Comprehensive Comparative Analysis Research Articles

Evaluation of machine learning and deep learning methods for early detection of internet of things botnets

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.

Strategic Siting of Direct Air Capture Facilities in the United States

Direct air capture (DAC) systems that capture carbon dioxide (CO2) directly from the atmosphere are garnering considerable attention for their potential role as negative emission technologies in achieving net-zero CO2 emission goals. Common DAC technologies are based either on liquid–solvent (L-DAC) or solid–sorbent (S-DAC) to capture CO2. A comprehensive multi-factor comparative economic analysis of the deployment of L-DAC and S-DAC facilities across various United States (U.S.) cities is presented in this paper. The analysis considers the influence of various factors on the favorability of DAC deployment, including local climatic conditions such as temperature, humidity, and CO2 concentrations; the availability of energy sources to power the DAC system; and costs for the transport and storage of the captured CO2 along with the consideration of the regional market and policy drivers. The deployment analysis in over 70 continental U.S. cities shows that L-DAC and S-DAC complement each other spatially, as their performance and operational costs vary in different climates. L-DAC is more suited to the hot, humid Southeast, while S-DAC is preferrable in the colder, drier Rocky Mountain region. Strategic deployment based on regional conditions and economics is essential for promoting the commercial adoptability of DAC, which is a critical technology to meet the CO2 reduction targets.

Enhancing healthcare data privacy and security: A comparative study of regulations and best practices in the US and Nigeria

The imperative for robust healthcare data privacy and security is escalating as healthcare systems worldwide are increasingly digitized. This review paper presents a comprehensive comparative analysis of the regulatory frameworks, challenges, and best practices related to healthcare data privacy and security in the United States and Nigeria. By examining the Health Insurance Portability and Accountability Act (HIPAA) in the US and the Nigeria Data Protection Regulation (NDPR) alongside other local regulations, this study highlights the nuances of each country's approach to safeguarding patient data. The analysis extends to the effectiveness of technological solutions like encryption and blockchain, and assesses the role of governance in policy implementation. Case studies from both nations offer insights into successful strategies and underscore the gaps and opportunities for cross-country learning and improvement. The paper concludes with targeted recommendations for policymakers and healthcare providers, aiming to strengthen the security measures and propose areas for further research and development in healthcare data management. This comparative study not only sheds light on current practices but also charts a course for future collaborative efforts to enhance data privacy and security in healthcare on a global scale.

Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol

The Industrial Internet of Things (IIoT) and Smart Manufacturing environments face significant challenges in machine identity authentication, crucial for ensuring data integrity and cybersecurity. Traditional methods like One-Class Support Vector Machines (OCSVM) often fall short in sensitivity, specificity, and explainability. This research introduces a novel Functional Data Analysis (FDA)-based model that leverages unique temporal patterns in machine-generated data to authenticate machine identities, adapting to equipment wear and tear. Through a comprehensive comparative analysis, the FDA model demonstrates superior performance and robustness in industrial settings. Additionally, we propose integrating this FDA-based approach with the MQTT 5.0 protocol, enhancing data security in IIoT communications. This work not only advances IIoT cybersecurity but also supports digital twins and predictive maintenance, contributing to a more secure and efficient Smart Manufacturing ecosystem.

Triterpenoid and trichothecenes from a rhizospheric soil-derived Paramyrothecium sp. KMU22107

A new phthalide derivative named paramlyktone (1) and a new arborinane-type triterpenoid named paramyrpenoid (2), together with ten previously described trichothecenes derivatives (3–12) were isolated and identified from a rhizospheric soil-derived Paramyrothecium sp. KMU22107 associated with Delphinium yunnanense. Their structural elucidation was achieved by the comprehensive analysis of spectroscopic data and comparison with literature values. Notably, paramyrpenoid (2) was the first example of an arborinane-type triterpenoid with a double bond at Δ12(13) and an additional methyl motif at C-8. This was the first report of arborinane-type triterpenoids from a fungus belonging to Paramyrothecium genus. In pharmacological studies, paramyrpenoid (2) demonstrated significant cytotoxic activity against the HL-60, SW480, A-549, MDA-MB-231 and SMMC-7721 cell lines, with IC50 values from 2.0 to 16.1 μM. Compounds 1 and 2 were also evaluated for anti-inflammatory, anti-acetylcholinesterase (AChE), and protein tyrosine phosphatase 1B (PTP1B) inhibitory activities in vitro.

Optimal robust sizing of distributed energy storage considering power quality management

AbstractThis paper proposes an optimal robust sizing model for distributed energy storage systems (DESSs) considering power quality management. The power conversion systems (PCSs) of DESSs with four‐quadrant operation characteristics can provide power quality management services to customers. To improve capacity utilization of the DESS, power quality management services are quantified and integrated into an optimal bi‐level sizing model, where the upper level addresses the sizing problem concerning battery and PCS capacities, while the lower level focuses on coordinating active/reactive power control of the DESS. A robust optimization approach for DESS scheduling is adopted to consider uncertainties of distributed photovoltaic (PV) power generation and power quality management requirements. The column and constraint generation (C&CG) algorithm is applied for efficient handling of this model. Ultimately, the effectiveness of the proposed approach is validated through comprehensive comparative analysis of four cases, resulting in a 12.44% increase in the net present value (NPV) over the entire lifecycle.

Experimental investigation of Catalan vault structures based on earthen materials

The Catalan vaulting have succeeded in reducing reliance on support centers, resulting in lowered costs and expedited construction processes. This research advances the study of Catalan vaulting by exploring innovative, environmentally friendly earth-based materials. The study conducts a comprehensive comparative analysis of load-bearing behaviors across distinct vault elements: those fabricated from terracotta, raw-earth tiles, and shot-earth. Constructed and tested at a 4-meter span, full-scale vault specimens are subjected to varying distributed load configuration, followed by rupture testing employing a linear load at a quarter of the span. Experimental evidence, corroborated by FE results, indicates that both terracotta and raw earth tile vaults offer commendable performance, yet their failure loads are surpassed by vaults constructed using shot-earth. Therefore, shot-earth emerges as a sustainable alternative for constructing vault elements. Furthermore, the study demonstrates that incorporating reinforcement within shot-earth vaults increases their strength and ductility.

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.

Invasion of the stigma by oomycete pathogenic hyphae or pollen tubes : striking similarities and differences.

Both filamentous pathogens' hyphae and pollen tube penetrate the host's outer layer and involve growth within the host tissues. Early epidermal responses are decisive for the outcome of these two-cell interaction processes. We identified a single cell type, the papilla of Arabidospis thaliana's stigma, as a tool to conduct a comprehensive comparative analysis on how an epidermal cell responds to the invasion of an unwanted pathogen or a welcomed pollen tube. We showed that Phytophtora parasitica, a root oomycete, effectively breaches the stigmatic cell wall and develops as a biotroph within the papilla cytoplasm. These invasive features resemble the behaviour exhibited by the pathogen within its natural host cells, but diverge from the manner in which the pollen tube progresses, being engulfed within the papilla cell wall. Quantitative analysis revealed that both invaders trigger reorganisation of the stigmatic endomembrane system and the actin cytoskeleton. While some remodelling processes are shared between the two interactions, others appear more specific towards the respective invader. These findings underscore the remarkable ability of an epidermal cell to differentiate between two types of invaders, thereby enabling it to trigger the most suitable response during the onset of invasion.

Phylogenetic variations in a novel family of hyperstable apple snail egg proteins: insights into structural stability and functional trends.

The relationship between protein stability and functional evolution is little explored in proteins purified from natural sources. Here we investigate a novel family of egg proteins (Perivitellin-1, PV1) from Pomacea snails. Their remarkable stability and clade-related functions in most derived clades (Canaliculata and Bridgesii) make them excellent candidates for exploring this issue. To that aim, we studied PV1 (PpaPV1) from the most basal lineage, Flagellata. PpaPV1 displays unparalleled structural and kinetic stability, surpassing PV1s from derived clades, ranking among the most hyperstable proteins documented in nature. Its spectral features contribute to a pale egg coloration, exhibiting a milder glycan binding lectin activity with a narrower specificity than PV1s from the closely related Bridgesii clade. These findings provide evidence for substantial structural and functional changes throughout the genus' PV1 evolution. We observed that structural and kinetic stability decreases in a clade-related fashion and was associated with large variations in defensive traits. For instance, pale PpaPV1 lectin turns potent in the Bridgesii clade adversely affecting gut morphology, while giving rise to brightly colored PV1s providing eggs with a conspicuous, likely warning signal in the Canaliculata clade. This work provides a comprehensive comparative analysis of PV1s from various apple snail species within a phylogenetic framework, offering insights into the interplay among their structural features, stability profiles, and functional roles. More broadly, our work provides one of the first examples from natural evolution showing the crucial link among protein structure, stability, and evolution of new functions.

Legal frameworks for digital transactions: Analyzing the impact of Blockchain technology

This study explores the legal frameworks governing digital transactions, with a specific focus on the transformative impact of blockchain technology. The primary aim is to elucidate the complexities and challenges posed by blockchain while examining the diverse regulatory approaches adopted internationally. Through a comprehensive literature review and comparative analysis, the research addresses key aspects such as the conceptual framework of digital transactions, the unique characteristics of blockchain, and the regulatory strategies implemented across different jurisdictions. The findings reveal that blockchain technology, characterized by its decentralized, immutable, and transparent nature, significantly disrupts traditional regulatory models. Identified challenges include jurisdictional ambiguities, enforcement difficulties, and privacy concerns. The comparative analysis shows divergent regulatory approaches: supportive frameworks in Japan and Switzerland contrast sharply with restrictive measures in China, highlighting the necessity for international cooperation and harmonization of regulations. The study concludes that effective regulation of blockchain technology requires innovative and flexible legal frameworks capable of adapting to rapid technological advancements. Policymakers must balance fostering innovation and protecting public interests, emphasizing the need for privacy-preserving technologies and international standards. Recommendations include developing global regulatory standards, enhancing privacy measures, and creating legal frameworks that accommodate the decentralized nature of blockchain systems. This research provides valuable insights for regulators, policymakers, and stakeholders, offering a pathway towards a secure, transparent, and innovative digital economy. Continuous adaptation and international collaboration are imperative to address emerging challenges and fully harness the potential of blockchain technology. The study advocates for proactive engagement and cooperation among nations to create a cohesive regulatory environment that promotes innovation while safeguarding public interests, enabling the global community to navigate the complexities of blockchain technology and unlock its full potential for economic and social advancement. Keywords: Blockchain Technology, Digital Transactions, Legal Frameworks, Regulatory Challenges, International Cooperation, Privacy Concerns.

A comparative metabolomic analysis reveals the metabolic variations among cartilage of Kashin-Beck disease and osteoarthritis.

The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA. Cartilage samples from patients with KBD (n = 10) and patients with OA (n = 10) were collected during total knee arthroplasty surgery. An untargeted metabolomics approach using liquid chromatography coupled with mass spectrometry (LC-MS) was conducted to investigate the metabolomics profiles of KBD and OA. LC-MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with R software. The online Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the metabolites by matching the exact molecular mass data of samples with those from the database. A total of 807 ion features were identified for KBD and OA, including 577 positive (240 for upregulated and 337 for downregulated) and 230 negative (107 for upregulated and 123 for downregulated) ions. After annotation, LC-MS identified significant expressions of ten upregulated and eight downregulated second-level metabolites, and 183 upregulated and 162 downregulated first-level metabolites between KBD and OA. We identified differentially expressed second-level metabolites that are highly associated with cartilage damage, including dimethyl sulfoxide, uric acid, and betaine. These metabolites exist in sulphur metabolism, purine metabolism, and glycine, serine, and threonine metabolism. This comprehensive comparative analysis of metabolism in OA and KBD cartilage provides new evidence of differences in the pathogenetic mechanisms underlying cartilage damage in these two conditions.

A Simulation Approach for Analysis of the Regenerative Potential of High-Speed Train Suspensions

This study primarily investigates the adaptability and performance of hydraulic–electric regenerative dampers for high-speed trains by substituting conventional primary dampers with hydraulic–electric regenerative dampers. The primary objectives are to develop a detailed model of primary suspension regenerative damper (PSRD) energy conversion that incorporates factors such as oil pressure loss, motor efficiency, and overall system efficiency, and to perform a comprehensive comparative analysis of vibration responses, wheel wear, comfort indices, and power generation using an integrated MATLAB and SIMPACK co-simulation platform. The results reveal that at an operational speed of 350 km/h, the dynamic responses of the carbody, bogie, wheelset, and dampers equipped with the proposed PSRD systems closely align with those of conventional primary vertical damper systems. The detailed PSRDs’ hydraulic–mechanical–electrical model effectively captures the subtleties of oil pressure fluctuations and their impacts. The wear distribution and magnitude across the vehicle remain consistent during acceleration, constant, and deceleration speeds, ensuring uniform wear characteristics. Under real-world railway operational conditions, the ride comfort metrics of vehicles fitted with regenerative dampers are comparable to those with conventional primary vertical dampers. Furthermore, each regenerative damper can generate up to 21.72 W of electrical power, achieving a generation efficiency of 45.28%. Finally, a test rig was designed and fabricated to validate the primary suspension regenerative damper (PSRD) model, showing good agreement between predicted and actual damping force and power regeneration, with results indicating a peak damping force of 12.5 kN and approximately 230 W of regenerated power. This research provides a theoretical foundation and experimental validation for implementing power regeneration mechanisms in railway transportation, demonstrating that the hydraulic–mechanical–electrical PSRD model can fulfil the performance criteria of conventional dampers while offering substantial energy harvesting capabilities. This advancement not only enhances energy efficiency but also contributes to the sustainable development of high-speed rail systems.

Quantum phase transitions in one-dimensional nanostructures: a comparison between DFT and DMRG methodologies.

In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density functional theory (DFT) and density matrix renormalization group (DMRG) have emerged as two powerful computational methods for addressing electronic correlation effects in diverse molecular systems. We compare ground-state energies ( ), density profiles ( ), and average entanglement entropies ( ) in metals, insulators and at the transition from metal to insulator, in homogeneous, superlattices, and harmonically confined chains described by the fermionic one-dimensional Hubbard model. While for the homogeneous systems, there is a clear hierarchy between the deviations, , and all the deviations decrease with the chain size; for superlattices and harmonic confinement, the relation among the deviations is less trivial and strongly dependent on the superlattice structure and the confinement strength considered. For the superlattices, in general, increasing the number of impurities in the unit cell represents lower precision in the DFT calculations. For the confined chains, DFT performs better for metallic phases, while the highest deviations appear for the Mott and band-insulator phases. This work provides a comprehensive comparative analysis of these methodologies, shedding light on their respective strengths, limitations, and applications. The DFT calculations were performed using the standard Kohn-Sham scheme within the BALDA approach. It integrated the numerical Bethe-Ansatz (BA) solution of the Hubbard model as the homogeneous density functional within a local-density approximation (LDA) for the exchange-correlation energy. The DMRG algorithms were implemented using the ITensor library, which is based on the matrix product states (MPS) ansatz. The calculations were performed until the energy reaches convergence of at least .

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

AbstractThe increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

Comparative genomic analysis of nickel homeostasis in cable bacteria

BackgroundCable bacteria are filamentous members of the Desulfobulbaceae family that are capable of performing centimetre‑scale electron transport in marine and freshwater sediments. This long‑distance electron transport is mediated by a network of parallel conductive fibres embedded in the cell envelope. This fibre network efficiently transports electrical currents along the entire length of the centimetre‑long filament. Recent analyses show that these fibres consist of metalloproteins that harbour a novel nickel‑containing cofactor, which indicates that cable bacteria have evolved a unique form of biological electron transport. This nickel‑dependent conduction mechanism suggests that cable bacteria are strongly dependent on nickel as a biosynthetic resource. Here, we performed a comprehensive comparative genomic analysis of the genes linked to nickel homeostasis. We compared the genome‑encoded adaptation to nickel of cable bacteria to related members of the Desulfobulbaceae family and other members of the Desulfobulbales order.ResultsPresently, four closed genomes are available for the monophyletic cable bacteria clade that consists of the genera Candidatus Electrothrix and Candidatus Electronema. To increase the phylogenomic coverage, we additionally generated two closed genomes of cable bacteria: Candidatus Electrothrix gigas strain HY10‑6 and Candidatus Electrothrix antwerpensis strain GW3‑4, which are the first closed genomes of their respective species. Nickel homeostasis genes were identified in a database of 38 cable bacteria genomes (including 6 closed genomes). Gene prevalence was compared to 19 genomes of related strains, residing within the Desulfobulbales order but outside of the cable bacteria clade, revealing several genome‑encoded adaptations to nickel homeostasis in cable bacteria. Phylogenetic analysis indicates that nickel importers, nickel‑binding enzymes and nickel chaperones of cable bacteria are affiliated to organisms outside the Desulfobulbaceae family, with several proteins showing affiliation to organisms outside of the Desulfobacterota phylum. Conspicuously, cable bacteria encode a unique periplasmic nickel export protein RcnA, which possesses a putative cytoplasmic histidine‑rich loop that has been largely expanded compared to RcnA homologs in other organisms.ConclusionCable bacteria genomes show a clear genetic adaptation for nickel utilization when compared to closely related genera. This fully aligns with the nickel‑dependent conduction mechanism that is uniquely found in cable bacteria.

Eye morphology contributes to the ecology and evolution of the aquatic avifauna.

Aquatic birds are notable among the global avifauna for living in environments exposed to large amounts of light. Despite growing evidence that visual adaptations to light underly the ecology and evolution of the avian tree of life, no comprehensive comparative analysis of visual acuity as approximated by eyes size exists for the global aquatic avifauna. Here, I use Stanley Ritland's unpublished dataset of measurements for axial length collected from museum specimens to explore the ecology and evolution of eye size variation for half of the aquatic avifauna (N = 464 species). After correcting for body mass allometry and incorporating phylogenetic relationships, aquatic species had significantly smaller eyes compared to terrestrial species. Furthermore, species using hyperopic foraging manoeuvres, exhibiting carnivorous and insectivorous diets, and displaying nocturnal behaviour had larger eyes. Plunge-divers (e.g. boobies and tropic birds) and stalkers (e.g. herons) had the largest relative eye sizes, especially species identifying prey at higher altitudes or longer distances. Underwater pursuit-divers foraging at greater depths had larger eyes, likely due to the dramatic attenuation of light in the deep ocean. Overall, residual eye size was phylogenetically conserved (l = 0.94), with phylogeny alone explaining 62% of residual eye size variation. Collectively, these results suggest that the relatively bright environments found in aquatic ecosystems negate the adaptive benefits of costly metabolic investments associated with developing and maintaining larger eyes, while also reducing the potential occurrence of disability glare. Strong correlations between eye size and foraging ecology in different aquatic environments corroborate similar comparative studies of terrestrial birds and underscore the central role that vision has played in driving the ecology and evolution of the global avifauna.

A Novel High-Gain Switched-Capacitor Multilevel Inverter with Reduced Components for Grid Integration

This paper introduces a novel Multi-Level Inverter (MLI) design which utilizes a single input and leverages capacitor voltages source to generate a four-fold increase in output voltage as the main problem that stays with the inverters is their low boost ability and efficiency while maintaining power quality at the same time. One capacitor is charged to match the input voltage magnitude, while the other two capacitors store twice this magnitude. Through a series-parallel combination with switching operations, all capacitors are effectively charged and discharged within each cycle, ensuring natural voltage balance. A comprehensive comparative analysis is conducted to highlight the advantages of this innovative approach, particularly in terms of component reduction and mitigation of voltage stress. Detailed assessment of power losses within the proposed circuit is undertaken, simulation studies are first carried out while extensive experimentation verifies its operational efficiency under diverse conditions such as varying modulation indices, loads, and supply-side fluctuations with an impressive maximum efficiency of 96.9 % at a 200 W power rating, our research contributes to advancing compact power converters, addressing crucial challenges in modern power electronics applications, and paving the way for enhanced performance and reliability in such systems.

Integrated analysis of DNA methylome and transcriptome revealing epigenetic regulation of CRIR1-promoted cold tolerance

BackgroundDNA methylation contributes to the epigenetic regulation of nuclear gene expression, and is associated with plant growth, development, and stress responses. Compelling evidence has emerged that long non-coding RNA (lncRNA) regulates DNA methylation. Previous genetic and physiological evidence indicates that lncRNA-CRIR1 plays a positive role in the responses of cassava plants to cold stress. However, it is unclear whether global DNA methylation changes with CRIR1-promoted cold tolerance.ResultsIn this study, a comprehensive comparative analysis of DNA methylation and transcriptome profiles was performed to reveal the gene expression and epigenetic dynamics after CRIR1 overexpression. Compared with the wild-type plants, CRIR1-overexpressing plants present gained DNA methylation in over 37,000 genomic regions and lost DNA methylation in about 16,000 genomic regions, indicating a global decrease in DNA methylation after CRIR1 overexpression. Declining DNA methylation is not correlated with decreased/increased expression of the DNA methylase/demethylase genes, but is associated with increased transcripts of a few transcription factors, chlorophyll metabolism and photosynthesis-related genes, which could contribute to the CRIR1-promoted cold tolerance.ConclusionsIn summary, a first set of transcriptome and epigenome data was integrated in this study to reveal the gene expression and epigenetic dynamics after CRIR1 overexpression, with the identification of several TFs, chlorophyll metabolism and photosynthesis-related genes that may be involved in CRIR1-promoted cold tolerance. Therefore, our study has provided valuable data for the systematic study of molecular insights for plant cold stress response.

Evolution of heritage and development in Liverpool's waterfront over 40 years

The recent delisting of Liverpool’s World Heritage Site has highlighted the adverse impacts of ongoing urban developments on the city’s heritage. This study aims to address the critical question of how Liverpool can safeguard its heritage following the loss of its World Heritage status. As this status withdrawal implies a lack of international policy consideration, it raises important questions: What additional measures should be implemented? How could we reassess Liverpool's heritage in this new context? To tackle these issues, we investigate the changing relationship between safeguarding heritage and urban development on Liverpool Waterfront’s Historic Urban Landscape over the past 40 years. This study analyses the impact of developments on Liverpool’s heritage values, specifically focusing on Integrity, Authenticity, and Outstanding Universal Value. By employing a novel, top-down heritage impact assessment methodology, we evaluate the influence of urban and architectural developments on these values. This methodology is distinguished by its comprehensive comparative analysis of diverse elements drawn from heritage policy documents, spanning both tangible and intangible domains. In addition, we conducted interviews with experts familiar with Liverpool's heritage context to deepen our understanding of developmental impacts. The analysis framework comprises three case study areas and their associated developments: the adaptive reuse of the Albert Dock Complex (1981–1988), the Pier Head Developments (2007–2013), and the Stanley Dock Conservation Area (2021 onwards). The findings indicate that adaptive reuse schemes have been reasonably successful in preserving heritage values, whereas certain urban interventions have had detrimental effects. This study underscores the need for stricter regulations and broader stakeholder involvement to protect Liverpool's heritage. Further research is recommended, incorporating more development case studies and examining successful urban interventions in other heritage cities, to strengthen Liverpool’s heritage safeguarding strategies amidst evolving urban development pressures.