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Comparing spatio-temporal patterns between planktonic and benthic algae is helpful for understanding their associations and differences. However, such studies are still rare especially in large rivers. We used a dataset collected in the upper reach of the Jinsha River in different seasons to explore biodiversity and assembly processes of planktonic and benthic diatom assemblages. We found that planktonic and benthic diatoms presented different seasonal variation in species richness and community compositions. We also found evidence that planktonic and benthic diatoms were coupled in the summer. Planktonic diatom assemblages were mainly affected by spatial processes via directional spatial dispersal, especially in the summer. By comparison, benthic diatom assemblages were more affected by environmental processes. Our findings suggest that mass effect and species sorting paradigms explain the assembly processes of planktonic and benthic diatom assemblages, respectively, but the explanatory powers of these two paradigms vary seasonally. To effectively monitor and assess ecological conditions of large rivers, we recommend using benthic algae as a biotic indicator group as they had stronger correlations with environmental factors.

Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb2S3 is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb2S3 solar cells is limited by nonradiative recombination, which is dependent on the quality of the absorber films. Additive engineering is an effective strategy to fine tune the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb2S3 films to be controlled, enabling the deposition of high-quality Sb2S3 absorbers with reduced grain boundary density, optimized band positions, and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb2S3 films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb2S3 absorber films, the MEA-modulated Sb2S3 solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5 G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb2S3 IPVs. Furthermore, we construct high performance large-area Sb2S3 IPV minimodules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb2S3 photovoltaics for indoor energy harvesting.We demonstrate an effective additive engineering strategy to construct Sb2S3 indoor photovoltaics (IPVs) with 17.55% efficiency under 1000 lux WLED illumination, enabling high-performance IPV minimodules for powering IoT wireless electronics.

Wafer transfer robots, serving as core components in semiconductor manufacturing equipment, are primarily responsible for the handling of semiconductor wafers. High-speed semiconductor equipment places stringent demands on the dynamic performance of these robots during operation. Existing control algorithms often fail to effectively balance the requirements for stability, precision, and rapid response in robotic motion control. To overcome this limitation, this paper presents a prescribed-time prescribed-performance event-triggered control algorithm. The proposed approach includes three key features: (1) a prescribed-time prescribed-performance function that explicitly constrains the error convergence time via parameter , enabling both fast response and overshoot suppression; (2) an extended state observer for real-time estimation and compensation of unmodeled dynamics and time-varying external disturbances; and (3) a relative threshold-based event-triggered mechanism that significantly reduces the frequency of control signal updates. Based on Lyapunov stability theory, it is proven that the closed-loop system achieves uniform ultimate boundedness despite model uncertainties and time-varying disturbances. Simulation results indicate that under identical disturbance conditions, the proposed algorithm ensures joint position errors converge within 0.0009 rad by the prescribed times , with overshoot limited to 1.2% of the relative reference trajectory amplitude, while reducing communication load by more than 64.6%. This method ensures deterministic transient response for wafer transfer robots, significantly improving disturbance rejection and communication efficiency.

To avoid too large exciting force in traditional linear vibrating screen and unstable working state in resonance screen, the disc spring system is applied in the linear vibration screen. The model of the disc spring system in vibration screen is established by simulation and experiment. The characteristics of modal and amplitude of the disc spring system in vibration screen are studied. We found that the disc spring system vibrates in vertical direction at the third-order natural frequency, which is consistent with the direction of the vibration screen when screening particles. Moreover, the third-order natural frequencies in simulation and experiment are basically consistent. Furthermore, the maximum amplitude of the disc spring system appears at 960 r/min (16 Hz), which is in accord with the third-order natural frequency. Meanwhile, the amplitude increases proportionally with the increase of exciting force, while the amplification factors are the same under three different exciting forces. This indicates that the disc spring system has excellent linearity. The results of research provide guidance for design and application of elastic components on the vibration screen.

Carbon fiber fabric reinforced composite laminates are widely used in the automotive and aerospace components, which are prone to suffering low velocity impacts. In this paper, helicoidal layups of fabrics inspired by the Bouligand type structure of the dactyl clubs of mantis shrimp are proposed to improve the impact resistance of carbon fiber fabric reinforced composite laminates. Low velocity impact tests and finite element simulation are carried out to investigate the effect of the rotation angle of helicoidal layups on the impact damage behaviors of composite laminates, including impact force response, energy absorption characteristics and damage mechanism. Results show that the simulation results of impact force–time response, absorbed energy–time response, and damage characteristics show good agreements with the experimental results. With the increase in impact energy, the maximum value of impact force, the absorbed energy and the energy absorption ratio for all specimens are all increased. Under all impact energies, the impact damage of specimens with helicoidal layups are lower than that of specimen QI1 (rotation angle of 0°), indicating that the helical layup of woven carbon fabric can sufficiently enhance the impact resistance of the composite material. Furthermore, the impact resistance of specimen HL2 (rotation angle of 12.8°) is the best, because it demonstrates the lowest impact damage and highest impact force under all energies. This work provides a bionic design guideline for the high impact performance of carbon fiber fabric reinforced composite laminate.

Current studies on the nonlinear dynamic modeling of bolted joints often neglect the influence of the effective contact area between interfaces. This assumption introduces significant deviations in the equivalent dynamic models due to the discrepancy in contact stiffness at the interface. To address this issue, this paper proposes a twin finite element modeling approach incorporating gradient stratification based on localized contact characteristics. First, a finite element model is established to analyze the contact pressure distribution in the bolted joint interface. A fourth-order polynomial function is used to fit the contact pressure distribution curve. Using 10 % of the peak contact pressure as the threshold, the validity of the selected pressure range is verified through an area integral formula, ensuring it accounts for over 99 % of the total pressure. This allows the effective and ineffective contact areas to be accurately identified. Based on the gradient distribution characteristics of contact pressure, the effective contact area is further divided into virtual material layers with gradient stratification. Subsequently, a deep neural network (DNN) model is employed to achieve a nonlinear mapping between the contact states of the bolted joint, the gradient-stratified regions, and the equivalent dynamic parameters. A twin finite element model is then constructed. Finally, a particle swarm optimization (PSO) algorithm is used to identify the equivalent dynamic parameters of the virtual material layers. The identification process minimizes the root-mean-square error between the first four predicted modal frequencies of the twin finite element model and experimentally measured values. The experimental results demonstrate that the proposed modeling method achieves a prediction error within 1 % for the first four modal frequencies compared to the measured values.

The detection of fish freshness is crucial for ensuring food safety. This study addresses the limitations of traditional detection methods, which rely on laboratory equipment and complex procedures, by proposing a smartphone-based detection method, termed FreshFusionNet, that utilizes a pitaya peel pH intelligent indicator film in conjunction with multimodal deep learning. The pitaya peel indicator film, prepared using high-pressure homogenization technology, demonstrates a significant color change from dark red to yellow in response to the volatile alkaline substances released during fish spoilage. To construct a multimodal dataset, 3600 images of the indicator film were captured using a smartphone under various conditions (natural light and indoor light) and from multiple angles (0° to 120°), while simultaneously recording pH values, total volatile basic nitrogen (TVB-N), and total viable count (TVC) data. Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. Experimental results indicate that the overall classification accuracy of FreshFusionNet reaches 99.61%, with a single inference time of only 142 ± 40 milliseconds (tested on Xiaomi 14). This method eliminates the need for professional equipment and enables real-time, non-destructive detection of fish spoilage through smartphones, providing consumers and the food supply chain with a low-cost, portable quality-monitoring tool, thereby promoting the intelligent and universal development of food safety detection technology.

Exercise is known to regulate the immune system. However, its prognostic value in hepatocellular carcinoma (HCC) remains largely unknown. This study aims to construct a machine learning-based prognostic signature using exercise-related immune genes (EIGs) to predict prognosis in HCC. We obtained mRNA-seq and scRNA of HCC from GeneCards, GEO, TCGA and ICGC. EIG were obtained using WGCNA, differential gene expression analysis and CIBERSORT. Univariate COX analysis and 101 combinations of 10 machine learning algorithms were used to construct EIG prognostic signature (EIGPS), and survival analyses were performed. Furthermore, we conducted molecular subtyping, qRT-PCR, biological functions, immune infiltration, drug sensitivity, and single cell analyses on EIGPS. Using WGCNA, differential gene expression analysis, and CIBERSORT, 59 EIGs were identified, of which 54 were associated with prognosis. EIGPS constructed by 7 EIGs (UPF3B, G6PD, ENO1, FARSB, CYP2C9, DLGAP5, SLC2A1) had the highest average C-index value (0.742), showing good predictive performance independent of clinical features. qRT-PCR results showed that CYP2C9 was lowly expressed in HCC cells, while all other genes were highly expressed. 7 EIGs were divided into two subtypes, with C2 exhibiting better anti-tumor immunity. Immunological biological differences between high- and low-risk groups based on EIGPS involved immune responses. EIGPS was mainly expressed in macrophages. The high-risk group had higher macrophage abundance and immune escape ability, as well as greater sensitivity to Afatinib and Alpelisib. We identified key EIGs and constructed an EIGPS that can effectively predict the prognosis of HCC, which offers avenues for better personalized treatments.

Hypoxia and adverse health outcomes might be affected by O3 pollution in the highland city of Lhasa. NOx emissions can amplify the role of volatile organic compounds (VOCs) in the secondary production of O3 under the conditions of high ultraviolet (UV) radiation levels and unfavourable dispersion patterns in the Lhasa River valley. Here, online C2–C11 VOC measurements, accompanied by other parameters concerning the O3 chemical budget, were first obtained and employed to identify the key VOC species and key sources of VOCs in terms of the loss rate against OH radicals (LOH), ozone formation potential (OFP), secondary organic aerosol potential (SOAP), and toxicity. Oxygenated VOCs (OVOCs) not only were the most abundant VOCs but also dominated LOH, OFP, and toxicity. Isoprene and anthropogenic VOCs were further identified as precursors of these OVOCs. Aromatics accounted for 5 % of the total VOCs (TVOCs) but contributed 88 % to the SOAP and 10 % to the toxicity. As the primary oxidative intermediates of aromatics were not well characterized by our measurements, the environmental impact of aromatics could be underestimated by our data. Source appointment and ternary analysis of benzene, toluene, and ethylbenzene confirmed the combined contribution of traffic emissions, solvent usage, and biomass burning. This suggests that O3 precursors are mainly from sources associated with residents' lives, except for solvent usage emissions which contribute to aromatics. Preliminary comparisons between source spectrums of transport sector emissions with positive matrix factorization (PMF)-decomposed ones and our measured ones suggest that vehicle emission patterns of VOCs at high altitude generally follow the ground-level impression. More quantitative data are required to further confirm this point though. Emission reduction strategy analysis for O3 pollution control highlighted multiple benefits of the simultaneous reduction in NOx originating from diesel vehicle emissions, biomass burning, and background sources (possibly dominated by incense burning). The notable biogenic emission contribution to the OFP was also first confirmed in our study, and this highlights the side effects of the government's pursuit of a greener city.

This paper investigates the tensile behavior of cotton ring-spun yarn through experimental testing, numerical simulation, and theoretical calculation. Firstly, scanning electron microscope testing of the microscopic geometric morphologies of yarns was performed for the development of basic finite element (FE) models. Then, the influences of tensile speed and yarn length on the tensile properties of yarn were studied using tensile experiments. Numerical simulations were further performed to investigate the effects of yarn diameter, twist angle, and friction between fibers on the tensile modulus of yarn. Finally, a modified ‘rule-of-mixtures’ equation was proposed to effectively calculate the tensile modulus of yarn through incorporating the friction correction factor. The experimental results show that the tensile modulus and strength of tested yarn are significantly affected by the yarn structure and are not sensitive to the yarn length and tensile speed. Furthermore, the tensile moduli of yarns obtained from the numerical simulations show a good fitting accuracy with those obtained from experimental tests when the friction coefficient is set to 0.5 in the FE models. The simulation results show that the twist angle and friction coefficient are two key factors affecting the tensile modulus of yarn. The modified ‘rule-of-mixtures’ equation presents better accuracy for the calculation of the tensile modulus of yarn compared with the traditional ‘rule-of-mixtures’ equation, which can be used to replace the FE modeling and simulation and reduce the computational cost. This work will provide a deeper understanding of the mechanical properties of cotton ring-spun yarns and enhance their application in the textile industry.