Infrastructure Protection Research Articles

REAL-TIME GIS FOR MONITORING AND SECURITY

Applications of geographic information systems in the security domain are diverse and cover various aspects such as crisis prevention and management, public safety, national security, critical infrastructure protection and military applications. Real-time GIS is essential for effective emergency response and management because it provides accurate information on the location and dynamics of events.

Planning and Construction Strategies for Infrastructure in Traditional Villages of the Grand Canal from the Perspective of Villager Needs

The traditional villages along the Jiangsu section of the Grand Canal are facing the threat of decline and disappearance due to the decline of the canal transportation function and the advancement of urbanization, and it is urgent to study the protection and renewal of these villages. Based on the needs of villagers, this study aims to explore the planning and construction strategies of traditional village infrastructure in the Jiangsu section of the Grand Canal. Through a literature review, field investigation, and qualitative and quantitative analysis methods, this paper conducts in-depth research and data analysis on the status quo of six types of infrastructure in 29 traditional villages, including road traffic, water supply and drainage, garbage sanitation, energy utilization, postal communication, and ecological disaster prevention. The research results show that, although the infrastructure construction has made some progress, there are still problems such as unreasonable planning, low construction quality, and the lack of cultural characteristics, which cannot fully meet the actual needs of villagers. Through a regional comparative analysis, it is found that the infrastructure condition of the Jiangnan Canal section is better than that of the Huaiyang Canal section and Middle Canal section, but they all face challenges of protection and renewal. Based on the needs of villagers, this study puts forward corresponding protection and optimization strategies, which provide theoretical guidance and practical methods for the sustainable development of traditional village infrastructure, and it has important reference significance for the infrastructure protection and development of traditional villages in other regions.

Importance of Cybersecurity/The Relevance of Cybersecurity to Diabetes Devices: An Update from Diabetes Technology Society.

As medical devices become more integrated with wireless technologies, the risks of cyberattacks and data breaches increase, making stringent cybersecurity measures essential. The implementation of rigorous cybersecurity standards is essential for enhancing the cybersecurity of devices. This article examines the evolving cyber threats faced by the medical technology industry, the role of IEEE 2621 in providing comprehensive security benchmarks for medical devices, and the need for continuous risk assessments and adherence to regulatory standards to mitigate future cyber risks. Adherence to cybersecurity standards establishes ensures the effective protection of sensitive data and critical infrastructure.

Multi-Step Ahead Water Level Forecasting Using Deep Neural Networks

Stream gauge height (water level) is a significant indicator for forecasting future floods. Flooding occurs when the water level exceeds the flood stage. Predicting imminent floods can save lives, protect infrastructure, and improve road traffic management and transportation. Deep neural networks have been increasingly used in this domain due to their predictive capabilities in capturing complex features and interdependencies. This study employs four distinct models—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), transformer, and LSTNet—with MLP serving as the baseline model to forecast water levels. The models are trained using data from 20 distinct river gages across the state of Missouri to ensure consistent performance. Random search optimization is employed for hyperparameter tuning. The prediction intervals are set at 4, 6, 8, and 10 (each interval equivalent to 30 min) to ensure that performance results are robust and not due to random weight initialization or suboptimal hyperparameters and are consistent throughout different prediction intervals. The findings of this study indicate that the LSTNet model leads to a better performance than the other models, with a median RMSE of 0.00724, 0.00959, 0.01204, and 0.01230 for the 4, 6, 8, and 10 intervals, respectively. As climate change leads to localized storms driven by atmospheric shifts, water level fluctuations are becoming increasingly extreme, further exacerbating data drift in real-world datasets. The LSTNet model demonstrates superior performance in terms of RMSE, MAE, and the correlation coefficient across all prediction intervals when forecasting water levels under data drift conditions.

Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations

Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.

Artificial Intelligence and the Development of “Specialized, Refined, Unique, and Innovative” Small‐ and Medium‐Sized Enterprises

ABSTRACT“Specialized, Refined, Unique, and Innovative” (SRUI) enterprises are a crucial part of the national innovation system and play a significant role in enhancing national core competitiveness by leveraging strengths and addressing weaknesses. Given the limited quantitative analysis on the productivity of SRUI enterprises in the academic field, this paper constructs an enterprise‐level artificial intelligence index based on text analysis and machine learning. Using panel data from 2018 to 2022, we investigate the impact of artificial intelligence on the productivity of SRUI small‐ and medium‐sized enterprises (SMEs). The research findings indicate that (1) artificial intelligence significantly enhances the productivity of SRUI SMEs, and for each one standard deviation increase in artificial intelligence, the productivity level of SRUI enterprises will increase by 6.82%. (2) Mechanism tests reveal that artificial intelligence improves productivity by optimizing labor structure, improving labor resource allocation, stimulating endogenous motivation and innovation vitality within enterprises, and enhancing management level and investment efficiency. (3) Heterogeneity analysis indicates that at the regional level, artificial intelligence significantly boosts the productivity of enterprises with well‐developed network infrastructure and robust intellectual property protection systems. At the industry level, AI has a more pronounced effect on the productivity of technology‐intensive and competitive industries. Additionally, we also find that the impact of AI on productivity is more significant in enterprises with younger executive teams and executives with digital and intelligent education backgrounds. This study expands the research scope of productivity and provides valuable insights for the government to optimize digital economy policies and for enterprises to formulate digital innovation strategies.

Transnational corporate counterinsurgency in the Colombian conflict and its legacies today

ABSTRACT This study challenges common-sense thinking on internal violent conflicts in two ways: that they are merely domestic, with limited influence from external actors; and that doing business in conflict-ridden places is detrimental to profits. Drawing on empirical research and theoretical lenses from critical security studies and the political ecology of counterinsurgency, the paper explores the transnational dimensions of Colombia’s violent conflict in relation to the extractive sector. Conceptualising two overall generations of counterinsurgency, the article shows how, during the conflict, several transnational corporations, especially those in the extractive and agro-industrial sector, not only established themselves in Colombia and expanded, they also developed their corporate security schemes. Given that energy projects were strategic targets in the guerrilla war, the protection of corporate infrastructure by the state was justified. The paper argues that the internal conflict not only facilitated corporate extractivism, it also provided innovative ways of ensuring corporate security. This ‘social warfare’ approach includes strategic efforts to win hearts and minds, which continue to be pursued today through practices known as ‘corporate social responsibility’ (CSR). The paper relates this to the current challenges to hold economic actors accountable in transitional justice.

Violations Committed by Israel in Armed Conflicts Under International Humanitarian Law

Introductioan: War has various aims and objectives, including under the pretext of self-defense to defend life, family, honor or to defend one's nation, to ideological issues. The wars that occur also often violate international humanitarian law. One of them is between Israel and Hamas.Purposes of the Research: To find out violations of international humanitarian law committed by IsraelMethods of the Research: This research uses an normative juridical research method. Normative Juridical Research is research on Norms, Principles, Rules and Principles based on statutory regulations related to the problem being studied. The approaches used are the statutory approach, the conceptual approach, and case aprroach.Results of the Research: The results of the research show that according to international humanitarian law, violations committed by Israel in armed conflict involve actions that violate the principles of protection of civilians and civilian infrastructure. Humanitarian law, as set out in the Geneva Conventions, prohibits attacks on non-combatants and requires protection of vital facilities such as hospitals and schools, as well as access to humanitarian aid.

A IMPORTÂNCIA DO ATERRAMENTO ELÉTRICO NA SEGURANÇA E EFICIÊNCIA DE SISTEMAS ELÉTRICOS

The following work will propose the electrical grounding, the voltage and the way in which electrical grounding is used and its proper installation tends to promote an impact on user safety, minimizing accidents, as most users do not know what the system is or even what the Fundamentals are for, the general objective of this article is to understand electrical grounding and how its performance contributes to user safety and as a secondary objective: Importance of grounding, types of grounding, materials and devices, basic principles and technical standards. The methodology used was bibliographic research, using sources such as books, magazines, theses, and publications on the internet, allowing the researcher to access existing material on the subject, being of the qualitative type. The results and discussion demonstrate the importance of electrical grounding for the safety and protection of electrical infrastructure, thus providing important details that can improve system efficiency and maintain safety, as well as regulatory standards. The conclusion is that electrical grounding plays a crucial and important role in safety and protection, so it is better to use the TN-S grounding system because the protective conductor will always be at zero potential.

Methodological Approach for the Development of Standard C-UAS Scenarios

The integration of unmanned aircraft systems (UAS), commonly referred to as drones, into various sectors including agriculture, healthcare, and military operations has transformed industries and services. While drones offer numerous benefits, they have also been exploited by terrorists and criminals for illicit activities, such as smuggling and surveillance. Incidents involving drones flying over restricted airspaces or sensitive locations have highlighted the vulnerabilities of critical infrastructure and limitations of current law enforcement capabilities. To address these challenges, there is a growing need for effective counter-UAS (C-UAS) systems including detection, tracking, and identification technologies. However, evaluating the capabilities of these systems poses a significant challenge, leading to the demand for standardized test methodologies. The COURAGEOUS project seeks to fill this gap by developing standardized scenarios covering various use cases, such as airport security, critical infrastructure protection, and border security. These scenarios are informed by a systematic approach involving a literature review, analysis of previous incidents, assessment of current C-UAS frameworks, and gathering feedback from end users. The resulting standardized test methodology aims to enhance the understanding of C-UAS system capabilities, improve preparedness against evolving threats, and facilitate the global dissemination of project results. Through validation trials and feedback from law enforcement agencies, the COURAGEOUS project strives to contribute to a broader network of counter-UAS initiatives, ultimately enhancing the lower airspace security.

Identification and assessment of the mudflat ecological vulnerability dominated by coastline evolution in Jiangsu

Coastlines have experienced profound alterations due to human disturbances and ocean dynamics against the backdrop of climate change and intensified anthropogenic activities. Significant modifications have occurred within the muddy coastal ecosystem, rendering it extremely susceptible to heightened levels of vulnerability. However, existing studies on coastal eco-environmental vulnerability have often overlooked crucial factors such as coastline erosion and siltation, particularly concerning coastline change. In this study, we assess coastline stability utilizing Landsat remote sensing imagery to investigate the vulnerability of coastal ecology alongside 24 economic and natural parameters, serving as indices for the Coastal Vulnerability Index (CVI) of the coastal eco-environment. Subsequently, we analyze the impact factors to vulnerability and predict trends in coastline change over the next 20 years to propose recommendations for sustainable development to safeguard the coastal eco-environment. Our findings reveal a trend towards coastline stabilization in Jiangsu, with significantly higher stability observed during 2010–2020 compared to 1985–2010. The overall CVI along the Jiangsu coast shows an downward trajectory over the past 15 years, primarily influenced by tidal action and coastal topography and geomorphology. Predictive modeling suggests Jiangsu coastline is bounded by the Sheyang estuary, with the coastline to the north dominated by stabilization and to the south dominated by siltation. Climate change, coupled with intensified human activities and future coastline alterations, may exacerbate vulnerability. Effective protection strategies for coastal ecosystems necessitate a comprehensive understanding of local natural conditions, resource availability, and socioeconomic dynamics. Governmental interventions should focus on reinforcing regulatory frameworks for coastal organism management, implementing coastal protection infrastructure, and enhancing supervision and management protocols for beachfront development, thereby fostering the sustainable management of muddy coastlines.

Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.

Analysis of Spatiotemporal Evolution of Coordinated Development Between Transportation Infrastructure and Ecological Environment in the Yangtze River Economic Belt

The coupling and coordinated development of transportation infrastructure and ecological environment is an important way to achieve win-win economic, social, and ecological benefits. This study analyzes the coupling and coordinated development between transportation infrastructure and ecological environment in the Yangtze River Economic Belt. Based on panel data from 11 provinces in the Yangtze River Economic Belt from 2007 to 2022, establish their respective indicator systems and use entropy method to measure the comprehensive level of the two. Through the perspective of spatiotemporal differentiation, this study analyzes the changes in the coupling and coordinated development level of transportation infrastructure and ecological environment in 11 provinces. It is found that the overall coupling and coordinated development level of each province has significantly improved, but there are obvious regional differences. In the future, each province should formulate more precise and effective policy measures based on its own actual situation, strengthen regional cooperation and communication, and jointly promote the coupling and coordinated development of transportation infrastructure and ecological environment protection in the Yangtze River Economic Belt.

An efficient convolution neural network method for copy-move video forgery detection

Unmanned systems play a pivotal role in military surveillance, critical infrastructure protection, law enforcement, search and rescue operations, and border security, showcasing their multifaceted importance across diverse applications. Video fraud detection is integral to multimedia security, where our task involves the precise identification of modified segments within video sequences. Current approaches to video fraud detection often rely on manual feature selection and models tailored to detect specific tampering types, such as copy-move or splicing. The general representation powers of deep learning models and the connection of multiple forensic characteristics are still not fully explored. This research uses a convolutional neural network (CNN) model to identify copy-move video forgeries. Copy-move forgery is a type of video tampering whereby a portion of the video is copied and pasted somewhere different in the same video to cover an essential video characteristic. The method that is being proposed involves dividing the video into individual frames, extracting features from each frame by using a CNN model that has already been trained, and then utilizing these features to train a new CNN model that would classify each frame as either legitimate or fabricated. The proposed method effectively detects copy-move video forgery with an exceptionally high accuracy rate, exceeding current methods of accuracy and computational effort. The proposed method outperformed all other approaches on the SULFA, GRIP, and VTD datasets. The model's accuracy was 85.42 %, 86.16 %, and 81.87 %, respectively, with the shortest times recorded being 9.6 sec, 11.4 sec, and 13.7 sec, respectively. Consequently, specialists can employ the suggested method as a machine-learning instrument for detecting fake videos in real-time.

Normative Approach in Forestry Law Enforcement in Papua Province: Challenges and Opportunities for Indigenous Peoples’ Participation

This study uses a normative-juridical approach to examine the enforcement of forestry crime laws in Papua Province. The research focuses on implementing Article 76 of Papua Perdasus No. 21 of 2008, which outlines legal enforcement mechanisms in the forestry sector. The regional government is responsible for preparing forestry police officers in Papua and involving customary law communities in forest protection efforts. These communities can act as Swakarsa forest police, participating in forest security within their customary areas. Additionally, the study highlights the role of Ministerial Regulation No. P.56/Menhut-II/2014, which governs the formation of the Forestry Police Partner Community (MMP). The MMP consists of local community groups collaborating with the Forestry Police to safeguard forest areas. Their duties include assisting in forest patrols, securing forest protection infrastructure, disseminating information on forestry, and reporting any threats or security disturbances. The MMP also plays a role in arresting suspects caught in the act and securing evidence for immediate handover to the forestry police or Civil Servant Investigators. This collaborative approach strengthens forest law enforcement by integrating local community participation in safeguarding forests.

Cyber Security Exploits and Management in Telecommunication Companies: The Case of Uganda

This study examined the risks faced by telecommunication companies due to cyber-attacks and threats to their critical information infrastructure. It suggests control measures to avert the vulnerabilities of such infrastructure to cyber attackers. Critical information infrastructure is important to the companies as a revenue enabler and reputation identifier. However, the infrastructure, if not well protected, can be a national security threat, affect the national economy, and disrupt societal integration and business globally. Cyber security is the protection of internet-connected systems, including hardware, software, people, and data, from cyber-attacks. Its threat exploits the increased complexity and connectivity of critical infrastructure systems. The identification of the critical information infrastructure was done through document analysis and discussion with the network technical teams. The infrastructure systems, such as devices, servers, data, database, firewall, and network, were identified in this study as vulnerable to attack. This study suggests a framework to support timely intrusion detection and for the protection of critical information infrastructure. To secure and safeguard the critical information infrastructure from inherent vulnerabilities, it is recommended that continuous monitoring of the critical infrastructure attack gates should be made part of the overall risk management of a telecommunication company, and innovations in cyber security detection and prevention should be harnessed.

Water-Enhancing Gels Exhibiting Heat-Activated Formation of Silica Aerogels for Protection of Critical Infrastructure During Catastrophic Wildfire.

A promising strategy to address the pressing challenges with wildfire, particularly in the wildland-urban interface (WUI), involves developing new approaches for preventing and controlling wildfire within wildlands. Among sprayable fire-retardant materials, water-enhancing gels have emerged as exceptionally effective for protecting civil infrastructure. They possess favorable wetting and viscoelastic properties that reduce the likelihood of ignition, maintaining strong adherence to a wide array of surfaces after application. Although current water-enhancing hydrogels effectively maintain surface wetness by creating a barricade, they rapidly desiccate and lose efficacy under high heat and wind typical of wildfire conditions. To address this limitation, unique biomimetic hydrogel materials from sustainable cellulosic polymers crosslinked by colloidal silica particles are developed that exhibit ideal viscoelastic properties and facile manufacturing. Under heat activation, the hydrogel transitions into a highly porous and thermally insulative silica aerogel coating in situ, providing a robust protective layer against ignition of substrates, even when the hydrogel fire suppressant becomes completely desiccated. By confirming the mechanical properties, substrate adherence, and enhanced substrate protection against fire, these heat-activatable biomimetic hydrogels emerge as promising candidates for next-generation water-enhancing fire suppressants. These advancements have the potential to dramatically improve the ability to protect homes and critical infrastructure during wildfire.

UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS

Introduction: Various changes in technologies have impacted the public health monitoring systems especially in the United States. AI and IoT, Big Data, block chains have enhanced the ways that diseases are monitored, vaccinations are administered and emergency responses are coordinated. Issues like the infrastructure constraints, privacy issues and variability in technological implementation graduality’s limit the extent to which these technologies can be scaled up in U.S. public health systems. Objective: The aim of this study is to evaluate the impact of technological improvement on the improvement of the public health monitoring systems in United States. It assesses the utilization of superior technologies and considers the observed enhancement of general health status together with the problems identified in the application of technologies. Methods: A quantitative cross-sectional survey was filled by 400 participants from the PH and technology workers working throughout the USA in the public health sector. Community and purposive sampling techniques were used and data were self-administered from the participants through an online structured questionnaire. The survey included questions about participants’ degrees of awareness of AI, IoTs, Big Data and blockchain, concerning their applicability to public health surveillance and the potential challenges to their application. Descriptive statistics, chi-square, independent sample t test, one way analysis of variance, Pearson correlation analysis and multiple regression analysis were conducted in SPSS V 26. Results: The current study revealed that the use of AI, IoT, Big Data and blockchain technologies has accelerated the enhancement of public health surveillance, including disease detection and vaccination management. Awareness of these technologies was directly related to perceived impact on public health to the extent of r = 0.75, p < 0.001. Other issues like lack of funding, lack of technical knowledge and issues surrounding privacy were cited as major reasons for inadequate scaling up. Conclusion: The results point out that new technology has a potential for advancing the system for monitor public health in the United States but there are some barriers. Challenges that prevent expansion and growth of these technologies include inadequate infrastructure, funding and data protection must be overcome in order to enhance the efficiency of the technologies to deliver on their intended health benefits. More work is necessary to expand understanding of longevity of these phenomena and ways of preventing these challenges.

Nitrogen Transport Pathways and Source Contributions in a Typical Agricultural Watershed Using Stable Isotopes and Hydrochemistry

The increasing global nitrogen input poses a significant threat to aquatic environments, particularly in agricultural watersheds, where intensive human activities and insufficient water protection infrastructure exacerbate the risk of nitrogen pollution. Accurate identification of nitrogen pollution sources and the associated transformation processes is essential for protecting watershed ecosystems. In this study, a combination of hydrochemical analysis, correlation and principal component analysis, and stable nitrate isotopes (δ15N-NO3− and δ18O-NO3−) were employed to trace nitrogen transport pathways and source contributions in both surface water and groundwater within a typical agricultural watershed. The results revealed the presence of nitrogen pollution, including total nitrogen (TN), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3−-N), with significant spatial and seasonal variations in both surface water and groundwater. The spatiotemporal evolution of hydrochemical indicators and nitrate isotope compositions highlighted multiple potential sources of nitrogen, including soil input, agricultural input, and manure and sewage input. The results from stable isotope analysis in an R (SIAR) model indicated that ammonium fertilizers (7.1~78.4%) and manure and sewage (2.6~69.7%) were the primary sources of nitrates in surface water, while manure and sewage were the main sources in groundwater (67.9~73.7%). This research demonstrated that nitrification, seasonal variations, and human activities significantly impact nitrogen migration and transformation in agricultural watersheds. However, the issue of groundwater severely polluted by manure and sewage has received insufficient attention. To effectively control nitrogen pollution in agricultural watersheds, it is necessary to improve septic tanks and sewage networks, as well as implement scientific fertilization practices.

Machine learning algorithms in SIEM systems for enhanced detection and management of security events

As cyber threats become increasingly sophisticated, traditional Security Information and Event Management (SIEM) systems face challenges in effectively identifying and responding to these dangers. This research presents the development of a SIEM system integrated with machine learning (ML) to enhance threat detection, anomaly identification, and automated incident response. The integration of ML allows the SIEM system to go beyond conventional rule-based approaches, enabling the detection of previously unknown threats by learning from historical data. The system employs advanced algorithms to analyze large-scale log data and network traffic, providing real-time insights and reducing false positives. Key features of this SIEM include anomaly detection, predictive analytics, and adaptive thresholds, which allow it to adjust dynamically based on contextual data. By adapting to new and evolving cyber threats, the system provides a more resilient and proactive defense against potential attacks. The results indicate that integrating machine learning into SIEM systems can offer organizations a more effective, scalable, and adaptive security solution, ensuring the protection of critical infrastructure and data in a rapidly changing digital landscape.