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Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.

Green tea is a non-fermented tea with flavor and polyphenols. Aroma is one of the important quality indicators of tea. Fermented green tea wine can solve the problem of low-grade tea, which has more bitterness and less aroma. In this study, Camellia sinensis var. pubilimba Hung T. Chang (Kaishan white tea 2) was screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) to benzyl alcohol and phenethyl alcohol presenting a fruity aroma, dimethyl sulfide presenting a green tea aroma, and rich tea polyphenols with contents of 2.08, 2.43, 12.26 and 3.72%, respectively. The optimal fermentation conditions for green tea wine were determined univariately as 1.5% yeast addition, 30 °Brix initial sugar, and fermentation temperature of 25 °C. The electronic sensory assessment showed that the saltiness, richness and umami were more prominent in green tea wine, while the response values of bitterness, astringency and aftertaste-A were lower. The order of aroma contribution can be seen as W1S > W5S > W2S > W2W > W1W > W3S > W6S. Kaisan white tea 2 gives green tea wine a clear tea aroma. This study provides better technical and theoretical strategies for the comprehensive quality assessment and control of fermented green tea wine quality.

Modern polymer coatings possess tremendous multifunctionalities and have attracted immense research interest in recent decades. However, with the expeditious development of technologies and industries, there is a vast demand for the flame retardancy and electrical conductivity of engineered polymer coatings. Traditional functional materials that render the polymer coatings with these properties require a sophisticated fabrication process, and their high mass gains can be a critical issue for weight-sensitive applications. In recent years, massive research has been conducted on a newly emerged two-dimensional (2D) nanosize material family, MXene. Due to the excellent electrical conductivity, flame retardancy, and lightweightness, investigations have been launched to synthesise MXene-based polymer coatings. Consequently, we performed a step-by-step review of MXene-involved polymer coatings, from solely attaching MXene to the substrate surface to the multilayered coating of modified MXene with other components. This review examines the performances of the fire safety enhancement and electrical conductivity as well as the feasibility of the manufacturing procedures of the as-prepared polymer composites. Additionally, the fabricated polymer coatings’ dual property mechanisms are well-demonstrated. Finally, the prospect of MXene participating in polymer coatings to render flame retardancy and electrical conductivity is forecasted.

A novel composite consisting of fluorine-doped carbon and graphene double-coated LiMn0.6Fe0.4PO4 (LMFP) nanorods was synthesized via a facile low-temperature solvothermal method that employs a hybrid glucose and polyvinylidene fluoride as carbon and fluorine sources. As revealed by physicochemical characterization, F-doped carbon coating and graphene form a ‘point-to-surface’ conductive network, facilitating rapid electron transport and mitigating electrochemical polarization. Furthermore, the uniform thickness of the F-doped carbon coating alters the growth of nanoparticles and prevents direct contact between the material and the electrolyte, thereby enhancing structural stability. The strongly electronegative F− can inhibit the structural changes in LMFP during charge/discharge, thus reducing the Jahn–Teller effect of Mn3+. The distinctive architecture of the LMFP/C-F/G cathode material exhibits excellent electrochemical properties, exhibiting an initial discharge capacity of 163.1 mAh g−1 at 0.1 C and a constant Coulombic efficiency of 99.7% over 100 cycles. Notably, the LMFP/C-F/G cathode material achieves an impressive energy density of 607.6 Wh kg−1, surpassing that of commercial counterparts. Moreover, it delivers a reversible capacity of 90.3 mAh g−1 at a high current rate of 5 C. The high-capacity capability and energy density of the prepared materials give them great potential for use in next-generation lithium-ion batteries.

Background and Objectives: This paper evaluates the efficacy and safety of ureteral access sheath (UAS) utilization in retrograde intrarenal surgery (RIRS). Materials and Methods: We searched PubMed, Embase, and the Cochrane Library up to 30 August 2023. The inclusion criteria comprised English-language original studies on RIRS with or without UAS in humans. The primary outcome was SFR, while the secondary outcomes included intraoperative and postoperative complications, the lengths of the operation and the hospitalization period, and the duration of the fluoroscopy. Subgroup analyses and a sensitivity analysis were performed. Publication bias was assessed using funnel plots and Egger’s regression tests. Dichotomous variables were analyzed using odds ratios (ORs) with 95% confidence intervals (CIs), while mean differences (MDs) were employed for continuous variables. Results: We included 22 studies in our analysis. These spanned 2001 to 2023, involving 12,993 patients and 13,293 procedures. No significant difference in SFR was observed between the UAS and non-UAS groups (OR = 0.90, 95% CI 0.63–1.30, p = 0.59). Intraoperative (OR = 1.13, 95% CI 0.75–1.69, p = 0.5) and postoperative complications (OR = 1.29, 95% CI 0.89–1.87, p = 0.18) did not significantly differ between the groups. UAS usage increased operation times (MD = 8.30, 95% CI 2.51–14.10, p = 0.005) and fluoroscopy times (MD = 5.73, 95% CI 4.55–6.90, p < 0.001). No publication bias was detected for any outcome. Conclusions: In RIRS, UAS usage did not significantly affect SFR, complications, or hospitalization time. However, it increased operation time and fluoroscopy time. Routine UAS usage is not supported, and decisions should be patient-specific. Further studies with larger sample sizes and standardized assessments are needed to refine UAS utilization in RIRS.

The two-step thermal stress calculation method (TTSCM) is commonly used to predict the cracking temperature of asphalt mixture. The aim of this study is to improve TTSCM’s mathematical model so as to enhance its prediction accuracy. First, this study evaluated the errors of predicted cracking temperatures of original TTSCM for AC-16 and AC-25 asphalt mixtures by thermal stress-restrained specimen test (TSRST). Then, an improved method called the extended creep compliance test (ECCT) was developed to modify the TTSCM. The test results show that the cracking predictions of the original TTSCM are not always accurate. Particularly for AC-16 asphalt mixture, the predicted cracking temperature is 2.9 °C (−10.6%) higher than the measured value by the TSRST. The ECCT method has been proven to be an effective way to enhance the prediction accuracy of the TTSCM. The predicted cracking temperatures modified by the ECCT method for both asphalt mixtures are relatively accurate, having an error within ±2%. The ECCT method changed the calculated thermal stress values at different temperatures of the TTSCM; however, they still conformed to a basic changing trend with respect to the initial temperature and cooling rate. Finally, a recommendation regarding the ECCT method was presented.

This study investigates the application of carbon quantum dots as tracers in inter-well connectivity monitoring. A new laboratory-made water-soluble carbon quantum dot fluorescent tracer (CQD-W) was studied using 3D fluorescence characterization, structural characterization, reservoir suitability evaluation, and core flow experiments. The experimental results showed that CQD-W has a size of about 2 nm, a minimum detection limit of 10−2 mg·L−1. It has good stability when the salinity is 200,000 mg·L−1, the concentration of Ca2+ is 1000 mg·L−1, the pH value is 1–9, and the temperature is 80 °C. Because CQD-W contains many functional groups, such as carboxyl and hydroxyl, it shows good water solubility and has a negative surface charge. In the process of formation flow, CQD-W has a small adsorption amount, high tracer resolution, and excellent injectivity and mobility, meaning it is less likely to cause reservoir damage. Through the study of this method, the application field of carbon quantum dots is broadened, and it is proved that the CQD-W fluorescent tracer has a high potential for application in the oil industry, laying the foundation for the popularization of this technology.

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.

With its rapid economic development, China has had to confront the serious issues of high energy consumption and air pollution. Natural ventilation is regarded as an effective method to reduce building energy consumption, but it is largely influenced by indoor and outdoor air pollution. However, most of the previous studies estimating natural ventilation potential (NVP) in China do not consider air pollution. This research estimated the NVP for residential buildings in major cities from four climate regions in China (Guangzhou, Chengdu, Shanghai, Beijing, and Shenyang) while considering the combined effect of indoor and outdoor air pollution. We compared the yearly NVP in three different scenarios, namely without considering air pollution, only considering outdoor air pollution, and considering both outdoor and indoor air pollution. The results show that Guangzhou had the highest yearly NVP, followed by Shanghai, Beijing, Shenyang, and Chengdu. The impact of air pollution could reduce the annual NVP in China by 78–95%. In addition, the main factors causing a low NVP differed between the four cities. The key factors for Chengdu and Guangzhou were natural ventilation flow rate and indoor air pollution, respectively. Beijing and Shenyang were mostly influenced by outdoor air pollution. Shanghai had two main factors with similar influence degrees, namely outdoor air pollution and indoor air pollution. The findings of this study will guide architects and policymakers in better forming natural ventilation strategies.

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

Wetlands provide important ecological services and aesthetic value at the landscape level. A landscape that makes people feel or appreciate the beauty of nature and is ecologically healthy and aesthetically beautiful can elicit positive emotions for people that are exposed to such landscapes. This then translates into protective environmental behaviors. Despite the growing importance of wetland conservation and human sensitivities to landscapes, little is known about the relationship between wetland landscapes and responsible environmental behaviors (REBs). This study was conducted at the wetlands at Guandu Nature Park (GNP), Taipei, Taiwan, using a five-point Likert scale questionnaire. Partial least squares structural equation modeling was used to test three hypotheses aiming to examine the influence of (1) landscape preference on REBs, (2) landscape preference on place attachment, and (3) place attachment on REBs. The findings indicated individuals displayed environmentally friendly behaviors because of the healthy environmental conditions of GNP wetlands and that an individuals’ sense of place attachment was influenced by the beauty of these wetlands. In addition, place attachment had a mediating effect on landscape preference and REBs. This study contributes to the limited resources associated regarding the effects of wetland landscapes on REBs and provides a basis for future comparative studies.

Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.

Single-pressure and ultrasonic action have their own unique advantages in the treatment of metal melts. When the two are combined into a composite field, the advantages of a single physical field can be fully utilized. So, the cavitation and acoustic streaming effects characteristics of an Al-5.0Cu alloy treated under different coupling process parameters, related to applied extrusion pressure and ultrasonic vibration, were analyzed by combining numerical simulation and experimental verification. The simulation results were experimentally verified by quantitative analysis of the microstructure, the melt, and its macro characteristics. The results show that the closing time t decreases with an increase in the extrusion pressure. In addition, when the ultrasonic power and extrusion force are increased simultaneously (100 MPa and 1 kW), the average grain size and the proportion of columnar grains reach the ideal effect. The influence of pressure parameters is greater, which will also lead to an increase in the proportion of columnar crystals. By optimizing the parameters, the grain size can be further reduced, the proportion of columnar crystal structure can be reduced, and fine and uniform equiaxed crystal structures can be easily obtained.

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms.

The ketone bodies, sodium and lithium salts of acetoacetate (AcAc) and sodium 3-hydroxybutyrate (3-HB; commonly called beta-hydroxybutyrate) have been found to inhibit the proliferation of cancer cells. Previous studies have suggested that lithium itself may be an inhibiting agent but may be additive or synergistic with the effect of AcAc. We previously found that sodium acetoacetate (NaAcAc) inhibits the growth of human colon cancer cell line SW480. We report here similar results for several other cancer cell lines including ovarian, cervical and breast cancers. We found that NaAcAc does not kill cancer cells but rather blocks their proliferation. Similar inhibition of growth was seen in the effect of lithium ion alone (as LiCl). The effect of LiAcAc appears to be due to the combined effects of acetoacetate and the lithium ion. The ketone bodies, when given together with chemotherapeutic agents, rapamycin, methotrexate and the new peptide anti-cancer agent, PNC-27, substantially lowers their IC50 values for cancer cell, killing suggesting that ketone bodies and ketogenic diets may be powerful adjunct agents in treating human cancers.

Automatic program repair techniques based on deep neural networks have attracted widespread attention from researchers due to the high degree of automation and generality. However, there is a scarcity of high-quality labeled datasets available for training program repair models. This study proposes a method of mining reasonable program repair examples from student program execution logs. Additionally, we introduce the Rookie Simulator (RS), which simulates the error patterns commonly made by novice programmers and generates a large number of program repair sample pairs. To address the issue of low repair rates for infrequent and complex error patterns in compilation errors, the study proposes the attention-enhanced capsule network for program repair (ACNPR), a program repair model that integrates compiler feedback information and utilizes capsule networks to capture complex semantic features. Experimental evaluations were conducted using publicly available datasets, including the DeepFix, TEGCER, and a real course dataset named SUES-COJ mined in this study. The results indicate that our method consistently outperforms current state-of-the-art models in terms of full repair rates.

Sweat, as a biofluid that is easy to extract and contains a variety of biomarkers, can provide various types of physiological information for health monitoring. In recent years, research on wearable sensors for sweat sensing has been emerging continuously. Wearable sweat sensing will probably become an alternative method to traditional chemical analysis. This is due to its advantages of portability, non-invasiveness, comfort, and continuous monitoring. Since the inception of this research field, wearable sweat sensors have achieved significant development in terms of materials, structures, systems, and application directions. Research interests are gradually evolving from single biomarker detection to the pursuit of multi-channel, multi-modal system-level architecture. The analysis of physiological signals has also developed from single signal characterization to omics analysis using multiple physiological information sources. Based on the changes mentioned above, this paper mainly introduces the latest researches of wearable sweat sensors from the aspects of strategy, architecture, material, system, data processing, etc., and tries to summarize the trends of sweat sensors. Finally, this paper analyzes the challenges faced by the sensing platform and possible methods for optimization.

To date, zinc-ion batteries (ZIBs) have been attracting extensive attention due to their outstanding properties and the potential to be the solution for next-generation energy storage systems. However, the uncontrollable growth of zinc dendrites and water-splitting issues seriously restrict their further scalable application. Over the past few years, solid polymer electrolytes (SPEs) have been regarded as a promising alternative to address these challenges and facilitate the practical advancement of zinc batteries. In this review, we revisit the research progress of SPEs applied in zinc batteries in the past few years and focus on introducing cutting-edge polymer science and technologies that can be utilised to prepare advanced SPEs for high-performance zinc batteries. The operating mechanism of SPEs and the functions of polymers are summarised. To highlight the polymer’s functions, SPEs are categorised into three types, homogenous polymer SPEs, hybrids polymer SPEs, and nanocomposites SPEs, which are expected to reveal the roles and principles of various polymers in zinc batteries. This review presents the current research progress and fundamental mechanisms of polymer-based SPEs in zinc batteries, outlines the challenging issues encountered, and proposes potential solutions for future endeavours.

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) is being studied and applied in more and more scenarios. However, there is still a certain distance away from real-time imaging using advanced algorithms. The traditional backpropagation algorithm (BPA) multi-accumulation integration is unsuitable for dealing with large-size scanning data, and the wavenumber domain algorithm requires the array to satisfy Nyquist sampling law in azimuth to avoid aliasing in imaging reconstruction. Based on these issues, a novel 3D imaging method is proposed for MIMO-SAR. An appropriate transformation and inverse Fourier transform (FT) is carried out for the frequency domain; thus, accumulation in the wavenumber domain is not required, which is easy to implement. The computational complexity of the algorithm is much lower than BPA and has better generalizability than the wavenumber domain algorithm. Coherence factor (CF) is also introduced to achieve sidelobe suppression. Proof-of-principle experiments were also carried out in the 92.5 GHz band based on the MIMO-SAR prototype system. Both simulation and experimental results of different distributed targets show good performance of imaging and do not lose the quality of image reconstruction.

In the present work, the effects of different die-casting process parameters on the mechanical properties and microstructure of A380 aluminum alloy casting with different wall thicknesses during the solidification process have been experimentally investigated. The experimental results show that both boost pressure and injection speed have a significant effect on the mechanical properties of the casting. As the injection speed increases, the changes in mechanical properties are more significant in the thin-walled area, while increasing the boosting pressure has a greater effect on the mechanical properties of the thick-walled area. In addition, the evolution of microstructure composition, including the α-Al phase, eutectic Si phase and Al-Si-Fe-Mn phase, has been analyzed and compared by energy-dispersive spectroscopy (EDS), optical microscopy (OM) and scanning electron microscopy (SEM). It was found that the α-Al phase in the thin-walled area is significantly refined with the increase of injection speed. Meanwhile, with the increase of boost pressure, the α-Al phase in the thick-walled area gradually becomes finer, and the distribution of the eutectic Si phase and the Al-Si-Fe-Mn phase in the alloy becomes more uniform. Thus, the injection speed and boost pressure have an important impact on the overall forming quality of the casting.

This is a review of approaches to the design of peptides and small molecules that selectively block the oncogenic RAS-p21 protein in ras-induced cancers. Single amino acid substitutions in this protein, at critical positions such as at Gly 12 and Gln 61, cause the protein to become oncogenic. These mutant proteins cause over 90 percent of pancreatic cancers, 40–50 percent of colon cancers and about one third of non-small cell cancers of the lung (NSCCL). RAS-p21 is a G-protein that becomes activated when it exchanges GDP for GTP. Several promising approaches have been developed that target mutant (oncogenic) RAS-p21 proteins in these different cancers. These approaches comprise: molecular simulations of mutant and wild-type proteins to identify effector domains, for which peptides can be made that selectively inhibit the oncogenic protein that include PNC-1 (ras residues 115–126), PNC-2 (ras residues 96–110) and PNC7 (ras residues 35–47); the use of contiguous RAS-p21 peptide sequences that can block ras signaling; cyclic peptides from large peptide libraries and small molecule libraries that can be identified in high throughput assays that can selectively stabilize inactive forms of RAS-p21; informatic approaches to discover peptides and small molecules that dock to specific domains of RAS-p21 that can block mitogenic signal transduction by oncogenic RAS-p21; and the use of cell-penetrating peptides (CPPs) that are attached to the variable domains of the anti-RAS-p21 inactivating monoclonal antibody, Y13 259, that selectively enters oncogenic RAS-p21-containing cancer cells, causing these cells to undergo apoptosis. Several new anti-oncogenic RAS-p21 agents, i.e., Amgen’s AMG510 and Mirati Therapeutics’ MRTX849, polycyclic aromatic compounds, have recently been FDA-approved and are already being used clinically to treat RAS-p21-induced NSCCL and colorectal carcinomas. These new drugs target the inactive form of RAS-p21 bound to GDP with G12C substitution at the critical Gly 12 residue by binding to a groove bordered by specific domains in this mutant protein into which these compounds insert, resulting in the stabilization of the inactive GDP-bound form of RAS-p21. Other peptides and small molecules have been discovered that block the G12D-RAS-p21 oncogenic protein. These agents can treat specific mutant protein-induced cancers and are excellent examples of personalized medicine. However, many oncogenic RAS-p21-induced tumors are caused by other mutations at positions 12, 13 and 61, requiring other, more general anti-oncogenic agents that are being provided using alternate methods.

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%.

We herein describe a general approach to 5-trifluoromethyl 1,2,4-triazoles via the [3 + 2]-cycloaddition of nitrile imines generated in situ from hydrazonyl chloride with CF3CN, utilizing 2,2,2-trifluoroacetaldehyde O-(aryl)oxime as the precursor of trifluoroacetonitrile. Various functional groups, including alkyl-substituted hydrazonyl chloride, were tolerated during cycloaddition. Furthermore, the gram-scale synthesis and common downstream transformations proved the potential synthetic relevance of this developed methodology.

The corrosion resistance of solder joints is a critical factor affecting the service life of electronic products during long-term operation. In this study, the corrosion behavior of Sn-0.7Cu-xNi@C (x = 0, 0.04, 0.08, and 0.12 wt.%) composite solders was investigated using a Tafel polarization curve in 3.5 wt.% NaCl solution, and the result demonstrated that it was the Ni@C that enhanced the corrosion resistance of the composite solder. The corrosion rate of the composite solders decreased with increasing Ni@C content and reached the lowest value of 0.205 mm/y when the content of Ni@C reached 0.08 wt.%. Ni@C changed the morphologies of corrosion products Sn3O(OH)2Cl2 from thick flakes to dense fine needles and flakes, which made it more difficult for Cl– to break down corrosion products. Thus, the corrosion resistance of composite solder was improved. The carbon skeleton in Ni@C served as an inert physical barrier to inhibit further corrosion. Furthermore, the potential difference between IMC and β-Sn decreased with the addition of Ni@C, which reduced the corrosion rate of the electric couple and enhanced the corrosion resistance of the composite solder.

The aim of the current study was to evaluate the subchronic toxicity of GmDREB3 gene modified wheat in the third generation rats. SPF Wistar rats were fed with transgenic wheat diet (Gm), parental wheat diet (Jimai22) and AIN-93 rodent diet (Control), respectively, for two generations, to produce the third generation rats which were used for this study. The selected fresh weaned offspring rats (20/sex/group) were given the same diet as their parents for 13 weeks. No toxicity-related changes were observed in rats fed with Gm diet in the following respects: clinical signs, body weights, body weight gains, food consumption, food utilization rate, urinalysis, hematology, serum biochemistry and histopathology. The results from the present study demonstrated that 13 weeks consumption of Gm wheat did not cause any adverse effects in the third generation rats when compared with the corresponding Jimai22 wheat.

Both waxy corn stover after fresh- (CF) and ripe-corn (CR) harvested are important byproducts of corn cropping system and have 20 d difference in harvest time. The study aimed to investigate the effects of prolonging harvest time on the nutritive value of corn stover silage by comparing CF with CR silages. In vitro ruminal experiment was firstly performed to investigate substrate degradation and fermentation of CF and CR silages. The CR diet was formulated by replacing 50% forage of CF silage with CR silage on a dry matter (DM) basis. Fourteen crossbred steers (Simmental × Limousin × local Chinese) aged 13 months with an average weight of 318.1 ± 37.1 kg were selected and randomly allocated into two dietary treatment groups. Although the CR silage had greater DM and fiber contents than CF silage, it did not alter in vitro degradation (p > 0.05), but with lower molar percentage of propionate and acetate to propionate ratio (p < 0.05). The cattle fed CR diet had a higher DM intake and lower fiber digestibility with reduction in 18S rRNA gene copies of protozoa and fungi and 16S rRNA gene copies of Fibrobacter succinogenes (p < 0.05). Further 16S rRNA gene amplicon analysis indicated a similar diversity of bacteria community between CR and CF treatments (p > 0.05). Few differences were observed in the abundance of genera larger than 1% (p > 0.05), except for the reduction in abundance of genera Ruminococcaceae_NK4A214_group in CR treatment (p < 0.05). In summary, prolonging 20 d harvest time of corn stover silage increases the forage fiber and DM content, which promotes feed intake with decreased fiber degradation, although rumen fermentation and growth performance are not changed in growing beef cattle.

A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was placed in a common path polarization interferometer by utilizing a heterodyne scheme. The measured phase variation of probe light was used to obtain the information regarding the concentration change in the fluidic liquids. The sensor was experimentally tested using different concentrations of sodium chloride solution showing a sensitivity of 3.52 ×104 deg./refractive index unit (RIU) and a detection resolution of 6.25 × 10−6 RIU. The estimated sensitivity and detection resolutions were 5.62 × 104 (deg./RIU) and 6.94 × 10−6 RIU, respectively, for the hydrochloric acid. The relationship between the measured phase and the concentration is linear with an R-squared value reaching above 0.995.

There have been few studies dealing with chemical elucidation and pharmacological potentials of water-soluble polysaccharides from jasmine tea, limiting their use in functional foods. In this study, water-soluble polysaccharides (named as JSP) were extracted from Jasminum sambac (L.) Aiton tea and fractionated to afford two sub-fractions (JSP-1 and JSP-2). The main structural characteristics of novel JSP sub-fractions were determined by high performance gel permeation chromatography, ultra-performance liquid chromatography-tandem mass spectrometry, Fourier transform infrared, and nuclear magnetic resonance analysis. Physiologically, the abilities of JSP-1 and JSP-2 to reduce ferric ions, scavenge DPPH and hydroxyl radicals, as well as protect islet cells were confirmed in vitro. JSP-1 exhibited better antioxidant and hypoglycemic activities than JSP-2. The molecular weights of JSP-1 and JSP-2 were 18.4 kDa and 14.1 kDa, respectively. JSP-1 was made up of glucose, galactose, rhamnose, xylose, arabinose, and galacturonic acid with molar ratios 1.14:4.69:1.00:9.92:13.79:4.09, whereas JSP-2 with a triple helical structure was composed of galactose, rhamnose, xylose, arabinose, and galacturonic acid as 3.80:1.00:8.27:11.85:5.05 of molar ratios. JSP-1 contains →1)-α-Galƒ-(3→, →1)-α-Galƒ-(2→, →1)-α-Araƒ-(5→, →1)-α-Araƒ-(3→, →1)-α-Araƒ-(3,5→, →1)-β-Xylp-(2→ and →1)-β-Xylp-(3→ residues in the backbone. These results open up new pharmacological prospects for the water-soluble polysaccharides extracted from jasmine tea.

In recent years, nanopore technology has become increasingly important in the field of life science and biomedical research. By embedding a nano-scale hole in a thin membrane and measuring the electrochemical signal, nanopore technology can be used to investigate the nucleic acids and other biomacromolecules. One of the most successful applications of nanopore technology, the Oxford Nanopore Technology, marks the beginning of the fourth generation of gene sequencing technology. In this review, the operational principle and the technology for signal processing of the nanopore gene sequencing are documented. Moreover, this review focuses on the applications using nanopore gene sequencing technology, including the diagnosis of cancer, detection of viruses and other microbes, and the assembly of genomes. These applications show that nanopore technology is promising in the field of biological and biomedical sensing.

The Western Liaoning area, where a large number of Jurassic-Cretaceous volcanic rocks are exposed, is one of the typical areas for studying the Mesozoic Paleo-Pacific and Mongolia-Okhotsk subduction process, and lithospheric destruction of North China Craton. The identification and investigation of Early Jurassic adakitic volcanic rocks in the Xintaimen area of Western Liaoning is of particular significance for exploring the volcanic magma source and its composition evolution, tracking the crust-mantle interaction, and revealing the craton destruction and the subduction of oceanic plates. Detailed petrography, zircon U–Pb dating, geochemistry, and zircon Hf isotope studies indicate that the Early Jurassic intermediate-acidic volcanic rocks are mainly composed of trachydacites and a few rhyolites with the formation ages of 178.6–181.9 Ma. Geochemical characteristics show that they have a high content of SiO2, MgO, Al2O3, and total-alkali, typical of the high-K calc-alkaline series. They also show enrichment of light rare earth elements (LREEs) and large ion lithophile elements (LILEs), depletion of heavy rare earth elements (HREEs) and high field strength elements (HFSEs), and have a high content of Sr and low content of Y and Yb, suggesting that they were derived from the partial melting of the lower crust. The εHf(t) values of dated zircons and two-stage model ages (TDM2) vary from −11.6 to −7.4 and from 1692 to 1958 Ma, respectively. During the Early Jurassic, the study area was under long-range tectonic effects with the closure of the Mongolia-Okhotsk Ocean and the subduction of the Paleo-Pacific plate, which caused the basaltic magma to invade the lower crust of the North China Craton. The mantle-derived magma was separated and crystallized while heating the Proterozoic lower crust, and part of the thickened crust melted to form these intermediate-acidic adakitic volcanic rocks.

Butyrylcholinesterase (BChE) is a biomarker in human blood. Aberrant BChE activity has been associated with human diseases. Here we developed a fluorescence resonance energy transfer (FRET) chemical probe to specifically quantify BChE activity in serum, while simultaneously discriminating against glutathione (GSH). The FRET chemical probe 11 was synthesized from a key trifunctional bicyclononyne exo-6 and derivatives of 5-(2-aminoethylamino)-1-naphthalenesulfonic acid (EDANS) and 4-[4-(dimethylamino)phenylazo]benzoic acid (DABCYL). EDANS fluorescence visualization and kinetic analysis of 11 in the presence of diverse compounds confirmed the outstanding reactivity and specificity of 11 with thiols. The thiol-dependent fluorescence turn-on property of 11 was attributed to a general base-catalyzed SN2 nucleophilic substitution mechanism and independent of metal ions. Moreover, all thiols, except GSH, reacted swiftly with 11. Kinetic studies of 11 in the presence of covalently modified GSH derivatives corroborated that the steric hindrance of 11 imposing on GSH was the likely cause of the distinguished reactivity. Since GSH commonly interferes in assays measuring BChE activity in blood samples, the 11-based fluorescent assay was employed to directly quantify BChE activity without GSH interference, and delivered a linear range of 4.3–182.2 U L−1 for BChE activity with detection limit of 4.3 U L−1, and accurately quantified serum BChE activity in the presence of 10 μM GSH. Finally, the 11-based assay was exploited to determine Ki of 5 nM for tacrine inhibition on BChE catalysis. We are harnessing the modulated characteristics of 6 to synthesize advanced chemical probes able to more sensitively screen for BChE inhibitors and quantify BChE activity in serum.

This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

Thermal error is one of the main reasons for the loss of accuracy in lathe machining. In this study, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy. The method used involves the collection of temperature data from the front and rear spindle bearings by means of embedded sensors in the bearing housings. Room temperature data were also collected as well as the thermal elongation of the main shaft. The data were used in a linear regression model to establish a robust model with strong predictive capability. Three methods were used: (1) Comsol was used for finite element analysis and the results were compared with actual measured temperatures. (2) This method involved the adjustment of the parameters of the linear regression model using the indicators of the coefficient of determination, root mean square error, mean square error, and mean absolute error, to find the best parameters for a spindle thermal displacement model. (3) The third method used system recognition to determine similarity to actual data by dividing the model into rise time and stable time. The rise time was controlled to explore the accuracy of prediction of the model at different intervals. The experimental results show that the actual measured temperatures were very close to those obtained in the Comsol analysis. The traditional model calculates prediction error values within single intervals, and so the model was divided to give rise time and stable time. The experimental results showed two error intervals, 19µm in the rise time and 15µm in the stable time, and these findings allowed the machining accuracy to be enhanced.

Malolactic fermentation is a vital red wine-making process to enhance the sensory quality. The objective of this study is to elucidate the starter cultures’ role in modifying phenolic compounds, biogenic amines, and volatile compounds after red wine malolactic fermentation. We initiated the malolactic fermentation in Cabernet Gernishct wine by using two Oenococcus oeni and two Lactobacillus plantarum strains. Results showed that after malolactic fermentation, wines experienced a content decrease of total flavanols and total flavonols, accompanied by the accumulation of phenolic acids. The Lactobacillus plantarum strains, compared to Oenococcus oeni, exhibited a prevention against the accumulation of biogenic amines. The malolactic fermentation increased the total esters and modified the aromatic features compared to the unfermented wine. The Lactobacillus plantarum strains retained more aromas than the Oenococcus oeni strains did. Principal component analysis revealed that different strains could distinctly alter the wine characteristics being investigated in this study. These indicated that Lactobacillus plantarum could serve as a better alternative starter for conducting red wine malolactic fermentation.

Phenolic compounds determine the color quality of fruit wines. In this study, the phenolic compound content and composition, color characteristics and changes during 6 months of bottle aging were studied in wines fermented with bog bilberry syrup under three different pHs. The total anthocyanins and total phenols were around 15.12–16.23 mg/L and 475.82 to 486.50 mg GAE/L in fresh wines and declined 22%–31% and about 11% in bottle aged wines, respectively. In fresh wines, eight anthocyanins, six phenolic aids and 14 flavonols, but no flavon-3-ols were identified; Malvidin-3-O-glucoside, petunidin-3-O-glucoside and delphinium-3-O-glucoside were the predominant pigments; Chlorogentic acid was the most abundant phenolic acid, and quercetin-3-O-galactoside and myricetin-3-O-galactoside accounted for nearly 90% of the total flavonols. During 6 months of bottle storage, the amounts of all the monomeric anthocyanins and phenolic acids were reduced dramatically, while the glycosidyl flavonols remained constant or were less reduced and their corresponding aglycones increased a lot. The effects of aging on blueberry wine color were described as the loss of color intensity with a dramatic change in color hue, from initial red-purple up to final red-brick nuances, while the pH of the fermentation matrix was negatively related to the color stability of aged wine.

Fabricating high-performance MXene-based polymer nanocomposites is a huge challenge because of the poor dispersion and interfacial interaction of MXene nanosheets in the polymer matrix. To address the issue, MXene nanosheets were successfully exfoliated and subsequently modified by long-chain cationic agents with different chain lengths, i.e., decyltrimethylammonium bromide (DTAB), octadecyltrimethylammonium bromide (OTAB), and dihexadecyldimethylammonium bromide (DDAB). With the long-chain groups on their surface, modified Ti3C2 (MXene) nanosheets were well dispersed in N,N-dimethylformamide (DMF), resulting in the formation of uniform dispersion and strong interfacial adhesion within a polystyrene (PS) matrix. The thermal stability properties of cationic modified Ti3C2/PS nanocomposites were improved considerably with the temperatures at 5% weight loss increasing by 20 °C for DTAB-Ti3C2/PS, 25 °C for OTAB-Ti3C2/PS and 23 °C for DDAB-Ti3C2/PS, respectively. The modified MXene nanosheets also enhanced the flame-retardant properties of PS. Compared to neat PS, the peak heat release rate (PHRR) was reduced by approximately 26.4%, 21.5% and 20.8% for PS/OTAB-Ti3C2, PS/DDAB-Ti3C2 and PS/DTAB-Ti3C2, respectively. Significant reductions in CO and CO2 productions were also obtained in the cone calorimeter test and generally lower pyrolysis volatile products were recorded by PS/OTAB-Ti3C2 compared to pristine PS. These property enhancements of PS nanocomposites are attributed to the superior dispersion, catalytic and barrier effects of Ti3C2 nanosheets.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

The fundamental advances in silicon isotope geochemistry have been systematically demonstrated in this work. Firstly, the continuous modifications in analytical approaches and the silicon isotope variations in major reservoirs and geological processes have been briefly introduced. Secondly, the silicon isotope fractionation linked to silicon complexation/coordination and thermodynamic conditions have been extensively stressed, including silicate minerals with variable structures and chemical compositions, silica precipitation and diagenesis, chemical weathering of crustal surface silicate rocks, biological uptake, global oceanic Si cycle, etc. Finally, the relevant geological implications for meteorites and planetary core formation, ore deposits formation, hydrothermal fluids activities, and silicon cycling in hydrosphere have been summarized. Compared to the thermodynamic isotope fractionation of silicon associated with high-temperature processes, that in low-temperature geological processes is much more significant (e.g., chemical weathering, biogenic/non-biogenic precipitation, biological uptake, adsorption, etc.). The equilibrium silicon isotope fractionation during the mantle-core differentiation resulted in the observed heavy isotope composition of the bulk silicate Earth (BSE). The equilibrium fractionation of silicon isotopes among silicate minerals are sensitive to the Si–O bond length, Si coordination numbers (CN), the polymerization degrees of silicate unites, and the electronegativity of cations in minerals. The preferential enrichment of different speciation of dissoluble Si (DSi) (e.g., silicic acid H4SiO40 (H4) and H3SiO4− (H3)) in silica precipitation and diagenesis, and chemical weathering, lead to predominately positive Si isotope signatures in continental surface waters, in which the dynamic fractionation of silicon isotope could be well described by the Rayleigh fractionation model. The role of complexation in biological fractionations of silicon isotopes is more complicated, likely involving several enzymatic processes and active transport proteins. The integrated understanding greatly strengthens the potential of δ30Si proxy for reconstructing the paleo terrestrial and oceanic environments, and exploring the meteorites and planetary core formation, as well as constraining ore deposits and hydrothermal fluid activity.

We demonstrate a refractive index sensor based on a long period grating (LPG) inscribed in a special photosensitive microfiber with double-clad profile. The fiber is tapered gradually enough to ensure the adiabaticity of the fiber taper. In other words, the resulting insertion loss is sufficiently small. The boron and germanium co-doped inner cladding makes it suitable for inscribing gratings into its tapered form. The manner of wavelength shift for refractive indices (RIs) differs from conventional LPG, and the refractive index detection limit is 1.67 × 10−5.