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To restore images with clear edge details and no staircase artifacts from degraded versions, this paper incorporates the ℓ 2 plus ℓ 0 data fidelity and non-convex high-order total variation regularizer to establish an optimization model for eliminating mixed Gaussian-impulse noise. Among them, the ℓ 2 fidelity is adopted to suppress Gaussian noise, while the ℓ 0 -norm is more suitable for detecting and removing impulse noise. In addition, the non-convex regularization displays excellent performance in overcoming the staircase effect and preserving edge details. Computationally, by using the iteratively reweighted ℓ 1 algorithm and variable splitting method, this work designs a modified alternating minimization method to solve the optimization problem we construct. In theory, the convergence proof of our resulting algorithm is also presented. Finally, in the experimental section, we conduct extensive numerical experiments on degraded images and compare with other existing techniques. From the intuitive effects and restoration accuracy, it follows that our newly proposed method is effective and competitive for image deblurring and mixed noise removal. [ABSTRACT FROM AUTHOR]

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover. • The work addresses the design of multi-stage processes (MSPs) to improve the efficiency of bioprocesses. • OptMSP: a new Python-based toolbox for identifying MSPs maximizing a given process metrics. • Input: ODE (or analytical) models for each stage (modules). • Output: optimal combination of modules together with switching times. • Application example: lactate production by E. coli with up to six combinable modules. [ABSTRACT FROM AUTHOR]

The Pogo suppression method can be divided into active suppression and passive suppression. For practical engineering, passive suppression is generally achieved through accumulators, but this method sometimes cannot meet the design requirements of liquid rockets with uncertain model parameters and time-varying parameters. As for active suppression methods, state variables are mostly used as feedback thus leading to poor practical feasibility. In this study, an adaptive Pogo active suppression controller design method is proposed where measured acceleration is used as feedback. The eigenspace transformation theory is utilized to design two types of dimensional reduced models: an observation-oriented dimensional reduced model (O-ODRM) and a control-oriented dimensional reduced model(C-ODRM). The former works for observer design, and its state variables can be reconstructed by measuring only the acceleration and can be input to the adaptive controller after matrix operations. The latter is employed for adaptive controller design, and it can effectively process the issues of model parameter uncertainty and time-varying parameters. To demonstrate the effectiveness of the proposed method, a certain type of rocket was selected as the research object for simulation analysis. The simulation results show that the proposed method, which uses measured acceleration as feedback, can achieve Pogo active suppression for liquid rockets with uncertain and time-varying parameters. • An active Pogo suppression method based on acceleration feedback considering practical applications has been proposed. • The proposed method effectively solves the problem of difficult controller design for Pogo systems by measuring structural acceleration. • The proposed dimensionality reduction models for observation and control are the basis for controller design. • The proposed method has excellent adaptability to Pogo systems with parameter uncertainty and time-variation. [ABSTRACT FROM AUTHOR]

In response to the coronavirus disease 2019 (COVID-19) pandemic, many cancer centers and clinics deployed remote work options for their employees. Due to the rapid response needed during this crisis, little to no feedback was obtained from dosimetrists. This study aimed to assess the productivity level and job satisfaction of medical dosimetrists in response to changes in working conditions due to the COVID-19 pandemic. With the assistance from the medical dosimetrists certification board (MDCB), critical data was gathered via an original instrument conducted and distributed by The University of Texas MD Anderson Cancer Center–School of Health Professions to all current practicing certified medical dosimetrists registered with the MDCB. Data were collected using Qualtrics and analyzed with IBM's SPSS. Most (326, 77.7%) participants indicated they transitioned to a version of remote work due to COVID-19. Almost half of the participants (208, 49.5%) reported increased job satisfaction due to the option to work remotely. The participants reported being extremely satisfied with the individual (247, 58.8%) and department (201, 47.9%) productivity levels even after implementing remote work options. Most participants (225, 53.6%), independent of age and years of experience, would prefer to stay in a hybrid role even after COVID-19 abates. These findings suggest that most dosimetrists prefer to perform their job remotely or asynchronously. A one size fits all job model design may make it difficult for organizations to attract, retain, and grow top dosimetrists. Industry leaders and employers may benefit by embracing this change as dosimetrists may value work-set-up flexibility over other employer-based benefits. Further research is needed to assess the unintended consequences of remote work environments in this profession. [ABSTRACT FROM AUTHOR]

Dielectric capacitors as energy storage electronics have drawn much attention due to ultrahigh power densities with quick charging and discharging rates. In this report, A-site Nd-doped (1- x)BiFeO 3 - x BaTiO 3 (x = 0.2–0.45) relaxor ferroelectric ceramics with superior storage efficiencies were prepared with 0.1 wt% MnO 2 additive. Energy-storage efficiency (η) increases from 63.7% to 89% at 190 kV/cm as BaTiO 3 increases accompanied by recoverable energy densities (W rec) in the range of 2.5–2.7 J/cm3. The energy-storage performance persists thermally stable up to 125 °C. The superior storage efficiency is associated with growth of the cubic Pm -3 m symmetry and the core-shell structure with increasing BaTiO 3 content. The formation of nanocluster/nanomosaic structure also plays an essential role as a barrier in suppressing the long-range polarization order. This work provides a design of binary rare-earth doped BiFeO 3 –BaTiO 3 dielectric ceramics for thermally stable and high-efficiency electrical energy storage. [ABSTRACT FROM AUTHOR]

Designing of phosphors with multi-emission peaks that show incongruent response to temperature is crucial to design high-efficiency optic fluorescence temperature sensors. Herein, Mn4+ and Dy3+ were co-doped into host lattice Ca 2 InSbO 6 (CIS) with bimodal emissions for fluorescence intensity ratio (FIR) thermometry. The Dy3+-luminescence is insensitive to temperature for its 4f→4f transitions whereas the Mn4+-luminescence is sensitive for its outer 3d→3d transitions. This feature causes almost unchanged intensity of Dy3+-luminescence with increasing temperature and fast decay of Mn4+-luminescence. Utilized the inconsistent temperature dependency of phosphor CIS:Mn4+/Dy3+, highly absolute sensitivity (S a) of 9.10% K−1 at 573 K and relative sensitivity (S r) of 2.16% K−1 at 498 K are realized. Additionally, the fluorescence lifetime of Mn4+ is also sensitive to the temperature with S a of 0.17% K−1 at 348 K and S r of 1.890 %K−1 at 423 K. This work gives a design idea for researchers in optic temperature sensor field that a phosphor with double activated ions of Dy3+ and Mn4+ is a good choice. Mn4+ and Dy3+ were co-doped into host lattice Ca 2 InSbO 6 with a bimodal emitting and a huge variation of Mn4+ lifetime for high sensitivity luminescent thermometry. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context. These methods utilize fine-label to optimize the model but ignore that fine-trained features are also precious training resources, which can introduce preferable distribution to hard pixels (i.e., misclassified pixels). Inspired by contrastive learning in unsupervised paradigm, we apply the contrastive loss in a supervised manner and re-design the loss function to cast off the stereotype of unsupervised learning (e.g., imbalance of positives and negatives, confusion of anchors computing). To this end, we propose P ositive- N egative E qual contrastive loss (PNE loss), which increases the latent impact of positive embedding on the anchor and treats the positive as well as negative sample pairs equally. The PNE loss can be directly plugged right into existing semantic segmentation frameworks and leads to excellent performance with neglectable extra computational costs. We utilize a number of classic segmentation methods (e.g., DeepLabV3, HRNetV2, OCRNet, UperNet) and backbone (e.g., ResNet, HRNet, Swin Transformer) to conduct comprehensive experiments and achieve state-of-the-art performance on three benchmark datasets (e.g., Cityscapes, COCO-Stuff and ADE20K). Our code will be publicly available at https://github.com/jingw193/PNE_Loss. [ABSTRACT FROM AUTHOR]

Energy process system positively contributes to the energy utilization efficiency, the energy complement, and the construction of a low-carbon sustainable energy system. The multiple energy subsystems are deep interdependent, therefore, significant operational risks exist in the energy process system. To avoid system risk and fulfill operation requirement, we propose the Safety-Index based model predictive control that coordinates the controller design and system safety in this work. The innovation of the method is to integrate the state-space model of process system with advanced controller design while accounting for system safety. Since the state-space model of the energy process system is described by coupled partial differential equations and ordinary differential equations, which leads to difficulties in modeling and solving, the main contribution of this work is to simplify the constrained optimization problem formed by model predictive controller. The paper addresses the compressed air energy storage system as case study. From the numerical simulations of the safety controller performance, it shows that the system safety can be guaranteed by control strategy which realizes the system operation target and reject the system external disturbances, which caused by environmental or operation change. • The paper addresses the modeling and dynamic safety control of compressed air energy storage system. • A control loop for safety operation that consists of controllers, system models, system constraints, and safety index is proposed. [ABSTRACT FROM AUTHOR]

[Display omitted] Amorphous solid dispersions (ASD) have been a successful formulation strategy to overcome the poor aqueous solubility of many novel drugs, but the development of pediatric formulations presents a special challenge due to variable gastrointestinal conditions in children. It was the aim of this work to design and apply a staged biopharmaceutical test protocol for the in vitro assessment of ASD-based pediatric formulations. Ritonavir was used as a model drug with poor aqueous solubility. Based on the commercial ASD powder formulation, a mini-tablet and a conventional tablet formulation were prepared. Drug release from the three formulations was studied in different biorelevant in vitro assays (i.e. MicroDiss, two-stage, transfer model, tiny-TIM) to consider different aspects of human GI physiology. Data from the two-stage and transfer model tests indicated that by controlled disintegration and dissolution excessive primary precipitation can be prevented. However, this advantage of the mini-tablet and tablet formulation did not translate into better performance in tiny-TIM. Here, the in vitro bioaccessibility was comparable for all three formulations. In the future, the staged biopharmaceutical action plan established herein will support the development of ASD-based pediatric formulations by improving the mechanistic understanding so that formulations are developed for which drug release is robust against variable physiological conditions. [ABSTRACT FROM AUTHOR]

The optimization of likelihood based inference through efficient experimental design is considered. A traditional framework for efficiency optimization is to maximize a concave function of expected Fisher information, commonly referred to as optimal design. There exists a substantial amount of literature that suggest that the observed Fisher information is a more relevant measure of the precision of the maximum likelihood estimate. Despite evidence in its favor limited effort has been made to find designs that are optimal with respect to this measure. In this work an adaptive design that incorporates observed Fisher information is proposed. The proposed design is shown to be more efficient than the optimal design with respect to inference. This theoretical result is used to develop a sample size calculation. It is found that fewer samples are required for the proposed design to obtain an equivalent level of precision as the optimal design. • Derivation of observed information adaptive designs for linear models. • Theoretical advantages associated with adaptively incorporating observed information. • Sample size calculation for fixed designs and observed information adaptive designs. [ABSTRACT FROM AUTHOR]

Skeleton-based action sequences are widely used for human behaviour understanding due to their compact characteristics. Most existing work designed Graph Convolutional Networks and integrated multiple input channels rather than the original motion sequence to improve the final performance. However, few of them have been reported on the detailed effects of such multiple input channels. In contrast to them, we systemically study the impact of different input channels and construct a more efficient GCN framework. We have identified the complementary effect between the local frame channel and global sequence channel, which is essential to improve the action recognition accuracy. By coupling local frame and global sequence information with a classical spatial–temporal graph neural network, e.g. MS-G3D, it achieves competitive performance compared with SOTA methods on challenging benchmark datasets. Related code would be available at https://github.com/movearbitrarily/multi-stream. • In this paper, we propose an effective multi-channel mechanism that exploiting global and local complementary information, which could significantly improve GCN models' performance on skeleton based action recognition. • We conduct comprehensive evaluations on two large scale benchmark datasets and achieved a competitive result compared with SOTA approaches. • We show that the performance of GCN models could be improved in a simple way. • Notably, the proposed multi-channel mechanism is generic and robust to seamlessly work with any existing GCN framework, which is essential that the reasonable performance could be achieved in a simple way. [ABSTRACT FROM AUTHOR]

[Display omitted] • Waste collection characteristics impact workers' health and safety. • Case study of a technical intervention in a door-to-door collection scheme. • Development of a theoretical framework for increasing sustainability. • Ergonomics evaluation not widely implemented for waste collection systems. • Ergonomics intervention on wheeled containers reduce the musculoskeletal disorders. Waste collection is the first step of waste management, and its characteristics impact workers' health and safety. Arising out of the challenge for waste collection operators to design sustainable systems of work, the authors review the literature on ergonomics and socio-economic sustainability and design a theoretical framework for assessing the sustainability of waste collection. The framework quantitatively assess the impact of the door-to-door collection system on the health and safety of the workers to provide indications to waste collection operators on how the load carried by workers can be minimised and the economic and social sustainability can be improved. As a case study, this paper investigates the musculoskeletal disorders derived from the manual material handling of waste containers affecting the workers in charge of door-to-door sorted collection of paper waste with the goal of optimizing the workers well-being and overall waste collection system performance The research study was conducted in collaboration with a company which operates in solid waste collection for Italian municipalities. For this purpose, the ergo-quality level of two paper waste collection systems is evaluated. For each system, ten scenarios of door-to-door paper waste collection are considered. The analysis is complemented by an economic analysis, which estimates the costs associated with the collection system under consideration, and a social life-cycle assessment. Results suggest that using 120-litres capacity bins would effectively improve ergonomics and optimise the costs of the investigated activity. More specifically, due to mechanised collection, the more limited number of lifting and carrying operations would expose the workers to lower ergonomic risk. [ABSTRACT FROM AUTHOR]

The aim of this work is to design implicit and semi-implicit high-order well-balanced finite-volume numerical methods for 1D systems of balance laws. The strategy introduced by two of the authors in some previous papers for explicit schemes based on the application of a well-balanced reconstruction operator is applied. The well-balanced property is preserved when quadrature formulas are used to approximate the averages and the integral of the source term in the cells. Concerning the time evolution, this technique is combined with a time discretization method of type RK-IMEX or RK-implicit. The methodology will be applied to several systems of balance laws. [ABSTRACT FROM AUTHOR]

• An ejector that can be adjusted without disassembly was designed. • Saturated/undersaturated regions of the ejector were found during the tuning process. • The concept of operating condition coefficient-Γ is proposed. • The control scheme for variable operating conditions of the ejector was provided. • Effects of AR and NXP on the vortices of the premixing chamber were analyzed. The ejector has a substantial impact on the performance of the solar-driven ejector refrigeration system. This work designs an adjustable ejector without disassembly and performs numerical modeling of the geometric model to enhance the ejector capacity to adapt to various operating conditions; the simulations dependability was also validated using experimental evidence. The findings indicate that within the given operating conditions, the adjustable ejector efficiency increased by an average of 82.4 %; the COP of the system increased by an average of 72.8 %. It is also found that the adjustable ejector has a saturation region, where the ejector performance rapidly declines as the nozzle exit position increases. Under the given operating conditions, the functional relationship between the optimal area ratio and nozzle exit position is obtained. By defining the operating condition coefficient (Γ), the function of the Γ and optimal area ratio is obtained, which provides the adjustable ejector control logic. From the energy perspective, the influence of structural changes on the vortex near the premixing chamber wall is analyzed, and the basic internal reasons for how structural alterations affect the ejector performance are initially revealed. [ABSTRACT FROM AUTHOR]

On-demand food delivery involves transport services based on gig-economy models. Food delivery services rely on motorcycles in many jurisdictions, resulting in safety risks. Motorcycles are generally-two-wheeled and therefore inherently unstable. They also lack rider restraint or roll cage to minimise the consequences of a collision. Given the risks of motorcycle food delivery, there is a need to understand how job design may influence safety behaviour on the roads and regulate this economic activity to minimise potential harmful health consequences on the riders. This study investigated the impact of job demands and resources on food delivery riders' compliance with road safety regulations. The job demands-resources (JD–R) model was used as the theoretical framework for this research. Data were collected using a cross-sectional design involving 550 motorcycle delivery riders in two megacities in Vietnam. A structural equation analysis indicated that job demands (e.g., time pressure, work/life imbalance, working environment) and job resources (e.g., social support, feedback) influence, directly and indirectly, job strain, risk-taking attitude, and road safety compliance. Control variables such as age, gender, and income also influenced road safety compliance. This study has critical implications for the food delivery industry that can help achieve sustainable development goals in the global south. [ABSTRACT FROM AUTHOR]

The changes employees make to everyday tasks, relationships, and perceptions within work boundaries can be described as job crafting. Job crafting provides a mechanism to help employees focus on job redesign from the bottom-up, where the power rests with individual workers. This research employed a mixed methods approach using a convergent parallel design to examine job crafting behavior among academic librarians in the United States before and during the COVID-19 pandemic. Additionally, work engagement was also examined as a potential moderating variable. Respondents self-selected participation (n = 1330) in completing an online survey that included questions from two established instruments (Job Crafting Questionnaire, Utrecht Work Engagement Scale-9) and open-ended questions developed by the researcher. The study found that many academic librarians embodied crafting behaviors, especially task crafting. Moreover, overall job crafting and work engagement resulted in a strong positive relationship. [ABSTRACT FROM AUTHOR]

• Work-related musculoskeletal disorders (WMSDs) represent one of the main causes of injury among healthcare professionals. • The account of interventions for reducing and preventing WMSD injuries and pain in healthcare professionals is still fragmented. • Based on the classification framework of Okaman et al. (2019) was provided a complete description of existing interventions on WMSD injuries and pain among healthcare professionals. • Multifaceted interventions showed a significant reduction of WMSD injuries and pain. Introduction: Work-related musculoskeletal disorders (WMSDs) are among the main causes of injury and pain in healthcare professionals. Previous reviews provided a fragmented view of the interventions available for WMSDs. This review aims to provide a comprehensive description of interventions for preventing and reducing work-related musculoskeletal injuries and/or pain among healthcare professionals, and to assess the methodological quality of studies. Methods: A systematic literature review was performed, based on the Effective Public Health Practice Project process. A comprehensive search was conducted on six peer-reviewed databases and manually. The methodological quality of the studies included was rated as weak, moderate, or strong. The studies were organized based on the 2019 classification of the interventions by Oakman and colleagues. Results: Twenty-seven articles were included reporting individual (n = 4), task-specific (n = 4), work organization and job design (n = 2), work environment (n = 1), and multifactorial (n = 16) interventions. Overall quality rating was strong for 6 studies, moderate for 16, and weak for 5. Individual interventions such as neuromuscular and physical exercise were effective in reducing pain. Task-specific and work organization interventions could prevent certain injuries. Significant reduction of both injuries and pain resulted from multifactorial interventions, which were reported by the majority of strong (n = 5) and moderate (n = 10) quality articles. Conclusions: This review provides healthcare professionals with evidence-based information to plan interventions targeted towards reducing WMSDs. In particular, more efforts are needed to implement and extend effective multifactorial interventions. Moreover, studies about each professional healthcare target group are needed. Practical Application: Our results can guide policy-makers, healthcare managers and professionals to choose the best strategies to prevent and reduce WMSDs and to shape continuous education programs. This study prompts clinicians to develop inter-professional collaborations and to practice physical activities in order to reduce WMSDs. [ABSTRACT FROM AUTHOR]

• Implementation of a simulation including visualizations. • Online study with N = 59 participants. • Results indicate that mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects regarding mode confusion. • A tintable windshield was negatively assessed. Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle's communication. To evaluate potential solutions to this problem, we conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tintable windshield. Our results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tintable windshield, however, was negatively assessed. Thus, our work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion. [ABSTRACT FROM AUTHOR]

A novel algorithm for collision-free navigation of a mobile robot on the campus road with pedestrian roaming is described in this paper. Proposed method uses the RGB-D depth sensor, utilizing optical flow estimation and object detection, to predict pedestrian locations. Given these environmental uncertainties, we present a Velocity Obstacle (VO) algorithm based on mobility rules to calculate the velocity has been presented. It is proposed to use the Markov Decision Process (MDP) for decision-making (to maneuver the robot whenever it approaches the target). The proposed algorithm is a hybrid combination of deep learning and model-based techniques and provides better results in terms of navigation time and collision avoidance success rate than conventional algorithms. The real-time performance of the proposed algorithm is highlighted using a real-world dynamic scenario for an up-to-date kid ride car that has been redesigned to work as an autonomous mobile robot. [ABSTRACT FROM AUTHOR]

The present work focuses on the design of an interval multiobserver for nonlinear systems with internal and external disturbances. The structure of the developed interval multiobserver is based on a discrete uncoupled multimodel. The used multimodel is known for its flexibility in the modeling step, since the state variables of the partial models can evolve in state spaces with different dimensions. Sufficient stability conditions are formulated in terms of linear matrix inequalities (LMIs), and nonnegativity constraints are imposed to ensure the cooperativity dynamic of the observation errors. A numerical example and an experimental validation on an esterification reactor are given to prove the significance of the proposed interval multiobserver. • This paper deal with the design of an interval multiobserver for nonlinear systems with internal and external disturbances. • The structure of the developed interval multiobserver is based on a discrete uncoupled multimodel. • Sufficient stability conditions are formulated in terms of linear matrix inequalities (LMIs), and nonnegativity constraints are imposed in order to ensure the cooperativity dynamic of the observation errors. [ABSTRACT FROM AUTHOR]

Motion blur can easily affect the quality of images. For example, Quick Response (QR) code is hard to be identified with severe motion blur caused by camera shaking or object moving. In this paper, a motion blur QR code identification algorithm based on feature extraction and improved adaptive thresholding is proposed. First, this work designs a feature extraction framework using a deep convolutional network for motion deblurring. The framework consists of a basic end-to-end network for feature extraction, an encoder-decoder structure for increasing training feasibility and several ResBlocks for producing large receptive fields. Then an improved adaptive thresholding method is used to avoid influence caused by uneven illumination. Finally, the proposed algorithm is compared with several recent methods on a dataset including QR code images influenced by both motion blur and uneven illumination. Experimental results demonstrate that the processing time and identification accuracy of the proposed algorithm are improved in executing motion blur QR code identification missions compared with other competing methods. [ABSTRACT FROM AUTHOR]

Performing pattern analysis over the symmetric positive definite (SPD) manifold requires specific mathematical computations, characterizing the non-Euclidian property of the involved data points and learning tasks, such as the image set classification problem. Accompanied with the advanced neural networking techniques, several architectures for processing the SPD matrices have recently been studied to obtain fine-grained structured representations. However, existing approaches are challenged by the diversely changing appearance of the data points, begging the question of how to learn invariant representations for improved performance with supportive theories. Therefore, this paper designs two Riemannian operation modules for SPD manifold neural network. Specifically, a Riemannian batch regularization (RBR) layer is firstly proposed for the purpose of training a discriminative manifold-to-manifold transforming network with a novelly-designed metric learning regularization term. The second module realizes the Riemannian pooling operation with geometric computations on the Riemannian manifolds, notably the Riemannian barycenter, metric learning, and Riemannian optimization. Extensive experiments on five benchmarking datasets show the efficacy of the proposed approach. • This work designs a Riemannian batch regularization module for SPD neural network. • A Riemannian pooling module is designed for SPD neural network in this article. • Extensive experiments certify the efficacy of the two Riemannian operation modules. [ABSTRACT FROM AUTHOR]

Due to their exceptional impact resistance capabilities, density-graded cellular materials have immense potential in applications where crashworthiness requirements are of prime importance. Under impact loading, the deformation in these materials is characterized by compaction front propagation. Previous studies have utilized numerical techniques to solve the equations governing compaction shock propagation for cellular materials with density gradation. In this study, analytical solutions are formulated using compaction front position as the independent variable. The expressions for the velocity of the impinging rigid mass, energy absorption, incident stress, and transmitted stress are determined. The analytical solutions are shown to be in excellent agreement with the cell-based finite element solutions. The effect of density gradient on energy absorption and stresses is studied. The impact resistance factor is employed to assess different density-graded cellular materials for their effectiveness in impact mitigation. This study demonstrates that density-graded cellular materials can offer superior impact protection to objects at both the incident and transmitted ends. Lower density toward the incident end enhances impact resistance to objects located there, and likewise, lower density at the transmitted end offers more impact resistance to objects at that end. • Analytical solutions for shock in density-graded cellular materials are derived. • Initial velocity resulting in consistent compaction front propagation is determined. • The density gradient significantly influences the energy absorption and stress. • An approach based on complete densification yields reliable and consistent results. • The work promotes the design and optimization of density-graded cellular materials. [ABSTRACT FROM AUTHOR]

In places where fissile materials are being handled, the risk of a criticality accident can never be absolutely eliminated. Criticality accidents require specialized dosimetry techniques, which differ markedly from those used in routine radiological protection. This is caused by the fact that one should be able to measure very high doses up to 10 Gy in mixed gamma and neutron fields with very high instantaneous dose rates up to 105 Gy/s and complex energy spectra. The criticality dosimetry system at the Belgian Nuclear Research Centre SCK CEN is accredited according to ISO 17025 by the Belgian accreditation body BELAC and is used to monitor about 4000 nuclear workers. The system is based on a dedicated criticality dosemeter with four activation detectors (gold foils with and without cadmium shielding, an indium foil and sulphur pellets) and an iterative unfolding algorithm starting from a guess neutron fluence energy spectrum. This work describes the design of the SCK CEN criticality dosemeters and the iterative unfolding algorithm. Furthermore, it also discusses two new developments. Firstly, effective neutron reaction cross sections were calculated by MCNP6.2 Monte Carlo radiation transport simulations to be used in the unfolding algorithm. These effective cross sections take into account the effects of the dosemeter, the potential presence of the body and the neutron angle of incidence. These effects were demonstrated to be important, especially for the gold foils and if the dosemeter is worn against the body. Secondly, a Bayesian unfolding algorithm based on a parametric description of the neutron fluence energy spectrum was developed and tested. The results of this new algorithm were in good agreement with the iterative algorithm. The uncertainty assessment was more realistic with the Bayesian algorithm because it includes also the uncertainty associated with the unfolding process. However, for spectra that cannot be well described by the parametric function, the Bayesian algorithm can lead to significant under- or overestimations. In case sufficient information is available, it is better to make use of the iterative algorithm with a realistic guess spectrum to start from. Finally, this work also demonstrated the compliance of the SCK CEN criticality dosimetry system with the IAEA criteria for criticality dosimetry systems. • Demonstration of SCK CEN criticality dosemeter compliance with IAEA criteria. • Development and testing of Bayesian unfolding algorithm. • Effective cross sections for taking into account effect of dosemeter and body. [ABSTRACT FROM AUTHOR]

This paper presents a 39-GHz broadband image-rejection down-conversion mixer for 5G millimeter-wave (mmWave) communication. To achieve a broadband image rejection, this paper proposes an on-chip compact transformer-based image-rejection filter, which exhibits the ability to track the pole and zero frequencies simultaneously. Moreover, the working mechanism and design guideline of the transformer-based image-rejection filter are illustrated with theoretical analysis, simulations and measurements. To enhance the mixer linearity performance, the transconductance stage of the conventional Gilbert mixer is utilized as a current source, and the radio frequency (RF) signal is directly fed into the switching stage. Fabricated in a 65-nm CMOS process, using a single-stage image-rejection filter with a compact area of only 0.013 mm2, the measurement results show that the proposed down-conversion mixer achieves a gain of 0 dB, a 3-dB RF bandwidth from 34 to 42 GHz, an input 1-dB compression point (IP 1dB) of 5.6 dBm, and an image rejection ratio (IRR) of about 25 dBc. [ABSTRACT FROM AUTHOR]

The conventional wisdom in point cloud analysis predominantly explores 3D geometries. It is often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these methods contain a significant number of learnable parameters, resulting in substantial computational costs and imposing memory burdens on CPU/GPU. Moreover, they are primarily tailored for object-level point cloud classification and segmentation tasks, with limited extensions to crucial scene-level applications, such as autonomous driving. To this end, we introduce PointeNet , an e fficient net work designed specifically for point cloud analysis. PointeNet distinguishes itself with its lightweight architecture, low training cost, and plug-and-play capability, while also effectively capturing representative features. The network consists of a Multivariate Geometric Encoding (MGE) module and an optional Distance-aware Semantic Enhancement (DSE) module. MGE employs operations of sampling, grouping, pooling, and multivariate geometric aggregation to lightweightly capture and adaptively aggregate multivariate geometric features, providing a comprehensive depiction of 3D geometries. DSE, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks, achieving notable performance improvements at a minimal cost. Extensive experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis. Notably, PointeNet outperforms PointMLP with significantly fewer parameters on ModelNet40, ScanObjectNN, and ShapeNetPart, and achieves a substantial improvement of over 2% in 3 D A P R 40 for PointRCNN on KITTI with a minimal parameter cost of 1.4 million. Code is publicly available at https://github.com/lipeng-gu/PointeNet. • We propose a representation learning framework for effective and efficient point cloud analysis, dubbed PointeNet. • We propose a lightweight Multivariate Geometric Encoding module to capture diverse 3D geometric features. • We propose an lightweight Distance-aware Semantic Enhancement module tailored for autonomous driving scenarios. [ABSTRACT FROM AUTHOR]

Introducing Laves phase into refractory multi-principal elements alloys (RMPEAs) is a new exploration direction to improve the alloy strength. In this work, we have prepared a series of (TiZr) 65-X Nb 15 Mo 20 Cr X (x = 5,10,15,20) multi-principal elements alloys, by the addition of Cr to introduce the Laves phase for excellent high-temperature performance. The microstructure, phase constituents, and mechanical properties of the refractory high-entropy alloy were studied in the present work. Most of these alloys exhibited typical dendrite morphology, except for the alloy Cr5. Excessive Cr results in the formation of TiZr-enriched zones. In addition, twins can appear in as-cast alloys as a result of a significant lattice mismatch between the matrix and the Laves phase, which is caused by the rapid cooling process during melting. All alloys demonstrate remarkable strength when exposed to high temperatures, with compressive yields surpassing 552 MPa, peaking at 712 MPa at 800 °C, and compressive strains exceeding 30%. Benefiting from the Laves phase, the alloys do not show significant softening, making it possible for this alloy to work stably at 800 °C. Analyses of high-temperature specimens under different degrees of deformation reveal that small-sized Laves phases containing nano-twins precipitate in the matrix. The small-sized Laves phases and nano-twins restrict the movement of dislocations, leading to a large amount of dislocation accumulation, which improves the strength and plasticity of the alloy. Therefore, the current work on designing novel alloys provides new ideas for developing RMPEAs with synergistic strength and plasticity. • The addition of excessive Cr elements resulted in the formation of TiZr-enriched regions in the Cr15 and Cr20. • At 800 °C, the precipitation of small-sized Laves phases and the formation of internal nano-twins restricts the mobility of dislocations, which results in excellent strength-plasticity synergistic effects and work-hardening ability. • The occurrence of twins in cast alloys can be attributed to a notable lattice mismatch between the matrix and the Laves phase rather than a phase transition in the Laves phase. [ABSTRACT FROM AUTHOR]

This work proposes a redesign method for nonlinear observers to reduce the effect of a particular class of output disturbances. Specifically, it is considered a disturbance composed of a term generated from a known system and an unstructured term. The proposed approach does not require modifying the original observer and is based on adding a simple filter that includes an internal model of the disturbance generator. Sufficient conditions for stability of the proposed filter-observer architecture are given. Moreover, the approach is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

Spatiotemporal prediction is a challenging topic because of uncertainty. Existing works attempt to design complicated systems to learn short-term and long-term dynamics. However, these models suffer from heavy computational burden on spatiotemporal data, especially for high-resolution frames. To reduce resource dependence, we propose FastNet, a novel and light encoder–decoder for predictive learning. We stack four ConvLSTM (Convolutional Long Short-Term Memory) based layers to construct a hierarchical framework. Based on this architecture, the feature aggregation module first aligns the temporal context, then decouples different frequency information, next gathers multi-level features and last synthesizes new feature maps alternately. We aggregate various and hierarchical features into predictions bringing two benefits: rich multi-level features and low resource usage. As for the unit blocks, depth-wise separable convolution is used to improve model efficiency and compress model size. Besides, we adopt perceptual loss as the cost function between ground truths and predictions, which helps our model to get higher similarity with true frames. In experiments, we evaluate the performances of FastNet on the MovingMNIST (Mixed National Institute of Standards and Technology) and Radar Echo datasets to verify its effectiveness. The quantitative metrics on the Radar Echo dataset show that FastNet reaches a slight increase in accuracy but up to 84% decline in computation compared with PredRNN-V2. Therefore, our FastNet achieves competitive results with lower resource usage and fewer parameters than the state-of-the-art model. [ABSTRACT FROM AUTHOR]

As a high and uniform wall temperature is desired for thermophotovoltaic applications, a novel micro planar combustor with multi inlets and outlets is designed in this work. Effects of injection strategies on thermal performance of the novel micro planar combustor fueled by hydrogen are analyzed and discussed. It is found that there are two straight flames under Injection Strategy A (Coflow mode), while there are two curved flames under Injection Strategy B and C (Counterflow mode), which is negative to the flame stabilization. In addition, the flame interactions under Injection Strategy B and C are much stronger than that under Injection Strategy A which benefits flame stabilization. Moreover, favorable and unfavorable effects of flame interactions under Injection Strategy B and C on flame stabilization are analyzed. Furthermore, the effects of inlet velocity, hydrogen/air equivalence ratio and solid wall materials on the thermal performance of the micro planar combustor are numerically examined under different injection strategies, the application conditions of the injection strategies are determined. This work will provide us significant suggestions for micro-thermophotovoltaic applications. [Display omitted] • A micro planar combustor with multi inlets and outlets is developed. • Effects of injection strategies on thermal performance are analyzed and discussed. • Favorable and unfavorable effects of flame interactions on flame stabilization are analyzed. • The application conditions of the optimal injection strategy are proposed. [ABSTRACT FROM AUTHOR]

This work aimed to design high-pressure homogenization process able to improve pea protein functionalities. To this purpose, pea protein concentrate suspensions (5% w/w) were homogenised at increasing pressures (up to 150 MPa) and number of passes (up to 3). Samples were analysed for protein structural and conformational modifications, technological functionalities, and in vitro digestibility. As HPH intensity increased, protein unfolding was favoured, leading to solubility and oil holding capacity increase and generating emulsions with rheological properties shifting from liquid-like pseudo-plastic behaviours to gel-like plastic characteristics. The best performing HPH treatment included 3 passes at 70 MPa and leaded to a novel plant-protein based ingredient with improved techno-functionalities and digestibility comparable to original pea proteins. A further increase in HPH intensity at 150 MPa for 3 passes caused protein aggregation with detrimental effects on technological functionalities and digestibility. Results demonstrate the remarkable effect of HPH in tailoring protein functionalities, highlighting the need of a process design step to define HPH process parameters allowing to generate novel high-added value. [ABSTRACT FROM AUTHOR]

[Display omitted] Additive manufacturing (AM) has rapidly changed both large- and small-scale production environments across many industries. By re-envisioning parts from the ground up, not limited to the challenges presented by traditional manufacturing techniques, researchers and engineers have developed new design strategies to solve large-scale materials and design problems worldwide. This is particularly true in the world of alloy design, where new metallic materials have historically been developed through tedious processes and procedures based primarily on casting methodologies. With the onset of directed energy deposition (DED) and powder bed fusion (PBF)-based AM, new alloys can be innovated and evaluated rapidly at a lower cost and considerably shorter lead time than has ever been achieved. This article details the advantages, challenges, applications, and perspectives of alloy design using primarily laser-based AM. It is envisioned that researchers in industry and academia can utilize this work to design new alloys leveraging metallic AM processes for various current and future applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • The BWBC*@Ni-F exhibited excellent H 2 O 2 accumulation of 303 mg L-1. • The DCEF system efficiently degraded contaminants in a wide pH range of 3–11. • The DCEF system displayed high TOC removal efficiency and low energy consumption. • The BCO 2 and C=O groups played a pivotal role in the activation of H 2 O 2. Electro-Fenton (EF) process stands out as an energy-efficient and highly promising advanced oxidation technology for organic contaminants degradation. However, achieving optimal heterogeneous catalytic conditions for simultaneous H 2 O 2 generation and spontaneous Fe2+ regeneration poses difficulties in conventional EF. This is due to the electric potential difference between H 2 O 2 generation and Fe2+ regeneration at a single cathode. Therefore, the boron doped biochar modified dual-cathode EF (DCEF) system was designed in this work to overcome the defect of conventional EF system, which not only demonstrated outstanding accumulation of H 2 O 2 (>300 mg L-1) but also exhibited a remarkable capability of degradation (>90 %). Furthermore, the system could also adapt to a broad pH range of 3–11. The superior doxycycline (DOX) degradation efficiency of DCEF system could be attributed to the dominant role played by C=O and BCO 2 groups in the activation of H 2 O 2 and subsequently generating reactive oxygen species (ROSs), as revealed by XPS analysis and DFT calculation. In addition, the DCEF system achieved an impressive TOC removal efficiency of 70.5 % and a minimal energy consumption (EC) of 0.32 kWh (g TOC)-1. Therefore, this work introduced a DCEF system to address the challenges of conventional EF system, which shed light on the design of wastewater treatment technology and lied the groundwork for a strategy of resource utilization. [ABSTRACT FROM AUTHOR]

Community detection aims to discover hidden communities or groups in complex networks and is essentially unsupervised clustering behavior. However, most of the existing unsupervised methods are designed for homogeneous networks; therefore, they cannot effectively handle heterogeneous structures and rich semantic information. Under such a situation, it is difficult to accurately detect communities in heterogeneous networks that better reflect the real world. Therefore, this work aims to design an unsupervised framework to fuse heterogeneous structure information and interpret the rich semantics of the network in the form of community semantics. Thus, a heterogeneous network community detection method, called HAESF, is introduced. It includes two modules: the Heterogeneous Auto-Encoder (HAE) and the Semantic Factorization (SF) modules. In more detail, the HAE module adopts a hierarchical attention scheme to represent and aggregate the heterogeneous structure of the network. And it proposes the concept of heterogeneous information combinatorial graphs for structural reconstruction to achieve unsupervised detection. Concerning the SF module, it focuses on learning the semantic information in the network from the community point of view. It uses nonnegative matrix factorization to decompose the network features for obtaining community semantics. Once both modules are implemented, the objective of restricting community segmentation based on these semantics is achieved. The constraint is based on community semantic homogeneity to correct inaccurate node delineation. Furthermore, to improve the algorithm efficiency, a unified framework is designed to optimize the HAE and SF modules jointly. Within this new framework, the SF loss is innovatively used as a judgmental loss for selective segmentation optimizations, helping to obtain more reliable community detection results. As for the results, extensive experiments are performed on three public datasets. The findings show that HAESF outperforms the other popular unsupervised methods, where the composite score of HAESF is 11.73% ahead of the next best, demonstrating the proposed method's effectiveness. • HAESF is an unsupervised community detection framework for heterogeneous networks. • HAE uses two-layer attention and HICG to achieve unsupervised detection. • SF uses semantic information to constrain community detection. • Performance is improved by judgmental loss and segmentation optimizations. [ABSTRACT FROM AUTHOR]

[Display omitted] In this work the design and synthesis of a chemical tool compound was carried out to inform studies toward selective and potent inhibitors of lymphocyte specific kinase, a member of the Src family of kinases (SFK), for the treatment of T-cell-mediated diseases. Synthesis of a thiophene-based analogue of the pan-kinase inhibitor dasatinib was achieved using a key Buchwald coupling in a highly convergent approach that avoided isolation of unstable intermediates that derailed other approaches. Biological testing showed abrogation of activity against the SFKs in biochemical assays together with an inability to suppress downstream signalling in activated Jurkat T-cells, when compared to dasatinib. Compound 8 was also found to be 4-fold less toxic than dasatinib in a cell viability assay. Non-kinase targets of kinase inhibitors have been shown to contribute to both desired and undesired activities of clinical agents, thus thiophene analogue 8 may be used as a matched pair with dasatinib in future mechanistic studies to assess the contribution of targets outside the SFK to observed activity. [ABSTRACT FROM AUTHOR]

Data centers (DCs) are gradually becoming one of the world's largest energy consumers. Owing to their spatial-temporal transferable characteristics, DCs show great potential in demand response (DR) programs. Considering the difference in task delay sensitivity (TDS), an elaborate DR model of DCs is proposed in this work. Furthermore, to ensure the TDS requirement and relieve the data transmission pressure, TDS-based unit migration cost matrices are designed in this work. Given that most DR dispatch for DCs is conducted relying on a central dispatching center, which imposes critical requests for the reliability of the dispatching system. In this work, we establish a novel 4-level joint optimal dispatch model for the DC operator, where dispatching centers installed at each level integrate district information from bottom to top and conduct optimal task dispatch from top to bottom. Moreover, an innovative dispatch autonomy mechanism is designed to realize district autonomy in the case of dispatching center breakdowns. Finally, simulation results prove that the proposed 4-level joint optimal dispatch model has a positive effect on enhancing renewable energy accommodation and improving the economic benefits of the DC operator. More specifically, by applying the proposed 4-level joint optimal dispatch model, the DCO's total cost is decreased by 32.25% in comparison with the result of no DR participation. It also proves that our designed dispatch autonomy mechanism can achieve district autonomy under the breakdown of dispatching centers at any level. • Propose a novel 4-layer joint optimal dispatch model for data center operator • Establish an elaborate demand response model of data centers based on task delay sensitivity • Design an innovative dispatch autonomy mechanism to realize district autonomy [ABSTRACT FROM AUTHOR]

[Display omitted] • Three novel NIR-II photothermal conversion agents (PCAs) were synthesized. • PCAs exhibit good optical properties. • NIR-II photothermal therapy (PTT). Second near-infrared (NIR-II,1000 ∼ 1700 nm) therapeutic window presents an increased tissue penetration and elevated maximal permissible exposure in the application of photothermal therapy (PTT). However, the lack of NIR-II photothermal conversion agents (PCAs) limit their further development. In this work, we rationally designed and successfully developed three novel indolium-like heptamethine cyanine dyes (NFs) by installing N,N- diethylamino on the terminal ends of a conjugated polyene backbone and replacing the middle chlorine atom with o- mercapto benzoic acid and p- mercapto benzoic acid. Notably, NF2 with stronger rotating group encapsulated in organic nanoparticles (NF2 NPs) exhibited high photothermal conversion efficiency (PCE), which could come up to (61.3 %). Then we conducted serial experiments to further investigate PTT capability of NF2 NPs 4 T1 cell line and nude mice bearing 4 T1 tumor. As expected, the resulting NF2 NPs presented the excellent photothermal conversion ability and superb PTT effect both in vivo and in vitro. This study will inspire more work for future design and clinical applications of NIR-II therapeutic agents. [ABSTRACT FROM AUTHOR]

The study of nano-heterostructures as light-capturing system applied in photocatalytic water splitting is a current research hotspot. In this study, a simple in situ anion exchange method was proposed for the synthesis of homologous heterostructure CdSe/CdS nanoflowers. Series of CdSe/CdS composites were obtained by controlling the amounts of Se powder. The optimum ratio 5 wt% CdSe loading on CdS nanosheets achieved the maximum H 2 production rate of 16.03 mmol g−1 h−1, which was 1.5 times higher than that of pure CdS. Mechanism study with UV–vis DRS, and photoluminescence revealed the excellent photocatalytic property was attributed to the expansion the absorption range of CdS with the introduction of CdSe, the construction of the Z-scheme heterojunction between CdS and CdSe, and the rich catalytic reaction interfacial sites provided by the hierarchical structure with 2D nanosheets and 3D nanoflowers. This work provided the design and synthesis of homologous heterostructure photocatalysts for high efficiency hydrogen production. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, the synchronization control of discrete-time complex dynamical networks (CDNs) is studied under the framework of intermittent networked communication. To reduce transmission frequency, a novel aperiodic intermittent event-triggered mechanism (ETM) with discontinuous characteristics is proposed. Under this mechanism, the controller only works within predetermined working intervals, and whether the control signal is updated depends on a preset triggering condition. In addition, an exponential term is designed in this mechanism to achieve integration and correlation between the intermittent mechanism and the ETM, which depends on the initial instants of the working intervals. Compared with the existing ETMs, this mechanism saves communication and computing resources effectively. To improve flexibility in the design of working/rest intervals, the concepts of average working time ratio (AWTR) and average working period (AWP) are introduced to describe intermittent aperiodic performance. A sufficient criterion for the synchronization of discrete-time CDNs is obtained, and the feasible domain of AWTR is calculated. Finally, a simulation example is given to verify the effectiveness of the proposed method. • An aperiodic intermittent event-triggered scheme is proposed primarily. • A working-interval-dependent exponential term is embedded in the intermittent ETM. • The average working time ratio is introduced to achieve complete aperiodicity. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) stability is an indispensable selection component to operate the device in RF range. An unstable device is susceptible to oscillating for any passive termination networks at input/output ports. In this article, the RF stability performance of junctionless stacked nanosheet FET (JL-SNSHFET) is deliberated by investigating the impact of its geometrical parameters, and ambient temperature (T A) on the performance of transit frequency (f T), maximum oscillation frequency (f max), stability (K), forward current gain (h 21) and unilateral gain (U) through extensive technology computer-aided design (TCAD) numerical simulations. It is discerned that the variations in nanosheet width (W NSH), thickness (t NSH), oxide thickness (t ox), spacer thickness (t sp), and ambient temperature exert substantial influence on JL-SNSHFET RF stability performance. The increase in W NSH , t NSH , and T A confer the stability of JL-SNSHFET at low frequency due to enhanced effective width. Whereas, the increase in t ox and t sp defers the stability of JL-SNSHFET to extended frequency. The proposed optimized device shows an improved f T of 200 GHz and f max of 467 GHz. It is unconditionally stable for the frequencies above 175 GHz, without any auxiliary network for stabilization. The results presented in this work provide design guidelines for JL-SNSHFET for RF applications. [ABSTRACT FROM AUTHOR]

CoSb 3 -based materials have surfaced as promising contenders for mid-temperature thermoelectric (TE) applications owing to their low cost and stability. The current work focuses on designing three different synthesis routes and their comparison to find out the most suitable route for synthesizing pristine CoSb 3. The designed synthesis routes are direct spark plasma sintering (SPS), conventional furnace melting followed by SPS, and arc melting followed by SPS. Based on structural and transport characterizations, the synthesis route of arc melting followed by SPS was handpicked amongst the three different synthesis routes. The key benefit of this synthesis route is the massive reduction in synthesis time and efforts without compromising the TE performance. Finally, to analyze the effectiveness of the optimized synthesis route, Ni-doped CoSb 3 alloys were also synthesized, and the properties were studied. The effect of "Ni doping" on TE properties of CoSb 3 alloys, resulted in n-type conduction, and an enhanced figure-of-merit (ZT) ∼0.61 at around 870 K for optimized composition Ni 0.07 Co 0.93 Sb 3. • Comparison of transport properties achieved by three different synthesis routes. • Arc melting followed by SPS delivers optimized ZT values and ensures a massive reduction in synthesis time and effort. • Enhanced ZT ∼0.61 was achieved at 870 K for optimized composition Ni 0.07 Co 0.93 Sb 3. • Electrical and thermal transport were explained using appropriate models. [ABSTRACT FROM AUTHOR]

This paper propose an inverted sequential load-modulated balanced amplifier (ISLMBA) for extending the dynamic power range of a power amplifier (PA) over a wide bandwidth. The proposed ISLMBA consists of a control amplifier (CA) and a balanced PA (BPA) pair. A composited impedance inverter is inserted between each BPA transistor and the output coupler. The composited impedance inverter is composed by the intrinsic elements and output matching network (OMN) of the BPA transistor. In this way, the intrinsic elements can be fully absorbed into the impedance inverter over a wide bandwidth. Meanwhile, the load modulation trajectories of the BPA transistors can be optimized over a wide bandwidth by tuning the characteristic impedance of the impedance inverter. The theoretical current, voltage and load-modulation profiles of the ISLMBA are comprehensively analyzed. It is illustrated that the proposed ISLMBA could maintain high efficiency at deep output back-off power level over a wide bandwidth. Moreover, this work designs and fabricates an ISLMBA operating over 1.3–2.55 GHz to verify the proposed PA architecture. The fabricated ISLMBA exhibits a saturation drain efficiency (DE) of 54.5%–73.8% and a saturation output power of 42.9–45.9 dBm. At the same time, the fabricated ISLMBA achieves a 10 dB back-off DE of 42.1%–63.9% over the frequency band of interest. When the fabricated ISLMBA is stimulated by a 20 MHz wideband signal with a peak-to-average power ratio (PAPR) of 10.2 dB at 2.0 GHz, the measured adjacent channel leakage ratios (after predistortion) are −47.7 and −48.8 dBc at the lower and upper bands, respectively. [ABSTRACT FROM AUTHOR]

In this work we design and analyse a Discrete de Rham (DDR) method for the incompressible Navier–Stokes equations. Our focus is, more specifically, on the SDDR variant, where a reduction in the number of unknowns is obtained using serendipity techniques. The main features of the DDR approach are the support of general meshes and arbitrary approximation orders. The method we develop is based on the curl–curl formulation of the momentum equation and, through compatibility with the Helmholtz–Hodge decomposition, delivers pressure-robust error estimates for the velocity. It also enables non-standard boundary conditions, such as imposing the value of the pressure on the boundary. In-depth numerical validation on a complete panel of tests including general polyhedral meshes is provided. The paper also contains an appendix where bounds on DDR potential reconstructions and differential operators are proved in the more general framework of Polytopal Exterior Calculus. [ABSTRACT FROM AUTHOR]

The monitoring of the neuromuscular blockade is critical for patient's safety during and after surgery. The monitoring of neuromuscular blockade often requires the use of Train of Four (TOF) technique. During a TOF test two electrodes are attached to the ulnar nerve, and a series of four electric pulses are applied. The electrical stimulation causes the thumb to twitch, and the amount of twitch varies depending on the amount of neuromuscular blockade in patient's system. Current medical devices used to assist anesthesiologists to perform TOF monitoring often require free hand movement and do not provide accurate or reliable results. The goal of this work is to design, prototype and test a new medical device that provides reliable TOF results when thumb movement is restricted. A medical device that uses a pressurized catheter balloon to detect the response thumb twitch of the TOF test is created. An analytical model, numerical study, and mechanical finger testing were employed to create an optimum design. The design is tested through a pilot human subjects study. No significant correlation is reported with subjects' properties, including hand size. [ABSTRACT FROM AUTHOR]

Recognition of passable areas of mine roads based on vehicle perspective is crucial for autonomous vehicles to drive in unmanned open-pit mine scenes. In the past few years, deep learning-based passable area recognition methods have been proven feasible. However, these works have mainly focused on structured urban road environments. Few works designed for mine road region detection due to dataset scarcity. In this article, we propose the mine road scapes segmentation dataset, which is collected from open-pit mine road environments. The dataset is collected from different mine areas, covers different daytime and dusk periods, and annotates the road and non-road areas in detail. Besides, a novel transformer-based road segmentation network is proposed, which is designed for the mine road characteristics of texture and distribution. The segmentation network is composed of an improved transformer feature extraction backbone, which can simultaneously aggregate global context and local detail information, and realize pixel-level recognition. We solve the problem of mine road segmentation benchmark under vehicle perspective. Moreover, by comparing the results of current mainstream methods on the mine road dataset, our method obtains state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Economic and environmental challenges are driving development towards more efficient and lighter materials. Polyethylene (PE) and polyamide (PA6) are among the most used polymers and their assembly in multilayer make them efficient protective materials. The aim of this work is to design new multilayer composite films based on polyethylene and polyamide with high barrier properties. A coextrusion process with layer multiplier elements (LME) made it possible to carry out 100 μm-thick multilayer films containing 5 up to 1025 layers. Loaded PE/PA6 multilayer films were made by incorporating Cloisite particles (organo-modified montmorillonite – C30B) at 5 wt% into the PA6 layers. For comparison PA6 films with and without fillers were also made by using the same coextrusion process. The structural and thermal properties of all multilayer films were correlated with the water and gas barrier properties. A good dispersion of exfoliated C30B in the PA6 phase was observed even for the thinnest confined layers of PA6 in PE/PA6 multilayers (∼90 nm). We showed the complexity of the multinanolayer structures involving interphases as well as the complexity of the transfer mechanisms. The serial model used for predicting permeability highlighted some significant improvements of the gas barrier properties of confined PA6 layers. The barrier effect on all the multilayer films was, however, limited due to the "on-edge" orientation of the crystalline phases and structural defects induced during the coextrusion process. Despite this, the confinement of nanofillers in PA6 multilayers and in PA6 layers of PE/PA6 multilayers allowed to increase the barrier properties of multilayers. [ABSTRACT FROM AUTHOR]

In this research, WS 2 nanomaterials were synthesized by a hydrothermal method at first. Then, partially oxidized WS 2 through calcination to obtain WO 3 /WS 2 binary materials via calcination. Among different calcination temperatures (400 °C, 350 °C and 300 °C), the WO 3 /WS 2 with the best performance calcined at 300 °C was selected to load Pd on the surface. Particularly, the 2 at% Pd-WO 3 /WS 2 exhibited the better hydrogen sensitivity compared to the load of Pd was 5 at%, displaying an ultrahigh response of 4227.35 at 125 ℃ with a faster response/recovery time of 1/25 s to 1000 ppm H 2. The property of the ternary nanocomposites was superior to previously reported binary WO 3 materials due to the synergistic effect of its unique microstructure, the formation of p-n-p heterojunction, and the electronic sensitization of palladium. This work designed a novel ternary material applied to rapid hydrogen detection for the first time. • Pd-WO 3 /WS 2 ternary nanocomposites were for the first time applied for H 2 detection. • Ultrahigh response (4227.35) for rapid H 2 detection based on 2 at% Pd-WO 3 /WS 2 sensor. • 2 at% Pd-WO 3 /WS 2 nanomaterials with relatively fast response/recovery time (1/25 s). [ABSTRACT FROM AUTHOR]

Presented here is the successful strategy of synthesis and unusual structural diversity of four new zinc tetrahedral tetrazolate frameworks (Zn-TTFs) depended on the rotation of carboxyl group. [Display omitted] • This work flourishes the design ideas of modulating tetrahedral tetrazolate structure with self-adjusting coordination of carboxylate. • One tetrahedral tetrazolate framework demonstrates super acid and alkali resistance. • The synthesized anionic framework has the potential to be used as fluorescent base material. • The tetrahedral tetrazolate framework shows microporous structure. Herein, a series of zinc tetrahedral tetrazolate frameworks (Zn-TTFs) are presented. The reactions of Zn(NO 3) 2 ·6H 2 O with 5-amino-1H-tetrazole and pyromellitic acid assemble four different Zn-tetrazolate porous networks, namely [Zn 2 (atz) 3 (PMA)]·guest (1), [Zn 2 (atz) 3.5 (PMA) 0.25 ]·guest (2), [Zn (atz) 1.5 (PMA) 0.25 ]·guest (3), [Zn 3 (atz) 3 (PMA)(Thiourea)(CH 3 OH)]·guest (4), (atz = 5-amino-1H-tetrazole, PMA = pyromellitic acid), whose results demonstrate that rotation of carboxyl group can generate the structural diversity. Interestingly, compound 3 shows a zeolite frl net, while compound 4 exhibits simultaneously super acid and alkali resistance. The luminescent properties based on compound 3 have been studied in the solid state at room temperature, and all of them display lanthanide-based luminescence. [ABSTRACT FROM AUTHOR]

Depression is now a prevalent mental illness and multimodal data-based depression detection is an essential topic of research. Internet of Medical Things devices can provide data resources such as text, audio, and vision, which is valuable for depression detection. Moreover, previous studies have concentrated on using single characteristics of each modality, such as low-dimensional pre-designed features and high-level deep representation, which cannot completely capture the emotional information included in the data. Against this background, we design an intra-modal and inter-modal fusion framework called IIFDD for Corpus-based depression detection. Intra-modal fusion module is designed to integrate low-dimensional pre-designed features and high-dimension deep representation from the same modality for better learning of the semantics information. Then, the inter-modal fusion module is proposed to fuse features from different modalities with attention mechanisms and use the fused result to complete the depression classification. Experiments on two Chinese depression corpus datasets with acoustics, textual, and visual features show that IIFDD can achieve state-of-the-art performance for depression detection. [Display omitted] An illustrative example of depression detection based on Internet of Medical Things (IoMT)-enabled multi-modal information fusion. Internet of Medical Things devices can provide data resources such as text, audio, and vision, which is valuable for depression detection. This work designs an intra-modal and inter-modal fusion framework called IIFDD for Corpus-based depression detection. Intra-modal fusion module is designed to integrate low-dimensional pre-designed features and high-dimension deep representations from the same modality for better learning of semantic information. Then, the inter-modal fusion module is proposed to fuse features from different modalities with attention mechanisms and use the fused result to complete depression classification. • Proposed Intra-modal fusion can bring improvement to depression detection. • The designed improved-MulT performs better than the original MulT. • Considering intra-modal and inter-modal fusion together achieves best performance. [ABSTRACT FROM AUTHOR]