Limitations Of Technology Research Articles

Peptide nucleic acids: Recent advancements and future opportunities in biomedical applications.

Peptide nucleic acids (PNA), synthetic molecules comprising a peptide-like backbone and natural and unnatural nucleobases, have garnered significant attention for their potential applications in gene editing and other biomedical fields. The unique properties of PNA, particularly enhanced stability/specificity/affinity towards targeted DNA and RNA sequences, achieved significant attention recently for gene silencing, gene correction, antisense therapy, drug delivery, biosensing and other various diagnostic aspects. This review explores the structure, properties, and potential of PNA in transforming genetic engineering including potent biomedical challenges. In Addition, we explore future perspectives and potential limitations of PNA-based technologies, highlighting the need for further research and development to fully realize their therapeutic and biotechnological potential.

Quaternized and boronic acid dual-functional CoAg nanozymes for label-free, stable, sensitive, and rapid quantitation detection of the bacterial total concentration.

Foodborne pathogenic bacteria lead to a significant increase in illnesses and fatalities annually. In the early stage of a pathogenic bacterial infection, the concentration of bacteria in food is lower than the detection limit of most technology which enhances the difficulty in diagnosis. It is a serious challenge for researchers to develop a rapid, sensitive, accurate, and stable pathogenic bacterial determination method without costly equipment and highly skilled operators. It is clear that the bacterial total concentration in food increased obviously could be attributed to microbial contamination in food. Herein, quaternized and boronic acid dual-functional capped CoAg nanozymes, known as CoAg@HTCC-B, were developed and synthesized to exhibit strong and stable chemiluminescence (CL) emission to detect bacterial total concentrations. The CoAg@HTCC-B nanozymes are capable of accurately quantifying the total concentrations of a mixed bacterial solution (S. aureus and E. coli) over a broad detection range and with a low limit of detection (LOD). These nanozymes were utilized for the detection of E. coli or S. aureus in fresh chicken samples with LOD values of 2.08colony-forming units per milliliter (CFU/mL) for E. coli and 1.71CFU/mL for S. aureus. This suggests that the CoAg@HTCC-B nanozymes have the potential for early-stage monitoring of microbial contamination in meat samples. The capability of CoAg@HTCC-B nanozymes in swiftly and accurately detecting known bacterial concentrations suggested their potential as a viable alternative to the traditional culture-based approach for quantitatively determining known bacterial concentrations in academic, research, or industrial settings.

Ways to Measure Metals: From ICP-MS to XRF.

This review explores the use of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and X-ray Fluorescence (XRF) for quantifying metals and metalloids in biological matrices such as hair, nails, blood, bone, and tissue. It provides a comprehensive overview of these methodologies, detailing their technological limitations, application scopes, and practical considerations for selection in both laboratory and field settings. By examining traditional and novel aspects of each method, this review aims to guide researchers and clinical practitioners in choosing the most suitable analytical tool based on their specific needs for sensitivity, precision, speed, and sample preparation. Recent studies highlight enhanced capabilities of both ICP-MS and XRF technologies, making them more adaptable to various analytical needs. ICP-MS is renowned for its unmatched sensitivity and precision in detecting ultra-trace metals and metalloids in complex biological samples, such as lead in plasma or seawater. XRF advancements include lower detection limits and reduced sample preparation time, enabling rapid, non-destructive analyses, ideal for quick field assessments. Portable XRF analyzers have revolutionized on-the-spot testing, providing robust data without traditional wet-lab constraints. Moreover, hybrid techniques combining ICP-MS and XRF features are emerging, offering rapid and precise metal analysis for environmental monitoring, clinical diagnostics, and epidemiological studies. Matching analytical methods to specific research demands is critical. ICP-MS is the gold standard for detailed quantitative analysis in laboratories, while XRF excels in non-destructive, immediate field applications. Selection should consider sample complexity, sensitivity, speed, and cost-efficiency. Integrating ICP-MS and XRF offers a versatile approach to metals analysis, transforming practices in environmental science and healthcare diagnostics. As these technologies evolve, they are promising to expand capabilities in detecting and understanding the roles of metals and metalloids in health and the environment.

Ethical perspective on telemedicine usage in sexual medicine: A friend or a foe?

AbstractTelemedicine has rapidly integrated into healthcare, overcoming initial barriers such as regulatory restrictions and technological limitations. Its role in sexual medicine, especially urology, has been accelerated by technological advances and the COVID‐19 pandemic. However, as telehealth continues to expand, key ethical challenges emerge, including concerns over privacy, healthcare equity, and informed consent. These challenges are particularly important in sexual medicine, where sensitive patient data and intimate conditions are involved. We aimed to examine the ethical implications of telemedicine in sexual medicine, focusing on data security, consent processes, and healthcare disparities. We further emphasize the importance of maintaining high ethical standards while integrating telemedicine as a complement to traditional care, ensuring that patient outcomes are not compromised.

Combining radio-telemetry and radar measurements to test optimal foraging in an aerial insectivore bird

Optimal foraging theory posits that foragers adjust their movements based on prey abundance to optimize food intake. While extensively studied in terrestrial and marine environments, aerial foraging has remained relatively unexplored due to technological limitations. This study, uniquely combining BirdScan-MR1 radar and the Advanced Tracking and Localization of Animals in Real-Life Systems biotelemetry system, investigates the foraging dynamics of Little Swifts (Apus affinis) in response to insect movements over Israel’s Hula Valley. Insect movement traffic rate (MoTR) substantially varied across days, strongly influencing swift movement. On days with high MoTR, swifts exhibited reduced flight distance, increased colony visit rate, and earlier arrivals at the breeding colony, reflecting a dynamic response to prey availability. However, no significant effects were observed in total foraging duration, flight speed, or daily route length. Notably, as insect abundance increased, inter-individual distances decreased. These findings suggest that Little Swifts optimize their foraging behavior in relation to aerial insect abundance, likely influencing reproductive success and population dynamics. The integration of radar technology and biotelemetry systems provides a unique perspective on the interactions between aerial insectivores and their prey, contributing to a comprehensive understanding of optimal foraging strategies in diverse environments.

Combining radio-telemetry and radar measurements to test optimal foraging in an aerial insectivore bird.

Optimal foraging theory posits that foragers adjust their movements based on prey abundance to optimize food intake. While extensively studied in terrestrial and marine environments, aerial foraging has remained relatively unexplored due to technological limitations. This study, uniquely combining BirdScan-MR1 radar and the Advanced Tracking and Localization of Animals in Real-Life Systems biotelemetry system, investigates the foraging dynamics of Little Swifts (Apus affinis) in response to insect movements over Israel's Hula Valley. Insect movement traffic rate (MoTR) substantially varied across days, strongly influencing swift movement. On days with high MoTR, swifts exhibited reduced flight distance, increased colony visit rate, and earlier arrivals at the breeding colony, reflecting a dynamic response to prey availability. However, no significant effects were observed in total foraging duration, flight speed, or daily route length. Notably, as insect abundance increased, inter-individual distances decreased. These findings suggest that Little Swifts optimize their foraging behavior in relation to aerial insect abundance, likely influencing reproductive success and population dynamics. The integration of radar technology and biotelemetry systems provides a unique perspective on the interactions between aerial insectivores and their prey, contributing to a comprehensive understanding of optimal foraging strategies in diverse environments.

Disaster Mitigation and Emergency Response in the School Environment

Natural disasters are a significant threat in vulnerable areas, including Indonesia which frequently experiences floods, earthquakes, and landslides. This study explores disaster mitigation and emergency response management in schools in Sembakung and Lumbis Districts, Nunukan Regency. The objectives are to understand the challenges, human resource (HR) readiness, availability of facilities and infrastructure, community participation, technology utilization, and post-disaster psychosocial support. Qualitative methods were used with in-depth interviews with school principals, teachers, and staff. The findings show limited disaster mitigation and simulation training, minimal disaster-resistant school infrastructure, and low community participation and technology limitations in remote areas. This study recommends a holistic approach to improving disaster preparedness through training, infrastructure strengthening, community participation, technology utilization, and psychosocial support. Collaboration between government, schools, and local communities is needed to create a safe and resilient educational environment in the face of disasters.

Investigation of Diverse Urban Carbon Emission Reduction Pathways in China: Based on the Technology–Organization–Environment Framework for Promoting Socio-Environmental Sustainability

In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across 81 cities in China. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), this research assesses the roles of technological, organizational, and environmental drivers in urban carbon reduction. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to uncover distinct carbon reduction pathways and causal asymmetries between cities. The findings reveal that technological, organizational, and environmental factors significantly drive carbon reduction, with technological and organizational factors playing the central roles. Environmental factors exert primarily indirect effects, interacting with technological and organizational drivers. This study categorizes cities into three distinct carbon reduction models: cities with high carbon-neutral potential primarily leverage technological innovation and energy efficiency optimization; cities with moderate potential integrate technology and policy, emphasizing green landscape planning to achieve balanced development; and cities with lower carbon reduction potential are mainly policy-driven, constrained by technological and resource limitations. This study underscores the role of computational modeling in providing valuable insights for the development of context-tailored carbon reduction strategies. It highlights the synergetic interactions among technological, organizational, and environmental factors, offering essential guidance for advancing sustainable development planning and facilitating the low-carbon transition of cities and communities.

Duchenne muscular dystrophy, one of the most complicated diseases for gene therapy

Gene therapy for Duchenne muscular dystrophy (DMD) is hindered by many pitfalls related in particular to the limitations of current technologies, the specificities of muscle and cardiac targets, and the disease itself, a chronic, multisystem, dystrophic and inflammatory disorder. Following RNA-based therapies, DNA gene transfer, mainly based on adeno-associated viral vectors, is now able to deliver therapeutic genetic sequences on a massive scale, and the first antisense and adeno-associated virus (AAV)-microdystrophin gene products are now reaching the marketing stage in Europe and/or in the US. However, only a subset of patients are eligible for those therapies. Many questions remain, such as the duration of the therapeutic effect, the burden of high doses of vectors, and the immunogenicity of viral capsids and therapeutic proteins, in the context of a disease-related inflammatory background. Evaluations of these treatments by the different biotech, pharma or non-for-profit sponsors also come up against the great clinical heterogeneity of patients. This review summarizes the significant progress made over the past three decades to optimize both the efficacy and safety of DMD gene therapies, as well as the remaining challenges, short-term prospects, and future directions such as more targeted vectors and combination therapies.

Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels.

Hybrid Learning Method in Teaching English for Medicine at the University of Bisha: Lecturers’ and students’ Perceptions

This study explores the implementation of hybrid learning, specifically through the Blackboard Learning Management System, as a pedagogical model in higher education. The purpose of the research is to examine the benefits and challenges associated with hybrid learning from the perspectives of lecturers and students, with a focus on its application in specialized disciplines such as medicine. The study adopts a qualitative design, employing semi-structured interviews, classroom observations, and document analysis to collect data from lecturers and students engaged in hybrid learning environments. The findings reveal that hybrid learning offers significant advantages, including enhanced flexibility, accessibility, and the integration of theoretical and practical knowledge. For lecturers, it facilitates innovative teaching strategies, fosters professional development, and enables efficient course management. For students, hybrid learning promotes self-discipline, time management, and active engagement while providing opportunities for real-world applications and lifelong learning. However, the study also identifies several challenges, including technological limitations, increased workload for lecturers, and skill gaps in navigating digital platforms. Additional issues, such as maintaining student engagement in asynchronous components, limited interaction in online activities, and assessment complexities, further underscore the need for targeted interventions. The research concludes that while hybrid learning is a transformative approach to modern education, its success requires robust technological infrastructure, comprehensive training for users, and institutional support. These measures can help address the identified challenges and optimize the benefits of hybrid models. The study highlights the importance of innovative pedagogical practices, tailored interventions, and a supportive digital ecosystem to enhance the effectiveness of hybrid learning, particularly in specialized and evolving academic fields such as medicine. This research provides valuable insights for educators, administrators, and policymakers aiming to implement or improve hybrid learning frameworks, contributing to the broader discourse on educational innovation in the digital age.

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this paper, we consider the challenge of scheduling a RT application modelled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTGs on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6 % and area usage reductions of 46.3 % are possible compared to full Software SW and HW execution, respectively.

The use of virtual reality in human training: trends and a research agenda

Traditional classroom training sessions can negatively impact participants’ engagement and knowledge retention. In response, several organisations are moving towards innovative and more engaging approaches, such as virtual reality (VR). By using VR, participants can immerse in a simulated environment, acquiring experiences in rare and hazardous situations that cannot be experienced in the natural environment without risk to the participants. VR has been applied in several areas, and this study aims to understand the applicability and impact of VR simulators on human training and perspectives of this emergent area. Drawn on a rigorous systematic literature review of 82 articles, the study presents findings from the thematic analysis. First, descriptive results outline the VR literature and its applications as an innovative training and learning tool. Second, a prescriptive thematic analysis has identified emerging themes related to behavioural, technological, and organisational benefits. The results also show the VR challenges related to technological limitations, restricted resources, and academic research. This study demonstrates the development of VR in human training, presenting contributions to theory and practice. The study presents some limitations related to the scope and focus of the study, and future pathways to address these limitations are suggested. For instance, the research protocol did not consider conference papers or articles that were not in English, and it was limited to a search period. Furthermore, emergent topics such as artificial intelligence and VR technology's physiological-emotional and sensory aspects in training should be addressed. Therefore, future studies should address these topics and use the research agenda suggested in this study.

SKILLS OF THE ENGLISH LANGUAGE TEACHER AS A SECOND LANGUAGE IN THE POST-PANDEMIC CONTEXT IN PUBLIC SCHOOLS

Abstract: This study investigates the challenges faced in English teaching during the Covid-19 pandemic, addressing the difficulties and strategies adopted by educators and students during this period. The research was conducted through a review of existing literature, utilizing original articles found in relevant databases. The results indicate that the lack of technological infrastructure and limitations in teacher training were the main obstacles, compromising student engagement and the effectiveness of pedagogical practices. Furthermore, the analysis emphasizes the need for new approaches and the importance of investments in technology and ongoing training for educators. It is concluded that to ensure quality English teaching, it is essential to promote methodological adaptations that meet the demands of the contemporary educational environment.

Sequencing technologies to measure translation in single cells.

Translation is one of the most energy-intensive processes in a cell and, accordingly, is tightly regulated. Genome-wide methods to measure translation and the translatome and to study the complex regulation of protein synthesis have enabled unprecedented characterization of this crucial step of gene expression. However, technological limitations have hampered our understanding of translation control in multicellular tissues, rare cell types and dynamic cellular processes. Recent optimizations, adaptations and new techniques have enabled these measurements to be made at single-cell resolution. In this Progress, we discuss single-cell sequencing technologies to measure translation, including ribosome profiling, ribosome affinity purification and spatial translatome methods.

Analysis of the Role of Regional Government in Optimizing the School Operational Assistance Program (BOSP) Policy in Surabaya as a Strategy for Improving Education Quality

The development of the implementation of the School Operational Assistance Policy (BOSP) in Surabaya faces various challenges that affect policy optimization. This study aims to analyze the role of local government in optimizing the BOSP policy using Edward III's policy implementation theory, which includes communication, bureaucratic structure, resources, and the disposition or attitudes of implementers. This research employs a qualitative method by collecting data through in-depth interviews with informants from the education office and educational institutions in Surabaya, as well as documentation studies related to the BOSP policy. The study results show that communication contributes 68% to the success of the BOSP policy implementation, although there are still shortcomings in responsiveness and socialization. Bureaucratic structure contributes 72%, with major challenges in fragmented coordination between units and complex decision-making procedures. Resources contribute the highest at 74%, but limitations in staff competence and technological infrastructure remain significant obstacles. The disposition or attitudes of implementers contribute 73% to success, though deficiencies in discipline, integrity, and initiative are still evident. These four aspects indicate that while most elements have performed well, there is still room for improvement requiring serious attention to enhance policy effectiveness.The conclusion of this study is that optimizing the BOSP policy in Surabaya requires strengthening the four analyzed aspects. To address communication shortcomings, more structured and responsive socialization mechanisms are needed. Bureaucratic fragmentation can be resolved by simplifying procedures and integrating data management systems. Human resource and technological limitations must be addressed through intensive training and infrastructure investment. Finally, implementer disposition can be improved through development programs and supervision. These strategic recommendations can support the Surabaya local government in optimizing the implementation of the BOSP policy in a transparent, accountable, and effective manner to achieve better educational goals.

Integration of Artificial Intelligence in Islamic Education: Trends, Methods, and Challenges in the Digital Era

This study employs a qualitative descriptive approach with a literature review method to analyze the application of Artificial Intelligence (AI) in Islamic education. Data were collected from 26 scholarly articles obtained through a systematic search in the SINTA database (1–6) and international journals (Q1–Q4), using keywords related to AI in Islamic education. Articles were selected based on their relevance to the research topic. Data analysis was conducted using descriptive analysis to identify AI usage trends and content analysis to evaluate key topics and challenges. The findings reveal a significant increase in AI usage since 2018, with descriptive analysis being the most commonly applied method. Key topics include AI integration in Qur'an and Hadith learning, the development of AI-based educational media, and the digitalization of Islamic manuscripts. Major challenges involve technological infrastructure limitations, ethical considerations, cultural sensitivities, and technological disparities between urban and rural areas.

Pentingnya SOP dalam Meningkatkan Kualitas Pelayanan Ketatausahaan di Sekolah Dasar

Standard Operating Procedures (SOPs) have an important role in creating information transparency, improving work efficiency, minimizing errors, and supporting the development of educators and education personnel's skills. This study uses a qualitative descriptive approach. The data used came from literature studies, direct observations, and interviews with administrative staff and teachers involved in school operations. The analysis process includes data reduction, data presentation, and conclusion drawing. The results of the analysis show that one of the components in managing administration in elementary schools to ensure the sustainability and quality of education is the Standard Operating Procedures or SOPs. Thus, a well-designed SOP is able to create information transparency, improve work efficiency, minimize errors, and support the development of the skills of educators and education personnel. The implementation of SOPs at SD IT Rocha Indonesia shows that although formal SOPs have not been fully implemented, initial steps such as digitization of archives, regular evaluations, staff training, and internal audits have supported more structured administrative management. Constraints such as technological limitations, manual attendance systems, and lack of formal guidelines can be overcome through the development of relevant SOPs and systematic implementation

TopoQual polishes circular consensus sequencing data and accurately predicts quality scores

BackgroundPacific Biosciences (PacBio) circular consensus sequencing (CCS), also known as high fidelity (HiFi) technology, has revolutionized modern genomics by producing long (10 + kb) and highly accurate reads. This is achieved by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently, the accuracy and quality value estimation provided by HiFi technology are more than sufficient for applications such as genome assembly and germline variant calling. However, there are limitations in the accuracy of the estimated quality scores when it comes to somatic variant calling on single reads.ResultsTo address the challenge of inaccurate quality scores for somatic variant calling, we introduce TopoQual, a novel tool designed to enhance the accuracy of base quality predictions. TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. Our results demonstrate that TopoQual corrects approximately 31.9% of errors in PacBio consensus sequences. Additionally, it validates base qualities up to q59, which corresponds to one error in 0.9 million bases. These improvements will significantly enhance the reliability of somatic variant calling using HiFi data.ConclusionTopoQual represents a significant advancement in genomics by improving the accuracy of base quality predictions for PacBio HiFi sequencing data. By correcting a substantial proportion of errors and achieving high base quality validation, TopoQual enables confident and accurate somatic variant calling. This tool not only addresses a critical limitation of current HiFi technology but also opens new possibilities for precise genomic analysis in various research and clinical applications.

A Magnetoelectric Distance Estimation System for Relative Human Motion Tracking.

Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value. Therefore, we propose a distributed magnetic tracking system based on early-stage demonstrators of novel magnetoelectric (ME) sensors. The system consists of two body-worn magnetic actuators and four ME sensor arrays (body-worn and fixed). It is enabled by a comprehensive signal processing framework with sensor-specific signal enhancement and a gradient descent-based system calibration. As a pilot study, we evaluated the technical feasibility of the described system for motion tracking in general (Scenario A) and for operation during treadmill walking (Scenario B). At distances of up to 60 cm, we achieved a mean absolute distance error of 0.4 cm during gait experiments. Our results show that the modular system is capable of centimeter-level motion tracking of the lower extremities during treadmill walking and should therefore be investigated for clinical gait parameter assessment.