Hierarchical Research Articles

Unveiling sextortion in sport: a global inquiry into the nexus of sexual violence, abuse of power, and corruption for enhanced safeguarding

Sextortion, a distinct form of sexual misconduct intersects with both sexual violence and corruption. Within the sphere of sport, marked by inherent power differentials and hierarchical structures, cases of sexual abuse and corruption persist, with sextortion emerging as a concerning manifestation of these pervasive issues. While sextortion shares commonalities with other forms of sexual abuse, such as harassment and assault, a distinguishing feature lies in how coercion is leveraged through authority or power imbalances. Unlike more overt forms of abuse, sextortion often involves subtle or implicit threats, where compliance is sought in exchange for perceived privileges or opportunities within the sporting environment. Leveraging Institutional Theory and Applied Ethics, this study aims to provide a comprehensive exploration of sextortion in sport. Despite increasing awareness, research on sextortion in sport remains limited. Previous studies lack data specific to this elusive misconduct, primarily relying on empirical data related to sexual abuse. This study represents the first empirical investigation into sextortion in sport, drawing on data collected from 49 countries and endeavours to quantifiably communicate the scale of sextortion. Through data analysis of over 500 elite athletes, community sport practitioners, and sport industry professionals aged 17 and above, the research sheds light on experiences related to abuses of entrusted power for sexual gain. Results found 20% (n = 96) of global respondents experienced sextortion, including 37 minors at the time of the incident, encompassing diverse genders, abilities, and identities. Sextortion was identified across 41 of the 49 surveyed nationalities and within 19 of 26 sport categories from grassroots to elite levels. This research deepens the understanding of sport-related sextortion and underscores the importance of addressing this pervasive issue through further theoretical and empirical inquiry. It emphasises the critical role of good governance, clear safeguarding protocols, increased awareness of power dynamics, consent, and the importance of diverse regional data in effectively combating sextortion in the sporting domain.

Symmetry Breaking: Case Studies with Organic Cage-Racemates.

ConspectusSymmetry is a pervasive phenomenon spanning diverse fields, from art and architecture to mathematics and science. In the scientific realms, symmetry reveals fundamental laws, while symmetry breaking─the collapse of certain symmetry─is the underlying cause of phenomena. Research on symmetry and symmetry breaking consistently provides valuable insights across disciplines, from parity violation in physics to the origin of homochirality in biology. Chemistry is particularly rich in symmetry breaking studies, encompassing areas such as asymmetric synthesis, chiral resolution, chiral structure assembly, and so on. Across different disciplines, a well-defined methodology is fundamental and necessary to analyze the symmetry or symmetry breaking nature behind the phenomenon, enabling researchers to uncover the underlying principles and mechanisms. Basically, three key points underpin symmetry-related research: the scale-dependency of symmetry/symmetry breaking, the driving force behind symmetry breaking phenomena, and the properties arising from symmetry breaking.This Account will focus on the three aforementioned key points elucidated with organic cages as proof-of-concept models, as organic cages exhibit shape-persistent 3D molecular frameworks, well-defined molecular motion, and a high propensity for crystallization.First, we examine racemization processes of organic cages with dynamic molecular motions to illustrate that symmetry and symmetry breaking are time-scale-dependent. Specifically, the racemization, driven by molecular motion, is influenced by hydrogen bonding and the rigidity of the cage framework, which may or may not be observable within the experimental temporal scale. This determines whether the enantiomeric excess system, namely, the symmetry broken system, can be detected experimentally. We also investigate the hierarchical structures self-assembled by racemic organic cages, demonstrating that symmetry and asymmetry manifest differently across spatial scales, from molecular to supramolecular and macroscopic levels. Second, we discuss the driving force behind spontaneous chiral resolution─a classic symmetry-breaking event during crystallization─from a thermodynamic perspective. We suggest that racemic compounds, compared to conglomerates, are more entropy-favored, explaining their greater prevalence in nature. Spontaneous chiral resolution can take place only when a favorable enthalpy compensates for unfavorable entropy. In conglomerates composed of organic cages, strong intermolecular interactions along the screw axes provide the necessary compensation. Finally, we explore the unique properties that emerge from symmetry-broken molecular packing within crystals of cage racemates, such as second-harmonic generation and piezoelectricity. It turns out that the symmetry operation in molecular packing plays a critical role in determining material properties. By comprehensively analyzing symmetry and symmetry-breaking in organic cage racemates, this Account provides insights into symmetry-related phenomena across scientific disciplines. It also paves the way for designing novel materials with tailored properties for applications in optics, electronics, and beyond.

Topological Supramolecular Complexation of Metal-Organic Polyhedra for Tunable Interconnected Hierarchical Microporosity in Amorphous Form.

The precise engineering of microporosity is challenging due to the interference at sub-nm scale from unexpected structural flexibility and molecular packing. Herein, the concept of topological supramolecular complexation is proposed for the feasible fabrication of hierarchical microporosity with broad tunability in amorphous form. The 2.5 nm metal-organic polyhedra (MOP) is complexed with quadridentate ligands via hydrogen and coordination bonding while the mismatch between MOPs' cuboctahedron and ligands' tetrahedron topology leads to frustrated packing with extrinsic microporosity. Amorphous supramolecular frameworks can be obtained that integrate the intrinsic microporosity of MOPs with the extrinsic porosity from the frustrated packing. The topologies, sizes and flexibility of ligands as well as ligand/MOP ratios are systemically varied, and the pore size distribution can be precisely adjusted. The hierarchical structures ranging from molecular packing to the morphologies of meso-scale assemblies are probed using ultra-small, small- and wide-angle X-ray scattering, enabling the quantitative evaluation of the micropores interconnectivity for the understanding of gas permeation performance. Gas separation membranes with permselectivity surpassing the Robeson upper bound can be designed. The findings not only put insight on the microscopic mechanism of supramolecular frustrated packing from topological design, but also pave new avenues for the cost-effective fabrications of microporous frameworks.

NH4+-Modulated Cathodic Interfacial Spatial Charge Redistribution for High-Performance Dual-Ion Capacitors

Compared with Zn2+, the current mainly reported charge carrier for zinc hybrid capacitors, small-hydrated-sized and light-weight NH4+ is expected as a better one to mediate cathodic interfacial electrochemical behaviors, yet has not been unraveled. Here we propose an NH4+-modulated cationic solvation strategy to optimize cathodic spatial charge distribution and achieve dynamic Zn2+/NH4+ co-storage for boosting Zinc hybrid capacitors. Owing to the hierarchical cationic solvated structure in hybrid Zn(CF3SO3)2-NH4CF3SO3 electrolyte, high-reactive Zn2+ and small-hydrate-sized NH4(H2O)4+ induce cathodic interfacial Helmholtz plane reconfiguration, thus effectively enhancing the spatial charge density to activate 20% capacity enhancement. Furthermore, cathodic interfacial adsorbed hydrated NH4+ ions afford high-kinetics and ultrastable C‧‧‧H (NH4+) charge storage process due to a much lower desolvation energy barrier compared with heavy and rigid Zn(H2O)62+ (5.81 vs. 14.90eV). Consequently, physical uptake and multielectron redox of Zn2+/NH4+ in carbon cathode enable the zinc capacitor to deliver high capacity (240mAhg-1 at 0.5Ag-1), large-current tolerance (130mAhg-1 at 50Ag-1) and ultralong lifespan (400,000 cycles). This study gives new insights into the design of cathode-electrolyte interfaces toward advanced zinc-based energy storage.

Identification of Key Risk Factors in Mechanical Cross Passage Construction Based on the Decision-Making Test and Evaluation Test Method and the Interpretation Structure Model

In order to solve the problem that the key risk factors in the construction of mechanical cross passages are relatively vague, the decision-making test and evaluation test method (DEMATEL) and the interpretation structure model (ISM) are combined to analyze safety risks in the construction of mechanical cross passages scientifically and reasonably. Based on the ‘personnel’, ‘material’, ‘machine’, ‘method’, and ‘environment’ of 4M1E comprehensive management, the construction safety risk index system of mechanical method cross passage, including 18 risk factors, is extracted. DEMATEL was used to divide the risk factors into four factor sets: strong cause, weak cause, strong result, and weak result. Furthermore, ISM is used to construct a hierarchical structure diagram of risk factors, and deep risk factors and risk factors with a high node degree are divided. The results show that the safety awareness of construction personnel, the safety technology level of construction personnel, the establishment and implementation of the safety construction system, the level of construction management, and the degree of geological complexity are strong cause-type risk factors with both high centrality and high causality. Additionally, the effect of reinforcement of the internal support system, the setting of the digging parameter, the effect of grouting, and the effect of assembling the pipe sheet comprise the strong result-type risk factors with high centrality. The risk factors with higher node degree in ISM are consistent with the risk factors with higher centrality in the DEMATEL model, which are key factors that play an important role in risk control.

A New Means to Generate Liposomes by Rehydrating Engineered Lipid Nanoconstructs

The concept and feasibility of producing liposomes by rehydrating engineered lipid nanoconstructs are demonstrated in this study. Nanoconstructs of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) were produced using a microfluidic delivery probe integrated with an atomic force microscope. The subsequent rehydration of these POPC constructs led to the formation of liposomes, most of which remained adhered to the surface. The size (e.g., diameter) of the liposomes could be tuned by varying the lateral dimension of the lipid constructs. Hierarchical liposomal structures, such as pentagons containing five liposomes at the corners, could also be designed and produced by depositing lipid constructs to designated locations on the surfaces, followed by rehydration. This new means allows for regulating liposomal sizes, distributions, and compositions. The outcomes benefit applications of liposomes as delivery vehicles, sensors, and building blocks in biomaterials design. The ability to produce hierarchical liposomal structures benefits numerous applications such as proto-cell development, multiplexed bio-composite materials, and the engineering of local bio-environments.

HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning

BackgroundDrug–drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.ResultsThis study introduces a novel framework for DDI prediction termed HDN-DDI. HDN-DDI integrates an explainable substructure extraction module to decompose drug molecules and represents them using innovative hierarchical molecular graphs, which effectively incorporates information from real chemical substructures and improves molecules encoding efficiency. Furthermore, the enhanced dual-view learning method inspired by the underlying mechanisms of DDIs enables HDN-DDI to comprehensively capture both hierarchical structure and interaction information. Experimental results demonstrate that HDN-DDI has achieved state-of-the-art performance with accuracies of 97.90% and 99.38% on the two widely-used datasets in the warm-start setting. Moreover, HDN-DDI exhibits substantial improvements in the cold-start setting with boosts of 4.96% in accuracy and 7.08% in F1 score on previously unseen drugs. Real-world applications further highlight HDN-DDI’s robust generalization capabilities towards newly approved drugs.ConclusionWith its accurate predictions and robust generalization across different settings, HDN-DDI shows promise for enhancing drug safety and efficacy. Future research will focus on refining decomposition rules as well as integrating external knowledge while preserving the model’s generalization capabilities.

Hierarchical Nanocellulose Photonic Design for Synergistic Colored Radiative Cooling.

Daytime radiative cooling (DRC) materials offer a sustainable, pollution-free passive cooling solution. Traditional DRC materials are usually white to maximize solar reflectance, but applications like textiles and buildings need more aesthetic options. Unfortunately, colorizing DRC materials often reduce cooling efficiency due to colorant sunlight absorption. Thus, this study reports a hierarchical photonic structure consisting of photoluminescent scatterer networks and a nanocellulose cholesteric structure. This design effectively addresses the trade-off between cooling efficiency and coloration, achieving enhanced cooling through synergistic all-day coloration. Sustainable nanocellulose with highly ordered cholesteric structures selectively reflects visible wavelengths, generating structural color and high mid-infrared (MIR) emission. The photoluminescent scatterer networks enhance reflectance and convert absorbed light into photoluminescent emission, further promoting cooling. This synergistic photonic interaction results in a significantly enhanced high reflectance of 92%, high MIR emissivity of >90%, stable and tunable structural color appearance, and hours-long afterglow photoluminescence. Consequently, a subambient cooling of up to 11.3 °C under sunlight is attainable, accompanied by the ability to produce programmable structural color and photoluminescent patterns for daytime and nighttime visibility. The enhanced cooling efficiency achieved through the synergistic interplay of four optical mechanisms from the UV to MIR region offers a promising design paradigm for other DRC materials.

Mechanism-Based Fire Hazard Chain Risk Assessment for Roll-On/Roll-Off Passenger Vessels Transporting Electric Vehicles: A Fault Tree–Fuzzy Bayesian Network Approach

Roll-on/Roll-off passenger vessels transporting electric vehicles (Ro-Ro EVs) face unique fire hazards, challenging traditional fire risk management strategies. This study integrates fault tree analysis (FTA) with Fuzzy Bayesian Network (FBN) to assess the fire risks of Ro-Ro EVs across the entire hazard chain. Given limited historical accident data, five experts familiar with the Shanghai Baoshan–Chongming ferry route refine fault tree models to visualize key fire hazard chain mechanisms and estimate risk probabilities. The FBN incorporates fault tree hierarchical structures, EV and Ro-Ro vessel-related risk factors, and applies a nine-level fuzzy scoring system to assess these risks. The FTA-FBN model offers a comprehensive framework for evaluating emerging fire risks specific to Ro-Ro EVs. Findings indicate that the highest risk occurs during the ignition phase. Primary triggers include external heat sources, improper vehicle securing, and vehicle collisions, leading to thermal runaway in lithium batteries. Failures in extinguishing and detecting lithium battery fires exacerbate fire spread. Effective fire compartmentalization and flammable material management are essential to prevent uncontrolled fires. Recommendations for fire prevention and control include shipboard battery level monitoring, charging restrictions, explosion-proof electrical installations, enhanced ventilation, lithium battery fire suppression systems, and vehicle securing.

Preparation and electrochemical dynamics simulation of cellulose-based composite films with different hierarchical structures applied in supercapacitors

Preparation and electrochemical dynamics simulation of cellulose-based composite films with different hierarchical structures applied in supercapacitors

Sound absorption and enhancement mechanism of hierarchical slit-embedded Helmholtz resonators

Abstract In this paper, the low frequency sound absorption structure based on Helmholtz resonator (HR) is deeply studied. The influence of hierarchical structure design on the broadband sound absorption is emphatically discussed. Through the introduction of the embedded slits and hierarchical structures, we design a new and efficient hierarchical slit-embedded HR (HSEHR) for sound absorption. This structure not only inherits the advantages of the classic HR, but also realizes the effective sound absorption(α > 0.97) in a lower frequency(225 Hz) range through the interaction of the embedded slit and the hierarchical structure. More interestingly, the thickness of HSEHR is only 1/50 of the corresponding wavelength. In order to verify the sound absorption effect of HSEHR, we have carried out a lot of theoretical analysis and numerical simulation. The results show that HSEHR has excellent sound absorption performance in the low frequency range, and with the introduction of the hierarchical structure, the sound absorption peak moves to a lower frequency, and a higher sound absorption coefficient is obtained. We also found that by adjusting the structural parameters of HSEHR (such as the depth and width of the primary embedded slit.), its resonance frequency can be precisely controlled. So it can better match the target noise frequency and improve the sound absorption efficiency. In addition, genetic algorithm is used to optimize the structural parameters of HSEHR to further improve its sound absorption performance. The optimization results show that HSEHR optimized by genetic algorithm has better sound absorption performance in the broadband low frequency range. It achieves excellent sound absorption at 260–480 Hz. The sound absorption coefficient is up to 0.92, which is infinitely close to perfect sound absorption. It provides an excellent solution to the noise problem.

Precision Metal Nanoclusters Meet Proteins: Crafting Next-Gen Hybrid Materials.

Metal nanoclusters (NCs), owing to their atomic precision and unique molecule-like properties, have gained widespread attention for applications ranging from catalysis to bioimaging. In recent years, proteins, with their hierarchical structures and diverse functionalities, have emerged as good candidates for functionalizing metal NCs, rendering metal NC-protein conjugates with combined and even synergistically enhanced properties featured by both components. In this Perspective, we explore key questions regarding why proteins serve as complementary partners for metal NCs, the methodologies available for conjugating proteins with metal NCs, and the characterization techniques necessary to elucidate the structures and interactions within this emerging bionano system. We also highlight the emerging applications of metal NC-protein conjugates in biomedicine, catalysis, and biosensing in which the hybrid conjugates demonstrate remarkable performance. Furthermore, key challenges hampering further development of metal NC-protein conjugates, which include understanding binding mechanisms, expanding the diversity of proteins used for conjugation, and exploiting the individual roles of metal NCs and proteins within the hybrid systems, are discussed. This Perspective aims to systemize current synthetic methodologies and design principles for metal NC-protein conjugates, adding to their acceptance in precision nanotechnology.

Derivation of human toxicokinetic parameters and internal threshold of toxicological concern for tenuazonic acid through a human intervention trial and hierarchical Bayesian population modeling.

Tenuazonic acid (TeA), a mycotoxin produced by Alternaria alternata, contaminates various food commodities and is known to cause acute and chronic health effects. However, the lack of human toxicokinetic (TK) data and the reliance on external exposure estimates have stalled a comprehensive risk assessment for TeA. To bridge this gap, a human TK trial and population-based TK (PopTK) modeling were applied to determine human TK parameters of TeA, and the results were applied for risk screening using population biomonitoring data and threshold of toxicological concern (TTC)-based approaches. Ten healthy volunteers participated in the TK trial during which the volunteers ingested a bolus dose of TeA at the (external) TTC (1500 ng/kg bw). Blood, urine, and fecal samples were collected over 48 h and analyzed using UPLC-MS/MS. Concentration-time profiles were fit with a multi-compartmental PopTK model using a hierarchical Bayesian population structure. Utilizing a probabilistic framework, fitted TK parameters were used to derive internal TTC (iTTC) values for comparison to blood and urine biomonitoring data. Risk screening with data from five diverse biomonitoring cohorts was performed using Hazard Quotient (HQ) and probabilistic individual margin of exposure (IMOE) approaches. TeA was estimated to have a population median half-life of 1.9 [90% CI: 1.4-2.7] hours and volume of distribution of 4.4 [3.1-6.1] L/kg, with inter-individual variability geometric standard deviations of 2.4- and 1.7-fold, respectively. Probabilistic lower confidence bound iTTCs were derived of 0.5 nmol/L in blood and 2.53 nmol/kg-d urinary excretion. Risk screening HQs were mostly >1 for the three blood biomonitoring cohorts and < 1 for the two urinary biomonitoring cohorts; results from probabilistic IMOE calculations were qualitatively consistent. A comprehensive human TK study was performed for TeA for the first time, demonstrating the importance of integrating TK and population variability for a more comprehensive risk evaluation, particularly for interpreting biomonitoring data. The results for TeA point to the critical need for toxicity data to move beyond TTC-based risk screening. A critical gap in food safety research was addressed studying the toxicokinetics of tenuazonic acid (TeA) in humans and using these data to derive an internal threshold of toxicological concern (iTTC) for comparison to human biomonitoring data. The innovative approach-combining a human intervention trial with population-based toxicokinetic modeling-accounts for inter-individual variability and provides a more comprehensive understanding of population exposure to TeA. The resulting probabilistic iTTC and risk screening methodologies offer improved tools for interpretation of biomonitoring data. These findings have significant implications for food safety regulations and public health protection, potentially influencing future mycotoxin risk assessment strategies.

Artificial Cornea Substitute Based on Hydrogel Skeletons with Natural Stromal Hierarchical Structure and Extracellular Matrix for Sutureless Transplantation.

Corneal substitutes with structural and compositional characteristics resembling those of natural corneas have attracted considerable attention. However, biomimicking the complex hierarchical organization of corneal stroma is challenging. In this study, humanized corneal stroma-like adhesive patches (HCSPs) are prepared through a multi-step process. First, polyethylene glycol diacrylate is cast and cured within decellularized porcine cornea (DPC) templates. The DPCs are then enzymatically digested to obtain hydrogel skeletons, which are finally integrated with human corneal extracellular matrix and methacrylate gelatin. HCSPs replicate the ultrastructure, protein components, and optical properties of human corneas and exhibit improved anti-swelling and anti-degradation capabilities compared with conventional DPCs and recombinant human collagen patches. HCSPs can deliver methacrylate gelatin at the ocular surface temperature (37°C) and achieve stable adhesion to the corneal stroma upon 405nm light irradiation. Furthermore, HCSPs promote the survival and migration of corneal epithelial and stromal cells while preserving their phenotypes. In rabbit models of lamellar keratoplasty and microperforation repair, HCSPs accelerate epithelial healing, minimize suture-associated complications, and maintain structural stability. These findings suggest that HCSPs are promising donor corneal substitutes for clinical applications.

From Pixels to Protection: Deep Learning Approaches for Plant Leaf Disease Detection

Plants are essential for human survival. However, diseases affecting plant leaves can lead to significant reductions in crop yield and economic losses. Detecting these diseases early is crucial in agriculture. To overcome these limitations, machine learning has been employed to automate the identification of plant leaf diseases. By analysing features such as colour, intensity, and shape, machine learning models classify diseases into specific categories, offering faster and more accurate results than conventional approaches. Various ML techniques are used to identify diseases in plant leaves, with deep learning gaining attention for its ability to automate learning and perform advanced feature extraction. CNNs have become a highly effective tool for plant leaf disease identification, thanks to their ability to automatically extract features from images and achieve high classification accuracy. Their hierarchical structure enables them to detect simple patterns in initial layers and progressively learn more complex features in deeper layers, capturing the intricate details of disease symptoms. Additionally, CNNs can process large datasets and classify multiple diseases accurately, even with limited labelled data, by leveraging pre-trained models through transfer learning.

Resilience of hierarchical actuators highlighted by a myosin-to-muscle mock-up.

Skeletal muscle is the main actuator of various families of vertebrates (mammals, fish, reptiles). It displays remarkable robustness to micro-damage, that supposedly originates both from its redundant hierarchical structure and its nervous command. &#xD;A bioinspired mock-up was designed and manufactured mimicking sarcomeres (micro-scale) and its series and parallel structure from fibre to muscle. First, the mechanical performances namely the force-velocity curve of the intact muscle mock-up were measured and modelled. Then, mimicking micro-damage by making some myosin heads inoperative, the mechanical performances were again measured to determine the resilience of the actuator. The mock-up is shown to be resilient: in the event of 10% damage of the mock-up, the mechanical performance of the mock-up was around 80% of the intact one. In this multi degrees of freedom actuator with hierarchical structure, the resilience is shown to be almost linear with the damage level for uniformly distributed damage (both maximal force and velocity decrease). Differently when micro-damage are clustered on a fibre, this decreases the maximal force with little effect on velocity.&#xD.

Bio-Inspired Highly Stretchable and Ultrafast Autonomous Self-Healing Supramolecular Hydrogel for Multifunctional Durable Self-Powered Wearable Devices.

As skin bioelectronics advances, hydrogel wearable devices have broadened perspectives in environment sensing and health monitoring. However, their application is severely hampered by poor mechanical and self-healing properties, environmental sensitivity, and limited sensory functions. Herein, inspired by the hierarchical structure and unique cross-linking mechanism of hagfish slime, a self-powered supramolecular hydrogel is hereby reported, featuring high stretchability (>2800% strain), ultrafast autonomous self-healing capabilities (electrical healing time: 0.3 s), high self-adhesiveness (adhesion strength: 6.92kPa), injectability, ease of shaping, antimicrobial properties, and biocompatibility. It is observed that by embedding with the highly hygroscopic salt LiCl in supramolecular hydrogel, the hydrogel not only showed excellent electrical conductivity but also presented favorable anti-freezing and water retention properties in extremely cold environments and natural settings. Given these attributes, the hydrogel served as a multifunctional durable self-powered wearable device with high sensitivity (gauge factor: 3.68), fast response time (160ms), low detection limit, and frequency sensitivity. Moreover, the multifunctional applicability of this supramolecular hydrogel is further demonstrated in long-term cold environments sensing, remote medical communication, and underwater communication. Overall, these findings pave the way for the sustainable development of hydrogel-based wearable devices that are self-powered, durable, and offer high performance, environmental adaptability, and multi-sensory capabilities.

Factors Influencing Science Teachers' Professional Development: A Cultural‐Historical Activity Theory Perspective

ABSTRACTExploring the factors influencing science teacher professional development (STPD) and their interactions may provide insight into promoting professional development and improving science education. However, existing research mainly focuses on STPD in larger domains, such as individual, external, and contextual, without paying systematic attention to the connections between internal elements within domains. Therefore, this study utilized the cultural‐historical activity theory (CHAT) as a framework to systematically explore the interactions between factors influencing STPD. Content analysis was used to identify the factors influencing STPD. Subsequently, we adopted a convergent parallel mixed research method, aiming at a more comprehensive exploration of how these factors affect STPD. The interpretive structural model (ISM) and cross‐influence matrix multiplication (MICMAC) method were used to explore the interrelationships among the factors. Furthermore, from the CHAT perspective, semi‐structured interviews were used to identify the tensions in STPD. The ISM results showed that the factors influencing STPD have a six‐level hierarchical structure. Level 1 represents factors that directly influence STPD. The influences at Level 1 were also directly or indirectly influenced by the factors at Levels 2–6. The primary and secondary contradictions in STPD were identified by integrating the MICMAC and interview results under the CHAT lens. This study established that ISM and MICMAC not only identifies the hierarchical relationship between the influencing factors but also better identifies the contradictions between and within different types of factors from the perspective of CHAT. Based on our results, we suggest a focus on professional practice and learning communities, rural and remote school teachers, and efficient use of technology to connect experts, teacher trainers, and peer groups to improve the reach and efficacy of STPD.

Analytic Hierarchy Process Optimization of Strategic Resource Management for Sustainable Development: Enhancing International Economic and Commercial Relations through Circular Economy Factors in Open Socio-Economic Systems

This article explores how the Analytic Hierarchy Process (AHP) can optimize strategic resource management for sustainable development, emphasizing circular economy factors in open socio-economic systems. A thorough literature review links existing research in financial security, innovation, and risk management to illustrate the significance of integrating environmental considerations into long-term resource allocation. The proposed AHP framework decomposes complex decision-making into a hierarchical structure that includes both quantitative metrics, such as financial stability and waste reduction, and qualitative concerns, like stakeholder engagement and regulatory compliance. Expert evaluations highlight the primacy of environmental and circular economy factors in resource management, closely followed by financial and risk considerations. Waste reduction, resource reuse, and supply chain resilience emerge as top-ranked sub-criteria, underscoring the importance of balancing ecological imperatives with economic viability. The study further highlights digital transformation as an essential enabler of real-time analysis and adaptive resource allocation. Integrating these insights within a broader policy and managerial context offers a roadmap for enhancing international economic and commercial relations, especially in turbulence-prone global markets. The findings suggest that holistic, multi-criteria decision-making—supported by AHP—provides a robust pathway toward sustainable development, promoting resilience and competitiveness in open socio-economic systems.

Research on ACC Control Strategy for Electric Vehicles Considering Cut-in Behavior

Background: Current adaptive cruise control systems primarily focus on the main target vehicle in the primary lane. However, there may be a delay in updating their targets after a side vehicle merges. This delay can impede the effective utilization of driving data from merging side vehicles, resulting in delayed responses and an increased risk of collision. Objective: Aiming at the problem of untimely control of traditional adaptive cruise control systems in cut-in scenarios, the authors establish a parallel model and collision analysis and design the control strategy of adaptive cruise under parallel behavior to improve the safety, following, and comfort of ACC. Methods: Initially, the vehicle model provided by CarSim is utilized and the power system parameters are adjusted to match the dynamic performance index. The permanent magnet synchronous motor model is then simplified to create an electric vehicle model. Following this, the cruise control system is designed using a PID control method, incorporating a hierarchical control structure. Simultaneously, a cut-in model characterized by sinusoidal acceleration is established, and a collision avoidance strategy based on cut-in behavior is formulated. Building on this foundation, an adaptive cruise control strategy is developed that takes cut-in behavior into account. Results: A test scenario utilizing the CarSim and Simulink co-simulation platforms was developed to simulate and validate the conditions for mode switching and lane cut-in. The ACC system, considering cut-in behavior recognition, demonstrated an ability to control the lead vehicle approximately 1 second earlier than traditional ACC systems. Notably, the distance error improved from - 0.1039 m to 0.040 m, the absolute value of velocity error decreased from 2.65 m/s to 2.02 m/s, the absolute value of main vehicle acceleration reduced by 34%, and the jerk response in acceleration diminished by approximately 14%. Conclusion: The proposed multi-objective cooperative adaptive cruise control system, which considers cut-in behavior, is effective.