Feasible Solution Research Articles

Collagen-Mesenchymal Stem Cell Microspheres Embedded in Hyaluronic Acid Solutions as Biphasic Stem Niche Delivery Systems for Pulmonary Differentiation.

Cell therapy has the potential to become a feasible solution for several diseases, such as those related to the lungs and airways, considering the more beneficial intratracheal administration route. However, in lung diseases, an impaired pulmonary extracellular matrix (ECM) precludes injury resolution with a faulty engraftment of mesenchymal stem cells (MSCs) at the lung level. Furthermore, a shielding strategy to avoid cell damage as well as cell loss due to backflow through the injection path is required. Here, an approach to deliver cells encapsulated in a biomimetic stem niche is used, in which the interplay between cells and physiological lung ECM constituents, such as collagen and hyaluronic acid (HA), can occur. To this aim, a biphasic delivery system based on MSCs encapsulated in collagen microspheres (mCOLLs) without chemical modification and embedded in an injectable HA solution has been developed. Such biphasic delivery systems can both increase the mucoadhesive properties at the site of interest and improve cell viability and pulmonary differentiation. Rheological results showed a similar viscosity at high shear rates compared to the MSC suspension used in intratracheal administration. The size of the mCOLLs can be controlled, resulting in a lower value of 200 μm, suitable for delivery in alveolar sacs. Biological results showed that mCOLLs maintained good cell viability, and when they were suspended in lung medium implemented with low molecular weight HA, the differentiation ability of the MSCs was further enhanced compared to their differentiation ability in only lung medium. Overall, the results showed that this strategy has the potential to improve the delivery and viability of MSCs, along with their differentiation ability, in the pulmonary lineage.

Research and Analysis on Policy Governance Based on Traffic Congestion in Beijing

This paper proposes three-game models to improve Beijing's traffic environment through the analysis method of game theory. First, the author examines the impact of improving drivers' moral character on alleviating traffic congestion. Second, this paper focus on the role of improving the public transportation environment in reducing road stress. Finally, this research examines the possibility of mitigating traffic congestion through a reward-punishment system. Comprehensive analysis of the results of these three game models, this paper conclude that a single policy of license restriction and traffic restriction is not enough to solve the traffic congestion problem in Beijing, and multiple policy measures need to be considered comprehensively. However, these policy measures also face some challenges and limitations, including time and education investment, people's travel needs and transportation network layout, and the design and implementation of the reward and punishment system. Therefore, future research and policy development need to further explore these issues and find more comprehensive and feasible policy solutions. Finally, this article recommends that the Beijing Municipal Government comprehensively consider the results of different game models and formulate comprehensive policy measures based on the actual situation, including improving road transportation facilities, encouraging green travel methods, and promoting intelligent transportation systems. Through these efforts, it is expected to improve Beijing's traffic environment, enhance citizens' travel experience, and achieve the goal of sustainable urban transportation.

Smartphone-Integrated Automated Sensor Employing Electrochemically Engineered 3D Bimetallic Nanoflowers for Hydrogen Peroxide Quantification in Milk.

Hydrogen peroxide (H2O2), one of the reactive oxygen species in living beings, serves as a regulator of various cellular processes. However, excessive peroxide concentrations are linked to oxidative stress and promptly disrupt cellular components, leading to several pathological conditions in the body. Moreover, it is extremely reactive and has a limited lifetime; thus, H2O2 sensing remains a prominent focus of research. Enzymatic sensing probes were widely employed to detect H2O2 in the recent past; however, they are susceptible to intrinsic chemical and thermal instabilities, which decrease the reliability and durability of the surface. This research was designed to come up with a feasible solution to this problem. Herein, a novel nonenzymatic peroxidase-mimic three-dimensional (3D) bimetallic nanoflower has been synergistically engineered for quick sensing of H2O2. The sensor platform showed minimal resistance or enhanced charge transfer properties as well as remarkable analytical capability, having a broad linear range between 0.01 and 1 nM and a detection limit of 1.46 ± 0.07 pM. The probe responded to changes in H2O2 concentration in just 2.10 ± 0.02 s, making it a quick sensing platform for H2O2 tracking. This peroxidase-mimic nanozyme probe showed minimal sensitivity to interferants often seen in real-world sample matrices and possessed good recoveries ranging from 92.88 to 99.09% in milk samples. Further, a facile and user-friendly smartphone application (APP) named "HPeroxide-Check" was developed and integrated into the sensor to check the milk adulteration by detecting H2O2. It processes the current output obtained from the sensing interface and provides real-time peroxide concentrations in milk. The entire procedure of fabricating the probe is a single, highly robust step that takes only 10 min and is coupled with a smartphone APP, highlighting the sensor's quick manufacturing and deployment for automated H2O2 monitoring in industrial and point-of-care settings.

An ultra-wideband power amplifier designed through a bandwidth expansion strategy

Abstract A bandwidth expansion strategy for ultra-wideband power amplifiers (PAs) is presented in this letter by adopting a parallel impedance matching architecture. This design strategy can effectively reduce the impedance conversion ratio between the load and the target impedance of the PA, thereby providing a feasible solution for broadband impedance matching. Subsequently, a commercially available 10 W gallium nitride device and a two-stage Wilkinson power divider network are combined to achieve the verification of the proposed theory. The results of the measurement show that within the target frequency band of 0.9–3.9 GHz, 58.5–71.2% of the drain efficiency and 9.1–12 dB of gain can be achieved with a saturated output power of 39.1–42 dBm.

Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Experimental study on long-term impermeability of recycled aggregate concrete mixed with crystalline admixture and waste glass powder

The impermeability of recycled aggregate concrete (RAC) presents challenges for underground engineering, particularly concerning its larger pore. Combining crystalline admixture (CA) with waste glass powder (WGP) offers a feasible solution to enhance impermeability. To understand the collaborative mechanism of CA and WGP in improving RAC impermeability and establish an accurate mixture ratio, we conducted compressive strength tests and a series of permeability tests on WGP and CA modified recycled aggregate concrete (WRC). These tests involved different CA contents (0%, 1%, and 2%) and varying WGP replacement rates (0%, 10%, 20%, and 30%). Our findings indicated that when the WGP substitution rate exceeds 10%, the strength of the WRC decreases by approximately 23%. In contrast, the overall decline is about 5% when the CA content increases from 0% to 2%. Therefore, the WGP emerges as the primary factor influencing the compressive strength of WRC. Meanwhile, The WGP and CA play a major role in enhancing the short-term and long-term impermeability of WRC, respectively. Additionally, When the WGP substitution rate increases from 0% to 30% on same CA content, the RC increases by approximately 2%, indicating that elevated WGP content enhances the effectiveness of CA. Finally, the optimal combination to improve the impermeability of WRC is 10% WGP substitution and 1% CA content. Microscopic tests analyses show that under the influence of CA and WGP, C–S–H gel, C-A-S-H gel, and Ettringite are produced inside the WRC, effectively blocking pores and cracks, thus improving the specimen's impermeability. This research not only holds the potential to elevate impermeability but also offers a solution to the issue of waste glass accumulation, contributing to global sustainable development.

PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios

Evolutionary algorithms exhibit flexibility and global search advantages in multi-UAV path planning, effectively addressing complex constraints. However, when there are numerous obstacles in the environment, especially narrow passageways, the algorithm often struggles to quickly find a viable path. Additionally, collaborative constraints among multiple UAVs complicate the search space, making algorithm convergence challenging. To address these issues, we propose a novel hybrid particle swarm optimization algorithm called PPSwarm. This approach initially employs the RRT* algorithm to generate an initial path, rapidly identifying a feasible solution in complex environments. Subsequently, we adopt a priority planning method to assign priorities to UAVs, simplifying collaboration among them. Furthermore, by introducing a path randomization strategy, we enhance the diversity of the particle swarm, thereby avoiding local optimum solutions. The experimental results show that, in comparison to algorithms such as DE, PSO, ABC, GWO, and SPSO, the PPSwarm algorithm demonstrates significant advantages in terms of path quality, convergence speed, and runtime when addressing path planning issues for 40 UAVs across four different scenarios. In larger-scale experiments involving 500 UAVs, the proposed algorithm also exhibits excellent processing capability and scalability.

“I don’t know if I can keep doing this”: a qualitative investigation of surgeon burnout and opportunities for organization-level improvement

IntroductionBurnout is a pressing issue within surgical environments, bearing considerable consequences for both patients and surgeons alike. Given its prevalence and the unique contextual factors within academic surgical departments, it is critical that efforts are dedicated to understanding this issue. Moreover, active involvement of surgeons in these investigations is critical to ensure viability and uptake of potential strategies in their local setting. Thus, the purpose of this study was to explore surgeons’ experiences with burnout and identify strategies to mitigate its drivers at the level of the organization.MethodsA qualitative case study was conducted by recruiting surgeons for participation in a cross-sectional survey and semi-structured interviews. Data collected were analyzed using reflexive thematic analysis, which was informed by the Areas of Worklife Model.ResultsOverall, 28 unique surgeons participated in this study; 11 surgeons participated in interviews and 22 provided responses through the survey. Significant contributors to burnout identified included difficulties providing adequate care to patients due to limited resources and time available in academic medical centers and the moral injury associated with these challenges. The inequitable remuneration associated with education, administration, and leadership roles as a result of the Fee-For-Service model, as well as issues of gender inequity and the individualistic culture prevalent in surgical specialties were also described as contributing factors. Participants suggested increasing engagement between hospital leadership and staff to ensure surgeons are able to access resources to care for their patients, reforming payment plans and workplace polities to address issues of inequity, and improving workplace social dynamics as strategies for addressing burnout.DiscussionThe high prevalence and negative sequalae of burnout in surgery necessitates the formation of targeted interventions to address this issue. A collaborative approach to developing interventions to improve burnout among surgeons may lead to feasible and sustainable solutions.

Ratio of photosynthetically active radiation to global solar radiation above forest canopy in complex terrain: measurements and analyses based on Qingyuan Ker Towers

BackgroundUnderstanding of the ratio of photosynthetic photon flux density (Qp) to global solar radiation (Rs) (Qp/Rs) is crucial for applying Rs to ecology-related studies. Previous studies reported Qp/Rs and its variations based on measurements from a single observatory tower, instead of multi-site-based measurements over complex terrains. This may neglect spatial heterogeneity in the terrain, creating a gap in an understanding of how terrain affects Qp/Rs and how this effect interacts with meteorological factors.MethodsHere the Qingyuan Ker Towers (three towers in a valley with different terrains: T1, T2, and T3) were utilized to measure Qp and Rs over mountainous forests of Northeast China. An airborne LiDAR system was used to generate a digital elevation model, and sky view factor of sectors (SVFs) divided from the field of view of tower’s pyranometer was calculated as a topographic factor to explain the variations of Qp/Rs.ResultsThe results identified significant differences in Qp/Rs of the three towers at both daily and half-hour scales, with larger differences on clear days than on overcast days. Qp/Rs was positively correlated with SVFs of T1 and T3, while this correlation was negative with that of T2. The effect of SVFs on Qp/Rs interacted with clearness index, water vapor pressure and solar zenith angle. Random forest-based importance assessment demonstrated that explanation (R2) on Qp/Rs was improved when SVFs was included in the predictor variable set, indicating that incorporating terrain effects enhances the prediction accuracy of Qp/Rs. The improvement in the R2 values was more pronounced on clear days than on overcast days, suggesting that the effect of terrain on Qp/Rs depended on sky conditions.ConclusionsAll findings suggested that Qp/Rs is affected by terrain, and integrating terrain information into existing Qp/Rs models is a feasible solution to improve Qp/Rs estimates in mountainous areas.

Hybrid optimization schemes for solving the piezoresistive inversion problem in self-sensing materials

Abstract Materials with electrically conductive nanofillers have the ability to 'sense' changes to their mechanical state. When these materials are subjected to deformation, the electrical networks formed by the nanofillers are disturbed causing a measurable change in the electrical conductivity of the material. This self-sensing property, known as piezoresistivity, has been leveraged in numerous engineering venues. Although this property has been thoroughly explored, prevailing self-sensing techniques are limited in that they provide little-to-no information about the underlying mechanical state of the material, such as the displacement and strain. This information must be indirectly obtained from the conductivity change. This limitation exists because obtaining mechanics from conductivity is an under-determined inverse problem with many possible mathematically feasible solutions. Previous work in this area used metaheuristic algorithms integrated with mechanics-based conditions to solve the piezoresistive inversion problem. Although these approaches were successful, they were computationally inefficient due to the stochastic search process and the need to perform multiple searches to find a converged solution. To overcome this limitation, we herein propose a hybrid optimization scheme for solving the piezoresistive inversion problem. This scheme is implemented in two steps. In the first step, a metaheuristic algorithm performs a single search for a suitable solution to the inverse problem. In the second step, a gradient descent algorithm searches for the final solution using the solution from the previous step as the starting point. We explore different norms for the fitness function of the metaheuristic search and demonstrate using experimental results that the proposed hybrid optimization scheme can accurately and efficiently solve the piezoresistive inversion problem. This exploration significantly advances the state of the art by enabling computationally efficient and highly accurate predictions of full-field mechanical condition in self-sensing materials for the first time, thereby paving the way for greater use of these principles in practice.

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.

Methods: We introduced PyHFO, which enables time-efficient HFO detection algorithms like short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors. It incorporates deep learning models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware. 

Main results: The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained deep learning model or use their EEG data for custom model training.

Significance: PyHFO successfully bridges the computational challenge faced in applying deep learning techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Minimizing Computation and Communication Costs of Two-Sided Secure Distributed Matrix Multiplication under Arbitrary Collusion Pattern

This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into some blocks, blocks of random matrices are appended to protect the security of the two input matrices, and encoded copies of the blocks are distributed to all computing nodes for matrix multiplication calculation. Our aim is to minimize the total cost, overall matrix splitting factors, number of appended random matrices, and distribution vector, while satisfying the security constraint of the two input matrices, the decodability constraint of the desired result of the multiplication, the storage capacity of the computing nodes, and the delay constraint. First, a strategy of appending zeros to the input matrices is proposed to overcome the divisibility problem of matrix splitting. Next, the optimization problem is divided into two subproblems with the aid of alternating optimization (AO), where a feasible solution can be obtained. In addition, some necessary conditions for the problem to be feasible are provided. Simulation results demonstrate the superiority of our proposed scheme compared to the scheme without appending zeros and the scheme with no alternating optimization.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

Unique ping-pong daisy-like catalysts: Efficient overall hydrolysis catalysis driven by vanadium-doped Fe2P and Co3(PO4)2 nanosheet composites

Balancing catalytic activity and durability remains a challenge in the development of high performance, inexpensive and durable electrocatalysts. In this study, fully hydrolyzed composite catalyst V–Fe2P@Co3(PO4)2/NF with ping-pong daisy-like morphology was synthesized using conventional hydrothermal and chemical vapor deposition (CVD) processes. In alkaline electrolyte, V–Fe2P@Co3(PO4)2/NF showed excellent electrochemical performance, achieving a current density of 10 mA cm−2 when the driving overpotentials for the hydrogen production reaction (HER) and oxidation reaction (OER) were 56 mV and 178 mV, respectively. Meanwhile, V–Fe2P@Co3(PO4)2/NF demonstrated good durability in terms of both HER and OER stability. Furthermore, the driving voltage required for the electrolyser assembled with V–Fe2P@Co3(PO4)2/NF as the electrode is lower than that of the RuO2/NF||Pt/C/NF electrode, both of which have driving voltages of 1.503 and 1.562 V (10 mA cm−2), respectively. Meanwhile, it achieves a Faraday efficiency of 95%. This study offers a feasible solution towards synthesizing and applying bifunctional water decomposition electrocatalysts.

This is SpArta: Rigorous Optimization of Regionally Resolved Energy Systems by Spatial Aggregation and Decomposition

Energy systems with high shares of renewable energy are characterized by local variability and grid limitations. The synthesis of such energy systems, therefore, requires models with high spatial resolution. However, high spatial resolution increases the computational effort. Here, we present the SpArta method for rigorous optimization of regionally resolved energy systems by Spatial Aggregation and decomposition. SpArta significantly reduces computational effort while providing a near-optimal solution at the full spatial resolution of sector-coupled energy systems.SpArta first reduces problem size by spatially aggregating the energy system using clustering. The aggregated problem is then relaxed and restricted to obtain a lower and an upper bound. The spatial resolution is iteratively increased until the difference between upper and lower bound satisfies a predefined optimality gap.Finally, each cluster of the aggregated problem is redesigned at full resolution. For this purpose, SpArta decomposes the original synthesis problem into subproblems for each cluster. Combining the redesigned cluster solutions yields an optimal feasible solution of the full-scale problem within a predefined optimality gap. SpArta thus optimizes large-scale energy systems rigorously with significant reductions in computational effort. We apply SpArta to a case study of the sector-coupled German energy system, reducing the computational time by a factor of 7.5, compared to the optimization of the same problem at full spatial resolution. As SpArta shows a linear increase in computational time with problem size, SpArta enables computing larger problems allowing to resolve energy system designs with improved accuracy.

Self-assembly Fe-Co MOF@BN and epoxy resin nano composites with highly enhanced flame retardancy and smoke suppression properties

A shuttle-shaped Fe-Co bimetallic metal organic frameworks (MOFs) on sheet-like hexagonal boron nitride (h-BN) was fabricated via an organic-inorganic hybrid strategy, forming a novel Fe-Co MOF@BN flame retardant, and the hybrids together with ammonium polyphosphate (APP) were incorporated into epoxy resins to explore their flame retardancy and smoke suppression effects. When combining 0.5 % Fe-Co MOF@BN with 9.5 % APP, the peak heat release rate, peak smoke release rate, CO production rate, and peak CO2 production rate of the EP composite (EP/APP/Fe-Co MOF@BN) were reduced by 73.7 %, 71.4 %, 70 %, and 80 % respectively, compared to epoxy. The limiting oxygen index increases to 30.2 %, indicating the EP/APP/Fe-Co MOF@BN composites have superior fire safety performance. The excellent fire performance of EP/APP/Fe-Co MOF@BN composites benefiting from the barrier effect of Fe-Co MOF@BN lamellar structure, the catalytic carbonization of Fe and Co, the gas phase dilution of APP and the enhancement of the cross-linking degree of the char layer. Meanwhile, Fe-Co MOF@BN improves the dispersion of APP in the EP composites, which broadens the application range of MOF and h-BN. Herein, the EP/APP/Fe-CoMOF@BN composites with high flame retardancy, low smoke production rate, and excellent mechanical properties provide a feasible solution for the use of EP under high temperatures.

A discrete Jaya algorithm for vehicle routing problems with uncertain demands

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

3D-AMM: a 3D artificial moment method for path planning of manipulator in multiple obstacles scenario

Purpose In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector. Design/methodology/approach A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints. Findings A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios. Originality/value This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Effective hydrogen supply chain management framework considering nonlinear multi-stage process uncertainties

The large amount of carbon emitted due to industrial development is posing serious climate change. Under these circumstances, hydrogen energy is being used as a major energy source and is attracting attention as an alternative energy to fossil fuels, characterized by high carbon emissions. While the use of hydrogen has the obvious benefit of a low carbon footprint, the hydrogen production process possesses a high carbon footprint difference depending on the source of utilized electricity. This study considers several situations arising in an electrolysis-based hydrogen supply chain network (HSCN). A multi-stage stochastic programming (SP) is modeled to consider the uncertainty of demand and transportation capacity generated in HSCN. Moreover, a typical stochastic model has a chain rule between the decision variables resulting from nonlinearity and uncertainty, and these features make it difficult to derive an effective solution. Thus, in this study, a new methodology, Weighted Scenario Sample Average Approximation (WSSAA), is proposed to derive an effective solution for the multistage SP model considering various scenarios under the hydrogen supply chain. While a general stochastic programming approach fails to generate feasible solutions with volatile HSCN uncertainties, the proposed WSSAA framework provides feasible decisions considering multiple scenarios. The effectiveness of the proposed model applied with WSSAA is demonstrated through comparative experiments over the models used in existing studies.