Search Results (832)

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. Although the aetiology of IBD remains largely unknown, several studies suggest that an individual’s genetic susceptibility, external environmental factors, intestinal microbial flora, and immune responses are all factors involved in and functionally linked to the pathogenesis of IBD. Beyond the gastrointestinal manifestations, IBD patients frequently suffer from psychiatric comorbidities, particularly depression and anxiety. It remains unclear whether these disorders arise solely from reduced quality of life or whether they share overlapping biological mechanisms with IBD. This review aims to explore the bidirectional relationship between IBD and depressive disorders (DDs), with a focus on four key shared mechanisms: immune dysregulation, genetic susceptibility, alterations in gut microbiota composition, and dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis. By examining recent literature, we highlight how these interconnected systems may contribute to both intestinal inflammation and mood disturbances. Furthermore, we discuss the reciprocal pharmacologic interactions between IBD and DDs: treatments for IBD, such as TNF-alpha and integrin inhibitors, have demonstrated effects on mood and anxiety symptoms, while certain antidepressants appear to exert independent anti-inflammatory properties, potentially reducing the risk or severity of IBD. Overall, this review underscores the need for a multidisciplinary approach to the care of IBD patients, integrating psychological and gastroenterological assessment. A better understanding of the shared pathophysiology may help refine therapeutic strategies and support the development of personalized, gut–brain-targeted interventions. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Objectives: To investigate the reliability and validity of the Singer Reflux Symptom Score (sRSS), a new patient-reported outcome questionnaire documenting the severity of reflux symptoms in singing voice is proposed. Methods: Amateur and professional singers consulting the European Reflux Clinic for laryngopharyngeal reflux disease (LPRD) symptoms and findings were prospectively recruited from January 2022 to February 2023. The diagnosis was based on a Reflux Symptom Score (RSS) > 13 and Reflux Sign Assessment (RSA) > 14. A control group of asymptomatic singer subjects was recruited from the University of Mons. The sRSS was rated within a 7-day period to assess test–retest reliability. Internal consistency was measured using Cronbach’s α in patients and controls. A correlation analysis was performed between sRSS and Singing Voice Handicap Index (sVHI) to evaluate convergent validity. Responsiveness to change was evaluated through pre- to post-treatment sRSS changes. The sRSS threshold for suggesting a significant impact of LPRD on singing voice was determined by receiver operating characteristic (ROC) analysis. Results: Thirty-three singers with suspected LPRD (51.5% female; mean age: 51.8 ± 17.2 years) were consecutively recruited. Difficulty reaching high notes and vocal fatigue were the most prevalent LPRD-related singing complaints. The sRSS demonstrated high internal consistency (Cronbach-α = 0.832), test–retest reliability, and external validity (correlation with sVHI: r = 0.654; p = 0.015). Singers with suspected LPRD reported a significant higher sRSS compared to 68 controls. sRSS item and total scores significantly reduced from pre-treatment to 3 months post-treatment except for the abnormal voice breathiness item. ROC analysis revealed superior diagnostic accuracy for sRSS (AUC = 0.971) compared to sRSS-quality of life (AUC = 0.926), with an optimal cutoff at sRSS > 38.5 (sensitivity: 90.3%; specificity: 85.0%). Conclusions: The sRSS is a reliable and valid singer-reported outcome questionnaire for documenting singing symptoms associated with LPRD leading to personalized management of Singers. Future large-cohort studies are needed to evaluate its specificity for LPRD compared to other vocal fold disorders in singers. Full article

Barrel tools are increasingly used in high-precision machining of free-form surfaces. However, limited studies evaluate their performance specifically on spherical geometries, where tool–surface contact characteristics differ significantly. Understanding how tool geometry and process parameters influence surface quality and cutting forces in such cases remains underexplored. This study evaluates how barrel cutter radius and varying machining parameters affect cutting forces and surface roughness when milling internal and external spherical surfaces. Machining tests were conducted on structural steel 1.1191 using two barrel cutters with different curvature radii (85 mm and 250 mm) on a 5-axis CNC machine. Feed per tooth and radial depth of cut were systematically varied. Cutting forces were measured using a dynamometer, and surface roughness was assessed using the Rz parameter, which is more sensitive to peak deviations than Ra. Novelty lies in isolating spherical surface shapes (internal vs. external) under identical path trajectories and systematically correlating tool geometry to force and surface metrics. The larger curvature tool (250 mm) consistently generated up to twice the cutting force of the smaller radius tool under equivalent conditions. External surfaces showed higher Rz values than internal ones due to less favorable contact geometry. Radial depth of the cut had a linear influence on force magnitude, while feed rate had a limited effect except at higher depths. Smaller-radius barrel tools and internal geometries are preferable for minimizing cutting forces and achieving better surface quality when machining spherical components. The aim of this paper is to determine the actual force load and surface quality when using specific cutting conditions for internal and external spherical machined surfaces. Full article

Purpose: To present the findings of our preliminary experience using daily image-guided radiotherapy (IGRT) supported by implanted fiducial markers (FMs) in the radiotherapy of the vaginal cuff, in a cohort of post-surgery endometrial cancer patients. Methods: Patients with vaginal cuff cancer requiring adjuvant radiation with external beams were enrolled. Five patients underwent radiation therapy targeting the pelvic disease and positive lymph nodes, with doses of 50.4 Gy in twenty-eight fractions and a subsequent stereotactic boost on the vaginal vault at a dose of 5 Gy in a single fraction. One patient was administered 30 Gy in five fractions to the vaginal vault. These patients underwent external beam RT following the implantation of three 0.40 × 10 mm gold fiducial markers (FMs). Our IGRT strategy involved real-time 2D kV image-based monitoring of the fiducial markers during the treatment delivery as a surrogate of the vaginal cuff. To explore the potential role of FMs throughout the treatment process, we analyzed cine movies of the 2D kV-triggered images during delivery, as well as the image registration between pre- and post-treatment CBCT scans and the planning CT (pCT). Each CBCT used to trigger fraction delivery was segmented to define the rectum, bladder, and vaginal cuff. We calculated a standard metric to assess the similarity among the images (Dice index). Results: All the patients completed radiotherapy and experienced good tolerance without any reported acute or long-term toxicity. We did not observe any loss of FMs during or before treatment. A total of twenty CBCTs were analyzed across ten fractions. The observed trend showed a relatively emptier bladder compared to the simulation phase, with the bladder filling during the delivery. This resulted in a final median Dice similarity coefficient (DSC) of 0.90, indicating strong performance. The rectum reproducibility revealed greater variability, negatively affecting the quality of the delivery. Only in two patients, FMs showed intrafractional shift > 5 mm, probably associated with considerable rectal volume changes. Target coverage was preserved due to a safe CTV-to-PTV margin (10 mm). Conclusions: In our preliminary study, CBCT in combination with the use of fiducial markers to guide the delivery proved to be a feasible method for IGRT both before and during the treatment of post-operative gynecological cancer. In particular, this approach seems to be promising in selected patients to facilitate the use of SBRT instead of BRT (brachytherapy), thanks to margin reduction and adaptive strategies to optimize dose delivery while minimizing toxicity. A larger sample of patients is needed to confirm our results. Full article

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. Full article

Compassion Focused therapy (CFT) is designed to reduce shame (internal and external) and self-criticism while enhancing the three flows of compassion (compassion to others, from others, and for the self). This systematic review evaluated the effectiveness of CFT on these core theoretical constructs in adult clinical populations. A systematic search of three databases (2000–2024) identified 21 studies (N = 450) meeting the inclusion criteria. The studies were narratively synthesised, and quality was assessed using the EPHPP tool. Consistent improvements in self-compassion (g = 0.23–4.14) and reductions in self-criticism (g = 0.29–1.56) were reported. Reductions in external shame were also observed (g = 0.54–1.22), though this outcome was examined in fewer studies. Limited and inconsistent evidence was found for internal shame and interpersonal compassion flows (compassion to and from others), with only a small number of low- to moderate-quality studies addressing these outcomes. Follow-up effects were rarely assessed, and comparator groups were limited. Most interventions were group-based and of variable methodological quality, with frequent selection bias, small sample sizes, and limited demographic diversity. Overall, CFT shows promise for targeting self-directed processes in clinical populations, though stronger evidence is needed to understand its effects on relational components of compassion. Future research should adopt standardised measures, improve methodological rigour, and recruit more diverse samples. Full article

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

Background: The management of end-stage renal disease (ESRD) is undergoing a paradigm shift, with increasing emphasis on kidney transplantation as a preferred treatment modality for elderly patients (≥65 years), who constitute a substantial portion of new ESRD cases. Transplantation offers markedly superior survival and quality of life (QoL) advantages compared to dialysis for this demographic. Nevertheless, key determinants such as frailty, physical functionality, and cognitive function have emerged as critical predictors of post-transplant success. Despite their relevance, standardized methodologies for evaluating these parameters in transplantation candidacy remain absent. This systematic review examines the influence of frailty, physical functionality, and cognitive function on outcomes in elderly kidney transplant recipients. Methods: Adhering to PRISMA guidelines, a rigorous literature search was conducted across PubMed, CINAHL, Embase, PsycINFO, and the Web of Science for studies published up to October 31, 2024. Relevant studies focused on elderly transplant candidates and examined correlations between frailty, physical functionality, or cognitive function and post-transplant outcomes. The Newcastle–Ottawa Scale was employed to evaluate studies quality. Results: Seven studies met the inclusion criteria. Five explored physical functionality, demonstrating that better pre-transplant physical performance predicts enhanced survival. Two studies addressed frailty, utilizing the Fried frailty phenotype, and linked frailty to elevated mortality and diminished QoL recovery. Notably, no studies explored cognitive function in elderly kidney transplant candidates or recipients and its association with post-transplant outcomes, exposing a salient gap in the literature. The included studies’ varied methodologies, reliance on single time-point assessments, and exclusive focus on kidney transplant recipients restrict both comparability among studies and the generalizability of findings to the broader end-stage renal disease (ESRD) population. Conclusions: These findings underscore the profound impact of physical functionality and frailty on transplant outcomes in the growing elderly kidney transplant population, illuminating the necessity for standardized assessment protocols and targeted pre-transplant interventions. The critical gap in cognitive function research underscores a vital direction for future investigation. This research received no external funding. This review is registered with PROSPERO under registration ID CRD42025645838. Full article

Background: Nursing students often experience heightened levels of stress during clinical training due to the dual demands of academic and clinical responsibilities. These stressors, compounded by environmental and organizational factors, can adversely affect students’ well-being, academic performance, and the quality of patient care they deliver. Aim: This study aimed to identify the key stressors influencing nursing students’ perceptions of single-shift quality (SSQ) during clinical training and to examine how well students can predict the quality of their shift based on pre-shift expectations. Methodology: A cross-sectional survey design was implemented, collecting pre- and post-shift data from 325 nursing students undergoing clinical training in Alabama. The survey measured 13 domains related to workload, environmental conditions, organizational interactions, coping strategies, and overall satisfaction. Paired t tests and linear regressions were used to assess changes in perception and identify key predictors of SSQ. Results: This study found significant discrepancies between students’ pre- and post-shift evaluations across multiple domains, including internal environment, organizational interaction with clinical faculty/preceptors, and coping strategies (p < 0.001). Students also accurately predicted stable factors such as patient characteristics and external environment. Pre-shift expectations did not significantly predict post-shift experiences. Post-shift perceptions revealed that stress-coping strategies and collegiality were the strongest predictors of shift quality. Conclusion: Students enter clinical shifts with optimistic expectations that often do not align with actual experiences, particularly regarding support and stress management. The SSQ framework offers a valuable tool for identifying gaps in clinical training and guiding interventions that foster resilience and better alignment between expectations and real-world practice. Full article

This study explores the optimisation of nozzle design for external twin fluid, single-stage atomisation in handling high-viscosity, shear-thinning polydimethylsiloxane (PDMS). A single PDMS grade was employed and atomised using unheated sonic air and the viscosity was varied by the fluid temperature. A systematic experimental approach was used, varying nozzle geometry—specifically apex angle, gas nozzle diameter, and number of gas nozzles—to identify the optimal nozzle configuration (ONC). The spray qualities of the nozzle configurations were evaluated via high-speed imaging at 75,000 FPS. Shadowgraphy was employed for the optical characterisation of the spray, determining the optimal volumetric air-to-liquid ratio (ALR), a key parameter influencing energy efficiency and operational cost, and for assessing droplet size distributions under varying ALR and viscosity of PDMS. The ONC yielded a Sauter mean diameter $d_{32}$ of 570 × 10⁻⁶$\mathrm{m}$, at an ALR of 8532 and a zero-shear viscosity of 15.9 $\mathrm{Pa}$ $\mathrm{s}$. The results are relevant for researchers and engineers developing twin fluid atomisation systems for challenging industrial fluids with similar physical properties, such as those in wastewater treatment and coal–water slurry atomisation (CWS). This study provides design guidelines for external twin fluid atomisers to enhance atomisation efficiency under such conditions. Full article