Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Lateral Heterostructure Formed by Highly Thermally Conductive Fluorinated Graphene for Efficient Device Thermal Management.

The continued miniaturization of chips demands highly thermally conductive materials and effective thermal management strategies. Particularly, the high-field transport of the devices built with 2D materials is limited by self-heating. Here a systematic control of heat flow in single-side fluorinated graphene (FG) with varying degrees of fluorination is reported, revealing a superior room-temperature thermal conductivity as high as 128 W m-1 K-1. Monolayer graphene/FG lateral heterostructures with seamless junctions are approached for device fabrication. Efficient in-plane heat removal paths from graphene channel to side FG are created, contributing significant reduction of the channel peak temperature and improvement in the current-carrying capability and power density. Molecular dynamics simulations indicate that the interfacial thermal conductance of the heterostructure is facilitated by the high degree of overlap in the phonon vibrational spectra. The findings offer novel design insights for efficient heat dissipation in micro- and nanoelectronic devices.

Pinecone derived hierarchical carbon nanostructure as a transducer in a solid-state ion-selective electrode for in vivo analysis of calcium ion.

Monitoring extracellular calcium ion (Ca2+) chemical signals in neurons is crucial for tracking physiological and pathological changes associated with brain diseases in live animals. Potentiometry based solid-state ion-selective electrodes (ISEs) with the assist of functional carbon nanomaterials as ideal solid-contact layer could realize the potential response for in vitro and in vivo analysis. Herein, we employ a kind of biomass derived porous carbon as a transducing layer to prompt efficient ion to electron transduction while stabilizes the potential drift. The eco-friendly porous carbon after activation (APB) displays a high specific area with inherit macropores, micropores, and large specific capacitance. When employed as transducer in ISEs, a stable potential response, minimized potential drift can be obtained. Benefiting from these excellent properties, a solid-state Ca2+ selective carbon fiber electrodes (CFEs) with a sandwich structure is constructed and employed for real time sensing of Ca2+ under electrical stimulation. This study presents a new approach to develop sustainable and versatile transducers in solid-state ISEs, a crucial way for in vivo sensing.

Soliton and dispersive wave generation with third-order dispersion and temporal boundary.

We investigate the pulse evolution and energy conservation condition at the temporal boundary under third-order dispersion. When the fundamental soliton crosses the temporal boundary and forms two reflected pulses and one transmitted pulse, the power of the transmitted pulse first increases and then decreases as the incident spectrum shifts toward the blue side. If the transmitted spectrum lies in the anomalous group-velocity dispersion region, second-order soliton is formed and dispersive wave is radiated. We present a modified phase-matching condition to predict the resonance frequencies. The predicted results are in good agreement with the results obtained by numerically solving the nonlinear Schrödinger equation.

Non-invasive assessment of periodontal inflammation by continuum-removal hemodynamic spectral indices.

BACKGROUND: Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied. METHODS: This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ( D HbO 2 ), deoxyhemoglobin ( D Hb ), total hemoglobin ( t Hb ) and tissue water ( D water ), and two normalized difference indices of oxyhemoglobin( N D HbO 2 I ) and deoxyhemoglobin ( N D Hb I ) from continuum-removal spectra (400-1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis. Typical statistical analyses were conducted to explore the effectiveness of the proposed HSIs. RESULTS: D Hb and t Hb exerted significant increasing trends as inflammation progressed, whereas D HbO 2 exhibited significant difference (P < 0.05) from the healthy sites only at moderate and severe periodontitis and D water presented unstable sensitives to disease severity. By contrast, N D HbO 2 I and N D Hb I showed more steadily downward trends as severity increased, and demonstrated the highest correlations with clinical gold standard parameters. Particularly, the proposed normalized HSIs ( N D HbO 2 I and N D Hb I ) yielded high correlations of - 0.49 and - 0.44 with probing depth, respectively, far outperforming results achieved by previous studies. The performances of the HSIs were also confirmed using the periodontal therapy group. CONCLUSIONS: These results indicated great potentials of combination optical spectroscopy and smart devices to non-invasively probe periodontitis at earlier stages using the simple and practical HSIs. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry on October 24, 2021, and the clinical registration number is ChiCTR2100052306.

The Effect of Comb Cell Size on the Development of Apis mellifera Drones.

The growth and development of honeybees are influenced by many factors, one of which is the cell size of the brood comb. Larger worker bees can be obtained by being raised in bigger cells. However, whether cell size has the same effect on drone development is still unknown. Here, using 3D-printed foundations, we observed the development of drones kept in comb cells of different sizes from the late larval stage through eclosion. The results showed that drones in larger cell-size combs had heavier body weights, longer body lengths, and larger head widths, thorax widths, and abdomen widths compared to those in smaller cell-size combs. Furthermore, regardless of developmental stages, the drones' body weights increased linearly with the comb's cell size. However, the other morphological changes of drones in different developmental stages were out of proportion to the cell-size changes, resulting in smaller cells with a higher fill factor (thorax width/cell width). Our findings confirm that comb cell size affects the development of honeybees; drones become bigger when raised in large cells.

Manipulation of Convection Using Infrared Light Emitted from Human Hands.

Control of convection plays an important role in heat transfer regulation, bio/chemical sensing, phase separation, etc. Current convection controlling systems generally depend on engineered energy sources to drive and manipulate the convection, which brings additional energy consumption into the system. Here the use of human hand as a natural and sustainable infrared (IR) radiation source for the manipulation of liquid convection is demonstrated. The fluid can sense the change of the relative position or the shape of the hand with the formation of different convection patterns. Besides the generation of static complex patterns, dynamic manipulation of convections can also be realized via moving of hand or finger. The use of such sustainable convections to control the movement of a floating "boat" is further achieved. The use of human hands as the natural energy sources provides a promising approach for the manipulation of liquid convection without the need of extra external energy, which may be further utilized for low-cost and intelligent bio/chemical sensing and separation.

Identification of potential pathogenic hepatic super-enhancers regulatory network in high-fat diet induced hyperlipidemia.

Hyperlipidemia (HLP) is a prevalent metabolic disorder and a significant risk factor for cardiovascular disease. According to recent discoveries, super-enhancers (SEs) play a role in the increased expression of genes that encode important regulators of both cellular identity and the progression of diseases. However, the underlying function of SEs in the development of HLP is still unknown. We performed an integrative analysis of data on H3K27ac ChIP-seq and RNA sequencing obtained from liver tissues of mice under a low-fat diet (LFD) and high-fat diet (HFD) from GEO database. The rank ordering of super enhancers algorithm was employed for the computation and identification of SEs. A total of 1,877 and 1,847 SEs were identified in the LFD and HFD groups, respectively. The SE inhibitor JQ1 was able to potently reverse lipid deposition and the increased intracellular triglyceride and total cholesterol induced by oleic acid, indicating that SEs are involved in regulating lipid accumulation. Two hundred seventy-eight were considered as HFD-specific SEs (HSEs). GO and KEGG pathway enrichment analysis of the upregulated HSEs-associated genes revealed that they were mainly involved in lipid metabolic pathway. Four hub genes, namely Cd36, Pex11a, Ech1, and Cidec, were identified in the HSEs-associated protein-protein interaction network, and validated with two other datasets. Finally, we constructed a HSEs-specific regulatory network with Cidec and Cd36 as the core through the prediction and verification of transcription factors. Our study constructed a HSEs-associated regulatory network in the pathogenesis of HLP, providing new ideas for the underlying mechanisms and therapeutic targets of HLP.

Risk factors associated with atrial fibrillation following lung cancer surgery: A multi-center case-control study.

BACKGROUND: Postoperative atrial fibrillation (POAF) is a common complication after major thoracic operations. The objective of this case-control study was to identify the risk factors for POAF following lung cancer surgery. METHODS: In total, 216 patients with lung cancer who were selected from three different hospitals were followed up between May 2020 and May 2022. They were divided into two groups: case group, patients with POAF and control group, patients without POAF (case-control). Risk factors associated with POAF were investigated using univariate and multivariate logistic regression analyses. RESULTS: Risk factors that were significantly associated with POAF were preoperative brain-type natriuretic peptide (BNP) levels [odds ratio (OR): 4.46; 95% confidence interval (CI): 1.52-13.06; P = 0.0064], sex (OR: 0.07; 95%CI: 0.02-0.28; P = 0.0001), preoperative white blood cell (WBC) count (OR: 3.00; 95%CI: 1.89-4.77; P < 0.0001), lymph node dissection (OR: 11.49; 95%CI: 2.81-47.01; P = 0.0007), and cardiovascular disease (OR: 4.93; 95%CI: 1.14-21.31; P = 0.0326). CONCLUSION: In summary, data from the three hospitals suggested that preoperative BNP levels, sex, preoperative WBC count, lymph node dissection, and hypertension/coronary heart disease/myocardial infarction were associated with a significantly high risk of POAF following lung cancer surgery.

Polymer-Entangled Spontaneous Pseudo-Planar Heterojunction for Constructing Efficient Flexible Organic Solar Cells.

Flexible organic solar cells (FOSCs) have attracted considerable attention from researchers as promising portable power sources for wearable electronic devices. However, insufficient power conversion efficiency (PCE), intrinsic stretchability, and mechanical stability of FOSCs remain severe obstacles to their application. Herein, an entangled strategy is proposed for the synergistic optimization of PCE and mechanical properties of FOSCs through green sequential printing combined with polymer-induced spontaneous gradient heterojunction phase separation morphology. Impressively, the toughened-pseudo-planar heterojunction (Toughened-PPHJ) film exhibits excellent tensile properties with a crack onset strain (COS) of 11.0%, twice that of the reference bulk heterojunction (BHJ) film (5.5%), which is among the highest values reported for the state-of-the-art polymer/small molecule-based systems. Finite element simulation of stress distribution during film bending confirms that Toughened-PPHJ film can release residual stress well. Therefore, this optimal device shows a high PCE (18.16%) with enhanced (short-circuit current density) JSC and suppressed energy loss, which is a significant improvement over the conventional BHJ device (16.99%). Finally, the 1 cm2 flexible Toughened-PPHJ device retains more than 92% of its initial PCE (13.3%) after 1000 bending cycles. This work provides a feasible guiding idea for future flexible portable power supplies.

Achieving Desired Pseudo-Planar Heterojunction Organic Solar Cells via Binary-Dilution Strategy.

The sequential deposition process has demonstrated the great possibility to achieve a photolayer architecture with an ideal gradient phase separation morphology, which has a vital influence on the physical processes that determine the performance of organic solar cells (OSCs). However, the controllable preparation of pseudo-planar heterojunction (P-PHJ) with gradient distribution has not been effectively elucidated. Herein, a binary-dilution strategy is proposed, the PM6 solution with micro acceptor BO-4Cl and the L8-BO solution with micro donor PM6 respectively, to form P-PHJ film. This architecture exists good donor (D) and acceptor (A) vertical gradient distribution and larger D/A interpenetrating regions, which promotes exciton generation and dissociation, shortens charge transport distance and optimizes carrier dynamics. Moreover, the dilution of PM6 by BO-4Cl promotes the regulation of active layer aggregation size and phase purity, thus alleviating energy disorder and voltage loss. As a result, the P-PHJ device exhibits an outstanding power conversion efficiency of 19.32% with an excellent short-circuit current density of 26.92 mA cm-2 , much higher than planar binary heterojunction (17.67%) and ternary bulk heterojunction (18.49%) devices. This research proves a simple but effective method to provide an avenue for constructing desirable active layer morphology and high-performance OSCs.

Nonlinear evolution of modulational instability under the interaction of Kerr nonlinearity with pure higher, even-order dispersion.

We investigate the nonlinear evolutions of modulation instability (MI) under the interaction of Kerr nonlinearity with pure higher, even-order dispersion (HEOD) by using the truncating method of three-wave mixing. For any HEOD, we find the phase-plane topological structure of the MI changes in three frequency regions whose ranges depend on the order of HEOD. And we present the novel types of nonlinear evolutions of the MI, which do not exist in the case of quadratic dispersion. Taking the pure-sextic dispersion as an example, the theoretical predictions of the MI evolutions are confirmed by numerically solving the modified nonlinear Schrödinger equation. Our results not only further deepen the understanding of MI, but also provide a universal guideline for experimental investigation of nonlinear waves, such as breather solitons or rogue waves excitation, in nonlinear Kerr media with pure HEOD.

Dispersive wave manipulation by the spectral Heaviside step phase modulation.

We investigate the dispersive waves (DWs) emitted from shaped pulses with spectral Heaviside step phases (HSPs). The spectrally HSP-modulated pulse exhibits a unique double-peak structure, where the intensity and separation of the twin peaks are determined by the modulation depth and frequency detuning. By tailoring the parameters of the HSP suitably, we can control the DW emission with regard to resonant frequency and conversion efficiency. As the intensity ratio or relative separation of neighboring peaks is elaborately chosen, the DW emission can be effectively boosted, or a solitonic cage can be constructed for realizing temporal reflections and refractions associated with spectral broadening and multi-peak spectra of the output DWs. These findings offer a straightforward and efficient approach for controlling the DW emission, which is highly relevant to the advancement of supercontinuum generation and wavelength conversion technology.

Directly seeding epitaxial growth of tungsten oxides/tungsten diselenide mixed-dimensional heterostructures with excellent optical properties.

Mixed-dimensional heterostructures have drawn significant attention due to their intriguing physical properties and potential applications in electronic and optoelectronic nanodevices. However, limited by the lattice matching, the preparation of heterostructures is experimentally difficult and the underlying growth mechanism has not been well established. Here, we report a three-step seeding epitaxial growth strategy for synthesizing mixed-dimensional heterostructures of one-dimensional microwire (MW) and two-dimensional atomic thin film. Our growth strategy has successfully realized direct epitaxial growth of WSe2 film on WOx MW and significantly improves the quality of the epitaxial WSe2 monolayer, which is evidenced by the remarkably enhanced photoluminescence (PL). More intriguingly, the as-synthesized WOx MWs exhibit a strong nonlinear optical response due to the enhancement effect of the core (WOx)-shell (WSe2) nanocavity. Our work provides a feasible route for direct growth of WOx-based mixed-dimensional heterostructures, which possess potential applications in high-performance optoelectronic devices.

The 10-m cotton maps in Xinjiang, China during 2018-2021.

Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84-0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach.

In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors. To address this issue, we introduce an innovative deep-learning-based multi-graph model using Beijing as the study case. This model consists of two key modules: firstly, the 'Meteorological Factor Spatio-Temporal Feature Extraction Module'. This module deeply integrates spatio-temporal features of hourly meteorological data by employing Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) for spatial and temporal encoding respectively. Subsequently, through an attention mechanism, it retrieves a feature tensor associated with air pollutants. Secondly, these features are amalgamated with PM2.5 concentration values, allowing the 'PM2.5 Concentration Prediction Module' to predict with enhanced accuracy the joint influence across multiple monitoring sites. Our model exhibits significant advantages over traditional methods in processing the joint impact of multiple sites and their associated meteorological factors. By providing new perspectives and tools for the in-depth understanding of urban air pollutant distribution and optimization of air quality management, this model propels us towards a more comprehensive approach in tackling air pollution issues.

Pt single atoms meet metal-organic frameworks to enhance electrocatalytic hydrogen evolution activity.

The electrochemical hydrogen evolution reaction (HER) effectively produces clean, renewable, and sustainable hydrogen; however, the development of efficient electrocatalysts is required to reduce the high energy barrier of the HER. Herein, we report two excellent single-atom (SA)/metal-organic framework (MOF) composite electrocatalysts (PtSA-MIL100(Fe) and PtSA-MIL101(Cr)) for HER. The obtained PtSA-MIL100(Fe) and PtSA-MIL101(Cr) electrocatalysts exhibit overpotentials of 60 and 61 mV at 10 mA cm-2, respectively, which are close to that of commercial Pt/C (38 mV); they exhibit overpotentials of 310 and 288 mV at 200 mA cm-2, respectively, which are comparable to that of commercial Pt/C (270 mV). Theoretical simulations reveal that Pt SAs modulate the electronic structures of the MOFs, leading to the optimization of the binding strength for H* and significant enhancement of the HER activity. This study describes a novel strategy for preparing desirable HER electrocatalysts based on the synergy between SAs and MIL-series MOFs. Using MIL-series MOFs to support SAs could be valuable for future catalyst design.

Metal-Insulator Transition of Single-Crystal V2O3 through van der Waals Interface Engineering.

Strongly correlated electron materials harbor interesting materials physics, such as high-Tc superconductivity, colossal magnetoresistance, and metal-insulator transition. These physical properties can be greatly influenced by the dimensionality and geometry of the hosting materials and their interaction strengths with underlying substrates. In a classic strongly correlated oxide vanadium sesquioxide (V2O3), the coexistence of a metal-insulator and paramagnetic-antiferromagnetic transitions at ∼150 K makes this material an excellent platform for exploring basic physics and developing future devices. So far, most studies have been focused on epitaxial thin films in which the strongly coupled substrate has a pronounced effect on V2O3, leading to the observations of intriguing phenomena and physics. In this work, we unveil the kinetics of a metal-insulator transition of V2O3 single-crystal sheets at nano and micro scales. We show the presence of triangle-like alternating metal/insulator phase patterns during phase transition, which is drastically different from the epitaxial film. The observation of single-stage metal-insulator transition in V2O3/graphene compared to the multistage in V2O3/SiO2 evidence the importance of sheet-substrate coupling. Harnessing the freestanding form of the V2O3 sheet, we show that the phase transition of V2O3 sheet can generate a large dynamic strain to monolayer MoS2 and tune its optical property based on the MoS2/V2O3 hybrid structure. The demonstration of the capability in tuning phase transition kinetics and phase patterns using designed hybrid structure of varied sheet-substrate coupling strengths suggests an effective knob in the design and operation of emerging Mott devices.

Soliton-sinc optical pulse propagation in the presence of high-order effects.

We investigate the propagation dynamics of the soliton-sinc, a kind of novel hybrid pulse, in the presence of higher-order effects with emphasis on the third-order dispersion (TOD) and Raman effects. At variance with the fundamental sech soliton, the traits of the band-limited soliton-sinc pulse can effectively manipulate the radiation process of dispersive waves (DWs) induced by the TOD. The energy enhancement and the radiated frequency tunability strongly depend on the band-limited parameter. A modified phase-matching condition is proposed for predicting the resonant frequency of the DWs emitted by soliton-sinc pulses, which is verified by the numerically calculated results. In addition, Raman-induced frequency shift (RIFS) of the soliton sinc pulse increases exponentially with a decrease of the band-limited parameter. Finally, we further discuss the simultaneous contribution of the Raman and TOD effects to the generation of the DWs emitted from the soliton-sinc pulses. The Raman effect can then either reduce or amplify the radiated DWs depending on the sign of the TOD. These results show that soliton-sinc optical pulses should be relevant for practical applications such as broadband supercontinuum spectra generation as well as nonlinear frequency conversion.

Study on the nitrogen content estimation model of cotton leaves based on "image-spectrum-fluorescence" data fusion.

Objective: Precise monitoring of cotton leaves' nitrogen content is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, the information expressed using nitrogen monitoring based on a single data source is limited and cannot consider the expression of various phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity. Methods: Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened. A multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion. Results: The determination coefficients (R2) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R2 increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively. Conclusion: The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for optimizing an accurate method of monitoring cotton leaf nitrogen content.