Number Of Complexes Research Articles

Analysis of different objective functions in petroleum field development optimization

Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.

Computation of polynomial and rational approximations in complex domains by the $$\tau $$-method

Computation of polynomial and rational approximations in complex domains by the $$\tau $$-method

SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models

Accurate inference of orthologous genes constitutes a prerequisite for comparative and evolutionary genomics. SonicParanoid is one of the fastest tools for orthology inference; however, its scalability and accuracy have been hampered by time-consuming all-versus-all alignments and the existence of proteins with complex domain architectures. Here, we present a substantial update of SonicParanoid, where a gradient boosting predictor halves the execution time and a language model doubles the recall. Application to empirical large-scale and standardized benchmark datasets shows that SonicParanoid2 is much faster than comparable methods and also the most accurate. SonicParanoid2 is available at https://gitlab.com/salvo981/sonicparanoid2 and https://zenodo.org/doi/10.5281/zenodo.11371108.

A ligand discovery toolbox for the WWE domain family of human E3 ligases

The WWE domain is a relatively under-researched domain found in twelve human proteins and characterized by a conserved tryptophan-tryptophan-glutamate (WWE) sequence motif. Six of these WWE domain-containing proteins also contain domains with E3 ubiquitin ligase activity. The general recognition of poly-ADP-ribosylated substrates by WWE domains suggests a potential avenue for development of Proteolysis-Targeting Chimeras (PROTACs). Here, we present novel crystal structures of the HUWE1, TRIP12, and DTX1 WWE domains in complex with PAR building blocks and their analogs, thus enabling a comprehensive analysis of the PAR binding site structural diversity. Furthermore, we introduce a versatile toolbox of biophysical and biochemical assays for the discovery and characterization of novel WWE domain binders, including fluorescence polarization-based PAR binding and displacement assays, 15N-NMR-based binding affinity assays and 19F-NMR-based competition assays. Through these assays, we have characterized the binding of monomeric iso-ADP-ribose (iso-ADPr) and its nucleotide analogs with the aforementioned WWE proteins. Finally, we have utilized the assay toolbox to screen a small molecule fragment library leading to the successful discovery of novel ligands targeting the HUWE1 WWE domain.

Structure of biomolecular films through advanced imaging and statistical analysis

This manuscript is dedicated to advancing the understanding of biomolecular film on solid target formation, with a particular emphasis on the intricacies of structure nonuniformities in amino acid and protein-based dehydrated films. Experimental and theoretical studying the structure of films at the quantitative level has been created and tested. By integrating advanced imaging and statistical analysis, we dissect the dynamics and intricacies of domain structures, including cellular and microcrystalline formations, that significantly influence dehydrated biomolecular film properties. The research process entails preparing amino acid and protein solutions, capturing high-resolution images during dehydration, and implementing advanced digital image processing techniques like Laplacian Pyramid Restoration and Gaussian Differential Scale-Invariance for enhanced image clarity. Analytical approach includes domain segmentation and texture analysis using Haralick features, enabling precise quantification of structural complexities in films formed under various dehydration conditions and protein concentrations. The suggested physical model describes the transition from equilibrium solutions through nonequilibrium processes to complex bubble vaporization, dehydration-induced Dewetting and domain differentiation, thus explained observed spiral formations within protein domains. This model provids a detailed mechanistic understanding of how environmental conditions and molecular dynamics drive the formation of domains, paving the way for developing films with tailored properties for diverse applications. These insights hold significant implications across various fields, including material science, nanotechnology, biomedical applications, environmental monitoring, electronics, and photonics.

Identifying point defects and ordering in the high-entropy layered oxide Li1.5MO3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li1.5MO3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery

With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.

X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFD

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at https://gapszju.github.io/X-SLAM.

Complex-valued recurrent neural network equalizer with low complexity for a 120-Gbps 50-km optical PAM-4 IM/DD system.

This paper introduces a novel complex-valued recurrent neural networks equalizer (RNNE) designed for a 120-Gbps, 50-km optical 4-level pulse-amplitude modulation (PAM-4) intensity modulation and direct detection (IM/DD) system. By mapping adjacent symbols of PAM-4 signals onto the complex domain, the correlation between two adjacent symbols of PAM-4 signals can be preserved. Based on experimental results, the proposed complex-valued RNNE outperforms the traditional real-valued RNNE with a 1.38-dB system power budget gain at the 7% overhead forward error correction BER threshold of 3.8 × 10-3. We believe that complex-valued RNNE has an advantage over real-valued RNNE in processing real-valued signals in IM/DD systems.

Analytical realization of complex thermal meta-devices

Fourier’s law dictates that heat flows from warm to cold. Nevertheless, devices can be tailored to cloak obstacles or even reverse the heat flow. Mathematical transformation yields closed-form equations for graded, highly anisotropic thermal metamaterial distributions needed for obtaining such functionalities. For simple geometries, devices can be realized by regular conductor distributions; however, for complex geometries, physical realizations have so far been challenging, and sub-optimal solutions have been obtained by expensive numerical approaches. Here we suggest a straightforward and highly efficient analytical de-homogenization approach that uses optimal multi-rank laminates to provide closed-form solutions for any imaginable thermal manipulation device. We create thermal cloaks, rotators, and concentrators in complex domains with close-to-optimal performance and esthetic elegance. The devices are fabricated using metal 3D printing, and their omnidirectional thermal functionalities are investigated numerically and validated experimentally. The analytical approach enables next-generation free-form thermal meta-devices with efficient synthesis, near-optimal performance, and concise patterns.

Investigating rheological characteristics of nonlinear fluid model in an asymmetric channel: A peristaltic pumping of blood with curvature and hydrodynamic impacts

Theoretical research is investigated for the steady rheology of incompressible blood from an asymmetric (penetrable) channel in the presence of curvature and non-linear hydrodynamic impacts. The motion takes place in an asymmetric channel due to a train of sinusoidal (complex nature) waves along the channel boundaries having different phases. In this study, the Casson fluid is used as blood. The governing equations are modeled in terms of Cartesian coordinates. The impacts of non-linear curvature become significant on the transportation of biological fluid in a complex domain such as the non-pregnant uterus at a higher Reynolds number. An approximate solution of flow models is obtained to the second order in the wavenumber (giving the curvature effect). A domain transformation is used to transform the variable cross-sectional of the flow regime into the uniform cross-section. This transformation helps to find the closed-form solution of transformed rheological equations in higher order. The physical impacts of the curvature parameter, Casson parameter, phase difference, flow rate, Reynolds number, and permeability parameter on the axial velocity, pressure gradient, stress tensor, trapping phenomena, and pressure rise are investigated. The velocity of blood is smaller as it is associated with the viscous fluid. The number of boluses was reduced by increasing the Casson parameter and Reynolds number in the trapping phenomenon. The stress tensor is an increasing function of the permeability parameter and the Reynolds number. The pressure gradient is affected by the permeability parameter. The comparison between viscous fluid and blood is also addressed in the current study. The outcomes of the current study arise in the medical domains, blood study, manufacturing of nano-blood pumps, bio-physical food processing, and chemical industry.

Harmonic Mean Optimizer (HMO) for global problems solving

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO’s applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains.

Non-local time evolution equation with singular integral and its application to traffic flow model

We consider an integro-differential equation model for traffic flow, which extends the Burgers equation model. To derive exact solutions to the equation, we first examine the features of integrable integro-differential equations and find that they are obtained through the residue formula from integrable equations in complex domains. Using this approach, we construct several new integrable equations with double singular integrals and elliptic singular integrals. Then, we discuss the traffic model and show that it exhibits interactions between free-flow and congested regions, as well as the deadlock phenomenon, depending on the non-locality parameter.

An overview on copper based heterometallic complexes in combination with d10 metal ions derived from N2O2 donor di-Schiff base ligands

A good number of heterometallic complexes based on various Cu(II) bound “metalloligands” of N2O2 donor di-Schiff bases and some of their reduced counterpart along with group 12 metal ions have been synthesized and characterized by different research groups in the literature. This review focuses on an overview of the synthetic strategies, X-ray crystallographic structures and a few important properties of these complexes. These have been discussed in three major sub-classes: Cu(II)-Zn(II), Cu(II)-Cd(II) and Cu(II)-Hg(II) heterometallic complexes. Judicious combinations of carbonyls and di-amines in an easy and effective manner gives 14 selected di-Schiff base ligands (symmetrical and unsymmetrical) with particular shape, geometry and denticity that direct the synthesis of these metal complexes totally or preferentially towards di-, tri- or poly-nuclear entities. Moreover, owing to their ability to assemble three metal ions in close proximity, these resulting oxido-bridged flexible coordination clusters adapted to various other ligand functionalities with particular shape and sometimes unusual coordination modes with certain degree of control. Such unique structural aspect of this system is immensely important in inorganic synthesis. The inherent flexibility of the di-oxido bridged trinuclear coordination cluster led to produce isomeric linear and bent structures that usually depend upon various factors e.g. the nature of the Schiff base, anionic co-ligands, temperature, solvent etc. The structural aspects of resulting discrete and polymeric self-assemblies have enriched the coordination library that extended to develop numerous heterometallic systems with significant applications and new scopes

Expanding model transparency and learning potential through structural and behavioural debriefings

AbstractSimulation‐based learning environments have been recommended by researchers as educational tools to support learning in complex domains. However, studies have evidenced that subjects may nevertheless have great difficulty understanding and managing dynamic systems, with several assertions are being made regarding the impact of employing transparent model simulations on learning and performance outcomes. This research, therefore, examines the moderating impacts of structural and behavioural debriefings, which are concentrated respectively on critical variables and relations, and on the connection between model structure, action patterns and system behaviour. We propose a series of hypotheses regarding the impact of model transparency and prior debriefing conditions on participants' performance and their comprehension of the system dynamics. Three out of the six hypotheses were validated: prior structural debriefing alongside model transparency positively impacts performance; prior behavioural debriefing, when combined with model transparency and prior structural debriefing, positively impacts both the comprehension of model dynamics and performance.

Two types of anti-noise integral enhanced recurrent neural networks for solving time-varying complex quadratic programming

The time-varying complex quadratic programming problem plays an important role in scientific research and engineering applications, and has received extensive attention. In this paper, by solving the problem in different number domains, two types of novel anti-noise integral enhanced recurrent neural networks (IERNNs) are proposed and analyzed. Specifically, the IERNN-A model solves the problems in the complex number domain, while the IERNN-B model solves the problems by converting the problems from the complex number domain into the real number domain. Both the IERNN-A and IERNN-B have the same design paradigm, i.e., an integral-type error function is first defined to obtain good anti-noise capability, and then substitute it into a differential-driven design formula to ensure convergence. Theory analysis and simulation results verify the global convergence and robustness of the proposed IERNNs. Furthermore, if a linear activation is used, the exponential convergence rate is obtained. Meanwhile, when time-varying bounded noise exists, the IERNNs are input-to-state stable. In addition, compared with other art-of-the-state methods, the proposed methods have faster convergence speed, stronger noise-suppression ability, and no overshoot, which are verified by the simulation and application of manipulator trajectory tracking.

Climate change-induced landslide vulnerability: Empirical evidence from Shimla district, Himachal Pradesh, India

Landslides as dynamic geological hazard have caused socio-economic and environmental vulnerabilities at various scales. Thus, landslide vulnerability assessment is essential for understanding the implications for society and the environment. The present study attempts to examine landslide vulnerability in Shimla District, Himachal Pradesh, India. It constructed a composite landslide vulnerability index (CLVI) by integrating 57 indicators of 7 different domains (social vulnerability index, physical vulnerability index, economic vulnerability index, climate change and environmental vulnerability index, early warning system index, emergency response system, and adaptation strategies index) at the household level. CLVI serves as a most effective tool for the quantification of complex domains of disaster vulnerability and identification of thematic areas for disaster risk reduction. The data on various indicators were derived through a field survey of 450 households from 30 villages. The relationship between CLVI and its domains was ascertained using multiple linear regression. The CLVI analysis revealed that nine villages were highly vulnerable to landslides, while six villages experienced moderate vulnerability. Climate change and environmental vulnerability, physical fragility, and low adaptation strategies induced high to moderate vulnerability. The regression analysis showed that the degree of vulnerability was mostly influenced by the physical domain, followed by the economic and environmental domains. Effective policy, institutional setups, provision of landslide-resistant buildings, and livelihood diversification may enhance adaptive capacity among the communities. Thus, CLVI has not only helped in identifying thematic areas for reducing vulnerability but has also been instrumental in recognizing priority areas.

Impact of global smoking prevalence on mortality: a study across income groups

BackgroundSmoking significantly contributes to the mortality rates worldwide, particularly in non-communicable and preventable diseases such as cardiovascular ailments, respiratory conditions, stroke, and lung cancer. This study aims to analyse the impact of smoking on global deaths, and its association with mortality across the main income groups.MethodsThe comprehensive analysis spans 199 countries and territories from 1990 to 2019. The study categorises countries into four income groups: high income, upper middle income, lower middle income, and low income.ResultsThe findings underscore the profound impact of global tobacco smoking on mortality. Notably, cardiovascular disease mortality is notably affected in both upper-middle-income and high-income groups. Chronic respiratory disease mortality rates show a significant impact across all income groups. Moreover, stroke-related mortality is observed in the lower-middle, upper-middle, and high-income groups. These results highlight the pervasive influence of smoking prevalence on global mortality, affecting individuals across various socioeconomic levels.ConclusionThe study underscores the critical implications of smoking on mortality rates, particularly in high-income countries. It emphasises the urgency of targeted interventions in these regions to address the specific challenges posed by tobacco smoking on public health. Policy recommendations include implementing prohibitive measures extending to indoor public areas such as workplaces and public transportation services. Furthermore, allocating funds for research on tobacco and health, is imperative to ensure policymakers are consistently informed about emerging facts and trends in this complex domain.

Structural and biochemical analyses of the nuclear IκBζ protein in complex with the NF-κB p50 homodimer.

As part of the efforts to understand nuclear IκB function in NF-κB-dependent gene expression, we report an X-ray crystal structure of the IκBζ ankyrin repeat domain in complex with the dimerization domain of the NF-κB p50 homodimer. IκBζ possesses an N-terminal α helix that conveys domain folding stability. Affinity and specificity of the complex depend on a small portion of p50 at the nuclear localization signal. The model suggests that only one p50 subunit supports binding with IκBζ, and biochemical experiments confirm that IκBζ associates with DNA-bound NF-κB p50:RelA heterodimers. Comparisons of IκBζ:p50 and p50:κB DNA complex crystallographic models indicate that structural rearrangement is necessary for ternary complex formation of IκBζ and p50 with DNA.

P-608 Cumulative live birth rates after 3 consecutive embryo transfers in freeze-all cycles following gonadotropin-releasing hormone antagonist protocol versus progesterone-primed ovarian stimulation

Abstract Study question Do cumulative live birth rates in freeze-all cycles differ between gonadotropin-releasing hormone (GnRH) antagonist protocol and progesterone-primed ovarian stimulation (PPOS)? Summary answer GnRH antagonist and PPOS protocols have similar cumulative live birth rates in freeze-all cycles. What is known already GnRH antagonists and progesterone have been used for pituitary suppression in ovarian stimulation cycles. While PPOS protocol has the advantage of oral usage and lower cost, data regarding the laboratory and clinical outcome is limited and confounding. Freeze-all cycles might provide a better model to assess the impact of PPOS protocol and GnRH antagonist protocol on laboratory, clinical outcomes and cumulative live birth rates. Study design, size, duration A retrospective cohort study on IVF cycles performed (n = 568) between January 2020 and May 2022.The entire cohort of embryos were cryopreserved at the blastocyst stage.Inclusion criteria were 18-42 years of age,ICSI for severe male factor infertility.Exclusion criteria were PGD and severe endometriosis.Indications for cryopreservation in the antagonist group were high ovarian response (n:102),endometrial polyps (n:34),high follicular phase P4(n:48) and elective purposes(n:260).PPOS was used due to a transient shortage of GnRH antagonist in the market (n:124). Participants/materials, setting, methods 568 women (GnRH antagonist:444; PPOS:124) were included in the analysis.The primary outcome was the cumulative live birth rate (CLBR) after 3 consecutive embryo transfer cycle, and the secondary outcome parameters were the number of M-II oocytes, fertilization rate and blastocyst utilization rate. Student’s t-test was used for normally distributed continuous data and Fisher’s exact tests for categorical data were used. A generalized estimating equation model and logistic regression analysis were performed to adjust for confounding factors. Main results and the role of chance The mean age of the GnRH antagonist group was 34.1 ± 5.3 and 34.5 ± 5.7 years in PPOS group (p = 0.38). The baseline characteristics including BMI, serum AMH levels, duration of infertility, duration of stimulation and gonadotropin ampules used were similar in both groups. Estradiol and progesterone levels at the time of ovulation trigger were higher in GnRH antagonist cycles (2383.5 ± 1375.4 vs 2034.5 ± 1218.3, p = 0.02 and 0.7 ± 0.6 vs 0.5 ± 0.4, p = 0.01, respectively) The mean number of cumulus-oophorus complexes (12.9 ± 7.4 vs 11.5± 10.0, p = 0.61), M-II oocytes (10.0± 6.1 vs 9.1± 6.1, p = 0.14), frozen blastocysts (4.8± 3.5 vs 4.7± 3.6, p = 0.71) were similar between GnRH antagonist and PPOS groups, respectively. Fertilization rates (84.4% vs 87.2%, p = 0.12) and blastocyst utilization rates (63.4% vs 65.9%, p = 0.34) were also similar between GnRH antagonist and PPOS groups, respectively. The number of transferred embryos and the number of unused blastocysts were similar between groups. The CLBRs after one IVF cycle and 3 consecutive frozen-thawed blastocyst transfers were comparable in GnRH antagonist and PPOS cycles (56.8% and 53.2%, p = 0.37, respectively). Limitations, reasons for caution This was a retrospective study with relatively small sample size performed at a single fertility center, which may limit the generalizability of our findings. Wider implications of the findings If the fresh embryo transfer is not intended, PPOS protocol can be used in all patient types with cost-effective and patient-friendly manner. Trial registration number 2024.013.IRB2.012