Search Results (49,593)

Background/Objectives: Compliance with healthcare standards is an absolute must for every healthcare organization seeking accreditation. Several factors were found to affect compliance, and in Saudi Arabia, certain standards were observed for non-compliance. Therefore, this systematic review and meta-analysis seeks to identify the factors associated with non-compliance with healthcare accreditation in Saudi Arabia. Methods: This study adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The population, intervention, comparison, and outcome (PICO) model was used to refine the research question. The Peer Review of Electronic Search Strategies (Press) guidelines were used to improve the search strategy. The databases used for the search were PubMed, Web of Science, Scopus, and Google Scholar. The dates searched were from 1 January 2000 to 1 November 2024. We used a data extraction form for study characteristics and outcome data, which was piloted on five studies in this review. The risk of bias was assessed by using the Joanna Briggs Institute (JBI) tool and the Mixed Methods Appraisal Tool (MMAT). The analysis was carried out using the Fisher r-to-z transformed correlation coefficient as the outcome measure. A random-effects model was fitted to the data. Results: A total of ten studies were included for qualitative synthesis and five for quantitative synthesis. Several factors were observed for non-compliance, including insufficient training organization hurdles, a lack of implementation strategies, and the attitudes of healthcare providers. The estimated average correlation coefficient based on the random-effects model was 0.2568 (95% CI: −0.1190 to 0.6326). Conclusions: The dimension of quality in healthcare through pooled correlations from various studies highlighted a weak association among these dimensions. Full article

The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments. Full article

Modern radar systems commonly utilize monopulse angle estimation techniques for target angle estimation, with the phase comparison method being one of the most widely adopted approaches. While the phase comparison method achieves high estimation precision, it is highly susceptible to noise and exhibits a suboptimal performance under low Signal-to-Noise Ratio (SNR) conditions, leading to a high SNR threshold. Moreover, conventional monopulse angle estimation methods provide limited target information, as a single measurement cannot reveal the target’s motion direction. To address these shortcomings, a novel approach based on the phase comparison method is proposed in this study, with the variation in the wave path difference modeled as a first-order motion model. By accumulating the conjugate-multiplied signals over multiple time steps, the Wave Path Difference Rate of Change Ambiguity Function (WPD-ROC AF) is constructed. A fast algorithm employing the 2D Chirp-Z Transform (2D-CZT) is proposed, enabling multi-pulse angle estimation through the identification of frequency and phase values corresponding to spectral peaks. Simulation results validate that the proposed method outperforms traditional monopulse angle estimation techniques under low-SNR conditions and effectively suppresses static clutter interference. Furthermore, the sign of the WPD-ROC AF is shown to be correlated with the target’s motion direction, providing practical utility for determining the direction of movement in remote sensing scenarios. Full article

In this paper, we introduce an Adams-type predictor–corrector method based on a modified graded mesh for solving Caputo fractional differential equations. This method not only effectively handles the weak singularity near the initial point but also reduces errors associated with large intervals in traditional graded meshes. We prove the error estimates in detail for both $0<\alpha <1$ and $1<\alpha <2$ cases, where $\alpha$ is the order of the Caputo fractional derivative. Numerical experiments confirm the convergence of the proposed method and compare its performance with the traditional graded mesh approach. Full article

Abstract: Background: The long-term effects of hormone replacement therapy (HRT) on inflammatory bowel disease (IBD) remain unclear, necessitating further investigations of the association between HRT and the development of ulcerative colitis and Crohn’s disease in postmenopausal women. Methods: This retrospective cohort study utilized Taiwan’s National Health Insurance claims (2001–2018) to identify postmenopausal women aged ≥50 years with HRT use. A one-year washout period was applied before the index date to ensure new HRT users. To address the immortal time bias, follow-up for HRT users began at HRT initiation. The non-HRT group was selected by 1:1 propensity score matching. Cox proportional hazards models with adjustments for comorbidities and medications were used to estimate hazard ratios. Results: A total of 10,126 postmenopausal women (5063 per group) were included. During a mean follow-up of 11.1 years, the incidence rates of ulcerative colitis were 0.14 and 0.11 per 1000 person-years in the HRT and non-HRT groups, respectively. The adjusted hazard ratios were 1.33 (95% CI, 0.46–3.83; p = 0.600) for ulcerative colitis and 0.72 (95% CI, 0.45–1.16; p = 0.177) for Crohn’s disease. Conclusions: This longitudinal study suggests that HRT use is not significantly associated with the risk of IBD among postmenopausal women. These findings indicate that IBD risk may not need to be a primary concern when considering HRT in this population. Full article

Sensorimotor training on an unstable base of support is considered to lead to improvements in balance and coordination tasks. Here, we intend to lay the groundwork for generating cost-effective real-time kinematic feedback for coordination training on devices with an unstable base of support, such as Sensopros or slacklines, by establishing a model for estimating relevant tape kinematic data from angle measurements alone. To assess the accuracy of the model in a real-world setting, we record a convenience sample of three people performing ten exercises on the Sensopro Luna and compare the model predictions to motion capture data of the tape. The measured accuracy is reported for each target measure separately, namely the roll angle and $XYZ$-position of the tape segment directly below the foot. After the initial assessment of the model in its general form, we also propose how to adjust the model parameters based on preliminary measurements to adapt it to a specific setting and further improve its accuracy. The results show that the proposed method is viable for recording tape kinematic data in real-world settings, and may therefore serve as performance indicator directly or form the basis for estimating posture and other measures related to human motor control in a more intricate training feedback system. Full article

In order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square dataset expansion method is also proposed to rapidly expand the training dataset. The ST-SDM computes sub-pixel displacement values of different scales through three-level classification tasks, and solves the problem of positive and negative displacement with the rotation relative tag value method. The accuracy of the ST-SDM is verified by simulation experiments, and its robustness is verified by real rigid body experiments. The experimental results show that the ST-SDM model has higher accuracy and higher efficiency than the comparison algorithm. Full article

Wireless powered communication networks (WPCNs) provide a sustainable solution for energy-constrained IoT devices by enabling wireless energy transfer (WET) in the downlink and wireless information transmission (WIT) in the uplink. However, their performance is often limited by interference in uplink communication and inefficient resource allocation. To address these challenges, we propose an RSMA-aided WPCN framework, which optimizes rate-splitting factors, power allocation, and time division to enhance spectral efficiency and user fairness. To solve this non-convex joint optimization problem, we employ the simultaneous perturbation stochastic approximation (SPSA) algorithm, a gradient-free method that efficiently estimates optimal parameters with minimal function evaluations. Compared to conventional optimization techniques, SPSA provides a scalable and computationally efficient approach for real-time resource allocation in RSMA-aided WPCNs. Our simulation results demonstrate that the proposed RSMA-aided framework improves sum throughput by 12.5% and enhances fairness by 15–20% compared to conventional multiple-access schemes. These findings establish RSMA as a key enabler for next-generation WPCNs, offering a scalable, interference-resilient, and energy-efficient solution for future wireless networks. Full article

Emotion recognition in video aims to estimate human emotions using acoustic, visual, and linguistic information. This problem is considered multimodal and requires learning different modalities, such as visual, verbal, and vocal cues. Although previous studies have focused on developing sophisticated deep learning models, this work proposes a different approach using dynamic restrained adaptive loss inspired by multitask learning to understand multimodal inputs jointly. This training strategy allows predictions from one modality to enhance the accuracy of predictions from other modalities, mirroring the concept of multitask learning, where the results of one task can improve the performance of related tasks. Furthermore, this work introduces the extended multimodal bottleneck transformer, an efficient and effective mid-fusion method designed for problems involving more than two modalities to enhance the performance of emotion recognition systems. The proposed method significantly improves results compared to other end-to-end multimodal fusion techniques on three multimodal benchmarks—Interactive Emotional Dyadic Motion Capture (IEMOCAP), Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), and the Chinese Multimodal Sentiment Analysis dataset with independent unimodal annotations (CH-SIMS). Full article

In this paper, we develop kernel-based estimators for regression functions under a functional single-index model, applied to censored time series data. By capitalizing on the single-index structure, we reduce the dimensionality of the covariate-response relationship, thereby preserving the ability to capture intricate dependencies while maintaining a relatively parsimonious form. Specifically, our framework utilizes nonparametric kernel estimation within a quasi-association setting to characterize the underlying relationships. Under mild regularity conditions, we demonstrate that these estimators attain both strong uniform consistency and asymptotic normality. Through extensive simulation experiments, we confirm their robust finite-sample performance. Moreover, an empirical examination using intraday Nikkei stock index returns illustrates that the proposed method significantly outperforms traditional nonparametric regression approaches. Full article

This research analyzes the technical feasibility and implementation of an appropriate technology for the production of briquettes from Pinus spp. waste (sawdust and shavings) in a rural community in Michoacán, Mexico. The results indicate that local small-scale briquette production in the Pichátaro community has the potential to boost a local economy based on the manufacturing and marketing of densified solid biofuels. The design of the manual briquetting machine was developed through a participatory approach with community users. Structural simplicity and locally accessible maintenance were prioritized, the aspects that were addressed little in previous studies. The machine allows for the production of briquettes using a low-cost mixture composed of sawdust and Pinus spp. shavings, corn starch, and water. Based on local conditions and production needs, parameters such as reduced processing times and simplified manufacturing methods were identified as essential to establishing an efficient regional production and supply chain. Furthermore, the valorization of solid waste through the production of alternative biofuels contributes to the diversification of the energy matrix in rural residential sectors and small industries in communities in Mexico. The estimated cost of the machine is USD 75.44, and most of its components are easily replaceable, which favors its sustainability and prolonged use. This study demonstrates that the implementation of a low-pressure briquette system based on appropriate rural technologies represents a viable strategy for the use of wood waste and the promotion of sustainable energy solutions in rural communities. Full article

As one of the key components in rotating machinery, rolling bearings have a crucial impact on the safety and efficiency of production. Acoustic signal is a commonly used method in the field of mechanical fault diagnosis, but an overlapping phenomenon occurs very easily, which affects the diagnostic accuracy. Therefore, effective blind source separation and noise reduction of the acoustic signals generated between different devices is the key to bearing fault diagnosis using acoustic signals. To this end, this paper proposes a blind source separation method based on an AFSA-FastICA (Artificial Fish Swarm Algorithm, AFSA). Firstly, the foraging and clustering characteristics of the AFSA algorithm are utilized to perform global optimization on the aliasing matrix W, and then inverse transformation is performed on the global optimal solution W, to obtain a preliminary estimate of the source signal. Secondly, the estimated source signal is subjected to CEEMD noise reduction, and after obtaining the modal components of each order, the number of interrelationships is used as a constraint on the modal components, and signal reconstruction is performed. Finally, the signal is subjected to frequency domain feature extraction and bearing fault diagnosis. The experimental results indicate that,  the new method successfully captures three fault characteristic frequencies (1f_i, 2f_i, and 3f_i), with their energy distribution concentrated in the range of 78.9 Hz to 228.7 Hz, indicative of inner race faults. Similarly, when comparingthe diffent results with each other, the denoised source signal spectrum successfully captures the frequencies 1f_o, 2f_o, and 3f_o and their sideband components, which are characteristic of outer race faults. The sideband components generated in the above spectra are preliminarily judged to be caused by impacts between the fault location and nearby components, resulting in modulated frequency bands where the modulation frequency corresponds to the rotational frequency and its harmonics. Experiments show that the method can effectively diagnose the bearing faults. Full article

Background: Pancreatic ductal adenocarcinoma is an aggressive neoplasm with a complex carcinogenesis process that must be understood through the interactions between tumor cells and tumor microenvironment cells. Methods: This study was retrospective with a chronological extension period of 16 years and included 56 cases of pancreatic ductal adenocarcinoma. This study identified, quantified, and correlated the cells of the tumor immune microenvironment in pancreatic ductal adenocarcinoma with major prognostic factors as well as overall survival, using an extensive panel of immunohistochemical markers. Results: Three tumor immunotypes were identified: subtype A (hot immunotype), subtype B (intermediate immunotype), and subtype C (cold immunotype). Patients with immunotype C exhibit considerably higher rates of both pancreatic fistulas and acute pancreatitis. Immunotypes B and C significantly increased the risk of this complication by factors of 3.68 (p = 0.002) and 3.94 (p = 0.001), respectively. The estimated probabilities of fistula formation for each immunotype are as follows: 2.5% for immunotype A, 25% for immunotype B, and 28% for immunotype C. There was a statistically significant difference in median survival times according to tumor immunotype (p < 0.001). Specifically, patients with immunotype C tumors had a median survival time of only 120.5 days, compared to 553.5 days for those with immunotype A and 331.5 for immunotype B tumors. Conclusions: The identification of the immunotype of pancreatic ductal adenocarcinoma can be a predictive factor for the occurrence of complications such as pancreatic fistula as well as for overall survival. Full article

Introduction: Ovarian cancer is a significant contributor to gynecological cancer-related mortality, necessitating innovative treatment strategies. This systematic review and meta-analysis aimed to assess the efficacy and safety of combining PARP inhibitors with anti-angiogenic agents (AAAs) in the treatment of ovarian cancer. Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and was registered on PROSPERO (CRD42022319461). A systematic search of three electronic databases, including MEDLINE (via PubMed), EMBASE, and Cochrane Library was conducted to identify relevant randomized controlled trials (RCT) that evaluated the efficacy and safety of the combination therapy. Subgroup analyses were based on BRCA mutation status. Meta-analysis was conducted to estimate pooled hazard ratios (HR) and risk ratios (RR) for progression-free survival (PFS) and adverse events, respectively. The combination therapy was compared to PARP inhibitors alone and to chemotherapy. Heterogeneity was assessed using Higgins and Thompson’s I² statistic where applicable. Results: Seven RCTs involving 2397 patients were included. Combination therapy did not show a statistically significant improvement in PFS compared to PARP inhibitor monotherapy in the general population (HR 0.63, CI 0.37–1.06), or in BRCA-mutated (HR 0.70, CI 0.30–1.63) and BRCA wild-type subgroups (HR 0.39, CI 0.14–1.07). When compared to chemotherapy, combination therapy produced no significant PFS benefit in recurrent ovarian cancer (HR 0.83, CI 0.42–1.63) in the total population. Safety analysis revealed that hypertension and diarrhea were significantly more frequent in combination therapy compared with PARP inhibitors alone (RR 6.80, CI 2.87–16.06 and RR 10.04, CI 2.25–44.75) or chemotherapy alone (RR 13.80, CI 3.43–55.57 and RR 6.57, CI 2.84–15.24). Conclusions: The combination of PARP inhibitors and AAAs did not demonstrate a statistically significant benefit in PFS compared to PARP inhibitors or chemotherapy alone in recurrent ovarian cancer. While the combination therapy was generally well tolerated, hypertension and diarrhea occurred significantly. These findings suggest that combination therapy may not provide a clear survival advantage in the recurrent setting. Further high-quality, biomarker-driven clinical trials are needed to refine patient selection, optimize toxicity management, and determine the potential role of combination therapy in ovarian cancer treatment. Full article

Factors related to catering distribution are typically characterized by local changes, but few studies have quantitatively investigated the inherent spatial nonstationarity correlations. In this study, a multiscale geographically weighted regression (MGWR) model was adopted to locally examine the impact of various factors on catering distribution, which were obtained through a novel method incorporating GeoDetector analysis and exploratory factor analysis (EFA) using point of interest (POI) data. GeoDetector analysis was used to identify the effective variables that truly contribute to catering distribution, and EFA was adopted to extract interpretable latent factors based on the underlying structure of the effective variables and thus eliminate multicollinearity. In our case study in Nanjing, China, four primary factors, namely commuting activities, shopping activities, tourism activities, and gathering activities, were retained from eight categories of POIs with respect to catering distribution. The results suggested that GeoDetector working in tandem with EFA could improve the representativeness of factors and infer POI configuration patterns. The MGWR model explained the most variations (adj. R²: 0.903) with the lowest AICc compared to the OLS regression model and the geographically weighted regression (GWR) model. Mapping MGWR parameter estimates revealed the spatial variability of relationships between various factors and catering distribution. The findings provide useful insights for guiding catering development and optimizing urban functional spaces. Full article