金融机构ESG投资的“漂绿”与“反漂绿”——基于DWSGroup的案例分析

深化绿色金融发展、助力经济绿色发展,是实现“双碳”目标的重要途径,特别是在ESG理念盛行的今天,金融机构的ESG投资行为因其深远影响而颇受投资者青睐,但层出不穷的“漂绿”现象也牢牢制约着绿色金融的发展,制约着ESG投资的发展。西方国家作为绿色金融发展先驱,从信息披露、制度监管、实践探索等方面对“漂绿”现象进行了研究和治理。本文在学习其先进经验的基础上,基于舞弊三角理论对DWSGroup案例进行分析,从压力、机会和自我合理化因素角度对金融机构ESG投资“漂绿”行为的动因进行分析,并据此提出我国“反漂绿”体系框架,以期为绿色金融的平稳发展和“双碳”目标的实现作出贡献。

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Combined Promoting Effects of Specific Organic Functional Groups and Alumina Surface Characteristics for the Design of a Highly Efficient NiMo/Al_(2)O_(3) Hydrodesulfurization Catalyst

To prepare a highly efficient NiMo/Al_(2)O_(3) hydrodesulfurization catalyst,the combined effects of specific organic functional groups and alumina surface characteristics were investigated.First,the correlation between the surface characteristics of four different alumina and the existing Mo species states was established.It was found that the Mo equilibrium adsorption capacity can be used as a specific descriptor to quantitatively evaluate the changes in surface characteristics of different alumina.A lower Mo equilibrium adsorption capacity for alumina means weaker metal-support interaction and the loaded Mo species are easier to transform into MoS2.However,the Mo-O-Al bonds still exist at the metal-support interface.The introduction of cationic surfactant hecadecyl trimethyl ammonium bromide(CTAB)can further improve Mo species dispersion through electrostatic attraction with Mo anions and interaction of its alkyl chain with the alumina surface;meanwhile,the introduction of ethylenediamine tetraacetic acid(EDTA)can complex with Ni ions to enhance the Ni-promoting effect on Mo.Therefore,the NiMo catalyst designed using alumina with lower Mo equilibrium adsorption capacity and the simultaneous addition of EDTA and CTAB exhibits the highest hydrodesulfurization activity for 4,6-dimethyl dibenzothiophene because of its proper metal-support interaction and more well-dispersed Ni-Mo-S active phases.

Reservoir characteristics and controlling factor of tight sandstone in Shuixigou Group in Taibei depression,Turpan-Hami basin

The positive structure belts surrounding the Taibei Sag,Turpan-Hami Basin,have been the main targets for oil and gas exploration for years and are now left with remaining resources scattering in reservoirs adjacent to source rocks in the sag,where the Shuixigou Group with substantial oil and gas potential constitutes the primary focus for near-source exploration.Consequently,characterization of development and key controlling factors of reservoir space becomes a must for future exploration in the area.This study investigates the development traits,genesis,and controlling factors of the Xishanyao and Sangonghe formations in the Shengbei and Qiudong Sub-sags of the Taibei Sag with techniques such as cast thin-section observation,porosity and permeability tests,high-pressure mercury injection,and saturation fluid NMR analysis of reservoir rocks.The findings reveal that the Shuixigou Group in the Taibei Sag consists of lithic sandstone.Reservoirs in the group are mostly poor in terms of physical properties,with undeveloped primary pores dominated by intergranular dissolved pores as a result of a strong compaction.Comparative analysis of key controlling factors of the Sangonghe Formation reveals significant distinctions in sandstone particle size,sand body thickness,genesis and distribution,provenance location,and source rock type between the Qiudong area and Shengbei area.Vertically,the coal seams of the Xishanyao Formation exhibit heightened development with shallower burial depth and lower maturity compared to those of the Sangonghe Formation.Consequently,this environment fosters the formation of organic acids,which have a stronger dissolution effect on minerals to develop secondary dissolution pores,and ultimately resulting in better reservoir physical properties.Overall,the reservoirs within the Qiudong area of the Taibei Sag demonstrate superior characteristics compared to those in the Shengbei area.Furthermore,the reservoir physical properties of the Xishanyao Formation are better than those of the Sangonghe Formation.The research findings will provide valuable guidance for the exploration and development of lithological oil and gas reservoirs within the Taibei Sag.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Response of phytoplankton functional group to spring drought in a large subtropical reservoir

Global warming has caused an increase in the frequency and duration of droughts worldwide.Droughts could trigger large changes in physico-chemical conditions and phytoplankton community in waterbodies,resulting in a shift in the phytoplankton community.Spring diatom blooms in reservoirs have been increasingly observed in the past decade in the Taihu Lake basin.The aim of the present study is to elucidate the impacts of droughts on aquatic environment and to determine the driving factors for the succession of the phytoplankton functional groups based on the analysis of data collected during spring from 2009 to 2020 in the Daxi Reservoir.The unimodal relationship between 1-month aggregated precipitation index and phytoplankton species richness indicated the competitive exclusion occurred in extremely drought period.The structural equation modeling indicated that drought-related low water level conditions intensified sediment resuspension,and increased the phosphorus-enriched nonalgal turbidity in the Daxi Reservoir.Concurrently,a steady shift in the Reynolds phytoplankton functional groups from L 0,TD,J,X 2,and A(phytoplankton taxa preferring low turbidity and nutrient conditions)to TB(pennate diatoms being adapt to turbid and nutrient-rich conditions)was observed.The increased TP and non-algal turbidity in addition to the lowered disturbance contribute to the prevalence of Group TB.Considering the difficulties in nutrient control,timely water replenishment is often a feasible method of controlling the dominance of harmful algae for reservoir management.Finally,alternative water sources are in high demand for ensuring ecological safety and water availability when dealing with drought.

Application of Specialized Group Management in the Quality Control of Perioperative Nursing

Objective:To explore the role of specialized group management in the quality control of perioperative nursing.Methods:45 surgical nurses from our hospital were selected as the research subjects.Traditional operating room management was adopted from July 2019 to June 2020,and specialized group management was adopted from July 2020 to June 2021.The surgeon’s satisfaction,surgical nurses’core professional competence,and surgical patients’satisfaction were obtained through surveys and the results were analyzed.Results:Surgeon satisfaction before the implementation of specialized group management was significantly lower than after its implementation(P<0.05).Besides,surgical nurses’core professional competency scores before the implementation of specialized group management were significantly lower than after its implementation(P<0.05).Lastly,surgical patients’satisfaction before the implementation of specialized group management was significantly lower than after its implementation(P<0.05).Conclusion:Specialized group management helps to improve the quality of perioperative care and should be applied in clinical practice.

Introducing hydroxyl groups to tailor the d-band center of Ir atom through side anchoring for boosted ORR and HER

Tuning the coordination atoms of central metal is an effective means to improve the electrocatalytic activity of atomic catalysts.Herein,iridium(Ir) is proposed to be asymmetrically anchored by sp-N and pyridinic N of hydrogen-substituted graphdiyne(HsGDY),and coordinated with OH as an Ir atomic catalyst(Ir_(1)-N-HsGDY).The electron structures,especially the d-band center of Ir atom,are optimized by these specific coordination atoms.Thus,the as-synthesized Ir_(1)-N-HsGDY exhibits excellent electrocatalytic performances for oxygen reduction and hydrogen evolution reactions in both acidic and alkaline media.Benefiting from the unique structure of HsGDY,IrN_(2)(OH)_(3) has been developed and demonstrated to act as the active site in these electrochemical reactions.All those indicate the fresh role of the sp-N in graphdiyne in producing a new anchor way and contributing to promote the electrocatalytic activity,showing a new strategy to design novel electrochemical catalysts.

Optimality of Group Testing with Differential Misclassification

Group testing is a method that can be used to estimate the prevalence of rare infectious diseases,which can effectively save time and reduce costs compared to the method of random sampling.However,previous literature only demonstrated the optimality of group testing strategy while estimating prevalence under some strong assumptions.This article weakens the assumption of misclassification rate in the previous literature,considers the misclassification rate of the infected samples as a differentiable function of the pool size,and explores some optimal properties of group testing for estimating prevalence in the presence of differential misclassification conforming to this assumption.This article theoretically demonstrates that the group testing strategy performs better than the sample by sample procedure in estimating disease prevalence when the total number of sample pools is given or the size of the test population is determined.Numerical simulation experiments were conducted to evaluate the performance of group tests in estimating prevalence in the presence of dilution effect.

Besov Estimates for Sub-Elliptic Equations in the Heisenberg Group

In this article, we deal with weak solutions to non-degenerate sub-elliptic equations in the Heisenberg group, and study the regularities of solutions. We establish horizontal Calderón-Zygmund type estimate in Besov spaces with more general assumptions on coefficients for both homogeneous equations and non-homogeneous equations. This study of regularity estimates expands the Calderón-Zygmund theory in the Heisenberg group.

Overstory functional groups indicate the legacy of land use in a secondary tropical forest in southwestern China

Anthropogenic disturbances are widespread in tropical forests and influence the species composition in the overstory.However,the impacts of historical disturbance on tropical forest overstory recovery are unclear due to a lack of disturbance data,and previous studies have focused on understory species.In this study,the purpose was to deter-mine the influence of historical disturbance on the diver-sity,composition and regeneration of overstory species in present forests.In the 20-ha Xishuangbanna tropical sea-sonal rainforest dynamics plot in southwestern China,the historical disturbance boundaries were delineated based on panchromatic photographs from 1965.Factors that drove species clustering in the overstory layer(DBH≥40 cm)were analyzed and the abundance,richness and composition of these species were compared among different tree groups based on multiple regression tree analysis.The coefficient of variation of the brightness value in historical panchro-matic photographs from 1965 was the primary driver of spe-cies clustering in the overstory layer.The abundance and richness of overstory species throughout the regeneration process were similar,but species composition was always different.Although the proportion of large-seeded and vigorous-sprouting species showed no significant differ-ence between disturbed and undisturbed forests in the tree-let layer(DBH<20 cm),the difference became significant when DBH increased.The findings highlight that historical disturbances have strong legacy effects on functional group composition in the overstory and the recovery of overstory species was multidimensional.Functional group composi-tion can better indicate the dynamics of overstory species replacement during secondary succession.

End-group modulation of phenazine based non-fullerene acceptors for efficient organic solar cells with high open-circuit voltage

Phenazine-based non-fullerene acceptors(NFAs)have demonstrated great potential in improving the power conversion efficiency(PCE)of organic solar cells(OSCs).Halogenation is known to be an effective strategy for increasing optical absorption,refining energy levels,and improving molecular packing in organic semiconductors.Herein,a series of NFAs(Pz IC-4H,Pz IC-4F,Pz IC-4Cl,Pz IC-2Br)with phenazine as the central core and with/without halogen-substituted(dicyanomethylidene)-indan-1-one(IC)as the electron-accepting end group were synthesized,and the effect of end group matched phenazine central unit on the photovoltaic performance was systematically studied.Synergetic photophysical and morphological analyses revealed that the PM6:Pz IC-4F blend involves efficient exciton dissociation,higher charge collection and transfer rates,better crystallinity,and optimal phase separation.Therefore,OSCs based on PM6:Pz IC-4F as the active layer exhibited a PCE of 16.48%with an open circuit voltage(Voc)and energy loss of 0.880 V and 0.53 e V,respectively.Accordingly,this work demonstrated a promising approach by designing phenazine-based NFAs for achieving high-performance OSCs.

Mineralogy and whole-rock geochemistry of the Oligocene Barail Group of rocks of Belt of Schuppen,Northeast India:Implications for tectono-provenance and paleo-weathering

The petrographic and geochemical attributes of the Oligocene Barail Group of rocks are used to decipher the likely source area(s)or tectonic domains,as this sequence of rocks was deposited in a foreland basin governed by orogenic domain,namely the North-east Arunachal Himalayas.The river system that gave rise to the Brahmaputra River(Yarlung-Tsangpo),which flowed through several tectonic domains of the Himalayan ranges,primarily from BomiChayu,Gangadese Granitoid,Higher Himalayan Leucogranites,and Namche Barwa into the proto Bengal Basin now a part of Assam Arakan Basin and Naga Schuppen Belt,was the main source of the sandstone formation of the Barail Group.The purpose of sandstone petrography,which combines modal analysis with XRF(Major Oxides)and HR-ICPMS(Trace&Rare Earth Elements)research,is to identify the type of source rock(s),their weathering pattern,and its paleo-environmental circumstances.These sandstones were formed from recycled orogen and include lithic and sublithic arenite variants with advanced texture and chemical maturity.The sediments were felsic(Th/Co:1.38,Cr/Th:9.78,La/Lu:11.58,Th/Sc:0.99,Eu/Eu*:0.66,La/Sc:3.05,La/Co:4.18),with contributions from intermediate source rocks and low-rank metamorphics deposited in an active continental margin to a continental island arc setting.Climatic conditions impacted the sediments of Barails,characterised by being warm and semi-humid to humid which resulted in moderate to a high degree of chemical weathering,as shown by weathering indices like CIA(79.14),PIA(85.47),CIW(86.9),WIP(32.50),ICV(0.71),and Th/U(6.03),which were further additionally supported by C-Value(1.01),PF(1.20),Sr/Cu(2.04),and Rb/Sr(0.97).

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Epidemiological observations of invasive group B Streptococcus infections in six major hospitals in Peninsular Malaysia

Objective:To address the lack of research on invasive group B Streptococcus(GBS)infections in Malaysia and Southeast Asia through a comprehensive analysis of GBS isolates obtained from hospitals.Methods:Medical records from patients with GBS infection isolated from the sterile site,such as blood and cerebrospinal fluid from 14 July 2019 to 15 December 2020,were reviewed from six major hospitals in Peninsular Malaysia.Inclusion criteria were invasive GBS,sterile sites and non-repeated GBS isolated from the same patients in the same admission.Viable isolates were re-identified for GBS and serotyped.Results:A total of 118 patients were eligible,with a majority of non-pregnant adults(76.3%).Over half of the patients(62.7%)had underlying medical conditions,with diabetes as the most common disease,followed by respiratory disease,renal disease,cardiovascular disease and skin and soft tissue disease.The most common manifestations were sepsis,followed by soft tissue abscess,diabetic foot ulcer,wet gangrene and cellulitis.The overall mortality was 7.6%.The most common serotype was serotype桋.Conclusions:Invasive GBS infection among non-pregnant adults showed a rising trend,particularly among diabetic individuals.The study underscores the importance of reducing risk factors and highlights the necessity of developing GBS vaccination as a preventive strategy for both infants and adults.

Molecular packing tuning via chlorinated end group enables efficient binary organic solar cells over 18.5%

Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.

Spherical Functions on Fuzzy Lie Group

Let G be a locally compact Lie group and its Lie algebra. We consider a fuzzy analogue of G, denoted by called a fuzzy Lie group. Spherical functions on are constructed and a version of the existence result of the Helgason-spherical function on G is then established on .

How Group Leaders Build Stable Community Buying Groups:A Perspective Based on the Differential Mode of Association

Since 2016,community group buying has grown significantly in China,largely driven by its efficient logistics,supply chains,low prices,and convenience.This model has been further popularized during the COVID-19 pandemic due to its effectiveness in meeting daily needs while minimizing human-to-human contact.A key component of this business model is the“group leaders”-influential individuals within a community responsible for managing group buying activities,which include order collection,supplier liaison,and goods distribution.Their primary task is to form and sustain a reliable community group buying consortium,a task that demands excellent organizational and interpersonal skills.This paper examines this phenomenon using the lens of the differential mode of association,a theoretical model explaining interpersonal relationships in traditional Chinese society.The research indicates that group leaders,through regular interaction with consumers,are able to alter their social network position,increase their influence,understand consumer needs,provide satisfying services,and enhance trust,thereby transforming consumers into loyal group buying participants.This transformation not only brings stability to group buying activities but also reinforces the community influence of group leaders,thus fostering the growth of community group buying.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.