Key Applications Research Articles

Optogenetic Tools for Regulating RNA Metabolism and Functions.

RNA molecules play a vital role in linking genetic information with various cellular processes. In recent years, a variety of optogenetic tools have been engineered for regulating cellular RNA metabolism and functions. These highly desirable tools can offer non-intrusive control with spatial precision, remote operation, and biocompatibility. Here, we would like to review these currently available approaches that can regulate RNAs with light: from non-genetically encodable chemically modified oligonucleotides to genetically encoded RNA aptamers that recognize photosensitive small-molecule or protein ligands. Some key applications of these optogenetic tools will also be highlighted to illustrate how they have been used for regulating all aspects of the RNA life cycle: from RNA synthesis, maturation, modification, and translation to their degradation, localization, and phase separation control. Some current challenges and potential practical utilizations of these RNA optogenetic tools will also be discussed.

Role of Fourier transform in quantum mechanics: applications and implications

The Fourier transform, as a fundamental mathematical tool, plays a pivotal role in quantum mechanics. Its significance extends to wave function analysis, solving the Schrdinger equation, and elucidating the relationship between position and momentum. In this review article, the primary objective is to summarize the diverse applications of the Fourier transform in quantum mechanics. This paper will delve into key applications, highlighting the uncertainty principle, the Planck-Einstein relation, and the Fourier transform solution of the Schrdinger equation. These theorems and relationships not only facilitate the mathematical manipulation of quantum states but also lay the theoretical foundation of quantum mechanics. By exploring these critical aspects, the author aims to provide a comprehensive understanding of how the Fourier transform underpins the core principles of quantum theory, offering valuable insights into the wave particle duality and the probabilistic nature of quantum systems. This discussion will emphasize the essential nature of the Fourier transform in both theoretical development and practical problem-solving within quantum mechanics

Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

One step from oxides to sustainable bulk alloys

Metallurgical production traditionally involves three steps: extracting metals from ores, mixing them into alloys by liquid processing and thermomechanical processing to achieve the desired microstructures1,2. This sequential approach, practised since the Bronze Age, reaches its limit today because of the urgent demand for a sustainable economy2–5: almost 10% of all greenhouse gas emissions are because of the use of fossil reductants and high-temperature metallurgical processing. Here we present a H2-based redox synthesis and compaction approach that reforms traditional alloy-making by merging metal extraction, alloying and thermomechanical processing into one single solid-state operation. We propose a thermodynamically informed guideline and a general kinetic conception to dissolve the classical boundaries between extractive and physical metallurgy, unlocking tremendous sustainable bulk alloy design opportunities. We exemplify this approach for the case of Fe–Ni invar bulk alloys6,7, one of the most appealing ferrous materials but the dirtiest to produce: invar shows uniquely low thermal expansion6,8,9, enabling key applications spanning from precision instruments to cryogenic components10–13. Yet, it is notoriously eco-unfriendly, with Ni causing more than 10 times higher CO2 emission than Fe per kilogram production2,14, qualifying this alloy class as a perfect demonstrator case. Our sustainable method turns oxides directly into green alloys in bulk forms, with application-worthy properties, all obtained at temperatures far below the bulk melting point, while maintaining a zero CO2 footprint.

Text analytics and new service development: a hybrid thematic analysis with systematic literature review approach

PurposeThis study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.Design/methodology/approachThis research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.FindingsThe study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.Originality/valueThis study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

Femtosecond pulse amplification on a chip

Femtosecond laser pulses enable the synthesis of light across the electromagnetic spectrum and provide access to ultrafast phenomena in physics, biology, and chemistry. Chip-integration of femtosecond technology could revolutionize applications such as point-of-care diagnostics, bio-medical imaging, portable chemical sensing, or autonomous navigation. However, current chip-integrated pulse sources lack the required peak power, and on-chip amplification of femtosecond pulses has been an unresolved challenge. Here, addressing this challenge, we report >50-fold amplification of 1 GHz-repetition-rate chirped femtosecond pulses in a CMOS-compatible photonic chip to 800 W peak power with 116 fs pulse duration. This power level is 2–3 orders of magnitude higher compared to those in previously demonstrated on-chip pulse sources and can provide the power needed to address key applications. To achieve this, detrimental nonlinear effects are mitigated through all-normal dispersion, large mode-area and rare-earth-doped gain waveguides. These results offer a pathway to chip-integrated femtosecond technology with peak power levels characteristic of table-top sources.

Nordic perspectives on the emerging biochar business

Biochar production offers various benefits related to climate change mitigation, circular economy, waste management, renewable energy, and reduced dependency on fossil carbon. Despite these advantages, the biochar market in the Nordic region is still developing. This study explores the current state and future potential of the Nordic biochar market, identifying existing and potential market segments, the role of biochar and its co-products, and factors affecting market growth. The study involved an online survey conducted in 2021, targeting key Nordic biochar stakeholders, including business actors, academic researchers, and other relevant groups (N = 72, representing 64 organizations). The findings reveal that the Nordic biochar market is in its nascent stages, with producers often considering biochar production as a side business. The market faces challenges such as inadequate legal and policy support, limited public awareness, lack of established norms, and uncertainties regarding profitability, technological efficiency, and market potential. However, the industry holds substantial growth potential due to its environmental and climate benefits, provided that current barriers are overcome. Key applications of biochar include carbon removal, water filtration, soil remediation, landscaping, and composting. Additionally, co-products such as energy-dense gases and bio-oil have the potential to enhance the economic viability of biochar production. To facilitate market development, integrating established scientific knowledge into industry standards and policies is crucial. The study underscores the importance of biochar networks and associations in advocating for industry development and highlights the need for enhanced collaboration among stakeholders to overcome existing barriers. Additionally, focused research on the varied applications of biochar is needed, with an emphasis on thoroughly evaluating its environmental, economic, and social impacts.

Application of Electrical Automation in Smart City

With the acceleration of urbanization, the concept of smart city came into being, aiming to improve the efficiency of urban management and the quality of life of residents through advanced information technology and integrated innovative technologies. Electrical automation is the foundation and support of smart city construction, and its intelligent and automated application is crucial to realize the functions of smart city. This article explores several key application areas of electrical automation in smart cities, including smart grids, smart buildings, smart transportation, smart security, etc., and analyzes how these technologies contribute to the development of smart cities, as well as the benefits they bring, and looks at future trends.

Polygenic scores and their applications in kidney disease.

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

A Review of Carbon Anode Materials for Sodium-Ion Batteries: Key Materials, Sodium-Storage Mechanisms, Applications, and Large-Scale Design Principles.

Sodium-ion batteries (SIBs) have been proposed as a potential substitute for commercial lithium-ion batteries due to their excellent storage performance and cost-effectiveness. However, due to the substantial radius of sodium ions, there is an urgent need to develop anode materials with exemplary electrochemical characteristics, thereby enabling the fabrication of sodium-ion batteries with high energy density and rapid dynamics. Carbon materials are highly valued in the energy-storage field due to their diverse structures, low cost, and high reliability. This review comprehensively summarizes the typical structure; energy-storage mechanisms; and current development status of various carbon-based anode materials for SIBs, such as hard carbon, soft carbon, graphite, graphene, carbon nanotubes (CNTs), and porous carbon materials. This review also provides an overview of the current status and future development of related companies for sodium-ion batteries. Furthermore, it offers a summary and outlook on the challenges and opportunities associated with the design principles and large-scale production of carbon materials with high-energy-density requirements. This review offers an avenue for exploring outstanding improvement strategies for carbon materials, which can provide guidance for future application and research.

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency. In this review, we provide an overview of the current landscape of image-based AI applications in psoriasis. Emphasis is placed on machine learning (ML) algorithms, a key subset of AI, which enable automated pattern recognition for various tasks. Key AI applications in psoriasis include lesion detection and segmentation, differentiation from other skin conditions, subtype identification, automated area involvement, and severity scoring, as well as personalized treatment selection and response prediction. Furthermore, we discuss two commercially available systems that utilize standardized photo documentation, automated segmentation, and semi-automated Psoriasis Area and Severity Index (PASI) calculation for patient assessment and follow-up. Despite the promise of AI in this field, many challenges remain. These include the validation of current models, integration into clinical workflows, the current lack of diversity in training-set data, and the need for standardized imaging protocols. Addressing these issues is crucial for the successful implementation of AI technologies in clinical practice. Overall, we underscore the potential of AI to revolutionize psoriasis management, highlighting both the advancements and the hurdles that need to be overcome. As technology continues to evolve, AI is expected to significantly improve the accuracy, efficiency, and personalization of psoriasis treatment.

Synthesis, Structure and Properties of Core‐Modified Calixporphyrinoids

AbstractCalix[n]porphyrinoids containing both sp2 and sp3 meso‐carbons can bind metals like porphyrinoids and anions like calix[n]pyrroles. Thus, calix[n]porphyrinoids are hybrid macrocycles of porphyrinoids and calix[n]pyrroles. The core‐modified calix[n]porphyrinoids resulted by replacing one or more pyrroles of calix[n]porphyrinoids with different heterocycles or carbacycles that possesses very attractive structural, spectral, and coordination properties. For the past two decades, several different types of core‐modified calix[n]porphyrinoids in which one or more pyrroles were replaced with other heterocycles such as thiophene, selenophene, tellurophene, silole, phosphole, pyridine, etc and carbacycles such as m/p‐phenylene have been synthesized and explored their structure, metal coordination and anion sensing properties. This review describes different synthetic strategies used for the synthesis of various types of core‐modified calix[4]phyrins and expanded calix[n]phyrins and discusses their key structural aspects, properties, coordination chemistry and applications.

From tradition to technological advancement: embracing blockchain technology in family businesses

PurposeDespite the rapid advancement of blockchain technology across various sectors, scholarly research on its application within family businesses remains significantly underdeveloped. This study aims to address this gap by examining the application of blockchain technology within family businesses to identify key application domains, benefits and implementation challenges.Design/methodology/approachThe study employs a conceptual approach, drawing on existing literature on family businesses and blockchain technology. This review aimed to identify the unique characteristics of family businesses, their challenges and the distinctive features of blockchain technology that can potentially be mapped to each other. Based on the literature review, we develop a conceptual framework exploring blockchain technology applications in family businesses. Real-world case studies of family businesses that have implemented blockchain technology were identified to provide practical insights and implementation challenges.FindingsBlockchain technology possesses transformative potential for family businesses across several critical domains. It includes enhancing trust and transparency in operations, improving governance and decision-making and facilitating succession planning and intergenerational wealth management. Case study evidence illustrates the tangible benefits of blockchain, including enhanced supply chain transparency, optimized business processes, increased customer trust and resultant business sustainability. Blockchain technology implementation challenges include data privacy concerns, integration with legacy systems, regulatory uncertainty and change management issues.Research limitations/implicationsThis study is limited by its reliance on existing literature and case studies. It may not capture the full spectrum of challenges and opportunities associated with blockchain applications in family businesses. Future research should focus on longitudinal and empirical research to provide a deeper understanding of the impact of blockchain technology application in family businesses.Originality/valueThis study contributes to the literature by exploring the intersection of family businesses and blockchain technology, an area that has received limited academic attention. It identifies potential application domains of blockchain technology in family businesses and develops a conceptual framework based on existing literature. Through case studies, the research provides practical insights and valuable lessons for family businesses considering blockchain implementation. It also addresses key considerations and challenges, providing a clear roadmap for blockchain technology integration in family businesses. The study lays the groundwork for further research and exploration in blockchain technology and family businesses.

Modular Synthesis of Azines Bearing a Guanidine Core from N-Heterocyclic Carbene (NHC)-Derived Selenoureas and Diazo Reagents.

N-Heterocyclic carbene (NHC)-derived selenoureas comprise a fundamentally important class of NHC derivatives, with key applications in coordination chemistry and the determination of NHC electronic properties. Considering the broad reactivity of chalcogen-containing compounds, it is surprising to note that the use of NHC-derived selenoureas as organic synthons remains essentially unexplored. The present contribution introduces a novel, straightforward transformation leading to azines bearing a guanidine moiety, through the reaction of a wide range of NHC-derived selenoureas with commercially available diazo compounds, in the presence of triphenylphosphine. This transformation offers a new approach to such products, having biological, materials chemistry, and organic synthesis applications. The guanidine-bearing azines are obtained in excellent yields, with all manipulations taking place in air. A reaction mechanism is proposed, based on both experimental mechanistic findings and density functional theory (DFT) calculations. A one-pot, multicomponent transesterification reaction between selenoureas, α-diazoesters, alcohols, and triphenylphosphine was also developed, providing highly functionalized azines.

Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

A tutorial on optical photothermal infrared (O-PTIR) microscopy.

This tutorial reviews the rapidly growing field of optical photothermal infrared (O-PTIR) spectroscopy and chemical imaging. O-PTIR is an infrared super-resolution measurement technique where a shorter wavelength visible probe is used to measure and map infrared (IR) absorption with spatial resolution up to 30× better than conventional techniques such as Fourier transform infrared and direct IR laser imaging systems. This article reviews key limitations of conventional IR instruments, the O-PTIR technology breakthroughs, and their origins that have overcome the prior limitations. This article also discusses recent developments in expanding multi-modal O-PTIR approaches that enable complementary Raman spectroscopy and fluorescence microscopy imaging, including wide-field O-PTIR imaging with fluorescence-based detection of IR absorption. Various practical subjects are covered, including sample preparation techniques, optimal measurement configurations, use of IR tags/labels and techniques for data analysis, and visualization. Key O-PTIR applications are reviewed in many areas, including biological and biomedical sciences, environmental and microplastics research, (bio)pharmaceuticals, materials science, cultural heritage, forensics, photonics, and failure analysis.

Key technologies and applications of collaboration between digital power grid and Internet of Things

AbstractThe integration of electricity technology and information technology, such as the Internet of Things (IoT), enables the construction of new power systems, along with the innovation of application scenarios and business scope. The key technologies of power IoT and the data flow process are summarised first. The IoT technology and application scenario requirements of power generation, transmission, loading, and storage of new power systems are studied. Thus, the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems. The key problems and solutions faced by the power IoT under the digital transformation are described, and the cross‐integration of key technologies and promotion of application scenario innovation are prospected. Finally, the key issues of future technological development were discussed, providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.

Diagnostic cytology: Key techniques and applications in disease diagnosis

Diagnostic cytology: Key techniques and applications in disease diagnosis