Cluster System Research Articles

The cosmic web around the Coma cluster from constrained cosmological simulations

Galaxy clusters in the Universe occupy the important position of nodes of the cosmic web. They are connected among them by filaments, elongated structures composed of dark matter, galaxies, and gas. The connection of galaxy clusters to filaments is important, as it is related to the process of matter accretion onto the former. For this reason, investigating the connections to the cosmic web of massive clusters, especially well-known ones for which a lot of information is available, is a hot topic in astrophysics. In a previous work, we performed an analysis of the filament connections of the Coma cluster of galaxies, as detected from the observed galaxy distribution. In this work we resort to a numerical simulation whose initial conditions are constrained to reproduce the local Universe, including the region of the Coma cluster to interpret our observations in an evolutionary context. We detect the filaments connected to the simulated Coma cluster and perform an accurate comparison with the cosmic web configuration we detect in observations. We perform an analysis of the halos’ spatial and velocity distributions close to the filaments in the cluster outskirts. We conclude that, although not significantly larger than the average, the flux of accreting matter on the simulated Coma cluster is significantly more collimated close to the filaments with respect to the general isotropic accretion flux. This paper is the first example of such a result and the first installment in a series of publications which will explore the build-up of the Coma cluster system in connection to the filaments of the cosmic web as a function of redshift.

Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications.

Formation and growth of atmospheric molecular clusters into aerosol particles impact the global climate and contribute to the high uncertainty in modern climate models. Cluster formation is usually studied using quantum chemical methods, which quickly becomes computationally expensive when system sizes grow. In this work, we present a large database of ∼250k atmospheric relevant cluster structures, which can be applied for developing machine learning (ML) models. The database is used to train the ML model kernel ridge regression (KRR) with the FCHL19 representation. We test the ability of the model to extrapolate from smaller clusters to larger clusters, between different molecules, between equilibrium structures and out-of-equilibrium structures, and the transferability onto systems with new interactions. We show that KRR models can extrapolate to larger sizes and transfer acid and base interactions with mean absolute errors below 1 kcal/mol. We suggest introducing an iterative ML step in configurational sampling processes, which can reduce the computational expense. Such an approach would allow us to study significantly more cluster systems at higher accuracy than previously possible and thereby allow us to cover a much larger part of relevant atmospheric compounds.

Research on the Collaborative Evaluation and Development of Digital Trade and Industrial Clusters: Taking Relevant Industries in Guangzhou as an Example

This paper constructs a synergy model between digital trade and industrial clusters from the perspective of system theory. Five major industrial clusters in Guangzhou, including textile and clothing, beauty and daily chemical, luggage and leather goods, jewelry, and food and beverage, are selected to analyze the evolution trend of the composite system between digital trade and industrial clusters. The study found that the luggage and leather goods industry is in the expansion stage of the synergy between digital trade and industrial clusters. The other four industries are still in the initial stage of digital trade and industrial cluster synergy, with low synergy level. The textile clothing industry has the least obvious growth momentum, showing the lowest level of synergy between digital trade and industrial clusters. By promoting the efficient evolution of the system, the upgrading and transformation of industrial clusters through digital trade can be achieved, and the deepening development of digital service trade based on industrial clusters can be effectively supported. This paper can provide recommendation for the collaborative development of Guangzhou's industrial digitization and digital industrialization.

Green development evaluation of China’s Yangtze River Economic Belt based on hierarchical clustering and composite ecosystem index system

Green development evaluation of China’s Yangtze River Economic Belt based on hierarchical clustering and composite ecosystem index system

Application of a Modified BPS Neural Network Based on Three-Way Decision Theory in an Effectiveness Evaluation for a Remote Sensing Satellite Cluster

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model’s training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field.

Fault Prediction of Control Clusters Based on an Improved Arithmetic Optimization Algorithm and BP Neural Network

Higher accuracy in cluster failure prediction can ensure the long-term stable operation of cluster systems and effectively alleviate energy losses caused by system failures. Previous works have mostly employed BP neural networks (BPNNs) to predict system faults, but this approach suffers from reduced prediction accuracy due to the inappropriate initialization of weights and thresholds. To address these issues, this paper proposes an improved arithmetic optimization algorithm (AOA) to optimize the initial weights and thresholds in BPNNs. Specifically, we first introduced an improved AOA via multi-subpopulation and comprehensive learning strategies, called MCLAOA. This approach employed multi-subpopulations to effectively alleviate the poor global exploration performance caused by a single elite, and the comprehensive learning strategy enhanced the exploitation performance via information exchange among individuals. More importantly, a nonlinear strategy with a tangent function was designed to ensure a smooth balance and transition between exploration and exploitation. Secondly, the proposed MCLAOA was utilized to optimize the initial weights and thresholds of BPNNs in cluster fault prediction, which could enhance the accuracy of fault prediction models. Finally, the experimental results for 23 benchmark functions, CEC2020 benchmark problems, and two engineering examples demonstrated that the proposed MCLAOA outperformed other swarm intelligence algorithms. For the 23 benchmark functions, it improved the optimal solutions in 16 functions compared to the basic AOA. The proposed fault prediction model achieved comparable performance to other swarm-intelligence-based BPNN models. Compared to basic BPNNs and AOA-BPNNs, the MCLAOA-BPNN showed improvements of 2.0538 and 0.8762 in terms of mean absolute percentage error, respectively.

A Novel Hybrid Energy Efficient Model using Clustering in Wireless Sensor Networks

An innovative hybrid approach is presented in this paper aimed at rapid predicting the optimum routing path selection in WSNs using the Lagrangian method and Clustering. The main motive of this work proposed is to maximize the clustering productivity, reduce the congestion in routing system and optimize the energy range in the network. By combining the route prediction through clustering and energy flow estimates in the routing protocol design-based algorithm, this will combine both the objectives of rapid route prediction and energy flow estimation. A Distributional Heterogeneous Pathfinder (DHP) based routing algorithm was used in this optimal clustering system to predict the best routing paths in the network and to optimize network energy consumption. In general, sensor communication in the Internet of Things takes place with tremendous amounts of data transmission and other interactions. The results drawn from simulation shows the higher efficiency and fast route detection with better energy transmission of data through the network.

Influence of planets on debris discs in star clusters – I. The 50 au Jupiter

ABSTRACT Although debris discs may be common in exoplanet systems, only a few systems are known in which debris discs and planets coexist. Planets and the surrounding stellar population can have a significant impact on debris disc evolution. Here, we study the dynamical evolution of debris structures around stars embedded in star clusters, aiming to determine how the presence of a planet affects the evolution of such structures. We combine NBODY6++GPU and REBOUND to carry out N-body simulations of planetary systems in star clusters ($N=8\, 000$; Rh = 0.78 pc) for a period of 100 Myr, in which 100 solar-type stars are assigned 200 test particles. Simulations are carried out with and without a Jupiter-mass planet at 50 au. We find that the planet destabilizes test particles and speeds up their evolution. The planet expels most particles in nearby and resonant orbits. Remaining test particles tend to retain small inclinations when the planet is present, and fewer test particles obtain retrograde orbits. Most escaping test particles with speeds smaller than the star cluster’s escape speed originate from cold regions of the planetary system or from regions near the planet. We identify three regions within planetary systems in star clusters: (i) the private region of the planet, where few debris particles remain (40–60 au), (ii) the reach of the planet, in which particles are affected by the planet (0–400 au), and (iii) the territory of the planetary system, most particles outside which will eventually escape (0–700 au).

Three-level image demonstration with optimized multi-feature fuzzy clustering and EPAPCNN system

Three-level image demonstration with optimized multi-feature fuzzy clustering and EPAPCNN system

Boron-Based Inverse Sandwich V2B7- Cluster: Double π/σ Aromaticity, Metal-Metal Bonding, and Chemical Analogy to Planar Hypercoordinate Molecular Wheels.

Inverse sandwich clusters composed of a monocyclic boron ring and two capping transition metal atoms are interesting alloy cluster systems, yet their chemical bonding nature has not been sufficiently elucidated to date. We report herein on the theoretical prediction of a new example of boron-based inverse sandwich alloy clusters, V2B7-, through computational global-minimum structure searches and quantum chemical calculations. This alloy cluster has a heptatomic boron ring as well as a perpendicular V2 dimer unit that penetrates through the ring. Chemical bonding analysis suggests that the inverse sandwich cluster is governed by globally delocalized 6π and 6σ frameworks, that is, double 6π/6σ aromaticity following the (4n + 2) Hückel rule. The skeleton B-B σ bonding in the cluster is shown not to be strictly Lewis-type two-center two-electron (2c-2e) σ bonds. Rather, these are quasi-Lewis-type, roof-like 4c-2e V-B2-V σ bonds, which amount to seven in total and cover the whole surface of inverse sandwich in a truly three-dimensional manner. Theoretical evidence is revealed for a 2c-2e Lewis σ single bond within the V2 dimer. Direct metal-metal bonding is scarce in inverse sandwich alloy clusters. The present inverse sandwich alloy cluster also offers a new type of electronic transmutation in physical chemistry, which helps establish an intriguing chemical analogy between inverse sandwich clusters and planar hypercoordinate molecular wheels.

DETERMINING COMMITMENT TO THE UNIVERSITY CLINIC OF O. O. BOGOMOLETS NATIONAL MEDICAL UNIVERSITY

Introduction. The article reveals the role of university clinics (hereinafter - UC) in the system of healthcare institutions in Ukraine and their role in implementing the right of every citizen to receive free medical care in state and communal healthcare institutions at the expense of the state budget under the program of medical guarantees. The results of a medical and social study to determine patients' attitudes towards UCs and identify risk factors for non-acceptance are presented.
 The aim. To determine the place of the University Clinic of the O.O. Bogomolets National Medical University in the medical-industrial cluster system of Kyiv city, conduct a medical and social study of patient attitudes towards the university clinic, and identify the main factors of non-acceptance.
 Materials and methods. The study was conducted on the principle of systematicity, using medical-statistical methods, methods of anonymous questionnaire survey, and analytical generalization. The construction of the sample of respondents is based on the fact that, based on the study of their opinion, it is possible to draw conclusions about the public opinion of potential clients.
 The results of the study show that university clinics, as a component of the healthcare system, meet the needs of citizens in terms of the effectiveness of services provided and accessibility to the population. With innovative equipment and the involvement of highly qualified specialists from the University's departments in the diagnostic and treatment process, university clinics are increasingly gaining the trust and support of patients.
 The results of the survey confirm that the university clinic of the National Medical University named after O.O. Bogomolets has successfully integrated into the medical-industrial cluster of Kyiv, establishing cooperation with the university, the Ministry of Health, other ministries and agencies, as well as with patients.
 Analysis of the socio-demographic indicators of the respondents in 2022 showed a significant volume of services provided by the clinic to the population of Kyiv and other regions of Ukraine (with the highest number of visits from pensioners - 83, and civil servants - 57 out of 213 surveyed), thus ensuring the implementation of the state program of medical guarantees for citizens. The risk of low probability recommendations by the clinic is influenced by increasing age (p=0.013) and decreasing educational level (p=0.040) of patients. The risk of low satisfaction with the comfort of the clinic increases (p=0.002) when patients visit the medical facility on the recommendation of acquaintances (patients of the clinic).
 Conclusions. The positive conclusion of the survey is the absence of a risk of probability of re-appealing to the UC based on the analyzed characteristics.

Development of Clusters in the Irkutsk Region in the Context of Import Substitution

The Irkutsk Region's economic growth in the context of import substitution requires modernizing industrial policy and developing a cluster system. Clusters can become a major factor of growth in the Irkutsk Region's economy. A cluster involves creating special conditions, assisting in funding and marketing the products of its member organizations. Cluster residents enjoy such benefits as tax preferences, support in the implementation of investment projects, and infrastructure provision in the form of technoparks. Introducing the cluster approach boosts efficiency of production and implementation of import substitution policies. The purpose of this study is to study special features of cluster formation in the Irkutsk region and the specifics of their functioning in the context of import substitution. The article studies the functioning features of the following clusters in the Irkutsk region: machinery, pharmaceutical, petrochemical, construction materials and technologies, Baikal cluster of light industry, agro-industrial, tourist and recreational clusters. The research methodology is based on a theoretical analysis of the main aspects of cluster development. The study analyzes the data on the development of clusters in the Irkutsk region. The problems of cluster development in the context of import substitution are outlined. The scientific novelty lies in identifying two approaches to the formation of clusters: one forming around an anchor resident and the other forming as a tandem of equivalent companies. It is proposed to expand the geography of sales by cluster companies via employment of anchor resident distribution centers in various territories. Interregional cooperation and establishing the Federal Center of Chemistry cluster are closely looked into. The relevance of a timber industry cluster and a cluster for the disposal of municipal solid waste in the Irkutsk region is substantiated.

Investigating the M GCS–M h Relation in the Most Massive Galaxies

The relation between the total mass contained in the globular clusters of a galaxy and the mass of its dark-matter halo has been found observationally to be nearly linear over five decades of mass. However, the high-mass end of this relation is not well determined from previous data and shows large scatter. We analyze the globular cluster systems (GCSs) of a homogeneous sample of 11 brightest cluster galaxies (BCGs) through DOLPHOT photometry of their deep Hubble Space Telescope images in the F814W filter. We standardize the definition of M GCS, the total GCS mass, by using the GC total population within a limiting radius of 0.1R virial, while the dark-matter halo mass M h is determined from the weak-lensing calibration of M h versus M bary. When these 11 BCGs are added to the previously studied homogeneous catalog of Virgo member galaxies, a total value for η = M GCS/M h is found to be (3.0 ± 1.8internal) × 10−5, slightly higher than previous estimates but with much reduced uncertainty. Perhaps more importantly, the results suggest that the relation continues to have a near-linear shape at the highest galaxy masses, strongly reinforcing the conclusion that accreted GCs make a major contribution to the GC populations at high galaxy mass.

Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques

Cluster managers play a crucial role in data centers by distributing workloads among infrastructure resources. Declarative Cluster Management (DCM) is a new cluster management architecture that enables users to express placement policies declaratively using SQL-like queries. This paper presents our experiences in scaling this architecture from moderate-sized enterprise clusters (102- 103nodes) to hyperscale clusters (104nodes) via query optimization techniques. First, we formally specify the syntax and semantics of DCM's declarative language, C-SQL, a SQL variant used to express constraint optimization problems. We showcase how constraints on the desired state of the cluster system can be succinctly represented as C-SQL programs, and how query optimization techniques like incremental view maintenance and predicate pushdown can enhance the execution of C-SQL programs. We evaluate the effectiveness of our optimizations through a case study of building Kubernetes schedulers using C-SQL. Our optimizations demonstrated an almost 3000× speed up in database latency and reduced the size of optimization problems by as much as 1/300 of the original, without affecting the quality of the scheduling solutions.

Blending “hard” and “soft” TQM for academic excellence: the University of Siena experience in the field of Life Sciences

PurposeFocusing on the adoption of Total Quality Management (TQM) principles in universities, this research paper explores how the “soft” dimensions of TQM trigger its “hard” dimensions considering them at the individual (micro-) and the university (meso-), and eventually at cluster (system-), levels.Design/methodology/approachAdopting a qualitative approach, this study presents an in-depth, longitudinal case study of University of Siena, one of the oldest Italian universities, that has been at the core of the research-based cluster on vaccines, today converged in the Tuscan Life Science Cluster. In particular, data were collected between 2018 and February 2022 and consists of archival data (press articles, websites, books), nine interviews to key informants, multiyear experience of the Life Sciences sector by two of the authors and other material put at disposal by university offices, and emails. Data analysis relied on a timeline, a coding procedure that considered three levels of analysis (individual, organization and cluster). Finally, the authors looked at the “how” and “why” the emerged themes have contributed to academic excellence.FindingsThis paper unveils how “soft” and “hard” sides of TQM are blended across multiple levels for reaching academic excellence. The grounded model emerged enlightens the importance of an individual “soft” dimension, academic passion (composed by its three subdimensions of individual research, teaching and entrepreneurial passion) and also sheds light on the organizational “soft” and “hard” sides that the university has been able to design for encouraging research, teaching and third mission quality. Academic excellence has been possible thanks to the capitalization of the individual and organizational “soft” sides into real outcomes as represented by the organizational and individual “hard” sides.Practical implicationsThe paper suggests the importance of TQM principles applied at universities' level, providing an in-depth description of “soft” and “hard” sides dimensions of TQM and their impact on all the three pillars of academic excellence. The study findings suggest implications for managers and professionals in the higher education domain as well as for policymakers emphasizing the importance of supporting the individual and organizational soft sides of TQM. The authors provide practical implications recommending universities to consider not only the organizational dimensions but also individual ones when pursuing higher education excellence. In particular, individual passion plays a crucial role and universities need to identify ways of nurturing it. The authors also recommend policymakers to think about new ways to sustain universities as crucial actors in boosting a cluster development, as well as to consider higher education institutions, especially in more rural areas, as a privileged player not only capable of nurturing academic excellence but also able of creating an internationally renowned cluster.Originality/valueTQM principles have been intensively analysed from an industrial perspective focusing on manufacturing and services, while this paper focuses on TQM in universities, presenting a grounded model that blends the individual and organizational “soft” and “hard” sides.

Diagnostic expression of the cotton pink bollworm Pectinophora gossypiella (Saunders) Ace1 exposed to some insecticide by the real-time PCR

The Pink Bollworm Pectinophora gossypiella (Saunders) is a devastating pest in Egyptian cotton field. Insecticide control failure may be happen due to its biological features of hide inside bolls and rarely exposed to insecticide application. Thus, a bulk of bolls infested with larvae collected from fields of Sharkia and Benysuef governorate. Moreover, exposed to py, Op sub lethal concentrations in laboratory bioassay. Toxicity features as LC50, LC90 and the slope, and RR was completed. Whereas the common target site of OP and pyrethroid is the acetylcholinesterase enzyme. Thus, the gene Ace1 encoding AChE relative expression was investigated using molecular marker of real-time PCR procedures via the steps of generating data was RNA isolation and characterization, cDNA synthesis, then generating normalization factors, and Ct data attained and analyzed using 2-ΔΔCT method to quantify the gene expression quantitatively of insecticide treated larvae tissue extracted. Data showed the most expressed ratio of samples was cypermethrin followed by deltamethrin compared with the control and the least was malathion and chlorpyrifos, thus, the most effective insecticide was malathion and the least were cypermethrin according to ct values. Hierarchical clustering combined with Heat map and principle component analysis based on various clustering systems and algorithm as distance measures were completed. Validation of reference gene in addition to gene expression stability analyzed by the three optimal gene finders and showed that deltamethrin and cypermethrin treated genes having lower mean weights and considered transcriptionally stable and ideal reference genes.

A database of low-energy atomically precise nanoclusters

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies.

Aluminum Josephson Junction Formation on 200mm Wafers Using Different Oxidation Techniques

This work focuses on the manufacturing of Al/AlOx/Al Josephson junctions (JJs), which are essential components of many quantum circuits. Two processes were studied to understand the oxidation of the aluminum surface. Static oxidation was performed by removing native AlOx in a cluster system with Ar-ion beam milling and controlling the final tunneling oxide by applying a specific O2 pressure in the chamber. Controlled plasma oxidation was performed by removing native AlOx with a H2 plasma followed by a defined reoxidation with an oxygen plasma. The resulting oxides had thicknesses up to 10 nm and their electrical properties were analyzed on wafer level, providing insight into the structure and composition of the aluminum oxides and their applicability for Qubits. This work is crucial for reliable industrial manufacturing on full-scale 200 mm wafers with a very high uniformity level.

Assessment of fuel management clusters in the development of a driving behavior control system model using lookup table mapping to improve fuel savings

Vehicle technology development to help reduce global energy consumption and prevent global warming has been the focus of attention. Therefore, engine development has focused on supporting energy-saving and low emissions policies. Yet, energy consumption in the transportation sector continues to increase, leading to studies on various efforts to realize energy-efficient vehicles. This process is carried out by adjusting the fuel management required to carry out an ideal combustion process and to fulfill the driver's needs. However, due to the influence of driving behavior in engine performance, adequate information is needed to use fuel management to choose the right control strategy. Therefore, this research provides a new design for fuel management clustering based on the lookup table mapping in accordance with the driving behavior profile, such as steering behavior, throttle valve opening, and braking. Fuel management cluster is designed to be monitored using Air to Fuel Ratio (AFR) cluster. The results showed that the output of the lookup table mapping system was in the form of AFR values and driving behavior, divided into two types, namely driving behavior with the normal scheme having an AFR of 14.67, fuel flow rate is 2.3 × 10−3 g/s and the economical scheme of 16.2, fuel flow rate is 2.1 × 10−3 g/s in 800 s period. Based on the simulation, it can be concluded that the clustering system provides an appropriate reference for control strategies because of driving behavior. In the driving behavior with the economical scheme, the control system can increase fuel saving.

PaScaL_TCS: A versatile solver for large-scale turbulent convective heat transfer problems with temperature-dependent fluid properties

PaScaL_TCS is an efficient and scalable solver for large-scale direct numerical simulations of natural convective flow that considers temperature-dependent fluid properties. For increased scalability on a massive-scale distributed memory system, the solver decomposes the computational domain into a cubic sub-domain. The numerical procedure is based on the monolithic projection method with staggered time discretization [1], which is an implicit and non-iterative solver for wall-bounded domains. The PaScaL_TDMA library is used to solve batched tridiagonal systems, which are partitioned according to domain decomposition. For parallel fast Fourier transform in a Fourier Poisson solver for pressure, two transpose schemes with different communicator sizes are proposed and compared according to the number of cores. An explicit intermediate aggregation scheme for MPI-IO is suggested to reduce the number of processes that simultaneously take part in parallel IO. The overall implementation was evaluated using the Nurion cluster system of the Korea Institute of Science and Technology Information with detailed performance profiling. With the efficient communication, the results showed good strong and weak scalability up to 131,072 cores. The file IO performance improved with the suggested MPI-IO scheme using explicit aggregation, especially when many processes are involved in parallel IO. The solver was verified by conducting large-scale differentially heated vertical convection flow simulations under the Oberbeck–Boussinesq (OB) approximation. The capability of the solver to capture and quantitatively describe the non-OB effect was demonstrated via glycerol Rayleigh–Bénard convection flow simulations. Program summaryProgram Title: PaScaL_TCSCPC Library link to program files:https://doi.org/10.17632/3b2bysrcxm.1Developer's repository link:https://github.com/MPMC-LabLicensing provisions: MITProgramming language: Fortran90Nature of problem: Solving the incompressible Navier-Stokes equations for natural convective flow considering the non-Oberbeck–Boussinesq effect, which considers the temperature dependence of fluids properties, in three-dimensional channel domain.Solution method: PaScaL_TCS uses the monolithic projection-based method with staggered time discretization (MPM-STD) [1]. It employs the Crank-Nicolson scheme along with staggered time which evaluates scalar and vector variables at the (n+1/2) and (n+1) time level, respectively. PaScaL_TDMA [2] is used to solve multiple tridiagonal matrix systems for decoupled energy and momentum equations in a distributed memory system. The Poisson equation solver for the pressure-correction scheme is based on Fourier diagonalization. MPI_Alltoallw is used for parallel FFT. PaScaL_TCS supports a single-file-based parallel IO for efficient data generation in large-scale computing.