To be successfully used in daily life situations, exoskeletons should be effective across multimodal scenarios, including walking on various terrains, and transitions between locomotion modes such as walking-to-stop. Correct continuous gait phase estimation is essential for high-level walking assistance control. Despite the impressive advances in gait phase estimation for a variety of locomotion modes, transition gait phase estimation is rarely researched, leading to the jittering of exoskeletons during walking-to-stop transitions. We propose an optimized phase oscillator (PO-opt) that estimates the gait phase correctly during transition gaits in multimodal locomotion, which is beneficial to eliminating the jittering. In the phase plane, a lateral axis extreme difference (LAED) is adopted to classify transition gaits. The threshold of LAED for transition gaits in multimodal locomotion was preliminarily determined by simulation, which was then applied and validated in experiments. Simulation results indicated that a threshold of 15.0 was suitable for transition gaits classification during treadmill walking, free walking, and ramp ascent/descent, while results of the experiment showed that a threshold between 6.5 and 10.5 was applicable for treadmill walking, free walking, and stair ascent/descent. In particular, the jittering elimination rates for 3, 4, and 5 km/h treadmill walking were improved from 29%, 21%, and 4% (PO model) to 100%, respectively, when the threshold of LAED was set at 15.0 in PO-opt model. The results indicated a significant increase in the rate of jittering elimination when the PO-opt model was applied. The model holds great promise in real-world applications for prostheses and other types of exoskeletons.

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9$ \% $ to 62.8$ \% $$ (p<0.001) $; the average shortest path length decreased from 1.53 to 1.14 $ (p<0.001) $; the average betweenness reduced from 0.65 to 0.11$ (p<0.001) $; the average cluster size dropped from 4.05 to 2.72 $ (p=0.004) $; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 $ (p=0.099). $ Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Arazyme, an alkaline metalloprotease, is produced by Serratia proteamaculans, a symbiotic bacterium isolated from the intestinal ecosystem of Nephila clavata. Arazyme is known to play a crucial role in facilitating the digestion process in N. clavata. Recently, there has been increasing interest in exploring invertebrate-associated gut symbionts as a valuable source of novel and biologically active enzymes. Animal husbandry has shown significant interest in this spider-derived bioactive enzyme. This paper aims to provide a comprehensive review of the current understanding and knowledge of arazyme in the context of animal husbandry, offering valuable references for potential applications of this enzyme.

Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

Objectives: In response to the COVID-19 pandemic, primary care swiftly transformed and re-established patient flow in clinics to red, orange, and green zones based on a set of screening criteria. To further manage the influx of suspected COVID-19 patients and their needs safely, a list of surveillance audit criteria was developed to ensure good infection control standards. Methods: The infection control team prepared the surveillance audit criteria based on recommended CDC/WHO guidelines for pandemic preparedness. These criteria were contextualized to the primary-care polyclinic setting. The surveillance audit criteria were grouped according to their category: screening, triage, early recognition and source control, inventory management of personal protective equipment (PPE), infection control measures in the red zone, precautionary measures during collection of nasopharyngeal swabs and environmental cleaning and disinfection for premises in the red, orange, and green zones, respectively. The infection control liaison nurses in each polyclinic were trained to use the checklist to ensure consistency in interpretation of the criteria. Results: Surveillance audits were conducted biweekly in the first 3 months then monthly once the compliance rate was steady at 90%–100% for all categories. The overall average compliance rate since commencing in March 2020 for all polyclinics was sustained at 90%–100%. Common findings included inappropriate use of PPE (eg, self-contamination during removal of gown or wrong sequence of doffing), inadequate ventilation, and inadequate cleaning processes. All findings were corrected immediately, and staff education was provided. Conclusions: Primary care plays an important role during a pandemic. It is essential that both patients and healthcare workers in the primary care setting are protected from infection risk during a pandemic. Having a good surveillance audit process helps ensure that primary care services can continue for the general population. Surveillance is an essential component of the health system’s response to a pandemic.

There is still controversy about optimal dietary iodine intake as the Universal Salt Iodization policy enforcement in China. A modified iodine balance study was thus conducted to explore the suitable iodine intake in Chinese adult males using the iodine overflow hypothesis. In this study, thirty-eight apparently healthy males (19·1 (sd 0·6) years) were recruited and provided with designed diets. After the 14-d iodine depletion, daily iodine intake gradually increased in the 30-d iodine supplementation, consisting of six stages and each of 5 d. All foods and excreta (urine, faeces) were collected to examine daily iodine intake, iodine excretion and the changes of iodine increment in relation to those values at stage 1. The dose–response associations of iodine intake increment with excretion increment were fitted by the mixed effects models, as well as with retention increment. Daily iodine intake and excretion were 16·3 and 54·3 μg/d at stage 1, and iodine intake increment increased from 11·2 μg/d at stage 2 to 118·0 μg/d at stage 6, while excretion increment elevated from 21·5 to 95·0 μg/d. A zero iodine balance was dynamically achieved as 48·0 μg/d of iodine intake. The estimated average requirement and recommended nutrient intake were severally 48·0 and 67·2 μg/d, which could be corresponded to a daily iodine intake of 0·74 and 1·04 μg/kg per d. The results of our study indicate that roughly half of current iodine intakes recommendation could be enough in Chinese adult males, which would be beneficial for the revision of dietary reference intakes.

We present a high-energy, hundred-picosecond (ps) pulsed mid-ultraviolet solid-state laser at 266 nm by a direct second harmonic generation (SHG) in a barium borate (BaB2O4, BBO) nonlinear crystal. The green pump source is a 710 mJ, 330 ps pulsed laser at a wavelength of 532 nm with a repetition rate of 1 Hz. Under a green pump energy of 710 mJ, a maximum output energy of 253.3 mJ at 266 nm is achieved with 250 ps pulse duration resulting in a peak power of more than 1 GW, corresponding to an SHG conversion efficiency of 35.7% from 532 to 266 nm. The experimental data were well consistent with the theoretical prediction. To the best of our knowledge, this laser exhibits both the highest output energy and highest peak power ever achieved in a hundred-ps/ps regime at 266 nm for BBO-SHG.

Coastal eutrophication and hypoxia remain a persistent environmental crisis despite the great efforts to reduce nutrient loading and mitigate associated environmental damages. Symptoms of this crisis have appeared to spread rapidly, reaching developing countries in Asia with emergences in Southern America and Africa. The pace of changes and the underlying drivers remain not so clear. To address the gap, we review the up-to-date status and mechanisms of eutrophication and hypoxia in global coastal oceans, upon which we examine the trajectories of changes over the 40 years or longer in six model coastal systems with varying socio-economic development statuses and different levels and histories of eutrophication. Although these coastal systems share common features of eutrophication, site-specific characteristics are also substantial, depending on the regional environmental setting and level of social-economic development along with policy implementation and management. Nevertheless, ecosystem recovery generally needs greater reduction in pressures compared to that initiated degradation and becomes less feasible to achieve past norms with a longer time anthropogenic pressures on the ecosystems. While the qualitative causality between drivers and consequences is well established, quantitative attribution of these drivers to eutrophication and hypoxia remains difficult especially when we consider the social economic drivers because the changes in coastal ecosystems are subject to multiple influences and the cause–effect relationship is often non-linear. Such relationships are further complicated by climate changes that have been accelerating over the past few decades. The knowledge gaps that limit our quantitative and mechanistic understanding of the human-coastal ocean nexus are identified, which is essential for science-based policy making. Recognizing lessons from past management practices, we advocate for a better, more efficient indexing system of coastal eutrophication and an advanced regional earth system modeling framework with optimal modules of human dimensions to facilitate the development and evaluation of effective policy and restoration actions.

An experimental investigation of the stereocamera's systematic error is carried out to optimize three-dimensional (3-D) dust observation on the HL-2A tokamak. It is found that a larger 3-D region occupied by all calibration points is able to reduce the 3-D reconstruction systematic error of the stereocamera. In addition, the 3-D reconstruction is the most accurate around the region where the calibration points are located. Based on these experimental results, the design of the stereocamera on the HL-2A tokamak is presented, and a set of practical procedures to optimize the 3-D reconstruction accuracy of the stereocamera are proposed.

This article introduces the 2D multilayer Laue lens (MLL) nanofocusing optics recently developed for high-resolution hard X-ray microscopy. The new optics utilized a micro-electro-mechanical-system (MEMS)-based template to accommodate two linear MLL optics in a pre-aligned configuration. Angular misalignment between the two lenses was controlled in tens of millidegrees, and the lateral position error was on a micrometer scale. Using the developed 2D MLLs, an astigmatism-free point focus of approximately 14 nm by 13 nm in horizontal and vertical directions, respectively, at 13.6 keV photon energy was obtained. The success of 2D MLL optics with an approaching 10 nm resolution is a significant step forward for the development of high-resolution hard X-ray microscopy and applications of MLL optics in the hard X-ray community.

In this study, we investigate the differences between two transient, three-dimensional, thermomechanically coupled ice-sheet models, namely, a first-order approximation model (FOM) and a ‘full’ Stokes ice-sheet model (FSM) under the same numerical framework. For all numerical experiments, we take the FSM outputs as the reference values and calculate the mean relative errors in the velocity and temperature fields for the FOM over 100 years. Four different boundary conditions (ice slope, geothermal heat flux, basal topography and basal sliding) are tested, and by changing these parameters, we verify the thermomechanical behavior of the FOM and discover that the velocity and temperature biases of the FOM generally increase with increases in the ice slope, geothermal heat flux, undulation amplitude of the ice base, and with the existence of basal sliding. In addition, the model difference between the FOM and FSM may accumulate over time, and the spatial distribution patterns of the relative velocity and temperature errors are in good agreement.

In this paper, an overall structure with the asymmetric constrained controller is constructed for human–robot interaction in uncertain environments. The control structure consists of two decoupling loops. In the outer loop, a discrete output feedback adaptive dynamics programing (OPFB ADP) algorithm is proposed to deal with the problems of unknown environment dynamic and unobservable environment position. Besides, a discount factor is added to the discrete OPFB ADP algorithm to improve the convergence speed. In the inner loop, a constrained controller is developed on the basis of asymmetric barrier Lyapunov function, and a neural network method is applied to approximate the dynamic characteristics of the uncertain system model. By utilizing this controller, the robot can track the prescribed trajectory precisely within a security boundary. Simulation and experimental results demonstrate the effectiveness of the proposed controller.