Search Results (14,024)

Cyber–physical systems (CPSs) increasingly rely on complex software components whose vulnerabilities may affect both digital services and physical processes. Fuzzing is a practical technique for discovering such vulnerabilities in CPS-facing parsers, protocol handlers, and edge services. Distributed fuzzing improves throughput, but outsourcing fuzzing tasks to multiple untrusted nodes introduces privacy risks: valuable seeds, especially crash-triggering samples, may reveal vulnerability information before affected users are protected. In this paper, we propose PrivFuzz, a privacy-preserving collaborative fuzzing framework. PrivFuzz allows organizations and individuals to collaborate and receive rewards while keeping fuzzing seeds confidential and enabling controlled encrypted seed reuse among untrusted fuzzing nodes. The key idea is to combine trusted execution environments (TEEs) with blockchain-based smart contracts to support confidentiality and fair reward settlement. We give game-based definitions and reduction-style arguments for seed confidentiality, worker soundness, outsourcer atomicity, and duplicate-claim resistance under an attested execution model. We implement a PrivFuzz prototype and evaluate it on four open-source parsing targets. Separately, native AFL++ sanity checks suggest that CPS-facing industrial protocol parsers such as Modbus and OPC UA fall within the same fuzzable target domain. Demonstrating end-to-end PrivFuzz on CPS control programs is left as future work. Using PrivFuzz, we discovered nine bugs and reported them to the developers. Full article

The semiconductor industry faces converging pressures from energy-constrained edge electronics and energy-bottlenecked high-performance computing, motivating heterogeneous monolithic three-dimensional (M3D) integration as a system-level response. M3D imposes a strict back-end-of-line (BEOL) thermal budget on upper-tier devices, restricting the channel materials and contact processes available and degrading conventional thin-film transistor performance. The source-gated transistor (SGT), in which drain saturation is set by gate-modulated injection across an engineered source barrier rather than by drain-side channel pinch-off, provides a device-level response: low saturation voltage, high output impedance, large intrinsic gain, and tolerance to channel-length variation, all achieved with moderate-mobility and nonideal-contact channel materials. This review organizes reported SGTs by source-barrier architecture and channel-material platform, develops a spatial characterization framework that complements electrical measurements for unambiguous identification of source-controlled operation, and surveys applications across standalone edge electronics and BEOL-compatible upper tiers in M3D stacks. Integrating non-volatile memory mechanisms into the source barrier further extends SGTs into a compute-in-memory and neuromorphic upper-tier role in which the voltage-invariant saturation current itself functions as a programmable, read-bias-robust state variable. Together, these considerations position SGTs as a flexible architectural primitive for heterogeneous M3D platforms that address the energy demands of both edge and high-performance computing. Full article

To date, there are no studies concerning the ear aging phases, nor adequate anti-aging protocols. The aims of this study are: (1) to propose an aesthetic classification of auricular aging; (2) to evaluate the aesthetic implications, efficacy, and safety levels of botulinum toxin application to the auricles as anti-aging treatment. A retrospective study was conducted in a single center on 40 patients’ charts to define an aesthetic ear aging classification, focusing on the muscles involved in the aging process; then an analysis on the results of the treatment of 50 healthy women (50–65 years), with the botulinum toxin type A for ear rejuvenation was conducted; the technique, visual aesthetic results, incidence of side effects, and efficiency of the treatment were presented. All procedures were performed in 2024, and subjects were monitored for the duration of the effects. Auricle aging process may be classified into 4 degrees. The treatment of 50 women with aging ears degree 1–2 permits correction of both ear protrusion and shape. In 1.5% of the auricles, mild swelling and transient bruising at the injection site were observed immediately after the treatment and resolved within 2–3 days. Patient satisfaction was high, and two external reviewers confirmed the treatment’s aesthetic efficiency. Aesthetic classification of auricle aging can be a useful tool in anti-aging decision-making. Ear treatment with botulinum toxin type A seems complementary to face treatment. The results are encouraging, but the number of subjects treated is limited. Further studies are required to outline age-related guidelines. Full article

Background/Objectives: The emotional impact of a cancer diagnosis may become particularly salient during survivorship, as survivors continue to face fears and concerns related to recurrence. Fear of cancer recurrence (FCR) is one of the most prevalent psychological challenges and significant unmet need among women with female cancer. The aim of this study was to assess the presence of FCR in predominantly breast cancer survivors, and to examine its relationship with certain demographic, clinical, and psychosocial factors. Methods: This cross-sectional study was conducted with a convenience sample of 120 women with a history of breast or gynecological cancer who were recruited over a two-month period through several cancer support associations across Croatia. Data were collected in person using a questionnaire comprising sociodemographic and clinical variables, as well as four self-reported instruments: the Fear of Cancer Recurrence Inventory–Short Form, Impact of Event Scale, Cancer Behavior Inventory–Brief Version, and Multidimensional Scale of Perceived Social Support. Data were analyzed using descriptive statistics, Pearson’s correlation coefficient and hierarchical regression analysis. Results: In the final sample of 110 women, FCR levels were moderate, with 66.4% of participants reporting clinically significant levels. FCR was positively associated with chemotherapy (r = 0.22, p < 0.05), intrusive thoughts (r = 0.56, p < 0.01) and avoidance tendencies (r = 0.39, p < 0.01), and negatively associated with age (r = −0.26, p < 0.01) and self-efficacy for coping (r = −0.41, p < 0.01). In the final regression model, intrusive thoughts, coping self-efficacy, and chemotherapy were the only variables independently associated with fear of cancer recurrence. Conclusions: These findings indicate the presence of FCR in the period after primary treatment, particularly among breast cancer survivors, and highlight cognitive processing of cancer-related experiences and self-efficacy as potentially relevant areas for further research. Full article

Today, the use of increasingly ubiquitous synthetic media, or ‘deepfakes’, has become a risk to online trust, information integrity and individual security and is being created by artificial intelligence (AI). The current approaches are mainly based on either spatial features of CNNs or high-level semantic representations of Vision Transformer; both have major drawbacks in effectively leveraging multi-domain forensic cues. This paper presents FAViT (Frequency-Aware Vision Transformer), a hybrid architecture capable of jointly utilizing spatial- and frequency-domain forensic information by the means of a bidirectional cross-attention fusion scheme. We use an 11-channel forensic tensor in each face image (including per-channel Fast Fourier Transform (FFT) magnitude maps, Discrete Wavelet Transform (DWT) sub-bands, channel noise residual maps, Sobel gradient magnitude and channels of Error Level Analysis (ELA)). A Frequency Branch CNN processes this multi-domain tensor and the original RGB image is encoded with a pretrained ViT-B/16 spatial branch. The two streams are combined through the bidirectional cross-attention which allows the model to localize both spatial and spectral manipulation artifacts. We also present an adversarial cleaning simulation pipeline which partitions the training process with five post-processing attack methods, namely GFPGAN neural face restoration, learned autoencoder cleaning, etc., to increase resistance to real-world forensic defenses. Tests of FaceForensics++ C23 (7926 images, consisting of four manipulation types) show that FAViT attains F1-score of 86.22, AUC-ROC of 94.26 and accuracy of 85.55 on the held-out test set. The strength analysis of 21 attack conditions shows that the max degradation in AUC is 30.3, with specific strengths in GFPGAN restoration (AUC = 98.51). Robustness is evaluated based on 21 post-processing attack cases that include JPEG compression, Gaussian blurring, down-sampling, and GFDGAN neural-based restoration; it should be noted that robustness against gradient-based adaptive attacks requires additional attention. Testing on the CIFAKE and Celeb-DF v2 datasets reveals some limitations of domain generalization. Full article

Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models. Full article

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article

This study examines how firms convert dynamic capabilities into green innovation (GI) by developing green core competence (GCC). Drawing on resource-based theory and dynamic capability theory and incorporating core competence logic, the study investigates the direct effects of absorptive capacity (AC) and technological capability (TC) on GI, the mediating role of GCC, and the moderating role of green organizational culture (GOC) in the capability-to-competence process. Survey data were collected from 324 managers in China’s electronic information manufacturing industry, a technology-intensive sector facing pressures for technological upgrading and environmental improvement. Partial least squares structural equation modeling was used to test the proposed mediation and moderation model. The results show that AC and TC both positively affect GI and GCC, while TC has stronger effects. GCC positively affects GI and partially mediates the relationships between AC and GI and between TC and GI. GOC further strengthens the positive effects of AC and TC on GCC. These findings suggest that GI depends not only on the possession of dynamic capabilities but also on their conversion into a green-specific competence base. The study offers a contextualized explanation of how technology-intensive manufacturing firms organize internal capabilities to support sustainable manufacturing practices. Full article

The construction of large-scale buildings often faces extended production cycles due to inefficiencies in scheduling processes. To address this challenge, a timed Petri net model was developed to analyze and optimize construction scheduling. Based on the Petri net transition sequence, a scheduling optimization model was proposed. To solve the model efficiently, an improved brainstorming optimization (BSO) algorithm was introduced. Compared with the classical BSO, two targeted enhancements were introduced: a problem-specific encoding and decoding method for Petri net transition sequences to ensure solution feasibility and an embedded simulated annealing local search mechanism to prevent premature convergence in later iterations. The proposed methodology was validated using real-world data from a large high-speed railway hub foundation pit construction project. Results demonstrated a significant reduction of 531 working hours in the total scheduling time, representing a 15.47% improvement in scheduling efficiency compared to traditional sequential scheduling methods. This approach not only shortened the scheduling cycle and enhanced production efficiency but also offered an innovative solution to address scheduling issues in complex construction processes. Full article

The ReFuelEU Aviation Regulation introduces mandatory targets for sustainable aviation fuels (SAF) from 2025 to 2050. However, hydrotreated esters and fatty acids (HEFA) technology based on waste oils alone is insufficient to meet targets beyond 2030, highlighting the need for alternative biocrude feedstocks to increase SAF production in the EU. Microalgae are promising feedstocks due to their biochemical composition and CO₂-utilization potential, although their high moisture content and nitrogen and oxygen levels require energy-efficient conversion technologies. Hydrothermal liquefaction (HTL) is a suitable process for converting wet microalgal biomass into biocrude, with an optimal temperature window of approximately 300–330 °C and typical biocrude yields ranging from 20 to 70 wt%, depending on feedstock composition and operating conditions. However, microalgal HTL remains at TRL 5–7 and faces challenges related to the high heteroatom content of the resulting biocrude. Hydrodeoxygenation (HDO) is a key upgrading step for converting biocrude into drop-in aviation fuels and commonly operates at approximately 250–400 °C and 10–30 MPa H₂ pressure. Nevertheless, few studies have addressed the HDO of microalgae-derived biocrude. This review examines microalgal HTL, pilot and demonstration facilities, biocrude yields and quality, and upgrading strategies for producing synthetic drop-in aviation biofuels. Full article

Background and Clinical Significance: Facial aging is a multifactorial process involving the skin, subcutaneous tissues, facial fat compartments, muscles, ligaments, and skeletal structures. Integrated minimally invasive protocols are increasingly used to improve facial harmony and skin quality while preserving natural expression. The PATH (Profundity, Action, Timing, and Home care) protocol combines chemical peeling, hyaluronic acid–succinate filler, intradermal biorevitalization, and post-procedural homecare in a sequential and individualized approach to facial rejuvenation; Case Presentation: Three female patients aged 52–58 years with clinical signs of facial aging were treated according to the PATH protocol. Assessments were performed at baseline and after 60 days using standardized photography, OBSERV 520^®, Antera 3D PRO^®, and QuantifiCare LifeViz^® Infinity Pro. No serious adverse events or systemic complications were reported during the 60-day follow-up period. Mild edema, erythema, and ecchymosis resolved spontaneously within 48–72 h. At 60 days, all patients showed natural improvement in facial appearance, with better midface and lower-face balance, increased skin brightness, improved texture, and no overcorrection or alteration of facial expression. Instrumental evaluations supported the clinical findings, showing improvements in skin regularity, chromatic uniformity, microrelief, and soft-tissue distribution; Conclusions: This preliminary case series suggests that the PATH protocol may represent a coherent multimodal strategy for integrated facial rejuvenation. The main clinical lesson is that a sequential, depth-oriented, and individualized approach may achieve natural aesthetic improvement. No serious adverse events were reported in the three patients during the 60-day follow-up period; however, the limited sample size and short follow-up do not allow definitive conclusions regarding safety. Further controlled studies with larger samples and longer follow-up are needed to confirm these exploratory findings. Full article

The conventional von Neumann architecture, constrained by the memory and power walls arising from the separation of storage and computation, faces significant limitations in computational efficiency and energy consumption. To address these challenges, this paper proposes a computing-in-memory (CiM) architecture based on a hybrid 2T-2C/4T-2C ferroelectric random-access memory (FRAM) array. The proposed architecture performs majority-based bitwise computation by simultaneously activating multiple word lines, enabling AND and OR operations in conventional 2T-2C FRAM cells. Selectively embedded 4T-2C FRAM cells further provide in-array inversion, extending the supported functions to NOT and functionally complete Boolean logic. The architecture also supports full-adder operations and stores input operands, intermediate data, and output results within the same FRAM subarray, thereby reducing data movement. Moreover, the architecture provides ADC-free bitwise computing with binary inputs and outputs, reducing peripheral-circuit overhead and power consumption. The internal computation, nevertheless, relies on analog charge sharing and differential sense-amplifier resolution. HSPICE simulations indicate PVT-evaluated sensing stability and computational efficiency under the evaluated conditions. The bit-line voltage difference reaches 337 mV under triple-row activation and 214 mV under quintuple-row activation, with the former being 5.2 times that of the reported DRAM implementation used for comparison. At 3.3 V, process–voltage–temperature (PVT) simulations show that the maximum deviation of ΔV from its mean value remains below 4.62% across the evaluated process corners and temperatures from −40 °C to 125 °C. Simulations of the 8 × 8 FRAM CiM compute-array circuit model yield an energy consumption of 1.94–3.46 pJ/bit and a calculation latency of 0.599–1.167 ns for the supported bitwise operations, corresponding to a 4.86×–5.90× reduction in energy consumption compared with the reported DDR3-based design. The architecture also supports parallel processing and mitigates data loss associated with destructive FRAM readout through an in-array replication mechanism. Finally, an 8 × 8 hybrid FRAM CiM prototype was fabricated in a 180 nm CMOS process as a physical implementation of the proposed hybrid architecture, and its basic array functionality was verified. Full article

Gastric cancer is one of the malignancies with the highest incidence and mortality worldwide. Helicobacter pylori (H. pylori) infection is the primary driving factor in its development. The progression of gastric mucosal malignancy follows the Correa cascade model: “chronic non-atrophic gastritis → chronic atrophic gastritis (CAG) → intestinal metaplasia (IM) → dysplasia (Dys) → gastric cancer.” Currently, clinical management faces major challenges, including increasing antibiotic resistance in H. pylori, limited pharmacological options for gastric precancerous lesions, and treatment resistance and toxicity in established gastric cancer. This review synthesizes current evidence on BBR, COP, EPI, PAL, and JAT and organizes their reported actions into a three-tier intervention framework. At the first tier, etiologic and inflammatory interception, individual alkaloids suppress H. pylori persistence through direct antibacterial injury, urease inhibition, and modulation of bacterial virulence and antibiotic susceptibility, while attenuating infection-driven inflammatory and immune responses. At the second tier, modulation of precancerous mucosal progression, preclinical studies indicate that these compounds can ameliorate gastric glandular injury and may attenuate biological processes associated with progression toward intestinal metaplasia and dysplasia. At the third tier, antitumor and adjunctive intervention in established gastric cancer, alkaloids inhibit proliferation, induce cell-cycle arrest and apoptosis, suppress invasion and metastasis, and regulate non-coding RNA and epigenetic networks; BBR-centered preclinical studies further suggest potential chemosensitizing and supportive effects. This review integrates the five alkaloids BBR, COP, EPI, PAL, and JAT and systematically elucidates their mechanisms of action across the pathological continuum from H. pylori infection and chronic inflammation to precancerous lesions and ultimately gastric cancer. It establishes a stage-oriented, compound-specific analytical framework to clarify the pharmacological positioning of these compounds, identify priorities requiring further validation, and guide future mechanistic and translational research. Full article