Single Network Research Articles

SITF: A Self-Supervised Iterative Training Framework for Point Cloud Denoising

Despite existing supervised point cloud denoising methods having made great progress, they require paired ideal noisy-clean datasets for training which is expensive and impractical in real-world applications. Moreover, they may perform the denoising process multiple times with fixed network parameters for better denoising results at test time. To address above issues, this paper proposes a self-supervised iterative training framework (SITF) for point cloud denoising, which only requires single noisy point clouds and a noise model. Given an off-the-shelf denoising network and original noisy point clouds, firstly, an intermediate noisier-noisy dataset is created by adding additional noises from the known noise model to noisy point clouds (i.e. learning targets). Secondly, after training on the noisier-noisy dataset, the denoising network is employed to denoise the original noisy point clouds to obtain the learning targets for the next iteration. The above two steps are iteratively and alternatively performed to get a better and better trained denoising network. Furthermore, to get better learning targets for the next round, this paper also proposes a novel iterative denoising network (IDN) architecture of stacked source attention denoising modules. The IDN explicitly models the iterative denoising process internally within a single network via reforming the given denoising network. Experimental results show that existing supervised networks trained through the SITF can achieve competitive denoising results and even outperform supervised networks under high noise conditions. The source code can be found at: https://github.com/VCG-NJUST/SITF.

Galactica’s dis-assemblage: Meta’s beta and the omega of post-human science

AbstractReleased mid-November 2022, Galactica is a set of six large language models (LLMs) of different sizes (from 125 M to 120B parameters) designed by Meta AI to achieve the ultimate ambition of “a single neural network for powering scientific tasks”, according to its accompanying whitepaper. It aims to carry out knowledge-intensive tasks, such as publication summarization, information ordering and protein annotation. However, just a few days after the release, Meta had to pull back the demo due to the strong hallucinatory tendencies or underwhelming performances of the model. This article aims to study, through a critical threefold argument, the potential impacts of LLMs once deployed in the scientific value chain. Our first argument is a technical one. By examining the technicity of Galactica, it is possible to explain the descripancies between its promotional corporate discourse and abysmal outputs. Second, by going back to debates in both computer science and computational philosophy on the automation of abduction, we argue from the epistemological front that LLMs indeed cannot produce strong abductions and, therefore, claims about the automation of hypothesis generation remains chambering. Finally, our third argument is a sociological one. By conceptualizing the scientific field through Nancy Katherine Hayles’ cognitive assemblage theory, we aim to outline the potential steering of science by LLMs, mainly through information ordering. The core of our argument rests on the assertion that excessive control on information risks contravening a certain serendipitous aspect inherent to scientific discoveries.

Conditional Adversarial Motion Priors by a Novel Retargeting Method for Versatile Humanoid Robot Control

ABSTRACTSignificant advancements have been made in the field of humanoid robot, particularly in walking control strategies. However, achieving straight‐legged walking remains a challenge. Both the traditional model‐based and the learning‐based control methods confront with difficulties in achieving natural humanoid gait feature. To address this issue, a general motion retargeting method is developed and also evaluated for humanoid robots with different structure, size and degrees of freedom. Moreover, a conditional adversarial motion priors method is proposed based on reinforcement learning and validated on the humanoid robot GTX‐III. Through various motion segments from the motion capture database, it is shown that this method can successfully enable the humanoid robot to perform straight‐legged walking with flexible and natural transitions between different gaits within a single discriminator network.

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

S2VSNet: Single stage V-shaped network for image deraining & dehazing

Producing high-quality, noise-free images from noisy or hazy inputs relies on essential tasks such as single image deraining and dehazing. In many advanced multi-stage networks, there is often an imbalance in contextual information, leading to increased complexity. To address these challenges, we propose a simplified method inspired by a U-Net structure, resulting in the “Single-Stage V-Shaped Network” (S2VSNet), capable of handling both deraining and dehazing tasks. A key innovation in our approach is the introduction of a Feature Fusion Module (FFM), which facilitates the sharing of information across multiple scales and hierarchical layers within the encoder-decoder structure. As the network progresses towards deeper layers, the FFM gradually integrates insights from higher levels, ensuring that spatial details are preserved while contextual feature maps are balanced. This integration enhances the image processing capability, producing noise-free, high-quality outputs. To maintain efficiency and reduce system complexity, we replaced or removed several non-essential non-linear activation functions, opting instead for simple multiplication operations. Additionally, we introduced a “Multi-Head Attention Integrated Module” (MHAIM) as an intermediary layer between encoder-decoder levels. This module addresses the limited receptive fields of traditional Convolutional Neural Networks (CNNs), allowing for the capture of more comprehensive feature-map information. Our focus on deraining and dehazing led to extensive experiments on a wide range of synthetic and real-world datasets. To further validate the robustness of our network, we implemented S2VSNet on a low-end edge device, achieving deraining in 2.46 seconds.

State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary. The efficiency of deep learning neural networks such as multi-layer perceptron (MLP) and Legendre neural network (LeNN) depends on the position of the measurements and the number of neural networks applied. Determining the applicable number of neural networks to ensure high estimation accuracy plays an important role in the estimation process. This aspect is addressed in this study, where multiple neural networks are used to improve performance compared to a single neural network. The results obtained indicate that determining the applicable number of neural networks depends on several factors such as the position of the measurements and the diversity of the data. The application of LeNN on state estimation of a 69-bus radial distribution network is used as an illustrative example to explain the distinctive feature of the proposed technique.

MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Arrhythmias, also known as irregular heartbeats, are important health problems that must be accurately identified to diagnose and treat cardiovascular disease. Within the scope of this study, a network for classifying arrhythmias, which are important in the diagnosis and treatment of cardiovascular diseases, was proposed by using one-dimensional convolutional neural network (1D CNN), one of the deep learning techniques. With the proposed 1D-CNN architecture, arrhythmia types and normal rhythm ECGs were subjected to a more detailed examination from general to specific according to urgency situations. In the classifications made, first of all, a binary classification was made and an evaluation was made as whether there was a life risk or not. In triple, quadruple and six-fold classification, the detection of arrhythmia status is detailed. More complex classifications have helped to define different types of arrhythmias in more detail. This study proposes a deep learning network for automatic identification and classification of arrhythmias and shows that different arrhythmia conditions can be diagnosed with a single network model by applying the proposed network structure to multi-class arrhythmia disorders.

Research on brain functional network property analysis and recognition methods targeting brain fatigue

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands’ node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

The authority file of names of agricultural organizations: Features and development

The experience of the CSAL in creating, maintaining and using the authority file of names of sectoral organizations is discussed. The characteristics of the authority file and information sources are given, methodological solutions for data generation are considered. The authority file is used to improve the quality of bibliographic entries by unifying access points, improving the processes of in- formation exchange and retrieval. Based on the authority file, the reference and information resource has been formed – the database “Authority File of Names of Scientific Institutions of the Agroindustrial Complex” is freely accessible via the Library’s website. The authority data on the names of scientific agricultural institutions include reference information and historical data, provide access to bibliographic information, connect resources within the single data network and enable to navigate the CSAL resources. The development of the authority file content and technology is reviewed. The content of the authority file is updated on the basis of the library collection and monitoring of data on organizations in external sources; besides, the new entries for the names of modern scientific agrarian institutions and agricultural institutions established in Russia in the 19th century are created. The OPAC-Global ILS replaced the earlier CSAL automated library and information system software, therefore the authority file was transferred into the new software environment.

Controlling the Relaxation Dynamics of Polymer Networks by Combining Associative and Dissociative Dynamic Covalent Bonds.

Dynamic polymer networks offer a promising solution to key challenges in polymers such as recyclability, processability, and damage repair. However, the trade-off between combining facile processability, fast self-healing, and high creep resistance remains a major obstacle to implementation. To overcome this, two very distinct dynamic covalent chemistries, Diels-Alder and transesterification, is combined in a single network. The resulting dual dynamic networks offer an unprecedented set of properties and control over the relaxation times. The system decouples the relaxation dynamics of the network from the spatial motifs, and the tuning of the ratio between chemistries enables to control of the relaxation dynamics over six orders of magnitude. Taking advantage of this control, the composition and rheological behavior is optimized to drastically improve the resolution for extrusion-based additive manufacturing of dynamic covalent networks. Additionally, two well-defined and separated stress relaxation peaks are observed at compositions close to 50% of each dynamic chemistry, accentuating the double character of the system's relaxation dynamics. This atypical situation, enables to preparation of self-healing materials with negligible creep, and with shape-memory properties solely leveraging the two distinct relaxation dynamics, instead of the glass transition temperature or the melting point.

Do Racial Disparities Impact Healthcare Costs and Resource Utilization after Total Joint Replacements?

Background Racial disparities currently exist in healthcare and can have a significant impact on patient outcomes and access to quality care. Previous studies have indicated that black patients are more likely to experience delays in treatment and increased surgical complications. Hispanic patients have more comorbidities and increased complications when undergoing orthopaedic surgeries. The purpose of this study was to evaluate the impact of racial disparities on postoperative complications and readmission rates following Total Joint Arthroplasty (TJA). Methods The largest single healthcare network database was queried to identify total joint arthroplasty patients treated in a single division between 2017 and 2021. Patient demographics including age, race, gender, and comorbidities were collected. Logistic regression and odds ratio point estimate analyses were utilized to assess for associations between race (defined as Whites, Blacks, Hispanics, Asians, and Others) and postoperative medical complications and surgical complications, which were collected for all patients. Patient outcomes included length of stay, hospital readmission status at 30 and 90 days postoperative, and emergency room (ER) visits up to 90 days. Results A total of 16,940 patients were included in this analysis consisting of 62% female, a mean age of 71 years and mean BMI of 29.4 kg/m2 . Racial demographics consisted of 61.6% White, 12.3% Black, 24.1% Hispanic, 0.8% Asian, and 1.2% Other. Race was not significantly associated with increased readmissions at 30 days (p=0.2215) and hispanic patients were 14% less likely to be readmitted within 90 days (p = 0.0208) but were not found to have differences in ER visits when compared to White patients. Black and Asian patients were not significantly more likely to visit the ER within 90 days or to be readmitted to the hospital within 90 days when compared to White patients. Race was not significantly associated with postoperative medical complications or surgical complications. Conclusions This study found a higher readmission rate of up to 20% for total joint arthroplasty patients within 90 days of follow-up. Although race did not appear to be a significant determinant of additional ER visits or readmissions, there was some variation seen amongst Hispanic patients undergoing TJR with a lower likelihood of readmission at 60 and 90 days postoperative.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

User-dedicated optical path switching with optical-wireless cooperative control for low-latency and seamless handover

We propose a user-dedicated optical path switching technique for low-latency and seamless handover in the radio access network (RAN) with the all-photonic network (APN). The APN is a network that connects end points directly with optical paths of dedicated wavelengths assigned to each user and provides large-capacity and low-latency connections. As mobile systems beyond 5th generation (5G) and 6th generation (6G) will require extremely low latency, applying the APN to the RAN is promising to achieve this. However, the RAN with the APN has a challenge in optical path switching during the handover procedure of mobile systems because optical switches that compose the APN do not have an Internet protocol (IP) layer function that enables routing control for handover in the conventional router network. Our proposed optical path switching technique to solve this challenge utilizes optical-wireless cooperative control and switches an optical path dedicated to user equipment (UE) executing handover in conjunction with its handover procedure. The key point of the proposed technique is that a next generation node B (gNB) transmits an optimal-timing optical path switching trigger to the optical switch. We experimentally demonstrated the generation of a dedicated optical path for UE in the RAN with the APN as a premise to enable the proposed optical path switching technique. To evaluate the feasibility and effectiveness of the proposed optical path switching technique, we experimentally compared the optical switch and router in terms of latency performance, packet loss, and packet misdirection in the path switching during handover. The results indicated successful optical path switching during handover without packet loss and packet misdirection while reducing the latency per single network equipment by 99%, from 4 µs for the router to 45 ns for the optical switch.

Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns. The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes. One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content.

Nested object detection using mask R-CNN: application to bee and varroa detection

AbstractIn this paper, we address an essential problem related to object detection and image processing: detecting objects potentially nested in other ones. This problem exists particularly in the beekeeping sector: detecting varroa parasites on bees. Indeed, beekeepers must ensure the level of infestation of their apiaries by the varroa parasite which settles on the backs of bees. As far as we know, there is no yet a published approach to deal with nested object detection using only one neural network trained on two different datasets. We propose an approach that fills this gap. Therefore, we improve the accuracy and the efficiency of bee and varroa detection task. Our work is based on deep learning, more precisely Mask R-CNN neural network. Instead of segmenting detected objects (bees), we segment internal objects (varroas). We add a branch to Faster R-CNN to segment internal objects. We extract relevant features for internal object segmentation and suggest efficient method for training the neural network on two different datasets. Our experiments are based on a set of images of bee frames, containing annotated bees and varroa mites. Due to differences in occurrence rates, two different sets were created. After carrying out experiments, we ended up with a single neural network capable of detecting two nested objects without decreasing accuracy compared to two separate neural networks. Our approach, compared to traditional separate neural networks, improves varroa detection accuracy by 1.9%, reduces infestation level prediction error by 0.22%, and reduces execution time by 28% and model memory by 23%. In our approach, we extract Res4 (a layer of the ResNet neural network) features for varroa segmentation, which improves detection accuracy by 11% compared to standard FPN extraction. Thus, we suggest a new approach that detects nested objects more accurately than two separate network approaches.

MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration

Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.

Abnormal Structural–Functional Coupling and MRI Alterations of Brain Network Topology in Progressive Supranuclear Palsy

BackgroundProgressive supranuclear palsy (PSP) can cause structural and functional brain reconstruction. There is a lack of knowledge about the consistency between structural–functional (S–F) connection networks in PSP, despite growing evidence of anomalies in various single brain network parameters.PurposeTo study the changes in the structural and functional networks of PSP, network's topological properties including degree, and the consistency of S–F coupling. The relationship with clinical scales was examined including the assessment of PSP severity, and so on.Study TypeRetrospective.SubjectsA total of 51 PSP patients (70.04 ± 7.46, 25 females) and 101 healthy controls (64.58 ± 8.84, 58 females).Field Strength/Sequence3‐T, resting‐state functional MRI, diffusion tensor imaging, and T1‐weighted images.AssessmentA graph‐theoretic approach was used to evaluate structural and functional network topology metrics. We used the S–F coupling changes to explore the consistency of structural and functional networks.Statistical TestsIndependent samples t tests were employed for continuous variables, χ2 tests were used for categorical variables. For network analysis, two‐sample t tests was used and implied an false discovery rate (FDR) correction. Pearson correlation analysis was used to explore the correlations. A P‐value <0.05 was considered statistically significant.ResultsPSP showed variations within and between modules. Specifically, PSP had decreased network properties changes (t = −2.0136; t = 2.5409; t = −2.5338; t = −2.4296; t = −2.5338; t = 2.8079). PSP showed a lower coupling in the thalamus and left putamen and a higher coupling in the visual, somatomotor, dorsal attention, and ventral attention network. S–F coupling was related to the number of network connections (r = 0.32, r = 0.22) and information transmission efficiency (r = 0.55, r = 0.28). S–F coupling was related to basic academic ability (r = 0.39) and disinhibition (r = 0.49).Data ConclusionPSP may show abnormal S–F coupling and intramodular and intermodular connectome in the structural and functional networks.Level of Evidence3Technical EfficacyStage 3

Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device Communication in Mobile Networks.

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

An approach to designing efficient implementation of 30×30 terrestrial conservation commitments

AbstractIn response to biodiversity declines worldwide, over 190 nations committed to protect 30% of their lands and waters by 2030 (hereafter, 30×30). Systematic conservation planning and return on investment analysis can be helpful tools for determining where protection efforts could deliver the most efficient and effective reserve design, and supporting decision‐making when trade‐offs among objectives are required. Here, we propose a framework for efficient “30×30” implementation and apply it to the state of California (USA). Because conservation of a region's full suite of biodiversity is the primary objective of the global initiative, we prioritized representation in our analysis. We used Zonation to identify networks that close the gap in representation of major habitat types in California's protected area network and that also conserve the places important for biodiversity or climate change mitigation. We identified networks that are efficient relative to metrics likely to be important in implementation including land acquisition cost, number of transactions, and conservation benefit per hectare, and we illustrate not only trade‐offs associated with these metrics but also differences in the co‐benefits achieved. Five of the eight major habitat types in California are not currently protected at a 30% level statewide, and if representation was achieved solely through private land acquisition, targets could be met for as little as $5.84 billion, with as few as 364 transactions, or with 2.18 million additional conserved hectares. Implementation of 30×30 will likely require more flexibility than a single network design. A “no regrets” action would be to protect properties that were prioritized across all networks and additional implementation should include properties with characteristics of any of the individual networks. Our analytical framework and implementation guidance can be applied to other geographies and jurisdictions to increase the likelihood of both meeting 30×30 targets and delivering the conservation benefits they aim to secure.