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Abstract Background Intrahospital transport of critically ill patients is a common practice in intensive care units (ICUs), where patients’ safety is constantly challenged in high-intensity and dynamic environments. While Intrahospital Transport Safety Scale (IHTSS) is widely used internationally to evaluate the intrahospital transport safety, it has not been introduced in China. Objectives This study aimed to assess the reliability and validity of the Chinese version of the IHTSS scale among critical care nurses in China. Methods A cross-sectional study was conducted using a cluster sampling method. A total of 544 critical care nurses from 25 ICUs in 10 tertiary hospitals were recruited. We employed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to examine the questionnaire’s underlying factor structure, ensuring construct validity. Additionally, internal consistency was assessed using Cronbach’s alpha coefficient, test-retest reliability, and corrected item-total correlation. Results The Chinese version of the scale displayed robust psychometric properties, with a Cronbach’s α coefficient of 0.976, a split-half reliability of 0.906, and a test-retest reliability of 0.856. EFA revealed a robust four-factor model that accounted for 75.970% of the variance, with the factor loadings of the items ranging from 0.433 to 0.951. CFA indicated a strong model fit, with a chi-square to degrees of freedom ratio (CMIN/DF) of 2.765, comparative fit index (CFI) of 0.943, incremental fit index (IFI) of 0.943, and goodness-of-fit index (GFI) of 0.845, supporting the efficacy of the four-factor model in assessing intrahospital transport safety for critically ill patients. Conclusion The Chinese version of the IHTSS demonstrated favourable reliability and validity among critical care nurses in China, making it a suitable tool for measuring the level of intrahospital transport safety for critically ill patients.

The dynamic interplay between socioeconomic development and climate change has brought about significant changes in drought vulnerability within the African socioeconomic system. However, there is a prevailing lack of understanding about how these changes will manifest in an increasingly globalized economy under global warming. To address this knowledge gap, this study used various climatic, geographical, and socioeconomic data from historical periods and future projections to comprehensively assess the drought vulnerability in North and West Africa. Firstly, the drought vulnerability index (DVI) is calculated for every country to determine its accuracy and reliability based on the 26 elements and five components (i.e. health, energy and infrastructure, water resources, society, and economy). The datasets are collected for North and West African regions from the World Bank, AQUASTAT (global information system on water resources and agricultural water management), and the Food and Agricultural Organization (FAO) of the United Nations during 1980–2020. The calculated DVI is then compared to earlier observations of drought consequences. Then, using historical variations and trends, a regression models are applied to the historical DVI time-series for each country and projected for the years 2020–2100 to produce three forecast scenarios for DVI in the future (high, medium, and low). The findings indicate that in North and West Africa, Tunisia, Egypt, and Algeria are the regions least affected by drought with a DVI value between 0.2 and 0.4, while Niger, Chad, and Mauritania are the most vulnerable regions with a DVI value between 0.6 and 1. Future DVI forecasts show that the gap between high - and low -vulnerable countries will enlarge, with most northern African regions becoming less drought-vulnerable and most western African regions showing increased drought vulnerability. The results of this study are primarily helpful in performing risk assessments, developing response plans or making adaptation decisions to help communities consider possible ways to address anticipated current and future threats resulting from drought and to reduce vulnerability and enhance resilience of the system, coping with recurring drought in the coming decades.

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The non-uniform reflectance characteristics of object surfaces and underwater environment disturbances during underwater laser measurements can have a great impact on laser stripe center extraction. Therefore, we propose a normalized grayscale gravity method to address this problem. First, we build an underwater structured light dataset for different illuminations, turbidity levels, and reflective surfaces of the underwater object and compare several state-of-the-art semantic segmentation models, including Deeplabv3, Deeplabv3plus, MobilenetV3, Pspnet, and FCNnet. Based on our comparison, we recommend PSPnet for the specific task of underwater structured light stripe segmentation. Second, in order to accurately extract the centerline of the extracted light stripe, the gray level values are normalized to eliminate the influence of noise and light stripe edge information on the centroids, and the weights of the cross-sectional extremes are increased to increase the function convergence for better robustness. Finally, the subpixel-structured light center points of the image are obtained by bilinear interpolation to improve the image resolution and extraction accuracy. The experimental results show that the proposed method can effectively eliminate noise interference while exhibiting good robustness and self-adaptability.

In the transition of China’s economy toward high-quality development, it will be increasingly important to coordinate the relationship between local government competition, environmental regulation, and the transformation of the economic growth mode. To explore the impact of local government competition and environmental regulation on total factor productivity (TFP), in this study empirical analysis models were established using panel data of 30 provinces in China from 2001 to 2017. Local government competition was measured from the perspectives of foreign direct investment (FDI), fiscal expenditure, and tax. The results show that local government competition has a positive impact on the improvement in TFP. Taking environmental regulation into consideration, the impact of local government competition on TFP is weakened. Furthermore, under the current intensity of environmental regulation, local government fiscal expenditure competition and tax competition both lead to improvement in TFP, while FDI competition has the opposite effect. This paper explores the heterogeneity of the impact of local government competition and environmental regulation on TFP under different local government competition levels. Under the influence of environmental regulations, local government FDI competition has a negative impact on the improvement in TFP, particularly in “low” FDI competition areas. Local government fiscal expenditure competition has a positive impact on the improvement in TFP, particularly in “low” fiscal expenditure competition areas. The current level of local government tax competition has a negative impact on the improvement in TFP in “high” tax competition areas, whereas it has a positive impact on the improvement in TFP in “low” tax competition areas.

ObjectiveInguinal lymph node metastasis (ILNM) is significantly associated with poor prognosis in patients with squamous cell carcinoma of the penis (SCCP). Patient prognosis could be improved if the probability of ILNM incidence could be accurately predicted at an early stage. We developed a predictive model based on machine learning combined with big data to achieve this.MethodsData of patients diagnosed with SCCP were obtained from the Surveillance, Epidemiology, and End Results Program Research Data. By combing variables that represented the patients' clinical characteristics, we applied five machine learning algorithms to create predictive models based on logistic regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor. Model performance was evaluated by ten-fold cross-validation receiver operating characteristic curves, which were used to calculate the area under the curve of the five models for predictive accuracy. Decision curve analysis was conducted to estimate the clinical utility of the models. An external validation cohort of 74 SCCP patients was selected from the Affiliated Hospital of Xuzhou Medical University (February 2008 to March 2021).ResultsA total of 1,056 patients with SCCP from the SEER database were enrolled as the training cohort, of which 164 (15.5%) developed early-stage ILNM. In the external validation cohort, 16.2% of patients developed early-stage ILNM. Multivariate logistic regression showed that tumor grade, inguinal lymph node dissection, radiotherapy, and chemotherapy were independent predictors of early-stage ILNM risk. The model based on the eXtreme Gradient Boosting algorithm showed stable and efficient prediction performance in both the training and external validation groups.ConclusionThe ML model based on the XGB algorithm has high predictive effectiveness and may be used to predict early-stage ILNM risk in SCCP patients. Therefore, it may show promise in clinical decision-making.

Objective(s): Endothelial dysfunction is a precursor of cardiovascular disease, and protecting endothelial cells from damage is a treatment strategy for atherosclerosis (AS). Curcumin, a natural polyphenolic compound, has been shown to protect endothelial cells from dysfunction. In the present study, we investigated whether curcumin could ameliorate high oxidized low-density lipoprotein (ox‐LDL)-induced endothelial lipotoxicity by inducing autophagy in human umbilical vein endothelial cells (HUVECs).Materials and Methods: HUVECs were treated with 50 μM high ox‐LDL alone or in combination with 5 μM curcumin for 24 hr. Cell viability and function were assessed by the cell counting kit-8 (CCK-8) assay, tube formation assay and cell migration experiments. Oil red O staining was used to detect lipid droplet accumulation in HUVECs. The change in reactive oxygen species (ROS) levels in HUVECs was measured with the probe DCFH-DA. Quantitative real-time PCR (qPCR) and Western blotting were used to evaluate the mRNA and protein levels of several inflammatory and autophagy-related factors.Results: Cell viability was restored, tube formation and migration ability were increased, and lipid accumulation, oxidative stress and inflammatory responses were decreased in the curcumin-treated group compared with the high ox‐LDL group. Furthermore, high ox‐LDL inhibited HUVEC autophagy, and this effect was reversed by curcumin. Moreover, curcumin regulated the expression of several key proteins involved in the AMPK/mTOR/p70S6K signaling pathway.Conclusion: Our findings suggest that curcumin is able to reduce endothelial lipotoxicity and modulate autophagy and that the AMPK/mTOR/p70S6K pathway might play a key role in these effects.

Background and objectivesPatients with muscle-invasive bladder cancer (MIBC) often experience a waiting period before radical surgery for numerous reasons; however, the COVID-19 outbreak has exacerbated this problem. Therefore, it is necessary to discuss the impact of the unavoidable time of surgical delay on the outcome of patients with MIBC.MethodsIn all, 165 patients from high-volume centers with pT2-pT3 MIBC, who underwent radical surgery between January 2008 and November 2020, were retrospectively evaluated. Patients’ demographic and pathological information was recorded. Based on the time of surgical delay endured, patients were divided into three groups: long waiting time (> 90 days), intermediate waiting time (30–90 days), and short waiting time (≤ 30 days). Finally, each group’s pathological characteristics and survival rates were compared.ResultsThe median time of surgical delay for all patients was 33 days (interquartile range, IQR: 16–67 days). Among the 165 patients, 32 (19.4%) were classified into the long waiting time group, 55 (33.3%) into the intermediate waiting time group, and 78 (47.3%) into the short waiting time group. The median follow-up period for all patients was 48 months (IQR: 23–84 months). The median times of surgical delay in the long, intermediate, and short waiting time groups were 188 days (IQR: 98–367 days), 39 days (IQR: 35–65 days), and 16 days (IQR: 12–22 days), respectively. The 5-year overall survival (OS) rate for all patients was 58.4%, and that in the long, intermediate, and short waiting time groups were 35.7%, 61.3%, and 64.1%, respectively (P = 0.035). The 5-year cancer-specific survival (CSS) rates in the long, intermediate, and short waiting time groups were 38.9%, 61.5%, and 65.0%, respectively (P = 0.042). The multivariate Cox regression analysis identified age, time of surgical delay, pT stage, and lymph node involvement as independent determinants of OS and CSS.ConclusionIn patients with pT2-pT3 MIBC, the time of surgical delay > 90 days can have a negative impact on survival.

ObjectiveThe purpose of our study is to construct and validate nomograms that effectively predict postoperative overall survival and cancer-specific survival for patients with chromophobe renal cell carcinoma (chRCC).MethodClinical, social, and pathological data from 6016 patients with chRCC collected from the SEER database were screened from 2004 to 2015. They were randomly assigned to a training cohort (n = 4212) and a validation cohort (n = 1804) at a 7:3 ratio. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were used to identify the prognostic factors affecting overall survival (OS) and cancer-specific survival (CSS) and establish nomograms. Their performance was validated internally and externally by calculating Harrell’s C-indexes, area under the curve (AUC), calibration, and decision curves. For external validation, samples from postoperative patients with chRCC at 3 independent centers in Xuzhou, China, were collected. Risk stratification models were built according to the total scores of each patient. Kaplan-Meier curves were generated for the low-risk, intermediate-risk, and high-risk groups to evaluate survival.ResultsThe C-indexes, AUC curves, and decision curves revealed the high ability of the nomograms in predicting OS and CSS, overall better than that of AJCC and TNM staging. Moreover, in internal and external validation, the calibration curves of 5-, 8-, and 10-year OS agreed with the actual survival. Kaplan-Meier curves indicated significant differences in survival rates among the 3 risk groups in OS or CSS.ConclusionThe nomograms showed favourable predictive power for OS and CSS. Thus, they should contribute to evaluating the prognosis of patients with chRCC. Furthermore, the risk stratification models established on the nomograms can guide the prognosis of patients and further treatment.

The concept of value co-creation brings about changes in tourists’ roles from value receivers to producers. Most existing studies take value co-creation behavior as a single dimension, ignoring the role switching of tourists. Starting from the precise constitution of value co-creation behavior, this paper, utilizing the method of structural equation modeling (SEM), discusses the mechanism of the effects of tourist participation behavior and citizenship behavior on destination loyalty through each sub-dimension of perceived value. The results showed the following: (1) The participation behavior (in-role) and citizenship behavior (extra-role) of tourists affect different value dimensions. (2) The value co-created by tourists presents a hierarchical state from the primary to the higher level. Among the types of value, novelty and social value belong to the primary level while quality, economic, knowledge, and emotional value belong to the higher level. (3) Destination loyalty is driven not only by tourist behavior (in-role) but also by citizenship behavior (extra-role). Based on the perspective of role switching, this paper establishes the theoretical framework of role switching in the era of value co-creation and proposes some strategic suggestions for marketing transformation.

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.

Recycling and energy recovery of waste plastic are major categories of modern solid waste management systems. Since the lower heating values of plastics are around 30 MJ/kg, equivalent to traditional solid fuels, such as petroleum coke, coal and charcoal, waste plastics are considered as potential fossil fuels alternatives in various industrial sectors. Already nowadays, energy recovery of waste plastic is typically conducted by combustion in incinerators or cement kilns. However, due to various types of waste plastics, their thermochemical behaviour has been rarely investigated. Even more, the thermochemical behaviour of waste plastics under oxy-combustion conditions has even rarely been studied. In this study non-isothermal thermogravimetric analyser was used to study the thermochemical behaviour of polyurethane plastic waste under seven different atmospheric conditions. Polyurethane decomposition kinetic constants have been estimated by the simultaneous evaluations of seven weight loss curves measured for the heating rate of 20 K/min and a final temperature of 1,073 K. The obtained results showed that the combustion of polyurethane is mainly composed of two stages, and the higher oxygen concentration slightly influenced the first stage but highly accelerates the second one, ascribing to a higher mean weight loss rate and an increasing activation energy. Replacing carbon dioxide with nitrogen slightly influences the first stage while positively influences the second stage, expressing in lower peak temperatures in differential thermogravimetry curves and higher peak values in differential scanning calorimetry curves.

The fast and reliable analysis of electrolytes such as K, Na, Ca in human blood serum has become an indispensable tool for diagnosing and preventing diseases. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated as a powerful analytical technique on elements. To apply LIBS to the quantitative analysis of electrolyte elements in real time, a self-developed portable laser was used to measure blood serum samples supported by glass slides and filter paper in this work. The partial least squares regression (PLSR) method was employed for predicting the concentrations of K, Na, Ca from serum LIBS spectra. Great prediction accuracies with excellent linearity were obtained for the serum samples, both on glass slides and filter paper. For blood serum on glass slides, the prediction accuracies for K, Na, Ca were 1.45%, 0.61% and 3.80%. Moreover, for blood serum on filter paper, the corresponding prediction accuracies were 7.47%, 1.56% and 0.52%. The results show that LIBS using a portable laser with the assistance of PLSR can be used for accurate quantitative analysis of elements in blood serum in real time. This work reveals that the handheld LIBS instruments will be an excellent tool for real-time clinical practice.

Background and ObjectivesDue to the inevitability of waiting time for surgery, this problem seems to have become more pronounced since the outbreak of COVID-19, and due to the high incidence of preoperative hydronephrosis in upper urinary tract urothelial carcinoma (UTUC) patients, it is particularly important to explore the impact of preoperative waiting time and hydronephrosis on upper urinary urothelial carcinoma.Methods316 patients with UTUC who underwent radical surgery at a high-volume center in China between January 2008 and December 2019 were included in this study. We retrospectively collected the clinicopathologic data from the medical records, including age, sex, smoking history, ECOG performance status (ECOG PS), body mass index (BMI), tumor location and size, number of lesions, T stage, N stage, surgical approach and occurrence of hydronephrosis, lymph node invasion, lymph node dissection, surgical margin, tumor necrosis, infiltrative tumor architecture, lymphovascular invasion and concomitant bladder cancer. Surgical wait time was defined as the interval between initial imaging diagnosis and radical surgery of UTUC. Hydronephrosis was defined as abnormal dilation of the renal pelvis and calyces due to obstruction of the urinary system. Firstly, all patients were divided into short-wait (90 days) groups according to the surgical wait time. The clinicopathological characteristics of each group were evaluated and the survival was compared. For patients with hydronephrosis, we subsequently divided them into two groups: short-wait (≤60 days) and long-wait (>60 days) groups according to the surgical wait time. Univariate and multivariate COX regression analysis were performed to evaluate the prognostic risk factor for patients with hydronephrosis.ResultsA total of 316 patients with UTUC were included in this study with a median surgical wait time of 22 days (IQR 11-71 days). Of the 316 patients, 173 were classified into the short-wait group (54.7%), 69 into the intermediate-wait group (21.8%) and 74 into the long-wait group (23.5%). The median follow-up time for all patients was 43 months (IQR 28-67months). The median surgical wait times of the short-wait, intermediate-wait and long-wait group were12 days (IQR 8-17days), 42days (IQR 37-65days) and 191days (IQR 129-372days), respectively. The 5-year overall survival (OS) of all patients was 54.3%. The 5-year OS of short-wait, intermediate-wait and long-wait groups were 56.4%, 59.3% and 35.1%, respectively (P=0.045). The 5-year cancer-specific survival (CSS) of short-wait, intermediate-wait and long-wait groups were 65.8%, 70.9% and 39.6%, respectively (P=0.032). In the subgroup analysis, we divided 158 UTUC patients with hydronephrosis into short-wait group (≤60 days) and long-wait group (> 60 days), 120 patients were included in the short-wait group and 38 patients in the long-wait group. The median surgical wait times of the short-wait and long-wait group were 14days (IQR 8-28days) and 174days (IQR 100-369days), respectively. The 5-year OS of long-wait group was significantly lower than the OS of short-wait group (44.2% vs. 55.1%, P =0.023). The 5-year CSS of long-wait and short-wait group were 49.1% and 61.7%, respectively (P=0.041). In multivariate Cox regression analysis of UTUC patients with hydronephrosis, surgical wait time, tumor grade, pathological T stage, and tumor size were independent risk factors for OS and CSS. Lymph node involvement was also a prognostic factor for CSS.ConclusionFor patients with UTUC, the surgical wait time should be limited to less than 3 months. For UTUC patients with hydronephrosis, the OS and CSS of patients with surgical wait time of more than 60 days were relatively shorted than those of patients with surgical wait time of less than 60 days.

With the increasing application of additive manufacturing (AM) in the industry, more attention has been paid to the performance of the additively manufactured part. How to maximize the structural efficiency of a part to achieve the desired performance has become the focus and challenge of design for additive manufacturing (DFAM). To give full play to the complex-geometry, multi-material, multi-scale, and multi-function manufacturing capabilities of AM, new approaches in DFAM have to be explored. The relationship between stress and growth in biology indicates that naturally evolved perfect structures are all optimal responses to their applied force. This work discusses a novel method to DFAM at the macroscale, mesoscale and microscale from a force-flow perspective, aiming to achieve an organic integration of force-flow based design with AM-driven manufacturing. Firstly, the characteristics of force-flow and its embodiment in nature and engineering are analyzed. Then, an overview of topology optimization, lattice/cellular structure design and infill pattern design are respectively provided in the combination of force-flow and AM. Finally, the future development directions of DFAM based on force-flow are proposed according to the limitations of current research.

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures.

Entrepreneurship, as one of the important factors to promote industrial innovation, is closely related to the development of the regional economy. Based on the methods of Kernel density and standard deviation ellipse, this article presents the spatio-temporal patterns of entrepreneurship and innovation performance. The article also examines the spatial spillover mechanism of entrepreneurship on innovation performance by establishing spatial Durbin models. The heterogeneous results of the spatial regression models in six clusters are also discussed. The final results show that the spatio-temporal patterns of entrepreneurship are gradually presenting three major hot spots and two secondary hot spots while the spatio-temporal patterns of innovation performance are presenting four major hot spots and a secondary hot spot; the spatial distribution of both entrepreneurship and innovation performance are changing regularly; the spillover effects of entrepreneurship and innovation performance are both significant; the spatial spillover mechanisms in six automobile industrial clusters are different. The results can provide empirical support for decision-making in the automobile industry in China in the future.

Replacing solid structures with lattice structure is a way enabled by additive manufacturing (AM) to realize part lightweight design. Conventional design optimization method based on homogeneous periodic lattice structure cannot achieve the optimal structure without taking the stress magnitude and orientation into account. A design optimization method of heterogeneous conformal lattice structure coupling constraints and anisotropy of AM based on principal stress lines (PSL) is proposed. (1) The PSL is calculated based on finite element analysis to visualize the path of load transfer. (2) The load-adapted lattice structure is generated guided by the PSL. (3) In order to further optimize lattice structure, the lattice structure optimization model is established by coupling anisotropy and constraints of AM based on the fused deposition modeling (FDM) experiment. Taking a cantilever beam as a case, the maximum force-to-weight ratio and the stiffness-to-weight ratio of the optimized heterogeneous conformal lattice structure are increased by 11.8% and 41.8% respectively compared with homogeneous conformal lattice structure, which verified the feasibility of the proposed method.

In an era of mobile internet, word-of-mouth marketing has become a powerful tool for optimizing tourist destination marketing. Extensive research studies on the mechanism of word-of-mouth have been carried out. However, there is little research on the mechanism of word-of-mouth concerning tourist destinations in crisis. This article focuses on the influence mechanism of word-of-mouth in the relationship between perceived images and behavioral intentions. A structural equation model was established, with “perceived image” as the independent variable, “word-of-mouth” and “psychological distance” as mediating variables, and “behavioral intention” as the dependent variable. Our study is based on a survey in Southern Xinjiang. The final results show that word-of-mouth plays a mediating role in the relationship between perceived image and behavioral intentions. The moderating effect of word-of-mouth plays two roles in the mechanism of the perceived image on the behavioral intention: the promotion mechanism and the repression mechanism. Tourists’ sense of psychological distance significantly mediates the relationship between perceived image and behavioral intention. The findings enrich word-of-mouth and the marketing theories for the destinations in crisis, which provide enlightening insights for the sustainable development of the destinations in crisis.

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.

Droughts are ranked among the most devastating agricultural disasters that occur naturally in the world. East Africa is the most vulnerable and drought-prone region worldwide. In this study, four drought indices were used as input variables for drought assessment from 1982 to 2015. This work applied the SMDM algorithm to the integrated approach of OLR and Hurst exponent. The Detrended Fluctuation Analysis (DFA) and Ordinary Least Square (OLR) were merged to compute the trend and persistence (Hurst exponent) of the drought indices. Result indicates that the OLR at time scale 1, 6, and 12 shows a similar distribution with positive (negative) trends scattered in the Northwest (Northeast and Southern) parts of the study area which differs with the OLR aggregated at a 3-month time scale. The percentage pixel distribution for OLR-1, OLR-3, OLR-6, and OLR-12 is 18.2 (81.8), 72.5 (27.5), 32.9 (67.1), and 36.9 (63.1) for increasing (decreasing) trends respectively. Additionally, results indicate that DFA-1 is highly persistent with few random pixels scattered around Ethiopia, South Sudan and Tanzania, with percentage pixels as 88.7, 11.3 and 0.1 representing h > 0.5, h = 0.5, and h < 0.5, respectively. DFA-6 shows high (low) pixels representing h > 0.5 (h > 1), respectively. Meanwhile, for DFA-3 and DFA-12, the distribution shows persistence and a random walk, respectively. Drought conditions may eventually persist, reverse or vary drastically in an unpredictable manner depending on the driving forces. Overall, the drought risk map at 1-, 3-, and 6-month aggregates has shown severe degradation in Southern Kenya and Tanzania while noticeable improvements are seen in western Ethiopia and South Sudan.

Peroxymonosulfate-based electrochemical advanced oxidation processes (PMS-EAOPs) have great potential for sustainable water purification, so an in-depth understanding of its catalytic mechanism is imperative to facilitate its practical application. Herein, the performance enhancement and mechanism of electroenhanced PMS activation by single-atom Fe catalyst modified carbon felt was investigated. Compared with the anode, the cathode exhibited faster bisphenol A degradation (kcathode = 0.073 vs. kanode = 0.015 min-1), increased PMS consumption (98.8 vs. 10.3%), and an order of magnitude reduction of Fe dissolution (0.068 vs. 0.787 mg L-1). Mass transfer is a key factor limiting PMS activation, while the electrostriction of water in the hydrophobic region caused by cathode electric field (CEF) significantly increased mass transfer coefficient (km, cathode = 1.49 x 10-4 vs. km, anode = 2.68 x 10-5 m s-1). The enhanced activation of PMS is a synergistic result between electroactivation and catalyst-activation, which is controlled by the applied current density. ¹O2 and direct electron transfer are the main active species and activation pathway, which achieve high degradation efficiency over pH 3 to 10. Density functional theory calculations prove CEF increases the adsorption energy, lengthens the O-O bond in PMS, and promotes charge transfer. A flow-through convection unit achieves sustainable operation with high removal efficiency (99.5% to 97.5%), low electrical energy consumption (0.15 kWh log-1 m-3), and low Fe leaching (0.81% of the total single atom Fe). This work reveals the critical role of electric fields in modulating Fenton-like catalytic activity, which may advance the development of advanced oxidation processes and other electrocatalytic applications. [ABSTRACT FROM AUTHOR]

This article systematically explores the strengthening effect of NaOH dosage on the fermentation acid production of Waste Activated Sludge (WAS) by the rhamnolipids‐alkali‐thermal combined pretreatment. Analyzed the dissolution characteristics of organic matter in sludge, changes in total volatile fatty acids (VFAs) content and components, and microbial community structure. Research has shown that increasing the amount of NaOH can promote the dissolution of sludge extracellular polymeric substances (EPS) and cell lysis. When NaOH dose was 60 mg/g VS, the dissolution of SCOD was the largest, reaching 5427.78 mg/L. It provides high‐quality substrate for anaerobic flora through the decomposition of refractory humus‐like substances and macromolecular substances, thus improving the production of VFAs. When NaOH dose was 80 mg/g VS, the maximum VFAs yield was 1663.76 mg C/L, and the fermentation type was butyric acid. The increase of NaOH dose promotes the effective enrichment of bacteria, and the dominant bacteria are Proteobacteria, Firmicutes, and Bacteroides at the gate level, with different abundance. The fermentation time plays a decisive role in the evolution of microbial population, and the increase of NaOH dose promotes the enrichment of acid‐producing bacteria, thus improving the production of VFAs. [ABSTRACT FROM AUTHOR]