Search Results (47)

Vertical farming allows for precise control of environmental conditions, including light quality, enabling the optimization of plant growth and the synthesis of specific phytochemicals. However, the effects of such conditions on postharvest quality remain underexplored. In this study, butterhead lettuce (Lactuca sativa cv. ‘Alyssa’) was grown for three weeks under light-emitting diode (LED) lighting (190 µmol m⁻² s⁻¹; 89% red, 11% blue), with or without supplemental far-red light (ca. 50 µmol m⁻² s⁻¹). Growth and quality parameters were assessed at harvest, followed by postharvest evaluation of fresh-cut lettuce stored under equilibrium modified atmosphere packaging (EMAP: 3% O₂, balance N₂) at 7 °C in darkness for 13 days. The respiration rate of the produce was also determined. Far-red light supplementation increased dry weight (+17%) and elevated glucose (+57%) and fructose (+64%) levels at harvest, without affecting fresh weight, pigment content, vitamin C, or sucrose levels. Although respiration rates during storage were about 54% higher for lettuce grown under far-red light, visual quality seemed slightly better preserved. Total aerobic psychrotrophic counts showed no significant differences between treatments at harvest or during storage. These findings suggest that far-red light can enhance certain quality traits of lettuce, particularly carbohydrate accumulation and dry weight, but the associated rise in respiration may limit these benefits postharvest. Further research is needed to clarify its long-term impact in vertical farming systems. Full article

As a core strategy for carbon emission reduction, carbon trading plays a critical role in policy guidance and market stability. Accurate forecasting of carbon prices is essential, yet remains challenging due to the nonlinear, non-stationary, noisy, and uncertain nature of carbon price time series. To address this, this paper proposes a novel hybrid deep learning framework that integrates dual-mode decomposition and a TKMixer-BiGRU-SA model for carbon price prediction. First, external variables with high correlation to carbon prices are identified through correlation analysis and incorporated as inputs. Then, the carbon price series is decomposed using Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) to extract multi-scale features embedded in the original data. The core prediction model, TKMixer-BiGRU-SA Net, comprises three integrated branches: the first processes the raw carbon price and highly relevant external time series, and the second and third process multi-scale components obtained from VMD and EWT, respectively. The proposed model embeds Kolmogorov–Arnold Networks (KANs) into the Time-Series Mixer (TSMixer) module, replacing the conventional time-mapping layer to form the TKMixer module. Each branch alternately applies the TKMixer along the temporal and feature-channel dimensions to capture dependencies across time steps and variables. Hierarchical nonlinear transformations enhance higher-order feature interactions and improve nonlinear modeling capability. Additionally, the BiGRU component captures bidirectional long-term dependencies, while the Self-Attention (SA) mechanism adaptively weights critical features for integrated prediction. This architecture is designed to uncover global fluctuation patterns in carbon prices, multi-scale component behaviors, and external factor correlations, thereby enabling autonomous learning and the prediction of complex non-stationary and nonlinear price dynamics. Empirical evaluations using data from the EU Emission Allowance (EUA) and Hubei Emission Allowance (HBEA) demonstrate the model’s high accuracy in both single-step and multi-step forecasting tasks. For example, the e_MAPE of EUA predictions for 1–4 step forecasts are 0.2081%, 0.5660%, 0.8293%, and 1.1063%, respectively—outperforming benchmark models and confirming the proposed method’s effectiveness and robustness. This study provides a novel approach to carbon price forecasting with practical implications for market regulation and decision-making. Full article

Wind turbine operation and maintenance (O&M) faces significant challenges due to the complexity of equipment, harsh operating environments, and the difficulty of real-time fault prediction. Traditional methods often fail to provide timely and accurate warnings, leading to increased downtime and maintenance costs. To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion. Specifically, it proposes a remote intelligent operation and maintenance (O&M) framework for wind turbines based on digital twin technology. Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). The WOA is used to optimize the hyperparameters of the TCN-Attention model. Then, the gearbox fault alarm threshold and warning threshold are set using the statistical characteristics of the residual values, and the absolute value of the residuals is used to determine the abnormal operating state of the gearbox. Finally, the proposed method was validated using operational data from a wind farm in Xinjiang. With input data from multiple sources, including seven key parameters such as temperature, pressure, and power, the method was evaluated based on EMAE, ERMSE, and EMAPE. The results demonstrated that the proposed method achieved the smallest prediction error and provided effective early warnings 18 h and 33 min prior to actual failures, enabling real-time and efficient operation and maintenance management for wind turbines. Full article

Strain tolerance is a crucial factor affecting the thermal life of coatings, and a higher strain tolerance can effectively alleviate the thermal stresses on coatings during thermal shock. To improve the strain tolerance, the coating structure was optimized by introducing an intermediate transition layer in this study. The intermediate transition layer material was prepared using a 1:1 volume ratio mixture of 6–8 wt. % Yttria-stabilized zirconia (YSZ) and NiCrAlY powders in the experiments. The coating structure consisted of an Al₂O₃-GdAlO₃ (AGAP) anti-erosion layer, a YSZ layer, an intermediate transition layer, and a bonding layer from top to bottom. After thermal shock experiments at 1500 °C, the coatings with the addition of the intermediate transition layer exhibited different failure modes, with the crack location shifting from between the YSZ and the bonding layer to within the intermediate transition layer, compared to the coatings without the intermediate transition layer. Finite element simulation analysis showed that the intermediate transition layer effectively increased the strain tolerance of the coating and significantly reduced the thermal stress. Furthermore, incorporating an embedded micron agglomerated particle-based (EMAP) thermal barrier coating structure into the intermediate transition layer effectively alleviated thermal stresses and enhanced the coating’s thermal insulation performance. Full article

With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method. Full article

This study aimed to develop an active biodegradable bio-based (polylactic acid/PLA) equilibrium modified atmosphere packaging (EMAP) containing a carvacrol-emitting sachet (created by poly-hydroxybutyrate) (PLA-PHB-CARV) to extend the shelf-life of cherry tomatoes at 15 °C and 25 °C. Cherry tomatoes in macro-perforated polypropylene (PP) films (mimicking the commercial packaging) or in PLA-based micro-perforated film without the carvacrol sachet (PLA) were also tested. Weight loss, decay, headspace gases, pH, titratable acidity (TA), total suspended solids (TSS), ripening index, color, texture, total viable counts (TVC), and sensory analysis were performed. Decay was 40% in PLA-PHB-CARV, and 97% in PP after 20 days at 25 °C. PLA-PHB-CARV showed lower weight loss (p < 0.05) and stable firmness compared to PP and PLA at both temperatures. TSS and TA were not affected by the packaging at 15 °C, while at 25 °C, the TSS accumulation was inhibited in PLA-PHB-CARV compared to in PLA and PP (p < 0.05), indicating a notable delay in the ripening process. PLA-PHB-CARV retained their red color during storage compared to PP and PLA. Carvacrol addition inhibited TVC compared to PP and PLA by ca. 2.0 log CFU/g during storage at 25 °C, while at 15 °C, the packaging did not reveal a significant effect. Overall, the results indicated that the developed active EMAP may be adequately used as an advanced and alternative packaging for tomatoes or potentially other fruits with a similar respiration rate versus their conventional packaging, showing several advantages, e.g., a reduction in petrochemical-based plastics use, shelf-life extension of the packaged food, and consequently, the perspective of limiting food waste during distribution and retail or domestic storage. Full article

In urban scenes, buildings are usually dense and exhibit similar shapes. Thus, existing autonomous unmanned aerial vehicle (UAV) localization schemes based on map matching, especially the semantic shape matching (SSM) method, cannot capture the uniqueness of buildings and may result in matching failure. To solve this problem, we propose a new method to locate UAVs via shape and spatial relationship matching (SSRM) of buildings in urban scenes as an alternative to UAV localization via image matching. SSRM first extracts individual buildings from UAV images using the SOLOv2 instance segmentation algorithm. Then, these individual buildings are subsequently matched with vector e-map data (stored in .shp format) based on their shape and spatial relationship to determine their actual latitude and longitude. Control points are generated according to the matched buildings, and finally, the UAV position is determined. SSRM can efficiently realize high-precision UAV localization in urban scenes. Under the verification of actual data, SSRM achieves localization errors of 7.38 m and 11.92 m in downtown and suburb areas, respectively, with better localization performance than the radiation-variation insensitive feature transform (RIFT), channel features of the oriented gradient (CFOG), and SSM algorithms. Moreover, the SSRM algorithm exhibits a smaller localization error in areas with higher building density. Full article

The development of gas turbine technology has led to an increase in the complexity of the geometric shape of the sprayed workpiece. Consequently, it has become more difficult to maintain the perpendicularity of the spraying angle during the spraying process, thereby impacting the structure and performance of the coating. This study uses the atmospheric plasma spraying method to simultaneously spray two types of powder for the preparation of embedded micro-agglomerated particle (EMAP) coatings. The spraying process is conducted at four different angles, ranging from 90° to 30°, in order to analyze the influence of the spray angle on the particle deposition and coating performance. The experimental results demonstrate that the relative deposition efficiency, hardness, and elastic modulus of the EMAP coatings decreased as the spray angle decreased. The porosity exhibited a reduction when the spraying angle dropped from 90° to 50°, followed by a significant rise at 30°. The greatest relative amount of second phase particles embedded in the coating appeared at a spraying angle of 90°, amounting to 10.8%. The smallest amount was found at a spraying angle of 30°, with a relative quantity of 2.2%. Furthermore, the molten droplets of the first phase matrix powder underwent extension and fragmentation along the angular direction at low angles. At an angle of 90°, the maximum average thermal shock life was 40.6 cycles, with the best stability of thermal shock life. The decrease in the spraying angle resulted in a deterioration in both the thermal shock life and its stability. Full article

This study investigates the impact of varying environmental conditions on the metal composition within the tissues of Sparisoma cretense, contributing to the understanding necessary to offer scientifically sound advice regarding the health status of this species. This knowledge extends beyond fishery production, encompassing implications for food security. The data span the years 2022 and 2023, encompassing both cold and warm climatic seasons. The concentrations of various metals, such as Al, Zn, Cd, Pb, Fe, and Cu, exhibited noteworthy variations across the islands, with significant increases recorded in 2023, particularly during the warm season. The intricate interplay between multiple factors shaped the availability of the analyzed elements in S. cretense. Factors such as rising temperatures during the warm season increased biological activity in marine ecosystems, seasonal fluctuations in weather conditions, water quality, and anthropogenic influences, all contributing to the observed variations in metal concentrations. Additionally, the geological composition of each island and the patterns of marine currents and sediment transport play pivotal roles in these differences. Comprehensive scientific research, monitoring, and environmental surveillance are essential for a holistic understanding of this variability and providing valuable insights for the conservation and management of marine ecosystems in the Canary archipelago. Full article

Ocean pollution poses a significant issue in the marine ecosystem. Coastal areas are particularly impacted by this pollution, and consequently, organisms associated with these coasts bear the brunt of its effects. Therefore, the presence of robust bioindicators, such as the shrimp species Palaemon elegans, is critically important. In this study, 20 P. elegans specimens were examined in each of the five areas on Gran Canaria Island. Water samples were collected to assess the potential existence of elevated concentrations. Significant discrepancies were observed in the levels of Al and Li across all zones, except those previously mentioned. The highest concentrations were recorded in Arguineguín (Southern sector), reaching 49.14 ± 4.51 mg/kg (Al) and 47.64 ± 2.86 mg/kg (Li). The authors contend that P. elegans proves to be a reliable bioindicator for tourist and port-related pollution, specifically for the metals Al, Zn, Cd, Pb, Ni, Fe, B, and Li analyzed in this research. Full article

This study aimed to address the issue of high-temperature challenges in asphalt pavement by developing two types of phase change materials (PCMs) for temperature control. Encapsulated paraffin wax particles (EPWP) and encapsulated myristic acid particles (EMAP) were synthesized using acid-etched ceramsite (AECS) as the carrier, paraffin wax (PW) or myristic acid (MA) as the core material, and a combination of epoxy resin and cement as the encapsulation material. The investigation encompassed leakage tests on PCMs; rutting plate rolling forming tests; SEM, FTIR, XRD, and TG-DSC microscopic tests; as well as heat storage and release tests and temperature control assessments using a light heating device. The study revealed the following key findings. Both types of PCMs exhibited no PCM leakage even under high temperatures and demonstrated low crushing ratios during rut-forming tests. Microscopic evaluations confirmed the chemical stability and phase compatibility of the constituents within the two types of PCMs. Notably, the phase change enthalpies of EPWP and EMAP were relatively high, measuring 133.31 J/g and 138.52 J/g, respectively. The utilization of AECS as the carrier for PCMs led to a substantial 4.61-fold increase in the adsorption rate. Moreover, the PCMs showcased minimal mass loss at 180 °C, rendering them suitable for asphalt pavement applications. The heat storage and release experiments further underscored the PCMs’ capacity to regulate ambient temperatures through heat absorption and release. When subjected to light heating, the maximum temperatures of the two types of phase change Marshall specimens were notably lower by 6.6 °C and 4.8 °C, respectively, compared to standard Marshall specimens. Based on comprehensive testing, EPWP displayed enhanced adaptability and demonstrated substantial potential for practical implementation in asphalt pavements. Full article

The innate immune system is the first line of defense against pathogens such as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The type I-interferon (IFN) response activation during the initial steps of infection is essential to prevent viral replication and tissue damage. SARS-CoV and SARS-CoV-2 can inhibit this activation, and individuals with a dysregulated IFN-I response are more likely to develop severe disease. Several mutations in different variants of SARS-CoV-2 have shown the potential to interfere with the immune system. Here, we evaluated the buffy coat transcriptome of individuals infected with Gamma or Delta variants of SARS-CoV-2. The Delta transcriptome presents more genes enriched in the innate immune response and Gamma in the adaptive immune response. Interactome and enriched promoter analysis showed that Delta could activate the INF-I response more effectively than Gamma. Two mutations in the N protein and one in the nsp6 protein found exclusively in Gamma have already been described as inhibitors of the interferon response pathway. This indicates that the Gamma variant evolved to evade the IFN-I response. Accordingly, in this work, we showed one of the mechanisms that variants of SARS-CoV-2 can use to avoid or interfere with the host Immune system. Full article

Nicotine is the one of the major addictive substances; the overdose of nicotine (NIC) consumption causes increasing heart rate, blood pressure, stroke, lung cancer, and respiratory illnesses. In this study, we have developed a precise and sensitive electrochemical sensor for nicotine detection in saliva samples. It was built on a glassy carbon electrode (GCE) modified with graphene (Gr), iron (III) phthalocyanine-4,4′,4″,4′′′-tetrasulfonic acid (Fe(III)Pc), and gold nanoparticles (AuNPs/Fe(III)Pc/Gr/GCE). The AuNPs/Fe(III)Pc/Gr nanocomposite was prepared and characterized by using FE-SEM, EDX, and E-mapping techniques to confirm the composite formation as well as the even distribution of elements. Furthermore, the newly prepared AuNPs/Fe(III)Pc/Gr/GCE-nanocomposite-based sensor was used to detect the nicotine in phosphate-buffered solution (0.1 M PBS, pH 7.4). The AuNPs/Fe(III)Pc/Gr/GCE-based sensor offered a linear response against NIC from 0.5 to 27 µM with a limit of detection (LOD) of 17 nM using the amperometry (i–t curve) technique. This electrochemical sensor demonstrated astounding selectivity and sensitivity during NIC detection in the presence of common interfering molecules in 0.1 M PBS. Moreover, the effect of pH on NIC electro-oxidation was studied, which indicated that PBS with pH 7.4 was the best medium for NIC determination. Finally, the AuNPs/Fe(III)Pc/Gr/GCE sensor was used to accurately determine NIC concentration in human saliva samples, and the recovery percentages were also calculated. Full article

The objective of this study was to study whether the metal concentrations in Sparisoma cretense and Serranus atricauda differ between different coastal areas around the island of Tenerife, Canary Islands and to study whether these species are good bioindicators of pollution. Thirty samples of each species were collected from three parts of the coastline around the island, and samples of muscle and liver tissue were taken from the collected specimens. The determination of the metal content (Al, Cd, Pb, Ca, K, Mg, Na, B, Ba, Cr, Cu, Fe, Li, Mn, Mo, Ni, Zn) was performed by inductively coupled plasma optical emission spectrometry (ICP-OES) before conducting a PERMANOVA analysis. The mean metal concentration was significantly higher in the liver tissue than in the muscle tissue of the two species studied. S. atricauda specimens had a larger number of metals with a higher concentration, and the samples from the northern and eastern zones were found to have a higher concentration of elements than those from the southern zone. The northern and eastern zones were found to have a higher concentration of metals and trace elements than the southern zone, which could be explained by the fact that these zones are more polluted due to their higher population density. Full article

Hyperspectral anomaly detection (HAD) plays an important role in military and civilian applications and has attracted a lot of research. The well-known Reed–Xiaoli (RX) algorithm is the benchmark of HAD methods. Based on the RX model, many variants have been developed. However, most of them ignore the spatial characteristics of hyperspectral images (HSIs). In this paper, we combine the extended multi-attribute profiles (EMAP) and RX algorithm to propose the Recursive RX with Extended Multi-Attribute Profiles (RRXEMAP) algorithm. Firstly, EMAP is utilized to extract the spatial structure information of HSI. Then, a simple method of background purification is proposed. That is, the background is purified by utilizing the RX detector to remove the pixels that are more likely to be anomalies, which helps improve the ability of background estimation. In addition, a parameter is utilized to control the purification level and can be selected by experiments. Finally, the RX detector is used again between the EMAP feature and the new background distribution to judge the anomaly. Experimental results on six real hyperspectral datasets and a synthetic dataset demonstrate the effectiveness of the proposed RRXEMAP method and the importance of using the EMAP feature and background purity means. Especially, on the abu-airport-2 dataset, the AUC value obtained by the present method is 0.9858, which is higher than the second one, CRD, by 0.0198. Full article