Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture, Classification & Monitoring Scheme, and Site Selection Algorithm

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the first two modules, especially the module on site selection. Specifically, regarding the medical waste classification & monitoring module, three main entities, i.e., relevant government departments, hospitals, and logistics companies, are identified, which are involved in the five management functions of this module. Detailed data flow diagrams are provided to illustrate the information flow and the responsibilities of each entity. Regarding the site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system and the remaining route optimization module can be found at https://shorturl.at/cdY59.Comment: 8 pages, 6 figures, submitted to and under review by the IEEE Intelligent Vehicles Symposium (IV 2023

Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.Comment: 6 pages, 4 figures, under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Adsorption and Desorption Characteristics of Arsenic on Soils: Kinetics, Equilibrium, and Effect of Fe(OH)3 Colloid, H2SiO3 Colloid and Phosphate

AbstractAdsorption and desorption of arsenic on different soils may affect the mobility, toxicity and bioavailability of arsenic in soil meia. In this study, laboratory batch experiments were carried out to study the adsorption and desorption of arsenic in three soils in China with different physicochemical properties. The results show that the adsorption was relatively fast for Beijing soil and Hainan soil, the reactions almost completed within the first few hours, while it was relatively slow for Jilin soil. The adsorption isotherms for three soils fitted very well to both the Langmuir and Freundlich models. The content of organic mater in the soils was of the major factor to determine the adsorption capacity. The thermodynamic parameters for the adsorption of arsenic were determined at three different temperatures of 283K, 303K and 323K. The adsorption reactions were endothermic and the process of adsorption was favored at high temperature. The adsorption behavior of arsenic on soils was strongly dependent on the concentrations of Fe(OH)3 and H2SiO3 colloid. Phosphate suppressed the adsorption of arsenite and arsenate, especially for BJ soil. The desorption data showed that desorption hysteresis occurred at the concentration studied. These findings improve our knowledge in modeling arsenic adsorption to common soil minerals

Local instabilities during capillary-dominated immiscible displacement in porous media

Fully understanding the mechanism of pore-scale immiscible displacement dominated by capillary forces, especially local instabilities and their influence on flow patterns, is essential for various industrial and environmental applications such as enhanced oil recovery, CO2 geo-sequestration and remediation of contaminated aquifers. It is well known that such immiscible displacement is extremely sensitive to the fluid properties and pore structure, especially the wetting properties of the porous medium which affect not only local interfacial instabilities at the micro-scale, but also displacement patterns at the macro-scale. In this review, local interfacial instabilities under three typical wetting conditions, namely Haines jump events during weakly-wetting drainage, snap-off events during strongly-wetting imbibition, and the co-existence of concave and convex interfaces under intermediate-wet condition, are reviewed to help understand the microscale physics and macroscopic consequences resulting in natural porous media.Cited as: Liu, Y., Iglauer, S., Cai, J., Amooie, M.A., Qin, C. Local instabilities during capillary-dominated immiscible displacement in porous media. Capillarity, 2019, 2(1): 1-7, doi: 10.26804/capi.2019.01.0

An ancestral NB-LRR with duplicated 3'UTRs confers stripe rust resistance in wheat and barley.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a global threat to wheat production. Aegilops tauschii, one of the wheat progenitors, carries the YrAS2388 locus for resistance to Pst on chromosome 4DS. We reveal that YrAS2388 encodes a typical nucleotide oligomerization domain-like receptor (NLR). The Pst-resistant allele YrAS2388R has duplicated 3' untranslated regions and is characterized by alternative splicing in the nucleotide-binding domain. Mutation of the YrAS2388R allele disrupts its resistance to Pst in synthetic hexaploid wheat; transgenic plants with YrAS2388R show resistance to eleven Pst races in common wheat and one race of P. striiformis f. sp. hordei in barley. The YrAS2388R allele occurs only in Ae. tauschii and the Ae. tauschii-derived synthetic wheat; it is absent in 100% (n = 461) of common wheat lines tested. The cloning of YrAS2388R will facilitate breeding for stripe rust resistance in wheat and other Triticeae species

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning

Siamese Transformer-Based Building Change Detection in Remote Sensing Images

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images

Facile Synthesis of PtCu Alloy/Graphene Oxide Hybrids as Improved Electrocatalysts for Alkaline Fuel Cells

Morphology-controllable preparation of Pt-based nanoalloys supporting on carbonaceous materials is a potential strategy to enhance the catalytic properties for oxygen reduction reaction (ORR) and ethanol oxidation reaction (EOR); they are recognized as irreplaceable electrode reactions in proton-exchange membrane ethanol fuel cells. Herein, we exhibit a facile, one-step synthesis method to directly prepare composition-tunable PtCu alloy/graphene oxide (GO) hybrids. The structure of the as-synthesized PtCu alloy/GO hybrids has been analyzed using transmission electron microscopy, high resolution transmission electron microscopy, energy-dispersive X-ray, X-ray diffraction, inductively coupled plasma, and X-ray photoelectron spectroscopy. In the PtCu alloy/GO hybrids, the PtCu alloy nanoparticles well disperse on GO, and the size is below 5.0 nm. The catalysis for ORR and EOR of the as-synthesized PtCu/GO hybrids has been evaluated in alkaline solution. Compared to commercial Pt/C, the PtCu/GO hybrids exhibit much higher mass activity and stability. The mass activities toward ORR/EOR on Pt_75.4Cu_24.6/GO hybrids are 5.3/2.36 times higher than the commercial Pt/C. This study proves that the as-synthesized PtCu/GO hybrids can be used as improved catalysts for ORR and EOR in alkaline medium

3D porous flower-like heterostructure of Fe doped Ni2P nanoparticles anchored on Al2O3 nanosheets as an ultrastable high-efficiency electrocatalyst

Water splitting is a vital candidate to efficiently prepare the green energy resource of hydrogen and solve the problem of energy shortage. Herein, this work firstly study a unique composite of 3D porous flower-like heterostructure of Fe doped NiP nanoparticles anchored on AlO nanosheets, which exhibits excellent electrochemical performance for hydrogen evolution reaction (HER) in all-PH medium. In this research, the doped Fe element can tune the electronic structure of NiP, which can remarkably improve the electrochemical activity. Besides, this unique 3D porous flower-like heterostructure possesses large specific surface area, which can expose a large number of active sites so as to further enhance the electrochemical property. Moreover, the AlO coating can further ensure the structure stability. The Ni(Fe)P@AlO displays splendid catalytic activity for HER with low overpotential of 53 mV and outstanding electrochemical durability of less than 3% increase in overpotential for 500 h at the constant density of 10 mA cm