Search Results (6,164)

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

Water quality monitoring is a fundamental element of sustainable aquaculture management, as changes in parameters of physicochemical and biological properties directly affect the health, growth performance, and productivity of the aquaculture systems. Although traditional machine learning (ML) methods have demonstrated effectiveness in water quality classification, their performance often depends on large amounts of labeled data, which can be challenging and expensive to collect in real-world aquaculture environments. This study explores a few-shot learning (FSL) framework for data-efficient water quality classification under limited supervision to address this limitation. Several FSL models, including prototypical networks (ProtoNet), Siamese Networks, and Matching Networks were developed and evaluated in a comparative experimental framework against the traditional machine learning classifiers logistic regression, random forest, support vector machine and extreme gradient boosting. Low-data learning scenarios were simulated using a structured episodic evaluation approach. Experimental results demonstrate FSL techniques outperform traditional machine learning methods across all evaluated scenarios. Among the tested methods, ProtoNet achieved the highest performance, attaining an accuracy of 94.46% and an ROC-AUC score of 98.65%, indicating superior discriminative capability and robustness. Siamese Networks also demonstrated competitive performance under highly constrained data conditions. Furthermore, latent-space visualization, confusion matrix analysis, paired t-test statistical analysis, and ablation studies confirmed that episodic meta-learning enables the learning of highly discriminative latent representations with strong generalization capability under limited labeled data conditions. The findings highlight that FSL provides a robust and scalable framework for intelligent water quality classification in aquaculture systems, particularly in scenarios where labeled data are scarce, offering significant potential for sustainable aquaculture monitoring applications. Full article

Although generative artificial intelligence (AI) has increasingly been used to create destination marketing images, tourists’ responses to such images remain unclear. Prior research has often attributed negative reactions to the visual characteristics of AI-generated images. However, limited attention has been paid to how tourists interpret these images within broader cultural contexts. Drawing on authenticity theory and cultural learning theory, this research examines the effect of image type (AI vs. human) on tourists’ perceived authenticity and visit intention, as well as the moderating roles of cultural learning cues in this process. Using three experiments, the results show that AI-generated images reduce perceived authenticity and visit intention compared with images taken by humans. Notably, while salient cultural learning cues enhance tourists’ perceived authenticity and visit intentions, different types of cues produce distinct outcomes: commodified cultural cues mitigate tourists’ negative responses to AI-generated images, whereas heritage cultural cues amplify authenticity concerns. These findings provide strategic insights for destination marketers on how to deploy AI-generated images effectively in tourism destination marketing. Full article

Prior to this study, knowledge on the evolutionary lineage of Garra remained inadequate, as previous phylogenetic investigations were primarily based on partial gene sequences. Although several mitogenomes of Garra species have been reported, their structural organization and comprehensive genomic characteristics have not been thoroughly evaluated. In this study, Garra manipurensis, endemic to the Indo-Burma biodiversity hotspot, was identified based on its detailed morphology and meristic counts. The circular mitogenome of G. manipurensis is 16,776 bp in length and contains the canonical set of 37 genes, along with duplicated control regions separated by tRNA-Proline. The comparative assessments across Garra species indicate predominantly conserved GTG start codons, occasional alternative ATA initiation codons, and incomplete stop codons. The selection pressure examinations within Garrini taxa reveal a purifying selection across all protein-coding genes. The control region comprises four conserved sequence blocks and species-specific tandem repeats, reflecting a balance between functional constraint and lineage-dependent evolutionary dynamics. The phylogenetic inference supports the monophyly of Garra and places G. manipurensis in close affinity with Garra flavatra, which is native to the western slope of Rakhine Yoma in Myanmar and Mizoram State in northeastern India. The genetic diversity analyses revealed haplotype differentiation, with shallow intraspecific genetic distances (0.000–0.011) observed samples between two distinct drainage systems in Manipur and Mizoram, northeastern India. The observed pattern of haplotype divergence in G. manipurensis may reflect the historical or seasonal hydrological connectivity among the western-slope drainages of the Chin Hills, with the subsequent geographic isolation potentially contributing to the emergence of distinct genetic lineages. Nevertheless, the extent and evolutionary significance of this differentiation remain uncertain and warrant further investigation through expanded geographic sampling and the incorporation of additional molecular data. Collectively, these findings provide in-depth insights into the mitogenomic architecture, comparative gene arrangements, phylogenetic patterns, and matrilineal evolutionary history of G. manipurensis and other congeners, thereby improving our understanding of the systematics and genetic diversity of this important cyprinid fish lineage. Full article

Triboelectric nanogenerators (TENG) show significant potential in pressure sensing by converting mechanical disturbances into electrical signals positively correlated with the magnitude of the applied force, yet their development as practical pressure sensors is severely hindered by the major drawback of only detecting transient mechanical inputs. Additionally, traditional dual-mode pressure sensors have typically required complex multilayer structures and time-consuming fabrication processes. Here, a simple dual-mode pressure sensor of novel structure integrated with TENG and anodic aluminum oxide (AAO) for both dynamic and static pressure detection is proposed. Nanoporous AAO is directly grown on an aluminum substrate to simplify the traditionally complex multi-layer structure of dual-mode pressure sensors. The AAO layer serves a dual functionality by acting as an active triboelectric layer that significantly enhances the triboelectric output performance while concurrently functioning as the capacitive dielectric layer. A polydimethylsiloxane (PDMS) film is employed as the elastic counterpart to pair with the AAO substrate. The influence of PDMS thickness on the charge accumulation and extraction of the TENG mode is investigated to optimize the device output. Under optimal configurations, the streamlined Al-AAO/PDMS sensor demonstrates good sensitivity and linearity (R² > 0.99) for both dynamic triboelectric voltage (1.05 V/kPa) and static capacitance (5.56 pF/kPa) over a wide sensing range of 1–73 kPa. This dual-mode sensor effectively overcomes the transient limitation of conventional single-mode TENGs and shows significant potential for future smart tactile applications. Full article

Background/Objectives: Optimal vascular access remains a critical determinant of outcomes in patients undergoing maintenance hemodialysis. While an arteriovenous fistula (AVF) is generally preferred over an arteriovenous graft (AVG), the impact of obesity and antithrombotic therapy on access-related complications remains incompletely defined. This study evaluated the association between vascular access type, obesity status, and adverse outcomes in a large real-world cohort. Methods: We conducted a retrospective cohort study using de-identified electronic health record data from the TriNetX Global Collaborative Network. Adult patients (≥18 years) receiving maintenance hemodialysis were stratified by vascular access type (AVF vs. AVG), body mass index (normal: 18.5–24.9 kg/m², obese: ≥30 kg/m²), and antithrombotic medication exposure. Propensity score matching (1:1) was performed within BMI strata. Primary outcomes included vascular access thrombosis, AVG failure, and AVF failure. Time-to-event analyses used Kaplan–Meier and Cox proportional hazards models. Results: AVG was associated with significantly higher rates of thrombosis and access failure compared with AVF in both obese and normal-weight cohorts (all p < 0.0001). In patients with obesity, thrombosis rates increased from 10.47% (AVF) to 17.54% (AVG) at 3 months to 34.32% versus 42.24% at 5 years. Kaplan–Meier analysis demonstrated early and persistent separation of thrombosis-free survival curves, with AVG associated with increased risk (HR 1.23; 95% CI, 1.07–1.41; log-rank p = 0.0001). Antithrombotic therapy reduced absolute risks but did not eliminate the relative disadvantage of AVG. Conclusions: In this large real-world cohort, AVG was consistently associated with higher risks of thrombosis and access failure compared with AVF, regardless of obesity status or medication exposure. These findings support preferential use of AVF and highlight the need for individualized vascular access strategies in patients undergoing hemodialysis. Full article

This study develops and validates risk-based emergency response scenarios for Biosafety Level 2 (BSL-2) and Animal Biosafety Level 2 (ABSL-2) facilities. Utilizing Bow-tie analysis, three multidimensional scenarios were constructed: infrastructure failure, biosecurity breach, and compound disaster. Four domain experts independently evaluated the scripts using the Content Validity Index (CVI), with an absolute consensus threshold of I-CVI = 1.00. To address operational gaps identified during initial evaluations, the revised protocols were strictly aligned with the Taiwan Centers for Disease Control (CDC) mandatory reporting thresholds for high-hazard incidents. Furthermore, the scripts explicitly defined the Incident Command System (ICS) to prevent communication fragmentation and integrated the NC3Rs tunnel handling technique to minimize occupational bite risks. Following these targeted refinements, all items achieved absolute expert consensus. This research translates static biosafety regulations into dynamic, stress-tested training tools. By providing a standardized instrument for resilience assessment, this study equips frontline personnel with the critical capacity to navigate cascading crises while strictly adhering to a “life safety first” paradigm. Full article

Because injuries are often vague and easily unnoticed, missed diagnosis of scaphoid fractures on emergency radiographs reveals a critical limitation of acute care imaging. In addition, owing to unremarkable radiographic features, scaphoid fractures are particularly challenging. Therefore, a deep learning-based scaphoid fracture detection (DLSFD) framework is developed in this study for predicting scaphoid fractures on anteroposterior wrist radiographs. A ten-year retrospective cohort of wrist radiographs including both fractures and non-fractures were collected and analyzed in the study. Furthermore, data augmentation and labeling were used to improve model performance. The proposed deep learning-based scaphoid fracture detection framework first applies the YOLOv8 algorithm to localize and segment the scaphoid region in anteroposterior wrist radiographs. Then, a U-Net-based classifier is employed to predict the fracture or non-fracture with 5-fold cross-validation to prevent overfitting. Instead of using heat maps to represent the regions of scaphoid fractures, this study carries out pixel-level segmentation and generates pixel-wise masks to clearly locate scaphoid fracture area. Numerical results indicate that the proposed DLSFD framework is a feasible and promising alternative in predicting scaphoid fractures in terms of classification performance. Moreover, overlay segmentation masks generated by the developed DLSFD framework provide visual assistance for clinical interpretation. Thus, the designed DLSFD framework is able to successfully identify scaphoid fractures and may be useful in clinical practice for assisting clinical assessment. Full article

Agricultural carbon reduction is increasingly important for advancing low-carbon and sustainable agricultural development. Using provincial panel data for the Yangtze River Economic Belt (YEB) from 2007 to 2022, this study examines the spatiotemporal evolution of the digital economy (DE) and agricultural carbon emissions (ACE), and applies a two-way fixed-effects model with mediation and moderation analyses. The results show that digital economy (DE) increased steadily across the YEB, while agricultural carbon emissions (ACE) showed clear spatiotemporal variation. Digital economy (DE) is significantly negatively associated with agricultural carbon emissions (ACE), indicating that digital development can support agricultural carbon reduction. The Bootstrap results show that technological innovation and industrial agglomeration are statistically supported mediating pathways. Technological innovation is the primary mechanism, accounting for 44.02% of the total effect, while industrial agglomeration has a smaller but significant mediation share of 0.25%. Industrial structure optimization and fiscal investment are not confirmed as robust indirect pathways. The moderation results show that environmental regulation weakens the negative DE–ACE relationship, whereas agricultural fiscal expenditure strengthens it. These findings highlight the importance of green innovation, agglomeration effects, and supportive fiscal conditions in digital agricultural carbon reduction. Full article

Environmental sustainability disclosure has become increasingly critical as climate risks intensify and regulatory and investor demands for transparent, decision-useful information continue to grow. It plays a key role in reducing information asymmetry and supporting capital allocation, risk assessment, and regulatory oversight. However, prior studies predominantly rely on aggregated ESG indicators and linear models, which often fail to capture the structural heterogeneity and nonlinear relationships inherent in environmental data. This study develops a machine learning-based analytical framework to examine environmental disclosure using corporate data from the Taiwan Economic Journal (TEJ) from 2022 to 2024. A polarized sampling design is employed by selecting firms in the top and bottom 20% of ESG performance to identify and compare the distinctive disclosure characteristics of companies with high versus low environmental performance. Five models are evaluated using Accuracy, Precision, Recall, F1-score, and AUROC. The results show that ensemble models outperform traditional approaches, with CatBoost achieving the most robust performance. Feature importance analysis reveals a concentrated structure dominated by carbon emissions, energy efficiency, and waste management, while the importance of renewable energy variables increases over time. These findings highlight the nonlinear and multidimensional nature of environmental disclosure and demonstrate the value of machine learning in enhancing environmental sustainability analysis, investment decision-making, and regulatory effectiveness. As this study is based on a single-country dataset (Taiwan), future research may incorporate cross-country datasets to improve external validity. Full article

Accurate detection of Campylobacter in chicken liver is hindered by strong matrix inhibition. This study evaluated sample-processing strategies to improve droplet digital PCR (ddPCR) quantification of Campylobacter coli and Campylobacter jejuni in chicken liver. Mechanical homogenization (Stomacher) and enzymatic/mechanical dissociation (gentleMACS), with and without 8 μm filtration, were compared. Particle-size analysis showed that filtration, especially following gentleMACS treatment, produced smaller, more uniform particles and reduced variability. Percent-degradation assays confirmed that gentleMACS achieved substantially greater tissue disruption than Stomacher homogenization. The multiplex ddPCR assay, which simultaneously targets C. coli and C. jejuni, produced droplet counts comparable to single-target reactions, indicating minimal interference between targets under the conditions tested. In inoculated liver samples, gentleMACS processing yielded droplet counts similar to those obtained from pure cultures, whereas unprocessed liver caused severe matrix interference and inconsistent quantification. Furthermore, gentleMACS-treated samples exhibited strong log-to-log linearity for quantifying C. coli and C. jejuni, enabling detection near 1 genome copy equivalent per reaction. Overall, the results indicate that enzymatic/mechanical dissociation combined with fine-pore filtration improves ddPCR detection of Campylobacter species in chicken liver. Full article

Background/Objectives: In Taiwan, more than 50% of patients with oral cancer seek medical help at a late stage. Reliable non-invasive biomarkers for pathological staging and disease monitoring are still lacking. This study aimed to identify a specific biomarker associated with late-stage oral cancer and to develop a non-invasive strategy for early pathological diagnosis and disease monitoring with improved patient acceptability. Methods: A total of 116 participants were enrolled, including 31 patients with early-stage oral cancer, 49 with late-stage oral cancer, and 36 healthy controls. Saliva samples were collected for proteomic analysis, and the findings were validated using ELISA and tissue immunohistochemistry. The identified biomarker was validated, and its tumor-promoting role was confirmed using malignant phenotype assays, including colony formation, soft agar, migration, and invasion assays. Results: Our results demonstrate that CD5L expression could not be distinguished between early- and late-stage groups using tissue immunohistochemistry. In contrast, salivary CD5L levels differentiated early-stage from late-stage patients noninvasively. Functional studies demonstrated that CD5L suppression markedly attenuated malignant phenotypes (colony formation, anchorage-independent growth, migration, invasion), suggesting its involvement in tumor aggressiveness and metastatic potential. Conclusions: These findings provide new insights into the pathological role of salivary CD5L in oral cancer progression and support its potential as a non-invasive biomarker for disease stratification. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

Background: Chung–Jansen syndrome (CHUJANS) is a rare autosomal dominant genetic condition caused by pathogenic variants in the PHIP gene, which encodes a protein involved in neurodevelopmental processes and IGF-1 signalling. The phenotype is characterised by variable degrees of intellectual disability, early-onset obesity or overweight, distinctive facial dysmorphisms, and behavioural disturbances. We here present a case of Chung–Jansen syndrome with a detailed endocrine work-up, highlighting the metabolic component of this syndrome. Case Presentation: We describe the case of a 21-year-old woman referred to our centre for evaluation of oligomenorrhea in the context of severe obesity (BMI 50.4 kg/m²), short stature (151 cm, <3rd percentile), and moderate-to-severe intellectual disability (full-scale IQ 38). Physical examination revealed dysmorphic features, including a round face, upslanting palpebral fissures, prominent zygomatic bones, anteverted nares, a prominent chin, and bilateral brachydactyly type E1. Laboratory investigations documented subclinical primary hypothyroidism of autoimmune origin, impaired glucose tolerance with associated hyperinsulinism, and polyendocrine metabolic ovarian syndrome (PMOS, previously known as PCOS). Exome analysis by next-generation sequencing (NGS) identified a heterozygous c.328C>T [p.(Arg110Cys)] variant in the PHIP gene, already reported in literature and classified as likely pathogenic (ACMG class 4). Segregation analysis in the mother (father was not available for the test) did not reveal the variant, suggesting a de novo origin in the patient. Concurrently, the same analysis revealed a variant of uncertain significance in the ANKRD17 gene, while array-CGH detected a maternally inherited microdeletion of uncertain significance on chromosome X (Xp11.23). Conclusions: This case confirms the association between the PHIP p.(Arg110Cys) variant and the phenotype of Chung–Jansen syndrome, providing a detailed characterisation of the endocrine and psychiatric comorbidities. Indeed, our report expands the knowledge on the endocrine phenotype providing further suggestion for personalised patient management. It underscores the importance of NGS in the diagnostic workup of syndromic obesity with intellectual disability, especially in the presence of negative family history and prior inconclusive genetic testing. This case suggests the inclusion of comprehensive endocrine evaluations in future studies on patients with Chung–Jansen syndrome, in order to support endocrine work-up and facilitate early identification and appropriate management of potentially treatable alterations. Full article

Endocrine therapy is an effective and common treatment strategy for estrogen receptor (ER)-positive breast cancers. However, the development of endocrine resistance, through genetic mutations and epigenetic alterations, in about 40% of treated patients remains a significant therapeutic challenge. Liver X receptors (LXRs) are nuclear receptors that regulate lipid metabolism and cholesterol homeostasis and have been implicated in metabolic reprogramming in breast cancers and other malignancies. We previously identified a novel LXR ligand GAC0001E5 (1E5), with potent antiproliferative activity across breast cancer subtypes. Here, we investigate its mechanisms of action in responsive (MCF-7) and endocrine-resistant (MCF-7-TamR) ER-positive breast cancer cells. Treatment with 1E5 resulted in the downregulation of LXR and its target genes, and significantly reduced ERα expression and the expression of ER-responsive genes. Aberrant expression of androgen receptor (AR) and human epidermal growth factor receptor 2 (HER2), both implicated in endocrine resistance, were downregulated following 1E5 treatment. siRNA-mediated knockdown of LXR expression only partially recapitulated the actions of 1E5, suggesting the involvement of LXR-dependent and independent mechanisms. Collectively, these findings reveal potential crosstalk between LXR and the genetic and epigenetic regulation of pathways involved in endocrine response and alternative signaling mechanisms, highlighting potential targets in endocrine-resistant breast cancer. Full article