Efficient Resource Utilization Research Articles

LEVERAGING BIG DATA TECHNOLOGIES FOR ENHANCED PUBLIC PARTICIPATION IN PUBLIC FINANCIAL MANAGEMENT

The article is devoted to the topical issues regarding the implementation of Big Data technologies in public finance management. The application of Big Data has the potential to enhance transparency and accountability in the use of budgetary resources, increase trust in government, improve the efficiency of budget resource utilization, better understand citizens' needs, and engage the public in public finance management. The purpose of the study is to explore theoretical, methodological, and practical aspects, as well as to develop recommendations for the implementation of Big Data processing and analysis technologies to enhance public participation in public financial management. The article examines traditional methods of civil engagement in the budgetary process, identifies their disadvantages, and explores Big Data technology potential based on Computational Linguistics and Machine Learning to strengthen public participation. Developments in sentiment analysis and opinion mining have been adapted to the field of public finance. A generative model for analyzing public sentiment on social networks regarding public finance management has been constructed and tested. The approaches developed for using Big Data technologies can be implemented in the field of public finance to enhance public participation in their management as advisory tools for the realization of representative democracy and require further theoretical elaboration and practical application to improve the analysis of alternative sentiments, prevent manipulation of public opinion, and abuse within the network.

PRODUCTION OF RECYCLED AGGREGATE FOR STRUCTURAL CONCRETE

Recycling demolished concrete is crucial for efficient resource utilization and environmental preservation. The study investigates the use of recycled aggregate in structural concrete production. This study focuses on the mix design of recycled aggregate concrete using the IS mix design procedure with a targeted design strength. Five different concrete proportions were investigated, ranging from 0% ,25% ,50% ,75% and 100% replacement of natural aggregates by recycled aggregates. Results were discussed, with a focus on comparing cube compressive strength to cylindrical compression strength and cylindrical compression strength to split tensile strength. The findings were compared with existing standards and provisions, and regression models were formulated to enhance understanding and prediction of concrete properties. This paper contributes valuable insights into the mix design of recycled aggregate concrete, facilitating sustainable construction practices in line with industry standards and regulations. Key Words: Natural Aggregate Concrete, Recycled Aggregate Concrete, Compressive Strength test, Split Tensile Strength test.

Exploration of Water Resource Protection and Utilization in Tibet from the Perspective of Ecological Civilization

Tibet, known as the “roof of the world” in China's southwest border region, is not only rich in water resources, but also regarded as an important national energy reserve base. However, on a global scale, Tibet is also one of the few places in the world that faces the challenge of water scarcity. In order to cope with this challenge, it is necessary to deeply understand and grasp the connotation, essential characteristics and basic requirements of the concept of ecological civilization, and on this basis, combined with the current situation of water resources in Tibet, analyze the existing problems and put forward improvement measures in the light of the concept of ecological civilization, to promote the sustainable development and management of water resources in Tibet. As one of the core elements of ecological civilization construction, the protection and rational utilization of water resources are directly related to the stability of the ecological environment and the sustainable development of human society. Therefore, it is necessary to curb water pollution from the source and strengthen the protection of water environment. In view of the shortage of water resources in Tibet, it is necessary to scientifically plan and manage water resources, rationally allocate water resources, and improve the efficiency of water resources utilization. At the same time, it is also necessary to use modern scientific and technological means, such as remote sensing technology and big data analysis, to carry out real-time monitoring and management of water resources in Tibet to better cope with the challenge of water shortage. Finally, it is necessary to adhere to the concept of harmonious coexistence between man and nature, respect nature, adapt to nature, and protect nature, in order to achieve the goal of harmonious coexistence between man and water, man and nature. In general, through the in-depth study of water resources in Tibet, we can better understand the connotation, essential characteristics and basic requirements of the concept of ecological civilization, so as to propose more scientific and reasonable improvement measures to promote the sustainable development and management of water resources in Tibet and to realize the harmonious coexistence between human beings and nature.

RESEARCH OF AEROSOL APPLICATION SYSTEMS FOR APPLYING LUBRICATING MATERIAL IN THE «WHEEL-RACK» CONTACT

The article discusses various methods of lubricating the «wheel-rail» contact zone aimed at reducing friction coefficient and improving the efficiency of energy resource utilization, safety of movement, and environmental indicators. Technologies for delivering lubricating materials to this zone, such as lubricating rods, stationary systems using brushes, or railhead applicators, are outlined. The article highlights issues with existing technologies, including excessive lubrication, the need for component replacements, high lubricant material consumption, and uneven or incorrect application. It is emphasized that precision in the application and dosage of lubricating material is crucial to prevent its transfer onto the wheel and rail surfaces, which could reduce traction. The aerosol method of applying lubricant to the wheel edge is stated to be more accurate and cost-effective. The article mentions the company «SKF» and their EasyRail systems utilizing nozzles with the SP9-2 and SP9-2-S7 metering valves. Schematic representations of the nozzle and its working principle are provided. Sécheron is also mentioned, offering a nozzle for spraying lubricant onto the wheel edge through a two-tube system for oil and oxygen supply. An image of this company's nozzle is included. The text delves into the equipment of the lubrication system by Sécheron, specifically detailing the product's technical characteristics. Information about the development and manufacturing of the locomotive wheel flange lubrication system by the company "Scientific and Production Firm «Yugtekhnotrans» is presented. The article introduces the TrainSys-AL system for lubricating the rail head/running surface, providing a detailed description of its features and technical specifications, including the use of friction modifiers «HeadLub 90» and «RailLub». The products of the company Dropsa are mentioned, specifically the spray valve of the 1132330 series for the lubrication system of railway rolling stock and tracks. The overall context of the text is oriented towards the technical aspects and features of modern lubrication systems in railway transport.The study reviews existing designs of rail and wheel lubrication devices, revealing that there is no ideal design that meets all requirements for material consumption and precision in applying lubricating material. Consequently, a new nozzle for aerosolized oil spray is developed, and a laboratory setup for researching this process is manufactured. The setup operates on compressed air and includes a compressor with a reservoir tank for stabilizing nozzle operation. The hydraulic scheme of the setup is described, with key elements such as a ball valve, pressure reducing valve, pressure gauge, tank, and nozzle. The preparation of the setup for operation and its functioning principle are demonstrated. Research results indicate that the developed setup effectively sprays both dense and liquid lubricants depending on the air pressure and nozzle diameter. Graphs illustrating lubricant consumption for different types of lubricants and various nozzle parameters are provided.

Optimizing the Conditions of Pretreatment and Enzymatic Hydrolysis of Sugarcane Bagasse for Bioethanol Production.

The agricultural waste sugarcane bagasse (SCB) is a kind of plentiful biomass resource. In this study, different pretreatment methods (NaOH, H2SO4, and sodium percarbonate/glycerol) were utilized and compared. Among the three pretreatment methods, NaOH pretreatment was the most optimal method. Response surface methodology (RSM) was utilized to optimize NaOH pretreatment conditions. After optimization by RSM, the solid yield and lignin removal were 54.60 and 82.30% under the treatment of 1% NaOH, a time of 60 min, and a solid-to-liquid ratio of 1:15, respectively. Then, the enzymolysis conditions of cellulase for NaOH-treated SCB were optimized by RSM. Under the optimal enzymatic hydrolysis conditions (an enzyme dose of 18 FPU/g, a time of 64 h, and a solid-to-liquid ratio of 1:30), the actual yield of reducing sugar in the enzyme-treated hydrolysate was 443.52 mg/g SCB with a cellulose conversion rate of 85.33%. A bacterium, namely, Bacillus sp. EtOH, which produced ethanol and Baijiu aroma substances, was isolated from the high-temperature Daqu of Danquan Baijiu in our previous study. At last, when the strain EtOH was cultured for 36 h in a fermentation medium (reducing sugar from cellulase-treated SCB hydrolysate, yeast extract, and peptone), ethanol concentration reached 2.769 g/L (0.353%, v/v). The sugar-to-ethanol and SCB-to-ethanol yields were 13.85 and 11.81% in this study, respectively. In brief, after NaOH pretreatment, 1 g of original SCB produced 0.5460 g of NaOH-treated SCB. Then, after the enzymatic hydrolysis, reducing sugar yield (443.52 mg/g SCB) was obtained. Our study provided a suitable method for bioethanol production from SCB, which achieved efficient resource utilization of agricultural waste SCB.

Application of AI Technology for the Development of Destination Tourism towards an Intelligent Information System

"The pervasive influence of Artificial Intelligence (AI) technology is fundamentally transforming industries and daily life. AI, an emulation of human intelligence in machines, endows systems with the capacity to autonomously learn reason and make decisions. By processing extensive datasets at unprecedented speeds, AI is ushering in a revolution across diverse domains, including healthcare, finance, education and manufacturing. Its applications span from elevating customer experiences through personalized recommendations to propelling advancements in areas like autonomous vehicles and precision agriculture. With the tourism industry in transformative evolution, AI is pivotal in enhancing visitor experiences, streamlining operations, and fostering sustainable destination management. AI-powered solutions personalize and simplify the entire traveller journey, offering tailored recommendations for pre-trip planning, on-site experiences and post-travel engagement. This not only boosts customer satisfaction but also elevates the overall appeal and competitiveness of destinations. Moreover, AI optimizes operational processes by utilizing predictive analytics and machine learning for demand forecasting, resource allocation, and crowd management. This facilitates data-driven decision-making, promoting efficient resource utilization and minimizing environmental impact. In sustainable tourism, AI monitors and manages environmental factors, employing smart sensors and analytics to assess ecological impact. This enables the implementation of proactive measures to preserve natural resources and protect biodiversity, contributing to smart infrastructure development for destination resilience. The integration of AI in destination tourism drives economic growth and enhances safety for travellers. AI-powered systems analyse real-time data to detect and respond to potential threats, ensuring a secure environment. As the tourism industry evolves, embracing AI becomes imperative for destinations to remain competitive, sustainable, and resilient. This abstract provides insights into the diverse applications of AI in destination tourism, underscoring benefits for tourists and destination management stakeholders."

Microbial Community Shifts in Tea Plant Rhizosphere under Seawater Stress: Enrichment of Beneficial Taxa.

Seawater intrusion has a significant impact on the irrigation quality of agricultural water, thereby posing a threat to plant growth and development. We hypothesized that the rhizosphere of tea plants harbors beneficial microorganisms, which may improve the tolerance of tea plants to seawater stress. This study utilized 16s and ITS techniques to analyze microbial community shifts in the tea plant rhizosphere and non-rhizosphere under seawater stress conditions. The findings suggest that seawater stress leads to a reduction in microbial diversity, although the rhizosphere microbial diversity in stressed soils showed a relatively higher level. Moreover, the rhizosphere of the tea plant under seawater stress exhibited an enrichment of plant growth-promoting rhizobacteria alongside a higher presence of pathogenic fungi. Network analysis revealed that seawater stress resulted in the construction of a more complex and stable rhizosphere microbial network compared to normal conditions. Predictions of bacterial potential functions highlighted a greater diversity of functional groups, enhancing resource utilization efficiency. In general, the rhizosphere microorganisms of tea plants are jointly selected by seawater and the host. The microorganisms closely related to the rhizosphere of tea plants are retained and, at the same time, attract beneficial microorganisms that may alleviate stress. These findings provide new insights into plant responses to saline stress and have significant implications for leveraging vegetation to enhance the resilience of coastal saline soils and contribute to economic progress.

Smart Agriculture Applications and Data Analysis with Industry 4.0

The world’s food demand is predicted to exceed its food supply significantly in the coming periods. Many physical factors, such as climate change in access to water, change the production amount and food prices. Efficient resource utilization can be achieved by developing smart agricultural applications using the IoT and image processing techniques. For this purpose, in the proposed study, a smart agriculture application was developed using IoT technology with image processing. In the proposed image processing algorithm, the crops’ images were processed, weight estimates were made, and their numbers were taken. The data we collect instantly can be uploaded to the cloud, and the data collected on the cloud side can be analyzed over the mobile application. Thanks to data analysis with the mobile application we have developed, we can see the current status of the crops and make instant decisions, and it also plays a role in determining market prices. With all these studies, it has been seen that agriculture can be digitalized with the technologies we use, and thus, productivity in agriculture can increase significantly.

Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

Constructing Ni-Pt Bimetallic Catalysts for Catalytic Hydrogenation and Rearrangement of Furfural into Cyclopentanone with Insight in H/D Exchange by D2O Labeling.

Developing a metallic catalyst for converting furfural (FAL) to highly valuable products such as cyclopentanone (CPO) is important for fine chemical synthesis by the efficient utilization of biomass resources. The presence of diverse unsaturated carbon atoms in FAL and the rearrangement of oxygen atoms hinder the production of CPO. We developed an optimal nickel (Ni)-to-platinum (Pt) molar ratio (1:0.007) for a bimetallic Ni-Pt/alumina (Al2O3) catalyst with a low Pt loading via an impregnation method to efficiently catalyze the selective hydrogenation of FAL in an aqueous solution to form CPO. The comprehensive characterizations by X-ray diffraction and X-ray absorption near edge structure analyses elucidated the formation of Ni0/Pt0 and Ni2+/Pt4+ after reduction by H2. The addition of a low amount of the Pt-Ni/Al2O3 catalyst resulted in an alleviation of H2 reduction behavior detected by hydrogen temperature-programmed reduction, accompanied by low H2 desorption ability observed by hydrogen temperature-programmed desorption. The catalytic activity of Ni-Pt/Al2O3 was higher than those of Ni/Al2O3 and Pt/Al2O3 catalysts. The maximum CPO yield was 66% with 93% FAL conversion under the optimized conditions (160 °C, 20 bar of H2 pressure, and 2 h). Isotopic deuterium oxide (D2O) labeling revealed the transfer of deuterium (D) atoms from D2O to the intermediates and products during hydrogenation and rearrangement, which confirmed that water was a medium for rearrangement and the source of hydrogen for the reaction. This study developed an efficient catalyst for the catalytic hydrogenation and ring rearrangement of FAL into CPO.

Cost Effective Photothermal Materials Selection for Direct Solar-Driven Evaporation.

The cornerstone of eco-friendly and affordable freshwater generation lies in harnessing solar energy for water evaporation. This process involves extracting vapor from liquid water using solar energy. Numerous innovative, low-cost materials have been proposed for this purpose. These materials aim to enable highly controllable and efficient conversion of solar energy into thermal energy while maintaining high cost-effectiveness. Here, in this review paper, we outline the advancements in solar-driven evaporation technology with a focus on optimizing synthesis methods and materials cost. It prioritizes refining evaporation efficiency and affordability using inventive manufacturing methods. By utilizing innovative reasonably priced materials, this process not only ensures efficient resource utilization but also fosters technological advancements in renewable energy applications. Moreover, the affordability of these materials makes solar-powered water evaporation accessible to a wider range of communities, empowering them to address water scarcity challenges.

Research on the Explosion Reaction Mechanism of Volatile Gases from Coal Dust Pyrolysis

ABSTRACT It is imperative to have an in-depth understanding of the coal dust explosion reaction mechanism to control and prevent coal explosions in the coal mining industry and promote the safe and efficient utilization of coal resources. The volatile gases are the primary components involved in coal dust explosion reactions. Therefore, studying the explosion reaction mechanism of volatile gases in coal dust is of great significance for revealing the process of coal dust explosion. This study conducted a thermogravimetric mass spectrometry (TG-MS) experiment to analyze the mass loss characteristics and volatile gas components during coal dust pyrolysis. Experimental results showed that the pyrolysis process of coal dust is mainly divided into three stages. The main components of volatile gases escaping from coal dust pyrolysis are CO, CH4, CO2, and H2O. Then, the Chemkin-Pro numerical simulation method was also introduced to analyze the mixture explosion reaction process of coal dust pyrolysis volatile gas. The sensitivity analysis and the rates of production analysis (ROP) of reactants, products, and intermediate products during the volatile gas explosion reaction were conducted. Furthermore, 14 components and 27 key elementary reactions involved in the reaction process were simplified and extracted. A simplified reaction pathway for the mixture explosion of coal dust pyrolysis volatile gas was constructed. This study introduces a novel approach to investigate the mechanism of homogeneous combustion of volatile gases in coal dust explosion reactions.

Efficient preparation of anisotropic cellulose sponge from cotton stalks: An excellent material for separation applications

Water pollution and solid waste resource reuse demand immediate attention and research. Here, we present a method to create anisotropic cellulose sponges from cotton stalk waste. Using the inherent structure of cotton stalks, we selectively remove lignin and hemicellulose via acid and alkali pretreatment. This process yields cellulose sponges with a natural pore structure. Our findings demonstrate that these sponges retain the original pore configuration of cotton stalks, providing excellent connectivity and compressibility due to their unique anisotropic three-dimensional structure. Moreover, these sponges exhibit exceptional super-hydrophilic and underwater super-oleophobic properties, with underwater oil contact angles exceeding 150° for all tested oils. External pressure can reduce the pore size of the cellulose sponge, facilitating the gravity-driven separation and removal of dyes and emulsions. Remarkably, removal efficiencies for Methylene Blue (MB), Congo Red (CR), water-in-oil (w/o) emulsions, and oil-in-water (o/w) emulsions exceed 99 %, 97 %, 99 %, and 99 %, respectively, highlighting superior removal and recyclability. Further investigation into the mechanisms of dye and emulsion removal employs X-ray photoelectron spectroscopy (XPS) characterization and molecular dynamics (MD) simulation. These insights lay the groundwork for the efficient recycling and resource utilization of waste cotton stalks, offering promising applications in water purification.

General equilibrium analysis of carbon tax policy on water-energy-food nexus efficiency

Carbon-reduction policy affects socio-economy and Water-Energy-Food (WEF) nexus contained therein. Existing studies concentrated on policy performances on resource consumption, but cannot quantify the impacts on resource utilization efficiency. To fulfill such gap, this study developed an integrated evaluation method through integrating computable general equilibrium model with data envelopment analysis to explore the impact of carbon tax on WEF environmental efficiency of China. Results show that increasing carbon taxes would improve the energy structure and reduce carbon emissions. While the increased cost of fossil fuels would have negative but limited impacts on economic output of water supply and food sectors through economic activity connections. Synergistic effect of WEF resource conservation could be observed in most production sectors, expect for clean energy sectors. Carbon tax would improve the WEF environmental efficiency expressed as the trade-offs between economic and carbon emission outputs and WEF resource inputs. There are significant differences in WEF efficiency and its growth rate among production sectors, with food sectors experiencing the most significant improvement in WEF efficiency at a carbon tax of 70 yuan/ton. The evaluation results raised the awareness of the performance of carbon tax on WEF nexus and provide a new perspective for promoting WEF nexus efficiency.

Artificial Intelligence for Sustainable Energy Transition: Optimising Renewable Energy Integration and Management

As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.

Standard-definition White-light, High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas: A Multicenter Randomized Controlled Trial.

This study aimed to compare the performance of standard-definition white-light endoscopy (SD-WL), high-definition white-light endoscopy (HD-WL), and high-definition narrow-band imaging (HD-NBI) in detecting colorectal lesions in the Chinese population. This was a multicenter, single-blind, randomized, controlled trial with a non-inferiority design. Patients undergoing endoscopy for physical examination, screening, and surveillance were enrolled from July 2017 to December 2020. The primary outcome measure was the adenoma detection rate (ADR), defined as the proportion of patients with at least one adenoma detected. The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression. Out of 653 eligible patients enrolled, data from 596 patients were analyzed. The ADRs were 34.5% in the SD-WL group, 33.5% in the HD-WL group, and 37.5% in the HD-NBI group (P=0.72). The advanced neoplasm detection rates (ANDRs) in the three arms were 17.1%, 15.5%, and 10.4% (P=0.17). No significant differences were found between the SD group and HD group regarding ADR or ANDR (ADR: 34.5% vs. 35.6%, P=0.79; ANDR: 17.1% vs. 13.0%, P=0.16, respectively). Similar results were observed between the HD-WL group and HD-NBI group (ADR: 33.5% vs. 37.7%, P=0.45; ANDR: 15.5% vs. 10.4%, P=0.18, respectively). In the univariate and multivariate logistic regression analyses, neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL (HD-WL: OR 0.91, P=0.69; HD-NBI: OR 1.15, P=0.80). HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients. It can be concluded that HD-NBI or HD-WL is not superior to SD-WL, but more effective instruction may be needed to guide the selection of different endoscopic methods in the future. Our study's conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources, especially advanced imaging technologies.

Projective quantum eigensolver via adiabatically decoupled subsystem evolution: A resource efficient approach to molecular energetics in noisy quantum computers.

Quantum computers hold immense potential in the field of chemistry, ushering new frontiers to solve complex many-body problems that are beyond the reach of classical computers. However, noise in the current quantum hardware limits their applicability to large chemical systems. This work encompasses the development of a projective formalism that aims to compute ground-state energies of molecular systems accurately using noisy intermediate scale quantum (NISQ) hardware in a resource-efficient manner. Our approach is reliant upon the formulation of a bipartitely decoupled parameterized ansatz within the disentangled unitary coupled cluster framework based on the principles of nonlinear dynamics and synergetics. Such decoupling emulates total parameter optimization in a lower dimensional manifold, while a mutual synergistic relationship among the parameters is exploited to ensure characteristic accuracy via a non-iterative energy correction. Without any pre-circuit measurements, our method leads to a highly compact fixed-depth ansatz with shallower circuits and fewer expectation value evaluations. Through analytical and numerical demonstrations, we establish the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems. This approach enables rapid exploration of emerging chemical spaces by the efficient utilization of near-term quantum hardware resources.

Development of Sustainable Integrated Farming Systems for Small and Marginal Farmers and Ecosystem Services -A Comprehensive Review

This review article shows a holistic approach regarding integrated farming system (IFS) for livelihood security and sustainable development of small and marginal households in India. Integrated farming systems are being developed for location specific because enormous problems are being aroused in the agriculture and allied sectors such as declined factor productivity, profitability, unscientific management of farm resources, decreases in crops and livestock productivity due to climate change, changes in food habits of the people, deprived soil health, low inputs use efficiencies, declined in crop diversification and biodiversity, increased cost of production, utilization of high energy inputs, produces low energy outputs and other problems are also coexisted in current farming system. The integrated farming system includes different components like crops, livestock, poultry, beekeeping, fisheries, mushroom, kitchen garden, boundary plantation and others to cultivate in an integrated way for efficient utilization of limited farm resources. Moreover, imperative need to resolve these ablaze issues with the help of development of location-specific integrated farming system modules/models. IFS is a tool of sustainable strategies for the meeting of assorted agrarian anxieties along with ensured food and nutritional security and conserved ecosystem services. The standardized and upscale IFS models increased the productivity and profitability of the farmers more than mono-cropping and single-farm enterprises. The conducted studies emphases about enhance soil quality indicators, recycling and saving plant nutrients by around 55.6% through proper management of farm-based waste and byproducts. A total of 265.18 kg nitrogen, 48.91 kg phosphorus and 269.48 kg potassium can be saved through recycled farm-based waste or byproducts of a 1.5 ha model which comprises nine components. In conclusion, IFS modules/models are emission-negative or low GHG emitters, paving the way for promoting of climate-friendly farming in India. IFS emerges as a holistic approach to increase climate-resilient, greenhouse gas emissions can be reduced, it is a potential for sustainable agriculture development requiring continued research, policy support and innovative strategies for widespread adoption. As regards to livelihood security, the IFS approach has adapted to meet the home-grown family needs of cereals, pulses, oils, fruits, milk, meat, eggs, and vegetables. The future direction for research includes vertical farming, climate-smart farming systems, and improving the quality and sustainability of the farming systems, especially for underprivileged farm householders.

Rapid quantitative analysis of multiple rare earth elements in NdFeB alloys based on laser-induced breakdown spectroscopy (LIBS) and random forest (RF)

NdFeB material has excellent comprehensive magnetic properties, which plays a crucial role in the field of rare earth magnetic materials, and is one of the important basic materials to support modern high-tech industries. In the production of NdFeB alloys, the control of rare earth elements (REEs) is directly related to the quality and resource utilization efficiency of NdFeB materials. Therefore, the prediction of REEs content in NdFeB alloys is of great research significance and application value for the quality control of products, the enhancement of production efficiency and reduction of energy consumption by the rare earth material industry. In this study, a combination of laser-induced breakdown spectroscopy (LIBS) and random forest (RF) was used to investigate the quantitative analysis of four REEs (Nd, Pr, Tb and Dy) in NdFeB alloys. Firstly, the original LIBS spectra were screened by principal component analysis-mahalanobis distance (PCA-MD). Then, the effects of different data processing methods on the screened LIBS spectra were explored, and next, the feature variables were extracted from the preprocessed spectral data by the variable importance measurement (VIM). In order to further verify the prediction performance of the model, the prediction results of the RF models based on the different methods were compared. Finally, a PCA-VIM-RF calibration model was established on the basis of the optimized spectra, selected feature variables and parameters. Leave-one-out cross validation (LOOCV) was used to optimize the parameters of PCA-MD method, spectral preprocessing method and variable importance thresholds during the construction of the calibration model. The results show that the PCA-VIM-RF model has better prediction performance than the RF calibration model based on the raw spectra. For the PCA-VIM-RF calibration method of Nd, Pr, Tb and Dy elements, the values of R2CV are 0.9991, 0.9998, 0.9986, and 0.9984, respectively, the values of RMSECV are 0.06296%, 0.02788%, 0.04647% and 0.05252%, respectively. The values of R2P are 0.9508, 0.9975, 0.9691 and 0.9457, respectively, and the values of RMSEP are 0.6082%, 0.09205%, 0.5776% and 0.2631%, respectively.The above results indicate that LIBS combined with PCA-VIM-RF algorithm is a promising method for rapid quantitative analysis of REEs in NdFeB alloys without complicated sample preparation, which can provide some new ideas or strategies for the future research, development and quality control of rare earth materials.