Limitations Of Technology Research Articles

Advanced multi-nozzle electrohydrodynamic printing: mechanism, processing, and diverse applications at micro/nano-scale

Abstract Electrohydrodynamic (EHD) jet printing represents a novel micro/nano-scale additive manufacturing process that utilises a high-voltage induced electric field between the nozzle and the substrate to print micro/nanoscale structures. EHD printing is particularly advantageous for the fabrication on flexible or non-flat substrates and of large aspect ratio micro/nanostructures and composite multi-material structures. Despite this, EHD printing has yet to be fully industrialised due to its low throughput, which is primarily caused by the limitations of serial additive printing technology. The parallel multi-nozzle array-based process has become the most promising option for EHD printing to achieve large-scale printing by increasing the number of nozzles to realise multichannel parallel printing. This paper reviews the recent development of multi-nozzle EHD printing technology, analyses jet motion with multi-nozzle, explains the origins of the electric field crosstalk effect under multi-nozzle and discusses several widely used methods for overcoming it. This work also summarises the impact of different process parameters on multi-nozzle EHD printing and describes the current manufacturing process using multi-nozzle as well as the method by which they can be realised independently. In addition, it presents an additional significant utilisation of multi-nozzle printing aside from enhancing single-nozzle production efficiency, which is the production of composite phase change materials through multi-nozzle. Finally, the future direction of multi-nozzle EHD printing development is discussed and envisioned.

Enhancing Thermoelectrical Properties of Silver-Nanowire-Embedded Heatable Textiles via Sputter-Mediated Nanowire Structural Modulation

This study addresses the fabrication of flexible, heatable fabrics via the integration of globally interconnected silver nanowires (Ag NWs) with sputter-deposited silver atoms. Conventional heatable fabrics, which utilize macroscale or nanoscale conductive wires, often face challenges in balancing flexibility, comfort, and structural durability. The proposed method leverages the advantages of nanoscale metallic wires and vacuum-based sputtering, maintaining fabric flexibility while enhancing heating efficiency. The fabrication process involves dip-coating polyester fabric with Ag NWs, followed by sputter deposition to modulate the nanowire morphology, thereby improving key electrical properties such as wire resistance and contact resistance between wires. The experimental results demonstrate that sputter-deposited Ag NW fabrics exhibit significantly enhanced heating capability compared to undeposited, otherwise identical counterparts. Further, the fabrics maintain their heating characteristics under repeated mechanical bending and prolonged electrical stress, highlighting their potential for use in wearable electronic applications. This approach offers a promising solution to the limitations of current heatable textile technologies, providing a pathway for the development of comfortable, efficient, and durable heatable fabrics.

Bioinspired Passive Cooling Hydrogel for Visualizing Hygroscopicity and Desorption Process

AbstractSelf‐hygroscopic hydrogels, characterized by high evaporation enthalpy, cooling efficiency, and self‐regulating properties, have garnered significant attention. However, most current research focuses on enhancing the hygroscopic and desorption performance, often overlooking the importance of monitoring the self‐regulation process, which limits its further application. Advanced visualization technologies, such as in situ electrical impedance tomography, low‐field nuclear magnetic resonance, and hyperspectral imaging, offer potential insights into this behavior, yet they often require additional devices, incur high costs, and involve complex sample preparation processes. Therefore, drawing inspiration from nature, humidity‐color‐sensitive hydrogels (HCSHs) strategy is proposed for visualized cooling. Benefiting from the strong polar responsiveness of the aggregation‐induced emission (AIE) molecules, the hydrogel's fluorescence significantly changes with varying interior water content, thereby its self‐regulation process is monitored easily. Further, the obtained hydrogel could be applied in the electronic device cooling owing to the polymer skeletons’ high swelling ratio, strong adhesion, and excellent self‐hygroscopic properties. This strategy overcomes current limitations in the visual technology of self‐hygroscopic materials and provides new insights into intelligent thermal management for electronic devices.

Implementing traceability technologies to promote ethical sourcing in Ghana’s fashion and apparel industry

ABSTRACT The demand for traceability has emerged as a significant imperative within the framework of an ethical supply chain that includes multiple stages. This study focusses on the implementation and impact of traceability technology in the Ghanaian fashion industry. The study employed descriptive and explanatory research, using a 700-stakeholder sample, a standardised questionnaire, and descriptive and inferential statistical methods. The findings of the study indicate that QR codes, GPS tracking, sustainable material tracing, and consumer interaction apps demonstrate a high level of effectiveness within the Ghanaian fashion business. The implementation of traceability technology faces challenges like limited knowledge, research insufficient, lack of coordination, policy absence, financial constraints, technological infrastructure limitations, data privacy concerns, opposition, and training gaps. Stakeholders generally support the implementation of coordination policies and regulatory compliance strategies, suggesting that factors like awareness, research, cost, infrastructure, data protection, opposition, and training should be prioritised.

Bridging the gap: opportunities, challenges, and future directions for teleradiology in rural healthcare

Teleradiology, the electronic transmission of radiological images and data for diagnostic purposes, holds significant promise for improving healthcare access in rural and underserved regions. This review explores the opportunities and challenges of implementing teleradiology in rural settings, where limited access to radiology services exacerbates healthcare disparities. Teleradiology offers multiple benefits, including improved access to timely diagnostics, enhanced decision-making, and cost savings for rural patients who would otherwise need to travel to urban centers for radiology services. It has shown positive outcomes globally, with examples in India, Mali, the Democratic Republic of Congo, and Brazil, demonstrating its feasibility and potential to reduce geographic health inequities. However, implementing teleradiology in these settings faces several challenges, such as technological limitations, workforce shortages, regulatory and legal barriers, data security concerns, and cultural acceptance issues. Solutions and recommendations include investments in infrastructure, workforce training, supportive regulatory frameworks, secure data management, and adaptable telehealth models that consider socioeconomic factors in rural areas. By addressing these challenges, stakeholders can enhance the impact of teleradiology in rural healthcare systems, ultimately contributing to reduced disparities and improved healthcare outcomes. This review provides a comprehensive overview of the current landscape and suggests actionable pathways for advancing teleradiology in rural settings worldwide.

New colleague or gimmick hurdle? A user-centric scoping review of the barriers and facilitators of robots in hospitals

Healthcare systems are confronted with a multitude of challenges, including the imperative to enhance accessibility, efficiency, cost-effectiveness, and the quality of healthcare delivery. These challenges are exacerbated by current healthcare personnel shortages, prospects of future shortfalls, insufficient recruitment efforts, increasing prevalence of chronic diseases, global viral concerns, and ageing populations. To address this escalating demand for healthcare services, healthcare systems are increasingly adopting robotic technology and artificial intelligence (AI), which promise to optimise costs, improve working conditions, and increase the quality of care. This article focuses on deepening our understanding of the barriers and facilitators associated with integrating robotic technologies in hospital environments. To this end, we conducted a scoping literature review to consolidate emerging themes pertaining to the experiences, viewpoints perspectives, and behaviours of hospital employees as professional users of robots in hospitals. Through screening 501 original research articles from Web-of-Science, we identified and reviewed in full-text 40 pertinent user-centric studies of the integration of robots into hospitals. Our review revealed and analysed 14 themes in-depth, of which we identified seven as barriers and seven as facilitators. Through a structuring of the barriers and facilitators, we reveal a notable misalignment between these barriers and facilitators: Finding that organisational aspects are at the core of most barriers, we suggest that future research should investigate the dynamics between hospital employees as professional users and the procedures and workflows of the hospitals as institutions, as well as the ambivalent role of anthropomorphisation of hospital robots, and emerging issues of privacy and confidentiality raised by increasingly communicative robots. Ultimately, this perspective on the integration of robots in hospitals transcends debates on the capabilities and limits of the robotic technology itself, shedding light on the complexity of integrating new technologies into hospital environments and contributing to an understanding of possible futures in healthcare innovation.

CNSC-11. INVESTIGATION OF GLIOBLASTOMA NEURON-TUMOR NETWORKS WITH RABIES-BASED RETROGRADE TRACING

Abstract Glioblastomas are primary brain tumors known for their whole-brain invasion and high therapeutic resistance. In previous studies, neuron-to-glioma synapses have been shown to promote glioblastoma progression. A deep characterization of this communication and of tumor-connected neurons has not been possible due to technological limitations. We adapted a rabies virus-based retrograde tracing approach to investigate and manipulate neuron-tumor networks. Interestingly, we found that glioblastoma cells rapidly integrated into neural circuits across the brain, engaging with distal neurons of diverse subtypes in very early stages of tumor development. Beyond glutamatergic neurons, we identified cholinergic neurons as drivers of glioblastoma cell invasion. We revealed patient- and tumor cell state-dependent differences in the expression of synaptogenic genes which also correlated with neuron-tumor connectivity and tumor cell invasivity. Notably, radiotherapy led to increased neuron-tumor connectivity mediated via neuronal hyperexcitability. The anti-epileptic drug perampanel alongside radiotherapy mitigated this effect and enhanced treatment efficacy by simultaneous neuronal activity inhibition. Utilizing our rabies-based approach, we genetically ablated tumor-connected neurons leading to a significantly decreased tumor progression offering a potential novel therapeutic avenue. Taken together, we developed a framework for dedicated analysis of multicellular neuron-tumor networks and propose our methodology as a versatile and adaptable approach to tackle glioblastoma.

The role of big data in transforming financial management in U.S. healthcare: A conceptual framework

This paper explores the transformative role of big data in financial management within the U.S. healthcare sector, highlighting its capacity to address long-standing financial inefficiencies, enhance decision-making, and improve regulatory compliance. Big data technologies, such as predictive analytics and real-time financial reporting, provide healthcare institutions with the tools to forecast financial trends, optimize budgeting, and mitigate risks. Despite the significant benefits, the adoption of big data in healthcare finance faces challenges, including data privacy concerns, regulatory complexities, and technological limitations. The paper examines these barriers and enablers, offering strategic recommendations for healthcare institutions and policymakers to optimize the use of big data. By improving data governance, upgrading infrastructure, and fostering collaboration across departments, healthcare organizations can maximize the impact of big data on their financial operations. The review concludes with a call for continued policy support and financial incentives to encourage the broader adoption of big data in healthcare finance, ultimately promoting cost efficiency and operational resilience in the sector. Keywords: Big Data, Healthcare Financial Management, Predictive Analytics, Data Governance, Regulatory Compliance, Financial Decision-Making.

Circular agro-economies (CAE): reducing waste and increasing profitability in agriculture

This paper explores the concept of Circular Agro-Economies (CAE), a sustainable agricultural model focused on recycling agricultural by-products to minimize waste and maximize profitability for farmers. In contrast to traditional linear agricultural systems, CAE promotes resource efficiency and the development of new value chains through the reuse of organic materials. The paper examines the environmental, economic, and social benefits of adopting CAE, including waste reduction, cost savings, rural development, and job creation. It also discusses the significant barriers to adoption, such as technological limitations, financial constraints, and policy-related challenges. Finally, practical recommendations for farmers, policymakers, and businesses are provided to support the successful implementation of CAE, highlighting the collaborative efforts needed to overcome existing challenges and realize the full potential of this model. Keywords: Circular Agro-Economies, Agricultural Waste Recycling, Sustainable Agriculture, Resource Efficiency, Rural Development, Profitability in Farming.

A review of carbon nanotube field effect transistor from structure and properties to applications

Abstract. In the pursuit of next-generation electronic devices that overweigh the limitations of traditional silicon-based technologies, Carbon Nano Tube Field Effect Transistors (CNTFETs) have garnered significant attention. Unlike conventional Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs), CNTFETs utilize the exceptional properties of Carbon Nanotubes (CNTs) as the channel material, promising substantial improvements in performance, power consumption, and scalability. This paper presents a comprehensive overview of CNTFETs, delving into their fundamental structures and underlying principles. It critically examines the unique characteristics that make CNTFETs an attractive alternative, including their superior carrier mobility, reduced leakage currents, and compatibility with nanoscale fabrication techniques. This review explores the application prospects of CNTFETs, spanning from high-speed digital circuits to energy-efficient sensors and beyond. Despite their promising potential, the paper also acknowledges recent challenges associated with CNTFETs, such as challenges in material synthesis, integration with existing semiconductor processes, and cost considerations. By addressing these challenges and fostering interdisciplinary research collaborations, the widespread adoption of CNTFETs in integrated circuits could revolutionize the electronics industry, enabling the development of more efficient, powerful, and sustainable devices for the future.

Integrating Vision: From Neuroscience to Artificial Intelligence

Abstract. This review article explores the complex interplay between neuroscience and artificial intelligence, focusing on vision processing and its applications. It starts by outlining the biological basis of vision, delving into how visual information is processed and encoded in the brain. The discussion then transitions to artificial intelligence, particularly machine vision, highlighting the advancements and technologies that mimic biological processes. A critical analysis compares how visual information is utilized in both biological organisms and artificial systems, with an emphasis on cognitive functions and neural encoding. The challenges of integrating vision across various sensory modalities are examined, underscoring the technological and cognitive limitations currently faced. The review culminates by identifying potential research paths aimed at closing the gap between neuroscience and AI. This involves enhancing the understanding and functionality of vision in multisensory contexts, striving to foster a more comprehensive approach to artificial intelligence that mirrors the complexity of human perception.

Advancing language learning: The impact and challenges of Computer-Assisted Language Learning (CALL)

Abstract. Computer-Assisted Language Learning (CALL) represents a transformative approach in modern education, integrating technology to enhance language acquisition effectively. This comprehensive review examines the various dimensions of CALL, focusing on its deployment through interactive software, online courses, mobile applications, and the accompanying challenges. Quantitative analyses reveal significant improvements in learner engagement and cognitive load management due to multimedia and interactive content. Moreover, the adaptability of CALL to cater to individual learning needs through personalized and adaptive learning systems demonstrates a superior proficiency acquisition rate. Despite these advancements, CALL implementation faces substantial barriers including technological limitations, the need for extensive teacher training, and concerns regarding data privacy and security. This article assesses the statistical impact of these elements on language learning outcomes and suggests strategies for overcoming the associated challenges. Through mixed-methods research and longitudinal studies, the effectiveness of CALL platforms is substantiated, highlighting their pivotal role in reshaping language education across diverse demographic and geographic spectrums.

Digital Gig Work Under Unstable Energy Infrastructures: Invisible Workarounds and Alternative Imaginaries

Digital upskilling and remote work are frequently presented as pathways for displaced communities who face political, economic crises and employment barriers. The viability and stability of available energy infrastructures for these communities is critical to the long-term success of these proposed job trajectories. In this paper, we investigate how 17 Syrian refugees living in Lebanon navigate their local unstable energy infrastructures to conduct digital gig work and receive digital training. We found that digital gig work and training are workarounds to the political and social inaccessibility of local labor markets for refugees and that participants rely on a series of material energy strategies to optimize their electricity access. We argue that to support refugees conducting digital gig work and training, we must recognize and account for ecological, social, and technological limitations and frailties in the development of technological solutions and draw our attention to how infrastructures are perpetually undergoing processes of breakdown, repair, and renewal. We argue that this attention to ongoing transformation and renewal creates new opportunities for productive and creative reconfiguration as well as modes through which CSCW may intervene in unstable energy contexts. From this perspective, we emphasize the importance of better resourcing displaced communities with information regarding energy access and supporting them in establishing and strengthening their own visions for alternative energy systems and employment pathways.

Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems

The fusion energy research field presents many intricate challenges that require resolution. Many diagnostic systems employed in experiments are approaching the limits of current technology. Implementing efficient measurements requires using an appropriate set of tools to facilitate the optimal utilization of hardware. Fusion energy measurements must provide low latency processing with the capacity for future improvements and the ability to handle complex data flows efficiently. The presented work addresses these requirements and describes the implementation of a high-performance, low-latency software platform with convenient development for soft X-ray (SXR) plasma impurities emission tracing—the Asynchronous Complex Computation Platform (AC2P). This article presents the architectural design, implementation details, and performance and latency measurements based on the raw data acquired from the WEST tokamak and laboratory tests. AC2P provides the tools to develop low-latency, multi-core, multi-device complex data flow graph scale-up solutions for measuring impurities in hot plasmas. The system has been designed to operate online during experiments, calculate the energy distribution, position and occurrence time of SXR photons, monitor the data stream’s quality and archive any abnormalities for subsequent offline verification and algorithm improvement. This article presents AC2P, which operates as part of the SXR measurement system on the WEST tokamak.

Removal of Inorganic Pollutants and Recovery of Nutrients from Wastewater Using Electrocoagulation: A Review

Water pollution is a major concern due to its detrimental effects on the environment and public health. The particular danger of inorganic pollutants arises from their persistent toxicity and inability to biodegrade. Recently, electrocoagulation (EC) has been demonstrated as an alternative sustainable approach to purifying wastewater due to the increasingly strict pollution prevention rules. In particular, EC has been used to remove inorganic pollutants, such as Cr, Zn, Pb, or As. EC has emerged as a sustainable tool for resource recovery of some inorganic pollutants such as N and P that, when recovered, have value as plant nutrients and are critical in a circular economy. These recovered materials can be obtained from diverse agricultural drainage water and recycled as fertilizers. In this work, a state-of-the-art technique is reviewed describing the advances in contaminant removal and nutrient recovery using EC through an in-depth discussion of the factors influencing the contaminant removal process, including operating pH, time, power, and concentration. Furthermore, limitations of the EC technology are reviewed, including the high-power consumption, fast deterioration of the sacrificial electrodes, and the types of contaminants that could not be efficiently removed. Finally, new emerging constructs in EC process optimization parameters are presented.

Extracellular Vesicle lncRNAs as Key Biomolecules for Cell-to-Cell Communication and Circulating Cancer Biomarkers

Extracellular vesicles (EVs) are secreted by almost every cell type and are considered carriers of active biomolecules, such as nucleic acids, proteins, and lipids. Their content can be uptaken and released into the cytoplasm of recipient cells, thereby inducing gene reprogramming and phenotypic changes in the acceptor cells. Whether the effects of EVs on the physiology of recipient cells are mediated by individual biomolecules or the collective outcome of the total transferred EV content is still under debate. The EV RNA content consists of several types of RNA, such as messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA), the latter defined as transcripts longer than 200 nucleotides that do not code for proteins but have important established biological functions. This review aims to update our insights on the functional roles of EV and their cargo non-coding RNA during cancer progression, to highlight the utility of EV RNA as novel diagnostic or prognostic biomarkers in cancer, and to tackle the technological advances and limitations for EV RNA identification, integrity assessment, and preservation of its functionality.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Mapping the future of oxidative RNA damage in neurodegeneration: Rethinking the status quo with new tools

Over two decades ago, increased levels of RNA oxidation were reported in postmortem patients with ALS, Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. Interestingly, not all cell types and transcripts were equally oxidized. Furthermore, it was shown that RNA oxidation is an early phenomenon, altogether indicating that oxidative RNA damage could be a driver, and not a consequence, of disease. Despite all these exciting observations, the field appears to have stagnated since then. We argue that this is a consequence of the shortcomings of technologies to model these diseases, limiting our understanding of which transcripts are being oxidized, which RNA-binding proteins are interacting with these RNAs, what their implications are in RNA processing, and as a result, what their potential role is in disease onset and progression. Here, we discuss the limits of previous technologies and propose ways by which advancements in iPSC-derived disease modeling, proteomics, and sequencing technologies can be combined and leveraged to answer new and decades-old questions.

Short cellulose acetate nanofibers: A novel and scalable coating for enhancing nanoparticles filtration efficiency of filter media

This study introduces a novel and scalable approach to significantly enhance the filtration efficiency of automotive cabin air filters using short cellulose acetate nanofibers applied via a spray coating method. The short nanofibers, produced through the mechanical fragmentation of electrospun mats, enable uniform dispersion and application onto microfibrous filter media, addressing a key limitation in nanofiber technology. This innovative process enhances the capture of ultrafine particles (7.4 to 250 nm), which are critical for improving air quality in confined environments such as vehicle cabins. The filters, coated with nanofiber weights ranging from 1 to 13 mg, showed a prominent improvement in filtration efficiency, achieving quality factors (Qf) from 0.01 to 0.0221 Pa−1, outperforming both uncoated filters (0.0025 Pa−1) and commercial HEPA filters (0.0212 Pa−1). Filtration efficiency increased from 18 % to 99.8 %, with corresponding pressure drops between 44.5 and 620 Pa. Remarkably, a filter with 9 mg of nanofibers achieved a superior quality factor of 0.0221 Pa−1, effectively optimizing both efficiency and pressure drop while outperforming HEPA filters. This method addresses the common challenge in conventional filters of balancing filtration efficiency with pressure drop. The scalable spray coating process, combined with the advantages of electrospinning, offers a practical, industrially viable solution to improve air filtration in automotive HVAC systems and other environments requiring high filtration efficiency and low-pressure drop.