Search Results (47)

Spherical magnetic nanoparticles consisting of an iron core and iron oxide shell (α-Fe/FeO_X) were fabricated by the electric explosion of the wire technique (EEW). The structure and magnetic properties of synthesized nanoparticles were experimentally investigated. Magnetic properties of an iron nanoparticle ensemble for individual defect-free, non-interacting iron-based nanoparticles having different diameters were calculated using micromagnetic modeling. Experimental and calculated magnetic hysteresis loops were comparatively analyzed. Full article

Addressing climate change requires deeper insight into the psychological drivers of pro-environmental behavior. This study investigates how personality traits, climate-related emotions, and demographic factors can predict sustainable consumption and climate action participation using a Bayesian regression approach. Drawing from the HEXACO personality model and key emotional predictors—Climate Change Worry (CCW) and environmental empathy (EE)—we analyzed data from 604 adults in Greece to assess both private and public climate-related behaviors. This research is novel in its integrative approach, combining dispositional traits and affective states within a Bayesian analytical framework to simultaneously predict both sustainable consumption and climate action. Bayesian model testing highlighted education as the most powerful and reliable predictor of sustainable consumption, with increasing levels—namely Doctoral education—linked to more environmentally responsible action. CCW produced small but reliable effects, supporting hypotheses that moderate emotional concern will lead to sustainable behavior when linked to efficacy belief. The majority of HEXACO traits, e.g., Honesty–Humility and Conscientiousness, produced limited predictive power. This indicates in this case that structural and emotional considerations were stronger than dispositional personality traits. For climate action involvement, Bayesian logistic models found no considerable evidence of any predictor, corroborating the perspective that public participation in high effort action is most likely to rely on contextual enablers instead of internal sentiments or attributes. A significant interaction effect between education and gender also indicated that the sustainability effect of education is moderated by sociocultural identity. Methodologically, this research demonstrates the strengths of Bayesian analysis in sustainability science to make sensitive inference and model comparison possible. The results highlight the importance of affect-related structural variables in behavioral models and have applied implications for theory-informed and targeted climate education and communication interventions to enable different populations to act sustainably. Full article

Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool to enhance the accuracy of these applications, enabling more efficient responses to seismic events of different magnitudes. This systematic review aims to provide researchers and professionals with a summary of the current state of ML applications in seismology, particularly on early earthquake magnitude estimations and related topics such as earthquake detection and seismic phase identification. A systematic search was conducted in Scopus, ScienceDirect, IEEE Xplore, and Web of Science databases, covering the period from early 2014 to 7 March 2025. The search terms included the following: (“earthquake magnitude” OR “earthquake early warning”) AND (prediction OR forecasting OR estimation OR forecast OR classification) AND (“machine learning” OR “deep learning” OR “artificial intelligence”). Out of the 472 articles initially identified, 28 were selected based on pre-defined inclusion criteria. The described methods and algorithms illustrate the strong performance of ML in earthquake magnitude estimation despite limited implementation in real-time systems. This highlights the need to develop standardized benchmark datasets to promote future progress in this field. Full article

In recent years, although a variety of deep learning models have been developed for magnitude estimation, the complex and variable nature of earthquakes limits the generalizability and accuracy of these models. In this study, we selected the waveform data of the Japan earthquake. We applied four deep learning techniques (MagNet combined with bidirectional long- and short-term memory network Bi-LSTM, DCRNN with deepened CNN layers, DCRNNAmp with the introduction of a global scale factor, and Exams with a multilayered CNN architecture) for real-time magnitude estimation. By comparing the estimation errors of each model in the first 3 s after the earthquake, it is found that the DCRNNAmp performs the best, with an MAE of 0.287, an RMSE of 0.397, and an R² of 0.737 in the first 3 s after the arrival of the P-wave, and the inclusion of S-wave seismic-phase information is found to significantly improve the accuracy of the magnitude estimation, which suggests that S-wave seismic-phase waveform features can enrich the model’s understanding of the relationship between the seismic phases. It shows that S-wave phase waveform features can enrich the model’s knowledge of the relationship between seismic fluctuations and magnitude. The epicentral distance positively correlates with the magnitude estimation, and the model can converge faster with the improved signal-to-noise ratio. Despite the shortcomings of model design and opaque internal mechanisms, this study provides important evidence for deep learning in earthquake estimation, demonstrating its potential to improve the accuracy of on-site earthquake early warning (EEW) systems. The estimation capability can be further improved by optimizing the model and exploring new features. Full article

The rapid technological advancements and the shift towards clean energy have significantly increased the demand for metals, leading to an increasing metal pollution problem. This review explores recent advances in bioelectrochemical systems (BES) for metal recovery from waste, especially Acid Mine Drainage (AMD) and Electrical, Electronic Wastes (EEW) and waste from smelters, highlighting their potential as a sustainable and economically viable alternative to traditional methods. This study addresses the applications and limitations of current BES recovery techniques. BES, including microbial fuel cells (MFCs), microbial electrolytic cells (MECs), and Microbial Desalination Cells (MDCs), offer promising solutions by combining microbial processes with electrochemical reactions to recover valuable metals while reducing energy requirements. This review categorizes recent research into two main areas: pure BES applications and BES coupled with other technologies. Key findings include the efficiency of BES in recovering metals like copper, chromium, vanadium, iron, zinc, nickel, lead, silver, and gold and the potential for integrating BES with other systems to enhance performance. Despite significant progress in BES application for metal recovery, challenges such as high costs and slow kinetics remain, necessitating further research to optimize materials, configurations, and operational conditions. The work also includes an economic assessment and guidelines for BES development and upscale. This review underscores the critical role of BES in advancing sustainable metal recovery and mitigating the environmental impact of metal pollution. Full article

This article introduces a novel smart deployable scissor lift brace system designed to mitigate earthquake risks in buildings prone to the soft-story effect. The system addresses the limitations of traditional retrofitting methods, providing an efficient solution for enhancing the structural integrity of buildings while preserving the functionality of open lower floors, commonly used for car parking or retail spaces. The soft-story effect, characterized by a sudden reduction in lateral stiffness in one or more levels of a building, often leads to catastrophic collapses during large earthquakes, resulting in significant structural damage and loss of life. The proposed system is triggered by signals from the Earthquake Early Warning (EEW) system, advanced technologies capable of detecting and broadcasting earthquake alerts within seconds which are currently implemented in countries and regions such as Japan, parts of the USA, and parts of Europe. The smart deployable system functions by instantly activating upon receiving EEW signals. Unlike traditional retrofitting approaches, such as adding braces or infill walls, which compromise the open layout of lower floors, this innovative device deploys dynamically during seismic events to enhance the building’s stiffness and lateral stability. The article demonstrates the system’s functionality through a conceptual framework supported by proof-of-concept experiments. Historical earthquake time histories are simulated to test its effectiveness. The results reveal that the system significantly improves the stiffness of the structure, reducing displacement responses during events of seismic activity. If properly proportioned and optimized, this system has the potential for widespread commercialization as a seismic risk mitigation solution for buildings vulnerable to the soft-story effect. Full article

To enhance real-time S-wave detection in the railway earthquake early warning (EEW) system, we improved the existing short-term average/long-term average (STA/LTA) algorithm. This enhancement focused on developing a more robust and computationally efficient method. Specifically, we introduced noise reflecting P-wave amplitude information before the P-wave to better distinguish between P- and S-waves. By applying this modified STA/LTA method, we achieved a significant improvement in S-wave detection accuracy. For seismic waveforms from stations located within 100 km of the epicenter of each earthquake, with magnitude of M5.5–6.5 and depths ≤ 100 km, the detection accuracy within 1.5 s of the correct time (manual picking) was 81.0%, compared to the 49.0% accuracy of the currently operational railway EEW system. Importantly, despite the improved accuracy, the computational cost of the new method remains comparable to the existing system, allowing for easy integration into the operational EEW system. This development is crucial for preventing false alarms, especially moderate earthquakes (~M6) because issuing warn-ings in unnecessary areas can have a significant social impact. Future plans involve implementing this method into the current system to further improve early warning capabilities and minimize false alarms. Full article

To improve the performance of earthquake early warning (EEW) systems, we propose an approach that utilizes the time-dependence of P-wave displacements to estimate the earthquake magnitude (M) based on the relationship between M and the displacement. The traditional seismological understanding posits that this relationship achieves statistical significance when the displacement reaches its final peak value, resulting in the adoption of time-constant coefficients. However, considering the potential for earlier establishment of the relationship’s significance than conventionally assumed, we analyze waveforms observed in Japan and determine the intercept in the relationship as a function of time from the P-wave onset. We demonstrate that our approach reduces the underestimation of M in the initial P-wave stages compared to the conventional technique. Consequently, we find a significant rise in the number of earlier warnings in the Japanese railway EEW system. Due to the inherent trade-off between the immediacy and accuracy of alarm outputs, the proposed method unavoidably leads to an increase in the frequency of alerts. Nonetheless, if deemed acceptable by system users, our approach can contribute to EEW performance improvement. Full article

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab’s EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab’s system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration. Full article

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs). Full article

In this work, the samples of the WC-Fe-Ni-Co composition were obtained and studied. Alloy NiCo 29-18 is used as a binder (Fe-Ni-Co). In this paper, a comparative analysis of the samples obtained using commercial micron-sized WC powder and the samples obtained is carried out using nano-WC synthesized via the electric explosion of wire (EEW) method. The samples were subjected to vacuum sintering, then their structure, density, and porosity, as well as microhardness and oxidation resistance, were studied. Five different additives were used to stabilize sintering: VC, Cr₃C₂, NbC, Y₂O₃, and Nd₂O₃. All these additives are described in the literature as additives that are used in the sintering of materials of the WC-Co system. Also, the samples from the WC-Fe-Ni-Co material were obtained using additive manufacturing technology with material extrusion. Bending strength and hardness of the additively fabricated samples were determined. Full article

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave “picking” directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution. Full article

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems. Full article

The early earthquake warning (EEW) system is essential for mitigating the effects of seismic incidents. However, in China, the design of EEW messages has not received much attention. This study employs affordance theory to examine the effectiveness of the EEW message generated by the Institute of Care-Life (ICL) in China, specifically by investigating four aspects of affordances: functional, cognitive, sensory, and emotional affordance. With 68 participants, we conducted an immersive virtual reality experiment. The results revealed that the ICL EEW message has a strong emotional affordance but inadequate functional, cognitive, and sensory affordance. These data provide recommendations for enhancing EEW messages, which could result in better interaction during earthquakes in China. This study investigated the viability of immersive virtual reality as a research tool for EEW. It increases understanding of the elements that determine the effectiveness of EEW communications, leading to better preparedness and response measures, reducing the impact of earthquakes and saving lives and property. Full article

As a group of naturally occurring peptides in various foods, γ-glutamyl peptides possess a unique Kokumi taste and health benefits. However, few studies have focused on the functionality of γ-glutamyl peptides. In this study, the γ-[glutamyl] _{(n=1, 2, 3)}-tryptophan peptides were synthesized from a solution of glutamine (Gln) and tryptophan (Trp) employing L-glutaminase from Bacillus amyloliquefaciens. Four different γ-glutamyl peptides were identified from the reaction mixture by UPLC-Q-TOF-MS/MS. Under optimal conditions of pH 10, 37 °C, 3 h, 0.1 mol/L Gln: 0.1 mol/L Trp = 1:3, and glutaminase at 0.1% (m/v), the yields of γ-l-glutamyl-l-tryptophan (γ-EW), γ-l-glutamyl-γ-l-glutamyl-l-tryptophan (γ-EEW) and γ-l-glutamyl-γ-l-glutamyl-γ-l-glutamyl-l-tryptophan (γ-EEEW) were 51.02%, 26.12% and 1.91% respectively. The antioxidant properties of the reaction mixture and the two peptides (γ-EW, γ-EEW) identified from the reaction media were further compared. Results showed that γ-EW exhibited the highest DPPH^•, ABTS^•+ and O₂^•−-scavenging activity (EC₅₀ = 0.2999 mg/mL, 67.6597 μg/mL and 5.99 mg/mL, respectively) and reducing power (EC₅₀ = 4.61 mg/mL), while γ-EEW demonstrated the highest iron-chelating activity (76.22%). Thus, the synthesized mixture may be used as a potential source of antioxidant peptides for food and nutraceutical applications. Full article