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In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) is a non-invasive molecular imaging technique. The limited spatial resolution of PET images, due to technological and physical imaging constraints, directly affects the precise localization and interpretation of small lesions and biological processes. The super-resolution (SR) technique aims to enhance image quality by improving spatial resolution, thereby aiding clinicians in achieving more accurate diagnoses. However, most conventional SR methods rely on idealized degradation models and fail to effectively capture both low- and high-frequency information present in medical images. For the challenging SR reconstruction of PET images exhibiting motion-induced artefacts, a degradation model that better aligns with practical scanning scenarios was designed by us. Furthermore, we proposed a PET image SR method based on the deep residual-in-residual network (DRRN), focusing on the recovery of both low- and high-frequency information. By incorporating multi-level residual connections, our approach facilitates direct feature propagation across different network levels. This design effectively mitigates the lack of feature correlation between adjacent convolutional layers in deep networks. Our proposed method surpasses benchmark methods in both full-reference and no-reference metrics and subjective visual effects across small animal PET (SAPET), phantoms, and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. The experimental findings confirm the remarkable efficacy of DRRN in enhancing spatial resolution and mitigating blurring in PET images. In comparison to conventional SR techniques, this method demonstrates superior proficiency in restoring low-frequency structural texture information while simultaneously maintaining high-frequency details, thus showcasing exceptional multi-frequency information fusion capabilities. [ABSTRACT FROM AUTHOR]

Identifying ecosystem service bundles and their long-term evolutionary characteristics is essential for the overall enhancement of regional ecosystem services, as well as the division and management of functional areas, providing a basis for decision-making in formulating ecological and environmental protection policies, as well as regional development planning. Based on land use, remote sensing, and meteorological data obtained from Anhui Province, this study assessed six important ecosystem service functions, including food production (FP), water yield (WY), carbon sequestration (CS), soil conservation (SC), habitat quality (HQ), and landscape aesthetics (LA), at the township scale in 2000, 2010, and 2020. On this basis, the k-means clustering method was used to identify ecosystem service bundles, analyze the spatio-temporal evolution trajectory of service bundles, and explore the driving factors of the spatio-temporal evolution of ecosystem service bundles using GeoDetector 2015 The results indicate the following: (1) At the spatial level, diverse ecosystem services demonstrate pronounced spatial differentiation. The distribution pattern of HQ, carbon fixation, and SC services is generally lower in the north and higher in the south, with areas of high value predominantly located in the western Dabie Mountains and the mountains of Southern Anhui. Conversely, FP services exhibit the reverse pattern, and WY services display a gradual increase from north to south, while cultural services are more dispersed, with areas of high value primarily located in the western Dabie Mountains, the Yangtze River Basin, and other locations. On the temporal scale, WY, SC, and FP services mainly exhibit an increasing trend, marked by a significant increase, whereas other services tend to present a decreasing trend. (2) Anhui Province can be categorized into four distinct types of service bundles: the grain production bundle (GPB), mountain ecological conservation bundle (MECB), urban living bundle (ULB), and core protection bundle (CPB). Ecosystem service bundles exhibit clear spatial differentiation, and identical service bundles demonstrate substantial clustering in space. Between 2000 and 2020, ecosystem service bundles displayed a marked spatio-temporal evolution, with the prevalence of GPBs diminishing, whereas the share of ULBs progressively increased, and the number of MECBs and CPBs remained largely stable. (3) In the spatio-temporal evolution process, the average annual precipitation, the proportion of forest land, and slope constitute the principal natural factors influencing the spatio-temporal evolution of ecosystem service bundles, while the proportion of construction land represents the primary socio-economic factor, with natural factors exerting a more significant influence than socio-economic factors. [ABSTRACT FROM AUTHOR]

Background: Our previous studies have shown that scorpion venom heat-resistant synthesized peptide (SVHRSP) induces a significant extension in lifespan and improvements in age-related physiological functions in worms. However, the mechanism underlying the potential anti-aging effects of SVHRSP in mammals remains elusive. Methods: Following SVHRSP treatment in senescence-accelerated mouse resistant 1 (SAMR1) or senescence-accelerated mouse prone 8 (SAMP8) mice, behavioral tests were conducted and brain tissues were collected for morphological analysis, electrophysiology experiments, flow cytometry, and protein or gene expression. The human neuroblastoma cell line (SH-SY5Y) was subjected to H2O2 treatment in cell experiments, aiming to establish a cytotoxic model that mimics cellular senescence. This model was utilized to investigate the regulatory mechanisms underlying oxidative stress and neuroinflammation associated with age-related cognitive impairment mediated by SVHRSP. Results: SVHRSP significantly ameliorated age-related cognitive decline, enhanced long-term potentiation, restored synaptic loss, and upregulated the expression of synaptic proteins, therefore indicating an improvement in synaptic plasticity. Moreover, SVHRSP demonstrated a decline in senescent markers, including SA-β-gal enzyme activity, P16, P21, SIRT1, and cell cycle arrest. The underlying mechanisms involve an upregulation of antioxidant enzyme activity and a reduction in oxidative stress-induced damage. Furthermore, SVHRSP regulated the nucleoplasmic distribution of NRF2 through the SIRT1-P53 pathway. Further investigation indicated a reduction in the expression of proinflammatory factors in the brain after SVHRSP treatment. SVHRSP attenuated neuroinflammation by regulating the NF-κB nucleoplasmic distribution and inhibiting microglial and astrocytic activation through the SIRT1-NF-κB pathway. Additionally, SVHRSP significantly augmented Nissl body count while suppressing neuronal loss. Conclusion: SVHRSP could remarkably improve cognitive deficiency by inhibiting oxidative stress and neuroinflammation, thus representing an effective strategy to improve brain health. [ABSTRACT FROM AUTHOR]

Corrosion behavior is critical to the application of lightweight aluminum/steel joints using new resistance spot welding (RSW) technology. The study investigated the corrosion mechanism and the shear strength of RSW joints comprising 1.2 mm 5182 aluminum and 1.5 mm DP780 galvanized steel. Electrochemical corrosion tests were conducted on the base materials and various positions of the welds in a 3.5% NaCl solution. This result revealed that the corrosion susceptibility of the interfacial intermetallic compound (IMC) layer was not accelerated by the aluminum nugget because of the noble corrosion potential. Subsequently, the spray acceleration test was employed to investigate the corrosion mechanism. It is noteworthy that microcracks, as well as regions enriched with silicon and oxygen at the interface front, are preferential to corrosion during salt spray exposure, instead of the IMC layer. Moreover, the shear strength of the joints decreases with the reduction in the effective joint area after the salt spray exposure of the weld joints. This research systematically explored the corrosion behavior and its relationship with the mechanical properties of Al alloy/steel RSW joints. [ABSTRACT FROM AUTHOR]

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications. [ABSTRACT FROM AUTHOR]

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid–high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by −0.15 days per year (days yr−1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr−1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr−1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change. [ABSTRACT FROM AUTHOR]

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance the wearer's operational capabilities. However, proposing additional methods to control and perceive the WRL when human limbs are heavily occupied with primary tasks presents a challenge. Existing interactive methods, such as voice, gaze, and electromyography (EMG), have limitations in control precision and convenience. To address this, we have developed an interactive device that utilizes the mouth and tongue. This device is lightweight and compact, allowing wearers to achieve continuous motion and contact force control of the WRL. By using a tongue controller and mouth gas pressure sensor, wearers can control the WRL while also receiving sensitive contact feedback through changes in mouth pressure. To facilitate bidirectional interaction between the wearer and the WRL, we have devised an algorithm that divides WRL control into motion and force-position hybrid modes. In order to evaluate the performance of the device, we conducted an experiment with ten participants tasked with completing a pin-hole assembly task with the assistance of the WRL system. The results show that the device enables continuous control of the position and contact force of the WRL, with users perceiving feedback through mouth airflow resistance. However, the experiment also revealed some shortcomings of the device, including user fatigue and its impact on breathing. After experimental investigation, it was observed that fatigue levels can decrease with training. Experimental studies have revealed that fatigue levels can decrease with training. Furthermore, the limitations of the device have shown potential for improvement through structural enhancements. Overall, our mouth and tongue interactive device shows promising potential in controlling the WRL during tasks where human limbs are occupied. [ABSTRACT FROM AUTHOR]

In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results. [ABSTRACT FROM AUTHOR]

Soil microorganisms are vital components of the karst terrestrial ecosystem. However, their responses to the vegetation succession on karst mountain peaks remain unclear as to whether soil microbial diversity and community compositions change with vegetation succession. We investigated the diversity and community compositions of soil bacteria and fungi and associated environmental factors along a vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks. The results indicated that soil organic carbon and total nitrogen generally increased, and soil pH changed in the range of 8.19–8.44 and slightly declined with vegetation succession. Overall, there was an increase in microbial biomass along the vegetation succession, with the dominant phyla of bacteria, including Proteobacteria, Acidobacteria, Actinobacteria, Choroflexi, Gemmatiomnadates, Bacteroidetes, and Planctomycetes, and the dominated phyla of fungi, including Basidiomycota and Ascomycota. Notably, both the bacterial and fungal community compositions were different among the three successional stages. Spearman's correction analysis showed that soil organic carbon and total nitrogen had stronger and more significant influences on the soil microbial community compositions compared to soil water content, pH, and C:N ratio. Overall, our results provide evidence for the changes and influencing factors of the microbial community with the succession vegetation on karst mountain peaks. [ABSTRACT FROM AUTHOR]

Herein, a Sc(OTf)3-catalyzed (3+2) annulation of 2-indolylmethanols with propargylic alcohols is reported. The reaction proceeds via a Friedel–Crafts-type allenylation/5-exo-annulation cascade. In the reaction, 2-indolylmethanol is used as a three-carbon synthon, and propargyl alcohol is used as a two-carbon synthon. This method provides a direct and high-yield pathway for synthetically useful cyclopenta[b]indoles. In general, the method features easily accessible substrates with broad scope and generality, the formation of multiple bonds with high efficiency, and easy scale-up. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common progressive neurodegenerative disease. The accumulation of amyloid-beta (Aβ) plaques is a distinctive pathological feature of AD patients. The aims of this study were to evaluate the therapeutic effect of chicoric acid (CA) on AD models and to explore its underlying mechanisms. APPswe/Ind SH-SY5Y cells and 5xFAD mice were treated with CA. Soluble Aβ1–42 and Aβ plaque levels were analyzed by ELISA and immunohistochemistry, respectively. Transcriptome sequencing was used to compare the changes in hippocampal gene expression profiles among the 5xFAD mouse groups. The specific gene expression levels were quantified by qRT-PCR and Western blot analysis. It was found that CA treatment reduced the Aβ1–42 levels in the APPswe/Ind cells and 5xFAD mice. It also reduced the Aβ plaque levels as well as the APP and BACE1 levels. Transcriptome analysis showed that CA affected the synaptic-plasticity-related genes in the 5xFAD mice. The levels of L1CAM, PSD-95 and synaptophysin were increased in the APPswe/Ind SH-SY5Y cells and 5xFAD mice treated with CA, which could be inhibited by administering siRNA-L1CAM to the CA-treated APPswe/Ind SH-SY5Y cells. In summary, CA reduced Aβ levels and increased the expression levels of synaptic-function-related markers via L1CAM in AD models. [ABSTRACT FROM AUTHOR]

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices. [ABSTRACT FROM AUTHOR]

As the depth of mines continues to increase, the problems of high temperature and potential heat damage become more prominent. In this study, the characteristics of natural and industrial heat sources in mines were reviewed, and then mainstream heat hazard governance technologies and corresponding utilization methods were discussed and compared. The first category of technologies comprises the optimization of ventilation systems, the insulation of roc heat, and artificial refrigeration. These cooling approaches are limited because the heat resources cannot be recovered. The second category is the utilization of waste industrial heat in mines, including the use of waste heat from the air compressors, drainage water, and foul airflow, but the current applications of these approaches have limited effectiveness in cooling the underground space. The third category is the application of geo-structures to recover natural heat in mines. Based on the principles of the chiller/heat pump cycle and the characteristics of heat sources and sinks in mines, the potential and constraints of each technology were discussed and summarized. This study provides a scientific reference for the selection of suitable heat governance and utilization technologies. [ABSTRACT FROM AUTHOR]

The modulation of a terahertz (THz) wave on amplitude, phase and polarization is important for the application of THz technology, especially in the field of imaging, and is one of the current research hotspots. Silicon-based, optically excited THz modulator is a wavefront modulation technique with a simple, compact and reconfigurable optical path. It can realize the dynamic modulation of THz wavefronts by only changing the projected two-dimensional pattern, but it still suffers from the problems of lower modulation efficiency and slower modulation rates. In this article, the Drude model in combination with the multiple thin layers structure model and Fresnel matrix method is used to compare the modulation efficiencies of three modulation modes and more factors. The method is more accurate than the popular proposed method, especially when the thickness of the excited photoconductive layers reaches a few hundred microns. In comparing the three modes, namely transmission, ordinary reflection and total internal reflection, it is found the total internal reflection modulation mode has the best modulation efficiency. Further, under this mode, the effects of three factors, including the lifetime of photo-excited carriers, the wavelength of pump light and the frequency of THz wave, on the performance of THz modulator are analyzed. The simulation results show that the realization of total internal reflection using silicon prisms is a simple and effective method to improve the modulation efficiency of a silicon-based optically excited THz modulator, which provides references for the design of a photo-induced THz modulator. [ABSTRACT FROM AUTHOR]

This study aims to investigate the long-term prognostic utility of circulating tumour DNA (ctDNA) KRAS mutations in colorectal cancer (CRC) patients and compare this with KRAS mutations in matched tissue samples. Tumour tissue (n = 107) and ctDNA (n = 80) were obtained from patients undergoing CRC resection and were analysed for KRAS mutations. The associations between KRAS mutation and overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) were analysed. All outcomes were measured in years (y). A total of 28.8% of patients had KRAS mutations in ctDNA and 72.9% in tumour tissue DNA. The high frequency of KRAS mutations in tissue samples was due to 51.4% of these being a detectable low mutation allele frequency (<10% MAF). Comparing KRAS mutant (KRASmut) to KRAS wild-type (KRASwt) in ctDNA, there was no association found with OS (mean 4.67 y vs. 4.34 y, p = 0.832), CSS (mean 4.72 y vs. 4.49 y, p = 0.747), or RFS (mean 3.89 y vs. 4.26 y, p = 0.616). Similarly, comparing KRASmut to KRASwt in tissue DNA there was no association found with OS (mean 4.23 y vs. 4.61 y, p = 0.193), CSS (mean 4.41 y vs. 4.71 y, p = 0.312), or RFS (mean 4.16 y vs. 4.41 y, p = 0.443). There was no significant association found between KRAS mutations in either tissue or ctDNA and OS, CSS, or RFS. [ABSTRACT FROM AUTHOR]

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country's carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m−2 a−1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods. [ABSTRACT FROM AUTHOR]

In this work, a fully discrete mixed finite element (MFE) scheme is designed to solve the multi-term time-fractional reaction–diffusion equations with variable coefficients by using the well-known L 1 formula and the Raviart–Thomas MFE space. The existence and uniqueness of the discrete solution is proved by using the matrix theory, and the unconditional stability is also discussed in detail. By introducing the mixed elliptic projection, the error estimates for the unknown variable u in the discrete L ∞ (L 2 (Ω)) norm and for the auxiliary variable λ in the discrete L ∞ ((L 2 (Ω)) 2) and L ∞ (H (div , Ω)) norms are obtained. Finally, three numerical examples are given to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

This study aimed to determine whether negative coping styles and negative physical self sequentially mediate the relationship between peer teasing and restrained eating among Chinese university students. In total, 1127 participants (66.9% women, average age = 18.43 years; age range, 14–26 years) completed the Perception of Teasing Scale, Coping Style Questionnaire, Negative Physical Self Scale, and the Chinese version of the Restraint Scale. The mediational analysis showed that, after controlling for age, sex, and body mass index (BMI), peer teasing was related to restrained eating behaviors through (a) the mediating effect of negative coping styles, (b) the mediating effect of negative physical self, and (c) the chain-mediating effect of negative coping styles and negative physical self. This study showed for the first time that negative coping styles and negative physical self may chain mediate the association between peer teasing and restrained eating. It also provides suggestions for clinical practices as to strategies for controlling restrained eating. [ABSTRACT FROM AUTHOR]

Ensiling density has significant importance for the quality and preservation of silage. Appropriate ensiling density could improve the nutritional value, extend the storage time of silage and reduce the risk of mold and spoilage. This work aimed to evaluate the effect of ensiling densities on the fermentation quality and aerobic stability of rice straw. The rice straw was obtained after threshing, then chopped and ensiled into a 10 L laboratory silo with three ensiling densities (high density at 700 g/L, medium density at 600 g/L and low density at 500 g/L). Five silos per density were opened after 3, 5, 7, 14, 30 and 60 days of ensiling, and then, the fermentation quality and aerobic stability were analyzed. During ensiling, high density had the highest lactic acid content, and the lowest pH and ammonia nitrogen. There was no difference (p > 0.05) in the propionic acid, butyric acid and ethanol contents among all silage, and the contents of propionic acid and butyric acid were trace amounts. On day 60 of ensiling, the Flieg's point of high density and medium density were higher than the low density. During aerobic exposure, the continuous lactic acid decrease and pH increase were observed in all silage. The aerobic bacteria and yeasts count in the high density and medium density were lower than that in the low density. The aerobic stability of the high density (26 h) and the medium density (24 h) were higher than that of the low density (13 h). It was suggested that if the ensiling density is higher than 600 g/L, it could effectively improve the fermentation quality and aerobic stability of rice straw. [ABSTRACT FROM AUTHOR]

GmSNAP18 and GmSHMT08 are two major genes conferring soybean cyst nematode (SCN) resistance in soybean. Overexpression of either of these two soybean genes would enhance the susceptibility of Arabidopsis to beet cyst nematode (BCN), while overexpression of either of their corresponding orthologs in Arabidopsis, AtSNAP2 and AtSHMT4, would suppress it. However, the mechanism by which these two pairs of orthologous genes boost or inhibit BCN susceptibility of Arabidopsis still remains elusive. In this study, Arabidopsis with simultaneously overexpressed GmSNAP18 and GmSHMT0 suppressed the growth of underground as well as above-ground parts of plants. Furthermore, Arabidopsis that simultaneously overexpressed GmSNAP18 and GmSHMT08 substantially stimulated BCN susceptibility and remarkably suppressed expression of AtPR1 in the salicylic acid signaling pathway. However, simultaneous overexpression of GmSNAP18 and GmSHMT08 did not impact the expression of AtJAR1 and AtHEL1 in the jasmonic acid and ethylene signaling pathways. GmSNAP18, GmSHMT08, and a pathogenesis-related (PR) protein, GmPR08-Bet VI, in soybean, and AtSNAP2, AtSHMT4, and AtPR1 in Arabidopsis could interact pair-wisely for mediating SCN and BCN resistance in soybean and Arabidopsis, respectively. Both AtSNAP2 and AtPR1 were localized on the plasma membrane, and AtSHMT4 was localized both on the plasma membrane and in the nucleus of cells. Nevertheless, after interactions, AtSNAP2 and AtPR1 could partially translocate into the cell nucleus. GmSNAP18 interacted with AtSHMT4, and GmSHMT4 interacted with AtSNAP2. However, neither GmSNAP18 nor GmSHMT08 interacted with AtPR1. Thus, no pairwise interactions among α-SNAPs, SHMTs, and AtPR1 occurred in Arabidopsis overexpressing either GmSNAP18 or GmSHMT08, or both of them. Transgenic Arabidopsis overexpressing either GmSNAP18 or GmSHMT08 substantially suppressed AtPR1 expression, while transgenic Arabidopsis overexpressing either AtSNAP2 or AtSHMT4 remarkably enhanced it. Taken together, no pairwise interactions of GmSNAP18, GmSHMT08, and AtPR1 with suppressed expression of AtPR1 enhanced BCN susceptibility in Arabidopsis. This study may provide a clue that nematode-resistant or -susceptible functions of plant genes likely depend on both hosts and nematode species. [ABSTRACT FROM AUTHOR]

Estimating the contact forces and moments (CFMs) between exoskeletons' feet and the ground is a prerequisite for calculating exoskeletons' joint moments. However, comfortable, portable, and high-precision force sensors for CFM detection are difficult to design and manufacture. In addition, there are many unknown CFM components (six force components and six moment components in the double-support phase). These reasons make it challenging to estimate CFMs precisely. In this paper, we propose a novel method for estimating these CFMs based on a proposed dynamic decoupled coordinate system (DDCS) and the minimum energy hypothesis. By decomposing these CFMs into a DDCS, the number of unknowns can be significantly reduced from twelve to two. Meanwhile, the minimum energy hypothesis provides a relatively reliable target for optimizing the remaining two unknown variables. We verify the accuracy of this method using a public data set about human walking. The validation shows that the proposed method is capable of estimating CFMs. This study provides a practical way to estimate the CFMs under the soles, which contributes to reducing the research and development costs of exoskeletons by avoiding the need for expensive plantar sensors. The sensor-free approach also reduces the dependence on high-precision, portable, and comfortable CFM detection sensors, which are usually difficult to design. [ABSTRACT FROM AUTHOR]

To improve the corrosion resistance of 6061 Al in electric vehicle battery packs, a composite coating of nano-SiO2/Micro-Arc oxidation (MAO) ceramic structure was prepared on its surface. Electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization curves (PDP) were used to evaluate the corrosion resistance of the specimens after 7 days immersion in a 3.5% NaCl solution. The corrosion resistance of the prefabricated coatings was measured via local electrochemical impedance spectroscopy (LEIS). Confocal microscopy, scanning electron microscopy (SEM), and X-ray diffraction (XRD) were used to characterize the microstructure and phase composition of the specimens. An energy dispersive spectrometer (EDS) was used to detect the elemental composition of the surface of the specimen. The results showed that the specimen with nano-SiO2/MAO composite coating had the least amount of micropores and superior corrosion resistance. The global electrochemical impedance of nano-SiO2/MAO composite coating was 1.1 times higher than that of the MAO coating and 8.4 times higher than that of the 6061 Al. When the coating was defective, the local electrochemical impedance of the nano-SiO2/MAO composite coating was still two times higher than that of the MAO coating. In the presence of scratches, the nano-SiO2/MAO composite coating still showed high corrosion resistance. The collapse corrosion mechanism of the nano-SiO2/MAO composite coating was proposed. [ABSTRACT FROM AUTHOR]

Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human–robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer's capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics. [ABSTRACT FROM AUTHOR]

The reliability of nuclear power plant electrical systems is an important guarantee of nuclear safety, and the common fault failure problem arising from redundant design and intelligent control may greatly affect reliability assessment results. Combined with the features of repairability, multi-state characteristics, and common fault failure of nuclear power plant electrical systems, a reliability analysis method of nuclear power plant electrical systems based on the GO-FLOW method considering common fault failure is proposed. This study firstly constructs the algorithmic model of combining operators of repairable components and the equivalent model of reliability parameters of multi-mode repairable components, then establishes a probability calculation model of common fault failure for repairable systems by considering the quantitative computation of the common signaling system model, and finally, quantitatively calculates the reliability of nuclear power plant electrical systems and their influencing factors. The example simulation calculates the reliability of the external power supply system and the electrical system of the nuclear power plant, analyzes the influence of the common signal processing and the common fault failure factors on the reliability of the electrical system of the nuclear power plant, and verifies the validity of the proposed method. The results show that the common fault failure factors have a large impact on the system reliability analysis; the common fault failure of the standby diesel generator set will seriously reduce the reliability of the electrical system, which can be improved by installing additional standby diesel generators. [ABSTRACT FROM AUTHOR]

Robots gradually have the ability to plan grasping actions in unknown scenes by learning the manipulation of typical scenes. The grasping pose estimation method, as a kind of end-to-end method, has rapidly developed in recent years because of its good generalization. In this paper, we present a grasping pose estimation method for robots based on convolutional neural networks. In this method, a convolutional neural network model was employed, which can output the grasping success rate, approach angle, and gripper opening width for the input voxel. The grasping dataset was produced, and the model was trained in the physical simulator. A position optimization of the robotic grasping was proposed according to the distribution of the object centroid to improve the grasping success rate. An experimental platform for robot grasping was established, and 11 common everyday objects were selected for the experiments. Grasping experiments involving the eleven objects individually, multiple objects, as well as a dark environment without illumination, were performed. The results show that the method has the adaptability to grasp different geometric objects, including irregular shapes, and it is not influenced by lighting conditions. The total grasping success rate was 88.2% for the individual objects and 81.1% for the cluttered scene. [ABSTRACT FROM AUTHOR]

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion. [ABSTRACT FROM AUTHOR]

A novel approach was developed to detect bed fluidity in gas–solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process. [ABSTRACT FROM AUTHOR]

In nature, some animals, such as snakes and octopuses, use their limited body structure to conduct various complicated tasks not only for locomotion but also for hunting. Their body segments seem to possess the intelligence to adapt to environments and tasks. Inspired by nature, a modular soft robot with integrated locomotion and manipulation abilities is presented in this paper. A soft modular robot is assembled using several homogeneous cubic pneumatic soft actuator units made of silicone rubber. Both a mathematical model and backpropagation neural network are established to describe the nonlinear deformation of the soft actuator unit. The locomotion process of the chain-type soft robot is analyzed to provide a general rhythmic control principle for modular soft robots. A vision sensor is adopted to control the locomotion and manipulation processes of the modular soft robot in a closed loop. The experimental results indicate that the modular soft robot put forward in this paper has both locomotion and manipulation abilities. [ABSTRACT FROM AUTHOR]

Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system's reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation. [ABSTRACT FROM AUTHOR]

With the increasing penetration of new energy sources, the volatility and uncertainty of new energy output can lead to interruptions or fluctuations in the supply of electricity. Power cuts to critical loads can have a significant impact on public safety, social stability, and the economy. Dealing with the effects of uncertainty from new energy sources means we need to find and strengthen the important loads and power sources when designing and operating the power system. Therefore, the assessment of the importance of loads, conventional power sources, and new energy sources is crucial. This paper proposes a power source importance evaluation method based on load importance and new energy uncertainty. The method constructs a load importance evaluation system considering structural characteristics, outage loss, regulation capability, and other factors. To determine the importance of each load, the method uses the ideal solution method and ranks them accordingly. Next, the method calculates the power supply coefficient, which represents the power supply capability of the power source to the critical loads. This calculation involves using the three-point estimation method, which combines the characteristics of the new energy output and the importance of each load. Following that, the evaluation of load importance and the power supply capacity to critical loads is accomplished based on the ideal solution method, taking into account the diversity of power supply characteristics. This comprehensive evaluation allows us to assess the significance of each load and the power supply capability to meet the needs of critical loads, considering the unique characteristics of each power source. Finally, an example analysis is carried out on the IEEE39 to calculate the importance of various types of power sources, which can accurately reflect the power supply capacity of power sources to important loads and verify the validity of the evaluation method. This method provides subsidies for future power system grid planning and operation. [ABSTRACT FROM AUTHOR]

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces' shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm. [ABSTRACT FROM AUTHOR]

The nation's transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts. [ABSTRACT FROM AUTHOR]

In vitro biomechanical experiments utilizing cadaveric specimens are one of the most effective methods for rehearsing surgical procedures, testing implants, and guiding postoperative rehabilitation. Applying dynamic physiological muscle force to the specimens is a challenge to reconstructing the environment of bionic mechanics in vivo, which is often ignored in the in vitro experiment. The current work aims to establish a hardware platform and numerical computation methods to reproduce dynamic muscle forces that can be applied to mechanical testing on in vitro specimens. Dynamic muscle loading is simulated through numerical computation, and the inputs of the platform will be derived. Then, the accuracy and robustness of the platform will be evaluated through actual muscle loading tests in vitro. The tests were run on three muscles (gastrocnemius lateralis, the rectus femoris, and the semitendinosus) around the knee joint and the results showed that the platform can accurately reproduce the magnitude of muscle strength (errors range from −6.2% to 1.81%) and changing pattern (goodness-of-fit range coefficient ranges from 0.00 to 0.06) of target muscle forces. The robustness of the platform is mainly manifested in that the platform can still accurately reproduce muscle force after changing the hardware combination. Additionally, the standard deviation of repeated test results is very small (standard ranges of hardware combination 1: 0.34 N~2.79 N vs. hardware combination 2: 0.68 N~2.93 N). Thus, the platform can stably and accurately reproduce muscle forces in vitro, and it has great potential to be applied in the future musculoskeletal loading system. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model's robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed. [ABSTRACT FROM AUTHOR]

The advancement of mobile internet technology has enabled companies to leverage social media for e-commerce, where some use personified images and language to communicate with consumers. This paper investigates how personified communication affects consumer behavior in social commerce and whether consumers are willing to accept this new form of communication. Specifically, the study explores consumers' willingness to accept personified communication in social commerce, considering the role of cognitive needs in regulating the internal mechanism. The paper proposes suggestions for enterprises to improve their social media communication and presents an improved model based on the Technology Acceptance Model (TAM). The model introduces perceived interaction as a new independent variable and adds cognitive needs as a regulatory variable, which is more suitable for social commerce. The study conducts a questionnaire survey online and analyzes the data using AMOS and SPSS. The results demonstrate that perceived usefulness and perceived interaction positively impact consumers' attitudes, which subsequently influences their willingness to purchase. Furthermore, cognitive needs as a regulatory variable significantly affect the path from perceived usefulness to attitude and purchase intention. [ABSTRACT FROM AUTHOR]

With the use of collaborative robots in intelligent manufacturing, human–robot interaction has become more important in human–robot collaborations. Human–robot handover has a huge impact on human–robot interaction. For current research on human–robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human–robot handover. [ABSTRACT FROM AUTHOR]

This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan. [ABSTRACT FROM AUTHOR]

The response of boreal vegetation to global warming has shown a weakening trend over the last three decades. However, in previous studies, models of vegetation activity responses to temperature change have often only considered changes in the mean daily temperature (Tmean), with the diurnal temperature range (DTR) being neglected. The goal of this study was to evaluate the temporal trends of the relationships between two temperature factors (Tmean and DTR) and the vegetation activity across the boreal regions on both annual and seasonal timescales, by simultaneously employing satellite and climate datasets. We found that the interannual partial correlation between the growing season (GS) NDVI and Tmean (RNDVI−Tmean) has shown a significant decreasing trend over the last 34 years. At the seasonal scale, the RNDVI−Tmean showed a significant upward trend in the spring, while in the summer and autumn, the RNDVI−Tmean exhibited a significant downward trend. The temporal trend characteristics of the partial correlation between the NDVI and DTR (RNDVI−DTR), at both the GS and seasonal scales, were fully consistent with the RNDVI−Tmean. The area with a significant decrease in the GS RNDVI−Tmean and RNDVI−DTR accounted for approximately 44.4% and 41.2% of the boreal region with the 17-year moving window, respectively. In stark contrast, the area exhibiting a significant increasing trend in the GS RNDVI−Tmean and RNDVI−DTR accounted for only approximately 22.3% and 25.8% of the boreal region with the 17-year moving window, respectively. With respect to the seasonal patterns of the RNDVI−Tmean and RNDVI−DTR, the area with a significant upward trend in the spring was greater than that with a significant downward trend. Nevertheless, more areas had a significant downward trend in the RNDVI−Tmean and RNDVI−DTR in summer and autumn than a significant upward trend. Overall, our research reveals a weakening trend in the impact of temperature on the vegetation activity in the boreal regions and contributes to a deeper understanding of the vegetation response to global warming. [ABSTRACT FROM AUTHOR]

Background and Objectives: The purpose was to compaSre medium-term clinical and radiological outcomes of Partial Pediculotomy, Partial Vertebrotomy (PPPV) Posterior Endoscopic Cervical Decompression (PECD) surgery versus Anterior Cervical Discectomy and Fusion (ACDF) for patients with cervical disc herniations and foraminal pathologies. Materials and Methods: A prospective registry of patients who had undergone either PPPV PECD surgery or ACDF surgery for cervical disc herniation or foraminal pathologies under a single fellowship-trained spine surgeon was performed. The baseline characteristics and operative details including complications were recorded for all included patients. The clinical outcomes evaluated include VAS, MJOA, motor score, and NDI and MacNab's score. The radiological parameters in neutral-measured facet length, facet area, disc height, C2–C7 angle, neck tilt angle, T1 slope and thoracic inlet angle were also evaluated. Results: A total of 55 patients (29 PPPV PECD, 26 ACDF) were included, with mean follow-up periods of 21.9 and 32.3 months, respectively. Each cohort was noted to have a single case of surgical complication. Statistically significant changes of facet area (49.05 ± 14.50%) and facet length (52.71 ± 15.11%) were noted in the PPPV PECD group. At neutral alignment of the neck on a lateral X-ray, compared to ACDF, PPPV PECD had a statistically significant change in neck tilt angle (−11.68 ± 17.35°) and T1 slope angle (−11.69 ± 19.58°). Whilst both PPPV PECD and ACDF had significant improvements in VAS, MJOA and NDI postoperatively, PPPV PECD was found to be superior across all above scores at various follow-up timepoints compared to its ACDF counterparts. Conclusions: PPPV PECD surgery achieved a satisfactory radiological correction of neck alignment and significantly improved clinical outcomes at medium-term follow-up for our cohort of patients, highlighting its feasibility in treating patients with cervical disc herniations and foraminal pathologies. [ABSTRACT FROM AUTHOR]

The continuous twin-screw wet granulation (TSWG) process was investigated and optimized with prediction-oriented I-optimal designs. The I-optimal designs can not only obtain a precise estimation of the parameters that describe the effect of five input process parameters, including the screw speed, liquid-to-solid (L/S) ratio, TSWG feed rate, and numbers of the 30° and 60° mixing elements, on the granule quality in a TSWG process, but it can also provide a prediction of the response to determine the optimum operating conditions. Based on the constraints of the desired granule properties, a design space for the TSWG was determined, and the ranges of the operating parameters were defined. An acceptable degree of prediction was confirmed through validation experiments, demonstrating the reliability and effectiveness of using the I-optimal design method to study the TSWG process. The I-optimal design method can accelerate the screening and optimization of the TSWG process. [ABSTRACT FROM AUTHOR]

Twin-screw wet granulation (TSWG) is a method of continuous pharmaceutical manufacturing and a potential alternative method to batch granulation processes. It has attracted more and more interest nowadays due to its high efficiency, robustness, and applications. To improve both the product quality and process efficiency, the process understanding is critical. This article reviews the recent work in process understanding and optimization for TSWG. Various aspects of the progress in TSWG like process model construction, process monitoring method development, and the strategy of process control for TSWG have been thoroughly analyzed and discussed. The process modeling technique including the empirical model, the mechanistic model, and the hybrid model in the TSWG process are presented to increase the knowledge of the granulation process, and the influence of process parameters involved in granulation process on granule properties by experimental study are highlighted. The study analyzed several process monitoring tools and the associated technologies used to monitor granule attributes. In addition, control strategies based on process analytical technology (PAT) are presented as a reference to enhance product quality and ensure the applicability and capability of continuous manufacturing (CM) processes. Furthermore, this article aims to review the current research progress in an effort to make recommendations for further research in process understanding and development of TSWG. [ABSTRACT FROM AUTHOR]

Most children with a neurogenic bladder (NB) have bladder fibrosis, which causes irreversible bladder dysfunction and damage to the upper urinary tract. However, the mechanism of bladder fibrosis remains unclear. This study aimed to investigate the underlying causes of bladder fibrosis. Here, the lumbar 6 (L6) and sacral 1 (S1) spinal nerves of Sprague Dawley rats were severed bilaterally to establish NB models. Using RNA-seq, we discovered that the NF-κB signaling pathway and inflammation were upregulated in spinal cord injury (SCI)-induced bladder fibrosis. Subsequent Western blotting, enzyme-linked immunosorbent assays, immunohistochemical staining, and immunofluorescence staining verified the RNA-seq findings. To further clarify whether the NF-κB signaling pathway and pyroptosis were involved in bladder fibrosis, a TGF-β1-treated urinary epithelial cell line (SV-HUC-1 cells) was used as an in vitro model. Based on the results of RNA-seq, we consistently found that the NF-κB signaling pathway and pyroptosis might play important roles in TGF-β1-treated cells. Further experiments also confirmed the RNA-seq findings in vitro. Moreover, using the NLRP3 inhibitor MCC950 rescued TGF-β1-induced fibrosis, and the NF-κB signaling pathway inhibitor BAY 11-7082 effectively rescued TGF-β1-induced pyroptosis and the deposition of extracellular matrix by SV-HUC-1 cells. In summary, our research demonstrated for the first time that the NF-κB signaling pathway inhibition rescued bladder epithelial cells pyroptosis and fibrosis in neurogenic bladders. [ABSTRACT FROM AUTHOR]

Soil food webs are extremely complex as they contain diverse organisms. Feeding preference, also known as prey selection, is an important determinant of soil community compositions. However, the feeding preferences of nematodes are commonly ignored in ecology research. In this paper, the population dynamics and feeding preferences of three bacterial-feeding nematodes (i.e., Caenorhabditis elegans, Protorhabditis spp., and Acrobeloides spp.) for eight bacterial prey species were evaluated. Protorhabditis and Acrobeloides were isolated from a paddy soil in subtropical China. C. elegans, the most common model system for biological research, was used as a control in this study, and it was revealed that C. elegans could feed on all the eight bacteria strains. Protorhabditis could only survive when fed E. coli and Bacillus thuringiensis. Acrobeloides could only survive when fed E. coli and B. aryabhattai. During 10 days of culture, C. elegans populations reached the maximum in 5–7 days, and most C. elegans populations exceeded 10,000 individuals. The two Protorhabditis populations on E. coli and Bacillus thuringiensis included less than 800 individuals during 10 days of culture. Acrobeloides population on B. aryabhattai reached the maximum (7799 individuals) on day 8, while on E. coli was its population included less than 500 individuals. These results indicate that different nematode species indeed have distinct feeding preferences. In addition, the population dynamics of the two soil nematodes isolated from soil could not fully match with their inferred life-history strategies (i.e., cp values, and a 1-5 colonizer-p-ersister series that range from r-strategists to K-strategists). Our findings highlights the existing deficiencies in the understanding of the feeding behavior and the life-history strategies of soil nematodes. [ABSTRACT FROM AUTHOR]

The connection method of lengthening the steel tube of hollow concrete-filled sandwich circular steel tubes and threaded connections is proposed. The length, depth and position are the basic parameters. Twelve hollow concrete sandwich circular steel pipes with threaded connections were designed and subjected to axial compression tests. The axial compressive loading–longitudinal compressive displacement curves, axial compressive loading strain of steel tube curves and failure mode of the specimens are analyzed, and the effects of different parameters on the axial compressive-bearing capacity and stiffness of the specimens are studied. The results showed that within the range of parameters studied, the axial compression load–longitudinal compression displacement curves of the specimens were the linear elastic stage and the elastic–plastic stage, which can be divided into a yield-strengthening stage and a decreasing stage. The bearing capacity and strength of the lined threaded connection specimen are not inferior to those of the ordinary specimen or the welded specimen. The bearing capacity and strength of the specimen increase with the increase of the thread length. The bearing capacity and strength of the specimens connected with inner liner screws at the ends are higher than those connected with inner liner bolts at the middle. [ABSTRACT FROM AUTHOR]

Lung ultrasound (LU) is increasingly used to diagnose and monitor neonatal pulmonary disorders; however, its role in hemodynamically significant patent ductus arteriosus (hsPDA) has not been elucidated. This prospective study investigated the predictive value of the LU score (LUS) for hsPDA in preterm infants with gestational age (GA) ≤ 25 weeks. Preterm infants with GA ≤ 25 weeks were enrolled in this study. LU was conducted on the fourth day of life (DOL). Six lung regions in every lung were scanned, with each region rated as 0–4 points. The performance of the LUS in predicting hsPDA among infants aged ≤25 weeks was analyzed by plotting the receiver operating characteristic (ROC) curve. A total of 81 infants were included in this study. GA, birth weight (BW), gender, Apgar score, delivery mode, antenatal steroids, meconium-stained amniotic fluid, premature rapture of membrane, and early-onset sepsis were not significantly different, but infants in the hsPDA group had increased LUS (38.2 ± 2.8 vs. 30.3 ± 4.3, p < 0.001) compared with non-hsPDA group. The area under the ROC curve (AUC) value of the LUS on the fourth DOL was 0.94 (95% CI: 0.93–0.99) in predicting hsPDA. The LUS threshold at 33 achieved 89% sensitivity and 83% specificity, with the positive and negative predictive values (PPV and NPV) being 87 and 86%, respectively. The LUS can predict hsPDA in extremely preterm infants at an early stage. [ABSTRACT FROM AUTHOR]

The stability of soil organic matter (SOM) plays a critical role in soil carbon (C) dynamics under global warming. However, the factors influencing SOM stability, particularly the significance of bedrock geochemistry and its hierarchical relationship with climate and soil properties, remain poorly understood. To address this gap, we conducted a study along a large climatic gradient (Δtemperature > 9 °C) in the subtropical karst forests of southwest China, quantifying SOM stability using thermal analysis and investigating the contributions of bedrock geochemistry, climate, and soil properties. Our results showed that SOM stability was positively correlated with mineral-associated organic C (MAOC) rather than particulate organic C. Hierarchical partitioning analysis further demonstrated that bedrock geochemistry was the predominant contributor to SOM stability variance, accounting for 23.7%. Following this, soil minerals contributed to 21.1%–22.6% of the variance, the mean annual temperature to 20.3%, and microbial biomass C to 17.2%. In particular, bedrock geochemistry—specifically the presence of calcium-rich bedrock—was found to enhance SOM stability by promoting the accumulation of exchangeable calcium and calcium carbonate in soils. Additionally, high temperature improved SOM stability by increasing the content and proportion of MAOC and soil pH. These results highlight the fundamental role of bedrock geochemistry in controlling SOM stability and emphasize the importance of considering hierarchical relationships among bedrock–soil–climate interactions for evaluating soil C dynamics. [ABSTRACT FROM AUTHOR]