Search

In this paper, the content of active ingredients and antioxidant capacity of different parts of Hoxylon and Red Willow Cistanche deserticola, from Hotan Cele County, Xinjiang, were evaluated. The results showed that the active substances, including total phenols, total flavonoids, total sugars, and verbascoside of Red Willow Cistanche deserticola, were the highest in the lower part of the plant, and decreased in the middle and upper parts. In terms of total phenols and total flavonoids, the contents of the lower part of Red Willow Cistanche deserticola (38.35 mg GAE/g DW, 35.61 mg RE/g DW) were 2.47 and 10.95 times compared to the lower part of Hoxylon Cistanche deserticola, respectively. The upper part of Hoxylon Cistanche deserticola showed the highest content of echinacoside with 92.93 mg/g DW. And the total triterpene content of the lower part-especially that of Hoxylon Cistanche deserticola was significantly higher than the upper and middle part (P

BackgroundWith the continuous progress and in-depth implementation of the reform of the medical and health care system, alongside the gradual enhancement of the standardized training framework for residents, such training has become a crucial avenue for cultivating high-level clinicians and improving medical quality. However, due to various constraints and limitations in their own capabilities, residents undergoing standardized training are often susceptible to job burnout during this process. Numerous factors contribute to job burnout, which is closely associated with depression and anxiety. To promote effective standardized training and develop high-quality resident personnel, it is essential to investigate the influencing factors of job burnout and implement strategies to mitigate its occurrence.ObjectiveExplore the job burnout and its influencing factors among standardized training residents of Affiliated Hospital of Yan ‘an University, and to provide favorable basis for effective prevention of job burnout.MethodsAn online questionnaire survey was conducted among all standardized training residents at the hospital from June 2020 to September 2022. The evaluation utilized a general situation questionnaire, the Job Burnout Inventory - General Survey (MBI-GS), the Self-Rating Anxiety Scale (SAS), and the Self-Rating Depression Scale (SDS).ResultsA total of 660 valid questionnaires were collected, and 315 people had positive reaction for job burnout, and the detection rate was 47.7%. Difference in gender, marital status, whether they held practicing certificates, participated in work before training, used sleep medication, age, grade, sleep state were the influencing factors of different dimensions of job burnout. 175 people were in a state of anxiety (26.5%), while 357 (54.1%) were depressed. Pearson correlation analysis showed that anxiety and depression were highly correlated with job burnout (p

Since the 21st century began, the frequency and intensity of extreme climate events have significantly increased globally, becoming a widely recognized phenomenon of global change. These extreme events, including droughts and heatwaves, have profound impacts on the structure and function of ecosystems. This study focuses on the Mongolian Plateau. It utilizes meteorological and remote sensing vegetation data, combined with consistency and sensitivity analyses. These approaches aim to provide an in-depth understanding of the relationship between various extreme climate events and vegetation low-growth. The study found that extreme drought and extreme heat events are the primary drivers affecting vegetation low-growth on the Mongolian Plateau. The analysis results indicate that the sensitivity of vegetation to these extreme climate events is regulated by regional hydrothermal conditions, with vegetation in long-term drought areas being more susceptible to the suppression of extreme drought, while humid areas exhibit some resistance. As the temperature gradient increases, the sensitivity of vegetation to extreme high temperatures increases, while sensitivity to extreme low temperatures decreases. Furthermore, the study also revealed differences in the responses of different vegetation types to extreme events under the same climatic conditions, highlighting the ecological basis of ecosystem resilience and adaptability. This research not only enhances our understanding of vegetation dynamics under the influence of extreme climate events but also provides scientific evidence for ecological management and climate adaptation in the Mongolian Plateau region.

ABSTRACTDeep learning (DL) models have been widely used for remote sensing-based landslide mapping due to their impressive capabilities for automatic information extraction. However, the large volumes of parameters and calculations have compromised the efficiency of DL models in extracting landslides from a large set of RS images. Lightweight convolutional neural networks (CNNs) exhibit promising feature representation abilities with fewer parameters. This study aims to introduce a new lightweight CNN called MS2LandsNet, designed to detect landslides with both high efficiency and accuracy. The MS2LandsNet consists of three down-sampling stages embedded with multi-scale feature fusion (MFF), aiming to decrease parameters while aggregating contextual features. Additionally, we incorporate multi-scale channel attention (MSCA) into MFF to improve performance. According to experimental results on three landslip datasets, MS2LandsNet obtains the highest F1 score of 85.90% and the highest IoU of 75.28%. Notably, MS2LandsNet accomplishes the resuts with the fewest parameters and the fastest inference speed, outperforming seven classical semantic segmentation models and three lightweight CNNs. The proposed lightweight model holds potential for application on a cloud computing platform for larger-scale landslide mapping tasks in future work.

In this study, static and dynamic desorption methods, infrared spectroscopy and, in vitro antioxidant modeling were used to isolate, purify, and investigate the bioactivity of melanoidins extracted from hypoheat-induced Lycium barbarum L. The results showed that melanoidin fractions with molecular weight in the range of 3−10 kDa were the dominant and most valuable fractions. In the purification phase, the optimal purification conditions were: a loading concentration of 4 mg·mL−1, elution volume of 6 BV, and an elution flow rate of 1 mL·min−1. Purified dominant melanoidin fractions (UF3) exhibited typical Maillard reaction (MR) characteristics in FTIR. The storage stability showed that sunlight and heat treatment exacerbated the instability of the purified UF3. At the same time it was relatively stable under dark conditions and incandescent light, with a retention rate of about 90%. After in vitro digestion, the purified UF3 still exhibited good antioxidant activity, and the DPPH scavenging activity and hydroxyl free radical scavenging ability reached more than 60%.

BackgroundGastric cancer (GC) is one of the most common malignant tumors of the digestive tract worldwide. Both environmental and genetic factors contribute to the occurrence and development of GC. Surgery and chemotherapy are the main treatment modalities for gastric cancer; however, some patients show insensitivity to chemotherapeutic agents. Chemotherapy resistance is one of the primary reasons for poor treatment outcomes and the high likelihood of recurrence and metastasis in gastric cancer patients. Numerous studies have confirmed a correlation between the dysregulation of microRNA expression and the development of various malignant tumors, as well as their resistance to chemotherapeutic agents. However, the role of microRNA-582-3p in gastric cancer cells and its mechanism in the resistance of gastric cancer cells to oxaliplatin have not been studied.MethodsWe first used q-PCR, CCK8, transwell, and scratch assays to validate the expression of microRNA-582-3p in gastric cancer tissues and cells, while also analyzing the relationship between its expression levels and the clinical pathological data of patients. Additionally, we further confirmed the impact of microRNA-582-3p on gastric cancer cell progression and oxaliplatin resistance through knockdown and overexpression experiments. Subsequently, to explore the specific mechanisms of microRNA-582-3p in gastric cancer, we verified the downstream target of microRNA-582-3p, ATG7, using dual-luciferase reporter assays and examined the effect of ATG7 on gastric cancer cell functions. Moreover, we conducted rescue experiments to further validate the interaction between microRNA-582-3p and ATG7.ResultsOur experimental results confirmed that microRNA-582-3p is lowly expressed in gastric cancer tissues and cells, and the expression level of miR-582-5p is correlated with the T stage of patients, while showing no correlation with the patients' gender, age, tumor size, degree of differentiation, or N stage. Additionally, we found that microRNA-582-3p functions as a tumor suppressor in gastric cancer cells, as its overexpression inhibits the biological functions of gastric cancer cells and increases their sensitivity to oxaliplatin. Furthermore, we identified binding sites between microRNA-582-3p and the autophagy-related gene ATG7, observing that knockdown of microRNA-582-3p increases ATG7 expression, while its overexpression reduces ATG7 levels. Moreover, ATG7 is overexpressed in gastric cancer cells; knockdown of ATG7 inhibits the biological functions of gastric cancer cells and increases their sensitivity to oxaliplatin, whereas overexpression of ATG7 reverses the inhibitory effect of miR-582-5p on gastric cancer.ConclusionOur study confirms that microRNA-582-3p acts as a tumor suppressor in gastric cancer cells, and its role may be mediated through the regulation of ATG7 expression levels. MicroRNA-582-3p may serve as a potential target for gastric cancer treatment and a predictive biomarker.

Abstract A twice‐input variable‐resolution single‐side switching scheme without reset energy is proposed for successive approximation register (SAR) analogue‐to‐digital converters (ADCs). The proposed switching scheme is based on semi‐resting DAC technology to design a four‐array architecture capable of handling twice the swing of the signal input while reducing the capacitor array size. The technique of top‐plate sampling and monotonic shift is utilized so that no switching energy is generated for the first three comparisons. The proposed scheme utilizes full‐capacitor split, bridged switch, and C‐2C dummy capacitor technology, which reduces average switching energy consumption by 99.8% compared to conventional schemes and enables ADC variable resolution function, making the SAR ADC more suitable for IoT applications.

The increasing frequency of drought events due to global climate change has significant impacts on ecosystems, often resulting in reduced vegetation productivity. However, vegetation responses to drought often exhibit non-linear threshold phenomena, where vegetation growth sharply decreases once water stress reaches a certain level. Our study, conducted on the Mongolian Plateau, focuses on revealing the drought response thresholds and distribution patterns of different vegetation types. These thresholds mark critical turning points from high drought resistance to high vulnerability in vegetation response and serve as early warning indicators, crucial for accurately assessing how terrestrial ecosystems adapt to climate change. We utilized remote sensing observation datasets and inversion datasets spanning from 2001 to 2020, including six representative vegetation indices. The results indicate that: (1) Over 70 % of the Mongolian Plateau experiences low vegetation growth (

In traditional studies on grassland degradation drivers, researchers often lacked the flexibility to selectively consider driving factors and quantitatively depict their contributions. Interpretable machine learning offers a solution to these challenges. This study focuses on Inner Mongolia, China, incorporating four categories and sixteen specific driving factors, and employing four machine learning techniques (Logistic Regression, Random Forest, XGBoost, and LightGBM) to investigate regional grassland changes. Using the SHAP approach, contributions of driving factors were quantitatively analyzed. The findings reveal the following: (1) Between 2015 and 2020, Inner Mongolia experienced significant grassland degradation, with an affected area reaching 12.12 thousand square kilometers. (2) Among the machine learning models tested, the LightGBM model exhibited superior prediction accuracy (0.89), capability (0.9), and stability (0.76). (3) Key factors driving grassland changes in Inner Mongolia include variations in rural population, livestock numbers, average temperatures during the growth season, peak temperatures, and proximity to roads. (4) In eastern and western Inner Mongolia, changes in rural population (31.4%) are the primary degradation drivers; in the central region, livestock number changes (41.1%) dominate; and in the southeast, climate changes (19.3%) are paramount. This work exemplifies the robust utility of interpretable machine learning in predicting grassland degradation and offers insights for policymakers and similar ecological regions.

Storing hydrogen in solid metal hydrides provides a safe and efficient storage approach. However, the large volume expansion of metal hydrides during hydrogen absorption imposes substantial stresses on the wall of a hydrogen storage tank. In this study, volume expansion behavior of a V-based hydrogen storage alloy, V61Cr24Ti12Ce3, with body-centered-cubic, was investigated using a self-developed in situ expansion testing device. The lattice expansion of the V61Cr24Ti12Ce3 alloy after full hydrogenation was determined to be 37.85% using X-ray diffraction(XRD). The powder bed, composed of alloy powder with an average size of 3.35 mm in diameter, displays a large volume expansion ratio of 131% at the first hydrogen absorption cycle and 40–45% in the following four cycles. The stable compact bed, made of alloy powders, organic silicone gel, and graphite flakes, shows significantly smaller volume expansion ratio, which is 97% at the first cycle and 21% at the second cycle, and stabilizes at 13% in the following cycles. Also, the compact bed shows similar hydrogen absorption capacity, but faster absorption kinetics compared to the powder bed.

Due to their ecological sensitivity and limited resource availability, grasslands are exceptionally vulnerable to environmental changes and human activities. However, comprehensive and in-depth analyses of the interplay between grasslands and human activities remain scarce. This study employed bibliometrics and critical paper analysis to scrutinize 4738 publications from 2000 to 2023. The findings reveal that: (1) Since 2000, research on grassland-human activity interactions has evolved and intensified, spanning diverse disciplines including environmental ecology, plant science, agricultural science, and biodiversity conservation. A notable trend is the relative clustering of scholars and research institutions within Eastern and Western countries and within national boundaries. China leads in publication volume, whereas the United States exerts greater influence in the international collaborative network. (2) Approximately 80% of the highly co-citation literature concentrates on elucidating scientific knowledge, particularly the impact of human activities on vegetation characteristics, grassland communities, and ecosystems. Research hotspots predominantly encompass European grasslands, North America’s Great Plains, and the Tibetan Plateau grasslands in China. The remaining 20% focuses on methodological advancements and tools, with frequent utilization of R language, PC-ORD, and Google Earth Engine (GEE). (3) Initial studies (2000–2005) primarily investigated the effects of specific anthropogenic activities on grassland biomes at a community level through experimental observations. Subsequent research (2006–2013) shifted towards exploring the overarching characteristics and transformations of grassland rangelands at regional scales, heavily relying on remote sensing technologies. More recent studies (2014–2023) have emphasized a comprehensive, quantitative appraisal of the multifaceted impacts of internal and external stressors on grassland ecosystems. This study enhances the understanding of the historical trajectory of grassland-anthropogenic relationship research, highlighting pivotal knowledge and collaborative tools.

Androgenetic alopecia (AGA) is a common clinical condition, affecting over 200 million people globally each year. For decades, Minoxidil (Mi) tincture has been the primary treatment for this disease, but its low utilization rate and significant side effects necessitate new therapeutic strategies. Nitric oxide (NO) is a signaling molecule in various physiological processes, including vasodilation, immune responses, and cell proliferation. Herein, we constructed a hyaluronic acid liposome (HL) complex as a novel transdermal delivery system (HL@Mi/NONOate) for NO and Mi, which displayed promising transdermal and hair-regrowth effects. In-depth mechanistic studies revealed three potential pathways of the synergistic AGA therapy. First, NO promoted capillary dilation and accelerated blood flow, thus achieving efficient penetration of Mi. Due to the structural advantage of liposomes, the residence time of the Mi in the skin was prolonged. Moreover, HL@Mi/NONOate promoted cell proliferation and angiogenesis, and upregulated the expression of regulatory factors involved in follicle stem cell differentiation. In the AGA model, HL@Mi/NONOate down-regulated the expression of inflammatory factors, inhibiting the inflammation of follicle and improving the microenvironment of hair regrowth. Concurrently, HL@Mi/NONOate upregulated the expression of Ki67 and PCNA proteins in follicle tissues, inducing follicle regeneration and development, ultimately achieving the synergistic multimodal AGA therapy.

With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-source data, such as nighttime light indices and road network density, various machine learning models—including random forest, XGBoost, and LightGBM—were employed to significantly improve prediction accuracy. Interpretable machine learning techniques were utilized to quantitatively analyze the contribution of various variables to population distribution. The results indicate that nighttime light indices and road network density are key influencing factors, revealing their complex nonlinear relationships with population density. This study provides new methodological support for predicting population density in Inner Mongolia and similar regions, demonstrating the potential of machine learning in regional population research. While machine learning models effectively capture correlations between variables, they do not reveal causal relationships. Future research should introduce more detailed data and causal inference models to deepen our understanding of population distribution and its influencing factors.

In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies.

Abstract History courses are an essential part of a national education. The application of traditional courseware's media forms in education still requires further development and refinement. Herein, we report on a history courseware mode that integrates various historical teaching media, including 360-degree VR, paintings, maps, infographics, text, audio, and videos, based on the SCORM standard. These media elements are used to provide learners with a multimodal learning experience in history courses. We monitor the learning effects using EEG and questionnaires. The results show a significant improvement in our multimodal courseware technology compared to traditional courseware.

This article presents a novel and feasible approach for researching the quantity of the ceramic phase and component optimization in high-temperature titanium alloys with small trace amounts of ceramic phases. Different near-α titanium alloys with varying yttrium and boron contents were prepared through the utilization of a vacuum non-consumable arc furnace melting method. The alloy used was a Ti-5.9Al-4Sn-3.9Zr-3.8Mo-0.4Si base. Its microstructure, texture, mechanical properties, and fracture behavior were studied. The observation of the as-cast structure shows that the addition of different doses of trace Y and B elements significantly refines both the original β grains and α grains. Moreover, the addition of the B element transforms the Widmanstätten structure in the titanium alloy structure into a basketweave structure. The addition of Y can refine the grain structure, improve the uniformity of the matrix structure, and act as a strong deoxidizer, which will take away the oxygen in the matrix and purify it. The TiB whiskers generated with the addition of B promotes dynamic recrystallization behavior and leads to more equiaxed α grains being precipitated around them, resulting in a significant refinement of the microstructure of the as-cast alloy. After adding a small amount of B, the texture strength of the α phase is significantly reduced, indicating that TiB whiskers inhibit the formation of texture. After conducting performance screening and structure analysis, the study supplements the analysis of Y’s regulation of the titanium alloy structure. The regulation is primarily explained by combining the results of the analysis of boron content, phase diagram analysis, mechanical properties, and fracture analysis. The mechanical analysis introduces the unique load transfer strengthening of TiB whiskers combined with an analysis of high-temperature mechanical properties, as the threshold for addition. The optimal amounts of Y and B additions are 0.6 wt% and 0.8 wt%, respectively. The optimized alloy obtained under this condition can achieve a tensile strength of 950 Mpa at 500 °C without any plastic deformation or heat treatment.

Objectives The study aimed to propose a multimodal model that incorporates both macroscopic and microscopic images and analyze its influence on clinicians’ decision-making with different levels of experience. Methods First, we constructed a multimodal dataset for five skin disorders. Next, we trained unimodal models on three different types of images and selected the best-performing models as the base learners. Then, we used a soft voting strategy to create the multimodal model. Finally, 12 clinicians were divided into three groups, with each group including one director dermatologist, one dermatologist-in-charge, one resident dermatologist, and one general practitioner. They were asked to diagnose the skin disorders in four unaided situations (macroscopic images only, dermatopathological images only, macroscopic and dermatopathological images, all images and metadata), and three aided situations (macroscopic images with model 1 aid, dermatopathological images with model 2&3 aid, all images with multimodal model 4 aid). The clinicians’ diagnosis accuracy and time for each diagnosis were recorded. Results Among the trained models, the vision transformer (ViT) achieved the best performance, with accuracies of 0.8636, 0.9545, 0.9673, and AUCs of 0.9823, 0.9952, 0.9989 on the training set, respectively. However, on the external validation set, they only achieved accuracies of 0.70, 0.90, and 0.94, respectively. The multimodal model performed well compared to the unimodal models, achieving an accuracy of 0.98 on the external validation set. The results of logit regression analysis indicate that all models are helpful to clinicians in making diagnostic decisions [Odds Ratios (OR) > 1], while metadata does not provide assistance to clinicians (OR 0.05). Conclusions The results of this study suggest that the multimodal model effectively improves clinicians’ diagnostic performance without significantly increasing the diagnostic time. However, further large-scale prospective studies are necessary.

Abstract In recent years, successive approximation register (SAR) analogue‐to‐digital converters (ADCs) have been widely applied in Internet of Things (IoT) sensor devices. To reduce the power consumption of ADCs, a bridged‐switch energy‐efficient switching scheme for SAR ADCs with a 2× input range is proposed. Due to the use of top‐plate sampling, overall shift and bridged switch, the energy consumption of the first three comparisons and reset is zero, meanwhile the energy consumption of the fifth comparison is greatly reduced. The proposed switching scheme has an average energy consumption of 13.084CVref2, which reduces the switching energy by 99.04% compared to the conventional switching scheme. The proposed switching scheme can achieve a 2× input range with the same supply voltage, effectively reducing ADC power consumption and making it more suitable for IoT applications.

ObjectiveThe primary objective of this research endeavor was to examine the underlying genetic causality between the age at first birth (AFB) and four prevalent esophageal diseases, namely oesophageal obstruction (OO), oesophageal varices (OV), gastro-oesophageal reflux (GOR), and oesophageal cancer (OC).MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to examine the causal association between AFB and four prevalent esophageal disorders. We employed eight distinct MR analysis techniques to evaluate causal relationships, encompassing random-effects inverse variance weighted (IVW), MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and fixed-effects IVW. The random-effects IVW method served as the primary approach for our analysis. Furthermore, we executed several sensitivity analyses to assess the robustness of the genetic causal inferences.ResultsThe random-effects IVW analysis revealed a significant negative genetic causal association between AFB and both GOR (P < 0.001, Odds Ratio [OR] 95% Confidence Interval [CI] = 0.882 [0.828-0.940]) and OC (P < 0.001, OR 95% CI = 0.998 [0.998-0.999]). Conversely, there was insufficient evidence support to substantiate a genetic causal link between AFB and OO (P = 0.399, OR 95% CI = 0.873 [0.637-1.197]) or OV (P = 0.881, OR 95% CI = 0.978 [0.727-1.314]). The results of sensitivity analyses underscore the robustness and reliability of our MR analysis.ConclusionThe findings of this investigation substantiate the notion that elevated AFB confers a protective effect against GOR and OC. In addition, no causative association was discerned between AFB and OO or OV at the genetic level.

AbstractThe selection and planning of the location of emergency shelters have a crucial impact on the safety of residents and cities. In this paper, based on multivariate open geographic data, the Gaussian two-step floating catchment area method, K-means clustering, and particle swarm optimization algorithm methods are utilized to carry out a spatial accessibility analysis and location optimization of emergency shelters in Deyang City, Sichuan Province, China. The study shows that: (1) Deyang City’s emergency shelters are higher than the government’s relevant standard requirements in terms of major indicators such as single building area, total area and per capita shelter area. (2) The spatial distribution of emergency shelters in the study area is uneven and unreasonable, with accessibility from the urban center outwards, exhibiting a “high-low” distribution pattern. (3) The study suggests that 10 new emergency shelters can reduce the number of accessible blind areas by 43.31%. The study recommends that the assessment and construction of emergency shelter facilities in rural areas in China and globally should be emphasized. Reliable recommendations for improvement of emergency shelter planning in Deyang city are provided in the study results.

A 9-10-bit adjustable and energy-efficient switching scheme for SAR ADC with one-LSB common-mode voltage variation is proposed. Based on capacitor-splitting technology and common-mode conversion techniques, the proposed switching scheme reduces the DAC switching energy by 96.41% compared to the conventional scheme. The low complexity and the one-LSB common-mode voltage offset of this scheme benefit from the simultaneous switching of the reference voltages of the capacitors corresponding to the positive array and the negative array throughout the entire reference voltage switching process, and the reference voltage of each capacitor in the scheme does not change more than two voltages. The post-layout result shows that the ADC achieves the 54.96 dB SNDR, the 61.73 dB SFDR, and the 0.67 μw power consumption with the 10-bit mode and the 48.33 dB SNDR, the 54.17 dB SFDR, and the 0.47 μw power consumption with the 9-bit mode in a 180 nm process with a 100 kS/s sampling frequency.

The farming–pastoral zone in northern China is one of the most ecologically sensitive areas globally, having experienced extensive cropland abandonment and land reclamation over decades, primarily influenced by policy adjustment and global warming. However, the spatiotemporal patterns and suitability of long-term cropland change remain poorly understood. Using the annual China land cover dataset (CLCD), we provide a cropland abandonment and land reclamation mapping approach based on actual land use processes (rather than land cover conditions) to investigate spatiotemporal features of abandonment and reclamation and evaluate the rationality. Our findings show that: (1) Returning farmland to forest and grassland has been a clear trend in the study area over the past 30 years. Specifically, cropland use has undergone three phases of change, i.e., cropland contraction and expansion alternately (before 2000), followed by substantial abandonment (after 2000), and low-intensity reclamation (after 2010). (2) In the last decade, the intensity of the abandonment of cropland with high and moderate suitability is low. The rate of abandonment decreased, while the intensity of land reclamation was relatively high. The rate of the reclamation increased, and the spatial distribution of cropland tended to be reasonable. Our study emphasizes the importance of monitoring actual cropland changes based on land use processes, and this method can be effectively extended to regional or global long-term cropland monitoring.

Abstract The microtubule (MT) plus-end binding protein Clip170 is associated closely with breast cancer invasion and migration. In this study, Clip170 tension observed by a newly designed cpstFRET tension probe was suggested to be positive related to breast cancer aggressiveness, which could be regulated by α-tubulin detyrosination-induced MT disassembly. Clip170 phosphorylation induced by Ribosomal protein S6 kinase (RSK) could also increase its tension and promote the conversion of a discrete comet-like Clip-170 distribution into a spotty pattern during cancer metastasis. Heightened Clip170 tension was correlated with the formation of cortactin-associated filopodia and lamellipodia, and then promoted invasion and metastasis both in vitro and in vivo. Meanwhile, Clip170 tension enhanced at the leading edge in directional migration, accompanying with IQGAP1 subcellular distribution variation. Our work indicates that the malignancy and directionality during breast cancer migration depend on the magnitude and polarization of Clip170 tension, and we suggest Clip170 tension as a new potential drug target for breast cancer therapy.

From land cover analysis, cropland expansion was a major driving factor for land use land cover changes in Nigeria from 2000 to 2020. This further highlights the food production needs in the country. While this land use change indicates a significant alteration in land cover, it was exigent to assess land suitability using a Multi-Criteria Decision Analysis (MCDA) combined with geospatial techniques to identify areas with agricultural suitability potential and to analyze cropland suitability. The results showed that the country had 8% of very high suitability, high suitability (25%), moderate suitability (29%), and marginal suitability (25%) croplands. However, low suitability accounts for 14% of the entire cropland. The spatial distribution of cropland suitability shows that most areas in the South East, South South, and South West, respectively, have the most suitable cropland as they meet the biophysical conditions for crop production, followed by the North Central regions, while most places in the North (North East and North West) have a higher share of moderate to low suitability. This study highlights the potential of the country to target localized self-sufficiency. Therefore, this study recommends using the cropland suitability map to launch food security programs across the six geopolitical zones to maximize their inherent environmental potentials to alleviate the country’s food production needs.

Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local governments with upgrading slums and alleviating urban poverty. Remote sensing (RS) technology, with its excellent Earth observation capabilities, can play an important role in slum mapping. Deep learning (DL)-based RS information extraction methods have attracted a lot of attention. Currently, DL-based slum mapping studies typically uses three optical bands to adapt to existing models, neglecting essential geo-scientific information, such as spectral and textural characteristics, which are beneficial for slum mapping. Inspired by the geoscience-aware DL paradigm, we propose the Geoscience-Aware Network for slum mapping (GASlumNet), aiming to improve slum mapping accuracies via incorporating the DL model with geoscientific prior knowledge. GASlumNet employs a two-stream architecture, combining ConvNeXt and UNet. One stream concentrates on optical feature representation, while the other emphasizes geo-scientific features. Further, the feature-level and decision-level fusion mechanisms are applied to optimize deep features and enhance model performance. We used Jilin-1 Spectrum 01 and Sentinel-2 images to perform experiments in Mumbai, India. The results demonstrate that GASlumNet achieves higher slum mapping accuracy than the comparison models, with an intersection over union (IoU) of 58.41%. Specifically, GASlumNet improves the IoU by 4.60~5.97% over the baseline models, i.e., UNet and ConvNeXt-UNet, which exclusively utilize optical bands. Furthermore, GASlumNet enhances the IoU by 10.97% compared to FuseNet, a model that combines optical bands and geo-scientific features. Our method presents a new technical solution to achieve accurate slum mapping, offering potential benefits for regional and global slum mapping and upgrading initiatives.

In recent years, due to the rise of the Internet of Things (IoT), various sensors have come to be in great demand for IoT devices. Analog-to-digital converters (ADCs) act as an important part of receivers in sensors. To improve the uptime of IoT devices, a bridged-switch energy-efficient switching scheme for successive approximation register (SAR) ADCs with a low-complexity capacitor drive circuit is proposed. The technique of top-plate sampling and closed-loop charge recycling is used in the proposed switching scheme so that neither the first nor the second comparison consumes switching energy. The third comparison uses bridge switches to connect the subarray to the main array, effectively reducing switching’s energy consumption. Only the least significant bit (LSB) is dependent on the accuracy of Vcm; thus, the last comparison consumes little switching energy. The proposed switching scheme achieves an average switching energy value of 47.5 CV2ref, which is 96.52% lower than that of the conventional capacitor switching scheme and reduces the area by 75%. The other major circuit modules employed are bootstrapped switches, a fully dynamic comparator, and dynamic SAR logic. The proposed ADC was simulated under the conditions of 180 nm CMOS process and 1 MS/s, resulting in a 9.8-bit effective number of bits (ENOB), a signal-to-noise and distortion ratio (SNDR) of 60.76 dB, a spurious-free dynamic range (SFDR) of 69.85 dB, a power consumption of 14.7 μW, and a figure of merit (FoM) of 16.55 fJ/conv.-step.

A low-power SAR ADC with capacitor-splitting energy-efficient switching scheme is proposed for wearable biosensor applications. Based on capacitor-splitting, additional reference voltage Vcm, and common-mode techniques, the proposed switching scheme achieves 93.76% less switching energy compared to the conventional scheme with common-mode voltage shift in one LSB. With the switching scheme, the proposed SAR ADC can lower the dependency on the accuracy of Vcm and the complexity of digital control logic and DAC driver circuits. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low sampling error sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrate that the ADC achieves a 61.77 dB SNDR and a 78.06 dB SFDR and consumes 4.45 μW of power in a 180 nm process with a 1 V power supply, a full-swing input signal frequency of 93.33 kHz, and a sampling rate of 200 kS/s.

A hybrid energy-efficient, area-efficient, low-complexity switching scheme in SAR ADC for biosensor applications is proposed. This scheme is a combination of the monotonic technique, the MSB capacitor-splitting technique, and a new switching method. The MSB capacitor-splitting technique, as well as the reference voltage Vaq allow for more options for reference voltage conversion, resulting in higher area savings and higher energy efficiency. In a capacitor array, the circuit performs unilateral switching during all comparisons except for the second and last two comparisons, reducing the difficulty in designing the drive circuit. The proposed switching scheme saves 98.4% of the switching energy and reduces the number of unit capacitors by 87.5% compared to a conventional scheme. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low-sampling error-sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrated that the proposed SAR ADC achieves 61.51 dB SNDR, 79.21 dB SFDR and consumes 0.278 μW of power in a 180 nm process with a 1 V power supply, a full swing input signal frequency of 23.33 kHz, and a sampling rate of 100 kS/s.

ObjectiveThe occurrence and development of oesophageal neoplasia (ON) is closely related to hormone changes. The aim of this study was to investigate the causal relationships between age at menarche (AAMA) or age at menopause (AAMO) and benign oesophageal neoplasia (BON) or malignant oesophageal neoplasia (MON) from a genetic perspective.MethodsGenome-wide association study (GWAS) summary data of exposures (AAMA and AAMO) and outcomes (BON and MON) were obtained from the IEU OpenGWAS database. We performed a two-sample Mendelian randomization (MR) study between them. The inverse variance weighted (IVW) was used as the main analysis method, while the MR Egger, weighted median, simple mode, and weighted mode were supplementary methods. The maximum likelihood, penalized weighted median, and IVW (fixed effects) were validation methods. We used Cochran’s Q statistic and Rucker’s Q statistic to detect heterogeneity. The intercept test of the MR Egger and global test of MR pleiotropy residual sum and outlier (MR-PRESSO) were used to detect horizontal pleiotropy, and the distortion test of the MR-PRESSO analysis was used to detect outliers. The leave-one-out analysis was used to detect whether the MR analysis was affected by single nucleotide polymorphisms (SNPs). In addition, the MR robust adjusted profile score (MR-RAPS) method was used to assess the robustness of MR analysis.ResultsThe random-effects IVW results showed that AAMA had a negative genetic causal relationship with BON (odds ratio [OR] = 0.285 [95% confidence interval [CI]: 0.130-0.623], P = 0.002). The weighted median, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with random-effects IVW (P < 0.05). The MR Egger, simple mode and weighted mode results showed that AAMA had no genetic causal relationship with BON (P > 0.05). However, there were no causal genetic relationships between AAMA and MON (OR = 1.132 [95%CI: 0.621-2.063], P = 0.685), AAMO and BON (OR = 0.989 [95%CI: 0.755-1.296], P = 0.935), or AAMO and MON (OR = 1.129 [95%CI: 0.938-1.359], P = 0.200). The MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with a random-effects IVW (P > 0.05). MR analysis results showed no heterogeneity, the horizontal pleiotropy and outliers (P > 0.05). They were not driven by a single SNP, and were normally distributed (P > 0.05).ConclusionOnly AAMA has a negative genetic causal relationship with BON, and no genetic causal relationships exist between AAMA and MON, AAMO and BON, or AAMO and MON. However, it cannot be ruled out that they are related at other levels besides genetics.

BackgroundThis study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness.MethodsWe collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model.ResultsThis study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve.ConclusionThe DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

A 10 bit 1 MS/s SAR ADC with one LSB common-mode shift energy-efficient switching scheme for image sensor is presented. Based on the two sub-capacitor arrays architecture and the common-mode technique, the proposed switching scheme achieves 98.45% less switching energy over the conventional architecture with common-mode shift in one LSB. The comparator uses a low power dynamic comparator. The sampling switch adopts a bootstrap circuit with low sampling error. SAR logic is composed of Bit-Slice circuit with low power consumption and few transistors. Simulated in 180 nm CMOS process and 1 MS/s sampling rate, the ADC achieves the 60.06 dB SNDR, the 75.43 dB SFDR and the 10.45 μW power consumption.

Accurate geographic data of slums are important for handling urban poverty issues. Previous slum mapping studies using high-resolution or very-high-resolution (HR/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency.

Composite micro-alloying is an important technique for developing cost-effective, high-performance aluminum alloys. Here, the effect of combined addition of Zr, Ti and rare-earth Y on the microstructure and tensile properties of an Al-7.6Zn-1.6 Mg-2.1Cu (wt.%) alloy is systematically investigated by means of X-ray diffraction, optical microscopy, scanning electron microscopy, transmission electron microscopy and room-temperature unixal tensile tests. After adding 0.12 %Zr, 0.04 %Ti and 0.28 %Y, L12-Al3Zr/(Al,Zn)3Zr, AlMgZnTiCuFe and (Al,Zn)8Cu4Y phases were induced; the as-T6 treated microstructure of the base alloy changed from fully recrystallized to fully unrecrystallized, accompanied by strong 〈100〉 + 〈111〉 fiber textures along extrusion direction. As a result, the Al-Zn-Mg-Cu-Zr-Ti-Y alloy with low Zn and Mg contents exhibits an ultimate tensile strength of 692 MPa, yield strength of 647 MPa (∼36 % higher than the base alloy) and 6.8 % elongation. The main strengthening mechanisms responsible for this high yield strength are Orowan dislocation bypassing strengthening (∼449 MPa), fiber texture strengthening, and dispersion strengthening. Moreover, the synergistic strengthening effect of rare-earth Y and transitional element(s) Zr and/or Ti was revealed, which may be related to the formation(s) of nanoscale network-structured Al8Cu4Y and/or L12-Al3(Zr,Y) phase(s). The massive formation of the Al8Cu4Y network structure requires a narrow process window.

In large-area dynamic imaging, an active pixel sensor (APS) is proposed. However, there is a trade-off between signal-to-noise ratio (SNR) and spatial resolution. To resolve this, a 256 × 256 active pixel image sensor array based on a 3-D dual-gate photosensitive thin-film transistor (TFT) is presented in this work, with a pixel pitch of 50 μm, pixel fill factor of 63%, photoconductive gain of 102–104 and spatial resolution of 505 ppi. An electronic global shutter is enabled by dual-gate biasing without additional a shutter TFT. Such an array is capable of dynamic imaging at a frame rate of 34 Hz.

Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction.

Stable growth in grain production is a critical challenge to ensure food security in North China Plain (NCP), an area dominated by smallholder farming. Food production and security of NCP largely depend on how smallholders farm their land. This study took Ningjin County of the NCP as an example to describe the characteristics of crop planting structure and the changes in crop production based on household surveys, statistics, various documents, and literature by descriptive statistics, calculation of crop self-sufficiency, and curve fitting, and aimed to reveal crop security and the influencing factors of crop production at the household level. The results were as follows: (1) Wheat and maize sown area accounted for 61.69% and 47.96% of the total sown area of crops during 2000–2020, increasing at a rate of 3.42% and 5.93%, respectively. Their planted areas increased from 27.52% and 15.54% in 2000 to 47.82% and 44.75% in 2020, respectively. (2) The self-sufficiency rate of maize showed a significant upward trend and reached its peak in 2019. the self-sufficiency rate of wheat also showed an increasing trend, from 192.87% to 617.37%, which indicates that wheat and maize can meet food self-sufficiency and the per capita grain yield is in a safe state. (3) The trends on wheat yield and fertilizer initially grew, then decreased, closely resembling an inverted “U”, while the maize yield showed a pattern of increasing first and then basically remaining stable, similar to an “S” shape. A turning point for fertilizer use (550 kg/ha) was identified, indicating the limits of fertilizer use to increase yield. The national agricultural production and environmental protection policies, continuous improvement of crop varieties, as well as the farmers’ traditional practices have significant impacts on crop production. This study will enhance management practices for improved yield, which can support the integrated management of agricultural production in intensive agricultural areas.

Ordos is an ecological fragile area in the upstream and midstream of the Yellow River and a component of the ecological security barrier on the northern frontier of China. With population growth in recent years, the contradiction between human beings and land resources has become increasingly prominent, leading to increased food security risks. Since 2000, the local government has implemented a series of ecological projects to guide farmers and herdsmen to transform from extensive production to intensive production, which has optimized the pattern of food production and consumption. It is necessary to study the balance between food supply and demand to evaluate food self-sufficiency. Here, we used the panel data from 2000 to 2020 collected based on random sampling surveys to reveal the characteristics of food production and consumption, the changes in food self-sufficiency rate and the dependence of food consumption on local production in Ordos. The results showed that food production and consumption dominated by grains have been increasing. The residents’ diets were characterized by excessive consumption of grains and meat, and insufficient consumption of vegetables, fruits, and dairy foods. On the whole, the locality has achieved self-sufficiency, because the food supply exceeded the demand during the two decades. However, the self-sufficiency of different food types varied greatly, as some foods, such as wheat, rice, pork, poultry, and eggs, have not been self-sufficient. Due to the increased and diversified food demand of residents, food consumption became less dependent on local production and more dependent on food imported from the central and eastern China, which threatened local food security. The study can provide a scientific basis for decision-makers for the structural adjustment of agricultural and animal husbandry and the structural adjustment of food consumption, to ensure food security and sustainable utilization of land resources.

High-strength 2xxx series aluminum alloys (Al-Cu system) have been favored by the aerospace and railway transportation industries. Traditionally, developing new materials with targeted properties is guided by extensive experiments and expert experience, causing the development process to be dismayingly slow and expensive. Here, a Kriging model-based efficient global optimization(EGO) lgorithm is applied to search for new 2xxx series aluminum alloys with high tensile strength in a huge search space. After four iterations, the alloy’s ultimate tensile strength increased by 60 MPa, which is higher than that of the best alloy in the initial data set. This study demonstrates the feasibility of using machine-learning to search for 2xxx alloys with good mechanical performance.

Four kinds of 7xxx series aluminum alloys with different Y elementcontents were obtained by ordinary gravity casting. The effect of Y content on the microstructure and mechanical properties of as-cast and as-solutionized Al-7.9Zn-3Mg-2.4Cu-0.13Zr (wt%) alloys were investigated by means of x-ray diffraction (XRD), electron backscattering diffraction (EBSD), scanning electron microscopy (SEM), energy dispersive spectrometer (EDS) and room-temperature tensile tests. The results shows that, in as-cast condition, Y element can refine the grain and reduce the content of Mg(Zn, Cu, Al) _2 lamellar phases at the interdendritic. (Al, Zn) _8 Cu _4 Y block-shaped phases form in interdendritic regions. After solution treatment, the undissolved Mg(Zn, Cu, Al) _2 phases evolved from lamellar to bulk-like which distribution in interdendritic, but no obvious change in (Al, Zn) _8 Cu _4 Y phase. The tensile testing results shows that the optimal yttrium content is 0.45 wt%. At 0.45 wt% Y, the ultimate tensile strength and elongation are 267 MPa and 2.4% in as-cast condition and 420 MPa and 3.6% in as-solutionized condition.

Land use/cover change (LUCC) research is important for regional ecological conservation and sustainable development. There is a lack of exploration of long-time-series dynamics and driving mechanisms at the national scale in the study of land use/cover change in Thailand. Based on the Global Land Cover product with Fine Classification System in 30m (GLC_FCS30) dataset and socioeconomic statistics, we studied the temporal and spatial evolution characteristics and driving mechanisms of LUCC in Thailand from 2000 to 2020 through indicators and methods such as land use dynamic attitude, mapping of a Sankey diagram, principal component analysis, and multiple linear stepwise regression analysis. The results showed that: (1) Thailand has developed in terms of agriculture and forestry. In 2020, the cropland and forest areas accounted for 53.77% and 32.15% of the land area, respectively. (2) From 2000 to 2020, the area of rainfed cropland, irrigated cropland, and forest continued to shrink; the area of impervious surfaces expanded rapidly, and the area of shrubland, other cropland, and wetlands increased. (3) The LUCC process mainly occurred in the two-way conversion between forest and shrubland, rainfed cropland and irrigated farmland, forest and rainfed cropland, and forest and other farmland. The LUC with the largest area transformed into other land types was forest (2.25 × 104 km2), and the LUC with the largest area transformed from other land types transferring into the area was shrubland (1.40 × 104 km2). (4) From 2000 to 2020, the LUCC process in Thailand was mainly influenced by socio-economics and tourism. Gross population, main grain output, industrial value added, passenger income, and urban population were the key factors driving the LUCC in Thailand. Our research can provide the basis and decision support for the future planning and management of land in Thailand.

A DAC switching scheme that combines energy efficiency and resolution reconfigurability is proposed. Compared with the conventional switching scheme, the proposed scheme achieves 93.8%, 96.1%, and 97.3% switching energy saving in 8-bit, 9-bit, and 10-bit modes, respectively. Based on the proposed switching scheme, an 8–10-bit resolution-reconfigurable SAR ADC for biosensor applications is designed. The ADC consists of resolution-reconfigurable binary-weighted capacitive DAC, a two-stage full dynamic comparator, sampling switch, and the resolution-control SAR logic. Simulated in 180 nm CMOS process and 100 kS/s sampling rate, the ADC achieves the 46.80/53.89/60.14 dB signal-to-noise and distortion ratio (SNDR), the 55.22/62.51/73.09 dB spurious-free dynamic range (SFDR) and the 0.81/0.91/1.01 μW power consumption in 8/9/10-bit mode, respectively.

Land use/cover (LUC) datasets are the basis of global change studies and cross-scale land planning. Data fusion is an important direction for correcting errors and improving the reliability of multisource LUC datasets. In this study, a new fusion method based on Bayesian fuzzy probability prediction was developed, and a case study was conducted in five countries of the Indochina Peninsula to form a fusion dataset with a resolution of 30 m in 2020 (BeyFusLUC30). After precision and uncertainty analysis, it was found that: (1) using accuracy validation information as prior knowledge and considering spatial relations can be well applied to LUC data fusion. (2) When compared to the four source datasets (LSV10, GLC_FCS30, ESRI10, and Globeland30), the accuracy indices of BeyFusLUC30 are all optimal. The average overall consistency increased by 6.42–13.61%, the overall accuracy increased by 4.84–7.11%, and the kappa coefficient increased by 4.98–7.60%. (3) The accuracy of the fusion result improved less for land types with good original accuracy (cropland, forest, water area, and built-up land), and the improved range of F1 score was at least 0.40–2.29%, and at most 6.66–9.88%. For the land types with poor original accuracy (grassland, shrubland, wetland, and bare land), the accuracy of the fusion result improved more, and the F1 score improved by at least 4.02–5.82%, and at most 14.41–48.35%. The LUC dataset fusion and quality improvement method developed in this study can be applied to other regions of the world as well. BeyFusLUC30 can provide reliable LUC data for scientific research and government applications in the peninsula.

It is still difficult to obtain high-resolution and fast-updated NDVI data, and spatiotemporal fusion is an effective means to solve this problem. The purpose of this study is to carry out the comparative analysis and comprehensive trade-off of spatiotemporal fusion models for NDVI generation and to provide references for scholars in this field. In this study, four spatiotemporal fusion models (STARFM, ESTARFM, FSDAF, and GF-SG) were selected to carry out NDVI image fusion in grassland, forest, and farmland test areas, and three indicators of root mean square error (RMSE), average difference (AD), and edge feature richness difference (EFRD) were used. A detailed evaluation and analysis of the fusion results and comprehensive trade-off were carried out. The results show that: (1) all four models can predict fine-resolution NDVI images well, but the phenomenon of over-smoothing generally exists, which is more serious in high-heterogeneity areas; (2) GF-SG performed well in the evaluation of the three indicators, with the highest comprehensive trade-off score (CTS) of 0.9658. Followed by ESTARFM (0.9050), FSDAF (0.8901), and STARFM (0.8789); (3) considering the comparative analysis and comprehensive trade-off results of the three test areas and the three indicators, among the four models, GF-SG has the best accuracy in generating NDVI images. GF-SG is capable of constructing NDVI time series data with high spatial and temporal resolution.

This paper presents a 10-bit successive approximation register analog-to-digital converter with energy-efficient low-complexity switching scheme, automatic ON/OFF comparator and automatic ON/OFF SAR logic for biomedical applications. The energy-efficient switching scheme achieves an average digital-to-analog converter switching energy of 63.56 CVref2, achieving a reduction of 95.34% compared with the conventional capacitor switching scheme for CDACs. With the switching scheme, the ADC can lower the dependency on the accuracy of Vcm and complexity of DAC control logic and DAC driver circuit. Moreover, dynamic circuits and automatic ON/OFF technology are used to reduce power consumption of comparator and SAR logic. The prototype is designed and fabricated in a 180 nm CMOS with a core size of 500 μm × 300 μm (0.15 mm2). It consumes 7.6 nW at 1 kS/s sampling rate and 1.8-V supply with an achieved signal-to-noise-and distortion ratio of 45.90 dB and a resulting figure of merit of 51.7 fJ/conv.-step.

Research on the spatial distribution and dynamic evolution of land use/cover (LUC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analyses of the evolution of LUC at the national scale in Cambodia. Based on the GLC_FCS30 dataset, this paper analyzes the temporal and spatial evolution of LUC in Cambodia from 2000 to 2020, as well as its relationship with the country’s economic and social development and climate change using dynamic degree, flow direction diagram, principal component analysis, and multivariate stepwise regression. The main findings are as follows. (1) Cropland and forest are Cambodia’s most significant land cover types. In 2020, the proportions of cropland and forest accounted for 47.67% and 42.22% of the total land area. Among the cropland area, rainfed cropland accounted for 35.90%, irrigated cropland accounted for 7.26%, and other cropland accounted for 4.51%. (2) From 2000 to 2020, rainfed cropland areas increased significantly (+4.69 × 103 km2, +7.77%), while irrigated cropland areas increased less (+0.37 × 103 km2, 2.91%). The forest area continued to shrink (−7.71 × 103 km2, −9.16%), and the area of impervious surfaces expanded most significantly (+161.16%). (3) In 2000–2020, the integrated dynamic degree of land use presented a spatial differentiation pattern of high in the northeast, low in the middle, and lowest in the plain area. The most critical LUC change processes were the conversion of forest and shrubland to rainfed cropland and the transformation of rainfed cropland to impervious surfaces. (4) The change in LUC in Cambodia has mainly been affected by economic development and human activities, especially regarding the agricultural added value, agricultural raw material export value, urban population, and urbanization rate. This study will help the Cambodian government to strengthen national land management and planning in a targeted manner and may provide a reference for the analysis of land use change processes in similar areas.

Land use/land cover change (LUCC) research is of great significance to land conservation and regional sustainable development. At present, there is a lack of research on the long-term timing of the change process and mechanisms of LUCC that accords with the national level in Laos. Based on the Global Land-Cover product with the Fine Classification System at 30 m (GLC_FCS30) data set as well as economic and social statistical data, the authors analyzed the spatiotemporal regularity and driving mechanism of LUCC in Laos from 2000 to 2020 by using dynamic degree, flow direction analysis, principal component analysis, correlation analysis and other methods. The results show that: (1) Laos is rich in natural ecological resources. In 2020, the forest and shrubland areas accounted for 53.3% and 32.4% of the land area, respectively; (2) from 2000 to 2020, the rate of LUCC across the country continued to rise, and the integrated dynamic degree of LUCC was 14.4%. The change in impervious surfaces is the most drastic. The area of evergreen broad-leaved forest, evergreen needle-leaved forest and grassland continued to shrink, while the area of rainfed cropland, irrigated cropland, deciduous broad-leaved forest, shrubland, wetland and the water body continued to expand; (3) the LUCC process mainly occurred between forest, shrubland and cropland. The LUC with the largest transfer out area is evergreen broad-leaved forest (8.91 × 103 km2), and the LUC with the largest transfer into the area is shrubland (8 × 103 km2); (4) in the past 20 years, the LUCC process in Laos has been mainly affected by macro-socioeconomic development, agricultural development, and forestry development. The population is the key factor driving LUCC in Laos. This study can provide decision-making support for the rational planning and utilization of land resources in Laos.

Research on the spatial distribution and dynamic evolution of land use/land cover (LULC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analysis on the evolution of LULC on the national scale in Vietnam. Based on the GLC_FCS30 dataset, this paper analyzed the temporal and spatial evolution of LULC in Vietnam from 2000 to 2020 as well as its driving mechanism using methods such as dynamicity, flow direction diagrams, principal component analysis, and multivariate stepwise regression. The results show that: (1) cropland, forest, and shrubland are Vietnam’s most important land-cover types. In 2020, the above three types of land area accounted for 34.77%, 32.36%, and 26.13% of the total land area, respectively. (2) From 2000 to 2020, the area of cropland and forest areas continued to shrink (−5.64%, −3.96%); the area of shrubland, water bodies, and other land areas expanded (+4.87%, +12.29%, +15.04%); and the area of impervious surfaces expansion was the most significant (+100.40%). (3) The integrated dynamic degree of LULC in Vietnam shows a spatial differentiation of high in the south, followed by the north, and lowest in the center. In the early period (2000–2010), the LULC rate of change in each region was rapid, while it gradually decreased in the later period (2010–2020). The most important LULC changes in Vietnam can be divided into two parts: (a) the mutual conversion of forest, cropland, and shrubland and (b) one-way conversion of cropland to impervious surfaces. (4) LULC changes in Vietnam are mainly affected by economic development and human activities, especially the GDP, population, and urbanization rate. There is no reliable statistical relationship between LULC and climatic factors. The results of this study contribute to the analysis of LULC processes in similar regions, and will also help the Vietnamese government strengthen national land management and planning in a targeted manner.

Open and high-temporal- and spatial-resolution global land use/land cover (LULC) mapping data form the foundation of global change research and cross-scale land management planning. However, the consistency and reliability of the use of multisource LULC datasets in specific regions need to be quantitatively assessed. In this study, we selected the Indochina Peninsula as the research area; considered four datasets: LSV10, GLC_FCS30, ESRI10, and Globeland30; and analyzed them from four dimensions: the similarity of composition type, the degree of category confusion, spatial consistency, and data accuracy. The results show that: (1) the land composition descriptions of the different datasets are consistent. The study area is dominated by forest and cropland, supplemented by grassland, shrubland, and other land types. (2) The correlation coefficient between datasets is between 0.905 and 0.972; the spatial consistency of datasets is good; and the high-consistency area accounts for 77.87% of the total. (3) The overall accuracy of LSV10 is the highest (83.25%), and that of GLC_FCS30 is the lowest (72.27%). The accuracy of cropland, forest, water area, and built-up land is generally high (above 85%); the accuracy of grassland, shrubland, and bare land is low (below 60%). Therefore, researchers must conduct validation for specific regions and specific land types before using the above datasets. Our findings provide a basis for selecting LULC datasets in related research on the Indochina Peninsula and a reference method for assessing the reliability of multisource LULC datasets in other regions.

High-accuracy, long-time-series and large-scale land classification mapping are essential for assessing the evolutionary patterns of land systems and developing sustainability studies. In this paper, using Google Earth Engine (GEE) and Landsat satellite remote sensing images, based on the Random Forest (RF) algorithm, we carried out remote sensing classification to obtain a year-by-year land use/cover data set in Vietnam over the past 21 years (2000–2020). Further applying principal component analysis and multiple linear regression methods, we examined the spatio-temporal characteristics, dynamic changes and driving mechanisms of land use change. The results show the following: (1) The RF classification algorithm supported by the GEE can quickly and accurately obtain a land use/cover data set. The overall classification accuracy is 0.91 ± 0.01. (2) The land cover types in Vietnam are dominated by woodland and cropland, with an area share of 54.62% and 37.90%, respectively. In the past 20 years, the area of built-up land has increased the most (+93.49%), followed by the area of water bodies (+54.19%), while the area of woodland has remained almost unchanged. (3) The expansion of built-up land is driven by regional economic development; the area changes in cropland, water bodies and woodland are influenced by both national economic development and climate change. The results of the study provide a basis for assessing land use policies in Vietnam and a reference methodological framework for rapid land mapping and analysis in other countries in the China–Indochina Peninsula.