Search Results (6,573)

The wake evolution of vertical-axis wind turbines (VAWTs) plays a critical role in turbine-array performance, yet the wake variations associated with rotor geometry, operating condition, and Reynolds-number-related factors remain insufficiently understood. In this study, the wake characteristics of H-type VAWTs were systematically investigated under varying rotor diameters (D = 2, 3, 4 m), chord lengths (c = 0.1–0.4 m), and incoming wind speeds ($v_{\infty }$ = 6–10 m/s) using a two-dimensional mid-span CFD approach based on Improved Delayed Detached Eddy Simulation (IDDES) built on the SST k-ω model. The simulations are intended to examine mid-span wake mechanisms rather than to directly predict full three-dimensional far-wake recovery, turbine-array interaction, or engineering layout performance involving tip vortices, spanwise momentum transport, and three-dimensional breakdown of coherent structures. The results show that, for the selected reference geometry and within the tested inflow-speed range, the lateral mean-velocity profiles at the same downstream location collapse reasonably well after normalization by $v_{\infty }$ and D, indicating weak sensitivity to incoming wind speed under these conditions rather than general Reynolds-number independence of VAWT wakes. For cases with different solidities, the observed wake differences should be interpreted as the combined effects of rotor solidity and the corresponding near-optimal operating condition. Overall, this study provides a mechanism-oriented numerical assessment of wake behavior in H-type VAWTs. Full article

Generative Artificial Intelligence (GenAI) is rapidly reshaping higher education, especially in technology-oriented programs where critical thinking and complex problem solving are core outcomes. This study triangulates global and local evidence on performance/efficiency, usage, autonomy, critical-thinking engagement, and ethics by combining a systematized review informed by Kitchenham and structured using selected PRISMA 2020 elements (2020–2025; last search: May 2025; 49 studies; Scopus, ACM Digital Library, IEEE Xplore, and SpringerLink; not prospectively registered) with an anonymous survey of 302 computing and engineering students from a single university in Ecuador. The expert-reviewed instrument showed acceptable internal consistency for most scale-based dimensions (McDonald’s $\omega$), whereas institutional and ethics-related items were analyzed primarily at the item level. Results showed near-universal academic GenAI use (96%), with 47% of students reporting weekly use and 26% daily use. Research-related work was the most frequent application (81.5%), followed by homework (48.3%), report writing (43.7%), and exam preparation (41.7%). Although students reported perceived efficiency gains, concerns persisted about reduced analytical engagement and technological dependence (84.1%). Ethical concerns centered on dependence, authenticity, and data privacy, while institutional responses pointed to the need for formal training (96.7%) and clearer guidance. Based on this triangulation, we propose a context-bounded interpretive framework suggesting that GenAI’s educational value depends on instructional and governance conditions that preserve autonomy, critical thinking, integrity, and equity. Full article

Background/Objectives: Disaster preparedness is central to public health resilience. This study examined the association between disaster-related digital technology engagement and Health Belief Model-based preparedness beliefs among adults attending a primary care clinic in Türkiye. Methods: In this cross-sectional study, 538 participants completed a sociodemographic form, a researcher-developed 10-item Disaster and Technology Use Questionnaire, and the 31-item General Disaster Preparedness Belief Scale. The technology questionnaire used five-point Likert-type response categories and was treated as a multidomain questionnaire; its prespecified equal-weighted sum formed an operational composite index rather than a unidimensional scale score. Supplementary internal-structure analyses described item covariance and clustering. Spearman correlations, false-discovery-rate correction, and hierarchical multiple linear regression with HC3 robust standard errors were used, adjusting for age, gender, education, income, marital status, and occupation. Results: The technology composite showed α = 0.793 and ω = 0.799, and exploratory and confirmatory analyses identified two correlated empirical item domains with adequate model fit (CFI = 0.952, TLI = 0.936, RMSEA = 0.055). Technology engagement was independently associated with total preparedness beliefs (B = 0.888, 95% CI 0.672–1.103; standardized β = 0.397; p < 0.001) and increased explained variance by 13.3% beyond sociodemographic factors. Adjusted associations remained significant for perceived susceptibility, perceived benefits, perceived low barriers, cues to action, and self-efficacy, but not for perceived severity. Conclusions: Greater disaster-related digital technology engagement was associated with stronger preparedness beliefs, but causal direction cannot be inferred. Primary care initiatives may promote official alert tools, practical application use, and digital-information literacy while maintaining non-digital alternatives for people with limited digital access. Full article

Background and Objectives: Persistent COVID-19 sequelae, often referred to as “long COVID”, still constitute a major global medical challenge. In particular, general fatigue is the most widely reported, serious symptom. In the framework of Japanese traditional (Kampo) medicine (JTM), post-infection fatigue is frequently considered a “Qi deficiency”, a significant loss of vital energy. In JTM, accurately diagnosing Qi deficiency is essential for determining the appropriate treatment. However, one challenge in JTM diagnosis is that mastering traditional diagnostic techniques takes time. We sought to validate the “Qi deficiency score”, a quantitative patient-reported outcome measure designed to assess Qi deficiency in patients suffering from long COVID. Materials and Methods: This was a methodological study. We conducted a cross-sectional study of 237 patients who sought treatment at the post-COVID-19 clinic (COVID-19 Aftercare Clinic) at Okayama University Hospital. The patient population was randomly split into two cohorts: one for Exploratory Factor Analysis (EFA, n = 122) and another for Confirmatory Factor Analysis (CFA, n = 115). The simple Qi-deficiency score comprises eight items derived from Terasawa’s diagnostic criteria, with a total range of 0–56. We evaluated internal consistency using Cronbach’s α/McDonald’s ω and examined structural validity. Convergent validity was established by examining correlations between the simple Qi deficiency score and the Fatigue Assessment Scale (FAS), the Self-rating Depression Scale (SDS), and Quality of Life (QOL) metric. Results: The score’s internal consistency (α = 0.64 and ω = 0.63) was considered modest for screening traditional clinical syndromes. EFA showed major factors representing Qi deficiency symptoms. CFA supported a one-factor model with modest fit indices (CFI = 0.87; RMSEA = 0.079; SRMR = 0.077; TLI = 0.82). Factor loadings revealed that “Body feels tired (now)” (0.98) and “Tires easily (tendency)” (0.73) were the most significant symptoms. Convergent validity showed a strong positive correlation with the FAS (r = 0.61, ρ = 0.60) and a moderate correlation with the SDS (r = 0.55, ρ = 0.55). A significant negative correlation was found with QOL scores (r = −0.42, ρ = −0.43). Conclusions: The simple Qi deficiency score may be useful for evaluating Qi deficiency in patients with post-COVID-19 condition. Full article

Many applications in finance and biology involve multiplicative noise geometric Brownian motion (GBM)-type stochastic differential equations (SDEs) whose drift carries a single dominant periodic component. Such structures arise in seasonal Black–Scholes option pricing, commodity derivatives with annual price cycles, and stochastic biological oscillators driven by a known frequency; the primary contribution of this paper is a high-precision numerical scheme validated on GBM-type test problems with periodic drift. This paper proposes a Strang operator splitting scheme within the Logarithmic Drift-Diffusion Splitting (LDDS) framework, which splits the SDE in y-space into a deterministic drift ODE sub-step (Step A) and an exactly solvable multiplicative diffusion sub-step (Step B). Step A employs the Periodically Fitted Adams–Bashforth–Moulton fourth-order predictor–corrector method (PABM4), which achieves zero local truncation error for trigonometric forcing terms by introducing additional shift terms and simultaneously imposing polynomial exactness conditions and trigonometric fitting conditions. When the Step A forcing belongs to the PABM4 exact function class ${\mathcal{F}}_{\omega }=\mathrm{span}\{1,t,t^2,\sin \omega t,\cos \omega t\}$, the Strang+PABM4 scheme achieves floating-point precision saturation. We investigate three test problems: the cosine-drift GBM (Test Problem 1), the polynomial–trigonometric mixed drift GBM (Test Problem 2), and a dual-frequency drift applicability test (Test Problem 3). Monte Carlo strong error experiments ($M=1000$ paths) validate that Strang+PABM4 achieves saturation at machine precision (≈${10}^{-15}$) on Test Problems 1 and 2, improving precision by ≈${10}^2\times$ over the best algebraically convergent reference. Test Problem 3 identifies the method’s applicability boundary: when the drift contains a second frequency outside ${\mathcal{F}}_{\omega }$, Strang+PABM4 degrades gracefully to order ≈ 4 without catastrophic failure. The floating-point saturation of Strang+PABM4 is contingent on the drift belonging to ${\widehat{\mathcal{F}}}_{\omega }$; when this condition is violated, the method degrades gracefully to algebraic order ≈ 4, as demonstrated in Test Problem 3. Full article

Background/Objectives: Baby-Led Weaning (BLW) is an increasingly adopted complementary feeding approach in which infants self-feed family foods rather than being spoon-fed purées. Caregivers’ attitudes play a central role in its adoption and implementation, yet no validated instrument has been specifically designed to assess caregivers’ attitudes toward BLW. This study aimed to develop and psychometrically validate the BLW Caregiver Attitudes Scale (BLW-CAS). Methods: A methodological psychometric study was conducted in two phases. Scale development involved a literature review, expert panel evaluation, and cognitive pretesting with caregivers. Psychometric validation was conducted among 256 caregivers, while temporal stability was assessed in an independent test–retest sample of 92 caregivers. Content validity, internal consistency, temporal stability, construct validity, and convergent validity were examined using established psychometric methods. Results: The final 16-item BLW-CAS comprised three dimensions: Predisposition, Self-efficacy, and Practical Skills. The revised scale achieved an S-CVI/Ave of 0.93. The three-factor exploratory solution accounted for 67.46% of the total item variance. The three-factor CFA model showed good overall fit (CFI = 0.995; TLI = 0.994; RMSEA = 0.052; SRMR = 0.066). Internal consistency was acceptable to excellent across subscales (α = 0.757–0.913; ω = 0.807–0.953), and test–retest reliability was good (ICC = 0.832). Positive small-to-moderate correlations with general self-efficacy (r = 0.298) and decision satisfaction (r = 0.350) supported convergent validity. Conclusions: The Spanish BLW-CAS is a concise, multidimensional instrument with promising psychometric properties for assessing caregivers’ attitudes toward BLW, supporting future research and the evaluation of interventions related to complementary feeding practices. Full article

Background: High-intensity and high-impact exercise may increase intra-abdominal pressure and loading of the pelvic floor. Although pelvic floor dysfunction (PFD) has been reported among physically active women, findings remain inconsistent, particularly in young nulliparous women. This study examined the relationship between exercise modality, pelvic floor distress, and sexual function in physically active nulliparous women. Methods: A cross-sectional survey was completed by 103 physically active nulliparous women aged 18–30 years. Participants completed the Pelvic Floor Distress Inventory-20 (PFDI-20) and Female Sexual Function Index (FSFI) and reported their primary exercise modality, exercise frequency, and duration. Statistical analyses included Welch’s ANOVA with effect sizes (ω²) and Spearman correlation analyses. Results: Participants engaged in aerobic training (10%), yoga/Pilates (6%), strength training combined with cardiovascular exercise (57%), powerlifting (14%), and CrossFit (14%). No statistically significant differences were detected across exercise modalities for total PFDI-20 scores (p = 0.209, ω² = 0.05) or total FSFI scores (p = 0.883, ω² = 0.00). Likewise, no significant differences were observed across individual PFDI-20 or FSFI domains (all p > 0.05), with effect sizes ranging from negligible to small (ω² = 0.00–0.07). Weak exploratory correlations were identified between pelvic floor distress and selected sexual function domains (ρ = 0.18–0.23, p < 0.05). Conclusions: No statistically significant differences in pelvic floor distress or sexual function were detected among self-reported exercise modalities in this cohort. These exploratory findings should be interpreted cautiously and warrant confirmation through adequately powered longitudinal studies incorporating objective assessments of pelvic floor function. Full article

Antimony (Sb) thin films are critical precursors for next-generation chalcogenide-based optoelectronics. However, the synergistic effects of sputtering power and pressure on their growth dynamics are not yet fully understood. The objective of this study is to establish the conditions for controlling the growth process of Sb films. To this end, we systematically investigated the interplay between RF-Sputtering parameters and film functionality. Samples were deposited as ~800 nm-thick films using RF-Sputtering, varying the power (50–70 W) and working pressure (10–25 mTorr), followed by comprehensive structural (XRD), morphological (SEM), chemical (XPS), and electrical characterization. A critical power threshold of 60 W was identified for the amorphous-to-crystalline transition, at which the films adopt a rhombohedral phase. Increasing the power to 70 W triggered a significant texture redistribution toward the (104) plane and an increase in grain size from 40 nm to 110 nm. Consequently, electrical resistivity dropped by several orders of magnitude, reaching a minimum of ~10⁻⁴ Ω cm due to enhanced grain connectivity and reduced boundary scattering and the transition from semiconductor-type conductivity to metallic-type conductivity. XPS analysis confirmed that these variations are driven by microstructural evolution rather than chemical changes, as the films maintain a stable metallic character beneath a nanometric surface oxide. These findings establish a direct correlation between plasma conditions and material properties. This strategic framework supports the optimization of Sb-based precursor layers in high-efficiency thin-film technologies. Full article

Vertical-axis wind turbines (VAWTs) are recognized as a viable option for wind energy farms due to their compact design and suitability for different wind settings, such as urban and offshore environments. VAWT wind farms have been studied with respect to various turbine spacing and configurations that demonstrate that the VAWT wind farm is well-suited for improving efficiency while requiring less land, compared to horizontal-axis wind turbines (HAWTs). Moreover, the use of combined dual-rotor configurations has recently given attention as a passive strategy to enhance the aerodynamic performance of VAWTs. Despite these advances, the optimal arrangement of VAWT farms, including inter-turbine distances, clustering configurations, and land-use efficiency of such dual rotors, has yet to be explored. This study investigates different clustering scenarios, including vertically aligned pairs and staggered clusters of three turbines, to evaluate their impact on power capture and land usage for a dual-rotor straight-blade vertical-axis wind turbine (DR-SBVAWT). The 2D-dimensional transient (URANS) numerical simulations are conducted using the k-$\omega$ SST turbulence model. Performance indices, namely, total power coefficient and improvement relative to standalone turbines, are analyzed. Wake effects are investigated through detailed velocity contour plots of the wind field. Results reveal that a DR-SBVAWT turbine arrangement can enhance wind farm performance by approximately 25% for two turbines and about 20% for three staggered turbines, with required spacing of 1.5 D and 2.5–3 D, respectively (Here, D is the outer diameter of the DR-SBVAWT). The study overall provides insights into the optimal placement and configuration of DR-SBVAWTs for maximizing energy output while minimizing land usage, offering guidance for the design of more efficient VAWT farms. Full article

Phase resistance unbalance in robotic joint drive systems can alter electromagnetic torque generation and degrade motion accuracy, but its early diagnosis is challenging because fault-related signatures are weak and coupled with operating dynamics. This study proposes a short-time Fourier transform (STFT)-based multi-scale attention ResNet for phase resistance-unbalance diagnosis using synchronized multi-sensor signals from a single industrial robotic joint. Controlled resistance-unbalance states were generated on an eRob70F100I-BM-18EN joint module by inserting 0.05 $\Omega$ and 0.1 $\Omega$ series resistors into one motor phase with a nominal single-phase resistance of 0.75 $\Omega$. Current, acceleration, rotational speed, and torque signals were segmented and converted into four-channel STFT log-amplitude maps. A modified ResNet18 backbone was integrated with feature pyramid network (FPN)-style multi-scale fusion and a convolutional block attention module (CBAM) to enhance discriminative time–frequency features. Under a window-level stratified split, the proposed model achieved 98.97% accuracy and 98.97% macro-F1, outperforming raw-signal, fast Fourier transform (FFT), wavelet, STFT-ResNet18, STFT-VGG11-BN, STFT-MobileNetV2, and STFT-ShuffleNetV2 baselines. Grouped validation was conducted using file-level, leave-one-speed-out, and leave-one-load-out splits to assess robustness under stricter data partitions. The proposed model achieved 91.75% macro-F1 under file-level splitting and average macro-F1 values of 89.77% and 84.22% under leave-one-speed-out and leave-one-load-out validation, respectively. Grad-CAM visualization further indicates that the model relies on non-uniform local time–frequency regions rather than uniformly using the entire spectrogram. These results demonstrate effective robotic-joint resistance-unbalance discrimination while revealing that unseen operating conditions, especially specific speed and load settings, remain challenging for robust cross-condition deployment. Full article

In conventional spaceborne Synthetic Aperture Radar (SAR) systems, raw echo data are usually downlinked to ground stations for image formation, resulting in substantial communication burden and processing delay. Although onboard SAR imaging can alleviate this problem, onboard processors are constrained by power consumption, memory capacity, thermal dissipation, and physical size. Therefore, low-power embedded GPU platforms have become a practical choice for onboard SAR processing. Existing onboard processing is mainly suitable for relatively low-complexity imaging modes and algorithms, while high-resolution sliding spotlight SAR requires more accurate frequency-domain processing due to its extended azimuth bandwidth, strong range–azimuth coupling, and nonlinear range cell migration. The $\omega$-k algorithm is well-suited for such high-resolution imaging scenarios, but its large-scale FFTs, phase compensation, and Stolt interpolation impose significant pressure on the memory capacity, memory bandwidth, and computational resources of embedded GPU platforms. To address these challenges, this paper presents a GPU-oriented $\omega$-k imaging framework for high-resolution sliding spotlight SAR on the Jetson AGX Orin embedded platform. The proposed framework formulates a complete sliding spotlight $\omega$-k processing flow and develops GPU-oriented optimization strategies for efficient execution on the embedded GPU platform. Specifically, hybrid data partitioning is designed to adapt memory access patterns to range- and azimuth-dominant stages, an asynchronous multi-stream pipeline with reusable GPU buffers is introduced to overlap data movement and computation, and customized kernels are developed for Stolt interpolation and FFT-related spectral centering. Experiments on simulated sliding spotlight SAR data demonstrate that the proposed method achieves well-focused imaging results with consistent impulse response characteristics. For a 32,768× 32,768 simulated SAR dataset, the proposed implementation achieves an end-to-end imaging time of 32.17 s on Jetson AGX Orin. These results demonstrate the feasibility of the proposed framework for onboard high-resolution SAR imaging on an embedded GPU platform. Full article

Single-line-to-ground (SLG) faults are the most frequent fault type in medium-voltage distribution networks. In 35 kV networks equipped with Y/Y/$\Delta$ transformers, the zero-sequence coupling with the transmission grid and inherent line asymmetry make reliable SLG faulted feeder identification highly challenging, especially under high fault resistance. To address these issues, this paper proposes an approximate cross-state apparent admittance method for identifying SLG faulted feeders in 35 kV asymmetric distribution networks. First, the pre-fault and post-fault zero-sequence current responses are analytically derived, accounting for the structural asymmetry of feeder phase-to-ground parameters and the neutral grounding branch. Then, a cross-state apparent admittance formulation is established using synchronized zero-sequence voltage and current phasors. Considering that the proposed criterion relies on the phase relationship of cross-state apparent admittances, its practical implementation requires a high-accuracy synchronized voltage and current measurement loop. Measurement-class CTs/PTs or equivalent high-precision acquisition channels are therefore recommended to ensure sufficient phase-angle accuracy for the admittance-plane criterion. For healthy feeders, the apparent admittance is represented by the inherent zero-sequence line-to-ground admittance under the adopted bus-voltage measurement configuration. For the faulted feeder, the nonlinear coupling term associated with the fault resistance is rigorously eliminated through system-level Kirchhoff’s current law analysis, yielding a fault-resistance-independent analytical expression. A quadrant-based identification criterion is subsequently developed in the complex admittance plane. Finally, a simulation model based on the real architecture and parameters of an actual 35 kV substation is established in PSCAD/EMTDC. The simulation case studies demonstrate that the proposed method can identify the faulted feeder under the tested severe-asymmetry and high-impedance fault conditions up to 10 k$\Omega$, provided that sufficiently accurate synchronized zero-sequence voltage and current phasors are available. In the simulation model, the zero-sequence voltage is measured at the bus or neutral grounding branch, and the feeder zero-sequence currents are measured at the outgoing feeder branches. Full article

Layered double hydroxide (LDH) has been widely used in the field of corrosion protection. LDH nanofillers can act as multifunctional additives in coatings based on the physical barrier effect and anion exchange effect. However, the preparation of the typical co-precipitation method needs to be simplified to realize large-scale industrialization. In this work, a wide range of synthesis parameters, including solution-mixing mode, reaction atmosphere, reaction pH control, titration rate and post-treatment on the obtained LDH, were systematically studied, and it was found that the one-step solution-mixing synthesis procedure and the adoption of N₂ atmosphere were able to alleviate the carbonate contamination to some degree. However, the one-step solution-mixing synthesis procedure has a negative influence on the formation of LDH, probably due to insufficient reaction, while the hydrothermal post-treatment is able to promote the continual growth of the LDH platelet and, thus, lead to an increase in crystallinity and particle size. Green inhibitor aspartates were used to modify LDH, and the electrochemical test results indicated that the adoption of N₂ atmosphere was able to clearly increase the corrosion protection ability of LDH, with an R_p value of 3.86 kΩ·cm² after immersion for 96 h in 0.1 mol/L NaCl, probably due to the intercalation of a relatively large amount of inhibitor anions since less carbonates were intercalated, followed by the LDH synthesized via co-precipitation without N₂ protection, pH control and hydrothermal post-treatment with an R_p value of 3.00 kΩ·cm². The LDH sample without inhibitor intercalation presented a corresponding R_p value of 2.45 kΩ·cm². Notably, the one-step solution-mixing method and a fast titration rate in co-precipitation would have a negative influence on the corrosion protection of LDH, probably due to insufficient reaction. This work could shed light on the simplification of LDH preparation, and it is able to provide a solid foundation and technical support for the wide promotion and application of LDH in anti-corrosion and other fields in the near future. Full article

Reducing aerodynamic drag on medium-duty freight trucks is essential for improving fuel efficiency; however, the relationship between local flow modification, aerodynamic loads, and propulsion-power demand has not yet been sufficiently quantified. This study evaluates the aerodynamic influence of a front deflector on a Chevrolet NQR 1015 box truck using steady RANS CFD with the k–ω SST turbulence model under zero-yaw conditions from 50 to 120 km/h. The numerical setup included near-wall inflation layers and mesh characterization, as well as grid-independence assessments based on C_D, and the Grid Convergence Index. The deflector produced consistent aerodynamic improvements, reducing average drag coefficient by 14.1%, while the average lift coefficient decreased by 73.5%. These aerodynamic changes reduced the average required propulsion power from 53.86 kW to 50.39 kW, corresponding to a 6.4% reduction, with a maximum saving of 8.1% at 120 km/h. Pressure, velocity, and pressure-coefficient C_P distributions indicate that the deflector promotes smoother flow redirection at the cab–box transition, attenuates suction peaks, and suggests lower pressure losses associated with the separated-flow and wake regions. Full article

This work presents the design evolution, fabrication, and characterisation of an additively manufactured ring-type thermal airflow sensor for in-pipe flow monitoring, developed employing exclusively Fused Deposition Modelling (FDM) additive manufacturing technology and a commercially available Carbon Nanotube (CNT)-enriched Biopolymer Polylactic Acid (PLA) composite filament. The design evolution proceeds through three progressive stages. In the first stage, a flat heater element is characterised through Constant-Current (CC) Joule heating experiments in order to derive the corresponding Temperature Coefficient of Resistance (TCR) and Thermal Resistance from the obtained experimental data. Consequently, a Finite Element Method (FEM) model implemented in COMSOL Multiphysics^® and calibrated with the extracted material parameters validates the experimental temperature–power relationship and predicts the convective cooling behaviour at various airflow velocities. In the second stage, the geometry is optimised by introducing a conductive trace with a reduced-cross-section central region; as a result, an equivalent thermal localisation is achieved at approximately 26% lower supplied power with respect to the initial heating element, enabled by the design freedom inherent in the FDM process. We should note that the specific sensing geometry can also be directly embedded into any 3D-printed structural component (e.g., a bracket or housing), enabling simultaneous local thermal heating and/or thermal monitoring together with structural functionality within a single printed part. In the third and final stage—the target device—a fully monolithic ring-type airflow sensor is directly integrated into a 3D-printed pipe segment during the printing process. Under constant-current excitation at 40 mA, the device exhibits a monotonically decreasing resistance with increasing airflow (ΔR ≈ 117 Ω over 0–4 m/s) due to convective cooling, while in a single flow-interruption cycle, approximately 79% of the flow-induced resistance change was recovered upon flow removal, with a residual offset of approximately 3% of the heated baseline. A coupled electrothermal FEM model of the device further supports the experimental response by comparing the simulated temperature rise with the values inferred from resistance measurements, while also clarifying the role of the effective internal convective cooling conditions imposed by the pipe geometry. Key features of the proposed device are low raw-consumables cost, fast on-site manufacturing employing a commercially available desktop 3D printer, monolithic construction free of wire-bonded interconnections, and simplicity, indicating its potential for flow monitoring and condition-based maintenance systems aboard vessels as well as in a wide range of industrial sectors. We should note that the present characterisation was performed under laboratory conditions employing a single prototype per design stage; the effects of humidity, salt exposure, vibration, temperature cycling, and material-batch variability remain to be assessed prior to shipboard deployment. Full article