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The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

In recent years, as wearable electronics continue to advance toward flexible, lightweight, and versatile designs, flexible pressure sensors with wide response ranges and high sensitivity have shown tremendous research value and application potential. In this study, we fabricated TPU-based flexible pressure sensors with a multistage gradient porous structure using layer-by-layer freezing and solvent templating techniques. Due to the layered differences in Young’s modulus from varying porosities, these sensors exhibit high pressure sensitivity ( S , S MAX  = 34.08 MPa −1 ) and can accurately distinguish stresses across a wide range (0–1.2 MPa). Additionally, they demonstrate rapid response and recovery times (140 ms), durability over 3000 compression cycles, and the ability to detect both subtle movements (facial expressions and swallowing) and larger actions (joint bends, walking, and running). Furthermore, we developed a smart glove using these gradient-structured pressure sensors combined with a K-nearest neighbor (KNN) algorithm, enabling accurate identification of various fruit types. Notably, the TPU sensors also exhibit excellent thermal insulation and Joule heating properties, making them effective for human thermal management even in extreme temperatures.

Indian Sign Language (ISL) is a vital means of interaction for the country's deaf and hard-of-hearing citizens. This highly developed visual-gestural system of language uses forms, gestures with the hands, and facial reactions to convey an extensive variety of complex ideas and viewpoints. Nevertheless, the automated ISL movement recognition technique has a great problem because of the varying characteristics and intricacy of sign lingo sentences. In order to overcome that, the researchers present a thorough approach to the recognition of Indian Sign Language (ISL), utilizing a multi-phase analytic framework that combines sophisticated signal processing techniques with machine learning to improve the accuracy and robustness of ISL gesture detection. The model employs the Central Limits Theorem-Discrete Wavelet Transform (CLT-DWT) eliminating strategies to reduce interference from noise in recorded sign language videos. It also improves the stage of pre-processing by using FICA-KMC for overlapping pixel division and concentrates on resilient extraction of features through the Manhattan Distance-based Histogram of Oriented Gradients (MD-HOG) and CCC-SURF strategies. Furthermore, researchers incorporate MIC-DT for choosing features in order to optimize the detection model's effectiveness. To achieve comprehensive motion recognition and categorization, the final procedure is to incorporate the powerful artificial neural network architecture GMT-Mask RCNN. The technique's possible efficacy in precisely and consistently comprehending Indian Sign Language for the visually impaired belonging is highlighted by the exploratory evaluation, which shows significant improvements in the rate of recognition as well as effective prevention of erroneous positive and negative incidents.

The seasonal movement of the upper-level jet plays a key role in the evolution of the East Asian summer monsoon (EASM). However, it remains unresolved how interannual changes in surface boundary conditions can influence the upper-level flow over East Asia, thereby modulating the onset of the EASM. Here we capture the timing of multistage evolution over East Asia using the upper-level zonal wind in a two-phase linear regression model. In addition, we show the impact of two surface boundary conditions on the timing of the EASM onset related to the strength of the upper-level zonal wind: 1) eastern Eurasian snow cover and 2) western North Pacific (WNP) sea surface temperature (SST) tendency. The eddy heat fluxes induced by the enhanced eastern Eurasian snow cover develop an anomalous anticyclonic circulation to the northwest, which causes anomalous warm southwesterly flow toward the north. These can make a reversal of the meridional temperature gradient, which results in the early monsoon onset via changes in the upper-level jet. The upper-level jet also responds to the SST tendency in April over the WNP via thermal wind balance and the resultant changes in transient eddy-induced heat transport. Our findings suggest potential sources for seasonal predictability in the interannual EASM onset dates.

Amorphous alloys are considered as an ideal material for complex micro component fabrication due to their extraordinary mechanical properties and thermoplastic formability. However, the conventional theories and thermoplastic forming (TPF) methods for amorphous alloys require a lower loading rate and a higher pressure which extremely prolong the processing time and reduce the mold life. In this study, an efficient and low-cost TPF-based method is developed to fabricate the micro impeller by controlling flow states of amorphous supercooled liquid at different forming stages. Based on the finite element simulation results of the filling process and strain distribution, the new multistage forming method enables thin-walled blade fabrication without a significant “dead zone” by adjusting the velocity gradient of the flow front, thus reducing the pressure requirement. Also, the non-Newtonian flow region is proved to be an appropriate TPF window at high temperatures. To verify the validity of the multistage forming method, technological experiments undergoing different conditions were performed. Finally, the full-filled thin-walled blades with a width of 508 μm and surface roughness of 0.25 μm were prepared in about 50 s. This research demonstrates a new TPF-based method for the rapid fabrication of micro components with a high-aspect-ratio characteristic structure in an economic way.

Axial turbines are the most common turbine configuration for electric power generation and propulsion systems due to their versatility in terms of power capacity and range of operating conditions. Mean-line models are essential for the preliminary design of axial turbines and, despite being covered to some extent in turbomachinery textbooks, only some scientific publications present a comprehensive formulation of the preliminary design problem. In this context, a mean-line model and optimization methodology for the preliminary design of axial turbines with any number of stages is proposed. The model is formulated to use arbitrary equations of state and empirical loss models and it accounts for the influence of the diffuser on turbine performance using a one-dimensional flow model. The mathematical problem was formulated as a constrained, optimization problem, and solved using gradient-based algorithms. In addition, the model was validated against two test cases from the literature and it was found that the deviation between experimental data and model prediction in terms of mass flow rate and power output was less than 1.2% for both cases and that the deviation of the total-to-static efficiency was within the uncertainty of the empirical loss models. Moreover, the optimization methodology was applied to a case study from the literature and a sensitivity analysis was performed to investigate the influence of several variables on turbine performance, concluding that: (1) the minimum hub-to-tip ratio constraint is always active at the outlet of the last rotor and that its value should be selected as a trade-off of aerodynamic performance and mechanical integrity; (2) that the total-to-static isentropic efficiency of turbines without diffuser deteriorates rapidly when the pressure ratio is increased; and (3) that there exist a loci of maximum efficiency in the specific speed and specific diameter plane (Baljé diagram) that can be predicted with a simple analytical expression.

Regenerative (peripheral) pumps offer compact, high-head solutions for low-flow applications; however, reliable performance data for viscous fluids and multistage configurations remain limited. This study employs high-fidelity geometry reconstruction and mesh-refined Computational Fluid Dynamics (CFD) to examine how fluid viscosity and stage number jointly influence the hydraulic performance of a commercial Pedrollo PKm60 pump. A laser scan of the impeller and side channel eliminated CAD simplifications and, together with a grid-convergence study, reduced the root-mean-square error between predicted and catalog heads to 1.71 m (5.8%). Simulations were performed using steady Reynolds-averaged Navier–Stokes (RANS) equations with a moving reference frame (MRF) formulation and the Realizable k–ε turbulence model. A single transient unsteady RANS (URANS) verification case with the SST k–ω model at Q = 10 L min −1 underpredicted the head (29.28 m) compared to the steady result (31.70 m) and manufacturer data (33.356 m). Because the steady solution matched the reference solution more closely, steady RANS (MRF) was adopted for all performance maps and analyses. Increasing viscosity shifted the best-efficiency point (BEP) from 23 to 17 L min −1 and reduced peak hydraulic efficiency from 34 to 20%. In the multistage configuration, the normalized head rose by 18% from Stage 1 to Stage 3, while power input increased by less than 5%, indicating efficient head development with diminishing internal losses. Flow visualizations of streamlines, turbulent kinetic energy, and pressure gradients revealed vortex suppression and boundary-layer thickening as the dominant mechanisms governing these performance changes. The resulting dimensionless correlations and validated flow-field data provide practical guidance for selecting and scaling regenerative pumps for handling viscous liquids, supporting Sustainable Development Goal 9 through energy-efficient, resource-conscious industrial fluid-handling design.

In the present study, a multistage route is proposed for the treatment of biodiesel industry wastewater (BWW) containing around 1000 mg L −1 of total organic carbon (TOC), 3500 mg L −1 of chemical oxygen demand (COD), and 1325 mg L −1 of oil and grease. Initially, BWW aerobic biodegradability was assessed via Zhan-Wellens biodegradability test to confirm the appropriate treatment route. Then, a hybrid moving bed bioreactor (MBBR) system was chosen as the first treatment stage. The hybrid MBBR achieved 69 and 68% removal of COD and TOC removals, respectively, and provided great conditions for biomass growth. The bacterial community present in the hybrid MBBR was investigated by PCR-DGGE and potential biodegraders were identified such as: members of Desulfuromonadales , Nocardioidaceae and Pseudomonadaceae . Since biodegradation in the hybrid MBBR alone was unable to meet quality requirements, advanced oxidation processes, such as Fenton and photo-Fenton, were optimized for application as additional treatment stages. Physicochemical properties and acute toxicity of BWW were analyzed after the multistage routes: hybrid MBBR + Fenton, hybrid MBBR + photo-Fenton and hybrid MBBR + UV-C 254nm /H 2 O 2 . Hybrid MBBR + Fenton or photo-Fenton showed overall COD removal efficiencies greater than 95% and removed acute toxicity, thus being appropriate integrated routes for the treatment of real BWW . Graphical abstract ᅟ

Rapid progresses in information and computer technology allow the development of more advanced optimal control algorithms dealing with real-world problems. In this paper, which is Part 1 of a two-part sequence, a multiple-subarc gradient-restoration algorithm (MSGRA) is developed. We note that the original version of the sequential gradient-restoration algorithm (SGRA) was developed by Miele et al. in single-subarc form (SSGRA) during the years 1968-86; it has been applied successfully to solve a large number of optimal control problems of atmospheric and space flight. MSGRA is an extension of SSGRA, the single-subarc gradient-restoration algorithm. The primary reason for MSGRA is to enhance the robustness of gradient-restoration algorithms and also to enlarge the field of applications. Indeed, MSGRA can be applied to optimal control problems involving multiple subsystems as well as discontinuities in the state and control variables at the interface between contiguous subsystems. Two features of MSGRA are increased automation and efficiency. The automation of MSGRA is enhanced via time normalization: the actual time domain is mapped into a normalized time domain such that the normalized time length of each subarc is 1. The efficiency of MSGRA is enhanced by using the method of particular solutions to solve the multipoint boundary-value problems associated with the gradient phase and the restoration phase of the algorithm. In a companion paper [Part 2 (Ref. 2)], MSGRA is applied to compute the optimal trajectory for a multistage launch vehicle design, specifically, a rocket-powered spacecraft ascending from the Earth surface to a low Earth orbit (LEO). Single-stage, double-stage, and triple-stage configurations are considered and compared.

In Part 1 (see Ref. 2), a multiple-subarc gradient-restoration algorithm (MSGRA) was developed with the intent of enhancing the robustness of gradient-restoration algorithms and also enlarging the field of applications. Indeed, MSGRA can be applied to optimal control problems involving multiple subsystems as well as discontinuities in the state and control variables at the interface between contiguous subsystems. In Part 2 (this paper), MSGRA is applied to compute the optimal trajectory for a multistage launch vehicle design, specifically, a rocket-powered spacecraft ascending from the Earth surface to a low Earth orbit (LEO). Single-stage, double-stage, and triple-stage configurations are considered. For multistage configurations, discontinuities in the mass occur at the interfaces between consecutive stages. The numerical results show that, given the current levels of the engine specific impulse and spacecraft structural factor, the single-stage version is not feasible at this time, while the double-stage and triple-stage versions are feasible. Further increases in the specific impulse and decreases in the structural factor are needed if the single-stage configuration has to become feasible. Also, the numerical results show that the optimal trajectory requires initially maximum thrust, followed by modulated thrust so as to satisfy the maximum acceleration constraint, followed by nearly zero thrust for coasting flight, followed by a final burst with moderate thrust so as to increase the spacecraft velocity to the circular velocity needed for LEO insertion. The above properties of the optimal thrust time history are useful for developing the guidance scheme approximating in real time the optimal trajectory for a launch vehicle design.