Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.the Strategic Priority Research Program of the Chinese Academy of Sciencesthe National Natural Science Foundation of ChinaPeer Reviewe

Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays

This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems are also taken into account and therefore, switched systems under consideration can take several switched systems as special cases. The comparison principle, the Halanay-like inequality, and the time-dependent switched Lyapunov function approach are used to obtain sufficient conditions to ensure that the considered SNNs with delays and partial state reset are exponentially stable. Numerical examples are provided to demonstrate the reliability of the developed results

Monitoring Epoxy Coated Steel under Combined Mechanical Loads and Corrosion Using Fiber Bragg Grating Sensors

Fiber Bragg grating (FBG) sensors have been applied to assess strains, stresses, loads, corrosion, and temperature for structural health monitoring (SHM) of steel infrastructure, such as buildings, bridges, and pipelines. Since a single FBG sensor measures a particular parameter at a local spot, it is challenging to detect different types of anomalies and interactions of anomalies. This paper presents an approach to assess interactive anomalies caused by mechanical loading and corrosion on epoxy coated steel substrates using FBG sensors in real time. Experiments were performed by comparing the monitored center wavelength changes in the conditions with loading only, corrosion only, and simultaneous loading and corrosion. The theoretical and experimental results indicated that there were significant interactive influences between loading and corrosion for steel substrates. Loading accelerated the progress of corrosion for the epoxy coated steel substrate, especially when delamination in the epoxy coating was noticed. Through the real-time monitoring from the FBG sensors, the interactions between the anomalies induced by the loading and corrosion can be quantitatively evaluated through the corrosion depth and the loading contact length. These fundamental understandings of the interactions of different anomalies on steel structures can provide valuable information to engineers for better management of steel structures

Filtered Convolution for Synthetic Aperture Radar Images Ship Detection

Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability

Filtered Convolution for Synthetic Aperture Radar Images Ship Detection

Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability

Early Diagnosis of Pine Wilt Disease in <i>Pinus thunbergii</i> Based on Chlorophyll Fluorescence Parameters

As the most severe forestry quarantine disease in several countries, pine wilt disease (PWD) causes substantial economic losses and poses a significant threat to the forest ecosystem. It is necessary to find a rapid and sensitive method for the early diagnosis of the disease to control the development of the disease effectively. This paper investigated the effect of Bursaphelenchus xylophilus (the pinewood nematode; PWN) on the chlorophyll fluorescence kinetic curve (OJIP curve) and the parameters of needles using four-year-old Pinus thunbergii as experimental materials and chlorophyll fluorescence analysis as a technical tool. It was shown by the results in the OJIP curve that the fluorescence intensity of the inoculated plants was significantly increased at points O and I. Additionally, the relative variable fluorescence intensity at points K and J was comparable to that of the control plants. Several chlorophyll fluorescence parameters of the treatment significantly increased or decreased with disease progression. At the same time, the control group had no significant changes in each parameter. Therefore, chlorophyll fluorescence parameters can be used as indicators for the early diagnosis of PWD, among which the DIo/RC parameter was the best. In summary, PWN invasion will produce fluorescence on the PSII of P. thunbergii, and its chlorophyll fluorescence parameters are expected to achieve early PWD diagnosis

Ferric Ion Driven Assembly of Catalase-like Supramolecular Photosensitizing Nanozymes for Combating Hypoxic Tumors

A facile approach to assemble catalase-like photosensitizing nanozymes with a self-oxygen-supplying ability was developed. The process involved Fe3+-driven self-assembly of fluorenylmethyloxycarbonyl (Fmoc)-protected amino acids. By adding a zinc(II) phthalocyanine-based photosensitizer (ZnPc) and the hypoxia-inducible factor 1 (HIF-1) inhibitor acriflavine (ACF) during the Fe3+-promoted self-assembly of Fmoc-protected cysteine (Fmoc-Cys), the nanovesicles Fmoc-Cys/Fe@Pc and Fmoc-Cys/Fe@Pc/ACF were prepared, which could be disassembled intracellularly. The released Fe(3+)could catalyze the transformation of H(2)O(2)enriched in cancer cells to oxygen efficiently, thereby ameliorating the hypoxic condition and promoting the photosensitizing activity of the released ZnPc. With an additional therapeutic component, Fmoc-Cys/Fe@Pc/ACF exhibited higher in vitro and in vivo photodynamic activities than Fmoc-Cys/Fe@Pc, demonstrating the synergistic effect of ZnPc and ACF

Effect of Associated Bacteria GD1 on the Low-Temperature Adaptability of <i>Bursaphelenchus xylophilus</i> Based on RNA-Seq and RNAi

To explore the effect of associated bacteria on the low-temperature adaptability of pinewood nematodes (PWNs), transcriptome sequencing (RNA-seq) of PWN AH23 treated with the associated bacterial strain Bacillus cereus GD1 was carried out with reference to the whole PWN genome. Bioinformatic software was utilized to analyze the differentially expressed genes (DEGs). This study was based on the analysis of DEGs to verify the function of daf-11 by RNAi. The results showed that there were 439 DEGs between AH23 treated with GD1 and those treated with ddH2O at 10 °C. There were 207 pathways annotated in the KEGG database and 48 terms annotated in the GO database. It was found that after RNAi of daf-11, the survival rate of PWNs decreased significantly at 10 °C, and fecundity decreased significantly at 15 °C. It can be concluded that the associated bacteria GD1 can enhance the expression of genes related to PWN low-temperature adaptation and improve their adaptability to low temperatures

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity&mdash;Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000&ndash;2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km&sup2; to 325.33 km&sup2; during the period 2000&ndash;2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth

A correlation study of the expression of resistin and glycometabolism in muscle tissue after traumatic brain injury in rats

Objective:To investigate the expression pattern of resistin (RSTN) in skeletal muscle tissue and its influence on glycometabolism in rats with traumatic brain injury (TBI). Methods:Seventy-eight SD rats were randomly divided into traumatic group (n=36), RSTN group (n=36) and sham operation group (n=6). Fluid percussion TBI model was developed in traumatic and RSTN groups and the latter received additional 1 mg RSTN antibody treatment for each rat. At respectively 12 h, 24 h, 72 h, 1 w, 2 w, and 4 w after operation, venous blood was collected and the right hind leg skeletal muscle tissue was sampled. We used real-time PCR to determine mRNA expression of RSTN in skeletal muscles, western blot to determine RSTN protein expression and ELISA to assess serum insulin as well as fasting blood glucose (FBG) levels. Calculation of the quantitative insulin sensitivity check index (Q value) was also conducted. The above mentioned indicators and their correction were statistically analyzed. Results:Compared with sham operation group, the RSTN expression in the skeletal muscle as well as serum insulin and FBG levels revealed significant elevation (P&lt;0.05), and reduced Q value (P&lt;0.05) in traumatic group. Single factor linear correlation analysis showed a significant negative correlation between RSTN expression and Q values (P&lt;0.001) in traumatic group. Conclusion:The expression of RSTN has been greatly increased in the muscular tissue of TBI rats and it was closely related to the index of glycometabolism. RSTN may play an important role in the process of insulin resistance after TBI. Key words: Brain injuries; Resistin; Insulin resistance; Blood glucose; Insulin sensitivit