Control List Research Articles

Wilderness Medicine Curricula in US Multidisciplinary Training Courses.

Wilderness medicine (WM) is the study of medicine in austere environments. There are several US multidisciplinary courses that teach WM to people from varying medical backgrounds. However, WM topics are covered to different extents. This study's purpose was to compare WM components among US multidisciplinary training courses. The American College of Emergency Physicians WM fellowship curriculum's 19 components and the Fellowship of the Academy of Wilderness Medicine's 12 core and 16 elective competencies and their credits were used as two control lists. Curricula from 10 US multidisciplinary courses were analyzed for WM components. Using descriptive analysis, each course curriculum was compared with the controls. This study examines WM components in 10 courses. The greatest number of American College of Emergency Physicians WM fellowship topics (14 of 19) was covered by the Paramedic course and the fewest number (4 of 19) by the Tactical Combat Casualty Care-Combat Lifesaver course. The greatest number of Fellowship of the Academy of Wilderness Medicine core credits (56) was offered by the Paramedic course and the fewest number (24) by the Tactical Combat Casualty Care-Medical Personnel course. The greatest number of Fellowship of the Academy of Wilderness Medicine elective credits (83) was offered by the Paramedic course and the fewest number (25) by the Tactical Combat Casualty Care-Combat Lifesaver course. This research analyzed WM components in US multidisciplinary courses and demonstrated that each covers WM topics to varying extents. This shows an opportunity for these courses to expand their WM education within their scope. It also demonstrates competencies offered by different courses for interested trainees.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The effect of formative assessment on students' clinical knowledge, skills and self-efficacy levels

AimThis research aims to examine the effect of using formative assessment methods in clinical education on students' knowledge, skills and self-efficacy levels. BackgroundFormative assessment is used as a method designed to identify areas where students fall short and provide feedback for improvement. Formative assessment and feedback represent fundamental characteristics of quality teaching in higher education and play a decisive role in learning in nursing education. Although educators observe students performing practical tasks during clinical education, evaluation is not made by a structured control list. Therefore, just as nursing students are evaluated with “Skill Checklists” in the OSCE exam, there is a need to evaluate nursing skills during patient care in the clinical field. DesignThe study was designed as a pre-test post-test randomized controlled experimental study. MethodBefore the research, both groups filled out the self-efficacy form. The experimental group received formative assessment throughout the course. At the end of the semester, all students were given a skills test and asked to fill out the self-efficacy form again. Finally, a knowledge test was administered to the entire class. ResultsThe average knowledge score of the experimental group is higher than the control group. It was determined that there was a statistical difference of 16.54 points in the average skill scores between the groups. Posttests showed significant differences in skills such as breathing-cough exercise, basic glycemic measurement, subcutaneous injection and blood collection skills. ConclusionIt was determined that the formative assessment method increased nursing students' knowledge, skills and self-efficacy levels regarding basic nursing skills.

The moderating effect of self-efficacy in the risk awareness and treatment compliance of hypertensive patients.

The WHO reported that 46% of adults with HT in the global statistics were unaware of the disease.The aim of this study was to examine the moderating role of self-efficacy in risk awareness and treatment compliance of hypertensive patients. Data for this descriptive and correlational study were collected between 22.08.2023 and 22.02.2024. A personal information form was used for data collection together with a risk awareness scale, self-efficacy scale, and antihypertensive treatment compliance scale. The data were collected from 169 patients in face-to-face intterviews. Multiple linear regression and PROCESS macro-Model vn.3.5 were used in the analyses. The STROBE control list was followed in the study. A moderate level of risk awareness (40.03 ± 7.98) and self-awareness (56.11 ± 10.18) of the study participants was determined, and there was seen to be treatment compliance (6.53 ± 2.34). Risk awareness was seen to be positively correlated with both treatment compliance and self-efficacy (p < 0.05). The moderating effect of the treatment points between risk awareness and self-efficacy was analyzed and the model formed was found to be statistically significant (F = 1.942, p = 0.006). Independent variables in the model explained 28% of the change in the dependent variable. Self-efficacy points were not found to have a moderating effect on the effect of cardiovascular risk awareness on antihypertensive treatment compliance (p = 0.144). Treatment compliance can increase with an increase in the self-efficacy and risk awareness of an individual. Increased self-efficacy may affect the moderating role.

Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control

Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

Configuration of multi‐shaper Time‐Sensitive Networking for industrial applications

AbstractIEEE 802.1 Time‐Sensitive Networking (TSN) has proposed several shapers, for example, time‐aware shaper (TAS, 802.1Qbv), asynchronous traffic shaping (ATS, 802.1Qcr), credit‐based shaper (CBS, 802.1Qav), and cyclic queuing and forwarding (CQF, 802.1Qch). The shapers have their advantages and disadvantages and can be used in isolation or in combination to address the varied timing requirements of industrial application streams. There is very limited work on how to analyse and configure shaper combinations. The authors are interested in the configuration optimisation of multi‐shaper TSN networks, targeting the TAS + CBS, TAS + ATS, and TAS + Multi‐CQF combinations. The authors first propose multi‐shaper integration approaches, focusing on a novel iterative delay analysis for TAS + ATS, an approach to integrate TAS and CQF by placing constraints on TAS scheduling as well as the TAS and CBS integration. We formulate the combinatorial optimisation problem of configuring multi‐shaper TSN networks, which consists, for example, of the routing of streams, the assignment of streams to the egress port queues, and the synthesis of gate control lists for TAS. Then, the authors propose a solution based on a simulated annealing metaheuristic. The proposed solutions are evaluated on large realistic test cases, up to tens of thousands of streams and devices.

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

U.S.–China trade conflicts and R&D investment: evidence from the BIS entity lists

The economic outcomes of U.S.–China trade conflicts on Chinese enterprises remain ambiguous. Using the staggered export control lists released by the Bureau of Industry and Security (BIS) of the U.S. Department of Commerce in conjunction with data from Chinese publicly listed manufacturing firms, we identify Chinese entities subjected to these export controls. Our Difference-in-Differences (DID) estimates show that export restrictions induced by the entity lists enhance the R&D investment intensity among Chinese firms by 16.58% in the next year. The mechanism is that controls increase firms’ R&D investment through government subsidies, firms’ inventory adjustment and firms’ risk-taking. Heterogeneity tests show that the policy effect is more significant for State-owned enterprises (SOEs), firms with executives with foreign experience. In addition, the R&D promotion of firms supported by China’s industrial policy is more pronounced. Although controls significantly increase R&D inputs, they have little or negative effect on firms’ innovation outputs. Our study provides micro-level causal evidence on the economic impacts of the U.S.–China trade war.

Pollution Characteristics and Ecological Impact of Screening Analysis of Fishing Port Sediments from Dalian, North China.

Environmental pollution from synthetic chemical mixtures has significant adverse impacts on marine ecosystems. However, identifying the main constituents of chemical mixtures that pose ecological threats is challenging due to the necessity of an integrated workflow for comprehensive identification and toxicological prioritization of pollutants. Here, an all-in-one mass spectrometric strategy integrating target, suspect, and nontarget analysis was used to investigate organic pollutants of concern in fishing port sediments, with 355 pollutants (32 from target analysis, 118 from suspect screening and 205 from nontarget analysis) identified in 11 categories. The chemical classes of polycyclic aromatic hydrocarbons (PAHs), pesticides, and intermediates were the extensively detected chemical classes. The ecological risks of absolutely quantified pollutants (i.e., 16 parent PAHs, 7 organophosphate esters (OPEs), 10 pesticides and 4 benzotriazole ultraviolet absorbers) were assessed using toxicity-weighted concentration ranking, with o,p'-DDT being the major contributor. Under the toxicological priority index (ToxPi) framework, an extended ranking of all identified pollutants was achieved by combining instrument response and detection frequency, with a priority control list of 15 pollutants obtained, of which benzo[ghi]perylene (BghiP) and p,p'-DDE had the highest risk priority. Due to frequent detection rates and significant environmental risks, routine monitoring of petroleum pollutants is considered essential. This study presents a general workflow that includes comprehensive identification and prioritization of pollutants, facilitating chemical management and ecological risk assessment.

Policy-Based Contextual Access Control Mechanism for Smart IoT Devices

Access control remained an important aspect of computer security, and it has been the focus of extensive research over the past several decades. Access control mechanisms generally composed of two fundamental components: authentication and authorization. Authentication refers to verifying the identity of an entity, and authorization guarantees that only authenticated entity or devices can access the permitted devices or other resources. Various traditional access control schemes, such as Access Control Lists (ACLs), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC) exist, but these have certain limitations that hinder their direct implementation in the Internet of Things (IoT). For instance, ACLs maintain user-specific access privilege lists, which are feasible for environments with limited users and devices, but impractical for large scale systems like IoT. RBAC assigns devices access through roles associated with permissions, however role management in dynamic IoT environments poses significant challenges. ABAC grants access based on user and devices attributes that requires certain attribute criteria for authorization. We advocate that IoT environments are dynamic in nature and consist of very large volumes of smart IoT devices (such as smart sensors, smart phones and gadgets) that which introduce unique access control challenges. One significant challenge is providing dynamic access to smart IoT devices, as opposed to relying on static rules, roles, or attributes. Considering these challenges, this research advocates for a novel access control scheme tailored for accessing smart IoT devices in internet of things environments. The prototype implementation of the proposed approach is carried out along with conducting the usability study to evaluate the performance and suitability of the proposed system for real world internet of things (IoT) scenarios.

The effect of simulation on auscultation skills, self-confidence and anxiety levels in nursing students: A randomized controlled trial

AimThis study evaluated the effect of simulation on auscultation skills, self-confidence and anxiety levels. BackgroundAuscultation is an essential topic in nursing education and patient care. Simulation is efficacious in improving auscultation skills and self-confidence and reducing anxiety levels. It is a valuable educational approach whose effects should be evaluated and disseminated in the context of auscultation. DesignThis study had a randomised controlled trial design. MethodsThe study was conducted with second-year nursing students enrolled at a university in Turkey. One group of students studied auscultation of heart, lung and bowel sounds with a simulated patient (n = 28), the second group with a high fidelity simulator (n = 30) and the third group with traditional education (n = 28). Data were collected using a demographic information form, auscultation control list and Nursing Anxiety and Self-Confidence with Clinical Decision Making Scale. The students' psychomotor auscultation skills, self-confidence and anxiety levels were evaluated. Data on auscultation skills were assessed after the intervention, while data on self-confidence and anxiety levels were collected at baseline, after the intervention and after clinical practices throughout the academic year. ResultsAnxiety and self-confidence levels showed statistically significant differences in intragroup evaluation. The self-confidence levels of the groups working with simulation did not change much in the follow-up test conducted after the end of clinical practices. The mean skill scores in the auscultation control list showed a statistically significant difference between the groups. ConclusionsSimulation and traditional approaches are effective in learning auscultation skills. However, the positive effects of simulation, particularly in enhancing students' self-confidence and reducing anxiety levels, appear to be more permanent and impactful than traditional education. Therefore, it is recommended that simulation be prioritized for teaching auscultation skills.

Implementation of Multi Extension in Blockchain-Based IoT Platform for Industrial IoT Devices

The rise of the Internet of Things (IoT) has led to the creation of technologies to improve human life. IoT involves integrating the Internet with the physical world, spanning applications like smart homes, industries, supply chains, academia, and more. By the end of 2020, around 212 billion IoT devices were globally deployed, presenting substantial opportunities for manufacturers and diverse applications. There have been numerous implementations of IoT across various fields, including Blockchain IoT (B-IoT), Artificial Intelligence of Things (AIoT), Digital Twin, and new communication protocols like the Matter protocol. We conducted a comprehensive testing of the blockchain (B-IoT) extension system on various bandwidths and scenarios, such as blockchain API execution time, speed, retention performance, and smart contract vulnerability testing. Our testing has been successful, and several messaging systems were used. Kafka was recommended to overcome the pending transaction problem caused by unprocessed messages. Our smart contract exhibited high severity. The Artificial Intelligence of Things extension, tested on real environments for person and vehicle counters, has shown successful results. Digital Twin, integrated into the IoT platform to perform and control 3D assets such as the postgraduate PENS building, has demonstrated efficient performance. Matter protocol achieved an average task execution speed of 0.48 tasks per second. Matter P2P communication was also successfully tested in this research by implementing the Access Control List (ACL) command.

ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies

Semantic generation of network access control policies can help network administrators accurately implement policies to achieve desired security objectives. Current semantic generation research mainly focuses on semantic generation of single grammar and lacks work on automatically generating semantics for different grammatical strategies. Generating semantics for different grammars is a tedious, inefficient, and non-scalable task. Inspired by sequence labeling in the field of natural language processing, this article models automatic semantic generation as a sequence labeling task. We propose a semantic generation algorithm named ADLBiLSTM. The algorithm uses a self-attention mechanism and double-layer BiLSTM to extract the features of security policies from different aspects, so that the algorithm can flexibly adapt to policies of different complexity without frequent modification. Experimental results showed that the algorithm has good performance and can achieve high accuracy in semantic generation of access control list (ACL) and firewall data and can accurately understand and generate the semantics of network access control policies.

Progress and Prospects for a Nucleic Acid Screening Test Set.

DNA synthesis companies screen orders to detect controlled sequences with misuse risks. Assessing screening accuracy is challenging owing to the breadth of biological risks and ambiguities in risk definitions. Here, we detail an International Gene Synthesis Consortium working group's rationale and process to develop a prototype DNA synthesis screening test dataset, aiming to establish a baseline of screening system accuracy to compare with various screening approaches. Construction of the prototype test dataset involved four tool developers screening nucleic acid sequences from three taxonomic clusters of controlled organisms (Orbivirus, Francisella tularensis, and Coccidioides). Results were mapped onto predefined, comparable categories, checking for consensus or conflicts. Conflicts were grouped based on gene annotation and resolved through discussion. The process highlighted several long-standing challenges in DNA synthesis screening, including the qualitative differences in approaches taken by screening tools. Our findings highlight the lack of clarity in assessing pathogen sequences with respect to regulatory control language, compounded by scientific uncertainty. We illustrate the current degree of consensus and existing challenges using classification statistics and specific examples. This prototype underscores the necessity of expert-regulator coordination in assessing gene-associated risks, offering a template for creating test sets across all taxonomic groups on international control lists. Expanding the working group would enrich dataset comprehensiveness, enabling a transition from species-focused to function-focused regulatory controls. This sets the foundation for quality control, certification, and improved risk assessment in DNA synthesis screening.

Profile of Electronic Scientific Journals in Angola

Scientific journals play an important role as a channel for communicating scientific research results around the world. In recent years, the number of electronic scientific journals in Angola has increased, despite their low visibility internationally. The objective of this article is to describe the profile of electronic scientific journals in Angola. This is an exploratory and documentary research, in which a total of 14 web pages of scientific journals were observed, using a form, containing a control list with the following items: year of creation, responsible institution, area of knowledge, periodicity of publications, last publication, indexing in databases, use of ORCiD by authors and information on the date of submission, acceptance and publication of originals. The results indicate that more than half of the scientific journals are multidisciplinary in nature and were created between 2012 and 2019; They are linked to Higher Education Institutions and are indexed in at least one database, with biannual publications. However, the study also shows that the majority have not published for at least a year. It is concluded that the profile of electronic scientific journals in Angola reflects the country's academic and scientific context, with many challenges to be overcome in this area.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

A security-enhanced scheme for MQTT protocol based on domestic cryptographic algorithm

Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT’s popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security-enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access Control List (ACL) and ensures end-to-end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol’s security deficiencies, achieving these improvements with small overhead.

Spatiotemporal distribution of ecological risk of antibiotics in seven major river basins of China: An optimized multilevel assessment approach.

Antibiotics have been recognized as emerging pollutants due to their ecological and human health risks. This paper aims to enhance the ecological risk assessment (ERA) framework for antibiotics, to illustrate the distribution of these risks across different locations and seasons, and to identify the antibiotics that pose high ecological risk. This paper focuses on 52 antibiotics in seven major basins of China. Relying on the optimized approach of ERA and antibiotic monitoring data published from 2017 to 2021, the results of ERA are presented in multilevel. Across the study area, there are marked variations in the spatial distribution of antibiotics' ecological risks. The Huaihe River Basin, the Haihe River Basin, and the Liaohe River Basin are the top three in the ranking of present ecological risks. The research results also reveal significant differences in temporal variation, underscoring the need for increased attention during certain seasons. Ten antibiotics with high contribution rates to ecological risk are identified, which is an important reference to formulate an antibiotic control list. The multilevel results provided both risk values and their ubiquities across a broad study region, which is a powerful support for developing ecological risk management of antibiotics.