Search Results (6,255)

Pedestrian inertial navigation is a pivotal technology for achieving seamless indoor and outdoor positioning. Traditional methods based on the Extended Kalman Filter (EKF) suffer from cumulative errors induced by inertial measurement unit (IMU) noise, which severely degrade the accuracy of pedestrian trajectory estimation over long durations. To address this critical limitation, a post-processing trajectory optimization approach for pedestrian inertial navigation based on the Birkhoff pseudospectral method is proposed in this paper. Leveraging the initial attitude and position estimates derived from the Zero-Velocity Update (ZUPT) technique and the EKF framework, the proposed method first parameterizes continuous-time acceleration measurements by adopting Chebyshev nodes as collocation points, and then formulates and solves the trajectory optimization problem via a Birkhoff pseudospectral framework, which effectively suppresses noise interference from the IMU accelerometer. Simulation experiments validate the superior noise suppression capability of the proposed algorithm. Furthermore, physical experiments conducted with a foot-mounted IMU demonstrate that the final position error is reduced by approximately 90% in comparison with the traditional EKF-based method. Full article

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

This 90-day trial evaluated the integrated benefits of biofloc technology (BFT) in lined ponds for intensive Penaeus vannamei culture, comparing it with a conventional water-exchange (WE) system. The BFT system maintained favorable water quality with a 68.4% reduction in cumulative water exchange. Concentrations of toxic total ammonia–nitrogen (TAN) and nitrite–nitrogen (NO₂⁻-N) were better controlled, and total suspended solids (TSS) stabilized within a beneficial range (150–200 mg L⁻¹). Microbial analysis indicated that BFT significantly increased total bacterial abundance in both culture water and shrimp hepatopancreas while reducing the total Vibrio-to-bacteria ratio in culture water to below 6%, significantly lower than in the WE system (>18%). Moreover, BFT significantly lowered the loads of specific pathogens, acute hepatopancreatic necrosis disease (AHPND)-causing Vibrio parahaemolyticus, and Enterocytozoon hepatopenaei (EHP) in both culture water and shrimp hepatopancreas. Regarding production performance, BFT significantly enhanced shrimp survival rate (82.4% vs. 71.5%), yield (3460 vs. 2948 kg pond⁻¹), and water productivity (0.85 vs. 0.28 kg m⁻³), while lowering the feed conversion ratio (1.16 vs. 1.33). In conclusion, BFT achieves stable water quality, effective pathogen suppression, and enhanced production efficiency through microbial regulation, offering a viable water-saving, environmentally sound, and disease-resilient strategy for intensive P. vannamei culture. Full article

The rapid expansion of the craft brewing sector has increased the number of small breweries, leading to rising organically rich waste across aquatic, terrestrial and atmospheric ecosystems. Although brewery by-products are frequently discussed in terms of valorisation and resource efficiency, their environmental implications remain insufficiently examined. The present review synthesises current knowledge on waste generated by small breweries (i.e., operations with annual production volumes typically below 20,000 hL of beer), including their composition and management, with an emphasis on the potential environmental consequences of inadequate handling. Waste, including wastewater, solid by-products, gaseous emissions, odours, and noise, is considered, and their mechanistic effects on aquatic, terrestrial, and atmospheric compartments are discussed. Particular attention is given to cumulative and localised impacts in ecosystems, such as oxygen depletion, nutrient enrichment, altered microbial processes, and downstream effects on soil biota, aquatic food webs, and biodiversity. Commonly proposed mitigation and valorisation strategies are critically evaluated, with attention to ecological trade-offs and constraints related to scale, infrastructure, and regulatory thresholds. The review highlights a pronounced bias in the research literature towards chemical and toxicological characterisation, alongside a lack of field-based and long-term monitoring studies. By identifying key knowledge gaps and framing small brewery waste within an environmental context, this review emphasises the need for biomonitoring, scale-appropriate management approaches, and regulatory frameworks tailored to small breweries. Full article

Resin-based dental composites are widely used in restorative dentistry; however, concerns persist regarding their potential release of Bisphenol-A (BPA), a compound with recognized endocrine-disrupting activity. This in vitro study evaluated the time-dependent release of BPA from four contemporary resin-based dental filling composites immersed in artificial saliva under different thermal conditions. Disk-shaped specimens (5.5 mm diameter and 2 mm thickness) of Estelite Sigma Quick, Clearfil Majesty ES-2, Omnichroma Flow, and Luna 2 were incubated in artificial saliva at physiological pH (6.8) at 37 °C and 44 °C. BPA concentrations were quantified after 1, 7, and 28 days using a validated UHPLC–MS/MS method. BPA release was observed for all materials except Luna 2, for which it remained below the limit of quantification (LOQ) at all time points and temperatures. Across all BPA-releasing composites, the highest concentrations were observed after 1 day of immersion, particularly at 44 °C. Estelite Sigma Quick exhibited the highest BPA release, followed by Clearfil Majesty ES-2 and Omnichroma Flow. BPA release decreased progressively over time for all materials. Statistical analysis confirmed significant effects of material type, temperature, and exposure duration on BPA release (p < 0.001). Within the limitations of this in vitro study, BPA release appears to be material-dependent and influenced by thermal conditions and immersion time. Although absolute BPA concentrations were low, these findings highlight the importance of material-specific evaluation and continued monitoring of potential sources of cumulative BPA exposure from restorative dental materials. Full article

The recurrent influx of pelagic Sargassum spp. along Caribbean coastlines poses a significant environmental challenge while offering potential as a resource-recovery agricultural input. However, agricultural reuse of Sargassum biomass raises concerns regarding salinity and trace-metal distribution within the soil–plant–food continuum. This study evaluated the short-term elemental response to a Sargassum-Based Liquid Biofertilizer (SBLB) produced via controlled anaerobic fermentation, using tomato (Solanum lycopersicum L.) grown under greenhouse conditions. Raw biomass, fermented biofertilizer, irrigation water, soils, vegetative tissues, and fruits were chemically characterized. Elemental concentrations were quantified by ICP–OES and ICP-MS and treatment effects were analyzed using one-way and two-way ANOVA (p < 0.05). Anaerobic fermentation resulted in lower measured concentrations of sodium, arsenic, and selected trace elements in the liquid fraction relative to raw biomass. SBLB application increased soil macronutrient availability (N, P, K, Ca, Mg), while soil trace-metal concentrations remained within international reference ranges during the experimental period. Metals of concern (As, Cd, Pb, Ni, Cr) showed no detectable short-term enrichment in soils, vegetative tissues, or fruits relative to controls. In tomato fruits, arsenic, cadmium, and lead were below the limit of quantification across all treatments. Within the experimental timeframe, SBLB application was not associated with detectable trace-element accumulation in the soil–plant system. Long-term field studies and detailed soil physicochemical characterization are required to evaluate cumulative effects under repeated applications. Full article

Retinoblastoma (Rb) is an intraocular tumor caused by genetic alterations in the RB1 and MYCN genes within developing retinal cells. Chemoresistance and metastasis are major challenges for treatment, with the bone marrow (BM) representing the most common metastatic site. We investigated the effect of tumor-derived sEVs (TDsEVs) on the crosstalk between metastatic site cells (BM-derived mesenchymal stem cells (BM-MSC)) and tumor cells, and characterized them according to MISEV guidelines. The uptake of sEVs and the associated phenotypic changes in the BM-MSCs were analyzed with confocal microcopy. The functional effects were assessed through MTT assays for viability, scratch and Transwell assays for migration, and colony- and sphere-formation assays to evaluate clonogenicity and self-renewal, while stemness marker expression was examined by immunoblotting. Secretome changes following sEV exposure were analyzed using dot blot assays. sEVs were taken up by both cells. TD-sEVs significantly enhanced BM-MSC migration and induced differentiation into a myofibroblast-like phenotype without affecting cell viability. Conversely, BM-MSC-derived sEVs promoted tumor cell viability, migration, and stemness marker expression. Both the BM-MSCs and tumor cells exhibited altered secretory profiles after sEV treatment. The in vitro findings provide cumulative evidence that sEV-mediated interactions contribute to a tumor-supportive milieu or premetastatic niche at the BM in Rb. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

This study investigates the thermo-hydro-mechanical (THM) coupling impacts on seepage and heat transfer characteristics to enhance the efficient utilization of hot dry rock resources in the Guanzhong Basin. A computational model of thermo-hydro-mechanical three-field coupling for an enhanced geothermal system is developed based on the geological context and rock thermophysical properties of the Huazhou-Huayin target area in the Guanzhong Basin. The effects of differential pressure during injection and production, injection temperature, and well configuration on the reservoir stress field, permeability variations, temperature distribution, and heat recovery efficiency of the system are carefully simulated and analyzed. Simulations indicate that increasing the injection–production pressure differential from ±1 MPa to ±7 MPa dramatically enhances heat recovery, yielding a fivefold increase in the extraction rate and an 11.54-fold rise in cumulative heat production. Conversely, this aggressive approach severely impacts long-term sustainability, accelerating thermal breakthrough and drastically cutting the operational lifespan by 93.30%. Lowering the injection temperature from 60 °C to 20 °C yields a 24.14% enhancement in heat output over the same duration, together with a 24.14% increase in the geothermal extraction rate. Increasing the number of injection–production wells from one to two broadens the heat extraction range and improves system heat production by 35.82%, concurrently diminishing lifespan by 39.50%. This work possesses theoretical importance for the progression of hot dry rock initiatives similar to those in the Guanzhong Basin and other geological settings. Full article

Landslides, often triggered by intense or prolonged rainfall, pose significant risks to communities and infrastructure. Identifying accurate rainfall thresholds is crucial for predicting landslide events and developing effective early warning systems. This study, conducted on São Miguel Island (Azores), aimed to improve the predictive capability of rainfall thresholds by integrating both rainfall preparatory and rainfall trigger thresholds. Using data from 61 landslide events and rainfall measurements recorded at four stations between 1977 and 2020, the study applied the Generalised Extreme Value (GEV) distribution with Maximum Likelihood Estimation (MLE) to identify the cumulative rainfall–duration pair with the highest return period for each event, thereby establishing a preparatory threshold. The trigger threshold was determined by analysing the rainfall amount recorded on the day of the event while also accounting for the duration of the preparatory rainfall period. The final threshold combines both the preparatory and trigger thresholds, and an event is detected when both thresholds are exceeded. Preparatory thresholds showed similar patterns across the stations, with Sete Cidades and Furnas recording the highest cumulative rainfall values, while Santana and Ponta Delgada exhibited lower thresholds. The trigger thresholds at Furnas reflected the highest daily rainfall intensities. The analysis also indicated that the rainfall intensity required to trigger landslides decreases with increasing durations of the antecedent rainfall. Performance of the thresholds using ROC metrics revealed that the combined threshold outperformed the preparatory threshold alone by reducing false positives (FPs) and improving predictive accuracy. In all cases, the combined threshold demonstrated superior performance in detecting landslide events, highlighting its effectiveness in landslide prediction. This study provides a detailed analysis of rainfall thresholds for landslides on São Miguel Island and underscores the advantages of the combined threshold approach for improving landslide prediction and supporting the development of robust early warning systems. Full article

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients. Full article

This systematic review and network meta-analysis aimed to compare the effects of dietary protein, creatine, and omega-3 fatty acid supplementation on muscle strength, endurance performance, and recovery outcomes in trained athletes. A comprehensive literature search across MEDLINE, Embase, Cochrane CENTRAL, Web of Science, SPORTDiscus, and Scopus identified randomized controlled trials evaluating these supplements in individuals engaged in structured training for a minimum of six months. Network meta-analysis employing a frequentist random-effects model synthesized direct and indirect evidence, with treatment rankings determined using Surface Under the Cumulative Ranking curve probabilities. The analysis incorporated 35 trials enrolling 1211 participants. Creatine supplementation demonstrated superior effects for muscle strength (SMD = 0.46, 95% CI: 0.29 to 0.63, SUCRA = 82.4%), protein supplementation proved most effective for endurance performance (SMD = 0.28, 95% CI: 0.08 to 0.48, SUCRA = 85.2%), and omega-3 supplementation yielded the greatest benefits for recovery outcomes (SMD = 0.40, 95% CI: 0.18 to 0.62, SUCRA = 88.7%). Network consistency assessment revealed no significant disagreement between direct and indirect evidence across all outcomes. These findings reveal an outcome-specific efficacy pattern supporting targeted supplementation strategies aligned with primary training objectives in athletic populations. Full article

Hurricanes have intensified and become more persistent under a changing climate, increasing the risk of infrastructure damage and property loss in coastal regions, threatening their sustainability. This study examines how hurricane intensity and persistence influence infrastructure loss, contributing to a more comprehensive understanding of climate-related risks. Using data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database from 1996 to 2024, we develop a series of machine learning models to predict property losses based on storm characteristics and contextual vulnerability factors. Narrative-based text analysis and time-series feature engineering were applied to extract meteorological and temporal attributes, while regression and ensemble models were used for predictive evaluation. Results show that storm intensity alone explains only a small portion of loss variance, with persistence influencing damage primarily through rainfall and hydrological effects. The findings highlight that vulnerability, exposure, and cumulative risk dynamics are essential for accurate long-term prediction and for assessing infrastructure sustainability. Overall, the study demonstrates that combining machine learning techniques with climate and vulnerability data can inform future research on infrastructure sustainability. The quantified vulnerability-versus-intensity breakdown presented here can support post-disaster resource allocation, insurance risk modeling, and the prioritization of infrastructure maintenance in hurricane-prone regions. Full article

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis. Full article

Rotational modulation improves strapdown inertial navigation system (SINS) by periodically reorienting the inertial measurement unit (IMU) to convert slowly varying sensor errors into manageable, cancelable components. However, existing dual-axis schemes may accumulate large total rotation angles and introduce delayed error balancing, which results in non-negligible residual attitude errors and degrades real-time navigation accuracy. To overcome these limitations, we propose an odd-symmetric dual-axis rotation strategy that jointly optimizes the rotation order and dwell positions to maximize error cancellation on each axis and across axes while constraining cumulative rotation. Based on this principle, we design a 64-position rotation scheme and derive its IMU error modulation/suppression characteristics, including gyroscope drift, accelerometer bias, scale-factor errors, and misalignment (installation) errors, and we quantify their effects on attitude and velocity. Simulations show that the proposed scheme reduces position and velocity errors by more than $60\%$ compared to a 16-position scheme, and decreases longitude error, east-velocity error, and yaw error by more than $30\%$ relative to a 32-position scheme. Experiments further validate consistent improvements in position, velocity, and attitude accuracy, demonstrating the effectiveness of the proposed rotational design for dual-axis SINS. Full article