A critical review towards the causes of the iron-based catalysts deactivation mechanisms in the selective oxidation of hydrogen sulfide to elemental sulfur from biogas

Hydrogen sulfide(H_(2)S) not only presents significant environmental concerns but also induces severe corrosion in industrial equipment,even at low concentrations.Among various technologies,the selective oxidation of hydrogen sulfide(SOH_(2)S) to elemental sulfur(S) has emerged as a sustainable and environmentally friendly solution.Due to its unique properties,iron oxide has been extensively investigated as a catalyst for SOH_(2)S;however,rapid deactivation has remained a significant drawback.The causes of iron oxide-based catalysts deactivation mechanisms in SOH_(2)S,including sulfur or sulfate deposition,the transformation of iron species,sintering and excessive oxygen vacancy formation,and active site loss,are thoroughly examined in this review.By focusing on the deactivation mechanisms,this review aims to provide valuable insights into enhancing the stability and efficiency of iron-based catalysts for SOH_(2)S.

Reusable salt-template strategy for synthesis of porous nitrogen-rich carbon boosts H_(2)S selective oxidation

Removing hydrogen sulfide(H_(2)S)via the selective oxidation has been considered an effective way to further purify the indusial sulfurcontaining due to it can completely transform residual H_(2)S into elemental sulfur.While N-doped porous carbon was applied to H_(2)S selective oxidation,a sustainable methodology for the synthesis of efficient and stable N-doped carbon catalysts remains a difficulty,limiting its future development in large-scale applications.Herein,we present porous,honeycomb-like N-doped carbon catalysts with large specific surface areas,high pyridinic N content,and numerous structural defects for H_(2)S selective oxidation prepared using reusable NaCl as the template.The asprepared NC-10-800 catalyst exhibits excellent catalytic performance(sulfur formation rate of 784 g_(sulfur) kg_(cat.)^(-1) h^(-1)),outstanding stability(>100 h),and excellent anti-water vapor,anti-CO_(2) and anti-oxidation properties,suggesting significant potential for practical industrial application.The characterization results and kinetic study demonstrate that the large surface areas and structural defects created by the molten salt at high temperature enhance the exposure of pyridinic N sites and thus accelerate the catalytic activity.Importantly,the water-soluble NaCl template could be easily washed from the carbon nanomaterials,and thus the downstream salt-containing wastewater could be subsequently reused for the dissolution of carbon precursors.This environment-friendly,low-cost,reusable salt-template strategy has significant implications for the development of N-doped carbon catalysts for practical applications.

Understanding the individual with Alzheimer’s disease: Can socioemotional selectivity theory guide us?

Individuals often get lost behind the diagnosis of Alzheimer’s disease (AD) while widespread differences between these patients are morecommon than similarities. Socioemotional Selectivity Theory (SST) suggests that as we age our goals change from future-oriented (acquiringnew information) to present-oriented (enhancing the emotional, especially positive, meaning of encounters). The goal of the current article is to examine whether the principles of SST might also apply for people with AD. Some aspects of SST are found especially in the early stages of AD when awareness is often intact and cognitive impairment is relatively limited. This review has clinical significance for the treatment of AD because it focuses on what is important to the individual rather than treating patients as a homogenous group. It also highlights the importance of social networks and emphasizes the role of the proxy in AD care. Lastly, it suggests that if those with AD (like the healthy elderly) have a positivity bias then positive emotional stimuli/wording/instructions could usefully be employed in their daily treatment. I suggest that SST may be a useful starting point when attempting to address what matters to individuals with AD and conclude by providing a few suggestions for future studies.

Effect of twine thickness on size-selectivity of driftnet for the yellow croaker Larimichthys polyactis in southwestern Sea of Korea

In order to determine the effect of twine thickness on the size-selectivity of the driftnet used for the yellow croaker, size-selectivity tests were conducted with three different twine thicknesses(monofi lament diameters of 0.279 mm(number's method; No. 3), 0.321 mm(No. 4), and 0.360 mm(No. 5)) of driftnets for the yellow croaker in the seas around Chooja-do, Jeju Islands. The selectivity curve was estimated by using Kitahara's method. In order to determine the physical properties of the twine used in the experimental fi shing nets, we measured the breaking load, elongation, and stiffness under both dry and wet conditions. In terms of physical properties, the thinnest twine(No. 3) had the strongest breaking strength per unit cross-sectional area, along with good elongation and excellent fl exibility. The thickest twine(No. 5) had the lowest fl exibility. In terms of selectivity, the net of No. 3 twine showed the broadest selection range and, thus, a relatively low selectivity compared with the other nets, while the less fl exible net of No. 5 twine showed the narrowest selectivity range and high selectivity. In addition, it was found that a thicker twine resulted in a smaller haul of small fi sh. Therefore, it can be inferred that the thickness of the twine affects the size of the catch and selectivity, and thus the size composition of the catch as well.

Comparative Analysis of Improving Effects of 12 Maize Populations with Improved S_1 Selection Method

ln this study, 12 maize populations were improved with the improved S1 selection method, aiming to increase the improvement efficiency of maize popula-tions. The results showed that number of rows per spike, number of grains per row and 100-grain weight were the three important component traits of maize yield. The highest genetic increment was found in Mengqun 2, fol owed by Mengqun 4, Mengqun 1 and other 7 maize populations. Negative genetic increment was shown in Mengqun 3 and 3 introduced foreign maize populations. Some changes were shown in spikes, plant traits and genetic diversity of maize populations. Based on our results, we concluded that Mengqun 2, Mengqun 4, Zhongzong 7, Mengqun C and Mengqun A could be directly used for the line breeding by selfing for their higher genetic increment of yield and better improvement effects of other agricultural traits. Compared with these 5 populations, the improvement potential of other maize populations was limited for their lower genetic increment.

Structural engineering of 3D hierarchical Cd0.8Zn0.2S for selective photocatalytic CO2 reduction

The solar-driven catalytic conversion of CO2 to useful chemical fuels is regarded as an environmentally friendly approach to reduce the consumption of fossil fuels and mitigate the greenhouse effect.However,it is highly intriguing and challenging to promote the selectivity and efficiency of visible-light-responsive photocatalysts that favor the adsorption of CO2 in photoreduction processes.In this work,three-dimensional hierarchical Cd0.8Zn0.2S flowers(C8Z2S-F)with ultrathin petals were successfully synthesized through an in-situ self-assembly growth process using sodium citrate as a morphology director.The flower-like Cd0.8Zn0.2S solid solution exhibited remarkable photocatalytic performance in the reduction of CO2,generating CO up to 41.4μmol g^−1 under visible-light illumination for 3 h;this was nearly three times greater than that of Cd0.8Zn0.2S nanoparticles(C8Z2S-NP)(14.7μmol g^−1).Particularly,a comparably high selectivity of 89.9%for the conversion of CO2 to CO,with a turnover number of 39.6,was obtained from the solar-driven C8Z2S-F system in the absence of any co-catalyst or sacrificial agent.Terahertz time-domain spectroscopy indicated that the introduction of flower structures enhanced the light-harvesting capacity of C8Z2S-F.The in situ diffuse reflectance infrared Fourier transform spectroscopy unveiled the existence of surface-adsorbed species and the conversion of photoreduction intermediates during the photocatalytic process.Empirical characterizations and predictions of the photocatalytic mechanism demonstrated that the flower-like Cd0.8Zn0.2S solid solution possessed desirable CO2 adsorption properties and an enhanced charge-transfer capability,thus providing a highly effective photocatalytic reduction of CO2.

Comparisons of Three Recurrent Selection Methods in the Improvement of Maize Populations

The maize population Csyn 4 was improved for three cycles with three recurrent selection methods MS1, MS1-HS, and MHSRRS in northern China. A total of 40 NC Ⅱ testcrosses were made by four testers with Csyn 4 and 10 improved populations, which were evaluated in four environments in the cropping season of 2005. Analysis of variance indicated a significant progress （P〈 0.05） in yield and other chief agronomic traits in the improved populations and testcrosses, demonstrating that the three recurrent selection methods were effective for increasing grain yield of testcrosses and improvement of general combining ability in maize population. The average grain yield increase of population Csyn 4 in MS1, MS1-HS, and MHSRRS recurrent selections was 266.7 kg ha^-1 （5.3%）, 288.0 kg ha^-1 （5.7%）, and 231.3 kg ha^-1 （4.6%） per cycle, while the grain yield of S 1 progeny of population for Csyn 4 was increased by 420.0 kg ha^-1 （10.9%）, 376.0 kg ha^-1 （9.8%）, and 414.7 kg ha^-1 （10.8%） per cycle in MS1, MS1-HS, and MHSRRS recurrent selections, respectively. Linear responses （b） in the MS1, MS1-HS, and MHSRRS recurrent selections were the same as the realized responses of the selection （G） for grain yield increase of Csyn 4 and S1 progeny of the populations. The grain yield of testcrosses increased by 3.3-5.2% on average per cycle. The value of heterosis did not display any differences amongst the different cycles of selections. The GCA for yield component traits of population Csyn 4 was improved significantly by means of three selection methods, whereas the GCA for plant height, ear height, days to silking, and days to pollen did not show significant increase in the recurrent selections. GCA for the number of tassel branches were decreased in the selections, while GCA for the number of plant leaves were increased, but the difference was not significant.

Selective serotonin reuptake inhibitors and Alzheimer’s disease

Given the failure to develop disease-modifying therapies for Alzheimer’s disease(AD),strategies aiming at preventing or delaying the onset of the disease are being prioritized.While the debate regarding whether depression is an etiological risk factor or a prodrome of AD rages on,a key determining factor may be the timing of depression onset in older adults.There is increasing evidence that untreated early-onset depression is a risk factor and that late-onset depression may be a catalyst of cognitive decline.Data from animal studies have shown a beneficial impact of selective serotonin reuptake inhibitors on pathophysiological biomarkers of AD including amyloid burden,tau deposits and neurogenesis.In humans,studies focusing on subjects with a prior history of depression also showed a delay in the onset of AD in those treated with most selective serotonin reuptake inhibitors.Paroxetine,which has strong anticholinergic properties,was associated with increased mortality and mixed effects on amyloid and tau deposits in mice,as well as increased odds of developing AD in humans.Although most of the data regarding selective serotonin reuptake inhibitors is promising,findings should be interpreted cautiously because of notable methodological heterogeneity between studies.There is thus a need to conduct large scale randomized controlled trials with long follow up periods to clarify the dose-effect relationship of specific serotonergic antidepressants on AD prevention.

Comparison of six statistical approaches in the selection of appropriate fish growth models

The performance of six statistical approaches,which can be used for selection of the best model to describe the growth of individual fish,was analyzed using simulated and real length-at-age data.The six approaches include coefficient of determination(R2),adjusted coefficient of determination(adj.-R2),root mean squared error(RMSE),Akaike's information criterion(AIC),bias correction of AIC(AICc) and Bayesian information criterion(BIC).The simulation data were generated by five growth models with different numbers of parameters.Four sets of real data were taken from the literature.The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data.The best supported model by the data was identified using each of the six approaches.The results show that R2 and RMSE have the same properties and perform worst.The sample size has an effect on the performance of adj.-R2,AIC,AICc and BIC.Adj.-R2 does better in small samples than in large samples.AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large.AICc and BIC have best performance in small and large sample cases,respectively.Use of AICc or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.

Corrosion Behavior of the S136 Mold Steel Fabricated by Selective Laser Melting

The selective laser melting(SLM) method has a great potential for fabricating injection mold with complex structure. However, the microstructure and performance of the SLM molds show significantly di erent from those manufac?tured by traditional technologies. In this study, the microstructure, hardness and especially corrosion behavior of the samples fabricated by SLM and casting were investigated. The XRD results exhibit that the γ?Fe phase is only obtained in the SLM parts, and the α?Fe peak slightly moves to low di raction angle compared with casting counterparts. Due to the rapid cooling rate, the SLM samples have fine cellular microstructures while the casting ones have coarse grains with obvious elements segregation. Besides, the SLM samples show anisotropy, hardness of side view and top view are 48.73 and 50.31 HRC respectively, which are 20% higher than that of casting ones. Corrosion results show that the SLM samples have the better anti?corrosion resistance(in a 6% FeCl3 solution for 48 h) but the deeper corrosion pits than casting ones. Finally, the performance of the SLM molds could meet the requirement of injecting production. Moreover, the molds especially present a significant decrease(20%) of cooling time and increases of cooling uniform?ity due to the customized conformal cooling channels.

Positions of selective leukocytapheresis in the medical therapy of ulcerative colitis

Ulcerative colitis （UC） and Crohn＇s disease （CD） are the major forms of idiopathic inflammatory bowel disease （IBD）. Both UC and CD are debilitating chronic disorders that afflict millions of individuals throughout the world with symptoms which impair function and quality of life. The etiology of IBD is inadequately understood and therefore, drug therapy has been empirical instead of being based on sound understanding of IBD pathogenesis. This is a major factor for poor drug efficacy and drug related side effects that often add to the disease complexity. The development of biologicals notably infliximab to intercept tumor necrosis factor （TNF）-α reflects some progress, albeit major concern about their side effects and lack of long-term safety and efficacy profiles. However, IBD seems to be perpetuated by inflammatory cytokines like TNF-α, interleukin （IL）-Iβ, IL-6 and IL-8 for which activated peripheral granulocytes and monocytes/macrophages （GH） are major sources. Further, in IBD, peripheral GHs are elevated with activation behavior, increased survival time and are found in vast numbers within the inflamed intestinal mucosa; they are suspected to be major factors in the immunopathogenesis of IBD. Hence, peripheral blood GMs should be appropriate targets of therapy. The Adacolumn is a medical device developed for selective depletion of GH by receptor-mediated adsorption （GHA）. Clinical data show GMA, in patients with steroid dependent or steroid refractory UC, is associated with up to 85% efficacy and tapering or discontinuation of steroids, while in steroid nai＇ve patients （the best responders）, GHA spares patients from exposure to steroids. Likewise, GMA at appropriate intervals in patients at a high risk of clinical relapse suppresses relapse thus sparing the patients from the morbidity associated with IBD relapse. Further, GHA appears to reduce the number of patients being submitted to colectomy or exposure to unsafe immunosupressants. First UC episode, steroid naivety and short disease duration appear good predictors of response to GMA and based on the available data, GMA seems to have an excellent safety profile.

Molecular Design of Crown Ethers. 19~1. Synthesis of Novel Disulfide and Diselenide-Bridged Bis (benzo-12-crown-4)s and their Ag^+ Selective Electrode Properties

Two novel heteroatom-bridged his (benzo-12-crown-4 ether)s, i.e. his [2-nitro-4,5 (1,4,7,10-tetraoxadecamethylene) disulfide 1 and diselenide 2, have been synthesized. X ray crystallographic structure was obtained for 1. Ion selective electrodes (ISE) for Ag+, containing 1 and 2 in PVC membrane as neutral carriers, were prepared, and their selectivity coefficients for Ag^+ (K_(Ag.M)^(pot)) were determined against other heavy metal ions, alkali and alkaline-earth metal ions. and ammonium ion. These ISEs showed excellent Ag^+ selectivities, log K_(Ag.M)^(pot) ≤ -3.8, against most of the interfering canons examined, except for Hg^+.

Ergodic Rate Analysis on Applying Antenna Selection in D2D Communication Underlaying Cellular Networks

By reusing the spectrum of a cellular network, device-to-device(D2D) communications is known to greatly improve the spectral efficiency bypassing the base station(BS) of the cellular network. Antenna selection is the most cost efficient scheme for interference management, which is crucial to D2D systems. This paper investigates the achievable rate performance of the D2D communication underlaying the cellular network where a multiple-antenna base station with antenna selection scheme is deployed. We derive an exact closed-form expression of the ergodic achievable rate. Also, using Jensen's inequality, two pairs of upper and lower bounds of the rate are derived and we validate the tightness of the two sets of bounds. Based on the bounds obtained, we analyze the ergodic achievable rate in noise-limited scenario, interference-limited high SNR scenario and larger-scale antenna systems. Our analysis shows that the presence of D2D users could be counter-productive if the SNR at cellular UE is high. Further analysis shows that the relationship between the ergodic rate and the number of antennas it positive, but keeps decreasing as the antenna number increasing. These show the inefficiency of antenna selection in D2D interference management.

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

Allocation of Repairable and Replaceable Components for a System Availability Using Selective Maintenance with Probabilistic Maintenance Time Constraints

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

Weapons system portfolio selection based on the contribution rate evaluation of system of systems

The weapons system portfolio selection problem arises at the equipment demonstration stage and deals with the military application requirements.Further,the contribution rate of the system is one of the important indicators to evaluate the role of a system,which can facilitate the weapons system portfolio selection.Therefore,combining the system contribution rate with system portfolio selection is the focus of this study.It also focuses on calculating the contribution rates of multiple equipment systems with various types of capabilities.The contribution rate is measured by establishing a hierarchical multi-criteria value model from three dimensions.Based on the value model,the feasible portfolios are developed under certain cost constraints and the optimal weapons system portfolios are obtained by using the classification optimization selection strategy.Finally,an illustrative example is presented to verify the feasibility of the proposed model.

Dual-functional marigold-like Zn_(x)Cd_(1-x)S homojunction for selective glucose photoreforming with remarkable H_(2)coproduction

The global commitment to pivoting to sustainable energy and products calls for technology development to utilize solar energy for hydrogen(H_(2))and value-added chemicals production by biomass photoreforming.Herein,a novel dual-functional marigold-like Zn_(x)Cd_(1-x)S homojunction has been the production of lactic acid with high-yield and H_(2)with high-efficiency by selective glucose photoreforming.The optimized Zn_(0.3)Cd_(0.7)S exhibits outstanding H_(2)generation(13.64 mmol h^(-1)g^(-1)),glucose conversion(96.40%),and lactic acid yield(76.80%),over 272.80 and 19.21 times higher than that of bare ZnS(0.05 mmol h^(-1)g^(-1))and CdS(0.71 mmol h^(-1)g^(-1))in H_(2)generation,respectively.The marigold-like morphology provides abundant active sites and sufficient substrates accessibility for the photocatalyst,while the specific role of the homojunction formed by hexagonal wurtzite(WZ)and cubic zinc blende(ZB)in photoreforming biomass has been demonstrated by density functional theory(DFT)calculations.Glucose is converted to lactic acid on the WZ surface of Zn_(0.3)Cd_(0.7)S via the photoactive species·O_(2)^(-),while the H_(2)is evolved from protons(H^(+))in H_(2)O on the ZB surface of Zn_(0.3)Cd_(0.7)S.This work paves a promising road for the production of sustainable energy and products by integrating photocatalysis and biorefine.

SQL-SELECT语句中谓词分析与应用

本文通过对SQL-SELECT语句中谓词的概念、功能及使用方法作出分析,并通过实例进行剖析,对读者合理使用它提供一些参考意见。

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

A Simple Strategy for Fabrication of In（OH）3/ZnIn2S4 Heterostructure as Visible-light-driven Photocatalyst for Selective Oxidation

The cubic In(OH)3/ZnIn2S4 heterostructures were successfully synthesized via a simple strategy of post-treatment of cubic ZnIn2S4(C-ZIS), and their structures and properties were characterized by X-ray diffraction(XRD), UV-vis diffuse reflectance spectra(DRS), field emission scanning electron microscopy(FESEM) and transmission electron microscopy(TEM). According to the results, the tofu-like In(OH)3 semiconductor as a new crystal phase appeared in the matrix of C-ZIS and formed a In(OH)3/ZnIn2S4 heterostructure. Compared with pure C-ZIS, the heterostructure exhibits higher photocatalytic activity towards selective oxidation of benzyl alcohol under visible light. The In(OH)3/ZnIn2S4 heterostructure manifests the best and the highest photocatalytic performance via 18 h post-treatment, which achieved nearly 100% selectivity, 35.5%conversion and 34.5% yield. This is ascribed to the formation of In(OH)3/ZnIn2S4 heterostructure to promote the transfer of photogenerated electron-hole pairs and thus efficiently inhibits their recombination, leading to the longer lifetime of photo-induced carriers. Furthermore, a possible photocatalytic mechanism is proposed and discussed. Our current work could boost more interest in researching the semiconductor materials of ternary chalcogenides and enlarging the applications based on cubic ZnIn2S4 heterostructure as visible-light-driven photocatalyst.