Search Results (2,805)

The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. However, the conventional and probabilistic methods are less adaptive to the acute, micro, and unusual changes in the demand trend. With the recent development of artificial intelligence (AI), machine learning (ML) has become the most popular choice due to its higher accuracy based on time-, demand-, and trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features to predict hourly load demand. The novelty of PredXGBR-1 lies in its focus on short-term lag autocorrelations to enhance adaptability to micro-trends and demand fluctuations. Validation across five datasets, representing electrical load in the eastern and western USA over a 20-year period, shows that PredXGBR-1 outperforms a long-term feature-based XGBoost model, PredXGBR-2, and state-of-the-art recurrent neural network (RNN) and long short-term memory (LSTM) models. Specifically, PredXGBR-1 achieves an mean absolute percentage error (MAPE) between 0.98 and 1.2% and an $R^2$ value of 0.99, significantly surpassing PredXGBR-2’s $R^2$ of 0.61 and delivering up to 86.8% improvement in MAPE compared to LSTM models. These results confirm the superior performance of PredXGBR-1 in accurately forecasting short-term load demand. Full article

This study explored the longitudinal dynamics of teacher violence and student wellbeing in rural Sierra Leone, West Africa. The participants, totaling 3170 children with an age range of 5 years to 11 years, were cluster-sampled from a large geographic area to ensure gender balance and representation from diverse linguistic backgrounds and religious affiliations. They were drawn from the Safe Learning Study, which spanned over 5 years and involved 100 schools in rural Sierra Leone. Data collection took place in four waves from November 2018 to May 2021. Participants completed self-report questionnaires pertaining to psychological wellbeing and experiences of violence from teachers. The study employed a random intercept cross-lagged panel model (RICLPM) to examine the relationship between violence and mental health across waves. Across children, a relationship between teacher violence and student wellbeing was observed over time. However, for individual children, higher wellbeing predicted lower instances of violence, and vice versa, although to a weak extent. These findings highlight the complex interplay between violence and wellbeing within the cultural sample. These insights contribute to a deeper understanding of the social dynamics surrounding violence and wellbeing, informing targeted interventions and policy initiatives aimed at creating safer and healthier environments for at-risk populations. Full article

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance. Full article

Non-bovine dairy animals, commonly referred to as non-traditional dairy species, include goats, sheep, yaks, buffalo, donkeys, alpacas, llamas, and other less commonly farmed species. These animals have been integral to livestock systems since ancient times, providing milk and other essential products. Despite their historical significance, dairy production from many of these species remains predominantly confined to rural areas in developing countries, where scientific advancements and technical improvements are often limited. As a consequence of this, the scientific literature and technological developments in the processing and characterization of dairy products from these species have lagged behind those for cow’s milk. This review aims to compile and analyze existing research on dairy products derived from non-traditional animals, focusing on their molecular characteristics, including proteins (alpha, beta, kappa, and total casein), fats (cholesterol and total fat), lactose, albumin, ash, total solids, and somatic cell count, among others, for each of these species. Additionally, we discuss emerging technologies employed in their processing, encompassing both non-thermal methods (such as high-pressure processing, pulsed electric fields, ultrasound processing, UV-C irradiation, gamma radiation, microfiltration, and cold plasma processing) and thermal methods (such as ohmic heating). This review also explores the specific potential applications and challenges of implementing these technologies. By synthesizing recent findings, we aim to stimulate further research into innovative technologies and strategies that can enhance the quality and yield of non-bovine dairy products. Understanding the unique properties of milk from these species may lead to new opportunities for product development, improved processing methods, and increased commercialization in both developing and developed markets. Full article

This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a nonparametric spatiotemporal trend, a spatial lag of the dependent variable, independent variables, noise, and time-series autoregressive noise. The primary functions in this package cover the estimation of P-Spline spatial econometric models using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) methods, as well as the computation of marginal impacts for both parametric and nonparametric terms. Additionally, the package offers methods for the graphical display of estimated nonlinear functions and spatial or spatiotemporal trend maps. Applications to cross-sectional and panel spatial data are provided to illustrate the package’s functionality. Full article

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends. Full article

Cancer immunotherapy has emerged as an effective, personalized treatment for certain patients, particularly for those with hematological malignancies. However, its efficacy in breast cancer has been marginal—perhaps due to cold, immune-excluded, or immune-desert tumors. Natural killer T (NKT) cells play a critical role in cancer immune surveillance and are reduced in cancer patients. Thus, we hypothesized that NKT cells could serve as a surrogate marker for immune function. In order to assess which breast cancer patients would likely benefit from immune cell-based therapies, we have developed a quantitative method to rapidly assess NKT function using stimulation with artificial antigen presenting cells followed by quantitative real-time PCR for IFN-γ. We observed a significant reduction in the percentage of circulating NKT cells in breast cancer patients, compared to healthy donors; however, the majority of patients had functional NKT cells. When we compared BC patients with highly functional NKT cells, as indicated by high IFN-γ induction, to those with little to no induction, following stimulation of NKT cells, there was no significant difference in NKT cell number between the groups, suggesting functional loss has more impact than physical loss of this subpopulation of T cells. In addition, we assessed the percentage of tumor-infiltrating lymphocytes and PD-L1 expression within the tumor microenvironment in the low and high responders. Further characterization of immune gene signatures in these groups identified a concomitant decrease in the induction of TNFα, LAG3, and LIGHT in the low responders. We next investigated the mechanisms by which breast cancers suppress NKT-mediated anti-tumor immune responses. We found that breast cancers secrete immunosuppressive lipids, and treatment with commonly prescribed medications that modulate lipid metabolism, can reduce tumor growth and restore NKT cell responses. Full article

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile. Full article

Tamibarotene is a synthetic retinoid that inhibits tumor cell proliferation and promotes differentiation. We previously reported on the safety and tolerability of tamibarotene in patients with recurrent or refractory solid tumors. Therefore, in this study, we aimed to evaluate the pharmacokinetic properties of tamibarotene and construct a precise pharmacokinetic model. We also conducted a non-compartmental analysis and population pharmacokinetic (popPK) analysis based on the results of a phase I study. Targeted pediatric and young adult patients with recurrent or refractory solid tumors were administered tamibarotene at doses of 4, 6, 8, 10, and 12 g/m²/day. Serum tamibarotene concentrations were evaluated after administration, and a popPK model was constructed for tamibarotene using Phoenix NLME. During model construction, we considered the influence of various parameters (weight, height, body surface area, and age) as covariates. Notably, 22 participants were included in this study, and 109 samples were analyzed. A two-compartment model incorporating lag time was selected as the base model. In the final model, the body surface area was included as a covariate for apparent total body clearance, the central compartment volume of distribution, and the peripheral compartment volume of distribution. Visual prediction checks and bootstrap analysis confirmed the validity and predictive accuracy of the final model as satisfactory. Full article

The rising frequency and severity of droughts requires accurate monitoring and forecasting to reduce the impact on water resources and communities. This study aims to investigate drought monitoring and categorization, while enhancing drought forecasting by using three machine learning models—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF). The models were trained on the study region’s historic precipitation and temperature data (minimum and maximum) from 1960 to 2021. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed for a time scale of 3, 6 and 12 months. The monthly precipitation data were used for creating lag scenarios and were used as input features for the models to improve the models’ performance and reduce overfitting. Statistical parameters like the coefficient of determination (R²), Mean Absolute Error (MAE), Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) were determined to evaluate the model accuracy. For forecasting, the SPEI3, ANN and SVM models show better performance (R² > 0.9) than the RF models when the 3-month lag data were used as input features. For SPEI6 and SPEI12, the 6-month lag and 12-month lag data, respectively, were needed to increase the models’ accuracy. The models exhibited RMSE values of 0.27 for ANN, 0.28 for SVM, and 0.37 for RF for the SPEI3, indicating the superior performance of the former two. The models’ accuracy increases as the lag period increases for SPI forecasting. Overall, the ANN and SVM models outperformed the RF model for forecasting long-term drought. Full article

Background. Alterations induced by direct oral anticoagulants (DOACs) or vitamin K antagonists (VKAs) to thromboelastometry and thrombin generation are not well defined. We performed a simultaneous investigation of thromboelastometry and thrombin generation for patients who were chronically anticoagulated with DOACs or VKAs. Methods. A total of 131 patients on DOACs [apixaban (n = 37), rivaroxaban (n = 34), dabigatran (n = 30), edoxaban (n = 30)] and 33 on VKAs were analyzed. Whole blood was analyzed for thromboelastometry and plasma was analyzed for thrombin generation. Results. While the thromboelastometry clotting time (CT) was responsive to the hypocoagulability induced by DOACs or VKAs, clot formation time and maximal clot formation were not. Cumulatively, the parameters denoting the velocity of thrombin generation (lag time, time-to-peak) were relatively less responsive to the hypocoagulability induced by VKAs than DOACs. Conversely, the parameters denoting the quantity of thrombin generation [peak-thrombin and the endogenous thrombin potential (ETP)] were more responsive to the hypocoagulability induced by VKAs than DOACs. Apixaban showed relatively small differences (peak vs. trough) in the plasma concentration and a relatively small (peak vs. trough) difference of hypocoagulability when assessed by the CT or the ETP. The CT and the ETP were strongly correlated with DOAC concentrations or with the VKA-INR. Conclusions. DOACs and VKAs altered thromboelastometry and thrombin generation to an extent that probably reflects the mode of action of these drugs and may also have practical implications for patients’ management. Apixaban showed a small difference of hypocoagulability (peak vs. trough), suggesting a more stable anticoagulation over the daily course of treatment. Based on the correlations of the CT or the ETP vs. the DOAC concentrations, we estimated that critical values of the CT or the ETP would correspond to DOAC concentrations of 400 or 20 ng/mL. Whenever dedicated tests for DOAC concentrations are not available, the CT or the ETP can be used as surrogates to evaluate the level of anticoagulation induced by DOACs. Full article

In this study, we investigated the sleep–wake rhythm of nursery school children with the aim of supporting their health and mental/physical development. We analyzed 4881 children from infancy to 6 years of age, using 2 week sleep tables recorded by their guardians. The tables contained night bedtimes, wake times, nighttime/daytime sleep duration, and the differences in these between weekdays and weekends. The total sleep decrement of children with increasing age is attributed to a decrease in daytime sleep, while nighttime sleep duration remains almost unchanged at about 10 h, which is, therefore, referred to as the nighttime basic sleep duration (NBSD). Although bedtime stabilizes at around 9:30 p.m. by the age of 2, wake-up times tend to be before 7 a.m., which results in sleep insufficiency during weekdays. This lack of sleep is compensated for by long naps on weekdays and by catching up on sleep on weekend mornings, which may contribute to future social jet lag. Guardians are encouraged to know their children’s exact NBSD and set an appropriate bedtime to be maintained on weekdays. This helps to prevent sleep debt and fosters a consistent daily rhythm of waking up at the same time both on weekdays and weekends. These conditions are believed to support mental/physical development and school and social adaptation. Full article

This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O₃) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air pollutants and meteorological factors during a typical sea–land breezes event from 22 to 23 July 2022. A coherent Doppler lidar (CDL) system was employed to continuously detect three-dimensional wind fields. The results revealed that nocturnal ozone levels were enhanced by a conversion of sea–land breezes. Initially, the prevailing northerly land breeze transported high concentrations of O₃ and other air pollutants from downtown to the Yellow Sea. As the sea breeze developed in the afternoon, the sea breeze front advanced northward, resulting in a flow of high O₃ concentrations back into inland areas. This penetration of the sea breeze front led to a notable spike in O₃ concentrations between 16:00 on 22 July and 02:00 on 23 July across downtown areas, with an average increase of over 70 μg/m³ within 10 min. Notably, a time lag in peak O₃ concentration was observed with southern downtown areas peaking before northern rural areas. During this period, combined pollution of O₃ and PM_2.5 was also observed. These findings indicated that the nighttime increase in O₃ concentrations, coupled with enhanced atmospheric oxidation, would likely promote the secondary conversion of gaseous precursors into PM_2.5. Full article

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness. Full article

Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Methods: Monthly confirmed malaria cases of children under five during the period 2015–2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Results: Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. Conclusions: The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems. Full article