Internet Research Articles

Lignin-modified cellulose nanofibers hydrogel under adjustable binary solvent systems with excellent adhesion, self-healing and anti-freeze properties

Hydrogels with remarkable flexibility have gained popularity as materials for current research. However, the unfavorable properties of short-term adhesion, susceptibility to damage, and freezing in low-temperature presented by conventional hydrogels have become bottlenecks for further applications. In this work, an anti-freezing hydrogel with excellent mechanical, adhesion, and self-healing properties were developed by constructing a persistent semiquinone/quinone-catechol redox equilibrium environment. The introduction of lignin-modified cellulose nanofibers (LCNFs) significantly improved the overall mechanical properties of the material, driven by strong hydrogen bond interactions. This enhancement was evident in the tensile properties (97.74 ± 1.72 kPa, 783 %) and compression properties (> 90 %). Within the internal network of the gel, the synergistic action of lignin and ammonium persulfate resulted in the production of catechol, which imparted the gel with excellent adhesion properties (28.26 ± 2.13 KPa) and broad adhesion applicability. In addition, the incorporation of ethylene glycol (EG) positively contributed to the strengthening of the gel while endowed with tunable anti-freezing properties. Given the exceptional advantages of the prepared hydrogels, they were used to assemble flexible strain sensors with outstanding sensitivity for monitoring human motions.

A Framework for Deploying IPV6 at Higher Education Institutions

Despite the fact that IPv6 was developed 30 years ago, its deployment within higher education institutions worldwide is still slow. While many have started transitioning from IPv4 to IPv6, a large number of them have deferred the transition due to the time needed to complete the transition, budget implications, and security issues. The main problem is how to deploy IPv6 without disrupting the teaching activities of academic departments. This paper introduces a phased framework for deploying IPv6 at the university campus networks of higher education institutions. The framework consists of three phases: preparation and assessment, perimeter network, and internal network. In addition to an independent phase to manage the security issues, a Venn diagram has been created to compare the proposed framework with a state-of-art framework. The results show that the proposed framework retains high value as a comprehensive IPv6 deployment framework at higher education institutions.

Business Process Management System of a Retail Network Enterprise Based on Big Data Analysis Tools

Business Process Management System of a Retail Network Enterprise Based on Big Data Analysis Tools

Investigating the Mediation of Powerful Entrepreneurial Networking in the Marketing Performance of Halal Smes in Indonesia Towards Achieving Sustainable Development Goals

Objective: This study introduces and analyzes a framework for Powerful Entrepreneurial Networking (PEN) to connect Entrepreneurial Orientation (EO) and Marketing Performance (MP) of halal Small and Medium Enterprises (SMEs) in Indonesia. It explores the direct impact of EO and Resource Availability (RA) on marketing performance and examines the mediating role of PEN in these relationships. The study aligns these factors with the Sustainable Development Goals (SDGs), aiming to enhance the economic and social performance of halal SMEs. Theoretical Framework: The research integrates concepts of entrepreneurial orientation, resource availability, and business networks, positing that PEN acts as a mediator, enhancing the influence of EO and RA on marketing performance. This holistic approach underscores the role of both internal capabilities and external networks in achieving business success, particularly within the halal sector. Method: A quantitative research approach was used, applying Structural Equation Modeling with Partial Least Squares (SEM-PLS). Data were collected from 354 halal-certified SMEs in North Sumatra, Indonesia, through structured questionnaires, and SEM-PLS was used to validate the research model. Results and Discussion: The findings reveal that entrepreneurial orientation and resource availability significantly improve the marketing performance of halal SMEs. Furthermore, PEN mediates these effects, enabling SMEs to leverage their entrepreneurial orientation and resources effectively, which is crucial for achieving superior marketing outcomes and contributing to the SDGs. Research Implications: The study offers practical implications for halal SMEs, suggesting the need to focus on entrepreneurial orientation, resource management, and networking to drive sustainable growth and align with SDGs. Originality/Value: This research contributes to the understanding of how PEN mediates the relationship between EO and marketing performance, providing insights into enhancing the capabilities of halal SMEs in line with SDGs.

Development of Secure Infrastructure for Advancing Generative AI Research in Healthcare at an Academic Medical Center.

The increasing interest in leveraging generative AI models in healthcare necessitates secure infrastructure at academic medical centers. Without an all-encompassing secure system, researchers may create their own insecure microprocesses, risking the exposure of protected health information (PHI) to the public internet or its inadvertent incorporation into AI model training. To address these challenges, our institution implemented a secure pathway to the Azure OpenAI Service using our own private OpenAI instance which we fully control to facilitate high-throughput, secure LLM queries. This pathway ensures data privacy while allowing researchers to harness the capabilities of LLMs for diverse healthcare applications. Our approach supports compliant, efficient, and innovative AI research in healthcare. This paper discusses the implementation, advantages, and use cases of this secure infrastructure, underscoring the critical need for centralized, secure AI solutions in academic medical environments.

Zero Trust Architecture: Enhancing Cybersecurity in Enterprise Networks

Zero Trust Architecture (ZTA) offers a robust approach to enhancing cybersecurity in enterprise networks, replacing the traditional perimeter-based security models. This paper examines the application, challenges, and effectiveness of ZTA in contemporary corporate environments, with a focus on hybrid and cloud infrastructures. By emphasizing key principles such as least-privileged access, continuous authentication, and network segmentation, ZTA directly addresses the security risks that organizations face today. This research includes a review of relevant literature and an analysis of case studies to explore the difficulties companies encounter when adopting ZTA, including financial costs, integration complexities, and resistance to change. The study also identifies strategies that organizations have successfully employed to overcome these obstacles, leading to improved security and operational efficiency. The findings highlight ZTA’s ability to reduce security incidents through automation and enhanced monitoring. While there are technical challenges to implementing Zero Trust, the research concludes that the framework is essential for maintaining a strong security posture. Future areas for exploration include the role of technologies like artificial intelligence and machine learning in further improving ZTA.

Ethogram of the predatory sequence of dogs (Canis familiaris)

Predatory behaviour is a relevant part of the behavioural repertoire of dogs, which often entails negative or desirable implications, depending on the context and target to which it is directed. It is also considered one of the most characterising aspects of breed-specific behaviour. Despite its relevance, there is surprisingly little scientific knowledge about the topic. In the present study we sought to develop an ethogram of the predatory sequence of dogs, with the aim of providing a sufficiently general structure to be applicable to dogs at large, without eluding peculiar aspects of the species’ specific behaviour. Sixty videos of dogs of different breeds expressing predatory acts, including both videos obtained from public internet repositories and videos made on purpose, were described in detail by two different observers. From descriptions, a general sequence was identified, consisting of four functional phases, i.e. search, approach, chase and bite. The descriptions of the behaviours included in each phase were organised into three levels, characterised by different levels of detail and focusing on different aspects of the behaviour. When appropriate, these descriptions highlighted differences in behaviour expressed by dogs belonging to different working groups. The ethogram proposed in the present paper could represent a useful starting point for improving knowledge about the predatory behaviour of dogs and answering relevant questions about both its evolution and ontogeny.

Enhanced edible films with Ferulic acid nanoemulsion: Fabrication, characterization, and preservation effects on fresh-cut Chinese water chestnuts

The cavitation produced by ultrasonication can generate strong shear forces and shock waves in the liquid medium, forming nanometer-sized droplets. In this study, ferulic acid (FA) nanoemulsions were prepared using ultrasonication for different durations. The FA nanoemulsion sonicated for 2.5 min exhibited the smallest droplet size (34.44 ± 3.38 nm) and the highest system stability. The compatibility of FA nanoemulsion with gelatin/starch edible films was further analyzed using Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The results indicated that the film containing the FA nanoemulsion sonicated for 2.5 min (G/S/FA2.5) demonstrated better film-forming ability, mechanical properties, and barrier performance. Moreover, due to its smallest droplet size and uniform distribution within the film matrix, the G/S/FA2.5 film also showed excellent antioxidant and antibacterial capabilities. Furthermore, the Rheological properties results of the film-forming solution indicate that G/S/FA2.5 possesses a better internal network structure and the most suitable viscosity value for application in food coatings. Finally, the film was applied to fresh-cut water chestnut stored at low temperatures (10 °C). The preservation experiment results showed that the FA nanoemulsion film can suppress up to 50.13% of the yellowing in fresh-cut Chinese water chestnuts by the 8 day of storage, effectively inhibiting the wrinkling on their surface. It significantly reduced the weight loss and spoilage of the water chestnut fruits, thereby extending their shelf life.

MICP treated sand: insights into the impact of particle size on mechanical parameters and pore network after biocementation

Microbiologically Induced Calcium Carbonate Precipitation (MICP) is a technology for improving soil characteristics, especially strength, that has been gaining increasing interest in literature during the last few years. Although a lot of influencing factors on the result of MICP are known, particle size and shape of the particles remain poorly understood. While destructive measuring of compressive strength or calcium carbonate content are important for the characterization of samples these methods give no insight into the internal structures and pore networks of the samples. X-ray microcomputed tomography (micro-CT) is a technique that is used to characterize the internals of rocks and to a certain degree MICP-treated soils. However, the impact of filtering and image processing of micro-CT Data depending on the type of MICP sample is poorly described in the literature. In this study, single fractions of local quarry were treated with MICP through the ureolytic microorganism Sporosarcina pasteurii to investigate the influence of particle size distribution on calcium carbonate content, unconfined compressive strength and the reduction of water permeability. Additionally, micro-CT was conducted to obtain insights into the resulting pore system. The impact of the Gauss filter und Non-local means filter on the resulting images and data on the pore network are discussed. The results show that particle size has a significant impact on the result of all tested parameters of biosandstone with lower particle size leading to higher strength and generally higher calcium carbonate content. Micro-CT data showed that the technology is feasible to gain valuable insights into the internal structures of biosandstone but the resolution and signal-to-noise ratio remain challenging, especially for samples with particle sizes smaller than 125 µm.

Multilayer asset association network, green technical cooperation innovation, and default risk: Evidence from China's high energy-consuming industries

High energy-consuming industries are expected to play a significant role in energy conservation and emissions reduction. This study collects data on China's high energy-consuming listed companies from 2009 to 2022. Using the complex network theory, we construct a multilayer asset association network for high energy-consuming enterprises and examine how different layers and totality affect default risk. The results revealed that node-weighted degree centrality in the asset association networks of high energy-consuming enterprises, particularly in the guarantee association network, significantly increased default risk. Further mechanistic analysis reveals that the level of green technical cooperation innovation mitigates the impact of increased weighted degree centrality on the default risk of high energy-consuming enterprises. For high energy-consuming enterprises located in the eastern region, central region or non-heavily polluting industries, the influence of weighted degree centrality in the asset association network on default risk is more significant. This study enriches complex network research and provides guidance for managing default risk in high energy-consuming enterprises. Additionally, these studies align with global trends toward enhanced asset transparency, sustainability, and financial resilience, and contribute emerging markets to the transition toward more sustainable energy practices.

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Institute for Data Valorisation, the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, the National Natural Science Foundation of China [Grants 12371456, 72101049, 72232001], the Sichuan Science and Technology Program [Grant 2024NSFJQ0038], and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045].

Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model

Given the significant impact of transportation-related carbon emissions on air quality and climate change, understanding the regional dynamics of these emissions is crucial. Despite numerous studies on carbon emissions, there is a lack of comprehensive analysis of China's interprovincial transport carbon emission correlation network. Based on China's provincial data from 2007 to 2021, we analyzed the network's basic structural characteristics and categorized it into four significant plates to investigate their interactions. Subsequently, motif analysis is employed to examine the micro-correlation patterns within the network, and the Exponential random graph model (ERGM) is utilized to analyze the network's formation mechanism. Findings reveal that: (1) Provinces with high correlation intensity are predominantly concentrated in the eastern region, such as Shanghai and Beijing. Additionally, provinces in the eastern region assume a central role in the transport carbon emission correlation network, mainly receiving carbon emissions from other provinces. In contrast, the western region primarily emits carbon emissions to other provinces, continuously converging towards the center. (2) The network is segmented into net beneficiary plate, net overflow plate, bidirectional spillover plate, and broker plate, with distinct roles and influences across different years. (3) Bidirectional correlation motif structures emerge as primary influencers within the network, although specific structures impede interregional communication and collaborative emission reduction. (4) Internal network's structural variables, such as mutuality, cyclic triple, and geometrically weighted edgewise shared partner, along with influencing factors including government intervention, urbanization rate, openness, fiscal expenditure on transport, and province adjacency significantly impact the formation of the transport carbon emission correlation network. The above transportation network research provides a theoretical basis for the country to promote low-carbon transportation and improve air quality, and also has important guiding significance for the cross-regional collaborative emission reduction work of provinces.

Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks.

The Influence of Morphological Elements of Urban Gated Communities on Road Network Connectivity: A Study of 120 Samples of the Central Districts of Jinan, China

Currently, the dominant gated communities (GCs) in Chinese cities have fragmented the urban road network, causing traffic congestion, energy consumption, carbon emissions, and environmental pollution. The morphological elements of GCs are key factors affecting road network connectivity. This paper aimed to explore the influence of the morphological elements of GCs on road network connectivity, to provide a quantitative basis for the evaluation and renovation of the connectivity of GCs, and to provide insights for urban planning and policy. This paper quantitatively analyzed the connectivity of GCs using 120 samples from the central districts of Jinan, China. Morphological elements were the independent variables, while route directness (RD) and the network distance (D) to the nearest entrance were the dependent variables. RD measured the internal connectivity, and D measured the connectivity between the internal and external road networks of GCs. GIS was used to measure RD and D, and SPSS was used to conduct a correlation analysis to identify significant variables. Multiple linear regression and LASSO regression were used to test the influence of these factors on RD and D. LASSO regression was employed to construct prediction models for RD and D. We found that intersection density had the greatest impact on RD, while the number of entrances and exits, and the scale of GCs, had the greatest impact on D. Using thresholds of D = 250 and RD = 1.3, the four types of GCs were classified and corresponding renovation measures were proposed.

Rapid and Resilient LoRa Leap: A Novel Multi-Hop Architecture for Decentralised Earthquake Early Warning Systems.

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.

Public service media and public service internet as a counterweight to media concentration

This article will discuss the question of whether and to what extent private ownership of media is the appropriate form for organising the infrastructures of social communication, or whether media oriented towards the common good and not profit-driven are better suited to the requirements of democratic communication in a pluralistic society. Starting from the dual character of media as merit goods and commodities, the connection between media concentration and democracy will be discussed based on concentration theories. In view of the fears that this connection might lead to negative excesses, media policy has long been attempting to curb media concentration. These efforts are shortly presented and discussed. Possibilities of balancing the dual character of the media within the framework of different ownership structures lead to the idea that public service media can form a counterweight to media concentration. This is as well an option for an internet oriented towards the common good. The suggestion of a Public Service Media and Public Service Manifesto that was published in 2021 and supported by more than 1000 subscribers from the academic and media community worldwide illustrates this possibility.

Co-Creating an Inclusion, Diversity, Equity, and Accessibility Strategy: Defining approach and outcomes in a health data research network

BackgroundHealth Data Research Network Canada (HDRN) has committed to strengthening data use to improve health equity. Key this priority is has been an increased operationalization of Inclusion, Diversity, Equity, and Accessibility (IDEA) across the organization and within the data research processes HDRN Canada supports. Missing, however, was a unifying strategy for embedding IDEA within all HDRN Canada initiatives. ApproachGuided by results from an internal environmental scan and network feedback, HDRN Canada began developing an IDEA strategy in 2023. With the end goal of embedding IDEA across all internal teams and across all HDRN Canada strategic goals, collaborative participation methods were chosen to involve all levels of the organization. A process was designed that included focus groups, interviews, and roundtables. ResultsCo-creating the IDEA strategy required broad buy-in including detailing expectations of the planning process in advance. A flexible management approach that enabled the team to adhere to the defined process allowed the Project Team to manage unanticipated challenges. 4 key strategies emerged focusing on learning, data quality and research, leadership, and community engagement. Developing the strategy with co-creative methods helped to identify practical approaches for HDRN to prioritize IDEA within its complex data research initiatives. ConclusionHDRN Canada has outlined its commitment to IDEA in the health data ecosystem. The co-created strategy supports efforts towards accomplishing its organizational objectives and priorities. It is also a valuable resource for those in the health data space working to ensure data research contributes to equitable health outcomes.

Proactively Defending Enterprise Computer Systems Against Threats and Vulnerabilities

Cybersecurity remains a critical concern, even amidst global events like the COVID-19 pandemic. The rise of COVID-19 has deepened cybersecurity threats, with phishing emails and phone scams attempting to exploit the situation. This paper focuses on proactive strategies to protect enterprise environments against threats and vulnerabilities, specifically in Windows-based systems. Tracing threats and vulnerabilities to their source aims to address them in their early stages rather than after an attack has occurred. This research tackles three prevalent issues: phishing emails, vulnerability patching, and industrial internet-connected devices. Through analyzing various cyber defense models and vulnerability databases, this paper proposes frameworks to mitigate these issues effectively. The study includes a detailed examination of sources of threats and vulnerabilities, aiming to develop methodologies for practical implementation. Ultimately, the goal is to summarize best practices to enhance tool utilization and process improvement and propose new proactive defense methods. The research emphasizes the shift from reactive to proactive defense strategies to better protect enterprise networks.

Effect of frozen storage on the quality of frozen instant soup rice noodles: From the moisture and starch characteristics

This study aimed to simulate frozen instant soup rice noodles (FISRN) and investigate the effects of long-term frozen storage (−18 °C, 180 days) on the quality characteristics, moisture status, and starch retrogradation of FISRN. The findings indicated that the extent of starch retrogradation gradually increased over 90 days, which elevated the RS rate and inhibited starch digestibility. However, recrystallization resulted in a gradual increase in ice crystal size after 90 days, which disrupted the ordered structure formed by starch retrogradation, reduced the degree of starch order, and accelerated the rate of starch digestion. Furthermore, a longer relaxation time (T24) was detected by NMR with increasing storage time. The weakly bound water in FISRN was gradually converted to free water. Texture results suggested that the hardness of FISRN experienced a general decrease. The cooking loss increased progressively from 3.66 % to 8.10 %. Scanning electron microscope demonstrated that the internal porous network structure of FISRN became inhomogeneous, and a significant number of apertures were formed on the surface. Overall, starch retrogradation and ice recrystallization significantly impact the quality of FISRN during long-term frozen storage. The findings may potentially influence the consumption and market circulation of FISRN positively.