Assignment Methods Research Articles

Evaluating China’s urbanization trajectory: an overextension or still in progress?

China’s urbanization has undergone a transformative journey since the initiation of the reform and opening-up policy in 1978, catalyzing economic growth while profoundly impacting its ecological and demographic landscapes. This study offers a systematic evaluation of China’s urbanization trajectory over the past 4 decades through the development of an indicator system encompassing three key dimensions: population urbanization, land urbanization, and economic urbanization. Using the entropy method for weight assignment, the analysis reveals a consistent upward trend in China’s comprehensive urbanization, with occasional sharp increases. Although population urbanization influences the overall index, land urbanization has outpaced both population and economic urbanization, indicating a pronounced dependence on land resources. This trend poses a potential risk of unsustainable urban growth if left unaddressed. The findings indicate that to achieve sustainable urban development, China must prioritize balancing the interplay between population and land, moving away from land-dependent strategies, and adopting a more balanced approach to urbanization. The quantitative analysis provides key insights into this imbalance, indicating that policies should integrate land use planning with population dynamics to promote holistic urban growth. Future research should investigate the long-term socio-economic and environmental impacts of land-centric urbanization and propose innovative strategies to guide China towards a sustainable urban future.

Redivision of reference intervals of the red blood cell distribution width in a healthy Chinese population based on natural geographic environment

Revealing the spatial distribution and regional differences of red blood cell distribution width (RDW) reference values in the healthy Chinese population is important for the monitoring, diagnosis and prevention of the top chronic diseases in the disease spectrum in China. Medical indicator data were matched with geographical data and environmental pollution data to predict RDW reference values for the whole region of China using ridge regression analysis, support vector machine and BP neural network. Taylor diagrams and assignment methods were used to select the optimal model, and the spatial distribution was mapped according to the prediction results of the optimal model, and the intervals of RDW reference values for different age groups of healthy people in China were redefined. It was suggested that the reference values of RDW in the young and middle-aged groups in China were divided into low-value areas in the north (12.31,12.80) and high-value areas in the south (12.80,13.18); the reference values of RDW in the elderly group were divided into low-value areas in the north (11.28,12.60) and high-value areas in the south (12.60,13.91).In turn, it can provide a more accurate reference basis in the monitoring, diagnosis and prevention of chronic diseases at the top of the disease spectrum.

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

The Influence of Project-Based Complex Instruction (CI) Collaborative Learning Models and Learning Motivation on Elementary School Students’ Problem Solving Skill

The problems solving skills of class V students in mathematics learning are still relatively low. One of the things that causes this is the teacher's tendency to carry out learning conventionally through lecture, question and answer and assignment methods. Teachers have a dominant role in finding, processing and explaining the material being taught. Students are not given the freedom to elaborate on the material taught through collaboration with their peers. The objectives of this research are: to describe the influence of the project-based CI Collaborative Learning Model on students' problems solving skills; Describe the interaction between the project-based CI Collaborative Learning Model and learning motivation on students' problems solving skills. The research method used was Quasi Experiment with a Nonequivalent Pretest-Posttest Only Control Group Design. The research instruments used were problem solving skills tests and learning motivation questionnaires. The results of the homogeneity test and normality test of problems solving skill show that the sample variance is homogeneous and normally distributed. After fulfilling the prerequisite tests, a hypothesis test is carried out and it can be concluded that the project-based CI Collaborative Learning Model has a significant effect on students' problems solving skills. Apart from that, there is no interaction between the project-based CI Collaborative Learning Model and learning motivation on students' problems solving skills

Automating thresholds in probabilistic linkage

ObjectiveThe Office for National Statistics aim is to create a platform containing a variety of datasets interlinked via data spines. The resource burden linking this many datasets is high, and so research to maximise automation is a key aim of the platform’s methodology. Probabilistic linkage is a standard linkage technique that utilises clerical resolution – a very costly process – to find acceptance threshold parameters. Here, we present research into automating threshold assignment. Every admin dataset is different, causing the probabilistically matched pairs scores to change, a set threshold cannot be used. Instead, a data-informed threshold must be set. ApproachA gold standard linked dataset was linked through Fellegi-Sunter, stopping at the point of finding the probabilistic threshold. To find the threshold, graphs plotting the number of records at each rounded score were created and reviewed, specifically looking at the spread of gold standard links, deterministic links, unique and non-unique probabilistic links. This review suggested a method of threshold assignment that provides multiple quality levels: a high-precision threshold, and a high-recall threshold. This presentation will showcase these graphs and explain how the two thresholds are calculated. The 2021 Census is one dataset linked within the gold standard linked dataset. The 2021 Census has high quality and relatively clean data, but is likely not representative of administrative data needed in the data platform. Therefore, next steps are testing this method on other datasets with a variety of quality, characteristics, and types.

Resilience of weighted networks with dynamical behavior against multi-node removal.

In many real-world networks, interactions between nodes are weighted to reflect their strength, such as predator-prey interactions in the ecological network and passenger numbers in airline networks. These weighted networks are prone to cascading effects caused by minor perturbations, which can lead to catastrophic outcomes. This vulnerability highlights the importance of studying weighted network resilience to prevent system collapses. However, due to many variables and weight parameters coupled together, predicting the behavior of such a system governed by a multi-dimensional rate equation is challenging. To address this, we propose a dimension reduction technique that simplifies a multi-dimensional system into a one-dimensional state space. We applied this methodology to explore the impact of weights on the resilience of four dynamics whose weights are assigned by three weight assignment methods. The four dynamical systems are the biochemical dynamical system (B), the epidemic dynamical system (E), the regulatory dynamical system (R), and the birth-death dynamical system (BD). The results show that regardless of the weight distribution, for B, the weights are negatively correlated with the activities of the network, while for E, R, and BD, there is a positive correlation between the weights and the activities of the network. Interestingly, for B, R, and BD, the change in the weights of the system has little impact on the resilience of the system. However, for the E system, the greater the weights the more resilient the system. This study not only simplifies the complexity inherent in weighted networks but also enhances our understanding of their resilience and response to perturbations.

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern next-generation sequencing data. We present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

Enhancing Morphological Measurements of the Cosmic Web with Delaunay Tessellation Field Estimation

The density fields constructed by traditional mass assignment methods are susceptible to irritating discreteness, which hinders morphological measurements of cosmic large-scale structure (LSS) through Minkowski functionals (MFs). To alleviate this issue, fixed-kernel smoothing methods are commonly used in the literature, at the expense of losing substantial structural information. In this work, we propose to measure MFs with the Delaunay tessellation field estimation (DTFE) technique, with the goal of maximizing the extraction of morphological information from sparse tracers. We perform our analyses starting from matter fields and progressively extending to halo fields. At the matter-field level, we elucidate how discreteness affects morphological measurements of LSS. Then, by comparing with the traditional Gaussian smoothing scheme, we preliminarily showcase the advantages of DTFE for enhancing measurements of MFs from sparse tracers. At the halo-field level, we first numerically investigate various systematic effects on MFs of DTFE fields, which are induced by finite voxel sizes, halo number densities, halo weightings, and redshift space distortions (RSDs), respectively. Then, we explore the statistical power of MFs measured with DTFE for extracting the cosmological information encoded in RSDs. We find that MFs measured with DTFE exhibit improvements by ∼2 orders of magnitude in discriminative power for RSD effects and by a factor of ∼3–5 in constraining power on the structure growth rate over the MFs measured with Gaussian smoothing. These findings demonstrate the remarkable enhancements in statistical power of MFs achieved by DTFE, showing enormous application potentials for our method in extracting various key cosmological information from galaxy surveys.

Continuation of Learning During COVID-19 Situation in Nepal

This study explored the obstacles and ways of continuation for learning faced by school teachers during and after the COVID-19 situation. The quantitative cross-sectional survey research design was adopted. All the school teachers of Nepal were the population for this study and the sample size was 226. The online survey method was adopted for the data collection tools. A set of closed and open-ended questionnaires were distributed to respondents and data was collected using Google Forms. The study seeks the relationship between the independent variables (age, experience of teaching, school location, teacher’s residence, access to the internet, access to digital devices, knowledge about ICTs interest in online teaching) and dependent variables (continuation of online teaching). It was found that the community teaching and homework/ assignment methods were applicable to the rural areas. Continuing online teaching would be inconvenient for the students who had no internet access. According to the teacher's response, it was found that about 90% of students had no access to the internet. Although online teaching was suitable for the COVID-19 situation, it was biased for the maximum number of students. The teacher's interest and the obstacles in the online class and continuation of learning are significantly co-related (0.003 and 0.000 respectively). There was no significant difference between male and female teachers in continuation for online learning but a greater percentage of males are involved in online classes than females. The experienced teachers equally continued their learning of digital skills. There was a problem with the internet/ wifi/ 3G/ 4G network in rural areas. Teacher adjusted their teaching and learning activities even if they had no access to a laptop. The study concludes that teachers are ready for online classes and they regularly update their digital skills with changing times but digital divides created a serious problem with conducting online classes. So, the Government of Nepal should pay attention to teacher training, integration of digital skills in the curriculum, and proper allocation of budget for infrastructure development.

Hardness-aware loss for object segmentation

In object segmentation, the existence of hard-classified-pixels limits the segmentation performance. Focusing on these hard pixels through assigning different weights to pixel loss can guide the learning of segmentation model effectively. Existing loss weight assignment methods perceive pixels hardness by current predicted information, pay less attention to past predicted information. While current studies show that samples with less improvement in predicted probability compared to the past are difficult to learn. To define hard pixels more accurately, a hardness-aware loss for object segmentation is proposed. Firstly, the metric of pixel hardness degree is defined, and a mapping function is proposed to quantitatively evaluate the hardness degree which is defined on the difference between current and past predicted probabilities. Then a new compound metric, hardness value, is defined based on hardness degree and the uncertainty. Based on the compound metric, a new loss function is proposed. Experiment results on four datasets using convolutional neural network and Transformer as the backbone models demonstrate that the proposed method effectively improves the accuracy of object segmentation. Especially, in the segmentation model based on ResNet-50, the proposed method improves mean Intersection over Union (mIoU) by almost 4.3 % compared to cross entropy on DUT-O dataset.

Tracing the geographic origin of Atlantic cod products using stable isotope analysis.

Increasing demand for fish and seafood means that the traceability of marine products is becoming ever more important for consumers, producers and regulators. Highly complex and globalised supply networks create challenges for verifying a stated catch region. Atlantic cod is one of the most commercially important species in the northeast Atlantic. Several regional fisheries supply cod into the trade network, of which some are at greater risk of overexploitation than others. Tools allowing retrospective testing of spatial origin would significantly assist sustainable harvesting of fish, reducing incentives for illegal fishing and fraud. Here, we investigate whether stable isotope ratios of carbon, nitrogen and sulphur can be used to retrospectively identify the catch region of Atlantic cod (Gadus morhua). We measured the isotopic composition of muscle tissue from 377 cod from 10 catch regions across the northeast Atlantic and then applied three different assignment methods to classify cod by region of most likely origin. The assignment method developed was subsequently tested using independently sourced, known-origin samples. Individual cod could be traced back to their true origin with an average assignment accuracy of 70-79% and over 90% accuracy for certain regions. Assignment success rates comparable to those using genetic techniques were achieved when assigning among restricted and pre-selected regions. However, assignment accuracy to the fishery region estimated from independent samples across the whole geographic range of cod averaged ~25% overall, highlighting the need for careful application of isotope-based approaches. Stable isotope techniques can provide effective tools to test for origin in Atlantic cod, but not all catch regions are isotopically distinct. Stable isotopes could be combined with genetic techniques to result in higher assignment accuracy than could be achieved using either method independently. Assignment potential can be estimated from reference datasets, but estimates of realistic assignment accuracy require independently collected data.

Parallel dual adaptive genetic algorithm: A method for satellite constellation task assignment in time-sensitive target tracking

The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm’s sensitivity to parameters, while the parallel mechanism increases the evolutionary process’s computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation.

Sustainable urban road planning under the digital twin-MCDM-GIS framework considering multidisciplinary factors

Sustainable urban road planning necessitates the formulation of functional, economic, people-friendly, eco-friendly, and engineering-reasonable road schemes within the confines of limited urban space. It is challenging to meet traffic demands, stimulate economic growth, foster social progress, and minimize adverse impacts on the social and natural environment. This study proposes a comprehensive approach for sustainable urban road planning, employing the Digital Twin-MCDM-GIS framework. Digital twins play a pivotal role in converting diverse factors into comprehensible expressions. MCDM (multi-criteria decision making) evaluates and assigns weights to these multidisciplinary factors. GIS (geographic information system) offers an integrated digital space for analysis. Various digitalisation and parsing methods, including direct assignment methods, spatial analysis methods, and complex professional methods, are introduced for traffic, development, cost, social, environmental, and engineering factors. By assigning distinct weights to these factors, diverse raster data are established to generate multiple road schemes using the least-cost wide path analysis. To validate the proposed approach, a case study was conducted in the London Borough of Bromley, resulting in the generation of 14 road schemes with varying widths and factor considerations in limited urban space.

Learnable Graph Matching: A Practical Paradigm for Data Association.

Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet. For point cloud registration, we also achieve competitive results.

Learning Management to Stimulate Critical Thinking in Islamic Religious Education Study Program Students

This research is motivated by the impact of learning management carried out by lecturers on students to think critically. because critical thinking is very necessary in everyday life. Critical thinking is a way for students to have an attitude of deeply about various things and problems that are different within their scope, which attitude involves activities in solving problems, making decisions, analyzing assumptions and organizing strategies and tactics. By having critical thinking skills, students can understand how to face, analyze and solve problems in facing challenges in the era of industrial revolution.Critical thinking learning management for students is effective in improving students' abilities in mastering concepts and subject matter as well as helping students' abilities in analyzing, interpreting and evaluating and being able to convey logical and systematic data presentations. Critical thinking learning management for students supports students' ability to know their own abilities, can interpret problems critically, strengthens students in searching for new information that was not previously known, in focusing a research, the researcher provides the formulation and objectives of the problem that will be reviewed in this research, namely: what lecturers do in the Islamic religious education study program to improve the critical thinking of students in the Islamic religious education study program. This research is included in descriptive qualitative research, with a qualitative approach. The results of the research show that lecturers in the Islamic religious education study program carry out 3 strategies in improving the critical thinking of students in the Islamic religious education study program: First, develop students' critical thinking skills in the aspect of identifying using methods, models and learning approaches. The methods used are lecture, discussion, question and answer, group assignment, project, collaborative, and inquiry methods by carrying out activities where students provide information to fellow students by explaining verbally and also through presentations from students. Second, the strategy for developing students' critical thinking skills in the analytical aspect is that students are presented with scientific problems and encouraged to analyze the material, then conclude and develop aspects of evaluating the problem, either cross-checking or checking the results of the conclusions. And third, the lecturer's strategy in developing students' critical thinking skills in the Islamic religious education study program in the aspect of evaluating problems.

Research on the Consistency Evaluation of the Spindle Unit Stiffness Based on Comprehensive Weights

The spindle unit is the core component of a machine, and its stiffness is a key performance characteristic. The stiffness consistency is a key index to measure the stiffness of spindle units; however, there is a lack of unified evaluation methods for the spindle unit stiffness consistency. This paper proposes a comprehensive method for the evaluation of the spindle unit stiffness consistency based on fluctuations and comprehensive weights. First, the consistency of the spindle unit stiffness evaluation indices is expressed based on the fluctuations. Secondly, the subjective and objective weights of the spindle unit stiffness evaluation indices are given based on the AHP and CRITIC weight assignment methods. To obtain the comprehensive weights of the evaluation indices, the subjective and objective weights are synthesized based on the linear weighting method. Then, the consistency level of the spindle unit stiffness is obtained by the weighted summation of the comprehensive weights and fluctuations of the evaluation indices. Finally, the stiffness consistency of a group of spindle units is evaluated based on this method, and the evaluation result shows a 10.63% fluctuation, which indicates that its stiffness consistency level is class B, in line with the factory production requirements.

Evaluating methods for B-cell clonal family assignment.

The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.

Real-world performance of the digital drug-assignment system for precision oncology in lung cancer.

e13590 Background: Precision medicine has profoundly transformed lung cancer treatment, an array of molecularly targeted agents (MTAs) are approved and used in clinical practice. Comprehensive molecular testing is key in treatment decisions, nevertheless, interpretation of complex molecular profiles can be challenging and subjective. Previously we demonstrated that digital drug assignment (DDA), an algorithmic computational reasoning model that ranks associated targeted therapies based on the totality of individual tumor genomic data, rather than matching one drug to one biomarker, was predictive of relative benefit of the agents as used in the SHIVA01 trial (1). Here, we collected real-world clinical outcome data from lung cancer patients who received decision support where DDA was integrated to aid a molecular tumor board (MTB) and investigated the effectiveness of administered therapies. Methods: Between 2018 and 2022, 111 lung cancer patients were involved in our precision oncology program. Following molecular diagnostic tests, analysis of the individual molecular profiles and scoring of associated drugs (DDA score) was performed by the DDA system as previously described. The output was assessed by the MTB that provided a strategy to the clinicians who made the final therapy decisions. Treatment courses and outcomes were collected from hospital health records and analyzed. Treatment lines were categorized as the following: high-score MTA (1000≤DDA score), low-score MTA (DDA score<1000), and standard of care (SOC). Results: Overall response rate (ORR) of high-score MTA lines administered following DDA-based decision support (n=13) was 69%, whereas of other treatment lines (low-score MTAs and SOC; n=55) it was 16% (p<0.001); and 26% in low-score MTA lines (n=34; p=0.017). Median progression-free survival (mPFS) of high-score MTA lines administered following DDA-based decision support (n=13) was 63 months (95% CI: 12.0 – 66.0), whereas it was 9 months both for any other treatment lines (low-score MTAs and SOC; n=44; 95% CI: 5.0 – 66.0; p<0.001) and for low-score MTA lines only (n=27; 95% CI: 5.0 – 66.0; p<0.001). Median overall survival (mOS) of patients who received at least one line of high-score MTA after decision support (n=13) was significantly longer than of those who only received non-high score treatments after decision support (patients only treated with low-score MTAs and/or SOC, n=34): not reached vs. 32 months (95% CI 24.0 – not reached), respectively (p=0.012). Conclusions: This study revealed that the DDA system is predictive of relative benefit of the various agents as used in lung cancer care, in line with our previous clinical validation. Validated algorithmic AI-guided drug assignment methods represent a new class of tools that can potentially address challenges in utilizing the results of complex molecular profiles in routine clinical setting. 1. Petak et al., 2021, npj Precis. Onc. 5, 59.

Guidelines for descriptor assignments for the solvation parameter model by separation techniques

The solvation parameter model uses six compound descriptors to model equilibrium properties in biphasic systems formally defined as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L. The V descriptor can be assigned from structure and the E descriptor for compounds liquid at 20 °C can be calculated from its refractive index and characteristic volume. The E descriptor for compounds solid at 20 °C and the S, A, B, and L descriptors are assigned from experimental properties traditionally obtained by chromatographic, liquid-liquid partition, and solubility measurements. Here I report an efficient experimental design using the Solver method for the accurate assignment of descriptors for neutral compounds that simultaneously minimizes laboratory resources. This multi-technique approach requires 3 retention factor measurements in a 60 °C temperature range per compound on four columns by gas chromatography, 3 retention factor measurements in a 30 % (v/v) acetonitrile composition range per compound on two columns by reversed-phase liquid chromatography, and eight partition constant measurements by liquid-liquid partition in totally organic and aqueous biphasic systems for a total of 26 experimental measurements. The accuracy of the descriptor assignments was validated by comparison with the values in the Wayne State University (WSU) descriptor database taken as the best estimate of the true descriptor values. The E, S, A, B and L descriptors were assigned simultaneously by the Solver method using the above approach without significant bias and with an average absolute deviation (AAD) of 0.054, 0.018, 0.015, 0.013, and 0.040, respectively, compared with the WSU database values, corresponding to a relative absolute average deviation in percent (RAAD) of 7.2, 1.9, 3.6, 5.1, and 0.84 %, respectively, for 32 varied compounds. This streamlined approach represents a significant improvement on earlier single-technique approaches used as the starting point for the development of the multi-technique approach. For compounds of variable hydrogen-bond basicity modifications to the multi-technique approach were implemented while maintaining the same number of experimental measurements. Acceptable descriptor assignments for B/B° were obtained for compounds liquid at 20 °C for which the E descriptor was available by calculation. For solid compounds at 20 °C the E and B/B° descriptors are restricted to qualitative application where approximate values may be acceptable.

Toxicity assessment of dioxins and their transformation by-products from inferred degradation pathways

Due to the significant POPs characteristics, dioxins caused concern in public health and environmental protection. Evaluating the toxicity risk of dioxin degradation pathways is critical. OCDD, 1,2,3,4,6,7,8-HpCDD, and 1,2,3,4,6,7,8-HpCDF, which are highly abundant in the environment and have strong biodegradation capabilities, were selected as precursor molecules in this study. Firstly, their transformation pathways were deduced during the metabolism of biometabolism, microbial aerobic, microbial anaerobic, and photodegradation pathways, and density function theory (DFT) was used to calculate the Gibbs free energy to infer the possibility of the occurrence of the transformation pathway. Secondly, the carcinogenic potential of the precursors and their degradation products was evaluated using the TOPKAT modeling method. With the help of the positive indicator (0–1) normalization method and heat map analysis, a significant increase in the toxic effect of some of the transformation products was found, and it was inferred that it was related to the structure of the transformation products. Meanwhile, the strength of the endocrine disrupting effect of dioxin transformation products was quantitatively assessed using molecular docking and subjective assignment methods, and it was found that dioxin transformation products with a higher content of chlorine atoms and molecules similar to those of thyroid hormones exhibited a higher risk of endocrine disruption. Finally, the environmental health risks caused by each degradation pathway were comprehensively assessed with the help of the negative indicator (1–2) standardization method, which provides a theoretical basis for avoiding the toxicity risks caused by dioxin degradation transformation. In addition, the 3D-QSAR model was used to verify the necessity and rationality of this study. This paper provides theoretical support and reference significance for the toxicity assessment of dioxin degradation by-products from inferred degradation pathways.