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Background: Limited data exists on the link between dietary iron intake and mortality in diabetes. Our investigation aimed to explore how dietary iron intake correlates with overall and cause-specific mortality in diabetic individuals. Methods: This analysis encompassed 5970 participants with diabetes from the National Health and Nutrition Examination Survey spanning 1999 to 2014. Baseline data were collected through surveys and examinations, with mortality status tracked via National Death Index records until December 31, 2015. Cox proportional hazard models were utilized to calculate hazard ratios (HR) and 95% confidence intervals (CI) for mortality from various causes, including cardiovascular disease (CVD) and cancer. Results: The average iron intake among the cohort was 14.1 ± 7.4 mg daily, with an average participant age of 61.3 and 3059 (51.3%) male adults. Over 41,425 person-years of follow-up, 1497 deaths were recorded. Following adjustments for multiple variables, an iron intake between 11.1 and 14.4 mg was associated with the lowest risk of all-cause mortality (HR 0.83 [0.70, 0.99], P < 0.05) compared to the reference group (<8.3 mg). Analysis of dose-response curves revealed an L-shaped pattern in men and a J-shaped pattern in women concerning the relationship between iron intake and all-cause mortality. Conclusions: Our findings suggest a nonlinear association between dietary iron intake and all-cause mortality in individuals with diabetes. Specifically, higher iron intake may increase all-cause mortality risk in men, while potentially exert a protective effect in women. [ABSTRACT FROM AUTHOR]

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model's convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 10 9 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model's generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering. [ABSTRACT FROM AUTHOR]

The Inconel 718 superalloy demonstrates the potential to fabricate high-temperature components using additive manufacturing. However, additively manufactured Inconel 718 typically exhibits low strength, necessitating post-heat treatments for precipitate strengthening. This study investigated the microstructures and mechanical properties of the Inconel 718 superalloy fabricated via laser powder bed fusion. The room-temperature and high-temperature tensile properties of the Inconel 718 alloy samples following various post-heat treatments were evaluated. The results indicate that the as-built samples exhibited columnar grains with fine cell structures. Solution treatment resulted in δ phase formation and grain recrystallization. Subsequent double aging led to finely distributed nanoscale γ′ and γ″ particles. These nanoscale particles provided high strength at both room and high temperatures, resulting in a balanced strength and ductility comparable to the wrought counterpart. High-temperature nanoindentation analyses revealed that the double-aging samples exhibited very high hardness and low creep rates at 650 °C. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) is a life‐threatening disease characterized by extensive membrane destruction in the vascular wall that is closely associated with vascular smooth muscle cell (VSMC) phenotypic switching. A thorough understanding of the changes in regulatory factors during VSMC phenotypic switching is essential for managing AAA therapy. In this study, we revealed the impact of NRF2 on the modulation of VSMC phenotype and the development of AAA based on single‐cell RNA sequencing analysis. By utilizing a murine model of VSMC‐specific knockout of nuclear factor E2‐related factor 2 (NRF2), we observed that the absence of NRF2 in VSMCs exacerbated AAA formation in an angiotensin II‐induced AAA model. The downregulation of NRF2 promoted VSMC phenotypic switching, leading to an enhanced inflammatory response. Through genome‐wide transcriptome analysis and loss‐ or gain‐of‐function experiments, we discovered that NRF2 upregulated the expression of VSMC contractile phenotype‐specific genes by facilitating microRNA‐145 (miR‐145) expression. Our data identified NRF2 as a novel regulator involved in maintaining the VSMC contractile phenotype while also influencing AAA formation through an miR‐145‐dependent regulatory mechanism. [ABSTRACT FROM AUTHOR]

Type I photodynamic therapy is considered to be a more promising cancer treatment than type II photodynamic therapy due to its non-oxygen-dependent characteristics. In this work, three D–A structure N,N′-dihydrophenazine (DHP)-based photosensitizers DP-CNPY, SMP-CNPY and DMP-CNPY were designed and synthesized by introducing different numbers of methyl groups in the backbone neighbor of DHP as the donor and combined with the typical strong electron acceptor 2-(pyridin-4-yl)acetonitrile. Among the three photosensitizers, SMP-CNPY with one methyl modification showed the best type I ROS (O2−˙, ˙OH) generation capacity and AIE performance. By encapsulation, SMP-CNPY was fabricated into nanoparticles, and SMP-CNPY NPs exhibited lipid droplet targeting ability with near-infrared (NIR) emission. Cell experiments have proved that SMP-CNPY NPs can effectively kill different kinds of cancer cells under normal oxygen conditions. Even under hypoxic and extreme hypoxic conditions, SMP-CNPY NPs can still produce ROS and kill cancer cells. This work holds significant potential in the field of type I AIE-active photosensitizers and provides a new strategy for overcoming the hypoxic dilemma in the malignant tumor microenvironment. [ABSTRACT FROM AUTHOR]

The color features of strawberries at different growth stages vary slightly and occluded during growth. To address these challenges, this study proposes a lightweight multi-stage detection method based on You Only Look Once version 7-tiny (YOLOv7-tiny) for strawberries in complex environments. First, the size of the model is reduced by replacing the ordinary convolution of the neck network used for deep feature extraction and fusion with lightweight Ghost convolution. Then, by introducing the Coordinate Attention (CA) module, the model's focus on the target detection area is enhanced, thereby improving the detection accuracy of strawberries. The Wise Intersection over Union (WIoU) loss function is integrated to accelerate model convergence and enhance the recognition accuracy of occluded targets. The advanced Adaptive nesterov momentum algorithm (Adan) is utilized for gradient descent, processing averaged sample data. Additionally, considering the small size of strawberry targets, a detection head specifically for small targets is added, performing detection on a 160 × 160 × 64 feature map, which significantly improves the detection performance for small strawberries. Experimental results demonstrate that the improved network model achieves an m A P @ 0.5 of 88.2% for multi-stage strawberry detection, which is 2.44% higher than the original YOLOv7-tiny algorithm. Meanwhile, G F L O P s and P a r a m s are reduced by 1.54% and 12.10%, respectively. In practical detection and inference, the improved model outperforms current mainstream target detection models, enabling a quicker and more accurate identification of strawberries at different growth stages, thus providing technical support for intelligent strawberry picking. [ABSTRACT FROM AUTHOR]

Titanium parts fabricated by additive manufacturing, i.e., laser or electron beam-powder bed fusion (L- or EB-PBF), usually exhibit columnar grain structures along the build direction, resulting in both microstructural and mechanical anisotropy. Post-heat treatments are usually used to reduce or eliminate such anisotropy. In this work, Ti-6Al-2Zr-1Mo-1V (TA15) alloy samples were fabricated by L-PBF to investigate the effect of post-heat treatment and load direction on the dynamic response of the samples. Post-heat treatments included single-step annealing at 800 °C (HT) and a hot isotropic press (HIP). The as-built and heat-treated samples were dynamically compressed using a split Hopkinson pressure bar at a strain rate of 3000 s−1 along the horizontal and vertical directions paralleled to the load direction. The microstructural observation revealed that the as-built TA15 sample exhibited columnar grains with fine martensite inside. The HT sample exhibited a fine lamellar structure, whereas the HIP sample exhibited a coarse lamellar structure. The dynamic compression results showed that post-heat treatment at 800 °C led to reduced flow stress but enhanced uniform plastic strain and damage absorption work. However, the HIP samples exhibited both higher stress, uniform plastic strain, and damage absorption work owing to the microstructure coarsening. Additionally, the load direction had a subtle influence on the flow stress, indicating the negligible anisotropy of flow stress in the samples. However, there was more significant anisotropy of the uniform plastic strain and damage absorption. The samples had a higher load-bearing capacity when dynamically compressed perpendicular to the build direction. [ABSTRACT FROM AUTHOR]

With the rapid development of portable electronic devices, there is an urgent need for the multifunctional composites like excellent electromagnetic interference (EMI) shielding performance, mechanical property, and thermal management capabilities. Here, due to the low thermal conductivity of phase change materials (PCMs), a sandwich structure carbon nanotube/phase change microcapsule/carbon nanotube (CNT/PMC/CNT (CPC)) film was prepared by vacuum‐assisted filtration (VAF) method. CNT, PMC, and cellulose nanocrystal (CNC) were used as functional fillers. The addition of CNCs was used to improve the mechanical performance of CPC film. Compared with PMC‐CNC (PC) film, CPC film with sandwich structure prevented leakage phenomenon efficiently. Subsequently, the use of ultrasonic‐assisted forced infiltration (UAFI) successfully infiltrated polydimethylsiloxane (PDMS) matrix in the filler skeleton to obtain CNT/PMC/CNT/PDMS (CPCP) composites. When filler mass ratio (CNT: CNC and PMC: CNC) reached 10:1, the thermal conductivity of CPCP composites was 2.32 W/(m·K) and total EMI shielding effectiveness (EMI SET) of it with 0.38 mm thickness reached 37.46 dB at 12.4 GHz. Besides, these sandwich composites exhibited the good mechanical properties and heat storage capacity. And we systematically observed the impact of different addition amounts of PC homogeneous dispersing solution on phase change ability of CPCP10:1 composites. In summary, CPCP phase change composites with sandwich structure shows potential application prospect in thermal management materials with EMI shielding of electronic devices. Highlight: The sandwich structure CPCP composites prepared by the combination of VAF and UAFI method, showing superior thermal conductivity and phase change ability.CPCP has excellent EMI shielding performance, which can meet the need of TMMs with EMI shielding performance.Compared with same thickness PC film, sandwich CPC film can prevent leakage phenomenon efficiently. [ABSTRACT FROM AUTHOR]

In recent years, flexible electronics technology has been developing rapidly, and flexible electronic devices have been widely used in wearable gadgets and robotics. Flexible wires are the basic components of these devices, which usually consist of metallic materials with flexibility and ductility. However, the introduction of flexible conductive materials has broadened the application scope of flexible wires. In this paper, PDMS/SCF conductive composites exhibiting different conductivity properties on both sides are fabricated by adding meshes based on the continuous spatial‐confining network assembly method. The conductivity of PDMS/SCF (6 wt%) composites can be exhibited as 173.45 S/m in the conductive layer and 6.25 × 10−11 S/m in the insulating layer, which can be used as core and surface layers of conductors, respectively. A mathematical model is also developed to predict the electrical conductivity of the PDMS/SCF conductive composites, and the experimental data are in good agreement with the predicted results. This work provides a method for the continuous preparation of multifunctional composites that can be molded in one step with conductive layer and insulating layer, which can be used as the core and insulating layers of wires, thus simplifying the preparation process of composite‐based wires and expanding the range of conductive composites applications. [ABSTRACT FROM AUTHOR]

Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology. [ABSTRACT FROM AUTHOR]