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For the first time, a novel dithiomaleimides (DTM) based tetra-antennary GalNAc conjugate was developed, which enable both efficient siRNA delivery and good traceability, without incorporating extra fluorophores. This conjugate can be readily constructed by three click-type reactions, that is, amidations, thiol-dibromomaleimide addition and copper catalyzed azide–alkyne cycloaddition (CuAAC). And it also has comparable siRNA delivery efficiency, with a GalNAc L96 standard to mTTR target. Additionally, due to the internal DTMs, a highly fluorescent emission was observed, which benefited delivery tracking and reduced the cost and side effects of the extra addition of hydrophobic dye molecules. In all, the simple incorporation of DTMs to the GalNAc conjugate structure has potential in gene therapy and tracking applications. [ABSTRACT FROM AUTHOR]

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations. [ABSTRACT FROM AUTHOR]

The highly focused laser beam is capable of confining micro-sized particle in its focus. This is widely known as optical trapping. The Janus particle is composed of two hemispheres with different refractive indexes. In a linearly polarized optical trap, the Janus particle tends to align itself to an orientation where the interface of the two hemispheres is parallel to the laser propagation as well as the polarization direction. This enables a controllable approach that rotates the trapped particle with fine accuracy and could be used in partial measurement. However, due to the complexity of the interaction of the optical field and refractive index distribution, the trapping trajectory of the Janus particle in the linearly polarized optical trap is still uncovered. In this paper, we focus on the dynamic trapping process and the steady position and orientation of the Janus particle in the optical trap from both simulation and experimental aspects. The trapping process recorded by a high speed camera coincides with the simulation result calculated using the T-matrix model, which not only reveals the trapping trajectory, but also provides a practical simulation solution for more complicated structures and trapping motions. [ABSTRACT FROM AUTHOR]

Healthy romantic relationships contribute to human physical health and emotional well-being. Technologies that catalyze human sexuality such as silicone sex toys and video-conferencing are increasingly common today, and disruptive sexological artifacts such as sexbots are speculated to eventually compete directly with human-human sexuality. The consequences of these evolutionary transitions in human sociosexual behavior are entirely unknown at the individual or collective scale. Here we introduce Partner Pen Play in Parallel (PPPiP), the act of simultaneous improvisational drawing on paper without clinical supervision. In this prospective article we sketch out what PPPiP is, then provide interdisciplinary evidence from art therapy, sexology, affective neuroscience, and aesthetics to support PPPiP as a useful strategy for relationship development. PPPiP combines the advantages of individuated artistic practice with the established frameworks of improvisation and dyadic relationship interventions. Relative to traditional art therapy practices, PPPiP is less clinically oriented, features fewer external constraints, and directly encourages the dynamic integration of artistic creation with relationship co-creation. PPPiP emphasizes the importance of narrative structure and controlled novelty at multiple scales in intimate partnerships, connecting art therapy practices more directly to recent neuropsychological research. Evidence from brain imaging in improvisational and aesthetic contexts supports a model in which PPPiP synergistically activates motor and cortico-limbic neural circuits associated with skilled emotive-creative processes. PPPiP thus represents a transdisciplinary answer to the question of what will we carry from our sociosexual past towards a healthier textosexual future. [ABSTRACT FROM AUTHOR]

What does it mean to be human in the 21st century? The pull of engineering on every aspect of our lives, the impact of machines on how we represent ourselves, the influence of computers on our understanding of free-will, individuality and species, and the effect of microorganisms on our behaviour are so great that one cannot discourse on humanity and humanities without considering their entanglement with technology and with the multiple new dimensions of reality that it opens up. The future of humanities should take into account AI, bacteria, software, viruses (both organic and inorganic), hardware, machine language, parasites, big data, monitors, pixels, swarms systems and the Internet. One cannot think of humanity and humanities as distinct from technology anymore. [ABSTRACT FROM AUTHOR]

The quantum model has been considered to be advantageous over the Markov model in explaining irrational behaviors (e.g., the disjunction effect) during decision making. Here, we reviewed and re-examined the ability of the quantum belief–action entanglement (BAE) model and the Markov belief–action (BA) model in explaining the disjunction effect considering a more realistic setting. The results indicate that neither of the two models can truly represent the underlying cognitive mechanism. Thus, we proposed a more realistic Markov model to explain the disjunction effect in the prisoner's dilemma game. In this model, the probability transition pattern of a decision maker (DM) is dependent on the information about the opponent's action, Also, the relationship between the cognitive components in the evolution dynamics is moderated by the DM's degree of subjective uncertainty (DSN). The results show that the disjunction effect can be well predicted by a more realistic Markov model. Model comparison suggests the superiority of the proposed Markov model over the quantum BAE model in terms of absolute model performance, relative model performance, and model flexibility. Therefore, we suggest that the key to successfully explaining the disjunction effect is to consider the underlying cognitive mechanism properly. [ABSTRACT FROM AUTHOR]

Rigorous peer-reviews are the basis of high-quality academic publishing. Thanks to the contribution of our reviewers, in 2021, the median time to first decision was 16 days and the median time to publication was 40 days. Thanks to the great efforts of our reviewers, I Sensors i was able to maintain its standards for the high quality of its published papers. [Extracted from the article]

One of the daunting features of the brain is its physiology complexity, which arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various cognitive functions. As a reflection of the complexity of brain physiology, the complexity of brain blood-oxygen signal has been frequently studied in recent years. This paper reviews previous literature regarding the following three aspects: (1) whether the complexity of the brain blood-oxygen signal can serve as a reliable biomarker for distinguishing different patient populations; (2) which is the best algorithm for complexity measure? And (3) how to select the optimal parameters for complexity measures. We then discuss future directions for blood-oxygen signal complexity analysis, including improving complexity measurement based on the characteristics of both spatial patterns of brain blood-oxygen signal and latency of complexity itself. In conclusion, the current review helps to better understand complexity analysis in brain blood-oxygen signal analysis and provide useful information for future studies. [ABSTRACT FROM AUTHOR]

With the rapid deployment of utility-scale photovoltaic (PV) plants, the impact of PV plants on the environment is a new concern of the scientific and social communities. The exchange of sensible and latent heat energy and mass between land and air in PV plants is crucial to understanding its impact. It is known that the near surface turbulence characteristics rule the exchange. Therefore, it is essential for understanding the impact to study the characteristics of near surface turbulence. However, it is not well recognized. Turbulent fluxes and strength characteristics for the PV plant and the adjacent reference site in the Xinjiang Gobi area were investigated in this study. Various surface layer parameters including friction velocity, stability parameter, momentum flux, and turbulent flux were calculated using eddy correlation system. Results indicate that compared to the reference site, near the surface boundary layer was more unstable during the daytime due to the stronger convection heating, while it was more stable at night in the PV plant. In the PV plant, I u was weakened and I v was strengthened during the daytime, and I u and I v were all weakened at night, while I w was strengthened across the whole day. The significant difference between I u and I v in the PV plant indicated that the horizontally turbulence strengths were affected by the plant layout. The turbulent kinetic energy of the PV plant was lower than the reference site and the momentum in the PV plant was higher than the reference site, especially during the daytime. Compared to the reference site, the PV plant had a higher sensible heat flux and less latent heat flux. The turbulent components of wind followed the 1/3 power law in the unstable conditions and stable conditions in the PV plant and the reference site. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time. [ABSTRACT FROM AUTHOR]

Complex dielectric constant (CDC) of bound water determines the accuracy of the complex dielectric constant of wet soil. According to electrical double-layer structure and dielectric properties, the bound water on clay particle surface is divided into strongly bound water and weakly bound water. Based on this classification, models for the complex dielectric constants of bound water and soil are established taking into consideration factors such as temperature, moisture, texture, and microwave frequency. The results show that the fundamental reason why the complex dielectric constant of bound water is between that of ice and free water is the adsorption force which forms the electrical double-layer structure on the surface of clay particles. Low-concentration cationic solution could exist in free soil water and was found as the reason for the higher salinity and conductivity of free soil water, as compared to the measured soil solution. Results of soil CDC model are in good agreement with measured data across a wide range of microwave frequencies and soil temperature, moisture, and texture. The absolute root mean square error analysis also shows that the soil CDC model in this paper compared to the other models is more accurate. [ABSTRACT FROM AUTHOR]

Fish-lighting complementary photovoltaic power station organically combines aquaculture and renewable energy. In this study we aimed to develop a solar photovoltaic that is not confined to land. We used a shade net to simulate photovoltaic panels, and studied the effects of different proportions of photovoltaic panels on water and fish. The results showed that the average light intensity of the unshaded area and the shaded area were 16,661.7 Lux and 2437.0 Lux. The average light intensity of the shaded area was 85.4% lower than that of the unshaded area. The effective range of the shaded area for the light intensity of the water layer was 0 cm~30 cm. The temperature and pH in the water body showed a linear decreasing trend with the increase of the photovoltaic deployment ratio, and the dissolved oxygen showed an inverted "U"-shaped change characteristic, but this effect was limited. When the photovoltaic deployment ratio was 25% to 75%, the dissolved oxygen in the water body increases by an average of 2.37% compared to the unshaded area. The appropriate proportion of photovoltaic deployment did not affect the dissolved oxygen content in the water body. When the photovoltaic deployment ratio reached 75%, the number of algae species and algae biomass was the largest, and the fish production was the highest at 8094.6 kg·acre−1, which is an increase of 166.2 kg·acre−1 compared to the average yield in unshaded areas. Therefore, photovoltaic layout will not affect the growth of fish, and the proportion of 75% is the best layout choice. [ABSTRACT FROM AUTHOR]

An algorithm to forecast very short-term (30–180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30–180 min horizon ranged from 26.78 to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting. [ABSTRACT FROM AUTHOR]

Aspergillus flavus is one of the most opportunistic pathogens invading many important oilseed crops and foodstuffs with such toxic secondary metabolites as aflatoxin (AF) and Cyclopiazonic acid. We previously used the DNA methylation inhibitor 5-azacytidine to treat with an AF-producing A. flavus A133 strain, and isolated a mutant (NT) of A. flavus, which displayed impaired abilities of AF biosynthesis and fungal development. In this study, gas chromatography–mass spectrometry (GC-MS) analysis was used to reveal the metabolic changes between these two strains. A total of 1181 volatiles were identified in these two strains, among which 490 volatiles were found in these two strains in vitro and 332 volatiles were found in vivo. The NT mutant was found to produce decreasing volatile compounds, among which most of the fatty acid-derived volatiles were significantly downregulated in the NT mutant compared to the A133 strain, which are important precursors for AF biosynthesis. Two antioxidants and most of the amino acids derived volatiles were found significantly upregulated in the NT mutant. Overall, our results reveal the difference of metabolic profiles in two different A. flavus isolates, which may provide valuable information for controlling infections of this fungal pathogen. [ABSTRACT FROM AUTHOR]

Fault arc detection is an important technology to ensure the safe operation of electrical equipment and prevent electrical fires. The high-frequency noise of the arc current is one of the typical arc characteristics of almost all loads. In order to accurately detect arc faults in a low-voltage alternating-current (AC) system, a novel differential high-frequency current transformer (D-HFCT) sensor for collecting high-frequency arc currents was proposed. The sensitivity and frequency band of the designed sensor were verified to ensure that the acquisition requirements of the high-frequency current were satisfied. A series arc fault simulation experiment system was built, and resistive, inductive, and non-linear load and high-power shielding load experiments were carried out. Experiments showed that the sensor output signal was close to zero in the non-arc state, and the sensor output response was a high-frequency glitch in the arc state. The results were consistent for different loads, and the discrimination between normal and fault states was obvious, which proved that the sensor is suitable for series arc fault detection. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively. [ABSTRACT FROM AUTHOR]