Cascading Path Research Articles

A framework for territorial spatial ecological restoration zoning integrating “Carbon neutrality” and “Human-geology-ecology”: Theory and application

Global climate change worsens the imbalance among human environment, geological environment and ecological environment (human-geology-ecology), leading to increasingly prominent ecosystem damage. Constructing a scientifically effective territorial spatial ecological restoration zoning (TSERZ) framework will promote the coordination between nature and human. However, the fundamental role of geological environment in TSERZ has been overlooked, and there is lack of theoretical frameworks and empirical research on TSERZ oriented towards carbon neutrality goal. Therefore, we have proposed a new TSERZ framework by integrating “carbon neutrality” and “human-geology-ecology”, which employs models such as CECM, LCA, IUEMS and ESP to divide primary zones, secondary zones and key areas, and also provides restoration directions for various zones. Taking the typical alpine-gorge region of Yulong County as an example, we conduct empirical research to verify the validity of the TSERZ framework. The findings indicate that, firstly, besides human and ecological factors, geological factors like lithology are indispensable factors for TSERZ, and understanding the dynamic balance relationship of human-geology-ecology is the prerequisite and basis for TSERZ. Secondly, the proposed TSERZ framework emphasizes the fundamental role of carbon source/sink and lithology in TSERZ, constructing the three-level zones division method oriented towards carbon neutrality goal with the research line of “natural background conditions—dominant ecological functions—ecological stress problems”. Thirdly, the framework has been successfully applied to TSERZ in Yulong County, dividing it into 8 primary zones, 17 secondary zones, and 7 types key areas. Given its complex geological conditions, fragile ecological environment and increasingly human interference, Yulong County urgently needs to focus on restoring ecological problems such as rocky desertification and geological disasters to improve carbon sink capacity. This study innovatively constructs a cascading path for TSERZ from theoretical cognition to practical application, which can provide a reference for solving the TSERZ problem in geologically complex regions, especially in alpine-gorge regions.

Retweeting behavior prediction based on dynamic Bayesian network classifier in microblogging networks

Nowadays, predicting user retweeting behavior in microblogging networks has gained considerable attention. Solving this problem allows for understanding the underlying mechanism of information diffusion and analyzing diffusion's evolution concerning a particular original microblog over time. Time-series data from microblogging networks are ubiquitous, and user retweeting behavior prediction is primarily considered a classification task. Consequently, there is a significant demand for accurate classification of time-series data. Several influential factors affect user retweeting behavior prediction in microblogging networks. However, much of the existing research focuses on studies that neglect the impact of users' indirect social influence strength. This neglect leads to inaccurate forecasts of indirect retweeting behavior for a particular original microblog. To address this, this paper presents a new time-sensitive measure of social influence strength based on a combination of users' temporal behavior patterns and the shortest cascade path length. Second, it proposes a prediction model based on Dynamic Bayesian Network derivative classifiers called V-DBNC to accurately predict user retweeting behavior over time. The proposed time-sensitive social influence strength factor and other factors, such as individual behavior and similarity-based driving factors, are applied to the V-DBNC model to achieve accurate user retweeting behavior prediction in the microblogging network. The joint density of factors in this classifier is estimated using a multivariate Gaussian kernel function with smoothing parameters. The V-DBNC model is empirically optimized by splitting the smoothing parameters into different-scale intervals and using the model averaging method to select the optimal classifier. Additionally, the proposed model's relatively simple structure helps overcome the overfitting problem and allows it to accumulate classification information through iterative evolution, promoting generalization. The real Twitter microblogging dataset is used to evaluate the performance of V-DBNC. The experimental results demonstrated that the proposed model outperforms other compared approaches when dealing with the classification of time-series data.

Towards rational design in electrochemical denitrification by analyzing pH dependence.

A small fraction of NOx (<1%) always exists in CO2 feedstock (e.g. exhausted gas), which can significantly reduce the efficiency of CO2 electroreduction by ∼30%. Hence, electrochemical denitrification is the precondition of CO2 electroreduction. The pH effect is a key factor, and can be used to tune the selectivity between N2 and N2O production in electrochemical denitrification. However, there has been much controversy for many years about the origin of pH dependence in electrocatalysis. To this end, we present a new scheme to accurately model the pH dependence of the electrochemical mechanism. An extremely small pH variation from pH 12.7 to pH 14 can be accurately reproduced for N2O production. More importantly, the obviously different pH dependence of N2 production, compared to N2O, can be attributed to a cascade path. In other words, the N2 was produced from the secondary conversion of the as-produced N2O molecule (the major product), instead of the original reactant NO. This is further supported by more than 35 experiments over varying catalysts (Fe, Ni, Pd, Cu, Co, Pt and Ag), partial pressures (20%, 50% and 100%) and potentials (from-0.2 to 0.2V vs. reversible hydrogen electrode). All in all, the insights herein overturn long-lasting views in the field of NO electroreduction and suggest that rational design should steer away from catalyst engineering toward reactor optimization.

Tunable Continuous‐Variable Tripartite Entanglement via Simultaneous and Ordinal Cascaded Nonlinear Processes

AbstractMultipartite quantum entanglement plays a vital role in both fundamental science and quantum applications. The cascaded four‐wave mixing (FWM) process is an effective method to prepare multipartite entanglement, however, the physical nature of entanglement based on different cascading paths, i.e., simultaneous cascaded FWM (SC‐FWM) and ordinal cascaded FWM (OC‐FWM), has not yet been conclusively determined. In this article, tunable continuous‐variable (CV) triple‐mode entanglement is proposed to be generated by using both the SC‐FWM and OC‐FWM schemes. The simulation results reveal that the absorption/dispersion properties of the two nonlinear processes can be efficiently tuned by varying the optical parameters (i.e., the photon detuning and the nonlinear susceptibility), resulting in triple‐mode CV entanglement with tunable properties. Compared with the OC‐FWM scheme, the SC‐FWM scheme has a broader entanglement bandwidth and a higher degree of entanglement, where the tripartite entanglement region is 4.38% larger than that of the OC‐FWM scheme. Furthermore, these results indicate that the SC‐FWM scheme has a more compact and stable mode structure with better entanglement performance. Such tunable optical triple‐mode entanglement may find applications in specific quantum communication protocols and pave the way for implementing and manipulating multichannel quantum networks.

Hierarchical Blocking Control for Mitigating Cascading Failures in Power Systems with Wind Power Integration

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase.

A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability.

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

Chemical bonding and facet modulating of p-n heterojunction enable vectorial charge transfer for enhanced photocatalysis

Heterojunctions have been proved to be the promising photocatalysts for hazardous contaminants removal, but the inferior interfacial contact, low carrier mobility and random carrier diffusion seriously hamper the photoactivity improvement of the conventional heterojunctions. Herein, SO chemically bonded p-n oriented heterostructure is fabricated via selectively anchoring of p-type Ag2S nanoparticles on the lateral facet of n-type Bi4TaO8Cl nanosheet. Such a p-n heterojunction engineering on specific facet of Bi4TaO8Cl semiconductor derives ingenious double internal electric field (IEF), which not only effectively creates the spatially separated oxidation and reduction sites, but also delivers the powerful driving forces for impactful spatial directed photocarrier transfer along the cascade path. Additionally, our experimental and theoretical analyses jointly signify that the interfacial SO bond could serve as an efficient atomic-level interfacial channel, which is conducive to encouraging the vectorial charge separation and migration kinetic. As a result, the Ag2S/Bi4TaO8Cl oriented heterojunction exhibits significantly enhanced visible light driven photocatalytic redox ability for tetracycline oxidation and hexavalent chromium reduction than those of single component and the traditional random/mixed heterojunctions. This study could provide a deeper insight into the synergistic effects of multi-IEF modulation and interfacial chemical bond bridging on optimizing the photogenerated carrier behaviors.

Empirical patterns of interdependencies among critical infrastructures in cascading disasters: Evidence from a comprehensive multi-case analysis

Critical infrastructures (CIs), such as electric power, transportation, and energy systems, offer essential goods and services to maintain societal health and well-being. Any interruptions in their functioning will result in catastrophic consequences. Besides, due to their growing interdependencies, a local failure can spread across CI networks, exacerbating the vulnerability of the whole system. This study aims to investigate the interdependency patterns of CIs from a comprehensive and holistic perspective. The interdependency natures, including cascading paths, interdependency types, and degrees were analyzed through processing data from 12 typical cascading disaster cases covering various disaster categories. Furthermore, the criticality of the recovery time for CIs was also discussed. A comparative analysis with an existing database was conducted to validate the applicability of the data used in this research. The results reveal that first, almost all cascading effects across CIs propagate within the first and second-order ranges, accounting for 80.4% and 17.9%, respectively. In a few cases, positive feedback loop events with a self-reinforcing effect may also occur. Second, physical interdependency is the most common type among CIs, followed by geographic, and more than 79% of interaction behaviors among CIs are highly interdependent. The same CI pairs can have different interdependency types and degrees depending on specific disruption scenarios and temporal or spatial locations of the CI components being affected. Third, CI failure durations are the key factor in determining interdependency impacts. Maintaining moderate redundancy in CIs and improving their recovery performances can significantly mitigate these impacts. This study extends existing research and enriches the knowledge system of CI interdependencies, its findings can help inform decision-makers on improving the preparedness level of CIs and mitigating interdependency impacts.

Tert-Butylhydroperoxide mediated radical cyanoalkylation/cyanoalkenylation of 2-anilino-1,4-naphthoquinones with vinylarenes/arylalkynes and azobis(alkylcarbonitrile)s

We disclosed a TBHP mediated cyanoalkylation/cyanoalkenylation reaction of 2-anilino-1,4-naphthoquinones with vinylarenes/arylalkynes and azobis(alkylcarbonitrile)s in a one-pot three-component fashion via a radical cascade pathway.

An approach for fast cascading failure simulation in dynamic models of power systems

The ground truth for cascading failure in power system can only be obtained through a detailed dynamic model involving nonlinear differential and algebraic equations whose solution process is computationally expensive. This has prohibited adoption of such models for cascading failure simulation. To solve this, we propose a fast cascading failure simulation approach based on implicit Backward Euler method (BEM) with stiff decay property. Unfortunately, BEM suffers from hyperstability issue in case of oscillatory instability and converges to the unstable equilibrium. We propose a predictor–corrector approach to fully address the hyperstability issue in BEM. The predictor identifies oscillatory instability based on eigendecomposition of the system matrix at the post-disturbance unstable equilibrium obtained as a byproduct of BEM. The corrector uses right eigenvectors to identify the group of machines participating in the unstable mode. This helps in applying appropriate protection schemes as in ground truth. We use Trapezoidal method (TM)-based simulation as the benchmark to validate the results of the proposed approach on the IEEE 118-bus network, 2383-bus Polish grid, and IEEE 68-bus system. The proposed approach is able to track the cascade path and replicate the end results of TM-based simulation with very high accuracy while reducing the average simulation time by ≈10−35 fold. The proposed approach was also compared with the partitioned method, which led to similar conclusions.

Circularity and LCA - material pathways: the cascade potential and cascade database of an in-use building product

Improving circularity in the building sector entails ensuring greater material efficiency to avoid virgin material extraction. To assist stakeholders in decisions regarding salvaging an in-use building product, the potential further uses of this salvaged product and its materials should be determined. In other words, its cascade potential should be determined given circular strategies, i.e. to reuse, repurpose, repair sequentially. To predict the cascade potential, a database is required that combines (acquired) product data, assembly characteristics and process data, e.g., info on the process to melt old steel. This database is referred to as a cascade database. The aim of this research is to study steps to form this database, specifically, the combination of product passports and an LCA process database. Hereto, major existing European data tools are considered. For product data, material passports and digital product passports initiatives are identified and compared. For process data, LCA databases are considered. Interactions between these data tools are a possible way to set up a partial cascade database. A workflow is then described- on how to acquire data on the cascade potential of a product from such a database. We note that the cascade database also relies on extensive (economic) forecast models, data collection and stakeholder interaction. Setting up such an extensive database and generating all possible cascading paths is a challenging, if not unachievable, feat. Nevertheless, even limited applications of this database might help reach a more circular economy.

Circularity and LCA - material pathways: cascade potential and cascade environmental impact of an in-use building product

Improving circularity in the building sector entails ensuring greater material efficiency to avoid virgin material extraction. To assist stakeholders in decisions regarding salvaging an in-use building product, requires to predict and assess the potential further productive uses of that product and its materials. The range of possible cascade material paths originating from the in-use building product X and their assessments comprise the cascade potential of product X. Method: To determine the cascade potential and impact, we work further on existing efforts done in the field of circularity and life cycle assessment (LCA). This entails discussing scenario models to predict cascade material pathways over time, and multifunctionality solutions to assess those pathways. Due to the fact that the environment is a complex system and long term forecasting is required, the cascade potential can never be exactly determined. Therefore, we first set up conceptual formulas and then discuss steps to make these formulas feasible. Furthermore, the effort to generate the cascade paths originating from a product, can also be used to form circular systems that adhere to carbon mitigation pathways.

Patterns of thermocline structure and the deep chlorophyll maximum feature in multiple stratified lakes related to environmental drivers

Thermal stratification and the deep chlorophyll maximum (DCM), two commonly related phenomena in stratified lakes, play fundamental roles in eco-environmental processes. However, the progressive linkages among multi-dimensional environmental factors, thermal stratification and DCM were poorly explored, which greatly constrains our understanding of cross-level governance in deep lakes. In this study, the thermocline structure (i.e., thermocline depth, thickness and strength) and DCM feature (depth and thickness) and their driving factors were investigated at regional scale using data from 18 stratified lakes differing in limnological characteristics, Southwest China. Our study showed that (1) DCM occurred close to the thermocline in most lakes (represented by their depth and thickness), (2) the depths of the thermocline and DCM were both shallower than the euphotic depth, and (3) spatial heterogeneity occurred the thermocline structure and the DCM feature, reflecting different environmental factors. Specifically, water depth and light penetration depths were both positively correlated with thermocline depth and thickness and the DCM feature, and ultraviolet radiation (UVR) was more important than photosynthetically active radiation (PAR) for thermocline depth, but PAR was more important for thermocline thickness; moreover, PAR played a more prominent role than UVR for the DCM feature. As there were interactions between some environmental factors, we built a cascading path using a partial least squares path modelling for the DCM feature: lake morphometry directly impacted the thermocline structure and surface water quality; the water quality further affected light penetration depths as well as the thermocline structure; light penetration depth and thermocline structure combined directly determined the DCM feature, where the importance of light was larger. Our findings provide information on the cascading drivers of the thermocline structure and DCM feature in deep lakes and also constitute a valuable reference for deep lake management under the dual pressure of climate change and eutrophication.

Submesoscale frontal waves and instabilities driven by sheared flows

Submesoscale processes at the frontal region are frequently observed in the form of submesoscale fronts, eddies, and filaments due to a variety of submesoscale instabilities. Previous studies tended to emphasize baroclinic conversions (e.g. mixed-layer instability) and few efforts have focused on the contribution of barotropic instability. In the present study, a series of idealized numerical simulations are implemented to examine how submesoscale structures are generated and evolve on conditions of different (horizontal and vertical) flow shear. The results show that barotropic instability would slow down or even restrain the growth of propagating submesoscale frontal waves, but instead, generate slower, larger phase-locked frontal waves. These waves with large amplitude significantly advect the denser, colder water to the warmer, lighter side. This provides active available potential energy and subsequently causes strong buoyancy production. In that context, barotropic instability also greatly contributes to the generation of submesoscale features, restratification, and vertical transport in the upper ocean. But this process cannot be identified in most time-averaged analyses. In this paper, the energy variation with time is also studied based on the spatial mean by Reynolds decomposition, the results of which suggest that the energy cascade paths caused by baroclinic instability and barotropic instability are different, but their contributions to downscale energy transition are equally important.

A Novel Cascade Path Planning Algorithm for Autonomous Truck-Trailer Parking

One of the most challenging tasks for truck drivers is maneuvering the truck-trailer system in different parking scenarios. This article presents a novel path planning approach for truck-trailer parking, where a realistic and deterministic parking behavior model, Iterative Analytical Method (IAM), is proposed and combined with Closed-Loop Rapidly Exploring Random Tree (CL-RRT) approach in a cascade path planning. Cascade path planning approach combining CL-RRT with the iterative analytical method (IAM) mimicking real-world parking practice enables the generation of both kinematically feasible and deterministic parking maneuvers with obstacle avoidance. For evaluation, different parking scenarios are generated and selected through a developed case generation tool. The performance of the proposed path planning approach is evaluated through MATLAB simulations. The results achieved a noticeable success with a high rate of generated feasible maneuvers for parking.

Cascade Path Augmentation Unet for bladder cancer segmentation in MRI

Treatment choices for patients with bladder cancer (BCa) are determined by the presence of muscular invasion. The precise segmentation of the inner and outer walls (IW and OW), as well as the bladder tumor (BT), is crucial for improving computer-aided diagnosis of muscle-invasive bladder cancer (MIBC). To propose a novel deep learning-based model to improve the segmentation accuracy of the IW, OW, and BT, which can be useful in clinical practice. We proposed a Cascade Path Augmentation Unet (CPA-Unet) network to conduct multi-regional segmentation of the bladder using 1545 T2-weighted MRI scans. The model employs a cascade strategy to eliminate the redundant information in the background. Unet is used to segment the bladder from the background in the rough segmentation. The path augmentation structure is used in the fine segmentation to mine multi-scale features. Additionally, the partial dense connection is adopted as the skip connection module to concatenate the low- and high-level sematic features. The CPA-Unet is trained using 1391 T2WI slices and tested using 154 T2WI slices. In comparison to previous deep learning-based methods, the CPA-Unet achieves superior segmentation results in terms of Dice similarity coefficient (DSC) and Hausdorff distance (HD) (IW: DSC =98.19%, HD =2.07mm; OW: DSC =82.24%, HD =2.62mm; BT: DSC =87.40%, HD =0.76mm). Our proposed CPA-Unet network is capable of segmenting the bladder into its IW and OW, as well as tumors. The segmentation results provide a reliable and effective foundation for computer-assisted clinical diagnosis of MIBC.

Development of Bullying and Victimization: An Examination of Risk and Protective Factors in a High-Risk Sample.

This prospective longitudinal study from birth to late adolescence investigated how early risk predicted subsequent aggression in middle childhood and bullying perpetration, bullying victimization, and violence victimization in adolescence. In addition, the moderating role of protective factors (i.e., maternal sensitivity, positive peers, and school connectedness) on these associations were examined. Caregiver-infant dyads (N = 216; 72% Black/African American) were recruited as part of a longitudinal study on substance exposed youth. Data using multiple methods and informants (observations, interviews, caregiver, and child/youth self-reports) were collected from dyads in early childhood (EC, birth to 48 months), middle childhood (MC, i.e., 84 months), early adolescence (EA, M = 13.26 years, SD = .83) and later adolescence (LA, M = 15.08 years, SD = .83). A developmental cascading path model was tested. There were direct associations between EC maternal harsh parenting and aggression in MC. In turn, MC aggression was associated with higher violence victimization and bullying in EA. Finally, EA violence victimization was then associated with higher levels of bullying as well as victimization from bullying in LA. Consistent with predictions, there was also evidence that protective factors (i.e., maternal sensitivity and positive peers) moderated the impact of predictor variables on aggression and bullying outcomes. Specifically, maternal sensitivity moderated the link between EC and MC aggression, such that those with moderately high levels of maternal sensitivity showed a negative relation between EC and MC aggression, whereas those with low levels of maternal sensitivity showed continuity in aggression. Positive peer influence moderated the link between violence victimization in EA and bullying in LA, such that children high on both violence victimization and positive peers had the highest levels of bullying victimization.

Double Cascading Charge Transfer at Integrated Perovskite/Organic Bulk Heterojunctions for Extended Near-Infrared Photoresponse and Enhanced Photocurrent.

Nowadays, nearly 48.7% near-infrared (NIR) irradiation (>800nm) of the full solar spectrum has actually not been fully utilized since the state-of-the-art perovskite film usually can only absorb the most UV-vis sunlight radiation. Herein, high efficiency integrated Cs0.15 FA0.85 PbI3 perovskite/organic bulk (PC61 BM:D18:Y6) heterojunction solar cells with enhanced low energy photon harvest until 931nm and a high maintained open circuit voltage of 1.04V is successfully obtained. In particular, the favorable double cascading charge transfer paths pave an interesting possibility to spatially separate electrons upon visible light excitation and holes upon NIR photon absorption simultaneously at interfaces, significantly suppressing non-radiative bimolecular recombination and reaching the photocurrent density as high as 27.48mA cm-2 and power conversion efficiency of 20.31%. Besides, the strong hydrophobicity of the ternary organic film has effectively prevented ambient humidity penetration and improves the stability of the perovskite in the continuous aging test (humidity > 60%) compared with the control device. This work has opened a significantly new window to improve the NIR light harvest for next generation highly efficient solar cells with full spectrum response.

Underactuated USV path following mechanism based on the cascade method

The path following control under disturbance was studied for an underactuated unmanned surface vehicle (USV) subject to the rudder angle and velocity constraints. For this reason, a variable look-ahead integral line-of-sight (LOS) guidance law was designed on the basis of the disturbance estimation and compensation, and a cascade path following control system was created following the heading control law based on the model prediction. Firstly, the guidance law was designed using the USV three-degree-of-freedom (DOF) motion model and the LOS method, while the tracking error state was introduced to design the real-time estimation of disturbance observer and compensate for the influence of ocean current. Moreover, the stability of the system was analyzed. Secondly, sufficient attention was paid to the rudder angle and velocity constraints and the influence of system delay and other factors in the process of path following when the heading control law was designed with the USV motion response model and the model predictive control (MPC). The moving horizon optimization strategy was adopted to achieve better dynamic performance, effectively overcome the influence of model and environmental uncertainties, and further prove the stability of the control law. Thirdly, a simulation experiment was carried out to verify the effectiveness and advancement of the proposed algorithm. Fourthly, the “Sturgeon 03” USV was used in the lake test of the proposed control algorithm to prove its feasibility in the engineering practices.