Batch Studies for Sorption of Ga(III), Cu(II), Ni(II) and Zn(II) Ions onto Synthetic Polymeric Resins

Poly(acrylamide-acrylic acid-dimethyl amino ethylmethacrylate), p(AM-AA-DMAEM) and Poly(acrylamide-acrylic acid)-ethylene diamine tetracetic acid disodium, p(AM-AA)-EDTANa2 were prepared by gamma radiation-induced template polymerization technique. The prepared polymeric materials were used for the sorption of Ga(III), Cu(II), Ni(II) and Zn(II) in aqueous solution. The effect of pH, weight of resins, metal ion concentrations and contact time on the sorption of these metal ions were studied.

Nonlinear Time History Analysis for the Different Column Orientations under Seismic Wave Synthetic Approach

The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National University, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Advances in Research on Synthetic Microbial Communities

The synthetic microbial community is a synthetic microbial system co-cultured with multiple species, which has the characteristics of clear composition and strong controllability. Compared with a single colony, it can achieve more complex functions and adapt to the changing environment more easily, so as to meet a wide range of needs. In this paper, the contents and concepts of microbial community and synthetic microbial community are briefly introduced, the principles that should be followed in the construction of microbial community are expounded, the methods and mathematical models used in the construction of synthetic microbial community are introduced, and the applications of synthetic microbial community in various fields are summarized. Finally, the challenges in the research of synthetic microbial communities are briefly described.

Preparation of a high-performance synthetic pitch from aromatic hydrocarbons containing N/Cl

The preparation of a synthetic pitch from aromatic monomers could easily regulate structure orientation at the molecu-lar level,which would be useful in fabrication.An isotropic synthetic pitch was prepared by a chlorine-and/or nitrogen-induced sub-stitution polymerization reaction method using aromatic hydrocarbon precursors containing Cl and N,which for this study were chloromethyl naphthalene and quinoline.This method was verified by investigating the structural changes under different synthesis conditions,and the synthesis mechanism induced by aromatics containing Cl was also probed.The result shows that the pyridinic N in quinoline contains a lone pair of electrons,and is an effective active site to induce the polymerization reaction by coupling with aromatic hydrocarbons containing Cl.The reaction between such free radicals causes strong homopolymerization and oligomeriza-tion.A higher reaction temperature and longer reaction time significantly increased the degree of polymerization and thus increased the softening point of the pitch.A linear molecular structure was formed by the Cl substitution reaction,which produced a highly spinnable pitch with a softening point of 258.6℃,and carbon fibers with a tensile strength of 1163.82 MPa were obtained.This study provides a relatively simple and safe method for the preparation of high-quality spinnable pitch.

Developing a highly scalable synthetic strategy for 5-amino-4-nitrobenzo[1,2-c:3,4-c']bis([1,2,5]oxadiazole)1,b-dioxide(CL-18)and investigating the influence of crystal engineering and positional isomerization on its safety and laser ignition performance

5-amino-4-nitrobenzo[1,2-c:3,4-c']bis([1,2,5]oxadiazole)1,6-dioxide(CL-18)exhibits significant potential as an initiating explosive.However,its current synthesis process remains non-scalable due to low yields and safety risks.In this study,we have developed a simple and safe synthetic route for CL-18.It was synthesized from 3,5-dihaloanisole in a four-step reaction with an overall yield exceeding 60%,surpassing all reported yields in the literature.Subsequently,recrystallization of CL-18 was successfully achieved by carefully selecting appropriate solvents and antisolvents to reduce its mechanical sensitivity.Ultimately,when DMF-ethanol was employed as the recrystallization solvent system,satisfactory product yield(>90%)and reduced mechanical sensitivity(IS=15 J;FS=216 N)were obtained.Additionally,CL-18 is derived from the rearrangement of oxygen atoms on i-CL-18 furoxan,and a comparative analysis of their physicochemical properties was conducted.The thermal stability of both compounds is similar,with onset decomposition temperatures recorded at 186 and 182℃respectively.Similarly,they exhibit 5 s breaking point temperatures of 236 and 237℃.Additionally,we present novel insights into the positional-isomerization-laser-ignition performance of CL-18 and its isomer i-CL-18 using laser irradiation for the first time.Remarkably,our findings demonstrate that i-CL-18 exhibits enhanced laser sensitivity,as it can be directly ignited by a 1064 nm wavelength laser,whereas CL-18 lacks this characteristic.

New food sources and production systems:a comparison of international regulations and China’s advancements in novel foods with synthetic biology

The global shift towards sustainable food systems has sparked innovations in food sources and production systems,including cell-based meat,plant-based food products,precision fermentation,and 3D food printing.These advancements pose regulatory challenges and opportunities,with China emerging as a critical player in adopting and regulating new food technologies.This review explores the international landscape of new food sources and production systems(NFPS),focusing on China’s role and regulatory approaches compared to global practices.Through this comparative analysis,we aim to contribute to the ongoing dialogue on food safety regulation,offering insights and recommendations for policymakers,industry stakeholders,and researchers engaged in the global food system’s evolution.This comprehensive overview underscores the dynamic nature of regulatory frameworks governing NFPS,highlighting the international efforts to ensure food safety,consumer protection,and the sustainable evolution of the food industry.

Life-threatening constrictive pericarditis accompanied by synthetic cannabinoid use:A case report and literature review

Rationale:Synthetic cannabinoids are increasingly used as recreational drugs and have been associated with adverse cardiovascular effects.However,reports of synthetic cannabinoids accompanied by constrictive pericarditis are limited.Patient’s concern:A 28-year-old male with a history of synthetic cannabinoid(Bonzai)abuse presented with chest discomfort,dyspnea,and lower extremity edema.Investigations revealed reduced left ventricular ejection fraction,elevated inflammatory markers,low electrocardiogram voltages,and atrial fibrillation.Diagnosis:Chest spiral computerized tomography scan and chest X-ray demonstrated pericardial calcification.Cardiac magnetic resonance imaging and right heart catheterization were done to confirm the possibility of constrictive pericarditis.Based on the patient’s addiction history and exclusion of rheumatologic and infectious causes,it was supposed that constrictive pericarditis and cardiomyopathy may be accompanied by synthetic cannabinoid use.Interventions:The patient received standard medical therapy,including loop diuretics for cardiomyopathy and constrictive pericarditis.Catheter ablation was recommended for his rhythm control,and he was planned for close monitoring of clinical and echocardiographic response and evaluation of the need for surgical pericardiectomy in the future.Outcomes:After 6 months follow-up,echocardiographic exam revealed no significant improvement in ventricular function.However,due to the high surgical risk,the patient’s poor compliance,and the continuation of drug abuse,he was not a good candidate for surgery according to our heart team’s decision.Lessons:Synthetic cannabinoids can trigger constrictive pericarditis,and clinicians should consider them when evaluating patients with compatible symptoms and exposure history.Further research on the cardiovascular effects of synthetic cannabinoids is needed and public education on potential harms is warranted.

The occurrence,inheritance,and segregation of complex genomic structural variation in synthetic Brassica napus

"Synthetic"allopolyploids recreated by interspecific hybridization play an important role in providing novel genomic variation for crop improvement.Such synthetic allopolyploids often undergo rapid genomic structural variation(SV).However,how such SV arises,is inherited and fixed,and how it affects important traits,has rarely been comprehensively and quantitively studied in advanced generation synthetic lines.A better understanding of these processes will aid breeders in knowing how to best utilize synthetic allopolyploids in breeding programs.Here,we analyzed three genetic mapping populations(735 DH lines)derived from crosses between advanced synthetic and conventional Brassica napus(rapeseed)lines,using whole-genome sequencing to determine genome composition.We observed high tolerance of large structural variants,particularly toward the telomeres,and preferential selection for balanced homoeologous exchanges(duplication/deletion events between the A and C genomes resulting in retention of gene/chromosome dosage between homoeologous chromosome pairs),including stable events involving whole chromosomes("pseudoeuploidy").Given the experimental design(all three populations shared a common parent),we were able to observe that parental SV was regularly inherited,showed genetic hitchhiking effects on segregation,and was one of the major factors inducing adjacent novel and larger SV.Surprisingly,novel SV occurred at low frequencies with no significant impacts on observed fertility and yield-related traits in the advanced generation synthetic lines.However,incorporating genome-wide SV in linkage mapping explained significantly more genetic variance for traits.Our results provide a framework for detecting and understanding the occurrence and inheritance of genomic SV in breeding programs,and support the use of synthetic parents as an important source of novel trait variation.

High-Precision DOA Estimation Method Based on Synthetic Aperture Technique

The existing direction-of-arrival(DOA)estimation methods only utilize the current received signals,which are susceptible to noise.In this paper,a method for DOA estimation based on a motion platform is proposed to achieve high-precision DOA estimation by utilizing past and present signals.The concept of synthetic aperture is introduced to construct a linear DOA estima-tion model.A DOA fine-tuning method based on the linear model is proposed to eliminate the lin-ear DOA variation,achieving a non-coherent accumulation of DOA estimations.Moreover,the baseband modulation and the phase modulation caused by the range history are compensated to achieve the coherent accumulation of all the DOA estimations.Simulation results show that the proposed method can significantly improve the DOA estimated accuracy at low signal-to-noise ratios(SNR).

A synthetic diagnostics platform for microwave imaging diagnostics in tokamaks

Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.

Exploration on Teaching Reform of Synthetic Biology Course Based on the New Engineering Concept

Synthetic biology is a new frontier of life science,which aims to design,transform and even synthesize organisms with engineering design concept.Doing a good job in the teaching of"synthetic biology"is of great significance to the cultivation and reserve of biotechnology professionals in China,and also has an important impact on students' employment competitiveness.Under the background of"new engineering",the course reform of"synthetic biology"was carried out in terms of the construction of teaching staff,teaching methods,students' participation and the innovation of course content,and specific reform suggestions were put forward,hoping to effectively promote the sustainable development of"synthetic biology"and effectively improve the quality of education.

CO_(2)capture costs of chemical looping combustion of biomass:A comparison of natural and synthetic oxygen carrier

Chemical looping combustion has the potential to be an efficient and low-cost technology capable of contributing to the reduction of the atmospheric concentration of CO_(2) in order to reach the 1.5/2°C goal and mitigate climate change.In this process,a metal oxide is used as oxygen carrier in a dual fluidized bed to generate clean CO_(2) via combustion of biomass.Most commonly,natural ores or synthetic materials are used as oxygen carrier whereas both must meet special requirements for the conversion of solid fuels.Synthetic oxygen carriers are characterized by higher reactivity at the expense of higher costs versus the lower-cost natural ores.To determine the viability of both possibilities,a techno-economic comparison of a synthetic material based on manganese,iron,and copper to the natural ore ilmenite was conducted.The synthetic oxygen carrier was characterized and tested in a pilot plant,where high combustion efficiencies up to 98.4%and carbon capture rates up to 98.5%were reached.The techno-economic assessment resulted in CO_(2) capture costs of 75 and 40€/tCO_(2) for the synthetic and natural ore route respectively,whereas a sensitivity analysis showed the high impact of production costs and attrition rates of the synthetic material.The synthetic oxygen carrier could break even with the natural ore in case of lower production costs and attrition rates,which could be reached by adapting the production process and recycling material.By comparison to state-of-the-art technologies,it is demonstrated that both routes are viable and the capture cost of CO_(2) could be reduced by implementing the chemical looping combustion technology.

A synthetic differencein-differences model for the influence of CR Express on Chongqing's economy

Purpose-The China-Europe Railway Express(CR Express)in Chongqing has operated regularly and undergone large-scale development.Its impact on Chongqing's economic growth has become increasingly evident,necessitating further research in this field.Design/methodology/approach-This study employs the opening of CR Express as a quasi-natural experiment,designating Chongqing,which inaugurated the CR Express in 2011,as the treatment group.13 provinces and cities that had not yet opened the CR Express until 2017 were selected as the control group.Utilizing panel data from 14 provinces across China spanning from 2006 to 2017,the synthetic control method(SCM)is employed to synthetically construct Chongqing.To quantify the difference in economic development levels between Chongqing with the operation of the CR express and Chongqing without its operation.Key metrics such as gross domestic product(GDP),per capita GDP,total retail sales of consumer goods,import and export value and the proportions of the secondary and tertiary industries are employed to measure urban economic development capabilities.Chongqing is designated as the experimental group,and a double-difference model is constructed to regress the operation of the CR Express against economic development capabilities.Robustness tests are conducted to validate the analytical results.Findings-The results indicate that,compared to provinces without the operation of the CR Express,the initiation of the CR Express in Chongqing significantly enhances the economic development level of the city.The opening of the CR Express exhibits a pronounced positive impact on Chongqing's economic development,and these findings remain robust and effective even after parallel trend tests and placebo tests.Originalitylvalue-The study represents an expansion of the theoretical framework.In contrast to previous studies that relied on a single indicator such as GDP,this study selects six indicators from the dimensions of economy,trade and industry to measure regional economic development capabilities.Furthermore,employing the grey relational analysis method,the study screens these indicators,thereby providing a theoretical basis for the selection of indicators for measuring regional economic development capabilities.

PHASE AUTOFOCUSING ALGORITHM FOR COMPRESSED INVERSE SYNTHETIC APERTURE RADAR IMAGING

As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.

Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data

Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features.Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping.Auto-encoder neural networks with convolutional layers have been applied to this problem,with encouraging results,but the problem of generalization to unseen data remains.Most published works have used supervised learning with training data constructed from field seismic data or synthetic seismic data generated based on measured well logs or based on seismic wavefield modelling.This leads to satisfactory results on datasets similar to the training data but requires re-training of the networks for unseen data with different characteristics.In this work seek to improve the generalization,not by experimenting with network architecture(we use a conventional U-net with some small modifications),but by adopting a different approach to creating the training data for the supervised learning process.Although the network is important,at this stage of development we see more improvement in prediction results by altering the design of the training data than by architectural changes.The approach we take is to create synthetic training data consisting of simple geometric shapes convolved with a seismic wavelet.We created a very diverse training dataset consisting of 9000 seismic images with between 5 and 300 seismic events resembling seismic reflections that have geophysically motived perturbations in terms of shape and character.The 2D U-net we have trained can boost robustly and recursively the dominant frequency by 50%.We demonstrate this on unseen field data with different bandwidths and signal-to-noise ratios.Additionally,this 2D U-net can handle non-stationary wavelets and overlapping events of different bandwidth without creating excessive ringing.It is also robust in the presence of noise.The significance of this result is that it simplifies the effort of bandwidth extension and demonstrates the usefulness of auto-encoder neural network for geophysical data processing.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

Reconstruction of lithofacies using a supervised Self-Organizing Map:Application in pseudo-wells based on a synthetic geologic cross-section

Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.

Effect of digital elevation models on monitoring slope displacements in open-pit mine by differential interferometry synthetic aperture radar

Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the potential to be a cost-effective method for monitoring surface displacements over extensive areas,such as open-pit mines.DInSAR requires the ground surface elevation data in the process of its analysis as a digital elevation model(DEM).However,since the topography of the ground surface in open-pit mines changes largely due to excavations,measurement errors can occur due to insufficient information on the elevation of mining areas.In this paper,effect of different elevation models on the accuracy of the displacement monitoring results by DInSAR is investigated at a limestone quarry.In addition,validity of the DInSAR results using an appropriate DEM is examined by comparing them with the results obtained by global positioning system(GPS)monitoring conducted for three years at the same limestone quarry.It is found that the uncertainty of DEMs induces large errors in the displacement monitoring results if the baseline length of the satellites between the master and the slave data is longer than a few hundred meters.Comparing the monitoring results of DInSAR and GPS,the root mean square error(RMSE)of the discrepancy between the two sets of results is less than 10 mm if an appropriate DEM,considering the excavation processes,is used.It is proven that DInSAR can be applied for monitoring the displacements of mine slopes with centimeter-level accuracy.