The outbreak of the COVID-19 pandemic in early 2020 introduced new challenges to social cohesion across Europe. Epidemiological control measures instituted in almost all European countries have impacted the possibility to provide help to others. In addition, individual characteristics contributed to whether individuals were able and willing to provide help to or receive help from others. Against this background, we focus on how private support networks of individuals aged 50 years and older across Europe were directly or indirectly affected by the COVID-19 pandemic. The focus of the paper is on the supply side. While the older population has been mainly perceived as recipients of instrumental help in the COVID-19 pandemic, the paper examines the patterns of providing instrumental help to others by the older generations and their changes during the pandemic. Has the provision of instrumental help increased or decreased in the course of the COVID-19 crisis? Have the groups of recipients changed during the pandemic? What were key determinants for helping others in 2021 as compared to the first phase of the pandemic 1 year before? And how did this differ across countries with different degrees of affectedness by COVID-19? To answer these questions, we analyzed representative data from the Survey of Health, Aging and Retirement in Europe (SHARE) and, in particular, the two waves of the SHARE Corona Survey, fielded in 27 European countries and Israel in 2020 and 2021. Results based on data from more than 45,000 respondents aged 50+ showed that help from children to parents has strongly increased in the first phase of the pandemic, while the opposite (parents helping their children) has decreased–especially in countries that have been hit hardest by the pandemic in 2020. This changed with the continuing crisis. Instrumental help provided to non-kin that was common in Western Europe in the first phase of the pandemic, yielding an optimistic view of increasing solidarity after the outbreak of COVID-19, strongly decreased 1 year later. Our findings provide a contribution to comparative research on micro- and macro-determinants that are crucial for the understanding of intergenerational support in times of crisis. The outbreak of the COVID-19 pandemic in early 2020 introduced new challenges to social cohesion across Europe. On the one hand, people had the perception of a widespread willingness to help each other and saw “a lot of the best in humanity” during the lockdown time  became even more important in the light of pandemic-related reduced availability of formal care service providers and social isolation especially for individuals under quarantine as well as high-risk groups such as older people. Therefore, the research on social cohesion and exchange of informal help in times of the still ongoing pandemic is highly relevant, especially research focusing on vulnerable social groups. The contribution of this paper is its focus on older people who are at highest risk of a severe course of the coronavirus disease and thus are highly affected by the pandemic.In particular, the COVID-19 pandemic has challenged the solidarity between generations resulting in intergenerational tensions due to changing mutual expectations and obligations of older and younger people Ayalon, . The polSince the beginning of the COVID-19 pandemic, researchers have been analyzing the impact of the pandemic on social cohesion and solidarity  still can be provided without face-to-face contact. The likelihood of providing informal instrumental help depends on a mixture of individual attributes and macro factors. Generally, it is interpreted as an interplay of the needs of the potential recipients on the one side and resources as well as ability and willingness of the givers to provide certain type of help on the other side  to compensate for reduced access to formal care provision and to social networks outside the family due to contact restriction policies during the peaks of the pandemic.During the pandemic, European countries were faced with completely new challenges regarding care provision for people aged 50+  if these were faced with an ongoing high (double) burden, leading to increasing difficulties in providing help the longer the pandemic and its accompanying restrictions continue. In addition, there was probably less need for help in the times of the “downtime” of the pandemic but also due to the vaccination of high-risk groups after the authorization of effective vaccines starting end of 2020.At the same time, the adherence to lockdown restrictions and general practice of social and physical distancing to reduce the risk of infection, especially during the peaks of the pandemic, made it more challenging to provide support that requires personal interactions. The provision of help that requires personal contact had to be carefully evaluated upon the possible risk of infection vs. benefit of the received help. Especially the representatives of the middle generations were faced with competing demands and the double burden of providing support to their own children and older parents simultaneously .With regard to socio-demographic and economic characteristics, previous research based on the cross-national SHARE data has shown that there is a general downward flow from the older to the younger generations for financial and practical assistance in European countries .Regarding health-related outcomes, individuals with long-term health conditions were found to have a less strong social network in general and tended to engage less in social activities during the COVID-19 pandemic .In addition, also behavioral characteristics are linked to the provision of help. People with a higher number of (in-person) social contacts seem to have more occasions to provide instrumental help to others. Vergauwen et al.  found thmore provision of instrumental help during the COVID-19 pandemic in countries with more strict pandemic-related policies and measures (Hypothesis 5).The ability and willingness of providing instrumental help is not only dependent on individual factors, some of which were mentioned above, but is also linked to macro factors. European countries introduced different policies as a response to the pandemic and the pandemic-related epidemiological control measures varied to a high extent with regard to their level of stringency across Europe  of 86 percent was achieved. To avoid selectivity, our analyses are based on 47,495 respondents who participated in both SHARE Corona Surveys. Among other pandemic-relevant content, both SHARE Corona Survey questionnaires contain a section on social networks that includes questions about providing and receiving instrumental help, which build the basis for our analyses. We further included country-specific information on epidemiological control measures using data from the Oxford COVID-19 Government Response Tracker :Since the outbreak of Corona, did you help others outside your home to obtain necessities, e.g., food, medications or emergency household repairs? Yes/No.Compared to before the outbreak of Corona, how often did you help the following people  from outside your home to obtain necessities: less often, about the same, or more often?Second SHARE Corona Survey (SCS2 in 2021):Since the outbreak of Corona, have you helped the following people  outside your home to obtain necessities, e.g., food, medications, or emergency household repairs? Please answer yes or no to each category.Compared to the first wave of the pandemic, how often did you help  to obtain necessities in the last 3 months, e.g., food, medications, or emergency household repairs? Less often, about the same, or more often?respondents' age at the respective interview in 2020 (SCS1) and 2021 (SCS2) to form three age groups  and respondents' sex  from the coverscreen data of the regular SHARE interview. Further, we coded the level of education attained based on the Internal Standard Classification of Education 1997 (ISCED-97) by using information from the respondents' baseline interview. Respondents were then grouped into two categories: primary education (ISCED-97 score: 0–2), secondary and post-secondary education (ISCED-97 score: 3–6). We further used information on the respondents' country of birth from the regular SHARE interview to determine whether they were born abroad or not as well as the type of living area . The latter information was updated during the second SHARE Corona Survey in case of moving. We further included a measure related to whether respondents were employed (including self-employment) or not at the time when Corona broke out (SCS1)/at the time of the interview (SCS2). In addition, we measured respondents' economic status by a question that asked the degree to which they were able to make ends meet  since the outbreak of Corona (SCS1)/since the last interview (SCS2)The multivariate analysis controls for a number of correlates known from previous research on intergenerational exchange mentioned above. As socio-demographic and economic characteristics, we used respondents' health, we used the reversed 5-point scale on their self-rated health  at the time of the respective SHARE Corona Survey and collapsed the categories poor and fair as well as good, very good and excellent to build a dichotomous indicator. In addition, we included a measure that indicates whether respondents were directly affected by COVID-19 (self-exposure) by using a set of questions on (a) having experienced symptoms, (b) having been tested for COVID-19 and (c) having been hospitalized. To determine whether someone close to the respondent was affected  by COVID-19, we used information on symptoms, tests, hospitalization and deaths due to COVID-19 with regard to the respondent's spouse/partner, parent, child, other household member, other relative outside the household, and neighbors, friend or colleague.To control for contact frequency of respondents and summed up the frequency of face-to-face contacts  with people from outside the household . Based on this metric indicator, we applied a median-split to separate respondents with lower/higher than median contact frequency since the outbreak of Corona (SCS1)/during the last 3 months (SCS2). To measure reciprocity of instrumental help, we used respondents' answers on the question whether they were helped by others from outside of home to obtain necessities, e.g., food, medications or emergency household repairs or not since the outbreak of Corona. To shed light on the heterogeneity across respondents of different age groups and possible consequences thereof during the different phases of the pandemic, we included an interaction of receiving instrumental help with age (< 65 vs. ≥65 years).As behavioral measures, we used the stringency index from the Oxford COVID-19 Government Response Tracker  as well as differentiated by type of relationship (2), considering the specific age structure of our sample. Afterwards, we investigated key determinants that were crucial for helping others during the first phase of the pandemic in 2020 compared to 1 year later to analyze substantial differences. For this, we used multivariate logistic models including a large set of individual respondent characteristics, such as respondents' age, sex, level of education, migration background, area of living , whether they were (self-) employed before the pandemic and subjective economic status. Furthermore, we analyzed respondents' self-rated health, their affectedness by COVID-19, their frequency of in-person contacts and whether they received help from others or not, which are closely linked to the provision of instrumental help. Finally, we included COVID-19-related policy measures  at the country level, which have been transferred to the individual level by matching the stringency index to the actual date of the respondents' interviewn = 7,452) of all respondents gave help to others outside the own household since the outbreak of the pandemic, the prevalence increased in the 2021 survey by more than ten percentage points to 32.4% . Regional variation showed the strongest relative increase in Southern Europe  and Eastern Europe . However, the increase was also considerable in the Baltic States  and in Western European countries . The smallest relative increase was found in Northern Europe , restrictions could be relaxed and social contact as well as support in general was possible again easier. Also, there was possibly less fear of suffering from severe COVID-19 symptoms for vaccinated individuals. In addition, in the course of the pandemic European countries introduced different policies to provide public support to their population which were not available in the beginning. Previous studies showed that European countries, in which families were relieved by welfare support provided by the state, were more stimulated to engage in the provision of informal intergenerational support as complementary help , the opposite was true with regard to older respondents (≥65 years). Interestingly, the positive correlation in the first phase of the pandemic for younger respondents was not statistically significant, indicating that providing help by this group was rather independent from receiving help during that time. This changed during the ongoing pandemic. Further, the found significant negative correlation for older people in 2020 and 2021 indicate that older people are more frequently the receivers (and not the providers) of instrumental help. Finally, there was some evidence that stricter control measures in the first phase of the pandemic were associated with providing more instrumental help, probably as a compensation of reduced formal help and care services. However, the negative correlation of the quadratic operationalization of the stringency index can be interpreted in the sense that very strict measures at the upper bound of the stringency index reduced the provision of help to some extent. This observation disappeared in 2021, meaning that continued control measures did not exhibit a significant effect on the provision of instrumental help anymore.a stronger provision of instrumental help can be expected in the beginning of the COVID-19 pandemic by younger cohorts of the older population). While this assumption seemed to hold for some specific groups of help recipients such as parents and non-relatives, the opposite was true for other groups of receivers like children and other relatives. In a study conducted by Silverstein et al.  was also supported. This change in the course of the COVID-19 pandemic demonstrate the dynamics of the intergenerational solidarity in times of crisis as well as changing patterns and dependencies that have to be carefully considered when drawing conclusions. In this respect, our findings can be seen as a starting point that need to be supplemented by other studies adding further information on younger people below 50 years. However, what should be additionally noted based on our findings is that while in some parts of Europe there was a decrease of instrumental help provided to others 1 year after the start of the pandemic, in other parts  there was, on contrary, an increase. This finding implies that it is not the time period per se that is relevant, but the state of the pandemic development in a given country at a certain point in time. Whereas, the pandemic was reaching another peak in Eastern Europe and the Baltic States in summer 2021  was largely confirmed by our analyses. However, the associations between providing instrumental help on the one side and gender and employment status on the other side became insignificant in summer 2021, which can be interpreted as a slightly decreasing impact of socio-demographic and economic characteristics on instrumental help as well as less need for help in the course of the ongoing pandemic in general. Only respondents' subjective economic situation did not exhibit the expected association, possibly due to a less clear link of subjective assessments with providing instrumental help. Future analyses should therefore focus more on objective measures, such as respondents' (household) income, which could not be included here due to questionnaire differences. The finding that older respondents had a significantly lower probability of providing instrumental help since the outbreak of the pandemic shows once again the age-related dynamics of the pandemic. Overall, our analyses clearly demonstrate the need for a differentiated consideration of a wide range of individual attributes when studying behaviors of older people during the pandemic instead of treating them as a homogeneous group.Hypothesis 2  was also largely supported. Knowing someone infected by COVID-19 was positively associated with providing instrumental help in both survey waves in 2020 and 2021.This finding provides an optimistic view of the development of social cohesion in European countries in the course of pandemic: after more than 1 year of coping with the pandemic and its consequences for individuals as well as society as a whole older Europeans were still willing and able to support those in need. However, it also has to be noted that a worse  health was clearly negatively associated with providing instrumental help to others. While this could be expected, being self-affected by the coronavirus, probably with negative consequences for respondents' own health, also had a negative effect on providing instrumental help to others in the first phase of the pandemic. Interestingly, this association turned around completely 1 year later, possibly due to increased protection against the coronavirus by a prior infection and/or vaccination.Hypothesis 3  was only partly confirmed. As expected, a high number of social contacts was clearly associated with a higher probability to provide instrumental help during the pandemic, possibly due to a combination of both a higher awareness of demands for support from others and easier possibilities to help. With regard to reciprocity, the results were not as clear-cut, indicating the importance of carefully considering age-related differences as well as changing conditions over the course of the pandemic. It thus became clear that the observation of a negative correlation between receiving and giving instrumental help was only true for respondents aged 65 years and older. It seems plausible that these respondents receiving help were in a more vulnerable position due to the pandemic and hence were probably not able to provide help to others vice versa. For younger respondents between 50 and 64 years a positive correlation was found, partly supporting previous pre-pandemic research. However, this correlation was much more pronounced in the second SHARE Corona Survey in 2021, again pointing out the very specific situation in the beginning of the pandemic. In this respect, our study can add important insights regarding relevant factors that affect the interplay between receiving and providing help.Hypothesis 4  was partly supported. In the first phase of the pandemic, stricter measures were associated with more provision of instrumental help but only up to a certain degree. Very strict measures at the upper bound of the stringency index again reduced the provision of help to some extent. One year later, the continued control measures did not exhibit a significant effect on the provision of instrumental help anymore. Therefore, the cross-national differences in providing instrumental help by older people in Europe cannot be explained only by the pandemic-related policies and measures. Other macro-factors should be taken into consideration as well. Future research could look, for example, at the role of welfare systems during the pandemic. In this respect, previous pre-pandemic research has demonstrated that country-specific patterns of intergenerational solidarity are associated with welfare systems , it has to be considered that our sample might underrepresent the actual degree of provided instrumental help in Europe. Moreover, the specific age group of our sample has to be considered when drawing generalized conclusions based on our results. Second, we did not include data on the exchange of instrumental help before the pandemic in our study. Further analyses could strongly benefit from such inclusion to get a more comprehensive picture of the development in providing help to others. However, the questions in the regular SHARE waves were not directly comparable to the questions in the SHARE Corona Surveys and we were restricted to focus on data collected exclusively during the pandemic. A further restriction to the data were the lack of measures regarding composition and intensity of provided instrumental help and how these differed with regard to pre-pandemic times. Finally, our study focusses primarily on the supply side of instrumental help and the determinants of providing help as we were primarily interested in better understanding how and to what degree the COVID-19 pandemic and its accompanying epidemiological control measures affected the provision of help to others outside the own household . Further research should also look more closely at the demand side and determinants of receiving help negotiated by involved actors in times of limited resources and restricted possibilities to offer help. Against this background, our findings provide new insights to the growing comparative research literature on intergenerational solidarity during the COVID-19 pandemic.http://www.share-project.org/data-documentation/share-data-releases.html. All data used in our study are available free of charge to all scientific users world-wide after individual registration (http://www.share-project.org/data-access/user-registration.html). Each wave and each release is assigned a persistent DOI. In our manuscript we use data from SHARE Waves 1, 2, 3, 4, 5, 6, 7, 8 and 9  that are fully available without restrictions.Publicly available datasets were analyzed in this study. This data can be found at: http://www.shareproject.org/fileadmin/pdf_documentation/MPG_Ethics_Council_SHARE_overall_approval_29.05.2020__en_.pdf. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Mannheim (Waves 1 to 4). Wave 4 of SHARE and the continuation of the project were reviewed and approved by the Ethics Council of the Max Planck Society. For more details please see: All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.www.share-project.org).The SHARE data collection has been funded by the European Commission, DG RTD through FP5 (QLK6-CT-2001-00360), FP6 , FP7 , and Horizon 2020  and by DG Employment, Social Affairs and Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, and VS 2020/0313. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging  and from various national funding sources is gratefully acknowledged (see The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models. Many applications reap the advance of artificial intelligence (AI) and deep learning (DL) methodologies to ease and facilitate the daily lives of humans. Recently, computer systems based on AI reached accuracy up to par with human experts. Applications of AI are spread over a broad spectrum of fields, such as banking transactions, financial affairs, manufacturing, retail, the supply chain, e-commerce, and healthcare . AI has The application of AI in medical science and health care is spreading rapidly. Common domains of applications in medical image analysis include different fields such as organ localization and abnormality detection ,4, gene Poxviruses are one of the most known vertebrate infectious viruses, including cowpox, vaccinia, variola , and monkeypox viruses ,12. HumaThe multinational monkeypox outbreak has choked the world while recovering from the aftershock of the COVID-19 pandemic. As of the end of June 2022, an abrupt and significant surge in the number of cases close to over 4900 human monkeypox instances has occurred which are non-endemic for the disease, spanning across the Western Hemisphere. Transmission has extended beyond cases linked to an initial exposure in Africa to human-to-human spread in each country. Virologists differentiate two variants of the monkeypox virus: the Central Africa clade and the West Africa clade. There is no proper treatment for the monkeypox virus to date ,15. AlthClinical identification of monkeypox from different pox viruses is difficult. The diagnosis procedure starts with visual observation of skin lesions and the existing history of exposure, followed by testing skin lesions using dermatoscopic images and then confirmation using a polymerase chain reaction (PCR) . This woClassification models assist physicians in making more accurate diagnoses of skin cancer, skin lesions, and psoriasis . Deep coAll proposed solutions for lesion detection problems lay on selecting the optimal parameters of the employed model. The selection process is often performed in two phases, namely mathematical modeling and optimization using an optimization method. Optimization is selecting optimal solutions from a pool of competing explanations for the problem under consideration. In general, a multi-dimensional search problem is turned into an optimization problem. In practice, this entails reducing or increasing the quality of the candidate solutions, often represented as a vector in the search space. In recent years, metaheuristic algorithms have been extensively applied among the optimization algorithms for solving classification problems . Their aNumerous types of AI have been proposed for use in different applications ,27. CombWe employ a metaheuristic algorithm for tuning the parameters of the neural network. A novel framework based on Al-Biruni Earth radius (BER) optimization fine-tunes the deep convolutional neural network (DCNN) layers. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space to learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human facial skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, outperforming numerous prior works on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.Based on the suggested BERSFS classification model, excellent prediction accuracy of monkeypox disease is achieved. The tested dataset is assessed using a classifier built with the specified BERSFS algorithm. The BERSFS algorithm is tested and compared to PSO , Grey WoOffer machine learning techniques for predicting monkeypox disease;A new Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) algorithm is suggested;To raise the tested dataset prediction accuracy, a BERSFS-based classifier is created.A comparison of the results of different algorithms to determine which is the most accurate is performed;The Wilcoxon rank-sum and ANOVA tests are used to determine the statistical significance of the BERSFS algorithm;It is possible to generalize and test the BERSFS-based classification algorithm for different kinds of datasets.The key contributions of this work can be summed up as follows:Here is how this paper is organized. Computer-aided methodologies for automatically identifying diseases from images reduce human error and increase detection speed, which helps physicians and radiologists diagnose conditions more easily, thus protecting the patient against medical threats in the early stages. Those methodologies can be categorized into two categories. The first, traditional methodologies, involves methods that apply only image processing techniques. In contrast, the other evolutionary methodologies are methods that apply AI and DL techniques. In the traditional methodologies ,44,45,46This section discusses the previously proposed evolutionary methodologies used in classifying skin lesions from a medical image. Arifin et al.  proposedEsteva et al.  describeThe majority of the studies that are conducted on skin diseases focus on melanoma, chickenpox, psoriasis, and measles, and very little research takes monkeypox into consideration. Data augmentation was utilized by Ali et al.  in orderSitaula and Shahi  analyzedAlthough the models for diagnosing monkeypox have been hopeful, they have increased in difficulty, making classical approaches so ineffective that they cannot solve certain NP-hard problems. Thus, we sought solutions to classic optimization methods’ issues. Metaheuristics are widely used in continuous optimization due to their robust and reliable performance, global search, lack of prerequisites for a continuously differentiable or differentiable objective function, simple implementation, implicit parallelism, and lack of specific information about the problem to be solved. This work offers machine learning techniques for effectively predicting monkeypox diseases compared with the classical approaches. A new Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) algorithm is presented to raise the tested dataset’s prediction accuracy.Convolutional neural networks are effective deep learning approaches. Although they can be used for text, signals, and other continuous data types, image inputs are where they shine the brightest when it comes to their applicability. Instead of being connected to one another, as neurons in traditional neural networks are, neurons in a convolutional layer are connected to the sub-regions of the layer that came before it. Neurons in a CNN create outputs that are spatially correlated because the sub-regions they represent may overlap. In contrast, neurons in fully connected neural networks provide outputs that are independent of one another. In addition, the number of parameters in a typical neural network can rapidly increase in response to an increase in the amount of inputs. A CNN, on the other hand, has a lower total number of network parameters since it has fewer connections, shared weights, and downsampling .A CNN comprises several layers: the convolutional, max-pooling, and fully connected layers. The CNN is made up of two phases: a phase for the extraction of features followed by a phase for the classification of binary options. The proposed CNN features three different convolutional subsections. Every segment is comprised of multiple fundamental units. The fundamental unit is made up of three layers: the convolution layer, the ReLu activation layer, and the batch normalization layer. After each block of convolution, a pooling layer is formed. The sections that use convolution are used as feature extractors. The classification process is carried out by the three most recent layers: the average pooling layer, the fully connected layer, and the softmax layer. The number of basic blocks is a tunable hyperparameter that, when set, influences the architecture of the CNN . The hypThe Al-Biruni Earth radius (BER) optimizes the situation by segmenting the population into groups that are focused on exploration and exploitation. A dynamic process of switching up the composition of agent subgroups is used to strike a healthy balance between exploitative and exploratory activities. The population is split between exploration (which makes up 70%) and exploitation 30%). In order to raise the overall global average fitness level of both the exploration and exploitation groups, the number of agents in each of those groups has been increased. The exploration team uses mathematics to search for potentially fruitful areas in the immediate vicinity of their current location. This is accomplished by searching through the available possibilities over and over again for one that has a better degree of fitness [%. In ordd is the optimization parameter or feature. It has been recommended that the fitness function F be used in order to evaluate the performance of an individual up to a specific point. During these phases of optimization, populations are searched in search of a fitness-optimal vector Algorithm 1 AL-Biruni Earth radius (BER) algorithm1:Initialize BER population d, iterations 2:Calculate fitness function 3:Find best solution as 4:whiledo5:for  to the others. In order to accomplish this, the research conducted by the BER makes use of the following equations:The group in charge of exploiting opportunities has the duty to improve upon the solutions that are currently in place. At the conclusion of each iteration, the BER identifies the participants who have achieved the highest levels of physical fitness and awards them accordingly. The purpose of exploitation is accomplished by the BER through the employment of two unique approaches, both of which are explained here.t, Moving in the direction of the optimal solution using the following equation, we can make progress in the direction of the optimal solution:z is a random number in the range For investigating the region surrounding the optimal solution, the territory surrounding the correct response is the most interesting potential option (leader). Because of this, some people try to find ways to enhance things by investigating possibilities that are somewhat near to the best option. In order to carry out the process described above, the BER makes use of the equation shown below:In order to ensure that the solutions have a high level of quality, the BER chooses the one that is going to be the most effective for usage in the subsequent cycle. However, due to the enhanced efficiency of the elitism technique, multi-modal functions might converge too soon. By taking a mutational approach and examining all around the members of the exploration group, the BER is able to deliver exceptional capabilities for the exploration process. The BER is able to delay convergence thanks to its powerful exploring capabilities. The BER pseudo-code can be viewed by referring to Algorithm 1 and looking at the section titles. To begin, we will provide the BER with basic information, such as the population size, mutation rate, and number of iterations. After that, the BER splits the participants up into two groups: those who undertake work that is exploratory and those who perform work that is exploitative. The BER method will automatically make adjustments to the size of each group as it works its way through the iterative process of finding the best answer. Each team completes its tasks using two different approaches. In order to ensure a diverse and in-depth inquiry, the BER will rearrange the order of the responses whenever it performs an iteration. For instance, a solution that was part of the exploration group in the previous iteration may migrate to the exploitation group in the iteration after that. Because of the exclusive nature of the BER’s selection procedure, the leader will not be changed while the process is ongoing.It is possible for random fractals to motivate a metaheuristic algorithm by making use of the time and accuracy of the conventional fractal technique. It is possible for a particle to have electrical potential energy, where each particle has the ability to diffuse. Other random particles can be created where the energy of the original particle is distributed among the new particles, and in each generation, the best particles are retained while the remaining particles are discarded. These three guidelines are the foundation of the fundamental fractal search (FS) procedure, which is utilized to locate a remedy for a specific issue that has been presented. The stochastic fractal search, also known as SFS, is a type of algorithm that was designed with the fractal paradigm as its foundation ,60. By uith point in the group that surrounds the point. Since there are fewer generations around the optimal solution, the value of Algorithm 2 Stochastic fractal search (SFS) algorithm1:Initialize SFS population d, iterations 2:Calculate fitness function 3:Find best solution as 4:whiledo5:for  algorithm can be found in Algorithm 3. The BERSFS algorithm mitigates the drawbacks of the BER and SFS algorithms while capitalizing on the benefits of both in order to produce the optimal answer for the whole world. The first step in the method is to find the starting positions of d preset agents using the notation n number of agents, one definition of the complexity is as follows:O(1);Initialize the parameters of the BERSFS algorithm: O(n);Calculate O (n);Obtain the best agent O.Obtain the best agent Algorithm 3 Proposed BERSFS algorithm1:Initialize BERSFS population d, iterations 2:Calculate fitness function 3:Find best solution as 4:whiledo5:if  results are included in The deep learning models of AlexNet , VGG19Nep value that is less than 0.05, demonstrated that there was a significant difference between the results of using the suggested BERSFS-CNN classifier and the results of applying alternative approaches. The p value for this test was less than 0.05. The classification results of the suggested algorithm are compared with the results of other CNN-based models, including the BER-CNN , SFS-CNNp value of less than 0.05. In addition to the homoscedasticity graphs, heat maps, residual plots, and QQ plots of the other fundamental models that are displayed in The box plot in To fine-tune the deep convolutional neural network (DCNN) layers, we offer a new framework that uses Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS). In the first stage of the proposed method, deep CNN-based models are utilized in order to acquire knowledge regarding the embedding of input images into Euclidean space. In the second stage, we calculate the distance between pairs of images in Euclidean space using an improved classification model that is based on the triplet loss function. This allows us to learn features that may be used to differentiate between different cases, including monkeypox cases. The method that has been proposed makes use of photographs of human facial skin illnesses that were collected from a hospital in Africa. The experimental findings of the study provide evidence that the proposed framework is effective, outperforming a large number of other studies that were conducted previously on skin disease issues. On the other hand, statistical experiments using the Wilcoxon and analysis of variance (ANOVA) tests were carried out in order to assess the proposed method with regard to its efficiency and consistency. When compared with other optimization algorithms and machine learning models, the findings that were recorded provided conclusive evidence that the method that was provided was superior.


This paper aims to investigate the impact of blockchain application on trust levels in supply chains. Through the systematic review of the relevant literature, three dimensions of trust, i.e., the trustor–trustee perspective, forms of trust, and time orientation, are investigated. Our findings show that, first, there are three pairs of trustors and trustees involved in blockchain implementation: (a) the user and the blockchain, (b) two supply chain partners, and (c) the consumer/public and a supply chain unit. Second, the two forms of trust, namely cognition-based and institution-based trust, are likely to be enhanced by blockchain execution, while affect-based trust may not be directly impacted by the technology. Third, the presence of blockchain technology would facilitate swift trust-building between unknown supply chain partners under specific circumstances. Moreover, we also find contradicting assertions among scholars on the implications of blockchain for trust in supply chains. While some studies pointed out that blockchain will enable a trustless trusted scheme, others expected the reinforcement of interorganizational trust. To test these assertions, we develop the blockchain-entrusted supply chain models to present the three-step process of how trust is developed through the blockchain and diffused to supply chain partners and external stakeholders.The online version contains supplementary material available at 10.1007/s10479-022-04868-0. Although trust has been considered one of the key success factors in supply chain management  Trustor–trustee perspective. Trust involves at least one trustor and one trustee entities, such as a buyer and a supplier. It is crucial to determine who the trustor and trustees are before conducting a trust investigation. Janowicz and Noorderhaven (derhaven  suggestederhaven  varies fderhaven .(2) Form of trust. Trust in supply chain management has variations in its form  Temporal orientation trust. In management research, there is a specific type of trust that can be built rapidly in certain conditions, in contrast to traditional trust that develops over the long term  which has been used for previous systematic literature reviews in supply chain management arenas .Step 1: We added selective keywords to the following search algorithm to look for keywords in titles, abstracts and keywords of the sources:To ensure maximum coverage of the literature on blockchain and trust in supply chains, the search strings are adopted from previous systematic literature reviews of blockchain for supply chains , 2019 (11 articles), 2020 (32 articles), and 2021 (49 articles). We adopted a combination of inductive and deductive approaches to conduct content analysis on the selected pool of articles. Initially, we utilized the theoretical ground of three trust dimensions as our underlying framework for analyzing the body of literature.As Janowicz and Noorderhaven  suggesteinstitution-based trust, cognition-based trust and affect-based trust to code forms of trust found in selected articles. The form of trust was either typically latent or not manifested clearly in the literature. In other words, we needed to delicately interpret the meanings contained within the articles to extract the exact forms of trust that each publication referred to. For the time orientation dimension, we used the categories of swift trust and long-term oriented trust in the coding process. This deductive approach to borrow theory outside the field of supply chain management to enrich phenomena in supply chains is considered a justified approach for theory extensions , in which two supply chain partners are trustors and the blockchain solution acts as a trustee. The other 38 articles discussed the impact of blockchain within the second subtheme of Interorganizational trust reinforcement (ITR), in which interorganizational trust between supply chain partners are enhanced and one supply chain partner acts as a trustor while the other partner acts as a trustee. In addition, 57 papers mentioned the direct impact of blockchain on users’ trust and 31 articles examined the impact of the blockchain supply chain on consumers and the public. The emergent three pairs of trustor–trustees showed different levels of units of analysis. Specifically, the blockchain solution and its user pairs, as well as the two supply chain partners pairs demonstrated the impact of blockchain on an organizational level, while the consumers/public and a unit of supply chain pairs demonstrated the impact of blockchain adoption on supply chain level. The summary of emergent themes under the framework of the trustor–trustee is shown in Table In terms of the number of papers, 78 papers investigated the impact of blockchain on two supply chain partners, in which 41 articles revolved around the first subtheme of the trust of users in the blockchain-based system. The enhanced trust stems from three main sources of the blockchain’s reinforced capacity, namely the superiority of information security  Trustless trusted scheme (TTS). Academics believe that the integration of blockchain into supply chain operating systems will allow supply chain partners to perform transactions in a trustless fashion  Interorganizational trust reinforcement (ITR). In contrast to TTS, scholars argue that one of the major contributions of blockchain adoption is the enhancement of trust between different trading parties  and cognition-based trust (knowledge from rating system and information sharing).From the six papers that discussed the implication of blockchain on trust formation in the long term, there were two main streams of discussion: the possibility and issue of blockchain implication on trust between supply chain partners in the long term . From our analysis of the literature, it is likely that the STD model is the emulation of the ITR model with modified components in step two, as shown in Fig. The first step of the TTS model expounds trust in a pair of blockchains and users. As shown in Fig. Increased cognition-based trust mainly stems from the two capabilities of blockchain to enhance system security strength and speed of data transmission which collectively create the visibility of nearly real-time, credible information  the self-executed governance mechanism, (2) direct transferring of blockchain capabilities to a trustee organization, and (3) the reinforcement of the information sharing paradigm. As presented in Fig. Similar to the TTS model, the blockchain application of smart contracts enables an effective mechanism to govern a transaction between two parties, thus increasing the level of institution-based trust to both supply chain partners Below is the link to the electronic supplementary material.


The digitalisation of finance influenced the emergence of new technological concepts for existing user needs. Financial technology, or fintech, provides improved services for customers and new economic value for businesses. As such, fintech services require on-demand availability on a 24/7 basis. For this reason, they are often deployed in cloud environments that allow connectivity with ubiquitous devices. This allows customers to perform online transactions, which are overseen by the respective financial institutions. However, such cloud-based systems introduce new challenges for information security. On one hand, they represent attractive targets for cyberattacks. On the other, financial frauds can still go unnoticed by the financial institutions in charge. This paper contributes to both challenges by introducing the concept for a cloud-based system architecture for fraud detection and client profiling in the banking domain. Therefore, a systematic risk assessment was conducted in this context, and exploitation probabilities were inferred for multiple attack scenarios. In addition, formal verification was accomplished in order to determine the effects of successful vulnerability exploits. The consequences of such security violations are discussed, and considerations are given for improving the resilience of fintech systems. The term fintech was introduced in 1972 and denotes the intersection of finance with information technology . ServiceUnfortunately, financial fraud also represents an issue for cloud-based fintech systems. For example, criminal acts such as credit card information theft happen on a daily basis and in large numbers . This frThe contributions of this paper are twofold: It introduces a cloud-based system architecture that serves as a representation of a fraud alerting system. This constituent part of a theoretical banking system is used to tackle the above security challenges. The conceptual system comes in two versions and relies on existing ML techniques. First, a systematic risk assessment is conducted to identify cyber threats that might occur in the system. Then, several cyberattack scenarios are defined, which are supported by their exploitation probability based on several metrics. The second challenge is the identification of malevolent users who had committed acts of financial fraud. In particular, a fintech component is introduced, whose task is to automatically detect credit card fraud in financial transactions. The proposed solution uses a ML-based scoring system that analyses user behaviour in fintech transactions. Finally, formal methods are applied to provide exposure assessment with the help of a state-of-the-art model checker. It is important to note that cyberattacks against the proposed system do not correlate with the emergence of financial frauds in the first place. However, an attacker can influence the functionality of the scoring system and cause false indications of fraud.The remainder of the paper is structured as follows: First, Several works exist that analyse cybersecurity in the emerging domain of cloud computing. In many cases, the focus lies on the application of existing tools and techniques to cover this issue. These papers describe frameworks to execute cyberattacks in simulated cloud environments and discuss resulting observations. In addition, the authors conducted risks and resilience assessments in this environment and discussed the impacts of attacks in the context of their technological solutions. A comparative table is given in The authors of  conducteThe work in  addresseIn , the autThe authors of  propose In , a cyberThe work in  analysesThe paper  discusseThe authors of  introducThe paper  addresseThe work in  addresseIn , a formaIn addition, an extensive survey on AI solutions for security threats is given in . There, An overview of cyber threats in the cloud environment can be found in . In addiThis section describes the methodology used and the proposed solution for a robust financial fraud alerting system.The main objective of this work includes a proposal of two system concepts and potential concepts’ implementations in a cloud service. It also includes an extensive analysis and comparison of two proposed concepts, which is based on formal modelling. This analysis covers two aspects—(i) the analysis of the fraud-detection reliability of the implemented concept, and (ii) the analysis of the concepts of implementation security and resilience in a cloud environment, including vulnerabilities and potential cyberattack analysis.The system itself represents a cloud service that contains two distinctive modules: One module handles fraud detection within financial (credit card) transactions, whereas the other is responsible for fraud alerting . It shouFraud detection robustness—ensured through the proposed parallel processing anomaly detectors;Alerting robustness—a novel scoring system is proposed, representing the users’ historical behaviour analysis;An extensive cyber resilience analysis—the system’s robustness during different cyberattacks targeting the cloud infrastructure, analysed by using formal methods.The novelty of this paper is reflected in several aspects:Part 1: Investigating and testing of different ML-based anomaly detection techniques for fraud detection in the fintech environment. This part was proposed and extensively reported in our previous paper in [paper in , which iExtensive SoA analysis covering automated fraud detection in the credit card , financial transactions and blockchain fintech domains;Analysis and case studies’ definitions based on publicly available datasets in this domain, including credit card fraud detection (CreditCard dataset), financial transactions fraud detection (PaySim dataset) and bank transactions fraud detection (BankSim dataset);Investigation and testing of suitable preprocessing techniques, including statistical analysis, feature engineering and feature selection based on information value;Analysis and testing of different applicable ML techniques, including outlier detection methods  and ensemble approaches ;Reliability analysis of anomaly detection algorithms based on layer-wise relevance propagation.Part 2: Resilience analysis of the system’s implementation possibilities in the cloud environment. This part presents the main focus of this paper. It involved findings and partial results from Part 1 and performance of a resilience analysis of implementation possibilities for the fraud detection service in the cloud environment. We also propose an additional scoring-based alerting logic which uses historical decisions of the ML models. It includes following aspects:SoA analysis covering risk and resilience assessment, formal verification and cybersecurity in cloud environments;Case study selection based on case studies in Part 1—credit card fraud detection (CreditCard dataset) based on outlier detection methods ;The fraud detection robustness is improved through the proposed implementation that includes parallel processing anomaly detectors;Alerting robustness is improved by proposing a novel ML-based scoring system representing the users’ historical behaviour analysis;The system’s resilience is analysed during different cyberattacks targeting the cloud infrastructure by using formal methods.The work in this paper reflects the partial results of a research project which deals with developing a ML-based cloud service for fintech fraud detection. The complete research pipeline in this project includes two logical and practical units which cover different aspects of the problem:It has to be noted that this paper, as it covers the second part of the research pipeline described above, does not include any additional analysis, training or testing of ML-based fraud detection techniques. As such, it utilises the findings from the selected case study from our previous work in  as inputThe proposed basic fraud alerting system, as depicted in The anomaly detection and alerting module is based on ML, and consists of some common components, such as a buffer, a feature extractor and trained ML-based anomaly detectors. Further, the proposed system utilises three different parallel anomaly detectors, instead of a commonly used one, and proposes a rule-based decision-making component. The input for one execution of the anomaly detection and alerting service is a feature vector , which represents a financial transaction originating from a certain user and at a given moment. Successively, the output represents a binary alert, which indicates a suspected fraudulent transaction.Formal checking of the influence of additional parallel detectors, with regard to true positive rate (TPR) and true negative rate (TNR), during the system’s functioning with uninterrupted by cyberattacks;Formal checking of the system behaviour and the influence of different cyberattacks on the system’s performance, reflected in missed alerts  and false alerts .The focus of the conducted experiments in the context of the proposed system was not to depict the best ML-based algorithm but to formally check the performance of such a system, including:It should be noted that the considered cyberattacks include different malicious activities that aim for the cloud infrastructure of the system. The potential financial frauds are separately considered, namely, in the form of modelled (expected) user behaviour. CreditCard: This credit card fraud detection dataset contains transactions made with credit cards of European cardholders in September 2013 (encoded as PCA components).PaySim: Synthetic financial datasets for fraud detection; the authors of this dataset used aggregated data from a private dataset to generate a synthetic one. The synthetic dataset resembles the common operation of transactions, but contains injected malicious behaviour to be able to evaluate the performance of fraud detection methods.BankSim dataset: Synthetic data from a financial payment system. In order to generate this dataset, its authors used an agent-based simulator of bank payments. This was based on a sample of aggregated transactional data that was provided by a bank in Spain.As already mentioned, the basis for this work is our previous research in , where wConsidering that only the CreditCard dataset contains representations of the real transactions, and thus represents the most credible data reflecting user behaviour, we decided to use the statistics of that dataset as inputs for probabilistic model checking. This dataset contains 492 fraudulent transactions out of 284,807 transactions within two days. However, this makes the dataset highly unbalanced, since the positive class’s percentage (fraud) of all transactions equals 0.172%.We also decided to use our outlier detection methods from  as the mTrue positive rate (TPR) represents the probability that fraudulent activity (transaction) will be correctly detected;True negative rate (TNR) represents the probability that a regular (non-fraudulent) transaction will be correctly classified.True positive and true negative rates for all three proposed methods are shown in t if at least two out of three detectors identified the anomaly. The logic of the applied decision-making is presented in the following equation.The decision-making component in the proposed system represents a simple rule based decision-making mechanism. This gives the positive output—referred to here as The extended variant of the proposed fraud alerting system, in addition to the module described in the previous subsection, includes a ML-based scoring module as an additional alerting module. This module considers historical user data, and its main purpose is to increase the resilience of the alerting system as a whole. The scoring system consists of a buffer and the score update decision component, and takes as inputs ML model outputs and the history data stored in a special instance of the service database—scores DB. While the output of the anomaly detection and alerting component is calculated with the same logic as the basic fraud alerting system, Equation ), the ouIn this work, two threshold values were tested in the experiments in order to test the influence on rising the alerts. The first one is the arithmetic average  were coThreat assessment: According to the obtained list of threats and the system’s architecture, possible entry points for an attacker are identified. Then, cyber vulnerabilities are attributed to certain entry points for an attacker and result in different types of exploits. Subsequently, corresponding attack scenarios are defined that depict the behaviour of an attacker in the given environment. For every vulnerability, exploitation probabilities are calculated according to several metrics, which each represents the likelihood of occurrence for a given attack.Model: The formal model was created in the PRISM modelling language based on the proposed architecture; we identified vulnerabilities with exploitation probabilities and modelled attacks. The non-deterministic model was developed as a Markov decision process (MDP). Formal attack properties were identified and modelled next using the probabilistic computation tree logic (PCTL) embedded in the PRISM model checker.Model checker (PRISM): Model checking was performed against the identified properties using the PRISM model checker. The required input elements for formal verification were the system model and the identified attack properties.Model checking results: This process resulted in the maximum likelihoods of successful attack attempts—risk exposure scores.The flowchart of the methodology proposed in this paper is given in Modelled input in our PRISM model is a state of the input transaction , where we define the probability of a transaction being fraudulent based on the case study (the CreditCard dataset used in our case study has the ratio of fraudulent transactions of 0.172%). All system components are modelled as modules in PRISM modelling language. Every module can change the inner state of our transaction; e.g., there is a probability of 0.8824 that ML1 is going to detect a fraudulent transaction (and leave it in the state 1 for the processing by the next module), and consequently a probability of 0.1176 that the transaction is going to be misclassified as regular (and its state is going to be changed to 0). Apart from the uncertainty that ML algorithms limitations bring to the alerting process, certain cyber-attacks (unrelated to the fraud originator) can also influence the alerting process (and cause changing of the state of our transaction in the model). The PRISM model checker checks the probability of every possible path in the defined system and calculates risk-exposure scores for the defined attack properties. For example, we model as an attack property that the client-side visualisation service is going to receive the information that the state of the transaction is 0 (regular), under certain conditions (active/no cyber-attacks), while the input transaction was in the state 1 (fraudulent).Different parts of a system, including the functional correctness of implementations, programming bugs, hardware Trojans and security properties, can be formally checked using formal methods. In general, they can provide both qualitative and quantitative analysis ,35,36. TQuantitative verification in general includes two main approaches, namely, probabilistic model checking and statistical model checking . Quantithttps://www-users.cs.york.ac.uk/~cap/FACT/ (accessed on 15 October 2022)), MODEST (http://www.modestchecker.net/ (accessed on 15 October 2022)), MRMC (http://mrmc-tool.org/ (accessed on 15 October 2022)), PASS (https://depend.cs.uni-saarland.de/tools/pass/ (accessed on 15 October 2022)), PARAM (https://depend.cs.uni-saarland.de/tools/param/ (accessed on 15 October 2022)), PRISM (http://www.prismmodelchecker.org/ (accessed on 15 October 2022)), UPPAAL ) and STORM (https://www.stormchecker.org/ (accessed on 15 October 2022)).Some of the available probabilistic modelling tools include: FACT  ,51. AddiSQL injection : This atDenial of service (DoS) attack : The goaAdversarial attacks on AI : In the Due to the distinctive nature of the components, an attacker can carry out different types of attacks. In the proposed fraud alerting systems, the following threats are considered:The threats are assigned to critical assets in the proposed cloud systems, which mark the individual entry points for cyberattacks. The following attack scenarios are defined in the context of the proposed cloud systems. The scenarios correspond to the entry points of their respective systems.Attack scenario 1In this scenario, the objective of a cyberattack is to cause an overflow in the buffer of the anomaly detection and alerting module. As a consequence, the posterior ML component will receive corrupted or no information. Theoretically, malicious code can be injected into the buffer, thereby causing damage to the system when executed.Attack scenario 2This attack scenario concerns the input module and targets its feature extraction functionality. As a result, incorrect features are derived from adversarial input data, which affects the further fraud alerting process. This happens by increasing significantly the number of false alerts, and potentially masking real frauds by doing so. While adversarial manipulation does not affect the functionality of ML, it does influence the outcomes of its classification process.Attack scenario 3In this scenario, an attacker can either insert, update or delete database entries. In the context of the proposed alerting system, the ML-generated message outcomes are stored in a database. In such way, the database becomes subject to this type of attack. In general, a SQLI directly affects the processing outcomes at their back-end.Attack scenario 4This attack scenario is the same as Attack Scenario 1 from a technical aspect but differs in its consequences. In this case, a DoS attack is executed against the buffer in the scoring system module. Similarly to the prior example, such attacks can disable further processing in this module. In cases of success, the module can be disabled completely, thereby blocking the user’s scoring functionality. Thus, a staged buffer overflow has an influence on the reputation of a client. This means that decisions on the fraudulent client level can be manipulated, which nullifies the outcome of the previous anomaly detection.Attack scenario 5Similarly to the third case, in this scenario, an attacker executes a SQLI against a database. Despite its similarity, this attack follows a different objective. This cyberattack is executed in the context of the extended system against its user scores database. This means that a successful SQLI leads to hacking of client information, which affects users’ reputations from the scoring system. This, in turn, helps an attacker to cover committed frauds and leads the fraud alerting system to provide incorrect information to financial institutions.In the next step, the exploitation probabilities for each cyberattack were calculated according to several score metrics. To this end, the already mentioned CVSS was used, which covers multiple characteristics of cyber vulnerability. This framework estimates the severity of a cyberattack and produces a numerical score in form of a probabilistic value. As already mentioned, the score for every cyberattack was determined according to basic vulnerability metrics. These include the proximity of an attacker to their target, the system’s complexity and attack complexity, required user privilege, scope and the implications for information security . BasicalHowever, it should be noted that some assumptions are valid for the risk assessment of both systems. First, it was assumed that an attacker had gained access to a network that was connected to the cloud system. Additionally, all target components were considered to be vulnerable, which means that they could be exploited. The assumption was that the attacker possessed sufficient domain knowledge and hacking skills in order to execute the mentioned attacks. It is important to note that the goal was not to compromise the system but to analyse the risks of particular attacks and their impact.The context of both DoS attacks, the attacker gains access to the target network and causes flooding to disable the target buffer. Eventually, no special user privilege and no additional user interaction is required for this attack. The exploited vulnerability’s scope remains unchanged with some loss of confidentiality and integrity. In this attack, the attacker is fully able to violate availability of the target service; thus, the base score equals 8.6. The target component for an adversarial attack is bound to the network stack and requires user privileges. In cases of success, this attack violates information confidentiality and integrity but does not affect its availability. The score for the adversarial attack equals 6.1. An SQLI in the given contexts does not require special physical proximity nor advanced attacker skills. However, in case of success, it does affect information security to a certain degree and results in a base score of 6.5.The base scores for all mentioned side-channel and adversarial attacks against the basic system are given in In the extended fraud alerting system, additional cyberattacks add two more values to the total base score, as presented in Missed alert—transaction was fraudulent but the system did not detect it,False alert—transaction was regular, but system created an alert.The outcomes of the previously selected cyberattacks were identified and modelled for the proposed systems in the following manner:Both outcomes were formally checked in two cases. In the first, no active attacks were present in the system. In the second, an attacker actively approached the system and possessed the means and knowledge to perform a successful attack. The previously described exploitation probabilities reflect the likelihood that an attack against a certain component will be successful. However, the attacker’s capability here was limited to the maximum number of two attacks against different components during the system’s run.The modelled attack outcomes are described in detail in The selected model checker, PRISM, was applied on two proposed fraud detection system architectures—basic and extended, in order to perform a formal risk and resilience analysis and to obtain an indication on how safety and security requirements are fulfilled within a given environment. Subsequently, the modelled attack scenarios were selected from the scenarios outlined in The first round of tests show the anomaly detection and alerting module’s behaviour in the case when no active cyberattacks are present. Nevertheless, potential financial frauds are considered in form of a fraud probability, which reflects the expected user behaviour.The anomaly detection and alerting module is modelled in the form of a non-deterministic system model, namely, a Markov decision process (MDP). The system’s architecture, attacker’s behaviour and existing vulnerabilities in the system, including their identified exploitation probabilities, are incorporated into the Markov model. Formal modelling and checking of the system is performed by using the PRISM model checker. This results in the system model, the identified attack properties and risk-exposure scores as quantifiable risk and resilience indicators.The conducted tests indicate that the use of three parallel anomaly detectors, with the logic described in As previously stated, it was assumed during the modelling process that an attacker actively tries to exploit the system and possesses the necessary skills and knowledge. In this case, the maximum number of vulnerabilities that can be exploited in one attack scenario—the cost value, measures an attacker’s skills. In this experiment, the cost value is set to two, which indicates a fairly skilled attacker. In addition, it is assumed that the attacker is aware of the availabilities from During the next step, attack properties are defined and added to the completed system model. In general, such properties define formal definitions and preconditions for a successful attack attempt within the modelled system. The attack properties are defined by using the probabilistic computation tree logic (PCTL), which is embedded in the PRISM model checker. The subsequent formal check results in the maximum likelihood of a successful attack attempt—the risk-exposure scores. All modelled attack properties are directly based on the given attack outcomes, as described in The obtained results show the maximum likelihood of a successful attack attempt, by considering different system architectures. Assuming that the goal of an attacker is to carry out an attack by exploiting the smallest amount of vulnerabilities, the presented analysis considers cases where the maximum number of exploited vulnerabilities is set to two.The results also show that an attack against the basic system architecture has a high likelihood to cause either missed or false alerts, where the system is more susceptible to missed alerts. Adding additional decision logic in form of the proposed scoring system has a positive impact on the system’s resilience. This is the case both when the system is under attack, and in a case of uninterrupted system operation. This effect is especially important when it comes to false alerts boosted by active cyberattacks  and in cases when the system is subject to active cyberattacks.The promise of the presented concept is its applicability to real-world environments. Additionally, it provides an overview of potential cybersecurity issues that must be considered when implementing a fraud alerting system. The proposed analysis considered realistic challenges to ensure correct and cyber secure functionality before its implementation. To our knowledge, the proposed solution represents one of the earliest contributions to the mentioned challenges. In this regard, the provided formal model constitutes an early specification for this type of system. As such, the proposed solution further contributes to security-by-design of anomaly detection systems in the fintech domain. In the future, the proposed architecture will be further extended by broadening the risk assessment in terms of cyber threats and vulnerabilities. Additional security properties that mirror realistic threats will be considered for formal specification. Finally, the produced knowledge will be useful for other disciplines, especially for the domain of anomaly detection and security testing.


Public safety and health cannot be secured without the comprehensive recognition of characteristics and reliable emergency response schemes under the disaster chain. Distinct from emergency resource allocation that focuses primarily on a single disaster, dynamic response, periodic supply, and assisted decision-making are necessary. Therefore, we propose a multiobjective emergency resource allocation model considering uncertainty under the natural disaster chain. Resource allocation was creatively combined with path planning through the proposed multiobjective cellular genetic algorithm (MOCGA) and the improved A* algorithm with avoidance of unexpected road elements. Furthermore, timeliness, efficiency, and fairness in actual rescue were optimized by MOCGA. The visualization of emergency trips and intelligent avoidance of risk areas were achieved by the improved A* algorithm. The effects of logistics performance, coupling of disaster factors, and government regulation on emergency resource allocation were discussed based on different disaster chain scenarios. The results show that disruption in infrastructure support, cascading effect of disasters, and time urgency are additional environmental challenges. The proposed model and algorithm work in obtaining the optimal solution for potential regional coordination and resilient supply, with a 22.2% increase in the total supply rate. Cooperative allocation complemented by political regulation can be a positive action for successfully responding to disaster chains. Public safety and health have become prominent livelihood issues nowadays. China’s 14th five-year development plan clearly suggests that the evolution mechanism and intelligent control theory of multidisaster coupling are important fields for priority development . This phPrevious studies have mainly concentrated on geologic genesis and multihazard framework of disaster chains ,6. ScholMoreover, only one objective function has been considered in most previous studies. Liu et al. addressed the problem of emergency medical vehicle allocation in urban areas to maximize the coverage of demand sites . Other eThe reliability and time costs of transportation paths are very important due to the urgency of resource availability after a disaster . DistancThe framework for disaster risk reduction sets out priority objectives for managing disaster risk and ultimately enhancing preparedness for effective response . ConsideIn light of the above considerations, a multiobjective emergency resource allocation model with path planning is proposed to be applied for the natural disaster chain in this study. From the results of this paper, the model performed well in dynamic decision, periodic supply, and cascading response. Although the A* algorithm has been widely used in many other fields, the provided perspective can be valuable in application for emergency management. The multiobjective cellular genetic algorithm (MOCGA) was applied to realize timeliness, effectiveness, and fairness of relief process and the A* algorithm was improved to support path planning in the multiechelon road network.The main contributions of this study are as follows: Firstly, an emergency resource allocation model that integrates the multiobjective, multiperiod, and multiechelon network is developed for the natural disaster chain under the uncertainties. Secondly, the interconnection of the improved A* algorithm and the MOCGA makes dynamic response and periodic supply feasible. Thirdly, through a series of numerical study, the key points of resource allocation in actual emergency relief are analyzed. The remainder of this paper is organized as follows: The formulation of the proposed model and improved path planning are described in This paper concentrates on the multiperiod and multiobjective problem of emergency resource allocation for the natural disaster chain, with the vehicle path problem for multiechelon logistics networks. Emergency resource allocation under the natural disaster chain is complex and often accompanied with uncertainty and conflicting objectives. In order to respond rapidly to the disaster chain, decision makers need better technical support and well-organized deployment of facilities. Responsiveness refers to distributing the maximum supplies to the maximum number of beneficiaries at the right time . GeneralGiven the time-dependent demand, generic and specific resources need to be transported from multiple warehouses to each disaster site. The time, location, type, and quantity of allocated resources must be given throughout the response phase. The purpose of this study is to develop a dynamic decision process and periodic supply strategy for the challenging problem. It is considered as a multiobjective model for emergency resource allocation under the natural disaster chain. A three-echelon emergency logistics network is constructed, consisting of warehouses (WHs), rescue stations (RSs), primary disaster points (PDPs), and secondary disaster points (SDPs).Sets.Although secondary disasters are induced by primary disasters in the chain structure, the disaster types and required emergency resources often differ. For example, the natural disaster chain of landslide-induced surge is a widespread form in mountainous and gorge areas, coastal areas, and reservoir areas . RelatedParameters.Variables.The allocation of emergency resources under the natural disaster chain is noted as inherently difficult and constitutes a spatiotemporal decision under uncertainty. As a method to describe uncertainty phenomena, fuzzy sets are frequently used in production and life, such as fuzzy control systems and fuzzy classification ,41,42. Iemployed . Take GiPath planning can support allocation decisions to avoid risky road sections and obtain optimal distribution trips and corresponding travel times. The regional road network is at risk of disaster chain disruption. It is necessary to optimize the timeliness and reliability of emergency resource transportation to ensure resources can safely reach primary and potential secondary disaster sites in time. From the actual disaster disposal, logistics access scenarios should be set up to reflect potential road interruptions and blockages., where The A* algorithm is organically combined with the raster map and road reliability is added to the traditional shortest path search. The purpose of path planning is to find the optimal path that meets the practical requirements from the starting nodes to the target nodes in the road network. The logistics network is abstracted as a directed connectivity graph Maps with arbitrary contours can be drawn with sufficiently fine raster to simulate the real road environment. Each raster is colored differently to characterize the specific physical entity. There are three directions for path planning based on raster maps: horizontal, vertical, and diagonal. For low-speed robots, the planned path can be followed exactly, while for medium- and high-speed robots, the planned path can be smoothed for non-fully-constrained systems.n to the target state. The search process of the evaluation function is shown in n in the state space, representing the information blocks near the start node; n to the target state, representing the information blocks near the target node. The total cost Based on the estimated cost, A* algorithm iterates from the initial state through state Based on the traditional shortest path planning, the A* algorithm is modified to overcome the poor flexibility and low applicability of single-objective pathfinding, which can only avoid static obstacles. The improved algorithm for the avoidance of random obstacles can simulate the unexpected road elements that may occur during the allocation of emergency resources, such as road disruption and blockage. Two main planning objectives are concerned, reflecting the timeliness and reliability of the trips. Trip efficiency refers to optimizing the actual travel time of the distribution vehicle, measured by the raster from start nodes to target nodes. Trip reliability refers to avoiding risky road sections and reducing trip exposure. Road section reliability is translated into a safety factor for each raster.The problem of emergency resource allocation is modeled to obtain a robust and scientific scheme to the disaster chain. The social and humanitarian attributes of the disaster chain relief dictate that the process is inevitably accompanied by multiple conflicting objectives. As an essential evaluation index of rescue schemes, timeliness determines whether the disaster sites can be allocated in time. According to the consensus of the rescue community, the golden relief time after a natural disaster is 72 h, after which the survival rate decreases significantly. It is necessary to avoid delays in the availability of resources. Furthermore, the complexity of the rescue network and the diversity of resource demand make it difficult to decide on a reasonable allocation solution. In the process of emergency resource allocation in the natural disaster chain, the demand comes from the primary disaster points and secondary disaster points, which is reflected in the types and quantity. Emergency resource allocation is a special logistics activity to scientifically distribute emergency resources from the warehouses to each disaster area. However, the supply of emergency resources from the warehouses is often limited at the initial stage. The excessive influx of emergency resources can result in wasted supplies, while insufficient resources can lead to further casualties and losses. Consequently, delivery time and unsatisfied demand are taken as multiple objectives for the model.Different from the commercial logistics problem, the proposed emergency resource allocation emphasizes both the timeliness and the effectiveness of schemes. The optimal allocation scheme is in compromise between different evaluation indexes. The multiobjective model for emergency resource allocation is proposed as follows:Subject toEquation (1) represents the timeliness of the model, labeled as F1, with the aim of minimizing the emergency travel time, which is related to transported resources and road conditions among sites. Equation (2) represents the efficiency of the model, labeled as F2, with the aim of minimizing the unsatisfied demand from PDPs and SDPs, which is related to the amount of supplied resources. Equations (3)–(13) are the feasibility constraints. Considering the availability of realistic assumptions, the transportation flow is ensured to be unidirectional by Equation (3). The quantity of transported resources is limited to the supply of WHs by Equation (4). Equations (5)–(8) are used to determine the quantity of unsatisfied demand of PDPs and SDPs at each period. Equations (9)–(12) are used to limit the quantity of resources to PDPs and SDPs no more than their demand at each period. The quantity of resources to PDPs and SDPs is no more than the resources available to RSs, as shown in Equation (13).The emergency resource allocation problem is NP-hard and many heuristic algorithms have been used to solve this problem. Compared to other algorithms, MOCGA is equipped with the domain structure and evolutionary mechanism to achieve the balance between local and global optimization ,45. The Real number coding is adopted because it can reflect resource flow during the rescue process. Each chromosome of MOCGA determines an allocation scheme and indicates the emergency resource flows between WHs, RSs, PDPs, and SDPs. Thus, each chromosome in period The chromosomes are initialized randomly to ensure the diversity. Constraints are attached to make chromosomes comply with the actual conditions. When the former part In order to evaluate the fitness for multiobjective optimization, the chromosomes undergo rapid nondominated sorting . The fitThere are two types of selection in the algorithm iteration. The first one is for the evolutionary operation. In the two-dimensional grids, the selection is conducted within the set quations :(14)αi={Since the proposed emergency resource allocation model is a constrained one, it is critical that the optimal scheme obtained is feasible. Accordingly, the evolutionary process is subject with constraints handling so that the generated chromosome In connection with the actual situation of the disaster chain, the in-depth study of the emergency resource allocation process is a prerequisite for effective control of catastrophe risks. Logistics network is an important element of emergency relief. The research trend on disaster chain is associated with the dynamic simulation of the cumulative amplification effect. Government regulation is the ultimate attribution of disaster management. To verify whether the above elements play a positive or negative role in disaster chain system, three numerical scenarios related to logistics performance, coupling of disaster factors, and government regulation were analyzed. The main practical problems faced in emergency response of the disaster chain were identified, and theoretical support and effective measures were explored.Road network construction and path planning are performed by means of the improved A* algorithm with the raster map. To model the practical road network, the grids represent different physical entities. Green grids represent rescue stations, red grids represent primary disaster points, yellow grids represent secondary disaster points, blue grids represent warehouses, gray grids represent fixed obstacles, and light-yellow grids represent unexpected road elements. It enables visualization of paths among points and the flexibility to avoid unexpected road conditions. The planned paths from Rescue Station The distances among WHs, RSs, PDPs, and SDPs are determined based on the planning results, as shown in The actual disaster chain fluctuates due to risk environment and system factors. The proposed model and algorithm are validated with numerical scenarios. The literature research is followed to summarize some key perspectives of general interest nowadays. Three scenarios related to disaster relief operations are included, i.e., logistics performance, coupling of disaster factors, and government regulation.In the algorithm’s preselection, it is found that the Pareto front of MOCGA fully dominates the nondominated sorting genetic algorithm (NSGA-II). The algorithm parameters of MOCGA are set as follows: The area of cell space is set to 12 × 10, the population size is set to 120, the maximum number of iterations is set to 500, the probability of crossover This section investigates the influence of logistics access performance on the objective functions. Through path planning, a specific area in the central–eastern part of the map is identified as having the highest traffic load. The transportation road network with the collapsed key hub is shown in The breakdown of the transportation hub results in an overall suboptimal allocation of emergency resources, both in terms of unsatisfied demand and the time cost. A comparison of the population scatter in period Notably, it validates the environmental challenges raised through semistructured interviews with disaster management agencies —that is,The overall disaster situation is often not a simple superposition of several individual disasters . The disIt is found that the gestation environment in a disaster chain is often modified by the preceding disaster type. With different hazards in the natural disaster chain, the vulnerability and exposure of the disaster-bearing body are not simply linear superposition. The causative factors chain formed by the interaction among hazards is often constrained by the gestation environment. The feedback effects of the disaster-bearing body on the causative factors chain are complex and it can be transformed into a causative factor under some conditions. The triggering of causative factors chain makes it possible for the disaster-bearing body to be subject to multiple forms of attacks, resulting in the concurrent and cascading phenomenon with disasters’ cumulative amplification.This section describes the challenges posed by a disaster’s cumulative amplification on the allocation of emergency resources. Due to disaster-bearing properties, Observing the initial relief capacity, the supply/demand ratio Comparing the results of emergency resource allocation under the scenarios, the supply increases to a certain extent when the proportion of demand increases at the corresponding disaster site. The total supply increases by 2.6% over the original, yet its overall unmet demand increases by 8.84% with a slight rise in travel time of 1.6%. As shown in In the events of supply–demand imbalance or disaster coupling amplification, as simulated above, intervention mechanisms are needed to bring them back into balance. Ultimately, political intervention can generate informative solutions, as disposal of disasters are political spaces . This seThe government’s newly assigned budget is maximized for its effectiveness. In the meantime, relief fairness, specifically regional allocation rates, is also an important factor of government regulation that decision makers should have to take into account. Government departments increase the supply of corresponding emergency resources by setting up additional warehouses at suitable locations. The objective expression of minimizing the maximum demand unsatisfied rate is introduced to measure the regional allocation fairness, as defined by Equation (16). As can be observed from As can be observed intuitively from the delivery flow displayed in A multiobjective emergency resource allocation problem under the natural disaster chain was presented from methodology and operational research. The key scientific issues in disaster chain response were realized, such as dynamic response system, multilevel resource coordination, and emergency auxiliary decision. The resource allocation was creatively combined with path planning through the proposed MOCGA and improved A* algorithm with the raster map. MOCGA was applied to achieve parallel optimization of timeliness, efficiency, and fairness in humanitarian relief. The A* algorithm was improved to visualize and intellectualize the resource flow and maintain the trip reliability. The proposed model and algorithm can provide the optimal and scientific solution for emergency resource allocation in response to the natural disaster chain, coping with the uncertainty in rescue.To further explore the identification and elimination of actual risks faced by decision makers, numerical studies were conducted under scenarios of logistics performance, coupling of disaster factors, and government regulation. When an important transportation hub in the original three-echelon logistics network fails, the efficiency of resource availability is greatly reduced. Thus, the overall performance of the scheme is affected by a 16.1% increase in unmet demand. Besides, the coupling of the causative factors chain, gestation environment, and disaster-bearing body in the disaster chain system brings additional resource allocation fluctuations. The overall unmet demand increases by 8.84% with a slight rise in travel time of 1.6%. If the supply cannot be kept up in time, it will trigger a serious break in the disaster relief loop. While neglecting regional distribution balance, the pursuit of resource supply efficiency at the macro level is also detached from the principle of postdisaster relief. In the subsequent analysis, the role of government regulation is added and reflects the fairness pursued by society. The government departments respond in real time and reallocate additional social resources as the disaster situation develops. The supply is balanced and the total supply rate increases by 22.2%. Therefore, the derivation of disaster situations is controlled by supplemental social resources, and forms potential regional coordination.Disruption in infrastructure support, cascading effect of disasters, and time urgency are challenges imposed by the environment. The results show that the interconnection of A* algorithms and MOCGA is suitable and practically usable. The critical transportation hub derived by the A* algorithm and resilient supply mechanism provided by MOCGA play a key role in risk avoidance and resource availability. Vehicle path planning is the prerequisite and emergency resource allocation is the goal, thus forming a closed loop for the response of the actual natural disaster chain. Resilient infrastructure and supply schemes shift from remediation to preparedness, contributing significantly to overcome environmental damage. In this way, resilient and sustainable regions are formed in disaster prevention. The potential coordination of regional facilities and supply routes can be quite beneficial to both managers and operators in creating a virtuous circle between regions and reducing the “marginal cost” of cross-regional transport. In conclusion, the optimal allocation solution complemented by political intervention is a new working idea for scientific response of the disaster chain.On future work, the study will be extended to other safety fields of the disaster chain and multiple transportation modes will be considered. The potential regional coordination identified in this paper could provide a basis for the next study on emergency response regarding biological disasters with regional sanitary barriers. Specific resource allocation strategies can also be relevant as future work.