Environmental Security Research Articles

On state securitization, artificial NGOs, and environmental journalism: An interview with Peter Schwartzstein, environmental security journalist, author, and researcher

This is an interview with environmental journalist Peter Schwartzstein. In the interview, Schwartzstein discusses how states are increasingly securitizing and weaponizing climate change and the environment. This can happen directly, for instance when militaries prepare for climate change or when governments use the environment in strategic ways to cover up their actions. But there are also indirect ways, such as artificial NGOs that serve governmental interests. Schwartzstein further discusses opportunities and challenges for environmental security journalists, including the need to work on the ground and the often-complex intersections between climate and conflict. He identifies declining state legitimacy as the most concerning development in the environmental security field in the coming years.

Factors associated with access and utilization of sexual abuse emergency healthcare services among street children in Zomba, Malawi: a qualitative study.

Sexual abuse among street children is a problem that renders a burden of sexually transmitted diseases, HIV infection, and early pregnancy. Literature shows that globally 95 million children experience sexual abuse with 1 in 5 girls and 1 in 7 boys exposed to sexual abuse before 18years of age in Malawi. Malawi adopted the World Health Organization guidelines for providing emergency health services for victims of sexual abuse, which include HIV Post Exposure Prophylaxis (PEP) and Emergency Contraceptive Pills (ECP) within 72h of exposure, Sexually Transmitted Infections (STI) treatment, and psychosocial services. However, there are challenges associated with the services that limit access and utilization among street children. This study explored the factors associated with access and utilization of sexual abuse emergency healthcare services among street children in Zomba. This was a cross-sectional phenomenological qualitative study conducted in Zomba City from 2022 to 2023 using in-depth and key informant interviews. We purposively selected street children between 10 to 17years who have been exposed to sexual abuse on the streets and social actors working with street children. The study employed a thematic analysis approach. The study found that street children did not utilize sexual abuse emergency health services. The major factors associated with utilization included the knowledge of sexual abuse, its associated health risks and sexual abuse emergency health services, and perceptions of utilizing sexual abuse emergency health services. The barriers to utilization of sexual abuse emergency health services included perceived shame, fear, discrimination, prolonged treatment process, and attitude of the health service providers. Sexual abuse and its health risks continue to be a challenge among street children. The absence of adequate connections and secure environments for street children to report sexual abuse and seek help without facing judgment has created significant obstacles for them in accessing sexual abuse emergency health services. To curb this challenge it is crucial for successful interventions to specifically address the health needs of street children and involve them in the decision-making processes related to their interventions.

Between Blocs And Blockades: Qatar’s Diplomatic Dilemma And Strategic Maneuvers

The article aims to analyze the dynamics of the 2017 Qatar diplomatic crisis in the context of the transforming geopolitics of the Gulf. The goal of the article is to examine the political causes of the crisis, which are directly related to regional competition and the change of power balance in the Gulf region, and to reveal why Qatar's diplomatic crisis was resolved years later without any concessions from Qatar. The article focuses on Qatar's foreign policy ambitions, Saudi Arabia's harsh opposition against it, and Doha's further expansion, which ultimately led to Qatar's five-year blockade. The study also examines the resolution of the crisis and the normalization of the relations between Saudi Arabia and its allies with Qatar, which was conditioned by the imperative to stabilize the security environment in the region. Saudi Arabia wanted to demonstrate that Riyad will continue to be the most influential player in the Gulf region. Qatar did not make tangible concessions but accepted the rules of the game.

Multi-condition hawser tension prediction of offshore offloading system based on long and short-term memory network and transfer learning

Real-time prediction of hawser tension of the side-by-side oil offloading system of Floating Production Storage and Offloading (FPSO) can provide early warnings for hawser breakages and ship collision risk, thus improving structural, property, and environmental security. Although offline numerical models and Long Short-Term Memory (LSTM) could offer substantial precision, they confront intensive time costs in recalibrating or retraining models. This paper proposes an integration method of LSTM networks and transfer learning for real-time tension prediction considering inputs of actual remote wave elevations. We use short-term environmental data and numerical simulation data of correlated hawser tensions during offloading operations to train a pre-trained benchmark model for transfer learning. Then, a highly generalized and efficient transferred model is constructed by using a small sample to realize short-term tension predictions in time-varying environments. The results show the occurrence time and value of extreme tensions predicted by transfer learning nearly match the reference data, and their maximum errors are 3 s and 0.11, respectively, superior to LSTM direct training. Therefore, it provides sufficient demand for real-time prediction and early collision risk prevention in dynamically changing ocean environment. The research results could provide an alternative framework for intelligent monitoring of large-scale marine structures.

MSC: A Unique Chameleon Hash‐Based Off‐Chain Storage Framework for Metaverse Applications

ABSTRACTBlockchain has evolved into a secure and trustworthy environment for decentralized applications, offering the advantages of tamper‐resistant, while simultaneously introducing on‐chain overhead issues. The development of metaverse related smart contracts on blockchain has given rise to a compelling research inquiry concerning the secure reduction of on‐chain storage overhead. In this research, the Metaverse Off‐chain Storage Framework based on Chameleon hash (MSC), a unique framework for decentralized system based on chameleon hash, supports code or stored data updates without changing on‐chain data is presented. The index of decentralized applications' data is calculated using the Chameleon hash to ensure that the index remains unchanged during the data modification process. Simultaneously, data can be stored outside of the blockchain with proper authentication mechanisms in place. The experimental results have shown that MSC exhibits reduced on‐chain storage requirements when compared to similar frameworks. Furthermore, MSC significantly reduced overhead as compared to the direct storage of data within a smart contract.

Alleviating Continuous Cropping Obstacles in Celery Using Engineered Biochar: Insights into Chemical and Microbiological Aspects

In the pursuit of environmental sustainability and food security, biochar has emerged as a promising soil conditioner to mitigate continuous cropping obstacles (CCOs). This study explored the use of engineered biochar (WP400) with high adsorption capacity for phenolic acids in celery cultivation. Using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) for both target and suspect analyses, along with Biolog EcoPlate™ to track the functional diversity of soil bacteria, the study examined chemical and microbiological interactions at varying WP400 application rates. WP400 enhanced celery growth, reduced disease severity, and adsorbed p-coumaric acid (COU), a potential autotoxin. Additionally, other potential allelochemicals, predominantly fatty acid-related, were identified, suggesting a broader role for fatty acids in allelopathy. WP400 also influenced soil bacterial carbon utilization and altered microbial communities. However, higher WP400 doses (0.8% w/w) may not be beneficial for celery growth and reduced bacterial metabolic potential, indicating limitations to its effectiveness. Proper application of WP400 provides a sustainable solution for alleviating continuous cropping issues, promoting both environmental sustainability and agricultural development.

Drought-tolerant wheat for enhancing global food security.

Wheat is among the most produced grain crops of the world and alone provides a fifth of the world's calories and protein. Wheat has played a key role in food security since the crop served as a Neolithic founder crop for the establishment of world agriculture. Projections showing a decline in global wheat yields in changing climates imply that food security targets could be jeopardized. Increased frequency and intensity of drought occurrence is evident in major wheat-producing regions worldwide, and notably, the wheat-producing area under drought is projected to swell globally by 60% by the end of the 21st century. Wheat yields are significantly reduced due to changes in plant morphological, physiological, biochemical, and molecular activities in response to drought stress. Advances in wheat genetics, multi-omics technologies and plant phenotyping have enhanced the understanding of crop responses to drought conditions. Research has elucidated key genomic regions, candidate genes, signalling molecules and associated networks that orchestrate tolerance mechanisms under drought stress. Robust and low-cost selection tools are now available in wheat for screening genetic variations for drought tolerance traits. New breeding techniques and selection tools open a unique opportunity to tailor future wheat crop with optimal trait combinations that help withstand extreme drought. Adoption of the new wheat varieties will increase crop diversity in rain-fed agriculture and ensure sustainable improvements in crop yields to safeguard the world's food security in drier environments.

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Co-benefits of agricultural diversification and technology for the environment and food security in China.

Co-benefits of agricultural diversification and technology for the environment and food security in China.

Activating Ultralong Room-Temperature Phosphorescence of Mono-Ring Arylboronic Acid by Hydrogen Bond for Tunable Afterglow Color and Stimulus Response.

Activating the ultralong room-temperature phosphorescence (RTP) of mono-ring arylboronic acid remains a great challenge, because the capacious free spaces shaped by a rubbery polymeric matrix allow the benzene ring skeleton to freely rotate. Herein, the ultralong RTP in mono-ring arylboronic acid derivatives embedded in a polyvinyl alcohol (PVA) matrix is activated, leveraging enhanced intermolecular and intramolecular hydrogen bonding and activating ultralong RTP. By incorporating diverse PBA derivatives into PVA via click chemistry, 3-aminophenylboronic acid (3A-PBA) doped PVA films showcase the most extended RTP lifetimes (2.24 s) and a high quantum yield (11.2%), alongside rapid and sensitive explosive vapor detection capabilities. These findings not only address the activation challenges of RTP in organic systems by highlighting the crucial role of hydrogen bonding and Förster resonance energy transfer in color tunability, but also mark a significant stride toward overcoming the limitations of current luminescent materials. This work heralds a new era in the development of organic phosphorescent materials, offering a promising strategy for broadening their practical applications and enhancing performance, particularly in environmental monitoring and security.

Exploring the impacts of risk perception and risk management planning on innovation orientation: a PLS-SEM approach

PurposeAlthough risk and innovation are often linked, current studies exploring the correlation between these terms are scarce. This study empirically examines the relationship between construction firms’ risk perception, risk management planning and innovation orientation.Design/methodology/approachA conceptual framework was formed through the literature and validated by the partial least squares structural equation modeling (PLS-SEM) technique, employing the data collected from 182 practitioners working in the Vietnamese construction sector.FindingsThis study highlighted that innovation orientation can be categorized as creation and adoption. The findings indicated that while risk perception can hinder innovation creation, effective risk management planning can mitigate the adverse effects. The findings also highlighted that the focus on innovation adoption will negatively affect innovation creation. Additionally, the findings showed that risk management planning has a positive relationship with innovation creation.Originality/valueThis study fills the research gap by explaining why innovation creation is often prioritized by innovation adoption, as risk perception acts as a deterrent. The results imply that construction firms should balance adopting innovations and fostering an environment conducive to innovation creation. It is recommended that these firms invest organizational resources in forming effective risk management plans to create a more secure environment for innovation creation.

Enhancing the security of edge-AI runtime environments: a fine-tuning method based on large language models

Enhancing the security of edge-AI runtime environments: a fine-tuning method based on large language models

Stabilizing food supply chains with Blockchain technology during periods of economic inflation

This study explores the potential of blockchain technology to stabilize food supply chains during periods of economic inflation. Economic inflation poses significant challenges to food security, as rising prices can disrupt supply chain transparency, efficiency, and trust among stakeholders. This research develops a conceptual model demonstrating how blockchain can enhance transparency and stability in food supply chains, ultimately improving resilience against inflationary pressures. By providing a decentralized, immutable ledger, blockchain technology allows for real-time tracking of food products from farm to table, enabling better inventory management and demand forecasting. The proposed model integrates key components, including smart contracts, which automate transactions and ensure compliance with quality and safety standards. This feature not only reduces delays but also fosters trust among supply chain participants, such as farmers, distributors, retailers, and consumers. Additionally, the model emphasizes the role of data analytics in harnessing blockchain data to provide insights into pricing trends and inventory levels, facilitating proactive decision-making in response to inflation. Future research directions are identified, focusing on innovations within blockchain technology that could further mitigate the effects of inflation on food supply chains. Potential areas of exploration include integrating artificial intelligence (AI) for predictive analytics, utilizing Internet of Things (IoT) devices for real-time data collection, and developing decentralized finance (DeFi) solutions to enhance liquidity and investment in the agricultural sector. This study contributes to the growing body of literature on blockchain applications in supply chain management by highlighting its potential as a tool for enhancing food security in inflationary environments. By ensuring transparency, improving stakeholder collaboration, and providing actionable insights, blockchain technology can play a pivotal role in stabilizing food supply chains amidst economic fluctuations. Ultimately, this research advocates for the adoption of blockchain solutions as part of comprehensive strategies to safeguard food systems from the adverse impacts of inflation. Keywords: Blockchain Technology, Food Supply Chains, Economic Inflation, Transparency, Stability, Smart Contracts, Predictive Analytics, Data Analytics, Food Security, Decentralized Finance.

An AI-Driven Model to Enhance Sustainability for the Detection of Cyber Threats in IoT Environments

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.

The Gray Zone and Its Place in Security Environment

The Gray Zone and Its Place in Security Environment

A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult. Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents the Hybrid approach based on the Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing approach to identifying cyber threats to the IoT. The HSVMR-D employs SVM to categorize known and unknown threats using attributes acquired from IoT data. Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. Moreover, protecting vital infrastructure and sensitive data, HSVMR-D provides a thorough and adaptable solution to improve the security posture of IoT deployments. Comprehensive experiment analysis and simulation results compared to the baseline study have confirmed the efficiency of the proposed HSVMR-D. Furthermore, increased resilience to completely novel changing threats, fewer false positives, and improved accuracy in threat detection are all outcomes that show the proposed work outperforms others. The HSVMR-D approach is helpful where the primary objective is a secure environment in the Internet of Things (IoT) when resources are limited.

Approaches to the Population Sheltering in Selected Countries in Relation to Threats and the Security Environment

Approaches to the Population Sheltering in Selected Countries in Relation to Threats and the Security Environment

Codes of Ethics and their Place in the New Security Environment as an Important Part of Human Resource Management

Codes of Ethics and their Place in the New Security Environment as an Important Part of Human Resource Management

Security and Privacy Perspectives of People Living in Shared Home Environments

Security and privacy (S&P) perspectives of people in a multi-user home are a growing area of research, with many researchers reflecting on the complicated power imbalance and challenging access control issues of the devices involved. However, these studies primarily focused on the multi-user scenarios in traditional family home settings, leaving other types of multi-user home environments, such as homes shared by co-habitants without a familial relationship, under-studied. This paper closes this research gap via quantitative and qualitative analysis of results from an online survey and qualitative content analysis of sampled online posts on Reddit. The study explores the complex roles of shared home users, which depend on various factors unique to the shared home environment, e.g., who owns what home devices, how home devices are used by multiple users, and more complicated relationships between the landlord and people in the shared home and among co-habitants. Half (50.7%) of our survey participants thought that devices in a shared home are less secure than in a traditional family home. This perception was found statistically significantly associated with factors such as the fear of devices being tampered with in their absence and (lack of) trust in other co-habitants and their visitors. We observed cyber-physical threats being a prominent topic discussed in Reddit posts. Our study revealed new user types and user relationships in a multi-user environment such as ExternalPrimary-InternalPrimary while analysing the landlord and shared home resident relationship with regard to shared home device use. Based on the results of the online survey and the Reddit data, we propose a threat actor model for shared home environments, which has a focus on possible malicious behaviours of current and past co-habitants of a shared home, as a special type of insider threat in a home environment. We also recommend further research to understand the complex roles co-habitants can play in navigating and adapting to a shared home environment's security and privacy landscape.

EXPLOITING OPTIMAL SELF-ATTENTION DEEP LEARNING-BASED RECOGNITION OF TEXTUAL EMOTIONS FOR DISABLED PERSONS

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.