Search Results (39)

Surgical site infections (SSIs) are a significant challenge in postoperative care, leading to increased morbidity, extended hospital stays, and elevated healthcare costs. Traditional antimicrobial dressings, such as those containing silver or iodine, have limitations, including cytotoxicity and the potential for antimicrobial resistance. Dialkylcarbamoyl chloride (DACC)-impregnated dressings offer a novel approach, employing a physical mechanism to bind and remove bacteria without the use of chemical agents, thereby reducing the risk of resistance. This review summarizes current evidence on the efficacy of DACC dressings in preventing SSIs and promoting wound healing. Findings from multiple studies indicate that DACC dressings reduce bacterial burden and SSI rates across various surgical procedures, including cesarean sections and vascular surgeries. Additionally, DACC dressings demonstrate potential in managing hard-to-heal wounds, such as diabetic foot ulcers, by reducing bacterial load and biofilm formation. Furthermore, they present advantages in antimicrobial stewardship and cost-effectiveness by minimizing the need for antibiotics and decreasing overall healthcare expenses. However, the current literature is limited by small sample sizes, methodological weaknesses, heterogeneity in study designs, and a lack of long-term data. Future research should focus on high-quality randomized controlled trials across diverse surgical populations, comprehensive cost-effectiveness analyses, and long-term outcomes to establish the full clinical impact of DACC dressings. With further validation, DACC-impregnated dressings could become a critical tool in sustainable postoperative wound care. Full article

This paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extracting key modal parameters such as natural frequencies, damping ratios, and mode shapes from vibration data. To address the limitations of traditional manual methods, several approaches have been developed to automate this process. Among these, SSI stands out as one of the most effective time-domain methods due to its robustness in handling noisy environments and closely spaced modes. This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. Advanced SSI implementations that incorporate real-time recursive estimation, adaptive stabilization criteria, and automated mode selection are also discussed. Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. This review concludes by highlighting the current challenges, such as computational demands and data management, and proposing future directions for research in automated modal analysis to support resilient, sustainable infrastructure. Full article

The Songnen Plain is a significant region in China, known for its high grain production and concentrated distribution of soda saline land. It is also considered a priority area for cropland development in the country. However, the Songnen Plain is now facing prominent issues such as soil salinization, soil erosion, and deteriorating cropland quality, which are exacerbated by climate change and intensified human activities. In order to address these challenges, it is crucial to adjust the quantitative structure and layout of different landscapes in a harmonious manner, aiming to achieve synergistic optimization, which is posed as the key scientific approach to guide comprehensive renovation policies, improve saline–alkaline land conditions, and promote sustainable agricultural development. In this study, four scenarios including natural development, priority food production (PFP), ecological security priority (ESP), and economic–ecological-balanced saline soil improvement were set up based on Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The results demonstrated that the SSI scenario, which focused on economic–ecological equilibrium, displayed the most rational quantitative structure and spatial layout of landscape types, with total benefits surpassing those of the other scenarios. Notably, this scenario involved converting unused land into saline cropland and transforming saline cropland into normal cropland, thereby increasing the amount of high-quality cropland and potential cropland while enhancing the habitat quality of the region. Consequently, the conflict between food production and ecological environmental protection was effectively mitigated. Furthermore, the SSI scenario facilitated the establishment of a robust ecological security and protection barrier, offering valuable insights for land use planning and ecological security pattern construction in the Songnen Plain, particularly in salt-affected areas. Full article

This article presents a model built for deep digitalization in the construction industry and for making building information modeling achieve a greater level of transparency, verifiability and effectiveness for the benefit of all stakeholders. Thanks to blockchain and the self-sovereign identity paradigm, the model guarantees data integrity and transaction reliability, enabling the generation of more efficient and productive businesses. The model includes a decentralized application for notarization of the information flow in building information modeling processes; the application is implemented and tested on a local blockchain. The proposed model represents a so-called digital twin and is, hence, a huge system that manages all the information flow associated with a building throughout its life cycle, returning to individuals the control of their own data. In this model, all stakeholders operate based on so-called decentralized identifiers and DID documents, which store on-chain the fingerprints of the information flow in a common data environment. Full article

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including safe access and data privacy, which are necessary with the advent of Metaverse technology. Traditional, centralized, and network-centered solutions fail to provide a resilient identity management solution. There are multifaceted security and privacy issues that hinder the secure adoption of this game-changing technology in contemporary cyberspace. Moreover, there is a need to dedicate efforts towards a secure-by-design Metaverse that protects the confidentiality, integrity, and privacy of the personally identifiable information (PII) of users. In this research paper, we propose a logical substitute for established centralized identity management systems in compliance with the complexity of the Metaverse. This research proposes a sustainable Self-Sovereign Identity (SSI), a fully decentralized identity management system to mitigate PII leaks and corresponding cyber threats on all multiverse platforms. The principle of the proposed framework ensures that the users are the only custodians and proprietors of their own identities. In addition, this article provides a comprehensive approach to the implementation of the SSI principles to increase interoperability and trustworthiness in the Metaverse. Finally, the proposed framework is validated using mathematical modeling and proved to be stringent and resilient against modern-day cyber attacks targeting Metaverse platforms. Full article

Following effective seismic codes, common buildings are considered to be made of the same material throughout the story distribution and based on an ideal rigid soil. However, in daily construction practice, there are often cases of buildings formed by a bottom part constructed with reinforced concrete (r/c) and a higher steel part, despite this construction type not being recognized by code assumptions. In addition, soil deformability, commonly referred to as the Soil–Structure Interaction (SSI), is widely found to affect the earthquake response of typical residence structures, apart from special structures, though it is not included in the normative design procedure. This work studies the seismic response of in-height mixed 3D models, considering the effect of sustaining deformable ground compared to the common rigid soil hypothesis, which has not been clarified so far in the literature. Two types of soft soil, as well as the rigid soil assumption, acting as a reference point, are considered, while two limit interconnections between the steel part on the concrete part are included in the group analysis. The possible influence of the seismic orientation angle is explored in the analysis set. Selected numerical results of the dynamic nonlinear analyses under strong near-fault ground excitations were plotted through dimensionless parameters to facilitate an objective comparative discussion. The effect of SSI on the nonlinear performance of three-dimensional mixed models is identified, which serves as the primary contribution of this work, making it unique among the numerous research works available globally and pointing to findings that are useful for the enhancement of the seismic rules regarding the design and analysis of code-neglected mixed buildings. Full article

Forest restoration and tree-planting projects initiated as a response to climate change and biodiversity loss are increasingly important around the globe. Small-scale and community-based tree nurseries have been promoted as a potential route to meeting some local or regional supply-side bottlenecks. A study in the UK used a mixed methods approach to assess the potential contribution of community tree nurseries (CTNs) to tree material supply. Semi-structured interviews (SSIs) were undertaken with 16 CTNs across the UK to generate a sector-wide characterisation of CTNs. A UK-wide online survey assessed the total number of CTNs, production methods and volumes, biosecurity practices and benefits of community involvement. Another 13 CTNs receiving support to establish and extend their operations took part in SSIs assessing their development. The results indicate that there are four broad types of CTN (Organisation- and project-based, Community-based, Enterprise and Network CTNs). A significant number of CTNs in Britain are new and establishing. The sustainability of CTNs relies in large part on grant support rather than income from tree sales. Production is almost exclusively native broadleaves and local provenances. There are policy implications concerning: i. the suitability of species being produced to future climatic and market conditions and ii. the tension around financial viability, grant dependence and CTN sustainability for those CTNs focused on social and environmental benefits rather than income generation. Identifying gaps in the provision of trees by commercial nurseries, e.g., uncommon or recalcitrant species, could be a feasible financial strategy. It is likely that financial support and skills development are likely to be required as social innovation in the community-supported tree nursery sector establishes and develops. Judging the success and impact of some types of CTNs in terms of production and finance measures may be unfair, as other social and environmental benefits may be as important and valuable. It is clear that more evaluative research is needed to quantify and better understand these kinds of outcomes and the added value they accrue. Full article

Soils are a source of natural capital that provide and regulate a range of ecosystem services (ES) and play an important role in sustaining human welfare. Nonetheless, the quality and quantity of soil ecosystem services (SES) delivery over the long term depend on the use of sustainable land management practices. In the present study, we assessed seven SES using a set of soil quality indicators in four production systems based on yerba mate (Ilex paraguariensis A. St.-Hil) in the Araucaria Forest biome of Southern Brazil: two sites were under traditional agroecological agroforestry management, one was a silvopastoral system with dairy pasture, and the last one was a monoculture yerba mate production system. The SES measured were soil fertility, carbon sequestration, erosion control, nutrient cycling, plant provision, biodiversity, and health. Soil samples were collected at various depths and analysed for chemical, physical, and biological attributes. A principal component analysis on the dataset showed that the soil quality indicators that best represent the variance between the systems at the 0–10 cm layer were acidity, microbial activity (FDA), total nitrogen, (TN), structural stability index (SSI), cation exchange capacity (CEC), pH, sum of bases (SB), microbial quotient (qMic), density of earthworms (EwD), bulk density (BD), and carbon stocks (C_stock). Soil quality indicators ranging from 0 to 1 were used to graphically represent the set of SES. The indicator-based approach used to explain the differences among the four production systems was able to capture the soil functions and offered a good starting point for quantifying SES provision. Full article

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future. Full article

Over the past three decades, a social dimension of sustainability has been established. Much like the environment, society also suffers from forms of pollution that pose a threat to community development. The phenomenon of information disorders, commonly referred to as fake news (FNs), represents an emblematic case of pollution of public discourse. This is particularly evident in the hybrid media ecosystem, where individuals construct their own information pathways in response to a process of deep mediatization. Considering this scenario, especially in the Italian context, research was conducted to investigate the impact of fake news on social sustainability. The survey was constructed around four research areas: sociodemographic factors, news consumption patterns, information disorders, and personal values. A non-probabilistic sample of the Italian population was drawn, totaling n = 399 individuals distributed throughout the country. The responses were analyzed using both single-variate and multivariate analyses. Specifically, a Social Sustainability Index (SSI) was synthesized from the analysis of sustainability indicators. This index revealed four levels of different behaviors concerning media use, political orientations, and especially, belief in fake news. Thus, a link seems to emerge between sustainability and the ability to inhabit the communication ecosystem properly. Full article

This paper examines a Business Model (BM) from a socio-economic system perspective to discern key factors and understand its interactions resulting in the Scaling of Social Impact (SSI) in Social Entrepreneurship (SE). Previously, studies have explained the importance of the BM in relation to SE. However, there is a lack of empirical studies on how a BM’s transitions through participation of various actors result in the SSI, causing a gap in this field’s research. This research applies a qualitative analysis on a single case study of a Japanese social startup, “mymizu”, the first water refill application platform in Japan. The findings show that collaboration amongst different stakeholders on the initial phase of the BM could increase awareness of responsible consumption, convert into actual users for sustainability, and change their behavior. Secondly, members of society could take on dual roles, both as users and collaborators in the BM, which results in an exponential scaling effect of the Social Impact (SI). This paper contributes towards adding a Participatory Stakeholder (PS) to the ecosystem of the SSI and building a Regenerative BM (RBM) that is relevant in SE towards sustainability. Full article

Centuries of cultivation in the Highland Aynoka of Puno, Peru, have endowed indigenous crops such as potato and quinoa with rich cultural and nutritional value deeply ingrained in local traditions. This study meticulously evaluates their economic viability, environmental implications, and cultural importance by employing a mixed-methods research approach involving surveys, interviews, and observations. The outcome reveals that while the Economic Sustainability Index (EKI) moderately supports potato and quinoa production sustainability, with a value of 2.98, it falls short of significant impact. Conversely, the Environmental Sustainability Index (ESI) and the Social Sustainability Index (SSI) exhibit moderate levels of sustainability, recording values of 4.04 and 3.38 for ESI and SSI, respectively. These crops demonstrate acceptable economic feasibility, marked by consistent sales, income generation, and manageable production expenses. The findings underscore the urgency of endorsing sustainable farming methods to safeguard cultural heritage, boost market prospects, and fortify regional ecological robustness. Rooted in ancestral sustainability, potato and quinoa cultivation is a cornerstone in local food systems. Recognizing the cultural, economic, and environmental significance inherent to these crops, efforts can be channeled towards nurturing sustainable agricultural systems that uphold community well-being, conserve biodiversity, and facilitate cultural resilience in Puno’s Highland Aynoka. Full article

More and more, educational researchers have advocated for the implementation of socio-scientific issues (SSI) education. To understand the current situation of, and future development trends in, social science issues-based education research, a content analysis method was used to review articles from SSI education research publications from 2004–2022. A total of 580 research publications were identified for content analysis through a keyword search in Web of Science. The basic characteristics of, and hot topics and future development trends in SSI education research were summarized based on the research analysis, including basic information (including the number of publications, journals, titles, and authors), and the coding of the research information (including the research participants, research methods, research topics, and research fields) in the articles. The results show that the number of publications has been increasing year by year, and the journal with the most publications is the International Journal of Science Education, while Science & Education has the fastest growing number of published articles. Researchers from the United States account for the largest number of authors of articles, and the focus has mainly been on research participants in K–12 education. The research methods are mainly qualitative or mixed research methods, and the research topics mainly focus on the teaching value and significance of social science issues, and the application and introduction of social science issues in subject teaching. The annual number of publications shows a significant increasing trend, while the teaching values and significance of SSI education, such as argumentation, reasoning, decision-making skills, and critical thinking, are hot topics. SSI teaching mainly focuses on environmental issues, and emphasizes the concept of sustainability. This study provides comprehensive discussions around both the basic information and research information, offering valuable insights into the current state of, and future trends in, SSI education research. It is particularly beneficial for individuals new to the SSI education field, and those with an interest in SSI. Full article

Even though the urge to transform educational practices towards sustainability has been widely recognized, teachers struggle with implementing socioscientific issues (SSI) such as climate change and loss of biodiversity into their lessons. While the research on SSI grows, the literature remains limited in terms of (i) the use of SSI in facilitating education for sustainable development (ESD), and (ii) teachers’ professional learning of SSI-based instruction as a means towards ESD. In this empirical study, we aimed at characterizing five STEM pre-service teachers’ pedagogical design capacity (PDC) by focusing on what resources they use and how they interact with these resources to design SSI-based instruction to teach about the sustainable development goals (SDGs). For this qualitative study, the data were collected through field notes, reflection reports, and semi-structured interviews. Our results reveal that pre-service teachers referred to teacher resources the most, followed by collaborative resources, and instructional resources during their design. Even though their use of resources shows strong connections between SSI and their pedagogical content knowledge, preservice teachers’ consideration regarding assessment remains inadequate. Furthermore, our study shows that professional development sessions have the potential to foster pre-service teachers’ use of PDC resources to address ESD. Full article

One goal of environmental civic education is preparing students, both as citizens and as professionals, to use effective arguments in public debates. Such debates include dominantly economic claims, which are multifaceted and rarely taught in schools. A learning unit that applied the pedagogical principles of socio-scientific issues was developed for ‘Israel’s Natural Gas Export Policy’, a real sustainability dilemma. The study aimed to understand how pre- and in-service science teachers craft their arguments, by comparing their written reasoned opinions on the gas export debate, before and after the learning unit. Content analysis was conducted using Grounded Theory on the two groups’ texts in a multiple case study design. Five reasoning rationales were found: ‘Profits and Risks’, ‘Ethics or Ideology’, ‘Pragmatic Objectives’, ‘Evidence Base’ and ‘Stakeholder Motivations’. Each rationale yielded different reasoning strategies, including ‘Costs/Benefits’, ‘the Trade-Off Dilemma’ or ‘Compromise’, ‘Compensatory Benefits’ and ‘Non-Compensatory Costs/Risks’. The findings show that both groups used more argument types in the post-task. The development of ‘Profits and Risks’ strategies, between the pre- and post-texts, shows how the teachers’ arguments became more complex and decisive. These results exemplify how the SSI-focused learning unit enables learners to enhance their critical citizenship thinking, one of the cornerstones of democracy. Full article