Search Results (111)

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs. Full article

Staphylococcus aureus is a common opportunistic pathogen found in various environments, with the potential for rapid spread, especially in densely populated indoor settings. Integrating traditional microbiological monitoring with molecular techniques is critical for the timely detection and control of such pathogens. The aim of this study was (1) to monitor the presence and spread of S. aureus in a crowded occupational environment and (2) to optimize a PCR protocol with sequence specific primers (PCR-SSP) for precise identification and early detection of this microorganism and its antibiotic resistance genes. Sampling was conducted in two different places: a call center and a healthcare facility room. All samples were collected from indoor areas at two different time points (T0 and T1) in May 2025 (mean temperature: 22.5 °C; humidity: 59.5%). Microbiological techniques and molecular analysis using PCR-SSP were employed to confirm the presence of S. aureus and detect antibiotic resistance genes such as mecA. A total CFU (colony-forming unit) count of 587 was recorded at the dental clinic corridor, and a total CFU count of 2008 was recorded at the call center corridor. PCR-SSP successfully confirmed the identity of S. aureus with an amplicon size 267 bp and enabled the detection of antibiotic resistance markers, validating its use as a complementary method to traditional microbiological techniques. This study highlights the importance of combining environmental monitoring with molecular biology tools to enhance the early detection and accurate identification of microbial pathogens such as S. aureus and provide an insight for our future direction of producing biosensors for digital air monitoring in crowded workplaces. Full article

Over the past two decades, museums have increasingly experimented with digital technologies to connect with broader contemporary culture. This review article investigates the role crowdsourcing can play in transforming museums into more engaged environments, raising visibility and inclusivity, and involving diverse voices and populations in knowledge-creation processes. Its contribution is to provide an overview of the history, definitions and concepts of crowdsourcing, and examples of crowdsourcing policies and practices that have been adopted by museums. Participation in crowdsourcing has been influenced by gender, education, and socio-economic and cultural background. In the past, historical structures and traditions and infrastructural complexities have stood in the way of wider diversity and inclusivity. As museums move increasingly online, the circulation of information outside the museum’s walls is just as important as the specialist knowledge held within. Museums can play a leading role in public communication by reaching those who constitute the ‘crowd’. This paper explores how museums, through strong collaboration and various forms of crowdsourcing, such as citizen science and participatory engagement, can offer more wide-ranging open access for the sharing and democratisation of knowledge. Full article

With climate governance playing an increasingly crucial role in driving the low-carbon transition and digital transformation being regarded as vital for sustainable development, China has formulated a comprehensive climate governance system with the Carbon Emissions Trading Scheme (CETS) at its core. This research utilizes the implementation of China’s CETS as a quasi-experimental setup to explore how climate governance enables corporate digital transformation to support the low-carbon transition. Findings show that climate governance remarkably boosts corporate digital transformation by 18.8%, mainly by relieving financing limitations and encouraging green technological innovation. Nonetheless, the “crowding-out impact” of regulatory environmental measures and the “policy replacement impact” of eco-friendly loans somewhat dampen these beneficial influences. Findings from the heterogeneity analysis indicate that the positive influence of climate governance is more evident in areas where the public has a greater awareness of the environment and in industries that are not major polluters, demonstrating differences in geographical and industrial features. Based on the research findings, this paper will provide comprehensive suggestions for improvement in institutional factors, financial and innovation support, differentiated implementation, and policy coordination. The suggestions will provide both theoretical and empirical insights for enterprises to advance towards achieving the integrated development of low-carbon economy and digitalization. Full article

The increasing frequency and severity of urban crowd disasters underscore a critical need for intelligent surveillance systems capable of real-time crowd anomaly detection and early warning. While deep learning models such as LSTMs, ConvLSTMs, and Transformers have been applied to video-based crowd anomaly detection, they often face limitations in long-term contextual reasoning, computational efficiency, and interpretability. To address these challenges, this paper proposes HiMeLSTM, a crowd anomaly detection framework built around a hippocampal-inspired memory-enhanced LSTM backbone that integrates Long Short-Term Memory (LSTM) networks with an Episodic Memory Unit (EMU). This hybrid design enables the model to effectively capture both short-term temporal dynamics and long-term contextual patterns essential for understanding complex crowd behavior. We evaluate HiMeLSTM on two publicly available crowd-anomaly benchmark datasets (UCF-Crime and ShanghaiTech Campus) and an in-house CrowdSurge-1K dataset, demonstrating that it consistently outperforms strong baseline architectures, including Vanilla LSTM, ConvLSTM, a lightweight spatial–temporal Transformer, and recent reconstruction-based models such as MemAE and ST-AE. Across these datasets, HiMeLSTM achieves up to 93.5% accuracy, 89.6% anomaly detection rate (ADR), and a 0.89 F1-score, while maintaining computational efficiency suitable for real-time deployment on GPU-equipped edge devices. Unlike many recent approaches that rely on multimodal sensors, optical-flow volumes, or detailed digital twins of the environment, HiMeLSTM operates solely on raw CCTV video streams combined with a simple manually defined zone layout. Furthermore, the hippocampal-inspired EMU provides an interpretable memory retrieval mechanism: by inspecting the retrieved episodes and their att ention weights, operators can understand which past crowd patterns contributed to a given decision. Overall, the proposed framework represents a significant step toward practical and reliable crowd monitoring systems for enhancing public safety in urban environments. Full article

Digitalization has reshaped economic systems worldwide, yet its distributional consequences remain uneven and raise new challenges for sustainable development. China, where digital infrastructure has expanded rapidly, provides a critical setting to examine these effects and their implications for sustainable and inclusive growth. Using a balanced panel of 285 prefecture-level cities from 2007 to 2023, this study constructs a text-based index of digital infrastructure from government work reports and applies two-way fixed effects, instrumental variables, nonlinear models, placebo tests, heterogeneity analysis, and spatial Durbin models. The results show that digital infrastructure significantly widens the urban–rural income gap, with the effect becoming increasingly convex as digital development deepens. Two mechanisms drive this pattern: the concentration of innovation resources in urban areas, which crowds out rural R&D, and a modest degree of wage-structure polarization. Spatial spillovers also matter; digital development in neighboring cities partially offsets local inequality by enhancing interregional connectivity and knowledge diffusion. These findings provide city-level causal evidence on the unequal distributional impacts of digitalization in large emerging economies and highlight the need for sustainability-oriented digital governance, inclusive innovation systems, and regionally coordinated strategies to prevent digital infrastructure from reinforcing structural disparities. Strengthening these policies is essential for achieving more sustainable urban–rural integration in the digital era. Full article

Following the Itaewon Halloween Crowd Crush of 29 October 2022, this study examines how public sentiment evolved on Naver, South Korea’s most influential digital platform. While prior research has focused on mainstream media and global social networks, little is known about localized discourse on Naver. To address this gap, we analyzed 2107 user-generated posts collected via Python-based web scraping across three time periods: the immediate aftermath, first anniversary, and passage of the Itaewon Special Law. Semantic network analysis, sentiment classification, and logistic regression were applied to uncover patterns in discourse and emotional tone. Results reveal a shift from grief and outrage in 2022 to demands for political accountability, safety reform, and memorialization by 2024. High-frequency keywords reflected media and government narratives, while low-frequency terms exposed grassroots voices and emotional nuance. Regression analysis confirmed statistically significant associations between sentiment, title length, and year. These findings suggest that digital platforms not only mirror public sentiment but also shape the emotional and political framing of national tragedies. By tracing sentiment over time, this study contributes to understanding how echo chambers, narrative framing, and temporal context interact in shaping collective responses to crisis. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article

Against the backdrop of the rapid development of digital technologies, such as mobile internet, big data, and cloud computing, the geographical agglomeration of industries is gradually shifting toward virtual agglomeration. In this paper, we examine the effect of both geographical and virtual agglomeration of the agricultural industry on farmers’ agricultural income, and we focus on the transmission mechanism of technological innovation in this process. In the empirical section, using the citrus industry as an example, we employed a moderated mediation effect model for verification and derived the following conclusions: (1) Both geographical and virtual agglomeration of the agricultural industry promote an increase in farmers’ agricultural income by enhancing technological innovation, respectively. (2) Virtual agglomeration of the agricultural industry has a negative moderating effect on the relationship between geographical agglomeration and farmers’ agricultural income, that is, virtual agglomeration alleviates the “crowding effect” and to some extent substitutes for geographical agglomeration. (3) In the mechanism where geographical agglomeration in the agricultural industry increases farmers’ agricultural income through technological innovation, virtual agglomeration has a positive moderating effect. This paper is important for enabling farmers to share the benefits of the digital economy and achieve continuous growth in agricultural income. It is also important for the sustainable development goals adopted by the United Nations, such as eliminating poverty (SDG1), eliminating hunger (SDG2), promoting sustainable economic growth and full employment (SDG8), and promoting innovation (SDG9). Full article

Cultural heritage museums, as integral components of the urban built environment and public cultural space, not only preserve historical memory but also subtly shape visitors’ psychological experiences and well-being. Yet the mechanisms linking museum environmental quality with visitor mental experiences remain insufficiently explored. Drawing on 10,684 visitor reviews collected from Dianping, Weibo, and Ctrip, this study applies text mining and semantic analysis to construct an evaluation framework of visitor behavioral preferences and psychological experiences in heritage museums. The findings show that attention to spatial remains, historical artifacts, and cultural symbols is closely associated with positive emotions such as mystery, awe, and beauty, while adverse environmental conditions such as queuing and crowding often trigger negative feelings including fatigue, disappointment, and boredom. Further analysis reveals a clear pathway linking objects, behaviors, and experiences: spatial remains evoke psychological resonance through immersive perceptions of authenticity; artifacts are primarily linked to visual pleasure and emotional comfort; and cultural symbols are transformed into cognitive gains and spiritual meaning through interpretation and learning. Cross-regional comparison highlights significant differences among museums with distinct cultural backgrounds in terms of architectural aesthetics, educational value, and emotional resonance. This study not only offers a practical framework for the refined management and spatial optimization of heritage museums, but also demonstrates that high-quality cultural environments can promote mental health and emotional restoration. The results extend the interdisciplinary framework of museum research and provide empirical evidence for environmental improvement and public health promotion in cultural heritage spaces in the digital era. Full article

This paper explores how digital crowdsourcing platforms communicate sustainability-oriented innovation and mobilise stakeholder engagement. Through a directed content analysis of three platforms (OpenIDEO, San Francisco, CA, USA; Enel Innovation Hub, Rome, Italy; and InnoCentive, Waltham, MA, USA). The study examines communication strategies, participation models, and alignment with the United Nations Sustainable Development Goals (SDGs). Results show that communication is not neutral but functions as a governance mechanism shaping who participates, how innovation is framed, and what outcomes emerge. OpenIDEO fosters inclusive co-creation and SDG alignment, Enel Innovation Hub highlights technical readiness and energy transition, and InnoCentive relies on rewards and competition. Word-frequency analysis confirms these emphases, while interpretation through Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory explains how motivational framing, legitimacy signals, and participation structures affect engagement. The study contributes to research on open innovation and platform studies by demonstrating the constitutive role of communication in enabling or constraining sustainable collective action. Practical implications are outlined for platform designers, marketers, and policymakers seeking to align digital infrastructures with systemic sustainability goals. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

In this paper, we report how digital resources support engineering students in the early stages of mathematical modelling within a Challenge-Based Education (CBE) course. The study was conducted in a second-year engineering course involving mathematics, physics, and ethics. Through a case study of two student teams, we analyze how a digital curriculum resource—specifically, a dashboard designed for feedback and progress monitoring—helped students identify, define, and begin modelling a real-world problem related to crowd flow on train platforms. Using the instrumental approach, we examined the dual processes of instrumentation (integration of resources) and instrumentalization (adaptation and repurposing of tools). Results show that the Dashboard played a central role in fostering self-regulated learning, interdisciplinary collaboration, and the iterative refinement of guiding questions. Students used data analysis, simulations, and modelling techniques to build and validate mathematical representations in answer to the guiding questions. Our findings contribute to ongoing discussions on how mathematics education in engineering can be enhanced through activity-based learning and targeted use of digital tools. We argue that digital feedback systems like dashboards can bridge the gap between abstract mathematical content and its meaningful application in engineering contexts, thus fostering engagement, autonomy, and authentic learning. Full article

Objectives: This study aimed to compare the skeletal and dentoalveolar effects of a fully customized elastodontic appliance with those of the traditional Twin Block appliance in growing patients with Class II malocclusion during the mixed dentition phase. Methods: A total of 35 patients were included: 18 treated with a customized elastodontic appliance (C-Ela group) and 17 with a Twin Block appliance (TB group). Digital dental models and lateral cephalometric radiographs were obtained at baseline (T1) and after 12 months of treatment (T2). All patients were treated by experienced clinicians according to standardized appliance protocols. Data analysis was performed by a blinded operator using Ortho Analyzer and Dolphin Imaging software. The Shapiro–Wilk test was applied to verify the normal distribution of the data. Paired-sample t-tests were used to assess within-group changes between T1 and T2. For intergroup comparisons two-tail independent-sample t-tests were used, and chi-square tests were used for categorical variables. Statistical significance was set at p < 0.05. Results: Both groups showed significant intragroup improvements in overjet (C-Ela: −2.77 ± 2.07; TB: −2.30 ± 2.72 mm), overbite (C-Ela: −1.79 ± 1.95; TB: −1.40 ± 2.65 mm), and sagittal molar relationship (p < 0.05) after treatment. The C-Ela group exhibited a significantly greater reduction in anterior dental crowding (p < 0.05) and better control of upper (C-Ela: −4.93 ± 7.65°; TB: −1.80 ± 5.72°) and lower incisor inclination (C-Ela: +1.70 ± 4.80°; TB: +4.35 ± 6.22°). In intergroup comparisons, the TB group showed a significantly greater proclination of the lower incisors at T2 (L1/Go-Gn: +4.35°; L1/A-Pog: +1.44 mm), whereas the C-Ela more effectively limited these changes (L1/Go-Gn: +1.70°; L1/A-Pog: +1.18 mm). Skeletal analysis revealed an increase in ANB angle in both groups (C-Ela: −1.49 ± 2.62°; TB: −1.78 ± 2.78°), with no statistically significant intergroup differences, and no other skeletal parameters showed significant between-group changes. Conclusions: Both appliances effectively corrected Class II malocclusions. However, the customized elastodontic device provided better dentoalveolar control, particularly in managing anterior crowding and incisor inclination. Its individualized fit may enhance biomechanical precision and improve overall treatment outcomes in growing patients. Full article

Amid growing demands for heritage conservation and precision urban governance, this study proposes a multimodal framework to analyze tourist perception and behavior in Macau’s Historic Centre. We integrate geotagged social media images and text, ultra-wideband (UWB) pedestrian trajectories, and a LiDAR-derived 3D digital twin to examine the interplay among spatial configuration, movement, and affect. Visual content in tourist photos is classified with You Only Look Once (YOLOv8), and sentiment polarity in Weibo posts is estimated with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. UWB data provide fine-grained trajectories, and all modalities are georeferenced within the digital twin. Results indicate that iconic landmarks concentrate visual attention, pedestrian density, and positive sentiment, whereas peripheral sites show lower footfall yet strong emotional resonance. We further identify three coupling typologies that differentiate tourist experiences across spatial contexts. The study advances multimodal research on historic urban centers by delivering a reproducible framework that aligns image, text, and trajectory data to extract microscale patterns. Theoretically, it elucidates how spatial configuration, movement intensity, and affective expression co-produce experiential quality. Using Macau’s Historic Centre as an empirical testbed, the findings inform heritage revitalization, wayfinding, and crowd-management strategies. Full article