Search Results (1,036)

Flexible strain sensors require both a responsive conductive network and a mechanically compliant supporting structure. In this study, a sandwich-structured carbon nanotube (CNT)/graphene–thermoplastic polyurethane (TPU) nanofiber strain sensor was fabricated by electrospinning, spray-coating, pre-stretching, and hot-pressing. Field-emission scanning electron microscopy was used to examine the nanofiber and coated-fiber morphology, while energy-dispersive X-ray spectroscopy was used only to describe elemental distribution. The electrical response was quantitatively evaluated under tensile deformation. The sensor exhibited piecewise gauge factors of approximately 47.3, 269.6, and 613.8 over strain ranges of 0–20%, 20–45%, and 45–60%, respectively. The response and recovery times were approximately 120 and 180 ms, and the electrical response remained observable over 5000 loading–unloading cycles at 20% strain. Qualitative demonstrations involving finger-bending, elbow-bending, pulse, grasping, walking, and repeated pressing produced distinguishable resistance-time patterns. These results indicate the potential of the CNT/graphene–TPU device for wearable deformation monitoring. Full article

Carbon black (CB) nanoparticles (~20 nm) offer high specific surface area and conductivity for self-sensing cementitious composites, but strong interparticle van der Waals forces drive agglomeration in alkaline pore solutions, limiting sensing reliability. This study introduces a nanoscale interfacial engineering strategy in which CB nanoparticles are adsorbed onto polypropylene (PP) fiber surfaces as spatially organized conductive elements, with EDS evidence of enhanced hydrate coverage at the fiber–matrix interface. Three CB dosages (0.5%, 1.0%, and 1.5% by binder mass) with 0.5% PP fiber were investigated. Nanoparticle coating and interfacial micro-structure were characterized by SEM-EDS, while FTIR was used to verify that the fiber backbone remained chemically unmodified; piezoresistive response and durability were assessed via cyclic compression, DIC, and hygrothermal cycling. The 1.0% CB nanocomposite lies within the effective percolation window (~0.9–1.2%), showing high linearity, a stable gauge factor (~100), and distinct FCR acceleration for early-warning sensing. The 1.5% CB composite yields higher sensitivity but scattered responses due to nanoparticle clustering; 0.5% CB remains below the percolation threshold with a discontinuous network. After 60 hygrothermal cycles, the 1.0% nanocomposite retains >93% of its gauge factor with minimal resistance drift. The nano-engineered CB–PP fiber architecture offers a scalable route integrating crack bridging, percolation networking, and durable self-sensing in cementitious nanocomposites for structural health monitoring. Full article

Railway track geometry degradation is a key determinant of infrastructure safety, ride comfort, maintenance demand, and lifecycle cost. This paper presents a measurement-based assessment of track geometry degradation on the analyzed section of railway line M300 in Romania, with emphasis on its practical use for maintenance prioritization. The study uses in situ track-geometry inspection records collected by a track-measuring vehicle (VMC) between 2020 and 2023 on a curved double-track sector, and evaluates the main geometric defect families used in railway practice, including alignment, gauge, longitudinal level, cross-level, and twist. In addition to conventional defect identification, severity grading, and penalty-point scoring, the paper uses a maintenance-oriented interpretation framework based on the campaign-level Defect Severity Index (DSI), the Defect Recurrence Factor (DRF), and the Maintenance Priority Matrix (MPM). In this framework, DSI corresponds to the normalized penalty score per kilometre and is used to compare inspection campaigns, while DRF and MPM extend the interpretation by identifying persistent defect families and translating measured degradation into maintenance priorities. The results show that degradation is non-uniform over time and defect-family-dependent, with cross-level and longitudinal-level defects dominating the cumulative penalty score. The DSI ranged from 467 points/km in September 2022 to 3183 points/km in March 2022, confirming a non-linear degradation and recovery pattern. The proposed indices provide an interpretable extension of the existing penalty-point framework and support condition-based maintenance planning on conventional railway lines. Full article

Climate-induced hydrometeorological extremes pose a persistent threat to the Ganges–Brahmaputra–Meghna (GBM) basin, where traditional univariate flood assessments systematically underestimate compound disaster risks. This study presents a transboundary risk approach that links basin-wide monsoon factors (GBM precipitation and soil moisture) with the seasonal flood footprint in the Bangladesh delta. While traditional assessments often focus on river discharge at specific gauges, this work provides an integrated assessment of the relationship between antecedent soil moisture and downstream inundation extent using a consistent 31-year satellite-derived record. Utilizing this longitudinal archive (1988–2021) of ERA5-Land drivers and JRC global surface water footprints, a bivariate copula framework was applied to characterize the non-linear dependency between monsoon precipitation, antecedent soil moisture, and seasonal flood extent. The analysis identified the Gumbel copula as the optimal model, revealing a significant upper-tail dependency (${\lambda }_u$ = 0.227) that confirmed a strong effect during extreme events. Hydrological attribution demonstrated that a saturated upstream basin acts as a significant antecedent factor, multiplying the probability of extreme flooding in the delta by a factor of 1.16 times compared to median conditions. Joint ‘AND/OR’ return period matrices established a probabilistic bivariate risk assessment model for compound disasters. These results provide a prospective yardstick for seasonal flood predictions and possible means of boosting support through transboundary basin-prior conditioning, enhancing the imminent likelihood of extreme inundation. Full article

Fused deposition modeling (FDM) is one of the most widely deployed additive manufacturing methods, and the mechanical performance of FDM-printed parts is governed by a small set of strongly coupled process parameters. Silk PLA, a PLA-based filament engineered to deliver a high-gloss finish with improved mechanical performance, has received far less attention than commodity PLA, and its parameter–property relationships remain incompletely characterized. In this work, the effects of three FDM printing parameters: (i) layer height (0.10, 0.15, and 0.20 mm), (ii) extrusion temperature (200, 210, and 220 °C), and (iii) print speed (100, 120, and 140 mm/s) on the tensile characteristic of Silk PLA were investigated through a full-factorial design consisting of 27 parameter combinations and 135 ASTM D638 Type-I specimens. Tensile tests were performed on an MTS E42 universal testing frame, while Digital Image Correlation (DIC) was employed to obtain full-field longitudinal and transverse strain distributions and to identify the onset and location of necking. Analysis of variance (ANOVA) was used to assess the statistical significance, while an interpretable machine learning (ML) pipeline combining extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP) values, and partial dependence plots (PDPs) was employed to quantify the relative influence of each parameter and elucidate its effect on the tensile response. Both analyses identified extrusion temperature as the dominant factor governing ultimate tensile strength and Young’s modulus. Partial dependence analysis further revealed that strength gains saturate above 210 °C and are maximized at an intermediate print speed of 120 mm/s, providing actionable guidance for process optimization. The highest tensile strength, 40.68 MPa, was achieved at a layer height of 0.20 mm, an extrusion temperature of 220 °C, and a print speed of 120 mm/s. DIC measurements showed that thinner layers (0.10 mm) produced higher breaking strains and necking that initiated at the gauge-section edges, whereas thicker layers (0.20 mm) shifted necking toward the mid-gauge. Together, the experimental, statistical, and ML results provide a consistent, mechanistically interpretable framework for optimizing FDM process parameters for Silk PLA functional parts. Full article

Quantitative precipitation estimation (QPE) is a fundamental task of hydrometeorological applications, ranging from flash-flood detection to water-resource management. While weather radars offer superior spatial coverage compared with rain gauges, traditional estimation based on the empirical $Z-R$ (reflectivity factor vs. rainfall rate) relationship fails to capture spatial variability and tends to underestimate extreme rainfall. The idea pursued here is to learn the radar-to-rainfall mapping by means of a convolutional neural network (CNN) trained on co-located radar and rain-gauge data; thus, once trained, the network can convert radar reflectivity measures into rainfall values, even where gauges are unavailable. Although machine learning techniques are promising for this task, they typically demand large training sets. To operate in a limited-data regime, we introduce a U-Net architecture that separates the analysis of space from that of time: each block first looks at the structure of the reflectivity field and then at how it changes over consecutive radar scans, extracting spatiotemporal features from volumetric data with fewer parameters than a full three-dimensional filter. The model is evaluated on a severe convective event that affected Tuscany, Italy, on 2 November 2023, benchmarking its performance against the classical Joss–Waldvogel $Z-R$ relationship (suitable for convective events), a data-driven log-regression of weather-radar and rain-gauge data, and a baseline CNN architecture. The main advantages are negative bias—i.e., underestimation of rainfall—more than halved and correlation with rain-gauge measures more than doubled, under the same operational conditions. What is noteworthy is the capability of learning the model from radar and rainfall data taken in different times and places, as well as the possibility of converting a reflectivity map into a rainfall map without the need for simultaneous rain-gauge measures. This is an asset of fixed parametric methods; however, they are far less accurate. Full article

Radar-based Quantitative Precipitation Estimation (QPE) in semi-arid Mediterranean climates is critically challenged by systematic underestimation of shallow precipitation, yet gauge–radar merging frameworks tailored to such environments remain poorly evaluated. This study develops and assesses a merging pipeline for Cyprus, combining X-band polarimetric observations from the Paphos and Larnaca operational radar network with accumulations from a 50-station rain gauge network across 11 rainfall events spanning the 2024 wet season (January and November–December 2024). Four approaches were evaluated: raw radar mosaic, global mean field bias (MFB) correction, spatially varying local inverse distance weighting (IDW) bias correction assessed through leave-one-out cross-validation (LOOCV), and a Random Forest (RF) machine-learning retrieval trained on polarimetric, geometric, and orographic predictors and evaluated through leave-one-event-out cross-validation (LOEO-CV). Raw radar exhibited severe and highly variable underestimation, with station-level bias factors ranging from 1.4 to 200×. Global MFB correction removed systematic offset but, as a single spatially uniform scalar, could not improve spatial correspondence; it was beneficial only where the bias field was spatially coherent. Local IDW correction provided cross-validated reduction in RMSE for most events (commonly 40–53%), but this improvement reflected removal of mean bias rather than recovery of spatial pattern: only 17 January 2024 combined RMSE reduction (24.07 mm to 11.31 mm) with genuine spatial skill (leave-one-out r = 0.850, bias-field coherence r = 0.742), while several events improved in RMSE yet retained near-zero spatial correlation, and 30 and 31 January degraded outright. These results characterise the limits of distance-weighted (IDW) interpolation specifically; whether geostatistical estimators incorporating topographic external drift can restore spatial skill where the present gauge network constrains the bias field remains to be tested. When re-evaluated on the same rainy matched-pair set (N = 2378), the Random Forest reduced 10 min RMSE by only 2.6% relative to the best classical Z-R estimator (from 10.38 mm to 10.10 mm) and reduced the systematic bias from −5.06 mm to −4.25 mm, but did not improve point-to-point spatial correspondence (r ≈ 0), indicating that this mean-regression Random Forest provides effective bias-correction skill without spatial-correspondence skill, leaving the fundamental representativeness gap between CAPPI sampling and gauge point measurements unresolved. Three pre-conditions for local bias correction skill are identified as empirical diagnostics under the sample conditions of this study: a minimum of approximately 40 contributing gauges, a spatially coherent bias field, and a moderate bias range. A formal bootstrap or resampling-based uncertainty estimate for these indicators was not attempted, because eleven events constitute too small a sample for stable resampling statistics; the per-event relationships between the number of contributing gauges, the bias-factor range, the bias-field spatial autocorrelation, and the LOOCV error are therefore presented as the empirical basis for these diagnostic indicators, which should be refined and tested for statistical robustness as longer event records become available. These findings demonstrate that the suitability of spatial merging can be diagnosed from network and bias field properties prior to correction, and that machine-learning retrieval offers complementary value through systematic bias removal where spatial interpolation fails. Probabilistic merging frameworks, denser gauge networks, and ML approaches that explicitly target spatial correspondence are identified as priority developments for eastern Mediterranean QPE. Full article

This research evaluates the accuracy and repeatability of a novel procedure for measuring pitch diameter using a touch-trigger probe in a production environment. Experiments were performed on a WFL M40 machine equipped with a Renishaw RMP600 strain gauge probe, using different thread types and machine tools. The results were validated by comparison with the traditional three-wire method, which is regarded as a high-precision reference for pitch diameter measurement. Statistical analyses, including the Shapiro–Wilk test, ANOVA, and Tukey HSD post hoc tests, were applied to assess error distribution and differences between thread types and machines. The proposed method demonstrated satisfactory accuracy and repeatability, with an error spread of approximately ±0.015 mm, achieving measurement results within the manufacturing tolerance range (±0.07 mm). The study also showed that measurement accuracy is influenced by factors such as thread angle and Z-axis positioning. Overall, the method proved to be reliable for production use, offering a good balance between measurement accuracy and operational efficiency, although minor adjustments may be needed for automatic correction routines. Full article

A Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ high-entropy alloy film was designed as a Cr- and Al-free metallic sensing layer for thin-film strain gauges in high-pressure hydrogen environments. CALPHAD calculations predicted a BCC/B2-type phase field, while XRD, EBSD and GIXRD results supported a BCC-type structure without direct confirmation of long-range B2 ordering. Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ films deposited on Si reference substrates at 150 and 300 W retained broad BCC-type diffraction features. The 300 W film showed a more continuous cross-sectional morphology, good metallic conductivity and a comparable nanomechanical response with slightly higher hardness. Device-level tests were then performed using Cr/AlN/Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ TFSGs on 316L stainless-steel substrates. The devices exhibited average absolute apparent zero shifts of 16.08 με in 12 MPa N₂ and 17.79 με in 12 MPa H₂, with an additional H₂-associated apparent response of only 1.71 με. Static tensile tests in 12 MPa H₂ confirmed a linear strain response with a gauge factor of 1.72 ± 0.01. Full article

Rainfall-triggered shallow landslides (SLs) are rapid slope failures affecting soil layers typically thinner than 3 m. Despite their limited extent, cumulative impact can be significant, particularly in vulnerable hilly environments. This study presents an empirical approach based on a 104-year historical inventory (1921–2025) covering an area of approximately 539 km², where 467 records were aggregated into 41 shallow landslide events. Because historical datasets primarily document damage, the most complete records are associated with densely populated municipalities. This exposure-driven bias is explicitly exploited to derive rainfall triggering conditions and assess their transferability to neighboring data-scarce areas. A reference municipality is used to define rainfall thresholds based on 1-, 3-, and 5-day cumulative precipitation. Results highlight the importance of rainfall accumulation over short to intermediate durations (1–5 days), particularly during intense rainfall episodes. The threshold derived from second-lowest values reduces the influence of individual minima and provides a more representative description of the observed triggering conditions. Event-based analysis shows that most rainfall events exhibit high spatial consistency, while lower consistency is limited to small number of cases. Station-based analysis confirms that several rain gauges are highly consistent with the reference station, supporting the transferability of rainfall-triggering conditions associated with the threshold, whereas a few stations show lower agreement due to local topographic and climatic factors. These findings suggest that the proposed approach provides a practical framework for landslide hazard assessment in regions characterized by fragmented historical datasets. Full article

Conductive polymer composite (CPC)-based wearable electronics and flexible strain sensors work in different environments, which require CPCs to show stable electrical performance at a wide range of temperatures. However, the resistance of most CPCs is generally temperature-dependent. In this work, a double-layer conductive framework was fabricated by coating highly conductive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on a graphene mesh. The resulting composite, PEDOT:PSS/GM/PDMS-0.75, exhibited outstanding conductivity (8.1 S/cm), a high gauge factor (~42), excellent reliability (1000 cycles) and stable sensing performance within −30~140 °C. This work highlights the importance of PEDOT:PSS in improving the conductive stability of graphene-based strain sensors at different temperatures. Moreover, applications of sensing for human joint movement in different environments open new opportunities for temperature-insensitive CPCs. Full article

High-g accelerometers are in high demand across sectors such as aerospace, defense, and industrial inspection. This paper presents a MEMS accelerometer based on graphene piezoresistors, designed for precise acceleration measurement under sudden impacts, intense vibrations, and extreme conditions, such as engine fault diagnosis and weapon impact testing. A step-by-step structural optimization and simulation analysis were conducted using finite-element simulation. Taking the peak strain at the beam root, the first-order natural frequency, and the maximum equivalent stress as optimization objectives, progressive parametric optimization was sequentially performed on four progressive architectures: a simple beam, a beam mass, a beam mass with stress concentration grooves, and a beam mass with stress concentration grooves and symmetric masses. The results indicate that the introduction of a central mass enhances the peak strain by more than 15 times compared to the simple beam. The addition of stress concentration grooves further increases the strain by approximately 30%. Finally, the incorporation of symmetric masses yields a further 9% strain enhancement while reducing cross-axis sensitivity by 5.6%, effectively suppressing off-axis interference. The final structure achieves maximized strain while maintaining a first-order natural frequency above 200 kHz, with the maximum equivalent stress staying within the allowable limit. This optimal comprehensive performance provides essential technical support for high-performance graphene-based accelerometers. In addition to the mechanical structural optimization, the graphene piezoresistors were treated as surface sensing regions at the beam-root locations, and the area-averaged longitudinal strain was extracted as the input of a piezoresistive transduction model. The simulated strain was converted to resistance variation and bridge output voltage using a graphene gauge-factor-based readout model incorporating contact-resistance effects, thereby providing a sensor-level electromechanical performance estimation for the proposed high-g accelerometer. Full article

Background: Pre-analytical errors account for the majority of failures across the total testing process. This study evaluated pre-analytical performance in a tertiary-care biochemistry laboratory in Riyadh, Saudi Arabia, using IFCC-aligned Quality Indicators (QIs), Six Sigma metrics, and structured Root Cause Analysis (RCA). Methods: A retrospective analysis of all biochemistry tests processed between January and December 2024 at a tertiary-care hospital was conducted. Rejected tests were classified into seven IFCC-aligned QI categories. Sigma metrics assessed process capability, Pareto analysis identified the vital few contributors, and RCA using the Ishikawa framework identified human, equipment, environmental, and process-related factors. Rejection patterns were described by department and work shift. Results: Of 845,647 tests performed, 10,783 (1.28%) were rejected, yielding an overall process capability of 3.89σ (Minimum Acceptable). Hemolysis was the leading cause (8186 tests; 75.92%) at 3.97σ, the only indicator classified as High against the IFCC WG-LEPS registry. The remaining six indicators demonstrated Good to Very Good performance (4.59σ–5.33σ). Pareto analysis identified hemolysis and inappropriate tube use (7.60%) as the vital few, jointly responsible for 83.52% of rejections. RCA implicated venipuncture technique, needle gauge selection, workload pressure, and prolonged tourniquet application as key contributors. The Emergency Department generated the highest inpatient rejection burden (38.7%). Conclusions: Although the overall rejection rate compared favorably with international benchmarks, hemolysis was the principal process vulnerability, with inappropriate tube selection as a secondary target. Recommended quality improvement strategies include structured phlebotomy training, real-time hemolysis index feedback, and Emergency Department-specific initiatives; structural solutions such as dedicated inpatient phlebotomy services warrant prospective evaluation alongside training-based interventions. Full article

Purpose: To address the measurement inaccuracies of weighing-type rain gauges caused by environmental disturbances such as vibration, temperature drift, and creep, this study aims to develop a robust error modeling and compensation framework adaptable to complex conditions. Method: A nonlinear error model was constructed by analyzing multi-source disturbance factors and incorporating both linear and nonlinear temperature terms. A BP neural network was employed to compensate for complex error patterns, and several intelligent optimization algorithms (a genetic Algorithm (GA), a particle swarm algorithm (PSO), and a GOOSE algorithm (GOOSE)) were used to enhance training performance. An improved adaptive GOOSE algorithm (ADGOOSE) was further proposed to optimize the BP network by integrating dynamic control coefficients and perturbation-based restart strategies. Results: Experiments under various rainfall intensities and temperatures demonstrated that the ADGOOSE-BP model outperformed traditional filtering and other optimization methods, achieving the lowest RMSE of 0.0494 and the highest R2 of 0.9835. Conclusion: The proposed method effectively models and compensates for environmentally induced errors in weighing rain gauges, demonstrating strong potential as a high-precision, adaptive compensation framework that provides a solid foundation for future field-deployable hydrological monitoring systems. Full article

Anaerobic digestion (AD) is an effective technology for sustainable waste management, renewable energy production and resource recovery within a circular economy. This study offers a systematic bibliometric review of global advances in AD research and assesses their relevance to Ghana. Using the PRISMA framework, the literature from 2011 to 2025 was sourced from the Scopus database and analysed through bibliometric and thematic methods. The review emphasises four key factors affecting AD performance: municipal solid waste as feedstock, pretreatment technologies, biochemical methane potential (BMP) assessment and process optimisation. Studies were included if they addressed any of these themes. Non-English publications, inaccessible full texts and papers lacking bibliographic metadata were excluded. After screening 3424 records, 61 studies were included in the systematic review. After metadata screening, 3374 of the 3424 retrieved records were retained for bibliometric analysis. Results show that municipal solid waste, food waste, agricultural residues and sewage sludge are promising sources for biogas generation. Pretreatment techniques, including thermal, chemical, mechanical and biological, significantly enhance substrate biodegradability and methane production. BMP assessment is a reliable way to gauge feedstock suitability and energy recovery potential. Optimising parameters like pH, temperature, organic loading, hydraulic retention time and co-digestion ratios improves process stability and biogas yield. The review highlights the increasing use of modelling and optimisation to boost digester performance and facilitate scale-up. In Ghana, abundant organic waste offers significant opportunities for biogas development. Employing advanced feedstock characterisation, pretreatment, BMP evaluation and optimisation can improve AD efficiency, support renewable energy, reduce waste disposal issues and promote Ghana’s shift toward a sustainable circular bioeconomy. Full article