Mass and Heat Balance of a Lake Ice Cover in the Central Asian Arid Climate Zone

To improve the understanding of the seasonal evolution of the mass and heat budget of ice-covered lakes in the cold and arid climate zone, in-situ observations were collected during two winters (2016–2017 and 2017–2018) in Lake Wuliangsuhai, Inner Mongolia, China. The mean snow thickness was 5.2 and 1.6 cm in these winters, due to low winter precipitation. The mean ice thickness was 50.9 and 36.1 cm, and the ice growth rate was 3.6 and 2.1 mm day−1 at the lower boundary of ice. Analyses of mass and heat balance data from two winters revealed that the surface heat budget was governed by solar radiation and terrestrial radiation. The net heat flux loss of the ice was 9–22 W m−2, affected by the snow and ice thickness. Compared to boreal lakes, Lake Wuliangsuhai received more solar radiation and heat flux from the water. The ice temperature had a strong diurnal variation, which was produced by the diurnal cycles of solar radiation, and air and water temperatures. These results expand our knowledge of the evolution of mass and heat balance in temperate lakes of mid-latitude arid areas

Solar radiation transfer for an ice-covered lake in the central Asian arid climate zone

Spectral albedo and light transmittance through snow, ice, and water were measured in Lake Wuliangsuhai (40 degrees 36 '-41 degrees 30 ' N, 108 degrees 43 '-108 degrees 70 ' E), Inner Mongolia, China, during winter 2016. Data on the weather, structure of lake ice, and geochemistry of water were also collected during the 60-day field program. The study lake is shallow (mean depth 1.0-1.5 m) with a large wetland area. Compared with polar lakes, solar elevation is higher, snow accumulation is much lower, and the ice has more sediment. The ice was all congelation ice with a mean thickness of 36.6 cm, corresponding to a mean air temperature of -9.6 degrees C. The mean daily broadband albedo and photosynthetically active radiation (PAR) band transmittance were 0.54 and 0.08 (bare ice), 0.74 and 0.04 (new snow), and 0.30 and 0.12 (melting period), respectively. The level of light allowed photosynthesis to occur to the bottom of the lake. The ice acted as a grey filter for the sunlight with a mean attenuation coefficient of 2.1 m(-1). These results expand our knowledge of the evolution of light transfer through ice and snow cover and its role in the ecology of lakes in temperate and arid areas.Peer reviewe

Diurnal Cycle Model of Lake Ice Surface Albedo : A Case Study of Wuliangsuhai Lake

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions

Facile in situ solution synthesis of SnSe/rGO nanocomposites with enhanced thermoelectric performance

Constructing nanostructured composite architectures has been considered as an effective strategy to reduce the lattice thermal conductivity (κL) and enhance the dimensionless figure of merit (ZT) of thermoelectric materials. Herein, a series of SnSe/reduced graphene oxide (rGO)-x (x = 0.1, 0.3, 0.5, 0.7 wt%) nanocomposites are controllably synthesised in situ via a facile single-step bottom-up solution method, where rGO nanosheets are incorporated intimately into the SnSe matrix. Nanocompositing performs two key functions: (i) significantly reducing the lattice thermal conductivity of the material, which can be attributed to enhanced phonon scattering from high-density SnSe/rGO interfaces, and (ii) improving the electrical conductivity over the low temperature range, as result of an increased carrier concentration. The subsequent thermoelectric performance of SnSe/rGO sintered pellets has been optimised by tuning the rGO mass fraction, with SnSe/rGO-0.3 achieving κL = 0.36 W m−1 K−1 at 773 K (cutting the κL of SnSe by 33%) to yield a maximum ZT of 0.91 at 823 K (representing a ∼47% increase compared to SnSe). This study provides a new pathway to improve the thermoelectric performance of polycrystalline SnSe by way of engineering metal chalcogenide/rGO composite architectures at the nanoscale

AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods

Molecular epidemiology and clinical characteristics of respiratory syncytial virus in hospitalized children during winter 2021–2022 in Bengbu, China

ObjectiveThis study aimed to study the molecular epidemiology and clinical characteristics of respiratory syncytial virus (RSV) infection from hospitalized children with ARTI in Bengbu.MethodsOne hundred twenty-four nasopharyngeal swab specimens and clinical data from children with ARTI cases were collected in Bengbu, China, during winter 2021–2022. The samples were detected by qPCR of 13 respiratory viruses. Phylogenetic analysis was constructed using MEGA 7.0. All analyses were performed using SAS software, version 9.4.ResultsIn winter 2021–2022, URTI, NSCAP, SCAP, and bronchiolitis accounted for 41.03%, 27.35%, 17.09%, and 14.53% of hospitalized children in Bengbu, China. The detection rates of the top three were RSV (41.94%), ADV (5.65%), and FluB (5.65%) in hospitalized children through 13 virus detection. RSV is the main pathogen of hospitalized children under 2 years old. Forty-eight sequences of G protein of RSV were obtained through PCR amplification, including RSV-A 37 strains and RSV-B 11 strains. Phylogenetic analysis showed that all RSV-A and RSV-B were ON1 and BA9 genotypes, respectively. ON1 genotypes were further divided into two clades. The majority of ON1 strains formed a unique genetic clade with T113I, V131D, N178 G, and H258Q mutations. Furthermore, RSV infection was an independent risk factor for ventilator use (OR = 9.55, 95% CI 1.87–48.64).ConclusionThere was a high incidence of RSV among hospitalized children during winter 2021–2022 in Bengbu with ON1 and BA9 being the dominant strains. This study demonstrated the molecular epidemiological characteristics of RSV in children with respiratory infections in Bengbu, China

High prevalence of Rickettsia spp. in ticks from wild hedgehogs rather than domestic bovine in Jiangsu province, Eastern China

BackgroundSpotted fever group Rickettsia (SFGR), containing various pathogenic Rickettsia spp., poses remarkable negative influences to public health by causing various severe or mild diseases. Information regarding prevalence of SFGR in ticks in Jiangsu province, Eastern China, is still limited and needs urgent investigations.MethodsHedgehog- and bovine-attached ticks were collected from Jiangsu province, Eastern China. DNA of individual ticks was extracted for nested polymerase chain reaction amplifications targeting gltA, 16S ribosomal RNA (rrs), ompA, ompB, and sca4 genes following with sequencing. SFGR-specific IgG antibodies in sera of local donators were evaluated using ELISA.ResultsOverall, 144 (83.2%) of the 173 ticks from hedgehogs and 2 (1.2%) of the 168 ticks from bovine were positive for one of the three identified Rickettsia spp., with significant difference between the two groups (P = 3.6e-52). Candidatus Rickettsia principis (9; 5.2%) and R. heilongjiangensis (135; 78.0%) were detected in Haemaphysalis flava rather than in H. longicornis ticks from hedgehogs. R. heilongjiangensis (1; 0.6%) and Candidatus R. jingxinensis (or Candidatus R. longicornii) (1; 0.6%) were identified in H. longicornis and Rhipicephalus microplus ticks from bovine, respectively. Phylogenetic analysis indicated Candidatus R. jingxinensis belonged to R. japonica subgroup, whereas Candidatus R. principis belonged to a novel subgroup. Higher serological prevalence of spotted fever and SFGR-specific IgG antibody level in humans were observed around the investigated area than in urban areas, without significant difference.ConclusionCandidatus R. principis and Candidatus R. jingxinensis were identified in Jiangsu province, Eastern China, and fully genetically characterized for the first time. The higher prevalence of SFGR in hedgehog-attached ticks as well as the higher SFGR-specific IgG antibody level and seropositive rate in humans around the investigated area suggested that more attention should be paid to SFGR. This pathogen is usually transmitted or harbored by wild animals and ticks. This study provides important epidemiological data for both physicians and public health officers in developing early prevention and control strategies against potential Rickettsia infections and in the preparation of suitable testing and treatment needs for rickettsiosis in the endemic areas