Critical Aspects Research Articles

Solar energy innovations in the USA: A comparative global review

This comparative global review delves into solar energy innovations in the United States, providing insights into the nation's advancements and positioning within the international landscape. The United States has emerged as a key player in the field of solar energy, characterized by rapid technological innovations, policy initiatives, and substantial investments. By comparing these developments with global trends, the study aims to shed light on the unique characteristics and impacts of solar energy innovations in the USA. The review begins by examining the historical evolution of solar energy technologies in the United States, highlighting key milestones and breakthroughs. Subsequently, it explores the policy frameworks and regulatory measures that have shaped the growth of the solar energy sector, drawing comparisons with strategies adopted by other leading nations. The study delves into financial incentives, government support, and the role of private-sector initiatives in fostering a conducive environment for solar innovation. A critical aspect of the review is the comparative analysis of solar energy adoption rates, capacity additions, and grid integration strategies across various countries. By benchmarking the USA's solar energy landscape against global benchmarks, the study aims to identify best practices, challenges, and opportunities for further advancements. The review also considers the socio-economic and environmental impacts of solar energy innovations in the USA, assessing job creation, economic growth, and carbon emissions reductions. Comparative case studies with other nations provide a comprehensive understanding of the broader implications of solar energy adoption on a global scale. As solar energy continues to gain prominence as a sustainable and scalable solution, this review contributes to the ongoing discourse by providing a nuanced perspective on the unique trajectory of solar innovations in the USA. The findings aim to inform policymakers, researchers, and industry stakeholders, offering valuable insights for the continued growth and optimization of solar energy technologies in both domestic and global contexts.

Evaluating the environmental impacts of smart vineyards through the Life Cycle Assessment

This study aimed to assess the environmental effectiveness of vineyards utilising on-site weather stations integrated with a decision support system (DSS), and to identify the critical hotspots in smart farms that have already obtained integrated or organic certification. For this purpose, Life Cycle Assessment (LCA) methodology was applied. The research comprised three smart farms employing on-site weather stations and a traditional farm without advanced technologies, which served as a benchmark. The analysis revealed variations in environmental footprints driven by differences in farm management practices and soil characteristics. The results highlighted that smart farms, in compliance with integrated or organic certifications, focus on reducing inputs such as agrochemicals or water consumption. However, these reductions could shift the environmental burden to other impacts, such as those related to machinery use, which remained the most critical aspect across all vineyards considered. In some smart farms, critical issues involve other aspects, such as irrigation and fertilisation. The lack of awareness about the potential environmental impacts of the adopted technical options could make smart farms more impactful than traditional farms. Interestingly, this study found that solely implementing advanced technologies could fall short of achieving ecological objectives. This study emphasises the significance of utilising LCA as a valuable tool to support farmers in making informed decisions while adopting technological strategies to achieve environmentally sustainable goals.

AMter: An end‐to‐end model for transcriptional terminators prediction by extracting semantic feature automatically based on attention mechanism

SummaryThe Terminator, a specific DNA sequence, provides the transcriptional termination signal to RNA polymerase, making it a critical aspect of transcriptional regulation. This article proposes AMter, the first end‐to‐end model designed for predicting transcriptional terminators, leveraging attention mechanisms. In AMter, rather than manual feature engineering, two distinct modules based on attention mechanism, known as Frequency‐Attention and Allkmer‐Attention, are employed to automatically learn efficient features. Frequency‐Attention generates informative features by autonomously determining the significance of various frequency features, while Allkmer‐Attention aims to capture the relationships among all k‐mers within a DNA sequence. Features generated by Frequency‐Attention and Allkmer‐Attention demonstrate high informativeness and discriminative capacity, allowing precise discrimination of whether a DNA sequence is a terminator through a simple prediction network. The results of the 5‐fold cross‐validation test indicate the remarkable achievement of our proposed method, attaining 100% accuracy in both the training and validation datasets. Furthermore, AMter demonstrates outstanding prediction accuracy on two independent datasets, with 100% accuracy for Escherichia coli and 99.30% for Bacillus subtilis, marking a significant 94.4% relative improvement over prior methods. Experimental results conclusively demonstrate that AMter surpasses existing approaches, establishing a new state‐of‐the‐art in transcriptional terminator prediction.

Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model's superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models.

PENGARUH FRAUD HEXAGON, INTERNAL CONTROL, DAN FINANCIAL DISTRESS TERHADAP PENDETEKSIAN KECURANGAN LAPORAN KEUANGAN

This research addresses issues related to the detection of financial statement fraud in non-cyclical consumer companies, focusing on the relationship between the hexagon fraud variables consisting of pressure, opportunity, rationalization, capability, arrogance, and collusions, followed by internal control and financial distress variables on the detection of financial statement fraud as the dependent variable. The analytical method employed by the researcher is multiple linear regression using purposive sampling technique. In this study, the author utilizes secondary data obtained from the annual reports of non-cyclical consumer companies listed on the Indonesia Stock Exchange for the years 2020-2022. From the conducted research, it is concluded that pressure and financial distress variables have a negative impact on the detection of financial statement fraud, while the opportunity variable has a positive influence on the detection of financial statement fraud. On the other hand, rationalization, capability, arrogance, and internal control variables do not affect the detection of financial statement fraud. The findings of this research can serve as a literature reference for future researchers to include citations, methods used, or other relevant information on topics related to and relevant to the opportunity variable that has been proven to have a positive impact in this study. This research contributes to the existing literature by focusing on critical aspects of financial statement fraud detection based on the latest fraud theory, internal control, and financial distress.

Knowledge graph reasoning for cyber attack detection

AbstractIn today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.

Operational experience with Adaptive Gain Integrating Pixel Detectors at European XFEL

The European X-ray Free Electron Laser (European XFEL) is a cutting-edge user facility that generates per second up to 27,000 ultra-short, spatially coherent X-ray pulses within an energy range of 0.26 to more than 20 keV. Specialized instrumentation, including various 2D X-ray detectors capable of handling the unique time structure of the beam, is required. The one-megapixel AGIPD (AGIPD1M) detectors, developed for the European XFEL by the AGIPD Consortium, are the primary detectors used for user experiments at the SPB/SFX and MID instruments. The first AGIPD1M detector was installed at SPB/SFX when the facility began operation in 2017, and the second one was installed at MID in November 2018. The AGIPD detector systems require a dedicated infrastructure, well-defined safety systems, and high-level control procedures to ensure stable and safe operation. As of now, the AGIPD1M detectors installed at the SPB/SFX and MID experimental end stations are fully integrated into the European XFEL environment, including mechanical integration, vacuum, power, control, data acquisition, and data processing systems. Specific high-level procedures allow facilitated detector control, and dedicated interlock systems based on Programmable Logic Controllers ensure detector safety in case of power, vacuum, or cooling failure. The first 6 years of operation have clearly demonstrated that the AGIPD1M detectors provide high-quality scientific results. The collected data, along with additional dedicated studies, have also enabled the identification and quantification of issues related to detector performance, ensuring stable operation. Characterization and calibration of detectors are among the most critical and challenging aspects of operation due to their complex nature. A methodology has been developed to enable detector characterization and data correction, both in near real-time (online) and offline mode. The calibration process optimizes detector performance and ensures the highest quality of experimental results. Overall, the experience gained from integrating and operating the AGIPD detectors at the European XFEL, along with the developed methodology for detector characterization and calibration, provides valuable insights for the development of next-generation detectors for Free Electron Laser X-ray sources.

Historical Climate Trends and Extreme Weather Events in the Tri-State Area: A Detailed Analysis of Urban and Suburban Differences

This study analyzes daily temperature and precipitation data collected from 44 weather stations throughout New York, New Jersey, and Connecticut to assess and quantify the historical climatic changes within these states. The study conducts a detailed examination of spatial and temporal trends, focusing on specific stations that best represent the climatic diversity of each area. A critical analysis aspect involves comparing temperature trends in urban and suburban areas, mainly focusing on New York City. The findings reveal a significant upward increasing trend in average temperatures across all seasons, with urban areas, especially NYC, exhibiting the most marked increases. This trend is notably sharp in the spring, reflecting climate change’s escalating influence. The study also observes an increase in the annual average temperatures and a concurrent decrease in the variability of temperature ranges, suggesting a stabilization of temperature fluctuations over time. Also, we identified a notable increase in heat wave frequency, more so in urban locales than in their suburban counterparts. Analysis of precipitation patterns, particularly in NYC, reveals a decline in snowfall days, consistent with the general warming trend. The results demonstrate significant trends in seasonal average temperatures, a decrease in the variability of temperatures, and a rise in heat wave occurrences, with urban areas typically experiencing warmer conditions. This comprehensive study highlights the need for a more in-depth analysis of spatial precipitation trends. It underscores the importance of continued research in understanding the multifaceted impacts of climate change, particularly in differentiating urban and rural experiences.

A Polysaccharide-Based Integrated Nutrient Management System Enhances the Antioxidant Properties in Origanum dictamnus (Lamiaceae), a Valuable Local Endemic Plant of Crete

Origanum dictamnus L. (Lamiaceae), a local endemic plant of Crete (Greece), creates polysaccharide-containing subcuticular compartments presenting biological activity against phytopathogenic fungi, and, among others, significantly affects the fungal cell wall polysaccharides. This field study introduces a fertilization scheme for O. dictamnus, which was developed and refined to optimize the yield as well as critical herbal quality aspects. Five fertilization schemes were investigated, based on a polysaccharide-based Integrated Nutrient Management (INM), a mixture of conventional inorganic fertilizers (ChF) and two biostimulants (not algae) via foliar and soil application. Plant growth, together with leaf chlorophyll fluorescence and color (SPAD meter, DA meter, Chroma Meter) were determined. The leaf content of chlorophyll, three critical antioxidant compounds (carotenoids, flavonoids, phenols) and nutrients were also assessed. Considering all three antioxidants together, the enhanced efficiency, non-toxic, water-soluble, polysaccharide-based INM by foliar application was the most stimulatory scheme, playing an important role in plant growth and development. The present field study provides, for the first time, baseline fertilization data improving key herbal quality features in O. dictamnus and unravels the attainment of high antioxidant properties. The latter may be exploited in favor of its further utilization as a raw material for tea preparation, medicinal purposes, natural food flavoring and/or food preservative.

Comparative analysis of digital holographic microscopy and digital lensless holographic microscopy for quantitative phase imaging

This study provides a detailed comparison of two widely used quantitative phase imaging (QPI) techniques: single-shot off-axis digital holographic microscopy (DHM) and digital lensless holographic microscopy (DLHM). The primary aim is to evaluate and contrast critical aspects of their imaging performance, including spatial phase sensitivity, phase measurement accuracy, and spatial lateral resolution. Employing typical configurations for both DHM and DLHM, the study utilizes a customized phase test target featuring linear phase changes introduced by a specially designed linear density attenuation filter. Ground truth data from an atomic force microscope is incorporated to validate the experimental findings. The comparative analysis reveals that DHM and DLHM exhibit nearly identical spatial phase sensitivity, with DHM demonstrating a minimal 3.2% measurement error compared to DLHM's 4% in height measurement accuracy. Notably, DHM achieves a finer spatial lateral resolution down to 3.1 µm, surpassing DLHM's 5.52 µm. While DHM outperforms DLHM in precision and resolution, the latter offers advantages in terms of portability and cost-effectiveness. These findings provide valuable insights for researchers and practitioners, aiding in the informed selection of QPI methods based on specific application requirements.

Opportunities and challenges of the nitride coatings for artificial implants: A review

With the continuous development of the economy and society, exacerbated by the growing challenges of an aging population, the demand for orthopedic implants has witnessed a considerable surge. To enhance the performance and lifespan of these implants, the deposition coating on their surfaces has emerged as a pivotal measure. Among the various coating options, nitride coatings have gained prominence due to their exceptional comprehensive properties and application in surface modification for orthopedic implants. This paper presents a succinct overview of the evolutionary trajectory and fundamental properties of materials employed in implants. It traces the progression from inert materials in the early stages to the second and third generations characterized by bioactivity (biodegradability), and currently, the fourth generation featuring bio-gene materials. Subsequently, a meticulous review delves into the detailed application of nitride coatings in the realm of implants, encompassing critical aspects such as mechanics, tribology, corrosion resistance, antibacterial properties, and biological responses. In conclusion, the article offers insightful perspectives on the future of implant coatings. It serves as a valuable reference for researchers engaged in nitride material studies and bio-coating research, providing a rapid and comprehensive understanding of the current research status surrounding nitride coatings for implants.

Algal-Based Hollow Fiber Membrane Bioreactors for Efficient Wastewater Treatment: A Comprehensive Review

The treatment of living organisms is a critical aspect of various environmental and industrial applications, ranging from wastewater treatment to aquaculture. In recent years, algal-based hollow fiber membrane bioreactors (AHFMBRs) have emerged as a promising technology for the sustainable and efficient treatment of living organisms. This review provides a comprehensive examination of AHFMBRs, exploring their integration with algae and hollow fiber membrane systems for diverse applications. It also examines the applications of AHFMBRs in various areas, such as nutrient removal, wastewater treatment, bioremediation, and removal of pharmaceuticals and personal care products. The paper discusses the advantages and challenges associated with AHFMBRs, highlights their performance assessment and optimization strategies, and investigates their environmental impacts and sustainability considerations. The study emphasizes the potential of AHFMBRs in achieving enhanced nutrient removal, bioremediation, and pharmaceutical removal while also addressing important considerations such as energy consumption, resource efficiency, and ecological implications. Additionally, it identifies key challenges and offers insights into future research directions. Through a systematic analysis of relevant studies, this review aims to contribute to the understanding and advancement of algal-based hollow fiber membrane bioreactors as a viable solution for the treatment of living organisms.

A multidisciplinary approach to analyze the antimicrobial resistance in natural ecosystems

Antimicrobial Resistance (AMR) poses a global threat to both human health and environmental well-being. Our study delved into Costa Rican wildlife reserves, uncovering a substantial human impact on these ecosystems and underscoring the imperative to pinpoint AMR hotspots. Embracing a One Health perspective, we advocated for a comprehensive landscape analysis that intricately intertwined geographic, climatic, forest, and human factors. This study illuminated the link between laboratory results and observed patterns of antimicrobial use, thereby paving the way for sustainable solutions. Our innovative methodology involved deploying open-ended questions to explore antimicrobial usage across livestock activities, contributing to establishing a comprehensive methodology. Non-invasive sampling in wildlife emerged as a critical aspect, shedding light on areas contaminated by AMR. Feline species, positioned at the apex of the food chain, acted as sentinels for environmental health due to heightened exposure to improperly disposed waste. Regarding laboratory findings, each sample revealed the presence of at least one antimicrobial resistance gene (ARG). Notably, genes encoding resistance to tetracyclines dominated (94.9%), followed by beta-lactams (75.6%), sulfonamides (53.8%), aminoglycosides (51.3%), quinolones (44.9%), phenicols (25.6%), and macrolides (20.5%). Genes encoding polymyxins were not detected. Moreover, 66% of samples carried a multi-resistant microbiome, with 15% exhibiting resistance to three antimicrobial families and 51% to four. The absence of a correlation between forest coverage and ARG presence underscored the profound human impact on wildlife reserves, surpassing previous estimations. This environmental pressure could potentially modify microbiomes and resistomes in unknown ways. As not all antimicrobial families encoding ARGs were utilized by farmers, our next step involved evaluating other human activities to identify the primary sources of contamination. This comprehensive study contributed crucial insights into the intricate dynamics of AMR in natural ecosystems, paving the way for targeted interventions and sustainable coexistence.

Analysis of The Construction Management Application to Project Success Using Importance Performance Analysis on Superflat Concrete Floor Work

In pursuit of Indonesia's equitable and sustainable development, the surge in industrial city development, notably in Karawang, has led to the construction of various facilities such as factories, warehouses, and workshops. Efficient planning methods, encompassing cost, time, and quality considerations, are imperative for these projects. A critical aspect is the construction of concrete floors, vital for supporting diverse loads like heavy vehicles, static loads from goods, storage racks, and large machines. The ongoing development of the Superflat Concrete Floor Method, with its productivity and precision advantages, underscores the need for effective construction management. This study aims to evaluate the substantial impact of construction management on project success and to map application indicators on the IPA diagram – Importance Performance Analysis. Employing both quantitative and qualitative methods, the research distributed questionnaires with Likert scales to 30 respondents, including contractors, owners, and design planners. Smartpls 3.0 software analysis reveals that Knowledge Management significantly and positively influences the success of the superflat concrete floor project at the Karawang Industrial Factory site. The IPA mapping places Knowledge Management and Total Quality Management in Quadrant I (Keep Up the Good Work), emphasizing their pivotal roles in ensuring project success. This study provides essential insights for stakeholders involved in industrial development, guiding them toward more effective construction management strategies for optimized outcomes.

Energy consumption prediction of industrial HVAC systems using Bayesian Networks

Predicting energy consumption has become a critical issue for energy-intensive industrial contexts. A significant contribution to their overall energy load is due to the Heating Ventilation and Air Conditioning (HVAC) systems. This work, therefore, aims to validate the applicability of a probabilistic graphical approach, the Bayesian Network, in predicting the HVAC systems' energy consumption. As a data-driven approach, it is compared with more common AI-based models like Support Vector Machine, Artificial Neural Networks and Random Forest. The graphical approach ensures a better interpretation of the main factors determining the energy consumption and the relationships underlying these dependences. After an initial contextualisation and an analysis of the state of the art, the design methodology of a Bayesian network is investigated in detail, deepening in the various solutions for each step and evaluating their performance through the application on two industrial case studies. The results show that Bayesian networks, despite not always providing the best results, are a valid solution, trading off between simplicity, flexibility, and performance. Moreover, the possibility to provide a physical interpretation of the results is one of its main strengths. The critical aspect encountered, instead, is the need for discretisation, which strongly influences the quality of the results.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING TAX COMPLIANCE AND FINANCIAL REGULATION

Artificial Intelligence (AI) has emerged as a transformative force in various domains, including tax compliance and financial regulation. This review explores the pivotal role of AI in enhancing these critical aspects of governance and economic stability. In the realm of tax compliance, AI-driven solutions offer unprecedented opportunities for governments to streamline tax administration processes, detect non-compliance, and mitigate tax evasion. Machine learning algorithms can analyze vast volumes of financial data with remarkable speed and accuracy, identifying patterns indicative of tax fraud or evasion. Furthermore, AI-powered predictive analytics enable tax authorities to anticipate taxpayer behavior and allocate resources effectively for enforcement purposes. By leveraging AI, governments can enhance revenue collection efficiency while minimizing compliance burdens on taxpayers. In financial regulation, AI technologies play a crucial role in monitoring and enforcing compliance with complex regulatory frameworks. With the exponential growth of financial transactions and the increasing sophistication of financial instruments, traditional regulatory mechanisms often struggle to keep pace. AI systems equipped with natural language processing capabilities can sift through immense volumes of regulatory documents and financial data to identify potential violations and assess systemic risks. Moreover, AI-based risk assessment models enable regulators to proactively identify emerging threats to financial stability, thereby facilitating timely interventions to prevent crises. However, the integration of AI in tax compliance and financial regulation also presents challenges and ethical considerations. The reliance on algorithmic decision-making raises concerns regarding transparency, accountability, and bias mitigation. Moreover, the proliferation of AI-driven solutions may exacerbate existing socio-economic disparities, as access to advanced technology remains uneven across jurisdictions and economic strata. While AI holds immense promise for enhancing tax compliance and financial regulation, its implementation must be accompanied by robust governance frameworks and ethical guidelines. Collaborative efforts between policymakers, regulators, and technology developers are essential to harnessing the full potential of AI while safeguarding against unintended consequences. Through responsible deployment and continuous refinement, AI can serve as a powerful tool in promoting fiscal transparency, regulatory effectiveness, and economic resilience.
 Keywords: Artificial Intelligence, Tax, Financial, Compliance, Regulation, Review.

Gender disparities in thermal responses under vertical air temperature differences

The effects of vertical temperature differences (VTD) on human thermal comfort have attracted growing attention from researchers when utilizing split air conditioners for winter heating. This study underscores the need to investigate the influences of gender disparities in the context of significant vertical temperature variations, a critical aspect that has not yet been comprehensively explored. Twenty gender-balanced subjects were exposed to a variety of stratified thermal environments created by a split air-conditioner. Their overall and local perceptual and physiological responses were measured at various operative temperatures and VTD. Results show that females have lower thermal sensation vote (TSV) and mean skin temperature under the same environments compared to males. Neutral operative temperatures of females and males are 23.7 ˚C and 20.8 ˚C, respectively. On the cold side, lower leg and foot thermal sensations are dominant whereas chest and back are dominant on the warm side. When females rated “slightly warm” (TSV equaled 1) or “warm” (TSV equaled 2), or males rated “slightly cool” (TSV equaled −1), increased VTD can lower thermal acceptability. The research findings have significant implications for the understanding of gender requirements of stratified heating environments and for maximizing the thermal comfort of the two genders.

Procedures for transferring organizational knowledge during generational change: A systematic review

The loss of organizational knowledge has emerged as a prevalent issue for 21st-century organizations. This systematic review aims to scrutinize knowledge transfer procedures applied to individuals in managerial and intermediate positions during generational change or knowledge management initiatives. Following the PRISMA statement [1], this review progressed through four stages, applying inclusion and exclusion criteria, and ultimately identifying 28 articles for the final analysis. Descriptive indicators and content-related metrics were employed in the study. Key findings include: (1) predominant investment in knowledge retention studies and procedure design is observed among large companies, primarily in the secondary sector; (2) digitalization emerges as a critical aspect of effective organizational knowledge transfer procedures and protocols; (3) intra-organizational communication styles are predominantly employed for knowledge transfer; (4) organizations prefer a collective approach to transferring both tacit and explicit knowledge. In summary, this research offers fresh insights into a pivotal area of business management, showcasing originality in its exploration of knowledge transfer within the realms of generational change and knowledge management.

Microplastic accumulation dynamics and risk assessment in dried fish processed with sea salt at different salting ratios

Global food security, particularly in South Asia and low-income coastal regions like India, relies on seafood consumption, both fresh and dried. Seafood, encompassing small pelagic fish, dried varieties, and salt-cured fish, absorbs xenobiotics like micro/nanoplastics and their associated contaminants during processing. This occurs due to factors such as processing conditions, the status of fish muscle biochemistry, and the additives used, presenting largely unexplored health risks that have received minimal study attention. This study investigates the impact of salting on microplastics (MPs) contamination in cured fish, exploring the intricate relationship between salting, muscle biochemistry, and MPs under controlled conditions. Commercial solar salt used for curing contained an average of 40.72 ± 5.97 MPs/g. Microplastic abundance in cured fish decreased significantly, from 65.7 ± 6 to 3.36 ± 1.5 MPs items/g, with a lower salt-to-fish ratio of 1:10. Characteristics of MPs such as morphotype, size, colour and polymer type in both cured fish and curing salt exhibited similar trends. Protein content and MPs showed a strong negative correlation, while the lipid content correlation with MPs presence was weak. Various MPs indices such as contamination factor, load index and polymer induced risk index emphasized salt's significant role in enhancing contamination in cured fish, posing safety risks to consumers. Given the limited number of studies conducted on comprehending the uptake of microplastics in processed seafood, a critical aspect of food safety, the present study aims to illuminate the intricate interplay between fish biochemistry and microplastic characteristics. Additionally, it explores the role of salt in this dynamic, highlighting implications for both food safety and human health.

Maritime boundaries in the energy transition: carbon dioxide (CO2) storage across maritime jurisdictions

Governments around the world are looking to ensure their transition to full low-carbon economies whilst tackling climate change by implementing offshore carbon capture and storage (CCS) – a method of permanently removing carbon dioxide (CO2) from the burning of fossil fuels and burying it deep below the seabed in suitable geological formations. Among the main targets of such geological formations are depleted, or nearly depleted, offshore oil and gas reservoirs within existing oil-producing fields. However, such reservoirs will frequently transcend maritime boundaries and jurisdictions, ie a shared geological formation could be used on both sides of the maritime boundary for CO2 sequestration and storage. This raises the potential for disputes among nations, transboundary environmental degradation, and business disruption. The paper discusses the role of CCS in the low-carbon energy transition, focusing specifically on international legal aspects related to the storage of CO2 in sub-seabed geological formations of the continental shelf. Ongoing CO2 storage projects are examined to showcase critical technical aspects, including reservoir selection, well design, and post-injection monitoring. The analysis highlights the need for international coordination in situations where CO2 storage occurs across an international boundary and the importance of consent from all states with jurisdiction over the relevant CO2 storage formations. The paper concludes that existing maritime boundary agreements, including those with straddling deposit clauses, are unlikely to cover transboundary CO2 storage operations, except if they are considered incidental to ongoing petroleum operations.