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The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach.

InAs/GaSb type-Ⅱ superlattices (T2SLs) are considered as high-quality material for the third-generation infrared detectors due to the special energy band structure and mature material growth technology. This paper introduces the optical properties of InAs/GaSb T2SLs for long-wave and short-wave infrared detection. The Raman vibration modes in the superlattice detector structure are obtained by Raman spectra at different layers, and the sub-band photoluminescence spectrum reveals that the device has dual band characteristics of near-infrared and far-infrared. The obvious absorption characteristics of short wave infrared and long wave infrared are characterized by reflection spectrum, photocurrent spectrum and wide band infrared transmission spectrum. The detection mechanism in the near, middle and far infrared bands is reasonably interpreted. The investigation can provide reference for the structural design and mechanism analysis of the development of dual/multi/broadband infrared detectors based on InAs/GaSb T2SLs.

We assessed the prognostic utility of the new perinephric fat adherence risk score - Mayo Adhesive Probability (MAP), in patients of East Asian ethnicity undergoing either laparoscopic partial nephrectomy (LPN) or laparoscopic radical nephrectomy (LRN). A retrospective analysis of clinical data was carried out on 169 patients who either underwent LPN or LRN surgery. These patients were categorized into two groups, group A (0-2 points) and group B (3-4 points) using the new MAP score. The overall clinical data between these two groups was compared and potential risk factors were investigated using logistic regression analyses. The new MAP score yielded an area under the curve of 0.761 (95 % CI: 0.691-0.831), indicating its effectiveness. Group B had a significantly higher incidence of adherent perirenal fat (APF) during surgery (p<0.001) and had a greater average age (p<0.001). There was an increased prevalence of hypertension (p=0.009), type 2 diabetes mellitus (p<0.001), and MAFLD (p<0.001) in group B. Additionally, there were significant differences in posterior perinephric fat thickness (p<0.05), lateral perinephric fat thickness (p<0.001), and perinephric stranding (p<0.001) between the two groups. The new MAP score holds significance in predicting APF in people of East Asian ethnicity undergoing LPN or LRN, and there is a strong correlation between elevated MAP scores and risk factors such as MAFLD and advanced age. [ABSTRACT FROM AUTHOR]

AIM: To examine the relationship between age-related cataracts(ARC)and comorbid hypertension and diabetes. METHODS: We analyzed the administrative records of 6 467 patients aged 50 years and older admitted to the ophthalmological department of a tertiary hospital from January 1st, 2011 to May 20th, 2017. With either eye considered, an ARC(n=3 343)was defined as the presence of lens opacity or previous cataract surgery without evidence of trauma, congenital anomalies or using certain medications. Patients admitted to the same department during the same period due to ocular traumas without clinical evidence of cataracts(n=379)were recruited as the cataract-free controls. Unconditional Logistic regressions were ran to obtain the odds ratio(OR)of hypertension and diabetes among ARC patients adjusted for age, sex and health care accessibility.RESULTS: Hypertension was diagnosed in 29.54% of men with any type of cataracts, in 30.12% of men with an ARC, and 10.82% of men of cataract-free controls. Diabetes was diagnosed in 16.64% of men with any type of cataracts, in 16.48% of men with ARC and 4.22% of men of cataract-free controls. Similar patterns were observed among women. After adjusting for age, sex, and health care accessibility, hypertension was weakly \〖OR=1.83(95% CI: 1.23, 2.74)\〗 and diabetes was strongly \〖3.38(1.86, 6.15)\〗 associated with ARCs. The adjusted OR of comorbid hypertension and diabetes among adults with ARC was 18.20(4.38, 75.59). CONCLUSION: Hypertension and diabetes were independently associated with ARC. Hypertension and diabetes, if co-existing, multiplicatively strengthened the association with ARC.

With the justification of a comprehensive matching function approach, this study analyzes the ride-sourcing market with heterogeneous users. A single origin-destination (O-D) ride-sourcing market model is first developed. The model fills the major research gap to extend general user heterogeneity modeling from the transit market to the two-sided ride-sourcing market. Sufficient conditions that guarantee a unique market equilibrium are given. Equilibrium market operation with a profit-maximizing platform is explored. Equilibrium transitions with respect to the changes in different exogenous market variables are investigated. Nonequilibrium modeling to understand the transition path is also analyzed, revealing how the detailed transition evolves. Extensions of the single O-D market model to aggregate and disaggregate markets and incorporation of travel time reliability are briefly discussed. Numerical experiments, which are based on real-world data in the city of Ningbo, China, and cover an aggregate/single O-D base case, sensitivity analysis, equilibrium transitions, and disaggregate market equilibrium, are presented to illustrate the theoretical model. The larger network of Sioux Falls is also tested as a demonstration of the disaggregate market equilibrium. Discussions are made regarding operation and policy implications., Author(s): Junlin Zhang [1,2]; Dong Mo [1]; Xiqun (Michael) Chen (corresponding author) [1,3] 1. Introduction Thanks to the development of smartphone applications and information and communication technologies, ride-sourcing service [1] [...]

An abundant hollow nanostructure is crucial for fast Li+ and K+ diffusion paths and sufficient electrolyte penetration, which creates a highly conductive network for ionic and electronic transport. In this study, we successfully developed a molecular-bridge-linked, organic–inorganic hybrid interface that enables the preparation of in situ nitrogen-doped hollow carbon nanospheres. Moreover, the prepared HCNSs, with high nitrogen content of up to 10.4%, feature homogeneous and regular morphologies. The resulting HCNSs exhibit excellent lithium and potassium storage properties when used as electrode materials. Specifically, the HCNS-800 electrode demonstrates a stable reversible discharge capacity of 642 mA h g−1 at 1000 mA g−1 after 500 cycles for LIBs. Similarly, the electrode maintains a discharge capacity of 205 mA h g−1 at 100 mA g−1 after 500 cycles for KIBs. Moreover, when coupled with a high-mass-loading LiFePO4 cathode to design full cells, the HCNS-800‖LiFePO4 cells provide a specific discharge capacity of 139 mA h g−1 at 0.1 C. These results indicate that the HCNS electrode has promising potential for use in high-energy and environmentally sustainable lithium-based and potassium-based batteries. [ABSTRACT FROM AUTHOR]

In a two-phase heat transfer device, achieving a high capillarity of the wick while reducing flow resistance within a limited space becomes the key to improving the heat dissipation performance. As a commonly used wick structure, mesh is widely employed because of its high permeability. However, achieving the desired capillary performance often requires multiple layers to be superimposed to ensure an adequate capillary, resulting in an increased thickness of the wick. In this study, an ultra-thin biomimetic copper forest structural modification of copper mesh was performed using an electrochemical deposition to solve the contradiction between the permeability and the capillary. The experiments were conducted on a copper mesh to investigate the effects of various conditions on their morphology and capillary performance. The results indicate that the capillary performance of the modified copper mesh improves with a longer deposition time. The capillary pressure drops can reach up to 1400 Pa when using ethanol as the working fluid. Furthermore, the modified copper mesh demonstrates a capillary performance value (ΔPc·K) of 8.44 × 10−8 N, which is 1.7 times higher than that of the unmodified samples. Notably, this enhanced performance is achieved with a thickness of only 142 μm. The capillary limit can reach up to 45 W when the modified copper mesh is only 180 μm. Microscopic flow analysis reveals that the copper forest modified structure maintains the original high permeability of the copper mesh while providing a greater capillary force, thereby enhancing the overall flow characteristics. [ABSTRACT FROM AUTHOR]