Search Results (1,944)

Organ-specific transcriptomes provide valuable insight into the genes involved in organ identity and developmental control. This study investigated transcriptomes of floral organs and subtending bracts in wild and domesticated Gossypium hirsutum, focusing on MADS-box genes critical for floral development. The expression profiles of A, B, C, D, and E class genes were analyzed, confirming their roles in floral organ differentiation. Hierarchical clustering revealed similar expression patterns between bracts and sepals, as well as between petals and stamens, while carpels clustered with developing cotton fibers, reflecting their shared characteristics. Beyond MADS-box genes, other transcription factors were analyzed to explore the genetic basis of floral development. While wild and domesticated cotton showed similar expression patterns for key genes, domesticated cotton exhibited significantly higher expression in carpels compared to wild cotton, which aligns with the increased number of ovules in the carpels of domesticated cotton. Functional enrichment analysis highlighted organ-specific roles: genes upregulated in bracts were enriched for photosynthesis-related GO terms, while diverse functions were enriched in floral organs, supporting their respective functions. Notably, A class genes were not significantly expressed in petals, deviating from the ABCDE model, which warrants further analysis. Lastly, the ABCDE class genes exhibited differential homoeolog expression bias toward each subgenome between two accessions, suggesting that the domestication process has influenced homoeolog utilization despite functional constraints in floral organogenesis. Full article

The schedule is a critical factor in the development of prefabricated buildings. This paper establishes the schedule risk influencing factors for prefabricated building projects across five dimensions—design, production, transportation, installation, and others—encompassing a total of 14 factors. By integrating DEMATEL and ISM, it constructs a hierarchical network model using expert knowledge and maps it to Bayesian networks (BN), and the node probabilities were calculated using fuzzy set theory combined with the noisy-OR gate model. This DEMATEL-ISM-BN model not only infers the probability of schedule risk occurrence in prefabricated construction projects through causal reasoning and controls the schedule risk of prefabricated construction projects, but it also deduces the posterior probabilities of other influencing factors when a schedule risk occurs through diagnostic reasoning. This approach identifies the key factors contributing to schedule risk and pinpoints the final influencing factors. Research has shown that the three influencing factors of “tower crane worker lifting level”, “construction worker component installation technology”, and “design changes” significantly affect project progress, providing a new risk assessment tool for prefabricated building project progress, effectively helping enterprises identify potential risks, formulate risk control strategies, improve project success rates, and overall benefits. Full article

Background/Objectives: The principal aim of this work was to prepare a liposomal drug delivery system based on the commercial drug doxorubicin (DOX) and a budding agent with promising anticancer activity, 9-(N-piperazinyl)-5-methyl-12(H)-quino [3,4-b][1,4]benzothiazinium chloride (9-PBThACl). Methods: A spectrophotometric methodology was used to meticulously investigate the drug entrapment and release characteristics of the new liposomal complexes (L) based on dipalmitoylphosphatidylcholine (DPPC) with human serum albumin (HSA) and its defeated analog (dHSA). Results: The impact of the operational parameters (temperature and pH) on the liposome/drug(s)/(d)HSA, namely [L_{DPPC/9-PBThACl/DOX} ]:(d)HSA] systems, as well as the polarity of the phospholipid bilayer, was examined. In order to compare the experimental findings, mathematical models were employed to specify the analytical factors controlling the process of drug release/potential drug release from liposomes. The observed variations in the drug encapsulation and release profiles were due to the combination of liposomal conjugates with human plasma protein. Conclusions: It was proven that changes in the environmental pH directly affect the percentage of drug entrapment in liposomes and the medicine release efficiency. Moreover, the grouping tendency of the liposomal combinations was investigated using a principal component analysis (PCA) and a hierarchical clustering analysis (HCA). Finally, an analysis of variance (ANOVA) confirmed the statistical impact of pH buffering and changing temperature factors on the drug release characteristics of liposomal conjugates. Full article

Natural gas-to-methanol plants are receiving renewed interest with the significant increase in shale gas availability. Methanol serves as a crucial raw material for producing various industrial and consumer goods as well as key platform chemicals, including acetic acid, methyl tertiary butyl ether, dimethyl ether, and methylamine. In this research, a dynamic model is developed for Natgasoline’s methanol manufacturing plant. A hierarchical control system comprising Dynamic Matrix Control (DMC) and a basic regulatory control loop is constructed using this dynamic model to minimize methanol losses and utility costs under various process upsets. A subspace identification methodology is used to develop rigorous DMCplus controller models. The simulation results in the ASPEN manufacturing software platform show that the DMCplus controller developed in this study can reduce methanol losses by 96% and utility requirements by 40%. The controller is robust to feed flow variations of ±10%. Furthermore, disturbances due to the variation in hydrogen content in the syngas are also successfully rejected by the controller. This hierarchical multivariable control system performs significantly better than the traditional regulatory PID control strategy in optimizing the methanol process under process constraints. Full article

Eco-efficiency is crucial for the European rubber and plastics industry to minimize production costs through effective resource management (e.g., energy management) and reduce environmental impacts like greenhouse gases (GHGs) emissions. Circular economy (CE) solutions can support the industry’s competitiveness while aligning with sustainability goals and regulatory requirements. In the present research, we employ a hybrid window data envelopment analysis (WDEA) methodology to measure panel data eco-efficiency via the application of the moving average principle. The examination of 27 European countries as decision-making units (DMUs), in the period 2014–2022, led to the conclusion that the average eco-efficiency is 70.33%, showing that most of the DMUs can ameliorate their performance regarding pollution control. The highest eco-efficiency in 2014 can be monitored in Ireland, Switzerland, Norway, and Poland, but in 2022, only Ireland and Switzerland kept their positions, whereas Norway dropped to the 16th position and Poland plummeted to the 24th hierarchical position. Geographical disparities can be spotted, as Northern and Western Europe have greater eco-efficiency than Eastern and Southern Europe. At a second level of analysis, the convergence between the 27 European countries in the period under consideration is examined using the log t regression test and club clustering. The analysis leads to three final clubs where conditional convergence dominates. Full article

This article presents the experimental validation of a model-based predictive control (MPC) strategy for the safe interconnection of voltage source inverters (VSI) with output LC filters for the grid connection of DC energy resources. The MPC is formulated as a quadratic programming (QP) problem and solved using the operator splitting quadratic programs (OSQP). The proposed approach incorporates integral action to achieve precise voltage magnitude reference tracking while accounting for modulated voltage limits and nominal current constraints within the control design. The effectiveness of the proposed strategy is validated through simulations conducted in MATLAB, demonstrating superior dynamic performance compared to the traditional hierarchical PI control. The implementation of the proposed MPC is experimentally verified on a VSI setup using the dSPACE MicroLabBox. The results confirm that the computational requirements are satisfied, establishing this approach as a practical alternative for modern power electronic systems. The proposed MPC for VSIs offers an effective approach to enforcing operational constraints, improving dynamic performance, and facilitating the robust integration of renewable energy sources in microgrids. Full article

Animal care workers in sheltering, rescue, and management are exposed to occupational stressors which negatively impact health. While the negative mental health impacts have been previously documented in this population, physical health, and its contributing factors, have not. This study investigated how job demands, job resources, and organisational justice relate to mental and physical health, and how they subsequently affect turnover intentions. Of the 285 participants, aged 19 to 94 years, who completed the online anonymous questionnaire, below average mental and physical health was reported. Mental health, but not physical health, was inversely related to intentions to leave. After controlling for age, hierarchical multiple regressions showed high emotional demands and direct euthanasia exposure significantly predicted poorer mental and physical health, while high levels of social support predicted better mental and physical health. No additional variance in either health domain was accounted for by organisational justice. It was concluded that both physical and mental health may be negatively impacted in those who work in shelter, rescue, and management environments which could lead to high turnover and poor outcomes for individuals and organisations. Monitoring through ongoing health records to enable early intervention and accommodations should be considered to promote the health of these workers. Full article

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions. Full article

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions. Full article

One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution algorithm is proposed on the basis of the original differential evolution algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (DOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6DOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution algorithm (SADE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization algorithms in maneuver decision problems. Full article

Although both prefectural governmental green investment (GGI_city) and provincial governmental green investment (GGI_prov) have potentially diverse impacts on prefectural cities’ carbon emission reduction (CER), previous studies have rarely examined the effects of governmental green investment (GGI) on different indicators of CER such as total carbon dioxide emissions (CE), carbon emissions intensity (CEI) and per capita carbon emissions (PCE) in the context of prefectural cities nested in provinces in China. In our research, six hierarchical linear models are established to investigate the impact of GGI_city and GGI_prov, as well as their interaction, on CER. These models consider eight control factors, including fractional vegetation coverage, nighttime light index (NTL), the proportion of built-up land (P_built), and so on. Furthermore, heterogeneous impacts across different groups based on provincial area, terrain, and economic development level are considered. Our findings reveal the following: (1) The three indicators of CER and GGI exhibit significant spatial and temporal variations. The coefficient of variation for CEI and PCE shows a fluctuating upward characteristic. (2) Both lnGGI_city and lnGGI_prov have promoted CER, but the impact strength of lnGGI_prov on lnCE and lnPCE is more pronounced than that of lnGGI_city. GGI_prov can strengthen the effect of GGI_city significantly for lnCE. Diverse control variables have exerted significant impacts on the three indicators of CER, albeit with considerable variation in their effects. (3) The effect of GGI on CER is significantly heterogeneous upon conducting grouped analysis by provincial area size, terrain complexity, and economic development level. The interaction term lnGGI_city:lnGGI_prov is stronger in the small provincial area group and simple terrain group. Among the control variables, economic Development Level (GDPpc), the logarithm of gross fixed assets investment (lnFAI), NTL, and P_built exhibit particularly pronounced differences across different groups. This study provides a robust understanding of the heterogeneous and interactive effects of GGI on CER, aiding in the promotion of sustainable development. Full article

Pre-trained models trained with internet-scale data have achieved significant improvements in perception, interaction, and reasoning. Using them as the basis of embodied grasping methods has greatly promoted the development of robotics applications. In this paper, we provide a comprehensive review of the latest developments in this field. First, we summarize the embodied foundations, including cutting-edge embodied robots, simulation platforms, publicly available datasets, and data acquisition methods, to fully understand the research focus. Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. In reinforcement learning, the pre-trained model is used to obtain the optimal reward function, which improves the learning efficiency and ability of reinforcement learning; (3) embodied agent: The pre-trained model adopts hierarchical or holistic execution to achieve end-to-end robot control. Finally, the challenges of the current research are summarized, and a perspective on feasible technical routes is provided. Full article

Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming (CLF-CBF-QP) approach. This framework enables a vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of simulations were conducted, starting with basic model-in-the-loop (MIL) non-real-time simulations, followed by real-time simulations. Multiple scenarios with different controller configurations and obstacle setups were tested, demonstrating the effectiveness of the proposed controllers in avoiding collisions. Real-time simulations in Simulink were used to demonstrate that the proposed controller could compute control actions for each state within a very short timestep, highlighting its computational efficiency. This efficiency underscores the potential for deploying the controller in real-world vehicle autonomous driving systems. Furthermore, we explored the feasibility of a hierarchical control framework comprising deep reinforcement learning (DRL), specifically a Deep Q-Network (DQN)-based high-level controller and a CLF-CBF-QP-based low-level controller. Simulation results show that the vehicle could effectively respond to obstacles and generate a successful trajectory towards its goal. Full article

Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an optimal approach by integrating hierarchical dimensionality reduction, stacking ensemble learning, and 1D-CNN models to estimate leaf chlorophyll content in S. chamaejasme using hyperspectral reflectance data. Field spectrometry analysis demonstrates that the combination of Pearson correlation, first derivative, and SPA algorithms can efficiently select the most chlorophyll-sensitive wavelengths, red-edge parameters, and spectral indices related to S. chamaejasme leaves. The stacking ensemble model outperforms the 1D-CNN model in predicting leaf chlorophyll content of S. chamaejasme over the whole growth stage, while the 1D-CNN excels at prediction in each individual growth stage. Comparatively, the 1D-CNN model achieved higher accuracy (R² > 0.5) in all five growth stages, with optimal performance during the flower bud stage (R² = 0.787, RMSE = 2.476). This study underscores the potential of combining feature spectra selection with machine learning and deep learning models to monitor S. chamaejasme growth, offering valuable insights for invasive species control and ecological management. Full article

The anisotropic mechanical behaviour of multi-phase TiAl alloys is intrinsically governed by the anisotropic crystal properties and morphology of their constituent phases, which control the initiation of local plasticity. To advance the understanding of macroscopic plastic anisotropy in multi-phase alloys, this study presents a comprehensive numerical investigation of a two-phase (α₂-Ti₃Al + γ-TiAl) lamellar TiAl alloy, with a focus on the evolution of plasticity across multiple structural scales. Utilizing the crystal plasticity finite element method (CPFEM), the influence of lamellar orientation (φ) and applied loading angles (θ) on plastic deformation and yield surface evolution was analysed in both the individual phases and in the combined two-phase system. The findings reveal that phase-specific anisotropy stems from the activation of distinct slip systems in the α₂ and γ phases, with the activation closely tied to the type of loading (e.g., proportional biaxial loading) and the direction of the load path. Furthermore, the anisotropy of the two-phase system is significantly influenced by the alignment between the lamellar interface orientation and the load-path direction. Analysis with varying load-path directions across different stress planes clarifies how local deformation constraints within the embedded phases modulate slip system activation, leading to either the enhancement or suppression of specific deformation mechanisms. This, in turn, alters the overall yield behaviour of the material. Based on these simulation results, this study provides a detailed understanding of the internal constraints within embedded phases and their role in the evolution of plasticity. It elucidates how anisotropy develops under diverse loading conditions and underscores the importance of hierarchical plasticity in shaping the global anisotropic response of TiAl alloys. Full article