A general nonparametric classification method for multiple strategies in cognitive diagnostic assessment.

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' strengths and weaknesses in terms of cognitive skills learned and skills that need study. In practice, it is not uncommon that questions can often be solved using more than one strategy, which requires CDMs capable of accommodating multiple strategies. However, existing parametric multi-strategy CDMs need a large sample size to produce a reliable estimation of item parameters and examinees' proficiency class memberships, which obstructs their practical applications. This article proposes a general nonparametric multi-strategy classification method with promising classification accuracy in small samples for dichotomous response data. The method can accommodate different strategy selection approaches and different condensation rules. Simulation studies showed that the proposed method outperformed the parametric CDMs when sample sizes were small. A set of real data was analyzed as well to illustrate the application of the proposed method in practice.

Air Pollutant Emission Inventory of Waste-to-Energy Plants in China and Prediction by the Artificial Neural Network Approach.

The waste-to-energy (WTE) plant has been deployed in 205 cities in China. However, it always faces public resistance to be built because of the great concerns on flue gas pollutants (FGPs). There are limited studies on the socioeconomic heterogeneity analysis and prediction models of WTE capacity/ FGP emission inventories (EIs) based on big data. In this study, the incinerator level emission factors (EFs) in 2020 of PM, SO2, NOx, CO, HCl, dioxins, Hg, Cd + Tl, and Sb + As+ Pb + Cr + Co + Cu + Mn + Ni were calculated based on 322,926 monitoring values of all the 481 WTE plants (1140 processing lines) operating in China, with uncertainties in the range of ±34.70%. The EFs were significantly 45-96% lower than the national standard (GB18485-2014) and had negative relationships with local socioeconomic elements, while WTE capacity and FGP EIs had significantly positive correlations. Gross domestic product, area of built district, and municipal solid waste generation were the main driving forces of WTE capacity. The WTE capacity increased by 150% from 2015 to 2020, while the total emission of PM, SO2, CO, dioxins, Hg, and Sb + As + Pb + Cr + Co + Cu + Mn + Ni decreased by 42.46-88.24%. The artificial neural network models were established to predict WTE capacity and FGP EIs in the city level, with the mean square errors ranging from 0.003 to 0.19 within the model validation limits. This study provides data and model support for the formulation of appropriate WTE plans and a pollutant emission control scheme in different economic regions.

Environmental, economic, and energy analysis of municipal solid waste incineration under anoxic environment in Tibet Plateau.

The first Municipal solid waste incineration (MSWI) plant in Lhasa, Tibet, the plateau region of China, started its operation in 2018. Considering the elevation and extreme climate (low pressure and low oxygen content) in Tibet, noticeable differences may be envisaged compared to MSWI elsewhere. The aim of this study is to evaluate the environmental impacts, economic benefits, and energy efficiency of this MSWI project with three representative MSWI case in plain region using Life cycle assessment (LCA), Cost-benefit analysis (CBA), and energy analysis methods. The result showed that enhancing blast volume and cross-sectional area of the boiler help adapt to the oxygen-deficient environment. GaBi model was employed based on the CML 2001 methodology to perform LCA. LCA shows that the Lhasa MSWI project has lower positive environment impacts than the projects in plain region. More attention is needed for the deficiencies in flue gas emissions of MSWI in the plateau region. CBA shows that the payback period is 11.97 years and the internal rate of return is 8.75%. The energy analysis indicates that the boiler energy efficiency is up to 81.92%. MSWI subject to minor changes seems suitable to Tibetan plateau, and can be deployed further.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses.

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

Investigation of enhanced H2 production from municipal solid waste gasification via artificial neural network with data on tar compounds.

An artificial neural network (ANN) is a biologically inspired computational technique that imitates the behavior and learning process of the human brain. In this study, ANN technique was applied to assess the gasification of municipal solid waste (MSW) with the aim of enhancing the H2 production. The experiments were conducted using a horizontal tube reactor under different parameters: temperatures, MSW loadings, residence times, and equivalence ratios. The input and output variables (released gases) were tested and trained using back-propagation algorithm, and the data distribution by K-fold contrivance. The values of the training (80% data) and validation (20% data) dataset were found satisfactory. The values of regression coefficient (R2) for the training phase were lied between 0.9392 and 0.9991, and 0.9363 and 0.993824 for the testing phase. Whereas; the values of root mean square error (RSME) for the training phase were lied between 0.4111 and 0.8422, and between 0.1476 and 0.7320 for the testing phase. Higher H2 production of 42.1 vol% was produced at the higher reaction temperature of 900 °C with LHV of 11.2 MJ/Nm3. According to the tar analysis, the dominant compounds were aromatics (17 compounds) followed by polycyclic aromatic, phenyl, aliphatic, aromatic heterocyclic, polycyclic, and aromatic ketone compounds.

Assessing the suitability of municipal sewage sludge and coconut bran as breeding medium for Oryza sativa L. seedlings and developing a standardized substrate.

The utilization of organic solid waste (OSW) for preparing standardized seedling substrates is a main challenge due to its temporal and spatial variability. This study aims to form models based on data from the literature and validate them through experiments to explore a standardized seedling substrate. The typical OSW in Hainan Province, including municipal sewage sludge (MSS), coconut bran (CB), seaweed mud (SM), and municipal sewage sludge biochar (MSSB), was used as raw material. A series of six mixing ratios was tested, namely: T1 (0% MSS: 90% CB), T2 (10% MSS: 80% CB), T3 (30% MSS: 60% CB), T4 (50% MSS: 40% CB), T5 (70% MSS: 20% CB), and T6 (90% MSS: 0% CB). SM and MSSB were added as amendment materials at 5% (w/w) for each treatment. The physicochemical properties of substrates, agronomic traits of rice seedlings and microbial diversity were analyzed. The results showed that the four kinds of OSW played an active role in providing rich sources of nutrients. The dry weight of the above-ground part was 2.98 times greater in T3 than that of the commercial substrate. Furthermore, the microbial analysis showed a higher abundance of Actinobacteria in T3, representing the stability of the composted products. Finally, the successful fitting of the results with the linear regression models could establish relationship equations between the physicochemical properties of the substrate and the growth characteristics of seedlings. The relevant parameters suitable for the growth of rice seedlings were as follows: pH (6.46-7.01), EC (less than 2.12 mS cm-1), DD (0.13-0.16 g cm-3), and TPS (65.68-82.73%). This study proposed relevant parameters and models for standardization of seedling substrate, which would contribute to ensuring the quality of seedlings and OSW resource utilization.

A multiple logistic regression-based (MLR-B) Q-matrix validation method for cognitive diagnosis models:A confirmatory approach.

Q-matrix is an essential component specifying the relationship between attributes and items, which plays a key role in cognitive diagnosis assessment. The Q-matrix is usually developed by domain experts and its specifications tend to be subjective and might have misspecifications. Many existing pieces of research concentrate on the validation of Q-matrix; however, few of them can be applied to saturated cognitive diagnosis models. This paper proposes a general and effective Q-matrix validation method by employing multiple logistic regression model. Simulation studies are carried out to investigate the performance of the proposed method and compare it with four existing methods. Simulation results indicate the proposed method outperforms the existing methods in terms of validation accuracy. In addition, a set of real data is used as an example to illustrate its application. Finally, we discuss the limitations of the current study and the directions of future studies.

A Nanocomposite of Bismuth Clusters and Bi2 O2 CO3 Sheets for Highly Efficient Electrocatalytic Reduction of CO2 to Formate.

The renewable-electricity-driven CO2 reduction to formic acid would contribute to establishing a carbon-neutral society. The current catalyst suffers from limited activity and stability under high selectivity and the ambiguous nature of active sites. Herein, we report a powerful Bi2 S3 -derived catalyst that demonstrates a current density of 2.0 A cm-2 with a formate Faradaic efficiency of 93 % at -0.95 V versus the reversible hydrogen electrode. The energy conversion efficiency and single-pass yield of formate reach 80 % and 67 %, respectively, and the durability reaches 100 h at an industrial-relevant current density. Pure formic acid with a concentration of 3.5 mol L-1 has been produced continuously. Our operando spectroscopic and theoretical studies reveal the dynamic evolution of the catalyst into a nanocomposite composed of Bi0 clusters and Bi2 O2 CO3 nanosheets and the pivotal role of Bi0 -Bi2 O2 CO3 interface in CO2 activation and conversion.

A Higher-Order Cognitive Diagnosis Model with Ordinal Attributes for Dichotomous Response Data.

Most existing cognitive diagnosis models (CDMs) assume attributes are binary latent variables, which may be oversimplified in practice. This article introduces a higher-order CDM with ordinal attributes for dichotomous response data. The proposed model can either incorporate domain experts' knowledge or learn from the data empirically by regularizing model parameters. A sequential item response model was employed for joint attribute distribution to accommodate the sequential mastery mechanism. The expectation-maximization algorithm was employed for model estimation, and a simulation study was conducted to assess the recovery of model parameters. A set of real data was also analyzed to assess the viability of the proposed model in practice.

Electrocatalytic reduction of CO2 and CO to multi-carbon compounds over Cu-based catalysts.

The electrocatalytic reduction of CO2 with H2O to multi-carbon (C2+) compounds, in particular, C2+ olefins and oxygenates, which have versatile applications in the chemical and energy industries, holds great potential to mitigate the depletion of fossil resources and abate carbon emissions. There are two major routes for the electrocatalytic CO2 reduction to C2+ compounds, i.e., the direct route and the indirect route via CO. The electrocatalytic CO2 reduction to CO has been commercialised with solid oxide electrolysers, making the indirect route via CO to C2+ compounds also a promising alternative. This tutorial review focuses on the similarities and differences in the electrocatalytic CO2 and CO reduction reactions (CO2RR and CORR) into C2+ compounds, including C2H4, C2H5OH, CH3COO- and n-C3H7OH, over Cu-based catalysts. First, we introduce the fundamental aspects of the two electrocatalytic reactions, including the cathode and anode reactions, electrocatalytic reactors and crucial performance parameters. Next, the reaction mechanisms, in particular, the C-C coupling mechanism, are discussed. Then, efficient catalysts and systems for these two reactions are critically reviewed. We analyse the key factors that determine the selectivity, activity and stability for the electrocatalytic CO2RR and CORR. Finally, the opportunities, challenges and future trends in the electrocatalytic CO2RR and CORR are proposed. These insights will offer guidance for the design of industrial-relevant catalysts and systems for the synthesis of C2+ olefins and oxygenates.

Fast Screening for Copper-Based Bimetallic Electrocatalysts: Efficient Electrocatalytic Reduction of CO2 to C2+ Products on Magnesium-Modified Copper.

Electroreduction of CO2 (CO2 RR) into high value-added chemicals is an attractive route to achieve carbon neutrality. However, the development of an efficient catalyst for CO2 RR is still largely by trial-and-error and is very time-consuming. Herein, we built an electrocatalyst testing platform featuring a home-built automatic flow cell to accelerate the discovery of efficient catalysts. A fast screening of 109 Cu-based bimetallic catalysts in only 55 h identifies Mg combined with Cu as the best electrocatalyst for CO2 to C2+ products. The thus designed Mg-Cu catalyst achieves a Faradaic efficiency (FE) of C2+ products up to 80 % with a current density of 1.0 A cm-2 at -0.77 V versus reversible hydrogen electrode (RHE). Systematic experiments with in situ spectroelectrochemistry analyses show that Mg2+ species stabilize Cu+ sites during CO2 RR and promote the CO2 activation, thus enhancing the *CO coverage to promote C-C coupling.

Preoperative Nutritional Risk Index Predicts Recurrence of Oligometastatic Prostate Cancer in Patients Undergoing Cytoreductive Radical Prostatectomy.

This study aims to investigate the specific predictive role of the preoperative Nutritional Risk Index (NRI) in oligometastatic prostate cancer (OM-PC) patients, who have undergone cytoreductive radical prostatectomy (cRP), and explored its prognostic index values. A total 89 OM-PC patients, who were identified between 2013 and 2019, were included in the present study. The Kaplan-Meier method and Cox regression analysis were used to separately assess the prostate specific antigen (PSA) progression-free survival (PFS). Overall accuracy was determined by analyzing the area under the receiver operating characteristic curve (AUC). The analysis of patients in these three different groups indicated that patients with lower NRI values were significantly associated with a higher Gleason score and more neoadjuvant androgen deprivation therapy (P < 0.05). In addition, the Kaplan-Meier curve analysis revealed that OM-PC patients in the preoperative high-risk group had shorter PSA-PFS (P < 0.001). Furthermore, the multivariate analysis further predicted that the high-risk NRI value is a common independent prognostic factor for shorter PSA-PFS (P < 0.001). Moreover, it was also observed that the AUC value of the NRI score was higher than other conventional nutritional indicators. The present study suggests that NRI can potentially be used as a new prognostic indicator for PSA-PFS for patients with OM-PC after cRP.

Value of pretreatment 18F-FDG PET/CT in prognosis and the reflection of tumor burden: a study in pediatric patients with newly diagnosed neuroblastoma.

Fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT has been commonly used in pediatric patients with newly diagnosed neuroblastoma (NB) for diagnosis. We retrospectively reviewed 40 pediatric patients with newly diagnosed NB who underwent 18F-FDG PET/CT. Clinicopathological factors and metabolic parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on PET/CT were evaluated as predictive factors for progression-free survival (PFS) and overall survival (OS) by univariate and multivariate analysis. Spearman rank correlation analyses were used to estimate the correlations between clinical factors and PET findings. The mean follow-up after 18F-FDG-PET/CT was 32.9 months. During the follow-up period 15 (37.5%) patients experienced progression, and 9 (22.5%) died. MTV (P=0.001) and TLG (p=0.004) remained significant predictive factors for tumor progression, along with lactate dehydrogenase (LDH), neuron-specific enolase (NSE) and bone metastasis. Univariate analysis showed that bone metastasis, LDH (>1064 IU/L), NSE (>364.4 ug/L), MTV (>191 cm3) and TLG (>341.41 g) correlated with PFS, and LDH (>1064 IU/L), NSE (>364.4 ug/L) and MTV (>191 cm3) correlated with OS (p<0.05). In multivariate analysis, MTV and bone metastasis were independent prognostic factors for PFS (p=0.001 and 0.023, respectively), and MTV remained the only independent prognostic factor for OS (p= 0.004). We also found that there were correlations between semiquantitative PET/CT parameters and clinical features in NB. Our results suggested that 18F-FDG PET/CT was a useful tool to predictive progression and to reflect tumor burden for patients with NB.

Enhanced glucose metabolism mediated by CD147 contributes to immunosuppression in hepatocellular carcinoma.

From a metabolic perspective, cancer may be considered as a metabolic disease characterized by reprogrammed glycolytic metabolism. The aim of the present study was to investigate CD147-mediated glucose metabolic regulation in hepatocellular carcinoma (HCC) and its contribution to altered immune responses in the tumor microenvironment. Several HCC cell lines and corresponding nude mice xenografts models differing in CD147 expressions were established to directly investigate the role of CD147 in the reprogramming of glucose metabolism, and to determine the underlying molecular mechanisms. Immunohistochemistry (IHC) analyses and flow cytometry were used to identify the relationship between reprogrammed glycolysis and immunosuppression in HCC. Upregulated CD147 expressions were found to be associated with enhanced expressions of GLUT1, MCT1 in HCC tumorous tissues. CD147 promoted the glycolytic metabolism in HCC cell lines in vitro via the PI3K/Akt/mTOR signaling pathway. A positive correlation existed between a profile of immunosuppressive lymphocytes infiltration and CD147 expression in HCC tissues. Accumulation of FOXP3-expressing regulatory T cells was induced under a stimulation with lactate in vitro. In conclusion, CD147 promoted glycolytic metabolism in HCC via the PI3K/Akt/mTOR signaling pathway, and was related to immunosuppression in HCC.

Combination of baseline total metabolic tumor volume measured on FDG-PET/CT and ß2-microglobulin have a robust predictive value in patients with primary breast lymphoma.

The aim was to build a prognostic model to stratify patients at diagnosis into different risk categories. We investigated the prognostic value of functional PET parameters and clinical features in 64 primary breast lymphoma (PBL) patients. With a median follow-up of 60 months, 5-year progression-free survival (PFS) and overall survival (OS) was 62.5% and 73.4%. In multivariate analysis, baseline total metabolic tumor volume (TMTV0) and ß2-microglobulin remained more reliable predictors of survival than other prognostic factors. The optimal TMTV0 cut-off value was 90 cm3 . Among 29 patients with high TMTV0, 5-year PFS and OS were 44.8% and 62.1%, respectively, while 5-year PFS and OS of 35 patients with low TMTV0 were 74.3% and 85.7%, respectively. TMTV0 combined with ß2-microglobulin identified three groups with very different prognosis, including low-risk group with low TMTV0 and ß2-microglobulin≤normal (n = 30), intermediate-risk group with high TMTV0 or ß2-microglobulin>normal (n = 20), and high-risk group with high TMTV0 and ß2-microglobulin>normal (n = 14). In the three groups, 5-year PFS rates were 80%, 55% and 28.6% (P = .003), and 5-year OS rates were 90%, 65%, and 50% (P = .023) respectively. We established a new prognostic model through TMTV0 and ß2-microglobulin, and can divide PBL at diagnosis into different risk categories.

Performance of chemical chelating agent stabilization and cement solidification on heavy metals in MSWI fly ash: A comparative study.

Heavy metal stabilization by chemical chelating agent and solidification by cement are technologies currently used to reduce the leaching of heavy metals in municipal solid waste incineration (MSWI) fly ash. This paper studied the physico-chemical properties of the fly ash, heavy metals content in the fly ash, and the leaching concentration of the heavy metals from fly ash. The effect of four chelating agents namely dithiocarbamate (1#), dithiocarbamic acid dipotassium salt (2#), amino dithiocarbamate chelating resin (3#) and thiourea (4#) on the stabilization and leaching of heavy metals were investigated. Different addition ratios (1%, 2% and 3% w/w) of the chelating agents, various curing time (7, 14, 28 days), and different amounts of the cement (10%, 15% and 20% w/w) were used in order to find the agent with the optimum stabilization and leaching of heavy metals as well as find the effect of combining the agent and cement. The results showed that the dithiocarbamate (1#) chelating agent had the best stabilizing performance due to the three-dimensional structure after their reaction. The addition of cement to the fly ash led to the immobilization of heavy metals due to the C-S-H gel formation. The technology of cement solidification and chelating agent stabilization was optimal from the point of economic cost and the complexity aspect.

Experimental Observation of a Generalized Thouless Pump with a Single Spin.

Adiabatic cyclic modulation of a one-dimensional periodic potential will result in quantized charge transport, which is termed the Thouless pump. In contrast to the original Thouless pump restricted by the topology of the energy band, here we experimentally observe a generalized Thouless pump that can be extensively and continuously controlled. The extraordinary features of the new pump originate from interband coherence in nonequilibrium initial states, and this fact indicates that a quantum superposition of different eigenstates individually undergoing quantum adiabatic following can also be an important ingredient unavailable in classical physics. The quantum simulation of this generalized Thouless pump in a two-band insulator is achieved by applying delicate control fields to a single spin in diamond. The experimental results demonstrate all principal characteristics of the generalized Thouless pump. Because the pumping in our system is most pronounced around a band-touching point, this work also suggests an alternative means to detect quantum or topological phase transitions.

Experimental Demonstration of Uncertainty Relations for the Triple Components of Angular Momentum.

The uncertainty principle is considered to be one of the most striking features in quantum mechanics. In the textbook literature, uncertainty relations usually refer to the preparation uncertainty which imposes a limitation on the spread of measurement outcomes for a pair of noncommuting observables. In this work, we study the preparation uncertainty for the angular momentum, especially for spin-1/2. We derive uncertainty relations encompassing the triple components of angular momentum and show that, compared with the relations involving only two components, a triple constant 2/sqrt[3] often arises. Intriguingly, this constant is the same for the position and momentum case. Experimental verification is carried out on a single spin in diamond, and the results confirm the triple constant in a wide range of experimental parameters.

Estimating cadmium concentration in the edible part of Capsicum annuum using hyperspectral models.

Hyperspectral remote sensing can be applied to the rapid and nondestructive monitoring of heavy-metal pollution in crops. To realize the rapid and real-time detection of cadmium in the edible part (fruit) of Capsicum annuum, the leaf spectral reflectance of plants exposed to different levels of cadmium stress was measured using hyperspectral remote sensing during four growth stages. The spectral indices or bands sensitive to cadmium stress were determined by correlation analysis, and hyperspectral estimation models for predicting the cadmium content in the fruit of C. annuum during the mature growth stage were established. The models were cross validated by taking the sensitive spectral indices in the bud stage and the sensitive spectral bands in the flowering stage as the input variables. The results indicated that cadmium accumulated in the leaves and fruit of C. annuum and leaf cadmium content in the three early growth stages were correlated with the cadmium content of the pepper in the mature stage. Leaf spectral reflectance was sensitive to cadmium stress, and the first derivative of the original spectral reflectance was strongly correlated with leaf cadmium content during all growth stages. Among the established models, the multiple regression model based on the sensitive spectral bands in the flowering stage was optimal for predicting fruit cadmium content of the pepper. This model provides a promising method to ensure food safety during the early growth stage of the plant.

Experimental Test of Heisenberg's Measurement Uncertainty Relation Based on Statistical Distances.

Incompatible observables can be approximated by compatible observables in joint measurement or measured sequentially, with constrained accuracy as implied by Heisenberg's original formulation of the uncertainty principle. Recently, Busch, Lahti, and Werner proposed inaccuracy trade-off relations based on statistical distances between probability distributions of measurement outcomes [P. Busch et al., Phys. Rev. Lett. 111, 160405 (2013); P. Busch et al., Phys. Rev. A 89, 012129 (2014)]. Here we reformulate their theoretical framework, derive an improved relation for qubit measurement, and perform an experimental test on a spin system. The relation reveals that the worst-case inaccuracy is tightly bounded from below by the incompatibility of target observables, and is verified by the experiment employing joint measurement in which two compatible observables designed to approximate two incompatible observables on one qubit are measured simultaneously.