Specific Learning Disorders (SLD) are a group of heterogeneous health deficits frequently diagnosed in early childhood that cause difficulties in general intellectual functioning. In the last decades in Italy new laws have been developed to give practical guidelines for the best education plans for children with SLD. Background: The aim of our study was to determine the efficacy of the educational treatment on SLD in Primary and Secondary schools in the Italian city of Barletta. We acquired valuable data to evaluate Special Education Needs during COVID-19. Methods: Our study was conducted from April to June 2021, during the second “lockdown” period in Italy. A fact-finding survey was conducted to schools with a questionnaire provided to the teachers to acquire data on the SEN applied in the management of distance learning for children. Results: The study involved 15 male and 6 female pupils with SLD in Primary Schools and 18 male and 6 female in Secondary Schools. The schools participating in the study organized distance learning programs with a support teacher with a 1:1 ratio. Data showed that all children with SLD needed a support teacher. Conclusions: The findings of this pilot study suggest that distance learning programs are able to achieve adequate educational goals, despite the difficulties of the lockdown period. Specific Learning Disorders (SLD) are a set of heterogeneous health deficits that may impair the ability to read, write, calculate, listen, and use verbal expression. They include dyslexia, dysgraphia, dyscalculia, and dyspraxia. The term specific means that the disorder only affects some abilities, leaving intact the general intellectual functioning. Moreover, they are evolutionary, because they vary with age of the subject. A child may have trouble in understanding instructions, carrying out tasks, and in interactions with others.A child may have to do more efforts in memorizing information, talking to others and understanding what others are saying to them. Consequently, they may have very low self-esteem or show symptoms of anxiety .Italian Law no. 170/2010 provides the right to education in the school to students with SLD. The Italian Minister of Education Decree no. 5669/2011 gives practical guidelines on what needs have to be guaranteed to students with SLD [Special education is therefore a training provision which is extra or different from what is needed by other children the same age. Some children may need sign language, worksheets in a larger font, or one-to-one/small group support . Children with SEN often have learning difficulties. Learning difficulties are classified in three grades: moderate if a child may take longer to learn skills than the majority of their peers; severe if a child has significant learning impairments and is likely to require high levels of specialist support in school; and profound and multiple learning difficulty when children have complex learning needs, physical difficulties, or severe medical conditions. In Italy there is an inclusive school system, in which students with and without SEN learn together. Students with SEN are supported by special education teachers, who prepare different educational plans for their own students .In different European countries, such as Germany, students with SEN attend a special school through the decision of the parents or legal guardians .There are different support focuses for students with SEN: learning, emotional, and social development; speech, sight, hearing; mental, physical, and motor development and instruction for sick students ,4. Data For students with SLD, the programs are to provide lessons and exams allowing additional time, oral rather than written tests, reading of texts in digital form, use of speech software, and the use of any other technology which was felt useful to facilitate the study and exams. Before the COVID-19 pandemic, in-person lessons supported students with SEN with the use of research tools and electronic resources available online, computers with speech programs, interlibrary lending services and audio books, and electronic copies of the articles . One of the most socially relevant conditions of SLDs is dyslexia, as it is characterized by difficulties that impact on reading and writing abilities. The severity of dyslexia can vary from mild to severe and this learning disability persists throughout life. However, although diagnosed and supported by specialists, dyslexia is frequently not emphasized in class during the early grades of school, in order to avoid children becoming frustrated or being discriminated against by their school-mates due their possible difficulty in learning to read.Our pilot study focused on SLD in Primary and Secondary schools in the Italian Southern city of Barletta. The aim was to evaluate whether the compensatory interventions provided to children in the pre-pandemic period and during the lockdown, as well as the educational treatment were able to ensure that effective preventive and protective measures were effective in helping children to better manage this condition, taking into account that psychological factors possibly caused by the pandemic may have influenced the results. The main objective was to acquire valuable data helpful to understand whether Special Education Needs programs, including distance learning, have been ensured during COVID-19. Finally, our survey investigated the possible negative impact and risks associated to distance teaching on SLD children. ADHD was not taken into account in the survey because COVID restriction legislation did not allow meetings in person.Our study was conducted from April to June 2021, during the second “lockdown” period in Italy. Out of the total 17 schools (8 Primary and 9 Secondary) active in the city of Barletta, 12 participated in the study (7 Secondary and 5 Primary). A fact-finding survey was conducted to schools. The questionnaire was designed in DanSurvey, a licensed product of Lime Survey 2.0 , which is a free and open-source online survey application written in PHP, based on a MySQL, PostgreSQL, or MSSQL database, and distributed under the GNU general public license . The queThe total number of children with SLD in Primary Schools was 21 out of a total of 726 subjects (2.8%), while in Secondary schools it was 24 children out of a total of 1160 (2.0%).The study involved 15 male and 6 female pupils with SLD in Primary Schools and 18 male and 6 female students with SLD in Secondary Schools. The mean age at diagnosis was 7 years in Primary Schools and 8 years in Secondary Schools. We observed a difference rate in sex: 73% of these participants were male and 27% were female.During COVID-19, the schools participating in the study organized distance learning programs with a support teacher with a 1:1 ratio. During the COVID-19 pandemic, a dedicated web platform was used for all children recruited in our studio. This teaching program was provided simultaneously with the rest of the classes. Data showed that all children with SLD needed a support teacher.Some children had more than one deficit. A total of 27 students presented 2 symptoms, dyslexia and dysgraphia, and 3 had dyslexia and dyscalculia .The results obtained in our study highlight the great impact that confinement has had on students with dyslexia and their families, but also the greater attention which Primary and secondary school examined in Barletta gave to children with SLD . Our surAhmed et al. defined COVID-19 as the most significant global health crisis of our generation . For ShaDuring COVID-19 there was a need for a reorganization of SED to ensure safety measures and distancing. In March 2020, in full health emergency, the first operational indications for distance learning were provided, some possible actions were declined, the concept of working coherently with the reference context was reaffirmed, and teachers were invited to make effective choices from a didactic point of view and above all inclusively, with a strong emphasis on planning and teamwork. In particular, with regard to students with SLD, it is necessary to devote, in the planning and implementation of remote activities, particular attention to the presence in the classroom of students with SLD and to the respective personalized teaching plans ,13.The relationship between distance education and pupils with learning disabilities is complex and challenging. During the months of closure there were many solicitations for teachers, students, and their families to face the many problems that arose in itinere to make distance learning inclusive and effective. In fact, SLD students have more difficulties in mastering new and changing learning environments, but many of them have some strengths that also help them to thrive in this unprecedented learning environment. Teachers and parents who recognize these strengths can encourage students to translate these skills into the classroom in the future ,15.During COVID-19, SLD experts invited teachers to record lessons which could be always available to be listened to according to the times of each child, thus promoting inclusion and respecting the needs of each learner, especially those with SLD, since in distance learning the time and pace of online lessons was pressing and not adequate for the teacher–pupil relationship. In fact, as their educational needs require continuous dedicated and systematic care on a daily basis, children diagnosed with SLD may be at increased risk for detrimental learning consequences during the COVID-19 lockdown. The suspension of school in-person activities may had impacted the continuity of educational care for children with dyslexia with the risk of increased emotional and psychological burden related to social distances, lack of dedicated specialist support, and isolation. Parents of children with special educational needs exhibited worries about the possibility for their children to fall even further behind in school because they did not feel adequate they could meet their specific needs during the COVID-19 emergency. Additionally, worries about the lack of supervised and specialist care for their children’s disability condition and rehabilitation was the most significant predictor of parents’ stress, depressive, and anxious symptoms during the COVID-19 lockdown ,16.There have been a few perplexities in the post-pandemic period with the resumption of studies in-person—the return to previous organization of learning materials, and new reorganization of time .The importance of diagnostic tests has been established subjected to children with SLD for the purposes of learning, but it is necessary to compare percentages of children with SLD between schools recruited in Barletta and those at a national level. Children with SLD in the participating schools were 5%, while at the national level, dating back to 2011, it was 4%. This difference, although not statistically significant, can be explained by the lack of knowledge of the condition studied. Finally, this study suggests that children suffering from LSD may have many opportunities to manage their condition thanks to the use of adequate compensatory tools, and thanks to diagnostic tests that enable families and caretakers to timely intervene in LSD children to properly manage their condition with the support of adequate supporting programs.The authors are aware of the limitations of this study. This is a cognitive investigation with a small sample size, which limits the generalizability of the results. The timing of data recruitment met the pandemic period with the due restrictions of the DPCM, which influenced the participation of schools and prevented a direct interaction and testing of the children. However, the methodology used allowed the adequate collection of data through teachers. Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.Students with Special Educational Needs (SEN) are an especially fragile group, as they have significantly greater difficulties in learning than other groups of children and young people of the same age. During the COVID-19 pandemic, children had major learning difficulties rather than before lockdown, due to the need to use distance learning platforms provided by their schools.Nonetheless, alternative forms of teaching, examination, and education dispensatory strategies were tailored to all students with SEN. There is, in general, a great need to identify the right tools and strategies to support students with difficulties in reading, writing, and mathematics, and provide them the right help to students with SEN. However, in our experience, thanks to the professional figure of the personal teacher who supported the children, they were able to reach the planned educational goals, despite the difficulties expected in pursuing their educational goals.The findings of this pilot study suggest that distance learning programs are able to achieve adequate educational goals if suitable supporting strategies and programs are provided to children. This type of investigation should be expanded, and comparisons between schools of different areas encouraged. Further studies would enable the acquisition of valuable information to help develop recommendations to be adopted by educational institutions for students with SEN.


Dashboards are being increasingly used in the health field, and literature points out that accurate and efficient dashboards require not only dealing with data issues, but also ensuring that dashboards are user-friendly and that incorporate users' views and needs. The integration of evidence and data into decision aiding tools, such as dashboards, to assess and monitor environmental health (EH) in urban settings requires careful design. Departing from EH evidence and making use of the views of EH stakeholders and experts, this study aimed at defining requirements for a dashboard to help decision-makers analyzing and visualizing EH information in the Lisbon urban context. In order to set those requirements, it was combined a user-centered with a design card approach to engage EH potential end-users so as to collect their visualization preferences and gather information related to dashboard requirements. Specifically, three online group semi-structured interviews, involving 11 potential end-users from different organizations, were conducted; design cards with a set of visualization options regarding 17 indicators of built and natural environment determinants were used in the interviews to capture participants' preferences and their rationale; questions about other dashboard features were also asked; and the results from the interviews were synthesized into four separate, but interrelated features, and operationalized into 11 requirements for a dashboard to monitor EH in Lisbon. This study contributes to EH literature by producing knowledge to inform dashboard construction, by highlighting issues related with the usability, analysis, and visualization of data to inform EH decision-making in urban contexts, and by designing an approach that can be replicated to other EH dashboard contexts. New digital tools are emerging rapidly, with health and environmental institutions increasingly collecting larger amounts of data and facing novel opportunities to adopt technologies to manage such data for improved decision-making –3. NeverCommon challenges related with health data have been data standardization; database linkage, integration and sharing; selection of key data for decision-making; and finding a balance between evidence and research data to be included within decision tools , 6. ChalMultiple health and EH institutions have been investing in tools using visual formats to help communicating information to EH stakeholders and enhance decision-making. For instance, the European Environment Agency has developed a visualization tool in which it is possible to track and compare national data (collected by European countries) concerning air quality, noise, mobility, and housing conditions indicators . This tyAmong such decision aiding tools using visual formats, dashboards have increasingly being adopted , with itDesigning a dashboard is far from being straightforward , and meeLisbon has been recently nominated as European Green Capital 2020 and promoting several EH-related initiatives, as well as expressing the need and scope to develop and implement tools like dashboards to evaluate EH . There bAiming to identify the requirements for a dashboard to monitor EH in urban settings, taking Lisbon as a case study, this work succeeds two previous studies: in a first study the evidence on which indicators are relevant for analyzing EH in urban settings was gathered through a systematic review of literature described in detail in Salgado et al. ; and in A systematic review of the literature reported in Salgado et al.  identifiDrawing on the evidence and data collected, a wide number of Portuguese EH experts were involved in selecting and validating built and natural environment indicators relevant to monitor EH in Lisbon [details in ]. A mixeThis study encompassed a series of semi-structured interviews with potential end-users from different local organizations. A user-centered approach was used to facilitate a ground-up conceptualization of data requirements from the user standpoint , 36. TheKey local government institutions engaged in environmental and health regulation, policymaking, and urban sustainability were firstly identified. Then a multidisciplinary group of potential end-users—with knowledge about architecture, geography, civil engineering, and public health—from those institutions was chosen due to their EH knowledge, taking into consideration their institutions' interest in EH and in having a dashboard to monitor EH, and due to their deep understanding of the EH-related databases and evidence. No prior experience with dashboards use was required. Four participants received a formal invitation via email with a description of the work. Due to the desire to capture the views of as many participants as possible, invited participants were given the opportunity to choose between a group interview and an individual interview. The members who consented to be interviewed had to communicate whether their co-workers would be part of the interview and provide their email addresses. All the participants received the instructions, the link for the interview platform, the interview template, and the informed consent before the interviews.A search in local open-access databases—“Lisboa Aberta” , QualAR Visualization is efficient if the large volume of data is perceived in a minimum amount of time . FollowiOnline semi-structured interviews were conducted using Microsoft Teams platform, between December 2020 and January 2021, and lasted for about 1 h. The screen was shared during the interview to allow the interviewees to add comments and interact with the interviewer and answer questions regarding the design cards. The interviewer wrote the answers in the template file. Due to institutional constraints of participants, the interviews were not recorded.The semi-structured interviews consisted of four parts. In the first part, the interviewees had the chance to see four examples of public dashboards –48. DespIn the second part, alternative design cards were presented for the group of indicators related to natural environment determinants see . The visIn the third part of the interview, participants were invited to analyze the indicators in the built environment determinant, using the same questioning protocol adopted for the natural environment indicators see . NeverthThe last part of the interview was based on a set of yes/no and short open-ended questions to explore issues concerning the data quality, periodicity, and potential limitations. The interviewees were asked, for example, to state if the inclusion of the data source of each indicator or demographic data of Lisbon would be appreciated in the dashboard.The answers provided by each participant were treated, analyzed, and aggregated to identify the requirements for an EH dashboard and emerging issues. Simple statistics, such as percentages, were calculated for the yes/no questions, and for the choice questions. For the short open-ended questions, a thematic content analysis was used to analyze the interviews. The analysis followed the principle of classifying and organizing data according to key and common concepts . SimilarN = 3). From the Sustainability Cities Organization, members from the urban planning department participated (N = 3). The interview with the Lisbon City Council integrated members from the department of environment, energy, and climate change (N = 4) and from the department of environment and green spaces (N = 1).Three online group interviews were performed between December 2020 and January 2021 with members from Lisbon and Tagus Valley Regional Health Authority, from the Sustainability Cities Organization, and from the Lisbon City Council led to the involvement of 11 participants. Each interview lasted between 60 and 90 min. The interview with the Regional Health Administration included members from the sanitation and public health department .In the first part of the interview the participants had the chance to analyse and compare four examples of city dashboards. The first identified requirement was the need to display the information in a way useful for the user. The dashboard should be designed to help the users to identify problems and to keep track of trends. These needs change according to the organization. Therefore, the dashboard must be customizable to enable the user to select which indicators to analyse and flexible enough to be used by different organizations (Requirement 2).In the second part of the interview, interviewees were asked about their preferences regarding visualization options for natural environment indicators. All the interviewees agreed that the indicators from air quality should be visualized in the same graph, but no clear consensus was reached about the most preferred way to visualize them. Bar plots were the choice of 54.5% of the interviewees, while 45.4% preferred line graphs. A similar choice was observed for the indicators of noise dimension. Only for the water indicator was clear the agreement to visualize it using a line graph. The pie chart was considered a “hard to read” option, while tables were considered only as an additional feature to the initial graphic. Overall, the features more appreciated by the interviewees to visualize natural environment indicators were the use of easy-to-understand graphics, with adequate font size, and minimal information displayed. It would help the users to acknowledge at a glance an overall picture of all the indicators (Requirement 3).In the third part of the interview, participants were asked about the built environment indicators. The data of these indicators were available in the public databases only in the map format. For the indicators without available data, a consensus was reached among the interviewees to use maps to visualize the data needed for the indicators. Design cards were presented to the interviewees including maps options with different types of legends and symbols to locate the area of the city under analysis. The maps in which colored circles identified the location with size proportional to the area were selected by 72.7% of the interviewees. The use of symbols with solid colors was a feature largely appreciated for the built environment indicators (Requirement 4). For instance, such feature would allow the user to see daily values in a specific location instead of monthly means for Lisbon city. The ability to depart from general data and overall assessments to more comprehensive and specific data would help understand the numbers' reasons.While discussing the options for the indicators, the need to disaggregate the data was frequently discussed among the interviewees. Regardless of the option chosen, a graph or map, all interviewees acknowledged that it was essential to add the ability to access additional and detailed information of a particular indicator (Requirement 5). Users from the political and sustainability organizations were especially concerned with this aspect. The inclusion of a benchmark would help the end-users follow the indicators' performance, identify EH issues, and design strategies to deal with the situation.Two monitorization requirements were identified related to which information should be provided in a dashboard and how it should be presented. One of the features discussed was the added value of comparing the data being collected against evidence-based standards, legal limits, or political goals legal standards (Requirement 6). This feature would be particularly important in the case of indicators in which they may expect to see fluctuations over seasons instead of daily or monthly alterations. Interviewees talked about wanting the possibility to choose a particular indicator and to select specific months and compare them with previous years.The second feature to monitor the data is the ability to access historical data selecting specific periods for trends analyse (Requirement 7). Another feature discussed by more than 70% of the interviewees was the ability to “drill down” into the data or “drill up” to view general data (Requirement 8). In a drill-down dashboard, the users would be able to navigate different data layers to see specific and detailed information of a particular indicator without overcrowding the dashboard. Moreover, regarding the indicators visualized using maps, a consensus was achieved related to the inclusion of a zoom option (Requirement 9). The dashboard should be designed with the feature to view the data on specific areas by simply dragging the mouse over the part of the map the user wishes to explore. By focusing on the area of interest, users could easily detect potential problems or acquire a deeper understanding of trends.Being able to identify causes or extreme events for any particular indicator was an element discussed in all the interviews. For 81.8% of the interviewees, the dashboard should be interactive and include features like the ability to see alerts whenever an outlier value was detected for a particular indicator selected by the user (Requirement 10). All the interviewees stated that public databases' data are often perceived as out-of-date to be useful for short-term decision-making. Furthermore, data reported in distinct formats and using different metrics, together with interoperability issues between distinct data sources, lead to increased time spent analyzing the data and difficulted proper inference about the performance of an indicator. Seventy two percentage of the interviewees expressed the relevance of including the data source (Requirement 11). Having access to the original database  would increase the users' trust as it would enable users to check inconsistent values or clarify potential doubts.In the last part of the interview, participants discussed the quality of data to include in the dashboard, with key requirements being the availability of timely data and access to data: a lack of standardized data sources and of timely data with standard metrics were constraints faced by the organizations involved in this study  EH interventions .The methodology implemented in this study was informed by existing related research , 67, 68 Despite these contributions, several important lessons were learned throughout this study. First, the pandemic required the authors to adapt the methods and adopt online and interactive interviews instead of face-to-face interviews. A series of design cards were prepared for friendly visualization, helping the participants to engage in the interview in such a remote setting. Another challenge that is common when implementing new tools in healthcare and in political organizations is the difficulty felt by users when envisioning the design of new tools that they never conceived before. For instance, designing an EH dashboard for the Lisbon city can be challenging for some users when asked to foresee how they wanted to view and use data. The use of design cards with visualization options was effective to overcome the difficulty of envisioning visualization format but could introduce bias since users often asked for a mix of data or agreed that all options would work well.Finally, some questions can be raised regarding the generalization of findings to other contexts or dashboards because of the small sample of interviewees. This study involved a non-probabilistic sample from organizations developing EH interventions and regulations in the Lisbon city. Such kind of sample may result in biases since those are the most concerned with implementing tools that help improve EH in Lisbon city and may be more prone to make time to participate. Although an agreed set of requirements was identified, the number of interviewees was too small to enable statistical analysis of the answers of the three groups of interviewees. Nevertheless, it was successful in obtaining from EH experts the requirements most relevant for a dashboard to be used by their organizations. The discussion format provided valuable insights about the dashboard's formats and contents and about EH monitoring in urban settings.In order to further understand if the requirements identified in this study are what is required for the dashboard to work, it would be valuable to build the dashboard using PowerBI®, implement it together with the involved organizations, and to further interact with the participants so as to adjust and validate the dashboard.Making available EH dashboards to environmental, healthcare, sustainable, and policy organizations has the potential to promote evidence-based policymaking and to contribute to the improvement of quality of life or health outcomes. It is undeniable that shortcomings in (dashboard) requirements elicitations can lead to inadequate implementations . The incThis study can be looked upon as a step within a participatory framework to build EH dashboards. The integrated approach described can contribute to the development of easily comprehensible visualizations of environmental data for urban settings, based on user and organizations' needs; and it can guide the design of health and/or urban dashboards and serve as a reference for other researchers to design new tools.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.MS and MO: conceptualization and writing—original draft preparation. MS: investigation and visualization. PN and MO: validation. MO: writing—review and editing. AT, MO, and PN: supervision. All authors have read and agreed to the published version of the manuscript.MS was supported by the Portuguese Foundation for Science and Technology (FCT) and Valorsul S.A. under the scholarship number PDE/BDE/120465/2016. MO was supported by the Portuguese Foundation for Science and Technology under the MEDI-VALUE project (Grant No. PTDC/EGE-OGE/29699/2017). MS and MO acknowledge support from the Center for Management Studies of Instituto Superior Técnico, a research center funded by FCT project UIDB/00097/2020. MS and PN acknowledge support from ISAMB, a research center funded by FCT project UIDB/04295/2020.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.


New techniques and tactics are being used to gain unauthorized access to the web that harm, steal, and destroy information. Protecting the system from many threats such as DDoS, SQL injection, cross-site scripting, etc., is always a challenging issue. This research work makes a comparative analysis between normal HTTP traffic and attack traffic that identifies attack-indicating parameters and features. Different features of standard datasets ISCX, CISC, and CICDDoS were analyzed and attack and normal traffic were compared by taking different parameters into consideration. A layered architecture model for DDoS, XSS, and SQL injection attack detection was developed using a dataset collected from the simulation environment. In the long short-term memory (LSTM)-based layered architecture, the first layer was the DDoS detection model designed with an accuracy of 97.57% and the second was the XSS and SQL injection layer with an obtained accuracy of 89.34%. The higher rate of HTTP traffic was investigated first and filtered out, and then passed to the second layer. The web application firewall (WAF) adds an extra layer of security to the web application by providing application-level filtering that cannot be achieved by the traditional network firewall system. One of the common difficulties in various disciplines of computer science is protecting computers and networks from infiltration, theft, and disturbance . The impMany attempts have been made to build various security solutions, such as intrusion detection systems (IDS) and firewalls . In mostMost of the current WAFs work on signature-based systems. Traditional firewall systems are not meeting the needs of the modern environment to properly filter out attacks over any kind of network ,8. AttacWAF layered architecture was proposed for DDoS, SQL injection, and XSS detection in the web-based service system.The proposed model’s performance was evaluated and achieved 97.57% accuracy with DDoS detection and 89.34% accuracy with XSS/SQL injection detection.We introduce a layered architecture of WAF. Generally, in the WAF, features from the incoming traffic are extracted and tested for different types of threat detection modules/signatures. Based on the incoming traffic, a higher rate of detection is filtered in the first layer, and only the filtered traffic from the first layer is processed in the second layer. As the nature of the attack is different for different attacks, extracting the required parameter based on the nature of the attack and predicting new requests using a pre-trained model would increase the performance and accuracy of the WAF. The major contributions of this research are as follows:The rest of this paper is organized as follows: Input protection provides a comprehensive application filter that accepts only valid user inputs.HTTP validation detects HTTP vulnerabilities and prevents attacks by setting up the validation rules.Policies tailored to widely used applications are set up according to specific requirements and need. Thus, it protects applications from vulnerabilities and also provides real-time insights about the traffic.Data leakage prevention provides an alert and prevents any kind of unusual traffic or data leakage by identifying, filtering, and shielding the private data.Automated attack blocking provides automation tools for blocking attacks by denying malicious traffic from entering the network.A web application firewall engine consists of two modules: (a) a configuration module (CM) and (b) packet analyzer module (PAM). When live packets are received from the internet, rule files filter them from CM and passes the traffic to PAM. PAM analyzes the packets and extracts the features from the packet. Using previously trained data, it tests and identifies the nature/character of that packet. Therefore, only analyzed and allowed packets are passed through the PAM to the web application server. WAF can be deployed as hardware devices on virtual appliances or software running on the same web server as the web application or through the cloud. It operates using a particular set of rules called policies . In eachWeb application security  is needeEvery website on the Internet is vulnerable to cyber-attacks. The dangers range from human error to sophisticated cyber-attacks carried out by an organized group of criminals. The major incentive for cyber attackers, according to Verizon’s data breach investigations report, is financial . WhetherDDoS attacks are deliberate attempts to interrupt the normal traffic of a targeted server, service, or network by flooding the target or its surrounding infrastructure with Internet traffic . A DDoS LSTM model is a special type of recurrent neural network. It is capable of learning long-term dependencies during the training of the module ,19. A LSThe structure of LSTM network is depicted in The second is the input gate that contains two layers, of which the first is the input gate sigmoid layer  tool and hulk tool in the Kali Linux environment in VMware was set up and implemented for data collection. We hosted a sample E-commerce site on a local host on a Windows system, then flooded the traffic from four LOIC instances consisting of two instances of LOIC on each machine. The forwarded traffic was captured with the Wireshark and then processed to extract useful information from it to train the WAF module.For the correlative analysis, the first layer of defense was for DDoS protection, whereas the second layer of protection was for XSS and SQL injection. SQL injection data acts like normal data in the DDoS attack layer of protection. Different payloads were passed from the browser as HTTP requests through the Burp Suite proxy and collected the HTTP traffic using Wireshark. The collected log in the respective tool was used for training the model. The collected data in Wireshark was taken as normal data, because it was collected by normally browsing the web. The correlated log was only applicable for the DDoS detection model, as the second layer of detection was applicable for XSS and SQL injection detection.To identify the features that could be used to distinguish normal traffic from DDoS attack traffic, we used the ISCX dataset, taking a sample of 100 K normal data and 100 K DDoS attack data. From the samples, we analyzed the features of attack traffic and normal traffic. As shown in Visualizations of the CIC dataset are presented in Referring to This dataset was collected in the simulation environment. A total of 100 K data samples were considered, out of which 50 K was the attack dataset and 50 K was the normal dataset. The flow rate sending the attack traffic from a single LOIC instance and the normal traffic flow from normal browsing are presented in We analyzed the CISC 2010 normal and attack traffic dataset . Normal We used the generated dataset for the analysis, training, and testing of the module. We standardized the collected data using the standard score of a sample (x), which was calculated as: Similarly, we applied the generated DDoS dataset to the LSTM model. After training the model up to 40 epochs, we observed an accuracy of 97.57%. The train and test accuracy of each epoch is shown in For the validation of the model with the new dataset, we passed the log of live DDoS traffic to the new site and applied it to our pre-trained model for prediction. It was able to correctly detect 96% of the live traffic. Moreover, traffic with XSS and SQL injection was applied to the DDoS module, as it detected it as good traffic and passed it to the second layer. The features for prediction in the second layer were extracted. It correctly detected 90% of HTTP packets with the payloads. A proxy was configured on the Jupiter notebook and listened to the specific port for live HTTP traffic input to the system. When traffic was routed from source to destination, it passed through the two modules: first, it was checked for DDoS attacks, then for XSS and SQL injection attacks. Only correctly classified traffic was routed to the web server.A confusion matrix of 2279 test datasets is shown in A confusion matrix is shown in Comparing the performance between the first and second layer of detection, as shown in Using LSTM as our deep learning approach, the proposed model detected DDoS, XSS, and SQL injection attacks with considerably good accuracy. The first detection layer was a DDoS attack detection model with an accuracy of 97.57%, and the second layer was for XSS and SQL injection attack detection with an accuracy of 89.34%. We analyzed features and parameters for attack detection, which reduced false positives during traffic filtering in the WAF. As DDoS traffic comes at a higher rate than normal traffic, the system’s performance imporves when we check the traffic in a layered format, i.e., first checking for DDoS before testing for SQL injection and XSS. Moreover, we analyzed the performance perspective of the web application when an extra layer of filtering was added and found a slight impact on performance. However, this difference was not distinguishable from a user experience perspective.This study focused on three types of web attacks: DDoS, SQL injection, and XSS. Future studies could include other types of common web attacks, such as RCE, malware, brute force, etc. Due to the similar detection properties, we examined SQL injection and XSS in a single module in this work. Further testing of models with other types of deep learning algorithms could lead to greater enhancements in WAF performance.


Background: Poland is facing the growing problem of overweight and obesity in the population, which makes it necessary to conduct a thorough assessment of the existing food environment policies. The aims of the study were: (1) to depict the strength of healthy food environment policies in Poland and identify implementation policies and infrastructure support gaps; (2) to identify and prioritise improvement policies, taking into account their importance, achievability and equity. Methods: We used the Healthy Food Environment Policy Index (Food-EPI). An experts’ panel rated Polish policies and infrastructure compared to international best practices and developed a list of recommended improvement actions addressing both components. Results: eight of the twenty-two policy and four of the twenty-two infrastructure indicators achieved the “no/very weak policy” result. Another four policy and five infrastructure indicators were considered “weak”. Another seven and eight indicators, respectively, were assessed as “moderate”. Among the identified actions, the highest priority was given to a food labelling system and training for persons involved in nutrition in schools. Conclusions: The Polish healthy food environment has been assessed as very weak or weak in most aspects. The infrastructure was assessed as slightly better compared to the policies domain, with more indicators receiving the “moderate” score. Like most developed countries, Poland faces a rising burden of obesity and diet-related non-communicable diseases (NCDs) ,2. In 20Over the past two decades, food policy in Poland has been considered inconsistent and unconsolidated . There aA number of challenges and difficulties are associated with implementing and evaluating food policies . These mOur study is an effort to address these deficiencies of food policy in Poland by applying the Healthy Food Environmental Policy Index (Food-EPI)  to evaluTo depict the overall strength of healthy food environment policies in Poland and to identify basic implementation gaps in terms of policies and infrastructure support.To identify and prioritise healthy food environment policies in Poland, taking into account their importance, achievability and equity.The two basic aims of this study are as follows:https://www.jpi-pen.eu/, accessed on 3 May 2022), funded by the Joint Programming Initiative Healthy Diet for Healthy Living (JPI-HDHL). To achieve the assumed study aims, we applied the Food-EPI [The study was a part of Work Package 1 (WP1) of the Policy Evaluation Network (PEN) project  and six are infrastructure-related  see . The domThe second step of the study was to develop a catalogue of international best practices by all PEN partners involved in the Food-EPI study. The basis for the catalogue was the benchmarks previously developed by INFORMAS in August 2017. The final version of the catalogue was approved by all study partners in November 2019. For details, see For each of the Food-EPI indicators, evidence for the implementation of policies in Poland was collected by three researchers  who reviewed official government documents and legal acts ,25,26,27Between March and August 2020, an online rating survey took part. The study participants were an expert panel consisting of independent experts representing diversified sectors, especially the academic and research sector, non-governmental organisations (NGOs) providing food-related and health-related activities, and practitioners in the field of nutrition and chronic disease prevention or treatment. The list of experts included specialists identified by the research team based on their scientific achievements (reflected in their publication output) or specialists recognised in Poland for their achievements in the field of food policy development and implementation. The list of experts has been reviewed and approved by the leaders of the PEN Project Work Package. Out of 63 invited experts, 21 responded by completing the survey questionnaire. The participants were informed of the aims of the survey and the voluntary nature of their participation in the study. They were given the opportunity to complete the questionnaire anonymously and to give contact details to obtain formal confirmation of their involvement in the study. The questionnaire was prepared using Google Forms and consisted of questions referring to each of the Food-EPI indicators. Each question was accompanied by an information package presenting the international best practices, in accordance with the catalogue developed in step 2 of the study, as well as information on the identified evidence of the implementation of a given policy in Poland. The experts assessed national policies against the international best practices for each indicator using a percentage-based scale translated into a five-point Likert scale, where 1 = very weak policy (0–20% implementation), 2 = weak policy (30–40% implementation), 3 = moderate policy (50–60% implementation), 4 = strong policy (70–80% implementation), and 5 = very strong policy (90–100% implementation). A “cannot rate” option was also given for selection, as well as the opportunity to comment on any of the indicators or policies. For each policy-related indicator, the participants were asked to assess whether its implementation would impact socio-economic inequalities. The rating was performed on a 5-point Likert scale, where 1 = a significant increase in inequalities, 2 = a moderate increase in inequalities, 3 = no impact, 4 = a moderate reduction in inequalities, and 5 = a significant reduction in inequalities.On 15 September 2020, a workshop was organised for the study participants with the aim of identifying actions to improve the Polish food environment. Invitations were sent to the experts who took part in the previous steps of the study, resulting in eleven positive responses. In addition to the invited experts, three members of the research team facilitated the workshop . Due to restrictions caused by the COVID-19 pandemic, the workshop was organised online using a videoconferencing platform. The workshop consisted of two main stages. The first was discussion in two subgroups that worked on the development of proposals for actions with regard to policies and infrastructure in line with the domains and indicators of the Food-EPI. It was followed by a plenary discussion and agreement on the final version of the catalogues of action for both areas. After the real-time workshop, the research team synthetised the plenary discussion results in the form of two catalogues of proposed actions, representing recommendations developed during the meeting. These catalogues were sent for approval to the workshop participants. After adjusting the catalogues based on the revision requests received, the final list approved by the workshop participants consisted of 15 actions with regard to policies and 15 actions with regard to infrastructure. These two catalogues were then subject to a prioritisation procedure, which was held asynchronously. The workshop participants received both catalogues along with the prioritisation questionnaire and were asked to rank the policy actions separately for three dimensions: importance, achievability and equity. In the case of infrastructure actions, the ranking was performed for two dimensions: importance and achievability. “Importance” was defined as the need, impact of the action, and other positive or negative effects of the action. “Achievability” referred to the feasibility, acceptability, affordability and efficiency of the action. “Equity” was defined as the impact of the action on socio-economic inequalities in dietary intake and health and on individual consumer choices. Additionally, the study participants were given an opportunity to weight the prioritisation dimensions individually for each of the actions in the catalogue, using 100 percentage points to be distributed between relevant dimensions.For the healthy food environment and infrastructure support indicators, the average level of implementation was calculated based on the mean values scored by 21 experts who completed the Food-EPI questionnaire. The ranking of actions in the prioritisation step was established by calculating the mean value of scores attributed to individual actions by members of the expert panel (with each expert assigning from 1 to 15 points). When calculating the ranking with all dimensions combined, we summed the mean values of the scores for each dimension, which were additionally modified with the weight attributed to a given dimension by each individual rater.We applied the bootstrapped Krippendorff’s alpha test to verify inter-rater reliability. The coefficient value was calculated using R software v. 4.0.4. All the remaining calculations were performed using MS Excel Professional Plus 2019.An invitation to take part in the Food-EPI survey was sent to 63 experts in Poland. The response rate was 30%, with a total of 21 questionnaires that were finally returned. Among the respondents, 16 participants were professionally linked with academic institutions, two represented health care providers, two represented local public administration units, and four were linked to third sector organisations. Since double entries were allowed, the sum of these numbers is not 21.The results for the assessment of the Polish health food environment in the policy-related domains are presented in Out of the 22 indicators evaluated, we obtained a “no/very weak policy” result for eight (36%) indicators. In the case of four indicators (18%), the result was “weak policy”, while for seven (32%) indicators, we obtained a “moderate policy” result. The remaining three indicators (14%) were evaluated as “strong policy”, and no indicator scored the highest rating. The best ratings were observed for indicators related to restrictions on the promotion of unhealthy food in places of collective stay of children, such as schools or kindergartens. The “strong policy” rating also appeared in the case of food subsidies for health foods and policies in schools to promote healthy food choices. These policies appear in practice, e.g., in the form of free fresh fruit or vegetables or milk products distributed for free among children ,29. The The Krippendorff’s alpha coefficient value for policy-related domains was 0.546  = 0.522–0.568), which shows a moderate level of agreement between raters.The results of the assessment of the Polish health food environment in the infrastructure-related domains are presented in The overall result of the evaluation of the infrastructure-related indicators is slightly better than that of policies. Of the 22 indicators in this group, only four (18%) received the lowest “no/very weak policy” rating, five (23%) were assessed as “weak policies”, eight (36%) were rated as “moderate policy”, and five (23%) received “strong policy” ratings. As was the case in the policy-related domains, no case of “very strong policy” was registered. The Krippendorff’s alpha coefficient value for the infrastructure-related domains was 0.403 (95% CI = 0.379–0.428), which means that the level of agreement was close to moderate.The study participants then assessed a set of policy-related indicators with regard to how the policies in a given area may impact socio-economic inequalities. For six indicators (27%), namely, restricting unhealthy food promotion to children , restricting unhealthy food promotion where children gather, food subsidies to favour healthy foods, policies in schools to promote healthy food choices, food-related income support for healthy foods, and support and training systems (public sector), the result was a “small reduction”. For the remaining 16 indicators, no impact on health inequalities appeared to be expected by the experts. However, since the Krippendorff’s alpha coefficient value for this group of questions was 0.019 (95% CI = −0.019–0.056), we observed no agreement between raters, and drawing any clear conclusions with regard to this issue is not possible, especially because many respondents marked the “cannot rate” answer as their choice.In the subsequent stages of the study, we identified a catalogue of actions to be recommended to the Polish government that would improve the national food environment in terms of policy and infrastructure support. For each of the two Food-EPI components (the policy and infrastructure domains), 15 actions were identified. Each expert ranked the identified action on a scale ranging from 15 (highest priority) to 1 (lowest priority) separately for each dimension. The highest priority has been attributed to the food labelling system, which mainly refers to the implementation of simple labelling systems, such as Nutriscore, followed by informational policy related to healthy food regulations and the change in legal regulations related to school meals. The lowest priority was given to restrictions on the density of fast-food bar restaurants in the public space. In total, one-third of the actions were related to the food provision domain, and one-fifth were related to the food retail domain. In this context, the implementation of a simple food labelling system was perceived not only as a priority action but also as the action that is the most important and the most achievable. In general, actions were assessed coherently in all three dimensions, including equity. Different situations were observed for several actions. For example, taxation policy (action No. 5) was considered very important but difficult to achieve. On the other hand, the rules for eating school meals (action No. 2) and the labelling of menus in restaurants (action No. 8) were assessed as highly achievable but as being of low importance. Importantly, the inter-rater agreement with regard to prioritisation was fair, with the Krippendorff’s alpha coefficient value being the highest in the case of scores for importance , followed by achievability . As was the case for socio-economic inequalities in the Food-EPI questionnaire, the level of agreement with regard to the equity dimension was lower .The highest priority was attributed to the implementation of healthy eating training sessions for people responsible for feeding children, followed by creating a system of promoting healthy eating habits in the media and including dietitian services in the basket of services covered by public health insurance. The lowest priority was attributed to regulations regarding food certification systems. Overall, domain No. 2 was addressed most frequently in the actions proposed by the expert panel, and it also appears most frequently in those actions that received the highest priority. We observed much higher diversity in terms of the scores for importance and achievability than in the case of policy support actions. Action No. 1 was assessed as having visibly higher importance than any of the other actions, but it had one of the lowest results in terms of achievability. In turn, action No. 6 obtained a relatively high score in terms of achievability but the lowest score in regard to importance. The overall level of agreement among the experts when prioritising the actions was somewhat lower than in the case of policy-related actions for importance , but it was slightly higher for achievability .Our study showed a number of gaps in food policies and infrastructure in Poland. In the case of policies, the largest deficiencies were found in the domains related to food promotion, food provision, and food retail. In the food promotion domain, the lowest score was given to “restricting unhealthy food promotion to children on social media” and “restricting unhealthy food promotion to children on packaging”. This observation seems to be in line with studies on public health-related informational policy in Poland, where it was found that public authorities neither utilise social media for health promotion  nor inteThe restrictions on packaging appear to be a similar case. The information located on product packaging has a considerable impact on consumer decisions . This isInterestingly, similar results have been obtained in Germany and The Netherlands, where application of the Food-EPI study revealed even more gaps existing in terms of food promotion, but with an equally low score in the case of restrictions related to social media and packaging ,37. SomeIn the food retail domain, all indicators received the lowest scores, which confirms that Polish public food policies do not propose any solutions aimed at retailers’ promotion of healthy consumer choices. While the large retail chains implement their own policies , no suchFurthermore, in this case, the result shows similarities with a number of other Food-EPI-based studies in other countries, including New Zealand and Germany, although in the first of the countries mentioned, a somewhat better result was observed with regard to the indicator assessing promotion of relative availability of healthy foods in food service outlets. A slightly better but still relatively low score for this domain has been observed in Japan and Canada ,39,44.In the case of infrastructure-related domains, we found that 4 out of 22 indicators were assessed with the lowest score. These domains were priorities for reducing health inequalities (domain: leadership), the use of evidence in food policies and transparency in the development of food policies (domain: governance) and assessing the public health impacts of non-food policies . The indicator addressing health inequalities was somewhat better scored in The Netherlands, Canada and Germany, while New Zealand and Japan have been assessed with considerably better scores ,38,39,44Assigning priority to efficient programmes for food subsidisation and the distribution of food aid has a high potential to contribute to reducing the existing inequalities in health . In PolaIn our study, the experts pointed out that tools for assessing the impact of non-food policies on the healthy food environment are lacking. An example in this respect is one of the flagship social policy projects implemented by the Polish government since 2016, i.e., the introduction of a new category of financial benefits for parents raising children. Both in the regulatory impact assessment and in the debate preceding its introduction, all impacts on the food environment have been narrowed down to reducing the scale of poverty and the need for government nutrition programmes. There was no reference to issues such as the impact on food prices and on the consumption of unhealthy food . The proThe priority actions identified in this study tend to be consistent with the identified implementation gaps, such as high deficiencies in relevant domains. Notably, however, actions that received the highest priority scores corresponded to subdomains that had already been assessed as relatively strong. It may be concluded that the expert panel members were leaning towards a strategy of improving good policies that existed in the first place rather than strengthening weak policies or implementing new policies .The priority actions with the highest priority were those related to food labelling and to creating a system of food-related information policies. Food labelling was given a high priority in the achievability dimension, which is in line with the experiences of other countries. Such a system does not require substantial financial outlays and provides rapid and measurable effects both among adults and among children and adolescents .The lack of a comprehensive informational strategy related to nutrition and food policies manifested after the implementation of the regulation on food sales at school in 2015, resulting in a discussion on the legitimacy of such arrangements, their feasibility, and economic, health, and educational consequences. The greatest controversies were the limitation of the use of salt and sugar in the food products sold at schools, as well as specific indications regarding the size of packaging, product quality, and the portioning of fruit and vegetables, which required producers, wholesalers and franchisees to implement new solutions and look for new suppliers . The resIn the case of infrastructure, our study found that the key priority actions, in general, correspond with the gaps identified in the healthy food environment evaluation see . The proThe participating experts tended to place great emphasis on issues related to health education. Since the area of education was not included in the original Food-EPI, the proposed actions were linked to the leadership domain as having potentially the highest impact in this area. Educational issues were addressed by the study participants in several ways, showing the general high priority of this issue. Their suggestions are in line with priority projects identified in local communities in Poland in recent years. The small number of recipients of those programmes  confirmsThe experts stated that nutritional education should also be present in popular media, including the use of influencers and celebrities to promote healthy choices. This finding remains consistent with suggestions for developing new forms of social communication and translating scientific knowledge, including the celebritisation of science as a solution to reverse the trend of the celebritisation of pseudoscience. There is evidence that this kind of solution might increase the effectiveness of healthy food-promoting activities and attract a wider audience .Incorporating dietitian counselling into the package of basic services financed by public health insurance was the action with the third highest priority in the infrastructure-related domains. Currently, in Poland, dietitian services are not covered by public health insurance, which is similar to most other developed countries. The rationale behind the postulated action is mainly the significantly higher effectiveness of dietitian counselling in terms of changing eating behaviours, as evidenced in a number of studies .The main strength of our study is the fact that it is the first comprehensive and systematic evaluation of Polish food environment policies. We have developed specific recommendations with regard to strengthening the food environment and eliminating the most important gaps in the national food policy system, which have been subject to multidimensional assessment. However, our study is not free of limitations, which result from both the study procedure and the characteristics of the tool we used. One of the basic limitations is the relatively small number of experts who decided to take part in the study. The results we obtained would more precisely reflect the views prevailing in the expert community if the participation rate was higher, especially in the case of the step for the identification and prioritisation of actions. The relatively low level of uniformity of the experts’ responses is also to be noted. A higher participation rate might also improve the credibility of the results obtained, although it seems that any shifts in the assessment results, in light of what has been included in the evidence document, would modify the results on a small scale only. The problem of a small number of participants in the study was limited to some extent due to the specific nature of the study group, which were experts, and also due to the fact that part of the study was qualitative.Another limitation is the arbitrary method of selecting the international best practices to constitute a benchmark for the assessment of the national food environment. Because assessing the effectiveness of the implemented solutions is possible only in the long term, such an approach appears inevitable; however, for the purpose of future research using the Food-EPI tool, creating a catalogue of practices with well-evidenced effectiveness seems justified. Moreover, the lack of international best practices identified in the case of the “monitoring NCD risk factors and prevalence” indicator prompted its exclusion from the study, which to some extent reduces the level of comprehensiveness of the assessment of the Polish food environment.Some limitations result from the degree of complexity of the questionnaire that the experts completed. This factor could have been among the main reasons for the low participation rate of the invited experts, and it also could have contributed to the discrepancies in the responses to individual questions. It seems advisable to conduct the study in the form of a workshop, that is, in direct contact with the participants, to reduce some unfavourable phenomena occurring in the course of the study. Although we did indeed conduct a workshop, the limitations resulting from the outbreak of the COVID-19 pandemic became limitations independent of the researchers and the tool used.Since the food environments are potentially subject to dynamic changes resulting from political decision-making processes, the reported findings might become partially obsolete between the time the study was conducted and the date of the publication. Nonetheless, to our best knowledge, no relevant action was taken to improve the healthy food environment in Poland in the months following the survey. The exception is the sugar-sweetened beverages tax, which started operating in January 2021. The COVID-19 pandemic became a national health policy priority in the period after this study, resulting in the slowing down of policy developments in other areas. Finally, the lack of agreement between the experts with regard to the inequality aspect is a limitation, which most likely shows the difficulty in decisively assessing the impact. Different ways of understanding the socio-economic aspect, where it might be understood as the impact on disposable income and welfare, but also as referring to the issue of health inequalities between socio-economic groups, might be one of the reasons for this result. This issue should be subject to improvement in future Food-EPI studies.The healthy food environment in Poland has been assessed as being very weak or weak in most cases of the indicators related to policies, with only three indicators being assessed as strong policies. The largest gaps were identified in the food retail domain, along with individual indicators related to other domains, namely, restricting unhealthy food promotion to children on social media, restricting unhealthy food promotion to children on packaging, healthy public procurement standards and support and training systems in private companies. Infrastructure was assessed as being slightly better, with more indicators being assessed as moderate policies and four indicators being assessed as strong policies. The main gaps identified here are the political support related to priorities for reducing health inequalities, the use of evidence in food policies, transparency in the development of food policies and assessing the public health impact of non-food policies.Taking into account the combined dimensions of importance, achievability and equity for policies and importance and achievability for infrastructure, the most prioritised actions identified to be implemented to improve the Polish health food environment are the implementation of a clear and simple labelling system for food products, including information on salt/sugar/trans fats, and the implementation of a system of training on healthy eating rules targeting people responsible for feeding children. These constitute the basic recommendations regarding food policy actions to be implemented by the public authorities in Poland.


The trade-off between the goals of promoting economic growth and protecting the ecological environment makes it possible for the government to constantly adjust the intensity of environmental regulation, leading to sharp fluctuations in environmental regulation in the short term. Fluctuations in environmental regulations may trigger concerns among firms and change their investment decisions. The theoretical model of corporate investment decision is used to analyze the inhibitory effect of environmental regulation fluctuations on investment through expected profits, which is empirically validated in this study by data from 255 Chinese prefecture-level cities. The results indicate that environmental regulation fluctuations reduce investors’ expected profits, which in turn inhibit investment. The heterogeneity analysis shows that environmental regulation fluctuations have no significant effect on investment in cities that are geographically closer to the provincial capital, while a greater inhibitory effect of it is revealed in other cities located further away. Therefore, this inhibitory effect should be weakened by reducing the intervention of administrative orders in environmental regulatory behavior, establishing environmental regulatory supervisory agencies, and taking into full consideration the public’s response to fluctuations in environmental regulation. This study can provide policy implications for optimizing government environmental regulation. Since their reform and opening up in 1978, China has achieved rapid economic growth. However, this has been accompanied by the pressure of environmental damage, which has gradually become a bottleneck limiting China’s sustainable economic development , as evidThe sharp fluctuations of environmental regulations in the short-term may cause concern to enterprises. This is because when regulations fluctuate, investors expect that environmental regulations may increase in the future. At that time, companies will be required to deal with pollutants more stringently, which will increase their costs; in addition, investors will not be able to recover their upfront investment when regulations rise due to the existence of sunk costs. Both factors ultimately lead to a decline in expected profits. Therefore, a rational investor may choose to “wait” when faced with this kind of uncertainty until the regulatory intensity stabilizes before making a decision. In other words, the uncertainty associated with regulatory fluctuations generates an “option value” for potential investors, resulting in a disincentive to potential investment behavior ,8. HowevThe importance of the topic of this paper is at least twofold. First, it provides empirical evidence on the investment effects of environmental regulatory fluctuations from an emerging market economy. Second, our results have important policy implications. If fluctuations in environmental regulations do lead to lower investment, then the government should focus on reducing the magnitude of regulatory fluctuations, guiding firms to form stable expectations, and maintaining investment consistency as a way to reconcile the tension between “promoting economic growth” and “protecting the ecological environment”.Our study has three main contributions. First, while the established literature mostly analyzes the impact of environmental regulations on investment ,10,11,12The rest of this paper is structured as follows. The design and implementation of environmental regulation policies is a dynamic process in which the drafters of the regulations need to continuously adjust and optimize . By the Hoffmann et al. defined regulatory uncertainty as “the perceived inability of individuals to predict the future state of the regulatory environment” , a stateHowever, other studies take a different view, arguing that regulatory uncertainty does not necessarily delay or inhibit firm investment, and may even have a catalytic effect on investment. The theoretical basis for this type of research is mainly the resource-based view of the firm ,26,27 anTaken together, the results of the above studies reveal that the impact of environmental regulatory fluctuations on investment depends on the firm’s response, which in turn is related to the firm’s tolerance and perception of uncertainty. Prior research has shown that there are differences in individuals’ perceptions and tolerance of uncertainty ,31, whicm. The product demand curve is assumed to be mp is the price of product m and i, mp, so that the actual revenue received by the firm after selling the good is m is S. When there are fluctuations in environmental regulations, that is, the intensity of the environmental regulations to which a firm is subject may change, investors may choose to wait or pay the sunk cost and enter the market. Investors determine whether to enter the market by observing the operating conditions of their peers in the previous period, the intensity of environmental regulations in the current period, and the model parameters at the beginning of each period. After entering the market, the survival probability of the firm is Drawing on Handley and Limão’s modeling framework , we assuHere we consider two scenarios: one without environmental regulation fluctuations and the other with environmental regulation fluctuations. When there is no environmental regulation fluctuation, by solving the profit maximization problem and the market entry condition When the intensity of environmental regulation may fluctuate, investors can choose to enter the market or continue to wait, and there exists a threshold marginal cost Equation (4) indicates that investors who choose to enter the market in the current period will earn S, enter the market, and earn the expected profit Equation (5) indicates that investors continue to wait in the current period, the profit is zero. If the firm survives with probability IN represents investment; VF denotes the fluctuation of environmental regulations and is the core explanatory variable of the equation; X represents the control variables; i and t represent city and time, respectively. In the theoretical analysis, we infer that the stronger the fluctuations of environmental regulation, the more pronounced its inhibitory effect on investment will be. To verify this point, we employ When we investigating the impact of environmental regulation volatility on investment, there may be endogeneity issues that investment may be driven by an unobservable component that affects regulation volatility, and this unobservable component can be captured by the lagged term of investment, OLS estimation as well as traditional panel estimation methods cannot obtain valid estimates. Therefore, we include the first-order lagged term of investment in the equation. In determining the optimal lag terms, Glen et al. and Gschwandtner argue that two lags are sufficient to capture persistence, and indeed ,34, we hBased on the analysis of the previous theoretical model, it is clear that environmental regulation fluctuations influence firms’ investment decisions by affecting their expected operating profits. This hypothesis is also supported by some theories. According to the option value theory, it pays for investors to wait when exposed to uncertainty. In this case, the uncertainty provides a “waiting option value” for the investor. That is, if the investor chooses to invest right away, then part of the investment cannot be recovered due to sunk costs when the economic environment deteriorates, which results in a potential loss for the investor. If the investor chooses to wait, the potential return when the economic environment gets better becomes a potential loss for this investor at the moment. Therefore, investors will choose to invest only if the expected return on investment under uncertainty is higher than the sunk cost; otherwise, rational investors will postpone investing ,8. In adEIN represents investors’ expected profits, In Equations (9)–(11),  proceed . If α2 pt by calculating the moving standard deviation of the intensity of environmental regulation from year t − 5 to year t − 1, with the larger the standard deviation the stronger the fluctuation. This indicator responds both to fluctuations in the intensity of environmental regulation over time and to trends in such fluctuations over time. The reason for choosing a 5-year moving standard deviation is that this length of time encompasses the cycle of official turnover and economic planning for local governments in China; when officials change and new plans are made, local policies will change accordingly and the intensity of environmental regulations may change. We begin by measuring the intensity of environmental regulation when measuring environmental regulation fluctuations. Among the existing research results, scholars have tried to measure the intensity of environmental regulation in terms of the emission intensity of industrial pollutants , pollutiThe established literature usually uses indicators such as social fixed asset investment, foreign direct investment (FDI), and investment in state-controlled enterprises to represent investment, but social fixed asset investment and investment in state-controlled enterprises are not applicable to the analysis framework of this paper. The reason is that social fixed asset investment includes not only enterprise investment, but also investment in fixed assets of institutions and administrative units, which are not profit-oriented and also have certain counter-cyclical characteristics (the “quadrillion plan” between 2008 and 2010 is a good example); state-controlled enterprises have policy burdens such as expanding employment in the process of operation, and their investment behavior does not fully reflect the changes in their expected operating profits. On the other hand, foreign direct investment is characterized by a high sunk cost and very sensitive to the investment environment, which is in line with the relevant assumptions of the theoretical research framework of this paper, and its data availability is better, so we chose the logarithm of the actual amount of foreign investment utilized in the year to reflect the investment level.In this paper, we choose the logarithmic value of the FDI contract amount in the current year to reflect the expected profit level. Since the contract amount of FDI is not the final actual amount of utilized foreign capital, but the investment plan estimated by foreign investors based on their expected business conditions after comprehensive analysis of the investment environment, the amount of FDI contract is directly related to the business expectation of the invested area, which can reflect the expected business profit of foreign investors to a degree. Therefore, this paper chooses the logarithm of the amount of foreign direct investment contracts in the current year to represent the expected profit of investors. In addition to the above variables, we also selected control variables from the following five aspects. A higher level of economic development in a region implies that consumers have stronger purchasing power and are more attractive to FDI, so we included the level of economic development expressed as the logarithm of GDP per capita in the equation. A region that is more open to the world will be more inclusive of FDI, so we controlled for the degree of openness of each city, expressed as a share of total imports and exports in GDP. For foreign investors, the availability of capital and the wage level of workers are two important factors. The higher the availability of capital and the lower the wage level, the more attractive cities are for FDI, so we added the size of the financial market and the average wage level to the equation, which are expressed as the logarithm of the balances of loans from financial institutions and the logarithm of the average wage of workers, respectively. In addition, FDI in China is mainly concentrated in the industrial sector, and the industrial structure of each city also has an impact on the investment decisions of foreign investors, so we control the share of added value of the secondary industry in GDP. All of the above variables exclude the effect of price changes.As of 2019, there are 293 prefectural-level cities in China, and excluding some cities with serious missing data, the sample in the baseline regression of this paper includes 255 prefectural-level cities, and the time interval of the original sample is 2005–2017. When measuring the environmental regulation fluctuations, a period of 5 years is used, so the actual time interval in the regression is 2010–2017. Due to the poor availability of data on the amount of foreign direct investment contracts in each city, the sample of 146 prefecture-level cities is included in the mechanism test. The data were obtained from the China City Statistical Yearbook, the China Regional Statistical Yearbook, the CEIC database, and some provincial and prefecture-level city statistical yearbooks. Since the regression results of the dynamic panel GMM estimator are more sensitive to the model settings, we gradually add control variables to the baseline regression model to observe the dynamic changes of the regression results, and combine the results of Arellano–Bond test and Hansen J test to judge the reasonableness of the model settings. The results are shown in As can be seen in The regression coefficient of environmental regulation fluctuation is always significantly negative when the control variables are continuously added, indicating that environmental regulation fluctuation will inhibit foreign investors from investing in the local area to some extent. In terms of control variables, the rise in per capita wage increases the cost of employing labor locally for foreign investors, which in turn decreases local FDI; while the rise in financial market size, openness level and industrial structure indicators significantly increase FDI.std4 and std6 as the environmental regulation fluctuations calculated with a 4-year and 6-year cycle. The results of columns (1) and (2) in It is clear from the benchmark regression that environmental regulation fluctuations would inhibit FDI. In the baseline regression, the period of environmental regulation fluctuations is measured to be 5 years, but investors may make investment decisions based on different information sets because of the different maturities of investment projects and differences in investors’ sensitivity to risk. Therefore, we test the robustness of the baseline results by narrowing and enlarging the calculation cycle of environmental regulation fluctuations, respectively. Since the average tenure of local officials in China is 4–6 years, we will test the inhibitory effects of environmental regulation fluctuations on investment with a 4-year and 6-year period, respectively. We note In the baseline regression, the environmental regulation intensity index is constructed based on the treatment of pollutants in each city. To test the robustness of the benchmark results, we construct environmental regulation intensity indicators here from the perspective of emission intensity per unit of output. We divide the industrial wastewater, industrial sulfur dioxide and industrial smoke emissions of each city by the added value of the secondary industry in that year to calculate the emission intensity of each pollutant per unit of output, and then add them up to obtain the environmental regulation intensity index through the entropy method. The weights of industrial wastewater emissions per unit output value, industrial sulfur dioxide emissions per unit output value and industrial smoke emissions per unit output value calculated by the entropy method are 0.268, 0.300 and 0.432, respectively. Finally, we calculate the 5-year moving standard deviation of this index to obtain the environmental regulation fluctuation index. As can be seen in column (3) of To test whether environmental regulation fluctuations affect investment through firms’ expectations, we conducted a mechanism test using the mediating model. The results of the mechanism test are presented in columns (1) to (3) in The regression coefficient of environmental regulation fluctuations in column (2) is significantly negative, indicating that environmental regulation fluctuations reduce the expected profits of foreign investors in the local area. The regression coefficient of expected profits in column (3) is significantly positive, which indicates that the mechanism in the previous theoretical analysis holds, that is, environmental regulation fluctuations will inhibit investment by reducing investors’ expected profits.Cities in China have different administrative hierarchies. The higher administrative hierarchy of provincial capitals enables them to gather various scarce resources more easily, which makes them ahead of other cities in the province in terms of capital, technology, talent and infrastructure. A higher gathering of resources is often accompanied by developed transportation, smooth information collection channels and convenient access to resources, which helps reduce the sunk cost of FDI and thus reduces the sensitivity of FDI to environmental regulation fluctuations. The cities closer to the provincial capital city are more radiated by the high-quality resources of the provincial capital city, the sunk cost of FDI is lower, and the inhibiting effect of environmental regulation fluctuation on FDI is relatively smaller; while for the cities farther away from the provincial capital city, because they are less radiated by the resources of the provincial capital city, the sunk cost of FDI is higher, and the inhibiting effect of environmental regulation fluctuation on FDI will be greater.To test this inference, we divided the total sample into two subsamples based on the average distance of each prefecture-level city from the capital city of the province. Columns (1) and (2) in In this paper, we studied the inhibitory effects of environmental regulation fluctuations on investment. We used a firm investment decision model to explore the impact of environmental regulation fluctuations on investment, and the model results show that environmental regulation fluctuations inhibit the expansion of investment by raising entry barriers and lowering investors’ profit expectations. Further, we used panel data of 255 prefecture-level cities in China from 2005–2017 to validate the findings of the theoretical model using a dynamic panel GMM estimator and tested the mechanism with the help of a mediating effects model. The results found that the fluctuation of environmental regulations in each city inhibits the scale of investment. This conclusion still holds after the relevant robustness tests. In addition, the heterogeneity analysis in this paper shows that the inhibitory effect of environmental regulation fluctuations on investment is insignificant in prefecture-level cities that are relatively close to the provincial capital city, while it is relatively larger in cities that are far from the provincial capital city. In contrast to much of the established literature analyzing the effects of environmental regulation on investment ,10,11,12First, the intervention of administrative orders in the government’s environmental regulation should be reduced to ensure that all environmental regulation policies are formulated and environmental regulation acts are carried out legally and compliantly. Reducing the direct or indirect intervention of administrative orders in the formulation of environmental regulation to ensure that environmental regulation policy formulation reflects its characteristics of improving the efficiency of resource allocation and compensating for the defects of the market operation mechanism. The direct or indirect intervention of administrative orders in the process of environmental regulation implementation should also be reduced, and the process of environmental regulation enforcement should be strictly regulated. By strengthening the relative independence of environmental regulation sectors, we can reduce the possibility of fluctuations in environmental regulation due to administrative power, thereby promoting the steady increase of investments.Second, we should establish environmental regulation supervisory agencies to reduce the possibility of environmental regulation fluctuations through before, during and after supervision over environmental regulation sectors. Before the environmental regulation policy is formulated, the agencies should monitor whether there are economic agents seeking rent from the environmental regulation sector for individual interests to ensure the fairness and reasonableness of the policy. During the process of environmental regulation policy formulation, the agencies shall monitor whether the environmental regulation department formulates environmental regulation policy in strict accordance with the legal procedures and within the scope of authority and responsibility stipulated in the law, and shall severely punish those who fail to formulate policy in accordance with the requirements of the law. After the environmental regulation policy is formulated, the agencies should monitor whether the environmental regulation department implements the environmental regulation acts according to the policy, and hold the responsible persons and departments accountable for the acts of not exercising the environmental regulation as required.Third, when carrying out environmental regulation reforms, the public’s reaction to environmental regulation fluctuations should be fully considered. Policymakers should be aware that although environmental regulation reform will reduce the burden of business operations, frequent fluctuations in environmental regulation will affect the expected profits of enterprises and inhibit investments in expansion. Therefore, in the process of deepening market-oriented reforms, the government should ensure that environmental regulation reforms are carried out smoothly and not too aggressively, and create a stable investment environment for enterprises by reducing the magnitude of environmental regulation fluctuations, so as to prevent enterprises from postponing their investments due to the changing investment environment.