The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding
The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data Fabrication/Falsification Statement
The author(s) declare that no data has been fabricated, falsified, or manipulated in this study.
Participant Consent
The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained.
Copyright and Licensing
For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Muhammad Attique Khan, Usama Shafiq, Ameer Hamza
et al.
Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computational inefficiencies due to numerous hyperparameters persist. This study aims to address these challenges by presenting a novel deep-learning framework for classifying and localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed framework begins with dataset augmentation to enhance training robustness. Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. These features are classified using a Shallow Wide Neural Network (SWNN) and traditional classifiers. Experimental evaluations on the Kvasir-V1 and Kvasir-V2 datasets demonstrate superior performance, achieving accuracies of 99.60% and 95.10%, respectively. The proposed framework offers improved accuracy, precision, and computational efficiency compared to state-of-the-art models. The proposed framework addresses key challenges in GI disease diagnosis, demonstrating its potential for accurate and efficient clinical applications. Future work will explore its adaptability to additional datasets and optimize its computational complexity for broader deployment.
Computer applications to medicine. Medical informatics
Alida Vallejo-López, Cesar Noboa-Terán, Juana Kou-Guzmán
et al.
Technology has become a global tool that allows us to obtain information and analyze data, streamlines communication, and allows us to share images, data, videos, texts, etc. Daily activities have gone from traditional to digital. Today, it is impossible to live without an electronic device. In this context, changes in people's health observed, with various complaints ranging from visual, neurological, and concentration problems to muscular, hearing, and sleep disorders. Society must be aware of the importance of using various technological devices responsibly to protect people's health in general. Keywords: Technology, activities, protect, electronic, Radiation, Health.
Artists and especially new media artists contribute to public perceptions and adoption of new technologies through their own use of emerging media technologies such as augmented and virtual reality, generative image systems, and high-resolution displays in the production of their work. In this way, art and media production can be understood as part of the larger issue of unsustainable computational consumption. As such, it is critical for artists to develop, share, and promote new and more sustainable methods of engaging with technology, especially within the context of higher education. This paper will explore how artists might implement more sustainable methods by considering the relationship between the technical approaches of compute reuse, sustainable web development, and frugal computing, and the concepts of material specificity , futurity, and media archaeology . Proposing three methods of less carbon-intensive artistic production and a set of guidelines for introducing sustainable methods into arts and technology curriculum, this paper will outline not only the technical viability of these approaches but also the rich conceptual opportunities these approaches might offer to artists and viewers alike. For each method, models for pedagogical implementation will be explored with an emphasis on how local resources and sustainability contexts should play a role.
Polycaprolactone (PCL) implants in large animals show great promise for tracheal transplantation. However, the longest survival time achieved to date is only about three weeks. To meet clinical application standards, it is essential to extend the survival time and ensure the complete integration and functionality of the implant. Our study investigates the use of three-dimensional (3D)-printed, biodegradable, PCL-based tracheal grafts for large-scale porcine tracheal transplantation, assessing the feasibility and early structural integrity crucial for long-term survival experiments. A biodegradable PCL tracheal graft was fabricated using a BIOX bioprinter and transplanted into large-scale porcine models. The grafts, measuring 20 × 20 × 1.5 mm, were implanted following a 2 cm circumferential resection of the porcine trachea. The experiment design was traditionally implanted in eight porcines to replace four-ring tracheal segments, only two of which survived more than three months. Data were collected on the graft construction and clinical outcomes. The 3D-printed biosynthetic grafts replicated the native organ with high fidelity. The implantations were successful, without immediate complications. At two weeks, bronchoscopy revealed significant granulation tissue around the anastomosis, which was managed with laser ablation. The presence of neocartilage, neoglands, and partial epithelialization near the anastomosis was verified in the final pathology findings. Our study demonstrates in situ regenerative tissue growth with intact cartilage following transplantation, marked by neotissue formation on the graft’s exterior. The 90-day survival milestone was achieved due to innovative surgical strategies, reinforced with strap muscle attached to the distal trachea. Further improvements in graft design and granulation tissue management are essential to optimize outcomes.
Rachael Fleurence, Jiang Bian, Xiaoyan Wang
et al.
This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.
Mohammad Ali Tareq, Piyush Tripathi, Nurhayati Md Issa
et al.
Although blockchain technology was first introduced in 2008 and materialised in 2009, the early usage of blockchain were mainly limited to financial technologies, particularly cryptocurrencies. Later, blockchain became a widespread emerging technology, utilised in multifaceted sectors and applications. In fact, various new and innovative application of blockchain and distributed ledger technologies are still continuously being researched and explored. On the other hand, smart-contracts were first introduced in 1990s, however, it did not gain enough popularity until being integrated with blockchain technologies lately. The duo lately been seen as the key to many innovations in various industries and sectors. So, we took data from 1445 blockchain-related patent documents and tried to map out the historical and current trends in patenting activities in the blockchain field. This helps us get a better grasp of how blockchain technologies are evolving and being tracked. In addition to serving as an indicator of science and technology growth, patents are also used to judge the research potential and development of a particular technology.
Z. Seskir, Steven Umbrello, Christopher Coenen
et al.
As quantum technologies (QT) advance, their potential impact on and relation with society has been developing into an important issue for exploration. In this paper, we investigate the topic of democratization in the context of QT, particularly quantum computing. The paper contains three main sections. First, we briefly introduce different theories of democracy (participatory, representative, and deliberative) and how the concept of democratization can be formulated with respect to whether democracy is taken as an intrinsic or instrumental value. Second, we give an overview of how the concept of democratization is used in the QT field. Democratization is mainly adopted by companies working on quantum computing and used in a very narrow understanding of the concept. Third, we explore various narratives and counter-narratives concerning democratization in QT. Finally, we explore the general efforts of democratization in QT such as different forms of access, formation of grassroot communities and special interest groups, the emerging culture of manifesto writing, and how these can be located within the different theories of democracy. In conclusion, we argue that although the ongoing efforts in the democratization of QT are necessary steps towards the democratization of this set of emerging technologies, they should not be accepted as sufficient to argue that QT is a democratized field. We argue that more reflexivity and responsiveness regarding the narratives and actions adopted by the actors in the QT field and making the underlying assumptions of ongoing efforts on democratization of QT explicit, can result in a better technology for society.
In this paper, we first define the notion of finitely-cosmall quotient (singly-cosmall quotient) morphisms. Then we give a characterization of this new concept. We show that an epimorphism p:Y→U is a finitely-cosmall quotient (singly-cosmall quotient) if and only if for any right R-module Z any morphism g:Z→Y such that pg is a finitely-copartial isomorphism (singly-copartial isomorphism) from Z to Y with codomain U is a finitely (singly) split epimorphism. We also investigate the relation between pure-cosmall quotient and finitely-cosmall quotient (singly-cosmall quotient) morphisms. We prove that over a right Noetherian ring R, an epimorphism p:Y→U is a pure-cosmall quotient morphism if and only if p is a finitely-cosmall quotient (singly-cosmall quotient) morphism. Moreover, we obtain an example of right minimal morphisms by using finitely-cosmall quotient (singly-cosmall quotient) morphisms.
Colin M. Gray, Shruthi Sai Chivukula, Janna Johns
et al.
Ethics as embodied by technology practitioners resists simple definition, particularly as it relates to the interplay of identity, organizational, and professional complexity. In this paper we use the linguistic notion of languaging as an analytic lens to describe how technology and design practitioners negotiate their conception of ethics as they reflect upon their everyday work. We engaged twelve practitioners in individual co-creation workshops, encouraging them to reflect on their ethical role in their everyday work through a series of generative and evaluative activities. We analyzed these data to identify how each practitioner reasoned about ethics through language and artifacts, finding that practitioners used a range of rhetorical tropes to describe their ethical commitments and beliefs in ways that were complex and sometimes contradictory. Across three cases, we describe how ethics was negotiated through language across three key zones of ecological emergence: the practitioner's "core" beliefs about ethics, internal and external ecological elements that shaped or mediated these core beliefs, and the ultimate boundaries they reported refusing to cross. Building on these findings, we describe how the languaging of ethics reveals opportunities to definitionally and practically engage with ethics in technology ethics research, practice, and education.
Vahidin Jeleskovic, David Alexander Behrens, Wolfgang Karl Härdle
Technology adoption research aims to determine the reasons why and how individuals, corporations, and industries start using new technology. Furthermore, technology adoption itself is decomposed into underlying sub-processes which are characterized by a finite number of sequential states in order to capture its evolutionary nature. Building upon that, in this paper a technology adoption index is being constructed that allows for statistical testing. This new framework is flexible with respect to the number of underlying models, and accounts for nonlinearities within the evolution of technology adoption. It can be considered as novel because it gives opportunity to a quantitative analysis of technology adoption that has not existed before. Subsequently, this framework is applied for an integrated model of technology adoption.
Yu-Kai Lin, Chih-Sung Liang, Chia-Kuang Tsai
et al.
Alzheimer’s disease (AD) involves the abnormal activity of transition metals and metal ion dyshomeostasis; however, the potential of trace metal biomarkers in predicting cognitive decline has not been evaluated. This study aimed to assess the potential of 36 trace elements in predicting cognitive decline in patients with amnestic mild cognitive impairment (aMCI) or AD. Participants (9 controls, 23 aMCI due to AD, and 8 AD dementia) underwent comprehensive cognitive tests, including the Mini-Mental State Examination (MMSE) and trace metal analysis. The correlations between the plasma trace element levels and annual MMSE changes during follow-up were analyzed. We found that an increase in disease severity was linked to lower plasma levels of boron (B), bismuth (Bi), thorium (Th), and uranium (U) (adjusted p < 0.05). Higher baseline calcium levels (r = 0.50, p = 0.026) were associated with less annual cognitive decline; those of B (r = −0.70, p = 0.001), zirconium (r = −0.58, p = 0.007), and Th (r = −0.52, p = 0.020) with rapid annual cognitive decline in the aMCI group; and those of manganese (r = −0.91, p = 0.035) with rapid annual cognitive decline in the AD group. Overall, our exploratory study suggests that plasma metal levels have great potential as in vivo biomarkers for aMCI and AD. Larger sample studies are necessary to confirm these results.
Abstract Post-secondary institutions are investing in and utilizing virtual reality (VR) for many educational purposes, including as a discretionary learning tool. Institutions such as vocational schools, community colleges, and universities need to understand what psychological factors drive students’ acceptance of VR for learning in discretionary contexts. The Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al. in MIS Quarterly 27:425–478, 2003) offers a theoretical framework for understanding students’ receptivity to VR for learning. Undergraduate university students (N = 300) read a description of VR and video training mediums, then indicated which they would choose to learn a novel task. Three psychological variables—performance expectancy, effort expectancy, and social influence—tended to be related to acceptance of VR, which was measured in two ways: (a) rated intentions to use VR and (b) preference for VR over a video-based alternative. Relative weight analyses compared the importance of the three predictors and revealed that performance expectancy tended to be the most influential antecedent of VR acceptance.
Special aspects of education, Information technology
Md Zahirul Islam Khan, Shing Yau Tam, Helen Ka Wai Law
Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.
Mauro Falconieri, Serena Gagliardi, Flaminia Rondino
et al.
Impulsive stimulated Raman scattering (ISRS) is a nonlinear pump–probe spectroscopy technique particularly suitable to study vibrational intermolecular and intramolecular modes in complex systems. For the latter, recent studies of ISRS microscopy with low-energy laser sources have attracted attention for investigation of photosensitive or biological samples. Following this stream of interest, in this paper, we report an investigation on the relationship between femtosecond ISRS data and pump–probe Z-scan measurements, showing that the latter technique is capable of capturing the Kerr nonlinearities induced by the molecular vibrational modes. To this aim, firstly, spectrally filtered and Raman-induced Kerr ISRS signals were simultaneously acquired to determine the sample nonlinear response and to establish the reference data for the Z-scan analysis. Then, by adopting a suitable experimental arrangement to avoid thermo-optical effects, we were able to unambiguously observe the Raman-induced effects in Z-scan measurements, thus obtaining a consistent picture between ISRS and Z-scan for the first time, to the best of our knowledge. Practical applications of the proposed method include calibrated measurements of the contribution of the internal (Raman) and external molecular modes to the nonlinear refractive index.
Johannes Sedlmeir, Hans Ulrich Buhl, Gilbert Fridgen
et al.
The enormous power consumption of Bitcoin has led to undifferentiated discussions in science and practice about the sustainability of blockchain and distributed ledger technology in general. However, blockchain technology is far from homogeneous - not only with regard to its applications, which now go far beyond cryptocurrencies and have reached businesses and the public sector, but also with regard to its technical characteristics and, in particular, its power consumption. This paper summarizes the status quo of the power consumption of various implementations of blockchain technology, with special emphasis on the recent 'Bitcoin Halving' and so-called 'zk-rollups'. We argue that although Bitcoin and other proof-of-work blockchains do indeed consume a lot of power, alternative blockchain solutions with significantly lower power consumption are already available today, and new promising concepts are being tested that could further reduce in particular the power consumption of large blockchain networks in the near future. From this we conclude that although the criticism of Bitcoin's power consumption is legitimate, it should not be used to derive an energy problem of blockchain technology in general. In many cases in which processes can be digitised or improved with the help of more energy-efficient blockchain variants, one can even expect net energy savings.
Abstract Peptide Nucleic Acid (PNA) are DNA/RNA synthetic analogs with 2‐([2‐aminoethyl] amino) acetic acid backbone. They partake unique antisense and antigene properties, just due to its inhibitory effect on transcription and translation; they also undergo complementary binding to RNA/DNA with high affinity and specificity. Hence, to date, many methods utilizing PNA for diagnosis and treatment of various diseases namely cancer, AIDS, human papillomavirus, and so on, have been designed and developed. They are being used widely in polymerase chain reaction modulation/mutation, fluorescent in‐situ hybridization, and in microarray as a probe; they are also utilized in many in‐vitro and in‐vivo assays and for developing micro and nano‐sized biosensor/chip/array technologies. Earlier reviews, focused only on PNA properties, structure, and modifications related to diagnostics and therapeutics; our review emphasizes on PNA properties and synthesis along with its potential applications in diagnosis and therapeutics. Furthermore, prospects in biomedical applications of PNAs are being discussed in depth.