A Study on Influencing Factors of Happiness of Urban Low-income Groups

With the completion of the strategic vision of building a moderately prosperous society in an all-round way in 2020,China’s economic development has entered a new normal.The concept of innovative,coordinated,green,open and shared development will lead China’s economic progress and development.However,the development of urban and rural areas and regions in China is still not coordinated,and the sudden COVID-19 outbreak has caused a serious impact on urban low-income groups.Due to the characteristics of poor employment stability,low income and single source,as well as greater family pressure of urban low-income groups,the author focuses on the influencing factors of their feelings of happiness.This paper assumes that the satisfaction degree of residents’needs is positively correlated with residents’feelings of happiness.Based on Maslow’s hierarchy of needs theory,this paper analyzes the factors that may affect the happiness of urban low-income groups in five levels,and explores the special needs and difficulties of urban low-income groups in the context of the epidemic.So as to find ways to improve the happiness of urban low-income people to provide reference.

Epidemiological, Clinical Aspects and Outcome of Measles in a Low-Income Country in 2023

Introduction: Measles remains a worrying health problem in sub-Saharan African countries. There have been measles outbreaks in Cameroon with the most recent occurring between October 2022 and September 2023. The Centre region of Cameroon was the most affected in the country and being at the frontline of the fight against this illness, we conducted this study in order to determine the epidemiological and clinical characteristics as well as the outcome of children who contracted the measles and were hospitalized at the Yaoundé Gynaeco-Obstetric and Pediatric Hospital. Methodology: We conducted a cross-sectional descriptive and prospective study for one year from October 2022 to November 2023 corresponding to the duration of the outbreak. We included all children admitted in the pediatric unit for measles and its complications during this period. Results: In total, 60 children were enrolled. Their caregivers were mostly their mothers who had a mean age of 34.71 ± 9.55 years living in significant precarious conditions for the majority. The median age of children was 16 months (09 - 30 months). Twenty six percent of children (16) were less than 9 months. Girls were predominant (55%). Most children were not up to date with their routine EPI vaccination (80%) and most did not receive the measles and Rubella vaccine (76.67%). Clinical manifestations, included fever, conjunctivitis, and cough. Skin rash and catarrh (98.33% and 86.67% respectively) are the case definition signs of measles. The outcome was unfavorable for 4 children. One had a neurological disorder: coma and three presented with pneumonia and severe respiratory distress (6.67%). Conclusion: Measles is still being a reality and claiming lives in our context, emphasis should be made on immunization coverage and if possible, advocacies should be formulated to decrease the age of measles vaccine administration.

Characteristics of Infertile Clientele Attending a Public in Vitro Fertilization Clinic: Appraising Priorities in a Low-Income Country

Introduction: Infertilityaffects one in six couples, and it is an important public health issue largely due to thepervasive effects on the emotional and psychological wellbeing of affected couples. In many developing nations emphasis is placed on childbirth and inability to fulfill this role can be very distressing. There is an unmet need for assisted reproductive technology (ART) in many developing countries and where facilities exist, they are mostly privately owned, expensive and concentrated in urban areas. To bridge this gap, public fertility clinics have been established to provide subsidized care. Evaluating the characteristics and peculiarities of clientele presenting at these public facilities will aid planning and prioritization of care. Methodology: A descriptive retrospective study of 116 infertile patients presenting to the fertility clinic of the University College Hospital, Ibadan, Nigeria from inception on the 14th of February 2019 and 31st of December 2022.Data was analyzed using the Statical Package for Social Sciences (IBM, SPSS, New York) version 23. Descriptive statistics were used to summarize the results which were presented with the aid of bar charts and frequency tables. Result: The mean age of the patients was 40.70 ± 6.62 years. Post-menopausal patients accounted for about one-fifth of the study population while 80.2% (93 women) were older than 35 years. The mean duration of infertility was 9.39 ± 6.11years and nine patients (7.8%) had a duration greater than 2 decades. Secondary infertility occurred in 67.2% of the women. Twenty-nine women (25%) had undergone myomectomy prior to presentation. Hypertension (11.2%) was the most prevalent comorbidity. Nineteen patients (16.4%) had used contraceptives in the past with the male condom (36.8%)being the most preponderant. Sixty-seven patients had experienced pregnancy losses before 28 weeks of gestation while just 16 patients (13.8%) had undergone ART, and none was successful. Conclusion: Secondary infertility was the prevalent type of infertility and may not be unconnected with the low contraceptive usage and high risk of sexually transmitted infection. Late presentation coupled with a large proportion of post-menopausal clientele suggests delayed health-seeking behavior most probably due to the prohibitive cost of ART. The need to streamline services offered in public fertility clinics is paramount in low-income countries grappling with scarce resources. A pragmatic approach will involve the provision of low-cost ART, while enhancing gamete donation programs through the implementation of gamete sharing policies. This will invariably bridge the unmet need and skewed access to ART in developing countries.

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。

Utilization of access to colorectal cancer screening modalities in low-income populations after medicaid expansion

BACKGROUND Colorectal cancer(CRC)remains a relevant public health problem.Current research suggests that racial,economic and geographic disparities impact access.Despite the expansion of Medicaid eligibility as a key component of the Affordable Care Act(ACA),there is a dearth of information on the utilization of newly gained access to CRC screening by low-income individuals.This study investigates the impact of the ACA’s Medicaid expansion on utilization of the various CRC screening modalities by low-income participants.Our working hypothesis is that Medicaid expansion will increase access and utilization of CRC screening by low-income participants.AIM To investigate the impact of the Affordable Care Act and in particular the effect of Medicaid expansion on access and utilization of CRC screening modalities by Medicaid state expansion status across the United States.METHODS This was a quasi-experimental study design using data from the Behavioral Risk Factor Surveillance System,a large health system survey for participants across the United States and with over 2.8 million responses.The period of the study was from 2011 to 2016 which was dichotomized as pre-ACA Medicaid expansion(2011-2013)and post-ACA Medicaid expansion(2014-2016).The change in utilization of access to CRC screening strategies between the expansion periods were analyzed as the dependent variables.Secondary analyses included stratification of the access by ethnicity/race,income,and education status.RESULTS A greater increase in utilization of access to CRC screening was observed in Medicaid expansion states than in nonexpansion states[+2.9%;95%confidence interval(95%CI):2.12,3.69].Low-income participants showed a+4.02%(95%CI:2.96,5.07)change between the expansion periods compared with higher income groups+3.19%(1.70,4.67).Non-Hispanic Whites and Hispanics[+3.01%(95%CI:2.16,3.85)vs+5.51%(95%CI:2.81,8.20)]showed a statistically significant increase in utilization of access but not in Non-Hispanic Blacks,or Multiracial.There was an increase in utilization across all educational levels.This was significant among those who reported having a high school graduate degree or more+4.26%(95%CI:3.16,5.35)compared to some high school or less+1.59%(95%CI:-1.37,4.55).CONCLUSION Medicaid expansion under the Affordable Care Act led to an overall increase in self-reported use of CRC screening tests by adults aged 50-64 years in the United States.This finding was consistent across all low-income populations,but not all races or levels of education.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Diabetes group medical visits and outcomes of care in low-income, rural, uninsured persons

Purpose: The purpose of this study was to test the effectiveness of Diabetes Group Medical Visits (DGMVs) verses usual care in a sample of low-income patients with diabetes receiving care at a rural free clinic. Methods: Data were collected through chart review, using direct data entry into Microsoft Access. Participants were included if they met the inclusion criteria: 1) age ≥ 18 years;2) diagnosis of diabetes;3) uninsured and received care between May 2007 and August 2009. Fifty-three participants attended DGMVs and were compared to 58 participants who received usual care. Results: The personal characteristics and biophysical measures of this population differed from previously studied Group Visit populations. The majority of patients were female (73.9%), white (95.5%), younger than 50 (53.2%), driving long distances to receive care (mean miles = 21, SD 20.4) and had a high school education or less (95.4%). Participants were severely obese (mean BMI = 37.6, SD 28.48) and had 5 co-morbid conditions other than diabetes (mean = 5.5, SD 2.1). Those attending DGMVs had higher baseline A1C, depression scores, BMIs, and more pain than usual care. There was a statistically significant decrease in systolic pressure from time one to time two in patients who attended DGMVs t(52) = 2.18, (p = 0.03). There was no significant impact on outcomes of patients who received usual care. However, it is important to note that the majority of patients attended three or fewer DGMVs visits in one year. Conclusion: Group visits may not be enough to improve outcomes in this population. Previous studies suggest that improvements are seen in those who attend more frequently. Hence, the lack of improvement in biophysical outcomes may be due to low attendance. The limited impact of this traditional style intervention in relation to low attendance argues the need to test alternative interventions to reach this population.

Metamorphic P-T conditions and ages of garnet-biotite schists in the Dahongshan Group from the southwestern Yangtze Block

The Dahongshan Group,situated at the southwestern margin of the Yangtze Block,represents a geological unit characterized by relatively high-grade metamorphism in the region.This paper investigates the garnet-biotite schist from the Laochanghe Formation of the Dahongshan Group,employing an integrated approach that includes petrological analysis,phase equilibrium modeling,and zircon U-Pb dating.The schist is mainly composed of garnet,biotite,plagioclase,quartz,rutile,and ilmenite.Phase equilibrium modeling revealed the peak metamorphic conditions of 8-9 kbar and 635-675°C.By further integrating the prograde metamorphic profile of garnet and geothermobarometric results,a clockwise P-T metamorphic evolution path is constructed,which includes an increase in temperature and pressure during the prograde stage.LA-ICP-MS zircon U-Pb dating and zircon Ti thermometry constrains the post-peak metamorphic age of 831.2±7.2 Ma.Integrated with previously reported results,it is revealed that the southwestern margin of the Yangtze Block experienced a large-scale regional metamorphism during the Neoproterozoic(890-750 Ma),which is related to the collisional orogenic process.This may be associated with the late-stage assembly of the Rodinia supercontinent or with local compression and subduction processes during the breakup of the Rodinia supercontinent.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.