Search Results (5,715)

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature. Full article

This study aimed primarily to evaluate the performance of two Conformité Européenne—In Vitro Diagnostic (CE-IVD) multiplex real-time PCR (rt-PCR) assays for the molecular identification of tick-borne pathogens (TBPs) of human interest on ticks removed from human skin and collected through a citizen science-based approach. As a secondary objective, the aggregated results were used to describe tick species distribution, developmental stages, and seasonal TBP circulation in 2024 in the considered area. The comparison was conducted on 116 tick samples collected in 2024 voluntarily delivered to a hospital in northeastern Italy. Detected TBPs were further confirmed with in-house-validated PCR methods and, where applicable, resolved to the species level. Clinically relevant pathogen species were identified as single infections or coinfections. Overall, 33.6% of tick samples tested positive for at least one TBP, and 6.9% showed coinfections. Kit B exhibited a higher detection rate for Borrelia spp. and Rickettsia spp. targets, partly reflecting its broader diagnostic specificity, while statistically significant differences in cycle threshold values were observed for Anaplasma phagocytophilum detection. The most frequently involved ticks were Ixodes ricinus nymphs, and the most represented area was Verona province. Late spring and early summer were identified as the periods with the highest tick conferment and pathogen diversity. Overall, the results support the use of multiplex real-time PCR commercial kits combined with citizen science-based tick collection as an effective approach for both diagnostic screening and regional surveillance of circulating ticks and TBPs. Full article

The expansion of tropical specialty livestock farming raises urgent concerns about airborne pathogen and antibiotic resistance dissemination. Ostrich farming, characterized by high-density stocking and feed exposure, yet their microbial ecology remain poorly characterized. This study analyzed 48 bioaerosols samples from an ostrich farm in Hainan, China, across dry and rainy seasons using 16S rRNA sequencing and metagenomics. The bacterial community were dominated by Firmicutes, Proteobacteria, and Actinobacteria, followed by Staphylococcus, Bacillus, and Acinetobacter as predominant genera, with particle size significantly shaping their structure. Large particles (>7.0 μm) carried higher species richness, while medium particles (2.1–3.3 μm) exhibited the highest diversity and evenness. Notably, small particles (0.65–1.1 μm), which can penetrate deep into the lungs, were enriched with Brevibacillus and Corynebacterium. Metagenomic analysis identified 638 antibiotic resistance genes (ARGs), dominated by efflux pump-associated determinants. The detection of clinically relevant ARGs (e.g., mcr-1 and bla_TEM) reflects the genetic potential of the airborne resistome, rather than confirmed resistance phenotypes or active horizontal gene transfer. Functional analysis revealed a strong potential for organic matter degradation, driven by abundant carbohydrate-active enzymes (CAZymes) and their corresponding CAZyme genes, as well as a nitrogen cycle dominated by assimilation and reduction pathways, while genes for nitrogen fixation and nitrification were absent. Our findings demonstrate that ostrich farming enhanced airborne microbial diversity and functional potential, facilitating the ARG dissemination and nitrogen transformation. This study provides critical insights into the ecological and health risks of bioaerosols in tropical livestock farms, informing environmental monitoring and risk management strategies. Full article

This study presents a structured multi-sensor predictive maintenance framework for CO₂ laser cutting machines based on real-time data acquisition and supervised machine learning. The proposed architecture integrates heterogeneous sensor signals—including vibration, temperature, humidity, and acoustic measurements—through synchronized feature-level fusion to characterize machine operational states. A statistically grounded thresholding strategy, validated using two years of operational observations and controlled experimental perturbations, is employed to distinguish normal and abnormal behavior. Sensor data are processed using a Decision Tree classifier implemented in Python with Scikit-learn, enabling short-horizon probabilistic fault prediction during operational cycles. The system is deployed in a real industrial environment and validated using cross-validation and structured dataset partitioning to assess generalization performance. Results demonstrate reliable fault discrimination capability under controlled operational conditions, highlighting the effectiveness of feature-level sensor integration for early anomaly detection. The modular hardware–software architecture supports adaptability to other CNC platforms with appropriate recalibration and retraining. The proposed framework provides a low-cost, interpretable, and computationally efficient solution for real-time industrial predictive maintenance applications. Full article

With the growing clinical use of ion-releasing resin composites, their repairability has become an important consideration in minimally invasive restorative dentistry. Therefore, this study investigated the repair bond strength of a universal composite restorative to commercially available and experimental ion-releasing resin composite materials. Specimens (n = 8 per group) were produced from three commercially available ion-releasing composite materials (ACTIVA BioACTIVE-RESTORATIVE, Cention Forte, Beautifil II), one experimental ion-releasing resin composite containing 20 wt% bioactive glass fillers, and two conventional resin composites (3M Filtek Supreme XTE, Ceram.x Spectra ST), and aged by thermal cycling in artificial saliva (5000 cycles, 5–55 °C). Substrate surfaces were sandblasted (Al₂O₃, 50 µm), silanized (Monobond Plus), and repaired using adhesive (OptiBond FL) and universal resin composite (Ceram.x Spectra ST). After further thermal cycling, micro-tensile repair bond strength was assessed and analyzed using one-way ANOVA followed by Tukey’s post hoc test. Failure modes were determined by stereomicroscopy (25× magnification) and statistically compared among the groups. Highest mean repair bond strength values were obtained for ACTIVA BioACTIVE-RESTORATIVE, Beautifil II, and 3M Filtek Supreme XTE (53.8, 46.2, and 43.0 MPa, respectively), which did not differ significantly among each other. ACTIVA BioACTIVE-RESTORATIVE attained significantly higher bond strength than the experimental composite, Ceram.x Spectra ST, and Cention Forte, and showed the highest incidence of cohesive failures (40%). No significant bond strength differences were detected among Beautifil II, 3M Filtek Supreme XTE, experimental composite, Ceram.x Spectra ST, and Cention Forte (36.2–46.2 MPa). In conclusion, ion-releasing resin composites can be repaired with conventional universal composite and show repair bond strength values at least as high as those of conventional composite materials. Full article

Antimony oxide nanoparticles (Sb₂O₃ NPs) were synthesized via a chemical vapor deposition (CVD) method and systematically characterized to evaluate their multifunctional performance. Powder X-ray diffraction (PXRD) confirmed the formation of an orthorhombic Sb₂O₃ phase with an average crystallite size of 53.50 nm, while SEM analysis revealed elongated nanostructures with diameters in the range of 20–100 nm. The stoichiometric composition of Sb₂O₃ (Sb:O ≈ 2:3) was verified by EDAX, and optical studies indicated a direct band gap of 3.10 eV. The electrochemical sensing capability of Sb₂O₃ NPs was investigated using a modified nickel mesh electrode for the detection of Metronidazole (MTZ) in 0.1 N KOH. The presence of Sb₂O₃ NPs resulted in an additional irreversible reduction peak at −0.14 V, confirming enhanced electrocatalytic activity toward MTZ, along with excellent cycling stability (94.36% retention after 10 cycles). In addition, the photocatalytic performance of Sb₂O₃ NPs was evaluated through the degradation of Acid Orange (AO) dye under UV-Vis irradiation, achieving a degradation efficiency of 73.31%. These results demonstrate that Sb₂O₃ nanoparticles are promising multifunctional materials for environmental remediation and electrochemical sensing applications, highlighting their potential for industrial implementation. Full article

Crop residues can harbor pathogens, making winter sanitation essential for sustainable viticulture. The grass–sheep–grape system could improve vineyard health through microbial optimization. To evaluate this, we assessed the effects of sheep feeding on fallen leaves on the occurrence of grape diseases through greenhouse experiments and used high-throughput-sequencing to compare microbial communities in grape fallen leaves and sheep feces, aiming to determine whether winter grazing reduces residue-borne pathogens. The results revealed that sheep grazing in vineyards significantly reduces the occurrence of grape leaf and cluster diseases, as well as a fundamental difference in microbial structures between leaves and feces, with no fungal taxa detected in the feces. The number of shared bacterial OTUs was minimal, while feces contained significantly more unique bacterial OTUs than fallen leaves. Additionally, bacterial diversity was significantly higher in feces than in fallen leaves. Sheep feces harbored a substantial number of highly efficient cellulose-degrading anaerobic bacteria, which may enhance organic matter conversion efficiency, and promote nutrient cycling in vineyards. Moreover, the grazing process directly reduced several pathogenic fungi associated with grape leaf, fruit, and root diseases. Functional analysis further indicated that fecal bacterial communities were primarily enriched in core metabolic and genetic processing functions, while leaf microbes were more involved in microbial interactions and secondary metabolism. More importantly, no function guilds of plant pathogenic fungi were present in feces. Overall, winter sheep grazing in vineyards can remove fallen leaves, not only reducing the risk of pathogen transmission but also potentially introducing beneficial bacterial communities. This study provides a feasible strategy for organic vineyard management in winter, and offers important insights for promoting sustainable vineyard production. Full article

Background/Objectives: Despite recent advances in breast cancer diagnostics, therapies and personalized medicine through genetic profiling, effective treatment of aggressive subtypes, particularly triple-negative breast cancer (TNBC), remains a considerable clinical challenge. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that influence tumor progression and are detectable extracellularly in biofluids, where they are typically protected within extracellular vesicles (e.g., exosomes) or associated with RNA-binding proteins and lipoprotein complexes. This review integrates current evidence on oncogenic and tumor-suppressive extracellular miRNAs in breast cancer, with emphasis on subtype-specific functions and potential clinical relevance as liquid-biopsy biomarkers and therapeutic targets. Methods: A PubMed-based literature review (January 2000–February 2026) was conducted using search terms combining “breast cancer” with “miRNA/microRNA” and “circulating/plasma/serum/exosomal/extracellular vesicle.” Studies were prioritized if they provided validated targets/mechanisms and/or human clinical evidence for diagnostic, prognostic, or predictive utility; discrepant findings were evaluated in a subtype-aware framework. Findings were organized into functional categories (e.g., EMT/metastasis, cell-cycle/DNA damage, immune modulation, and hormone/growth factor signaling). Clinical and translational studies evaluating circulating miRNAs for diagnosis, prognosis, treatment response, and toxicity prediction were synthesized, together with key pre-analytical and analytical variables that affect reproducibility. Results: Across mechanistic and clinical studies, miR-21 and miR-155 recur as prominent oncogenic miRNAs, whereas miR-205 is frequently reported as a tumor-suppressive miRNA that is reduced in breast cancer and in circulation in several cohorts. Panels combining these miRNAs show promise for sensitive and specific breast cancer diagnostics. Additionally, several miRNAs show context- or subtype-dependent effects, with opposing activities reported between TNBC and estrogen receptor (ER)-positive disease (e.g., miR-17-92, miR-425, miR-181 family members, miR-31, and miR-24). Conclusions: Circulating miRNAs represent a promising class of minimally invasive biomarkers and potential therapeutic targets; however, translation is constrained by biological context dependence and by pre-analytical and analytical variability. Standardized protocols and rigorously validated, subtype-aware biomarker panels will be essential for clinical implementation and for enabling miRNA-informed precision oncology in breast cancer. Full article

As climate change increasingly impacts the water cycle across the Alpine region, monitoring surface soil moisture is essential for hydrological models and drought early warning. Yet operational products either mask steep terrain, or lack the spatial resolution to capture the surface soil moisture (SSM) spatial variability of the Alpine catchments. This study presents a novel retrieval approach aggregating Sentinel-1 radiometric terrain-corrected backscatter (${\gamma }^0$) into 100 m elevation bands per sub-basin and aspect across the Austrian Alps. The resulting Alpine backscatter product is processed through an orbit-wise change detection to derive over 34,000 SSM timeseries, evaluated using ERA5-Land and compared to 264 precipitation stations from Geosphere for the period from 2016 to 2024. The results show satisfactory agreement with ERA5-Land (Pearson correlation > 0.46 below 400 m) and capture in situ precipitation-driven anomalies with the strongest performance below 400 m (Spearman correlation > 0.47), particularly over grasslands and south-facing slopes. Despite its limitations at high elevation and over dense vegetation, Sentinel-1 provides consistent and elevation-stratified information across more than 80% of the Austrian Alps, typically excluded from operational products. The new Alpine SSM product highlights Sentinel-1’s potential to support hydrological modeling, drought monitoring, and water resource management across complex topography such as the Alps. Full article

This study develops and validates the Saharan Dust Flux and Transport Index (SDFTI) using a 22-year dataset (2003–2024) of dust-related and dynamical variables across the Mediterranean. The index integrates six components (surface-particulate matter, satellite-derived desert-dust optical depth, free-tropospheric dust mass, transport score, North-Atlantic Oscillation and Oceanic Niño Indices) combined through a physically calibrated weighting scheme. To assess the stability of the formulation, three alternative variants are constructed (dust-enhanced, dynamics-enhanced, and equal-weight) and evaluated across four Mediterranean sub-regions using seasonal means, inter-annual anomalies, component correlations, and extreme-event detection. The results show that the SDFTI is highly robust over the full 2003–2024 period. Across all regions, the calibrated variants reproduce nearly identical seasonal cycles (e.g., spring–summer peaks of +0.53 to +0.58 in Western Mediterranean), identify the same dusty and non-dusty years (2008–2012 minima, 2021–2022 maxima), and capture the same major dust outbreaks (e.g., March 2022, June 2021). SDFTI consistently provides the most balanced representation of dust-mass loading and transport dynamics, while the equal-weight variant diverges as expected due to its lack of physical calibration. Overall, the SDFTI offers a stable and regionally coherent measure of Saharan dust transport. The methodological framework (variable selection, normalisation, weighting, and sensitivity testing) is general and can be adapted to other dust-affected regions worldwide. Full article

TaC/amorphous-carbon (TaC/a-C) composite coatings with varied a-C contents were deposited on graphite by dual-target magnetron sputtering to mitigate the thermal-expansion mismatch that commonly triggers cracking and spallation in TaC coatings on carbon substrates during rapid thermal cycling. However, existing TaC–C (often termed “free carbon”) approaches rarely identify the carbon’s structural state and spatial distribution explicitly, and a clear correlation between carbon fraction, thermal-shock-driven microstructural evolution, and cyclic damage remains insufficiently established. Increasing the a-C fraction progressively refines the TaC grain structure and introduces an a-C phase along grain boundaries, thereby lowering the effective coefficient of thermal expansion (CTE) and improving compatibility with the graphite substrate. Under laser thermal cycling, coatings with higher a-C contents exhibit markedly enhanced resistance to cracking and spallation. After 15 cycles, the high-a-C (~28.99 at.%) coating remains free of through-thickness cracks, maintains its thickness, and retains a single-phase TaC structure without detectable Ta₂C, whereas the low-a-C coating shows severe thinning, through-cracks, and partial TaC → Ta₂C transformation. Microstructural observations indicate that the a-C phase forms a compliant, stress-relaxing boundary network and promotes a porous, mechanically interlocked TaC architecture, synergistically redistributing thermal stresses and deflecting crack propagation. Full article

Melatonin, an indoleamine crucial for regulating circadian rhythms, sleep–wake cycles, and immune–endocrine homeostasis, is present in biological fluids at extremely low concentrations, making its quantification analytically challenging. This narrative review provides a critical comparative assessment of current methodologies for melatonin determination across various biological matrices—plasma, urine, saliva, breast milk, and hair. The discussed techniques include immunoassays, colorimetric and spectrophotometric methods, chromatographic–mass spectrometric platforms (LC–MS/MS, UHPLC–MS/MS), and emerging biosensors. Each approach is evaluated regarding analytical sensitivity, specificity, reproducibility, cost, and clinical applicability. While immunoenzymatic and colorimetric techniques offer accessible, low-cost solutions for large-scale or preliminary studies, LC–MS/MS remains the benchmark for reference analysis, providing sub-picogram detection limits and multiplexing capability. However, its high cost, procedural complexity, and inter-laboratory variability limit routine implementation. New developments, including molecularly imprinted polymers, dispersive microextraction, and nanomaterial-based biosensors, suggest a shift toward hybrid, sustainable, and portable analytical platforms. By synthesizing recent methodological advances and identifying key limitations, this review aims to guide researchers and clinicians in selecting the most appropriate analytical approach for clinical, pharmacological, and circadian biomonitoring applications. Full article

The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa⁻¹ within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection. Full article

Two-dimensional (2D) materials are competitive in a diverse range of areas, spanning from electronic and optoelectronic devices to wearable devices, due to their unique physical and chemical characteristics, as well as remarkable flexibility. As a typical 2D material, lead iodide (PbI₂), featuring a high atomic number and tunable band gap, has been extensively studied in many applications of electroluminescent (EL) devices, photodetectors, and perovskite solar cells. However, high-performance PbI₂-based photodetectors remain a challenge. Herein, we present a high-performance flexible photodetector based on 2D layered PbI₂ nanoplates, which were synthesized via a straightforward air sublimation method. The PbI₂-based photodetector exhibits an excellent photoresponse and the highest responsivity peaks at 34 A/W at 405 nm, together with an ultrahigh transient switching on/off current ratio of 10⁷. Due to a low dark current (10⁻¹⁴ A), the device exhibits an extremely low noise level (<10⁻²⁶ A²Hz⁻¹) and acceptable detectivity (2 × 10¹⁰ Jones). Furthermore, remarkable mechanical flexibility was observed in the device on a PET substrate, preserving both its electrical conductance and photoresponse stability after 560 bending cycles. Finally, high-resolution imaging applications were implemented under a 100 Hz modulated light signal. This work highlights the superior optoelectrical properties of 2D PbI₂ growth by the in-air sublimation method and proves its promising future in flexible and wearable optoelectronic devices. Full article

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector. Full article