Limited Capabilities Research Articles

Magnetic recyclable g-C3N4/CuFe2O4/MnO2 activated peroxymonosulfate process via dual Z-scheme heterojunction for photodegradation of ciprofloxacin

Composite heterojunctions effectively enhance the separation efficiency of photoelectrons and holes, thereby activating the peroxymonosulfate (PMS) process to degrade recalcitrant pollutants. Consequently, a g-C3N4/CuFe2O4/MnO2 (CN/CFO/MN) composite with a dual Z-scheme heterojunction was developed for the first time and coupled with PMS for the degradation of ciprofloxacin. CN/CFO/MN exhibited strong binding ability to ciprofloxacin with an adsorption capacity of 38mg/g, reaching adsorption equilibrium within 60min with high efficiency, which is crucial for effective degradation. Additionally, isolated CN/CFO/MN and bare PMS (0.3g/L) demonstrated limited degradation capabilities with ciprofloxacin removal rate of 51% and 41% under illumination, respectively. Notably, the elimination efficiency of ciprofloxacin was enhanced to nearly 100% by integrating CN/CFO/MN photodegradation with PMS activation. Furthermore, compared to pure CN, CFO, MN, and the binary CN/CFO, CN/CFO/MN provided superior photocatalytic degradation, improving the removal efficiency from 45% to 63%. The dual Z-scheme heterojunction in CN/CFO/MN furnishes ample photoelectrons to activate the PMS process for the generation of free radicals. Active species exploration experiments suggest that the CN/CFO/MN-PMS-light method primarily degrades ciprofloxacin by catalyzing the generation of holes, superoxide, hydroxyl and sulfate radicals. The CN/CFO/MN-PMS-light method achieved complete removal of 0.02–0.1g/L ciprofloxacin within 5min in tap, pond and river water systems. Ultra-performance liquid chromatography-mass spectrometry analysis-determined the degradation pathways of ciprofloxacin to involve the piperazine ring, defluorination reaction, and oxidation reaction of quinolone. Assessment via toxicity estimation software indicated that the toxicity of the degradation products was significantly reduced reaching harmless level. This work demonstrates that the CN/CFO/MN-PMS-light method with a dual Z-scheme heterojunction holds considerable potential for environmental purification.

AutoQuo: An Adaptive plan optimizer with reinforcement learning for query plan selection

An efficient execution plan generation is crucial for optimizing database queries. In exploring large table spaces to identify the most optimal table join orders, traditional cost-based optimizers may encounter challenges when confronted with complicated queries. Thus, learning-based optimizers have recently been proposed to leverage past experience and generate high-quality execution plans. However, these optimizers demonstrate limited generalisation capabilities for workloads with diverse distributions.In this study, an adaptive plan selector based on reinforcement learning is proposed to address these issues. However, three challenges remain: (1) How to generate optimal multi-table join orders? We adopt an exploration-exploitation strategy to traverse the vast search space composed of candidate tables, thereby evaluating the significance of each table. Long short-term memory (LSTM) networks are subsequently used to predict the performance of join orders and generate high-quality candidate plans. (2) How to automatically learn new features in novel datasets? We employ the Actor-Critic strategy, which involves jointly cross-training the policy and value networks. By adjusting the parameters based on real feedback obtained from the database, the new datasets are automatically learnt. (3) How to automatically select the best plan? We introduce a constraint-aware optimal plan selection model that captures the relationship between constraints and plans. This model guides the selection of the best plan under constraints of execution time, cardinality, cost, and mean-squared error (MSE). The experimental results on real datasets demonstrated the superiority of the proposed approach over state-of-the-art baselines. Compared with PostgreSQL, we observed a reduction of 29.73% in total latency and 28.36% in tail latency.

Dispersal ability of the predatory mites Blattisocius tarsalis and Cheyletus malaccensis in grain piles and storage facilities

Predatory mites can be released into storage facilities to control various stored product pests. However, their ability to disperse within grain piles and structures (warehouses and processing facilities) has not been well assessed. This study aimed to evaluate two predatory mites, Blattisocius tarsalis and Cheyletus malaccensis, as biological control agents by examining their dispersal abilities in piles of rice grains and storage facilities. The mites were evaluated for their capacity to navigate vertically through polyvinyl chloride (PVC) tubes filled with paddy rice at depths of 40 and 100 cm to determine if they could reach the eggs of various lepidopteran pests at the bottom. Additionally, their dispersal ability over varying time intervals and their preferences concerning light intensity were assessed in 2-metre-long channels coated with rice flour residues. An integrated assessment of their vertical and horizontal dispersal capacities, along with the influence of light, was conducted in a controlled pilot storage room (4 × 3 × 3 m). Blattisocius tarsalis demonstrated impressive dispersal capabilities, reaching pest eggs at vertical depths of 40 and 100 cm through rice and dispersing 2 m horizontally in just 4 h. This proficiency extended to pilot storage room conditions, where it successfully navigated all distances, both horizontally and vertically, showing a preference for navigating away from the light. Cheyletus malaccensis proved to be an effective predator at close range to pest eggs but demonstrated limited dispersal capabilities in complex setups. This study underlines the suitability of B. tarsalis as a biological control agent capable of dispersing in various environments. Conversely, the utility of C. malaccensis may be more specialised and effective within a narrow movement range. These findings highlight the importance of understanding mite dispersal behaviours for optimising pest management strategies and contributing to the more sustainable and effective storage of rice grains.

An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network

<p>Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools.</p>

Sea food by-products valorization for biomedical applications: evaluation of their wound regeneration capabilities in an Ex vivo skin model

IntroductionThe skin is often exposed to harmful stimuli that might compromise its integrity and functionality. After an injury, the skin has a limited capability to restore its complex structure, and in the case of severe skin damage, surgical operations and rapid application of wound dressings are often required to promote optimal wound healing. Nowadays, collagen-based biomaterials are widely used in combination with bioactive molecules able to prevent excessive inflammation and possible infections. In line with a circular economy and blue biotechnology approach, it was recently demonstrated that both collagen and bioactive molecules (i.e., antioxidant compounds) can be sustainably obtained from sea food by-products and effectively used for biomaterial development. Herein, we describe and compare the application of two marine collagen-based wound dressings (CBWDs), produced with materials obtained from sea urchin food waste, for the treatment of skin lesions in a wound healing organ culture (WHOC) model.MethodsThe ex vivo WHOC model was set up starting from rat skin explants and the induced lesions were assigned into three different groups: control (CTRL) group, not treated, marine collagen wound dressing (MCWD) group, and antioxidants-enriched marine collagen wound dressing (A-MCWD) group. After 5 and 10 days, specimens were examined for organ maintenance and assessed for the healing process.ResultsImmunohistochemical results showed that both CBWDs were similarly successful in prolonging skin repair, preserving the epidermal barrier up to 5 days under static culture conditions. Histological and gene expression analysis highlighted that the A-MCWD might support and accelerate skin wound healing by exerting antioxidant activity and counteracting inflammation.DiscussionOverall, these findings underline the potential of sea urchin food waste as a novel resource for the development of functional medical devices for the treatment of skin wounds.

Image Dehazing Technique Based on DenseNet and the Denoising Self-Encoder

The application value of low-quality photos taken in foggy conditions is significantly lower than that of clear images. As a result, restoring the original image information and enhancing the quality of damaged images on cloudy days are crucial. Commonly used deep learning techniques like DehazeNet, AOD-Net, and Li have shown encouraging progress in the study of image dehazing applications. However, these methods suffer from a shallow network structure leading to limited network estimation capability, reliance on atmospheric scattering models to generate the final results that are prone to error accumulation, as well as unstable training and slow convergence. Aiming at these problems, this paper proposes an improved end-to-end convolutional neural network method based on the denoising self-encoder-DenseNet (DAE-DenseNet), where the denoising self-encoder is used as the main body of the network structure, the encoder extracts the features of haze images, the decoder performs the feature reconstruction to recover the image, and the boosting module further performs the feature fusion locally and globally, and finally outputs the dehazed image. Testing the defogging effect in the public dataset, the PSNR index of DAE-DenseNet is 22.60, which is much higher than other methods. Experiments have proved that the dehazing method designed in this paper is better than other algorithms to a certain extent, and there is no color oversaturation or an excessive dehazing phenomenon in the image after dehazing. The dehazing results are the closest to the real image and the viewing experience feels natural and comfortable, with the image dehazing effect being very competitive.

A Prospective Longitudinal Study to Demonstrate the Utility of the Palliative Prognostic Index in Forecasting the Short-term Survival of Patients with Advanced Cancer in India

Objectives: In this study, our primary objectives were to validate the palliative prognostic index (PPI) tool in the context of palliative care for patients with advanced cancer. Specifically, we aimed to assess the accuracy of the PPI in predicting actual survival in these patients through prospective validation. Materials and Methods: To achieve our objectives, we enrolled a cohort of 227 advanced cancer patients receiving palliative care. The study population comprised 132 (58.1%) men and 95 (41.9%) women, with a median age of 52 years (Range: 20–81). Among them, 56 (24.7%) underwent chemotherapy, and 26 (11.5%) underwent palliative radiotherapy. We utilised the PPI score to categorise patients into three prognostic groups: (a) PPI score <4 indicating likely survival of more than 6 weeks; (b) PPI score 4–6 indicating likely survival shorter than 6 weeks; and (c) PPI score >6 indicating likely survival <3 weeks. Results: Through our analysis, we found that the PPI demonstrated limited predictive capabilities, particularly for short-term survival (<3 weeks). The PPI’s performance metrics included a positive predictive value of 45.24%, a negative predictive value of 100%, a sensitivity of 100.00% and a specificity of 88.94%. Conclusion: In conclusion, our study establishes the limited reliability of the PPI in predicting short-term survival (<3 weeks) among patients in palliative care with advanced cancer. These findings underscore the PPI’s potential as a valuable tool for healthcare professionals, aiding in the development of treatment plans and facilitating discussions on end-of-life care options with patients and their families. In addition, the PPI may assist healthcare professionals in identifying individuals who could benefit from more aggressive interventions or those approaching the end of life, thereby guiding the provision of additional support and care.

Semantic Web-Driven Targeted Adversarial Attack on Black Box Automatic Speech Recognition Systems

The susceptibility of Deep Neural Networks (DNNs) to adversarial attacks in Automatic Speech Recognition (ASR) systems has drawn significant attention. Most work focuses on white-box methods, but the assumption of full transparency of model architecture and parameters is unrealistic in real-world scenarios. Although several targeted black-box attack methods have been proposed in recent years, due to the complexity of ASR systems, they primarily rely on query-based approaches with limited search capabilities, leading to low success rates and noticeable noise. To address this, we propose DE-gradient, a new black-box approach using differential evolution (DE), a population-based search algorithm. Inspired by Semantic Web ideas, we introduce modulation noise to preserve semantic coherence while enhancing imperceptibility. In experiments on two public datasets, DE-gradient improved attack success rates by 19% and increased the signal-to-noise ratio (SNR) of silent parts from 27 dB to 54 dB, establishing a strong baseline for evaluating black-box adversarial attacks in ASR systems.

Multi-strategy cooperative enhancement dung beetle optimizer and its application in obstacle avoidance navigation.

Path planning is a crucial component of unmanned driving systems designed to address the challenge of autonomous navigation. This article introduces a novel approach called the multi-strategy cooperative enhanced dung beetle optimization algorithm (RCDBO) and applies it to dispose of path planning issues. Its primary aim is to mitigate the shortcomings of the fundamental dung beetle optimization algorithm (DBO), namely, its tendency to prematurely converge to local optima and its limited global planning capability. Initially, population initialization leverages Bernoulli-based chaotic mapping to enhance diversity and ensure uniform randomness, thereby improving both the quality of initial solutions and optimization efficiency. Subsequently, a random walk strategy is employed to perturb the rolling behavior of the dung beetle population during the initial stage, thus mitigating potential algorithmic local stagnation. Finally, adopting a vertical and horizontal crossover strategy introduces perturbations to the current best value of the dung beetle population, thereby enhancing the DBO method's global optimization capability during the later stages of evolution. The RCDBO method was evaluated against several well-established swarm intelligence algorithms using twelve benchmark test functions, the CEC2021 test suite, the Wilcoxon rank-sum test, and the Friedman test, to rigorously assess its feasibility. Additionally, the effectiveness of each strategy within the RCDBO framework was validated, and the algorithm's capability to achieve an optimal balance between global exploration and local exploitation was systematically analyzed through the CEC2021 test suite. Furthermore, in path planning simulation experiments targeting maps of varying sizes and complexities, the RCDBO algorithm, compared to the basic DBO algorithm, results in shorter planned path lengths. In simpler map environments, it exhibits fewer turning points and smoother paths, indicating that the RCDBO algorithm also possesses certain advantages in addressing path-planning challenges.

A genetic algorithm with tabu search method for 3 dimensional detect profile reconstruction using MFL measurement.

In nondestructive testing, magnetic flux leakage (MFL) inspection is extensively employed for the inversion of pipeline defects. Exact reconstruction of the defect plane with measurements is a pressing issue in the field of MFL detection. This article proposes a method that incorporates a layer-by-layer genetic algorithm with tabu search (GA-TS). During the iterative process, our objective is to utilize a tabu search based on evolutionary algorithms to avoid local optimal solutions, obtain global optimal solutions, and reconstruct defects with enhanced accuracy. In addition to enhancing the accuracy of pipeline defect categorization, our approach has two significant limitations: its limited global search capability and slow convergence rate. Finally, the method is evaluated via experiments in which MFL signals obtained from an experimental platform and simulation signals are used. Practical results and a thorough comparative study of alternative approaches confirm the model's superiority.

A modified artificial electric field algorithm and its application

Abstract As an efficient meta-heuristic technique, artificial electric field algorithm (AEFA) has been extensively applied to tackle various challenging tasks posed by practical scenarios. However, in the classical AEFA, the fitness function has a cumulative effect on the charge, resulting in limited search capability. To address this issue, a modified AEFA (MAEFA) is presented in this paper. More specifically, a novel charge calculation scheme is introduced to overcome the cumulative effect by gradually distinguishing the charges of particles during the evolutionary process. Further, an alternating search strategy is developed to calculate the total electrostatic force, thereby reinforcing the guiding effect of excellent individuals on the entire population. Subsequently, the performance of MAEFA is investigated using 42 well-benchmarked functions, two chaotic time series prediction problems, and two engineering design problems. Experimental results reveal that MAEFA is more competitive in comparison with several established AEFAs and 20 popular meta-heuristic techniques.

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.

Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates the precision of ChatGPT-4 in interpreting multimodal images for paediatric cardiology knowledge assessment, including echocardiograms, angiograms, X-rays, and electrocardiograms. One hundred multiple-choice questions with accompanying images from the textbook Pediatric Cardiology Board Review were randomly selected. The chatbot was prompted to answer these questions with and without the accompanying images. Statistical analysis was done using X2, Fisher's exact, and McNemar tests. Results showed that ChatGPT-4 answered 41% of questions with images correctly, performing best on those with electrocardiograms (54%) and worst on those with angiograms (29%). Without the images, ChatGPT-4's performance was similar at 37% (difference = 4%, 95% confidence interval (CI) -9.4% to 17.2%, p = 0.56). The chatbot performed significantly better when provided the image of an electrocardiogram than without (difference = 18, 95% CI 4.0% to 31.9%, p < 0.04). In cases of incorrect answers, ChatGPT-4 was more inconsistent with an image than without (difference = 21%, 95% CI 3.5% to 36.9%, p < 0.02). In conclusion, ChatGPT-4 performed poorly in answering image-based multiple-choice questions in paediatric cardiology. Its accuracy in answering questions with images was similar to without, indicating limited multimodal image interpretation capabilities. Substantial training is required before clinical integration can be considered. Further research is needed to assess the clinical reasoning skills and progression of ChatGPT in paediatric cardiology for clinical and academic utility.

PUBLIC ANTI-CRISIS MANAGEMENT AND COMMUNICATIONS WHEN PROVIDING EMERGENCY MEDICAL AID IN THE MEDICAL INDUSTRY OF UKRAINE

In this article, the authors focus on several key problems that arise in the process of management and communication in crisis situations related to the provision of emergency medical care. The article examines approaches to managing crisis situations in the sphere of health care of Ukraine in conditions of increased uncertainty and instability. The authors emphasize the importance of effective communication between medical institutions, government structures and the public to ensure prompt response to emergency situations. The article examines various aspects of public crisis management, including planning, organization of resources, and coordination of actions during the provision of emergency medical care. The article analyzes the tools of public management, which allow to minimize various consequences of incoherence of actions between state institutions and medical institutions. Special attention is paid to the importance of prompt decision-making and establishment of communication links between state bodies, medical workers and the population. The possibilities of applying international experience in the field of anti-crisis management and its adaptation to Ukrainian realities are also discussed. The conclusions of the article emphasize the need to reform the health care system in the direction of strengthening its resilience to crises, improving communication mechanisms, and implementing innovative management solutions to increase the effectiveness of providing emergency medical care in emergency situations. The article emphasizes the need for quick decision-making, building effective communication chains between medical staff, patients and other stakeholders. Special attention is paid to the challenges associated with the lack of resources, the limited capabilities of the health care system in the context of crises and the role of leadership in implementing changes. The article also offers recommendations for improving communication processes and management approaches that will contribute to increasing the effectiveness of medical care during emergency situations, particularly in wartime.

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

A Review of Data-Driven Intelligent Monitoring for Geological Drilling Processes

The exploration and development of resources and energy are fundamental to human survival and development, and geological drilling is a key method for deep resource and energy exploration. Intelligent monitoring technology can achieve anomaly detection, fault diagnosis, and fault prediction in the drilling process, which is crucial for ensuring production safety and improving drilling efficiency. The drilling process is characterized by complex geological conditions, variable working conditions, and low information value density, which pose a series of difficulties and challenges for intelligent monitoring. This paper reviews the research progress of the data-driven intelligent monitoring of geological drilling processes, focusing on the above difficulties and challenges. It mainly includes multivariate statistics, machine learning, and multi-model fusion. Multivariate statistical methods can effectively handle and analyze complex geological drilling data, while machine learning methods can efficiently extract key patterns and trends from a large amount of geological drilling data. Multi-model fusion methods, by combining the advantages of the first two methods, enhance the ability to handle complex multivariable and nonlinear problems. This review shows that existing research still faces problems such as limited data processing capabilities and insufficient model generalization capabilities. Improving the efficiency of data processing and the generalization capability of models may be the main research directions in the future.

Rising orthotospovirus incidence in India: Challenges and advances in management

Orthotospoviruses (OV) have emerged as a significant agricultural threat in India, posing a severe risk to critical crops, including tomato, chilli, watermelon and groundnut. This review explores the rising incidence of OV, including Groundnut Bud Necrosis Orthotospovirus (GBNV), Watermelon Bud Necrosis Orthotospovirus (WBNV), Capsicum Chlorosis Orthotospovirus (CaCV), Peanut Yellow Spot Orthotospovirus (PYSV), Iris yellow spot Orthotospovirus (IYSV) and Tomato Spotted Wilt Orthotospovirus (TSWV), along with the challenges in their management. These viruses have led to severe yield losses, sometimes causing complete crop failure due to their wide host range and the polyphagous nature of thrips. This further complicates control efforts by facilitating rapid spread across diverse crops and regions. The review highlights the multifaceted challenges in managing OV and thrips, including the lack of durable host resistance, limited diagnostic capabilities, and difficulties in controlling thrips populations. Current management strategies, including cultural practices, chemical control, biological control and resistant genotype development, have shown limited efficacy in providing long-term solutions. Recent advancements in biotechnological approaches, such as RNA interference, are discussed as promising pathways for improved virus management. The review underscores the need for genome editing techniques, such as CRISPR/Cas9, which offer the capacity to develop virus-resistant plants by targeting essential viral or vector genes to disrupt transmission cycles. Additionally, using nanoparticles for targeted delivery of antiviral compounds and novel detection tools presents another innovative solution to effectively mitigate the impact of OV on Indian agriculture.

Flight-Based Control Allocation: Towards Human–Autonomy Teaming in Air Traffic Control

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.

Molecular Weaving Towards Flexible Covalent Organic Framework Membranes for Efficient Gas Separations.

Covalent organic frameworks (COFs) exhibit considerable potential in gas separations owing to their remarkable stability and tunable pore structures. Nevertheless, their application as gas separation membranes is hindered by limited size-sieving capabilities and poor processability. In this study, we propose a novel molecular weaving strategy that combines hydroxyl polymers and 2D TpPa-SO3H COF nanosheets, achieving high gas separation efficiency. Driven by the strong electrostatic interactions, the hydroxyl chains thread through the COF pores, effectively weaving and assembling the composites to achieve exceptional flexibility and high mechanical strength. The penetrated chains also reduce the effective pore size of COFs, and combined with the "secondary confinement effect" stemming from abundant CO2 sorption sites in the channels, the PVA@TpPa-SO3H membrane demonstrates a remarkable H2 permeance of 1267.3 GPU and an H2/CO2 selectivity of 43, surpassing the 2008 Robson upper bound limit. This facile strategy holds promise for the manufacture of large-area COF-based membranes for small-sized gas separations.

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography

Electrical Tomography (ET) technology is widely used in multiphase flow detection due to its advantages of low cost, visualization, fast response, non-radiation, and non-invasiveness. However, ill-posed solutions lead to low image reconstruction resolution, which limits its practical engineering applications. Although existing interpolation approximation algorithms can alleviate the effects of the ill-posed solutions to some extent, the imaging results remain suboptimal due to the limited approximation capability of these methods. This paper proposes a Bivariate Reproducing Kernel-Based Best Interpolation (BRKBI) method, which offers smaller approximation errors and clearer image reconstruction quality compared to existing methods. The effectiveness of the BRKBI method is validated through theoretical analysis and experimental comparisons.