Search Results (43,015)

Flexible electrode materials with tailored electrical and mechanical properties are essential for reliable electrocardiographic (ECG) sensing. In this work, p-toluenesulfonic-acid-doped polypyrrole (PPy–TSA) films were modified using polymeric and inorganic fillers, as well as their combinations (polyethylene glycol, graphene, carbon nanotubes, and zeolite), to tune their functional performance. The reference PPy–TSA film exhibits typical morphological and chemical characteristics of doped polypyrrole and serves as a reliable baseline for comparison. All composite films retain electrical conductivity within the range required for ECG applications while showing improved mechanical compliance (i.e., enhanced ability to conform to the skin and sustain deformation). Based on the optimized balance between electrical and mechanical properties, flexible ECG electrodes were fabricated using the TSA-doped PPy-based composite film. ECG recordings obtained with the several proposed electrodes show good agreement with those acquired using a commercial ECG electrode, demonstrating the potential of PPy-based composite films for flexible bioelectronic sensing applications. Full article

A novel sensitive and selective electrochemiluminescence (ECL) sensor using nitrogen-doped graphyne as the platform was proposed for kanamycin (KAN) detection. First, nitrogen-doped graphyne nanomaterial (1N-GY) with high conductivity was synthesized using a high-energy ball milling method. Compared with ordinary graphyne, the addition of nitrogen atoms can improve the conductivity of the material and reduce the electronic migration energy barrier. Then it was used as a substrate material of the ECL sensor, not only increasing the conductivity of the biosensor but also improving the sensitivity of the ECL sensor by providing more immobilization space for the luminescent probe of Nafion-coated mesoporous silica adsorbed Ru(bpy)₃²⁺ (mSiO₂@Nafion@Ru(bpy)₃²⁺). On this basis, mSiO₂@Nafion@Ru(bpy)₃²⁺ functionalized DNA probes were used as luminescent and capture probes to specifically recognize different concentrations of KAN to produce ECL signals. Under optimal conditions, the proposed ECL sensor exhibited good linearity (10⁻¹²–10⁻⁶ M KAN) and a low detection limit of 1.08 pM. The prepared biosensor with good stability and selectivity successfully detected KAN in honey and milk samples, with spiked recovery rates ranging from 98% to 111.79%. This method not only expands the application of 1N-GY as a novel graphitic material in ECL biosensors but also provides an effective way to check antibiotics in dairy products. Full article

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework’s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure. Full article

Crop fungal and viral diseases cause annual economic losses exceeding USD 150 billion globally, demanding rapid, sensitive, and field-deployable diagnostic technologies. This review critically evaluates recent advances in electrochemical biosensing platforms for early crop pathogen detection, focusing on immunosensors, genosensors, aptasensors, and VOC-based systems. Reported analytical performances demonstrate ultralow detection capabilities, including 0.3 fg mL⁻¹ for viral coat proteins, 15 DNA copies for bacterial pathogens, 0.5 fg µL⁻¹ RNA detection for viroids, and nanomolar-level VOC sensing (35–62 nM), with response times ranging from 2 to 60 min. Comparative analysis reveals that genosensors and aptasensors generally achieve the lowest LODs due to nucleic acid amplification or high-affinity recognition, while immunosensors provide robust protein-level specificity validated against ELISA. Volatile organic compound (VOC) sensors enable non-invasive, pre-symptomatic monitoring but face specificity challenges. Despite strong laboratory performance, practical adoption is limited by matrix-derived electrochemical interference, environmental instability of biorecognition elements, workflow complexity, and insufficient standardization across studies. Emerging innovations, including magnetic bead enrichment, nanoporous and graphene-based electrodes, microfluidic integration, AI-assisted impedance interpretation, and biodegradable substrates, are progressively addressing these bottlenecks. This review emphasizes that successful field translation requires holistic workflow engineering, matrix-matched validation, and harmonized performance metrics rather than incremental sensitivity improvements alone. By integrating analytical chemistry, nanomaterials engineering, and agricultural decision-support frameworks, electrochemical biosensing platforms hold significant potential to enable decentralized, rapid, and sustainable crop disease management. Full article

Polyether Ether Ketone (PEEK) is a high-performance polymer increasingly utilized in additive manufacturing due to its exceptional thermal, chemical, and mechanical properties. Thus, they are used to produce aerospace brackets, fuel system parts, seals, compressor valve plates, etc. This study investigates the mechanical performance of both neat PEEK and glass fiber-reinforced PEEK (PEEK + GF) composites fabricated via fused deposition modeling (FDM). The effects of print speed, print orientation, and post-heat treatment were systematically evaluated. Among the tested orientations, the 0° print direction with post-heat treatment at 250 °C yielded highest tensile strength of ~80 MPa, outperforming the 45° and 90° orientations. Print speeds ranging from 5 to 20 mm/s and annealing temperatures between 250 °C and 300 °C significantly influenced material properties. For neat PEEK, both tensile strength and microhardness improved with increasing print speed and post-heat treatment, peaking at 20 mm/s and 250 °C. However, annealing at 300 °C led to performance degradation, attributing to gas-induced porosity within the material. The PEEK + GF composites achieved a maximum ultimate tensile strength (UTS) of approximately 83 MPa under the same optimal conditions (20 mm/s print speed and 250 °C post-treatment). This enhancement is attributed to improved fiber alignment along the print path, increased crystallinity, and superior interfacial bonding. Notably, the composites did not exhibit the microstructural damage observed in neat PEEK at the higher annealing temperature. Full article

Background: Non-small cell lung cancer (NSCLC) constitutes about 80–85% of lung cancers, and ~60–70% of NSCLC patients are diagnosed at an advanced stage of the disease. Concurrent chemoradiotherapy (cCRT) followed by consolidation durvalumab has become the standard of care for unresectable stage III NSCLC, following the phase III PACIFIC trial, which demonstrated significantly improved progression-free survival (PFS) and overall survival (OS) with durvalumab. Methods: We conducted a single-center retrospective study with unresectable stage III NSCLC patients who received cCRT between January 2021 and December 2025 at the Białystok Oncology Center (Poland). Patients with an ECOG performance status of 0–1 and no disease progression (PD) after cCRT were treated with durvalumab consolidation up to 12 months or until PD or unacceptable toxicity. The clinical outcomes and treatment tolerance were analyzed. Results: Out of 94 cCRT-treated patients (pts), 78 received durvalumab consolidation therapy. The median age of the pts was 66.5 years; 64.1% pts were >65 years old. Squamous carcinoma was the predominant histology (56.4%). The median time from cCRT completion to durvalumab initiation was 45 days (range: 15–85). A majority (57.7%) of patients completed the full 12 months of durvalumab. With a median follow-up of 40 months, the median PFS was ~1224 days (40.2 months). At 3 years, PFS was 52.8%. There were no significant differences in PFS by age (<65 vs. ≥65 years), HR:0.65, clinical stage (IIIA vs. IIIB/IIIC) HR:1.01, histology (squamous vs. non-squamous carcinoma), HR:0.76; sex HR:0.6, ECOG 0 vs. 1 HR:0.82; or initiation of durvalumab ≤42 vs. >42 days after cCRT, HR:0.62 (p > 0.05 for all). The sole factor significantly affecting PFS was smoking status: ever-smokers had a longer PFS than never-smokers (median ~46 months vs. ~21 months, HR:2.11, p = 0.04). Durvalumab consolidation was generally well tolerated. Grade 3–4 adverse events (mainly pneumonitis and esophagitis) leading to permanent durvalumab discontinuation occurred in 7 patients (9%), almost all over 65 years old. Conclusions: Real-world data from our single-center study confirm that consolidation durvalumab therapy after cCRT provides substantial clinical benefit in unresectable stage III NSCLC, even in older patients. The PFS and safety outcomes in our cohort, which had a higher proportion of elderly and locally advanced cases, were comparable with those reported in clinical trials (PACIFIC) and observational studies (PACIFIC-R), underscoring the effectiveness and tolerability of this approach in routine practice. We acknowledge the limitations of the retrospective design and sample size, but our findings support the use of cCRT followed by durvalumab in eligible stage III NSCLC patients and highlight the need for further research on optimizing outcomes (e.g., the impact of smoking and other biomarkers). Full article

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

This study explores how various factors—including motivation, emotion, demographics, and institutional characteristics—interrelate to shape academic success among Greek university students. Based on Self-Determination Theory (SDT) and Tinto’s model of integration, it fills a gap in research by addressing the specific characteristics of the Greek higher education system. While prior research emphasizes the importance of motivation and integration, few studies have combined these with factors like program alignment, student type, and gender in a structural model. A sample of 284 students, aged 18–28, completed validated Greek versions of the AMS, PASS, and SACQ. Structural Equation Modeling (SEM) was used to assess both the direct and indirect effects on academic success. Key variables included gender, traditional vs. non-traditional student status, first-choice program enrollment, intrinsic and extrinsic motivation, academic and social integration, emotional adjustment, institutional attachment, and procrastination. Gender (female) was the strongest predictor of academic success (β = 0.819), affecting outcomes through intrinsic motivation, emotional adjustment, and procrastination. Academic integration (β = 0.424) and traditional student status (β = 0.300) also significantly predicted GPA. Social integration had an indirect effect through academic engagement. Procrastination (β = −0.228) and emotional maladjustment (β = −0.143) were major obstacles. While selecting a first-choice program affected institutional attachment, it did not directly impact academic performance. Conclusion: Academic success in Greek universities is influenced by a range of personal, motivational, and contextual factors. Improving integration, reducing procrastination, and fostering intrinsic motivation can boost academic outcomes. Interventions should consider gender and student pathways to be more effective. Full article

We aim to elucidate the sedimentary cyclicity of the Middle Jurassic Bath–Bajocian Formation in the northern S Oilfield of the West Siberian Basin, address the lack of high-resolution Milankovitch cycle research in this region, and support hydrocarbon exploration and development. This study employs the gamma-ray (GR) logging data of Well 79 as the primary dataset. Using Acycle V2.8 software implemented on the MATLAB 2020b platform, we conducted a systematic astrochronological analysis. After improving data quality through preprocessing procedures—including outlier removal, linear interpolation, and detrending—we identified significant cyclic signals via spectral analysis. These cyclicities were subsequently validated using multitaper spectral analysis (MTM), sliding spectral analysis, COCO correlation testing, and wavelet analysis. Band-pass filtering was then applied to facilitate sequence subdivision and sedimentation rate estimation. The results reveal well-preserved Milankovitch cyclicity in the Bath–Bajocian Formation of Well 79. The observed cycle thicknesses corresponding to the 405 kyr long eccentricity, 100 kyr short eccentricity, 41 kyr obliquity, and 20 kyr precession are 34.57 m, 8.26 m, 3.44 m, and 1.73 m, respectively, with thickness ratios deviating by less than 5% from the theoretical 20:5:2:1 proportion. Sliding spectral analysis indicates an alternating pattern of increasing and decreasing sedimentation rates. Based on the identified orbital signals, 12 fourth-order sequences and 52 fifth-order cycles were recognized. Sedimentation rates among the three wells range from 6.49 to 12.08 cm/kyr, averaging 9.29 cm/kyr, and exhibit a decreasing trend from west to east. These findings provide a robust astrostratigraphic framework for refined stratigraphic division and reservoir prediction in the study area. Full article

Background/Objectives: Advancements in digital dentistry have led to new approaches for soft tissue management aimed at improving impression accuracy. This pilot split-mouth study included a single 39-year-old male patient with 19 abutment teeth (114 measurement points). Sulcus width gain was measured at six standardized points per abutment tooth (mesio-buccal, centro-buccal, disto-buccal, disto-oral, centro-oral, mesio-oral) using Exocad software. Methods: Nineteen abutment teeth (114 measurement sections) from one patient were included in a randomized split-mouth design. Gingival displacement was performed either with a Nd: YAG laser or astringent retraction paste. Sulcus width gain was measured at six standardized points per abutment using Exocad software version 3.1 on superimposed STL files obtained by intraoral (IOS) and laboratory (LABSCAN) scanners. Statistical analysis was conducted in JASP (α = 0.05). Results: Both gingival displacement methods achieved sufficient sulcus widening above the clinical threshold of 0.20 mm. Mean gains ranged from 0.270 mm (LASER, IOS) to 0.378 mm (PASTE, LABSCAN). Intergroup comparisons revealed no statistically significant differences between the two gingival displacement methods (p > 0.05), whereas a significant difference was found between scanning modalities (p < 0.001), with higher values recorded for the laboratory scanner. The results should be interpreted cautiously due to the pilot design and limited sample size. Conclusions: Both techniques proved clinically effective for soft tissue displacement, with the laboratory scanner yielding higher sulcus width measurements. As a preliminary investigation, these findings should be interpreted cautiously due to the pilot nature of the study and the inclusion of a single participant. Full article

Woody biomass crops are increasingly considered a promising alternative to conventional agricultural systems due to their potential for sustained carbon sequestration under accelerating climate change. Optimizing management practices in such systems is therefore critical to enhance biomass production and carbon storage. In this study, we investigated how management influences biomass allocation in four poplar plots differing in planting density, variety, and harvest-rotation design during their 6th and 7th year of growth. Biomass stocks were quantified for crown, stem, coarse roots, and fine roots. Management effects were most pronounced in aboveground biomass, whereas belowground responses were less consistent. The highest aboveground biomass was observed in the high-density system within the first rotation (MxHD1), reaching 55.32 Mg ha⁻¹ in 2024 and 94.91 Mg ha⁻¹ in 2025. Belowground biomass ranged from 8.12 to 18.35 Mg ha⁻¹ across plots and years. The root:shoot ratio declined with increasing shoot basal diameter and was highest in the year following harvest. Based on these data, we developed general and management-specific allometric models to predict aboveground and belowground biomass from diameter at breast height. Including management factors improved prediction accuracy, supporting more precise quantification of biomass allocation under different cultivation strategies. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

In recent times, increasing attention has been devoted to the investigation of atmospheric processes through remote sensing in order to improve our understanding of climate dynamics and atmospheric physics. This requires accurate simulation of the spectra emitted by the Earth, from which atmospheric composition and thermodynamic conditions can be retrieved. The FORUM mission focuses on observations of the Earth’s outgoing radiation in the far-infrared spectral region, which has been only sparsely explored due to observational challenges, despite its significant contribution to the characterization of atmospheric processes. As part of the mission activities, dedicated simulations of the measurements expected from the FORUM instrument are required. Different models and codes can be employed for this purpose. Fast radiative transfer models, such as SIGMA-FORUM, efficiently simulate all-sky conditions, whereas detailed line-by-line models, such as KLIMA, have generally been limited to clear-sky applications. In this context, SIGMA-FORUM, an all-sky fast radiative transfer model operating in the 10–2760 cm⁻¹ spectral range and KLIMA, a FORTRAN-based line-by-line algorithm extensively validated under clear-sky conditions, are used to simulate FORUM radiances in both clear and cloudy atmospheres. This study extends the comparison between SIGMA-IASI/F2N and KLIMA to cloudy-sky scenarios by incorporating cloud optical properties into KLIMA using the same parametrization approach adopted in SIGMA-FORUM version 2.4. By combining complementary modeling approaches, this work enables KLIMA to simulate atmospheric radiances under all-sky conditions, thereby broadening its applicability. Full article

Phase-change materials (PCMs), such as paraffin wax, are widely used in latent heat storage (LHS) because they store substantial thermal energy at nearly constant temperature; however, their low thermal conductivity limits heat transfer and slows melting/solidification. In this work, two flat-plate solar collectors are coupled with a paraffin-based LHS unit for low-temperature solar heating, and the design is optimized by introducing improved fin-geometry combinations on both the heat transfer fluid (HTF) tube and shell side. The M-shaped fins combined with rectangular fins significantly enhanced convective heat transfer by generating localized vortices, while the extended surface area improved conduction within the solid PCM, facilitating efficient heat dissipation and accelerating the phase transition. The LHS unit without fins showed complete melting in 67 min. However, fin introduction remarkably mitigated charging duration to 44 min, 52.3% faster than bare tubes having no fins. The experimental melting process exhibited a 7 min delay by comparing experimental and numerical results, achieving complete melting in 51 and 44 min, respectively. Discharging was completed in 48 min. During PCM charging, sensible heating produces a rapid temperature rise with only a small energy increase, but once the PCM entered into the melting range (320–324 K), the energy changed more steeply. Adding fins boosts stored energy from 2.10 MJ to 3.25 MJ (54.8%) and exergy from 0.15 MJ to 0.27 MJ (80.0%), yet exergy remains far smaller than energy (92.9% lower without fins and 91.7% lower with fins), indicating fins enhance total heat storage more than recoverable work potential. Full article

This narrative critical review addresses class imbalance in medical imaging, particularly within the context of multi-sensor and multi-modal environments, poses a critical challenge to developing reliable AI diagnostic systems. The integration of heterogeneous data from sources like CT, MRI, and PET presents a unique opportunity to address data scarcity for rare conditions through fusion techniques. This review provides a structured analysis of strategies to tackle class imbalance, categorizing them into data-centric (e.g., advanced resampling like SMOTE-ENC for mixed data types, GAN-based synthesis) and model-centric (e.g., loss function engineering, transfer learning, and ensemble methods) approaches. Crucially, we highlight how multi-sensor feature fusion and decision-level fusion paradigms can inherently enrich representations for minority classes, offering a powerful frontier beyond single-modality learning. We evaluate each method’s merits, clinical viability, and compliance considerations (e.g., FDA). Finally, we identify emerging trends where imbalance-aware learning synergizes with multi-sensor fusion frameworks, federated learning, and explainable AI, charting a roadmap toward robust, equitable, and clinically deployable diagnostic tools. Our quantitative synthesis shows that data-centric strategies can improve minority class recall by 12–35% in datasets with imbalance ratios (majority:minority) ≥10:1, while model-centric strategies achieve an average AUC improvement of 0.08–0.21 in multi-sensor medical imaging tasks with sample sizes ranging from 50 to 50,000. Full article