Hierarchical Research Articles

Elliptic geometry-based kernel matrix for improved biological sequence classification

Protein sequence classification plays a pivotal role in bioinformatics as it enables the comprehension of protein functions and their involvement in diverse biological processes. While numerous machine learning models have been proposed to tackle this challenge, traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences. These limitations stem from operating within high-dimensional non-Euclidean spaces. To address this issue, we introduce the application of the elliptic geometry-based approach for protein sequence classification. First, we transform the problem in elliptic geometry and integrate it with the Gaussian kernel to map the problem into the Mercer kernel. The Gaussian-Elliptic approach allows for the implicit mapping of data into a higher-dimensional feature space, enabling the capture of complex nonlinear relationships. This feature becomes particularly advantageous when dealing with hierarchical or tree-like structures commonly encountered in biological sequences. Experimental results highlight the effectiveness of the proposed model in protein sequence classification, showcasing the advantages of utilizing elliptic geometry in bioinformatics analyses. It outperforms state-of-the-art methods by achieving 76% and 84% accuracies for DNA and Protein datasets, respectively. Furthermore, we provide theoretical justifications for the proposed model. This study contributes to the burgeoning field of geometric deep learning, offering insights into the potential applications of elliptic representations in the analysis of biological data.

A nanoporous nickel–iron alloy seamlessly anchored into hierarchical porous nickel for efficient and extremely stable water splitting

The development of a robust, low-cost electrode for electrochemical water splitting is a challenging, but essential task for realizing hydrogen production under industrial conditions. Here, we seamlessly anchor nanoporous nickel–iron into a hierarchical porous nickel foam (npNi–Fe/HPNF) by a designed gaseous oxidation–reduction engineering for a commercial nickel foam. The three-dimensional hierarchical porous structure considerably enhances the specific surface area of the electrode, yielding abundant electrocatalytic sites that promote the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Moreover, owing to its mechanically robust, seamlessly anchored architecture, and remarkable superhydrophilicity/superaerophobicity, the electrolyzer composed of self-standing npNi–Fe/HPNF and its electro-activation derivative displays a current density of 10 mA cm−2 at 1.54 V, ultralong durability of more than 2000 h at 500 mA cm−2 in a 1 M KOH electrolyte, and high performance of 2 V at 1 A cm−2 in a quasi-industrial environment (5 M KOH and 70 °C), outperforming most alkaline electrolyzers based on state-of-the-art electrodes. Exceptional electrochemical performance and the ability to be prepared at a large scale (2500 cm2) make npNi–Fe/HPNF a promising candidate for industrial water splitting applications.

Determination of seven phenoxy carboxylic acid herbicides in water using dispersive solid phase extraction coupled with ultra-high performance liquid chromatography-tandem mass spectrometry based on metal-organic framework composite aerogel

Phenoxy carboxylic acid herbicides (PCAs) are difficult to degrade and, thus, pose significant threats to the environment and human health. The limit for 2,4-dichlorophenoxyacetic acid is 30 μg/L in China's standards for drinking water quality, 70 μg/L in the United States' drinking water standards, and 30 μg/L in the World Health Organization's guidelines for drinking water quality. Therefore, the development of an effective detection method for trace PCAs in water is a crucial endeavor. Metal-organic frameworks (MOFs) are novel porous materials that possess advantages such as a large specific surface area, adjustable pore size, and abundant active sites. They exhibit excellent adsorption capability for various compounds. However, the applications of MOFs as adsorbents are limited. For example, the process of isolating powdered MOFs from aqueous solutions is laborious, and microporous MOFs exhibit limited surface affinity, which decreases their mass transfer efficiency in the liquid phase. MOF crystals can be embedded in a substrate to overcome these limitations. Aerogels are obtained by drying hydrogels, which are hydrophilic polymers with a three-dimensional crosslinked network structure. Spongy aerogel materials exhibit unique structural properties such as high porosity, large pore volume, ultralow density, and easy tailorability. When MOFs are combined with an aerogel, their efficient and selective adsorption properties are preserved. In addition, MOF aerogels exhibit a hierarchical porous structure, which enhances the affinity and mass transfer efficiency of the MOF for target molecules. At present, MOF aerogels are primarily prepared by freeze-drying or using supercritical carbon dioxide. These drying processes require significant amounts of energy and time. Hence, the development of greener and more efficient methods to prepare skeleton aerogels is urgently needed. In this study, we prepared an environment-friendly aerogel at ambient temperature and pressure without the use of specialized drying equipment. This ambient-dried MOF composite aerogel was then used for the dispersive solid phase extraction (DSPE) of seven PCAs from environmental water, followed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The key parameters affecting the efficiency of DSPE, including the extraction conditions, ratio of MIL-101(Fe)-NH2 to sodium alginate, pH of the aqueous samples, extraction time, ionic strength (salinity), and elution conditions, such as the elution solvent ratio, elution time, and elution volume, were investigated to obtain optimal extraction efficiency. The adsorbent could adsorb the target contaminants within 12 min, and the analytes could be completely desorbed within 30 s by elution with 4 mL of 1.5% (v/v) formic acid in methanol solution. The water samples could be analyzed without pH adjustment. The main adsorption mechanisms were electrostatic interactions and π-π conjugation. Thus, a new method based on MOF aerogels coupled with UHPLC-MS/MS was developed for the determination of the seven PCA residues in water. The calibration curves for the seven PCAs showed good linearity (r2≥0.9986), with limits of detection (LODs) and quantification (LOQs) ranging from 0.30 to 1.52 ng/L and from 1.00 to 5.00 ng/L, respectively. Good intra- and inter-day precision values of 6.5%-17.1% and 7.4%-19.4%, respectively, were achieved under low (8 ng/L), medium (80 ng/L), and high (800 ng/L) spiking levels. The developed method was applied to the detection of PCAs in surface water, seawater, and waste leachate, and the detected mass concentrations ranged from 0.6 to 19.3 ng/L. Spiked recovery experiments were conducted at mass concentrations of 8, 80, and 800 ng/L, and the recoveries ranged from 61.7% to 120.3%. The proposed method demonstrates good sensitivity, precision, and accuracy, and has potential applications in the detection of trace PCAs in environmental water.

Interfacial engineering of 0D–2D hierarchical MXene@PEI@AgNC nanohybrids to achieve flame retardant, mechanical and antibacterial properties of ABS nanocomposites

It is an intractable challenge that the hierarchical MXene-based nanohybrid fillers are intended to modify the inherent flammability and easily breeding bacteria property of ABS polymeric materials, thereby facilitating wide applicability of ABS composites. Here, an interface engineering strategy was proposed, wherein polyethyleneimine (PEI)-modified silver nanocubes (AgNCs) was firmly assembled on MXenes via electrostatic interaction to construct 0D–2D hierarchical MXene-based nanohybrids (MXene@PEI@AgNC). The MXene was protected and the interface compatibility between MXene-based nanohybrid and ABS matrix was enhanced. Results revealed that the unique hierarchical structure of the composite promoted enhanced dispersion of MXene@PEI@AgNC in the ABS matrix. Combustion tests showed that 1.0 wt%MXene@PEI@AgNC provided the ABS with effective fire safety. Moreover, ABS-1.0 wt%MXene@PEI@AgNC exhibited a 31.88 % decrease in peak heat release rate, a 39.60 % decrease in peak smoke release rate, a 41.50 % decrease in peak CO2 production rate, and a 50.04 % reduction in the smoke factor. Meanwhile, 1.0 wt%MXene@PEI@AgNC improved the tensile strength of ABS-1.0 %MXene@PEI@AgNC (+18.1 %) without reducing the elongation at break. They also enhanced the antibacterial properties of ABS-1.0 %MXene@PEI@AgNC. This study proposed a novel and feasible approach to address the interfacial stability of MXene and to construct MXene-based hierarchical nanoblends, thereby facilitating the wide practical applications of polymeric nanomaterials in industry.

Hybrid 2D/3D carbon framework confined Si with optimized reaction kinetics for highly stable Li-Ion storage

Three-dimensional (3D) conductive skeletons can optimize electron/Li-ion migration kinetics and alleviate stress accumulation for silicon (Si)-based electrodes. In this study, the modified carbon nanotubes (MCNs) are used as a stress-buffering and high-speed conducting framework, and a hierarchical structure of 3D MCNs interspersed with flour-derived 2D N-doped C layer is designed for Si anode via molecular self-assembly and in-situ carbonization strategies. Experimental and theoretical calculations show that the charge redistribution occurred in the fabricated SiOx and N-doped C interfaces, which induced an electric field response and increased the interfacial electron/Li-ion transfer rate. Multiphysics simulations show that the 2D/3D hierarchical structures of carbon can optimize the physicochemical properties, such as a favorable local electronic environment, flexible stress dissipation mechanisms and good thermal stability. The prepared electrode with 77.1 wt% Si@SiOx shows a low volume expansion rate of 23 % and has an excellent Li-ion storage capability (972.1 mAh g−1 at 4000 mA g−1). Moreover, the structural stability of fabricated Si-based electrodes is enhanced, achieving 0.04 % per cycle capacity decay for 500 cycles at 2000 mA g−1. Such integrating the 2D/3D carbon framework to manipulate Si interfacial properties provides fundamental research for other electrodes plagued by significant volume expansion.

Optimizing University administrative structures: Strategies for enhancing efficiency and effectiveness in higher education institutions in Nigeria

This study investigated the inefficiencies within Nigerian university administrative structures, prompted by ongoing challenges such as bureaucratic bottlenecks, poor coordination between academic and administrative units, and insufficient technological integration. These issues have hindered the effectiveness of university operations and negatively impacted staff and student satisfaction. The research adopted a mixed-methods approach, using both quantitative surveys and qualitative interviews to gather data from select Nigerian universities. The survey measured perceptions of administrative inefficiencies through Likert-scale questions, while interviews with key stakeholders provided deeper insights into structural challenges. The findings revealed significant delays in decision-making, caused by hierarchical administrative structures, as well as a lack of communication between academic and administrative units. Additionally, technological integration was found to be inconsistent and inadequate, exacerbated by financial constraints and insufficient training. These results were consistent with prior studies, highlighting the need for structural reforms to enhance efficiency. The study concluded that optimizing university administrative structures requires decentralizing decision-making, improving communication between departments, investing in modern technology, and implementing clear accountability mechanisms. Based on these findings, the study recommended promoting collaborative practices, adopting digital solutions, and fostering transparency to rebuild trust within the university community. Addressing these inefficiencies is crucial for improving institutional performance and ensuring Nigerian universities remain competitive in a rapidly evolving global educational landscape.

Scalable Macroscopic Engineering from Polymer-Based Nanoscale Building Blocks: Existing Challenges and Emerging Opportunities.

Natural materials exhibit exceptional properties due to their hierarchical structures spanning from the nano- to the macroscale. Replicating these intricate spatial arrangements in synthetic materials presents a significant challenge as it requires precise control of nanometric features within large-scale structures. Addressing this challenge depends on developing methods that integrate assembly techniques across multiple length scales to construct multiscale-structured synthetic materials in practical, bulk forms. Polymers and polymer-hybrid nanoparticles, with their tunable composition and structural versatility, are promising candidates for creating hierarchically organized materials. This review highlights advances in scalable techniques for nanoscale organization of polymer-based building blocks within macroscopic structures, including block copolymer self-assembly with additive manufacturing, polymer brush nanoparticles capable of self-assembling into larger, ordered structures, and direct-write colloidal assembly. These techniques offer promising pathways toward the scalable fabrication of materials with emergent properties suited for advanced applications such as bioelectronic interfaces, artificial muscles, and other biomaterials.

Preparation of Novel Hierarchical Catalysts by Simultaneous Generation of β-Zeolite and Mesoporous Silica for Catalytic Cracking.

The gel skeletal reinforcement (GSR) method was applied at the preparation stage of β-zeolite to prepare a novel hierarchical catalyst. A solution of hexamethyldisiloxane (HMDS) and acetic anhydride, a GSR reagent, was added to the mixture of colloidal silica, sodium aluminate, tetraethylammonium hydroxide, sodium hydroxide and water, and successive aging and hydrothermal treatment gave microporous β-zeolite surrounded by mesoporous silica like core-shell structure. Its properties were characterized by XRD, nitrogen adsorption and desorption, NH3-TPD, TEM, and TG-DTA measurements, and further characteristics of the catalysts produced were clarified by the catalytic cracking of n-dodecane. The hierarchical structure of microporous zeolite and mesoporous silica was shown from GSR-2.9HS-H-Beta to GSR-3.2HS-H-Beta, where the molar ratio of HMDS and silica source of β-zeolite was 2.9~3.2 : 100. It was found that in the catalytic cracking of n-dodecane, the relative activity (the conversion per the amount of zeolite crystals) increased with the increase in mesopore volume and surface area. The result indicated that the introduction of mesopores was effective even in catalytic cracking of small molecule of n-dodecane.

PASCL: supervised contrastive learning with perturbative augmentation for particle decay reconstruction

Abstract In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Continuous Phase Separation Induced Tough Hydrogel Fibers with Ultrahigh Conductivity for Multidimensional Soft Electronics

AbstractConductive hydrogel fibers exhibit great potential in soft robots, bioelectronics, and human–machine interfaces due to the unique combination of electrical conductivity, high water content, tissue‐like mechanical properties, and 1D structure. Despite significant advances in hydrogel technologies, the typical conductive hydrogel fibers show low conductivity (<10 S cm−1), weak mechanical properties, and water stability, which makes it challenging to satisfy the requirements of practical applications. Here, a facile strategy is proposed to construct hydrogel fibers with ultrahigh conductivity and toughness by exploiting the synergistic effects of freezing‐thawing, salting‐out, and drying‐annealing. The continuous phase separation induced by the combined processes results in hierarchical structures, promoting the formation of interconnected conductive networks and increasing the fiber's crystallinity and crystal domain size. The prepared conductive hydrogel fibers exhibited ultrahigh conductivity (≈958 S cm−1), excellent mechanical properties (strength (≈6.2 MPa), stretchability (>300%), and toughness (≈10 MJ m−2)), high water content (≈75%), outstanding water stability, and fatigue resistance properties. In addition, the processibility of conductive hydrogel yarns and fabrics are demonstrated and their potential application in bioelectronics. Overall, this work presents a preparation strategy for conductive hydrogel fibers, which will facilitate the advancement of soft electronics and may inspire structural construction in other polymers.

Overlapping community detection in weighted networks via hierarchical clustering.

In real-world networks, community structures often appear as tightly connected clusters of nodes, with recent studies suggesting a hierarchical organization where larger groups subdivide into smaller ones across different levels. This hierarchical structure is particularly complex in trade networks, where actors typically belong to multiple communities due to diverse business relationships and contracts. To address this complexity, we present a novel algorithm for detecting hierarchical structures of overlapping communities in weighted networks, focusing on the interdependency between internal and external quality metrics for evaluating the detected communities. The proposed Graph Hierarchical Agglomerative Clustering (GHAC) approach utilizes maximal cliques as the basis units for hierarchical clustering. The algorithm measures dissimilarities between clusters using the minimal closed trail distance (CT-distance) and the size of maximal cliques within overlaps, capturing the density and connectivity of nodes. Through extensive experiments on synthetic networks with known ground truth, we demonstrate that the adjusted Silhouette index is the most reliable internal metric for determining the optimal cut in the dendrogram. Experimental results indicate that the GHAC method is competitive with widely used community detection techniques, particularly in networks with highly overlapping communities. The method effectively reveals the hierarchical structure of communities in weighted networks, as demonstrated by its application to the OECD weighted trade network, which describes the balanced trade value of bilateral trade relations.

Hierarchically Structured Conductive Lanthanide Metal Organic Framework Nanorods for Ultrastable Flexible Magnesium Ion Capacitors

AbstractAqueous multivalent metal ion capacitors are very attractive owing to their high capacitance, low cost, environmental benignity, and safety. Herein, hierarchically structured, conductive lanthanide metal‐organic frameworks (MOFs) assembled by nanorods for ultrastable flexible magnesium ion capacitors (MICs) is designed. Three MOFs, consisting of lanthanide metals (La, Ho, and Yb) and 2,3,6,7,10,11‐hexahydroxytriphenylene (HHTP), are structurally stabilized through the covalent bonds and electronically conductive due to π‐d conjugation. Among 3 MOFs, Ho‐HHTP electrodes in a full‐cell system achieved long‐term cyclic stability with a high capacitance retention of 65.0% after 12 000 cycles and a high Coulombic efficiency of 96.9%. Furthermore, flexible pouch cells are fabricated using Ho‐HHTP as anode, reduced graphene oxide as cathode, and poly(vinyl alcohol) (PVA)/Mg(ClO4)2 as gel electrolyte, demonstrating a low capacitance decay rate of 0.00444% per cycle during 10 000 cycles owing to the hierarchical structure and intrinsic electronic conductivity. As indicated by density functional theory and spectroscopic characterizations, Mg2+ ions are faradaically stored in the catechol part of the ligand, and Mg2+ inserted into the vacant Ho sites contributes to structural stability. Furthermore, operando 2D correlation Raman spectroscopy identified how Mg2+ ions interact with the ligand of Ho‐HHTP and demonstrated the sequential change of peak change.

Achievements and Challenges in Surfactants-Assisted Synthesis of MOFs-Derived Transition Metal-Nitrogen-Carbon as a Highly Efficient Electrocatalyst for ORR, OER, and HER.

Metal-organic frameworks (MOFs) are excellent precursors for preparing transition metal and nitrogen co-doped carbon catalysts, which have been widely utilized in the field of electrocatalysis since their initial development. However, the original MOFs derived catalysts have been greatly limited in their development and application due to their disadvantages such as metal atom aggregation, structural collapse, and narrow pore channels. Recently, surfactants-assisted MOFs derived catalysts have attracted much attention from researchers due to their advantages such as hierarchical porous structure, increased specific surface area, and many exposed active sites. This review mainly focuses on the synthesis methods of surfactants-assisted MOFs derived catalysts and comprehensively introduces the action of surfactants in MOFs derived materials and the structure-activity relationship between the catalysts and the oxygen reduction reaction, oxygen evolution reaction, and hydrogen evolution reaction performance. Apparently, the aims of this review not only introduce the status of surfactants-assisted MOFs derived catalysts in the field of electrocatalysis but also contribute to the rational design and synthesis of MOFs derived catalysts for fuel cells, metal-air cells, and electrolysis of water toward hydrogen production.

Micro/Meso‐Porous Double‐Shell Hollow Carbon Spheres through Spatially Confined Pyrolysis for Supercapacitors and Zinc‐Ion Capacitor

AbstractEnergy storage in supercapacitors and hybrid zinc ion capacitors (ZIC) using porous carbon materials offers a promising alternative method for clean energy solutions. The unique combination of hierarchical porous structure and nitrogen doping in these materials has demonstrated significant capacity for energy storage. Nevertheless, the full potential of these materials, particularly the relationship between pore structure configuration and performance, remains underexplored. Herein, a confined pyrolysis strategy based on the polymerization characteristics of polydopamine (PDA) was developed to construction of hollow carbon spheres with microporous/mesoporous dual shell structure. The depth of micropores and cavity can be controlled by adjusting the duration of heat treatment and hydrothermal treatment, in accordance with the decomposition and polymerization characteristics of PDA. Due to the elasticity of this structure, the relationship between the micro/mesoporous depth of the prepared carbon spheres and the energy storage performance in supercapacitors and ZIC is established. Through optimizing the ion transport capacity of carbon spheres and considering the influence of its internal cavity structure on energy storage, the resulting carbon spheres exhibit high specific capacitance of 389 F g−1 in supercapacitor and specific capacitance of 260 F g−1 and excellent stability with 99.3 % retention after 30000 chare/discharge cycles in ZIC.

Electrochemical Sacrificial Alchemy: Crafting Hollow Hydroxide Hierarchy for Practical Aqueous Energy Storage Solution

AbstractEffective utilization of internal active sites in high‐mass loading electrode materials is essential for advancing practical energy storage. Herein, a novel electrochemical sacrificial alchemy for the first time to directly transform the solid Co‐Ni‐Zn carbonate hydroxide (CH) into its hollow structure with a strategic hierarchical architecture is introduced. In contrast to the complex and often cumbersome procedures of traditional methods, the approach is built on the simple electrochemically induced in situ etching and remodeling process. The experimental and theoretical analyses highlight the significant role of electric field force‐induced Zn sacrificial etching and hydroxide redeposition in creating internal cavities and regenerating external active sites, leading to the final hollow hierarchical structure with primary 1D hollow nanorod arrays and secondary reconstructed 2D nanoflakes, effectively exposing abundant energy storage sites. Benefiting from the synergistic effect, the resulting Co‐Ni‐Zn CH exhibited exceptional electrochemical performance. As proof of concept, a modular pouch‐type hybrid supercapacitor (HSC) composed of Co‐Ni‐Zn CH cathode achieved an ultrahigh energy density of 46.9 Wh kg−1. This device can efficiently power consumer electronics with a faster charging speed compared to commercial shared power banks. This research unveils a facile electrochemical approach for structuring electrode materials in energy storage solutions.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BackgroundIn randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.MethodsWe conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.ResultsWe identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.ConclusionsWe found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

A system dynamics model based on ISM for risk perception in emergency by employees in chemical industrial parks

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

Biomimetic Superhydrophobic Triboelectric Surface Prepared by Interfacial Self‐Assembly for Water Harvesting

AbstractExtracting water from air offers a promising route to address the global challenge of water scarcity. However, bionic engineering surfaces for water harvesting often struggle with efficiently coordinating droplet nucleation and desorption. The recently emerging triboelectric effect at the liquid–solid interface offers a novel approach for developing bionic fog harvesting surfaces. In this study, inspired by Namib Desert beetles and lotus leaves, a biomimetic superhydrophobic surface with nanoscale hydrophilic domains is prepared via interfacial self‐assembly, exhibiting heterogeneous wettability for effective water harvesting. The essence of interfacial self‐assembly lies in the synergy of non‐covalent interaction forces, driving the self‐assembly of nanoparticles into surface micro‐nano hierarchical structures, thereby regulating wettability and increasing potential nucleation sites. Additionally, the triboelectric effect can directly utilize the triboelectric charges to facilitate droplet migration. The triboelectric effect can be generated by flexible mechanical input induced by walking, which enhances interfacial mass transfer, thereby rapidly improving droplet removal and achieving a 39.02% increase in water harvesting efficiency. This study has opened up a new breakthrough for the design of portable and efficient water harvesting systems.

Stakeholder-based management of ecosystem services in agricultural areas: Integrating the leader-follower game and a conflict resolution model

Given the substantial effects of agricultural practices on the environment, this paper introduces a novel stakeholder-based framework for assessing the ecosystem services (ESs) provided by agricultural areas. Ecosystem services include essential functions such as water supply, food production, carbon storage, soil erosion control, and habitat support. In addition to ESs, water footprint is also taken into account to evaluate the impacts of agricultural activities on water resources. Some of the mentioned ESs are assessed using the Soil and Water Assessment Tool (SWAT). Then, by extending and combining the Conflict Resolution Model with the Composition of Probabilistic Preferences (CRMCPP) method and the leader-follower game (LFG), while considering the hierarchical structure of decision-makers, the best scenario for enhancing the ESs is selected.The Zarrinehroud River Basin (ZRB) in Iran has been chosen as a case study to evaluate the performance of the proposed framework, as this basin is vital for supplying water to Lake Urmia, the largest hypersaline lake in the Middle East. In this paper, 16 Water and Environmental Resources Management (WERM) scenarios have been defined according to the Urmia Lake Restoration National Committee (ULRNC) projects. Then, the mentioned ESs have been evaluated under different WERM scenarios. Ultimately, by utilizing the CRMCPP-LFG method and taking into account the hierarchical structure of decision-makers, we can identify the optimal WERM scenario. The criteria for making this decision include various factors, such as ecosystem services and the costs involved in implementing the WERM scenarios. In the selected scenario, the average water inflow into Lake Urmia is projected to rise to 1329 million cubic meters per year, which is 6.3% more than the average inflow in the current condition. Key initiatives in this scenario include reducing cultivated areas, altering irrigation methods, changing crop patterns, and incorporating water-efficient plant species.

Collapsible tree: interactive web app to present collapsible hierarchies.

A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-SNE and UMAP, require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells. Here, we present a JavaScript framework and an interactive web app named Collapsible Tree, which presents values jointly with interactive, expandable, and collapsible lineage structures. For example, the Collapsible Tree presents cellular states and gene expression from single-cell transcriptomics within a single hierarchical plot, enabling comparisons of gene expression across lineages and subtle patterns between sub-lineages. Our framework can facilitate the exploration of complicated value patterns that are not evident in UMAP or t-SNE plots. The Collapsible Tree web interface is available at https://collapsibletree.data2in.net. The JavaScript library source code is available at https://github.com/data2intelligence/collapsible_tree.