Search Results (159)

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal biometric system that combines fingerprint (FP) and finger vein (FV) modalities to improve accuracy and security. The system explores three fusion strategies: feature-level fusion (combining feature vectors from each modality), score-level fusion (integrating prediction scores from each modality), and a hybrid approach that leverages both feature and score information. The implementation involved five pretrained convolutional neural network (CNN) models: two unimodal (FP-only and FV-only) and three multimodal models corresponding to each fusion strategy. The models were assessed using the NUPT-FPV dataset, which consists of 33,600 images collected from 140 subjects with a dual-mode acquisition device in varied environmental conditions. The results indicate that the hybrid-level fusion with a dominant score weight (0.7 score, 0.3 feature) achieved the highest accuracy (99.79%) and the lowest equal error rate (EER = 0.0018), demonstrating superior robustness. Overall, the results demonstrate that integrating deep learning with multimodal fusion is highly effective for advancing scalable and accurate biometric authentication solutions suitable for real-world deployments. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article

Participation in horse-related activities frequently involves relocating horses from the home stable to an activity venue, which might require local, regional, or international travel. In these circumstances, horses are exposed to unfamiliar surroundings and experience changes to their daily routines, which could have negative welfare impacts. An online survey was conducted in 2021 to ask experienced horse sector participants about the horse management practices that they perceived worked well and provided for positive horse welfare when undertaken at venues. Qualitative analysis identified four themes: ‘managing venues’, ‘monitoring fitness to participate’, ‘maintaining a healthy equine digestive system’, and ‘using horse behaviors to inform decision-making’. The findings indicate that activity-related individuals selected practices that assisted horses to adapt to venue surroundings, remain calm, and stay healthy. The co-authors propose that experienced participants recognize that practices include both provisions (inputs) and outcomes (the horse’s subjective experiences), resonating with the Five Freedoms and Five Domains models. For horse activity organizations proposing to implement the Five Domains model, the findings indicate that reviewing practices and implementing updates is timely and achievable. The authors propose that continuously updating practices will contribute to safeguarding horses and maintaining the sector’s social license to operate. Full article

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches still suffer from limited generalization and suboptimal accuracy. To address these challenges, this study proposes an improved rice pest detection model, MTD-YOLO, based on the YOLOv8 framework. First, the original backbone is replaced with MobileNetV3, which leverages optimized depthwise separable convolutions and the Hard-Swish activation function through neural architecture search, effectively reducing parameters while maintaining multiscale feature extraction capabilities. Second, a Cross Stage Partial module with Triplet Attention (C2f-T) module incorporating Triplet Attention is introduced to enhance the model’s focus on infested regions via a channel-patial dual-attention mechanism. In addition, a Dynamic Head (DyHead) is introduced to adaptively focus on pest morphological features using the scale–space–task triple-attention mechanism. The experiments were conducted using two datasets, Rice Pest1 and Rice Pest2. On Rice Pest1, the model achieved a precision of 92.5%, recall of 90.1%, mAP@0.5 of 90.0%, and mAP@[0.5:0.95] of 67.8%. On Rice Pest2, these metrics improved to 95.6%, 92.8%, 96.6%, and 82.5%, respectively. The experimental results demonstrate the high accuracy and efficiency of the model in the rice pest detection task, providing strong support for practical applications. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

The gut microbiota has emerged as a critical modulator in metabolic diseases, with substantial evidence supporting its role in attenuating diabetes-related nephropathy. Recent investigations demonstrate that strategic manipulation of intestinal microflora offers novel therapeutic avenues for safeguarding renal function against diabetic complications. This investigation sought to determine the nephroprotective potential of Lactobacillus rhamnosus GG (LGG) administration in diabetic nephropathy models. Six experimental cohorts were evaluated: control, probiotic-supplemented control, diabetic, diabetic receiving probiotic therapy, diabetic with antibiotics, and diabetic treated with both antibiotics and probiotics. Diabetic conditions were established via intraperitoneal administration of streptozotocin (50 mg/kg) following overnight fasting, according to validated protocols for experimental diabetes induction. Probiotic therapy (3 × 10⁹ CFU/kg, bi-daily) began one month before diabetes induction and continued throughout the study duration. Glycemic indices were monitored at bi-weekly intervals, inflammatory biomarkers, renal function indices, and urinary albumin excretion. The metabolic profile was evaluated through the determination of HOMA-IR and the computation of metabolic syndrome scores. Microbiome characterization employed 16S rRNA gene sequencing alongside metagenomic shotgun sequencing for comprehensive microbial community mapping. L. rhamnosus GG supplementation substantially augmented microbiome richness and evenness metrics. Principal component analysis revealed distinct clustering of microbial populations between treatment groups. The Prevotella/Bacteroides ratio, an emerging marker of metabolic dysfunction, normalized following probiotic intervention in diabetic subjects. Results: L. rhamnosus GG administration markedly attenuated diabetic progression, achieving glycated hemoglobin reduction of 32% compared to untreated controls. Pro-inflammatory cytokine levels (IL-6, TNF-α) decreased significantly, while anti-inflammatory mediators (IL-10, TGF-β) exhibited enhanced expression. The renal morphometric analysis demonstrated preservation of glomerular architecture and reduced interstitial fibrosis. Additionally, transmission electron microscopy confirmed the maintenance of podocyte foot process integrity in probiotic-treated groups. Conclusions: The administration of Lactobacillus rhamnosus GG demonstrated profound renoprotective efficacy through multifaceted mechanisms, including microbiome reconstitution, metabolic amelioration, and inflammation modulation. Therapeutic effects suggest the potential of a combined probiotic and pharmacological approach to attenuate diabetic-induced renal pathology with enhanced efficacy. Full article

The Great Wall of China is a cultural monument of profound historical significance and a testament to the evolution of various historical periods. As a living heritage, it holds exceptional value. However, due to inadequate protection measures in recent years, numerous sections of the Great Wall have been subject to continuous degradation. While damage to its main structural components and explicit heritage elements has been widely acknowledged, the more critical issue lies in the ambiguous recognition and insufficient safeguarding of its implicit heritage elements. This study explores the composition and classification of protective elements associated with the Great Wall, proposing a framework that emphasizes the dual safeguarding of both its tangible structures and intangible cultural significance. Employing big data collection through search engine optimization (SEO) techniques and questionnaire surveys, this research analyzes recent trends in the prioritization of heritage conservation efforts related to the Great Wall. Furthermore, by constructing a mathematical model based on the “grey relational analysis” method, the study classifies and stratifies various heritage elements to highlight the Wall’s core values and propose targeted protection strategies. The findings reveal that (1) certain regions possess considerable development potential and can be restored and planned as cultural tourism destinations; (2) conservation efforts should prioritize material restoration while preserving the intrinsic spiritual and cultural values; (3) a living heritage transmission strategy should underpin the overall protection framework. Ultimately, the study establishes a classification and grading system for conservation elements centered on the sustainable development of the Great Wall heritage. By concretely mapping the concept of living heritage protection onto the various protective elements of the Great Wall, this research offers valuable insights and recommendations for enhancing conservation practices. Full article

(1) Background: With the deep integration of e-commerce and video technology, live-streaming marketing has emerged globally and maintained rapid growth. However, most of the current research on live-streaming e-commerce marketing focuses on merchants’ sales strategies and consumers’ purchase intentions, and there is relatively little research related to the risks of live-streaming e-commerce marketing. Nevertheless, with the development of live-streaming e-commerce marketing and its integration with technologies such as artificial intelligence and virtual reality (VR), live-streaming e-commerce marketing still faces challenges such as unclear subject responsibility, difficulty in verifying the authenticity of marketing information, and uneven product quality. It also harbors problems such as the ethical misbehavior of AI anchors and the excessive beautification of products by VR technology. (2) Methods: This study systematically analyzes the scenarios of live-streaming marketing to elucidate the mechanisms of risk formation. Utilizing fault tree analysis (FTA) and risk checklist methods, risks are identified based on the three core elements of live-streaming marketing: “people–products–scenes”. Subsequently, the Delphi method is employed to refine the initial risk indicator system, resulting in the construction of a comprehensive risk indicator system comprising three first-level indicators, six second-level indicators, and 16 third-level indicators. A hesitant fuzzy multi-attribute group decision-making method (HFMGDM) is then applied to calculate the weights of the risk indicators and comprehensively assess the live-streaming marketing risks in live broadcast rooms of three prominent celebrity anchors in China. Furthermore, a detailed analysis is conducted on the risks associated with the six secondary indicators. Based on the risk evaluation results, targeted recommendations are proposed. This study aims to enhance consumers’ awareness of risk prevention when conducting live-streaming transactions and pay attention to related risks, thereby safeguarding consumer rights and fostering the healthy and sustainable development of the live-streaming marketing industry. (3) Conclusions: The results show that the top five risk indicators in terms of weight ranking are: Ethical Risk of the AI Anchor (A4), VR Technology Promotion Risk (F3), Anchor Reputation (A1), Product Quality (D1), and Logistics Distribution Service Quality (D2). The comprehensive live-streaming marketing risk of each live broadcast room is Y > L > D. Based on the analysis results, targeted recommendations are provided for anchors, MCN institutions, merchants, supply chains, and live-streaming platforms to improve consumer satisfaction and promote sustainable development of the live-streaming marketing industry. Full article

Smart home devices and home automation systems, which control features such as lights, blinds, heaters, door locks, cameras, and speakers, have become increasingly popular and can be found in homes worldwide. Central to these systems are smart home hubs, which serve as the primary control units, allowing users to manage connected devices from anywhere in the world. While this feature is convenient, it also makes smart home hubs attractive targets for cyberattacks. Unfortunately, the average user lacks substantial cybersecurity knowledge, making the security of these systems crucial. This is particularly important as smart home systems are expected to safeguard users’ privacy and security within their homes. This paper synthesizes eight prevalent cybersecurity challenges associated with smart home hubs through a systematic literature review. The review process involved identifying relevant keywords, searching, and screening 713 papers in multiple rounds to arrive at a final selection of 16 papers, which were then summarized and synthesized. This process included research from Scopus published between January 2019 and November 2024 and excluded papers on prototypes or individual features. The study is limited by scarce academic sources on open-source smart home hubs, strict selection criteria, rapid technological changes, and some subjectivity in study inclusion. The security of extensible smart home hubs is a complex and evolving issue. This review provides a foundation for understanding the key challenges and potential solutions, which is useful for future research and development to secure this increasingly important part of our everyday homes. Full article

Bearings are essential rotational components that enable mechanical equipment to operate effectively. In real-world industrial environments, bearings are subjected to high temperatures and loads, making failure prediction and health management critical for ensuring stable equipment operations and safeguarding both personnel and property. To address long-tail defect identification, we propose a coupled time–frequency attention model that accounts for the long-tail distribution and pervasive noise present in production environments. The model efficiently learns amplitude and phase information by first converting the time-domain signal into the frequency domain with the Fast Fourier Transform (FFT) and then processing the data using a real–imaginary attention mechanism. To capture dependencies in long sequences, a multi-head self-attention mechanism is then implemented in the time domain. Furthermore, the model’s ability to fully learn features is enhanced through the linear coupling of time–frequency domain attention, which effectively mitigates noise interference and corrects imbalances in data distribution. The performance of the proposed model is compared with that of advanced models under the conditions of imbalanced label distribution, cross-load, and noise interference, proving its superiority. The model is evaluated using the Case Western Reserve University (CWRU) and laboratory bearing datasets. Full article

Background: The issue of digital piracy is increasingly prevalent, with its proliferation further fueled by the widespread use of social media outlets such as WhatsApp, Snapchat, Instagram, Pinterest, and X. These platforms have become hotspots for the unauthorized sharing of copyrighted materials without due recognition to the original creators. Current techniques for digital watermarking are inadequate; they frequently choose less-than-ideal locations for embedding watermarks. This often results in a compromise on maintaining critical relationships within the data. Purpose: This research aims to tackle the growing problem of digital piracy, which represents a major risk to rights holders in various sectors, most notably those involved in entertainment. The goal is to devise a robust watermarking approach that effectively safeguards intellectual property rights and guarantees rightful earnings for those who create content. Approach: To address the issues at hand, this study presents an innovative technique for digital video watermarking. Utilizing the 2D-DWT along with the KAZE feature detection algorithm, which incorporates the Accelerated Segment Test with Zero Eigenvalue, scrutinize and pinpoint data points that exhibit circular symmetry. The KAZE algorithm pinpoints a quintet of stable features within the brightness aspect of video frames to act as central embedding sites. This research selects the chief embedding site by identifying the point of greatest intensity on a specific arc segment on a circle’s edge, while three other sites are chosen based on principles of circular symmetry. Following these procedures, the proposed method subjects videos to several robustness tests to simulate potential disturbances. The efficacy of the proposed approach is quantified using established objective metrics that confirm strong correlation and outstanding visual fidelity in watermarked videos. Moreover, statistical validation through t-tests corroborates the effectiveness of the watermarking strategy in maintaining integrity under various types of assaults. This fortifies the team’s confidence in its practical deployment. Full article

In recent decades, religious spaces have increasingly become subject to heritage processes, encompassing both their tangible dimension and the emerging concept of intangible cultural heritage. This article examines the legal strategies available for protecting the (in)tangible cultural heritage that minority religious communities can employ to safeguard their religious sites. Focusing on the case of African-derived religions in Brazil, this study argues that the recognition of their (in)tangible heritage serves as a strategic legal instrument for protecting their religious spaces, despite the conflicts that such recognition may provoke. This article contributes to the field of cultural heritage law, engaging with religious studies and exploring the complexities of legally safeguarding minority cultural practices. Full article

The preservation of historical heritage requires a deep understanding of the interactions between physical and structural phenomena. This study investigates the influence of bells acoustic waves propagation on the dynamic behavior for the historical bell tower of the San Michele Arcangelo Cathedral in Caserta Vecchia, Italy. By combining environmental acoustic measurements with finite element modeling in ABAQUS, the research analyzes vibroacoustic coupling and identifies vibrational modes affected by the bell sounds. Harmonic analysis reveals the relationships between sound frequencies and structural vibrational modes, providing insights into the dynamics of historic towers subjected to repetitive acoustic loads. The findings demonstrate how sound can induce complex structural responses, offering valuable guidance for targeted conservation strategies. This interdisciplinary approach can be extended to other historic buildings, highlighting the critical role of vibroacoustic considerations in safeguarding cultural heritage. Full article

With the Republic of Türkiye signing the “Convention on Intangible Cultural Heritage” in 2006, awareness and sensitivity towards the safeguarding of cultural heritage has also begun to develop in the Turkish Republic of Northern Cyprus (TRNC), which has close relations with the Republic of Türkiye. However, the intangible cultural heritage convention could not be signed in the TRNC due to political reasons, and this has led to a lack of sufficient change and awareness about it on the island. Regrettably, the limited number of academic studies carried out in universities in this field has proven to be inadequate in terms of creating general public awareness about the subject. However, the knowledge and experience of both the community and individuals working in this field are crucial for the safeguarding and sustainability of cultural heritage elements. In this context, this study collected the knowledge and opinions of personnel affiliated with the TRNC Ministry of Culture on the subject of intangible cultural heritage (ICH), and it investigated whether the adequacy of the personnel’s knowledge and opinions around cultural heritage had a direct impact on its safeguarding and sustainability. This research used a type of mixed method called “Convergent Mixed-Methods Design”. The sample of this research consisted of 30 personnel working in different units affiliated with the Ministry of Culture in Northern Cyprus. This study included personnel working in different units and positions affiliated with the ministry. An interview form was used to obtain the data that formed the basis of this research. The interview form consisted of 10 structured and 3 semi-structured questions prepared by the researchers. Descriptive statistics were used in the analysis of the quantitative data, and content analysis was used in the analysis of the qualitative data. This study, which aimed to determine the knowledge and opinions of Ministry of Culture officials working in the TRNC regarding the safeguarding and transfer of intangible cultural heritage elements, revealed that the employees were not fully informed about the scope, objectives, and protection of ICH. Full article