Longer Run Times Research Articles

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

B-316 Evaluation of an FDA-cleared Chromogranin A Assay on an Automated Analyzer

Abstract Background Chromogranin A (CgA) is a hydrophilic and acidic protein, present in the chromaffin granules of neuroendocrine cells. It is the preferred tumor marker for the monitoring of neuroendocrine tumors (NETS), which are often found in the lungs, appendix, pancreas, and gastrointestinal tract. Previous CgA assays were performed by manual ELISA testing, and were not FDA-approved, leading to a longer, more rigorous validation process and longer run times. The introduction of an automated, FDA-cleared CgA assay allows the laboratory to process patient samples more quickly and reliably, shortening a multiple hour process to minutes. The objective of this study was to evaluate the performance of an automated CgA assay recently cleared by the FDA. Methods We validated the the B·R·A·H·M·S™ Chromogranin A II (CGAII) assay on the automated B·R·A·H·M·S KRYPTOR Compact PLUS (Thermo Fisher Scientific Inc.). The CGAII assay is an immunofluorescent assay, which uses Time-Resolved Amplified Cryptate Emission (TRACE) to quantitate CGA in human serum. Assay performance characteristics were evaluated including measuring range (MR) and maximum dilution, precision, method comparison, stability, reference interval, stability, and carryover. Results The MR for CGAII was established with triplicate analysis of serum at 8 levels, ranging from 25.8 - 2,700 ng/mL. Linearity was demonstrated with a slope of 0.907, intercept of 1.948 and observed error of 2.54 ng/mL. Recovery at each point ranged from 83.3-110.3%. Carryover (<5.0 ng/mL) was not observed at a concentration approximately 2 times the upper limit of the MR. A 1:25 or 1:500 automatic dilution was determined to be acceptable (% bias <16%). Intraday precision coefficient of variation (CV) ranged from 4.1-1.7% (SDs from 2.32-6.64 ng/mL) at concentrations of 56.7 and 393.0 ng/mL. The interday CVs ranged from 6.0-7.1% (SDs from 3.43 - 28.84 ng/mL) at concentrations of 57.5 and 403.8 ng/mL. Twenty samples with concentrations ranging from 56.4-2,907 ng/mL were compared to a reference laboratory utilizing a similar methodology. The correlation coefficient was 0.9934, with a slope of 1.014, and intercept of -32.95. An additional twenty samples with concentrations ranging from 67.0-2,446 ng/mL were compared to a CgA ELISA kit (CGA-ELISA-NG, CISBIO) to determine the bias between the two methodologies. The correlation coefficient was 0.9902, with a slope of 0.845, and intercept of -15.31, and a bias of -19.9%. The serum samples were stable for 3 days when stored at 2-8°C. Recovered CgA concentrations fell outside of the 95% confidence interval after day 3 at 2-8°C. The manufacturer provided reference interval of <187 ng/mL was verified by testing 20 healthy adult samples, all of which fell within this range. Conclusions This assay has been validated and shown to be an accurate and precise method for monitoring CgA. Further, the method was validated on an automated analyzer to allow expeditious turnaround time in a core laboratory.

Influence of Fractal Disc Filter Flow Channel Parameters on Filtration Performance

The research and development of a new disc filter is a key link in intelligent irrigation systems, the core of efficient and water-saving irrigation development, and also an important joint effort to ensure a clean water source in micro-irrigation systems. In this paper, the independent research and development of the fractal flow passage disc filter was taken as the research object, and the disc filter numerical simulation cell (FLUENT) and artificial intelligence technology (Back Propagation Neural Network) were combined to optimize the filter flow channel parameters, including the tilt angle, the length and height of the bottom of the internal section triangle, the taper, the position and number of buffer slots, etc. A new type of disc filter with lower head loss, larger flow capacity, higher filtration efficiency, and longer running time is proposed. It has certain reference value and promotion significance for the future development and design of high-performance disc filters and their wide use.

Temporal heterogeneity in the performance of machine learning models for PM2.5 concentration estimation

Machine learning (ML) methods have been applied extensively to simulate air pollutant concentrations and assess individual exposure in epidemiological studies. However, there is still a paucity of research on the temporal heterogeneity of ML model performance and the impact of dataset size. To explore the temporal heterogeneity in model performance when estimating daily concentrations of fine particulate matter (PM2.5) across China in 2021, we compared five decision tree-based ML models (Random Forest (RF), Categorical Boosting (CatBoost), Gradient Boost Regression Tree (GBRT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)) across daily scales within three distinct timeframes. The performance of all models was evaluated using cross-validation. We observed that the performance of ML models varied with time, which showed a significant correlation with PM2.5 concentration. Among the 365 days in 2021, RF model performed best, the annual mean R2 was 0.86, with a minimum of 0.84 and a maximum of up to 0.95. For RF, we chose a cubic polynomial curve to fit the relationship between model performance and PM2.5 concentrations, and based on this, we devised a model selection strategy for different time scales, achieving an accuracy rate of up to 79.45 %, with the selected models having an average R2 of 0.85, and a maximum of up to 0.95. Additionally, we found that increasing the dataset size did not significantly improve model performance. Instead, it resulted in considerably longer runtime and increased memory usage. The methodology and findings of this study hold significant value for advancing the development of more efficient and precise modeling approaches for air pollutant concentrations. Furthermore, this research provides a foundation for regional air pollutant governance and future health-related research.

On the search for efficient face recognition algorithm subject to multiple environmental constraints

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BackgroundCOVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.ResultsIn this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.ConclusionOur proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

Probing fractured reservoir of enhanced geothermal systems with fuzzy-genetic inversion model: Impacts of geothermal reservoir environment

Our previous study (Zhou et al., 2023) proposed a fuzzy inference model to inverse the predominant flow area in fractured reservoirs. Yet, geothermal reservoir parameters in the direction problem model are hard to make exactly consistent with the actual parameters. This study aims to discuss the impacts of discrepancies between actual geothermal reservoir environment and simulated reservoir environment on the inversion results. An inversion model (including direction problem model, fuzzy inference model, and genetic algorithm) is established to explore the location of predominant flow area under different guess geothermal reservoir environments. The effects of running time, guess reservoir environment (temperature field, pressure field, and Hot Dry Rock property) and guess predominant fracture area property on inversion results are studied. Results show that longer running time can obtain good inversion results, and the inversion speed decreases as running time increases. In addition, the inversion model has good inversion accuracy under different guess geothermal reservoir environments. Lastly, the inversion accuracy is very poor when the guess permeability of predominant fracture area is extremely low. However, the inversion model still needs improvement in the initial stage. It cannot inverse the location of predominant flow area when the running time is less than 1×107 s.

Impact of a multidisciplinary team "ECMO Team" on the prognosis of patients undergoing veno-arterial extracorporeal membrane oxygenation for refractory cardiogenic shock and cardiac arrest

Veno-arterial Extracorporeal membrane oxygenation (VA ECMO) is a salvage intervention in patients with cardiogenic shock (CS), and cardiac arrest (CA) refractory to standard therapies. The design of ECMO Teams has achieved the standardization of processes, although its impact on survival and prognosis is unknown. We aimed to analyze whether the creation of an ECMO Team has modified the prognosis of patients undergoing VA ECMO for refractory CS or CA. . We conducted a single-center retrospective cohort study. Patients with refractory CS or CA who underwent VA ECMO were divided in two consecutive periods: from 2014 to April 2019 (pre-ECMO T) and from May 2019 to December 2022 (Post ECMO T). The main outcomes were survival on ECMO, in-hospital survival, complications, and annual ECMO volume. Eighty-three patients were included (36 pre-ECMO T and 47 post-ECMO T). The mean age was 53 +/-13 years. The most common reason for device indication was different: postcardiotomy shock (47.2%) pre-ECMO T and refractory cardiogenic shock (29.7%) post-ECMO T. The rate of extracorporeal cardiopulmonary resuscitation was 14.5%. The median duration of VA ECMO was longer after ECMO team implementation: 8 days (IQR 5-12.5) vs. five days (IQR 2-9, p=0.04). Global in-hospital survival was 45.8% (38.9% pre-ECMO T vs. 51.1% post-ECMO T; p=0.37), and the survival rate from VA ECMO was 60.2% (55.6% pre-ECMO T vs 63.8% post-ECMO T; p= 0.50). The volume of VA ECMO implantation was significantly higher in the post-ECMO team period (13.2 +/3.5 per year vs. 6.5 +/-3.5 per year, p: 0.02). The rate of complications was similar in both groups. After the implementation of an ECMO team, there was no statistical difference in the survival rate of patients treated with VA ECMO. However, a significant increase in the number of patients supported per year was observed after the implementation of this multidisciplinary team. Post-ECMO T, the most common reason for device indication was cardiogenic shock, with longer run times and a higher rate of extracorporeal cardiopulmonary resuscitation.

Enhancing an existing algorithm for small-cardinality constrained portfolio optimisation

The efficient frontier (EF) allows an investor to (in theory) maximise their return for a given level of risk. Portfolios on the EF may contain many assets, making their management difficult and possibly expensive. An investor may wish to impose an upper bound on the number of assets in their portfolio, leading to so-called cardinality constrained efficient frontiers (CCEFs). Recently, a new algorithm was developed to find CCEFs for small cardinalities. Relative to other algorithms for this problem, this algorithm is very intuitive, and its authors demonstrated that it performs at nearly the state-of-the-art. However, we have found that the algorithm seems to struggle in certain situations, particularly when faced with both bonds and equities. While preserving its intuitiveness, we modified the algorithm to improve its CCEFs. This improvement comes with longer runtimes, but we think many practitioners will prefer the algorithm with modifications. Some practitioners may prefer other algorithms, due to the runtimes or because some points on our CCEFs still fall short of optimality. However, in addition to its intuitiveness, our modified algorithms (and the original version) find low-risk points on the CCEF that a state-of-the-art algorithm does not.

Drain-to-Drain Abutment-Aware Detailed Placement Refinement for Power Staple Insertion Optimization

Power staple insertion is an effective means to mitigate IR drop in the advanced technology process. Previous works have shown that detailed placement refinement can greatly enhance the power staple insertion rate. However, with FinFET, it is necessary to consider the drain-to-drain abutment (DDA) constraints. We note that extra source node insertion for resolving DDA violations can interfere with power staple insertion. To handle DDA constraints and optimize power staple insertion at the same time, we formulate and solve a new DDA-aware placement refinement problem in this work. Given an initial nonoverlapping placement optimized for other conventional objectives, we compute a refined placement with cell shifting subject to DDA constraints such that the number of staple insertion slots can be maximized. An effective approach is proposed that supports a nonrestricted cell displacement range during placement refinement. It provides the flexibility to adjust the displacement bound of each cell dynamically in order to resolve all DDA violations. As a result, our algorithm guarantees that a placement solution with no DDA violation can always be computed without using a large displacement range for each cell which would require much longer runtime and larger average cell displacement. In addition to cell shifting, we incorporate concurrent cell flipping to reduce the required cell displacement to satisfy the DDA constraints and to facilitate power staple insertion. The experimental results showed the effectiveness of the proposed approach.

Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic–continuum framework

Despite their success in microscale modeling of materials, atomistic methods are still limited by short time scales, small domain sizes, and high strain rates. Multiscale formulations can capture the continuum-level response of solids over longer runtimes, but using such schemes to model highly dynamic, nonlinear phenomena is very challenging and an active area of research. In this work, we develop novel techniques within the concurrent atomistic–continuum (CAC) multiscale framework to simulate shock wave propagation through a two-dimensional, single-crystal lattice. The technique is described in detail, and two moving window methods are incorporated to track the shock front through the domain and thus prevent spurious wave reflections at the atomistic–continuum interfaces. We compare our simulation results to analytical models as well as previous atomistic and CAC data and discuss the apparent effects of lattice orientation on the shock response of two materials. We then use the moving window techniques to perform parametric studies which analyze the shock front’s structure. Finally, we compare the efficiency of our model to molecular dynamics simulations. This work showcases the framework’s capability for simulating dynamic shock evolution over long runtimes and opens the door to more complex studies involving shock propagation through composites and alloys.

Application of Intelligent Optimization Algorithms in the Distribution Network Planning and Evaluation Models

In order to reasonably plan distribution network evaluation strategies and improve the efficiency and accuracy of power system planning, this study aims to explore the application of intelligent optimization algorithms in distribution network planning and evaluation. It includes the principle, method and model construction of intelligent optimization algorithm, and designs a distribution network planning evaluation model using Ant colony optimization algorithms combined with GIS (Geographic Information System). This article applies its example to the distribution network planning of a certain area, and the results show that compared with traditional algorithms, the intelligent optimization algorithm has a voltage value of 230.2V at the highest and 220.2V at the lowest; The highest load value is 2.75 Megawatt, and the lowest is 1.19 Megawatt; The longest running time is 8913ms, surpassing traditional algorithms in all aspects. This algorithm improves the voltage stability and load balance of the distribution network, and reduces operating time. This study has certain promotion and application value for the research and practical application of power system planning, which can improve the efficiency and accuracy of power system planning and provide scientific basis for the reliability and effectiveness of power supply.

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.

The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. Supplementary data are available at Bioinformatics online.

Prediction of TOC in Lishui–Jiaojiang Sag Using Geochemical Analysis, Well Logs, and Machine Learning

Total organic carbon (TOC) is important geochemical data for evaluating the hydrocarbon generation potential of source rocks. TOC is commonly measured experimentally using cutting and core samples. The coring process and experimentation are always expensive and time-consuming. In this study, we evaluated the use of three machine learning (ML) models and two multiple regression models to predict TOC based on well logs. The well logs involved gamma rays (GR), deep resistivity (RT), density (DEN), acoustic waves (AC), and neutrons (CN). The ML models were developed based on random forest (RF), extreme learning machine (ELM), and back propagation neural network (BPNN). The source rock of Paleocene Yueguifeng Formation in Lishui–Jiaojiang Sag was taken as a case study. The number of TOC measurements used for training and testing were 50 and 27. All well logs and selected well logs (including AC, CN, and DEN) were used as inputs, respectively, for comparison. The performance of each model has been evaluated using different factors, including R2, MAE, MSE, and RMSE. The results suggest that using all well logs as input improved the TOC prediction accuracy, and the error was reduced by more than 30%. The accuracy comparison of ML and multiple regression models indicated the BPNN was the best, followed by RF and then multiple regression. The worst performance was observed in the ELM models. Considering the running time, the BPNN model has higher prediction accuracy but longer running time in small-sample regression prediction. The RF model can run faster while ensuring a certain prediction accuracy. This study confirmed the ability of ML models for estimating TOC using well logs data in the study area.

Optimization of a GC-MS method for the detection of bioactive compounds from the green peel of pomelo cultivar ‘PO52’

Pomelo is the largest and most popular citrus fruit known to possess diverse health benefits. Sabah’s fruit basket, Tenom, is famous for pomelo production. Among the pomelo cultivars being planted in Tenom, the PO52 cultivar is the most abundant one. To date, the bioactive compounds of the PO52 cultivar are not well studied. Although numerous aroma-active compounds from the peels of Malaysian pomelo have been reported, there are still many potential non-aromatic bioactive compounds that are yet to be discovered. In this study, we analysed the peel extract of pomelo PO52 using gas chromatography coupled to a single quadrupole mass spectrometry (GC-MS) approach. We first analysed the pomelo peel extracts using two different GC-MS methods, one can detect 45 organic compounds with good separation, while the other can detect phytosterols and pentacyclic triterpenoids in Clinacanthus nutans roots. In our study, these two methods were able to detect bioactive compounds from the pomelo peels but with a longer running time and the generated total ion chromatograms (TIC) were also not smooth. Therefore, we further optimized the two methods by changing the temperature ramping and holding times of the GC-MS. Compared to the first two methods, the optimized method had the shortest total running time (38 min) and the total ion chromatogram (TIC) produced was smooth throughout the run time. Furthermore, bioactive compounds that have not been reported for pomelo peel before, such as osthole, α-tocopherol, gamma-sitosterol, friedelan-3-one and others were also detected using this optimized method. In conclusion, the optimized method is suitable for the detection of non-aromatic compounds present in the PO52 peels that can be subsequently applied in the next stage for large-scale analysis.

Increased Longevity of a Novel Gas Exchanger System for Low-Flow Veno-Venous Extracorporeal CO2 Removal in Acute Hypercapnic Respiratory Failure

Introduction: Low-flow veno-venous extracorporeal CO2 removal (ECCO2R) is an adjunctive therapy to support lung protective ventilation or maintain spontaneous breathing in hypercapnic respiratory failure. Low-flow ECCO2R is less invasive compared to higher flow systems, while potentially compromising efficiency and membrane lifetime. To counteract this shortcoming, a high-longevity system has recently been developed. Our hypotheses were that the novel membrane system provides runtimes up to 120 h, and CO2 removal remains constant throughout membrane system lifetime. Methods: Seventy patients with pH ≤ 7.25 and/or PaCO2 ≥9 kPa exceeding lung protective ventilation limits, or experiencing respiratory exhaustion during spontaneous breathing, were treated with the high-longevity ProLUNG system or in a control group using the original gas exchanger. Treatment parameters, gas exchanger runtime, and sweep-gas VCO2 were recorded across 9,806 treatment-hours and retrospectively analyzed. Results: 25/33 and 23/37 patients were mechanically ventilated as opposed to awake spontaneously breathing in both groups. The high-longevity system increased gas exchanger runtime from 29 ± 16 to 48 ± 36 h in ventilated and from 22 ± 14 to 31 ± 31 h in awake patients (p < 0.0001), with longer runtime in the former (p < 0.01). VCO2 remained constant at 86 ± 34 mL/min (p = 0.11). Overall, PaCO2 decreased from 9.1 ± 2.0 to 7.9 ± 1.9 kPa within 1 h (p < 0.001). Tidal volume could be maintained at 5.4 ± 1.8 versus 5.7 ± 2.2 mL/kg at 120 h (p = 0.60), and peak airway pressure could be reduced from 31.1 ± 5.1 to 27.5 ± 6.8 mbar (p < 0.01). Conclusion: Using a high-longevity gas exchanger system, membrane lifetime in low-flow ECCO2R could be extended in comparison to previous systems but remained below 120 h, especially in spontaneously breathing patients. Extracorporeal VCO2 remained constant throughout gas exchanger system runtime and was consistent with removal of approximately 50% of expected CO2 production, enabling lung protective ventilation despite hypercapnic respiratory failure.

Train Operation Curve Optimization for an Urban Rail Interval With Multi-Parameter Adjustment

This paper proposes an energy consumption optimization method of train operation by controlling multiple parameters for an urban rail interval. The parameters include the maximum operation speed, the ratio of the minimum coasting speed to the maximum speed, the maximum traction, and the braking force. Based on the mass belt model, an optimization model of train operation is built with these parameters as decision variables. The objective function is to minimize the energy consumption and the penalty on the running time delays for the observed rail interval. A simulated annealing algorithm is designed to solve the model, which adopts synergic adjustment of multiple parameters based on the influence of the running time with variation of each parameter. A numerical example of Guangzhou Metro Line 8 is adopted to test the method. The results show that the maximum operation speed has a decisive influence on the optimal train operation strategy, interval running time, and energy consumption; the train traction has a direct effect on energy consumption; the train braking force has multiple optimal solutions; moreover, the energy-saving strategy has a significant effect on energy saving in a longer running time.

Transition of fouling characteristics after development of membrane wetting in membrane-aerated biofilm reactors (MABRs)

The practical applications of water treatment techniques based on hydrophobic aeration membrane are limited due to membrane pores blocking. Various studies have revealed that both biofilm and microbial secretion can exacerbate membrane fouling. Recently, we constructed a membrane-aerated biofilm reactor (MABR) system for treating micro-polluted surface water in order to identify the primary cause for oxygen transfer rate (OTR) decline. It was found that microbial secretion had a more prominent negative effect than that caused by biofilm, as manifested by the fact the effect of microbial secretion (66.49%) was greater than the resistance of biofilm (38.83%). Fouling decreased the total pore volume of all membrane. The peak location of adsorption capacity was more likely to occur at smaller pore sizes with longer running time. Notably, continuous fluorescence distribution between the separating layer and pores like finger in MABR system exhibited an increasing trend with the operation time, indicating a gradual increase of microbial viability. Core protein structure was revealed by different bond peaks (0–90 d). Specifically, for different organic components of EPS, the hydrophilic HIS was the main content, while the mass transfer resistance caused by the gel increased, which reduced the contact angle and increased the bubble point pressure. Therefore, effects of EPS content and composition should be considered during the application of water treatment techniques based on MABR.

Low electric field assisted surface conductive membrane in AnMBR: Strengthening effect and fouling behavior

• The novel surface conductive membrane as cathode mitigated membrane fouling. • A unimodal pattern of SFR led to flocs on the surface of membrane to gel layer. • Basophilic methanogens and metabolic activity were influenced by electricity. • High specific surface area and biocompatibility contributed to form MEC system. This study investigated the possibility of using a novel type of surface conductive hollow fiber membrane during the Anaerobic membrane bioreactor process (AnMBR). In particular, the membrane fouling control mechanisms of Surface electric field AnMBR (S-AnEMBR) were evaluated through comparing with the operation of Conventional AnMBR (AnMBR) and Electric field AnMBR (AnEMBR). Results showed that S-AnEMBR had longer running time, stronger anti-fouling capability, and more stable Total organic carbon (TOC) removal efficiency. And S-AnEMBR could extend the running time of the rapid fouling phase (Stage 2). Electrochemical analysis and microbial community analysis showed that enhanced electron transfer in S-AnEMBR accelerated the decomposition of organic matter and further improved acidification efficiency from 83.75% to 234.78%, indicating the methane concentration ranged between 24.56 mmol/L and 36.99 mmol/L. Cake layer compression was reduced because significant changes were observed in increased particle size and more negative Zeta potential. In addition, basophilic methanogens (48%) were enriched at cathode, which improved electrochemical activity of carbon electrode biocathode and promoted anaerobic digestion. Therefore, it was found that S-AnEMBR was more conducive to controlling the properties and structure of the gel layer, and further achieving the resistance towards protein and polysaccharide adhesion in membrane. This work demonstrated that the surface conductive hollow fiber membrane possessed good anti-fouling ability in AnMBR, and provided an alternative membrane material for AnMBR application.

The Off by 100% Bias: The Effects of Percentage Changes Greater than 100% on Magnitude Judgments and Consumer Choice

Abstract Percentage changes greater than 100% are frequently used in consumer contexts; for example, a cordless vacuum cleaner may boast “125% longer runtime” compared to competitors. Via six studies (n = 2,395) and 11 supplementary studies (n = 3,249), the current research shows that consumers systematically underestimate the magnitude of percentage changes greater than 100%. Specifically, many consumers apply the relative size usage (e.g., “125% of,” equivalent to 25% more) instead of the appropriate relative change (e.g., “125% more,” equivalent to 100% more + 25% more), which leads them to be off by exactly 100% in their magnitude estimates. The rate of bias decreases when the difference between these two usages is emphasized. The Off by 100% bias occurs across a variety of consumer contexts, influencing behavioral intentions and incentive-compatible choice. The findings make theoretical contributions to research on processing of percentages, probability versus frequency formats, and magnitude judgments. Finally, understanding how different presentation formats of the same information can lead to different magnitude judgments enables marketers and policymakers to ensure more effective communication.