Vibrio is a bacterial genus widely distributed in natural aquatic systems. Some Vibrio species are pathogenic and can cause severe diseases in both marine organisms and humans. Previous studies revealed a link between the current climate change and increased incidence of the Vibrio-associated diseases recently causing sanitary, economic and/or ecological problems worldwide. The conventional culture-based methods (e.g. cultivation of TCBS agar) used to monitor the presence of Vibrio spp. in environmental samples are not always straightforward and can underestimate the number of cells, especially if the microbial population contains a fraction of ‘dormant’ cells (e.g. cells in Viable but not Culturable (VBNC) state). However, this problem can be overcome by using alternative culture-free approaches such as Catalyzed Reporter Deposition-Fluorescence In situ Hybridization (CARD-FISH). To optimize CARD-FISH for efficient and reliable detection of Vibrio spp. in environmental samples, we have used both computer-assisted and experimental approaches. Our results demonstrate that the use of the optimized protocol along with a very specific probe, ViB572a, can offer the high sensitivity and selectivity of CARD-FISH detection of marine vibrios in natural seawater.

Vibrio is a bacterial genus widely distributed in natural aquatic systems. Some Vibrio species are pathogenic and can cause severe diseases in both marine organisms and humans. Previous studies revealed a link between the current climate change and increased incidence of the Vibrio-associated diseases recently causing sanitary, economic and/or ecological problems worldwide. The conventional culture-based methods (e.g. cultivation of TCBS agar) used to monitor the presence of Vibrio spp. in environmental samples are not always straightforward and can underestimate the number of cells, especially if the microbial population contains a fraction of ‘dormant’ cells (e.g. cells in Viable but not Culturable (VBNC) state). However, this problem can be overcome by using alternative culture-free approaches such as Catalyzed Reporter Deposition-Fluorescence In situ Hybridization (CARD-FISH). To optimize CARD-FISH for efficient and reliable detection of Vibrio spp. in environmental samples, we have used both computer-assisted and experimental approaches. Our results demonstrate that the use of the optimized protocol along with a very specific probe, ViB572a, can offer the high sensitivity and selectivity of CARD-FISH detection of marine vibrios in natural seawater.

Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully.

Computer vision is a fast-expanding discipline focusing on analyzing, altering, and comprehending images at a high level. Its goal is to figure out what's going on in front of a camera and use that knowledge to manage a computer or robotic system or to show people new visuals that are more instructive or attractive than the original camera photos. Video surveillance, biometrics, automotive, photography, movie production, Web search, medicine, augmented reality gaming, new user interfaces, and many other applications are all possible with computer vision technologies. This paper aims to describe how computer vision will be used to play a winning game of blackjack.

Biometric based access control system acts as a weapon against the challenges for reliable authentication mechanism. Although there are many advantages of biometric system, still it is vulnerable to attacks, which decline its security. This leads to propose a new approach to address these open issues and challenges for biometric data security. Smart card have been already used in past with various degree of success to store biometric credentials. Match-on-card biometric framework locks the biometric data inside the microcontroller of the smart card, which leads to minimal risks if card is lost or stolen. It is fast and accurate, balancing security and convenience by never exchanging user data outside the system of smart card and performs fingerprint enrollment as well as matching within a tamperproof environment of smart card.

Wireless sensor networks are widely used in many applications such as environmental monitoring, health care, smart grid and surveillance. Many security protocols have been proposed and intensively studied due to the inherent nature of wireless networks. In particular, Wu et al. proposed a promising authentication scheme which is sufficiently robust against various attacks. However, according to our analysis, Wu et al.'s scheme has two serious security weaknesses against malicious outsiders. First, their scheme can lead to user impersonation attacks. Second, user anonymity is not preserved in their scheme. In this paper, we present these vulnerabilities of Wu et al.'s scheme in detail. We also propose a new scheme by fixing such vulnerabilities and improving the performance of the protocol.

With quick improvement in the field of network, everything becomes online. Remote user authentication is a mechanism in which remote server verify the correctness of user over common or public channel. Remote user authentication can be Single server authentication or Multi server authentication. The disadvantage of single server authentication is that the user needs to recall user id and password for each service, he/she need to utilize, however it can overcome by Multi-server authentication in which user needs to register himself with Registration Center (RC) for the first run through and after that onwards he can utilize each service (which are given by servers, associated with RC) by recollecting only one user id and password. In this paper, we analyze Chen's authentication scheme (based on multi server authentication) and show that this scheme is vulnerable to password guessing attack, replay attack, RC spoofing attack, session key verification attack and perfect forward secrecy attack. In this Paper, we propose a biometric based remote user authentication scheme in multi-server environment. Proposed scheme is more secure and efficient as compared to chen's scheme\cite{23}.

Tumors of the central nervous system produce adverse outcomes in everyday life; however, low-grade gliomas are uncommon in adults. For this reason, the objective is to describe the executive function of a 59-year-old adult neuro-oncological patient. Pre- and postintervention evaluation of neurological and neuropsychological functioning was performed through neuroimaging techniques (iRM, Spectroscopy, Electroencephalography), hospital history, psychological interview, and Wisconsin Card Classification Test (WCST). Executive performance exhibited a noticeable decline in typical and centile WCST results, as evidenced by deterioration in persistent scores, failure to maintain an attitude and difficulty learning from clinical samples. This information coincides with the evolution of the neuroimage over time. Finally, this topic is discussed in the literature.

Background: In 2020, the incidence of cancer was 844,778 cases among the population aged 0 to 19 years. Ninety percent of the population under 18 years of age lives in low- and middle-income countries, where cancer survivors report adverse outcomes. disease, which negatively impacts individuals’ general health, emotional state and external factors such as academic performance. Therefore, the objective of this article was to report the performance of the WCST and the sociodemographic characteristics of oncopediatric patients from Cali, Colombia. We report the reliability of the WCST instrument and indicate the association between the type of cancer and executive functioning in patients. Methods: In a cross-sectional observational study, 24 oncopediatric patients were interviewed and evaluated via the WCST. Results: The mean age was 12.08 years (3.98), 20.8% were women, 70.8% had a diagnosis of leukemia, 8 patients had acquired brain deficits, and more than 75% had adequate functioning in terms of the indicators of executive functions. Robust statistics were used to explore the differences between the types of diagnoses and performance in executive functions, and no statistically significant differences were found (p = 0.156). We accumulated evidence that the WCST has a reliable Cronbach's α of 0.804 and that oncopediatric patients without brain deficits had high scores for executive functions (p = 0.002) with a moderate effect size (0.727). Conclusions: The WCST is reliable for discriminating executive function among pediatric cancer patients. The evidence suggests that there are no differences between the types of cancer evaluated and executive functioning in the participants.

There is a lack of information on vaccination for pregnant women in the Brazilian literature. Our aim was to evaluate the data on vaccines recorded on the pregnancy card and knowledge about the importance of vaccination for women admitted for labor in a reference public tertiary maternity unit in Presidente Prudente, São Paulo, Brazil. Vaccine data as prescribed by the National Immunization Program of Brazil (NIP) in prenatal cards for patients in labor from the 45 municipalities of the western region of São Paulo state were obtained. A questionnaire relating to knowledge about the types and importance of vaccination in pregnancy was applied. All vaccines indicated by the NIP were registered in only 12.0% of the prenatal cards, and 33.0% of cards registered any vaccines. In this group, 16.6% were aged between 14 and 21 years, and tetanus and COVID-19 were the most prevalent vaccines that they had heard of during prenatal care. Data on vaccines was poorly recorded in the cards, suggesting a lack of attention and unpreparedness of professionals working in primary care. Our study has countrywide and global relevance, and may be applied mainly in Latin American countries facing similar difficulties and limitations with low rates of vaccination during pregnancy.

Objectives Asylum seekers in Germany represent a highly vulnerable group from a health perspective due to a variety of risk factors. At the same time their access to healthcare is restricted. While the introduction of the Electronic Health Insurance Card (EHIC) for asylum seekers instead of healthcare-vouchers is discussed controversially using politico-economic reasons, there is hardly any empirical evidence on its actual impact on the use of medical services Study design Thus, the aim of this study is to examine the influence of the possession of the EHIC on the use of medical services by asylum seekers as measured by their consultation rate of ambulant physicians (CR). For this purpose, a standardized survey was carried out to 260 asylum seekers in different municipalities of which some have introduced the EHIC for asylum seekers, while others have not. Methods Various CR were differentiated considering possible third variables as well as confounding factors. The period prevalence was compared between the groups "with EHIC" and "without EHIC" using a two-sided t-test. Multivariate analysis was done using a linear OLS regression model. Results Asylum seekers who are in possession of the EHIC are significantly more likely to seek ambulant medical care than those receiving healthcare-vouchers. Their CR, however, does not differ significantly from the age-corrected CR of the autochtonous population. Taking into account relevant covariables, the possession of the EHIC can be viewed as an independent influencing factor on the asylum seekers' use of medical care. Conclusions The results of this study suggest that having to ask for healthcare-vouchers at the social security office could be a relevant barrier for asylum seekers. Nevertheless, the ownership of the EHIC does not seem to lead to an overuse of medical services.

The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviours and urban mobility. Here we propose a framework, ActivityNET, using machine learning (ML) algorithms to predict passengers’ trip purpose from smart card data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed ‘activity-POIs consolidation algorithm’. Phase II feeds the extracted features into an artificial neural network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

General Linear Modelling (GLM) has been widely employed to estimate the hemodynamic changes evoked by cognitive processing, which are more likely to be nonlinear than linear. First, this study re-analyzed the fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected in the Mixed-Order Design Dimensional Change Card Sort (DCCS) task. The results indicated that the quadratic equation was better than GLM to model HbO changes in this task. Second, analysis of a new set of data indicated that the Habit-DisHabit design of DCCS was more effective in identifying the neural correlates of cognitive shifting than the Mixed-Order Design. Third, this study found that the Non-users were more attentive and engaged than the Heavy-users, with a slower but more steady increase of brain activation in BA8 and BA9.

This study aims to examine the impact of tablet use on preschoolers’ executive function during the Dimensional Change Card Sort Task (DCCS) task using the functional near-infrared spectroscopy (fNIRS). Altogether 38 Chinese preschoolers (Mage = 5.0 years, SD = 0.69 years, 17 girls) completed the tasks before the COVID-19 lockdown. Eight children never used tablets, while 16 children were diagnosed as the ‘heavy-user'. The results indicated that: (1) the 'Non-user' outperformed the 'Heavy-user' with a significantly higher correct rate in the DCCS task; (2) the two groups differed significantly in the activation of the prefrontal cortex (BA 9): the 'Non-user' pattern is normal and healthy, whereas the 'Heavy-user' pattern is not normal and needs further exploration.

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, crucial as credit card fraud techniques continue to evolve.

Background: Animal-assisted therapy has positive effects on cognitive function, depression, performance ability, and social functioning in elderly patients. The aim of this study was to evaluate the effects of rearing pet insects on the cognitive function of healthy elderly participants, with fMRI (functional magnetic resonance imaging) being used for this purpose. Methods: Community-dwelling elderly women (≥60 years) with normal cognitive function were enrolled during April 2015. They were randomized at a 1:1 ratio into two groups: insect-rearing and control (n=16) groups, with the insect-rearing group being further classified into two groups for analysis according to the subjects’ scores in the Wisconsin Card Sorting Test, WCST) at the first fMRI: insect-rearing group I with a relatively high score (n=13), and insect-rearing group II with a relatively low score (n=6). All subjects were educated on a healthy lifestyle for better cognitive function at every visit, and the insect-rearing groups received and reared crickets as pet insects. The fMRI was performed at baseline and after 8 weeks using the WCST as a stimulus. The WCST consisted of two variations, a high level baseline (HLB) and semi-WCST version. Results: There were no significant differences in the baseline characteristics among the three groups. There was a significant difference accuracy of the HLB–semi-WCST (p<0.05) in insect-rearing group II after 8 weeks from the baseline test. In the fMRI analysis involving the WCST reaction test, increased activation was observed in the right dorsal lateral prefrontal cortex and parietal cortex in insect-rearing group II when the semi-WCST, rather than the HLB, was performed. There were no significant differences in the other groups. Conclusion: The rearing of pet insects as an animal-assisted therapy is cost-effective, easy, and occupies little space. In this study, it showed positive effects on executive functions and performance improvement in elderly women. Further larger studies on the effects of pet insects on cognitive function are warranted.

Cucurbitacin phytonematicides consistently suppress population densities of plant nematodes, but have no effect against bacteria, fungi and insects. This study investigates the mechanism through which cucurbitacin phytonematicides consistently exert nematicidal properties on nematodes. Post 72-h exposure of second-stage juveniles (J2) of Meloidogyne enterolobii to geometric concentration of Nemafric-BL phytonematicide, J2 total proteins were measured using TruspecCHNS Macro. Total proteins versus phytonematicide exhibited a negative quadratic relation (R² = 0.97), with the minimum total proteins achieved at x = 4.9% phytonematicide. Beyond the minimum, total proteins increased gradually, depicting the existence of both isoprenylation (breakdown) and farnesylation (biosynthesis) of total proteins. In conclusion, cucurbitacins and nematode cuticles are both chemically structured, which provides the mechanism through which the test chemicals consistently decrease population densities of plant nematodes.

Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.