Search Results (34,159)

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor and non-motor symptoms. Several studies have reported varying prevalence of Carpal Tunnel Syndrome (CTS) among individuals with PD. Objective: This study aimed to estimate the pooled prevalence of CTS in people with PD and explore any potential association between the two conditions. Methods: This systematic review and meta-analysis was conducted and reported in accordance with the PRISMA 2020 guidelines. A systematic search was performed across PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science (WoS), Scopus, and EMBASE from inception to April 2024. Studies reporting CTS prevalence data in individuals with PD were included. Methodological quality was assessed using the National Institutes of Health (NIH) quality assessment tool. Pooled prevalence estimates were calculated using a random-effects model. Risk difference (RD) and risk ratio (RR) were calculated to assess the association between PD and CTS compared with control groups. Results: A total of 7 studies involving 411 participants (343 with PD and 68 controls) met the inclusion criteria, with 679 wrists assessed. The pooled prevalence of CTS in PD was estimated at 15% (95% CI: 0.07–0.28) with significant heterogeneity (p < 0.001, I² = 91%). The RD was 10% (95% CI: 0.04–0.16, p = 0.002), with low heterogeneity (p = 0.29, I² = 19%). The RR of CTS in PD compared with controls was 3.31 (95% CI: 0.60–18.42, p = 0.17), with moderate heterogeneity (p = 0.13, I² = 52%). Conclusions: This meta-analysis provides preliminary pooled estimates indicating a potentially increased prevalence of carpal tunnel syndrome in individuals with PD. Although the findings suggest a possible association, clinicians should maintain increased vigilance for CTS symptoms in patients with PD presenting with upper-limb sensory or motor complaints. From a biomechanical and functional perspective, these findings highlight the importance of routine upper-limb screening and the implementation of rehabilitation strategies targeting hand use, dexterity, and sensorimotor control within physiotherapy practice. Further high-quality studies with larger, well-characterised samples are required to confirm this relationship and clarify its clinical and functional implications. Full article

Objective: This study evaluated the effects of a 12-week High-Intensity Functional Training (HIFT) program combined with thylakoid supplementation on plasma adipo-myokine levels (Decorin, Myostatin, Follistatin, Activin A, and TGF-β1) in men with obesity. Secondary outcomes included anthropometric indices, lipid profiles, and insulin resistance markers. Methods: Sixty men with obesity (age: 27.6 ± 8.4 years; BMI: 32.6 ± 2.6 kg/m²) were randomly assigned to four groups (n = 15 per group): Placebo (PG), Supplement (SG), HIFT + placebo (TPG), and HIFT + supplement (TSG). To ensure robustness against the 27% attrition rate, statistical analyses included both per-protocol and intention-to-treat (ITT) models. HIFT was performed for 3 sessions/week (Borg scale: 15–17). Results: Following Bonferroni correction for multiple endpoints, repeated-measures ANOVA showed significant Time × Group interactions for most adipo-myokines and metabolic markers. Both training groups (TPG and TSG) demonstrated improvements in body composition and insulin sensitivity compared to PG (p < 0.05). While no significant differences were observed between TPG and TSG for systemic metabolic markers, preliminary data suggested that thylakoid supplementation might provide modest complementary modulations in specific myokines (e.g., decorin and follistatin). However, these observed trends did not reach clinical superiority over exercise alone in the broader metabolic profile. Conclusions: Twelve weeks of HIFT is an effective primary driver for modulating the adipo-myokine network in obese men. Although thylakoid supplementation showed potential for selective complementary effects on certain myokines, these findings are exploratory given the small sample size. The clinical significance and long-term complementary value of thylakoid-exercise interactions require further validation in larger, more diverse cohorts. Full article

Background: Conservative management of port-related bacteremia often includes locally administered antimicrobials, known as antimicrobial lock therapy (ALT). Current guidelines recommend daily replacement of antimicrobial lock solutions (ALSs). We aimed to evaluate whether ALSs could remain effective with extended dwell times of up to 10 days. Methods: In this randomized clinical trial, patients with noninfected, recently implanted ports were assigned to one of five ALS dwell-time groups, ranging from 1 to 10 days. Each ALS contained heparin plus an antimicrobial at standard intraluminal concentrations: vancomycin 2 mg/mL, teicoplanin 10 mg/mL, linezolid 1.8 mg/mL, daptomycin 5 mg/mL, or tigecycline 4.5 mg/mL. The primary endpoint was the time at which intraluminal drug concentrations decreased below 1 mg/mL (ClinicalTrials.gov NCT01592032). Results: Vancomycin and linezolid concentrations fell significantly below 1 mg/mL after 3 days of dwell time. Daptomycin and tigecycline concentrations decreased significantly after 7 days but remained above 1 mg/mL. Teicoplanin concentrations did not decline significantly after 7 days. Conclusions: Optimal ALS dwell time depends on the antimicrobial agent. Vancomycin and linezolid locks require daily replacement, whereas daptomycin, tigecycline, and teicoplanin locks maintain therapeutic concentrations for up to 7 days. These findings support individualized ALS replacement strategies, potentially reducing the need for daily interventions. Full article

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. Full article

Monitoring spatiotemporal dynamics of aboveground carbon (AGC) storage at the tree species level is crucial for evaluating the ecological impacts of large-scale infrastructure projects and facilitating accurate ecological environmental management. However, existing studies heavily rely on interannual coarse-spatial-resolution forest-type products, leading to significant uncertainties in carbon estimation, particularly in fragmented linear engineering zones. This study integrated Sentinel-1/2 data with a random forest (RF) model to map tree species distribution (overall accuracy = 85.18%; Kappa = 0.8319) and AGC estimation (R² = 0.7057; RMSE = 13.35 Mg ha⁻¹) at a 10 m resolution in the Pinglu Canal Basin from 2019 to 2024. The results revealed a total AGC decline of 16.88% across the watershed. Spatially, the Environmental Impact Area (EIA) functioned as the primary disturbance core (experiencing a 28.91% loss), while the Ecological Buffer Area (EBA) acted as a regional carbon stabilizer. At the species level, while Eucalyptus grandis accounted for the majority of carbon depletion, Pinus massoniana exhibited a resilience-driven rebound in the mid-construction phase. Meanwhile, Litchi chinensis and other native species demonstrated steady gains. Cumulatively, these species-specific carbon gains associated with natural growth and restoration initiatives effectively offset 34.45% of the carbon loss. These findings provide quantitative evidence supporting the potential of green engineering to mitigate the ecological footprint of infrastructure development. This study offers a robust monitoring tool for low-carbon infrastructure and directly supports the United Nations Sustainable Development Goal 15 (SDG 15) related to forest conservation and ecological restoration. Full article

The project cycle of complex construction projects covers the whole process from project decision-making, design, bidding, construction, completion acceptance, and the initial stage of operation. Among them, the investment risk assessment of complex construction projects focuses on the early decision-making stage of the project, aiming to provide a basis for investment feasibility analysis. The investment risk of complex construction projects is highly nonlinear and uncertain, and the traditional risk assessment methods have limitations in model generalization ability and prediction accuracy. To improve the accuracy and reliability of quantitative risk assessment, this study proposed a novel investment risk assessment model based on the perspective of investors. Firstly, through literature research, a multi-dimensional comprehensive risk assessment index system covering policies and regulations, economic environment, technical management, construction safety, and financial cost was systematically identified and constructed. Subsequently, the Least Squares Support Vector Machine (LSSVM) was used to establish a nonlinear mapping relationship between risk indicators and final risk levels. Aiming at the problem that the parameter selection of the standard LSSVM model has a significant impact on the performance, this paper proposed an improved Firefly Algorithm (IFA) to automatically optimize the penalty factor and kernel function parameters of LSSVM, so as to overcome the blindness of artificial parameter selection and improve the convergence speed and generalization ability of the model. Compared with the classical Firefly Algorithm, IFA strengthens learning and adaptive strategies by adding depth. The conclusions are as follows. (1) Compared with the Backpropagation Neural Network (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), this model showed higher prediction accuracy on the test set, and its accuracy was reduced by about 3%. (2) Compared with FA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), IFA had a stronger global retrieval ability. (3) The model could effectively fit the complex risk nonlinear relationship, and the risk assessment results were highly consistent with the actual situation. Therefore, the risk assessment model based on the improved LSSVM constructed in this study not only provides a more scientific and accurate quantitative tool for investment decision-making of construction projects, but also has important theoretical and practical significance for preventing and resolving significant investment risks. Full article

Endometriosis is a chronic, estrogen-dependent inflammatory condition affecting approximately 10% of women of reproductive age worldwide. It is characterized by the presence of endometrial-like tissue outside the uterine cavity, which frequently results in dysmenorrhea, chronic pelvic pain, dyspareunia, and infertility. While hormonal medications and surgical procedures are common treatments, they are often constrained by adverse effects and high recurrence rates. The aim was to systematically identify, critically appraise, and synthesize randomized controlled trials evaluating vitamin D, C, and E supplementation in women with endometriosis, focusing on their effects on pelvic pain, dysmenorrhea, dyspareunia, quality of life, oxidative and inflammatory biomarkers, and fertility-related outcomes, and to highlight methodological gaps that can inform future research and integrated therapeutic strategies. Following PRISMA guidelines, seven eligible RCTs were identified from databases including PubMed, Scopus, and ScienceDirect. The quality of these studies was assessed using the Jadad Scoring System and Cochrane RoB 2 tool. High-dose supplementation of vitamin D (50,000 IU) was found to significantly reduce pelvic pain and improve biochemical markers such as hs-CRP and total antioxidant capacity (TAC). Vitamin D appears to modulate endometrial pathways by reducing active β-catenin protein activity, which may disrupt signaling associated with lesion invasion and survival. Additionally, combined Vitamin C and E therapy (typically 1000 mg/day of Vitamin C and 800 IU/day of Vitamin E) acts synergistically to scavenge free radicals. This intervention significantly decreased oxidative stress markers, including malondialdehyde (MDA) and reactive oxygen species (ROS). Patients reported significant improvements in symptoms, including a 43% reduction in daily pelvic pain and a 37% reduction in dysmenorrhea. Despite physiological improvements, there was no statistically significant increase in pregnancy rates observed across the trials. Vitamin supplementation with D, C, and E represents a safe, low-cost adjunct therapy that can effectively mitigate endometriosis-related oxidative stress and pelvic pain. While these vitamins show promise for symptom relief, further research with larger sample sizes is required to determine their long-term impact on fertility outcomes and lesion regression. Full article

Against the backdrop of global climate change, remote subtropical mountainous power grids face severe operational challenges due to their fragile infrastructure and complex climatic conditions. However, existing research has insufficiently addressed load forecasting in data-sparse regions, particularly lacking systematic analysis of the “meteorology–load–failure” coupling mechanism. To address this gap, this study focused on 10 kV distribution lines in a typical subtropical monsoon region of southern China. Based on hourly load and meteorological data from 2016 to 2025, we propose a two-stage hybrid model combining “Random Forest (RF) feature selection + Long Short-Term Memory (LSTM) time series forecasting”. Through deep feature engineering, composite, lagged, and interactive features were constructed. Using the RF algorithm, we quantitatively identified the core drivers of load variation across different time scales: at the hourly scale, variations are dominated by historical inertia (with weights of 0.5915 and 0.3757 for 1-h and 24-h lagged loads, respectively); at the daily scale, the logic shifts to meteorological triggering and cumulative effects, where the composite feature load_lag1_hi_product emerged as the most critical driver (weight of 0.8044). Experimental results demonstrate that the hybrid model significantly improved forecasting accuracy compared to the full-feature LSTM benchmark: on a daily scale, RMSE decreased by 13.29% and MAE by 16.67%, with R² reaching 0.8654; on an hourly scale, R² reached 0.9687. Furthermore, correlation analysis with failure data revealed that most grid faults occurred during intervals of extremely low load variation (0–5%), suggesting that “chronic stress” from environmental exposure in hot and humid conditions is the primary cause, with lightning identified as the leading external threat (26.90%). The interpretable forecasting framework proposed in this study transcends regional limitations. It provides a strategic “low-cost, high-resilience” prototype applicable to power systems in humid-subtropical zones worldwide, particularly for developing regions facing the dual challenges of data sparsity and climate vulnerability. Full article

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article

Background: Periodontitis is a chronic inflammatory condition characterized by progressive destruction of tooth-supporting tissues and sustained microbial dysbiosis. Increasing evidence suggests that chronic oral inflammation may be associated with oral and oropharyngeal carcinogenesis, although findings across epidemiological and prognostic studies remain heterogeneous. Objective: To systematically evaluate the epidemiological association between clinically defined periodontitis and the risk of oral and/or oropharyngeal cancer, and to explore, in a distinct analytical component, the prognostic association between tumor-associated periodontal pathogens, particularly Porphyromonas gingivalis, and survival outcomes in affected patients. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines and registered in PROSPERO (CRD420251273975). Observational studies evaluating periodontitis and oral/oropharyngeal cancer risk (Arm 1) and prognostic studies assessing tumor-associated periodontal pathogens and survival outcomes (Arm 2) were identified through comprehensive database searches. Random-effects meta-analyses were performed to pool adjusted effect estimates. Risk of bias was assessed using the Newcastle–Ottawa Scale and the QUIPS tool. Results: Six observational studies were included in the epidemiological meta-analysis. Periodontitis was significantly associated with an increased risk of oral and/or oropharyngeal cancer (pooled HR = 2.14; 95% CI: 1.53–2.98), with substantial heterogeneity; trial sequential analysis supported the statistical robustness of this association. In the separate prognostic analysis, three studies evaluating intratumoral Porphyromonas gingivalis were included. A higher presence or expression of P. gingivalis was associated with poorer overall survival (HR = 2.89; 95% CI: 1.93–4.32), with no observed heterogeneity. Sensitivity and influence analyses confirmed the stability of these findings. Conclusions: This systematic review and meta-analysis demonstrate a consistent epidemiological association between periodontitis and an increased risk of oral and/or oropharyngeal cancer. In addition, exploratory prognostic evidence suggests that the presence of Porphyromonas gingivalis within tumor tissue may be associated with adverse survival outcomes. These findings should be interpreted as addressing distinct clinical and microbiological constructs, underscoring the need for further well-designed prospective and mechanistic studies. Full article

Background: The Mediterranean–DASH Intervention for Neurodegenerative Delay (MIND) study was a 3-year randomized controlled trial to test the effects of the MIND diet on cognitive decline in individuals at risk for Alzheimer’s dementia (AD). Here we examine whether genetic differences in (a) AD predisposition and (b) nutrient metabolism modify the effect of MIND on cognitive change. Methods: This secondary analysis included 494 trial participants of genetically inferred European ancestry with genetic data. Genetic scores (GS) were derived from prior genome-wide studies of AD and nutrient biomarkers. Linear regression and linear mixed models were used to examine the main effects of GS and interactions with diet assignment on cognition. An exploratory genome-wide interaction analysis was also performed. Results: We observed a statistically significant interaction (p = 0.002) between the COMT Val158Met variant and diet assignment for the 3-year change in perceptual speed. Met/Met (lower enzyme activity) carriers’ perceptual speed improved more on the MIND than the control diet, while no difference by diet was observed for Val carriers. Conclusions: Catechol-O-methyltransferase (COMT) metabolizes catecholamines as well as polyphenols unique to the MIND diet. Individuals with genetically impaired COMT activity may be especially responsive to the cognitive benefits of the MIND diet. Full article

Background/Objectives: Evidence regarding the effect of malocclusion on periodontal health is contradictory. This systematic review with meta-analysis seeks to summarize the available scientific evidence on the relationship between malocclusion and periodontal health in children and adolescents. Methods: A review of four electronic databases (PubMed, Scopus, Embase, and Web of Science) was performed. Observational studies were included if they investigated the link between malocclusion and periodontal health in children and adolescents. The quality of the studies included in the review was determined using the Newcastle–Ottawa Scale (NOS). A meta-analysis was conducted on binary outcomes using random-effect models. The Grading Recommendations Assessment, Development and Evaluation (GRADE) tool was used to determine the certainty of the evidence for each outcome. Results: The initial search yielded 774 potential articles. Nineteen articles were selected for the final qualitative analysis, and four of these were included in the meta-analysis. Certain malocclusion traits appear to be associated with less favorable periodontal health indicators in children and adolescents. Quantitative synthesis restricted to studies using the Gingival Bleeding Index (GBI) suggests that malpositioned teeth, such as crowding or lack of spacing, and Class II or Class III molar relationships may be associated with a higher odds of gingivitis in individuals under 18 years of age. The overall quality of evidence of the studies was very low, according to the GRADE criteria. Conclusions: Observational cross-sectional evidence of very low certainty suggests an association between certain malocclusions (crowding, lack of spacing, Class II or Class III molar relationships) and increased odds of gingivitis in children and adolescents. Evidence regarding dental plaque accumulation is inconsistent and limited by substantial heterogeneity across studies. Causality cannot be inferred, and further high-quality longitudinal studies are required. Full article

Background/Objectives: Fibromyalgia syndrome (FMS) is a chronic condition characterized by widespread pain, fatigue, sleep disturbances, and psychological symptoms. Aquatic exercise offers the benefits of physical activity with reduced mechanical stress. This meta-analysis evaluated the effectiveness of AE on pain, functional physical status, and health-related quality of life. Methods: A PRISMA-guided systematic review and meta-analysis (PROSPERO CRD42025115158) included randomized and non-randomized trials up to October 2025 from MEDLINE (PubMed), Cochrane Library, PEDro, CINAHL Complete, SPORTDiscus, and Academic Search Ultimate. Eligible participants were adults diagnosed with FMS undergoing AE programs, alone or combined with other modalities. Standardized mean differences (SMD) with 95% confidence intervals were pooled using random- or fixed-effects models. Methodological quality, risk of bias, and certainty of evidence were evaluated using the PEDro scale, the RoB 2.0 tool, and the GRADE approach. Results: 27 trials (n = 1785; >95% women; mean age 44–62 years) were included. AE significantly improved pain (SMD = −0.92; 95% CI: −1.03 to −0.80; p < 0.00001), physical function (SMD = −0.74; 95% CI: −0.84 to −0.63; p < 0.00001), and HRQoL (SMD = 0.57; 95% CI: 0.42 to 0.72; p < 0.00001). Effects were consistent across time frames, though overall heterogeneity was considerable (Tau² = 4.93; I² = 97%). The mean PEDro score was 5.2/10, and RoB 2.0 indicated moderate methodological limitations mainly due to a lack of blinding. Evidence certainty was low for the main outcomes and moderate for adverse events. Conclusions: Aquatic exercise is an effective and safe complementary therapy for patients with FMS, alleviating pain while enhancing function and quality of life. However, methodological variability and small sample sizes warrant further high-quality trials to confirm these findings and explore underlying mechanisms. Full article

Background/Objectives: Orthodontically induced root resorption (OIRR) is a well-documented but undesired consequence of orthodontic treatment. This systematic review and meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) models applied to cone-beam computed tomography (CBCT) for detecting and quantifying OIRR while evaluating their agreement with manual reference standards and the impact of model architecture, validation design, and quantification strategy. Methods: Comprehensive searches were conducted across PubMed/MEDLINE, Scopus, Web of Science, and EMBASE up to November 2025. Studies were included if they employed AI for OIRR diagnosis using CBCT and reported relevant performance metrics. Following PRISMA guidelines, data were extracted and a random-effect meta-analysis was performed. Subgroup analyses explored the influence of model design and validation. Results: Seven studies were included. Pooled sensitivity from three eligible studies was 0.903 (95% CI: 0.818–0.989), suggesting excellent true positive rates. Specificity ranged from 82% to 98%, and area under the receiver operating characteristic curve values reached up to 0.96 across studies using EfficientNet, U-Net, and other convolutional neural network (CNN)-based architectures. The pooled intraclass correlation coefficient for agreement with manual quantification was 1.000, reflecting near-perfect concordance. Subgroup analyzes showed slightly superior performance in CNN-only models compared to hybrid approaches, and better diagnostic metrics with internal validation. Linear assessments appeared more sensitive to early apical shortening than volumetric methods. Conclusions: AI models applied to CBCT demonstrate excellent diagnostic accuracy and high concordance with expert assessments for OIRR detection. These findings support their potential integration into clinical orthodontic workflows. Full article