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In order to improve the automation and safety of underground steel belt support in coal mines, a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors is proposed. Using STM32F407ZGT6 as the main control chip, tasks such as data acquisition and processing, motor motion control, etc., are assigned based on the real-time operating system FreeRTOS. Using the XY mobile platform equipped with inductive sensors to detect steel belts, The collected data includes coordinate values and voltage values. Adaptive threshold generation and correction strategies are used for threshold segmentation and extraction of anchor hole boundary points. The principle of Hough circle transformation is used to fit the extracted boundary points into circles. The results show that this method can perform anchor hole positioning with a positioning error of within 5 mm, meeting the design requirements. [ABSTRACT FROM AUTHOR]

With the development of microelectronics products with high density and high power, it is urgent to improve the electrical and thermal conductivity of electronic paste to achieve the new requirements of packaging materials. In this work, a new synthesis method of Ag-MWCNTs was designed: Firstly, carboxylated MWCNTs and stannous chloride were used as raw materials to prepare high-loading-rate Sn-MWCNT composite material to ensure the high loading rate of metal on the MWCNT surface. Then, Ag-MWCNT composite material was prepared by the chemical displacement method to solve the problem of the low loading rate of silver nanoparticles on the MWCNT surface. On the basis of this innovation, we analyzed and compared the electrical, thermal, and mechanical properties of Ag-MWCNT composite electronic paste. Compared with the electronic paste without adding Ag-MWCNTs, the resistivity was reduced by 77%, the thermal conductivity was increased by 66%, and the shear strength was increased by 15%. Therefore, the addition of Ag-MWCNTs effectively improves the electrical, thermal, and mechanical properties of the paste, making it a promising and competitive choice for new packaging materials in the future. [ABSTRACT FROM AUTHOR]

Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized complex modules to improve their performance for skin disease image segmentation. However, limited computational resources have hindered their practical application in clinical environments. To address this challenge, this paper proposes a lightweight model, named GSCEU-Net, which is able to achieve superior skin lesion segmentation performance at a lower cost. GSCEU-Net is based on the U-Net architecture with additional enhancements. Firstly, the partial convolution (PConv) module, proposed by the FasterNet model, is modified to an SConv module, which enables channel segmentation paths of different scales. Secondly, a newly designed Ghost SConv (GSC) module is proposed for incorporation into the model's backbone, where the Separate Convolution (SConv) module is aided by a Multi-Layer Perceptron (MLP) and the output path residuals from the Ghost module. Finally, the Efficient Channel Attention (ECA) mechanism is incorporated at different levels into the decoding part of the model. The segmentation performance of the proposed model is evaluated on two public datasets (ISIC2018 and PH2) and a private dataset. Compared to U-Net, the proposed model achieves an IoU improvement of 0.0261 points and a DSC improvement of 0.0164 points, while reducing the parameter count by 190 times and the computational complexity by 170 times. Compared to other existing segmentation models, the proposed GSCEU-Net model also demonstrates superiority, along with an advanced balance between the number of parameters, complexity, and segmentation performance. [ABSTRACT FROM AUTHOR]

For the demand of rapid monitoring of pollution gas disorganized emissions in industrial parks, this paper studies the solar fast tracker system of vehicle-mounted SOF-FTIR (Solar Occultation Flux Fourier Transform Infrared Spectroscopy) system, where the spectrometer directly measures the broadband absorption spectrum of solar radiation light. A fast portable solar tracking system based on PSD (position sensitive detector) is designed, the mathematical model of solar spot position on the PSD surface source is established, and the optimal optical design parameters are simulated using the model. The dead-zone integral separation PID (Proportion Integration Differentiation) control algorithm is used to track the trajectory of the solar, and the light spot position model is used to nonlinearly compensate the output of PID control so that the PID controller has the same control precision and response speed in different error areas. Experimental analysis of the solar tracking performance of the vehicle-mounted SOF-FTIR under static and dynamic conditions, as well as the spectral effects on the measurements under static vehicle, constant speed, and turning driving conditions. The remote sensing application experiment of vehicle-mounted SOF-FTIR pollution gas emission flux was carried out in a tire factory in Hefei City, Anhui Province. A vehicle-mounted SOF-FTIR system realized the qualitative and quantitative analysis of the pollution gas at the boundary of the tire plant and calculated the flux of each component pollution gas. The emission flux of pollution gas was highly consistent with the actual pollution distribution of the tire plant. The results show that the positioning accuracy of PSD in the vehicle tracking experiment can also meet SOF-FTIR requirements for solar tracking. The remote sensing system will be useful in the field of atmospheric environment monitoring, and the mobile monitoring of regional pollutant gases based on solar infrared spectroscopy has application value. [ABSTRACT FROM AUTHOR]

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction-based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method's effectiveness in practice. [ABSTRACT FROM AUTHOR]

Coastal urban megacities across Asia face significant risks from climate change, including coastal flooding, high temperatures, urban heat island impacts and air pollution. These hazards are associated with negative impacts on infrastructure, communities and the environment. To identify the current intensity of climate change impacts in coastal urban megacities, an integrated evaluation method is needed. Firstly, the present study assesses the climate change impacts of Guangzhou, a Chinese coastal urban megacity, for both physical and social aspects. This study includes 60 years of time-series data for 1960–2020 to examine temperatures, precipitation, humidity and air pollution in Guangzhou city. At the same time, a survey was conducted between April and July 2022 in this megacity and collected the views of 336 people on climate change and its associated environmental impacts. Secondly, the Ganzhou city results are compared with existing data from similar nearby cities to evaluate the diverse climate change trends. Results show that during 1961-1990, the city received the most rainfall in May, reaching 283.6 mm. From 1990 to 2020, June recorded the highest rainfall of 356.6 mm and shows an increase of 73 mm during that period. The very severe monsoon season brought an increased risk of flooding. Results also revealed that the warmest month is July, and the coldest month is January, and both months showed increased temperatures of 0.60 ℃. Comparison results revealed that Guangzhou is not the only city which scored increased highest temperatures; other nearby cities including Heyuan, Shantou and Shaoguan also scored increased highest temperatures. The survey reveals that the majority of respondents (75%) perceived the increased frequency of extreme weather, including typhoons, heavy rainfall and multiple days of hot weather, such as higher temperatures and an increased number of hot days. In the responses to the questions related to the heat island effect, more than 80% of residents are aware of the existence of the heat island and its impacts. People believe that the primary causes of the urban heat island problem are industrial production and anthropogenic heat generated by the city. These results will be helpful to local and national policy and decision makers to revise and/or develop new strategies to improve the environment and quality of life in coastal megacities, particularly Ganzhou. [ABSTRACT FROM AUTHOR]

Saprolegniasis, which is caused by Saprolegnia parasitica, leads to considerable economic losses. Recently, we showed that metalaxyl, bronopol and copper sulfate are good antimicrobial agents for aquaculture. In the current study, the efficacies of metalaxyl, bronopol and copper sulfate are evaluated by in vitro antimicrobial experiments, and the mechanism of action of these three antimicrobials on S. parasitica is explored using transcriptome technology. Finally, the potential target genes of antimicrobials on S. parasitica are identified by protein–protein interaction network analysis. Copper sulfate had the best inhibitory effect on S. parasitica, followed by bronopol. A total of 1771, 723 and 2118 DEGs upregulated and 1416, 319 and 2161 DEGs downregulated S. parasitica after three drug treatments (metalaxyl, bronopol and copper sulfate), separately. Additionally, KEGG pathway analysis also determined that there were 17, 19 and 13 significantly enriched metabolic pathways. PPI network analysis screened out three important proteins, and their corresponding genes were SPRG_08456, SPRG_03679 and SPRG_10775. Our results indicate that three antimicrobials inhibit S. parasitica growth by affecting multiple biological functions, including protein synthesis, oxidative stress, lipid metabolism and energy metabolism. Additionally, the screened key genes can be used as potential target genes of chemical antimicrobial drugs for S. parasitica. [ABSTRACT FROM AUTHOR]

The treatment of phenolic-containing wastewater has received increased attention in recent years. In this study, the N-doped porous carbons were prepared from lignite with tripolycyanamide as the N source, and their phenol adsorption behaviors were investigated. Results clearly showed that the addition of tripolycyanamide largely improved the surface area, micropore volume, N content and thus the phenol adsorption capacity of lignite-based carbons. The N-doped sample prepared at 700 °C showed a surface area of 1630 m2/g and a phenol adsorption capacity as high as 182.4 mg/g at 20 °C, which were 2.0 and 1.6 times that of the lignite-based carbon without N-doping. Pseudo-second order and Freundlich adsorption isotherm models could better explain the phenol adsorption behaviors over lignite-based N-doped porous carbon. Theoretical calculations demonstrated that phenol adsorption energies over graphitic-N (−72 kJ/mol) and pyrrolic-N (−74 kJ/mol) groups were slightly lower than that over the N-free graphite layer (−71 kJ/mol), supporting that these N-containing groups contribute to enhance the phenol adsorption capacity. The adsorption mechanism of phenol over porous carbon might be interpreted by the π–π dispersion interactions between aromatic-ring and carbon planes, which could be enhanced by N-doping through increasing π electron densities in the carbon plane. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) is one of the most prevalent malignant tumors of the urinary system, accounting for around 2% of all cancer diagnoses and deaths worldwide. Clear cell RCC (ccRCC) is the most prevalent and aggressive histology with an unfavorable prognosis and inadequate treatment. Patients' progression-free survival is considerably improved by surgery; however, 30% of patients develop metastases following surgery. Identifying novel targets and molecular markers for RCC prognostic detection is crucial for more accurate clinical diagnosis and therapy. Glycosylation is a critical post-translational modification (PMT) for cancer cell growth, migration, and invasion, involving the transfer of glycosyl moieties to specific amino acid residues in proteins to form glycosidic bonds through the activity of glycosyltransferases. Most cancers, including RCC, undergo glycosylation changes such as branching, sialylation, and fucosylation. In this review, we discuss the latest findings on the significance of aberrant glycans in the initiation, development, and progression of RCC. The potential biomarkers of altered glycans for the diagnosis and their implications in RCC have been further highlighted. [ABSTRACT FROM AUTHOR]

In recent years, China has promoted a series of legal norms to reduce the environmental impact of air pollutants from vehicles. The three main vehicle emission species (carbon monoxide, hydrocarbons, nitrogen oxides) contribute significantly to air pollution. In this study, the emission factor method was used to estimate air pollutants from vehicles in 31 provinces from 2006 to 2016. The results show a trend of total vehicle carbon monoxide (CO) and hydrocarbons (HC) emissions decreasing with time; the vehicle nitrogen oxides (NOx) emission trend is divided into two stages: an upward trend between 2006 and 2012 and a downward trend after 2012. Based on a panel threshold, a regression method was used to divide the vehicle NOx and CO emissions in China into four emission zones: low emissions, medium emissions, high emissions, and extra-high emissions. Vehicle HC emissions were divided into three emission zones, which corresponded to low emissions, medium emissions, and high emissions. Overall, vehicle pollution emission efficiency and per capita GDP have a significant inhibitory effect on the three main air pollutants from vehicles (NOx, HC, CO). Both passenger and freight turnover have significant roles in promoting the three air pollutants from vehicles (NOx, HC, CO). Road density and road carrying capacity have a significant role in promoting vehicle HC and CO emissions. Increasing truck proportion inhibits vehicle CO emissions and promotes vehicle NOx emissions. The urbanization rate has a positive effect on vehicle HC and CO emissions. Moreover, there is obvious heterogeneity in different emission zones of the three air pollutants from vehicles (NOx, HC, CO). [ABSTRACT FROM AUTHOR]

Simple Summary: Pasteurella multocida (Pm) infection can cause pasteurellosis in goats. Currently, the mechanisms of Pm infection in goats are limited. In this study, the effects of Pm infection to goats were characterized from the aspects of histopathology, miRNA and mRNA expression dynamics in lung of goats infected for 1, 2, 5 and 7 days and the clinical symptoms of animals. We further revealed the potential role of differentially expressed RNAs, which are commonly involved in and regulate the host immune reaction to the Pm infection. The differentially expressed genes were mapped to marker genes of the immune cells in the lung, which further confirmed the cells detected by the histopathological method. Pasteurella multocida (Pm) infection causes severe respiratory disease in goats. We investigated the effects of the Pm infection intratracheally on the histopathology, miRNA and mRNA expression dynamics in the lung of goats infected for 1, 2, 5 and 7 days. Pm infection caused fever, which significantly (p < 0.05) increased the body temperature of the goats from day 1 to 5. Haemotoxylin–eosin staining of the infected lung tissue showed characteristics of suppurative pneumonia with inflammatory cells infiltration and the lung structure destruction. During the Pm infection of the goats, compared with the control group, there were 3080, 3508, 2716 and 2675 differentially expressed genes and 42, 69, 91 and 108 significantly expressed miRNAs (|log2Fold Change| > 1, p < 0.05) in the Pm_d1, Pm_d2, Pm_d5 and Pm_d7 groups, respectively. Five miRNAs and nine immune-related genes were selected for confirmation by reverse transcription–polymerase chain reaction. The results indicated that the expression patterns of the miRNAs and genes were consistent with those determined by next-generation sequencing. The differentially expressed genes were enriched in cytokine–cytokine receptor interaction, cell adhesion molecules, complement and coagulation cascades, tight junction and phagosome Kyoto Encyclopedia of Genes and Genomes pathways and cytokine production, leukocyte migration, myeloid leukocyte migration, cell periphery, plasma membrane, extracellular region part, extracellular region and other Gene Ontology terms. The differentially expressed genes were mapped to marker genes in human and mouse lung cells. The results showed the presence of some marker genes of the immune cells. Compared with the CK group, five miRNAs and 892 common genes were differentially expressed in the Pm_d1, Pm_d2, Pm_d5 and Pm_d7 groups. The target relationships between the common 5 miRNAs and 892 differentially expressed genes were explored and the miRNAs involved in the host immune reaction may act through the target genes. Our study characterized goats' reaction in the lung from histopathological and molecular changes upon Pm infection, which will provide valuable information for understanding the responses in goats during Pm infection. [ABSTRACT FROM AUTHOR]

Common buckwheat (Fagopyrum esculentum M.) is known for its adaptability, good nutrition, and medicinal and health care value. However, genetic studies of buckwheat have been hindered by limited genomic resources and genetic markers. In this study, Illumina HiSeq 4000 high-throughput sequencing technology was used to sequence the transcriptome of green-flower common buckwheat (Gr) with coarse pedicels and white-flower Ukrainian daliqiao (UD) with fine pedicels. A total of 118,448 unigenes were obtained, with an average length of 1248 bp and an N50 of 1850 bp. A total of 39,432 differentially expressed genes (DEGs) were identified, and the DEGs of the porphyrins and chlorophyll metabolic pathway had significantly upregulated expression in Gr. Then, a total of 17,579 sequences containing SSR loci were detected, and 20,756 EST-SSR loci were found. The distribution frequency of EST-SSR in the transcriptome was 17.52%, and the average distribution density was 8.21 kb. A total of 224 pairs of primers were randomly selected for synthesis; 35 varieties of common buckwheat and 13 varieties of Tartary buckwheat were verified through these primers. The clustering results well verified the previous conclusion that common buckwheat and Tartary buckwheat had a distant genetic relationship. The EST-SSR markers identified and developed in this study will be helpful to enrich the transcriptome information and marker-assisted selection breeding of buckwheat. [ABSTRACT FROM AUTHOR]

A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time. [ABSTRACT FROM AUTHOR]

This review discusses different forms of nanomaterials generated from chitosan and its derivatives for controlled drug delivery. Nanomaterials are drug carriers with multiple features, including target delivery triggered by environmental, pH, thermal responses, enhanced biocompatibility, and the ability to cross the blood-brain barrier. Chitosan (CS), a natural polysaccharide largely obtained from marine crustaceans, is a promising drug delivery vector for therapeutics and diagnostics, owing to its biocompatibility, biodegradability, low toxicity, and structural variability. This review describes various approaches to obtain novel CS derivatives, including their distinct advantages, as well as different forms of nanomaterials recently developed from CS. The advanced applications of CS-based nanomaterials are presented here in terms of their specific functions. Recent studies have proven that nanotechnology combined with CS and its derivatives could potentially circumvent obstacles in the transport of drugs thereby improving the drug efficacy. CS-based nanomaterials have been shown to be highly effective in targeted drug therapy. [ABSTRACT FROM AUTHOR]

We have recently shown that endogenous hydrogen sulfide (H2S), an important cellular gaseous mediator, exerts an antiviral and anti-inflammatory activity in vitro and in vivo, and that exogenous H2S delivered via the synthetic H2S-releasing compound GYY4137 also has similar properties. In this study, we sought to extend our findings to a novel class of H2S donors, thiol-activated gem-dithiol-based (TAGDDs). In an in vitro model of human respiratory syncytial virus (RSV) infection, TAGDD-1 treatment significantly reduced viral replication, even when added up to six hours after infection. Using a mouse model of RSV infection, intranasal delivery of TAGDD-1 to infected mice significantly reduced viral replication and lung inflammation, markedly improving clinical disease parameters and pulmonary dysfunction, compared to vehicle treated controls. Overall our results indicate that this novel synthetic class of H2S-releasing compounds exerts antiviral and anti-inflammatory activity in the context of RSV infection and represents a potential novel pharmacological approach to ameliorate viral-induced lung disease. [ABSTRACT FROM AUTHOR]

The high variability and somatic stability of DNA fingerprints can be used to identify individuals, which is of great value in plant breeding. DNA fingerprint databases are essential and important tools for plant molecular research because they provide powerful technical and information support for crop breeding, variety quality control, variety right protection, and molecular marker-assisted breeding. Building a DNA fingerprint database involves the production of large amounts of heterogeneous data for which storage, analysis, and retrieval are time and resource consuming. To process the large amounts of data generated by laboratories and conduct quality control, a database management system is urgently needed to track samples and analyze data. We developed the plant international DNA-fingerprinting system (PIDS) using an open source web server and free software that has automatic collection, storage, and efficient management functions based on merging and comparison algorithms to handle massive microsatellite DNA fingerprint data. PIDS also can perform genetic analyses. This system can match a corresponding capillary electrophoresis image on each primer locus as fingerprint data to upload to the server. PIDS provides free customization and extension of back-end functions to meet the requirements of different laboratories. This system can be a significant tool for plant breeders and can be applied in forensic science for human fingerprint identification, as well as in virus and microorganism research., Competing Interests: The authors declare no conflict of interest.