Comparison of the Effects of LDPE and PBAT Film Residues on Soil Microbial Ecology.

The plastic film is extensively applied with limited recycling, leading to the long-run residue accumulation in soil, which offers a distinctive habitat for microorganisms, and creates a plastisphere. In this study, traditional low-density polyethylene (LDPE) plastic film and biodegradable polybutylene adipate terephthalate (PBAT) plastic film materials were selected to test their effects on soil microbial ecology. Based on high-throughput sequencing, compared to the soil environment, the alpha-diversity of bacterial communities in plastisphere was lower, and the abundance of Actinobacteria increased. Plastic film residues, as bacterial habitats, exhibited greater heterogeneity and harbor unique bacterial communities. The communities were distinguished between plastisphere and soil environment by means of a random-forest (RF) machine-learning model. Prominent distinctions emerged among bacterial functions between soil environment and plastisphere, especially regarding organics degradation. The neutral model and null model indicated that the constitution of bacterial communities was dominated by random processes except in LDPE plastisphere. The bacterial co-occurrence network of the plastisphere exhibited higher complexity and modularity. This study contributes to our comprehending of characteristics of plastisphere bacterial communities in soil environment and the associated ecological risks of plastic film residues accumulation.

Experimental Investigation of Water Jet-Guided Laser Micro-Hole Drilling of Cf/SiC Composites.

In this paper, water jet-guided laser (WJGL) drilling of Cf/SiC composites was employed and the effects of the processing parameters on the depth and quality of the micro-holes were systematically investigated. Firstly, the depth measurement showed that the increase in processing time and power density led to a significant improvement in micro-hole drilling depth. However, the enhancement of the water jet speed resulted in a pronounced decrease in the depth due to the phenomenon of water splashing. In contrast, the scanning speed, path overlap ratio, pulse frequency, and helium pressure exhibited less effect on the micro-hole depth. Secondly, the microstructural analysis revealed that the increase in power density resulted in the deformation and fracture of the carbon fibers, while the augmentation in water jet speed reduced the thermal defects. Finally, based on the optimization of the processing parameters, a micro-hole of exceptional quality was achieved, with a depth-to-diameter ratio of 8.03 and a sidewall taper of 0.72°. This study can provide valuable guidance for WJGL micro-hole drilling of Cf/SiC composites.

The ecology of the sewer systems: Microbial composition, function, assembly, and network in different spatial locations.

Microbial induced concrete corrosion (MICC) is the primary deterioration affecting global sewers. Disentangling ecological mechanisms in the sewer system is meaningful for implementing policies to protect sewer pipes using trenchless technology. It is necessary to understand microbial compositions, interaction networks, functions, alongside assembly processes in sewer microbial communities. In this study, sewer wastewater samples and microbial samples from the upper part (UP), middle part (MP) and bottom part (BP) of different pipes were collected for 16S rRNA gene amplicon analysis. It was found that BP harbored distinct microbial communities and the largest proportion of unique species (1141) compared to UP and MP. The community in BP tended to be more clustered. Furthermore, significant differences in microbial functions existed in different spatial locations, including the carbon cycle, nitrogen cycle and sulfur cycle. Active microbial sulfur cycling indicated the corrosion risk of MICC. Among the environmental factors, the oxidation‒reduction potential drove changes in BP, while sulfate managed changes in UP and BP. Stochasticity dominated community assembly in the sewer system. Additionally, the sewer microbial community exhibited numerous positive links. BP possessed a more complex, modular network with higher modularity. These deep insights into microbial ecology in the sewer system may guide engineering safety and disaster prevention in sewer infrastructure.

Deciphering recovery paradigms: Foam rolling's impact on DOMS and lactate dynamics in elite volleyball athletes.

This study examines the effects of Self-Myofascial Release (SMR) techniques on post-exercise recovery in elite volleyball athletes. Through a controlled investigation involving eighteen Chinese Men's National Volleyball Team athletes, the research assessed the impact of foam rolling (FR) versus passive recovery (PAS) on blood lactate clearance and Delayed Onset Muscle Soreness (DOMS), as measured by Visual Analogue Scale (VAS) scores. Findings indicated that FR significantly reduces VAS scores and facilitates lactate clearance when compared to PAS, suggesting foam rolling may enhance post-exercise recovery. While confirming foam rolling's benefits, this research calls for further exploration into recovery mechanisms, emphasizing a cautious interpretation of foam rolling as part of a comprehensive recovery strategy.

Diversity, composition, metabolic characteristics, and assembly process of the microbial community in sewer system at the early stage.

Sewer systems play vital roles in wastewater treatment facilities, and the microbial communities contribute significantly to the transformation of domestic wastewater. Therefore, this study conducted a 180-day experiment on a sewer system and utilized the high-throughput sequencing technology to characterize the microbial communities. Additionally, community assembly analysis was performed to understand the early-stage dynamics within the sewer system. The results demonstrated that the overall diversity of microbial communities exhibited fluctuations as the system progressed. The dominant phyla observed were Chloroflexi, Bacteroidetes, Firmicutes, and Proteobacteria, accounting for over 85.4% of the total relative abundances. At the genus level, bacteria associated with fermentation displayed a high relative abundance, particularly during days 75 to 180. A random-forest machine-learning model identified a group of microbes that confirmed the substantial contribution of fermentation. During the process of fermentation, microorganisms predominantly utilized propionate formation as the main pathway for acidogenesis, followed by acetate and butyrate formation. In terms of nitrogen and sulfur cycles, dissimilatory nitrate reduction and assimilatory sulfate reduction played significant roles. Furthermore, stochastic ecological processes had a dominant effect during the experiment. Dispersal limitation primarily governed the assembly process almost the entire experimental period, indicating the strong adaptability and metabolic plasticity of microorganisms in response to environmental variations. This experiment provides valuable insights into the metabolic mechanisms and microbial assembly associated with sewer systems.

Tetracycline hydrochloride degradation in polarity inverted microbial fuel cells: Performance, mechanisms and microbiology.

Tetracycline antibiotics are widely used in veterinary medicine, human therapy and agriculture, and their presence in natural water raises environmental concerns. In this study, more than 94% of tetracycline hydrochloride (TCH) could be rapidly degraded within 48 h in polarity-inverted microbial fuel cells. The electrochemically active bacteria had the best electrochemical performance at 1 mg/L of TCH with the minimum internal resistance of 77.38 Ω. The electron-rich functional groups of TCH were continuously attacked and finally degradated into small molecules in three possible degradation pathways. Microbial community structure analysis showed that Comamonas and Shinella were enriched at the electrode as polarity-inverted bacteria. Genomic analysis showed that both direct and indirect electron transfer participated in the degradation of TCH in polarity-inverted microbial fuel cell (MFC) and the functional genes related to electrical conductivity in polarity-inverted MFC were more enriched on the electrode surface than non-polarity-inverted MFC. This study can facilitate further investigations about the biodegradation of TCH in polarity-inverted microbial fuel cell.

A flame-retardant and conductive fabric-based triboelectric nanogenerator: Application in fire alarm and emergency evacuation.

The fabrics commonly used in architectural decorative materials pose significant fire hazards due to their flammability and rapid fire spread. Moreover, the traditional fire-alarm systems may fail to function properly in complex fire environments owing to power supply disruptions. In this study, we developed a low-cost and eco-friendly flame-retardant conductive fabric-based triboelectric nanogenerator (FCF-TENG) by integrating flame-retardant conductive nylon fabric and polytetrafluoroethylene soaked cotton fabric. This nanogenerator exhibits excellent flame-retardant properties and remarkable energy-harvesting capabilities. The nylon fabric, treated with layer-by-layer self-assembly method, possesses outstanding self-extinguishing capability and melt-dripping resistance. Additionally, the electrical performance of FCF-TENG significantly improves, with a 10-fold boost in conductivity, and the open-circuit voltage increases by 84% to 92 V. Besides, by incorporating the rectifier circuit, the FCF-TENG is capable of completely charging a 1 µF capacitor within 30 s. Furthermore, the FCF-TENG was successfully applied as a self-powered sensor in the fire-alarm system and served as a safety exit indicator for evacuees and fire rescue. This work presents an effective and innovative application of multifunctional smart textiles for energy harvesting and self-powered sensing.

Exceptional Performance of Flame-Retardant Polyurethane Foam: The Suppression Effect on Explosion Pressure and Flame Propagation of Methane-Air Premixed Gas.

A self-built gas explosion testing platform was used to explore the quenching effect of flame-retardant polyurethane foam on a gas explosion. The effect of the foam's filling position and length on the explosion suppression performance was explored. The results demonstrate that polyurethane foam exhibits an excellent flame-quenching performance, with a minimum of a 5 cm length of porous material being sufficient to completely quench the flame during propagation. Furthermore, the attenuation function of this porous material on the pressure wave is insignificantly affected by the change in ignition energy. Compared with the explosive state of the empty pipeline, the best suppression effect is obtained when the polyurethane foam is 20 cm in length with a filling position at 1.8 m, and the maximum explosion pressure and maximum rise rate are attenuated by 86.2% and 84.7%, respectively. This work has practical significance for the application of porous materials in explosion suppression and explosion-proof technologies in the chemical industrial processing and oil (gas) storage fields.

Role of the ISKpn element in mediating mgrB gene mutations in ST11 hypervirulent colistin-resistant Klebsiella pneumoniae.

Background: Colistin has emerged as a last-resort therapeutic against antibiotic-resistant bacterial infections, particularly those attributed to carbapenem-resistant Enterobacteriaceae (CRE) like CRKP. Yet, alarmingly, approximately 45% of multidrug-resistant Klebsiella pneumoniae strains now manifest resistance to colistin. Through our study, we discerned that the synergy between carbapenemase and IS elements amplifies resistance in Klebsiella pneumoniae, thereby narrowing the existing therapeutic avenues. This underscores the instrumental role of IS elements in enhancing colistin resistance through mgrB disruption. Methods: From 2021 to 2023, 127 colistin-resistant Klebsiella pneumoniae isolates underwent meticulous examination. We embarked on an exhaustive genetic probe, targeting genes associated with both plasmid-mediated mobile resistance-encompassing blaKPC, blaNDM, blaIMP, blaVIM, blaOXA-48-like, and mcr-1 to mcr-8-and chromosome-mediated resistance systems, including PhoP/Q, PmrA/B, and mgrB. PCR amplification revealed the presence of virulence-associated genes from the pLVPK plasmid, such as rmpA, rmpA2, iucA, iroB, and peg344. mgrB sequencing was delegated to Sangon Biotech, Shanghai, and the sequences procured were validated using BLAST. Our search for IS elements was navigated through the IS finder portal. Phenotypically, we harnessed broth microdilution (BMD) to ascertain the MICs of colistin. To sketch the clonal lineage of mgrB-mutated CoR-Kp isolates, sophisticated methodologies like MLST and PFGE were deployed. S1-PFGE unraveled the intrinsic plasmids in these isolates. Our battery of virulence assessment techniques ranged from the string test and capsular serotyping to the serum killing assay and the Galleria mellonella larval infection model. Results: Among the 127 analyzed isolates, 20 showed an enlarged mgrB PCR amplicon compared to wild-type strains. These emerged over a three-year period: three in 2021, thirteen in 2022, and four in 2023. Antimicrobial susceptibility tests revealed that these isolates consistently resisted several drugs, notably TCC, TZP, CAZ, and COL. Additionally, 85% resisted both DOX and TOB. The MICs for colistin across these strains ranged between 16 to 64 mg/L, with a median of 40 mg/L. From a genetic perspective, MLST unanimously categorized these mgrB-mutated CoR-hvKp isolates as ST11. PFGE further delineated them into six distinct clusters, with clusters A and D being predominant. This distribution suggests potential horizontal and clonal genetic transmission. Intriguingly, every mgrB-mutated CoR-hvKP isolate possessed at least two virulence genes akin to the pLVPK-like virulence plasmid, with iroB and rmpA2 standing out. Their virulence was empirically validated both in vitro and in vivo. A pivotal discovery was the identification of three distinct insertion sequence (IS) elements within or near the mgrB gene. These were:ISKpn26 in eleven isolates, mainly in cluster A, with various insertion sites including +74, +125, and an upstream -35.ISKpn14 in four isolates with insertions at +93, -35, and two upstream at -60.IS903B present in five isolates, marking positions like +74, +125, +116, and -35 in the promoter region. These diverse insertions, spanning six unique locations in or near the mgrB gene, underscore its remarkable adaptability. Conclusion: Our exploration spotlights the ISKpn element's paramount role in fostering mgrB gene mutations in ST11 hypervirulent colistin-resistant Klebsiella pneumoniae. Employing MLST and PFGE, we unearthed two primary genetic conduits: clonal and horizontal. A striking observation was the ubiquitous presence of the KPC carbapenemase gene in all the evaluated ST11 hypervirulent colistin-resistant Klebsiella pneumoniae strains, with a majority also harboring the NDM gene. The myriad mgrB gene insertion locales accentuate its flexibility and the overarching influence of IS elements, notably the pervasive IS5-like variants ISKpn26 and IS903B. Our revelations illuminate the escalating role of IS elements in antibiotic resistance within ST11 hypervirulent colistin-resistant Klebsiella pneumoniae, advocating for innovative interventions to counteract these burgeoning resistance paradigms given their profound ramifications for prevailing treatment modalities.

Enantioselective synthesis of 3a-azido-pyrroloindolines by copper-catalyzed asymmetric dearomative azidation of tryptamines.

Copper-catalyzed asymmetric dearomative azidation of tryptamines using azidobenziodoxolone as an azidating reagent was developed, which affords a variety of 3a-azido-pyrroloindolines in good to high enantioselectivities under mild reaction conditions. The azides could be readily transformed into the corresponding 3a-amino-pyrroloindolines via reduction and 1,2,3-triazole derivatives via a click reaction.

Toluene catalytic oxidation over gold catalysts supported on cerium-based high-entropy oxides.

A series of cerium-based high-entropy oxide catalysts (the ratio of CeO2 and HEO is 1:1) was prepared by a solid-state reaction method, which exploit their unique structural and performance advantages. The Ce-HEO-T samples can achieve 100% toluene conversion rate above 328°C when they were used as catalysts directly. Subsequently, the Ce-HEO-500 exhibited the lowest temperature for toluene oxidation was used as a support to deposit different amounts of Au for a further performance improvement. Among all of prepared samples, Au/Ce-HEO-500 with a moderate content of Au (0.5 wt%) exhibited the lowest temperature for complete combustion of toluene (260°C), which decreased nearly 70°C compared with Ce-HEO-500 support. Moreover, it also showed excellent stability for 60 h with 98% toluene conversion rate. Most importantly, under the condition of 5 vol.% H2O vapour, the toluene conversion rate remained unchanged and even increased slightly compared with that in dry air, exhibiting excellent water resistance. Combined with the characterizations of XRD, SEM, TEM, BET, Raman, H2-TPR and XPS, it was found that the high dispersion of active Au NPs, the special high-entropy structure and the synergistic effect between Au and Ce, Co, Cu are the key factors when improving the catalytic performance in the Au/Ce-HEO-500 catalyst.

Insights into the Role of Nanorod-Shaped MnO2 and CeO2 in a Plasma Catalysis System for Methanol Oxidation.

Published papers highlight the roles of the catalysts in plasma catalysis systems, and it is essential to provide deep insight into the mechanism of the reaction. In this work, a coaxial dielectric barrier discharge (DBD) reactor packed with γ-MnO2 and CeO2 with similar nanorod morphologies and particle sizes was used for methanol oxidation at atmospheric pressure and room temperature. The experimental results showed that both γ-MnO2 and CeO2 exhibited good performance in methanol conversion (up to 100%), but the CO2 selectivity of CeO2 (up to 59.3%) was much higher than that of γ-MnO2 (up to 28.6%). Catalyst characterization results indicated that CeO2 contained more surface-active oxygen species, adsorbed more methanol and utilized more plasma-induced active species than γ-MnO2. In addition, in situ Raman spectroscopy and Fourier transform infrared spectroscopy (FT-IR) were applied with a novel in situ cell to reveal the major factors affecting the catalytic performance in methanol oxidation. More reactive oxygen species (O22-, O2-) from ozone decomposition were produced on CeO2 compared with γ-MnO2, and less of the intermediate product formate accumulated on the CeO2. The combined results showed that CeO2 was a more effective catalyst than γ-MnO2 for methanol oxidation in the plasma catalysis system.

Multi-Objective Optimization of Liquid Silica Array Lenses Based on Latin Hypercube Sampling and Constrained Generative Inverse Design Networks.

Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and "Constraint Generation Inverse Design Network (CGIDN)" to achieve multi-objective optimization of the injection process, shorten the time to find the optimal process parameters, and improve the production efficiency of plastic parts. Taking the LSR lens array of automotive LED lights as the research object, the residual stress and volume shrinkage were taken as the optimization objectives, and the filling time, melt temperature, maturation time, and maturation pressure were taken as the influencing factors to obtain the optimization target values, and the response surface models between the volume shrinkage rate and the influencing factors were established. Based on the "Constraint-Generated Inverse Design Network", the optimization was independently sought within the set parameters to obtain the optimal combination of process parameters to meet the injection molding quality of plastic parts. The results showed that the optimal residual stress value and volume shrinkage rate were 11.96 MPa and 4.88%, respectively, in the data set of 20 Latin test samples obtained based on Latin hypercube sampling, and the optimal residual stress value and volume shrinkage rate were 8.47 MPa and 2.83%, respectively, after optimization by the CGIDN method. The optimal process parameters obtained by CGIDN optimization were a melt temperature of 30 °C, filling time of 2.5 s, maturation pressure of 40 MPa, and maturation time of 15 s. The optimization results were obvious and showed the feasibility of the data-driven injection molding process optimization method based on the combination of Latin hypercube sampling and CGIDN.

Regularized impurity reduction: accurate decision trees with complexity guarantees.

Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. Thus, although these traditional methods are accurate in practice, there has been no theoretical guarantee that they will produce small trees. In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees. We consider a general setting, where objects to be classified are drawn from an arbitrary probability distribution, classification can be binary or multi-class, and splitting tests are associated with non-uniform costs. As a measure of tree complexity, we adopt the expected cost to classify an object drawn from the input distribution, which, in the uniform-cost case, is the expected number of tests. We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. This approximation factor is tight up to a constant factor under mild assumptions. The algorithm recursively selects a test that maximizes a greedy criterion defined as a weighted sum of three components. The first two components encourage the selection of tests that improve the balance and the cost-efficiency of the tree, respectively, while the third impurity-reduction component encourages the selection of more discriminative tests. As shown in our empirical evaluation, compared to the original heuristics, the enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity.

A Node Detection Method Based on Johnson-Cook and Thin-Film IMD Characteristic Model Armor Damage Detection Repair and Subsequent Optimization.

In this paper, a node detection method is proposed for the detection of battle damage to armor. This experiment uses the special nature of the film to virtualize the surface of the armor IMD film coverage. The die index is a large area and is easy to damage, but with the use of a unique IMD film stamping die, the possibility of damage decreases, which provides a damage prediction function for the armor. In addition, for the damaged armor, the same method can be used to detect because the damaged part more easily causes the surface film to rupture after being impacted, so it is possible to optimize the design of the armor and the molding through the die index. The die index can also detect the degree of damage to the damaged part of the damaged armor. Therefore, the IMD die index is introduced to quantify the data, and the degree of damage is judged by the IMD die index. The novelty of this work is that each node can efficiently detect the vulnerable damage position of the armor using the die index and then pass through the COMSOL. The Johnson-Cook stress model simulates the battle loss, obtains the stress deformation that occurs after the battle loss, and verifies the experiment by comparing the results obtained. Finally, the repair method is used to repair all the predicted battle damage parts based on additive manufacturing to ensure that they can be used again after repair.

Predicting of Process Parameters for Theoretical Concentrated Stress of Fatigue Notch Coefficient of Auto Parts Using Virtual Recognizable Performance Evaluation Research.

This paper analyzes the structure of the key parts of the car belt guide, and the average stress of the vulnerable parts is simulated by analysis software. The theoretical stress of the section is calculated. The theoretical stress concentration factor (Kt) is given. The relation between the gap radius and the notch coefficient (Kf) was studied according to a previous Kf calculation formula. The tensile tests of real products are used as reference data. The results showed that Kf and Kt are linear in most cases, but there are also cases of non-compliance. The relationship between the fatigue notch coefficient Kf and the theoretical stress concentration coefficient Kt was closely related to the service life and fatigue strength of the product. In addition, we found that the size and direction of warpage improved significantly with the increase of fillet size, which was not consistent with the effect of adding glass fiber material. The rounded corners of ordinary PP materials usually displayed forward warping, but the addition of glass fiber into PP materials made the degree of warping smaller, or even led to reverse warping. The size of rounded corners is an important optimization parameter. The relationship between Kf and Kt was studied from the perspectives of virtual measurement (VM) and recognizable performance evaluation (RPM). According to abnormal filling pressure, these relationships were compared with filling data to generate a fracture initiation control model. Based on a large amount of normal process data and quality inspection data, the historical data (causes) and quality inspection data (results) were combined.

Non-Dominant Genetic Algorithm for Multi-Objective Optimization Design of Unmanned Aerial Vehicle Shell Process.

This paper uses Pareto-optimized frames and injection molding process parameters to optimize the quality of UAV housing parts with multi-objective optimization. Process parameters, such as melt temperature, filling time, pressure, and pressure time, were studied as model variables. The quality of a plastic part is determined by two defect parameters, warpage value and mold index, which require minimal defect parameters. This paper proposes a three-stage optimization system. In the first stage, the main node position of the electronic chip in the module is collected by the unified sampling method, and the chip calculation index of these node positions is analyzed by the mold flow analysis software. In the second stage, the kriging function predicts the mathematical relationship between the mold index and warpage value and the process parameters, such as melt temperature, filling time, packing pressure, and packing time. In the third stage, using LHD sampling and non-dominant rank genetic algorithm II, a convergence curve of warp value is found near the Pareto optimal frontier. In the fourth stage, the fitting degree of Pareto optimal leading edge curve points was verified by analytical experiments. According to experimental verification, it can be seen that the injection molding factors are pressure and pressure time, because the injection molding time and pressure time are completely positively correlated with the mold indicators, the correlation is the strongest, the mold temperature and glue temperature are not the main influencing factors, and the mold temperature shows a certain degree of negative correlation. In this experiment, the die index is mainly improved by injection time and pressure, optimal injection parameter factor combination and minimum injection index, the optimization rate of the die index is up to 96.2% through genetic algorithm optimization nodes and experimental verification, the average optimization rate of the four main optimization nodes is 91.2%, and the error rate with the actual situation is only 8.48%, which is in line with the needs of actual production, and the improvement of the UAV IME membrane is realized.

Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling.

This paper uses a multi-objective optimization method to optimize the injection-molding defects of automotive pedals. Compared with the traditional automotive pedal material, aluminum alloy, the polymer pedal containing glass fibers not only reduces the aluminum pedal by at least half, but also improves the strength and hardness of the fibers by adjusting the orientation of the fibers in all directions. Injection factors include: filling time, filling pressure, melt temperature, cooling time, injection time, etc. For the optimization process influencing factors, herein, we focus on warpage analyzed via flow simulation, and setting warpage parameters and cycle time as discussed by setting different cooling distributions, pressures and temperature schemes. The multi-objective optimization design was mainly used to describe the relationship between cycle time and warpage, and the Pareto boundary was used for cycle time and warpage to identify the deviation function and radial-basis-function network. We worked with a small DOE for building the surface to run SAO programming-which improved the accuracy of the response surface by adding sampling points-terminating the time when the warpage value met the solution requirements, to find out the global optimal solution of the warpage value under different cooling times. Finally, the results highlighted four influencing parameters that match the experimental image of the actual production.

Ranking with submodular functions on a budget.

Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items. On the other hand, many real-world applications require a solution that is a ranking over a set of items. The problem of ranking in the context of submodular function maximization has been considered before, but to a much lesser extent than item-selection formulations. In this paper, we explore a novel formulation for ranking items with submodular valuations and budget constraints. We refer to this problem as max-submodular ranking ( MSR ). In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints. For the MSR problem with cardinality- and knapsack-type budget constraints we propose practical algorithms with approximation guarantees. In addition, we perform an empirical evaluation, which demonstrates the superior performance of the proposed algorithms against strong baselines.

Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation.

In the process of injection molding, a certain percentage of recycled material is usually used in order to save costs. The material properties of recycled materials can change significantly compared with raw materials, and the quality of their molded products is more difficult to control. Therefore, it is crucial to propose a method that can effectively maintain the yield of the recycled material products. In addition, the variation of clamping force during the injection molding process can be determined by measuring the tie-bar elongation of the injection molding machine. Therefore, this study proposes a real-time product quality monitoring system based on the variation of clamping force during the injection molding process for the injection molding of recycled materials for plastic bottle caps. The variation of clamping force reflects the variation of cavity pressure during the injection molding process and further maps the variation of injection parameters during the injection molding process. Therefore, this study evaluates the reliability of the proposed method for three different injection parameters (residual position, metering end point and metering time). Experiments have shown that there is a strong correlation between the quality (geometric properties) and weight of the product under different molding parameters. Moreover, the three main injection parameters have a strong influence on the weight and quality of the plastic caps. The variation of the clamping force is also highly correlated with the weight of the plastic bottle cap. This demonstrates the feasibility of applying the variation of clamping force to monitor the quality of injection molded products. Furthermore, by integrating the clamping force variation index with the calibration model of the corresponding injection parameters, it is possible to control the weight of the plastic cap within the acceptable range of the product in successive production runs.