Search Results (182)

The endogenous security paradigm has emerged to address the limitations of traditional cybersecurity, which relies on reactive “patching” and struggles against unknown threats, APTs, and supply chain attacks. Centered on the principle that “structure determines security”, it diverges from detection-based approaches by employing systems theory and cybernetics to architect closed-loop systems with “heterogeneous execution, multimodal adjudication, and dynamic scheduling”. This is realized through intrinsic architectural constructs such as dynamism, heterogeneity, and redundancy. Theoretically, it transforms deterministic component-level attacks into probabilistic system-level events, thereby shifting the security foundation from a “cognitive contest” to an “entropy-driven confrontation”. This paper provides a comprehensive review of this paradigm. We begin by elucidating its philosophical foundations and core axioms, focusing on the Dynamic Heterogeneous Redundancy (DHR) model, which converts attacks on specific vulnerabilities into probabilistic events under the core assumption of independent heterogeneous execution entities. Next, we trace the architectural evolution from early mimic defense prototypes to a universal framework, analyzing key developments including expanded heterogeneity dimensions, intelligence-driven dynamic policies, and enhanced adjudication mechanisms. We then explore essential enabling technologies and their integration with cutting-edge trends such as artificial intelligence, 6G, and cloud-native computing. Through case studies of the 5G core network and intelligent connected vehicles, the engineering feasibility of the endogenous security paradigm has been validated, with quantifiable security gains demonstrated. In a live-network pilot of the endogenous security micro-segmentation system for the 5G core, resource consumption (CPU/memory usage) of network function virtual machines remained below 3% under steady-state service loads. The system concurrently maintained microsecond-level forwarding performance and achieved carrier-grade core service availability of 99.999%. These results demonstrate that the endogenous security mechanism delivers high-level structural security with an acceptable performance cost. The paper also critically summarizes current theoretical, engineering, and ecosystem challenges, while outlining future research directions such as “Endogenous Security as a Service” and convergence with quantum-safe technologies. Full article

Developmental dyslexia (DD) often involves difficulties in phonological processing of speech. Objectives: While underlying neural changes have been identified in terms of stimulus- and task-related responses within specific brain regions and their neural connectivity, there is still limited understanding of how these changes affect the overall organization of brain networks. Methods: This study used EEG and functional network analysis, focusing on small-world propensity across various frequency bands (from δ to γ), to explore the global brain organization during the auditory discrimination of words and pseudowords in children with DD. Results: The main finding revealed a systemic inefficiency in the functional network of individuals with DD, which did not achieve the optimal small-world propensity. This inefficiency arises from a fundamental trade-off between localized specialization and global communication. During word listening, the δ-/γ1-networks (related to impaired syllabic and phonemic processing of words) and the θ-/β-networks (related to pseudoword listening) in the DD group showed lower local clustering and connectivity compared to the control group, resulting in reduced functional segregation. In particular, the θ-/β-networks for words in the DD group exhibited a less optimal balance between specialized local processing and effective global communication. Centralized midline hubs, such as the postcentral gyrus (PstCG) and inferior frontal gyrus (IFG), which are crucial for global coordination, attention, and executive control, were either absent or inconsistent in individuals with DD. Consequently, the DD network adopted a constrained, motor-compensatory, and left-lateralized strategy. This led to the redirection of information flow and processing effort toward the left PstCG/IFG loop, interpreted as a compensatory effort to counteract automatic processing failures. Additionally, the γ1-network, which is involved in phonetic feature binding, lacked engagement from posterior sensory hubs, forcing this critical process into a slow and effortful motor loop. The γ2-network exhibited unusual activation of right-hemisphere posterior areas during word processing, while it employed a simpler, less mature routing strategy for pseudoword listening, which further diminished global communication. Conclusions: This functionality highlights the core phonological and temporal processing deficits characteristic of dyslexia. Full article

Internationally, there are several studies that examined the relationship between core executive functions (working memory, inhibitory control, and cognitive flexibility) and mind wandering. These studies focused mostly on adult samples and there are fewer studies that examined this relationship with children and adolescent samples. Therefore, the current systematic review aims to identify and critically examine the existing peer-reviewed literature on the relationship between the core executive functions and mind wandering. Journal articles reporting quantitative results were identified through keyword searches in PsycINFO, Scopus, and PubMed. In total, 750 references were identified using the specified keywords. Among those, only ten studies were deemed to fit the inclusion criteria. The majority of the studies employed behavioural measures. The evidence on the relationship between the core executive functions and mind wandering was rather scarce and mixed. Most of the studies suggest that working memory capacity is critical for reduced mind wandering. The evidence regarding inhibitory control is rather mixed. Cognitive flexibility may underpin adaptive reallocation of attention between internal and external states, producing performance declines. The directional nature of the relationship between the three core executive functions and mind wandering is largely an unresolved matter, which requires further research. Full article

The exponential growth and complexity of legal agreements pose significant Big Data challenges and strategic risks for modern organizations, often overwhelming traditional, manual contract management workflows. While AI has enhanced legal research, most current applications require extensive domain-specific fine-tuning or substantial annotated data, and Large Language Models (LLMs) remain susceptible to hallucination risk. This paper presents an AI-based Agreement Management System that addresses this methodological gap and scale. The system integrates a Python 3.1.2/MySQL 9.4.0-backed centralized repository for multi-format document ingestion, a role-based Collaboration and Access Control module, and a core AI Functions module. The core contribution lies in the AI module, which leverages zero-shot learning with OpenAI’s GPT-4o and structured prompt chaining to perform advanced contractual analysis without domain-specific fine-tuning. Key functions include automated metadata extraction, executive summarization, red-flag clause detection, and a novel feature for natural-language contract modification. This approach overcomes the cost and complexity of training proprietary models, democratizing legal insight and significantly reducing operational overhead. The system was validated through real-world testing at a leading industry partner, demonstrating its effectiveness as a scalable and secure foundation for managing the high volume of legal data. This work establishes a robust proof-of-concept for future enterprise-grade enhancements, including workflow automation and predictive analytics. Full article

Conventional sensing expends energy at three stages: powering dedicated sensors, transmitting measurements, and executing computationally intensive inference. Wireless sensing re-purposes WiFi channel state information (CSI) inherent in every packet, eliminating extra sensors and uplink traffic, though reliance on deep neural networks (DNNs) often trained and run on graphics processing units (GPUs) can negate these gains. This review highlights two core energy efficiency levers in CSI-based wireless sensing. First ambient CSI harvesting cuts power use by an order of magnitude compared to radar and active Internet of Things (IoT) sensors. Second, integrated sensing and communication (ISAC) embeds sensing functionality into existing WiFi links, thereby reducing device count, battery waste, and carbon impact. We review conventional handcrafted and accuracy-first methods to set the stage for surveying green learning strategies and lightweight learning techniques, including compact hybrid neural architectures, pruning, knowledge distillation, quantisation, and semi-supervised training that preserve accuracy while reducing model size and memory footprint. We also discuss hardware co-design from low-power microcontrollers to edge application-specific integrated circuits (ASICs) and WiFi firmware extensions that align computation with platform constraints. Finally, we identify open challenges in domain-robust compression, multi-antenna calibration, energy-proportionate model scaling, and standardised joules per inference metrics. Our aim is a practical battery-friendly wireless sensing stack ready for smart home and 6G era deployments. Full article

Instruction Set Architecture (ISA) extensions, particularly scalar cryptography extensions (Zk), combine the performance advantages of hardware with the adaptability of software, enabling the direct and efficient execution of cryptographic functions within the processor pipeline. This integration eliminates the need to communicate with external cores, substantially reducing latency, power consumption, and hardware overhead, making it especially suitable for embedded systems with constrained resources. However, current scalar cryptography extension implementations remain vulnerable to physical threats, notably power side-channel attacks (PSCAs). These attacks allow adversaries to extract confidential information, such as secret keys, by analyzing the power consumption patterns of the hardware during operation. This paper presents an optimized and secure implementation of the RISC-V scalar Advanced Encryption Standard (AES) extension (Zkne/Zknd) using Domain-Oriented Masking (DOM) to mitigate first-order PSCAs. Our approach features optimized assembly implementations for partial rounds and key scheduling alongside pipeline-aware microarchitecture optimizations. We evaluated the security and performance of the proposed design using the Xilinx Artix7 FPGA platform. The results indicate that our design is side-channel-resistant while adding a very low area overhead of 0.39% to the full 32-bit CV32E40S RISC-V processor. Moreover, the performance overhead is zero when the extension-related instructions are properly scheduled. Full article

Background/Objectives: Chronic Obstructive Pulmonary Disease (COPD) is frequently associated with dyspnea, impaired health-related quality of life (HRQoL), and cognitive dysfunction. Although pulmonary rehabilitation (PR) is considered a core therapeutic strategy, its specific effects on cognitive function, dyspnea, and dysphonia remain unclear. This systematic review aimed to evaluate the impact of PR and respiratory or cognitive-focused rehabilitative interventions on dyspnea, quality of life, cognitive performance, and voice outcomes in adults with COPD. Methods: This review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420251131325). A systematic search of PubMed, Scopus and Web of Science identified studies published between 2010 and 21 August 2025. Eligible designs included randomized and non-randomized controlled studies, cohort, and mixed-method studies involving adults with COPD undergoing rehabilitative interventions targeting dyspnea, cognition, dysphonia, or swallowing. Outcomes included cognitive measures, dyspnea scales, voice parameters, and HRQoL indices. Results: Twelve studies (n ≈ 810 participants) met inclusion criteria. Most PR and exercise-based programs showed improvements in global cognition and executive functions, particularly when combined with cognitive training or high-intensity exercise modalities. Dyspnea improved consistently following short- to medium-term PR or respiratory muscle training, whereas low-frequency long-term programs yielded limited benefit. HRQoL improved across structured PR programs, especially in multidimensional interventions. Only one study assessed dysphonia, reporting transient improvements in maximum phonation time following inspiratory muscle training. No included study evaluated dysphagia-related outcomes. Conclusions: PR and respiratory muscle training can enhance cognition, dyspnea, and HRQoL in COPD, although evidence for dysphonia remains scarce and dysphagia is entirely unaddressed. Future high-quality trials should adopt standardized outcome measures, include long-term follow-up, and integrate voice and swallowing assessments within PR pathways. Full article

Background and Clinical Significance: Giant sphenoid wing meningiomas are generally viewed as skull base masses that compress frontal centers and their respective pathways gradually enough to cause a dysexecutive–apathetic syndrome, which can mimic primary neurodegenerative disease. The aim of this report is to illustrate how bedside phenotyping and multimodal imaging can disclose similar clinical presentations as surgically treatable network lesions. Case Presentation: An independent, right-handed older female developed an incremental, two-year decline of her ability to perform executive functions, extreme apathy, lack of instrumental functioning, and a frontal-based gait disturbance, culminating in a first generalized seizure and a newly acquired left-sided upper extremity pyramidal sign. Standardized neuropsychological evaluation revealed a predominant frontal-based dysexecutive profile with intact core language skills, similar to behavioral-variant frontotemporal dementia (bvFTD). MRI demonstrated a large, right fronto-temporo-basal extra-axial tumor attached to the sphenoid wing with homogeneous postcontrast enhancement, significant vasogenic edema within the frontal projection pathways, and a marked midline displacement of structures with an open venous pathway. With the use of a skull-base flattening pterional craniotomy with early devascularization followed by staged internal debulking, arachnoid preserving dissection, and conservative venous preservation, the surgeon accomplished a Simpson Grade I resection. Sequential improvements in the patient’s frontal “re-awakening” were demonstrated through postoperative improvements on standardized stroke, cognitive and functional assessment scales that correlated well with persistent decompression and symmetric ventricles on follow-up images. Conclusions: This case illustrates the possibility of a non-dominant sphenoid wing meningioma resulting in a pseudo-degenerative frontal syndrome and its potential for reversal if recognized as a network lesion and treated with tailored, venous-sparing skull-base surgery. Contrast-enhanced imaging and routine frontal testing in atypical “dementia” presentations may aid in identifying additional patients with potentially surgically remediable cases. Full article

This study examined how negative emotions influence three core components of cognitive control, inhibition, updating, and shifting, as assessed through a Go/No-Go, 2-back, and set-switching task, respectively. Participants performed these three tasks under both negative and neutral emotional conditions. Negative emotions led to slower response times on false-positive trials, suggesting increased interference during inhibitory demands rather than a direct impairment of inhibition. In the 2-back task, accuracy decreased on Non-Match trials under negative emotions, indicating difficulties in updating working memory and disengaging from irrelevant information. In the switching task, participants showed higher error rates under negative emotions regardless of trial type, pointing to a broader decline in performance when cognitive flexibility is required. Correlation analyses indicated that emotion-related effects were associated between updating and shifting, but not with inhibition, suggesting that negative emotions preferentially affect partially overlapping control processes depending on their cognitive demands. These findings highlight that the impact of negative emotions is not uniform across executive functions and underscore the importance of investigating emotion–cognition interactions across multiple domains within individuals. Full article

With an aging population and the high incidence of neurological diseases, rehabilitative lower limb exoskeleton robots, as a wearable assistance device, present important application prospects in gait training and human function recovery. As the core of human–computer interaction, control strategy directly determines the exoskeleton’s ability to perceive and respond to human movement intentions. This paper focuses on the control strategies of rehabilitative lower limb exoskeleton robots. Based on the typical hierarchical control architecture of “perception–decision–execution,” it systematically reviews recent research progress centered around four typical control tasks: trajectory reproduction, motion following, Assist-As-Needed (AAN), and motion intention prediction. It emphasizes analyzing the core mechanisms, applicable scenarios, and technical characteristics of different control strategies. Furthermore, from the perspectives of drive system and control coupling, multi-source perception, and the universality and individual adaptability of control algorithms, it summarizes the key challenges and common technical constraints currently faced by control strategies. This article innovatively separates the end-effector control strategy from the hardware implementation to provide support for a universal control framework for exoskeletons. Full article

The conceptual understanding of genetic information has evolved from early philosophical speculation to the molecular precision of contemporary biology. Initial debates over the nature of heredity, including Mendel’s hereditary factors and the longstanding protein versus nucleic acid controversy, underscored the difficulty of identifying the true substrate of inheritance. Subsequent discoveries, including reverse transcription, protein-based infectivity (prions), transposable elements, and the regulatory functions of non-coding RNAs, revealed molecular processes that operate at the boundaries of, or alongside, Crick’s original formulation of the Central Dogma of Molecular Biology. Importantly, these findings do not violate the directional rules of information transfer defined by the Central Dogma (DNA → RNA → protein), but instead reshape how, when, and under what constraints these canonical flows are executed in living systems. Epigenetic and epigenetic-like mechanisms, including DNA methylation, histone modifications, chromatin topology, non-canonical DNA conformations, and cytoplasmic inheritance, introduce regulatory layers that modulate information flow without constituting independent information matrices. In parallel, genome innovation, through de novo gene birth, and genome erosion, through pseudogenization, demonstrate that the repertoire of DNA → RNA → protein pathways is itself evolutionarily dynamic. This narrative integrative review reconstructs the historical milestones that culminated in the Central Dogma and synthesizes subsequent discoveries that expand its functional realization. By revisiting the Central Dogma through an extended, holistic lens, this article argues that DNA, RNA, and proteins function not only as carriers of genetic information, but also as active participants in its regulation, contextualization, and evolutionary diversification, without departing from the core directional principles originally articulated by Crick. For reader convenience, a dedicated section entitled “Abbreviations and Key Molecular Terms” is provided at the end of the manuscript to facilitate navigation and interdisciplinary accessibility. Full article

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

Digital twin technology is emerging as a critical enabler for the lifecycle management of complex engineering equipment, yet its implementation faces significant hurdles. Generic, one-size-fits-all digital twin platforms often fail to address the unique characteristics of this domain—such as tightly coupled multi-physics, high-fidelity modeling requirements, and the need for real-time model execution under harsh operating conditions. This creates a critical need for a structured, reusable blueprint. However, a dedicated reference architecture that systematically guides the development of such specialized digital twins is notably absent. To bridge this gap, this paper proposes MetaD-DT, a reference architecture designed to enable and streamline the development of digital twins specifically for complex engineering equipment. We detail its comprehensive four-layer architecture, core functional modules, and streamlined graphical development workflow. The MetaD-DT’s efficacy and practical value are validated through two distinct industrial case studies: a health management system for diesel engine Diesel Particulate Filter (DPF) and an intelligent control optimization system for Indirect Air-Cooled (IAC) towers. These applications validate the framework’s ability to support the creation of robust digital twins that can effectively handle complex industrial dynamics and improve O&M (Operation And Maintenance) efficiency. This work provides a systematic architectural blueprint for the future development of specialized and efficient digital twins in the engineering equipment domain. Full article

Public procurement in many European Union member states remains strongly price-oriented, often at the expense of delivery performance, innovation, and effective risk management. This study examines how the Best Value Approach (BVA) operates within a post-transition, legality-focused administrative environment through a document-based embedded case study of a major public construction contract in the Czech Republic. By analysing artefacts from the Selection, Clarification, and Execution phases, the study traces how BVA’s core governance mechanisms—expert signalling, vendor-led risk ownership, and information-centric oversight—functioned under locally constraining conditions. The findings show that BVA improved capability sorting, surfaced risks earlier, and enhanced transparency through structured reporting instruments such as Weekly Risk Reports (WRRs), Directors’ Reports (DRs), and Key Performance Indicators (KPI)s. However, the performance effects were partial. Three boundary conditions attenuated BVA’s mechanisms: a 40% price weighting that constrained qualitative differentiation, the omission of a formal Value-Added (VA) pathway for supplier-initiated optimisation, and the absence of continuous expert facilitation to support methodological fidelity. A documented execution-phase cost variance of approximately five percent further indicates residual volatility where key BVA complements are incomplete. The study integrates Principal–Agent theory, New Public Governance, and institutional isomorphism to explain why BVA’s governance architecture activated only in attenuated form and identifies the institutional conditions that moderate its effectiveness. While limited to a single revelatory case, the findings support analytical generalisation to similarly price-dominant, audit-driven procurement regimes in post-transition EU member states and offer practical guidance for evaluation design, innovation pathways, and facilitation models. Full article