Search Results (644)

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

This paper aims to analyze the performance of a tilt-rotor fixed-wing RPAS (Remotely Piloted Aircraft System) using a segmented approach, focusing on a nominal mission for SAR (Search and Rescue) applications. The study employs optimization techniques tailored to each segment to meet power consumption requirements, and the results highlight the accuracy of the physical characterization, which incorporates nonlinear propulsive and aerodynamic models derived from wind tunnel test campaigns. Critical segments for this nominal mission, such as the vertical take off or the transition from vertical to horizontal flight regimes, are addressed to fully understand the performance response of the aircraft. The proposed framework integrates experimental models into trajectory optimization procedures for each segment, enabling a realistic and modular analysis of energy use and aerodynamic performance. This approach provides valuable insights for both flight control design and future sizing iterations of convertible UAVs (Uncrewed Aerial Vehicles). Full article

Eye movements (EMs) offer valuable insights into cognitive and perceptual processes, serving as potential biomarkers for disordered thinking. This study explores the relationship between EM indices and perception and thinking problems in the Rorschach Performance Assessment System (R-PAS). Sixty non-clinical participants underwent eye-tracking while completing the Rorschach test, focusing on variables from the Perception and Thinking Problems Domain (e.g., WSumCog, SevCog, FQo%). The results reveal that increased cognitive disturbances were associated with greater exploratory activity but reduced processing efficiency. Regression analyses highlighted the strong predictive role of cognitive variables (e.g., WSumCog) over perceptual ones (e.g., FQo%). Minimal overlap was observed between performance-based (R-PAS) and self-report measures (BSI), underscoring the need for multi-method approaches. The findings suggest that EM patterns could serve as biomarkers for early detection and intervention, offering a foundation for future research on psychotic-spectrum processes in clinical and non-clinical populations. Full article

Accurate analysis of medical videos remains a major challenge in deep learning (DL) due to the need for effective spatiotemporal feature mapping that captures both spatial detail and temporal dynamics. Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. To bridge this gap, a novel DL-based framework is proposed for spatiotemporal feature extraction from medical video sequences. As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. To address data imbalance, 60 matched pairs of videos are randomly selected across 20 experimental runs. Videos are resized to 224 × 224, and the 3D CNN captures spatiotemporal information. A 3D version of the parallel spatial and channel squeeze-and-excitation (P-scSE) is implemented, and a new block called the residual with parallel attention (RPA) block is proposed by combining P-scSE3D with a residual block. To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. The model achieves an average accuracy of 0.933, precision of 0.932, recall of 0.944, F1-score of 0.935, and AUC of 0.933. It is also observed that the integration of P-scSE3D increased the F1-score by 7%. This preliminary work opens avenues for exploring various GI endoscopic video-based prospective studies. Full article

Efficient procurement processes are pivotal for strategic performance in digital organizations, requiring continuous refinement driven by automation, integration, and performance monitoring. This research investigates and demonstrates the potential for synergies between RPA and BPM in procurement processes. The primary objective is to analyze and evaluate a manual procurement-intensive process to enhance efficiency, reduce time-consuming interventions, and ultimately diminish costs and cycle time. Employing Design Science Research Methodology, this research yields a practical artifact designed to streamline procurement processes. An artifact was created using BPM methods and RPA tools. The RPA was developed after applying BPM Redesign Heuristics to the current process. A mixed-methods approach was employed for its evaluation, combining quantitative analysis on cycle time reduction with a qualitative Confirmatory Focus Group of department experts. The analysis revealed that the synergy between BPM and RPAs can leverage procurement processes, decreasing cycle times and workload on intensive manual tasks and allowing employees time to focus on other functions. This research contributes valuable insights for organizations seeking to harness automation technologies for enhanced procurement operations, with the findings suggesting promising enduring benefits for both efficiency and accuracy in the procurement lifecycle. Full article

The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances cognitive decision-making, challenges such as organizational resistance, interoperability, and ethical governance hinder scalable and sustainable implementation. The envisioned scenario involves seamless RPA-AI integration, fostering human–machine collaboration, operational resilience, and sustainability. Expected outcomes include (1) hyperautomation for efficiency gains, (2) agile, data-driven decision-making, (3) sustainable resource optimization, and (4) an upskilled workforce focusing on innovation. This study proposes a structured five-stage framework for RPA-AI deployment in Industry 5.0, combining automation, cognitive enhancement, and human–machine symbiosis. A systematic literature review (PICO method) identifies gaps and supports the framework’s design, validated through operational, human-impact, and sustainability metrics. Incorporating ethical governance and continuous upskilling, the model ensures technological advancement aligns with societal and environmental values. Results demonstrate its potential as a roadmap for responsible digital transformation, balancing efficiency with human-centricity. Future research should focus on empirical validation and sector-specific adaptations. Full article

Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R² = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R² values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems. Full article

Background/Objectives: Pleomorphic adenomas (PAs) are the most common salivary gland tumors, with a known risk of recurrence, especially after inadequate surgical excision. Understanding how surgical approach influences recurrence remains essential to optimize management. This study aimed to synthesize recurrence rates of PAs based on different surgical techniques and to illustrate the implications of surgical strategy through a representative case of multifocal deep lobe recurrence. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. Three electronic databases (PubMed, Cochrane, and Google Scholar) were searched for studies published in the last ten years, reporting recurrence rates of PAs by surgical approach. Data were extracted on recurrence, complications, and tumor characteristics. Additionally, a complex clinical case of recurrent deep lobe PA (DLPA) was presented to contextualize the findings. Results: Fifteen studies were included, comprising a total of 2095 patients. Recurrence rates were 3.27% after extracapsular dissection (ED), 0.73% after partial superficial parotidectomy (PSP), and 2.41% after superficial parotidectomy (SP). Recurrent PA (RPA) is often multifocal and associated with increased risks of facial nerve palsy and positive surgical margins. The presented case involved five surgical procedures, with ultimate total parotidectomy and facial nerve preservation despite infiltrative recurrence in the prestyloid space. Conclusions: Techniques such as ED and PSP have demonstrated their efficacy and safety compared to more invasive approaches, although their application should be carefully evaluated based on tumor size and location. RPA remains a challenging entity to treat. Avoiding outdated techniques and ensuring evidence-based decision making may improve long-term outcomes in PA management. Full article

Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating under visual line-of-sight (VLOS) conditions. Two groups of undergraduate students performed distance estimation tasks at 20 and 50 m. One group used direct observation only to estimate the exocentric distance between the drone and an obstacle. The second group, as well as direct observation, had access to a live video feed from the drone’s onboard camera via a ground control station. At 20 m, there was no statistically significant difference in estimation accuracy between the groups. However, at 50 m, the camera-assisted group demonstrated significantly improved accuracy in distance estimation and reduced variance in estimation error. These findings suggest that a ubiquitous and low-cost technology, originally intended for imaging, can offer measurable benefits for depth perception at greater operational distances. The inclusion of camera-assisted perception training during early-stage licensing may enhance safety and spatial judgement in RPAS operations. Full article

PoRV is a significant etiological agent of neonatal diarrhea in piglets, resulting in substantial economic losses within the global swine industry due to elevated mortality rates and reduced productivity. To address the urgent need for accessible and rapid diagnostics in resource-limited settings, we have developed a CRISPR/Cas12a-based assay integrated with recombinase polymerase amplification (RPA) for the visual detection of PoRV. This platform specifically targets the conserved VP6 gene using optimized RPA primers and crRNA, harnessing Cas12a’s collateral cleavage activity to enable dual-readout via fluorescence or lateral flow dipsticks (LFDs). The assay demonstrates a detection limit of 10² copies/μL within 1 h, exhibiting no cross-reactivity with phylogenetically related pathogens such as Transmissible Gastroenteritis Virus (TGEV). By eliminating reliance on thermal cyclers or specialized equipment, this method is fully deployable in swine farms, veterinary clinics, or field environments. The lateral flow format provides immediate colorimetric results that require minimal technical expertise, while the fluorescence mode allows for semi-quantitative analysis. This study presents a robust and cost-effective platform for decentralized PoRV surveillance in swine populations, addressing the critical need for portable diagnostics in resource-limited settings and enhancing veterinary health management. Full article

With increasing complexity and the demand for electronic components, evaluation boards that allow the customer to assess the components themselves are required. However, the evaluation boards are normally tested manually, and manual testing methods have challenges in terms of time efficiency, human error, and scalability. Therefore, we formulated an automated testing system based on robotic process automation (RPA) to address the issues. The system integrates RPA with existing testing hardware for high-speed analog-to-digital converter (ADC) evaluation boards to simplify processes of configuration, data logging, and the analysis of key parameters such as the signal-to-noise ratio full scale (SNRFS) and spurious-free dynamic range (SFDR). The current hardware setup was modified for automation to develop an RPA-based software solution for efficient testing, and its performance was compared with traditional methods in terms of time and repeatability. A marked improvement in test time efficiency was observed, with a reduction of up to 69.68% for inexperienced operators and 41.4% for experienced ones. The RPA-based method demonstrated a high accuracy (99.9603%) and repeatability, with minimal variance between test runs. The system provides an efficient and cost-effective test process that minimizes human intervention. This reduces process complexity for evaluation board functional testing, providing an effective solution to meet the growing demands of electronic components. Full article

The growing human population has increased the need for food beyond what terrestrial sources can provide. This boosts aquaculture demand for molluscs, fish, and crustaceans. Molluscs are popular for their nutritional benefits, making them a profitable industry. Despite a 3% annual growth in mollusc populations, recent high mortality rates and population losses due to poor feeding practices and water pollution have made them more disease-prone. Limited treatment options exist for mollusc diseases in aquaculture systems. Hence, developing rapid, sensitive, and cost-effective diagnostic tools for field use is essential to identify and prevent infections promptly. Recently developed isothermal nucleic acid amplification technologies, like loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA), offer rapid results within an hour. This review examines these isothermal diagnostic techniques for mollusc pathogens and their potential for field application. Full article

Novel methods of coffee processing, including animal-assisted fermentation, are gaining popularity—among them, elephant dung coffee stands out for its rarity and high price, making it a likely target for adulteration. This study aims to discover candidate biomarkers for elephant coffee by comparing the chemical composition, antioxidant activity, and volatile profiles of Arabica coffee processed by three methods: conventional, civet-derived, and elephant-derived (all originated from Southeast Asia, medium roast). Analytical methods included HPLC-UV and GC-SPME-MS, along with in vitro antioxidant assays (DPPH, ORAC, ABTS, total phenolics, and total flavonoids). Principal Component Analysis (PCA) was used to evaluate differences between the samples. While elephant coffee showed lower caffeine (0.93%) and antioxidant capacity across all assays, it was richer in selected volatile compounds, such as pyrazines (e.g., 3-ethyl-2,5-dimethylpyrazine; 3.73% RPA), 2- and 3-methybutanal (1.18 and 0.19% RPA), and furfuryl acetate (18.00% RPA; p < 0.05). These changes are likely to be due to fermentation in the gastrointestinal tract. Despite differences, no definitive biomarker of elephant coffee was found, suggesting that discrimination from other coffee samples may not be as simple as previous studies indicated. More studies with a higher number of samples that employ an extensive analytical approach (e.g., omics or NMR) to thoroughly analyze the phytochemical profile of coffee beans before and after digestion by the elephant are needed. Full article

Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time process optimization, and enhanced knowledge management capabilities. However, key challenges remain, particularly regarding explainability, user engagement, and behavioral integration. Methods: This study presents a novel framework that synergistically integrates the Socialization, Externalization, Combination, and Internalization knowledge model (SECI), Agile methods (specifically Scrum), and cutting-edge AI technologies, including explainable AI (XAI), process mining, and Robotic Process Automation (RPA). The framework enables the formalization, verification, and sharing of knowledge via a well-organized, user-friendly software platform and collaborative practices, especially Communities of Practice (CoPs). Results: The framework emphasizes situation-aware explainability, modular adoption, and continuous improvement to ensure effective human–AI collaboration. It provides theoretical and practical mechanisms for aligning AI capabilities with organizational knowledge management. Conclusions: The proposed framework facilitates the transition from traditional BPMSs to more sophisticated ABPMSs by leveraging structured methodologies and technologies. The approach enhances knowledge exchange and process evolution, supported by detailed modeling using BPMN 2.0. Full article

Background/Objectives: The methylation of the hypermethylated oncological region (THOR) of human telomerase reverse transcriptase (hTERT) may forecast tumour aggressiveness. This pilot study aimed to evaluate THOR methylation as a potential biomarker for recurrence/malignant transformation in salivary gland pleomorphic adenomas (PA). Methods: THOR methylation was assessed by quantitative pyrosequencing in 96 parotid tissue samples (benign and malignant), including non-neoplastic parotid tissue, PA, recurrent PA (rPA), and carcinomas, along with their adjacent tissues. TERT promoter mutations (TPMs) were analysed by Sanger sequencing. Results: THOR methylation significantly differed across the seven groups. Malignant tissues showed higher THOR methylation than non-neoplastic tissues, whereas benign tumours showed no significant difference from non-neoplastic tissue. THOR methylation in rPA was closer to carcinoma than to normal tissue, similar in rPA and tissues adjacent to rPA, and higher in tissues adjacent to carcinomas than in non-neoplastic tissues. A subset of PA-adjacent tissues showed epigenetic alterations, suggesting an increased risk of recurrence or malignant transformation (5–15%). No TPMs were detected. Conclusions: THOR methylation may add information to differentiate normal from carcinogenic tissues and, as such, may be included in a biomarkers panel. Epigenetic alterations in PA-adjacent tissues with normal histology highlight the need for improved diagnostic markers. Full article