Search Results (4,163)

Background/Objectives: Permanent cardiac pacemakers (PPMs) are small electronic implanted devices that regulate cardiac rhythm. Measurement of quality of life (QoL) serves as a powerful tool for gaining in-depth insights into pacing therapy and ultimately guiding patient-centered management strategies. The aim of the present study was to evaluate factors affecting QoL among PPM patients by applying the two generic questionnaires: SF-36 and EQ-5D-5L. Materials and Methods: A total of 120 patients with PPM were enrolled. QoL data were collected through interviews using the 36-Item Short Form Health Survey (SF-36) and the Euro QoL 5-Dimensions 5-Levels Health Questionnaire (EQ-5D-5L). Patients’ characteristics were also recorded. Results: The majority of participants were male (54.2%), retired (83.3%) residents in urban areas (75.5%), had a DDD pacemaker (82.5%), had rate response programmed on (77.5%), and had comorbidities (83.3%). Regarding QoL measured by SF-36, the Physical Component Summary Score (PCS) was significantly associated with programming rate response in their pacemaker (p = 0.046), comorbidities (p = 0.047), and the NYHA functional class (p = 0.047). The Mental Component Summary Score (MCS) was significantly associated with sex (p = 0.034), place of residence (p = 0.003), NYHA functional class (p = 0.001), and patients’ level of information about the device (p = 0.039). Patients’ QoL, as measured by the EQ-5D-5L, was significantly associated with sex (p = 0.001), age (p = 0.019), occupation (p = 0.040), pacing mode (p = 0.034), comorbidities (p = 0.019), NYHA functional class (p = 0.047), and level of information about the device (p = 0.005). Conclusions: NYHA functional class, comorbidities, and level of information as reported by patients were the factors associated with QoL, as shown by the two scales. All three factors guide a personalized care plan since NYHA class shows the burden of disease, comorbidities add to the complexity, and patient information determines the effectiveness of management. Full article

In recent years, oil and gas exploration in the Lower Cambrian of the central–northern Sichuan Basin, China, has demonstrated enormous resource potential. As a potential interval of high-quality hydrocarbon source rocks, the Canglangpu Formation of the Lower Cambrian remains underdeveloped in exploration and lacks in-depth research. Affected by tectonics, sedimentary environment, and diagenesis, the genetic mechanisms and genetic models of carbonate reservoirs in the Canglangpu Formation within the study area need further clarification. This study utilizes petrological characteristics of dolomite and geochemical data to clarify diagenetic fluids of different reservoir rocks and identifies the main controlling factors and development models of the reservoirs. The results show that the dolomites in Member 1 of the Canglangpu Formation (Cang-1 Member) in central–northern Sichuan are mainly classified into three types: silty–fine crystalline dolomite (D1), granular dolomite (D2), and residual-texture dolomite (D3). The reservoir spaces are dominated by intercrystalline pores, intergranular pores, and structural fractures. The porosity of the Cang-1 Member in the area is relatively low, with an average porosity of 5% or lower. The reservoir porosity average is 3.63%, belonging to low-porosity reservoirs. The permeability average is 2.94 × 10⁻³ mD. Analysis of different geochemical indicators indicates that the diagenetic fluids of the three dolomite types are mainly syndepositional seawater. D1 is formed by penecontemporaneous dolomitization, while both D2 and D3 are formed during the shallow-to-middle burial stage. The main controlling factors of dolomite reservoirs include sedimentary facies, diagenesis, and tectonic movement. This study clarifies the genesis and development model of dolomite reservoirs in the Cang-1 Member, aiming to provide reliable and valuable references for the exploration of dolomite reservoirs in the Canglangpu Formation of the Sichuan Basin. Full article

To solve common defects such as warpage deformation, interface debonding, and uneven filling during the two-color injection molding of medical goggles while meeting their multi-performance requirements, including high light transmittance, impact resistance, chemical corrosion resistance, and structural stability, this study conducts research on the process optimization of two-color injection molding. Firstly, based on the principle of material compatibility and Moldflow simulation, a suitable material combination was selected: the first-shot frame adopts Apec 1745 PC material, and the second-shot lens uses Makrolon 2858 PC material, which effectively avoids the risk of interface non-fusion. Subsequently, a high-precision 3D simulation model was established using Moldflow software, and the injection sequence of “frame first, lens second” was optimized and determined. A gating system with double-gate (for the frame) and single-gate side feeding (for the lens), as well as a cooling system with an 8 mm diameter, was designed, and all key indicators of mesh quality meet the simulation requirements. Taking the mold and melt temperatures, holding pressures, and holding times of the two shots as design variables and warpage deformation as the optimization objective, sample data were obtained through an L₃₂ (7⁴) orthogonal test. A BP neural network was constructed to describe the nonlinear relationship between parameters and quality, and the Sparrow Search Algorithm (SSA) was combined to optimize the weights and thresholds of the network, forming a BP-SSA intelligent optimization model. The results show that the mean absolute percentage error (MAPE) of the proposed model is only 2.28%, which is significantly better than that of the single BP neural network (14.36%). The optimal process parameters obtained by optimization are a mold temperature of 130 °C, first-shot melt temperature of 311 °C, second-shot melt temperature of 310 °C, first-shot holding pressure of 83 MPa, second-shot holding pressure of 70 MPa, first-shot holding time of 14 s, and second-shot holding time of 8 s. Simulation and mold test verification indicate that after optimization, the warpage deformation of the goggles is reduced to 0.8956 mm (simulation) and 0.944 mm (measured), with a relative error of only 5.4%, which is 67.9% lower than the initial simulation result. The integrated method of “material selection—CAE simulation—orthogonal test—BP-SSA intelligent optimization” proposed in this study provides technical support for the high-precision manufacturing of thin-walled transparent multi-material medical products. Full article

The taste and quality of kiwifruit are key factors affecting consumers’ purchase intention and satisfaction. As an important indicator for measuring kiwifruit quality, sugar content is crucial for quality grading. Accurate and rapid kiwifruit grading based on sugar content is of great significance for ensuring product quality and enhancing market competitiveness. Traditional grading methods mostly adopt destructive sampling, which are cumbersome, low in efficiency, and difficult to meet the needs of modern large-scale production. Therefore, this paper proposes a kiwifruit classification method based on the Hierarchical 3D Convolution and Attention Mechanism Network (H3DAMNet). This method performs 3D convolution operations on multiple dimensions of hyperspectral data blocks simultaneously to deeply extract spatial–spectral features. It assigns weights to each channel through the channel attention mechanism to weaken attention to irrelevant information, and introduces the bottleneck self-attention mechanism to capture the positional dependence in input features, thereby effectively modeling global information. Referring to industry standards, kiwifruit are classified into three grades based on sugar content: first-grade (≥14.5 °Brix), second-grade (13.5–14.5 °Brix), and third-grade (≤13.5 °Brix). On the test set containing 280 kiwifruit samples, the overall accuracy (OA) of this method reaches 97.5% and the average accuracy (AA) is 97.3%, successfully realizing the accurate classification of kiwifruit according to sugar content and setting a reference example for the classification of other similar fruits. Full article

Intelligent vehicles represent a frontier in technological innovation. Effectively identifying their key core technologies and primary competitors is a crucial prerequisite for overcoming industrial technological bottlenecks, playing a pivotal role in promoting sustainable industrial development and enhancing global market competitiveness. This study is based on 46,373 authorized invention patents in the field of intelligent vehicles from 1950 to 2024 and based on four core characteristics of key core technologies: technological centrality, technological value, economic value, and competitive monopoly. Combining the entropy weight method and gray correlation analysis method, it effectively identifies 15 key core technologies in the field of intelligent vehicles, including G05D1, B60W30, G08G1, etc. These technologies cover four core domains: autonomous driving and vehicle control, intelligent transportation and vehicle–road coordination, onboard computing and data processing, and powertrain system integration and optimization. Building on this foundation, the study analyzes the technological competitive landscape from both national and corporate perspectives. The results show that the United States and Japan, with their profound technological accumulation, demonstrate strong competitive strength. China leads globally with 25.56% of worldwide patents, exhibiting rapid growth in R&D scale. However, the technological influence of key core technology patents held by major Chinese enterprises still lags significantly behind that of the United States and Japan, indicating room for improvement in R&D quality. By precisely identifying core R&D directions for intelligent vehicles, this study provides strategic guidance and practical references for optimizing green innovation resource allocation within the industry. It aims to overcome key technological bottlenecks in low-carbon intelligent vehicles, thereby achieving breakthroughs in key core technologies and enabling high-quality, sustainable industrial development. Full article

Drought stress severely constrains cotton yield and fiber quality, but conventional evaluation methods are inefficient and time-consuming. To address this, we developed a high-throughput, non-destructive phenotyping framework by integrating UAV-based multispectral remote sensing with machine learning, using 225 upland cotton (Gossypium hirsutum L.) accessions. The accessions were subjected to well-watered (CK) and drought stress (DS) treatments at the flowering and boll-setting stage. Canopy multispectral imagery (Green/Red/Red_edge/Near-infrared bands) was acquired via DJI Mavic 3 Multispectral UAV, and 16 vegetation indices (VIs) were derived. Concurrently, 15 agronomic and fiber quality traits were measured to calculate drought resistance coefficients (DRCs), which were used for principal component analysis (PCA) and comprehensive drought tolerance index (D) construction. Hierarchical clustering categorized the accessions into 6 drought tolerance grades (Groups I–VI). Variable importance analysis identified GNDVI, NGRVI, and NDRE as the most drought-sensitive VIs (%IncMSE > 11). Among four regression models (LR, KNN, LGBM, XGBoost), XGBoost achieved the best performance for D prediction (test set: R² = 0.785, RMSE = 0.032, MAE = 0.024). This study demonstrates that UAV multispectral data coupled with XGBoost enables accurate, efficient drought tolerance assessment, providing a robust tool for high-throughput germplasm screening and smart agricultural management. Full article

The Italian peninsula is, as shown by satellite and ground-based measurements, a pollution hotspot. In recent years, ground-based MAX-DOAS commercial systems have been installed in the Po Valley and the area surrounding Rome to monitor NO${}_2$ tropospheric column densities and validate coincident satellite (e.g., TROPOMI) products. Three of the instruments are located in the Po Valley at San Pietro Capofiume (Bologna), Bologna city, and Mount Cimone (Modena), and one is located in Tor Vergata (Rome). The chosen system is the SkySpec-2D from Airyx. All the recorded spectra are saved in the FRM4DOAS format and processed with QDOAS software to obtain slant column densities (SCDs) of NO${}_2$, O${}_4$, and other trace gases. The MAX-DOAS SCD sequences are then analysed with the DEAP code to retrieve tropospheric profiles of NO${}_2$ and aerosol extinction, while zenith-sky SCDs are used to retrieve NO${}_2$ total columns. A dedicated campaign, involving the network instruments, has been conducted in the Po Valley to compare the performance of the individual instruments in the network with respect to the one that participated in the CINDI-3 campaign (Cabauw, the Netherlands). The results of the intercomparison campaign indicated that all instruments showed comparable performance. As an example of obtainable products, one year (from September 2024 to August 2025) of NO${}_2$ tropospheric columns, as well as their comparison with TROPOMI measurements, is presented, highlighting the potential of this network for both air quality studies and satellite validation. Due to Italy’s location in the highly complex Mediterranean hotspot region, these data represent an important contribution to satellite validation efforts, particularly in view of upcoming missions such as Copernicus Sentinel-4, Sentinel-5, and the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) constellation. We found a negative TROPOMI bias relative to SkySpec-2D for NO${}_2$ tropospheric columns ranging from −13% in San Pietro Capofiume, to −25% in Bologna and −44% in Rome Tor Vergata. The comparison between NO${}_2$ total columns from TROPOMI and SkySpec-2D at Mount Cimone shows generally good agreement, with TROPOMI being 15% higher. Full article

Background/Objectives: Low back pain (LBP) is a leading cause of disability and work absenteeism worldwide. Lumbar facet joint degeneration is a common source of chronic LBP, and when conservative treatment fails, interventional procedures may be indicated. Cryodenervation is a minimally invasive option that remains less extensively studied. This study aims to evaluate clinical outcomes, cost–utility, and return-to-work rates following lumbar facet joint cryodenervation. Methods: A retrospective study included 42 professionally active patients treated with lumbar facet joint cryoablation between 2020 and 2022 at a tertiary neurosurgical center. All patients had facet-mediated LBP confirmed by a positive diagnostic medial branch block. Pain (VAS), disability (ODI), and work status were assessed before and after treatment. ODI scores were converted to SF-6D utilities to estimate quality-adjusted life years (QALYs). Cost data were obtained from institutional records. Results: Mean ODI improved from 48.5 ± 12.8 to 36.6 ± 17.8, and mean VAS from 7.0 ± 1.7 to 3.8 ± 2.0. Mean SF-6D increased from 0.53 to 0.59, corresponding to a gain of 0.0103 QALYs over four months (annualized 0.0309). The mean procedure cost was 1905 PLN, resulting in approximately 185,000 PLN per QALY, which is within the national cost-effectiveness threshold. Overall, 58.5% of patients returned to work, with the highest rate in those aged 30–39 years (83.3%). Conclusions: Lumbar facet cryoablation provides meaningful pain relief and functional improvement at a favorable cost-effectiveness profile. Younger patients show higher return-to-work rates. Larger prospective studies are required to confirm these findings. Full article

Background and Objectives: Paraneoplastic hypercalcemia represents a rare but clinically significant complication of penile squamous cell carcinoma (PSCC); however, limited information is available for this condition. Therefore, this systematically conducted narrative review aimed to comprehensively evaluate the pathophysiological mechanisms, clinical presentation, therapeutic strategies and prognostic outcomes of tumor-induced hypercalcemia in PSCC. Methods: A comprehensive literature search was conducted across PubMed/MEDLINE and Scopus databases from their inception to December 2024. Cases were included if they documented histopathologically confirmed PSCC with biochemically verified hypercalcemia and objective evidence of paraneoplastic etiology. Data extraction included tumor characteristics, severity of hypercalcemia, mechanistic classification, therapeutic interventions and survival outcomes. The search methodology followed PRISMA 2020 guidelines adapted for narrative synthesis. Given the absence of comparative studies for this rare condition, all study types were eligible for inclusion, resulting in an evidence base that consisted exclusively of case reports and case series. Results: Twelve published cases spanning six decades (1965–2024) met the inclusion criteria. The median age at presentation was 56 years, with 91.6% of patients presenting with advanced disease. Severe hypercalcemia (≥14 mg/dL) occurred in 66.7% of cases, with a median calcium level of 15.45 mg/dL. Two established pathophysiological mechanisms were identified: PTHrP-mediated humoral hypercalcemia and bone metastasis-associated hypercalcemia. By contrast, three cases with unmeasured PTHrP levels had an undetermined mechanism. Despite biochemical correction, median overall survival was 9 weeks following diagnosis of hypercalcemia. Conclusions: Paraneoplastic hypercalcemia in PSCC represents a rare metabolic emergency. While aggressive management can achieve biochemical correction, the occurrence of hypercalcemia uniformly indicates advanced tumor biology with limited survival benefit. Early recognition and prompt multidisciplinary intervention remain essential for symptomatic relief and preserving quality of life. Reporting future cases and collaborating with international registries will be necessary to improve understanding of this rare paraneoplastic entity. Full article

In this work we investigate the behavior of a toroidal photoacoustic resonator to provide compact, physics-guided analytical relationships that link its geometry to two key parameters: resonance frequency and quality factor. Finite-element data are combined with reduced-order analytical models to refine a corrected toroidal-resonance frequency model that accounts for effective propagation length and thermo-viscous effects. For the quality factor, a simple law motivated by a boundary-layer dissipation model is proposed. Derived models are validated by experimental tests performed using three 3D printed toroidal resonators in different sizes. Experimental results confirm the prediction both for the first and third resonance frequencies with an average relative error below 1%, outperforming cylindrical and uncorrected baseline models available in the literature. The results also confirm the predicted trend of the quality factor with respect to the torus’s minor radius, highlighting a direct relationship between the cross-sectional area and acoustic losses, which governs the balance between stored acoustic energy and thermo-viscous dissipation. Overall, the framework provides quick, interpretable design rules that reduce dependence on extensive finite-element method simulation campaigns for first-pass estimation of resonant behavior during the early design phase and guiding the optimization of high-performance PAS devices while preserving accuracy. Full article

Soil salinization represents a critical constraint to sustainable agriculture in arid and semi-arid regions, where salinity threatens soil productivity, water quality, and ecosystem resilience. Soil salinity pattern prediction is complicated by tightly coupled landscape hydro-climatic processes, wherein the central Sabkha acts as a persistent salt sink, episodic inundation and intense evaporation concentrate dissolved salts, and a shallow saline groundwater table interacts with the semi-arid climate to drive surface salinization. Conventional mapping is laborious and lacks the precision needed to capture the spatio-temporal dynamics of soil salinity across landscapes. This study developed an integrated framework uniting multi-temporal Landsat imagery (2000–2025), hypsometric data, climatic indicators, and in situ soil electrical conductivity (ECe) measurements to model soil salinity dynamics using machine learning (ML), over the Sehb El Masjoune (SEM) semi-arid region, Morocco. A total of 233 soil samples were collected in the investigated area in 2022, 2023, 2024, and 2025 to assess the spatial variability to calibrate and validate modeling findings. To this end, three predictive algorithms, i.e., Gradient-Boosted Trees (GBT), Support Vector Regression (SVR), and Random Forest (RF) were assessed. Our findings showed that SVR achieved the highest predictive capability (R² = 0.76; RMSE = 32.91 dS/m), whereas SVR-based salinity maps revealed a distinct spatial organization of salinization processes, characterized by extremely saline soils (≥64 dS/m) concentrated in the central study area (i.e., SEM center) and a progressive decline toward adjacent agricultural lands (0–8 dS/m). Our results demonstrated that from 2000 to 2025, moderately to highly saline areas (≥16 dS/m) expanded by nearly 10%, driven by recurrent droughts and inefficient drainage. Hydroclimatic analysis confirmed that dry years (SPI: Standardized Precipitation Index ≤ −0.5) promoted net salinity build-up through the expansion and persistence of moderate-to-high salinity classes (≥16 dS/m), whereas wet years (SPI ≥ +0.5) favored temporary leaching and partial recovery, mainly within the low-to-moderate range. This integrative remote sensing–ML approach provides a robust and scalable framework for operational soil salinity monitoring, offering valuable insights for sustainable land-use planning in similar Sabkha’s data-scarce agroecosystems. Full article

Assessing human embryo quality is a critical step in in vitro fertilization (IVF), yet traditional manual grading remains subjective and physically limited by the shallow depth-of-field in conventional microscopy. This study develops a novel “soft optical sensor” architecture that transforms standard optical microscopy into an automated, high-precision instrument for embryo quality assessment. The proposed system integrates two key computational innovations: (1) a multi-focal image fusion module that reconstructs lost morphological details from Z-stack focal planes, effectively creating a 3D-aware representation from 2D inputs; and (2) a retrieval-augmented generation (RAG) framework coupled with a Swin Transformer to provide both high-accuracy classification and explainable clinical rationales. Validated on a large-scale clinical dataset of 102,308 images (prior to augmentation), the system achieves a diagnostic accuracy of 94.11%. This performance surpasses standard single-plane analysis methods by 9.43%, demonstrating the critical importance of fusing multi-focal data. Furthermore, the RAG module successfully grounds model predictions in standard ESHRE consensus guidelines, generating natural language explanations. The results demonstrate that this soft sensor approach significantly reduces inter-observer variability and offers a robust tool for standardized morphological assessment, though prospective validation against live birth outcomes remains essential for clinical adoption. Full article

A notable drawback of Double Random Phase Encryption (DRPE), a prominent optical cryptography technique, is its low data efficiency. This is because both the encrypted image and the decryption key are represented as complex numbers. To address this issue, a conventional method was proposed that encrypts two images simultaneously by treating the first image as amplitude and the second image as phase. Nevertheless, processes such as integral imaging, which extract 3D object information from images, utilize vast amounts of imagery, necessitating further enhancements in data efficiency. The objective of this research is to enhance DRPE and improve data efficiency by increasing the number of images that can be processed simultaneously. This paper incorporates the information from a third image into the random phase mask used in conventional methods, enabling the simultaneous processing of three images. It also proposes a method to synthesize two images by extracting their high-order bits and combining them. The combination of this image composition method as a preprocessing step with the proposed DRPE method enables the simultaneous processing of six images. As a result, the proposed method achieves a data efficiency approximately six times that of the basic DRPE and approximately three times that of conventional methods. The quality of the decrypted images was evaluated using PSNR and SSIM, while the encryption strength was assessed in terms of key space, key sensitivity, entropy, and correlation coefficients. Full article

Fractal image compression achieves high compression ratios but suffers from prohibitively long encoding times and limited reconstruction quality. To address these limitations, we propose fast fractal image compression using non-uniform partition (FFICNUP), a hybrid algorithm that adaptively partitions range blocks (R-blocks) and domain blocks (D-blocks) based on local texture and edge content. Smaller R-blocks are employed in texture-rich regions or edge-dense areas to preserve fine details, while larger R-blocks are adopted in smooth regions to accelerate encoding. By integrating a Task-Serial Workflow with Data-Parallel Vectorization and adaptive block partitioning, FFICNUP substantially accelerates both encoding and decoding processes while enhancing reconstruction fidelity and compression ratios. Experimental results demonstrate that the proposed FFICNUP method significantly outperforms conventional fractal image compression (FIC) approaches. By leveraging vectorized parallelization, the proposed FFICNUP achieves state-of-the-art (SOTA) decoding speed with a 14× acceleration, reduces encoding latency by three orders of magnitude, improves the Peak Signal-to-Noise Ratio (PSNR) by up to 5.19 dB, and attains a compression ratio 2.21 times higher than that of conventional FIC. Validated across both CPU and GPU platforms, FFICNUP dynamically balances encoding speed, reconstruction quality, compression ratio, and latency across varying image sizes, demonstrating its suitability for practical engineering applications. Full article

The study proposes a methodological integration of machine vision and image processing based on color-based object detection. The primary goal of the study is to use the color vision method to simplify the process of transforming real objects into 3D digital twins for application in Sustainable Agriculture 4.0. The experiment solves several related problems: (1) Color analysis and methodology for quantifying the color representation of a 3D model. Representation quality was determined using colorimetric methods with sRGB and L*a*b* models in relation to the D65 standard. Colors with accurate color values on the object surface and in the 3D model were identified. (2) The process of capturing and creating digital twins using the SfM method is time-consuming and requires manual work. The study solves this problem by partially automating the entire process. The proposed DSLR system with an automated method for capturing, storing, and sorting data significantly accelerates the entire process. (3) To create a digital color scale, it is necessary to define the color values of 3D digital twins. A color segmentation procedure based on points on the surface of a 3D model is proposed. These color values form a basic color form corresponding to the color value changes in the coloring process of a real object. The proposed procedure uniquely integrates methodologies and has potential for use in Sustainable Agriculture 4.0. The proposed colorimetric method quantifies representation quality and could be deployed in other 3D model digitization and automation processes, especially in image processing and computer vision. Full article