Preparation of Green Biosorbent using Rice Hull to Preconcentrate, Remove and Recover Heavy Metal and Other Metal Elements from Water

Sodium hydroxide treated rice hulls were investigated to preconcentrate, remove, and recover metal ions including Be2+, Al3+, Cr3+, CO2+, Ni2+, Cu2+, Zn2+, Sr2+, Ag+, Cd2+, Ba2+, and Pb2+ in both batch mode and column mode. Sodium hydroxide treatment significantly improved the removal efficiency for all metal ions of interest compared to the untreated rice hull. The removal kinetics were extremely fast for Co, Ni, Cu, Zn, Sr, Cd, and Ba, which made the treated rice hull a promising economic green adsorbent to preconcentrate, remove, and recover low-level metal ions in column mode at relatively high throughput. The principal removal mechanism is believed to be the electrostatic attraction between the negatively charged rice hulls and the positively charged metal ions. pH had a drastic impact on the removal for different metal ions and a pH of 5 worked best for most of the metal ions of interest. Processed rice hulls provide an economic alternative to costly resins that are currently commercially available products designed for metal ion preconcentration for trace metal analysis, and more importantly, for toxic heavy metal removal and recovery from the environment

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure

Explaining the differences of gait patterns between high and low-mileage runners with machine learning

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns

A new method applied for explaining the landing patterns:interpretability analysis of machine learning

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis

New insights for the design of bionic robots:adaptive motion adjustment strategies during feline landings

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy

Altering the Alkaline Metal Ions in Lepidocrocite-Type Layered Titanate for Sodium-Ion Batteries

The relatively large ionic radius of the Na ion is one of the primary reasons for the slow diffusion of Na ions compared to that of Li ions in de/intercalation processes in sodium-ion batteries (SIBs). Interlayer expansion of intercalation hosts is one of the effective techniques for facilitating Na-ion diffusion. For most ionic layered compounds, interlayer expansion relies on intercalation of guest ions. It is important to investigate the role of these ions for material development of SIBs. In this study, alkali-metal ions (Li+, Na+, K+, and Cs+) with different sizes were intercalated into lepidocrocite-type layered titanate by a simple ion-exchange technique to achieve interlayer modulation and those were then evaluated as anode materials for SIBs. By controlling the intercalated alkaline ion species, basal spacings of layered titanates (LTs) in the range of 0.68 to 0.85 nm were obtained. Interestingly, the largest interlayer spacing induced by the large size of Cs did not yield the best performance, while the Na intercalated layered titanate (Na-ILT) demonstrated a superior performance with a specific capacity of 153 mAh g–1 at a current density of 0.1 A g–1. We found that the phenomena can be explained by the high alkaline metal ion concentration and the efficient utilization of the active sites in Na-ILT. The detailed analysis indicates that large intercalating ions like Cs can hamper sodium-ion diffusion although the interlayer spacing is large. Our work suggests that adopting an appropriate interlayer ion species is key to developing highly efficient layered electrode materials for SIBs

An online tool for predicting ovarian responses in unselected patients using dynamic inhibin B and basal antimüllerian hormone levels

BackgroundReliable predictive models for predicting excessive and poor ovarian response in controlled ovarian stimulation (COS) is currently lacking. The dynamic (Δ) inhibin B, which refers to increment of inhibin B responding to exogenous gonadotropin, has been indicated as a potential predictor of ovarian response.ObjectiveTo establish mathematical models to predict ovarian response at the early phase of COS using Δinhibin B and other biomarkers.Materials and methodsProspective cohort study in a tertiary teaching hospital, including 669 cycles underwent standard gonadotropin releasing hormone (GnRH) antagonist ovarian stimulation between April 2020 and September 2020. Early Δinhibin B was defined as an increment in inhibin B from menstrual day 2 to day 6 through to the day of COS. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 5-fold cross-validation was applied to construct ovarian response prediction models. The area under the receiver operating characteristic curve (AUC), prevalence, sensitivity, and specificity were used for evaluating model performance.ResultsEarly Δinhibin B and basal antimüllerian hormone (AMH) levels were the best measures in building models for predicting ovarian hypo- or hyper-responses, with AUCs and ranges of 0.948 (0.887–0.976) and 0.904 (0.836–0.945) in the validation set, respectively. The contribution of the early Δinhibin B was 67.7% in the poor response prediction model and 56.4% in the excessive response prediction model. The basal AMH level contributed 16.0% in the poor response prediction model and 25.0% in the excessive response prediction model. An online website-based tool (http://121.43.113.123:8001/) has been developed to make these complex algorithms available in clinical practice.ConclusionEarly Δinhibin B might be a novel biomarker for predicting ovarian response in IVF cycles. Limiting the two prediction models to the high and the very-low risk groups would achieve satisfactory performances and clinical significance. These novel models might help in counseling patients on their estimated ovarian response and reduce iatrogenic poor or excessive ovarian responses