Two-stage Process Research Articles

American Indian and Alaska Native recruitment strategies for health-related randomized controlled trials: A scoping review.

Significant health disparities exist among American Indian and Alaska Natives (AI/ANs), yet AI/ANs are substantially underrepresented within health-related research, including randomized controlled trials (RCTs). Although research has previously charted representation inequities, there is however a gap in the literature documenting best practice for recruitment techniques of AI/ANs into RCTs. Therefore, the aim of this review was to systematically gather and analyze the published literature to identify common strategies for AI/AN participant recruitment for RCTs in the US. A scoping review methodology was engaged with a systematic search operationalized within relevant databases to February 19, 2022, with an additional updated search being carried out up until January 1, 2023: PubMed, Embase, Web of Science, PsycINFO, CINAHL, and Google Scholar. A two-stage article review process was engaged with double reviewers using Covidence review software. Content analysis was then carried out within the included articles by two reviewers using NVivo software to identify common categories within the data on the topic area. Our review identified forty-one relevant articles with the main categories of recruitment strategies being: 1) recruitment methods for AI/ANs into RCTs (passive advertising recruitment approaches, individual-level recruitment approaches, relational methods of recruitment); 2) recruitment personnel used within RCTs; and, 3) relevant recruitment setting. The majority of the included studies used a culturally relevant intervention, as well as a community-involved approach to operationalizing the research. Increasing AI/AN representation in RCTs is essential for generating evidence-based interventions that effectively address health disparities and improve health outcomes. Researchers and funding agencies should prioritize the engagement, inclusion, and leadership of AI/AN communities throughout the RCT research process. This includes early community involvement in study design, implementation of culturally tailored recruitment strategies, and dissemination of research findings in formats accessible to AI/AN communities.

Optimization of Integrated Energy System Considering Electricity and Hydrogen Coordination in the Context of Carbon Trading

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy’s ability to achieve a low-carbon economy of the scheduling strategy.

Boosted Bio-Jet production: Unveiling the potential of a one-stage concurrent process of deoxygenation/upgrading versus two-stage sequential processes using Pt/Cl-Al2O3@Ni/ZSM-5 catalysts

Bio-jet fuel production from renewable bioresources is an increasingly attractive approach for mitigating the environmental impacts of aviation. Among the different methods available for deriving bio-jet fuel from fatty acids, catalytic hydroprocessing has emerged as a promising and commercially viable technique. Recent studies have focused on identifying suitable catalysts to produce kerosene-type bio-jet fuel, which can serve as blend stocks for aviation fuel after further cracking and isomerization. In this study, a bi-layer catalyst Pt/Cl-Al2O3@Ni/ZSM-5 catalyst was prepared and characterized using various techniques (including XRD, TEM, NH3–TPD, FTIR, H2–TPR, XPS, surface area, and pore size measurements). The thus-prepared catalyst is tested for bio-jet fuel production from Palm kernel oil (PKO). A novel one-stage process yields a high iso-alkane content with a low freezing point of –57 °C. The proposed Pt/Cl-Al2O3@Ni/ZSM-5 bi-layer catalysts in a one-stage process provided satisfactory results, with a conversion rate of 96%. The primary component of the resulting bio-jet fuel was alkanes in the C8-C16 range, with an isomers/normal-alkane (I/N) ratio of 2 and a proportion of 75.6 wt%. Interestingly, the produced bio-jet fuel using the one-stage strategy has significant potential for use as a blend stock to enhance the properties of traditional jet fuels (Jet A and Jet A-1, JP-5, and JP-8).

Two-stage co-pyrolysis of Kraft lignin and palm oil mixture to biofuels: The role of lignin as a methylation agent for methyl ester formation

In this work pyrolysis of palm oil and lignin has been investigated using a two-stage process at 550 °C, with a first step configuration of continuous condensation for vapors and separation of gases, and a second step with distillation of vapors. Experiments were realized as pyrolysis of palm oil, palmitic acid, and Kraft lignin, as well as co-pyrolysis of palm oil/lignin, palmitic acid/lignin and palmitic acid/guaiacol. It has been shown that the addition of lignin improves the quality of palm oil pyrolysis bio-oil, thanks to the conversion of fatty acids coproduct to fatty acid methyl esters (FAME). The production of methyl esters in the reaction environment using palm oil and lignin was studied by conducting experiments with palmitic acid and lignin, as well as palmitic acid and guaiacol (the main product obtained from lignin pyrolysis). The results highlighted that during pyrolysis, the production of FAME is a consequence of a direct esterification reaction on palmitic acid. The formation of FAMEs during pyrolysis presents a promising avenue to optimize the utilization of palm oil by generating FAMEs as supplementary fuel products, Furthermore, it is possible to consider the application of the studied process for the conversion of free fatty acids into FAME.

Development and validation of an interprofessional collaborative educational module on the self-management of foot for individuals with type II diabetes mellitus

Background Insufficient awareness of foot self-care among diabetic individuals results in diabetic foot ulcers. The management of diabetes and diabetic foot ulcers demands a well-coordinated approach that involves multiple healthcare providers (HCPs). The present study aims to develop and validate an interprofessional collaborative (IPC) educational program involving HCPs to efficiently oversee and instruct the public on appropriate strategies for self-managing diabetic foot health. Methods The research group worked on creating an educational module titled ‘An Interprofessional Collaborative Educational Module on Self-Management of Foot for Individuals with Type II Diabetes Mellitus.’ The objective of this module was to promote the adoption of proper practices in self-managing foot health for individuals with type 2 diabetes mellitus. A panel of 13 experts participated in a two-stage validation process using the Delphi method to assess the module and its educational resources. Subsequently, the module was tested on a group of 30 participants, i.e., individuals with diabetes, with its efficacy evaluated through conversation analysis and in-depth interviews. Results The three-month-long module included three sessions • 1: Diabetes and its health implications • 2: Diabetic foot and self-management • 3: Interprofessional education in diabetic foot care The mode of content delivery was via Whatsapp, and the educational resources, in the form of pamphlets, flowcharts, handouts, case-based cartoons, and videos on diabetes, including diabetic foot, its risks, and self-management, were shared regularly. All participating experts consensually validated the module and educational resources. Analysis of in-depth interviews revealed that the module immensely benefitted the participants and helped them improve their knowledge and practices of foot care in diabetes. Conclusions The study concludes that IPC educational modules can enhance adherence to proper diabetic foot care practices, potentially reducing the occurrence of foot ulcers and amputations, and ultimately improving the quality of life for individuals with diabetes.

Characterization of Bioadsorbents from Organic Municipal Waste.

This article describes the production of bioadsorbents coming from seven different kinds of organic waste, produced in huge quantities in households, in a two-stage process. In order to determine the influence of the process parameters of carbonization (I stage) and activation with potassium hydroxide solution (II stage), the following analysis of the physicochemical properties of each sample at each stage processing was performed: base elemental composition, structure properties, surface morphology, thermal stability, crystallinity, and transmittance spectra characteristic bands. There was a lack of research on samples after each stage of waste processing in the literature. Addressing this allowed us to evaluate the transformative potential of each kind of organic waste included in the research and select the best waste for the production of bioadsorbents commonly used in environmental protection. Moreover, the results were compared with the ones in the literature. The utilization of particular kinds of organic waste seems to be especially important taking into account the strategy of waste management and sustainable development.

Lactate-mediated medium-chain fatty acid production from expired dairy and beverage waste

Fruits, vegetables, and dairy products are typically the primary sources of household food waste. Currently, anaerobic digestion is the most used bioprocess for the treatment of food waste with concomitant generation of biogas. However, to achieve a circular carbon economy, the organics in food waste should be converted to new chemicals with higher value than energy. Here we demonstrate the feasibility of medium-chain carboxylic acid (MCCA) production from expired dairy and beverage waste via a chain elongation platform mediated by lactate. In a two-stage fermentation process, the first stage with optimized operational conditions, including varying temperatures and organic loading rates, transformed expired dairy and beverage waste into lactate at a concentration higher than 900 mM C at 43 °C. This lactate was then used to produce >500 mM C caproate and >300 mM C butyrate via microbial chain elongation. Predominantly, lactate-producing microbes such as Lactobacillus and Lacticaseibacillus were regulated by temperature and could be highly enriched under mesophilic conditions in the first-stage reactor. In the second-stage chain elongation reactor, the dominating microbes were primarily from the genera Megasphaera and Caproiciproducens, shaped by varying feed and inoculum sources. Co-occurrence network analysis revealed positive correlations among species from the genera Caproiciproducens, Ruminococcus, and CAG-352, as well as Megasphaera, Bacteroides, and Solobacterium, indicating strong microbial interactions that enhance caproate production. These findings suggest that producing MCCAs from expired dairy and beverage waste via lactate-mediated chain elongation is a viable method for sustainable waste management and could serve as a chemical production platform in the context of building a circular bioeconomy.

Systemic Inflammation Response Index (SIRI) and Monocyte-to-Lymphocyte Ratio (MLR) Are Predictors of Good Outcomes in Surgical Treatment of Periprosthetic Joint Infections of Lower Limbs: A Single-Center Retrospective Analysis.

Periprosthetic joint infection (PJI) is a devastating complication that develops after total joint arthroplasty (TJA), whose incidence is expected to increase over the years. Traditionally, surgical treatment of PJI has been based on algorithms, where early infections are preferably treated with debridement, antibiotics, and implant retention (DAIR) and late infections with two-stage revision surgery. Two-stage revision is considered the "gold standard" for treatment of chronic prosthetic joint infection (PJI) as it enables local delivery of antibiotics, maintenance of limb-length and mobility, and easier reimplantation. Many studies have attempted to identify potential predicting factors for early diagnosis of PJI, but its management remains challenging. In this observational retrospective study, we investigated the potential role of inflammatory blood markers (neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI)) as prognostic factors in two-stage exchange arthroplasty for PJI. A single-center retrospective analysis was conducted, collecting clinical data and laboratory parameters from patients submitted to prosthetic explantation (EP) for chronic PJI. Laboratory parameters (PCR, NLR, MLR, PLR, SIRI, SII, and AISI) were evaluated at the explantation time; at 4, 6, and 8 weeks after surgery; and at reimplantation time. The correlation between laboratory parameters and surgery success was evaluated and defined as infection absence/resolution at the last follow-up. A total of 57 patients with PJI were evaluated (62% males; average age 70 years, SD 12.14). Fifty-three patients with chronic PJI were included. Nine patients underwent DAIR revision surgery and chronic suppressive therapy; two patients died. Nineteen patients completed the two-stage revision process (prosthetic removal, spacer placement, and subsequent replanting). Among them, none showed signs of reinfection or persistence of infection at the last available follow-up. The other twenty-three patients did not replant due to persistent infection: among them, some (the most) underwent spacer retention; others (fewer in number) were submitted to resection arthroplasty and arthrodesis (Girdlestone technique) or chronic suppressive antibiotic therapy; the remaining were, over time, lost to follow-up. Of the patients who concluded the two-stage revision, the ones with high SIRI values (mean 3.08 SD 1.7 and p-value 0.04) and MLR values (mean 0.4 SD 0.2 and p-value 0.02) at the explantation time were associated with a higher probability of infection resolution. Moreover, higher variation in the SIRI and PCR, also defined, respectively, as delta-SIRI (mean -2.3 SD 1.8 and p-value 0.03) and delta-PCR (mean -46 SD 35.7 and p-value 0.03), were associated with favorable outcomes. The results of our study suggest that, in patients with PJI undergoing EP, the SIRI and MLR values and delta-SIRI and delta-PCR values could be predictive of a favorable outcome. The evaluation of these laboratory indices, especially their determination at 4 weeks after removal, could therefore help to determine which patients could be successfully replanted and to identify the best time to replant. More studies analyzing a wider cohort of patients with chronic PJI are needed to validate the promising results of this study.

Ultra-Dense Supported Ruthenium Oxide Clusters via Directed Ion Exchange for Efficient Valorization of 5-Hydroxymethylfurfural.

Maximizing the loadings of active centers without aggregation for a supported catalyst is a grand challenge but essential for achieving high gravimetric catalytic activity, especially toward multi-step reactions. The oxidation of 5-hydroxymethylfurfural (HMF), a key biomass-derived platform molecule, into 2,5-furandicarboxylic acid (FDCA), a promising alternative to polyester monomer, is such a multi-step reaction that involves 6 proton and electron transfers. This process often demands strong alkaline environment but also suffers from the alkali-driven polymerization side-reaction. Meanwhile, neutral media ameliorates the polymerization, but lacks efficient catalyst toward deep oxidation. Herein, we devised a strategy of creating ultra-dense supported Ru oxide clusters via directed ion exchange in a Co hydroxyanion (CoHA) support material. Pyrimidine ligands were first incorporated into the CoHA interlayers, and the subsequent evacuation of pyrimidines created porous channels for the directed ion exchange with the built-in anions in CoHA, which allowed the dense and mono-disperse functionalization of RuCl6 2- anions and their resulting Ru oxide clusters. These ultra-dense Ru oxide clusters not only enable high HMF electrooxidation currents under neutral conditions but also create microscopic channels in-between the clusters for the expedited re-adsorption and oxidation of intermediates toward highly oxidized product, such as 5-formyl-2-furoic acid (FFCA) and FDCA. A two-stage HMF oxidation process, consisting of ambient conversion of HMF into FFCA and FFCA oxidation into FDCA under 60 °C, was eventually developed to first achieve a high FDCA yield of 92.1 % under neutral media with significantly reduced polymerization.

Mapping of Data-Sharing Repositories for Paediatric Clinical Research—A Rapid Review

The reuse of paediatric individual patient data (IPD) from clinical trials (CTs) is essential to overcome specific ethical, regulatory, methodological, and economic issues that hinder the progress of paediatric research. Sharing data through repositories enables the aggregation and dissemination of clinical information, fosters collaboration between researchers, and promotes transparency. This work aims to identify and describe existing data-sharing repositories (DSRs) developed to store, share, and reuse paediatric IPD from CTs. A rapid review of platforms providing access to electronic DSRs was conducted. A two-stage process was used to characterize DSRs: a first step of identification, followed by a second step of analysis using a set of eight purpose-built indicators. From an initial set of forty-five publicly available DSRs, twenty-one DSRs were identified as meeting the eligibility criteria. Only two DSRs were found to be totally focused on the paediatric population. Despite an increased awareness of the importance of data sharing, the results of this study show that paediatrics remains an area in which targeted efforts are still needed. Promoting initiatives to raise awareness of these DSRs and creating ad hoc measures and common standards for the sharing of paediatric CT data could help to bridge this gap in paediatric research.

A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection.

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.

Exploring Allied Health Models of Care for Children with Developmental Health Concerns, Delays, and Disabilities in Rural and Remote Areas: A Systematic Scoping Review.

Access to appropriate healthcare is essential for children's healthy development. This is lacking in rural and remote areas, impacting health outcomes. Despite efforts to improve access for these communities, to date, no review has systematically mapped the literature on allied health models of care for children with developmental needs. This scoping review seeks to address this knowledge gap. Adhering to the PRISMA-ScR and Joanna Briggs Institute guidelines, a systematic search was conducted. A total of 8 databases (from inception to May 2023) and 106 grey literature sources were searched. Two reviewers independently undertook a two-stage screening process. Data were extracted using customised tools and narratively synthesised utilising the Institute of Medicine's quality domains. This review is registered a priori via Open Science Framework. Twenty-five citations were identified within the literature. Varied models of care were reported from five mostly Western countries. Models of care identified in these areas were classified as screening services, role substitution, consultative services, or online-based services. Positive impacts on quality of healthcare were reported across all quality domains (apart from safety) with the domain of effectiveness being the most commonly reported. Multiple models of care are currently in operation for children with developmental needs in rural and remote areas and appear to improve the quality of care. Due to complexities within, and limitations of, the evidence base, it is unclear if one model of care is superior to another. This review provides a basis for further research to explore why some models may be more effective than others.

Spacial regulation of precise aperture in metal–organic frameworks towards ultrahigh-selective mixed matrix membranes for low-energy-consumption C3H6/C3H8 separation

Membrane technology based on metal–organic framework (MOF) with molecular sieving properties is a promising alternative to energy-intensive conventional distillation for C3H6/C3H8 separation. However, precisely regulating the aperture of MOF materials for industrial separation processes with low energy consumption remains a significant challenge. Herein, we introduce a post-synthetic defect exchange, using ortho-trifluoromethylbenzoic acid (o-TBA) in defective UiO-66, resulting in a mixed matrix membrane (MMM) with a C3H6/C3H8 ideal selectivity of ∼ 104 and C3H6 permeability of ∼ 293 Barrer, and exhibiting a C3H6/C3H8 selectivity ∼ 36 with C3H6 permeability of ∼ 188 Barrer for a C3H6/C3H8 (50:50) mixture. Computational simulations confirm impact of o-TBA-UiO on reducing pore sizes and providing moderate propylene affinity. Static adsorption and solution-diffusion experimental results show that the o-TBA-UiO/6FDA-DAM membrane provides fast C3H6 diffusion rates but retards C3H8 diffusion, leading to ultrahigh C3H6/C3H8 selectivity. A large-area membrane of 2400 cm2 was prepared to promote practical applications, mainly because of the considerably enhanced affinity between o-TBA-UiO and 6FDA-DAM. At a feed pressure of 1 bar, the 1812-type membrane module exhibited a modest decline in single gas separation performance with a C3H6 permeability of ∼ 175 Barrer and a C3H6/C3H8 ideal selectivity of ∼ 96. Finally, a potential two-stage membrane process with low energy consumption was designed. This study provides a robust design for MOF-based MMMs with molecular sieving for energy-efficient gas separation.

The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

Approach for contact medical device development via integrated testing, skeletal muscle modeling, and finite element analysis

Development of novel medical devices for the treatment of musculoskeletal pain associated with neuro-muscular trigger points requires a model for relating the mechanical responses of in vivo biological tissues to applied palliative physical pressures and a method to design treatments for optimal effects. It is reasonable to hypothesize that the efficacy of therapeutic treatment is proportional to the maximum tensile strain at trigger point locations. This work presents modeling of the mechanical behavior of biological tissue structures and treatment simulations, supported by indentation experiments and finite element (FE) modeling. The steady-state indentation responses of the tissue structure of the posterior neck were measured with a testing device, and an FE model was constructed using a first-order Ogden hyperelastic material model and calibrated with the experimental data. The error between experimental and FE-generated displacement-load curves was minimized via a two-stage optimization process comprised of an Optimal Latin Hypercube design-of-experiments analysis and a Bayesian optimization loop. The optimized Ogden model had an initial shear modulus (μ) of 5.16 kPa and a deviatoric exponent (α) of 11.90. Another FE model was developed to simulate the deformation of the tissue structures in the posterior neck adjacent to the C3 vertebrae in response to indentation loading, in order to determine the optimal location and angle to apply an indentation force for maximum therapeutic benefit. The optimal location of indentation was determined to be 28° lateral from the sagittal plane along the surface of the skin, measured from the centerline of the spine, at an angle of 8° counterclockwise from the surface normal vector. The optimized spatial orientation of the indentation corresponded to the average of the maximum principal strain across the deep muscle region of the model.

Reconceptualizing selective moral disengagement mechanisms as continuums of moral influence: a theoretical expansion

Abstract Mass communication researchers have applied Bandura’s selective moral disengagement (SMD) concept in diverse contexts to explain audiences’ acceptance of moral violations and reduced condemnation. Recent updates to moral psychology—specifically the application of social intuitionist theories—challenge SMD’s underlying assumptions, necessitating a reconceptualization. In this article, we incorporate modern social intuitionist theories of moral judgment into SMD’s underlying assumptions. We further propose a two-stage moral signal detection process consistent with current decision-making theory in order to explain how SMD reflects a modulation of both moral condemnation and moral commendation. Building on these extensions, we reconceptualize SMD’s eight original mechanisms as expanded continuums of moral influence applicable to diverse forms of moral perceptions and moral judgments. This reconceptualization of SMD removes unnecessary boundary conditions, improves the internal consistency of the theory, and can begin to resolve scholarly disagreements. We conclude the article by providing concrete suggestions for future empirical research.

Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

Understanding the two-stage degradation process of peach gum polysaccharide within ultrasonic field

This study investigated the dynamic degradation process of peach gum polysaccharide (PGPS) within ultrasonic field. The results show that the molecular weight, intrinsic viscosity, and polydispersity of PGPS were rapidly reduced within the initial 30 min and then gradually decreased. The solubility of PGPS was drastically improved from 3.0% to 40.0–42.0% (w/w) after 120 min. The conformation of PGPS changed from an extended chain to a flexible random coil within initial time of ultrasound, and gradually tended to be compact spheres. The apparent viscosity of PGPS significantly decreased after 30 min, and PGPS solution exhibited a near-Newtonian fluid behavior. It is possible that these above changes are a result of random cleavage of the decrosslinking and the backbone of PGPS, resulting in the preservation of its primary structure. The results will provide a fundamental basis for orientation design and process control of ultrasonic degradation of PGPS.

A Decentralized Multi-Agent Path Planning Approach Based on Imitation Learning and Selective Communication

Abstract Multi-agent path planning (MAPP) is crucial for large-scale mobile robot systems to work safely and properly in complex environments. Existing learning-based decentralized MAPP approaches allow each agent to gather information from nearby agents, leading to more efficient coordination among agents. However, these approaches often struggle with reasonably handling local information inputs for each agent, and their communication mechanisms between agents need to be further refined to treat those congested traffic scenarios effectively. To address these issues, we propose a decentralized MAPP approach based on imitation learning and selective communication. Our approach adopts an imitation learning architecture that enables agents to rapidly learn complex behaviors from expert planning experience. The information extraction layer is integrated with convolutional neural network (CNN) and gated recurrent unit (GRU) for capturing features from local field-of-view observations. A two-stage selective communication process based on graph attention neural network (GAT) is developed to reduce the required neighbor agents in inter-agent communication. In addition, an adaptive strategy switching mechanism utilizing local expert-planned paths is designed to support robots to escape from local traps. The effectiveness of our proposed approach is evaluated in simulated grid environments with varying map sizes, obstacle densities, and numbers of agents. Experimental results show that our approach outperforms other decentralized path planning methods in success rate while maintaining the lowest flowtime variation and communication frequency. Furthermore, our approach is computationally efficient and scalable, making it suitable for real-world applications.

Hyperspectral target detection using self-supervised background learning

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels’ contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold.