Search Results (101)

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support. Full article

The wet chemical auto-combustion technique was used to synthesize barium zirconate ceramic (BaZrO₃). Many strategies were applied to regulate the functional properties of the perovskite-structured sample which was calcinated at 800 °C for 9 h. A Fourier-transform IR spectrometer, an X-ray diffractometer, a scanning electron microscope (SEM)-EDAX, an LCR meter, and a UV–visible spectrometer were employed to study the structural, morphological, optical, and electrical properties of the prepared barium zirconate sample. Using data derived from XRD, the perovskite phase was confirmed, and the average value of the crystallite size was found to be 17.68 nm. The lattice constant, crystallinity, unit cell volume, tolerance factor, and X-ray density were also calculated. SEM-EDAX confirmed the elemental composition of the product and verified that it contained only the major constituents (Ba, Zr, and O). The vibrational modes of the prepared sample were investigated using FTIR in wavelengths ranging from 400 to 4000 cm⁻¹. Energy bandgap was observed using Tauc’s plot, where a graph was prepared for photon energy (hυ) and (αhυ)². The powder sample was blended with PVA and made into pellets of 13 mm diameter using a pelletizer to explore dielectric parameters like the dielectric constant, while the loss factor was recorded at a frequency ranging from 100 Hz to 4 MHz at room temperature. With its high dielectric constant and low dielectric loss factor, barium zirconate ceramic stands as an excellent material for several microwave applications. Full article

Agropyron michnoi is a perennial grass with rhizomes in the genus Agropyron. It has a strong tolerance to drought and low temperature, and it is an established species in sandy flat and hilly slope lands, which constitute sandy grassland. So, it is an important forage species in dry grassland and desert steppes. Rhizomes not only enable asexual reproducibility but also confer strong resilience to stresses in A. michnoi. However, during production and utilization, it has been found that there are significant differences in the development of rhizomes among individuals of A. michnoi, yet the regulatory mechanism remains unclear. Therefore, in this study, the A. michnoi ‘Baiyinxile’ was used as the material, and the anatomical structures of the rhizomes, roots, and stems were analyzed using the paraffin sectioning technique. The results showed that the anatomical structure composition of the cross-section of the rhizome was similar to that of the root, while the arrangement of the vascular bundles in the stele was different from that of the root but similar to that of the stem. Subsequently, the Agropyron michnoi plants were classified into two types: plants with rhizomes and plants without rhizomes. Root, stem, and rhizome samples were collected from each type, and RNA sequencing was conducted. De novo transcriptomic analysis was performed to identify the candidate genes involved in rhizome development. From the RNA sequencing, a total of 103.73 Gb clean bases were obtained, from which 215,282 unigenes with an average length of 905.67 bp were assembled. Among these unigenes, 161,175 (74.87%) were functionally annotated based on seven common public databases. From pairwise comparisons of differentially expressed genes between the five samples, 129 candidate genes that are potentially specifically expressed in rhizomes were selected. Pathway enrichment analysis revealed that the rhizome-expressed genes are highly enriched in pathways of phenylpropanoid biosynthesis and starch and sucrose metabolism. The rhizome-specific expression pattern of 10 of the 129 candidate genes was further validated using qRT-PCR. Through the analysis of metabolites, 11 metabolites closely related to rhizome development, such as choline and betaine, were successfully identified. CYP family genes were selected for functional verification, and phylogenetic analysis revealed that CYP86B1 was grouped with CYP 86B1 of species such as Triticum aestivum and Lolium rigidum and was named AmrCYP86B1. The cloning results showed that its size was 1599 bp, and its subcellular localization was in the endoplasmic reticulum. Through stable genetic transformation, the study found that AmrCYP86B1 can promote the development of plant roots and stems and increase the dry matter content of the roots. Hormone detection showed that overexpression of AmrCYP 86B1 decreased the content of ABA hormone and increased the content of GA3 hormone in the plants. Combined with previous studies, it was determined that AmrCYP 86B1 promoted rhizome elongation by regulating ABA and GA3 hormones. The selected candidate genes involved in rhizome development, along with the preliminary functional verification, provide a preliminary mechanistic interpretation of rhizome development. This will contribute to in-depth research on the molecular mechanism of rhizome development in A. Michnoi. Full article

The community of epibiotic yeasts significantly influences the quality of Docynia delavayi (Franch.) Schneid. wine. The yeast diversity in four different Docynia delavayi (Franch.) Schneid. wines during the brewing stage was investigated using pure culture methods and high-throughput sequencing technology. A total of 229,381,292 sequencing bases were generated, yielding 323,820 valid sequences with an average length of 708 nt and identifying 93 operational taxonomic units (OTUs) from naturally fermented samples of Docynia delavayi (Franch.) Schneid. wine for classification purposes. At the early fermentation stage, Hanseniaspora sp. was identified as the dominant species, whereas at the late fermentation stage, Hanseniaspora sp., Saccharomyces sp., and Candida californica became predominant. From these samples, a total of 109 yeast strains were isolated from Docynia delavayi (Franch.) Schneid. wine. Three specific strains—LZX-76, LZX-89, and LZX-104—were further selected based on their growth characteristics along with hydrogen sulfide production, ester production, ethanol production, and tolerance levels. Through morphological examination and molecular biology techniques, these strains were identified as Pichia fermentans and Hanseniaspora spp. Additionally, a total of 29 volatile compounds were detected through simulated fermentation processes; these included 12 esters, 6 alcohols, 2 acids, 4 aldehydes, and 5 other compounds. When compared to commercial yeasts used as starters in winemaking processes, it was observed that utilizing yeast strains LZX-76, LZX-89, and LZX-104 resulted in an increased number of volatile compounds, which enhanced the aromatic profile characteristics of Docynia delavayi (Franch.) Schneid. wine by making its aroma richer and more complex. The findings from this study hold significant potential value for both the production practices and research endeavors related to Docynia delavayi (Franch.) Schneid. wine. Full article

The high penetration of distributed generation (DG) in the distribution system poses a challenge to the protection techniques and strategies of active distribution networks, making it difficult to adapt traditional methods to the fault diagnosis of the new power system. A method based on the improved subtractive optimiser algorithm for fault diagnosis is proposed to address this situation. Firstly, a fault localization model applicable to DG grid connection is constructed, which can effectively deal with the impact of the dynamic switching of DGs on the system and make up for the shortcomings of the traditional single-power network model; secondly, to solve the model, the original algorithm is improved using multi-strategy fusion, and the improved subtraction-average-based optimizer (ISABO) is obtained. Through the test of classical functions, its excellent solving performance and decoupling ability are verified; finally, the ISABO algorithm is applied to the 33-node test system to make it operate in various complex fault conditions. The results show that the ISABO algorithm is feasible in solving the fault location problem and can adapt to the connect/disconnect state of the interconnection switch and the dynamic casting and cutting of multiple DGs. Compared with the original SABO algorithm, its positioning accuracy can always be maintained at 100%, and the positioning speed is increased by 46.68%, symmetrically improving positioning speed, positioning accuracy, and fault tolerance. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

According to the climate projections, drought will increase in frequency and severity. Since water stress (WS) impacts a grapevine’s physiology and yield negatively, the evaluation and selection of tolerant genotypes are needed. To analyse the WS effects on the morphology and cell division of three grapevines (Vitis vinifera L.) varieties, “Touriga Franca” (TF), “Touriga Nacional” (TN) and “Viosinho” (VS), in vitro-grown plants were exposed to 10% polyethylene glycol 6000 (PEG) (−0.4 MPa) or 20% PEG (−0.8 MPa), incorporated in the culture medium, for four weeks. Control plants were kept in culture media without PEG. The VS and TN plants showed the highest mean numbers of nodes, shoots and leaves and average mitotic indexes under 20% PEG. The TF and TN plants showed the lowest frequencies of mitotic anomalies under 10% PEG. The VS plant growth was less affected by WS, but TF and TN presented more regular mitosis under moderate WS. Globally, in vitro culture constitutes a cost-effective experimental system for studying grapevine responses to WS and the preliminary selection of resilient genotypes. These approaches could be applied to study plant responses to other abiotic stresses based on additional evaluation techniques (e.g., transcriptional analyses or genome-wide association studies). Full article

Background: Caloric vestibular stimulation (CVS) is a well-established neurological diagnostic technique that also induces many phenomenological modulations, including reductions in phantom limb pain (PLP), spinal cord injury pain (SCIP), and central post-stroke pain. Objective: We aimed to assess in a variety of persistent pain (PP) conditions (i) short-term pain modulation by CVS relative to a forehead ice pack cold-arousal control procedure and (ii) the duration and repeatability of CVS modulations. The tolerability of CVS was also assessed and has been reported separately. Methods: We conducted a convenience-based non-randomised single-blinded placebo-controlled study. Thirty-eight PP patients were assessed (PLP, n = 8; SCIP, n = 12; complex regional pain syndrome, CRPS, n = 14; non-specific PP, n = 4). Patients underwent 1–3 separate-day sessions of iced-water right-ear CVS. All but four also underwent the ice pack procedure. Analyses used patient-reported numerical rating scale pain intensity (NRS-PI) scores for pain and allodynia. Results: Across all groups, NRS-PI for pain was significantly lower within 30 min post-CVS than post-ice pack (p < 0.01). Average reductions were 24.8% (CVS) and 6.4% (ice pack). CRPS appeared most responsive to CVS, while PLP and SCIP responses were less than expected from previous reports. The strongest CVS pain reductions lasted hours to over three weeks. CVS also induced substantial reductions in allodynia in three of nine allodynic CRPS patients, lasting 24 h to 1 month. As reported elsewhere, only one patient experienced emesis and CVS was widely rated by patients as a tolerable PP management intervention. Conclusions: Although these results require interpretative caution, CVS was found to modulate pain relative to an ice pack control. CVS also modulated allodynia in some cases. CVS should be examined for pain management efficacy using randomised controlled trials. Full article

Analyzing the uncertainty of outcomes based on estimates of the data’s membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices of the fuzzy set’s fuzziness provide an assessment of the degree of uncertainty of the results. It is feasible to bring the fuzzy sets’ fuzziness below a tolerable level by suitably redefining the strong fuzzy partition. Significant differences in the original fuzzy partition, however, result in disparities concerning the decision maker’s approximative reasoning and the interpretability of the results. In light of this, we provide in this study a technique applied to trapezoidal strong fuzzy partitions that, while not appreciably altering the original fuzzy partition, reduces the fuzziness of its fuzzy sets. The fuzziness of the fuzzy sets is assessed using the De Luca and Termini fuzzy entropy. An iterative process is then executed, with the aim of modifying the cores of the trapezoidal fuzzy partitions to decrease their fuzziness. This technique is tested on datasets containing average daily temperatures measured in various cities. The findings demonstrate that this approach strikes a great balance between the goal of lessening the fuzziness of the fuzzy sets and the goal of not appreciably altering the original fuzzy partition. Full article

Olive Quick Decline Syndrome (OQDS) associated with Xylella fastidiosa subsp. pauca is one of the most destructive diseases of olive trees in the Salento Peninsula (Southern Italy), particularly on the cultivars Cellina di Nardò and Ogliarola Salentina. This study proposes the NuovOlivo protocol as a management strategy to permit coexistence between X. fastidiosa subsp. pauca and olive drupes and extra-virgin oil production. Thirty-two private olive orchards affected by OQDS and cultivated following the standard agronomic techniques in use in the area were surveyed during the 2019–2023 olive-growing seasons. Tested cultivars included Cellina di Nardò, Ogliarola Salentina, Coratina, Ascolana Tenera, Nociara, Leccino, and Bella di Cerignola. At the beginning of the protocol application, the susceptible plants showed OQDS symptom severity of 40–80% and did not produce olives or oil, while the resistant(?)/tolerant cultivars exhibited a 2–8% leaf scorch and a drupe production less than 1–2 kg/plant. After the removal of dry branches in January–February, plants were sprayed two times per year (preferably in March and October) with NuovOlivo^®, a mixture of aqueous botanical extracts esterified in the presence of sodium hydroxide with vegetable oils and activated at the time of use with sodium bicarbonate. In all the orchards, a slow-release fertilizer was distributed, and weeds were controlled by mowing or chopping. Upon eventual appearance, the dry twigs were removed. Treated olive trees produced new vegetation, rebuilt their foliage, reduced OQDS symptoms, and turned out cluster inflorescence and drupes. The drupes yield was 6.67–51.36 kg per plant, with an average of 13.19% in extra-virgin olive oil (free acidity 0.01–0.2%). Plants used as controls showed OQDS symptoms and were unproductive, and newly formed shoots were desiccated. The proposed protocol promotes, supports, and restores new vegetation, flowers, fruits, and oil production of the treated olive plants affected by OQDS without losing susceptible olive plants. The Apulian landscape and economy, based on olive presence and production, could be also safeguarded. Full article

This work introduces a novel fault-tolerance technique for data fusion in Unmanned Aerial Vehicles (UAVs), designed to address sensor faults through a deep learning-based framework. Unlike traditional methods that rely on hardware redundancy, our approach leverages Long Short-Term Memory (LSTM) networks for state estimation and a moving average (MA) algorithm for fault detection. The novelty of our technique lies in its dual strategy: utilizing LSTMs to analyze residuals and detect errors, while the MA algorithm identifies faulty sensors by monitoring variations in sensor data. This method allows for effective error correction and system recovery by replacing faulty measurements with reliable ones, eliminating the need for a fault-free prediction model. The approach has been validated through offline testing on real sensor data from a hexarotor UAV with simulated faults, demonstrating its efficacy in maintaining robust UAV operations without resorting to redundant hardware solutions. Full article

Reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing have emerged as a significant area of research in privacy protection. However, existing algorithms have limitations, such as low embedding capacity and insufficient privacy protection. To address these challenges, this paper proposes an RDH-EI scheme based on adaptive median edge detection (AMED) and matrix-based secret sharing (MSS). The algorithm creatively leverages the AMED technique for precise image prediction and then integrates the (r, n)-threshold MSS scheme to partition the image into n encrypted images. Simultaneously, it embeds identifying information during segmentation to detect potential attacks during transmission. The algorithm allows multiple data hiders to embed secret data independently. Experimental results demonstrate that the proposed algorithm significantly enhances the embedding rate while preserving reversibility compared to current algorithms. The average maximum embedding rates achieved are up to 5.8142 bits per pixel (bpp) for the (3, 4)-threshold scheme and up to 7.2713 bpp for the (6, 6)-threshold scheme. With disaster-resilient features, the algorithm ensures $(n-r)$ storage fault tolerance, enabling secure multi-party data storage. Furthermore, the design of the identifying information effectively evaluates the security of the transmission environment, making it suitable for multi-user cloud service scenarios. Full article

Forage maize is one of the most important feed crops for livestock production, and is mainly grown in northwest China. However, their growth is often stressed by limited soil water availability due to the arid climate. To provide more soil moisture, a high-efficiency tillage technique was required to make crops effectively use soil moisture in deep soil layers. Deep vertical rotary tillage is a promising choice for this purpose. In this study, a long-term (2020–2022) field experiment consisting of three treatments, i.e., traditional tillage (TT), deep rotary tillage (DT), and deep vertical rotary tillage (VRT), was carried out in semiarid areas of Loess Plateau, northwest China, to investigate the effects of VRT on soil water storage (SWS), phase crop evapotranspiration (ET_c) during the pre- and post-flowering periods, dry matter accumulation, grain yields and the water use efficiency (WUE) of forage maize. The results showed that VRT significantly improved the absorption of soil moisture from deep layers, especially in dry years. During the pre-flowering period of a dry year (2020), VRT decreased SWS by 7.6%–10.0% in the 60–180 cm layer, and by 17.6%–18.5% in the 180–300 cm layer, respectively, compared to DT and TT. As a result, VRT increased ET_c during the pre-flowering period by 6.1% and 9.2%, respectively. In wet years (2021 and 2022), VRT increased total ET_c by 2.0%–7.9% in 2021, and by 10.1%–14.9% in 2022, respectively. On average, VRT increased the dry matter weight per plant by 1.0%–7.8%, grain yields by 2.4%–38.6%, biomass yields by 3.4%–16.2%, and WUE by 10.1%–30.0%, respectively. Particularly, the benefit of VRT for increasing yields and WUE was more noticeable in dry years. It can be concluded that VRT is a drought-tolerant and yield-boosting tillage technique that is suitable for rain-fed forage maize in semiarid areas of Loess Plateau, northwest China. Full article

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm², peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality. Full article

The tomato is a fruit vegetable rich in nutritional and medicinal value grown in greenhouses and fields worldwide. It is severely sensitive to heat stress, which frequently occurs with rising global warming. Predictions indicate a 0.2 °C increase in average surface temperatures per decade for the next three decades, which underlines the threat of austere heat stress in the future. Previous studies have reported that heat stress adversely affects tomato growth, limits nutrient availability, hammers photosynthesis, disrupts reproduction, denatures proteins, upsets signaling pathways, and damages cell membranes. The overproduction of reactive oxygen species in response to heat stress is toxic to tomato plants. The negative consequences of heat stress on the tomato have been the focus of much investigation, resulting in the emergence of several therapeutic interventions. However, a considerable distance remains to be covered to develop tomato varieties that are tolerant to current heat stress and durable in the perspective of increasing global warming. This current review provides a critical analysis of the heat stress consequences on the tomato in the context of global warming, its innate response to heat stress, and the elucidation of domains characterized by a scarcity of knowledge, along with potential avenues for enhancing sustainable tolerance against heat stress through the involvement of diverse advanced technologies. The particular mechanism underlying thermotolerance remains indeterminate and requires further elucidatory investigation. The precise roles and interplay of signaling pathways in response to heat stress remain unresolved. The etiology of tomato plants’ physiological and molecular responses against heat stress remains unexplained. Utilizing modern functional genomics techniques, including transcriptomics, proteomics, and metabolomics, can assist in identifying potential candidate proteins, metabolites, genes, gene networks, and signaling pathways contributing to tomato stress tolerance. Improving tomato tolerance against heat stress urges a comprehensive and combined strategy including modern techniques, the latest apparatuses, speedy breeding, physiology, and molecular markers to regulate their physiological, molecular, and biochemical reactions. Full article