Genome editing techniques, especially clustered regularly interspaced short palindromic repeats (CRISPR), brought researchers into a new era of molecular plant breeding because it enabled them to make targeted modifications in plant genomes and transcriptomes. However, the successful incorporation of large DNA segments into plant genomes, necessary for high genetic gains and desired traits, remains a critical challenge. As there is an increasing demand for technologies that support chromosomal integration of large DNA inserts suitable for application in synthetic biology and plant breeding, PrimeRoot editors have presented a revolutionary solution to this issue, as they integrate enhanced prime editing guide RNA (PegRNA) designs, improved plant prime editor systems and advanced recombinases, helping in the precise insertion of DNA fragments of up to 11.1kb into the plant genomes. Third-generation PrimeRoot editors further enhanced the precision and efficiency of transformation under different gene delivery systems. This technology holds enormous promise for accurately inserting long DNA sequences across different species of plants. This review highlights expected developments, opportunities, applications, advantages and challenges associated with PrimeRoot editors as well as the significance of expanding their applicability to more varieties of plants.

Renewable hydrogen is increasingly promoted as a key component of sustainable low-emission energy systems; however, its realistic role remains highly dependent on national system conditions. This review examines under what circumstances renewable hydrogen can effectively contribute to Poland’s low-emission energy transition, given its coal-dominated electricity mix, energy-intensive industrial structure, and evolving regulatory environment. The article adopts a system-oriented review approach that integrates recent European Union and national policy developments, including RED III and related delegated acts, with technological pathways, infrastructure readiness, safety considerations, and sectoral demand. Particular attention is given to electricity–hydrogen–industry coupling and the system-level conditions that determine the technical feasibility, efficiency losses, and economic viability of renewable hydrogen deployment. The review demonstrates that renewable hydrogen in Poland is unlikely to become a universal decarbonization solution. Its effective deployment is conditional on accelerated renewable electricity expansion, coordinated development of hydrogen transport and storage infrastructure, and regulatory alignment with EU frameworks. In the short and medium term, the highest system value lies in substituting fossil-based hydrogen in existing industrial applications, while in the longer-term hydrogen may support system flexibility and the decarbonization of hard-to-electrify sectors. Technology-neutral policy approaches may facilitate early market formation but risk reinforcing technology lock-in effects if maintained in the long term. These findings suggest that renewable hydrogen should be positioned as a complementary element of Poland’s low-emission energy system, requiring targeted, system-integrated policy and investment strategies rather than broad, technology-neutral deployment.

ABSTRACT Emerging wireless technologies demand high bit rate with lower latency and a guaranteed quality-of-service. Such technologies support real-time applications in fifth-generation and beyond networks under various delay constraints. In Shannon’s capacity, it is not assumed that no delays are introduced in the system. The effective bit rate (EBR) of delay-sensitive communication systems operating over shadowed Beaulieu–Xie (SBX) fading channels is analysed. SBX is a composite fading channel suitable for describing a channel encountered by millimetre-wave and terahertz wireless technologies. The mathematical expressions of EBR over SBX fading channels are expressed by using the probability density function method. The EBR analysis covers the influence of different fading and shadowing conditions, as well as varying delay constraints. The truncation error expression for the corresponding EBR expression is also derived. Further, the asymptotic expressions for high signal-to-noise ratio (SNR) as well as low SNR regimes are also derived and analysed to reveal the nature of the system. It also includes a comparison between capacity with no delays and capacity with delay constraint on the system.

Driven by the demand for green ammonia and sustainable energy technologies, electrochemical ammonia synthesis has attracted increasing attention due to its mild operating conditions, compatibility with renewable energy, and low-carbon potential. However, prior studies have focused primarily on catalyst development, while systematic analyses of reactor engineering remain limited, constraining industrial translation. This review provides a comparative examination of electrochemical ammonia synthesis reactors, including single-chamber and H-type electrolyzers, continuous-flow reactors, and membrane electrode assemblies, with an emphasis on mass transfer, current density, faradaic efficiency, and scalability. Key reactor engineering strategies-such as interface optimization, three-phase regulation, and membrane and flow-channel design-are summarized. Finally, future perspectives are discussed, highlighting durable catalysts, stable and low-cost membranes, modular continuous reactors, and integration with renewable energy systems to enable efficient, low-carbon, and scalable ammonia production.

T4 DNA ligase-mediated RAA coupled with RNA aptamer-driven cascade signal amplification for ultra-sensitive monkeypox virus detection.

Interfacial-stabilized quasi-solid-state Li-metal batteries enabled by electrospun eLATP nanosheets composite electrolyte.

Abstract The demand for advanced photonic technologies to enhance augmented reality (AR) devices has led to a significant interest in new optical materials suitable for operation in sometimes challenging environmental conditions, such as elevated temperatures levels. Holographic optical lenses (HOLs), fabricated using holographic recording, offer several advantages over traditional refractive lenses, including greater versatility, lower cost, lower weight, and increased adaptability. In this study, we investigated the optical and thermal performance of HOLs recorded on four different photosensitive materials: Bayfol HX200, Bayfol HX120, a PVA/acrylamide-based photopolymer (AA/PVA), and a photopolymerisable hybrid sol–gel (PHSG). The lenses were exposed to thermal cycles 20 ∘ C–100 ∘ C–20 ∘ C to simulate real-world conditions in AR systems, where heat generation from processors, sensors, displays, and the proximity to poweful LED light sources can affect their optical performance. The results highlight that Bayfol HX200 showed the highest thermal stability, while the PHSG demonstrated good recovery properties after thermal stress. Optical and image quality were evaluated using parameters such as root mean square wavefront error, Strehl ratio and spatial resolution, which revealed significant variations in lens performance as a function of temperature. These results provide valuable information on the material selection for durable, high-performance holographic lenses in AR applications.

Recent advances and future perspectives in ceramic-based nanofiltration membranes: Material innovations, applications, and sustainability challenges.

Recent advances in lithium extraction from brine via solar-driven interfacial evaporation: Advanced strategies and challenges.

Mixed reality (MR) devices, such as Microsoft HoloLens, offer immersive and interactive learning opportunities that can enhance vocational education. While MR technologies are increasingly used in K–12 settings, their application in vocational schools remains limited, despite the potential to improve workplace readiness of graduates in alignment with Industry 5.0 standards. This study investigates the effectiveness of MR in automotive vocational schools using a quasi-experimental pre-test/post-test design to measure student performance and a SPRADO assessment tool to assess user experience. The study involved 42 students and 9 teachers from 4 vocational high schools (from grades 9 and 10) divided into MR and non-MR groups. Academic performance was assessed through standardized pre- and post-tests, and the MR group also completed a survey using the SPRADO assessment tool, which evaluates six dimensions of MR learning systems: usability, system functionality, pedagogical alignment, realism, accessibility, and user satisfaction. The findings show a significant improvement in academic performance among students in the MR group compared to the non-MR group and revealed positive perceptions from both students and teachers in the MR group regarding the capabilities of MR in supporting learning. These results provide empirical support for integrating MR into vocational curricula as a strategic response to evolving technological and industrial demands.

GaN is a key material in microelectronics that requires precise etching to meet technological demands. Plasma Atomic layer etching (ALE) is a promising process to achieve a defectless surface. However, the plasma/surface interaction mechanisms involved remain complex. In this work, we propose a multiscale model of GaN plasma ALE in chlorinated plasmas that combines a plasma module using a dynamic global model to describe Cl2/Ar plasmas, a sheath module described by a Monte Carlo model, and a surface module considering a kinetic Monte Carlo model. In addition, density functional theory calculations of Cl adsorption on GaN surfaces are used for a better description of the adsorption mechanisms. The surface module allows simulations of the GaN surface during ALE with an atomic-scale resolution. This multiscale approach enables comprehensive evolution of surface morphology, material composition, and etching products over time. Results reveal a self-limited etching behavior consistent with ALE principles and show that residual chlorine penetrating in shallow layers significantly impacts the etch per cycle. Our simulator provides a currently offline predictive framework that could contribute as a step toward future digital twin development to optimize and control GaN ALE processes.

ABSTRACT With the escalating global demand for sustainable, wearable electronics and clean energy harvesting technologies, Textile‐based triboelectric nanogenerators (TENGs) have attracted significant attention as a novel energy conversion device. Due to the versatile nature of textile structures, combined with their lightweight, flexible, and wearable characteristics, textiles serve as excellent substrates for TENGs. To promote the in‐depth research and practical applications of textile‐based TENGs, this review classifies them into two main categories based on the dimensional scale of the textile materials: fiber‐based TENGs and fabric‐based TENGs. Further, based on the structural features and weaving techniques of the materials, a more detailed classification is provided, highlighting the unique advantages and limitations of different fiber and fabric structures. This review explores and summarizes the roles and performance differences of these structures in energy harvesting and sensing capabilities, intending to provide a reference for the design optimization and practical application scenarios of smart textile sensor devices.

Tourism Micro, Small, and Medium Enterprises (MSMEs) in underdeveloped regions play a crucial role in driving local economic development and sustaining the tourism ecosystem. Yet they face limitations in innovation capacity and organizational performance. This study aims to develop and test a green innovation model to improve MSME organizational performance and strengthen the tourism ecosystem in East Sumba Regency, Indonesia. This study employed a quantitative approach, collecting data through questionnaires from tourism MSMEs, which were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that green innovation, represented by product value, technology, networking, marketing, and market demand, is positively and significantly associated with organizational performance, which, in turn, is positively associated with perceived ecosystem performance, as reflected in productivity and resilience. These findings support the view that the relationship between green innovation and perceived tourism ecosystem performance operates indirectly and is dependent on strengthening the operational and financial performance of MSMEs. The novelty of this study lies in integrating the empirical PLS-SEM model with an implementation approach, including the development of training modules and the digitalization of learning, in the context of 3T regions (Frontier, Outermost, and Underdeveloped). The limitations of this study include the use of data from a single time period; further research is recommended to use multi-period data to capture the dynamics of change better.

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

Deep learning (DL) is a machine learning technique that processes data in a manner influenced by the functioning of the human brain. It is an effective tool for deciphering complicated data and may be applied to many other processes, such as decision-making, image recognition, and natural language processing. The requirement to process large amounts of data rapidly and precisely drives the demand for deep learning technologies in the healthcare industry. Deep learning can find patterns in medical data, including genomic data, patient records, and medical imaging. Additionally, it can be utilized to create prediction models that can aid clinicians in selecting the course of treatment for patients. This article employed deep learning models to examine medical data for better diagnoses. DL models efficiently improve accuracy, handle complicated medical data, and detect subtle trends. A comparative analysis of deep learning architectures revealed that DL helps boost diagnostic accuracy and recognize subtle disease patterns. However, issues like the need for vast training data, overfitting, model interpretability, and high computational resources exist. Also, we presented the applications in diagnosing heart disease, cancer, Alzheimer’s, and other specific diseases, demonstrating the potential of deep learning in predictive modeling for clinical decision support. This article comprehensively reviews deep learning architectures and comparative research for disease identification and prediction, and explores emerging solutions such as federated learning and explainable artificial intelligence (AI). The study also tackles research obstacles and potential advantages by presenting the current status and probable future directions of deep learning in disease diagnosis and prognosis.

Cargo transport in Brazil is composed of road, rail, waterway, pipeline and air modes. The road modal stands out, representing about 64.8% of the total cargo transported in the country, according to data from the National Transport Confederation (CNT) in 2023. The rail modal configures 14.9%, while other modes, such as waterway, correspond to smaller portions, with cabotage and waterways, adding up to 12%. In the transport of dangerous products, the choice of the most appropriate modal must consider not onl y technical and safety aspects but also costs and environmental impacts. Green hydrogen (which is characterized as a dangerous product) has been consolidated as an essential element in the global energy transition. Ensuring its economic viability in Brazil requires optimization in transpor tation methods. Although the road modal is predominant in the transport chain of dangerous products, the other modes, such as rail, pipeline and waterway, can contribute significantly to the chain, reflecting even more relevance due to their greater efficiency, safety and contribution to the reduction of environmental impacts. To enable the use of the various modes available, it is essential to invest in infrastructure, such as the expansion of road and rail networks, retrofit or construction of new pipelines, as well as the modernization of ports and waterways. These investments are essential to improve efficiency, increase safety and enhance the contribution of these modes in reducing environmental impacts, offering more sustainable alternatives in a context of a low-carbon economy. This article proposes a detailed comparison of the modes for transporting green hydrogen. The advantages, limitations and environmental impact of each modal were analyzed. Qualitative research was used as a methodology, based on bibliographic research and observations. The article aims to contribute to the analysis of the most appropriate mode of transport for the transport of green hydrogen, in terms of sustainability and efficiency, comparing the operating scenarios (involving costs, infrastructure, among others), in order to identify viable solutions that meet the economic, environmental and technological demands for the global energy transition.

This study discusses the economic threats facing Iraq because of the global shift towards a low-carbon economy. It focuses on the concept of "stranded assets"—oil resources or infrastructure that may lose their economic value before being fully depleted due to changes in climate policies, technology, or global demand. Oil constitutes more than 90% of Iraq's revenues, making the economy vulnerable to any decline in global oil demand. The study employs a theoretical and analytical approach, reviewing three potential climate scenarios and their projected impacts up to 2050. It indicates that the most severe scenario (a 1.5° trajectory) could lead to a collapse in oil revenues exceeding 80%, rendering a significant portion of Iraq's reserves unusable. The study recommends restructuring the Iraqi economy by diversifying income sources, investing in renewable energy, establishing a sovereign wealth fund to manage oil revenues, and restructuring oil contracts to align with the global shift. It also calls for the establishment of a national council for economic transformation. The study emphasizes that ignoring these transformations is a gamble with Iraq's future, and that successful models like Norway and Saudi Arabia should be studied as examples, while Nigeria serves as a cautionary tale about the failure of wealth management. This research constitutes a call for a radical rethinking of Iraq's development model to ensure the sustainability of its post-oil economy.

ABSTRACT This study examines the integration of Industry 4.0 digital skills into vocational training for electric vehicle (EV) technicians, with the aim of aligning conventional curricula with emerging technological demands. Using a PRISMA-based systematic literature review, 62 articles from 2024–2025 were analyzed from an initial pool of 421 Scopus-indexed documents. Findings indicate that technical competencies remain focused on battery management and fault detection, whereas newer technologies such as digital twins and predictive maintenance are still emerging. Though pedagogical evaluations are scarce, vocational training models include embedded, hybrid, and transformative approaches. Bibliometric analysis reveals strong clusters in energy storage and AI diagnostics research, dominated by East Asian authors, but with weak links to vocational education research. This study underscores the need to balance technical and digital competencies within EV-focused vocational curricula to enhance workforce readiness.

The scalding emergence of digital technologies, artificial intelligence, big data environments, and international data flows have completely changed the methods of collecting, processing, and regulating personal data. The paper will touch on the legal problems of the rights of data protection and privacy in a digital era, the transforming regulatory provisions, the ethical concerns and technological demands that are shifting its present day paradigm of regulation. It investigates tensions between innovation, surveillance, cybersecurity and individual agency and observes that the traditional beliefs of the law are failing with the algorithmic decision-making, biometric identification and the platform-based data economy. The paper unites the newest principles of regulation incorporating risk-oriented regulation, human rights-oriented artificial intelligence regulation, accountability principles, and regulatory convergence between countries based on comparative legal theory and recent interdisciplinary literature. An analytical framework is given in a manner that it is arranged in a sequence to gauge on the adequacy of the existing legal regimes and a loophole that requires a reform. The findings indicate that although increasing attention is now paid to the fact that privacy is one of the central rights of the modern models, implementation, territorial issues and technological lopses remain factors that threaten effective protection. The paper is concluded by stating that the adaptive and harmonized and technology-neutral regulatory models are so fundamental in that the privacy protection can be enhanced in a context of the ever-growing digital transformation

ABSTRACT Current volatility and unpredictability of our global environment and recent advances in info-communication technologies demand the work of reflective professionals more than ever: professionals who are truly innovative and resourceful in responding to the complexities of the emerging environmental and social challenges. Professionalism reduced to non-reflected use of technologies, and its possible consequence, control work instead of empowerment in social work maintain the existing power relations and serve the short-term interests of the ruling regimes only – today’s lucky few, but in the long run, sharers in humankind’s predictable losses. This article describes a class design with a focus on a method, Reflective Portrait, which facilitates the development of students’ systemic, person-in-environment orientation and enables them to reflect on the synergies between the societal, cultural, relational, and individual factors influencing social problem definition and resolution. Students’ knowledge, skills, attitudes, and values, as the core constituents of their professional identity, are the key targets of the learning process.