Linear Aggregates Research Articles

Decision making for renewable energy source selection using q-rung linear Diophantine fuzzy hypersoft aggregation operators

Decision making for renewable energy source selection using q-rung linear Diophantine fuzzy hypersoft aggregation operators

Preliminary Study on Multi-Scale Modeling of Asphalt Materials: Evaluation of Material Behavior through an RVE-Based Approach

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official’s maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions.

Vibrator Rack Pose Estimation for Monitoring the Vibration Quality of Concrete Using Improved YOLOv8-Pose and Vanishing Points

Monitoring the actual vibration coverage is critical for preventing over- or under-vibration and ensuring concrete’s strength. However, the current manual methods and sensor techniques fail to meet the requirements of on-site construction. Consequently, this study proposes a novel approach for estimating the pose of concrete vibrator racks. This method integrates the Linear Spatial Kernel Aggregation (LSKA) module into the You Only Look Once (YOLO) framework to accurately detect the keypoints of the rack and then employs the vanishing point theorem to estimate the rotation angle of the rack without any 3D datasets. The method enables the monitoring of the vibration impact range for each vibrator’s activity and is applicable to various camera positions. Given that measuring the rotation angle of a rack in reality poses is challenging, this study proposes employing a simulation environment to validate both the feasibility and accuracy of the proposed method. The results demonstrate that the improved YOLOv8-Pose achieved a 1.4% increase in accuracy compared with YOLOv8-Pose, and the proposed method monitored the rotation angle with an average error of 6.97° while maintaining a working efficiency of over 35 frames per second. This methodology was successfully implemented at a construction site for a high-arch dam project in China.

Assessment of the Climate Change Vulnerability of the Cities in Turkey

Climate-related vulnerability indices are increasingly being utilized to enhance the creation of better disaster management strategies and to better understand and anticipate the effects of disasters related to climate change. This study evaluates the climate change vulnerability of the cities in Türkiye through focusing on their exposure, susceptibilities and adaptive capacities to climate change. Data from social, economic and environmental sub-indicators were assessed and most relevant indicators were aggregated with the goal of constructing a composite Climate Change Vulnerability Index (CCVI). The CCVI includes six forms of capital leading to socio-economic and environmental sustainability i.e. social, public utility and transport, economics, land cover, meteorology and atmospheric conditions, and natural disaster capitals, and will be assessed combining each of these forms of capitals and its three dimensions (exposure, susceptibility and adaptive capacity). Stakeholder-driven structured methodology that discovers and ranks context-relevant indicators and sets weights for aggregating indicator scores by using the Best-Worst method (BWM) and stepwise weight assessment ratio analysis (SWARA) method are utilised. The indicators are aggregated through application of the BWM and SWARA weights using a linear aggregation method. From BWM and SWARA, the highest weights were obtained for meteorological conditions and land cover which are more than 0.36 and 0.22, respectively. The lowest weights were assigned to social characteristics and economy both of which were smaller than 0.10. The findings indicated that the cities on the northern, western and southern coasts as well as the cities in south-east region are the most vulnerable to climate change. The construction of CCVI can be used as part of decision-making process to minimize hazards and exposure to risk of climate change for the cities of Türkiye.

Compact modeling of highly excited linear aggregates using generalized quantum particles

Calculation of nonlinear spectra of chromophore aggregates using response function theory when the number of contributing chromophores is large, and the level of excitation is high is extremely complicated. The main limitation is due to the exponential growth of computational time due to the aggregate size and number of excitations when considering an arbitrary excitation intensity. Non-perturbative calculation of spectra in this case becomes advantageous. We revisit our proposed model with exciton - exciton annihilation terms and apply it to large aggregates. We generalize the equations for both paulions and bosons with a parameter that allows smooth transition from one description to another. Intermediate statistics may also be valuable as molecular electronic excitations do not strictly obey either boson or paulion statistics. Specific approximations allow efficient calculation of pump-probe spectra for a large J aggregate.

A linear programming aggregation method based on generalized Zhenyuan integral in q-ROFN environment and the application of talent recruitment in universities

The reasonable ranking of binary pairs that characterize fuzzy information in many fuzzy decision problems is very important. To overcome some defects of the existing score functions for the q-rung orthopair fuzzy numbers (q-ROFNs), a novel score function and ranking criterion are proposed by the q-compression transformation and hesitation factor. The main motivation is to introduce the generalized Zhenyuan (GZ)-integral into the q-ROFN environment, and cleverly transform the aggregation operations into a linear programming problem through the arithmetic operations of q-ROFNs. The main contribution is to solve the aggregation problem of q-rung orthopair fuzzy generalized Zhenyuan integral ordered weighted average (q-ROFGZIOWA) operator through the optimization technique of linear programming, and a new decision making method is established by using the q-ROFGZIOWA operator and ranking criterion. The main innovation is to map all q-ROFNs to the unit triangle in the first quadrant (converted into intuitionistic fuzzy numbers, IFNs) according to the q-compression transformation in geometric significance, and the novel score function and its ranking criterion are proposed by combining hesitation factor, and then the aggregation operation based on generalized Z-integral is converted to an optimization problem in linear programming. Finally, the superiority of the proposed method are verified by comparing the aggregation results of two integral operators through an example, and apply the proposed method to the optimal decision-making of talent recruitment in universities. The proposed method can not only correct some flaws in the ranking of existing q-ROFNs, but also overcomes some defects of existing Choquet integral average (geometric) operators in a q-ROFN environment. These results are of great significance for further research on the widespread application of q-ROFNs.

Synergistic effects of biological stimuli and flexion induce microcavities promote hypertrophy and inhibit chondrogenesis during in vitro culture of human mesenchymal stem cell aggregates.

Interzone/cavitation are key steps in early stage joint formation that have not been successfully developed in vitro. Further, current models of endochondral ossification, an important step in early bone formation, lack key morphology morphological structures such as microcavities found during development in vivo. This is possibly due to the lack of appropriate strategies for incorporating chemical and mechanical stimuli that are thought to be involved in joint development. We designed a bioreactor system and investigated the synergic effect of chemical stimuli (chondrogenesis-inducing [CIM] and hypertrophy-inducing medium [HIM]) and mechanical stimuli (flexion) on the growth of human mesenchymal stem cells (hMSCs) based linear aggregates under different conditions over 4 weeks of perfusion culture. Computational studies were used to evaluate tissue stress qualitatively. After harvesting, both Safranin-O and hematoxylin & eosin (H&E) staining histology demonstrated microcavity structures and void structures in the region of higher stresses for tissue aggregates cultured only in HIM under flexion. In comparison to either HIM treatment or flexion only, increased glycosaminoglycan (GAG) content in the extracellular matrix (ECM) at this region indicates the morphological change resembles the early stage of joint cavitation; while decreased type II collagen (Col II), and increased type X collagen (Col X) and vascular endothelial growth factor (VEGF) with a clear boundary in the staining section indicates it resembles the early stage of ossification. Further, cell alignment analysis indicated that cells were mostly oriented toward the direction of flexion in high-stress region only in HIM under flexion, resembling cell morphology in both joint cavitation and hypertrophic cartilage in growth plate. Collectively, our results suggest that flexion and HIM inhibit chondrogenesis and promote hypertrophy and development of microcavities that resemble the early stage of joint cavitation and endochondral ossification. We believe the tissue model described in this work can be used to develop in vitro models of joint tissue for applications such as pathophysiology and drug discovery.

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), named as VCBA, for spatio-temporal fusion of multi-site data to forecast daily PM2.5 concentrations in a city. The approach involves integrating air quality data from the target site with data from neighboring sites, applying mathematical techniques for dimensionality reduction, decomposing PM2.5 concentration data using VMD, and utilizing Causal CNN and BiLSTM models with an attention mechanism to enhance performance. The final prediction results are obtained through linear aggregation. Experimental results demonstrate that the VCBA model performs exceptionally well in predicting daily PM2.5 concentrations at various stations in Taiyuan City, Shanxi Province, China. Evaluation metrics such as RMSE, MAE, and R2 are reported as 2.556, 1.998, and 0.973, respectively. Compared to traditional methods, this approach offers higher prediction accuracy and stronger spatio-temporal modeling capabilities, providing an effective solution for accurate PM2.5 daily concentration prediction.

Development of Aczel-Alsina t-norm based linear Diophantine fuzzy aggregation operators and their applications in multi-criteria decision-making with unknown weight information

Aczel-Alsina t-norm and t-conorm are intrinsically flexible and endow Aczel-Alsina aggregation operators with greater versatility and robustness in the aggregation process than operators rooted in other t-norms and t-conorm families. Moreover, the linear Diophantine fuzzy set (LD-FS) is one of the resilient extensions of the fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PyFSs), and q-rung orthopair fuzzy sets (q-ROFSs), which has acquired prominence in decision analysis due to its exceptional efficacy in resolving ambiguous data. Keeping in view the advantages of both LD-FSs and Aczel-Alsina aggregation operators, this article aims to establish Aczel-Alsina operation rules for LD-FSs, such as Aczel-Alsina sum, Aczel-Alsina product, Aczel-Alsina scalar multiplication, and Aczel-Alsina exponentiation. Based on these operation rules, we expose the linear Diophantine fuzzy Aczel-Alsina weighted average (LDFAAWA) operator, and linear Diophantine fuzzy Aczel-Alsina weighted geometric (LDFAAWG) operator and scrutinize their distinctive characteristics and results. Additionally, based on these aggregation operators (AOs), a multi-criteria decision-making (MCDM) approach is designed and tested with a practical case study related to forecasting weather under an LD-FS setting. The developed model undergoes a comparative analysis with several prevailing approaches to demonstrate the superiority and accuracy of the proposed model. Besides, the influence of the parameter Λ on the ranking order is successfully highlighted.

The concentration limit of stability for individual gold nanoparticles in aqueous colloid during water evaporation

The work established the presence of a concentration limit for the stability of aqueous colloids of gold nanoparticles obtained using laser ablation. It has been shown that in aqueous colloid of gold nanoparticles with a size of 15 nm, upon evaporation of water, the concentration of nanoparticles monotonically increases to a certain limit. When the limit is reached (the concentration of nanoparticles is about 1013 NPs/ml), a threshold process of decreasing the concentration of nanoparticles due to aggregation is observed. In this case, the ζ-potential of the entire colloid does not change. It has been shown that the aggregation process for some gold nanoparticles is irreversible; linear aggregates in the form of dimers, trimers and chain aggregates of greater length are observed in the colloid.

Perceptual point cloud qality assessment for immersive metaverse experience

Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task. Firstly, because point cloud is formed by unstructured 3D points that makes the topology more complex. Second, the quality impairment generally involves both geometric attributes and color properties, where the measurement of the geometric distortion becomes more complex. We proposed a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System (HVS) and the intrinsic characteristics of the point cloud. The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling, and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors (KNN) clustering. For geometric distortion, the Point Feature Histogram (PFH) is extracted as the feature descriptor, and the Earth Mover's Distance (EMD) between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement. For color distortion, the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement. Finally, the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement. The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time. Meanwhile, the experimental results also demonstrate the robustness of the proposed method across various distortion types. The source codes are available at https://github.com/llsurreal919/PointCloudQualityAssessment.

On Risk Evaluation and Control of Distributed Multi-agent Systems

Abstract In this paper, we deal with risk evaluation and risk-averse optimization of complex distributed systems with general risk functionals. We postulate a novel set of axioms for the functionals evaluating the total risk of the system. We derive a dual representation for the systemic risk measures and propose new ways to construct families of systemic risk measures using either a collection of linear scalarizations or non-linear risk aggregation. The proposed framework facilitates risk-averse sequential decision-making by distributed methods. The new approach is compared theoretically and numerically to other systemic risk measurements from the existing literature. We formulate a two-stage decision problem for a distributed system using a systemic measure of risk. The structure accommodates distributed systems arising in energy networks, robotics, and other practical situations. A distributed decomposition method for solving the two-stage problem is proposed and applied to a problem arising in communication networks. We have used this problem to compare the methods of systemic risk evaluation. We show that the risk evaluation via linear scalarizations of outcomes leads to less conservative risk evaluation and results in a substantially better solution to the problem at hand than aggregating the risk of individual agents.

Differentially private distributed estimation and learning

We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks. Our novel algorithms extend the existing distributed estimation literature and enable the participating agents to estimate the expected value of a complete sufficient statistic from private signals acquired offline or online over time and to preserve the privacy of their signals and network neighborhoods. This is achieved through linear aggregation schemes with adjusted randomization schemes that add noise to the exchanged estimates subject to differential privacy constraints, both in an offline and online manner. We provide convergence rate analysis and tight finite-time convergence bounds. We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent’s sensitivity to their signal and network characteristics. Our algorithms are amenable to dynamic topologies and balancing privacy and accuracy trade-offs. Finally, to supplement and validate our theoretical results, we run experiments on real-world data from the US Power Grid Network and electric consumption data from German Households to estimate the average power consumption of power stations and households under all privacy regimes and show that our method outperforms existing first-order, privacy-aware, distributed optimization methods.

Investigation of the structure and multi-stimuli-responsive behavior of ion-associated salt solvates of binary crystals

Investigation of the intermolecular interactions and crystal packing to understand crystal growth is a prime objective of crystal engineering. Solvent molecules within crystal assist the supramolecular aggregation, however, control over their lattice incorporation remains uncertain. In this work, we report three solvates of an ionic organic salt of a diazo-naphthol sulfonate (1) and 1,10-phenanthroline with one (2), three (3) and four (4) lattice water molecules. Attempts to isolate the dihydrate form were unsuccessful, while the monohydrate of 1 has been crystallised for comparative structural investigations. Structural studies reveal the existence of intriguing sulfonate-diazo intermolecular interactions responsible for centrosymmetric dimerization of the organosulfonate 1, which also persist in 2–4, validating higher preference and robustness of the synthon. Repetitive aggregation of the sulfonate-diazo interaction in 1 leads to the formation of linear π-stacked aggregate, which further aggregates through the lattice water molecules. The structural comparison indicates the preferential formation of the organo-sulfonate dimers in hydrated salts 2–4. Further, sandwich tetramers in 2 and 4 are formed by charge transfer interaction of the organo-sulfonate dimers with pyridinium coformer on either side, which are further associated by the lattice water molecules into disparately packed 3-dimensional supramolecules. The organo-sulfonates in 3 form a similar type of extended π-stacked chains as in 1, which are flanked by pyridinium ions to form hydrogen-bonded tapes, which are helped by the lattice water molecules to grow as segregated stacked layer solid. The characteristic absorption frequency of the diazo functionality is blue-shifted in infrared spectra of the cocrystal forms, indicating the presence of charge transfer interactions. The presence of dynamic solvent and protons in the lattice makes the solid forms multi-stimuli-responsive to exhibit mechano and vapochromic response. These phenomena have been studied in detail with the help of thermal, spectroscopic, and powder-diffraction studies.

Linear diophantine fuzzy Aczel-Alsina aggregation operators by using fuzzy-C means clustering algorithm

This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes.

Measuring food system sustainability in Ethiopia: Towards a Multi-Dimensional perspective

In the face of continuous population growth, environmental degradation, climate extremes and enduring abject poverty, ensuring sustainability of the food system is of crucial importance. As such, developing a comprehensive measure of sustainability has been of a great interest for both researchers and policy makers so as to measure progresses and evaluate the outcomes overtime. This paper attempts to assess the food system sustainability (FSSI) in Ethiopia over the past two decades from 2001 to 2020. A composite index was constructed by aggregating 24 selected food system indicators in the four food system dimensions: environmental, economic, social, and food and nutrition security. ‘Distance to a Reference’ method was employed for data normalization, whereas equal weighting and linear aggregation methods were used for aggregating the indicators in each dimension. The result showed that in a scale from 0 to 1, better score was achieved in the social dimension (0.66), followed by environmental dimension (0.52), and food and nutrition dimension (0.43), with the least recorded in the economic dimension (0.31) in 2020. The overall Food System Sustainability Index for the most recent year (2020) was estimated to be 0.46 which is far below a satisfactory level of food system sustainability (0.75) as per OECD Classification of Sustainability Indices. The result calls for policy interventions aimed at improving the overall food system sustainability in light of the four food system dimensions, in general, and the weak dimensions, in particular. Given the predictable need for more food production in the face of growing populace, interventions that enhance synergies and limit the potential trade-off among the food system outcomes, as well as striking the right balance should be the policy direction for Ethiopia.

Engineering heterogeneous hierarchical hydrogels based on Artemisia sphaerocephala Krasch polysaccharide/whey protein isolate fibrils aqueous two-phase emulsion

Emulsion-templating techniques have been used to develop structurally anisotropic hydrogels. This paper proposes a method to couple biopolymer assembly behavior with a phase-separated aqueous two-phase system (ATPS) to construct a hierarchical structure on protein-polysaccharide hydrogels. Based on the phase separation of Artemisia sphaerocephala Krasch polysaccharide (ASKP) and whey protein isolate (WPI) fibrils, a bi-continuous hydrogel was fabricated through spontaneous iron-facilitated molecular chelation. The impact of WPI fibrils under ultrasonic treatment for varying durations on gel structurization and physicochemical properties was also exploited. Shorter length of WPI fibrils facilitated stepwise cross-linking gelation during the oxidation of ferrous ions to ferric ions. Specifically, ultrasonic treatment for 6 min and 8 min promoted the accumulation of shorter fibrils, resulting in the formation of a more intensive clockwise-oriented hierarchical network. Compared to the hydrogels with longer linear aggregates without the ultrasonic treatment, the incorporation of the shorter aggregates resulting from ultrasonic treatment for 6 min and 8 min significantly increased the gel's water holding capacity and mechanical properties. Additionally, the fibrils that underwent ultrasonic treatment for 6 min resulted in a more stable three-dimensional network with optimal elastic performance. This study presents a new water-in-water emulsion-templated strategy for structural regulation and stabilization of multiform hydrogel.

Molecular dynamics study of water molecules nucleation for fine particle removal: Effects of wettability and aggregation modes and comparison with experiment

Water vapor nucleation on particle's surface plays an important role in dust removal, cloud formation, and particle measurement. However, the selectivity of nucleation sites and the nucleation characteristic of water molecule on the particle's surface are still unclear, especially for the aggregated particles. In this paper, the effects of particle wettability and aggregation modes on the selectivity of nucleation sites and the nucleation characteristics were investigated using molecular dynamics simulation. The results were compared with our earlier experimental findings. It illustrates how the contact angle of clusters, the growth velocity, and the growth duration are all influenced by the interaction coefficient between water and particles. Moreover, the nucleation sites of water molecules on the particle aggregation surface exhibit a definite selectivity. The primary indicator of this selectivity is the preferential nucleation of water molecules at the interfaces of linear chain aggregation particles, at the inner side of non-linear chain aggregation particles, and at the centers of ring aggregation. These results are in good agreement with our previous experimental findings. More significantly, additional research has revealed that subcritical-size clusters typically aggregate on two-particle surfaces spacing when the spacing smaller than the critical cluster size.

Analysis of Accounting Information Systems on MSME Performance with Emotional Intelligence as Moderation

Good Emotional Intelligence (comprising self-awareness, motivation, and social skills) enables one to make appropriate decisions under a variety of circumstances. An organization's accounting information system is a device utilized to streamline the majority of business operations, regardless of the company's size (small, medium, or large). Due to the fact that MSMEs are a primary driver of economic expansion, the creation of new employment is crucial to their ability to make sound decisions.The analysis technique refers to a process of data analysis designed to yield information that can be utilized in this study. In order to facilitate researchers' comprehension of the data as a foundation for decision making, it is necessary to analyze the data for interpretation. The present study employed the Partial Least Squares (PLS) methodology is carried out in in three stages: Outer model analysis, Inner model analysis, Hypothesis testing. In accordance with the formal model, latent variables are defined explicitly as linear aggregates of observed variables or their indicators. In order to generate weight estimates for latent variable score components, the inner and outer model specifications are compared.Determine the impact of emotional intelligence (self-awareness, motivation, and social skills) on the performance of micro, small, and medium-sized enterprises (MSME) in relation to the implementation of accounting information systems. The primary data utilized in this study were collected through the distribution of questionnaires to MSME entrepreneurs in Jember Regency, who provided their responses. Clearly, the accounting information system plays a crucial role in the progress and growth of MSMEs. Emotional Intelligence has no appreciable impact on the efficacy of MSME businesses as it relates to accounting information systems. Nonetheless, Emotional Intelligence significantly impacts the business performance of SMEs.

Anisotropic span embeddings and the negative impact of higher-order inference for coreference resolution: An empirical analysis

Abstract Coreference resolution is the task of identifying and clustering mentions that refer to the same entity in a document. Based on state-of-the-art deep learning approaches, end-to-end coreference resolution considers all spans as candidate mentions and tackles mention detection and coreference resolution simultaneously. Recently, researchers have attempted to incorporate document-level context using higher-order inference (HOI) to improve end-to-end coreference resolution. However, HOI methods have been shown to have marginal or even negative impact on coreference resolution. In this paper, we reveal the reasons for the negative impact of HOI coreference resolution. Contextualized representations (e.g., those produced by BERT) for building span embeddings have been shown to be highly anisotropic. We show that HOI actually increases and thus worsens the anisotropy of span embeddings and makes it difficult to distinguish between related but distinct entities (e.g., pilots and flight attendants). Instead of using HOI, we propose two methods, Less-Anisotropic Internal Representations (LAIR) and Data Augmentation with Document Synthesis and Mention Swap (DSMS), to learn less-anisotropic span embeddings for coreference resolution. LAIR uses a linear aggregation of the first layer and the topmost layer of contextualized embeddings. DSMS generates more diversified examples of related but distinct entities by synthesizing documents and by mention swapping. Our experiments show that less-anisotropic span embeddings improve the performance significantly (+2.8 F1 gain on the OntoNotes benchmark) reaching new state-of-the-art performance on the GAP dataset.