Search Results (248)

This study examines the mechanisms through which ecological public art influences pro-environmental behavior, addressing the urgent challenges of the global ecological crisis and sustainable urban development. Using the 5th Shanghai Urban Space Art Season (SUSAS) as a case study, a serial multiple mediation model was established, with ecological public art perception as the independent variable, environmental psychological ownership and ecological awareness as mediators, and pro-environmental behavior as the dependent variable. Based on 326 valid responses, structural equation modeling (SEM), which integrates confirmatory factor analysis (CFA) and path analysis, demonstrates that ecological public art perception significantly enhances pro-environmental behavior. Environmental psychological ownership and ecological awareness function not only as independent mediators but also jointly constitute a serial mediation pathway. The findings reveal a multidimensional process whereby ecological public art enhances pro-environmental behavior through “perceptual activation–emotional identification–cognitive enhancement–behavioral transformation”. Building on these insights, the study proposes intervention strategies focusing on multi-sensory integration, emotional narrative, digital technology application, and community-based practices to reinforce the role of ecological public art in urban environmental governance and sustainable development. Overall, this research advances the theoretical understanding of the social functions of public art and offers a valuable perspective for fostering ecological awareness and action. Full article

Collaborative experiences are enriched through cross-platform interactions in the context of eXtended Reality (XR) systems. In this paper, we introduce SRVS-C (Spatially Referenced Virtual Synchronization for Collaboration), a centralized framework designed to support co-located, real-time AR (on smartphone) and VR (in headset) interactions over local networks. The framework adopts an architecture of interactive asymmetry, where the interaction roles, input modalities, and rendering responsibilities are adapted to the unique capabilities and constraints of each device. Concurrently, the framework maintains perceptual symmetry, guaranteeing a coherent spatial and semantic experience for all users. This is achieved through anchor-based spatial registration and unified data representations. Compared to prior work that relies on cloud services or symmetric platforms (e.g., VR–VR, AR–AR, and PC–PC pairings), SRVS-C supports seamless communication between AR and VR endpoints, operating entirely over TCP sockets using serialization-agnostic message formats. We evaluated SRVS-C in a dual-user scenario involving a mobile AR and a VR headset, using shared freehand drawing tasks. These tasks include simple linear strokes and geometry-rich drawing content to assess how varying interaction complexity—ranging from low-density sketches to intricate, high-vertex structures— impacted the end-to-end latency, state replication timing, and collaborative fluency. The results show that the system sustains latency between 35 ms and 175 ms, even during rapid, continuous drawing actions that generate a high number of stroke updates per second, and when handling drawings composed of numerous vertices and complex shapes. Throughout these conditions, the system maintains perceptual continuity and spatial alignment across users by applying platform-specific interactive asymmetry. Full article

The development of serial crystallography (SX), including serial synchrotron crystallography (SSX) at synchrotron sources and serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs), has facilitated the collection of high-resolution diffraction data from micron-sized crystals, providing unique insights into the structures and dynamics of biomolecules at room temperature. Standard samples are essential for the commissioning of new XFEL instruments and the validation of experimental setups. In this review, we summarize currently used standard proteins and describe representative microcrystal preparation workflows for four widely adopted models, lysozyme, myoglobin, iq-mEmerald, and photoactive yellow protein (PYP), drawing on established methodologies and accumulated experience from their applications at the European XFEL. By consolidating existing knowledge and integrating protocols that have been systematically refined and optimized through our experimental efforts, this review aims to provide practical guidance for the serial crystallography community, thereby enhancing reproducibility and ensuring consistent experimental performance across facilities. Full article

Changes in the soil microbial community for studies of different novel communities can be promoted by different methodologies, among which soil autoclaving stands out as a quick and readily available tool. However, this procedure may also directly or indirectly alter nitrogen (N) and phosphorus (P) dynamics. The purposes of this study were as follows: (i) to characterize microbial activity after soil autoclaving through microbial ¹⁴CO₂-respiration; and (ii) to evaluate the effect of microbial manipulation and autoclaving on soil N and ³³P dynamics. For this, two sets of soil samples from two areas (forest and cultivated area) were used in the laboratory. Firstly, ¹⁴C-glucose was added to the soils and after 24 h five soil microbiomes were generated: AS (autoclaved soil), and AS re-inoculated with serial dilutions (w/v) prepared by successive mixing of soil suspensions in sterile deionized water obtaining 10⁻¹, 10⁻³, and 10⁻⁶, which generated the treatments AS + 10⁻¹, AS + 10⁻³, and AS + 10⁻⁶; and the treatment NS (non-autoclaved control), all incubated for 28 d. ¹⁴CO₂ emission was used to characterize microbial activity; additionally, N dynamics were assessed at the end of incubation. In a second assay, ³³P was applied to the soil before autoclaving and re-inoculation. Following further incubation (14 d), a ³³P chemical fractionation was performed. The following are based on the results: (i) ¹⁴CO₂ emission: microbial activity in the autoclaved soil is null, but after a reinoculation of AS + 10⁻¹ and AS + 10⁻³ soil dilution suspension, the ¹⁴CO₂-respiration is higher than in an NS. (ii) regarding the N dynamics, in autoclaved soils, the microbial levels increased N-NH₄⁺ concentration, with an evident increase in the AS + 10⁻³ and AS + 10⁻¹, and a reduction in the N-NO₃⁻ concentration in comparison to the NS. For ³³P, the autoclaving procedure itself reduced the ³³P lability, regardless of the levels of microbial community reinoculated. Full article

Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels. Full article

Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. Full article

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

In this study, to enhance the concentration of caproic acid generated from maize straw fermentation and clarify the structures of bacterial and fungal communities within the serially subcultured rumen microbial fermentation system, maize straw was used as the substrate. In a continuous subculture system, the impacts of ethanol addition on pH and gas production were explored, with a focus on the caproic acid yield in the final (eighth generation) generation and alterations in bacterial and fungal communities. The results showed that the relative abundances of unidentified_Clostridiales, Shuttleworthia, and Syntrophococcus in ethanol-driven caproic acid production were enriched by 5.36-fold, 2.61-fold, and 2.25-fold, respectively. This consequently increased the concentration of caproic acid in the fermentation broth to 1492 mg/L, representing a 3.7-fold increase. These findings are highly significant for the high-value utilization of maize straw waste to produce caproic acid via the carboxylic acid platform using rumen microorganisms in industrial processing. Full article

Wetlands are the most important natural sources of methane. Studies on the distribution and diversity of methanotrophs, especially in tropical wetlands, are limited. The studies on wetland methanotrophs help bridge the gap in the literature for understanding the community structure of methanotrophs in tropical wetlands. Our present study documents the methanotroph diversity from various wetland habitats across Western India. Samples from various sites, such as freshwater ponds, lake sediments, mangroves, etc., located in Western India, were collected and enriched for methanotroph isolation. An established protocol for the isolation of methanotrophs from Indian rice fields, involving serial dilution and long-term incubations, was slightly modified and used. Obtaining entirely pure cultures of methanotrophs is a labor-intensive and technically challenging process. Hence, for primary level characterization, ‘methanotroph monocultures’, which have a single methanotroph culture with minimal contamination, were established. Twenty monocultures and eight pure cultures of methanotrophs were obtained in this study. The pmoA gene has been used for the phylogenetic characterization of methanotrophs for the last 25 years. Monocultures were from seven genera: the Methylomonas, Methylocystis, Methylosinus, Methylocaldum, Methylocucumis, Methylomagnum, and Methylolobus genera. Eight pure cultures were obtained, which were strains of Methylomonas koyamae, Methylosinus sporium, and Methylolobus aquaticus. A maximum number of cultures belonged to the Type I genus Methylomonas and to the Type II genus Methylocystis. Thus, the cultivation-based community studies of methanotrophs from wetland habitats in India expanded the current knowledge about the methanotroph diversity in such regions. Additionally, the cultivation approach helped us obtain new methanotrophs from this previously unexplored habitat, which can be used for further biotechnological and environmental applications. The isolated monocultures can either be used as MMCs (mixed methanotroph consortia) for environmental applications or further purified and used as pure cultures. Full article

Firmware vulnerabilities in embedded devices have caused serious security incidents, necessitating similarity analysis of binary program instruction embeddings to identify vulnerabilities. However, existing instruction embedding methods neglect program execution semantics, resulting in accuracy limitations. Furthermore, current embedding approaches utilize independent computation across models, where the lack of standardized interaction information between models makes it difficult for embedding models to efficiently detect firmware vulnerabilities. To address these challenges, this paper proposes a firmware vulnerability detection scheme based on statistical inference and code similarity fuzzy matching analysis for resource-constrained vehicular network environments, helping to balance both accuracy and efficiency. First, through dynamic programming and neighborhood search techniques, binary code is systematically partitioned into normalized segment collections according to specific rules. The binary code is then analyzed in segments to construct semantic equivalence mappings, thereby extracting similarity metrics for function execution semantics. Subsequently, Google Protocol Buffers (ProtoBuf) is introduced as a serialization format for inter-model data transmission, serving as a “translation layer” and “bridging technology” within the firmware vulnerability detection framework. Additionally, a ProtoBuf-based certificate authentication scheme is proposed to enhance vehicular network communication reliability, improve data serialization efficiency, and increase the efficiency and accuracy of the detection model. Finally, a vehicular network simulation environment is established through secondary development on the NS-3 network simulator, and the functionality and performance of this architecture were thoroughly tested. Results demonstrate that the algorithm possesses resistance capabilities against common security threats while minimizing performance impact. Experimental results show that FirmPB delivers superior accuracy with 0.044 s inference time and 0.932 AUC, outperforming current SOTA in detection performance. Full article

Background and Objectives: Blood-borne inflammatory ratios have been proposed as inexpensive prognostic tools across a range of diseases, but their role in pulmonary tuberculosis (TB) remains uncertain. In this retrospective case–control analysis, we explored whether composite indices derived from routine haematology—namely the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic immune–inflammation index (SII) and a novel CRP–Fibrinogen Index (CFI)—could enhance risk stratification beyond established cytokine measurements among Romanian adults with culture-confirmed pulmonary T. Materials and Methods: Data were drawn from 80 consecutive TB in-patients and 50 community controls. Full blood counts, C-reactive protein, fibrinogen, and four multiplex cytokines were extracted from electronic records, and composite indices were calculated according to standard formulas. The primary outcomes were in-hospital mortality within 90 days and length of stay (LOS). Results: Among TB patients, the median NLR was 3.70 (IQR 2.54–6.14), PLR was 200 (140–277) and SII was 1.36 × 10⁶ µL⁻¹ (0.74–2.34 × 10⁶), compared with 1.8 (1.4–2.3), 117 (95–140) and 0.46 × 10⁶ µL⁻¹ (0.30–0.60 × 10⁶) in controls. Those with SII above the cohort median exhibited more pronounced acute-phase responses (median CRP 96 vs. 12 mg L⁻¹; fibrinogen 578 vs. 458 mg dL⁻¹), yet median LOS remained virtually identical (29 vs. 28 days) and early mortality was low in both groups (8% vs. 2%). The CFI showed no clear gradient in hospital stay across its quartiles, and composite ratios—while tightly inter-correlated—demonstrated only minimal association with cytokine levels and LOS. Conclusions: Composite cell-count indices were markedly elevated but did not predict early death or prolonged admission. In low-event European cohorts, their chief value may lie in serving as cost-free gatekeepers, flagging those who should proceed to more advanced cytokine or genomic testing. Although routine reporting of NLR and SII may support low-cost surveillance, validation in larger, multicentre cohorts with serial sampling is needed before these indices can be integrated into clinical decision-making. Full article

Background: The mechanisms through which information and communication technology (ICT) use influences chronic disease self-management remain unclear. Method: This cross-sectional investigation examined the mediating effects of health literacy, social support, and self-efficacy on the association between ICT use and self-management behaviors among older adults (≥60 years) with comorbidities in China (n = 520). The participants were recruited from a tertiary hospital in Shanghai (July 2023–June 2024), and data on sociodemographics, self-management, health literacy, social support, self-efficacy, and ICT use were collected via structured questionnaires. Results: Pearson’s correlation analysis revealed a significant association between ICT use, health literacy, social support, self-efficacy, and chronic disease self-management (p < 0.001). Multiple mediation modeling revealed a direct positive effect of ICT use on self-management (b = 1.3314, 95% CI = 0.6629, 2.0002). Furthermore, significant indirect effects were observed, mediated independently by both health literacy and social support. Additional serial mediation pathways included health literacy → social support, health literacy → self-efficacy, social support → self-efficacy, and a comprehensive pathway from health literacy through social support to self-efficacy. Conclusions: These findings collectively indicate that ICT use directly enhances chronic disease self-management among older adults with comorbidities. Moreover, ICT use indirectly improves self-management by enhancing health literacy, augmenting social support, and bolstering self-efficacy. Full article

Turbo Codes (TCs) are a family of convolutional codes that provide powerful Forward Error Correction (FEC) and operate near the Shannon limit for channel capacity. In the context of modern communication systems, such as those conforming to the DVB-RCS2 standard, Turbo Encoders (TEs) play a crucial role in ensuring robust data transmission over noisy satellite links. A key computational bottleneck in the Turbo Encoder is the non-uniform interleaving stage, where input bits are rearranged according to a dynamically generated permutation pattern. This stage often requires the intermediate storage of data, resulting in increased latency and reduced throughput, especially in embedded or real-time systems. This paper introduces a vector processing algorithm designed to accelerate the interleaving stage of the Turbo Encoder. The proposed algorithm is tailored for vector DSP architectures (e.g., CEVA-XC4500), and leverages the hardware’s SIMD capabilities to perform the permutation operation in a structured, phase-wise manner. Our method adopts a modular Load–Execute–Store design, facilitating efficient memory alignment, deterministic latency, and hardware portability. We present a detailed breakdown of the algorithm’s implementation, compare it with a conventional scalar (serial) model, and analyze its compatibility with the DVB-RCS2 specification. Experimental results demonstrate significant performance improvements, achieving a speed-up factor of up to 3.4× in total cycles, 4.8× in write operations, and 7.3× in read operations, relative to the baseline scalar implementation. The findings highlight the effectiveness of vectorized permutation in FEC pipelines and its relevance for high-throughput, low-power communication systems. Full article

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks. Full article

Rape is typically committed as a one-on-one crime. However, a relatively high number of rapes (2–27%) involve a single victim and multiple perpetrators. These cases are often referred to as “gang” rapes but are also termed Multiple Perpetrator Rape (MPR). Despite these data, there is a scarce amount of legal decision-making research on this issue. This study investigated legal decision making in an acquaintance rape case involving multiple perpetrators. This study was a 2(Defendant Number: one vs. three) × 2(Victim Intoxication: intoxicated vs. sober) × 2(Participant Gender: women vs. men) between-participants design. Online community members (N = 171) were randomly assigned to read a trial summary involving one of four conditions. The primary results showed that, when the case involved multiple (vs. one) perpetrators, mock jurors were more likely to vote guilty, perceived the victim to be more helpless, and reported less sympathy for the defendant and lower defendant credibility. Cognitive networks showed that jurors in the MPR condition emphasized the number of perpetrators as a primary reason for voting guilty. Finally, there was evidence of a serial indirect effect involving victim helplessness and defendant blame that explained the relation between the number of defendants and verdicts, as well as parallel indirect effects of defendant credibility, sympathy, and anger, and victim helplessness on verdicts. Implications for prosecuting MPR cases are discussed. Full article