Search Results (282)

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

The utilization of steel slag (SS) in construction materials represents an effective approach to improving its overall recycling efficiency. This study incorporates SS into a conventional ground granulated blast-furnace slag (GGBS)–fly ash (FA)-based binder system to develop a ternary system comprising SS, GGBS, and FA, and investigates how this system influences the static mechanical properties of ultra-high-performance geopolymer concrete (UHPGC). An axial point augmented simplex centroid design method was employed to systematically explore the influence and underlying mechanisms of different binder ratios on the workability, axial compressive strength, and flexural performance of UHPGC, and to determine the optimal compositional range. The results indicate that steel slag has a certain negative effect on the flowability of UHPGC paste; however, with an appropriate proportion of composite binder materials, the mixture can still exhibit satisfactory flowability. The compressive performance of UHPGC is primarily governed by the proportion of GGBS in the ternary binder system; an appropriate GGBS content can provide enhanced compressive strength and elastic modulus. UHPGC exhibits ductile behavior under flexural loading; however, replacing GGBS with SS significantly reduces its flexural strength and energy absorption capacity. The optimal static mechanical performance is achieved when the mass proportions of SS, GGBS, and FA are within the ranges of 9.3–13.8%, 66.2–70.7%, and 20.0–22.9%, respectively. This study provides a scientific approach for the valorization of SS through construction material applications and offers a theoretical and data-driven basis for the mix design of ultra-high-performance building materials derived from industrial solid wastes. Full article

Memory impairment, ranging from mild memory impairment to neurodegenerative diseases such as Alzheimer’s disease, poses an escalating global health challenge that necessitates multi-targeted interventions to prevent progression. Health functional foods (HFFs), which include bioactive dietary compounds that not only provide basic nutrition but also function beyond that to modulate physiological pathways, offer a promising non-pharmacological strategy to preserve memory function. This review presents an integrative framework for the discovery, evaluation, and clinical translation of biomarkers responsive to HFFs in the context of preventing memory impairment. We examine both established clinical biomarkers, such as amyloid-β and tau in the cerebrospinal fluid, neuroimaging indicators, and memory assessments, as well as emerging nutritionally sensitive markers including cytokines, microRNAs, gut microbiota signatures, epigenetic modifications, and neuroactive metabolites. By leveraging systems biology approaches, we explore how network pharmacology, gut–brain axis modulation, and multi-omics integration can help to elucidate the complex interactions between HFF components and memory-related pathways such as neuroinflammation, oxidative stress, synaptic plasticity, and metabolic regulation. The review also addresses the translational pipeline for HFFs, from formulation and standardization to regulatory frameworks and clinical development, with an emphasis on precision nutrition strategies and cross-disciplinary integration. Ultimately, we propose a paradigm shift in memory health interventions, positioning HFFs as scientifically validated compounds for personalized nutrition within a preventative memory function framework. Full article

Precise grading of manufactured sand is vital to concrete performance, yet standard sieve tests, though accurate, are too slow for online quality control. Thus, we devised an image-based inspection method combining a dual-camera module with a Temporal Interval Sampling Strategy (TISS) to enhance throughput while maintaining precision. In this design, a global wide-angle camera captures the entire particle field, whereas a local high-magnification camera focuses on fine fractions. TISS selects only statistically representative frames, effectively eliminating redundant data. A lightweight segmentation algorithm based on geometric rules cleanly separates overlapping particles and assigns size classes using a normal-distribution classifier. In tests on ten 500 g batches of manufactured sand spanning fine, medium, and coarse gradations, the system processed each batch in an average of 7.8 min using only 34 image groups. It kept the total gradation error within 12% and the fineness-modulus deviation within ±0.06 compared to reference sieving. These results demonstrate that the combination of complementary optics and targeted sampling can provide a scalable, real-time solution. Full article

Black garlic is a thermally processed product derived from fresh garlic through controlled high-temperature and -humidity conditions. During this process, the formation of 5-hydroxymethylfurfural (5-HMF), a potentially harmful byproduct, is a major quality and safety concern in food processing. This study systematically investigated the distributions of 5-HMF and key process-related biochemical components in black garlic samples from three major production regions in China—Jiangsu, Yunnan, and Shandong. Additionally, correlations between 5-HMF and biochemical components—reducing sugars, amino acids, and organic acids—were analyzed to inform process optimization strategies. Results showed significant regional variation in 5-HMF content, with Jiangsu black garlic exhibiting the highest levels, followed by Yunnan and Shandong (p < 0.05). Partial least squares regression analysis (PLSR) indicated that the key biochemical factors regulating 5-HMF accumulation are primarily organic acids. Among them, citric acid was identified as the most important negative regulator (VIP = 3.11). Although acetic acid (VIP = 1.38) and malic acid (VIP = 1.03) showed positive correlations with 5-HMF, aspartic acid (VIP = 0.41) and fructose (VIP = 0.43) exhibited a weak positive correlation, and arginine (VIP = 0.89) showed weak negative correlations, their effects were far less significant than that of citric acid. Based on these findings, we propose a potential strategy for reducing 5-HMF content in black garlic—selecting raw material cultivars with higher endogenous citric acid levels or exploring the exogenous addition and regulation of citric acid during processing. This study provides a theoretical foundation for understanding the accumulation mechanism of 5-HMF in black garlic and suggests new potential regulatory directions for controlling its content. Full article

A vanadium dioxide (VO₂)-based layered metastructure is proposed that enables dynamic optical encoding in the range of 15.5 GHz to 16 GHz through synergistic temperature and magnetic field modulation. By utilizing sequential temperature control, an optical date flip-flop (DFF) functionality can be achieved. The VO₂ component of the metastructure exhibits an insulator-to-metal phase transition under thermal regulation, accompanied by significant changes in its optical properties. Furthermore, by optimizing the sequential temperature-control strategy, an optical DFF is successfully implemented whose output state can be dynamically controlled by the data input (D), timing control port (T), and state control port (B). A novel technical approach is provided for programmable photonic devices, dynamic optical information storage, and optical computing systems. Full article

In this paper, we investigate a class of fuzzy partially differentiable models for Caputo–Hadamard-type Goursat problems with generalized Hukuhara difference, which have been widely recognized as having a significant role in simulating and analyzing various kinds of processes in engineering and physical sciences. By transforming the fuzzy partially differentiable models into equivalent integral equations and employing classical Banach and Schauder fixed-point theorems, we establish the existence and uniqueness of solutions for the fuzzy partially differentiable models. Furthermore, in order to overcome the complexity of obtaining exact solutions of systems involving Caputo–Hadamard fractional derivatives, we explore numerical approximations based on trapezoidal and Simpson’s rules and propose three numerical examples to visually illustrate the main results presented in this paper. Full article

Plant phenology is closely related to plant function, ecosystem services, and climate balance. Solar-induced chlorophyll fluorescence (SIF) offers new perspectives on plant phenology at regional and global scales. However, the effect of SIF products at different scales on phenology extraction is still unclear. Understanding of the mechanisms underlying phenological responses to environmental factors remains incomplete. Therefore, in this study, two phenological metrics for the Start of Growing Season (SOS) and the End of Growing Season (EOS) were extracted from the phenology of deciduous forests in the middle and high latitudes of the Northern Hemisphere, utilizing SIF products at scales of 1 km, 5 km, and 50 km, and applying the Savitzky-Golay filtering method along with the dynamic threshold method. Our results showed that the 1-km resolution SIF had a significant advantage over the 5-km and 50-km resolution SIFs in terms of consistency with the extracted phenology results from the Gross Primary Productivity (GPP) sites, with mean absolute errors (MAEs) of 4.48 and 15.49 days for SOS and EOS, respectively. For the 5-km resolution SIF, the MAEs for the same phenological metrics were 9.2 and 21.07 days. For the 50-km resolution SIF, the MAEs were 58.94 and 42.73 days. Meanwhile, this study analyzed the trends of phenology utilizing the three scales of SIF products and found a general trend of advancement. The coarser spatial resolution of the SIF data made the trend of advancement more obvious. Using SHapley Additive exPlanations (SHAP) analysis, we investigated the phenological responses to environmental factors at different scales. We found that SOS/EOS were mainly regulated by soil and air temperature, whereas the scale effect on this analysis’ results was not significant. This study has implications for optimizing the use of data, understanding ecosystem changes, predicting vegetation dynamics under global change, and developing adaptive management strategies. Full article

Background: Predicting malignancy in small lung nodules (SLNs) across diverse populations is challenging due to significant demographic and clinical variations. This study investigates whether transfer learning (TL) can improve malignancy prediction for SLNs using low-dose computed tomography across datasets from different countries. Methods: We collected two datasets: an Asian dataset (669 SLNs from Cathay General Hospital, CGH, Taiwan) and an American dataset (600 SLNs from the National Lung Screening Trial, NLST, America). Initial U-Net models for malignancy prediction were trained on each dataset, followed by the application of TL to transfer model parameters across datasets. Model performance was evaluated using accuracy, specificity, sensitivity, and the area under the receiver operating characteristic curve (AUC). Results: Significant demographic differences (p < 0.001) were observed between the CGH and NLST datasets. Initial models trained on one dataset showed a substantial performance decline of 15.2% to 97.9% when applied to the other dataset. TL enhanced model performance across datasets by 21.1% to 159.5% (p < 0.001), achieving an accuracy of 0.86–0.91, sensitivity of 0.81–0.96, specificity of 0.89–0.92, and an AUC of 0.90–0.97. Conclusions: TL enhances SLN malignancy prediction models by addressing population variations and enabling their application across diverse international datasets. Full article

In this paper, we propose a novel alternating direction method of multipliers based on acceleration technique involving two symmetrical inertial terms for a class of nonconvex optimization problems with a two-block structure. To address the nonconvex subproblem, we introduce a proximal term to reduce the difficulty of solving this subproblem. For the smooth subproblem, we employ a gradient descent method on the augmented Lagrangian function, which significantly reduces the computational complexity. Under appropriate assumptions, we prove subsequential convergence of the algorithm. Moreover, when the generated sequence is bounded and the auxiliary function satisfies Kurdyka–Łojasiewicz property, we establish global convergence of the algorithm. Finally, effectiveness and superior performance of the proposed algorithm are validated through numerical experiments in signal processing and smoothly clipped absolute deviation penalty problems. Full article

Inflammatory bowel disease (IBD), comprising ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic, immune-mediated inflammatory disorder of the gastrointestinal tract. Immunosuppressive therapy administration increases the risk of hepatitis B virus (HBV) and hepatitis C virus (HCV) reactivation. This study aimed to investigate the hepatitis screening rate, serological status, and protective antibody levels among the Taiwanese IBD population. This single-center retrospective study included patients with IBD from January 2016 to December 2024. Hepatitis serological markers were analyzed. Patients were categorized into active HBV infection (HBsAg-positive), resolved HBV infection (HBsAg-negative and anti-HBc-positive), and non-HBV-infected groups, with prevalences of 7.5%, 32.5%, and 0.9%, respectively. This study included 347 patients with IBD (UC: 68.3%; CD: 31.7%), with a mean age of 47.1 ± 16.4 years. Patients born after 1984 demonstrated a significantly reduced HBsAg positivity (0.9% vs. 11.0%; p < 0.05) and resolved HBV infection (52.2% vs. 1.0%; p < 0.05). However, among non-HBV-infected individuals, only 42.0% had protective anti-HBs levels (≥10 mIU/mL), despite vaccination program initiation. In this study, we found an overall HBsAg positivity rate of 7.5% and an anti-HCV seropositivity rate of 0.9% in our IBD population. Taiwan’s HBV vaccination program has effectively reduced the HBV prevalence. However, a significant proportion of vaccinated individuals lack sufficient protective antibody levels, thereby requiring continued HBV screening and booster vaccinations. Full article

Hyperspectral imagery (HSI), with its rich spectral information across continuous wavelength bands, has become indispensable for fine-grained land cover classification in remote sensing applications. Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. Experimental results from four common hyperspectral data sets validate the effectiveness of the proposed method. Specifically, when 10, 10, 10 and 25 samples from each class are selected for training, ASLNN achieves the highest overall accuracy (OA) of 81.12%, 85.88%, 80.62%, and 97.97% on the four data sets, outperforming other methods with increases of more than 1.70%, 3.21%, 3.78%, and 2.70% in OA, respectively. Full article

The selection of sound-source signals is a pivotal aspect of temperature measurement in stored grain using the acoustic method, as their characteristics directly influence the propagation effects of sound waves in grain media and the accuracy of temperature measurement. To identify a sound-source signal with optimal propagation performance, this study focused on analyzing the signal attenuation levels of typical sound sources, including simulated pulse signals and linear swept signals, during propagation. The results demonstrated that the linear swept signal exhibited superior propagation characteristics in grain media, with significantly lower signal attenuation compared to other sound-source signals. Specifically, a linear swept signal with a duration of 0.5 s and a frequency range of 200 Hz to 1000 Hz showed the best propagation performance. Finally, based on this rational signal, the temperature of grain samples was measured, yielding a mean absolute error of 1.62 °C. Full article

Groundwater depletion has emerged as a pressing global challenge, yet the low spatial resolution (0.25°) of Gravity Recovery and Climate Experiment (GRACE) satellite data limits its application in regional groundwater monitoring. In this study, based on 0.25° spatial resolution groundwater storage anomalies (GWSAs) data derived from GRACE satellite observations and GLDAS hydrological model outputs, supplemented with hydrological data, humanities data, and other geographic parameters, we constructed a Stacking-based ensemble machine learning model that achieved a 1 km spatial resolution of GWSAs distribution data across the contiguous United States (CONUS) from 2010 to 2020. The ensemble model integrates eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) models using an Attention-Based Dynamic Weight Allocation (ADWA) approach, along with a ridge regression model. The results indicate that our ensemble model outperforms individual machine learning (ML) models, achieving a coefficient of determination (R²) of 0.929, root mean square error (RMSE) of 25.232 mm, mean absolute error (MAE) of 19.125 mm, and Nash–Sutcliffe efficiency (NSE) of 0.936, validated by 10-fold cross-validation. In situ measurements indicate that, compared with the original data, approximately 61.7% of the monitoring wells (266 out of 431) exhibit a higher correlation after downscaling, with the overall correlation coefficient increasing by about 18.7%, which suggests that the downscaled product exhibits an appreciable improvement in accuracy. The ensemble model proposed in this study, by integrating the advantages of various ML algorithms, is better able to address the complexity and uncertainty of groundwater storage variations, thus providing scientific support for the sustainable management of groundwater resources. Full article

With the rapid development of remote sensing technology, optical remote sensing images are increasingly being used in areas such as military reconnaissance, environmental monitoring, and urban planning. Due to the small number of pixels, fuzzy features, and complex background, it is difficult for conventional convolutions to effectively extract features from small objects. To address this problem, we propose to use multi-scale dilated convolutions to increase the receptive field size of the model to adapt to changes in object size, capture multi-scale contextual information of the feature map, and extract richer object features. First, we propose a Dilated Convolutional Residual (DCR) module for high-level feature extraction in the network. Second, the context aggregation (CONTEXT) module uses remote interaction to perform associative computation on images using contextual aggregation, allowing the model to understand the global semantic information of the image. We propose a novel object detection method, DCN-YOLO, which achieves an AP50 of 56.6 on the AI-TOD dataset, effectively improving the detection accuracy and robustness of small objects in remote sensing images. It provides a new technical approach to the detection of small objects in remote sensing. Full article