Search Results (286)

Human viral entry receptor mouse models exist to overcome a fundamental experimental barrier: many clinically important viruses bind their human entry factors far more efficiently than the corresponding murine orthologs, leaving conventional mice unable to support authentic infection, physiological tissue tropism, or meaningful countermeasure evaluation. This review is organized around the receptor-humanization concept rather than around a single coronavirus model. Engineering strategies compared here include random transgenesis, endogenous-locus knock-in, minimal receptor-interface humanization, conditional and inducible expression, and transient vector-mediated delivery. Receptor systems covered span human angiotensin-converting enzyme 2 (hACE2)-dependent sarbecoviruses, human dipeptidyl peptidase 4 (hDPP4)-dependent Middle East respiratory syndrome coronavirus (MERS-CoV), human cluster of differentiation 4/human C-C chemokine receptor type 5 (hCD4/hCCR5)-dependentt human immunodeficiency virus type 1 (HIV-1), adenovirus receptor models, human intercellular adhesion molecule 1 (hICAM-1) rhinovirus systems, hepatitis C virus (HCV), hepatitis B virus (HBV), and hepatitis D virus (HDV) entry-factor models, measles receptor models, poliovirus receptor/CD155 (PVR/CD155) models, human scavenger receptor class B member 2 (hSCARB2) enterovirus systems, and human transferrin receptor 1 (hTfR1) arenavirus models. We then discuss how these platforms support antibody evaluation, Fc-effector analysis, vaccine protection, variant benchmarking, and safety assessment. These models yield the most reliable data when the experimental question is explicitly entry-dependent and when receptor expression level, anatomical distribution, pathology window, and immune context have all been independently validated. They are least informative when receptor expression is non-physiological, when disease readouts are driven by promoter artifacts, or when post-entry species barriers remain the dominant bottleneck. A validation-centered framework is therefore proposed to guide the selection of each model for the specific antiviral antibody or vaccine question it can legitimately answer. Full article

This article reports the design, implementation, and formative perception-based evaluation of an early-stage virtual reality (VR) prototype that integrates a virtual cane, localized haptic feedback, 3D audio, and a three-layer bioinspired sensing framework. The prototype was implemented in Unity 2022.3 using the XR Interaction Toolkit and URP and was structured according to design science research methodology (DSRM). The bat–whisker–contact framework was used as a functional abstraction to organize distal auditory reference or warning, proximal haptic feedback, and contact confirmation; it was not evaluated against a non-bioinspired baseline. The completed evaluation consisted of an anonymous, voluntary, post-use questionnaire administered to 25 sighted participants who could select which visual-input configurations to experience. The analysis focused on reported clarity, tolerability, initial signal interpretability, and design feedback; it did not include objective navigation metrics or assess clinical efficacy, training transfer, accessibility outcomes, or orientation-and-mobility performance in blind or low-vision users. General responses suggested favorable perceived clarity and multimodal usefulness, while cane length, floor-versus-wall/obstacle differentiation, and reported discomfort identified priorities for technical refinement. In the simulated no-vision condition (n = 21), participants reported high reliance on the cane response, whereas reported initial insecurity or doubt and basic mental map ratings remained mixed. The study contributes an early-stage technological artifact and a formative basis for subsequent controlled evaluations with objective performance measures, reference conditions, and target users or orientation and mobility specialists. Full article

Digital forensics increasingly operates under extreme data growth, where exhaustive bit-wise imaging of modern storage is constrained by time, storage, and processing cost. Selective imaging and artifact prioritization methods reduce acquisition volume, but they typically order artifacts by content, location, or offense type rather than by the relationships that make evidence interpretable. As a result, they risk discarding the contextual artifacts on which attribution and corroboration depend. This paper introduces the High-Value Evidence Area (HVEA) pyramid, a dependency-oriented abstraction that organizes Windows forensic artifacts into ten operational layers (A–J) grouped into four evidentiary tiers: attribution anchor, primary payload, behavioral contextualization, and structural corroboration, in which acquisition order follows interpretive prerequisites rather than artifact salience. The model is evaluated on nine Windows forensic images spanning Windows XP, Vista, and Windows 11, combining retrospective analysis of the M57-Patents corpus with a controlled fourteen-day behavioral experiment. Across systems and operating system generations, HVEA layers exhibit stable evidentiary function despite changing artifact implementations; behavioral execution telemetry persists even where user content is sparse or deliberately concealed; and a three-source timestamp corroboration pattern consistently converges within thirty seconds across independent OS mechanisms, providing an empirically grounded defensibility threshold for event reconstruction. The results support a dependency-ordered, NIJ-aligned selective acquisition strategy that preserves interpretive context while reducing acquisition footprint by approximately 95% (a 15–20× reduction) on the controlled Windows 11 image. The present evaluation is scoped to Windows environments; extending the tier mappings to other platforms is identified as future work. Full article

The rise of 3D cell culture systems—including organoids, spheroids, and organ-on-a-chip models—has transformed our understanding of tumor biology, disease pathology, and tissue development. However, accurately analyzing spatial phenotypic content within these complex architectures remains a formidable challenge. While contemporary protocols strive for precise protein localization, their reliability is frequently undermined by technical artifacts and the structural degradation of the 3D matrices. These distortions are often induced by invasive harvesting, harsh clearing agents, and frequent sample transfers. To bridge the gap between complex 3D tissue architectures and reliable assay readouts, this study establishes a systematic troubleshooting framework for whole-mount 3D immunofluorescence staining. Utilizing a diverse panel of 24 distinct antibodies targeting membrane, cytoplasmic, and nuclear proteins across human airway organoids and liver cancer spheroids, we executed comprehensive mono-, double-, and triple-labeling configurations. To evaluate workflow boundaries, we conducted a series of controlled whole-mount experiments where specific, common technical mistakes were deliberately introduced. By documenting the exact imaging artifacts, structural distortions, and aberrant signal profiles generated by these intentional procedural errors, this study provides a unique visual “atlas of failure” paired directly with validated methodological solutions. The study offers a practical, high-throughput diagnostic resource to eliminate technical error and experimental noise for whole-mount immunofluorescence labeling experiments, thereby facilitating high-quality imaging and consistent phenotypic validation. Full article

Real-world speech restoration must handle coupled distortions, including acoustic noise and reverberation, codec artifacts, clipping, and artifacts left by upstream enhancement systems. Token-based generative systems offer a flexible route for such universal restoration, but discrete audio tokens can discard fine acoustic detail, and aggressive generative decoding may over-process inputs that are already close to clean speech. We propose AudioVAE-MASR, a continuous-latent masked autoregressive framework for multi-distortion speech restoration. A frozen AudioVAE maps clean and degraded speech into paired continuous latent sequences; a Conformer-based branch extracts the degraded-condition sequence $C_y$ from degraded latents; a two-stream masked autoregressive encoder-decoder conditions masked clean-latent recovery on both degraded context and visible clean tokens; and a lightweight diffusion head models the masked clean tokens in the continuous latent space. On the released CCF AATC 2025 blind test set, the main inference setting ($K=16$, temperature $0.5$) achieved WAcc 0.793, SIG 3.401, BAK 3.987, OVRL 3.111, PESQ 1.780, and ESTOI 0.798. Relative to the degraded input, these results improved WAcc and DNSMOS but did not improve PESQ; relative to the organizer baseline, they improved WAcc, SIG, OVRL, and PESQ but remained lower in BAK. A local subjective MOS evaluation with five listeners gave an overall mean score of 4.08 for AudioVAE-MASR, compared with 3.70 for the degraded input and 4.59 for the clean reference. Distortion-type, ablation, and parameter-sensitivity analyses further show that codec inputs remain vulnerable to over-restoration and that longer iterative decoding does not provide a consistent gain. The study therefore presents AudioVAE-MASR as a transparent continuous-latent restoration framework and identifies the fidelity-control problems that must be solved before such generative restoration can match the strongest lightweight discriminative systems. Full article

Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproducible benchmark and validation workflow for the public SASB-Aligned ESG Sentences corpus, a sentence-level sustainability disclosure dataset organized around standards-based categories such as those used in Sustainability Accounting Standards Board (SASB) analytics. Using the downloaded 6460-row version of the corpus, we construct fixed train/validation/test splits, map released child labels to parent categories, and evaluate label reuse through supervised classifiers, prompted GPT-4o classification, blind and candidate-visible Claude annotation, and Monte Carlo aggregation into ESG/Non-ESG category profiles. The reproducibility artifacts provide split metadata, label mappings, prompt templates, model predictions, LLM annotation outputs, profile sensitivity outputs, figure inputs, and scripts for reproducing the reported tables and figures. Results show that label reproduction is strongest at coarser label levels, blind annotation flags 40.3% of held-out sentences as ambiguous, candidate-visible annotation increases agreement while changing the task format, and aggregate profiles remain sensitive to label source. The benchmark supports transparent reuse of sentence-level ESG labels by reporting label source, annotation condition, prompt family, and aggregation level. Full article

Understanding the neural dynamics underlying expressive musical performance remains a major challenge at the intersection of neuroscience, music cognition, and computational modeling. While Electroencephalogram (EEG) studies of emotion have largely focused on passive exposure to affective stimuli, comparatively little research has examined oscillatory brain activity during active musical expression. The present single-subject study investigated whether band-limited EEG activity recorded during expressive piano performance by a professional concert pianist contains sufficient discriminative structure to support supervised multi-class classification of musically defined emotional categories. EEG was recorded from 128 scalp sites while a professional concert pianist performed emotionally characterized excerpts from Bach, Beethoven, and Chopin in a continuous naturalistic session. Musical excerpts had been previously categorized and perceptually validated according to emotional valence, tempo, energy/arousal, and tonal structure. From the continuous EEG recording, 180 non-overlapping 2 s artifact-free segments were extracted, yielding 30 segments for each emotional category. Mean spectral power was computed within theta (3.5–7.5 Hz), alpha (7.5–12.5 Hz), and high-beta (24–30 Hz) frequency bands across selected centro-parietal and posterior electrodes, resulting in 24 EEG-derived features per segment. Linear Support Vector Machine, Random Forest, and Gradient Boosting classifiers were evaluated using an 80/20 train-test split combined with five-fold cross-validation. EEG-only classification achieved above-chance performance across models, with Random Forest yielding the highest accuracy (0.42), macro F1-score (0.414), and Cohen’s κ (0.30), exceeding the theoretical chance level of 0.167. Feature importance analysis revealed distributed contributions across theta, alpha, and high-beta oscillatory activity, particularly over parietal and occipital regions, without evidence for a single dominant neural marker. Inclusion of an additional binary arousal-related feature substantially improved Random Forest performance (accuracy = 0.58; macro F1 = 0.579; κ = 0.50), indicating that arousal organization contributed strongly to category separability within the classification framework. These findings suggest that oscillatory EEG activity accompanying expressive musical action contains measurable statistical structure associated with emotionally differentiated performance states. Rather than identifying discrete neural correlates of emotion, the present results provide a computational characterization of distributed oscillatory dynamics emerging during expressive motor-acoustic interaction, extending affective EEG research beyond passive perception paradigms toward ecologically grounded musical performance contexts. Full article

This multi-site ethnographic study focuses on the utility of using student growth data generated from teacher evaluations as a data-use intervention. Findings suggest that teachers developed a common perspective about evaluation but organizational contexts shaped teacher responses at the three sites differently. At two of the schools, teachers were responsible for administering their own assessments, organizing results, and presenting these results to the principal. In both schools, the data-use process was similar. Teachers administered a variety of assessments, chose from among the results, constructed artifacts metrics, and presented these metrics to the principal. At the third school, student achievement was calculated using an externally developed assessment and student growth scores were calculated at the district level. At this school, teachers maintained a unified front that disestablished the link between their performance and their students’ assessment results. The teachers maintained this stance in their interactions with colleagues and with the principal. Full article

This paper proposes a model for account-holding proofs across multiple authorities and presents a concrete construction from JWT-derived evidence, enabling a verifier to evaluate the resulting artifact under specified system assumptions and acceptance policies. Conventional account linkage approaches often depend on particular identity providers (IdPs) or linkage mechanisms and may expose or correlate more credential information than is necessary for the verifier’s policy. To address this, we formalize a scheme-agnostic abstract model and organize its security, disclosure-related, and deployment requirements. As a concrete instantiation, we apply an RSA-based synchronized aggregate signature scheme to encoded messages derived from the RS256 preprocessing step of JWT signing inputs. The resulting artifact is not a standard JWT and is not intended for direct verification by existing JWT/OIDC verifiers; rather, it provides a single aggregate signature component over multiple JWT-derived evidence items. Through analytical and prototype-based evaluation, we show that the signature-component data to be presented is reduced from n individual components to a single aggregate component and that the exponentiation applied to the presented signature component is reduced from $O\left(n\right)$ to $O\left(1\right)$, while the overall verification remains dominated by per-message public-key terms. The results of prototype implementation indicate that aggregate verification is not faster than ordinary RS256 JWT verification under the evaluated parameters; therefore, the construction is better suited to one-time or low-frequency account-holding proof scenarios in which the additional latency is tolerable. Full article

Agile teams often struggle to translate business requirements into maintainable, high-quality software due to the persistent ambiguity in the roles and relationships of behavior-driven development (BDD), Acceptance Test-driven Development (ATDD), and Test-driven Development (TDD). These approaches are frequently misunderstood, inconsistently applied, and only loosely connected within a unified delivery lifecycle. This article introduces BLOW (Behavior-Driven Development in a Layered Organization of Work-Centers), a systematic approach that establishes BDD as the coordinating methodology between ATDD (business-focused) and TDD (technology-focused). BLOW structures scenario-driven development across layered domains of accountability with clearly defined roles and responsibilities, organizing delivery through nested work-centers that transform user stories into executable specifications and production code. This approach integrates two complementary collaboration practices: the Three Amigos for discovering and formulating business scenarios, and the proposed Technical Three Amigos for linking those scenarios to Technical Domain Contexts, identifying required Enablers, and deriving technical scenarios when additional architectural support is needed. The proposed operating model emphasizes observability through executable scenarios as first-class artifacts, introducing native, test-anchored metrics that support reasoning about progress, technical effort, and value delivery within scenario-driven development. An exploratory longitudinal case study, consisting of a single-sprint proof of concept followed by an 18-month production deployment, reports patterns in which technical enablement precedes business value delivery and reusable infrastructure supports sustained growth of business scenarios over time. The findings also indicate that changes in the applied operating model are associated with measurable shifts in scenario evolution and internal quality indicators. Overall, BLOW provides a governance-compatible, end-to-end approach for organizing scenario driven development and improving alignment between stakeholder intent and technical implementation in complex software systems. Full article

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

The documentation and reconstruction of fragile underwater organic artifacts remain among the most challenging tasks in digital heritage practice. This study presents a conservation-first, contact-minimizing protocol applied to a rare Byzantine leather bag recovered from the commercial port of Rhodes, Greece. Due to its incomplete preservation and structural instability, exclusively non-invasive methodologies were employed. High-resolution close-range photogrammetry and structured-light 3D scanning were integrated to capture both micro-topographic detail and metrically stable geometry. Quantitative deviation analysis (nearest-neighbor cloud-to-mesh distances) indicated that most geometric differences remain below 0.5 mm. The resulting models were processed through controlled mesh optimization, UV remapping, and conservation-oriented digital completion workflows. In addition, radiance field visualization techniques such as Gaussian Splatting were explored as complementary visualization approaches for incomplete geometries. These methods were evaluated primarily in terms of visual continuity and interpretative support rather than as reconstruction tools. The study demonstrates that the integration of photogrammetry, structured-light scanning, and Gaussian Splatting can significantly enhance the documentation and visualization of fragile underwater organic heritage. At the same time, it highlights the necessity of methodological transparency and ethical framing when incorporating probabilistic reconstructions into conservation workflows. Full article

This study presents the development and field testing of a Pressurized Driving-Down Air Multilevel Sampler (PDA-MLS), an integrated groundwater sampling device designed for depth-discrete sampling in boreholes affected by floating non-aqueous phase liquids (NAPLs). Conventional sampling methods—such as low-flow pumps, bailers, and packer-isolated systems—often fail under these conditions due to limited accessibility, cross-contamination, or disturbance of the water column. The proposed system addresses these limitations through a controlled pressurized-gas actuation mechanism that transfers groundwater from multiple PTFE-membrane chambers installed at discrete depths. This configuration enables low-disturbance sampling below floating contaminant layers. The use of chemically inert materials (stainless steel and PTFE) minimizes sampling artifacts and ensures compatibility with volatile organic compound (VOC) analyses. A simplified hydraulic conceptual framework describing inflow, outflow, and pressure-driven displacement was developed to support purge-duration estimation and operational parameter definition. The device was tested in a 90 m deep fractured limestone aquifer contaminated by tetrachloroethylene (PCE), where floating hydrocarbons limited the applicability of conventional sampling techniques. Field testing showed stable discharge conditions (~145–160 mL/min), repeatable sampling cycles, and successful collection of depth-discrete groundwater samples under the investigated site conditions. No evidence of sampler-related hydrocarbon entrainment was observed in the collected samples within the analytical detection limits of the adopted laboratory methods. To the authors’ knowledge, the PDA-MLS represents one of the few groundwater sampling systems specifically designed to combine low-disturbance multilevel sampling with operation in wells affected by floating NAPL. These features make it a promising tool for environmental monitoring, high-resolution characterization of fractured aquifers, and long-term assessment of contaminated sites. Full article

Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behavior changes faster than conventional probability baselines. This article proposes a method for operationalizing likelihood through a cyber exposure profile that integrates external cyber knowledge and organization-specific telemetry into a graph-based representation. The contribution is a formally specified artifact chain—from unified data model through organization-specific profiling, metric registry, likelihood scoring, and control prioritization—that operationalizes four constructs grounded in incident evidence: exposure, traceability, motivation, and systems update. The pipeline provides a pathway from heterogeneous source evidence to a bounded likelihood indicator comparable across organizations and observation periods. An evaluation in 15 real organizations shows that those implementing the cyber exposure profile were associated with reduced incident frequency and faster detection and response times, providing preliminary empirical support for the framework’s directional claims. Full article