Specific Quantities Research Articles

A sensitive Ag+-mediated magnetic relaxation and colorimetry dual-mode sensing platform

To address the relatively low sensitivity of current redox reagent-mediated magnetic relaxation sensing methods, we present a novel Ag+-mediated magnetic sensing platform that enhances the sensitivity by three orders of magnitude. The new sensing platform is based on Ag+-catalyzed oxidation of Mn2+ to KMnO4, accompanied by a distinct color change, which facilitates colorimetric detection. In the case of insufficient Ag+ ions, MnO2 is an additional oxidation product and the KMnO4/MnO2 ratio is dependent on the concentration of Ag+. When combined with a specific quantity of reducing agent, both KMnO4 and MnO2 are reduced to Mn2+ with a large relaxivity, and the concentration of Mn2+ in the resultant solution inversely correlates with the amount of KMnO4 since KMnO4 consumes more reductant during reduction. Consequently, the transverse relaxation rate of the solution exhibits a negative correlation with the Ag+ concentration. Thus, by coupling this Ag+-mediated Mn2+ to KMnO4 transformation with reactions that modulate Ag+ concentration, a dual-mode sensing platform for magnetic relaxation and colorimetry can be realized. Herein, we take H2O2 as an example to verify the detection performance of this sensing platform since H2O2 can oxidize Ag0 in Ag@Fe3O4 nanoparticles to Ag+. Experimental findings demonstrate detection limits of 10 nM and 20 nM for the magnetic relaxation and colorimetry modes, respectively, affirming the excellent sensitivity and the potential practical application of this strategy.

Effect of Enzymes, Biotic, Abiotic Stresses and Metal Toxicity in Brassica napus

Canola is an important crop with low levels of fatty acid and genetically produced from rapeseed. Canola oil is obtained by extract sources. Heavy metals are a unique form of toxin because they may exist in the environment for a longer period. Exogenous administration of gibberellic acid has been proven in several studies to increase crop output by affecting an important physiological process. This study of the aimed and objectives included the nickel sulphate stress in quantity 300 µM and gibberellic acid in quantity 0.25 mM in specific quantity and their effects on different plant functions. Super canola under stress condition when 0.25 mM GA3 was applied. Whereas, minimum reduction was observed in cv. Sherilla under stress condition when 0 mM GA3 was applied. Maximum Catalase was observed in cv. Results showed the Peroxidase (POD), catalase (CAT) and superoxide dismutase (SOD) are the most important protective enzyme systems in plants, and also the most important reactive oxygen species clearance system. Maximum Total Amino acid was observed in cv. Sherilla under control condition when 0 mM GA3 was applied. However, effect of GA3 application was observed non-significant and same for as varietal difference among two genotypes was observed non-significant. Overall, super canola performed better under stress or non-stress condition. Mustard plants exposed to nickel showed decreased morphological characteristics, which might be related to the degradation of plant architecture, which limits the capacity of water content and minerals absorption by plants, resulting in total development of plant loss.

Hydrothermal synthesis of Ce-doped SnS2 2D nanoplates with enhanced photocatalytic CO2 reduction performance

CO2 emissions caused by human activities seriously influence the climate of earth and hinder the sustainable development of humans and nature. Photocatalytic CO2 reduction is an effective method to convert CO2 into other chemicals and reduce CO2 emissions. Here, Ce doped SnS2 photocatalyst has been obtained by using a hydrothermal method. The phase and structure of obtained samples have been characterized and the photocatalytic CO2 reduction performance has been evaluated. The results show that Ce dopants can improve visible light absorption, increase the specific surface area, especially increase the adsorption of CO2. The influence of Ce doping concentration was investigated to optimize the photocatalytic performance. Compared to the pristine SnS2 photocatalyst, the Ce–SnS2 reached 46.9 μmol g−1 h−1 in the CO2 photo-reduced test, which is 3.21 times that of pristine SnS2 photocatalyst. The enhanced photocatalytic performance can be attributed to Ce dopant which increases the specific surface and CO2 absorption quantity, and reduces the recombination of photogenerated electrons and holes.

Smart Water ATM with Arduino Integration, RFID Authentication, and Dynamic Dispensing for Enhanced Hydration Practices

This proposal outlines the development of a comprehensive solution to address hydration challenges through the creation of a Smart Water ATM with Arduino integration, RFID authentication, and dynamic dispensing capabilities. Traditional water dispensers often fall short in monitoring water intake and promoting optimal hydration practices. In response, our project aims to revolutionize hydration practices by integrating Arduino Mega and Uno boards into a Smart Water ATM with a Bottle Dispenser. This innovative system is designed to dispense specific water quantities based on user preferences, encourage the use of personal water bottles, display water temperature, and operate independently without the need for a water line connection. Prior to prototype fabrication, an extensive literature review and survey were conducted to understand existing water dispenser shortcomings and gather public perspectives in Singapore. The Smart Water ATM addresses identified issues by incorporating sensors to monitor water levels, dispense specific quantities, and measure water temperatures. The prototype fabrication involves designing a stainless-steel housing, 3D printing the Smart Water dispenser unit, and installing tanks, tubing, and electronic components. To enhance user interaction, the Smart Water ATM requires RFID authentication through Identity Cards, tracking daily water consumption. An LCD screen displays the dispensed water volume over the ATM’s lifespan, motivating users to be mindful of their water consumption and minimize wastage. Adjustments will be made for deployment in public spaces, such as train stations, where access to permanent water dispensers is limited. This proposal presents an innovative solution to promote enhanced hydration practices, encouraging users to adopt mindful water consumption habits.

Activated carbon modification for real diesel adsorptive deep desulfurization: experiments and modeling

This study examined the desulfurization capacity of a high specific surface area activated carbon (AC) and its modifications for real diesel fuel of different sulfur concentrations. Adsorbent’s evaluation strategy included desulfurization experiments under batch and continuous conditions. Batch experiments were conducted by a homogeneous stirring of adsorbent and diesel fuel, while for the continuous tests, a fixed bed reactor was used to generate breakthrough desulfurization curves. In the fixed bed reactor’s tests, the sulfur removal – considering an initial concentration of about 6 parts per million weight (ppmw) – was substantial for the first 30 ml fuel per gram of adsorbent. For this specific quantity, the total sulfur content of the cumulatively processed fuel was less than 2 ppmw. The batch experimental results are elaborated using a mechanistic kinetic model for batch adsorption considering the total sulfur mass balance. Mass transfer coefficients are derived this way for all the conditions and adsorbents tested. The adsorption equilibrium data from batch experiments are utilized to model the fixed bed experimental results. In this way, mass transfer coefficients for the fixed bed are derived. In any case, the modeling results indicate that sulfur is not the only adsorbate of the process, but other adsorbates act competitively to sulfur, thereby complicating the process.

'Dust you shall eat': The complex nutritional and functional considerations underlying a simple diet.

Animals assimilate macronutrients and mineral nutrients in specific quantities and ratios to maximise fitness. To achieve this, animals must ingest different foods that contain the needed nutrients or facilitate the digestion of those nutrients. We explored how these multidimensional considerations affect the desert isopods (Hemilepistus reaumuri) curious food selection, using field and laboratory experiments. Wild isopods consumed three-fold more macronutrient-poor biological soil crust (BSC) than plant litter. Isopods tightly regulated macronutrient and calcium intake, but not phosphorus when eating the two natural foods and when artificial calcium and phosphorus sources substituted the BSC. Despite the equivalent calcium ingestion, isopods performed better when eating BSC compared to artificial foods. Isopods that consumed BSC sterilised by gamma-radiation ate more but grew slower than isopods that ate live BSC, implying that ingested microorganisms facilitate litter digestion. Our work highlights the need to reveal the multifaceted considerations that affect food-selection when exploring trophic-interactions.

Nutrigenomics and nutrigenetics: Special emphasis on optimization of human health and animal performance

Environmental factors, food’s nutrients, and genetic makeup play an important role in phenotypic appearance of a trait via the principles of biological sciences. The nutritional genomics focuses on the interaction between bioactive food components and the genome, which includes nutrigenetics and nutrigenomics. The influence of nutrients on genes expression is called nutrigenomics, while the heterogeneous response of gene variants to dietary nutrients is called nutrigenetics. Nutrigenomics is providing the effects of ingested food’s nutrients on gene expression and gene regulation, i.e., diet-gene interaction in order to identify the dietetic components having beneficial or detrimental health effects. It elucidates that how DNA and genetic code influence the need for specific nutrients and also within specific quantities are required for maintaining good health throughout life. It is helpful to determine the personalized diet that means individual nutritional requirements based on the genetic makeup of the person as well as clarifies some etiological perspectives of chronic diseases like type-2 diabetes, cancer, obesity and cardiovascular diseases (CVD) because of associations between diet and chronic diseases. Rearing of farmed animals is a vital component of global food production systems, its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. The goal of discussing these issues is to get a better understanding of basic biological issues and to apply their knowledge to practical problems such as medicine, and improvement of animal performance. Therefore, this review article is focus on the role of genes and the use of genomic techniques in human health, as well as animals’ well-being and welfare.

The pioneer of intelligent and sustainable construction in tunnel shotcrete applications: a comprehensive experimental and numerical study on a self-sensing and self-heating green cement-based composite

In this study, a self-sensing and self-heating natural fibre-reinforced cementitious composite for the shotcrete technique was developed using Kenaf fibres. For this purpose, a series of Kenaf fibre concentrations were subjected to initial chemical treatment, followed by integration into the cement-based composite containing hybrid carbon nanotubes (CNT) and graphene nanoplatelets (GNP). The investigation encompassed an examination of mechanical, microstructural, sensing, and joule heating performances of the environmentally friendly shotcrete mixture, with subsequent comparisons drawn against a counterpart blend featuring a conventionally synthesized polypropylene (PP) fibre. Following the experimental phase, a comprehensive 3D nonlinear finite difference (3D NLFD) model of an urban twin road tunnel, completed with all relevant components, was meticulously formulated using the FLAC3D (fast lagrangian analysis of continua in 3 dimensions) code. This model was subjected to rigorous validation procedures. The performances of this green shotcrete mixture as the lining of the inner shell of the tunnel were assessed comparatively using this 3D numerical model under static and dynamic loading. The twin tunnel was subjected to a harmonic seismic load as a dynamic load with a duration of 15 s. The laboratory findings showed a reduction in the composite sensing and heating potentials in both cases of Kenaf and PP fibre reinforcement. Incorporating a specific quantity of fibre yields a substantial enhancement in both the mechanical characteristics and microstructural attributes of the composite. An analysis of digital image correlation demonstrated that Kenaf fibres were highly effective in controlling cracks in cement-based composites. Furthermore, based on the static and dynamic 3DNLFD analysis, this green cement-based composite demonstrated its potential for shotcrete applications as the lining of the inner shell of the tunnel. This study opens an appropriate perspective on the extensive and competent contribution of natural fibres for multifunctional sustainable, reliable and affordable cement-based composite developments for today’s world.

The Lie point symmetry criteria and formation of exact analytical solutions for Kairat-II equation: Paul-Painlevé approach

The renowned non-linear Kairat-II equation, which expresses the surface curves, is examined meticulously in this work. There has never been a study before that discussed conserved quantities, sensitivity analysis, Hamiltonian function, Lie symmetry invariance criteria, and invariant solutions of Kairat-II equation The Lie invariance criteria are taken into consideration by the symmetry generators. The suggested technique leads to in a four-dimensional Lie algebra, where the translation point symmetries in space and time correlate with the conservation of mass and the energy, respectively, and remaining point symmetries are dilation and scaling. Firstly, we use symmetry reduction of Lie subalgebras to obtain closed-form invariant solutions. In specific reduction cases, we transform the Kairat-II equation into a spectrum of non-linear ordinary differential equations, which have the benefit of being able to offer an extensive selection of closed-form solitary wave solutions. For this equation, the Cauchy problem cannot be solved by the inverse scattering transform, therefore, traveling wave exact solutions are generated using the analytical Paul-Painlevé approach. In both two and three dimensions, the graphical behavior of specific solutions is presented for specific quantities of physical factors of examined equation. Utilizing conservation laws multipliers, the study culminates by determining an extensive set of local conservation laws for the non-linear Kairat-II equation that are applicable to arbitrary constant coefficients. Instead of focusing on the physical aspects of conservation laws, this study computed the conserved quantities using a mathematical perspective that can be used to identify potential symmetries of partial differential equations, guiding readers toward the solutions of partial differential equations. The existence of the Hamiltonian function is presented. The model’s sensitivity to various starting conditions is highlighted by the sensitivity analysis.

Multiscale Thermodynamics-Informed Neural Networks (MuTINN) towards fast and frugal inelastic computation of woven composite structures

The complex behavior of inelastic woven composites stems primarily from their inherent heterogeneity. Achieving accurate predictions of their linear and nonlinear responses, while considering their microstructures, appears feasible through the application of multi-scale modeling approaches. However, effectively incorporating these methodologies into real-scale applications, particularly within FE2 analyses, remains challenging due to the significant computational requirements they entail. To overcome this issue, while considering the scale effects, this study introduces an alternative approach based on Artificial Neural Networks (ANNs) to perform a macroscopic surrogate model of composites. This model, referred to as Multiscale Thermodynamics Informed Neural Networks (MuTINN), is founded on thermodynamic principles and introduces specific quantities of interest that serve as internal state variables at the macroscopic level. This captures efficiently the state and evolution laws governing the history-dependent behavior of these composites while retaining the thermodynamic admissibility and the physical interpretability of their overall responses. Moreover, to facilitate its numerical implementation within a FE code, a Meta-UMat has been developed, streamlining the application of multiscale FE×MuTINN approach for composite structure computations. The prediction capabilities of the proposed approach is demonstrated across the material scales, exemplified through diverse instances of woven composite structures. These applications account for anisotropic yarn damage and an elastoplastic polymer matrix behavior. The numerical results and the related comparison with experimental findings and FE computations demonstrate remarkable consistency across a wide range of non-proportional loading paths. This promises a potential solution to alleviate the computational challenges associated with multiscale simulations of large-scale composite structures.

Size, material type, and concentration estimation for micro-particles in liquid samples

The on-site examination and characterization of microparticles are becoming crucial due to the significant rise in plastic pollution in natural resources. Hence, identifying the specific microplastic composition and quantity would enable the implementation of preventive measures. This paper presents a cost-effective setup that utilizes the Random Forest algorithm to detect the size and refractive index of micro particles, hence facilitating the identification of the material type. The system utilizes the scattering patterns of laser light from the dispersion of microparticles, namely within the concentration range of 0.05 fM to 3.00 fM. The refractive indices and particle sizes of melamine (Me8) spheres with a size of 8 μm, as well as polystyrene (PS8) spheres with a size of 8 μm and (PS10) 10 μm, were estimated using the Random Forest algorithm and recorded scattering patterns. The proposed method may deliver findings with an average deviation of 0.23 μm for particle size and 0.015 for particle refractive index. The statistical analysis indicated that there was no notable disparity between the experimental findings and the predictions derived from the machine learning system. The existing configuration can be readily converted into a point-of-use system that can be employed on-site for the purpose of monitoring and identifying microplastic contamination.

Vacancies‐regulated Prussian Blue Analogues through Precipitation Conversion for Cathodes in Sodium‐ion Batteries with Energy Densities over 500 Wh/kg

AbstractPrussian blue analogues (PBAs) have been widely applied in many fields, especially as cathode materials of sodium‐ion batteries on account of their low cost and open framework for fast ions transport. However, the capacity of reported PBAs has a great distance from its theoretical value. Herein, we proposed that [Fe(CN)6] vacancies are crucial point for the high specific capacity for the first time. The [Fe(CN)6] vacancies may create net electrons and reduce obstacles to ionic transport, which is conducive to rate performance of PBAs by increasing electronic and ionic conductivity to some extent. As a proof of concept, a series of PBAs have been prepared by co‐precipitation method. And then, a novel precipitation conversion method has been designed, by which unique PBAs with a specific quantity of [Fe(CN)6] vacancies was successfully synthesized. Remarkably, the as‐prepared PBAs possessing hierarchical hollow morphology have reached a unprecedent level of high capacity (168 mAh g−1 at 25 mA g−1, close to PBAs’ theoretical capacity 170 mAh g−1), high rate performance (90 mAh g−1 at 5 A g−1), and high energy density (over 500 Wh kg−1).

Vacancies-regulated Prussian Blue Analogues through Precipitation Conversion for Cathodes in Sodium-ion Batteries with Energy Densities over 500 Wh/kg.

Prussian blue analogues (PBAs) have been widely applied in many fields, especially as cathode materials of sodium-ion batteries on account of their low cost and open framework for fast ions transport. However, the capacity of reported PBAs has a great distance from its theoretical value. Herein, we proposed that [Fe(CN)6] vacancies are crucial point for the high specific capacity for the first time. The [Fe(CN)6] vacancies may create net electrons and reduce obstacles to ionic transport, which is conducive to rate performance of PBAs by increasing electronic and ionic conductivity to some extent. As a proof of concept, a series of PBAs have been prepared by co-precipitation method. And then, a novel precipitation conversion method has been designed, by which unique PBAs with a specific quantity of [Fe(CN)6] vacancies was successfully synthesized. Remarkably, the as-prepared PBAs possessing hierarchical hollow morphology have reached a unprecedent level of high capacity (168 mAh g-1 at 25 mA g-1, close to PBAs' theoretical capacity 170 mAh g-1), high rate performance (90 mAh g-1 at 5 A g-1), and high energy density (over 500 Wh kg-1).

On the spin dependence of detection times and the nonmeasurability of arrival times

According to a well-known principle of quantum physics, the statistics of the outcomes of any quantum experiment are governed by a Positive-Operator-Valued Measure (POVM). In particular, for experiments designed to measure a specific physical quantity, like the time of a particle’s first arrival at a surface, this principle establishes that if the probability distribution of that quantity does not arise from a POVM, no such experiment exists. Such is the case with the arrival time distributions proposed by Das and Dürr, due to the nature of their spin dependence.

Multidimensional Multi-Connected Fuzzy Interval-Logic Controller

The key features and the principle of operation of a multidimensional fuzzy interval-logic controller with interconnected adjustable parameters developed by the authors are considered. The scheme of interpretation of continuous physical quantities by an equivalent set of terms and the basic version of the block diagram of the regulator are presented. The main elements of the logical output block are described, including the block of production rules, which includes a predefined database of values of control actions on control objects in the form of ordinal numbers of terms of output continuous quantities. The advantages and features of setting up the mutual connections between the input and output continuous values of the regulator are considered, which is expressed in determining the functional dependencies between them. The set of parameters possessed by the terms of input continuous quantities in relation to specific output continuous quantities of the regulator is described. The methods of defuzzification of the values of output continuous quantities within the boundaries of their terms are shown. The possibility of combining input and output continuous quantities into groups when setting up mutual connections between them is described. The mechanism of determining groups of interrelated continuous quantities is illustrated, the total number of which is determined by forming a matrix of relationships, according to the author’s scheme. The algorithm of sequential processing of the contents of the cells of the matrix of relationships, consisting of three steps, is considered. Expressions are given for calculating the basic parameters of a multidimensional fuzzy interval-logic controller with interrelated adjustable parameters. The results of a computational experiment to reduce the system of production rules of the regulator in determining the relationships between input and output continuous quantities are presented.

Rare Event Probability Estimation for Groundwater Inverse Problems With a Two‐Stage Sequential Monte Carlo Approach

AbstractBayesian inversions followed by estimations of rare event probabilities are often needed to analyze groundwater hazards. Instead of focusing on the posterior distribution of model parameters, the main interest lies then in the distribution of a specific quantity of interest contingent upon these parameters. To address the associated methodological challenges, we introduce a two‐stage Sequential Monte Carlo approach. In the first stage, it generates particles that approximate the posterior distribution; in the second stage, it employs subset sampling techniques to assess the probability of the rare event of interest. By considering two hydrogeological problems of increasing complexity, we showcase the efficiency and accuracy of the resulting PostRisk‐SMC method for rare event probability estimation related to groundwater hazards. We compare the performance of the PostRisk‐SMC method with a traditional Monte Carlo approach that relies on Markov chain Monte Carlo samples. We showcase that our estimates align with those of the traditional method, but the coefficients of variation are notably lower for the same computational budget when targeting more rare events. Furthermore, we highlight that the PostRisk‐SMC method allows estimating rare event probabilities approaching one in a billion using less than one hundred thousand forward simulations. Even if the presented examples are related to groundwater hazards, the methodology is well‐suited for addressing a wide range of topics in the geosciences and beyond.

Human brain representations of internally generated outcomes of approximate calculation revealed by ultra-high-field brain imaging

Much of human culture’s advanced technology owes its existence to the ability to mentally manipulate quantities. Neuroscience has described the brain regions overall recruited by numerical tasks and the neuronal codes representing individual quantities during perceptual tasks. Nevertheless, it remains unknown how quantity representations are combined or transformed during mental computations and how specific quantities are coded in the brain when generated as the result of internal computations rather than evoked by a stimulus. Here, we imaged the brains of adult human subjects at 7 Tesla during an approximate calculation task designed to disentangle in- and outputs of the computation from the operation itself. While physically presented sample numerosities were distinguished in activity patterns along the dorsal visual pathway and within frontal and occipito-temporal regions, a representation of the internally generated result was most prominently detected in higher order regions such as angular gyrus and lateral prefrontal cortex. Behavioral precision in the task was related to cross-decoding performance between sample and result representations in medial IPS regions. This suggests the transformation of sample into result may be carried out within dorsal stream sensory-motor integration regions, and resulting outputs maintained for task purposes in higher-level regions in a format possibly detached from sensory-evoked inputs.

Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity.

Obesity is an established risk factor for cancer. However, conventional measures like body mass index lack precision in assessing specific tissue quantities, particularly of the two primary abdominal fat compartments, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Computed tomography (CT) stands as the gold standard for precisely quantifying diverse tissue types. VAT, distinguished by heightened hormonal and metabolic activity, plays a pivotal role in obesity-related tumor development. Excessive VAT is linked to aberrant secretion of adipokines, proinflammatory cytokines, and growth factors, fostering the carcinogenesis of obesity-related tumors. Accurate quantification of abdominal fat compartments is crucial for understanding VAT as an oncological risk factor. The purpose of the present research is to elucidate the role of CT, performed for staging purposes, in assessing VAT (quantity and distribution) as a critical factor in the oncogenesis of obesity-related tumors. In the field of precision medicine, this work takes on considerable importance, as quantifying VAT in oncological patients becomes fundamental in understanding the influence of VAT on cancer development-the potential "phenotypic expression" of excessive VAT accumulation. Previous studies analyzed in this research showed that VAT is a risk factor for clear cell renal cell carcinoma, non-clear cell renal cell carcinoma, prostate cancer, and hepatocarcinoma recurrence. Further studies will need to quantify VAT in other oncological diseases with specific mutations or gene expressions, in order to investigate the relationship of VAT with tumor genomics.

Analisis Peran Petani Kopi Sukamakmur Dalam Mewujudkan Kontinuitas Pasokan Kopi Yang Berkualitas Untuk Home Coffee Roastery Bogor

Indonesia has many rich natural resources, especially plantations and agriculture. This country has great potential and is considered the second largest producer in the world after Brazil. In addition, coffee agrotourism and coffee plantations promote sustainable aspects of tourism. Farmers are the main actors who play a role in increasing crop productivity. With demand for coffee increasing every year, how do farmers maintain continuity of coffee supply? This then became the basis for the birth of research entitled "Analysis of the Role of Sukamakmur Coffee Farmers in Realizing Continuous Supply of Quality Coffee for Home Coffee Roastery Bogor". The aim of this research is to analyze the role of farmers in realizing sustainable coffee production. This research brings qualitative closer. The data collection method was carried out by interviewing informants with direct field observation and documentation. The results of this research are seen from the aspect of farmers from the general community group in Sirnajaya Village, Sukamakmur Regency, who succeeded in producing quality coffee beans to supply at Home Coffee Roastery Bogor, by paying attention to the post-harvest process, special training for farmers, and prioritizing specific quality and quantity. Keywords : Coffee farmer, Productivity, Coffee Industry.

Solution of the discrete Boltzmann equation: Based on the finite volume method

Mesoscopic methods serve as a pivotal link between the macroscopic and microscopic scales, offering a potent solution to the challenge of balancing physical accuracy with computational efficiency. Over the past decade, significant progress has been made in the application of the discrete Boltzmann method (DBM), which is a mesoscopic method based on a fundamental equation of nonequilibrium statistical physics (i.e., the Boltzmann equation), in the field of nonequilibrium fluid systems. The DBM has gradually become an important tool for describing and predicting the behavior of complex fluid systems. The governing equations comprise a set of straightforward and unified discrete Boltzmann equations, and the choice of their discrete format significantly influences the computational accuracy and stability of numerical simulations. In a bid to bolster the reliability of these simulations, this paper utilizes the finite volume method as a solution for handling the discrete Boltzmann equations. The finite volume method stands out as a widely employed numerical computation technique, known for its robust conservation properties and high level of accuracy. It excels notably in tackling numerical computations associated with high-speed compressible fluids. For the finite volume method, the value of each control volume corresponds to a specific physical quantity, which makes the physical connotation clear and the derivation process intuitive. Moreover, through the adoption of suitable numerical formats, the finite volume method can effectively minimize numerical oscillations and exhibit strong numerical stability, thus ensuring the reliability of computational results. Particularly, the MUSCL format where a flux limiter is introduced to improve the numerical robustness is adopted for the reconstruction in this paper. Ultimately, the DBM utilizing the finite volume method is rigorously validated to assess its proficiency in addressing flow issues characterized by pronounced discontinuities. The numerical experiments encompass scenarios involving shock waves, Lax shock tubes, and acoustic waves. The results demonstrate the method's precise depiction of shock wave evolution, rarefaction waves, acoustic phenomena, and material interfaces. Furthermore, it ensures the conservation of mass, momentum, and energy within the system, as well as accurately measures the hydrodynamic and thermodynamic nonequilibrium effects of the fluid system. Compared with the finite difference method, the finite volume method is also more convenient and flexible in dealing with boundary conditions of different geometries, and can be adapted to a variety of systems with complex boundary conditions. Consequently, the finite volume method further broadens the scope of DBM in practical applications.