergocristinine; ergocryptine/ergocryptinine; ergome-trine/ergometrinine; ergosine/ergosinine; ergotamine/ergotaminine), after QuEChERS (Z-Sep/C18) purification. The analyzed sclerotia showed significant differences in total alkaloids content that varies between 0.01-0.5% (w/w), according to the results of 2017 EFSA scientific report. EAs de-tected in sclerotia were up to 4951 mg/kg, in wheat ears up to 33 mg/kg, and in kernels were 1 mg/kg. Additional mycotoxins including ochratoxin A, deoxynivalenol, zearalenone, fumonisins, T2-HT2 toxins, and aflatoxins were investigated in wheat kernels, after purification with immu-noaffinity columns (IAC). The analysis revealed the presence of deoxynivalenol in wheat kernels at a concentration of 2251 µg/kg. It is expected that climate change will increase the frequency of extreme weather events. In order to mitigate the potential risks associated with mycotox-in-producing fungi and to ensure the protection of human health, it is suggested to implement of-ficial controls in the field.

Oil and petroleum are strategic resources of great importance to national security. Petroleum and hydrothermal gold deposits may form together, with similar evolutionary trends in their formation, migration, and enrichment. Petroleum reservoirs and gold deposits are closely coupled under certain geological conditions. The solubility of gold in crude oil and its forms of occurrence are important in determining the mechanisms of interaction between gold and petroleum and in facilitating the recovery of gold from gold-bearing petroleum. In this study, the occurrence of gold in crude oil from the Linnan Depression in the Bohai Bay Basin, China, was studied using inductively coupled plasma–mass spectrometry and X-ray photoelectron spectroscopy. Concentrations of gold in crude oil from the Linpan and Shanghe oilfields averaged 44.5 ppb, which is well above the minimum concentration required for hydrothermal gold mineralization. Gold has an affinity with carbon, oxygen, and sulfur, and its concentration in crude oil is positively correlated with total acid and sulfur contents. We speculate that gold may exist in crude oil as complexes with organic acids or thiols, with crude oil thus being a transport medium for gold.

Water mist fire suppression technology has attracted an increasing interest from the field of fire protection services such as fire safety for buildings, ships, spacecraft, libraries and museums due to its non-toxic and high efficiency in the suppression of a wide variety of fires. To support the technological development of water mist fire suppression system and its application areas, this review introduces the concept of water mist system and discusses its suppression mechanisms in comparison with other fire protection systems. The recent application areas of water mist system are surveyed for class A fires involving combustible solid materials such as wood, paper and textiles; class B fires involving flammable liquids such as petrol, oils, lubricants, paints and waxes; class C fires involving flammable gasses such as natural gas and liquefied petroleum gas; fires involving electrical (class E) equipment such as computers and information technology facilities; and the class F fires involving flammable cooking oils and fats. Finally, the paper concludes the review by identifying the current research trends, and providing the future direction for water mist technology and applications.

Based on Chinese CDCP report on COVID-19, 14% of patients presented severe disease and 5% critical conditions. The average case-fatality rate was 2.3%, but mortality was as high as 49% in patients with critical illness. Serious life threatening thromboembolic complications have been found in 71·4% of non-survivors and micro/macro angiopathic coagulopathy has been found, also at autopsy, with highly increased neutrophil number, fibrinogen, concentrations of D-dimer and FDPs and NETs, ATIII decrease and normal number of platelets. A cytokine storm and interaction between inflammation and coagulation has been advocated as explanation of hypercoagulability. In this paper, it’s hypothesised that SARS-CoV-2 infection of alveolar cells induces recruitment of innate responder neutrophils, which release proteases and NETs inducing endothelial damage/endotheliopathy and imbalance of the four major proteolytic cascades (coagulation, complement, fibrinolysis and kallikrein) with prevalence of activators over inhibitors and consequent thrombotic complications. Platelets adhesion to damaged endothelium and the presence of ULVWF multimers, due to decreased ADAMTS13, contributes to the state of hypercoagulability. Neutrophil innate “unfriendly fire” response can be identified as the trigger of a “proteolytic storm”, responsible for subsequent well known prothrombotic condition and “cytokine storm”. The hypothesis explains also the pathology of recently described systemic “Kawasaki Disease like” vasculitis cases in Covid-19 young ill patients.

CD74 is a type II cell surface receptor found to be highly expressed in several hematological and solid cancers, due to its ability to activate pathways associated with tumor cell survival and proliferation. Over the past 16 years, CD74 is emerging as a commonly detected fusion partner in multiple oncogenic fusion proteins. Studies have found CD74 fusion proteins in a range of cancers including lung adenocarcinoma, inflammatory breast cancer, and pediatric acute lymphoblastic leukemia. To date, there are five known CD74 fusion proteins, CD74-ROS1, CD74-NTRK1, CD74-NRG1, CD74-NRG2a, and CD74-PDGFRB, with a total of 16 different variants, each with unique genetic signatures. Importantly, the occurrence of CD74 in the formation of fusion proteins has not been well explored despite the fact that ROS1 and NRG1 families utilize CD74 as the primary partner for the formation of oncogenic fusions. Fusion proteins known to be oncogenic drivers, including those of CD74, are typically detected, and targeted after standard chemotherapeutic plans fail and the disease relapses. The analysis reported herein provides insights into early intervention of CD74 fusions and highlight the need for improved routine assessment methods so that targeted therapies can be applied while they are most effective.

The Victorian Government Inquiry into wildfires that killed 173 people in 2009 has driven an Australian policy shift from self-evacuation or staying and defending a well-prepared property (‘go or stay’) to self-evacuation under catastrophic fire weather (‘leave early’). The Inquiry also led to the establishment of national ‘performance standards’ for Private Fire Shelters (PFS, that also known as bunkers). Nonetheless, incorporation of PFS into national bushfire policy remains embryonic with only Victoria having streamlined accreditation and planning approval processes. Arguments against PFS include potentially engendering complacency about preparing dwellings to survive fire and encourage risky behavior in response to a fire threat. Counteracting these arguments is research that shows that residents without PFS have low engagement with bushfire preparation and typically delay evacuation. In any case, because wildfire is unpredictable it is accepted that self-evacuation plans must have fallback positions that include sheltering ‘in place’ from the bushfire, making properly used and well maintained PFS an important element of bushfire safety. A less discussed barrier to PFS uptake outside Victoria appears to hinge on a lack of clarity about obligations for their design, certification, and consistency with planning approvals. The escalating Australian fire crisis demands much greater research and development in legal frameworks, policy and planning processes for PFS, as well as design and construction standards.

This study aims to spatially analyze the distribution characteristics of vacant homes in Asan-si, Chungcheongnam-do, and understand the differences in factors causing vacant homes by type. We use a variety of statistical methods to examine these factors and identify key influencing factors such as the number of vacant stores, changes in overall population, land use complexity, and physical characteristics of land and buildings. The findings reveal that the factors contributing to the occurrence of vacant homes vary significantly by region and property type, highlighting the need for region-specific policies. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural village, the average number of floors and land use complexity play a major role. This study underscores the diversity of factors influencing vacant homes, suggesting that policies should be tailored to the unique characteristics of different regions to effectively address this issue.

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent application, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This research focuses on implementing IoT technology to enhance decision-making for crop yield optimization and sustainable practices on a real farm. By collaborating with a farm in the southern region of Zagreb, Croatia, farmers were trained on sensor usage and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial skills. The DMAIC methodology was applied to define the problem and measure relevant parameters. The analysis demonstrated consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It suggests the potential for estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, the PCA biplot analysis highlighted that pH value behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

The company strategic procurement function makes a significant contribution to overall corporate success, and yet remains under-researched in terms of digitalisation and digital maturity. This research adopts an inductive case study approach, using qualitative data from in-depth inter-views with industry practitioners, to develop and apply a digital maturity model for the deployment of strategic procurement analytics. The research presents a snapshot of the digital maturity of the strategic procurement function of a global aircraft manufacturer, and finds that current exploitation of analytics remains constrained by a range of factors, including the need for close compliance with regulatory norms. Thematic analysis of the interview material provides the basis for the development of the maturity model, which - although geared to a specific industry context - is nevertheless of relevance in other business environments. The research thus con-tributes to the existing literature in this field, and will also be of interest to procurement professionals. However, the research clearly has its limitations, not least in that it is based on just one industry case, and cross-industry generalisations from the findings must therefore be treated with caution.

The rate of fires, and particularly residential-fires, is a serious concern in industrialized countries. However, there is considerable uncertainty regarding the reported numbers of residential-fire incidents as official figures are based on fires reported to fire response agencies only. This population-based study aims to quantify the total number of residential-fire incidents regardless of reporting status. The cohort comprised linked person-level data from Fire and Rescue New South Wales (FRNSW) and health system and death records. It includes all persons residing at a residential address in New South Wales, Australia, that experienced a fire between 1 January 2005 – 31 December 2014. Capture-Recapture method was used to estimate the underreporting number of residential fire-related incidents. Over the study period, 43,707 residential-fire incidents were reported to FRNSW and there were 2,795 residential fire-related health service utilization of which 2,380 were not reported. Using Capture-Recapture method, the total number of residential-fire incidents was estimated at 267,815 to 319,719 which is more than six times the official records. This study found that 15% of residential-fire incidents that were identified in health administrative dataset were reported. The residential-fire incidents that were not reported occurred mainly in socio-economically disadvantaged areas among the males and adults.

The planning of bicycle infrastructure across our cities remains is a complex task involving many key stakeholders including the community, who traditionally have had limited involvement in the planning process. This research develops an interactive bicycle prioritisation index tool which includes participatory spatial and textual citizen feedback. The research involves three components. Firstly, a survey of current cyclists in Sydney, their current level of participation, priorities in investment in cycling, and preferred locations for cycling infrastructure. Secondly, it documents the development of an interactive digital bicycle planning tool which is informed through citizen feedback. Thirdly, it evaluates the approach in conversation with potential end-users, including government, planning practitioners, and advocacy group members. The research proposes the combination of multiple passive and active data traces with end-user evaluation to legitimise the citizen co-design of bicycle investment prioritisation initiatives. A case study approach has been taken, focusing on the city of Sydney, Australia. The bicycle planning support system can be used by cities when engaging in cycle prioritisation initiatives, particularly with a focus on integrating citizen feedback and navigating new and complex data landscapes introduced through recent, passively collected big data sets.

One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.

Dietary factors are one of the risk factors that can impact the brain chemistry, which leads to mental distress. Based on our data mining approach, we found that mental distress in men is associated with eating unhealthy food. Our aim in this paper is to apply results from our big data analytics approach to inform system dynamics (SD) modeling to investigate the causal relationships between brain structures, nutrients from food and dietary supplements, and mental health. We perform descriptive analysis based on a large data set to estimate the SD modeling parameters. Finally, we calibrate the model towards a time series data collected for individuals on their dietary and distress patterns. The results reveal that bridging these different methodologies leads to further insights from the SD model and decreases the error of calibrated parameter values. Future research is needed to validate our initial results for investigating the relationship between mental distress and dietary intake.

Drought is a long period of unusually low rainfall and can have serious effects on people's lives. It is caused by various factors such as climate change, deforestation, and the overuse of water re-sources. In the Somali region of Ethiopia, Kebri Dehar is the most at risk and most likely to have a drought. The lack of rainfall and high temperatures in Kebri Dehar have led to drought. Man-aging drought is the most challenging and complex problem. The purpose of this study is to as-sess household perception of drought hazards occurrence and adaptation. Additionally, the study aims to design a drought resilience mechanism matrix to mitigate its impact on the local commu-nity. This study used the Heckman two-stage model, which is useful in measuring drought per-ception as it accounts for potential selection bias and allows for the identification of factors that influence individuals' perceptions of drought. Gender, severity of drought, water shortage, tem-perature, drought frequency, and long dry season all have a positive effect on adaptation of resil-ience mechanisms to drought hazards, while food availability and rainfall have a negative effect. It is important for governments and individuals to act to mitigate the effects of the drought. Ef-forts to address the issue require a multi-participation approach that involves household, com-munity, and institutional level participation is needed for adopting drought resilience mecha-nisms.

For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.

The Drinking Water Directive (EU) 2020/2184 includes the parameter microcystin-LR, a cyanotoxin, which drinking water producers need to analyze if the water source has potential for cyanobacterial blooms. In light of the increasing occurrences of cyanobacterial blooms worldwide and given that more than 50 percent of the drinking water in Sweden is produced from surface water, both fresh and brackish, the need for improved knowledge about cyanotoxin occurrence and cyanobacterial diversity has increased. In this study a total of 98 cyanobacterial blooms were sampled in 2016-2017 and identified regarding their toxin production and taxonomical compositions. The surface water samples from freshwater lakes throughout Sweden including brackish water from 8 east coast locations along the Baltic Sea were analyzed for their toxin content with LC-MS/MS and taxonomic composition with 16S rRNA amplicon sequencing. Both the extracellular and the total toxin content was analyzed. Microcystin´s prevalence was highest with presence in 82% of blooms, of which as free toxins in 39% of blooms. Saxitoxins were found in 36% of blooms in which the congener decarbamoylsaxitoxin (dcSTX) was detected for the first time in Swedish surface waters at four sampling sites. Anatoxins were most rarely detected, followed by cylindrospermopsin, which were found in 6% and 10% of samples, respectively. As expected, nodularin was detected in samples collected from the Baltic Sea only. The cyanobacterial operational taxonomic unit (OTU) with the highest abundance and prevalence could be annotated to Aphanizomenon NIES-81 and the second most profuse cyanobacterial taxon to Microcystis PCC-7914. In addition, two correlations were found, one between Aphanizomenon NIES-81 and saxitoxins and another between Microcystis PCC-7914 and microcystins. This study is of value to drinking water management and scientists involved in recognizing and controlling toxic cyanobacteria blooms.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Business Intelligence, defined by [1] as "the ability to understand the interrelations of the facts that are presented in such a way that it can guide the action towards achieving a desired goal", has been used since 1958 for the transformation of data into information, and of information into knowledge, to be used when making decisions in a business environment. But, what would happen if we took the same principles of business intelligence and applied them to the academic environment? The answer would be the creation of Academic Analytics, a term defined by [2] as the process of evaluating and analyzing organizational information from university systems for reporting and making decisions, whose characteristics allow it to be used more and more in institutions, since the information they accumulate about their students and teachers gathers data such as academic performance, student success, persistence, and retention [5]. Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, using ASP.Net technology and trusting storage in the database engine Microsoft SQL Server, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, without having direct access to institutional databases, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of self-evaluation, and adaptation of content and dynamics in the classrooms, and finally for the head of the program to make decisions.

The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18\% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

This study investigated the presence and characteristics of Juglans neotropica in three ecosystems in Southern Ecuador: Montane Evergreen Forest, Evergreen Seasonal Lower Montane Forest, and Semideciduous Foot Montane Forest. The main focus was on the species' multipurpose nature as both a fruit and timber source. Six study sites, totaling at least 0.5 hectares each, were established, with four in Loja province and two in Zamora Chinchipe province.The results showed significant differences in dendrometric variables across the sites, with the most favorable growth recorded in The Tundo, where trees exhibited an average diameter at breast height (DBH) of 45.16 cm, basal area (G) of 1.41 m2, total height (TH) of 19.22 m, canopy height (CH) of 13 m, cubic volume (CV) of 3.55 m3, and total volume (TV) of 5.22 m3. The species displayed a clumped distribution pattern, as indicated by a Morisita index greater than 1. Regarding abundance, the highest density of 297 trees per hectare was found in Argelia, while Victoria had the lowest density of 46 trees per hectare. This research highlights the importance of Juglans neotropica as a multipurpose species, providing both valuable fruits and wood resources in the studied ecosystems of Southern Ecuador.

In this review paper, the occurrence in the plant kingdom and the biological activities associated to two specific phenyl-ethanoid glycosides i.e., leucosceptoside A and leucosceptoside B, were shown and discussed. This is the first work ever done on such subject. Analysis of the literature data clearly indicates that leucosceptoside A is much more common in plants and exerts many more biological activities than leucosceptoside B even if this also presents some important elements. All of this was widely discussed in this paper.

Social media data is widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple data sets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API.

Invasive macrophytes are considered problematic in natural environments and hydroelectric reservoirs. Climate changes, the occurrence of watercourses, and biotic interactions influence the biological invasion of macrophytes. Native species can correlate with invasives positively or negatively. Urochloa subquadripara is an invasive in natural or disturbed habitats co-occurring with the natives Eichhornia crassipes and Salvinia minima. Aquatic plant communities can be altered by climate change, so species distribution models (SDMs) are important tools for predicting in-vaded areas. This work aimed to make an SDM for U. subquadripara correlating with the poten-tial distribution of native species E. crassipes and S. minima. Occurrence data for U. subquadripara, E. crassipes, and S. minima were collected from databases and in consultation with the published literature. Parameters encompassing biological information of the species were entered into the CLIMEX software and used to generate the Ecoclimatic Index (EI). The species co-occurrence was performed based on multicriteria decision-making (MCDM), and weights were assigned using the analytical hierarchy process (AHP). It was observed that U. subquadripara, E. crassipes, and S. minima have a higher occurrence in tropical and subtropical regions; however, it is predicted that, from climatic changes, these species may move to high latitudes. According to climate change, the risk of invasion by U. subquadripara in the northern hemisphere is mainly in lakes, while in the southern hemisphere, the areas conducive to invasions are rivers and reservoirs. In general, emerging and floating macrophyte species such as U. subquadripara, E. crassipes, and S. minima will be favored, causing suppression of submerged species. Therefore, identifying the potential distribution of these species allows the creation of pre-invasion intervention strategies.

Microplastics (of size < 5mm) pollution in our environment is of current concern by researchers, public media and non-governmental organizations. Implications by their presence in aquatic and soil ecosystems have been well studied and documented, but less attention has been paid on airborne microplastics (MPs). Studies concerning airborne microplastics started from 2016 and only a few (n=7) have been published till date. Although, studies may increase in the following years, since air is very important for human survival. Microplastics have been observed in atmospheric fallouts in indoor and outdoor environments using a sampling or vacuum pump, rain sampler and/or particulate fallout collector. Identification and quantification have been carried out by visual, spectroscopic and spectrometric techniques. Factors such as meterological, climatic and anthropogenic influence the distribution and movement of airborne MP. Human exposure may be through inhalation or dermal route with their potential biopersistence and translocation. Ingestion may cause localized inflammation and cancer due to responses by the immune cells, especially in individuals with compromised metabolism and poor clearance mechanisms. Ecological risks involve possible contamination of the ecosystem through a dynamic relationship of MPs in soil, water and air forming a MP contamination cycle. The present review aimed at providing a comprehensive overview of current knowledge or information regarding microplastics in air, identifies gap in knowledge and give suggestions for future research.

The high reliable correlations between CBR and DCP in general proposed are presented in this paper. We analyzed the test data and correlations from many researchers to find the best equation for prediction the CBR from DCP test. The findings show that all correlations from existing paper are based on local material and cannot apply to soil in a different location in general purpose. New correlations between CBR and DCP were proposed and validated in this paper for general uses. The validation confirmed that these new correlations can predict well with higher R2.

Background: In long-term care facilities, there are many residents who do not have the ability to seek shelter by themselves in the case of an emergency. Thus, it is extremely important that the staff of nursing homes are equipped with correct disaster prevention concepts, emergency survival responses, and hazard mitigation measures. Purpose: Discuss the intervention effectiveness of different fire prevention and emergency response trainings at nursing homes and the relationship and predictivity of awareness to self-efficacy. Method: Recruit staff from two nursing homes through purposive sampling, using a two-team pre-and post-test design to collect results from 41 individuals in the experiment group and 40 individuals in the control group. The research tool is the “Nursing Home Fire Prevention and Emergency Response Awareness and Self-Efficacy Scale,” to compare the effectiveness of advanced and general fire safety trainings. Result: After receiving improved advanced fire safety training, the total score and the result of the experiment group on fire prevention and emergency response awareness and self-efficacy had both performed better than the control group who received general fire safety training (p < .001); fire prevention and emergency response awareness has significant and positive correlation with self-efficacy (r=.601, p < .001), and awareness is a significant predictor variable to self-efficacy (p < .001). Conclusion/Practical Application: This study finds that the key to improving learning effectiveness includes adding fire science concept chapter when creating fire safety training material in order to strengthen basic awareness; fire safety training should comprehensively introduce all related duty responsibilities of staff fire defense formation, in turn enabling mutual responsive support for the needs of the site; also, to become familiarized with the knowledge requires appropriate frequency of training and enhancing the staff’s awareness to fire prevention and emergency response, which is the most important key of learning effectiveness.

This work presents the dynamics of forest clearing in the Brazilian Amazon during the period 2006–2019 in which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State and in the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess greenhouse gas (GHG) emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was also observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, a significant intensification comparatively higher than the previous ten years which was on an average of 6,760 km2. The steady increase of deforestation in the Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon (295 million tons of net CO2 equivalent in the year 2019). Better correlation of deforestation and fires occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, which degrades primary forest surrounding the deforested areas.

The peri-urban and urban forests in Greece occupy a total area of 105.353 ha. In these vulnerable ecosystems, fire constitutes a natural disaster presenting particular challenges and specific difficulties. These include the high number of visitors as well as forests characteristics - such as the presence of particularly flammable tree species and high accumulation of combustible biomass – that make the on-start of fires more likely. The main purpose of the current research is to identify the optimum combination of silvicultural treatments to efficiently reduce potential forest fire severity and to facilitate their successful suppression by firefighting crews. In order to simulate the basic fire environment of urban forests, two main experimental plots were established and several tree and topographical characteristics were measured. Additionally, the NEXUS wildfire system was used to simulate forest fire potential behavior before and after the adoption of the silvicultural treatments that altered critical characteristics of the forest fire environment. The results clearly show that specific silvicultural prescriptions altered the type of forest fire spreading potential, revealing the overall efficiency of preventing actions during forest management.

Fires are the leading cause of death, serious injury and property damage. In the past, schools, temples and government offices had more frequent fires than they should. Statistics showed that the number of fires between 2017 to 2022 amounted 13,593 cases which mostly occurred in the school, temple and government offices (40.0% of all buildings). Moreover, it causes more damage among disabled especially the blinds who has a limited vision. Therefore, this cross-sectional purpose of this study was to assess fire risk including management model in school for the blind. The fire checklists, brainstorming and Analytic Hierarchy Process (AHP) were applied to estimate the fire risk in school for the blind building. The findings revealed an inherent fire hazard factors with a risk score equal to 3.2830 and evacuation factors with a risk score equal to 3.3178 were acceptable risk except the fire control factors with a risk score equal to 1.4320 was unacceptable risk may cause an impact on life, health, property and public communities. Eventually, efforts should be made to supervise those risk factors by designing suitable activities to reduce undesirable conditions in school for the blind.

. Gut microbiome reflects healthy and predicts possible disease in hosts. A holistic view of this community is needed, focused on identification species, and on the dissection of how species interact with their host and with each other, regardless of whether their presence is beneficial, inconsequential, or detrimental. Distribution of gut-associated eukaryotes within and across NHPs is likely driven by host behavior and ecology. To ascertain the existence of free-living amoebae (FLA) in the gut of wild and captive non-human primates (NHPs), 01 sample stools were collected and submitted to culture-dependent and microscopy examination and DNA sequencing. FLAs were detected in 45.4% (46/101) of fecal samples analysed and their morphological characteristics matching those of Acanthamoeba spp, Vermamoeba spp, and fan-shaped amoebae of the family Vannellidae. Sequence analysis of the PCR- products revealed that the suspected amoebae are highly homologous (99% identity and 100% query coverage) with Acanthamoeba genotype T4 and Vermamoeba vermiformis amoebae. The results showed a great diversity of FLAs in the NHP microbiome, which may pose a potential risk to the health of NHPs. To the best of our knowledge, this is the first report the presence of FLAs in NHPs naturally infected. However, it is not known whether gut-borne FLA are exploiting a viable ecological niche, or whether they are simply transient residents in the gut.

Balínt Syndrome is an acquired disorder manifesting in the inability to recognize several objects at once (simultagnosia), inaccurate visually guided limb movements despite intact motor function (optic ataxia) and the inability to make accurate voluntary saccades to visual targets despite demonstrating unrestricted range of eye movements (ocular motor apraxia). Here we report the first case of a patient presenting with Balínt Syndrome caused by a platelet-derived growth factor receptor A mutation (PDGFRA)-induced Hypereosinophilic Syndrome (HES).

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.

Ensuring fire safety is an important condition for the successful operation of industrial enterprises. It is necessary to detect the threat of fire before ignition. An indicator of danger is the appearance of gas, which occurs as a result of unauthorized heating of equipment. Gas sensors make it possible to detect the appearance of gases characteristic of the decomposition of materials at the stage of smouldering and pyrolysis, which cause a change in the composition of the atmosphere. This paper presents a mathematical method for the rational placement of gas analyzers for premises of potentially hazardous industries. It is the rational placement of gas sensors, which ensures safety, taking into account economic feasibility that determines the effectiveness of their use. For the first time, the analysis of gas accumulation during the movement of the flow from the source to the ceil-ing, then propagation to the location of the sensors, is used for the first time to select the placement of gas sensors. Depending on the permissible values of the gas volume, the placement and number of gas sensors are selected. The calculation of the conditions for the rational placement of gas sen-sors for the most common indicators, such as carbon monoxide and hydrogen at different sizes and heights of rooms, is carried out. The method of calculating the location of gas analyzers makes it possible to diagnose and predict fire hazard at technological facilities on the basis of gas control, and compare the obtained values with the maximum permissible values. This method provides cal-culation of the coordinates of the gas source from the coordinates of the triggered sensors. Knowing the coordinates of all sensors, the coordinates of the sensors that gave the alarm, and the formula for determining the volume of the gas cloud under these sensors, it is possible to assess the localization of the source of dangerous gas in the protected room. This increases the efficiency of the fire safety system. The method makes it possible to control the layout of sensors depending on the volume of protected premises, types of production, the creation of new sensors, changes in the composition of hazardous gas in different technological conditions.

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), has emerged as a significant health concern following the COVID-19 pandemic. Molecular mechanisms underlying the occurrence and progression of long COVID include viral persistence, immune dysregulation, endothelial dysfunction, and neurological involvement, and highlight the need for further research to develop targeted therapies for this condition. While a clearer picture of the clinical symptomatology is shaping, many molecular mechanisms are yet to be unraveled, given their complexity and high level of interaction with other metabolic pathways. This review summarizes some of the most important symptoms and associated molecular mechanisms that occur in long COVID, as well as the most relevant molecular techniques that can be used in understanding the viral pathogen, its affinity towards the host and the possible outcomes of host-pathogen interaction.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

This study aimed to detect picobirnavirus (PBV) in the fecal samples of wild and domestic animals from 2014 to 2016 in the Amazon biome. Fecal samples from different animals, including birds (n=41) and mammals (n=217) were used. The PAGE test showed negativity for PBV. However, 32 samples (12.4%, 32/258) showed positive results in RT-PCR analyses. Among the positive sam-ples, pigs and cats, both with 28.12% (9/32), registered the highest frequencies. In phylogenetic analysis, eight sequences from positive samples grouped in the Genogroup 1 of PBV (PBV GI). PBV occurrence was significantly related to cats and pigs, but not other mammals or birds, in-dependently of their geographical origin. Nucleotide analysis demonstrated similarity among the feline group, but the absence of a defined structure between the clades. PBVs are highly wide-spread viruses that can affect the most diverse types of hosts in the Amazon biome, including humans.

Antarctica and its surrounding environment are considered untouched and thought that it is free from microplastics (MPs) pollution. However, recent studies and science projects have reported MPs in both water and sediment in the South Polar Regions. The reports state that the MP’s pollution occurs in this region due to fishing, tourism, and research activities by the nearby countries and nature's circulation is also part of it. The Antarctic Treaty System (ATS) has received attention on MP’s pollution and initiated research on it. MPs are tiny plastic particles with a size of less than 5 mm. It has two types, 1. Primary MPs have been manufactured directly for various applications like cosmetics and scrubbing etc 2. The secondary MPs are generated by photochemical degradation of large plastics.Although several studies have been done there is a quite gap in our understanding of the concentration, characteristics, and impact of plastics on the ecosystem of the Antarctic Region. The impact of MP’s pollution in this region may be very high. The presence of MP is a serious issue that is affecting not only the aquatic environment but also humans. It is an alarming situation that causes environmental damage. The main objective of this paper is to review MP's introduction, occurrence, sources, harmful effects, and detection methods. This review highlighted the various methodologies and analyses like density separation, microscope observation of MP’s properties Fourier-transform infrared spectroscopy (FTIR), and Raman spectrometer respectively, and urged for more research in the future and gave several recommendations to maintain the pristine region near Antarctica.

The Gulong shale oil reservoir is formed in freshwater to slightly saline lacustrine basins, mainly consisting of pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of fluid occurrence space and the variation of fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores, and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, the porosity measured by saturation method was calibrated using NMR T2 spectra. Combined with the identification of fractures in shale cores using micro-CT and the analysis of porosity and permeability parameters, it was found that the permeability of shale cores was related to the development of fractures in shale cores. Through the testing and analysis of NMR T1-T2 two-dimensional spectra of the shale cores before and after saturation with oil, it was found that the shale mainly contains heavy oil, light oil, and clay-bound water, and they were distributed in different positions in the T1-T2 spectrum. Finally, the T1-T2 two-dimensional spectra of the shale core at different imbibition stages were analyzed, and it was found that the saltwater mainly entered the minuscule inorganic pores of clay minerals during the imbibition process, and squeezed the larger-sized inorganic pores containing light oil through the hydration expansion effect, thus expelling the light oil from the shale core and achieving the purpose of enhanced oil recovery.

Food and nutrition insecurity, affecting 30% of the global population in 2020, poses a significant challenge to achieving Sustainable Development Goal (SDG) 2 - Zero Hunger. Urbanization, particularly in sub-Saharan African cities e.g. Lagos, Nigeria, exacerbates these issues, impacting resources and contributing to informal settlements. There is emphasis on innovative solutions in urban farming, with Recirculating Aquaculture Systems (RAS) emerging as one of the promising approach due to its efficiency in space and capital utilization. However, maintaining optimal conditions in RAS, crucial for SDG 2 and 11, requires robust water quality monitoring. This study explores the availability of digital tools for water quality monitoring in small-scale urban RAS, evaluating handheld devices and IoT sensors' reliability through t-test statistical method. The results aim to guide practitioners in selecting effective monitoring tools, contributing valuable insights for sustainable aquaculture in urban areas, particularly in sub-Saharan Africa, where access to affordable digital solutions is pivotal for success and can attract youth to agri-food technologies.

Global climate change and associated environmental extremes present a pressing need to understand and predict social-environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we use the Wisdom-Knowledge-Information-Data (WKID) Innovation framework to inform solutions-oriented science in an integrated Research to Commercialization (R2C) model that explores market viability for value-added analytics. We conduct a case study using this R2C model to address the wicked wildfire problem. By integrating WKID and human centered design (HCD), through an industry-university partnership called the Climate Innovation Collaboratory between the University of Colorado Boulder and Deloitte Consulting, we systematically evaluated 39 different user stories across 8 user personas and identified common gaps, how to define information technologies that add value, and how to develop a marketable product. This R2C model could enable transition of knowledge to operational implementation informing policy and decision makers, tasked with addressing some of our most challenging environmental problems.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.