Search Results (21)

An efficient and reliable heat dissipation system is essential for the safe and stable operation of high-power water-cooled couplers. However, thermal analysis methods accounting for the centrifugal effects on coolant flow remain limited. This paper presents a high-accuracy equivalent thermal network model (ETNM) for analyzing the temperature distribution in water-cooled permanent magnet couplers (WPMCs), based on fluid–structure interaction and rotational centrifugal flow-field inversion. First, the ETNM is established based on key assumptions. Subsequently, an eddy current loss calculation method based on permanent magnet mapping is proposed to accurately determine the heat source distribution. The convective heat transfer coefficient of the coolant is then precisely derived by inverting the flow field obtained from fluid–structure coupling simulations under rotational centrifugal conditions. Finally, the model is applied for temperature analysis, and its accuracy is verified through both finite element simulations and experimental tests. The calculated results show errors of only 3.2% compared to numerical simulation and 5.6% compared to experimental data, indicating strong agreement of the proposed thermal analysis method. The accuracy of copper conductor (CC) temperature prediction is improved by 32.73%, and that of permanent magnet (PM) prediction by 33.33%. Furthermore, this method enables accurate estimation of individual component temperatures, effectively preventing operational failures such as PM demagnetization, CC softening, and severe vibrations caused by overheating. Full article

Accurate prediction of air pollutant concentrations, particularly fine particulate matter (PM_2.5), is essential for controlling and preventing heavy pollution incidents by providing early warnings of harmful substances in the atmosphere. This study proposes a novel spatiotemporal model for PM_2.5 concentration prediction based on a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP). The framework incorporates three key design components: First, the generator employs an Inception-style Convolutional Long Short-Term Memory (ConvLSTM) network, integrating parallel multi-scale convolutions and hierarchical normalization. This design enhances multi-scale spatiotemporal feature extraction while effectively suppressing boundary artifacts via a map-masking layer. Second, the discriminator adopts an architecturally enhanced U-Net, incorporating spectral normalization and shallow instance normalization. Feature-guided masked skip connections are introduced, and the output is designed as a raw score map to mitigate premature saturation during training. Third, a composite loss function is utilized, combining adversarial loss, feature-matching loss, and inter-frame spatiotemporal smoothness. A sliding-window conditioning mechanism is also implemented, leveraging multi-level features from the discriminator for joint spatiotemporal optimization. Experiments conducted on multi-source gridded data from Dongguan demonstrate that the model achieves a 12 h prediction performance with a Root Mean Square Error (RMSE) of 4.61 μg/m³, a Mean Absolute Error (MAE) of 6.42 μg/m³, and a Coefficient of Determination (R²) of 0.80. The model significantly alleviates performance degradation in long-term predictions when the forecast horizon is extended from 3 to 12 h, the RMSE increases by only 1.84 μg/m³, and regional deviations remain within ±3 μg/m³. These results indicate strong capabilities in spatial topology reconstruction and robustness against concentration anomalies, highlighting the model’s potential for hyperlocal air quality early warning. It should be noted that the empirical validation is limited to the specific environmental conditions of Dongguan, and the model’s generalizability to other geographical and climatic settings requires further investigation. Full article

Blossom-end enlargement (BEE) is a physiological disorder in cucumbers (Cucumis sativus L.) that affects postharvest quality and results in commercial loss due to reduced product value. Pre-cooling using modified atmosphere packaging (MAP) has been encouraged as a preventive method of BEE; however, BEE can still be observed under actual distribution conditions. This study reexamined the process from harvesting in midsummer to arriving at the market (550 km) and storage, while considering the impact of packaging materials, packaging methods, and human factors on BEE occurrence. More than 18 h were required from harvest to delivery at the pre-cooling warehouse at the common shipping site; however, despite using a refrigerated truck, the temperature inside the packaging increased again during transportation. The temperature then dropped during 24 h of pre-cooling; however, it did not reach 10 °C, the appropriate storage temperature for cucumbers. MAP suppressed the occurrence of BEE compared to conventional film packaging; however, the BEE index varied greatly between individuals who performed the packaging. We determined that tying both ends of the packaging film increases the degree of airtightness as individual differences decrease and is more effective at suppressing BEE. Porous mineral-containing film (PM) packaging, which generates a modified atmosphere (MA), significantly suppressed BEE compared to conventional perforated film (C). In 2019 transport trials, the BEE index at 6 DAH for C film was 77.3, while for PM film it was only 12.0. Furthermore, we found that the effectiveness of PM film was significantly affected by human-related operational factors. The novel packaging method of tying both ends of the film (PM-T) provided the most consistent BEE suppression and lowest BEE index regardless of the packaging worker, demonstrating its superior potential in standardizing airtightness and minimizing human-related operational variability. Full article

Lightning is associated with severe thunderstorm events and causes hundreds of deaths annually in Brazil. Additionally, it is responsible for losses amounting to millions in Brazil’s electricity and telecommunication sectors. Between November 2011 and March 2012, the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign was conducted in the Vale do Paraíba region and the Metropolitan Area of São Paulo (MASP), located in southeastern São Paulo state, Brazil, to enhance the understanding of cloud processes, including lightning. During the campaign, several instruments were available: a meteorological radar, lightning location systems, rain gauges, a vertical-pointing radar, a surface tower, and others. In this context, the main goal of this study was to evaluate the temporal evolution of lightning properties, such as frequency, type (cloud-to-ground (CG) and intracloud (IC) lightning), peak current, length, and duration, in the MASP between November 2011 and March 2012. To achieve this objective, lightning data from the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) were utilized. The maximum amount of lightning for the BrasilDAT (322,598 events/month) occurred in January, while for the SPLMA (150,566 events/month), it occurred in February, suggesting that thunderstorms displayed typical summer behavior in the studied region. Most of lightning registered by the BrasilDAT were concentrated between 2:00 and 5:00 pm local time, with a maximum of 5.0 × 10⁴, 6.2 × 10³, and 95 events/month.hour for IC, −CG, and +CG lightning, respectively. These results are associated with the favorable conditions of diurnal atmospheric instability caused by surface heating. Regarding the lightning properties from the SPLMA, longer-duration lightning (up to 0.4 s) and larger spatial extension (up to 14 km) occurred during the nighttime period (0–6:00 am local time), while the highest lightning frequency (up to 9 × 10⁴ events month⁻¹ h⁻¹) was observed in the afternoon (3–4:00 pm local time). Full article

Powdery mildew has become a significant disease affecting the yield and quality of rubber trees in recent years. It typically manifests on the leaf surface at an early stage, rapidly infecting and spreading throughout the leaves. Therefore, early detection and intervention are essential to reduce the resulting losses due to this disease. However, the conventional methods of disease detection are both time-consuming and labor-intensive. In this study, we proposed a novel deep-learning-based approach for detecting powdery mildew in rubber trees, even in complex backgrounds. First, to address the lack of existing datasets on rubber tree powdery mildew, we constructed a dataset comprising 6200 images and 38,000 annotations. Second, based on the YOLO framework, we integrated a multi-scale fusion module that combines a Feature Focus and Diffusion Mechanism (FFDM) into the neck of the detection architecture. We designed an overall focus diffusion architecture and introduced a Dimension-Aware Selective Integration (DASI) module to enhance the detection of small powdery mildew targets, naming the model PM-YOLO. Furthermore, we proposed an automatic grading detection algorithm to evaluate the severity of powdery mildew on rubber tree leaves. The experimental results demonstrated that the proposed method achieved 86.9% mean average precision (mAP) and 85.6% recall, which outperformed the standard YOLOv10 by 7.6% mAP and 8.2% recall. This approach offered accurate and real-time detection of powdery mildew rubber trees, providing an effective solution for early diagnosis through automated grading. Full article

Excessive fine particulate matter (PM_2.5) and ozone (O₃) are invisible killers affecting our wellbeing and safety, which cause great harm to people’s health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM_2.5 and O₃ exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM_2.5 and O₃ pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM_2.5 and O₃ control in the BTH region. Full article

Powdery mildew is a severe wheat disease that causes substantial yield losses in wheat production worldwide. The Chinese wheat landrace Changanhongmai (CAHM) exhibits high resistance to the physiological race E09 of powdery mildew. In this study, we characterized the powdery mildew resistance gene in CAHM, and developed molecular markers for wheat marker-assisted selection. To investigate the genetic characteristics of this resistant gene, we developed F₁ plants, F₂ generation population, and F_2:3 families by crossing CAHM with SY225 (Shaanyou ‘225’ as susceptible male parent). Genetic analysis demonstrated that all F₁ plants were resistant to the disease, while the ratio of resistant to susceptible plants was 3: 1 in both the F₂ population and F_2:3 families, indicating that CAHM is inherited in a manner of a single dominant powdery mildew resistance gene, which was tentatively designated as PmCAHM. By using bulk segregation analysis, we constructed a genetic map encompassing Xgwm273, Xwmc626, Xgwm11, Xgwm18, Xgdm28, Xgpw7812, Xgpw5195, Xwmc694, and PmCAHM. Among these markers, Xgpw7812 and Xgpw5195 are flanking markers that are tightly linked to PmCAHM at a genetic distance of 2.5 cM and 8.4 cM, respectively. Furthermore, nullisomic-tetrasomic analysis revealed that PmCAHM is located on chromosome 1B. These results indicate that PmCAHM differs from the internationally recognized powdery mildew resistance genes in both location and source. In addition, a new germplasm/line NW1748 with stronger powdery mildew resistance and large grains was developed from the cross and backcross populations of Fengyou1718 (FY1718)/CAHM/5/FY 1718. Therefore, PmCAHM can serve as a novel powdery mildew resistance source for breeding of wheat by using NW1748 as the donor in the future. Full article

This paper presents coupled electromagnetic (EM)–thermal modelling of the steady-state dynamic performances, such as torque speed curve and the efficiency map, for surface-mounted permanent magnet machines. One important feature of such a model is that it considers the demagnetization caused by magnet temperature rise at different rotor speeds. EM-only simulations, which often assume that the machines operate under constant temperature, have been widely used in the literature. However, the interaction between EM and thermal performances could lead to very different dynamic performance prediction. This is because the material properties, e.g., magnet remanence, coercivity, and copper resistivity are temperature-dependent. The temperature rise within electrical machines reduces torque/power density, PM eddy current losses, and iron losses but increases copper loss. Therefore, the coupled EM–thermal modelling is essential to determine accurate temperature variation and to obtain accurate EM performances of electrical machines. In this paper, the coupled EM–thermal modelling is implemented for both modular and non-modular machines to reveal the advantages of the modular machine under different operating conditions. The results show that the modular machine generally has better dynamic performance than the non-modular machine because the introduced flux gaps in alternate stator teeth can boost both EM and thermal performance. Full article

Fresh-cut broccoli is a highly demanded product due to its convenience and high content of bioactive compounds. Unfortunately, this product shows rapid senescence and anoxia generation problems, especially when storage temperature varies. Therefore, perforation-mediated modified atmosphere packaging (PM-MAP) of broccoli florets, in different temperature scenarios, was studied. Polypropylene films with different levels of laser perforation were evaluated. After packaging, florets were stored at two temperatures: 2 °C, and 2 °C + 7 °C (during 2 d before sampling). PM-MAP slightly modified the internal composition of O₂ (14–20 kPa) and CO₂ (0.9–5 kPa) and allowed us to preserve the external quality and bioactive compounds of broccoli florets throughout 21 d, even at 7 °C. The generation of anoxia was avoided at both temperatures. PM-MAP kept broccoli mass loss below 0.5% and preserved its sensory quality. The perforation level affected evolution of firmness and glucosinolate content, especially with increasing temperature. Broccolis packaged in the film with fewer perforations showed higher firmness (0.73 ± 0.09 N/mm) and total glucosinolate content (10 ± 0.3 mg/g) compared to broccolis packaged in films with higher perforations (0.59 ± 0.05 N/mm and 8.60 ± 0.2 mg/g). Therefore, the perforation level should be taken into account in the design of packaging for fresh-cut products. Full article

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect and cooling structure optimization. Firstly, the electromagnetic–thermal coupling model considering electromagnetic harmonics is established using the subdomain model and Bertotti’s iron loss separation theory. Combined with the finite element (FE) simulation model established by Ansoft Maxwell software platform, the winding copper loss, stator core loss and PM eddy current loss under the action of complex magnetic flux are analyzed, and the transient temperature distribution of each component is obtained through coupling. Secondarily, the influence of the waterway structure parameters on the heat dissipation effect of the PM-IWM is analyzed by the thermal-fluid coupled relationship. On the basis, the optimization design of waterway structure parameters is carried out to improve the heat dissipation effect of the cooling system based on the proposed chaotic mapping ant colony algorithm with metropolis criterion. The comparison before and after optimization shows that the temperature of key components is significantly improved, the average convection heat transfer coefficient (CHTC) is increased by 23.57%, the peak temperature of stator is reduced from 95.47 °C to 82.73 °C, and the peak temperature of PM is decreased by 14.26%, thus the demagnetization risk in the PM is improved comprehensively. The research results can provide some theoretical and technical support for the structural optimization of water-cooled dissipation in the PM motor. Full article

PM_2.5 in the atmosphere causes severe air pollution and dramatically affects the normal production and lives of residents. The real-time monitoring of PM_2.5 concentrations has important practical significance for the construction of ecological civilization. The mainstream PM_2.5 concentration prediction algorithms based on electrochemical sensors have some disadvantages, such as high economic cost, high labor cost, time delay, and more. To this end, we propose a simple and effective PM_2.5 concentration prediction algorithm based on image perception. Specifically, the proposed method develops a natural scene statistical prior to estimating the saturation loss caused by the ’haze’ formed by PM_2.5. After extracting the prior features, this paper uses the feedforward neural network to achieve the mapping function from the proposed prior features to the PM_2.5 concentration values. Experiments constructed on the public Air Quality Image Dataset (AQID) show the superiority of our proposed PM_2.5 concentration measurement method compared to state-of-the-art related PM_2.5 concentration monitoring methods. Full article

The large availability of both air pollution and COVID-19 data, and the simplicity to make geographical correlations between them, led to a proliferation of ecological studies relating the levels of pollution in administrative areas to COVID-19 incidence, mortality or lethality rates. However, the major drawback of these studies is the ecological fallacy that can lead to spurious associations. In this frame, an increasing concern has been addressed to clarify the possible role of contextual variables such as municipalities’ characteristics (including urban, rural, semi-rural settings), those of the resident communities, the network of social relations, the mobility of people, and the responsiveness of the National Health Service (NHS), to better clarify the dynamics of the phenomenon. The objective of this paper is to identify and collect the municipalities’ and community contextual factors and to synthesize their information content to produce suitable indicators in national environmental epidemiological studies, with specific emphasis on assessing the possible role of air pollution on the incidence and severity of the COVID-19 disease. A first step was to synthesize the content of spatial information, available at the municipal level, in a smaller set of “summary indexes” that can be more easily viewed and analyzed. For the 7903 Italian municipalities (1 January 2020—ISTAT), 44 variables were identified, collected, and grouped into five information dimensions a priori defined: (i) geographic characteristics of the municipality, (ii) demographic and anthropogenic characteristics, (iii) mobility, (iv) socio-economic-health area, and (v) healthcare offer (source: ISTAT, EUROSTAT or Ministry of Health, and further ad hoc elaborations (e.g., OpenStreetMaps)). Principal component analysis (PCA) was carried out for the five identified dimensions, with the aim of reducing the large number of initial variables into a smaller number of components, limiting as much as possible the loss of information content (variability). We also included in the analysis PM_2.5, PM₁₀ and NO₂ population weighted exposure (PWE) values obtained using a four-stage approach based on the machine learning method, “random forest”, which uses space–time predictors, satellite data, and air quality monitoring data estimated at the national level. Overall, the PCA made it possible to extract twelve components: three for the territorial characteristics dimension of the municipality (variance explained 72%), two for the demographic and anthropogenic characteristics dimension (variance explained 62%), three for the mobility dimension (variance explained 83%), two for the socio-economic-health sector (variance explained 58%) and two for the health offer dimension (variance explained 72%). All the components of the different dimensions are only marginally correlated with each other, demonstrating their potential ability to grasp different aspects of the spatial distribution of the COVID-19 pathology. This work provides a national repository of contextual variables at the municipality level collapsed into twelve informative factors suitable to be used in studies on the association between chronic exposure to air pollution and COVID-19 pathology, as well as for investigations on the role of air pollution on the health of the Italian population. Full article

Rusts and powdery mildew are diseases that have a major effect on yield loss in barley. Adult Plant Resistance (APR) is a post-seedling resistance mechanism and its expression is influenced by many factors, including host susceptibility and weather conditions, as well as the timing and severity of disease outbreaks. There are two mechanisms associated with APR: non-hypersensitive and minor gene APR. In this study, 431 European barley accessions were evaluated phenotypically over 2 years (2018–2019) under field conditions, scoring APR to powdery mildew (PM), barley brown rust (BBR), and stem rust (SR), and genotypically using DArTseq. Accessions were grouped into sub-collections by cultivation period (group A—cultivated prior 1985, B—cultivated after 1985, and C—Polish landraces) and by European country of origin or European region. GWAS was conducted for PM, BBR, and SR, and scored at the heading (HA) and milky-waxy (MW) seed stages in 2019 and maximum scores across all replicates were obtained 2018–2019. Disease severity was sufficient to differentiate the collection according to cultivation time and country of origin and to determine SNPs. Overall, the GWAS analysis identified 73 marker–trait associations (MTAs) with these traits. For PM resistance, we identified five MTAs at both the HA stage and when considering the maximal disease score across both growth stages and both years. One marker (3432490-28-T/C) was shared between these two traits; it is located on chromosome 4H. For BBR resistance, six MTAs at HA and one MTA at the MW stage in 2019 and seven MTAs, when considering the maximal disease score across both growth stages and both years, were identified. Of the 48 markers identified as being associated with SR resistance, 12 were on chromosome 7H, 1 was in the telomeric region of the short arm, and 7 were in the telomeric region of the long arm. Rpg1 has previously been mapped to 7HS. The results of this study will be used to create a Polish Gene Bank platform for precise breeding programs. The resistant genotypes and MTA markers will serve as a valuable resource for breeding for PM, BBR, and SR resistance in barley. Full article

In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed. Full article

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence. Full article