Search Results (595)

In the context of digital construction, responsibility management in smart city building information modeling (BIM) projects spans the entire building lifecycle. The involvement of numerous BIM designers in project management and frequent data exchanges pose significant challenges for the traceability, immutability, and responsibility attribution of BIM models. To address these issues, this study proposes a blockchain-based responsibility management and collaboration framework for BIM projects using non-fungible tokens (NFTs), aiming to enhance the management of responsibilities and accountability in BIM projects. This research adopts a design science methodology, strictly adhering to scientific research procedures to ensure rigor. First, NFTs based on blockchain technology were developed to generate corresponding digital signatures for BIM model files. This approach ensures that each BIM model file has a unique digital identity, enhancing transparency and traceability in responsibility management. Next, the interplanetary file system (IPFS) was used to generate digital fingerprints, with the content identifier generated by IPFS uploaded to the blockchain to ensure the immutability of BIM model files. This method guarantees the integrity and security of BIM model files throughout their lifecycle. Finally, the proposed methods were validated through a blockchain network. The experimental results indicate that the proposed framework is theoretically highly feasible and demonstrates good applicability and efficiency in practical production. The constructed blockchain network meets the actual needs of responsibility management in smart city BIM projects, enhancing the transparency and reliability of project management. Full article

Our article analyzes data from a broader exploratory Italian study on youth imaginaries and the role of narratives in attributing meaning to the world. The research gathered responses from 872 young people (aged 18 to 23) through a digital questionnaire. The data were analyzed with quantitative methodology using descriptive statistics. Our research questions can be formulated as follows: What level of familiarity do respondents have with biblical narratives? What narrative themes and categories do they use to define those stories? In relation to these elements, what are the characteristics of the respondents’ subgroups that defined themselves as “Religious”, “Indifferent/Agnostic”, and “Atheist”? The questionnaire items analyzed in this article provide an account of the respondents’ familiarity with some biblical narratives and their characters (Abraham, Jacob, and Ruth), as well as their choices related to the stories’ narrative themes and categories. The results from our sample open the field for further investigations, particularly in contexts characterized by different religious backgrounds (e.g., Protestant contexts), which may offer more nuanced interpretations of the educational process in relation to religious identity. Full article

A novel bacterial strain, designated 24SJ04S-52^T, was isolated from bank-side soil in the Osucheon Stream of the Seomjin River, Republic of Korea. This strain is aerobic, Gram-stain-positive, and short-rod-shaped, with optimal growth observed at 30 °C, pH 7, and 0% salinity, and growth occurring across a temperature range of 15–37 °C, pH 5–9, and salinity of 0–4%. Phylogenetic analysis of the 16S rRNA gene showed that strain 24SJ04S-52^T shares 98.3% sequence similarity with Oryzobacter terrae PSGM2-16^T. However, the average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values were 85.0% and 50.1%, respectively, which are well below the species delineation thresholds of 95–96% for ANI and 70% for dDDH, confirming the novelty of this species. Genomic analysis identified a genome size of 3.98 Mb with a G+C content of 72.9 mol%. Functional annotation revealed various genes involved in amino acid, carbohydrate, and protein metabolism, suggesting metabolic versatility that may support adaptation to nutrient-variable environments. Chemotaxonomic analyses revealed distinctive profiles, including major fatty acids such as C_17:1 ω8c, iso-C_16:0, and iso-C_14:0, with MK-8(H₄) as the predominant menaquinone. The polar lipids included diphosphatidylglycerol, phosphatidylethanolamine, and phosphatidylinositol, and the peptidoglycan was of type A4γ with meso-diaminopimelic acid as the diagnostic diamino acid. These comprehensive analyses support the classification of strain 24SJ04S-52^T as a novel species within the genus Oryzobacter, for which the name Oryzobacter telluris sp. nov. is proposed. The type strain is 24SJ04S-52^T (=KACC 23836^T = FBCC-B16192^T). Full article

Blockchain technology is an important technical basis for ensuring carbon trading and plays a fundamental role in maintaining fairness in the carbon trading market. This paper proposes a carbon emission metering and settlement method and a system based on blockchain technology which creates the digital identity of electric meters and stores it in the blockchain. Verifiable credentials are generated based on the digital identity, energy data, and time stamp. The system records the energy data read by the verified meter to the blockchain cloud platform for carbon emission statistics. In the payment and settlement stage, through application of the blockchain and its combination with a digital payment wallet, the regional energy network consumption settlement value is generated according to the regional power supply and electricity consumption, and the settlement value is used as the benchmark to measure the carbon emissions in the region. Through the data analysis of practical application cases in an industrial park in China, this study concludes that the carbon emission statistical settlement method based on blockchain technology solves the problems of untrustworthiness, unreliability, and inconsistency in the statistical and settlement methods during the statistical settlement of electric energy statistics and energy consumption carbon emissions. Full article

The Space Physics Data Facility (SPDF) is a digital archive of space physics data and is useful for the storage, analysis, and dissemination of data. We discuss the process used to create an amended dataset and store it on the SPDF. The operational software to generate the archival data software uses the open-source Python package pysat, and an end-user module has been added to the pysatNASA module. The result is the addition of data products to the Mars Global Surveyor (MGS) magnetometer (MAG) dataset, its archival location on SPDF, and pysat compatibility. The primary and metadata format increases the convenience and efficiency for users of the MGS MAG data. The storage of planetary and heliophysics data in one location supports the use of data throughout the solar system for comparison, while pysat compatibility enables loading data in an identical format for ease of processing. We encourage the use of the outlined process for past, present, and future space science missions of all sizes and funding levels. This includes balloons to Flagship-class missions. Full article

Phytophthora-induced crop diseases, commonly known as “plant plagues”, pose a significant threat to global food security. In this study, strain ASG80 was isolated from sisal roots and demonstrated a broad-spectrum antagonistic activity against several Phytophthora species and fungal pathogens. Strain ASG80 was identified as Streptomyces luteireticuli via phylogenetic analysis, digital DNA–DNA hybridization (dDDH), and average nucleotide identity (ANI). Whole-genome sequencing identified 40 biosynthetic gene clusters (BGCs) related to secondary metabolite production, including antimicrobial compounds. Strain ASG80 extract exhibited broad-spectrum inhibitory activity against Phytophthora nicotianae, P. vignae, P. cinnamomi, and P. sojae. Pot experiments showed that strain ASG80 extract significantly reduced sisal zebra disease incidence, with an efficacy comparable to the fungicide metalaxyl. These findings suggest that strain ASG80 is a promising biocontrol agent with substantial potential for managing Phytophthora-related diseases in agriculture. Full article

The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset. As a transitional phase towards Cognitive Multi-Functional Radar (CMFR), Adaptive Multi-Function Radar (AMFR) possesses the capability to emit identical waveform signals across different working modes and states for task completion, with dynamically adjustable waveform parameters that adapt based on scene information. From a reconnaissance perspective, the valid signals received exhibit sparsity and localization in the time series. To address this challenge, we have redefined the reconnaissance-focused research priorities for radar systems to emphasize behavior analysis instead of pattern recognition. Based on our initial comprehensive digital system simulation model of a radar, we conducted reconnaissance and analysis from the perspective of the reconnaissance side, integrating both radar and reconnaissance aspects into environmental simulations to analyze radar behavior under realistic scenarios. Within the system, waveform parameters on the radar side vary according to unified rules, while resource management and task scheduling switch based on operational mechanisms. The target in the reconnaissance side maneuvers following authentic behavioral patterns while adjusting the electromagnetic space complexity in the environmental aspect as required. The simulation results indicate that temporal annotations in signal flow data play a crucial role in behavioral analysis from a reconnaissance perspective. This provides valuable insights for future radar behavior analysis incorporating temporal correlations and sequential dependencies. Full article

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms. Full article

Water surface micro-amplitude waves (WSMWs) of identical frequency are elicited as acoustic waves propagating through water. This displacement can be translated into an intermediate frequency (IF) phase shift through transmitting a frequency modulated continuous wave (FMCW) towards the water surface by a millimeter-wave radar, and information transmission across the water–air interface is achieved via the signal reconstruction method. In this paper, a novel mathematical model based on energy conversion from underwater acoustic to vibration (ECUAV) is presented. This method was able to obtain WSMW vibration information directly by measuring the sound source level (SL). An acoustic electromagnetic wave-based information transmission (AEIT) system was integrated within the water tank environment. The measured distribution of SL within the frequency range of 100 Hz to 300 Hz exhibited the same amplitude variation trend as predicted by the ECUAV model. Thus, the WSMW formation process at 135 Hz was simulated, and the phase information was extracted. The initial vibration information was retrieved through a combination of phase unwinding and Butterworth digital filtering. Fourier transform was applied to the vibrational data to accurately reproduce the acoustic frequency of underwater nodes. Finally, the dual-band binary frequency shift keying (BFSK) modulated underwater encoding acoustic signal was effectively recognized and reconstructed by the AEIT system. Full article

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability. Full article

The renovation and revitalization of vernacular architecture are pivotal in sustainable rural development. In regions like Shangri-La, traditional structures not only safeguard cultural heritage but also provide a foundation for enhancing local communities’ living conditions. However, these villages face growing challenges, including infrastructure decay, cultural erosion, and inadequate adaptation to modern living standards. Addressing these issues requires innovative research approaches that combine heritage preservation with the integration of contemporary functionality. This study employs a research-by-design approach, focusing on the Shanpian House as a case study, to explore how pre-planning and post-evaluation methods can revitalize traditional vernacular architecture. The pre-planning phase utilizes field surveys and archival research to assess spatial, cultural, and environmental conditions, framing a design strategy informed by field theory. In doing so, it evaluates how traditional architectural elements can be preserved while introducing modern construction techniques that meet current living standards. The post-evaluation phase, conducted through questionnaires and semi-structured interviews, assesses user satisfaction, focusing on the impact of architectural esthetics, structural stability, and material choices. Key findings from an OLS regression highlight the strong positive correlation between architectural style, structural choices, and cultural relevance with resident satisfaction. The research emphasizes that design elements such as structural details, materials, and infrastructure upgrades are critical in shaping perceptions of both functionality and cultural identity. Interestingly, the model reveals that improving architectural esthetics, alongside modern indoor features such as network connectivity, has a significant impact on enhancing overall resident satisfaction (significance level: 0.181). This study contributes to the broader discourse on sustainable building renovation by demonstrating how traditional architecture can be thoughtfully adapted for contemporary use and also proposes a paradigm shift in the renovation of historic buildings, advocating for a balance between preservation and modernization. The application of sustainable materials, digital modeling, and innovative construction techniques further ensures that these traditional structures meet the demands of modern civilization while maintaining their cultural integrity. Full article

As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers. Full article

A polyphasic taxonomic study was carried out on the rod-shaped, orange-pigmented strain C11^T, isolated from gold mine tailings. Sequencing of the 16S rRNA gene showed a relatedness to Brevundimonas, with a 98.4% and 98.2% similarity to Brevundimonas bacteroides and Brevundimonas variabilis, respectively. The average nucleotide identity and a digital DNA–DNA hybridization with the closest phylogenetic neighbor of strain C11^T indicate distinction at the species level, further confirmed by the differences in physiology. C_18:1 ω7c is the dominant cellular fatty acid. Its DNA G + C content is 68.3 mol %. Its predominant ubiquinone is Q-10; 1,2-Di-O-acyl-3-O-α-D-glucopyranuronosyl glycerol, phosphatidylglycerol, 1,2-di-O-acyl-3-O-α-D-glucopyranosyl glycerol, and 1,2-di-O-acyl-3-O-[D-glucopyranosyl-(1→4)-α-D-glucopyranuronosyl] glycerol are its major polar lipid constituents. This bacterium produces bacteriochlorophyll a and tolerates high concentrations of (μg/mL) the following: tellurium (>1500), selenium (1000 to >5000), and vanadium (>5000) oxyanions. The data support the inclusion of the strain C11^T into the genus Brevundimonas as a new species with the proposed name Brevundimonas aurifodinae sp. nov. (C11^T = NRRL B-61758^T; =DSM 118059^T). Full article

The increasing ubiquity of digital devices in childhood had outpaced the understanding of their effects on cognitive development, creating a significant research gap regarding their long-term impact. Objective: The present narrative overview explored the complex relationship between digital device usage and cognitive development in childhood. Methods: We conducted a comprehensive literature search across multiple databases, including PubMed, Embase, Scopus, and Web of Science, to critically assess cognitive domains such as attention, memory, executive functions, problem-solving skills, and social cognition. Incorporating over 157 peer-reviewed studies published between 2001 and 2024, we used strict inclusion and exclusion criteria to ensure scientific rigor. Results: The review integrated empirical findings with established theoretical frameworks, particularly from cognitive development and media psychology, to highlight both the advantages and risks of early, frequent exposure to technology. The potential for digital devices to enhance cognitive skills, such as multitasking and information processing, was weighed against risks such as cognitive overload, diminished attention spans, and impaired social skills. We also examined psychological and behavioral outcomes, including identity formation, emotional regulation, and maladaptive behaviors associated with excessive screen time. Additionally, we identified strategies to mitigate negative effects, emphasizing structured digital engagement and parental involvement to support healthy cognitive and psychological growth. Our findings provided actionable recommendations for parents, educators, and policymakers, promoting optimal digital practices that enhanced cognitive development while safeguarding against potential harms. Conclusions: The review offered essential insights for stakeholders in child development, education, and policy-making, highlighting the need for balanced integration of digital tools in childhood learning environments. Full article

From mobile health and wearables to implantable medical devices and neuroprosthetics, the integration of machines into human biology and cognition is expanding. This paper explores the technological advancements that are pushing the human–machine boundaries further, raising profound questions about identity and existence in digital realms. The development of robots, androids, and AI–human hybrids promises to augment human capabilities beyond current limits. However, alongside these advancements, significant limitations arise: biological, technical, ethical, and legal. This paper further discusses the existential implications of these technological strides. It addresses the philosophical dimensions of mortality, forgiveness, and the significance of death in a world where technological immortality may be within reach. By addressing these questions, the paper seeks to provide a comprehensive analysis of the potential for these advancements to reshape our understanding of existence and the quest for immortality. Full article