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Abstract Monovalent salts are generally believed to stabilize DNA duplex by weakening inter-strand electrostatic repulsion. Unexpectedly, our force-induced hairpin unzipping experiments and thermal melting experiments show that LiCl, NaCl, KCl, RbCl, and CsCl at concentrations beyond ~1 M destabilize DNA, RNA, and RNA-DNA duplexes. The two types of experiments yield different changes in free energy during melting, while the results that high concentration monovalent salts destabilize duplexes are common. The effects of these monovalent ions are similar but also have noticeable differences. From 1 M to 4 M, DNA duplex is destabilized by about 0.3 k B T/bp and the melting temperature decreases by about 10 oC. Our all-atom simulations reveal this effect is caused by overcharging, where excessive ion absorption inverts the effective DNA charge from negative to positive. Furthermore, our coarse-grained simulations obtain a phase diagram that indicates whether DNA overcharging occurs at a given cation valence and concentration. These findings challenge the traditional belief that DNA overcharging occurs only with multivalent ions and have significant implications for polyelectrolyte theory, DNA nanomaterials, DNA nanotechnology, and DNA biophysics.

Abstract Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)—based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box–Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model.

Abstract In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

Abstract Background Elevated interstitial fluid pressure within tumors, resulting from impaired lymphatic drainage, constitutes a critical barrier to effective drug penetration and therapeutic outcomes. Results In this study, based on the photosynthetic characteristics of algae, an active drug carrier (CP@ICG) derived from Chlorella pyrenoidosa (CP) was designed and constructed. Leveraging the hypoxia tropism and phototropism exhibited by CP, we achieved targeted transport of the carrier to tumor sites. Additionally, dual near-infrared (NIR) irradiation at the tumor site facilitated photosynthesis in CP, enabling the breakdown of excessive intratumoral interstitial fluid by generating oxygen from water decomposition. This process effectively reduced the interstitial pressure, thereby promoting enhanced perfusion of blood into the tumor, significantly improving deep-seated penetration of chemotherapeutic agents, and alleviating tumor hypoxia. Conclusions CP@ICG demonstrated a combined effect of photothermal/photodynamic/starvation therapy, exhibiting excellent in vitro/in vivo anti-tumor efficacy and favorable biocompatibility. This work provides a scientific foundation for the application of microbial-enhanced intratumoral drug delivery and tumor therapy. Graphical Abstract

In this study, a mixture of ultrasound or microwave pretreated walnut meal protein peptides and CaCl2 was used for the preparation of walnut peptide-calcium chelate. The effects of different pretreatments on the calcium chelating capacity, structural changes and stability of walnut meal protein peptides were analyzed. The results showed that compared with walnut meal protein peptide-calcium chelate (WPP-Ca), the chelation rates of ultrasound-pretreated walnut meal protein peptide-calcium chelate (UP-WPP-Ca) and microwave-pretreated walnut meal protein peptide-calcium chelate (MP-WPP-Ca) were enhanced, which indicated that ultrasound and microwave pretreatments improved the calcium-chelating capacity of the peptides effectively. Using ultraviolet-visible (UV-Vis) absorption spectroscopy and Fourier transform infrared (FTIR) spectroscopy, it was found that ultrasound and microwave pretreatments mainly affected the calcium ion binding sites such as amino groups, carbonyl groups, carboxyl groups, amide bonds and carboxylate groups of walnut meal protein peptides. The results of X-ray diffraction (XRD) showed that ultrasound and microwave treatments changed the molecular arrangement of walnut meal protein peptides, thereby making the structure of walnut peptide-calcium chelate more ordered. Fluorescence spectroscopy showed that ultrasonic and microwave treatments promoted the chelation between aromatic amino acids and calcium ions. In addition, UP-WPP-Ca and MP-WPP-Ca showed good stability toward different pH values, temperatures, and gastrointestinal digestion. In short, ultrasonic and microwave pretreatments can improve the calcium-chelating capacity and stability of walnut meal protein peptides, which is of guiding significance for the processing of walnut peptide-calcium chelate and the development of calcium supplements.

In the context of cooperative vehicle-infrastructure systems (CVIS), vehicles often offload computational tasks to roadside units (RSUs) for execution due to their own constrained computing resources. A novel approach that has garnered increased attention involves the "vehicle-edge-cloud" framework, wherein RSUs decide whether to process tasks locally or to offload them to the cloud. The open and unpredictable nature of the wireless channels on the "edge-cloud" interface necessitates the incorporation of a security mechanism to safeguard the integrity of information transmission. However, integrating such a security mechanism can escalate the energy consumption of RSUs. Addressing the challenge of jointly optimizing roadside energy consumption and information security utility, without compromising task delay constraints, is a complex issue. To tackle this, an energy-packets queue tradeoff of task scheme and encryption strategy (EPTS) was introduced. This approach involved the development of a vehicle speed state model, a task encryption model, a data cache queue model, and a task calculation model, along with the formulation of an optimization objective function. The optimization model was subsequently transformed using Lyapunov optimization theory and was reformulated as a knapsack problem for resolution. The simulation results confirm the EPTS's commendable convergence and effectiveness. The average objective value achieved by the proposed EPTS was found to be 17% superior to that of the Equal Allocation Strategy (EAS) and 21% superior to the queue-weighted strategy (QS).

Microelectromechanical systems (MEMSs) and nanoelectromechanical systems (NEMSs) are revolutionary technologies that merge mechanical and electronic components on microscopic and nanoscopic scales [...]

Large magnification factors near gravitational lensing caustics of galaxy-cluster lenses allow the study of individual stars or compact stellar associations at cosmological distances. We study how the presence of sub-galactic subhalos, an inevitable consequence of cold dark matter, can alter the property of caustics and hence change the interpretation of highly magnified sources that lie atop them. First, we consider a galaxy-cluster halo populated with subhalos sampled from a realistic subhalo mass function calibrated to N -body simulations. Then, we compare a semianalytical approximation and an adaptive ray-shooting method that we employ to quantify the property of the caustics. As a case study, we investigate Earendel, a z = 6.2 candidate of magnified single- or multiple-star system with a lone lensed image atop the critical curve in the Sunrise Arc. We find that the source size constraint (≲0.3 pc) previously derived from macrolens models should be relaxed by a factor of a few to 10 when subhalos are accounted for, therefore allowing the possibility of a compact star cluster. The subhalos could introduce an astrometric perturbation that is ≲0 $\mathop{.}\limits^{\unicode{x02033}}$ 5, which does not contradict observation. These conclusions are largely robust to changes in the subhalo population. Subhalos therefore should be seriously accounted for when interpreting the astrophysical nature of similar highly magnified sources uncovered in recent high- z observations.

The first James Webb Space Telescope (JWST) Near InfraRed Camera imaging in the field of the galaxy cluster PLCK G165.7+67.0 ( z = 0.35) uncovered a Type Ia supernova (SN Ia) at z = 1.78, called “SN H0pe.” Three different images of this one SN were detected as a result of strong gravitational lensing, each one traversing a different path in spacetime, thereby inducing a relative delay in the arrival of each image. Follow-up JWST observations of all three SN images enabled photometric and rare spectroscopic measurements of the two relative time delays. Following strict blinding protocols which oversaw a live unblinding and regulated postunblinding changes, these two measured time delays were compared to the predictions of seven independently constructed cluster lens models to measure a value for the Hubble constant, H _0 = 71.8 + 9.2 − 8.1 km s ^−1 Mpc ^−1 . The range of admissible H _0 values predicted across the lens models limits further precision, reflecting the well-known degeneracies between lens model constraints and time delays. It has long been theorized that a way forward is to leverage a standard candle, but this has not been realized until now. For the first time, the lens models are evaluated by their agreement with the SN absolute magnifications, breaking degeneracies and producing our best estimate, H _0 = ${75.7}_{-5.5}^{+8.1}$ km s ^−1 Mpc ^−1 . This is the first precise measurement of H _0 from a multiply imaged SN Ia and only the second from any multiply imaged SN.

Effort-aware just-in-time software defect prediction (JIT-SDP) aims to effectively utilize the limited resources of software quality assurance (SQA) to detect more software defects. This improves the efficiency of SQA work and the quality of the software. However, there is disagreement regarding the representation of the key feature variable, SQA effort, in the field of effort-aware JIT-SDP. Additionally, the most recent metaheuristic optimization algorithms (MOAs) have not yet been effectively integrated with multi-objective effort-aware JIT-SDP tasks. These deficiencies, in both feature representation and model optimization (MO), result in a significant disparity between the performance of effort-aware JIT-SDP techniques and the expectations of the industry. In this study, we present a novel method called weighted code churn (CC) and improved multi-objective slime mold algorithm (SMA) (WCMS) for effort-aware JIT-SDP. It comprises two stages: feature improvement (FI) and MO. In the FI phase, we normalize the two feature variables: number of modified files (NF) and distribution of modified code across each file (Entropy). We then use an exponential function to quantify the level of difficulty of the change. The equation is as follows: DD = NFEntropy, where DD is an acronym for the degree of difficulty, NF denotes the base number, and Entropy denotes the index. We define change effort as the product of the difficulty degree in implementing the change and CC, with weighted CC representing the change effort. During the MO stage, we improve the SMA by incorporating multi-objective handling capabilities and devising mechanisms for multi-objective synchronization and conflict resolution. We develop a multi-objective optimization algorithm for hyperparameter optimization (HPO) of the JIT-SDP model in WCMS. To evaluate the performance of our method, we conducted experiments using six well-known open-source projects and employed two effort-aware performance evaluation metrics. We evaluated our method based on three scenarios: cross-validation, time-wise cross-validation, and across-project prediction. The experimental results indicate that the proposed method outperforms the benchmark method. Furthermore, the proposed method demonstrates greater scalability and generalization capabilities.

Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

IntroductionIn order to explore the correlation between discharge readiness and Harris score or self-care ability of patients undergoing total hip arthroplasty (THA) based on the enhanced recovery after surgery (ERAS) concept. We carried out this single center retrospective study.MethodsWe enrolled 331 patients who underwent THA. These patients were divided into the higher score group and the lower score group according to median discharge readiness score. After the baseline data of these patients were compared, the effect factors of discharge readiness of these patients was analyzed through univariate and multivariate logistic regression analyses and mixed effects models.ResultsThe results demonstrated that there was a correlation between discharge readiness and changes in Harris score 30 days after discharge (compared with that before surgery) in these patients. Besides, the Harris score and self-care ability 30 days after discharge were higher than those at the time of discharge. In addition, patients in the higher score group exhibited a higher Harris score compared with those in the lower score group. From the evaluation at different time points after discharge, there was a significant difference in the Harris score between both groups.DiscussionIt can be inferred that the discharge readiness of patients undergoing THA was correlated with the Harris score but not with the self-care ability. These results are expected to provide guidance for the physical and mental recovery of patients undergoing total hip replacement under the ERAS concept. Furthermore, these findings may contribute to higher diagnosis, treatment, and nursing levels of orthopedic medical staff.

Energy recovery from low-strength wastewater through anaerobic methanogenesis is constrained by limited substrate availability. The development of efficient methanogenic communities is critical but challenging. Here we develop a strategy to acclimate methanogenic communities using conductive carrier (CC), electrical stress (ES), and Acid Orange 7 (AO7) in a modified biofilter. The synergistic integration of CC, ES, and AO7 precipitated a remarkable 72-fold surge in methane production rate compared to the baseline. This increase was attributed to an altered methanogenic community function, independent of the continuous presence of AO7 and ES. AO7 acted as an external electron acceptor, accelerating acetogenesis from fermentation intermediates, restructuring the bacterial community, and enriching electroactive bacteria (EAB). Meanwhile, CC and ES orchestrated the assembly of the archaeal community and promoted electrotrophic methanogens, enhancing acetotrophic methanogenesis electron flow via a mechanism distinct from direct electrochemical interactions. The collective application of CC, ES, and AO7 effectively mitigated electron flow impediments in low-strength wastewater methanogenesis, achieving an additional 34% electron recovery from the substrate. This study proposes a new method of amending anaerobic digestion systems with conductive materials to advance wastewater treatment, sustainability, and energy self-sufficiency.

Background: Breastfeeding provides several positive health benefits for the newborn child, yet breastfeeding rates remain low in the United States (US). Theory-based approaches have the potential to improve breastfeeding promotion interventions. Hence, the study examined the correlates of intention to breastfeed among US pregnant women based on the multi-theory model (MTM) of health behavior change. Methods: Using a cross-sectional design, a 36-item online survey was administered to a nationally representative sample of 315 pregnant women in the US. The instrument was psychometrically validated for face, content, and construct validity by a panel of six experts over two rounds. Further, construct validation was done by confirmatory factor analysis (CFA). Hierarchical regression modeling was employed to explain the intention to start breastfeeding and sustain exclusive breastfeeding for up to six months and with complementary foods for up to 24 months. Results: Internal consistency using Cronbach’s alpha was found to be acceptable. It was found that behavioral confidence and changes in the physical environment positively affected the initiation of breastfeeding (P

Abstract This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

Abstract Background The poor targeting delivery efficiency and limited efficacy of single therapeutic approach have consistently posed significant challenges in tumor management. Results In this research, we have conceived and synthesized a dual-targeting nanodrug delivery system denoted as PDA-DEM-Fe3O4@M, which incorporates a polydopamine nanoparticle (PDA) with photothermal properties, diethyl maleate (DEM) as a chemotherapy agent accelerating tumor apoptosis, iron oxide nanoparticles (Fe3O4) eliciting magnetic targeting effects, and tumor cell membranes (M) contributing to homologous targeting capabilities. The synergistic effect of PDA-induced photothermal therapy and DEM-mediated chemotherapy has been demonstrated in this study to exert a robust inhibitory and cytotoxic influence on tumor cells. Additionally, the biocompatibility of this system has also been demonstrated. Conclusions Through the synergistic effects of PDA’s photothermal therapy and DEM’s chemotherapy, this system demonstrated excellent inhibition and killing effects on tumor cells. Furthermore, we established its excellent biological safety profile. This study demonstrated the potential of this nanomaterial for clinical application in tumor therapy.

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Pressure pulsations are crucial data within the flow field of a pump, and the characteristics of these pulsations can reveal changes in the internal flow. Based on model experiments, this paper obtained pressure pulsation data under two blade conditions and compared direct time-domain observations, peak-to-peak value changes, and the VMD decomposition method. The results show that even when it is known that one blade condition has changed, it is not possible to determine this through direct observation of pressure pulsation changes. The peak-to-peak value changes indicate that under special flow conditions, they are easily affected by different operating conditions, which can interfere with the results. In contrast, the VMD method, which decomposes the signal into low-frequency components, can better display anomalies within the pressure pulsation cycle and is less susceptible to the interference of flow conditions, offering some reference significance for diagnosing the blade operating conditions of the main pump.

Abstract This year marks the 20th anniversary of the discovery of LaAlO3/SrTiO3 (LAO/STO) oxide heterointerfaces. Since their discovery, transition metal oxide (TMO) interfaces have emerged as a fascinating and fast‐growing area of research, offering a variety of unique and exotic physical properties which has provided a strong impetus for the rapid advances and actualization of oxide electronics. This review revisits the fundamental mechanisms accounting for the two‐dimensional (2D) conducting interfaces, and how new models proposed to better account for the unique interfacial effects. Recent breakthroughs in the theoretical and experimental domains of oxide interfaces are also discussed including the detection and investigation of 2D quasiparticle. Moving beyond the well‐known LAO/STO interface, this review delves into other systems where unconventional interfacial superconductivity, interfacial magnetism, and spin polarization are dealt with in greater detail. In terms of device applications, this review proceeds with a treatment on the recent developments in domains including field effect transistors and freestanding heterostructure membranes. By emphasizing the opportunities and challenges of integrating oxide interfaces with existing technologies, the review will end off with an outlook projecting the progress and the trajectory of this research domain in the years to come.

Abstract Background Marfan syndrome (MFS) is a rare genetic disorder caused by mutations in the Fibrillin‐1 gene (FBN1) with significant clinical features in the skeletal, cardiopulmonary, and ocular systems. To gain deeper insights into the contribution of epigenetics in the variability of phenotypes observed in MFS, we undertook the first analysis of integrating DNA methylation and gene expression profiles in whole blood from MFS and healthy controls (HCs). Methods The Illumina 850K (EPIC) DNA methylation array was used to detect DNA methylation changes on peripheral blood samples of seven patients with MFS and five HCs. Associations between methylation levels and clinical features of MFS were analyzed. Subsequently, we conducted an integrated analysis of the outcomes of the transcriptome data to analyze the correlation between differentially methylated positions (DMPs) and differentially expressed genes (DEGs) and explore the potential role of methylation‐regulated DEGs (MeDEGs) in MFS scoliosis. The weighted gene co‐expression network analysis was used to find gene modules with the highest correlation coefficient with target MeDEGs to annotate their functions in MFS. Results Our study identified 1253 DMPs annotated to 236 genes that were primarily associated with scoliosis, cardiomyopathy, and vital capacity. These conditions are typically associated with reduced lifespan in untreated MFS. We calculated correlations between DMPs and clinical features, such as cobb angle to evaluate scoliosis and FEV1% to assess pulmonary function. Notably, cg20223687 (PTPRN2) exhibited a positive correlation with cobb angle of scoliosis, potentially playing a role in ERKs inactivation. Conclusions Taken together, our systems‐level approach sheds light on the contribution of epigenetics to MFS and offers a plausible explanation for the complex phenotypes that are linked to reduced lifespan in untreated MFS patients.

Many microlensed stars discovered by JWST closely follow the winding critical curve of A370 along the “Dragon Arc” with m _AB > 26.5, which we show comprises asymptotic giant branch stars microlensed by the observed level of diffuse cluster stars, corresponding to ≃1% of the dark matter density. Most events appear along the inner edge of the critical curve, following an asymmetric band of width ≃4.5 kpc that is skewed by −0.7 ± 0.2 kpc. This asymmetry, we argue, follows from the parity difference in caustic structure inherent to microlensing that extends to higher magnification in the negative parity regions. This parity difference predicts a modest net shift of −0.04 kpc to the inside of the cluster critical curve within a narrower band of ≃1.4 kpc than observed. Adding cold-dark-matter-like subhalos of 10 ^6−8 M _⊙ doubles the width, but detections are predicted to favor the outside of the critical curve, where the subhalos generate local Einstein rings, and subhalos inside the critical curve depress the magnification, reducing microlensing. Instead, the density perturbations of “wave dark matter” as a Bose–Einstein condensate ( ψ DM) can generate a wide band of corrugated critical curves with a large negative asymmetry. We find that a de Broglie wavelength of ≃10 pc reproduces the observed width of 4.5 kpc, with a negative skewness ≃−0.6 kpc, like the data, corresponding to a boson mass of ≃10 ^−22 eV, in agreement with dwarf galaxy dynamical estimates. Independently, we also find clear asymmetry in the Jupiter Arc, with 12 microlensed stars lying along the inside of the critical curve, like the Dragon Arc.

Background and aimsFew studies have examined the relationship between daytime napping and risk of kidney diseases. We aimed to investigate the association of daytime napping with the incidence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD). We also examined whether sleep duration modified the association of nap with CKD or ESKD.MethodsWe recruited 460,571 European middle- to older-aged adults without prior CKD or ESKD between March 13, 2006, and October 1, 2010, in the UK Biobank. Sleep behavior data were obtained through questionnaires administered during recruitment. The analysis of the relationship between napping and the occurrence of CKD and ESKD utilized Cox proportional hazards regression models. The modification role of sleep duration on the effect of nap on CKD and ESKD was also examined.ResultsAfter a mean follow-up of 11.1 (standard deviation 2.2) years, we observed 28,330 incident CKD cases and 927 ESKD cases. The daytime napping was associated with incident CKD (P for trend = .004). After fully adjusted, when compared with participants who did not take nap, those in sometimes and usually nap groups had higher risk of CKD. Nevertheless, the available evidence did not support a link between daytime napping and ESKD (P for trend = .06). Simultaneously, there was insufficient evidence suggesting that sleeping duration modified the association of daytime napping with incident CKD or ESKD.ConclusionDaytime napping was associated with an increased risk of CKD. However, the absence of conclusive evidence did not indicate a connection between daytime napping and ESKD.

Abstract Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning‐based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high‐image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non‐local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi‐scale feature information from the echo and image data. PixelShuffle is utilised for up‐sampling to generate high‐quality echo data. The experimental results on MSTAR and Sentinel‐1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.

Abstract Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.

Abstract Background Magnetic resonance imaging (MRI) is the gold standard for evaluating body composition. However, the reference ranges have not been established. Methods Three lean tissue and seven adipose tissue parameters based on MRI data from the UK Biobank were used in this study. Participants with European ancestry and data on at least one parameter were screened. Age‐ and sex‐specific percentile curves were generated using the lambda–mu–sigma method. Three levels of reference ranges were provided, which were equivalent to the mean ± 1 standard deviation (SD), 2 SDs and 2.5 SDs. Results The final analysis set for each parameter ranged from 4842 to 14 148 participants (53.4%–56.6% women) with a median age of 61. For lean tissue parameters, compared with those at age 45, the median total lean tissue volume and total thigh fat‐free muscle volume at age 70 were 2.83 and 1.73 L, and 3.02 and 1.51 L lower in men and women, respectively. The median weight‐to‐muscle ratios at age 45 were 0.51 and 0.83 kg/L lower compared with those at age 70 in men and women, respectively. Adipose tissue parameters showed inconsistent differences. In men, the median muscle fat infiltration, visceral adipose tissue (VAT) volume, total abdominal adipose tissue index and abdominal fat ratio were 1.48%, 0.32 L, 0.08 L/m2 and 0.4 higher, and the median abdominal subcutaneous adipose tissue (ASAT) volume and total adipose tissue volume were 0.47 and 0.41 L lower, respectively, at age 70 than at age 45. The median total trunk fat volume was approximately 9.53 L at all ages. In women, the median muscle fat infiltration and VAT volume were 1.68% and 0.76 L higher, respectively, at age 70 than at age 45. The median ASAT volume, total adipose tissue volume, total trunk fat volume, total abdominal adipose tissue index and abdominal fat ratio were 0.35 L, 0.78 L, 1.12 L, 0.49 L/m2 and 0.06 higher, respectively, at age 60 than at age 45. The medians of the former three parameters were 0.33 L, 0.14 L and 0.20 L lower, at age 70 than at age 60. The medians of the latter two parameters were approximately 3.64 L/m2 and 0.55 at ages between 60 and 70. Conclusions We have established reference ranges for MRI‐measured body composition parameters in a large community‐dwelling population. These findings provide a more accurate assessment of abnormal adipose and muscle conditions.

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale optimization algorithm, and RVM tuned with Manta Ray foraging optimization concerning root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R2) and Nash-Sutcliffe Efficiency (NSE) and new graphical inspection methods. The models were assessed using data acquired from two stations in China and data were divided into three equal parts. The models were tested using each data set. The application outcomes revealed that the proposed algorithm considerably improved the accuracy of a single RVM in monthly pan evaporation prediction by an average improvement in RMSE, MAE, R2, and NSE as 27.65%, 27.53%, 8.40% and 8.63%, respectively. It is also found that the proposed algorithm showed significant dominance over others models with respect to improvement in overall mean values of RMSE, MAE, R2, and NSE statistics from 34.7–38.2 to 18.2–19.5, 36.2–36.4 to 19.1–18.5, 12.5–13.8 to 3.6–3.7, and 12.4–14.6 to 3.6–3.9%, for both climatic stations, respectively. Importing extraterrestrial radiation and periodicity component (month number of the data) into the model inputs improved the prediction accuracy of the implemented models. The outcomes revealed that the RVM-IMRFO performed superior to the other methods in predicting monthly pan evaporation using only temperature data which is essential, especially in developing countries where other climatic data are missing or unavailable. The RVM model was also compared with standard multi-layer perceptron neural networks (MLPNN) and found that the first acts better than the latter in monthly pan evaporation prediction.

This meta-analysis examined the predictive value of admission serum and urine carboxypeptidase-B activation peptide (CAPAP) in patients with severe acute pancreatitis. PubMed, Embase, and the Cochrane Library were searched for relevant studies published up to February 21, 2022. Seven studies (439 acute pancreatitis patients) were included. The summary sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the receiver operating characteristic curve (AUC) of admission serum CAPAP were 0.86 (95%CI: 0.76-0.93), 0.78 (95%CI: 0.68-0.85), 3.89 (95%CI: 2.56-5.91), 0.18 (95%CI: 0.09-0.33), 18.56 (95%CI: 6.99-49.30), and 0.90 (95%CI: 0.87-0.92), respectively; these values for admission urine CAPAP were 0.82 (95%CI: 0.50-0.95), 0.78 (95%CI: 0.68-0.86), 3.75 (95%CI: 2.40-5.85), 0.23 (95%CI: 0.07-0.79), 9.87 (95%CI: 3.71-26.22), and 0.84 (95%CI: 0.80-0.87), respectively. There were no significant differences between admission serum and urine CAPAP in sensitivity (1.05, 95%CI: 0.75-1.47; P = 0.781), specificity (1.00, 95%CI: 0.85-1.18; P = 1.000), PLR (1.04, 95%CI: 0.56-1.91; P = 0.906), NLR (0.78, 95%CI: 0.20-3.10; P = 0.727), and DOR (1.88, 95%CI: 0.47-7.49; P = 0.370). The AUC of serum CAPAP (1.07, 95%: 1.02-1.13; P = 0.007) was higher than that of urine CAPAP in predicting acute pancreatitis severity. Both admission serum and urine CAPAP had moderate predictive values for assessing the severity of acute pancreatitis. Key highlights Both admission serum and urine carboxypeptidase-B activation peptide (CAPAP) had moderate predictive values for assessing the severity of acute pancreatitis. The predictive value of admission serum CAPAP was higher than that of admission urine CAPAP in predicting acute pancreatitis severity. Including the measurement of serum CAPAP at admission in patients with suspected pancreatitis could help triage the patients and determine the course of management. Hospital policymakers could consider including it among the routine tests at admission for such patients.

Drought modeling is vital for designing and managing water resource systems due to its significant effects on agriculture and other components of the environment. This study evaluates the prediction accuracy of two newly developed heuristic methods, optimally pruned extreme learning machine (OP-ELM) and dynamic evolving neural-fuzzy inference system (DENFIS) in drought modeling based on four standard precipitation indexes (SPI), i.e., SPI3, SPI6, SPI9 and SPI12 calculated for three meteorological stations of Pakistan. The prediction capability of the two methods is tested using the different time lag input combinations of each drought index and compared with multivariate adaptive regression spline (MARS). For the SPI3, SPI6, and SPI12 drought indexes, it was found that the DENFIS model provided better accuracy than the OP-ELM and MARS models. However, in the case of SPI6, the OP-ELM model performed better than the other models. The effect of periodicity on the prediction accuracy of the models is also evaluated. It was found that adding periodicity as inputs for models generally gave good forecasting results; however, importing this component negatively affects models’ accuracy in some cases. The best DENFIS model improved the accuracy of the OP-ELM by 8.35%, 6.06% and 2.51% predicting SPI3, SPI6, and SPI12 of Drosh Station with respect to root mean square error criterion, respectively. This study also demonstrated that the DENFIS heuristic models which have not been previously used for this issue generally provided more accurate predictions than the other models in drought prediction.

The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In June 2019, during the reservoir filling process of the Huangdeng Hydroelectric Station, a large-scale reactivation of ancient landslides occurred in this area, posing severe threats to riverside infrastructure and human safety, including ground cracking, house cracking, foundation settlement, and road collapse. The reactivation mechanism of ancient landslides at reservoir banks is highly complex due to fluid dynamics. This study conducted field investigations in the Cheyipin landslide area, monitored surface and subsurface deformations using GNSS and inclinometers, and analyzed the distribution characteristics, destruction features, and reactivation mechanisms of the landslides through correlation analysis and numerical calculations. The results indicate that the instability pattern of the slopes manifests as traction-type sliding failure. The slopes do not slide along the ancient sliding surface but along a newly formed arcuate sliding surface, with the direct impact area mainly concentrated near the waterline. The stability of the slopes in this project is closely related to the reservoir water level. It can be assumed that the lowering of the reservoir water level triggered the reactivation of the ancient landslides in the Cheyipin section, while the influence of rainfall can be ignored. To prevent the reactivation of ancient landslides, attention should be focused on the changes in reservoir water level, avoiding rapid adjustments in water level during the initial lowering and final raising of the water level.

The sulfidation of nanoscale zerovalent iron (nZVI) has received increasing attention for reducing the oxidizability of nZVI and improving its reactivity toward heavy metal ions. Here, a sulfide (S)-modified attapulgite (ATP)-supported nanoscale nZVI composite (S-nZVI@ATP) was rapidly synthesized under acidic conditions and used to alleviate Cd2+ toxicity from an aqueous solution. The degree of oxidation of S-nZVI@ATP was less than that of nZVI@ATP, indicating that the sulfide modification significantly reduced the oxidation of nZVI. The optimal loading ratio was at an S-to-Fe molar ratio of 0.75, and the adsorption performance of S-nZVI@ATP for Cd2+ was significantly improved compared with that of nZVI@ATP. The removal of Cd2+ by S-nZVI@ATP was 100% when the adsorbent addition was 1 g/L, the solution was 30 mL, and the adsorption was performed at 25 °C for 24 h with an initial Cd2+ concentration of 100 mg/L. Kinetics studies showed that the adsorption process of Cd followed the pseudo-second-order model, indicating that chemisorption was the dominant adsorption mechanism. The adsorption of Cd2+ by S-nZVI @ATP is dominated by the complexation between the iron oxide or iron hydroxide shell of S-nZVI and Cd2+ and the formation of Cd(OH)2 and CdS precipitates. HIGHLIGHTS S-nZVI was loaded on ATP, effectively preventing nZVI agglomeration, and the sulfide modification significantly reduced the oxidation of nZVI.; Cd2+ removal was mailly attributed to complex with the iron oxides or iron hydroxide shell of S-nZVI.; S-nZVI@ATP showed a stable adsorption effect on Cd2+ over a wide pH range.; The removal of Cd2+ by S-nZVI@ATP follows the quasi-second-order kinetic adsorption model.;