Specific Types Research Articles

Help-Seeking Patterns Among Students Experiencing Sexual Harassment: A Latent Class Analysis.

Sexual harassment continues to be a pervasive problem in institutes of higher education. Despite this, there are significant gaps in research and our understanding related to students' help-seeking associated with sexual harassment. Understanding students' help-seeking patterns is critical in improving and streamlining campus-wide resources. The following study uses a latent class analysis to examine whether unique patterns of help-seeking exist among students experiencing sexual harassment and whether there are meaningful differences between help-seeking groups with respect to incident characteristics, campus climate, and demographic profiles. Data used in this analysis are from an anonymous, web-based campus climate survey across a university system that included 7,318 undergraduate and 3,484 graduate students. Of these, 704 undergraduates and 229 graduate students reported experiencing sexual harassment. Our results indicated four help-seeking groups: Comprehensive help-seeking group (engaged in multiple types of formal and informal help-seeking), Informal help-seeking group (relied exclusively on friends as sources of support), Low help-seeking group (individuals in this group told virtually no one about their experience, including friends or family), and Unsure group (reached out to friends in large numbers but universally characterized themselves as not knowing what to do). Across classes, findings highlight significant differences related to incident characteristics (offender identity and incident location), student status, and racial identity. Our results point to the heterogeneity of patterns and responses in help-seeking for students experiencing sexual harassment. Variations in help-seeking across different classes highlight that students' perceptions and preferences for formal and informal support depend on their specific type. Our study is a reminder that survivors access support through diverse ways; understanding these distinct patterns in help-seeking behaviors based on specific subgroups will help universities tailor programs that better align with students' contextual needs and realities.

제주도 당신앙 속 여산신(女山神)의 흔적과 변주

In this Thesis, we looked back on the traces of the “female mountain god” that were rarely dealt with in the category of community beliefs on Jeju Island. In particular, the discussion aimed to concretely reconstruct the traces of the “sequence of female mountain gods” Jeju Island's Belief in Dang. which has a close relationship with “Mt. Halla” and can be assumed as a “specific type” and “the sequencing of Belief in Dang” It is a common academic belief to regard the Seonma-Seonpa(Seon-Mago) in Jang Han-cheol'’s “Pyo-Hae-rok” as the same existence. but there were not a few discussions refuting this. As a result, based on the existence of the “Madame of Seonnyeo-guk” of Guayangdang, a person most similar to the Seonma-Seonpa(Seon-Mago), Jeju Island, which has been handed down, and the place of Gongju Mt.Mudeung, it was heard that similar gods can be found in several villages related to the god's faith in the middle of Jeju Island. These female mountain gods were closely related to the west direction as “female mountain gods with a history of overseas,” which had been differentiated and expanded as spouses of male gods of Mt. Halla descent. In addition, based on this assumption, the correlation between these female mountain gods and the goddess se-myung-ju was solved in relation to the goddess Beck-ju-to served by Songdang-Bonhyangdang and Sehwa-Bonhyangdang.

A multi-type vulnerability detection framework with parallel perspective fusion and hierarchical feature enhancement

A core problem of vulnerability detection is to detect multi-type vulnerabilities simultaneously by characterizing vulnerabilities of high diversity and complexity in real program source code. Current methods mainly adjust and compromise multiple code representations such as code sequence and code graph based on composite graph. However, sequential features extracted by graph are hardly sufficient to model the contextual semantic associations of the token sequence. Meanwhile, structural features of the code graph extracted by models based on Euclidean Graph Neural Network are difficult to fit the tree-like calling relationships between code lines. These limitations make it difficult to detect diverse vulnerabilities. In addition, most of the existing models ignore the type of code statement, which is closely associated with some specific vulnerability types. In this paper, we propose a Parallelism Framework with Hierarchical feature Enhancement for Multi-type Vulnerability Detection (PFHE-MVD). PFHE-MVD models program code from three parallel perspectives, containing sequence, code graph, and Abstract Syntax Tree statistic. Hyperbolic Graph Convolutional Neural Network is integrated to model the top-down hierarchical calling structure in program code graph through hyperbolic space mapping. Besides, the statement type of code is embedded along with the code text to strengthen the identification ability for different types of vulnerabilities. Experimental results show that PFHE-MVD achieves new state-of-the-art results in multi-type vulnerability detection. PFHE-MVD captures tree-like hierarchical code structure feature and enhances the distinguishing ability for vulnerabilities by code statement type embedding.

Inflammatory bowel disease activity threatens ankylosing spondylitis: implications from Mendelian randomization combined with transcriptome analysis.

Inflammatory bowel disease (IBD) and ankylosing spondylitis (AS) share common traits of chronic recurrent inflammation affecting both the intestines and joints. Epidemiological studies have revealed that the incidence of AS has jumped from 0.3% to 3% among patients with IBD. However, these findings do not definitively establish a causal relationship whereby IBD directly leads to the development of AS. Moreover, whether the activity of IBD will have an impact on this process remains a pending question. Two-sample Mendelian randomization (MR) analyses were employed across multiple datasets to investigate the potential of IBD as a risk factor for AS. The pathogenic genes of AS were identified by MR analysis of expression quantitative trait locus. Risk scores for active and inactive patients were calculated by single-sample gene set enrichment analysis. Comparative assessments encompassing alterations in risk transcription factor activity, shifts in signaling pathways, and variances in immune cell profiles were conducted between active and inactive patients. Moreover, the correlation of immune cells and risk genes was quantified. A total of 6 MR analyses, conducted across 3 exposure datasets and 2 outcome datasets, consistently revealed that IBD substantially elevates the risk of AS development. The MR analysis of the two outcome datasets identified 66 and 54 risk genes, respectively. Notably, both the risk scores computed from the two distinct sets of risk genes were notably higher in active patients compared to their inactive counterparts. Discernible variations in the activity of risk-associated transcription factors were observed between active and inactive patients. In addition, three inflammatory pathways exhibited marked activation in active patients. Moreover, seven specific immune cell types, closely linked to disease activity, exhibited statistically significant correlations with the identified risk genes. By combining Mendelian randomization with transcriptome analysis, this study postulates IBD as a significant risk factor for AS, and further presents innovative evidence for the impact of IBD activity on the progression of AS.

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

The development of a prototype footbridge composed of biological self-healing and ultra-high-performance concretes

Developments in concrete technologies have allowed engineers to design lightweight, slender, and aesthetically attractive structures. The application of new concrete types in real projects, however, is uncommon. The lack of regulation, uncertainty in material performance, and the absence of successfully implemented projects hinders the use of modern concretes in everyday design projects. The present paper examines the application of two specific concrete types in a prototype footbridge: ultra-high-performance concrete (UHPC), and biological self-healing concrete (BSHC). The material properties of UHPC were selected and tailored specifically for the prototype structure, applying the principles of performance-based design. To evaluate the efficiency of self-healing under real environmental conditions, BSHC beams were designed as a structural part of the bridge. The step-by step presentation of the bridge development demonstrates the specifics in material design and a structural analysis of the prototype structure. The prototype structure serves as demonstrative example of the use of BSH and UHP concretes, encouraging engineers towards the wider application of advanced materials in construction projects.

Predicting Time-to-Healing from a Digital Wound Image: A Hybrid Neural Network and Decision Tree Approach Improves Performance

Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a controlled study in dogs, as well as human wound images that are annotated for the tissue type composition. In the context of a hybrid-learning approach (neural network segmentation and decision tree regression) for the image-based prediction of time-to-healing, we tested whether explicitly incorporating tissue type-derived features into the model would improve the accuracy for time-to-healing prediction versus not including such features. We tested four deep convolutional encoder–decoder neural network models for wound image segmentation and identified, in the context of both original wound images and an augmented wound image-set, that a SegNet-type network trained on an augmented image set has best segmentation performance. Furthermore, using three different regression algorithms, we evaluated models for predicting wound time-to-healing using features extracted from the four best-performing segmentation models. We found that XGBoost regression using features that are (i) extracted from a SegNet-type network and (ii) reduced using principal components analysis performed the best for time-to-healing prediction. We demonstrated that a neural network model can classify the regions of a wound image as one of four tissue types, and demonstrated that adding features derived from the superpixel classifier improves the performance for healing-time prediction.

Identifying failure types in cyber-physical water distribution networks using machine learning models

Abstract Water cyber-physical systems (CPSs) have experienced anomalies from cyber-physical attacks as well as conventional physical and operational failures (e.g., pipe leaks/bursts). In this regard, rapidly distinguishing and identifying a facing failure event from other possible failure events is necessary to take rapid emergency and recovery actions and, in turn, strengthen system's resilience. This paper investigated the performance of machine learning classification models – support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) – to differentiate and identify failure events that can occur in a water distribution network (WDN). Datasets for model features related to tank water levels, nodal pressure, and water flow of pumps and valves were produced using hydraulic model simulation (WNTR and epanetCPA tools) for C-Town WDN under pipe leaks/bursts, cyber-attacks, and physical attacks. The evaluation of accuracy, precision, recall, and F1-score for the three models in failure type identification showed the variation of their performances depending on the specific failure types and data noise levels. Based on the findings, this study discussed insights into building a framework consisting of multiple classification models, rather than relying on a single best-performing model, for the reliable classification and identification of failure types in WDNs.

Passaged Articular Chondrocytes From the Superficial Zone and Deep Zone Can Regain Zone-Specific Properties After Redifferentiation

Background: Bioengineered cartilage is a developing therapeutic to repair cartilage defects. The matrix must be rich in collagen type II and aggrecan and mechanically competent, withstanding compressive and shearing loads. Biomechanical properties in native articular cartilage depend on the zonal architecture consisting of 3 zones: superficial, middle, and deep. The superficial zone chondrocytes produce lubricating proteoglycan-4, whereas the deep zone chondrocytes produce collagen type X, which allows for integration into the subchondral bone. Zonal and chondrogenic expression is lost after cell number expansion. Current cell-based therapies have limited capacity to regenerate the zonal structure of native cartilage. Hypothesis: Both passaged superficial and deep zone chondrocytes at high density can form bioengineered cartilage that is rich in collagen type II and aggrecan; however, only passaged superficial zone–derived chondrocytes will express superficial zone–specific proteoglycan-4, and only passaged deep zone–derived chondrocytes will express deep zone–specific collagen type X. Study Design: Controlled laboratory study. Methods: Superficial and deep zone chondrocytes were isolated from bovine joints, and zonal subpopulations were separately expanded in 2-dimensional culture. At passage 2, superficial and deep zone chondrocytes were seeded, separately, in scaffold-free 3-dimensional culture within agarose wells and cultured in redifferentiation media. Results: Monolayer expansion resulted in loss of expression for proteoglycan-4 and collagen type X in passaged superficial and deep zone chondrocytes, respectively. By passage 2, superficial and deep zone chondrocytes had similar expression for dedifferentiated molecules collagen type I and tenascin C. Redifferentiation of both superficial and deep zone chondrocytes led to the expression of collagen type II and aggrecan in both passaged chondrocyte populations. However, only redifferentiated deep zone chondrocytes expressed collagen type X, and only redifferentiated superficial zone chondrocytes expressed and secreted proteoglycan-4. Additionally, redifferentiated deep zone chondrocytes produced a thicker and more robust tissue compared with superficial zone chondrocytes. Conclusion: The recapitulation of the primary phenotype from passaged zonal chondrocytes introduces a novel method of functional bioengineering of cartilage that resembles the zone-specific biological properties of native cartilage. Clinical Relevance: The recapitulation of the primary phenotype in zonal chondrocytes could be a possible method to tailor bioengineered cartilage to have zone-specific expression.

Analysis of flavonoid metabolism of compounds in succulent fruits and leaves of three different colors of Rosaceae

Red flesh apple (Malus pumila var. medzwetzkyana Dieck), purple leaf plum (Prunus cerasifera Ehrhar f), and purple leaf peach (Prunus persica ‘Atropurpurea’) are significant ornamental plants within the Rosaceae family. The coloration of their fruits and leaves is crucial in their appearance and nutritional quality. However, qualitative and quantitative studies on flavonoids in the succulent fruits and leaves of multicolored Rosaceae plants are lacking. To unveil the diversity and variety-specificity of flavonoids in these three varieties, we conducted a comparative analysis of flavonoid metabolic components using ultra-high-performance liquid phase mass spectrometry (UPLC-MS/MS). The results revealed the detection of 311 metabolites, including 47 flavonoids, 105 flavonols, 16 chalcones, 37 dihydroflavonoids, 8 dihydroflavonols, 30 anthocyanins, 14 flavonoid carbon glycosides, 23 flavanols, 8 isoflavones, 11 tannins, and 12 proanthocyanidins. Notably, although the purple plum and peach leaves exhibited distinct anthocyanin compounds, paeoniflorin and corythrin glycosides were common but displayed varying glycosylation levels. While the green purple leaf peach fruit (PEF) and red flesh apple leaf (AL) possessed the lowest anthocyanin content, they exhibited the highest total flavonoid content. Conversely, the red flesh apple fruit (AF) displayed the highest anthocyanin content and a diverse range of anthocyanin glycosylation modifications, indicating that anthocyanins predominantly influenced the fruit's color. Purple PLF, PLL, and PEL showcased varying concentrations of anthocyanins, suggesting that their colors result from the co-color interaction between specific types of anthocyanins and secondary metabolites, such as flavonols, flavonoids, and dihydroflavonoids. This study provides novel insights into the variations in tissue metabolites among Rosaceae plants with distinct fruit and leaf colors.

Индивидуально-типологические детерминанты конфликтологической компетентности сотрудников полиции

The relevance of the study is due to the high level of conflict in the professional activities of police officers and insufficient development of the issue of conflictological competence in the context of the individual typological approach. In the article the author discusses conflictological competence as a specific type of professional competence. The psychological content and scheme of the research analysis of the components of conflicto-logical competence of the police officers are revealed. The indicators of conflictological competence / incom-petence for reflexive-evaluative, emotional-volitional, motivational-value and behavioral components are rec-orded. The severity of the police officers’s of individual typological characteristics has been analyzed. Negative characteristics provoking manifestations of conflictological incompetence of police officers by type of character accentuations (hyperthymic and emotive), which are expressed at the level of tendency or have a high degree of representation, are considered. Based on the results of the correlation analysis, it is shown that conflictologi-cal competencies are represented to varying degrees among police officers, taking into account their individual typological characteristics.

Understanding Food Waste in Bulk in Incheon: Based on Naver Blog Big Data

Purpose: This study aims to provide insight into the contemporary food waste issue for the better management of food waste in large quantities in Incheon, one specific region in Korea, based on the analysis of the Naver Blog corpus, employing analysis techniques including text mining and LDA topic modeling.
 Design/methodology/approach: In order to achieve the aforementioned objectives, the current study has employed the R program for the analysis (e.g., TF, tf-idf, correlation, and LDA) of 868 Naver Blog posts which included information about food waste, and/or garbage produced in bulk by foodservice operators in the context of Incheon.
 Findings: The frequently addressed keywords in the dataset include food, waste, garbage, processor, workplace, food waste dewaterer, microorganism, pulverizer, plastic, compressor, restaurant, sink, cafeteria, and bean sprouts. The correlation analysis demonstrated that waste is largely generated by food material, and food material is closely related to specific types of private companies (e.g., cafeterias) and public places (e.g., military bases, prisons, hospi-tals). The LDA identified three topics: the implications for food waste produced by the workplace, recent equipment and technologies used for food waste processing, and effect of waste on the environment and call for remedies.
 Research limitations/implications: While this study has shed light on contemporary issues in relation to food waste in Incheon, it is suggested that the involved parties in the waste management industry pay more attention to the development of effective waste management strategies by hospitality operators in a specific region.
 Originality/value: This study responds to a lack of understanding underpinning foodservice operators who produce a large quantity of food waste in Incheon, albeit much attention has been paid to some recent research on food waste.

Project management as a value creator agent of innovation and business ecosystems

This study introduces a structural model that examines the intricate correlation between innovation and business ecosystems, with a special emphasis on the role of project management as a pivotal integration factor. Furthermore, we propose an exploration of how this model aligns with the life cycle of products, services, or outcomes. In this research, we employ a hybrid methodology that seamlessly combines content and network analysis, bibliometrics, and statistical techniques to provide theoretical underpinnings for our correlation model. Our investigation reveals a robust correlation between the nature of a given ecosystem and the interconnections that companies within it establish. Furthermore, we demonstrate that the overall health and vitality of an ecosystem are closely linked to its specific type. We acknowledge that certain limitations within this study stem from the methodological choices made by our research team. This research underscores several practical advantages, including the potential for value creation through the development of novel products and services, as well as the enhancement of existing projects, all while minimizing waste and optimizing profitability. However, it should be noted that one limitation of our study is the absence of real-world application of the framework to validate our theoretical-conceptual construct. The concept of Innovation Ecosystems continues to garner significant attention among practitioners, academics, and businesses alike, offering a promising avenue for the rapid and flexible development of innovative solutions within existing markets.

The emergence of circadian timekeeping in the intestine.

The circadian clock is a molecular timekeeper, present from cyanobacteria to mammals, that coordinates internal physiology with the external environment. The clock has a 24-h period however development proceeds with its own timing, raising the question of how these interact. Using the intestine of Drosophila melanogaster as a model for organ development, we track how and when the circadian clock emerges in specific cell types. We find that the circadian clock begins abruptly in the adult intestine and gradually synchronizes to the environment after intestinal development is complete. This delayed start occurs because individual cells at earlier stages lack the complete circadian clock gene network. As the intestine develops, the circadian clock is first consolidated in intestinal stem cells with changes in Ecdysone and Hnf4 signalling influencing the transcriptional activity of Clk/cyc to drive the expression of tim, Pdp1, and vri. In the mature intestine, stem cell lineage commitment transiently disrupts clock activity in differentiating progeny, mirroring early developmental clock-less transitions. Our data show that clock function and differentiation are incompatible and provide a paradigm for studying circadian clocks in development and stem cell lineages.

Cell Type-Specific Extracellular Vesicles and Their Impact on Health and Disease.

Extracellular vesicles (EVs), a diverse group of cell-derived exocytosed particles, are pivotal in mediating intercellular communication due to their ability to selectively transfer biomolecules to specific cell types. EVs, composed of proteins, nucleic acids, and lipids, are taken up by cells to affect a variety of signaling cascades. Research in the field has primarily focused on stem cell-derived EVs, with a particular focus on mesenchymal stem cells, for their potential therapeutic benefits. Recently, tissue-specific EVs or cell type-specific extracellular vesicles (CTS-EVs), have garnered attention for their unique biogenesis and molecular composition because they enable highly targeted cell-specific communication. Various studies have outlined the roles that CTS-EVs play in the signaling for physiological function and the maintenance of homeostasis, including immune modulation, tissue regeneration, and organ development. These properties are also exploited for disease propagation, such as in cancer, neurological disorders, infectious diseases, autoimmune conditions, and more. The insights gained from analyzing CTS-EVs in different biological roles not only enhance our understanding of intercellular signaling and disease pathogenesis but also open new avenues for innovative diagnostic biomarkers and therapeutic targets for a wide spectrum of medical conditions. This review comprehensively outlines the current understanding of CTS-EV origins, function within normal physiology, and implications in diseased states.

Altering the substitution and cross-linking of glucuronoarabinoxylans affects cell wall architecture in Brachypodium distachyon.

The Poaceae family of plants provides cereal crops that are critical for human and animal nutrition, and also, they are an important source of biomass. Interacting plant cell wall components give rise to recalcitrance to digestion; thus, understanding the wall molecular architecture is important to improve biomass properties. Xylan is the main hemicellulose in grass cell walls. Recently, we reported structural variation in grass xylans, suggesting functional specialisation and distinct interactions with cellulose and lignin. Here, we investigated the functions of these xylans by perturbing the biosynthesis of specific xylan types. We generated CRISPR/Cas9 knockout mutants in Brachypodium distachyon XAX1 and GUX2 genes involved in xylan substitution. Using carbohydrate gel electrophoresis, we identified biochemical changes in different xylan types. Saccharification, cryo-SEM, subcritical water extraction and ssNMR were used to study wall architecture. BdXAX1A and BdGUX2 enzymes modify different types of grass xylan. Brachypodium mutant walls are likely more porous, suggesting the xylan substitutions directed by both BdXAX1A and GUX2 enzymes influence xylan-xylan and/or xylan-lignin interactions. Since xylan substitutions influence wall architecture and digestibility, our findings open new avenues to improve cereals for food and to use grass biomass for feed and the production of bioenergy and biomaterials.

Non-severe thermal burn injuries induce long-lasting downregulation of gene expression in cortical excitatory neurons and microglia.

Burn injuries are devastating traumas, often leading to life-long consequences that extend beyond the observable burn scar. In the context of the nervous system, burn injury patients commonly develop chronic neurological disorders and have been suggested to have impaired motor cortex function, but the long-lasting impact on neurons and glia in the brain is unknown. Using a mouse model of non-severe burn injury, excitatory and inhibitory neurons in the primary motor cortex were labelled with fluorescent proteins using adeno-associated viruses (AAVs). A total of 5 weeks following the burn injury, virus labelled excitatory and inhibitory neurons were isolated using fluorescence-activated cell sorting (FACS). In addition, microglia and astrocytes from the remaining cortical tissue caudal to the motor cortex were immunolabelled and isolated with FACS. Whole transcriptome RNA-sequencing was used to identify any long-lasting changes to gene expression in the different cell types. RNA-seq analysis showed changes to the expression of a small number of genes with known functions in excitatory neurons and microglia, but not in inhibitory neurons or astrocytes. Specifically, genes related to GABA-A receptors in excitatory neurons and several cellular functions in microglia were found to be downregulated in burn injured mice. These findings suggest that non-severe burn injuries lead to long lasting transcriptomic changes in the brain, but only in specific cell types. Our findings provide a broad overview of the long-lasting impact of burn injuries on the central nervous system which may help identify potential therapeutic targets to prevent neurological dysfunction in burn patients.

A Machine learning approach for Post-Disaster data curation

Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development.

ABC‐Auxetics: An Implicit Design Approach for Negative Poisson's Ratio Materials

A novel methodology is introduced for designing auxetic (negative Poisson's ratio) structures based on topological principles and is demonstrated by investigating a new class of auxetics based on two‐dimensional (2D) textile weave patterns. Conventional methodology for designing auxetic materials typically involves determining a single deformable block (a unit cell) of material whose shape results in auxetic behavior. Consequently, patterning such a unit cell in a 2D (or 3D) domain results in a larger structure that exhibits overall auxetic behavior. Such an approach naturally relies on some prior intuition and experience regarding which unit cells may be auxetic. Second, tuning the properties of the resulting structures is typically limited to parametric variations of the geometry of a specific type of unit cell. Thus, most of the currently known auxetic structures belong to a selected few classes of unit cell geometries that are explicitly defined in accordance with a specified topological (i.e., grid structure). Herein, a new class of auxetic structures is demonstrated that, while periodic, can be generated implicitly, i.e., without reference to a specific unit cell design. The approach leverages weave‐based parameters (A–B–C), resulting in a rich design space for auxetics that is previously unexplored.

SME’s export intensity: enhancers, inhibitors and firm characteristics

This study aims to investigate the factors explaining the variation in SMEs’ degree of internationalization, measured by export intensity (i.e. the share of export revenue in total revenue). Primary data were collected through survey administered using questionnaire in seven provinces in Indonesia, yielding 497 usable responses. Principal component analysis was used to solve dimensionality issues of perceptual variables while Generalized Linear Model (fractional logit regression) and the Ordinary Least Square regression were used in the estimation. The findings show that SMEs’ export expansion is positively affected by firm characteristics namely firm age and total employees, as well as some exhibiting factors namely owners’ international work experience, central government assistance, network relationships with non-government actors, location, export market of choices and years of exporting. By contrast, SMEs’ export continuation is adversely affected by perceived difficulties in overcoming informational and human resources barriers, distribution, logistics and promotional barriers, financial barriers, foreign government barriers, procedural barriers and price barriers. The findings suggest that post-export assistance is as important as creating new exporters. Further, the findings provide insights on specific types of assistances required by exporting SMEs to exploit their export-exhibiting factors while also overcoming the export-inhibiting factors.