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An evaluation of the role of inductive confirmation in relation to the conjunction fallacy
Get PDFInductive confirmation has been proposed as a mechanism giving rise to the conjunction fallacy. For each of five separate vignettes, probability estimates were obtained for a neutral event, for a second event: i.e. the “added conjunct”, and for their conjunction. The added conjunct was selected such that it was inductively confirmed, either by some background evidence provided in the vignette or by the other component event. So as to achieve sufficient statistical power, multilevel models were used to analyse the data. For the added conjunct, the level of confirmation and the posterior probability were significantly associated such that higher levels of confirmation were associated with larger probability estimates. However, there was no significant association between the level of confirmation on the one hand and the incidence of the fallacy and the conjunctive probability on the other
Personhood as projection: The value of multiple conceptions of personhood for understanding the dehumanisation of people living with dementia
Get PDFWe examine the concept of personhood in relation to people living with dementia and implications for the humanity of care, drawing on a body of ethnographic work. Much debate has searched for an adequate account of the person for these purposes. Broad contrasts can be made between accounts focusing on cognition and mental faculties, and accounts focusing on embodied and relational aspects of the person. Some have suggested the concept of the person is critical for good care; others suggest the vexed debates mean that the concept should be abandoned. We argue instead that the competing accounts illuminate the very tensions in personhood which are manifest for all of us, but especially for people living with dementia, and argue that our account has explanatory power in shedding light on how precisely dehumanisation and constraints on agency may arise for people living with dementia, and for staff, within an institutional context
Blockchain Security Encryption to Preserve Data Privacy and Integrity in Cloud Environment
Get PDFBlockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest
Effects of propofol and sevoflurane on social and anxiety-related behaviours in sleep-deprived rats
Get PDFBackground
Sleep disorders can profoundly affect neurological function. We investigated changes in social and anxiety-related brain functional connectivity induced by sleep deprivation, and the potential therapeutic effects of the general anaesthetics propofol and sevoflurane in rats.
Methods
Twelve-week-old male Sprague–Dawley rats were subjected to sleep deprivation for 20 h per day (from 14:00 to 10:00 the next day) for 4 consecutive weeks. They were free from sleep deprivation for the remaining 4 h during which they received propofol (40 mg kg–1 i.p.) or sevoflurane (2% for 2 h) per day or no treatment. These cohorts were instrumented for EEG/EMG recordings on days 2, 14, and 28. Different cohorts were used for open field and three-chambered social behavioural tests, functional MRI, nuclear magnetic resonance spectroscopy, and positron emission tomography imaging 48 h after 4 weeks of sleep deprivation.
Results
Propofol protected against sleep deprivation-induced anxiety behaviours with more time (44.7 [8.9] s vs 24.2 [4.1] s for the sleep-deprivation controls; P<0.001) spent in the central area of the open field test and improved social preference index by 30% (all P<0.01). Compared with the sleep-deprived rats, propofol treatment enhanced overall functional connectivity by 74% (P<0.05) and overall glucose metabolism by 30% (P<0.01), and improved glutamate kinetics by 20% (P<0.05). In contrast, these effects were not found after sevoflurane treatment.
Conclusions
Unlike sevoflurane, propofol reduced sleep deprivation-induced social and anxiety-related behaviours. Propofol might be superior to sevoflurane for patients with sleep disorders who receive anaesthesia, which should be studied in clinical studies
Novel approaches to prevent and treat intracellular bacterial infections
No full textInfectious diseases caused by intracellular bacterial pathogens present a considerable health threat, requiring innovative strategies to overcome the limitations of traditional antibiotics, which cannot easily penetrate and accumulate inside host cells. This Research Topic brings together a collection of papers that explore “Novel approaches to prevent and treat intracellular bacterial infections”, highlighting common themes that emerge from the research
A Novel AI-based Approach for Modelling the Fate, Transportation and Prediction of Chromium in Rivers and Agricultural Crops: A Case Study in Iran
Get PDFChromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves
Event-based flood data imputation for infilling missing data in real-time flood warning systems
Get PDFReal-time flood warning systems as part of digital and innovative non-structural solutions have been widely used to prepare decision makers, operators, and affected population to alleviate socio-economic flooding consequences [1]. Many models have been introduced recently to provide more accurate flood forecasts with longer lead times. However, they rely highly on availability of input data which may contain missing values in measurement for one or more timesteps mainly due to wide range of reasons such as random/systematic errors and blunders. Hence, real-time early warning systems cannot be operated properly unless these missing data are properly infilled [2]. Despite data imputation techniques have been mainly employed in pre-processing step of historical data i.e., models training and validation, they have not been properly elaborated in real-time operation practically [3].
This paper aims to propose a new event-based data imputation method for infilling rainfall and water level missing data appearing in real-time operation of flood early warning systems. Event identification is first used to divide the real-time data into the wet or dry weather conditions which then are used for selecting the best strategy of infilling missing data. Imputation decision framework takes advantage of various imputation techniques including t-copula, move-median, and kriging based on external available benchmarks and temporal location of missing data. Proposed methodology is tested in real-world case study of urban drainage system in London, UK. Conventional techniques such as linear regression, kriging, nearest neighbourhood, t-copula, inverse distance, and similar calendar are first compared together and best techniques are then tested with proposed methodology in three real-time scenarios as (1) missing rainfall intensity, (2) missing water level, (3) missing both rainfall and water level. Recurrent neural network model used for flood forecasting and results are demonstrated for the next 3hr-ahead predictions.
Results show the proposed method can reduce root mean square error (RMSE) from 55% to 13%, 43% to 12%, and 97% to 17% for the above scenarios, respectively. Furthermore, using external benchmark data resources, i.e. other near rainfall/water level stations, shows very efficient when missing data appears at early steps of rainfall events where selected conventional techniques suffer from predicting rainfall pattern. Finally, when both water level and rainfall intensity were missing, the proposed imputation method can reduce RMSE from 197mm to 117mm (RMSE was originally 100 for no missing data) for 3hr-ahead predictions. Generally, this study shows the proposed imputation method can better infill the missing data, especially those in the flood event by using correlated data in other weather/gauging stations and flexibility in applying different methods.
References
[1] Piadeh, F., Behzadian, K., Alani, A.M. (2022). Multi-Step Flood Forecasting in Urban Drainage Systems Using Time-series Data Mining Techniques. Water Efficiency Conference, West Indies, Trinidad and Tobago, repository.uwl.ac.uk/id/eprint/9690 [Accessed 31/12/2022].
[2] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, 127476.
[3] Ben Aissia, M., Chebana, F., Ouarda, T. (2017). Multivariate missing data in hydrology–Review and applications. Advances in Water Resources, 110, pp.299-309.
How to cite: Piadeh, F., Behzadian, K., and Rizzuto, J. P.: Event-based Flood Data Imputation for Infilling Missing Data in Real-time Flood Warning Systems, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4524, https://doi.org/10.5194/egusphere-egu23-4524, 2023
Estimating Anthropometric Soft Biometrics: An Empirical Method
Get PDFFollowing the success of soft biometrics over traditional biometrics, anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video. Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics. However, one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video. The level of accuracy is usually affected by several contextual factors such as overlapping body components, an angle from the camera, and ambient conditions. Exploring and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or retrieval. For this purpose, anthropometric soft biometrics, which are originally geometric measurements of the human body, can be computed with ease and higher accuracy using landmarks information from the human body. To this end, several key contributions are made in this paper; i) summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body, ii) a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field, iii) an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system, and iv) finally, a novel bag of anthropometric soft biometrics containing a list of anthropometrics is presented those are practically possible to measure from an image or video. To the best of our knowledge, anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained and unconstrained scenarios and they also minimize the approximation level of feature value estimation than traditional biometrics. In our opinion, anthropometric soft biometrics constitutes a practical approach for recognition using closed-circuit television (CCTV) or retrieval from the image dataset, while the bag of anthropometric soft biometrics presented contains a potential collection of biometric features which are less sensitive to contextual factors
Niosomal Delivery of Celecoxib and Metformin for Targeted Breast Cancer Treatment.
Get PDFBreast cancer continues to be a prominent worldwide health concern and requires continued investigation into innovative therapeutic approaches. Here, we report the first investigation into the therapeutic efficacy of combining Metformin (MET) and Celecoxib (CXB), both in free and niosomal form, for the treatment of breast cancer. Our investigation encompassed the characterization of these niosomal drug carriers, their stability assessment, and their effect on breast cancer cell models. The thin-film hydration technique was employed to prepare niosomes with spherical, uniform-size distributions and high encapsulation efficiencies. The niosomes were characterized by TEM, particle size analyzer, and ATR-FTIR. The niosomes with an average size of 110.6 ± 0.6 and 96.7 ± 0.7, respectively, for MET and CXB were stable when stored at 4 °C for three months with minimal drug leakage, minor changes in encapsulation efficiency and size, and unchanged physicochemical parameters. Evaluation in two-dimensional (2D) and three-dimensional (3D) viability assays demonstrated an increased cytotoxicity of encapsulated drugs when compared to their free-drug counterparts. Additionally, the combination of Metformin Niosomal Particles (MET NPs) and Celecoxib Niosomal Particles (CXB NPs) led to decreased cell viability in both 2D and 3D models compared to each drug administered individually. When comparing the effect of the niosomal versus the free combination of the drugs on cell migration, we found that both interventions effectively prevented cell migration. However, the efficacy of the niosomes’ combination was not superior to that of the free drug combination (p < 0.05). In conclusion, the results of this study provide valuable insights into the potential application of combining MET and CXB nanoparticle delivery systems to breast cancer treatment. Exploring the in vivo application of this drug delivery system could open new avenues for more effective and targeted therapeutic approaches for breast cancer patients