Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Changes in the Supply and Demand of Ecosystem Services in the Kaidu-Kongque River Basin, China

The assessment of ecosystem service (ES) supply and demand is crucial for the sustainable development of dryland drainage basins. The natural ecosystems in the Kaidu-Kongque River Basin have experienced severe ecological degradation in recent years, and the ES supply and demand were contradicted due to water scarcity and excessive water utilization. In this paper, the supply–demand of five key ecosystem services were evaluated, and their spatial matching was also analyzed to provide total insights. The services assessed were food supply, water yield, carbon sequestration, habitat quality, and windbreak and sand fixation. We utilized various models, including InVEST, RWEQ, and GeoDa, to quantify and analyze the spatial and temporal patterns of ecosystem service supply and demand between 1990 and 2020. Our findings indicate that the supply and demand for all ecosystem services in the basin have increased over the last 30 years. However, the spatial distribution of supply and demand for each ecosystem service is not completely consistent. Except for windbreak and sand fixation, where supply exceeds demand, there is a spatial mismatch between supply and demand for each service. Furthermore, we observed a positive and synergistic correlation between the supply and demand of each ecosystem service, with water yield services being the dominant and limiting factor. The spatial correlation between the supply and demand of ecosystem services was dominated by “low supply—low demand”, “high supply—high demand” spatial matching, and “low supply—high demand” mismatch, which could explain the variation in water yield from upstream to downstream. Based on our findings, we recommend policies and recommendations for ecological conservation and sustainable development in the Kaidu-Kongque River Basin. The ES supply and demand will become more reliable by increasing water supplies in the middle and lower reaches of the basin. Our results provide illumination for the maintenance and sustainability of ecosystem services in arid regions.
Clarification of Phylogenetic Relationships among Chinese Nemacheilids with Tube-Shaped Anterior Nostrils, with a Description of a New Genus and Two New Species

Nostril structures are important characteristics for generic diagnosis in Nemacheilidae and Cobitidae. Based on phylogenetic analysis, we found that 10 genera within Nemacheilidae (Eonemachilus, Guinemachilus, Lefua, Micronemacheilus, Oreonectes, Paranemachilus, Protonemacheilus, Traccatichthys, Troglonectes, and Yunnanilus) shared tube-shaped anterior nostrils and formed a monophyletic group. Morphologically, the location between the anterior and posterior nostrils was divided into three types: (i) separated, i.e., a distance greater than 1.5 times the diameter of the posterior nostril; (ii) adjacent, i.e., a distance shorter than the diameter of the posterior nostril; and (iii) closely set, i.e., posterior margin of the anterior nostril connected to the anterior margin of the posterior nostril. Thus, the 10 genera can be distinguished based on nostril features. Additionally, we describe one new genus, Guinemachilus gen. nov., and two new species, Guinemachilus pseudopulcherrimus sp. nov. and Paranemachilus chongzuo sp. nov.
Quantification of Extramyocellular Lipids and Intramuscular Fat from Muscle Echo Intensity in Lower Limb Muscles: A Comparison of Four Ultrasound Devices against Magnetic Resonance Spectroscopy

This study aimed to compare different ultrasound devices with magnetic resonance spectroscopy (MRS) to quantify muscle lipid content from echo intensity (EI). Four different ultrasound devices were used to measure muscle EI and subcutaneous fat thickness in four lower-limb muscles. Intramuscular fat (IMF), intramyocellular (IMCL) and extramyocellular lipids (EMCL) were measured using MRS. Linear regression was used to compare raw and subcutaneous fat thickness-corrected EI values to IMCL, EMCL and IMF. IMCL had a poor correlation with muscle EI (r = 0.17–0.32, NS), while EMCL (r = 0.41–0.84, p < 0.05–p < 0.001) and IMF (r = 0.49–0.84, p < 0.01–p < 0.001) had moderate to strong correlation with raw EI. All relationships were improved when considering the effect of subcutaneous fat thickness on muscle EI measurements. The slopes of the relationships were similar across devices, but there were some differences in the y-intercepts when raw EI values were used. These differences disappeared when subcutaneous fat thickness-corrected EI values were considered, allowing for the creation of generic prediction equations (r = 0.41–0.68, p < 0.001). These equations can be used to quantify IMF and EMCL within lower limb muscles from corrected-EI values in non-obese subjects, regardless of the ultrasound device used.
HPAR: deep neural network-based approach for human pose-activity recognition

Abstract. Human pose activity recognition (HPAR) offers a wide range of applications due to the widespread use of collection devices such as smartphones and video cameras, as well as its capacity to gather human activity data. Electronic devices and applications continue to evolve, and breakthroughs in artificial intelligence (AI) have transformed the capacity to extract deeply buried information for accurate recognition and interpretation. We propose a systematic design for integrating conventional networks and constraints into the attention framework for learning long-range dependencies, thereby achieving end-to-end pose estimation with flexibility and scalability. The proposed method modifies the temporal receptive field using a multi-scale structure of dilated convolutions and can be adapted to a causal model for real-time performance. Our approach achieves state-of-the-art performance on the task of three-dimensional HPAR and outperforms previous methods while maintaining a lower complexity cost.
Machine Learning: A Suitable Method for Biocatalysis

Biocatalysis is currently a workhorse used to produce a wide array of compounds, from bulk to fine chemicals, in a green and sustainable manner. The success of biocatalysis is largely thanks to an enlargement of the feasible chemical reaction toolbox. This materialized due to major advances in enzyme screening tools and methods, together with high-throughput laboratory techniques for biocatalyst optimization through enzyme engineering. Therefore, enzyme-related knowledge has significantly increased. To handle the large number of data now available, computational approaches have been gaining relevance in biocatalysis, among them machine learning methods (MLMs). MLMs use data and algorithms to learn and improve from experience automatically. This review intends to briefly highlight the contribution of biocatalysis within biochemical engineering and bioprocesses and to present the key aspects of MLMs currently used within the scope of biocatalysis and related fields, mostly with readers non-skilled in MLMs in mind. Accordingly, a brief overview and the basic concepts underlying MLMs are presented. This is complemented with the basic steps to build a machine learning model and followed by insights into the types of algorithms used to intelligently analyse data, identify patterns and develop realistic applications in biochemical engineering and bioprocesses. Notwithstanding, and given the scope of this review, some recent illustrative examples of MLMs in protein engineering, enzyme production, biocatalyst formulation and enzyme screening are provided, and future developments are suggested. Overall, it is envisaged that the present review will provide insights into MLMs and how these are major assets for more efficient biocatalysis.
Flowery math: a case for heterodiscoursia in mathematics problems solving in recognition of students’ authorial agency

In an innovative, progressive school, students were asked to solve a fairly routine mathematical problem using real money in a “real-world” scenario. Even though the school values students’ ideas, the reaction of the teacher to one student’s alternative modelling of the problem suggests that he was expecting a particular answer to be provided using routine mathematical models and thinking while not being interested in exploring the student’s unexpected alternative. We place his reasoning for doing so within broad pedagogical discourses that we think define the “allowable” responses of teachers and students in ways that inhibit meaning-making for both. These broad discourses are defined as the progressive constructivist approach, the scaffolding discursive approach, the situation modelling approach and the dialogic approach. We consider the advantages and the potential consequences each might bring to the case. We suggest that extensive consideration of pedagogical discourses in mathematics classes must be reconsidered both for how we understand students’ mathematical meaning-making and how we construct student agency in relationship to culture, whether as apprentices or authors.
A low‐drift and real‐time localisation and mapping method for handheld hemispherical view LiDAR‐IMU integration system

This paper proposes a simultaneous localisation and mapping (SLAM) framework that uses a handheld hemispherical view LiDAR‐IMU integration system. Inspired by the specific characteristic of the hemispherical view LiDAR, a ground segmentation module based on seed points is designed. The ground points are then downsampled to eliminate redundant vertical constraints. The IMU data and the pre‐processed point cloud are used to perform state estimation via a tightly coupled iterative extended Kalman filter (iEKF) to obtain the pose estimation. The automatically detected loop closures provide closed‐loop constraints for the odometry, and a factor graph ensures the global consistency of the map. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are carried out to verify the framework's performance. According to the experimental results, our framework achieves low‐drift, high‐coverage and real‐time performance, outperforming the state‐of‐the‐art LiDAR SLAM methods in our handheld hemispherical view LiDAR‐IMU test sites. For the research community's benefit, the dataset is publicly provided for other researchers to compare against.
Carbon-Neutral Steel Production and Its Impact on the Economies of China, Japan, and Korea: A Simulation with E3ME-FTT:Steel

The iron and steel industry is a large emitter of CO2 globally. This is especially true for the iron and steel industries in China, Japan, and Korea due to their production volumes and the prevalence of carbon-based steel production. With few low-carbon and commercially available alternatives, the iron and steel industry is truly a hard-to-abate sector. Each of the countries of interest have committed to a net-zero future involving the mitigation of emissions from steel production. However, few studies have investigated the means by which to achieve decarbonization beyond the inclusion of price signalling policies (e.g., carbon tax or emission trading schemes). Here, we use E3ME-FTT:Steel to simulate technology diffusion in the ISI under several policy environments and we investigate the likely impacts on the wider economy. The results show that penalizing carbon intensive processes can incentivize a transition towards scrap recycling, but it is relatively unsuccessful in aiding the uptake of low carbon primary steelmaking. A combination of support and penalizing policies can achieve deep decarbonisation (>80% emission reduction compared with the baseline). Mitigating the emissions in the iron and steel industry can lead to economic benefits in terms of GDP (China: +0.8%; Japan: +1.3%; Korea: +0.1%), and employment (Japan: +0.7%; Korea: +0.3%) with China, where job losses in the coal sector would negate job gains elsewhere, as the exception.
Poisonous Plants of the Genus Pimelea: A Menace for the Australian Livestock Industry

Pimelea is a genus of about 140 plant species, some of which are well-known for causing animal poisoning resulting in significant economic losses to the Australian livestock industry. The main poisonous species/subspecies include Pimelea simplex (subsp. simplex and subsp. continua), P. trichostachya and P. elongata (generally referred to as Pimelea). These plants contain a diterpenoid orthoester toxin, called simplexin. Pimelea poisoning is known to cause the death of cattle (Bos taurus and B. indicus) or weaken surviving animals. Pimelea species are well-adapted native plants, and their diaspores (single seeded fruits) possess variable degrees of dormancy. Hence, the diaspores do not generally germinate in the same recruitment event, which makes management difficult, necessitating the development of integrated management strategies based on infestation circumstances (e.g., size and density). For example, the integration of herbicides with physical control techniques, competitive pasture establishment and tactical grazing could be effective in some situations. However, such options have not been widely adopted at the field level to mitigate ongoing management challenges. This systematic review provides a valuable synthesis of the current knowledge on the biology, ecology, and management of poisonous Pimelea species with a focus on the Australian livestock industry while identifying potential avenues for future research.
Craniofacial Cephalometric Characteristics and Open Bite Deformity in Individuals with Amelogenesis Imperfecta—A Systematic Review and Meta-Analysis

Background: Individuals with amelogenesis imperfecta (AI) often present with malocclusions, especially a dental or skeletal anterior open bite (AOB). Objectives: To evaluate the craniofacial characteristics in individuals with AI. Material and methods: A systematic literature search was conducted with the PubMed, Web of Science, Embase and Google Scholar databases to identify studies relating to the cephalometric characteristics of individuals with AI, without any language or publication date restrictions. The grey literature was searched using Google Scholar, Opengrey and Worldcat. Only studies with a suitable control group for comparison were included. Data extraction and a risk of bias assessment were carried out. A meta-analysis was performed using the random effects model for cephalometric variables that were evaluated in at least three studies. Results: The initial literature search yielded 1857 articles. Following the removal of duplicates and a screening of the records, seven articles were included in the qualitative synthesis, representing a total of 242 individuals with AI. Four studies were included in the quantitative synthesis. The meta-analysis results showed that individuals with AI present a smaller SNB angle and larger ANB angle than those of control groups in the sagittal plane. In the vertical plane, those with AI present a smaller overbite and larger intermaxillary angle than those without AI. No statistically significant differences were found for the SNA angle when comparing the two groups. Conclusions: Individuals with AI seem to present with more vertical craniofacial growth, leading to an increased intermaxillary angle and decreased overbite. This possibly leads to a more retrognathic mandible with a larger ANB angle due to an anticipated posterior mandibular rotation.
Diagnosis and Management of Uterine Rupture in the Third Trimester of Pregnancy: A Case Series and Literature Review

Background: Uterine rupture is associated with clinically significant uterine bleeding, fetal distress, expulsion or protrusion of the fetus, placenta or both into the abdominal cavity requiring prompt cesarean delivery and uterine repair or hysterectomy. Previous cesarean section is the most common risk factor. The most consistent early indicator of it is the onset of prolonged and profound fetal bradycardia. Objective: In this study, we present six cases of uterine rupture highlighting risk factors, and challenges in diagnosis and management, along with a review of the literature. Method: A retrospective case series identified eight cases during the five-year study period. All cases from January 1, 2018 to December 31, 2022 were reviewed. Cases with multiple previous cesarean sections were excluded. Result: Six cases meeting the study criteria were included in our case series. Uterine rupture was a rare occurrence with a prevalence of nine in 31,315 births representing 0.03% of deliveries. No maternal mortality or need for hysterectomy occurred in our study. Fifty percent of uterine ruptures were associated with stillbirths. The most common risk factor was a previous cesarean section in 83.3%. The most common presenting sign was non-reassuring fetal status patterns in 66.6%. A single case had a silent rupture. Conclusion: Signs and symptoms of uterine rupture are nonspecific making diagnosis challenging. Delay in definitive management causes significant fetal morbidity and mortality. For best outcomes, vaginal birth after a previous cesarean section needs close monitoring in appropriately prepared units with the ability to perform immediate cesarean delivery and provide advanced neonatal support.
A Descriptive Review of the Action Mechanisms of Berberine, Quercetin and Silymarin on Insulin Resistance/Hyperinsulinemia and Cardiovascular Prevention

Insulin resistance (IR) and the associated hyperinsulinemia are early pathophysiological changes which, if not well treated, can lead to type 2 diabetes, endothelial dysfunction and cardiovascular disease. While diabetes care is fairly well standardized, the prevention and treatment of IR lacks a single pharmaceutical approach and many lifestyle and dietary interventions have been proposed, including a wide range of food supplements. Among the most interesting and well-known natural remedies, alkaloid berberine and the flavonol quercetin have particular relevance in the literature, while silymarin—the active principle of the Silybum marianum thistle—was traditionally used for lipid metabolism disorders and to sustain liver function. This review describes the major defects of insulin signaling leading to IR and the main properties of the three mentioned natural substances, their molecular targets and synergistic action mechanisms. The actions of berberine, quercetin and silymarin are partially superimposable as remedies against reactive oxygen intermediates generated by a high-lipid diet and by NADPH oxidase, which is triggered by phagocyte activation. Furthermore, these compounds inhibit the secretion of a battery of pro-inflammatory cytokines, modulate intestinal microbiota and are especially able to control the various disorders of the insulin receptor and post-receptor signaling systems. Although most of the evidence on the effects of berberine, quercetin and silymarin in modulating insulin resistance and preventing cardiovascular disease derive from experimental studies on animals, the amount of pre-clinical knowledge strongly suggests the need to investigate the therapeutic potential of these substances in human pathology.
Improving Gaze Tracking in Large Screens With Symmetric Gaze Angle Amplification and Optimization Technique

Many gaze tracking applications focus on use in personal devices such as mobile phones and PCs. However, gaze tracking in large screens poses challenges because with an increase in screen size, gaze tracking in the edge region decreases owing to the restricted range of human eye movement. In addition, as large screens are often exposed to the public, anyone can use the gaze tracking module. This makes it difficult to apply personalized calibration as in personal devices. To acquire accurate gaze in the edge region, we propose a novel approach–symmetric angle amplifying function–for the gaze angle, which amplifies angles when a user is looking at the edge area of the large screen. Our function is designed particularly for the case where the screen is divided into grid-shaped regions. Furthermore, for the better user experience, we optimize neural networks using the network-optimization framework and also propose a center gravity function that pulls gaze coordinates presented on the screen to the predefined center of the region to compensate for the person-wise difference in movement of the human eyes. Experimental results revealed the superiority of the proposed methods over the baseline and different types of fitting functions. The gaze tracking module serves as a part of an aggregated system and is implemented for use in autonomous vehicles.
Nanowires-Based Stretchable Strain Sensor for Wearable Applications

Flexible, stretchable strain sensors are needed to measure physiological parameters or joint angles in wearable systems and robotics, respectively. This letter presents the stretchable strain sensor that is directly fabricated on environment-friendly Ecoflex® substrate. The sensor uses stretchable serpentine-shaped Au interconnects and Ag nanowires (NWs) as the strain-sensitive active channel. The sensor structure has been evaluated under stretching conditions that provided insights about the material integrity during device operation. The device characteristics show good linearity under low strain range (<40%), average gauge factor of 5.8, cyclic stability for more than 5000 cycles, and response time of 200 ms. Finally, the sensor was placed on human finger, as a proof of concept, and measurements were taken to understand the bending strain during finger movements and potential application in smart glove and robotics.
The elasticity of substitution and labor-saving innovations in the Spanish regions

This paper performs a nonlinear estimation of a normalized CES production function within a system of equations with a panel of Spanish regions for the period 1964-2013. It obtains an elasticity of substitution below one and identifies different rates of factor-augmenting technical progress. The results support for labor-saving innovations hypothesis for the Spanish case. Nevertheless, they do not support a relationship between the elasticity of substitution and the initial regional capital per worker. The results do not change if labor is adjusted by human capital.
Burnout and Brownout in Intensive Care Physicians in the Era of COVID-19: A Qualitative Study

The health crisis has had a strong impact on intensive care units. The objective of this study was to investigate the experience of resuscitation physicians during the COVID-19 health crisis to understand the associated determinants of quality of life, burnout, and brownout. This qualitative, longitudinal study covered two periods (T1, February 2021, and T2, May 2021). The data were collected in individual semi-directed interviews with 17 intensive care physicians (ICPs) (T1). Nine of the latter also participated in a second interview (T2). The data were examined using grounded theory analysis. We identified a multiplication of burnout and brownout indicators and factors already known in intensive care. In addition, burnout and brownout indicators and factors specific to the COVID-19 crisis were added. The evolution of professional practices has disrupted the professional identity, the meaning of work, and the boundaries between private and professional life, leading to a brownout and blur-out syndrome. The added value of our study lies in identifying the positive effects of the crisis in the professional domain. Our study revealed indicators and factors of burnout and brownout associated with the crisis among ICPs. Finally, it highlights the beneficial effects of the COVID-19 crisis on work.