Effects of Mixing Techniques and Material Compositions on the Compressive Strength and Thermal Conductivity of Ultra-Lightweight Foam Concrete.

The research focuses on ultra-lightweight foam concrete with a dry density below 200 kg/m3, primarily used as insulation material. Factors that may affect material properties are categorized into mixing techniques and material composition, and experimental investigations were conducted on the impact of these factors on the rheological properties of cement slurry, density at different time intervals, compressive strength, and thermal conductivity of foam concrete samples. The experimental results indicate the influence of mixing speed and mixing duration on the instrument during the cement slurry production and mixing process with foam. Additionally, variations in foam concrete sample properties are observed due to the water-to-cement ratio, foam content, and foam density in the selected material compositions. By analyzing the material density at different time intervals, the relationship between the ambient air trapped during the mixing process and the viscosity of the material can be indirectly observed. This analysis can also reveal the correlation between the unplanned air content and the properties of the material.

Copper-Catalyzed Difunctionalization of Propargylic Carbonates through Tandem Nucleophilic Substitution/Boroprotonation.

Highly functionalized organic molecules are in high demand, but their preparation is challenging. Copper-catalyzed transformation of alkynyl- and allenyl-containing substrates has emerged as a powerful tool to achieve this objective. Herein, an efficient copper-catalyzed difunctionalization of propargylic carbonates through tandem nucleophilic substitution/boroprotonation has been developed, affording the formation of thiol-, selenium-, and boron-functionalized alkenes with high yield and stereoselectivity. Two distinct catalytic mechanisms involving a single reaction without any requirement of catalyst change were successfully demonstrated.

Pollutant removal in an experimental bioretention cell situated in a northern Chinese sponge city.

To assess the viability and effectiveness of bioretention cell in enhancing rainwater resource utilization within sponge cities, this study employs field monitoring, laboratory testing, and statistical analysis to evaluate the water purification capabilities of bioretention cell. Findings indicate a marked purification impact on surface runoff, with removal efficiencies of 59.81% for suspended solids (SS), 39.01% for chemical oxygen demand (COD), 37.53% for ammonia nitrogen (NH3-N), and 30.49% for total phosphorus (TP). The treated water largely complies with rainwater reuse guidelines and tertiary sewage discharge standards. Notably, while previous research in China has emphasized water volume control in sponge city infrastructures, less attention has been given to the qualitative aspects and field-based evaluations. This research not only fills that gap but also offers valuable insights and practical implications for bioretention cell integration into sponge city development. Moreover, the methodology and outcomes of this study serve as a benchmark for future sponge city project assessments, offering guidance to relevant authorities.

Investigation of thia-Diels-Alder Reactions by Ultrafast Transient Absorption Spectroscopy and DFT Calculations.

The thia-Diels-Alder reaction represents a versatile synthetic method for the preparation of six-membered sulfur-containing compounds. However, the mechanism of the thia-Diels-Alder reactions remains unclear. In this work, time-resolved spectroscopic experiments and DFT calculations demonstrate that phenacyl sulfide undergoes Norrish II cleavage to produce thioaldehyde, and ortho-hydroxy benzhydryl alcohol occurs in a dehydration reaction to generate o-QMs using diphenylphosphate as the catalyst. Then, the thia-Diels-Alder reaction takes place between thioaldehyde and o-QMs by an asynchronous concerted mechanism. The illustration of the thia-Diels-Alder reaction mechanism not only provides important support for organic synthesis and drug design but also enhances fundamental insights into reaction pathways and catalytic processes in the field of chemical synthesis.

Effect of femtosecond laser process parameters on the thermal denaturation degree of skin tissue.

The influence of femtosecond laser parameters on the degree of thermal denaturation was studied experimentally. The relationship between the degree of thermal denaturation and the characteristic parameters of skin microstructure and the secondary structure of skin tissue proteins in characterizing the degree of thermal damage was analyzed. The results showed the interaction of laser power, laser power, and scanning speed had a significant effect on the degree of thermal denaturation; greater degrees of thermal denaturation were associated with larger second-order moments of the texture angle of the skin microtissue and smaller entropy values and contrast, indicating a greater degree of thermal damage; and higher peak temperature, the lower peak intensity of Raman spectra, decrease in the percentage area of α-helix fitted curves and increase in the percentage area of ß-sheet and ß-turn fitted curves indicate that the protein is denatured to a large extent that means thermal damage is large.

RPIOSL: construction of the radiation transfer model for rice leaves.

The radiative transfer model of vegetation leaves simulates the transmission mechanism of light inside the vegetation and simulates the reflectivity of blades according to the change law of different components in the process of plant growth. Based on the PIOSL model, this paper combines PIOSL with the structure of rice leaves to construct a radiation transfer model for rice leaves. The parameters of each layer of the RPIOSL model are determined by the Non-dominated Sorting Genetic Algorithm-III. (NSGA-III.) algorithm. The reflectance spectra of 218 rice leaf samples in different periods were simulated using the RPIOSL model. The results show that the mean (RMSE) between the simulated and measured spectra of the constructed RPIOSL model is 0.1074, which is 0.0191 lower than that of the PROSPECT model. Among them, the spectral simulation effect of RPIOSL model in yellow and red light band is the best, and the RMSE at tillering period, jointing period, heading period and grouting period are 0.0584, 0.0576, 0.0724 and 0.0820, respectively. Therefore, the establishment of the RPIOSL model can accurately describe the interaction mechanism between light, which is of great significance for the rapid acquisition of rice growth information and accurate crop management.

Experimental study on the mechanical properties and thermal damage of laser welding the ruptured flexor digitorum longus tendons.

To investigate the influence of laser parameters on the performance of tendon tissue, experiments were conducted and the process of laser-assisted tendon welding was studied. Several conclusions were drawn by analyzing the effects of laser parameters on the tensile strength, microstructure, and collagen content of tendon tissue incisions. The optimal parameters for laser welding tendon tissue were found to be a laser power of 5 W, a scanning speed of 150 mm/s, and a defocus amount of 0 mm, resulting in a laser energy density of 32.164 J/cm2 . At these parameters, the percentage of inactivated cells due to thermal damage was only 23.78%, and the tensile strength of the tendon tissue incisions reached 0.61 MPa. Additionally, the collagen content around the incision was measured to be 33.679%, composed of type I and type III collagens, with the latter accounting for 50.714% of the total collagen content.

How to promote the sustainable development of virtual reality technology for training in construction filed: A tripartite evolutionary game analysis.

In recent years, virtual reality training technology (VRTT) has been considered by many scholars as a new training method instead of traditional training (TT) to reduce unsafe behaviors ascribed to construction workers (CWs) and corporate accident rates. However, in this process, a conflict of interest arises among the government, construction enterprises (CEs), and CWs. Therefore, this study introduces a quantitative research method, the three-party evolutionary game and creatively combining them with the product life cycle (PLC) to solve this problem by analyzing the equilibrium and evolutionarily stable strategies of the system. Finally, collaborative players' decision-making behaviors and their sensitivity to critical factors are examined. This paper will illustrate these in each stage through numerical simulations. The results of the study indicate that the government plays a dominant role in the VRTT introduction stage. When the government gives CEs appropriate subsidies, CEs will eventually realize the importance of VRTT for CWs. Then the government will gradually reduce the amount of the subsidies in this process. In addition, we also find that the continually high cost will lead to negative policies by the government, which requires the active cooperation and attitude change from CEs and CWs. Ultimately, the government, CEs and CWs adopt the best strategy in the evolutionary process to facilitate the promotion, application and sustainability of VRTT in the construction industry.

An Integrated Microfluidic Biosensing System Based on a Versatile Valve and Recombinase Polymerase Amplification for Rapid and Sensitive Detection of Salmonella typhimurium.

Detecting foodborne pathogens on-site is crucial for ensuring food safety, necessitating the development of rapid, cost-effective, highly sensitive, and portable devices. This paper presents an integrated microfluidic biosensing system designed for the rapid and sensitive detection of Salmonella typhimurium (S. typhimurium). The biosensing system comprises a microfluidic chip with a versatile valve, a recombinase polymerase amplification (RPA) for nucleic acid detection, and a customized real-time fluorescence detection system. The versatile valve combines the functions of an active valve and a magnetic actuation mixer, enabling on-demand mixing and controlling fluid flow. Quantitative fluorescence is processed and detected through a custom-built smartphone application. The proposed integrated microfluidic biosensing system could detect Salmonella at concentrations as low as 1.0 × 102 copies/µL within 30 min, which was consistent with the results obtained from the real-time quantitative polymerase chain reaction (qPCR) tests. With its versatile valve, this integrated microfluidic biosensing system holds significant potential for on-site detection of foodborne pathogens.

Palladium/Lewis Acid Co-catalyzed Cyclocarbonylation of (2-Aminoaryl)(aryl)methanols: An Access to 3-Aryl-indolin-2-ones.

A benign approach to valuable 3-aryl-indolin-2-ones was developed based on palladium(II)/Lewis acid-cocatalyzed cyclocarbonylation of readily available (2-aminoaryl)(aryl)methanols. The protocol features producing water as the only byproduct, mild reaction conditions, and good efficiency, constituting an array of 3-arylindolin-2-ones in yields of 35 to 90%. The reaction can be easily scaled up to the gram scale in good yields.

Estimating the rice nitrogen nutrition index based on hyperspectral transform technology.

Background and objective: The rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance. Methods: This study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables. Results: 1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1). Conclusion: The logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.

Mechanism of human chorionic gonadotropin in endometrial receptivity via the miR-126-3p/PI3K/Akt/eNOS axis.

Human chorionic gonadotropin (hCG) might affect endometrial receptivity, exerting integral roles in embryo implantation. This study explored the action of hCG in endometrial receptivity via the miR-126-3p/PIK3R2/PI3K/Akt/eNOS axis. The embryo implantation dysfunction (EID) mouse models were established by administrating mifepristone and human endometrial epithelial cells (EECs) were used for in vivo experiments, both followed by hCG treatment. Expression level of CD105 and protein levels of cadherin CD144 and CD146 in mice were determined by immunohistochemistry and Western blot. The levels of miR-126-3p and PIK3R2 mRNA and PIK3R2, p-PI3K p85 α, PI3K p110 α, p-Akt, Akt, p-eNOS, and eNOS protein levels were measured. Cell proliferation was evaluated by CCK-8 and EdU assays. The binding sites of miR-126-3p and PIK3R2 were predicted and verified. hCG-treated EECs were further transfected with miR-126-inhibitor for functional rescue experiments. hCG ameliorated endometrial receptivity in EID mice. Moreover, hCG promoted miR-126-3p and suppressed PIK3R2 in EID mice and EECs. miR-126-3p targeted PIK3R2. EEC proliferation was enhanced after hCG treatment but inhibited by miR-126-3p downregulation. Both in vivo and in vitro experiments validated that hCG activated the PI3K/Akt/eNOS pathway through the miR-126-3p/PIK3R2 axis. Collectively, hCG improves endometrial receptivity by activating the PI3K/Akt/eNOS pathway via regulating miR-126-3p/PIK3R2.

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot.

The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic, and fast plant point cloud segmentation. Besides, a few methods can easily integrate the global structural features and local morphological features of point clouds relatively at a reduced cost. In this paper, a distance field-based segmentation pipeline (DFSP) which could code the global spatial structure and local connection of a plant was developed to realize rapid organ location and segmentation. The terminal point clouds of different plant organs were first extracted via DFSP during the stem-leaf segmentation, followed by the identification of the low-end point cloud of maize stem based on the local geometric features. The regional growth was then combined to obtain a stem point cloud. Finally, the instance segmentation of the leaf point cloud was realized using DFSP. The segmentation method was tested on 420 maize and compared with the manually obtained ground truth. Notably, DFSP had an average processing time of 1.52 s for about 15,000 points of maize plant data. The mean precision, recall, and micro F1 score of the DFSP segmentation algorithm were 0.905, 0.899, and 0.902, respectively. These findings suggest that DFSP can accurately, rapidly, and automatically achieve maize stem-leaf segmentation tasks and could be effective in maize phenotype research. The source code can be found at https://github.com/syau-miao/DFSP.git.

Photorelease Reaction Mechanism Study of a Diarylethene Caged Dual Functions Compound.

Femtosecond transient absorption, nanosecond transient absorption, and nanosecond resonance Raman spectroscopy techniques coupled with density functional theory calculations were performed to unravel the photocyclization and photorelease mechanisms of a diarylethene based compound (1o) containing two caged groups (OMe and OAc). Since the ground state parallel (P) conformer of 1o with a large dipole moment is stable in DMSO, the fs-TA transformations observed for 1o in DMSO mainly have contributions from the P conformer, which undergoes an intersystem crossing to generate a corresponding triplet state species. In a less polar solvent like 1,4-dioxane, in addition to the P path behavior of 1o, an antiparallel (AP) conformer can also take place to give a photocyclization reaction from the Franck-Condon state and finally give a deprotection via this pathway. This work provides a deeper understanding for these reactions, which not only facilitate the applications of diarylethene compounds but also help for the future design of functionalized diarylethene derivatives for particular applications.

Copper-Catalyzed Asymmetric Boroprotonation of Phosphinylallenes.

Synthesis of chiral phosphorus compounds from readily available substrates by a facile method is an attractive strategy. In this study, an efficient route for copper-catalyzed asymmetric boroprotonation of phosphinylallenes with bis(pinacolato)diboron with high regioselectivity was developed, affording chiral allylphosphine oxides in high yields with high enantioselectivities of up to 98% ee. The synthetic utility was further demonstrated by the facile transformation of the chiral allylphosphine oxides to several stereospecific products.

Classification of rice leaf blast severity using hyperspectral imaging.

Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.

Research on weed identification method in rice fields based on UAV remote sensing.

Rice is the world's most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVI NIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVI NIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVI NIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVI NIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.

BACKGROUND: Rice blast, which is prevalent worldwide, represents a serious threat to harvested crop yield and quality. Hyperspectral imaging, an emerging technology used in plant disease research, is a stable, repeatable method for disease grading. Current methods for assessing disease severity have mostly focused on individual growth stages rather than multiple ones. In this study, the spectral reflectance ratio (SRR) of whole leaves were calculated, the sensitive wave bands were selected using the successive projections algorithm (SPA) and the support vector machine (SVM) models were constructed to assess rice leaf blast severity over multiple growth stages. RESULTS: The average accuracy, micro F1 values, and macro F1 values of the full-spectrum-based SVM model were respectively 94.75%, 0.869, and 0.883 in 2019; 92.92%, 0.823, and 0.808 in 2021; and 88.09%, 0.702, and 0.757 under the 2019-2021 combined model. The SRR-SVM model could be used to evaluate rice leaf blast disease during multiple growth stages and had good generalizability. CONCLUSIONS: The proposed SRR data analysis method is able to eliminate differences among individuals to some extent, thus allowing for its application to assess rice leaf blast severity over multiple growth stages. Our approach, which can supplement single-stage disease-degree classification, provides a possible direction for future research on the assessment of plant disease severity during multiple growth stages.

To photodeprotect or not: effect of the oxidation state of the sulfur atom of thiochromone derivatives.

Time-resolved spectroscopic experiments, assisted with DFT calculations, were employed to study the photochemical reaction mechanism of (4-oxo-3-phenyl-4H-thiochromen-2-yl) methoxycarbonyl-caged ethanol (TC) and (1,1-dioxido-4-oxo-3-phenyl-4H-thiochromen-2-yl) methoxy carbonyl-caged ethanol (TS-PPG) in different solvents. TC went through an intersystem crossing to form the triplet state with π-π* character in acetonitrile (MeCN) and protic solvents. While the n-π* triplet state was generated for TS-PPG in MeCN, which further underwent Paternò-Büchi reaction to give a biradical intermediate. Then, the C-O bond was cleaved, followed by deprotonation. Besides the similar deprotection route in MeCN, another reaction pathway existed in protic solvents, where the C-O bond heterolysis took place via the singlet excited state. The unambiguous mechanism would not only guide the efficient application of TS-PPG, but also help develop more excellent PPGs based on the thiochromone framework.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.