Search Results (10,120)

This study examined the impact of entrepreneurship education in sustainability on entrepreneurial intention using the theory of planned behavior (TPB). The MEMORE macro was used to analyze within-subject mediation and enabled us to examine how entrepreneurial intention is affected by changes in the factors of the theory of planned behavior (attitude, subjective norms, perceived behavioral control). The survey follows a questionnaire-based, pre-test-post-test design (the research involved 271 business administration students in Athens). A paired sample t-test was used to analyze changes in attitude, subjective norms, perceived behavioral control, and entrepreneurial intention before and after education. The results indicated that after the entrepreneurship course in sustainability, students indicated a significant positive change in entrepreneurial intention, attitude, and perceived behavioral control. MEMORE macro indicated that only the change in perceived behavioral control positively influenced the increase in entrepreneurial intention levels. Based on these findings, entrepreneurship education in sustainability enhances students’ entrepreneurial intentions by increasing their perceived behavioral control. As a result, students’ confidence and knowledge regarding sustainable entrepreneurship are fundamental to the development of sustainable entrepreneurial mindsets. This study emphasizes the importance of integrating targeted pedagogical approaches that enhance perceived behavioral control in sustainable entrepreneurship education by equipping students with practical knowledge and skills to overcome psychological barriers. The use of the MEMORE macro highlights this study’s innovation, uncovering new relationships between the examined variables. Full article

Proper subcellular localization is essential to exert the designated function of a protein, not only for endogenous proteins but also transgene-encoded proteins. Post-translational modification is a frequently used method to regulate the subcellular localization of a specific protein. While there are a number of tags that are widely used to direct the target protein to a specific location within a cell, these tags often fail to emulate the dynamics of protein trafficking, necessitating an alternative approach to the direct subcellular localization of transgene-encoded proteins. Here, we report the development of a new synthetic polypeptide protein tag comprised of ten amino acids, which promotes membrane localization of a target protein. This short synthetic peptide tag, named “Palmito-Tag”, induces ectopic palmitoylation on the cysteine residue within the tag, thereby promoting membrane localization of the target proteins without affecting their innate function. We show that the target proteins with the Palmito-Tag are incorporated into the membranous organelles within the cells, including the endosomes, as well as extracellular vesicles. Given the reversible nature of palmitoylation, the Palmito-Tag may allow us to shift the subcellular localization of the target protein in a context-dependent manner. With the advent of therapeutic applications of exosomes and other extracellular vesicles, we believe that the ability to reversibly modify a target protein and direct its deposition to the specific subcellular milieu will help us explore more effective venues to harness the potential of extracellular vesicle-based therapies. Full article

Hyperspectral image classification faces challenges with high-dimensional spectral data and complex dependencies between bands. This paper proposes HyperspectralMamba, a novel architecture for hyperspectral image classification that integrates state space modeling with adaptive recalibration mechanisms. The method addresses limitations in existing techniques through three key innovations: (1) a novel dual-stream architecture that combines SSM global modeling with parallel convolutional local feature extraction, distinguishing our approach from existing single-stream SSM methods; (2) a band-adaptive feature recalibration mechanism specifically designed for hyperspectral data that adaptively adjusts the importance of different spectral band features; and (3) an effective feature fusion strategy that integrates global and local features through residual connections. Experimental results on three benchmark datasets—Indian Pines, Pavia University, and Salinas Valley—demonstrate that the proposed method achieves overall accuracies of 95.31%, 98.60%, and 96.40%, respectively, significantly outperforming existing convolutional neural networks, attention-enhanced networks, and Transformer methods. HyperspectralMamba demonstrates an exceptional performance in small-sample class recognition and distinguishing spectrally similar terrain, while maintaining lower computational complexity, providing a new technical approach for high-precision hyperspectral image classification. Full article

Accurate classification of chest X-ray (CXR) images is crucial for diagnosing lung diseases in medical imaging. Existing deep learning models for CXR image classification face challenges in distinguishing non-lung features. In this work, we propose a new segmentation-assisted fusion-based classification method. The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. Then, a fusion of the masked CXR image with the original image enables classification using the improved lightweight ShuffleNetV2 model. The proposed method is trained and evaluated on segmentation datasets including the Montgomery County Dataset (MC) and Shenzhen Hospital Dataset (SH), and classification datasets such as Chest X-Ray Images for Pneumonia (CXIP) and COVIDx. Compared with seven segmentation models (U-Net, Attention-Net, SegNet, FPNNet, DANet, DMNet, and SETR), five classification models (ResNet34, ResNet50, DenseNet121, Swin-Transforms, and ShuffleNetV2), and state-of-the-art methods, our PCSNet model achieved high segmentation performance on CXR images. Compared to the state-of-the-art Attention-Net model, the accuracy of PCSNet increased by 0.19% (98.94% vs. 98.75%), and the boundary accuracy improved by 0.3% (97.86% vs. 97.56%), while requiring 62% fewer parameters. For pneumonia classification using the CXIP dataset, the proposed strategy outperforms the current best model by 0.14% in accuracy (98.55% vs. 98.41%). For COVID-19 classification with the COVIDx dataset, the model reached an accuracy of 97.50%, the absolute improvement in accuracy compared to CovXNet was 0.1%, and clinical metrics demonstrate more significant gains: specificity increased from 94.7% to 99.5%. These results highlight the model’s effectiveness in medical image analysis, demonstrating clinically meaningful improvements over state-of-the-art approaches. Full article

This paper presents a meta-analysis of social research on water, offering a novel methodological contribution to the study of emerging interdisciplinary research fields. We propose and implement a mixed methods framework that integrates quantitative network analysis with qualitative research, aiming to enhance both to give access to new emerging empirical fields and enhance the analytical depth of empirical social research. Drawing on a dataset of publications from the Web of Science over four distinct time intervals, we identify thematic clusters through keyword co-occurrence networks that reveal the evolving structure and internal dynamics of the field. Our findings show a clear trend toward increasing interdisciplinarity, responsiveness to global events, and contemporary challenges such as the emergence of COVID-19 and the continued centrality of topics related to water management and evaluation. By uncovering latent structures, our approach not only maps the field’s development but also lays the foundation for targeted qualitative analysis of articles representative of identified clusters. This methodological design contributes to the broader discourse on mixed methods research in the social sciences by demonstrating how computational tools can enhance the transparency and reliability of qualitative inquiry without sacrificing its interpretive richness. Furthermore, this study opens new avenues for critically reflecting on the epistemic culture of social water research, particularly in relation to its proximity to applied science and governance-oriented perspectives. The proposed method holds potential relevance for both academic researchers and decision makers in the water sector, offering a means to systematically access dispersed knowledge and identify underrepresented subfields. Overall, the study showcases the potential of mixed methods designs for navigating and structuring complex interdisciplinary research landscapes. Full article

The two-stage proportional valve is a key control component in heavy-duty equipment, where its signal-flow characteristics critically influence operational performance. This study proposes an innovative flow characteristic regulation method using variable-gain feedback grooves. Unlike conventional throttling notch optimization, the core mechanism actively adjusts pilot–main valve mapping through feedback groove shape and area gain adjustments to achieve the desired flow curves. This approach avoids complex throttling notch issues while retaining the valve’s high dynamics and flow capacity. Mathematical modeling elucidated the underlying mechanism. Subsequently, trapezoidal and composite feedback grooves are designed and investigated via simulation. Finally, composite feedback groove spools tailored to construction machinery operating conditions are developed. Comparative experiments demonstrate the following: (1) Pilot–main mapping inversely correlates with area gain; increasing gain enhances micro-motion control, while decreasing gain boosts flow gain for rapid actuation. (2) This method does not significantly increase pressure loss or energy consumption (measured loss: 0.88 MPa). (3) The composite groove provides segmented characteristics; its micro-motion flow gain (2.04 L/min/0.1 V) is 61.9% lower than conventional valves, significantly improving fine control. (4) Adjusting groove area gain and transition point flexibly modifies flow gain and micro-motion zone length. This method offers a new approach for high-performance valve flow regulation. Full article

In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media platforms necessitates new approaches to address cyberbullying using images. This domain has been largely overlooked. In this paper, we present a novel dataset specifically designed for the detection of visual cyberbullying, encompassing four distinct classes: abuse, curse, discourage, and threat. The initial prepared dataset (cyberbullying visual indicators dataset (CVID)) comprised 664 samples for training and validation, expanded through data augmentation techniques to ensure balanced and accurate results across all classes. We analyzed this dataset using several advanced deep learning models, including VGG16, VGG19, MobileNetV2, and Vision Transformer. The proposed model, based on DenseNet201, achieved the highest test accuracy of 99%, demonstrating its efficacy in identifying the visual cues associated with cyberbullying. To prove the proposed model’s generalizability, the 5-fold stratified K-fold was also considered, and the model achieved an average test accuracy of 99%. This work introduces a dataset and highlights the potential of leveraging deep learning models to address the multifaceted challenges of detecting cyberbullying in visual content. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

This paper provides research that shows that the scale and quantification of the degree of fracturing in a rock mass should and can be considered when estimating geological strength index (GSI) ratings for rock mass strength and deformability estimates. In support of this notion, a brief review is provided to demonstrate why it is imperative that scale is considered when using GSI in engineering design. The impact of scale and scale effects on the engineering response of a rock mass typically requires a definition of fracture intensity relative to the volume or size of rock mass under consideration and the relative scale of the project being built. In this research three volume scales are considered: the volume of a structural domain, a representative elemental REV, and unit volume. A theoretical framework is established that links these three volume scales together, how they are estimated, and how they relate to parameters used to estimate engineering behaviour. Analysis of data from several examples and case histories for real rock masses is presented that compares and validates the use of a new and innovative but practical method (a sphere of unit volume) to estimate fracture intensity parameters VFC or P30 (fractures/m³) and P32 (fracture area—m²/m³) that is included on the vertical axis of the volumetric V-GSI chart. The research demonstrates that the unit volume approach to calculating VFC and P32 used in the V-GSI system compares well with other methods of estimating these two parameters (e.g., discrete fracture network (DFN) modelling). The research also demonstrates the reliability of the VFC-correlated rating scale included on the vertical axis of the V-GSI chart for use in estimating first-order strength and deformability estimates for rock masses. This quantification does not negate or detract from geological logic implicit in the original graphical GSI chart. Full article

A new, simple machine was developed to address a long-standing challenge in biomedical and mechanical engineering: how to enhance the primary stability and long-term integration of screws and implants in low-density or heterogeneous materials, such as bone or composite substrates. Traditional screws often rely solely on external threading for fixation, leading to limited cohesion, poor integration, or early loosening under cyclic loading. In response to this problem, we designed and built a novel device that leverages a unique mechanical principle to simultaneously perforate, collect, and compact the substrate material during insertion. This mechanism results in an internal material interlock, enhancing cohesion and stability. Drawing upon principles from physics, chemistry, engineering, and biology, we evaluated its biomechanical behavior in synthetic bone analogs. The maximum insertion (MIT) and removal torques (MRT) were measured on synthetic osteoporotic bones using a digital torquemeter, and the values were compared directly. Experimental results demonstrated that removal torque (mean of 21.2 Ncm) consistently exceeded insertion torque (mean of 20.2 Ncm), indicating effective material interlocking and cohesive stabilization. This paper reviews the relevant literature, presents new data, and discusses potential applications in civil infrastructure, aerospace, and energy systems where substrate cohesion is critical. The findings suggest that this new simple machine offers a transformative approach to improving fixation and integration across multiple domains. Full article

Memory is a fundamental neurological function, essential for animal survival. Over the course of vertebrate evolution, elaborations in the forebrain telencephalon create new memory mechanisms, meaning basal vertebrates such as amphibians must have a less sophisticated system of memory acquisition, storage, and retrieval than the well-known hippocampal-based circuitry of mammals. Personality also appears to be a fundamental vertebrate trait and is generally defined as consistent individual behavior over time and across life history stages. In anuran amphibians (frogs), personality studies generally ask whether adult frogs retain the personality of their tadpole stage or whether personality shifts with metamorphosis, an idea behavioral ecologists term adaptive decoupling. Using a multidisciplinary perspective and recognizing there are ~7843 species of frogs, each with some molecular, morphological, physiological, or behavioral feature that makes it unique, we review, clarify, and provide perspective on what we collectively know about memory and personality and their mechanisms in anuran amphibians. We propose four working hypotheses: (1) as tadpoles grow, new telencephalic neurons become integrated into functional networks, producing behaviors that become more sophisticated with age; (2) since carnivores tend to be more bold/aggressive than herbivores, carnivorous anuran adults will be more aggressive than herbivorous tadpoles; (3) each amphibian species, and perhaps life history stage, will have a set point on the Shy–Bold Continuum; and (4) around this set point there will be a range of individual responses. We also suggest that several factors are slowing our understanding of the variety and depth of memory and personality possibilities in anurans. These include the scala natura approach to comparative studies (i.e., the idea that one frog represents all frogs); the assumption that amphibians are no more than simple reflex machines; that study species tend to be chosen more for convenience than taxonomic representation; and that studies are designed to prove or disprove a construct. This latter factor is a particular hindrance because what we are really seeking as scientists is not the confirmation or refutation of ideas, but rather what those ideas are intended to produce, which is understanding. Full article

The implementation of Building Information Modelling (BIM) in public design processes enhances efficiency, transparency, and sustainability. However, public agencies often encounter significant barriers, particularly regarding organisational and managerial readiness. This study develops a BIM implementation framework tailored to the specific needs of an Italian public agency. The research adopts a qualitative approach, combining 15 semi-structured interviews with process mapping Using (Business Process Modeling Notation) BPMN. The current as-is workflows were analysed and validated by internal stakeholders. Based on this analysis, strategic objectives were defined, relevant (Building Information Modelling) BIM uses were selected, and revised to-be processes were proposed, integrating new roles and responsibilities according to the standards. The framework addresses both technical and organisational dimensions of BIM adoption, highlighting the need for training, coordination, and stakeholder engagement. The main outcomes include a structured process model, a priority-based selection of BIM uses, and a role matrix supporting organisational transformation. The added value for researchers lies in the replicable methodology that combines empirical process mapping with implementation planning. For practitioners, especially consultants in sustainable design, the study offers a practical roadmap for aligning BIM adoption with project goals, regulatory compliance, and environmental performance targets in complex public sector contexts. Full article

This study introduces a new method for modeling the nonlinear behavior of adhesively bonded composite steel–concrete T-beam systems. The model characterizes the interfacial behavior between the steel beam and the concrete slab using a strain compatibility approach within the framework of linear elasticity. It captures the nonlinear distribution of shear stresses over the entire depth of the composite section, making it applicable to various material combinations. The approach accounts for both continuous and discontinuous bonding conditions at the bonded steel–concrete interface. The analysis focuses on the top flange of the steel section, using a T-beam configuration commonly employed in bridge construction. This configuration stabilizes slab sliding, making the composite beam rigid, strong, and resistant to deformation. The numerical results demonstrate the advantages of the proposed solution over existing steel beam models and highlight key characteristics at the steel–concrete interface. The theoretical predictions are validated through comparison with existing analytical and experimental results, as well as finite element models, confirming the model’s accuracy and offering a deeper understanding of critical design parameters. The comparison shows excellent agreement between analytical predictions and finite element simulations, with discrepancies ranging from 1.7% to 4%. This research contributes to a better understanding of the mechanical behavior at the interface and supports the design of hybrid steel–concrete structures. Full article

This article demonstrates how positioning residents as active co-designers fundamentally transforms both the process and outcomes of carbon-positive building development. Through structured collaborative workshops, shared decision-making protocols, and continuous partnership throughout the building lifecycle, the REN+HOMES Positive Carbon Building methodology challenges the conventional expert-driven approach to sustainable construction. Developed and validated through the H2020 REN+HOMES project, this resident-centered approach achieved remarkable technical performance—65.9% reduction in final energy demand—while simultaneously enhancing community ownership and long-term sustainability practices. By integrating participatory design with Zero Emissions Building (ZEB) criteria, renewable energy systems, and national carbon offset programs, the methodology proves that resident collaboration is not merely beneficial but essential for creating buildings that truly serve both environmental and human needs. This research establishes a new paradigm where technical excellence emerges from authentic partnership between residents and sustainability experts, offering a replicable framework for community-driven environmental regeneration. Full article

Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established residential suburbs in Western Sydney, Australia. Established areas featured larger housing lots and mature street trees, while newly developed suburbs had smaller lots and limited vegetation cover. Microclimate data were collected during summer 2021 under both heatwave and non-heatwave conditions in full sun, measuring air temperature, relative humidity, wind speed, and wet-bulb globe temperature (WBGT) as an index of heat stress. Daily maximum air temperatures reached 42.7 °C in new suburbs, compared to 39.3 °C in established ones (p < 0.001). WBGT levels during heatwaves were in the “extreme caution” category in new suburbs, while remaining in the “caution” range in established ones. These findings highlight the benefits of larger green spaces, permeable surfaces, and lighter roof colours in the context of urban heat exposure. Maintaining mature trees and avoiding dark roofs can significantly reduce summer heat and improve outdoor thermal comfort across a range of conditions. Results of this work can inform bottom-up approaches to climate-responsive urban design where informed homeowners can influence development outcomes. Full article