Browse Articles

An integration of bacterial probiotic extract and green nanoparticles as a safe approach for conservation of historical manuscript against fungal deterioration: an applied study

Abstract This study focuses on integrations of an eco-friendly, cost effectiveness, and safe approach for conservation of historical manuscripts to avoid the risks from utilizing traditional conservation methods and synthetic fungicides, which pose dangerous effects on environment, human health, and materials of historical museums. Therefore, the deterioration aspects of an 11th-century historical manuscript, including its paper and leather binding, were assessed in relation to the influence of the corresponding fungal communities. Photographic documentation, scanning electron microscopy (SEM), X-ray diffraction (XRD), attenuated total reflectance Fourier-transform infrared (ATR-FTIR), color changes, and pH values in comparison to a control sample were used to examine the deterioration aspects. Dust, dirt, corrosion, stains, weakness, missing parts, decreasing paper crystal, changing cellulosic band wavenumbers, color change, and acidity were identified as the most degrading elements. Thirteen fungal strains were isolated from the historical manuscript and identified morphologically and molecularly as Aspergillus flavus (five isolates), A. chinensis (one isolate), Penicillium chrysogenum (one isolate), Cladosporium velox (one isolate), Paecilomyces variotii (two isolates), Paecilomyces brunneolus (one isolate), Curvularia tamilnaduensis (one isolate), and Curvularia geniculate (one isolate). These strains showed high activity for producing different hydrolytic enzymes involved in biodeterioration, including cellulase, amylase, gelatinase, and pectinase. The conservation of the historical manuscript against fungal deterioration was achieved using innovative and pioneering environmentally friendly biomaterials: Lactobacillus plantarum extract (100 µg mL⁻¹) followed by green-synthesized TiO₂-NPs (100 µg mL⁻¹). Other conservation techniques, such as cleaning, consolidation, completion of missed parts, and other necessary methods for the studied manuscript, were also applied and revealed the aesthetic value of the manuscript.

ENRICHED trial of preservative-free lubricants in dry eye disease

Energy-efficient secure routing in wireless sensor networks using fuzzy inference clustering and attention-based multi-scale deep learning

Time–temperature superposition of silicone rubber embedded with irregular-shaped magnetic particles under different magnetic fields

Learning multi-granularity skeleton representations via hierarchical graph contrastive learning

A hardware-efficient Berkeley gate for superconducting quantum processors

Abstract The Berkeley gate is a high-performance, two-qubit entangling operation with particular potential for quantum error correction and fault-tolerant protocols. However, harnessing this potential on current noisy intermediate-scale quantum (NISQ) processors, requires efficient compilation and robust performance under realistic noise conditions. In this work, we demonstrate a hardware-efficient implementation of the Berkeley gate on a superconducting quantum processor. Using quantum process tomography (QPT), we experimentally characterize its performance and benchmark it against a noiseless quantum simulator to evaluate its practical reliability in the NISQ era. Experimental measurements confirm the gate’s correct logical action, producing the target partially entangled state with a subspace confinement probability of $$P_{\text {succ}}^{\text {hardware}} \approx 95.96\%$$ on real quantum hardware compared to 100% in quantum simulation. Results from QPT experiments show a simulated process fidelity of $$\mathcal {F}_{\text {process}}^{\text {sim}} = 98.23\%$$ , while the experimental process fidelity on hardware is $$\mathcal {F}_{\text {process}}^{\text {hardware}} = 91.76\%$$ . The observed discrepancy is analyzed in the context of device-specific noise sources, including qubit relaxation, dephasing, and state preparation and measurement (SPAM) errors. Our work provides a concrete fidelity benchmark for the Berkeley gate on superconducting hardware and quantify the impact of realistic noise on a non-trivial two-qubit operation, supporting its use in near-term algorithmic and error-correction applications.

A novel approach to soil nutrients prediction model for Bezuidenhout Park, Johannesburg, Gauteng Province, South Africa: attention temporal neural networks (ATNN)

Impact of epidemiological and nutritional factors on outcomes following traumatic brain injury in a prospective cohort study

Experimental evaluation of fly ash and lime stabilization on the geotechnical characteristics of expansive clay for road subbase applications

A novel cascade neural network with heuristic computational analysis for thermal dynamics of rectangular fin model with surface stretching/shrinking

Integrating gene expression and morphological traits for drought stress adaptation in maize hybrids

Non-thermal plasma treatment as a sustainable approach for wastewater reuse in hydroponic cultivation of Lactuca Sativa

Comparative evaluation of XGBoost, TabNet, and FT transformer models for fatal crash prediction under extreme class imbalance

Gadd45α silencing alleviates cerebral ischemia–reperfusion injury by suppressing FOXO1 signaling

Assessing subtle brain injury related to cognitive impairment in obese pediatric obstructive sleep apnea using 3D MRI texture analysis

Factors associated with cyberbullying and depression in cybervictimized adolescents and the role of internet gaming disorder

Postpartum-specific anxiety among working mothers in relation to maternity leave perceptions: a cross-sectional study from Turkey

Development and internal validation of a clinical prediction model for hemodialysis-related headache using LASSO and Boruta feature selection

High-fidelity CNN-based surrogate modeling for wildfire spread forecasting

Abstract The rising frequency of large-scale, destructive wildfires has significantly affected not only natural ecosystems but also critical infrastructure, human lives, and properties. Given the devastating and often irreversible environmental and financial consequences, researchers from diverse fields are actively seeking solutions to improve resilience against wildfires. Fire suppression, one of the most effective strategies to mitigate wildfire damage, relies heavily on rapid and high-fidelity forecasting of fire spread. These predictions are essential for planning evacuations by state or local emergency management agencies, implementing preemptive de-energization strategies for electric utilities, and coordinating fire containment efforts by firefighting teams. However, a significant bottleneck across all these planning processes is the significant computational burden imposed by high-resolution wildfire modeling, the demand for improved predictive accuracy, and the need to integrate diverse and large-scale datasets. Since response time is crucial for wildfire risk management, this paper proposes a deep learning-based surrogate model to predict fire spread in just a fraction of a second. We developed and trained a convolutional neural network (CNN) model that efficiently predicts wildfire propagation. The proposed model demonstrates high efficiency, achieving an F1 score of 0.92. The contributions of this paper are twofold: (1) a fast, high-resolution CNN model that can support wildfire-related public safety power shutoff (PSPS) planning for electric utilities, and (2) a practical tool for firefighting and evacuation teams to support rapid and data-informed risk assessment.

A dual-layer vector map encryption scheme using 4D hyperchaos and SM4

Abstract Vector map data, as a fundamental component of national geographic information, plays a strategic role in areas such as national security, urban planning, and smart city development. However, with the rapid advancement of information technology, vector map data faces increasing security challenges during transmission and storage. On the one hand, traditional coordinate-scrambling methods are vulnerable to reverse analysis since the numerical values remain essentially unchanged. On the other hand, some existing approaches rely on low-dimensional chaotic systems, which are prone to short periods and dynamical degradation, thereby reducing randomness and security. To address these challenges, this paper proposes a dual-layer encryption method for vector maps that integrates a four-dimensional hyperchaotic system with the national cryptographic algorithm SM4. In this method, the four-dimensional hyperchaotic system generates multiple high-randomness sequences, from which sequences are dynamically selected to scramble coordinates and thereby alter the spatial topological relationships. Furthermore, the chaotic sequences are employed to generate dynamic SM4 keys and initialization vector, enabling deep encryption of coordinate values. Experimental results demonstrate that the proposed method not only effectively protects the three types of vector data–points, polylines, and polygons–but also exhibits significant advantages in resisting brute-force attacks, differential attacks, and low-dimensional chaotic degradation. Overall, it substantially enhances the security and applicability of vector map data during transmission and storage.