Search Results (563)

Encryption-then-compression (EtC) based on block scrambling enables privacy-preserving image sharing while maintaining compatibility with standard image codecs, yet it disrupts the spatial correlations and synchronization cues required by conventional reversible data hiding (RDH). This difficulty is further amplified in grayscale-based EtC pipelines, where RGB-to-YCbCr conversion and component serialization introduce representation shifts and non-bijective rounding/clamping effects, complicating reliable embedding and extraction. This paper presents a unified RDH framework compatible with both RGB-based and grayscale-based block-scrambling EtC systems, without altering the underlying encryption procedures. The core idea is to restore embedding and extraction synchronization directly in the encrypted domain using two encryption-invariant cues: diagonal pixel absolute difference (DPAD) and an encryption-invariant synchronization index (EISI), together with domain-consistent handling of the grayscale conversion pipeline. Experimental results on standard datasets demonstrate perfect reversibility and stable embedding performance under the evaluated settings, with negligible impact on lossless compressibility. We further observe that the proposed embedding can increase statistical dispersion within encrypted blocks; although not designed as a security enhancement, this effect degrades the performance of representative texture-based analyses in the considered ciphertext-only setting. Full article

Lattice-based post-quantum cryptography relies on fast polynomial multiplication. The Number-Theoretic Transform (NTT) is the key operation that enables this acceleration. To provide high throughput and low latency while keeping the area overhead small, hardware implementations of the NTT is essential. This is particularly true for resource-constrained devices. However, existing NTT accelerators either achieve high throughput at the cost of large area overhead or provide compact designs with limited pipelining and low operating frequency. Therefore, this article presents a compact, seven-stage pipelined NTT accelerator architecture for post-quantum cryptography, using the CRYSTALS–Kyber algorithm as a case study. The CRYSTALS–Kyber algorithm is selected due to its NIST standardization, strong security guarantees, and suitability for hardware acceleration. Specifically, a unified three-stage pipelined butterfly unit is designed using a single DSP48E1 block for the required integer multiplication. In contrast, the modular reduction stage is implemented using a four-stage pipelined, lookup-table (LUT)-only Barrett reduction unit. The term “LUT-only” refers strictly to the reduction logic and not to the butterfly multiplication. Furthermore, two dual-port BRAM18 blocks are used in a ping-pong manner to hold intermediate and final coefficients. In addition, a simple finite-state machine controller is implemented, which manages all forward NTT (FNTT) and inverse NTT (INTT) stages. For validation, the proposed design is realized on a Xilinx Artix-7 FPGA. It uses only 503 LUTs, 545 flip-flops, 1 DSP48E1 block, and 2 BRAM18 blocks. The complete FNTT and INTT with final rescaling require 1029 and 1285 clock cycles, respectively. At 200 MHz, these correspond to execution times of 5.14 µs for the FNTT and 6.42 µs for the INTT. Full article

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology. Full article

In complex wireless communication environments, automatic modulation classification (AMC) faces two critical challenges: the lack of robustness under low-signal-to-noise ratio (SNR) conditions and the inefficiency of integrating multi-scale feature representations. To address these issues, this paper proposes CAIC-Net, a robust modulation classification network that integrates a dynamic cross-attention mechanism with a cross-SNR contrastive learning strategy. CAIC-Net employs a dual-stream feature extractor composed of ConvLSTM2D and Transformer blocks to capture local temporal dependencies and global contextual relationships, respectively. To enhance fusion effectiveness, we design a Dynamic Cross-Attention Unit (CAU) that enables deep bidirectional interaction between the two branches while incorporating an SNR-aware mechanism to adaptively adjust the fusion strategy under varying channel conditions. In addition, a Cross-SNR Contrastive Learning (CSCL) module is introduced as an auxiliary task, where positive and negative sample pairs are constructed across different SNR levels and optimized using InfoNCE loss. This design significantly strengthens the intrinsic noise-invariant properties of the learned representations. Extensive experiments conducted on two standard datasets demonstrate that CAIC-Net achieves competitive classification performance at moderate-to-high SNRs and exhibits clear advantages in extremely low-SNR scenarios, validating the effectiveness and strong generalization capability of the proposed approach. Full article

Planar multi-loop fractionated kinematic chains (FKCs)—kinematic chains that can be decomposed into two or more coupled subchains by separating joints or links—are widely used in heavy-duty manipulators, yet their large design space makes automatic synthesis and application-oriented screening challenging. The novelty of this paper is a general automated synthesis-and-screening framework for planar fractionated kinematic chains, regardless of whether multiple joints are present; multiple-joint chains are handled via an equivalent transformation to single-joint models, enabling the construction of a deduplicated topological graph atlas. In the mine scaler manipulator case study, an 18-link, 5-DOF (N18_M5) FKC with two multiple joints is taken as the target and converted into a single-joint equivalent N20_M7 model consisting of three subchains (KC1–KC3). Atlases of the required non-fractionated kinematic chains (NFKCs) for KC1 and KC3 are generated according to their link counts and DOFs. The subchains are then combined as building blocks under joint-fractionation (A-mode) and link-fractionation (B-mode) to enumerate fractionated candidates, and a WL-hash-based procedure is employed for isomorphism discrimination to obtain a non-isomorphic N20_M7 atlas. Finally, a connectivity-calculation-based screening is performed under task-driven structural and functional constraints, yielding 249 feasible configurations for the overall manipulator arm. The proposed pipeline provides standardized representations and reproducible outputs, offering a practical and transferable route from large-scale enumeration to engineering-feasible configuration sets for planar multi-loop FKCs, including those with multiple joints. Full article

The two-spotted spider mite, Tetranychus urticae Koch, is a major agricultural pest whose control is increasingly constrained by resistance to synthetic acaricides. This study evaluated the long-term field efficacy of three commercial entomopathogenic fungal (EPF) biopesticides—Velifer^® (Beauveria bassiana), Metab^® (B. bassiana + Metarhizium anisopliae), and Botanigard^® (B. bassiana)—against larval and protonymph stages of T. urticae on two host plants, Italian cypress (Cupressus sempervirens) and sweet orange (Citrus sinensis). Two foliar applications were conducted during the 2023 growing season (25 May and 25 July), and mite populations were monitored for 140 days after the final application. A randomized complete block design was used, and efficacy was calculated using the Henderson–Tilton formula. All EPF treatments significantly reduced mite populations compared with the untreated control throughout the monitoring period. Velifer consistently achieved the highest suppression of larval populations, particularly on C. sinensis, with efficacy comparable to the chemical standard. Botanigard showed more gradual but sustained population reduction over time, whereas Metab exhibited lower but stable efficacy in all cases. Treatment performance was strongly influenced by host plant species and mite developmental stage, with larvae consistently more susceptible than protonymphs. On C. sinensis, Velifer achieved the highest larval suppression (84.6%), comparable to the chemical standard abamectin, while Botanigard and Velifer were most effective on C. sempervirens. Survival analysis confirmed isolate- and host-dependent differences in hazard effects over time. These results demonstrate that EPF-based products can provide sustained, long-term suppression of T. urticae under field conditions, supporting their integration into integrated pest management programs. Full article

Background: The macrogeometry and shape of dental implants strongly influence primary stability, which may at times result in excessively high insertion torque. This in vitro study aimed to evaluate whether increasing coronal thread density could reduce insertion torque without compromising primary stability. Methods: Two conical implants with identical macrogeometry and surface characteristics (Ø 4.2 × 11.5 mm) differed only in the thread pitch of the coronal 3 mm: a modified version (27% more coronal threads; Group 1) and a standard, commercially available version (Group 2). Thirty implants of each design were inserted into high-density (D1; 40 PCF; pounds per cubic foot) and low-density (D3; 20 PCF) polyurethane blocks (n = 120). Insertion torque (IT) and implant stability quotient (ISQ, measured by resonance frequency analysis) were recorded. Group comparisons used the Kruskal–Wallis test, and a generalized linear model (GLM) assessed the independent effects of IT and design on ISQ in D1 bone. Results: In D1 bone, Group 2 showed higher IT (median 74.0 vs. 63.5 N·cm; p < 0.001) and ISQ (mean 79.1 vs. 77.4; p ≤ 0.030). The GLM identified IT as a negative predictor of ISQ (β = −0.267 per 1 N·cm; p < 0.001), and Group 2 was associated with higher ISQ (+3.90; p < 0.001). In D3 bone, Group 2 again exhibited higher IT (median 37.5 vs. 33.0 N·cm; p < 0.001), while ISQ values were similar between designs (all p > 0.35). Conclusions: Increasing coronal thread density lowers insertion torque without reducing stability in softer bone and maintains sufficient ISQ for immediate loading in dense bone, making the design advantageous for varied bone qualities. Full article

In object detection, boundary blurring caused by occlusion and background interference often hinders effective feature extraction. To address this challenge, we propose Edge Aware-YOLO, a novel framework designed to enhance edge awareness and efficient feature fusion. Our method integrates three key contributions. First, the Variable Sobel Compact Inverted Block (VSCIB) employs convolution kernels with adjustable orientation and size, enabling robust multi-scale edge adaptation. Second, the Spatial Pyramid Shared Convolution (SPSC) replaces standard pooling with shared dilated convolutions, minimizing detail loss during feature reconstruction. Finally, the Efficient Downsampling Convolution (EDC) utilizes a dual-branch architecture to balance channel compression with semantic preservation. Extensive evaluations on public datasets demonstrate that Edge Aware-YOLO significantly outperforms state-of-the-art models. On MS COCO, it achieves 56.3% mAP₅₀ and 40.5% mAP_50–95 (gains of 1.5% and 1.0%) with only 2.4M parameters and 5.8 GFLOPs, surpassing advanced models like YOLOv11. Full article

This study aims to evaluate the potential of circular economy principles in earth-based construction using an experimental rammed earth building located in Pasłęk, Poland as a case study. The research focuses on end-of-life scenarios for earth materials, with particular emphasis on rammed earth, adobe, and compressed earth blocks stabilized with Portland cement. A scenario-based life-cycle assessment (LCA) was conducted to compare alternative demolition and reuse strategies, including manual and mechanical deconstruction, as well as on-site and off-site material reuse. Greenhouse gas emissions associated with demolition (Module C1) and transport (Module C2) were estimated for each scenario. The results indicate that manual deconstruction combined with local, on-site reuse leads to the lowest carbon footprint, whereas off-site reuse involving long-distance transport significantly increases greenhouse gas emissions. In addition, qualitative reuse pathways were identified for wood, glass, ceramics, and insulation materials. The study reveals a lack of standardized technical procedures for the recovery and reuse of stabilized earthen materials after demolition and highlights the importance of integrating end-of-life planning into the early design phase using digital tools such as material passports and BIM. The findings demonstrate that properly designed rammed earth systems can provide a viable low-tech solution for reducing construction waste and supporting circular material flows in the built environment. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

Self-supervised monocular depth estimation has demonstrated high practical utility, as it can be trained using a photometric image reconstruction loss between the original image and a reprojected image generated from the estimated depth and relative pose, thereby alleviating the burden of large-scale label creation. However, this photometric image reconstruction loss relies on the Lambertian reflectance assumption. Under non-Lambertian conditions such as specular reflections or strong illumination gradients, pixel values fluctuate depending on the lighting and viewpoint, which often misguides training and leads to large depth errors. To address this issue, we propose a multitask learning framework that integrates albedo estimation as a supervised auxiliary task. The proposed framework is implemented on top of representative self-supervised monocular depth estimation backbones, including Monodepth2 and Lite-Mono, by adopting a multi-head architecture in which the shared encoder–decoder branches at each upsampling block into a Depth Head and an Albedo Head. Furthermore, we apply Intrinsic Image Decomposition to generate albedo images and design an albedo supervision loss that uses these albedo maps as training targets for the Albedo Head. We then integrate this loss term into the overall training objective, explicitly exploiting illumination-invariant albedo components to suppress erroneous learning in reflective regions and areas with strong illumination gradients. Experiments on the ScanNetV2 dataset demonstrate that, for the lightweight backbone Lite-Mono, our method achieves an average reduction of 18.5% over the four standard depth error metrics and consistently improves accuracy metrics, without increasing the number of parameters and FLOPs at inference time. Full article

Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques—Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)—and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems. Full article

This study evaluated the effects of nitrogen fertilization and different types of planting material on the yield and fruit quality of pineapple (Ananas comosus var. comosus) cv. Smooth Cayenne under the edaphoclimatic conditions of the Northern region of Rio de Janeiro State, Brazil. The experiment was conducted in a randomized block design, arranged in a factorial scheme with four nitrogen rates, six types of planting material, and two harvest seasons (winter and summer). Based on the results, it can be inferred that slips provided higher yields and heavier fruits, whereas plants derived from crowns and suckers showed lower productivity. Increasing nitrogen rates promoted greater fruit mass and length, higher pulp percentage, and increased production of vegetative propagules. Fruits harvested in the summer showed higher soluble solids content (15.5 °Brix), greater pulp and juice percentages, and lower titratable acidity, which are desirable characteristics for fresh consumption. Despite the seasonal differences, fruit mass ranging from 1.5 to 2.0 kg met commercial standards for both processing and domestic markets. The soluble solids/titratable acidity ratio (15.8) was below the ideal range for fresh consumption. The combination of appropriate planting material and nitrogen fertilization contributes to higher production efficiency, cost reduction, and improved fruit quality. Full article

The present study describes a digitally guided workflow for the Split Bone Block Technique (SBBT) using standardized cortical and particulate allogeneic grafts in combination with custom-designed, 3D-printed surgical guides. The aim was to illustrate the feasibility of a donor-site-free alternative to the conventional autologous approach, which remains technically demanding and associated with increased morbidity. A narrative literature review and a single clinical case report were conducted to contextualize the proposed workflow. Digital planning was performed by merging DICOM and STL datasets to design cutting boxes for standardized allogeneic laminae and a transporter guide for intraoperative positioning. The technique was applied in a patient with severe horizontal ridge atrophy. Primary wound closure and uneventful healing were achieved. Six-month CBCT evaluation demonstrated an increase in horizontal ridge width from 2 mm to 8 mm. Within the limitations of a single illustrative case, this report suggests that a fully guided allogeneic SBBT workflow is feasible and may facilitate controlled graft adaptation while avoiding autologous bone harvesting. Further controlled clinical studies are required to evaluate accuracy, reproducibility, and long-term outcomes. Full article

Compressing a mixture of soil, water, and stabilizer forms compressed stabilized earth blocks (CSEBs), a modernized earthen construction material capable of performance similar to that of engineered masonry with added sustainability achieved by usage of raw materials on-site, reduction in transportation costs of bulk materials to the build site, and improved thermal performance of built CSEB structures. CSEBs have a wide range of potential physical properties due to variations in base soil, mix composition, stabilizer, admixtures, and initial compression achieved in CSEB creation. While CSEB construction offers several opportunities to improve the sustainability of construction practices, assuring codifiable, standardized mix design for a target strength or durability remains a challenge as the mechanical character of the primary base soil varies from site to site. Quality control may be achieved through creation and testing of CSEB samples, but this adds time to a construction schedule. Such delays may be reduced through development of predictive CSEB compressive strength estimation models. This study experimentally determined CSEB compressive strength for six different mix compositions. Compressive strength predictive models were developed for 7-day and 28-day CSEB samples through multiple numerical models (i.e., linear regression, back-propagation neural network) designed and implemented to relate design inputs to 7-day and 28-day compressive strength. Model results provide insight into the predictive performance of linear regression and back-propagation neural networks operating on designed data streams. Performance, robustness, and significance of changes to the model dataset and feature set are characterized, revealing that linear regression outperformed neural networks on 28-day data and that inclusion of downstream data (i.e., cylinder compressive strength) did not significantly impact model performance. Full article