Search Results (13)

Global low-carbon innovation networks face increasing vulnerabilities amid growing geopolitical tensions and technological competition. The interdependent structure of low-carbon innovation networks and the risk propagation dynamics within them remain poorly understood. This study investigates vulnerability patterns by constructing a two-layer interdependent network model based on Chinese low-carbon patent data, comprising a low-carbon collaboration network of innovation entities and a low-carbon knowledge network of technological components. Applying dynamic shock propagation modeling, we analyze how risks spread within and between network layers under various shocks. Our findings reveal significant differences in vulnerability distribution: the knowledge network consistently demonstrates greater susceptibility to cascading failures than the collaboration network, reaching complete system failure, while the latter maintains partial resilience, with resilience levels stabilizing at approximately 0.64. Critical node analysis identifies State Grid Corporation as a vulnerability point in the collaboration network, while multiple critical knowledge elements can independently trigger system-wide failures. Cross-network propagation follows distinct patterns, with knowledge-network failures consistently preceding collaboration network disruptions. In addition, propagation from the collaboration network to the knowledge network showed sharp transitions at specific threshold values, while propagation in the reverse direction displayed more gradual responses. These insights suggest tailored resilience strategies, including policy decentralization approaches, ensuring technological redundancy across critical knowledge domains and strengthening cross-network coordination mechanisms to enhance low-carbon innovation system stability. Full article

Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). However, their widespread adoption raises concerns about intellectual property (IP) protection, as unauthorized use can undermine innovation. Watermarking has emerged as a promising solution for model ownership verification, but its application to NLP models presents unique challenges, particularly in ensuring robustness against fine-tuning and preventing interference with downstream tasks. This paper presents a novel watermarking scheme, TIBW (Task-Independent Backdoor Watermarking), that embeds robust, task-independent backdoor watermarks into pre-trained language models. By implementing a Trigger–Target Word Pair Search Algorithm that selects trigger–target word pairs with maximal semantic dissimilarity, our approach ensures that the watermark remains effective even after extensive fine-tuning. Additionally, we introduce Parameter Relationship Embedding (PRE) to subtly modify the model’s embedding layer, reinforcing the association between trigger and target words without degrading the model performance. We also design a comprehensive watermark verification process that evaluates task behavior consistency, quantified by the Watermark Embedding Success Rate (WESR). Our experiments across five benchmark NLP tasks demonstrate that the proposed watermarking method maintains a near-baseline performance on clean inputs while achieving a high WESR, outperforming existing baselines in both robustness and stealthiness. Furthermore, the watermark persists reliably even after additional fine-tuning, highlighting its resilience against potential watermark removal attempts. This work provides a secure and reliable IP protection mechanism for NLP models, ensuring watermark integrity across diverse applications. Full article

The classrooms in Israel are very diverse, with students differing in learning styles, their handling of literacy tasks, personal and socioeconomic backgrounds, and more. These differences significantly impact the curriculum aimed at promoting literacy resilience, explicit teaching processes in the classroom, and imparting metacognitive strategies and actions to overcome learning difficulties. This qualitative-interpretative study reveals the pedagogical perceptions, challenges, and coping strategies of fourteen Hebrew teachers in five elementary schools in central Israel regarding integrating home literacy in language lessons and cultivating literacy resilience among their students. The research data were collected through in-depth interviews with the teachers. The analysis of the teachers’ reports reveals two main perceptions regarding literacy resilience: (1) Literacy resilience is a tool for life; (2) Home literacy significantly contributes to fostering literacy resilience. Furthermore, cultivating literacy resilience presents three significant challenges for the teachers: (1) Teaching in a heterogeneous classroom, (2) Encouraging parental involvement, and (3) Fostering independent learners. To cultivate literacy resilience in a heterogeneous classroom, teachers must be sensitive to each student’s unique needs and plan teaching-learning processes based on principles of self-directed learning and peer dialogue. They must establish a personal-emotional connection that is a significant anchor for the students and outlines a path for integrating and strengthening the sense of competence in handling literacy tasks. It was also found that parental involvement is a significant factor influencing the cultivation of literacy resilience, and teachers undertake various actions to increase their level of involvement. This study adds an essential layer to the body of knowledge regarding the understanding of the factors affecting the development of pedagogical literacy perceptions that promote the integration of home literacy in the classroom. These perceptions may promote the nurturing process of literacy resilience among students from various cultures, accepting and understanding them. In this way, we can attempt to address literacy and language challenges in Israel. Full article

The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks. Full article

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over $GF\left(2^n\right),n\ge 8$. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important. Full article

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide range of modalities, each of which offers a different and complementary window into the thoughts and emotions of the speaker. In this way, a more complete picture of a person’s emotional state may emerge through the fusion and analysis of data from several modalities. The research suggests a new attention-based approach to multimodal emotion recognition. This technique integrates facial and speech features that have been extracted by independent encoders in order to pick the aspects that are the most informative. It increases the system’s accuracy by processing speech and facial features of various sizes and focuses on the most useful bits of input. A more comprehensive representation of facial expressions is extracted by the use of both low- and high-level facial features. These modalities are combined using a fusion network to create a multimodal feature vector which is then fed to a classification layer for emotion recognition. The developed system is evaluated on two datasets, IEMOCAP and CMU-MOSEI, and shows superior performance compared to existing models, achieving a weighted accuracy WA of 74.6% and an F1 score of 66.1% on the IEMOCAP dataset and a WA of 80.7% and F1 score of 73.7% on the CMU-MOSEI dataset. Full article

Technological breakthroughs have offered innovative solutions for smart parking systems, independent of the use of computer vision, smart sensors, gap sensing, and other variations. We now have a high degree of confidence in spot classification or object detection at the parking level. The only thing missing is end-user satisfaction, as users are forced to use multiple interfaces to find a parking spot in a geographical area. We propose a trustless federated model that will add a layer of abstraction between the technology and the human interface to facilitate user adoption and responsible data acquisition by leveraging a federated identity protocol based on Zero Knowledge Cryptography. No central authority is needed for the model to work; thus, it is trustless. Chained trust relationships generate a graph of trustworthiness, which is necessary to bridge the gap from one smart parking program to an intelligent system that enables smart cities. With the help of Zero Knowledge Cryptography, end users can attain a high degree of mobility and anonymity while using a diverse array of service providers. From an investor’s standpoint, the usage of IPFS (InterPlanetary File System) lowers operational costs, increases service resilience, and decentralizes the network of smart parking solutions. A peer-to-peer content addressing system ensures that the data are moved close to the users without deploying expensive cloud-based infrastructure. The result is a macro system with independent actors that feed each other data and expose information in a common protocol. Different client implementations can offer the same experience, even though the parking providers use different technologies. We call this InterPlanetary Smart Parking Architecture NOW—IPSPAN. Full article

There are world tendencies to implement interconnected infrastructures of energy-water-waste-transportation-food-health-social systems to enhance the overall performance in normal and emergency situations where there are multiple interactions among them with possible conversions and improved efficiencies. Hybrid energy systems are core elements within interconnected infrastructures with possible conversions among electricity, thermal, gas, hydrogen, waste, and transportation networks. This could be improved with storage systems and intelligent control systems. It is important to study resiliency of hybrid energy systems within interconnected infrastructures to ensure reduced risks and improved performance. This paper presents framework for the analysis of resiliency layers as related to protection layers. Case study of hybrid energy system as integrated with water, waste, and transportation infrastructures is presented where different resiliency and protection layers are assessed. Performance measures are modeled and evaluated for possible interconnection scenarios with internal and external factors that led to resiliency demands. Resiliency layers could trigger protection layers under certain conditions, which are evaluated to achieve high performance hybrid energy systems within interconnected infrastructures. The proposed approach will support urban, small, and remote communities to achieve high performance interconnected infrastructures for normal and emergency situations. Full article

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually. Full article

The physiological performance of early-maturing nectarine trees in response to water deficits was studied during the postharvest period. Two deficit irrigation treatments were applied, moderate and severe, and these were compared with a control treatment (fully irrigated). Stem water potential and leaf gas exchange (net CO₂ assimilation rate, A_CO2; transpiration rate, E; and stomatal conductance, g_s) were measured frequently. Drought avoidance mechanisms included a decrease in stomatal conductance, especially in the case of the severe deficit treatment, which also showed a strong dependence of A_CO2 on g_s. Intrinsic water-use efficiency (A_CO2/g_s) was more sensitive than instantaneous water-use efficiency (A_CO2/E) as an indicator to detect water deficit situations in nectarine trees. However, in contrast to the results obtained for other deciduous fruit trees, a poor correlation was found between A_CO2/E and A_CO2/g_s, despite the important relation between E and g_s. A_CO2/E was also weakly correlated with g_s, although this relationship clearly improved when the vapor pressure deficit (VPD) was included, along with g_s as the independent variable. This fact reveals that apart from stomatal closure, E depends on the boundary layer conductance (g_b), which is mediated by VPD through changes in wind speed. This suggests low values of the decoupling coefficient for this water-resilient species. Full article

Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale. Full article

The European climate strategy has encouraged the usage of the External Thermal Insulation Composite System (ETICS) to increase the energy efficiency of external building envelopes. This externally and relatively easily applicable façade solution must meet various technical requirements. This paper develops a technical severity evaluation model of on-site construction activities of ETICS to prioritize the risks of the construction process. The model can be used independently by any stakeholder of the construction process. The relevance of the activities is assessed with the Failure Mode Effects Analysis method. The model weights the impact of the essential technical requirements and simulates an integrated weighted technical severity value, which is derived from the analysis of experts’ judgments validated with the non-parametric Friedman’s test. The data collection for probability of occurrence and difficulty of detectability follows the Delphi technique to quantify the opinions of a group. The simulation, conducted on 103 degradation factors, shows that the on-site construction activities of ETICS strongly influence the decrease in the technical resilience of long-term durability, mechanical resistance, and stability, as well as the ability to bypass tensions. The highest risk is detected by the shortcomings in the layers of substrate, reinforcement, adhesive, and additional details. Full article

Micro energy grids (MEGs) are used extensively to meet the combined electricity, heating, and cooling energy demands for all types of customers. This paper develops a hazard matrix for a MEG and utilizes two advanced risk modeling approaches (fault tree and layer of protection analysis (LOPA)) for MEGs’ risk analysis. A number of independent protection layers (IPLs) have been proposed to achieve a resilient MEG, hence increasing its safety integrity level (SIL). IPLs are applied using co-generators and thermal energy storage (TES) techniques to minimize the hazards of system failure, increase efficiency, and minimize greenhouse gas emissions. The proposed modeling and risk assessment approach aims to design a resilient MEG, which can utilize those potentials efficiently. In addition, an energy risk analysis has been applied on each MEGs’ physical domains such as electrical, thermal, mechanical and chemical. The concurrent objectives achieve an increased resiliency, reduced emissions, and sustained economy. Full article