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This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

The optimization of the inlet layout in aquaculture systems is essential to ensure minimal solid waste discharge into the environment and improve fish production efficiency. In the present study, laboratory experiments were carried out to investigate the effects of the jetting position d/a (where d is the distance from the pipe axis to the tank side and a is the side length of the tank wall) and the jetting angle θ (the acute angle between the jetting direction and the nearest tank wall) on the solid waste removal efficiency in single-inlet and dual-inlet octagonal Recirculating Aquaculture System (RAS) tanks. To this end, three jetting positions (d/a) of 1/50, 1/8, and 1/4 and ten jetting angles (θ) of 0° to 80° were considered in the experiments. The Particle Image Velocimetry (PIV) technique was applied to measure the flow characteristics in the tank and analyze the solid waste removal under different working conditions. Residual mass of the solid waste, time of complete removal of solid waste, average velocity (vavg), and uniformity coefficient of velocity distribution (DU50) were analyzed to evaluate the solid waste removal efficiency. The obtained results indicate that adjustments of the inlet layout significantly affect the solid waste removal efficiency. It was found that a single-inlet tank with a d/a of 1/8 and θ in the range 10° to 40° has a good solid wastes removal performance, and the optimal efficiency occurs at a jetting angle of 30°. Moreover, the optimal solid waste removal efficiency in a dual-inlet tank can be achieved with a d/a ratio of 1/8 and a θ of 20°. The performed analyses reveal that from the aspect of solid waste removal efficiency, a tank with a d/a ratio of 1/8 outperforms a tank with a d/a ratio of 1/4 or 1/50. The results of this article offer novel insights in the layout of octagonal RAS tanks and provide a guideline to improve self-cleaning features of aquaculture tanks.

Locus coeruleus (LC) sends widespread outputs to many brain regions to modulate diverse functions, including sleep/wake states, attention, and the general anesthetic state. The paraventricular thalamus (PVT) is a critical thalamic area for arousal and receives dense tyrosine-hydroxylase (TH) inputs from the LC. Although anesthesia and sleep may share a common pathway, it is important to understand the processes underlying emergence from anesthesia. In this study, we hypothesize that LC TH neurons and the TH:LC-PVT circuit may be involved in regulating emergence from anesthesia. Only male mice are used in this study. Here, using c-Fos as a marker of neural activity, we identify LC TH expressing neurons are active during anesthesia emergence. Remarkably, chemogenetic activation of LC TH neurons shortens emergence time from anesthesia and promotes cortical arousal. Moreover, enhanced c-Fos expression is observed in the PVT after LC TH neurons activation. Optogenetic activation of the TH:LC-PVT projections accelerates emergence from anesthesia, whereas, chemogenetic inhibition of the TH:LC-PVT circuit prolongs time to wakefulness. Furthermore, optogenetic activation of the TH:LC-PVT projections produces electrophysiological evidence of arousal. Together, these results demonstrate that activation of the TH:LC-PVT projections is helpful in facilitating the transition from isoflurane anesthesia to an arousal state, which may provide a new strategy in shortening the emergence time after general anesthesia.

While substantial progress has been made in finding genetic variants associated with nicotine dependence (ND), a large proportion of the genetic variants remain undiscovered. The current research focuses have shifted toward uncovering rare variants, gene-gene/gene-environment interactions, and structural variations predisposing to ND, the impact of genetic heterogeneity in ND has been nevertheless paid less attention. The study of genetic heterogeneity in ND not only could enhance the power of detecting genetic variants with heterogeneous effects in the population but also improve our understanding of genetic etiology of ND. As an initial step to understand genetic heterogeneity in ND, we applied a newly developed heterogeneity weighted U (HWU) method to 26 ND-related genes, investigating heterogeneous effects of these 26 genes in ND. We found no strong evidence of genetic heterogeneity in genes such as CHRNA5. However, results from our analysis suggest heterogeneous effects of CHRNA6 and CHRNB3 on nicotine dependence in males and females. Following the gene-based analysis, we further conduct a joint association analysis of two gene clusters, CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6. While both CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6 clusters are significantly associated with ND, there is a much stronger association of CHRNB3-CHRNA6 with ND when considering heterogeneous effects in gender (p-value = 2.11E-07).

Palygorskite is an environmentally friendly, inexpensive, and promising silicate mineral. In this research, nanoscale palygorskite additive powders (NPA) were prepared by high energy ball milling with the modifier γ -aminopropyltriethoxysilane (KH550). The NPA was sustainably dispersed in base oil 150 N, exhibiting no particle sedimentation for one week. The self-healing behavior and tribological performance of NPA as lubricant additive were investigated by a ring-on-disc tribometer under different concentrations for 50 h. The results indicate that NPA possesses excellent friction-reducing and anti-wear performance. The 3.0 wt.% and 4.0 wt.% concentrations of NPA displayed the best friction-reducing and anti-wear effects, respectively. The average friction coefficient and wear mass loss decreased by 31.93% and 26.92%, in contrast to that of base oil 150 N. It was also found that different NPA concentrations possessed different friction states. A tribo-film with bilayer structure was formed during the friction process, the first one of which contained compound made up of multiple elements, and the second one of which contained NPA nanoparticles deposited on the friction interface. Moreover, the friction coefficient increased dramatically during the formation of the tribo-film.

In this work, a versatile strategy was proposed to fabricate translucent superhydrophobic coatings by spraying the suspension of modified SiO _2 /polydimethylsiloxane (PDMS) on various substrates. The wettability can be adjusted by changing the mass ratio of modified SiO _2 nanoparticles in the suspension. By optimizing the parameters in the spraying process, a superhydrophobic surface with a water contact angle of 164.1 ± 3.4° and a sliding angle of 4.2 ± 0.4° was achieved. As prepared translucent superhydrophobic SiO _2 /PDMS coatings show good mechanical durability, chemical stability, and self-cleaning performances.

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unmanned vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the Typhoon Sinlaku center from July 24 to August 2, 2020, over the South China Sea. The in-situ and high temporal resolution(1-min) observations obtained from MWO were analyzed and evaluated by comparing with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period and even less difference after the typhoon's arrival, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height of the sensor. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminary demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

Hierarchical layered double hydroxide (HLDH) was synthesized by using sodium dodecyl sulfate (SDS) as a soft-template agent for the removal of two charged organic dyes (i.e. methylene blue (MB; cationic dye) and methyl orange (MO; anionic dye)). The experimental results based on response surface methodology (RSM) demonstrated distinct removal behaviours of HLDH towards these two dyes: (1) the maximum capacity was 416.7 mg g- for MO and 58.7 mg g- for MB at 25°C; (2) the increase in temperature could enhance MO removal significantly, whereas it had a negligible effect on the MB treatment process; and (3) rapid removal of MB (5 min) com- pared to MO (480 min) was observed. In addition, the removal process for both dyes was pH-independent. Multiple characterization techniques further revealed the removal mechanisms, demonstrating that SDS played a significant role in the removal of both dyes; that is, MO replaced SDS to be intercalated into the HLDH interlayer via anion exchange. MB could influence the --SO3 group of SDS, resulting in it modifying the electrodensity of SDS. It could then be further combined with an SDS anion (DS-) via hydrophobic and electrostatic interactions to form DS-MB monolayers. This work not only provides an efficient capture agent for charged dyes, but also offers a deep insight into the underlying removal mechanism. [ABSTRACT FROM AUTHOR]

Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In this paper, a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network (CNN) in deep learning in order to improve the analysis accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods, e.g., the support vector machine method. [ABSTRACT FROM AUTHOR]

Steroid cell tumors (SCT), not otherwise specified (NOS) are particularly rare ovarian sex cord-stromal tumors, which comprise <0.1% of all ovarian tumors. These tumors are uncommon in patients' prior to puberty without any typical syndromes involving hirsutism, virilization and hypertension. We here in present the case of a 5-year-old female patient who presented with sudden abdominal pain, repeated vomiting and a pelvic mass. Our patient underwent urgent exploratory laparotomy and right salpingo-oophorectomy and the histopathological examination revealed an ovarian SCT-NOS. The patient has been followed up for 5 years since the surgery, without evidence of disease recurrence. The purpose of this study was to discuss the available information on the presentation, diagnosis and recommended treatment of ovarian SCT-NOS; and describes the immunohistochemical characteristics of these tumors. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the feasibility of utilizing near-infrared spectroscopy to determine the water content of corn stover silage across a wide range. The water contents of 208 samples were measured, and their corresponding near-infrared spectra were simultaneously collected. The effects of different preprocessing methods, such as derivation, standard normal variety (SNV), multiplicative scatter correction (MSC), and non-preprocessing methods for the obtained near-infrared spectra on the performance of calibration models were compared. The calibration models were established by modified partial least squares (MPLS) regression. The results showed that the calibration model developed from the successive preprocessing of MSC and first-order derivation (1-D) achieved the optimal performance. The correlation coefficients of the calibration and validation subset were 0.974 and 0.949, respectively, and the standard errors of the calibration and cross validation were 4.249% and 4.256%, respectively. External validation was performed on 60 samples. The correlation coefficient between the measured and predicted values of the calibration model was 0.973 and the prediction model's relative percent deviation was 4.317. This indicated that the mathematical model of near-infrared spectroscopy predicted the water content in corn stover silage with high accuracy. The study showed that the near-infrared spectroscopy technology can be used for rapid and non-destructive testing across a wide range of water contents in the corn stover silage. [ABSTRACT FROM AUTHOR]

Vegetation segmentation from images is an essential issue in the application of computer vision in agriculture. In this paper, we present a new vegetation segmentation method based on Particle Swarm Optimisation (PSO) clustering and morphology modelling in CIE L*a*b* colour space. At the off-line learning stage, a new method is put forward to determine the clustering number. Secondly, the tools of morphological dilation and erosion are employed to establish the vegetation colour model. At the online segmentation stage, the PSO-based k-means is used to cluster the vegetation image into vegetation classes and non-vegetation classes. Afterwards, the established colour model is used to distinguish the vegetation classes and give the segmentation result. In the experiments, the proposed method was applied to segment 200 smaller regions of the full camera images of rice and 100 smaller regions of the full camera images of cotton. The means of segmentation qualities reached 88.1% and 91.7% respectively. Moreover, the proposed method was compared with three well-known vegetation segmentation methods and two skin segmentation methods. Experiments demonstrate that the proposed method yielded the highest mean of segmentation qualities and lowest standard deviations of segmentation qualities. In addition, the vegetation colour models built with different structuring element types are analysed. [ABSTRACT FROM AUTHOR]