Theoretical Comparison Research Articles

A coupled two-dimensional–three-dimensional hydrodynamic model for simulating flood wave evolution in reservoirs and riverbeds

The geometric scale of real reservoirs is immense, and their boundaries are intricate. Consequently, two-dimensional numerical simulations of the entire reservoir cannot accurately capture the three-dimensional flow characteristics in localized regions. Conversely, comprehensive three-dimensional simulations are often unfeasible due to the substantial computational resources they demand. This paper proposes a two-dimensional-three-dimensional coupled hydrodynamic model by combining a two-dimensional shallow water model and a three-dimensional free surface model based on the Lattice Boltzmann Method. The model introduces algorithms that establish unified requirements for computational parameters, ensure the conservation of physical quantities at the interface, and facilitate the inversion of two-dimensional data to three-dimensional formats, as well as the integration of three-dimensional data into two-dimensional formats. The model was applied to meticulously simulate the evolution of flood waves resulting from dam breaches over uneven riverbeds. Through theoretical analysis and experimental comparisons, a detailed examination of the conservation and coherence of physical quantities at the interface was conducted, thereby validating the accuracy of the proposed approach. Finally, the large-scale evolution of wave surges in a riverbed hydropower station reservoir was simulated, effectively demonstrating the capability to address practical hydrodynamic challenges. Additionally, the discussion highlights the computational savings achieved by this model relative to conventional three-dimensional simulations, establishing a foundation for its application in real-world engineering cases.

The importance of therapeutic and preventive measures in the elimination of non-communicable diseases of cattle

The purpose of the study is to establish the importance of therapeutic and preventive measures in the elimination of non –communicable diseases of cattle, based on the data of summary reports on their detection and explanatory notes to them for 2022-2023, provided by the State Budgetary Institution "St. Petersburg Mining Station". In the process of conducting research using empirical (internal qualitative and quantitative analyses) and theoretical (classification, specification, analogy, generalization and comparison) methods, an analysis of statistical reporting on form No. 2-vet "Information on non-communicable animal diseases" covering agricultural organizations of St. Petersburg was carried out. It has been established that an increase in the volume of therapeutic and preventive measures carried out by veterinary specialists can significantly reduce the incidence of animal pathologies of non-infectious origin. First of all, this concerns medical examination, which allows timely identification and elimination of etiological factors that cause their occurrence.

A coupled dynamics model for studying the outer ring fault characteristics and mechanisms of axle-box bearings in urban rail vehicles

With the rapid and large-scale development of urban rail transit in China, the operational safety of urban rail vehicles is highly concerned. The axle-box bearing, as the core component of vehicle bogies, directly determines the operational safety and quality of vehicles. To ensure the service performance of axle-box bearings, their fault characteristics should be monitored and diagnosed in time. A more realistic and in-depth understanding of fault characteristics is a prerequisite for fault diagnosis. Thus, a double-rows tapered roller bearing (DTRB)-vehicle-track coupled dynamic model was built via the co-simulation in this paper, which comprehensively considers a DTRB model with the localised outer ring raceway fault, as well as the flexibilities of the wheelset, axle-box, and bogie frame. Its correctness and rationality were verified by the theoretical comparison and the field test. The results indicate that the distribution of contact load is consistent with the theoretical value, the fault features of the outer ring raceway can be found in the periodic impulse of the roller-raceway contact load. The slip rates of rolling elements are less than 4.3 %. Compared with the field test, the fault order harmonics in the order spectrum of the simulation are closer to the theoretical fault characteristic order under variable and constant speed conditions, and the maximum error is only 0.37 %. Therefore, the proposed coupled dynamic is correct and can be employed to investigate the fault characteristics of axle-box bearings in urban rail vehicles.

A new improved estimator for the gamma regression model

Modeling with the gamma regression model is common when the dependent variable is positively skewed and follows a gamma distribution. The maximum likelihood (ML) estimator is used to estimate the parameters for this model. However, when there is multicollinearity, the ML estimator is inefficient. As a result, we propose a new improved gamma (NIG) estimator for this model’s parameter estimation in the presence of multicollinearity. Theoretical comparisons of the NIG estimator with some existing gamma estimators are done via mean squared error. Additionally, a real-data example and a simulation study are conducted to provide a clear view of the NIG estimator’s superior performance over other available ones. The results show that the NIG estimator outperforms previous gamma estimators in terms of mean squared error.

The employerʼs brand for generation Z

Building an employerʼs brand is a strategic objective for modern companies, as it allows them to attract talented employees who are motivated to achieve the companyʼs goals. This helps to improve corporate culture and enhance the companyʼs business reputation. In a world where competition for qualified personnel is growing and job requirements are evolving, a strong employerʼs brand is becoming a key competitive advantage. The research is based on the use of methods such as theoretical generalization, comparison and ranking, and statistical analysis. The content of the employerʼs brand value proposition for Generation Z is determined. The components of the employerʼs brand value proposition among the representtatives of generation Z are ranked. The components of the value proposition of the employerʼs brand of the researched enterprises in the retail sector are ranked in terms of their degree of development. The degree of compliance of the components of the employerʼs brand value proposition with the expectations of the representatives of generation Z at the researched enterprises is determined. Proposals aimed at shaping the development of the employerʼs brand for the researched enterprises are developed. The hypotheses about the expediency of ranking the components of the employerʼs brand value proposition to determine their importance for representatives of generation Z are formulated and empirically tested; ranking the components of the employerʼs brand value proposition in terms of the level of their development in the researched enterprises; Spearmanʼs rank correlation coefficient allows to determine the level of compliance of the employerʼs brand of the researched enterprises with the requirements of the representatives of generation Z and to propose proposals for its further development

Evaluation of Portfolio Assessment of Student Work in Arabic Language Learning

This research discusses the evaluation of portfolio assessment in Arabic learning, especially related to students' ability to understand fi'il madhi and fi'il mudhari' in the form of mabni majhul and mabni ma'lum at Pomdok Pesantern Al-Anwar Muara Jambi. The background of this study focuses on the importance of a comprehensive evaluation that not only measures the final results, but also the overall learning process of students. The purpose of the research is to evaluate work portfolios as an assessment tool in measuring student development related to Arabic grammar. The research used a qualitative approach of case study type, with data collection techniques through semi-structured interviews and documentation analysis. The data analysis technique used is the Miles and Huberman model, which involves collecting, reducing, and presenting data, as well as drawing conclusions. This study used primary data sources and secondary data sources. Primary data sources were obtained from semi-structured interviews with Ustadzah Fitriawati as well as analysis of student portfolios to assess understanding of fi'il madhi and fi'il mudhari' in the form of mabni majhul and mabni ma'lum. Secondary data sources include archived academic grade documents and learning outcome reports, as well as literature related to portfolio assessment in language education to provide a theoretical foundation and comparison with previous research. The results showed that the use of portfolio work assessment was effective in monitoring the continuous development of students' understanding, both in theory and application of concepts. Portfolios encourage collaboration and discussion between students, and improve critical thinking skills.

Effect of polymer emulsion and calcium sulfate whisker on an-corrosion of cement stone for CO2 multiple gas injection and production wells

Abstract An oilfield in the South China Sea will be developed by CO2 injection to enhance oil recovery and carbon sequestration. It is challenging to prevent corrosion in the cementing operation of injection wells, and high requirements are put forward for the anti-corrosion performance of cementing slurry. In this paper, aiming at the sealing problem of the cement ring, which is easy to corrode under the condition of stratified gas injection, the rheological properties and thickening properties of cement slurry, as well as the tensile strength, compressive strength, elastic modulus, and porosity permeability of the cement slurry system, are simulated under the condition of an underground field with polymer emulsion and calcium sulfate whiskers as the research objects. Through experimental and theoretical comparison, the results show that the addition of polymer emulsion and calcium sulfate whisker can significantly improve the tensile strength and tensile strength and reduce porosity and permeability, elastic modulus has been controlled below 7.5 GPa, and the performance has been dramatically improved. In addition, according to electron microscope scanning and other experimental methods, it is found that the cement stone cured by cement slurry with polymer emulsion and calcium sulfate whisker is more compact and compact in structure and has higher strength and toughness, which fully meets the requirements of stratified gas injection conditions, providing theoretical basis and data support for the corrosion prevention of cementing cement slurry in oil fields. Promoting the safe and efficient development of oil and gas resources is of great significance.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

Long-range laser ranging echo signal processing method based on non-uniform multi-pulse superposition

Abstract When a laser fuze detects a long-distance target, the noise drowns out the echo signal, which makes the signal-to-noise ratio of the echo signal attenuate severely, seriously limiting its detection distance. Based on theoretical analysis and experimental comparison, this paper proposes a non-uniform multi-pulse superposition method to improve the signal-to-noise ratio and enhance the fuzes’ long-range target detection capability. The superposition algorithm is simulated in a MATLAB environment, and the test results prove the method’s effectiveness.

A New Modified Estimator for Population Mean using Auxiliary Character and Attribute in Double Sampling

In sample surveys, when information on two auxiliary characters is available, several authors prefer chain-type estimators to improve the performance of estimators of the population mean. These authors have assumed that the information (population mean, total, etc.) on the first auxiliary or auxiliary character is not available but on the second or additional auxiliary character. However, there are instances where the latter and former may also be unavailable. In such cases, we have suggested a novel modified regression-type estimator that uses the data from the auxiliary character and the auxiliary attribute in double sampling to estimate the population mean. We have derived the expressions for bias and mean square error of the suggested estimator using the large sample approximation. After mathematical comparisons, we have obtained mathematical conditions under which the suggested estimator has lower mean square error than the existing estimators. Further, we supported these theoretical comparisons using a numerical analysis with a real-life dataset.. KEYWORDS :Bias, Double sampling, Mean square error, Auxiliary attribute, Auxiliary character.

Optimization and Thermodynamic Analysis of CO2 Refrigeration Cycle for Energy Efficiency and Environmental Control

Supermarket applications are significant contributors to greenhouse gas emissions, necessitating efforts to reduce carbon footprints in the food retail sector. Carbon dioxide (R744) is recognized as a viable long-term refrigerant choice due to its favorable properties, including low Global Warming Potential, non-toxicity, non-flammability, affordability, and widespread availability. However, enhancing the energy efficiency of pure CO2 systems in basic architecture units, particularly in warm regions like India, remains a challenge. To address this, modern refrigeration systems must prioritize low energy consumption and high coefficient of performance (COP) while meeting environmental standards. This study investigates different operating conditions to determine the optimal parameter range for maximizing COP and improving the efficiency of conventional CO2 refrigeration configurations. It examines both subcritical and transcritical refrigeration cycles under varying parameters, emphasizing the importance of understanding COP’s relationship with factors such as subcooling, superheating, ambient temperature, and evaporator temperature. The study advises against superheating in CO2 systems but highlights the substantial COP increase with higher degrees of subcooling, leading to enhanced system performance. Additionally, it provides a comprehensive theoretical comparison between advanced pure CO2 supermarket applications and commonly used hydrofluorocarbons-based systems, offering insights into energy efficiency and environmental impacts for informed decision-making in the industry.

X-Shooting ULLYSES: Massive Stars at low metallicity

Context. Current implementations of mass loss for hot, massive stars in stellar evolution models usually include a sharp increase in mass loss when blue supergiants become cooler than Teff ∼ 20 − 22 kK. Such a drastic mass-loss jump has traditionally been motivated by the potential presence of a so-called bistability ionisation effect, which may occur for line-driven winds in this temperature region due to recombination of important line-driving ions. Aims. We perform quantitative spectroscopy using UV (ULLYSES program) and optical (XShootU collaboration) data for 17 OB-supergiant stars in the LMC (covering the range Teff ∼ 14 − 32 kK), deriving absolute constraints on global stellar, wind, and clumping parameters. We examine whether there are any empirical signs of a mass-loss jump in the investigated region, and we study the clumped nature of the wind. Methods. We used a combination of the model atmosphere code FASTWIND and the genetic algorithm (GA) code Kiwi-GA to fit synthetic spectra of a multitude of diagnostic spectral lines in the optical and UV. Results. We find an almost monotonic decrease of mass-loss rate with effective temperature, with no signs of any upward mass loss jump anywhere in the examined region. Standard theoretical comparison models, which include a strong bistability jump thus severely overpredict the empirical mass-loss rates on the cool side of the predicted jump. Another key result is that across our sample we find that on average about 40% of the total wind mass seems to reside in the more diluted medium in between dense clumps. Conclusions. Our derived mass-loss rates suggest that for applications such as stellar evolution one should not include a drastic bistability jump in mass loss for stars in the temperature and luminosity region investigated here. The derived high values of interclump density further suggest that the common assumption of an effectively void interclump medium (applied in the vast majority of spectroscopic studies of hot star winds) is not generally valid in this parameter regime.

Making waves: Harnessing anammox bacteria coupled with dissimilatory nitrate reduction to ammonium for sustainable wastewater management.

Anaerobic ammonia oxidation (anammox) which converts nitrite and ammonium to dinitrogen gas is an energy-efficient nitrogen removal process. One of the bottlenecks for anammox application in wastewater treatment is the stable supply of nitrite for anammox bacteria. Dissimilatory nitrate reduction to ammonium (DNRA) is a process that converts nitrate to nitrite and then to ammonium. Significantly, it has been reported that some anammox bacteria can perform DNRA by reducing nitrate to nitrite and ammonium nitrogen with little low-molecular-weight organic acids such as volatile fatty acids. Here, we propose an innovative nitrogen removal process, i.e., nitrification and anammox coupled with partial DNRA (i.e., NPDA), and make a theoretical comparison with previously accepted partial nitrification and anammox (PNA) and partial denitrification and anammox (PdNA) for nitrogen removal. Under similar conditions of oxygen consumption, removal efficiency, external carbon source addition, and greenhouse gas emission, the novel NPDA process can better facilitate resource-effective and environment-friendly wastewater treatment. Thermodynamic analysis indicates that partial DNRA-anammox appears to be preferred, oxidizing per mole of NH4 +produces higher energy gain than that of conventional anammox alone. The carbon source limitation rather than nitrate limitation is the key to the realization of NPDA process. This perspective highlights the positive role of DNRA for sustainable wastewater management.

Simulating a Temperature, Stress And Strain FBG Sensor for Troposphere Layer

In this study, a simulation and design for a temperature (T) sensor are investigated by using the fiber Bragg gratings (FBG). The study investigated and characterized the FBG regarding maximum reflectivity, bandwidth, and the influence of applied strain on the shift in Bragg wavelength (λB). This is via measuring sensitivity of wavelength shift under strain in an optical sensing system. The response of FBG sensors was also investigated and optimized. Uniform FBG spectra and theoretical comparisons were conducted considering the influence of several selected external strain/stress (S) values: 101.325, 89.874, 79.495, 70.108, 61.640, 54.019, 47.181, 41.060, 35.599, 30.742, 26.436. The Optisystem 16 software is analyzing these two types of effects. The measured parameters, such as sensitivity, were determined by using a white light source. The light source operating wavelength was 1550 nm. This is to design the sensor especially for troposphere that have high sensitivity (1279.7472 pm/oC. The sensitivity obtained exhibited varying values between 1279.7472 and 1239.7551 for strain. The peak sensitivity observed is associated with 550 nm of wavelength. Result for measured sensitivity against S by the FBG sensor was 15.5087 pm/oC with sensor length 20 mm. While it was found equals 15.5131 pm/oC with sensor length 10 mm. T sensing is simulated for selected values (15, 8.5, 2, -4.5, -11, -17.5, -24, -30.5, -37, -43.5, -50 oC). Resulted sensitivity was found to fluctuate fitting sine, Long Normal, and Boltzmann functions, indicating large sensitivity for the simulated sensor responses.

Deployment of power-to-protein technology in Ethiopia to provide drought-related emergency relief and mitigate food insecurity

Food insecurity in Ethiopia is an immediate humanitarian crisis that is expected to worsen due to population growth and climate change. This study applied GIS-based approaches to evaluate the feasibility of deploying an emergent type of single-cell protein (SCP) technology to supplement the nutritional needs of Ethiopian citizens who are most vulnerable to drought. The technology—power-to-protein (PtP)—uses H2 and O2 from water electrolysis and CO2 from woody biomass combustion in a two-stage bioprocess to produce nutrient-rich protein powder for human consumption. Population density, land use, and other geographical data were used to identify optimal site locations for these PtP systems based on two deployment strategies: large centralized plants vs. small decentralized units. The model also accounted for biomass availability, collection, and distribution logistics. The analysis revealed three sites that are both (highly) vulnerable/food-insecure and accessible within walking distance. The identified sites are proximate to the urban areas of Mekele in northern Ethiopia, Addis Ababa in central Ethiopia, and Hawasa south of Addis Ababa. If centralized PtP were deployed, the protein requirements of these populations could be sustained for several months, assuming a modest biomass collection radius of 35 km. Decentralized PtP deployment was similarly effective, requiring a distribution density of 5.4–11.0 PtP units per km2 under conservative estimates and 0.76–1.1 units per km2 under optimistic estimates. Lastly, a theoretical comparison showed that PtP is more efficient than conventional agricultural food production regarding biomass-to-protein conversion yields. Overall, our study suggests that PtP technology would be a feasible approach to supplement the nutritional needs of Ethiopian people in times of drought-related emergencies. However, given logistical limitations and considering social preference factors, it would be more practical to implement PtP in conjunction with standard emergency food aid measures.

Tunning the omnidirectional bandgap of nanoporous silicon using a semi-sinusoidal refractive index profile

We report the theoretical comparison of the omnidirectional bandgap in a 1-D photonic crystal using sinusoidal and semi-sinusoidal refractive index profiles. It is found that the corresponding omnidirectional bandgap of the semi-sinusoidal widens and shifts to a higher wavelength range as a function of the asymmetric ratio of semi-sinusoidal profile. The asymmetric ratio plays an essential role in tunning the width of the omnidirectional bandgap due to the changed average refractive index and optical thickness. The semi-sinusoidal refractive index is experimentally achieved by changing the current waveform. Novel omnidirectional nanoporous silicon mirrors with an omnidirectional bandgap covering optical communication wavelength (1550 nm) were fabricated using a semi-sinusoidal current waveform. The experimental analogy was carried out by building up the multilayered dielectric structures of omnidirectional mirrors by anodic etching using a semi-sinusoidal current waveform. The experimental results were compared with the theoretical results investigated by the Transfer matrix method. It was shown that the distorted current profile impacts the quality of the omnidirectional bandgap although it does not affect the porous size range.

17O NMR relaxation measurements for investigation of molecular dynamics in static solids using sodium nitrate as a model compound

17O NMR methods are emerging as a powerful tool for determination of structure and dynamics in materials and biological solids. We present experimental and theoretical frameworks for measurements of 17O NMR relaxation times in static solids focusing on the excitation of the central transition of the 17O spin 5/2 system. We employ 17O-enriched NaNO3 as a model compound, in which the nitrate oxygen atoms undergo 3-fold jumps. Rotating frame (T1ρ), transverse (T2) and longitudinal (T1) relaxation times as well as line shapes were measured for the central transition in the 280 to 195 K temperature range at 14.1 and 18.8 T field strengths. We conduct experimental and theoretical comparison between different relaxation methods and demonstrate the advantage of combining data from multiple relaxation time and line shape measurements to obtain a more accurate determination of the dynamics as compared to either of the techniques alone. The computational framework for relaxation of spin 5/2 nuclei is developed using the numerical integration of the Liouville − von Neumann equation.

Comparative Analysis of Tetravalent Actinide Schiff Base Complexes: Influence of Donor and Ligand Backbone on Molecular Geometry and Metal Binding.

A series of isostructural early actinide AnIV complexes was synthesized in order to investigate the influence of a conjugated framework in the ligand backbone on An bonding. Therefore, the AnIV complexes [An(pyrophen)2] (An = Th, U, Np, and Pu) with the pure N-donor ligand bis(2-pyrrolecarbonylaldehyde)-o-phenylenediamine referred to as pyrophen, were synthesized and characterized. Solid state analysis via single-crystal X-ray diffraction (SC-XRD) reveals two sets of ligands binding in an almost orthogonal arrangement to the actinide center. For the larger actinides Th and U, the coordination sphere allows for additional coordination by a solvent molecule. Nuclear magnetic resonance spectroscopy (NMR) studies show the presence of highly symmetrical complexes in solution in good agreement with the solvent-free solid structures. While SC-XRD suggests mainly ionic binding, an analysis of paramagnetic NMR contributions and quantum chemical bond analysis hint towards significant covalency in the U, Np, and Pu compounds. This series of An complexes allowed for a thorough structural and theoretical comparison of a conjugated system to a closely related N-donor ligand (pyren)[1] as well as to the mixed N,O Schiff base ligands salophen (conjugated) and salen (non-conjugated).

Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model

Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions.

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography.

Electrical Tomography (ET) technology is widely used in multiphase flow detection due to its advantages of low cost, visualization, fast response, non-radiation, and non-invasiveness. However, ill-posed solutions lead to low image reconstruction resolution, which limits its practical engineering applications. Although existing interpolation approximation algorithms can alleviate the effects of the ill-posed solutions to some extent, the imaging results remain suboptimal due to the limited approximation capability of these methods. This paper proposes a Bivariate Reproducing Kernel-Based Best Interpolation (BRKBI) method, which offers smaller approximation errors and clearer image reconstruction quality compared to existing methods. The effectiveness of the BRKBI method is validated through theoretical analysis and experimental comparisons.