Sorting Strategy Research Articles

Next-generation sequencing protocol of hematopoietic stem cells (HSCs). Step-by-step overview and troubleshooting guide.

Populations of very small embryonic-like stem cells (VSELs) (CD34+lin-CD45- and CD133+lin-CD45-), circulating in the peripheral blood of adults in small numbers, have been identified in several human tissues and together with the populations of hematopoietic stem cells (HSCs) (CD34+lin-CD45+) and CD133+lin-CD45+constitute a pool of cells with self-renewal and pluripotent stem cell characteristics. Using advanced cell staining and sorting strategies, we isolated populations of VSELs and HSCs for bulk RNA-Seq analysis to compare the transcriptomic profiles of both cell populations. Libraries were prepared from an extremely small number of cells; however, their good quality was preserved, and they met the criteria for sequencing. We present here a step-by-step NGS protocol for sequencing VSELs and HSC with a description of troubleshooting during library preparation and sequencing.

Pembelajaran Interaktif Dengan Mengunakan Strategi Card Sort Pada Mata Pelajaran Ipas Kelas V Mis Miftahul Khoir Rasau Jaya

This study aims to determine the impact of implementing the Card Sort strategy in Science learning (IPA) in grade V at MIS Miftahul Khoir Rasau Jaya. The Science learning at this school previously faced challenges related to low student engagement and difficulty in understanding abstract concepts. The Card Sort strategy was applied to improve student involvement, understanding of the material, and social skills such as collaboration and communication. This research used a qualitative approach with a descriptive research type. The results showed that the implementation of the Card Sort strategy significantly increased student engagement in Science learning. Students, who were previously passive, became more active in group discussions and concept sorting. This strategy also helped students understand Science material more deeply, particularly in connecting related concepts. Furthermore, Card Sort encouraged students to collaborate in groups, enhancing their collaboration and communication skills. Student motivation also improved, as the learning became more interactive and enjoyable. The implementation of this method also led to improved learning outcomes, as evidenced by tests showing better comprehension of Science material. Therefore, the Card Sort strategy was proven effective in enhancing the quality of Science learning in grade V at MIS Miftahul Khoir Rasau Jaya.

Can we enhance investment with ESG?

Given evidence of low abnormal returns to ESG stock investment, growth in ESG focused stock investment suggests a wider utility from holding higher ESG performance stocks. We add detail and granularity through a double-sorted portfolio approach across two ESG measures and 24 anomalies. Traditional anomaly factor sort strategies may be enhanced by ESG information to produce an annualised ESG tilted alpha of more than 6% and provide an up to 7% alpha gain over the unconditional factor sort strategy. Investors using our strategies may increase ESG exposure and gain abnormal return with no alpha cost relative to traditional factor investing.

The Strategy of the Yogyakarta City Government in Implementing a Sustainable Zero Inorganic Waste Policy

This research aims to analyze the implementation of the Zero Inorganic Waste policy enacted by the Yogyakarta City Government, focusing on waste sorting strategies, infrastructure support, and challenges encountered in managing inorganic waste. The qualitative methodology involves field observations, in-depth interviews, and focus group discussions with various stakeholders. The research findings indicate that while this policy has successfully raised public awareness about waste separation, significant challenges remain, such as low public participation, limited infrastructure, and insufficient education efforts. Therefore, the government must increase resource allocation for waste management infrastructure and develop more effective educational programs to encourage behavioral change. This study’s contribution to urban waste management literature lies in its emphasis on the importance of a participatory and systematic approach to waste management and the need for public behavioral change to achieve sustainability goals. The conclusions of this study provide valuable insights for policymakers and practitioners in formulating more effective waste management strategies in Yogyakarta and other urban areas. This research also offers substantial theoretical contributions, particularly regarding the importance of participatory and systematic approaches in urban waste management and the necessity for societal behavioral changes to achieve sustainability objectives.

A Microfluidic Multiplex Sorter for Strain Development

AbstractSelecting strains with superior traits from strain improvement strategies is challenging, as it involves navigating the fitness landscape by applying selective pressures that drive variants from peaks of improvement to valleys over time. In recent years, the screening and selection is conducted via droplet microfluidic methods due to its high throughput capabilities. However, the oft‐used binary strategy, targeting only the high levels of improved traits, may not reflect the overall enhancement. A multiplexed sorting method capable of applying an additional threshold to sort traits by phenotypic strength is reported. The novel approach uses a droplet‐digital microfluidic sorter to screen different volumes of droplets using the same device design and sorting parameters. This method is used to sort glucoamylase enzyme mutants with two levels of activity (medium and high) from libraries of diastatic yeast that have been mutated with non‐genetically modified techniques. Using the multiplex system, medium‐performing strains with enhanced (up to 60%) fermentation kinetics in synthetic beverage media, which would have been missed with a binary screening approach, are identified. The multiplex sorting strategy efficiently finds strains with superior fermentation traits in the fitness landscape without requiring extensive screening rounds and mutations.

Artificial Intelligence of Things: Quick Sort Strategy for Medical Supply Chain

Artificial Intelligence of Things: Quick Sort Strategy for Medical Supply Chain

Enhancing Bucket Sort Efficiency: A Combinatorial Approach to Algorithm Optimization

Abstract: The efficiency of sorting algorithms is critical to numerous computational processes, including data management, search operations, and real-time applications. Among these algorithms, Bucket Sort is notable for its linear-time performance when data is uniformly distributed over a defined range. However, its efficiency suffers significantly with non-uniform data distributions, resulting in unbalanced buckets and increased sorting time. In our research, we explore how combinatorial algorithms can enhance Bucket Sort’s efficiency by optimizing bucket partitioning, dynamic allocation, and internal sorting strategies. Through rigorous mathematical proofs and analysis, we demonstrate that the application of combinatorial techniques yields substantial improvements in average-case performance, particularly in handling skewed data distributions. Practical case studies in large-scale data processing illustrate the adaptability and effectiveness of these optimizations, supporting their potential in real-world applications.

A platform for the early selection of non-competitive antibody-fragments from yeast surface display libraries.

In this work, we report the development of a platform for the early selection of non-competitive antibody-fragments against cell surface receptors that do not compete for binding of their natural ligand. For the isolation of such subtype of blocking antibody-fragments, we applied special fluorescence-activated cell sorting strategies for antibody fragments isolation from yeast surface display libraries. Given that most of the monoclonal antibodies approved on the market are blocking ligand-receptor interactions often leading to resistance and/or side effects, targeting allosteric sites represents a promising mechanism of action to open new avenues for treatment. To directly identify these antibody-fragments during library screening, we employed immune libraries targeting the epidermal growth factor receptor as proof of concept. Incorporating a labeled orthosteric ligand during library sorting enables the early selection of non-competitive binders and introduces an additional criterion to refine the selection of candidates exhibiting noteworthy properties. Furthermore, after sequencing, more candidates were identified compared to classical sorting based solely on target binding. Hence, this platform can significantly improve the drug discovery process by the early selection of more candidates with desired properties.

Are Parameterized Entrainment Rates as Scale‐Dependent as Those Estimated From Cloud Resolving Model Simulations?

AbstractEntrainment rates at varying horizontal grid spacing were diagnosed and analyzed based on Large Eddy Model (LES) and Cloud Resolving Model (CRM) simulations of shallow and deep convection. Results show the estimated entrainment rates increase with increasing resolution, and the growth rate is roughly power‐law related to resolution. However, all commonly used parameterizations except for the buoyancy sorting scheme fail to reproduce the monotonic increase of entrainment with resolution, but rather a slight decrease instead. For these schemes, it is suggested the power‐law fitting formula derived from LES/CRM simulations can be used as a scaling function for high‐resolution entrainment correction. Preliminary tests show that the application of entrainment scaling largely reduces the parcel buoyancy and thus the convective available potential energy, favoring the reduction of parameterized convection at high resolution.

A Multi-Pointer Network for Multiple Agile Optical Satellite Scheduling Problem

With the rapid growth in space-imaging demands, the scheduling problem of multiple agile optical satellites has become a crucial problem in the field of on-orbit satellite applications. Because of the considerable solution space and complicated constraints, the existing methods suffer from a huge computation burden and a low solution quality. This paper establishes a mathematical model of this problem, which aims to maximize the observation profit rate and realize the load balance, and proposes a multi-pointer network to solve this problem, which adopts multiple attention layers as the pointers to construct observation action sequences for multiple satellites. In the proposed network, a local feature-enhancement strategy, a remaining time-based decoding sorting strategy, and a feasibility-based task selection strategy are developed to improve the solution quality. Finally, extensive experiments verify that the proposed network outperforms the comparison algorithms in terms of solution quality, computation efficiency, and generalization ability and that the proposed three strategies significantly improve the solving ability of the proposed network.

Detection of Short-Chain Chlorinated Aliphatic Hydrocarbons through an Engineered Biosensor with Tailored Ligand Specificity.

Short-chain chlorinated aliphatic hydrocarbons (SCAHs), commonly used as industrial reagents and solvents, pose a significant threat to ecosystems and human health as they infiltrate aquatic environments due to extensive usage and accidental spills. Whole-cell biosensors have emerged as cost-effective, rapid, and real-time analytical tools for environmental monitoring and remediation. While the broad ligand specificity of transcriptional factors (TFs) often prohibits the application of such biosensors. Herein, we exploited a semirational transition ligand approach in conjunction with a positive/negative fluorescence-activated cell sorting (FACS) strategy to develop a biosensor based on the TF AlkS, which is highly specific for SCAHs. Furthermore, through promoter-directed evolution, the performance of the biosensor was further enhanced. Mutation in the -10 region of constitutive promoter PalkS resulted in reduced AlkS leakage expression, while mutation in the -10 region of inducible promoter PalkB increased its accessibility to the AlkS-SCAHs complex. This led to an 89% reduction in background fluorescence leakage of the optimized biosensor, M2-463, further enhancing its response to SCAHs. The optimized biosensor was highly sensitive and exhibited a broader dynamic response range with a 150-fold increase in fluorescence output after 1 h of induction. The detection limit (LOD) reached 0.03 ppm, and the average recovery rate of SCAHs in actual water samples ranged from 95.87 to 101.20%. The accuracy and precision of the proposed biosensor were validated using gas chromatography-mass spectrometry (GC-MS), demonstrating the promising application for SCAH detection in an actual environment sample.

Density-guided and Adaptive Update Strategy for Multi-objective Particle Swarm Optimization

Abstract As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy to fall into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.

Validation of a PGNAA sensor model for bulk material sorting

Global mineral demand is forecast to increase significantly to achieve the transition to renewable energy. Greater volumes of ore of lower grade will have to be mined to meet demand. Techniques to process large volumes of low-grade ore efficiently are being investigated to reduce the cost and impact of mining. One technique is to use sensor information to sort mined material, allowing waste to be discarded early in mineral processing. Prompt gamma neutron activation analysis (PGNAA) is a sensing technique that can provide information on the multi-elemental composition of a bulk sample which can be used for bulk material sorting. This paper presents the development of a Monte Carlo simulation model of a PGNAA sensor for bulk sorting using the Geant4 toolkit. The GEOSCAN sensor (Scantech Australia) was used as a case-study to demonstrate the application of the model. The sensor responses for a range of pure mineral samples (Fe2O3, SiO2, S, Na2CO3 and MnO2) were measured to validate the developed model. The sensitivity of the simulation results to the hadronic and electromagnetic physics models used was tested. It was determined that the PGNAA sensor model can reproduce measurements obtained from the GEOSCAN sensor. In particular, the model can provide a good reproduction of the overall spectral shape and the locations of distinct characteristic peaks. The differences between simulated and experimental results are within 30% on average. It was found that the Geant4 HP neutron model best reproduces the activation peaks observed in experimental measurements. Additionally, the PGNAA spectrum was found to be insensitive to the choice of electromagnetic model for the photon interactions. The validated sensor model provides a useful tool for investigating PGNAA sensor applications including a bulk sorting strategy for new materials, sensor calibration, improvements in signal analysis and optimised sensor design.

Pengembangan Kompetensi Nilai dan Sikap Kebersamaan dalam Pembelajaran PPKn Melalui Strategi Card Sort

This research aims to describe the development of value competencies and attitudes of togetherness in Civics learning through the Card Sort strategy for XC class students at SMA Muhammadiyah Boarding School Zam-Zam Cilongok. This research uses descriptive research with a qualitative approach. This research design uses case studies of data collection techniques interviews with sources, observation of learning activities, and photo documentation of activities which are then carried out triangulation data validity. The results of the research obtained by the researcher that the teacher has applied the card sort strategy in Civics learning with material related to elements in the indicators of togetherness. So that by presenting the results of student answers in turn and providing mutual feedback, students can increase their self-confidence. The obstacles to the application of this strategy are the lack of time allocation, students do not understand the direction of the teacher, the lack of conducive learning space and there is still individualism, less efficient discussion time, lack of social attitudes in students so that they look indifferent. Solutions that can be done based on these obstacles are that the teacher ensures that the learning instructions reach the students by going around the group in turn, giving a warning to students who do not participate in the discussion, the teacher reminds the rules and attitudes of the correct discussion.

Multi-stage deep sorting strategy for retired batteries based on the clustering of static and dynamic features

Echelon utilization is the optimal solution for handling retired lithium-ion batteries. How to ensure the economy and safety of retired batteries for echelon utilization, consistency sorting technology is crucial and valuable. However, batteries are influenced by multiple factors and the mapping of internal and external parameters is complex. Finding external features with discrimination and interpretability, and using effective sorting methods for consistency sorting is still challenged. To address the above problem, a multistage deep sorting strategy for retired batteries based on the clustering of static and dynamic features is proposed in this paper. Firstly, the density-based spatial clustering of applications with noise is used to remove abnormal batteries in the initial sorting stage, which ensures the safety of battery echelon utilization and the efficiency of energy utilization. Secondly, low-correlation static features are selected by Pearson correlation coefficient analysis, and the WK-means clustering algorithm was employed for the secondary sorting of retired batteries. Finally, the sliding window method is used to extract the time-square dynamic features from the dynamic stress test data, and the clustering algorithm is employed to subdivide the retired batteries again with precision. The experimental results indicate that the proposed method in this paper can effectively achieve consistency assessment and sorting of retired batteries. After sorting, the capacity, resistance, and open-circuit voltage of the battery are compactly distributed, resulting in a reduction of their corresponding standard deviations by 36.76 %, 62.97 %, and 71.62 %, respectively. Furthermore, the cut-off time and voltage drop of dynamic profile are reduced by 8.87 % and 65.36 %, respectively.

Post-Truth and Information Warfare in their Technological Context

As citizens are faced with an overabundance of information, their reliance on intuitive sorting strategies and platform-enabled content selection and delivery increases correspondingly. Under such circumstances, political action tends to be based on haphazard encounters with opinion-congruent content than on anything else, giving rise to so-called post-truth condition and, in turn, opening up conditions for manipulating such information encounters as part of information warfare operations. In particular, this novel environment necessitates a rethinking of informational agency, locating it within interactions between humans and technological artefacts, whereby humans as generators of data and algorithms as tools that structure the information domain based on such data co-construct political and social spaces. The impact of digital technologies is further amplified by the advent of synthetic (Artificial Intelligence-generated) media, which is foreseen to bring about epistemic confusion, that is, increasing inability to separate between reality and fiction. Under such conditions, and in any situations of actual or perceived crisis and tension, audiences are inclined to rely on narratives as coping strategies, which is where information warfare operations come to the fore. Either capitalising on the existing fertile ground or having manufactured a condition of crisis and distrust, such operations are geared towards hijacking audience cognitive processes with narratives that suit their perpetrators.

The Impact of Surface Modifier on Magnetic Nanoparticle Properties and Their Application in CD3+T Cell Separation.

Fe3O4 nanoparticles occupy a pivotal position in the realm of nanobiology due to their nontoxic, biocompatible, and superparamagnetic properties. This study examines the influence of surface modifiers on the properties of magnetic nanoparticles. Poly(methacrylic acid) (PMAA), poly(4-styrenesulfonic acid-co-maleic acid) sodium salt (PSSM), trisodium citrate (TSC), carboxymethylcellulose (CMC), and carboxymethylated-dextran 40 (CMD40) were introduced into a one-pot solvothermal method to synthesize magnetic nanoparticles. TEM, the 4-(bromomethyl)-6,7-dimethoxy coumarin (BMMC) absorption assay, and the Bradford method were employed to characterize the diameter, carboxyl content, and protein immobilization ability of the nanoparticles, respectively. The findings revealed that CMD40-modified magnetic nanoparticles (CMD40-MNPs) exhibited the highest carboxyl content and streptavidin (SA) immobilization content, reaching 6.5 × 10-7 mol/mg and 375 μg/mg, respectively. In contrast, CMC-modified magnetic nanoparticles displayed opposite trends. This is primarily attributed to dextran's unique molecular structure, which enhances its water solubility and biocompatibility, thereby facilitating contact with Fe3O4 nanoparticles in aqueous solutions. CMD40-MNPs possess a saturation magnetization value of 60.90 emu/g and can be collected within (60 ± 5) s using a standard magnetic separator. Cytotoxicity assays demonstrated that CMD40-MNPs are nontoxic to cells. A cell sorting strategy utilizing the binding of SA-CMD40-MNPs and biotin antihuman CD3 antibody-modified cell suspensions was employed to isolate CD3+T cells. The results indicate that the purity and efficiency of targeted CD3+T cells are 85.2% and 61.5%, respectively.

Super-agile satellites imaging mission planning method considering degradation of image MTF in dynamic imaging

Super-agile satellites with dynamic imaging capabilities can significantly improve the imaging efficiency of space-borne Earth observation. However, the image modulation transfer function (image MTF) of dynamic imaging varies dramatically with changes in the attitude maneuver status while improving observation efficiency. Therefore, both the observation efficiency and image MTF should be considered in the imaging mission planning of super-agile satellites. Existing imaging mission planning methods are mainly aimed at traditional agile satellites that are not capable of dynamic imaging, and the mainly optimization objectives are coverage revenue, spatial resolution, and task performance time, without considering the observation efficiency and image MTF as the optimization objectives. To overcome the problem of image MTF degradation caused by dynamic imaging, a mission planning method which considers the degradation of the image MTF and observation efficiency was proposed. Firstly, a mapping relationship between the angular velocity of the attitude maneuvering and the image MTF was constructed. On this basis, a Pareto multi-objective imaging mission planning model of partial order based was established. Considering the particularity of the model, the non-dominated sorting strategy of the NSGA-II algorithm was redesigned when solving the model using NSGA-II algorithm. Finally, the imaging tasks of different scales in single satellite and multi-satellite simulation scenes were set, and the proposed imaging mission planning method was verified. The planning results showed that the proposed method can achieve a balanced optimization of the image MTF and task efficiency under the premise of ensuing maximum observation coverage revenue.

Isolation of Intestinal Macrophage Subpopulations for High-Quality Total RNA Purification in Zebrafish.

Intestinal macrophages have been poorly studied in fish, mainly due to the lack of specific molecular markers for their identification and isolation. To address this gap, using the zebrafish Tg(mpeg1:EGFP) transgenic line, we developed a fluorescence-activated cell sorting strategy (FACS) that allows us to isolate different intestinal macrophage subpopulations, based on GFP expression and morphological differences. Also, we achieved the purification of high-quality total RNA from each population to perform transcriptomic analysis. The complete strategy comprises three steps, including intestine dissection and tissue dissociation, the isolation of each intestinal macrophage population via FACS, and the extraction of total RNA. To be able to characterize molecularly different macrophage subpopulations and link them to their functional properties will allow us to unravel intestinal macrophage biology.

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.