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The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, de; heading deviation, θe; and bending degree, c, of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld, was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery's pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments. [ABSTRACT FROM AUTHOR]

A structural optimization scheme for the prototype is proposed to improve the driving performance of tree transplanting machines operating in hilly and mountainous environments. Through theoretical analysis of the tree transplanting machine's stability and passability, we determined the relationship between the center of gravity offset distance and overall performance when climbing slopes and crossing steps and trenches. This analysis determined the optimal parameter for the center of gravity offset distance. Subsequently, the prototype structure was optimized, and the center of gravity position was adjusted accordingly. The stability and passability of the prototype were simulated using the multi-body dynamics analysis software RecurDyn V9R4. Finally, the driving performance of the optimized prototype is evaluated through extensive tests. The test results revealed that the optimized prototype achieves the maximum lateral travel and longitudinal climbing angle of more than 30°, the maximum height over steps of 330 mm, and the maximum width across trenches of 890 mm. The outcomes of the real-vehicle tests are largely consistent with the simulation results, and the test outcomes meet the driving performance requirements for transplanting operations in hilly and mountainous terrains. [ABSTRACT FROM AUTHOR]

Optimizing irrigation profit is essential for enhancing agricultural productivity and ensuring sustainable water resource management, especially under the uncertainties caused by climate variability. It signifies that maximizing irrigation profits has become the primary consideration of current agricultural irrigation systems. By discussing the change in irrigation profits with irrigation fraction f , which represents the proportion of irrigated farmland per unit area of cultivated land, a new method of qualitative diagnosis of irrigation supply and demand balance was proposed by means of the optimal irrigation fraction f 0 corresponding to the objective function y f and the correlation coefficient ρ between irrigation water demand anomalies D ^ and irrigation water withdrawal anomalies I ^ ( ρ D ^ , I ^ = 0 ). It will serve as a new attempt to optimize irrigation profits. We argue that the optimal objective function y f 0 represents the state of equilibrium of irrigation supply and demand. This study proposes a reverse optimization idea, which includes two comparison methods, to estimate the comprehensively optimal irrigation fraction f 0 that maximizes irrigation profits and irrigation profit per unit area while minimizing risks associated with climatic anomalies. By demonstrating that f 0 performs exceptionally well in meeting the objectives of maximizing irrigation profits and minimizing risks across various climatic scenarios, we suggest a robust method for optimizing irrigation profits, which simply calculates ρ D ^ , I ^ f = 0 because ρ D ^ , I ^ is the function of f. Since f 0 could capture the optimal solution to the greatest extent. It can serve as a simple and effective solution to determine the optimal objective function y f 0 and diagnose the state of balance between irrigation water demand and supply. This method offers practical implications for water resource management and agricultural planning in the face of climate change. [ABSTRACT FROM AUTHOR]

Background: Measuring non‐parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes. Purpose: We developed a data‐driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB‐1 labeling index (LI) levels and molecular status of gliomas. Methods: An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR ∈[30,300]$ \in [ {30,\ 300} ]$. Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ±$ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo–echo planar imaging (SE‐EPI) diffusion‐weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C‐means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (fn,n=1−6${{f}_n},\ n = 1 -6$) and diffusivities of the maximum peaks (Dpeak${{D}_{peak}}$). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann–Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation. Results: The simulation results showed that the FCN‐based MDD approach (FCN‐MDD) achieved higher CSS than non‐negative least squares‐based MDD (NNLS‐MDD). For in vivo data, the spectra of ET and NET obtained by FCN‐MDD are more distinguishable than NNLS‐MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non‐enhancing tumor, edema, and necrosis). f3,f4,Dpeak${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB‐1 respectively (r=0.568,r=−0.521,r=−0.654$r = 0.568,\ r = - 0.521,\ r = - 0.654$, all p<0.001$p < 0.001$). The AUC of Dpeak${{D}_{peak}}$ for predicting MIB‐1 LI levels was 0.900 (95% CI, 0.826–0.974), versus 0.781 (0.677–0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f1+f3${{f}_1} + {{f}_3}$) was 0.873 (95% CI, 0.802–0.944). Conclusion: The proposed FCN‐MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS‐MDD method. FCN‐MDD also enabled qualitative and quantitative evaluation of the composition of gliomas. [ABSTRACT FROM AUTHOR]

Monofunctional Pt(II) complexes with potent efficacy to overcome the drawbacks of current platinum drugs represent a promising therapeutic approach for triple negative breast cancer (TNBC). A heterocyclic-ligated monofunctional Pt(II) complex PtL with a unique action of mode was designed and investigated. PtL induced DNA single-strand breaks and caused genomic instability in TNBC cells. Mechanism studies demonstrated that PtL disrupted lysosomal acidity and function, which in turn triggered lysosome-dependent cell death. Furthermore, PtL showed convincing suppression in the tube forming and cell migratory abilities against the metastatic potential of TNBC cells. The synthesis and investigation of PtL revealed its potential value as an anti-TNBC drug and extended the family of monofunctional Pt(II) complexes. [ABSTRACT FROM AUTHOR]

It is challenging to identify the features of subsurface fresh bruises of fruits and vegetables by traditional uniform imaging techniques due to the mild symptoms. Owing to the capability of depth-varying and signal-enhanced characterization, spatial-frequency domain imaging was used to detect the fresh bruising (within 30 minutes) of 10 kinds of fruits and vegetables (carrot, Fuji apple, cucumber, golden delicious apple, fruit cucumber, delicious apple, mango, yellow peach, rose plum, and honey peach). Through the procedures of image acquisition, image demodulation, whiteboard correction and inverse estimation, optical properties of the fruits and vegetables were obtained. The results indicated that there were great differences of optical properties between non-bruised and bruised samples, with decreased values of reduced scattering coefficients for the bruised ones, which visually realized the non-destructive detection of early bruising. The novel method proposed in this paper is promising for non-destructive detection of early bruising of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Background: Angelman syndrome (AS) is a genetic disorder that affects neurodevelopment. The investigation of changes in the brain white matter network, which would contribute to a better understanding of the pathogenesis of AS brain, was lacking. Purpose: To investigate both local and global alterations of white matter in patients with AS. Study Type: Prospective. Subjects: A total of 29 AS patients (6.6 ± 1.4 years, 15 [52%] females) and 19 age‐matched healthy controls (HC) (7.0 ± 1.5 years, 10 [53%] females). Field Strength/Sequence: A 3‐T, three‐dimensional (3D) T1‐weighted imaging by using gradient‐echo‐based sequence, single shell diffusion tensor imaging by using spin‐echo‐based echo‐planar imaging. Assessment: Network metrics including global efficiency (Eg), local efficiency (Eloc), small world coefficient (Swc), rich‐club coefficient (Φ), and nodal degree (ND) were estimated from diffusion MR (dMR) data. Connections among highly connected (hub) regions and less connected (peripheral) regions were also assessed. Correlation between the topological parameters and age for each group was also calculated to assess the development of the brain. Statistical Tests: Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction. Results: AS patients showed significantly lower Eg and higher swc compared to HC. Φn significantly increased at higher k‐levels in AS patients. In addition, the connections among hub regions and peripheral regions were significantly interrupted in AS patients. Data Conclusion: The AS brain showed diminished connectivity, reflected by reduced network efficiency compared to HC. Compared to densely connected regions, less connected regions were more vulnerable in AS. Evidence Level: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

It is difficult to obtain high-protein contents from rice straw using direct fermentation due to its low nitrogen content. This study investigates the effects of ammonification–steam explosion pretreatment of rice straw on the protein content after solid-state fermentation (SSF). The pretreatment is carried out under multi-strain inoculation conditions. The samples of rice straw after ammonification (TA), steam explosion (TSE), and ammonification and steam explosion (TA-SE) were compared to the control group (TC). The results indicate that both ammonification and steam explosion could disintegrate rice straw's lignocellulosic structure, releasing nutrients that can be used for microbial reproduction. In addition, amino compounds are formed along with depolymerization products, thus effectively promoting the true protein content. Post-fermentation, total crude protein contents of TA, TSE, and TA-SE samples were 2.56, 1.83, and 4.37 times higher than that of Tc samples, respectively, and true protein contents were 2.52, 1.83, and 5.03 times higher. This study shows that the true protein content by combined ammonification and steam explosion pretreatment of rice straw during 96 h of solid-state fermentation was 46.7% of its total matter, rendering it a suitable alternative to high-protein animal feed. [ABSTRACT FROM AUTHOR]