Search Results (317)

Unhealthy dietary patterns are a major modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD). International guidelines recommend reducing saturated fatty acid intake while increasing polyunsaturated and monounsaturated fatty acids (MUFAs) to mitigate cardiovascular risk. However, evidence regarding MUFAs and risk of ASCVD remains conflicting, with recent studies raising concern about a potential higher risk associated with MUFA intake. The aim of this narrative review is to provide an overview of current knowledge and gaps in the literature regarding MUFAs and the risk of ASCVD with a focus on intake, individual types, and content in adipose tissue as a biomarker of endogenous exposure. Main findings reveal that most studies have inappropriately combined all MUFAs together, despite individual MUFA types having different biological effects and showing varying correlations between dietary intake and adipose tissue content. Adipose tissue composition may serve as a biomarker of long-term MUFA exposure, reflecting cumulative intake over one to two years while minimizing biases inherent in dietary assessments. However, tissue levels reflect both dietary intake and endogenous synthesis, complicating interpretation. Importantly, the source of MUFAs appears critical, with plant-derived MUFAs potentially offering advantages over animal-derived sources. In conclusion, we suggest that future research should focus on individual MUFA types rather than treating them as a homogeneous group, investigate their specific dietary sources and associations with ASCVD risk, and use adipose tissue biomarkers to improve exposure assessment and clarify causal relationships while considering overall dietary patterns. Full article

The construction and maintenance of an ecological security pattern (ESP) are important for promoting the regional development of ecological civilizations, realizing sustainable and healthy development, and creating a harmonious and beautiful space for human beings and nature to thrive. Traditional construction methods have the limitations of a single dimension, a single method, and excessive human subjective intervention for source and corridor identification, without considering the multidimensional quality of the sources and the structural connectivity and resilience optimization of the corridors. Therefore, an ecological civilization demonstration area (Jiangxi Province) was used as the study area, a new research method for ESP was proposed, and an empirical study was conducted. To evaluate ecosystem service (ES) importance–disturbance–risk and extract sustainability sources through the deep embedded clustering–self-organizing map (DEC–SOM) deep unsupervised learning clustering algorithm, ecological networks (ENs) were constructed by applying the minimum cumulative resistance (MCR) gravity model and circuit theory. The ENs were then optimized to improve performance by combining the comparative advantages of the two approaches in terms of structural connectivity and resilience. A comparative analysis of EN performance was constructed among different functional control zones, and the ESP was constructed to include 42 ecological sources, 134 corridors, 210 restoration nodes, and 280 protection nodes. An ESP of ‘1 nucleus, 3 belts, 6 zones, and multiple corridors’ was constructed, and the key restoration components and protection functions were clarified. This study offers a valuable reference for ecological management, protection, and restoration and provides insights into the promotion of harmonious symbiosis between human beings and nature and sustainable regional development. Full article

Melissa officinalis L. (Lamiaceae) is a perennial plant species widely used in the pharmaceutical and food industries, particularly valued for its sedative properties. This study investigates the impact of synthetic mulch film and planting density as two experimental factors on agronomic performance, raw material quality, and economic efficiency in lemon balm production. The experiment was conducted at three locations in Serbia (L1: Bačko Novo Selo, L2: Bavanište, L3: Vilandrica) from 2022 to 2024, using two planting densities on synthetic mulch film (F1: 8.3 plants m⁻²; F2: 11.4 plants m⁻²) and a control treatment without mulch (C). The synthetic mulch film used was a synthetic black polypropylene film (Agritela Black, 90 g/m²), uniformly applied in strips across the cultivation area, covering approximately 78% of the soil surface. The results showed consistent increases in morphological parameters and yield across the years. Plant height in F1 and F2 treatments ranged from 65 to 75 cm, while in the control it reached up to 50 cm (2022–2024). Fresh biomass yield varied from 13.4 g per plant (C) to 378.08 g per plant (F2), and dry biomass yield from 60.3 g (C) to 125.4 g (F2). The highest essential oil content was observed in F2 (1.2% in 2022), while the control remained at 0.8%. The F2 treatment achieved complete weed suppression throughout the experiment without the use of herbicides, demonstrating both agronomic and ecological advantages. Economic evaluation revealed that F2 generated the highest cumulative profit (€142,164.5) compared to the control (€65,555.3). Despite higher initial investment, F2 had the most favorable cost–benefit ratio in the long term. This study highlights the crucial influence of mulching and planting density on optimizing lemon balm production across diverse climatic and soil conditions, while also underscoring the importance of sustainable, non-chemical weed management strategies in lemon balm cultivation. Full article

Prior research often portrays Corporate Social Responsibility (CSR) as a coercive institutional force compelling firms to passively conform for legitimacy. More recent studies, however, suggest firms actively pursue CSR to gain sustainable competitive advantages. Yet, how and when CSR buffers firms against adverse shocks of crises remains insufficiently understood. This study addresses this gap by using multiple regression analysis to examine the buffering effects of CSR investments during the COVID-19 crisis, which severely disrupted capital markets and firm valuation. Drawing on signaling theory and CSR literature, we analyze the stock market performance of China’s A-share listed firms using a sample of 2577 observations as of the end of 2019. Results indicate that firms with higher CSR investments experienced significantly greater cumulative abnormal returns during the pandemic. Moreover, the buffering effect is amplified among firms with higher debt burdens, greater financing constraints, and those operating in regions with stronger social trust and more severe COVID-19 impact. These findings are robust across multiple robustness checks. This study highlights the strategic value of CSR as a resilience mechanism during crises and supports a more proactive view of CSR engagement for sustainable development, complementing the traditional legitimacy-focused perspective in existing literature. Full article

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying theoretical drought CDF models, such oversights lead to biased representations of drought evaluation and characteristics. This research compares the parametric theoretical and empirical CDFs for a comprehensive evaluation of standardized Drought Indices. Additionally, it examines the advantages, disadvantages, and limitations of both empirical and theoretical distribution functions in drought assessment. Three drought indices, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Standardized Precipitation Evapotranspiration Index (SPEI), cover meteorological and hydrological droughts. The assessment spans diverse applications, covering different climates and regions: Durham, United Kingdom (SPEI, 1868–2021); Konya, Türkiye (SPI, 1964–2022); and Lüleburgaz, Türkiye (SDI, 1957–2015). The findings reveal that theoretical and empirical CDFs demonstrated notable discrepancies, particularly in long-term hydrological drought assessments, where underestimations reached up to 50%, posing risks of misinformed conclusions that may impact critical drought-related decisions and policymaking. Root Mean Squared Error (RMSE) for SPI3 between empirical and best-fitted CDF was 0.087, and between empirical and Gamma it was 0.152. For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. Additionally, it concludes that empirical CDFs provide more reliable and conservative drought assessments and are free from the constraints of model assumptions. Both approaches gave approximately the same drought duration with different intensities regarding drought characteristics. Due to the complex process of drought events and different definitions of drought events, each drought event must be studied separately, considering its effects on different sectors. Full article

This paper presents a Multimodal Hidden Markov Model (MHMM) framework specifically designed for real-time human proficiency assessment, integrating physiological (Heart Rate Variability (HRV)), behavioral (Task Completion Time (TCT)), and subjective (NASA Task Load Index (NASA-TLX)) data streams to infer latent human proficiency states in industrial settings. Using published empirical data from the surgical training literature, a comprehensive simulation study was conducted, with the MHMM (Trained) achieving 92.5% classification accuracy, significantly outperforming unimodal Hidden Markov Model (HMM) variants 61–63.9% and demonstrating competitive performance with advanced models such as Long Short-Term Memory (LSTM) networks 90%, and Conditional Random Field (CRF) 88.5%. The framework exhibited robustness across stress-test scenarios, including sensor noise, missing data, and imbalanced class distributions. A key advantage of the MHMM over black-box approaches is its interpretability by providing quantifiable transition probabilities that reveal learning rates, forgetting patterns, and contextual influences on proficiency dynamics. The model successfully captures context-dependent effects, including task complexity and cumulative fatigue, through dynamic transition matrices. When demonstrated through simulation, this framework establishes a foundation for developing adaptive operator-AI collaboration systems in Industry 5.0 environments. The MHMM’s combination of high accuracy, robustness, and interpretability makes it a promising candidate for future empirical validation in real-world industrial, healthcare, and training applications in which it is critical to understand and support human proficiency development. Full article

Background: Radioligand therapy with [¹⁷⁷Lu]Lu-PSMA-617 represents an emerging treatment for metastatic castration-resistant prostate cancer (mCRPC). Its clinical positioning relative to standard therapies remains under discussion. Objective: To compare overall survival (OS), radiographic progression-free survival (rPFS), PSA response, and treatment burden across randomised trials evaluating [¹⁷⁷Lu]Lu-PSMA-617 versus androgen receptor pathway inhibitors (ARTA), Cabazitaxel, or standard of care (SOC). Evidence Acquisition: We conducted a meta-analysis of five randomised controlled trials, including 2073 patients with PSMA-positive metastatic castration-resistant prostate cancer (mCRPC). We assessed survival endpoints, baseline comparability, and treatment intensity. Evidence Synthesis: [¹⁷⁷Lu]Lu-PSMA-617 significantly improved rPFS and PSA response. While modest overall, the OS benefit was more pronounced in taxane-naïve populations. Compared with Cabazitaxel, [¹⁷⁷Lu]Lu-PSMA-617 was associated with similar or better survival despite shorter treatment duration and potentially lower cumulative toxicity and cost. Economic modelling suggests it could offer a more sustainable therapeutic option under typical willingness-to-pay thresholds. Conclusions: [¹⁷⁷Lu]Lu-PSMA-617 shows clinical effectiveness and economic value in mCRPC, with potential advantages over Cabazitaxel and ARTA. Its use could be prioritised in early treatment lines. Patient Summary: This study suggests that PSMA-targeted radioligand therapy is at least as effective as other treatments for advanced prostate cancer, with potential benefits in terms of toxicity, duration, and overall cost. Full article

In this work, we propose an automated, real-time optical scanning approach to assessing catalyst performance in the process of nitro-to-amine reduction using well-plate readers to monitor reaction progress. This approach takes advantage of a simple on–off fluorescence probe that gives a shift in absorbance and strong fluorescent signal when the non-fluorescent nitro-moiety is reduced to the amine form. The combination of an affordable probe and a low barrier-to-entry technique provides an accessible approach to high-throughput catalyst screening. Under this paradigm, we screened 114 different catalysts and compared them in terms of reaction completion times, material abundance, price, recoverability, and safety. Using a simple scoring system, we plotted the catalysts in terms of cumulative scores, along with some intentional biases, including an emphasis on preference for catalysts with potential as green catalysts, considering environmental issues and possible geopolitical preferences. Full article

Four new minor monosulfated triterpene penta- and hexaosides, cladolosides S (1), S₁ (2), T (3), and T₁ (4), were isolated from the Vietnamese sea cucumber Cladolabes schmeltzii (Sclerodactylidae, Dendrochirotida). The structures of the compounds were established based on extensive analysis of 1D and 2D NMR spectra as well as HR-ESI-MS data. Cladodosides S (1), S₁ (2) and T (3), T₁ (4) are two pairs of dehydrogenated/hydrogenated compounds that share identical carbohydrate chains. The oligosaccharide chain of cladolosides of the group S is new for the sea cucumber glycosides due to the presence of xylose residue attached to C-4 Xyl1 in combination with a sulfate group at C-6 MeGlc4. The oligosaccharide moiety of cladolosides of the group T is unique because of the position of the sulfate group at C-3 of the terminal sugar residue instead of the 3-O-Me group. This suggests that the enzymatic processes of sulfation and O-methylation that occur during the biosynthesis of glycosides can compete with each other. This can presumably occur due to the high level of expression or activity of the enzymes that biosynthesize glycosides. The mosaicism of glycoside biosynthesis (time shifting or dropping out of some biosynthetic stages) may indicate a lack of compartmentalization inside the cells of organism producers, leading to a certain degree of randomness in enzymatic reactions; however, this also offers the advantage of providing chemical diversity of the glycosides. Analysis of the hemolytic activity of a series of 26 glycosides from C. schmeltzii revealed some patterns of structure–activity relationships: the presence or absence of 3-O-methyl groups has no significant impact, hexaosides, which are the final products of biosynthesis and predominant compounds of the glycosidic fraction of C. schmeltzii, are more active than their precursors, pentaosides, and the minor tetraosides, cladolosides of the group A, are weak membranolytics and therefore are not synthesized in large quantities. Two glycosides from C. schmeltzii, cladolosides D (18) and H₁ (26), display selectivity of cytotoxic action toward triple-negative breast cancer cells MDA-MB-231, while remaining non-toxic in relation to normal mammary cells MCF-10A. Quantitative structure–activity relationships (QSAR) were calculated based on the correlational analysis of the physicochemical properties and structural features of the glycosides and their hemolytic and cytotoxic activities against healthy MCF-10A cells and cancer MCF-7 and MDA-MB-231 cell lines. QSAR highlighted the complexity of the relationships as the cumulative effect of many minor contributions from individual descriptors can have a significant impact. Furthermore, many structural elements were found to have different effects on the activity of the glycosides against different cell lines. The opposing effects were especially pronounced in relation to hormone-dependent breast cancer cells MCF-7 and triple-negative MDA-MB-231 cells. Full article

This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We extend several widely used optimization algorithms—including Adam, RMSprop, SGD, Adadelta, FTRL, Adamax, and Adagrad—by incorporating fractional derivatives into their update rules. This novel approach leverages the memory-retentive properties of fractional calculus to improve convergence behavior and model efficiency. Our experimental analysis evaluates the performance of fractional-order optimizers on LSTM networks tasked with forecasting stock prices for major companies such as AAPL, MSFT, GOOGL, AMZN, META, NVDA, JPM, V, and UNH. Considering four metrics (Sharpe ratio, directional accuracy, cumulative return, and MSE), the results show that fractional orders can significantly enhance prediction accuracy for moderately volatile stocks, especially among lower-cap assets. However, for highly volatile stocks, performance tends to degrade with higher fractional orders, leading to erratic and inconsistent forecasts. In addition, fractional optimizers with short-memory truncation offer a favorable trade-off between computational efficiency and modeling accuracy in medium-frequency financial applications. Their enhanced capacity to capture long-range dependencies and robust performance in noisy environments further justify their adoption in such contexts. These results suggest that fractional-order optimization holds significant promise for improving financial forecasting models—provided that the fractional parameters are carefully tuned to balance memory effects with system stability. Full article

Background/Objectives: This study evaluated the efficacy and safety of the ab externo open-conjunctiva (AEO) approach with adjunctive Ologen collagen matrix (OCM) compared to ab interno closed-conjunctiva (AIC) techniques for XEN45 gel stent implantation in patients with refractory open-angle glaucoma. The goal was to determine whether the AEO with OCM approach offers advantages in intraocular pressure (IOP) control and postoperative outcomes. Methods: A retrospective, comparative case series was conducted on 76 eyes from 76 patients with open-angle glaucoma who underwent XEN45 implantation between 2017 and 2022 at a single tertiary center. The patients were divided into Group 1 (AEO with OCM, n = 47) and Group 2 (AIC, n = 29). Postoperative IOP, the number of glaucoma medications, surgical complications, bleb revisions, and failure rates were recorded over 12 months. The AEO technique, supported by OCM, was assessed for its potential to reduce postoperative fibrosis and improve long-term outcomes. Results: Both groups experienced significant IOP reductions over time compared to baseline. However, Group 1 had superior outcomes, requiring fewer glaucoma medications postoperatively (p < 0.05), and demonstrated lower rates of complications (10.6% vs. 31.0%, p = 0.026) and bleb revisions (8.5% vs. 34.5%, p = 0.005). Kaplan–Meier survival analysis showed significantly greater cumulative surgical success in Group 1 compared to Group 2 (p < 0.001). Conclusions: The AEO with OCM approach to XEN45 implantation may provide improved safety and efficacy compared to the AIC approach. It appears to be beneficial in minimizing postoperative fibrosis, reducing the medication burden, and lowering complication and failure rates. Prospective randomized trials are needed to validate these findings. Full article

The detection efficiency evaluation of sonars is crucial for optimizing task planning and resource scheduling. The existing static evaluation methods based on single indicators face significant challenges. First, static modeling has difficulty coping with complex scenes where the relative situation changes in real time in the task process. Second, a single evaluation dimension cannot characterize the data distribution characteristics of efficiency indicators. In this paper, we propose a multidimensional detection efficiency evaluation method for sonar search paths based on dynamic spatiotemporal interactions. We develop a dynamic multidimensional evaluation framework. It consists of three parts, namely, spatiotemporal discrete modeling, situational dynamic deduction, and probability-based statistical analysis. This framework can achieve dynamic quantitative expression of the sonar detection efficiency. Specifically, by accurately characterizing the spatiotemporal interaction process between the sonars and targets, we overcome the bottleneck in entire-path detection efficiency evaluation. We introduce a Markov chain model to guide the Monte Carlo sampling; it helps to specify the uncertain situations by constructing a high-fidelity target motion trajectory database. To simulate the actual sensor working state, we add observation error to the sensor, which significantly improves the authenticity of the target’s trajectories. For each discrete time point, the minimum mean square error is used to estimate the sonar detection probability and cumulative detection probability. Based on the above models, we construct the multidimensional sonar detection efficiency evaluation indicator system by implementing a confidence analysis, effective detection rate calculation, and a data volatility quantification analysis. We conducted relevant simulation studies by setting the source level parameter of the target base on the sonar equation. In the simulation, we took two actual sonar search paths as examples and conducted an efficiency evaluation based on multidimensional evaluation indicators, and compared the evaluation results corresponding to the two paths. The simulation results show that in the passive and active working modes of sonar, for the detection probability, the box length of path 2 is reduced by 0∼0.2 and 0∼0.5, respectively, compared to path 1 during the time period from T = 11 to T = 15. For the cumulative detection probability, during the time period from T = 15 to T = 20, the box length of path 2 decreased by 0∼0.1 and 0∼0.2, respectively, compared to path 1, and the variance decreased by 0∼0.02 and 0∼0.03, respectively, compared to path 1. The numerical simulation results show that the data distribution corresponding to path 2 is more concentrated and stable, and its search ability is better than path 1, which reflects the advantages of the proposed multidimensional evaluation method. Full article

In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments. Full article

Background and Objectives: Regional anesthesia is a key component of multimodal analgesia following minimally invasive gynecologic surgery. However, single-port laparoscopic adnexal surgery differs anatomically and physiologically from multiport or open approaches, particularly in terms of incision site, tissue handling, and pain characteristics. Despite its increasing use, evidence supporting procedure-specific regional analgesic protocols for this approach remains limited. This study aimed to compare the analgesic efficacy of quadratus lumborum block (QLB) and rectus sheath block (RSB) in this surgical context. Materials and Methods: In this randomized controlled trial, 68 patients undergoing single-port laparoscopic adnexal surgery were randomly assigned to receive either QLB or RSB at the end of surgery. Four patients were excluded due to missing patient-controlled analgesia (PCA) data, resulting in 64 patients analyzed (QLB group: n = 32; RSB group: n = 32). The primary outcome was cumulative opioid consumption over the first 24 postoperative hours. Secondary outcomes included interval-based opioid consumption, time to first PCA bolus, postoperative pain scores, and incidence of postoperative nausea and vomiting (PONV). Results: The RSB group demonstrated significantly lower cumulative opioid consumption at 24 h postoperatively (132.9 [61.3, 338.4] µg vs. 453.0 [253.1, 811.0] µg, p < 0.001). This trend persisted across most postoperative time points up to 48 h. Interval-based opioid consumption was also lower in the RSB group during 0–24 h and 32–48 h intervals (each comparison p < 0.05). The time to first PCA bolus was significantly longer in the RSB group (56.5 [41.0, 340.3] minutes vs. 40.5 [33.3, 68.8] minutes; p = 0.014), and Kaplan–Meier analysis confirmed a delayed first bolus request in the RSB group (log-rank p = 0.007). Pain scores and postoperative nausea and vomiting incidence were comparable between groups. Conclusions: Compared with QLB, RSB provided similar pain relief with significantly lower opioid consumption following single-port laparoscopic adnexal surgery. These findings highlight the potential advantages of RSB in enhancing analgesic efficiency and support the development of procedure-specific regional analgesia protocols tailored to this surgical approach. Full article

The combination of GNSS RTK and INS offers complementary advantages but faces significant challenges in urban canyons. Frequent cycle slips in carrier phase measurements and ambiguity resolution algorithms increase computational burden without improving positioning accuracy. Additionally, environmental interference introduces noise into observations, potentially leading to complete signal loss. To address these issues, this paper proposes a factor graph optimization (FGO) positioning algorithm incorporating predictive observation factors. First, a penalized spline (P-spline) is constructed to predict and smooth Doppler measurements. The predicted Doppler is then fused with the dynamics model predictions to enhance robustness. Using predictive Doppler, carrier phase and pseudorange observations are reconstructed, generating predictive constraint factors to improve positioning accuracy. Real-world tests conducted in urban canyons, including Shanghai, demonstrate that the proposed method maintains stable positioning performance even under short-term signal outages, effectively mitigating cumulative positioning errors caused by data loss. Compared to traditional methods that rely solely on available observations, the proposed algorithm improves northward and dynamic positioning accuracy by 35% and 29%, respectively, providing a highly robust navigation solution for complex urban environments. Full article