Cost Efficiency Research Articles

Iranian Traditional Medicine (ITM) and Natural Remedies for Treatment of the Common Cold and Flu.

Traditional Iranian medicine is usually used for both prevention and relief of cold and flu symptoms in China, Iran, and many other Asian countries all over the world. There are 4 kinds of influenza viruses. Unlike type B, which may cause seasonal epidemics, type A viruses can cause pandemics, and influenza C may lead to mild human infection with little public health effects. A literature review was done by using multiple databases such as ISI Web of knowledge, PubMed, Science Direct and Google Scholar. The most notable antiviral medicinal plants for flu and cold are honeysuckle flowers, thyme leaf, green chiretta, andrographis, peppermint oil and leaf and calendula. The most important expectorant medicinal plants for cold and flu are snake root, tulsi, licorice root, slippery elm, clove, and sage leaf. Recommended immunostimulant medicinal plants for cold and flu are eucalyptus, Echinacea root, ginseng, garlic, slippery elm, marshmallow, Usnea lichen, Isatis root, ginger root, and myrrh resin. Iranian traditional medicine, which is one of the oldest schools of traditional medicine, is one of the main concepts of disease and health, and it can be considered as an important complementary and alternative medicine, as in some cases, modern medicine has many side effects, low efficiency, and high costs. Medicinal plants and herbs, which are included in many traditional systems, have significant and promising bioactive components in organic life.

Simulation on the influence of the shape of the carabiner as a hanging accessory on stress distribution using Autodesk Fusion 360

Engineering and innovation in the manufacturing process will continue to be carried out. This aims to ensure that there are always improvements in every product made, both in terms of design, materials used, and how the production process is carried out. Product design innovation is often also aimed at efficiency and reducing product production costs. Innovation in a product must be to improve, not reduce the value and usefulness of the product being made. The aim of this research is to determine the distribution of stress and strain as well as the safety factor of carabiners as hanging accessories using polypropylene polymer material. The research uses experimental methods, namely observing the simulation results that occur in the form of stress, strain and safety factors, as well as knowing the cause and effect phenomena that occur in the design of a carabiner for an accessory. By changing the shape of each design to the upper end of the frame in the direction of the carabiner gate, R15, R30 and R45. As well as varying the load given by 10 N to 100 N, with an increase in force of 10 N in each simulation carried out, with axial and vertical loading directions. By ignoring the type of gate and the shape of the connection on the carabiner gate. The different shapes in each carabiner design cause differences in the tension distribution that occurs. The R15 design has a maximum stress value at a load of 100 N, namely 25.03 MPa, the R30 design is 33.78 MPa, and the R45 design is 63.61 MPa. The vertical loading direction achieves a good safety level of 4.0 at a load of 20 N in the R15 and R30 designs. Meanwhile, axial loading does not achieve product safety targets. The difference in calculating the factor of safety is 4.0 between the results of computer computing and the results of analytical calculations using a formula, namely 1% of the maximum limit of 5%.

Analysis of Business Intelligence Technology in the Big Data Era A Case Study of Alibaba Group

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibabas cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibabas success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.

Rapid Detection of Broad-Spectrum Pathogenic Bacteria Based on Highly Sensitive Proton Response of the Nucleic Acid Amplification SPQC Platform.

Pathogenic bacteria significantly contribute to elevated morbidity and mortality rates, highlighting the urgent need for early and precise detection. Currently, there is a paucity of effective broad-spectrum methods for detecting pathogenic bacteria. We have developed an innovative proton-responsive series piezoelectric quartz crystal (PR-SPQC) platform for the broad-spectrum identification of pathogenic bacteria. This was achieved by retrieving and aligning sequences from the NCBI GenBank database to identify and validate 16S rRNA oligonucleotide sequences that are signatures of pathogenic bacteria but absent in humans or fungi. The hyperbranched rolling circle amplification, activated exclusively by the screened target, exponentially generates protons that are detected by SPQC through a 2D polyaniline (PANI) film. The PR-SPQC platform demonstrates broad-spectrum capabilities in detecting pathogenic bacteria, with a detection limit of 2 CFU/mL within 90 min. Clinical testing of blood samples yielded satisfactory results. With its advantages in miniaturization, cost efficiency, and suitability for point-of-care testing, PR-SPQC has the potential to be extensively used for the rapid identification of diverse pathogenic bacteria within clinical practice and public health sectors.

Optimizing Foundations: The Case for Slab Selection in Multi-Story Residential Buildings

This study investigates the optimal foundation design for multi-story residential buildings through a comparative analysis of two foundation variants under specific geological conditions. Employing the finite element method via SCAD software, we assessed the structural integrity and load-bearing capacities of the foundation options. Material consumption was quantified for each variant, and the consistency of settling calculations for the slab foundation was evaluated. The results indicated that the slab foundation not only minimizes material usage but also simplifies design complexity according to normative standards. Based on technical and economic assessments, the slab foundation is recommended as the most cost-effective and structurally efficient option for such construction projects. This research provides a pivotal foundation design perspective that could influence future architectural and engineering practices in residential building construction.
 Highlights:
 
 SCAD software validates foundation integrity via finite element analysis.
 Slab foundations offer superior cost and material efficiency.
 Simplifies design, enhancing construction process efficiency.
 
 Keywords: Structural Integrity, Slab Foundation, Reinforcement, Monolith, Building

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation.

A Structural Equation Model Study for Adoption of Internet of Things for the Growth of Manufacturing Industries in Australia

This research study delves into the integration of IoT within the Australian manufacturing sector, analyzing five key factors influencing adoption and drawing comparisons with Rogers' innovation theory. Its objectives encompass assessing how competitiveness, regulatory support, management support, manufacturing growth, and cost savings affect IoT adoption. Methodologically, the research journey includes defining the problem, conducting a thorough literature review, crafting a research proposal, executing pilot and main studies, validating findings, and ultimately composing the final thesis. Quantitative data obtained from a large pool of over 500 respondents shed light on the positive impacts of competitiveness, regulatory support, and cost savings on IoT adoption. Additionally, the study employs Importance-Performance Map Analysis (IPMA) to underscore the significance of management support in successful IoT implementation. Despite facing limitations such as common method bias and constraints on generalizability, the research underscores the transformative potential of IoT in enhancing operational efficiency and decision-making within Australian manufacturing. Overall, the study's findings confirm the significant influence of key factors on IoT adoption, affirming its potential to drive competitiveness, regulatory compliance, cost efficiency, and growth within the Australian manufacturing landscape.

Application of ChatGPT in Information Literacy Instructional Design

Recent developments in generative artificial intelligence tools have prompted immediate reactions in the academic library community. While most studies focus on the potential impact on academic integrity, this work explored constructive applications of ChatGPT in the design of instructional materials for courses in academic information literacy. The starting point was the use of openly licenced information resources or content infrastructure as facilitators in the creation of educational materials. In the first phase, course teaching material was developed using a prompt engineering strategy, predefined standards, and a prompt script. As a second step, we experimented with designing a custom chatbot model connected to a pre-defined corpus of source documents. The results demonstrated that the final teaching material required careful revision and optimisation before use in an actual instructional programme. The experimental design of the custom chatbot was able to query specific user-defined documents. Taken together, these findings suggest that the strategic and well-planned use of ChatGPT technology in content creation can have substantial benefits in terms of time and cost efficiency. In the context of information literacy, the results provide a practical and innovative solution to integrate the new technology tool into instructional practices.

Multiplex fluorescent amplification-refractory mutation system PCR method for the detection of 10 genetic defects in Holstein cattle and its comparison with the KASP genotyping assay.

The common deleterious genetic defects in Holstein cattle include haplotypes 1-6 (HH1-HH6), haplotypes for cholesterol deficiency (HCD), bovine leukocyte adhesion deficiency (BLAD), complex vertebral malformation (CVM) and brachyspina syndrome (BS). Recessive inheritance patterns of these genetic defects permit the carriers to function normally, but homozygous recessive genotypes cause embryo loss or neonatal death. Therefore, rapid detection of the carriers is essential to manage these genetic defects. This study was conducted to develop a single-tube multiplex fluorescent amplification-refractory mutation system (mf-ARMS) PCR method for efficient genotyping of these 10 genetic defects and to compare its efficiency with the kompetitive allele specific PCR (KASP) genotyping assay. The mf-ARMS PCR method introduced 10 sets of tri-primers optimized with additional mismatches in the 3' end of wild and mutant-specific primers, size differentiation between wild and mutant-specific primers, fluorescent labeling of universal primers, adjustment of annealing temperatures and optimization of primer concentrations. The genotyping of 484 Holstein cows resulted in 16.12% carriers with at least one genetic defect, while no homozygous recessive genotype was detected. This study found carrier frequencies ranging from 0.0% (HH6) to 3.72% (HH3) for individual defects. The mf-ARMS PCR method demonstrated improved detection, time and cost efficiency compared with the KASP method for these defects. Therefore, the application of mf-ARMS PCR for genotyping Holstein cattle is anticipated to decrease the frequency of lethal alleles and limit the transmission of these genetic defects.

Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use. Thus far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of some of the critical features, including power consumption, coverage, data rate, security, cost, and quality of service (QoS). This comparative study shows that low-power wide area network (LPWAN)-based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an Industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable IoT communication protocol for an industrial and production field.

Estimation of gross calorific value of coal: A literature review

ABSTRACT The gross calorific value (GCV), also known as the higher heating value (HHV) of coal is the amount of heat emitted upon complete combustion of coal. The GCV of coal is used to estimate various technoeconomic parameters such as boiler efficiency, combustion values, and production costs. This study seeks to examine the methods that are used to estimate the GCV of coal. These methods can be classified as either mathematical, experimental, or online methods.” It was found that linear and non-linear regression, differential scanning calorimetry (DSC), artificial intelligence (AI) methods have all been frequently used to estimate the GCV. The challenge with these approaches is that they include sophisticated machinery that demand expert operation, and the task is time consuming. Accurate and timely analysis of the GCV coal is a crucial stage in power plants, which can be achieved with online monitoring of the GCV of coal. There is not much literature on the topic of online monitoring of the coal GCV because it hasn’t been studied extensively. In power plant operation, online monitoring of the GCV of coal is a promising technology that hasn’t been studied extensively.

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Evaluation of free and immobilized cellulase on chitosan-modified magnetic nanoparticles for saccharification of sorghum residue.

Enzymatic hydrolysis plays a pivotal role in transforming lignocellulosic biomass. Addressing alternate techniques to optimize the utilization of cellulolytic enzymes is one strategy to improve its efficiency and lower process costs. Cellulases are highly specific and environmentally benign biocatalysts that break down intricate polysaccharides intosimple formsofsugars. In contrast to the most difficult and time-consuming enzyme immobilization processes, in this research, we studied simple, mild, and successful techniques for immobilization of pure cellulase on magnetic nanocomposites using glutaraldehyde as a linker and used in the application of sorghum residue biomass. Fe3O4 nanoparticles were coated with chitosan from the co-precipitation method, which served as an enzyme carrier. The nanoparticles were observed under XRD, Zeta Potential, FESEM, VSM, and FTIR. The size morphology results presented that the Cs@Fe3O4 have 42.2nm, while bare nanoparticles (Fe3O4) have 31.2nm in size. The pure cellulase reaches to 98.07% of loading efficiency and 71.67% of recovery activity at optimal conditions. Moreover, immobilized enzyme's pH stability, thermostability, and temperature tolerance were investigated at suitable conditions. The kinetic parameters of free and immobilized enzyme were estimated as Vmax; 29 ± 1.51 and 27.03 ± 2.02µmolmin-1mg-1, Km; 4.7 ± 0.49mM and 2.569 ± 0.522mM and Kcat; 0.13s-1, and 0.89s-1. Sorghum residue was subjected to 2% NaOH pre-treatment at 50℃. Pre-treated biomass contains cellulose of 64.8%, used as a raw material to evaluate the efficiency of reducing sugar during hydrolysis and saccharification of free and immobilized cellulase, which found maximum concentration of glucose 5.42g/L and 5.12g/L on 72h. Thus, our study verifies the use of immobilized pure cellulase to successfully hydrolyze raw material, which is a significant advancement in lignocellulosic biorefineries and the reusability of enzymes.

Are Lentil (Lens culinaris) Farms Productive, Profitable, and Efficient in Resource Allocation? A Cross‐Sectional Study From Nepal

ABSTRACTThe study was based on primary data from 473 lentil farmers selected randomly to analyze productivity, profitability, efficiency, and sensitivity of lentil farms in Nepal. Methods like benefit–cost, break‐even, margin safety, and sensitivity analysis, scaling technique, Cobb–Douglas type of production function, and stochastic frontier were adopted to derive farm economics, allocative, and cost efficiency levels. With average productivity of 672 kg/ha, lentil farmers in the study area were earning about 41% profit as of gross return with a profitability index of 0.78. About 45% margin of safety and estimates of benefit–cost ratio above one on all sensitivity measures is indicative of low risk and robust enterprise. Resources allocated in lentil production were found inefficient, and to achieve maximum return, expenses on land preparation, seed, nutrient, and plant protection cum irrigation should be increased by 27.6%, 80%, 33.1%, and 97%, respectively. Similarly, expenses on labor and harvesting activities need to be decreased by 30.1% and 23.6%. Labor cost and seed cost were the most important variables, and a 1% increase would surge the total production costs by 0.42% and 0.19%, respectively. The cost efficiency was estimated as 1.137 mean value, meaning that over 13.7% of the costs in lentil farms is wasted while comparing best‐practiced farm. Only about 48% of farms is fairly efficient at efficiency levels 1.0 to 1.09, but the majority is inefficient, which needs to minimize the waste of resources. Although suffering from climatic risks and production‐related problems, lentil enterprise is profitable, less risky, less sensitive, and fairly to inefficient in resource use. Wise attention is need on the part of farm management and resource utilization. Farmers are suggested to maintain farm size around 0.5 ha or below 1 ha, use only improved varietal seed, cut labor expenses with the use of machinery, and perform adequate tillage during sowing followed by effective disease management practices.

Energy Budgeting and Efficiency Analysis of Organic Cotton: A DEA Approach

Background: Cotton (Gossypium hirsutum L.) is crop of economic importance in global and national scale. India is the greatest producer of quality cotton, the third largest exporter and the second largest consumer. Conventional cotton farming creates environmental pressures and negative impact on public health. Organic farming is the sustainability option in cotton production. Cotton is the energy intensive crop, which uses high amount of input energy to produce seed cotton. Hence, energy budgeting is highly essential for better energy utilization and resource conservation and also measuring the input use efficiency of a farm/agronomic practices is vitally important in present day Indian agriculture. Methods: Sensing the economic importance of cotton, ten number of eco-friendly, ecologically safe organic nutrient management (ONM) practices were framed and field experiment was conducted in split plot design with two main plots (M) and five subplots (S) and replicated thrice. The direct and indirect energy used in different ONM practices were computed and energy coefficients were computed from energy equivalents and that has been used as inputs to generate efficiency coefficients by using an input oriented DEA approach. Result: Energy budgeting on field operation basis reported that, the field preparation and irrigation operation uses most of the energy (in non-renewable forms) which needs attention and there is the scope to find alternative energy conservation systems. Results on input use efficiency reported that the ONM practice, double green manuring followed by cotton and application of well decomposed poultry manure and foliar application of fermented fish extract at 5% concentration at 25 and 35 DAS are 100% efficient in terms of technical CRS, technical VRS, scale, allocative and cost efficiencies. This organic nutrient management practice would produce optimum output from the least amount of input and would be ideal for sustainable cotton production.

Contribution of a C-Terminal Extension to the Substrate Affinity and Oligomeric Stability of Aldehyde Dehydrogenase from Thermus thermophilus HB27.

Aldehyde dehydrogenase enzymes (ALDHs) are widely studied for their roles in disease propagation and cell metabolism. Their use in biocatalysis applications, for the conversion of aldehydes to carboxylic acids, has also been recognized. Understanding the structural features and functions of both prokaryotic and eukaryotic ALDHs is key to uncovering novel applications of the enzyme and probing its role in disease propagation. The thermostable enzyme ALDHTt originating fromThermus thermophilus, strain HB27, possesses a unique extension of its C-terminus, which has been evolutionarily excluded from mesophilic counterparts and other thermophilic enzymes in the same genus. In this work, the thermophilic adaptation is studied by the expression and optimized purification of mutant ALDHTt-508, with a 22-amino acid truncation of the C-terminus. The mutant shows increased activity throughout production compared to native ALDHTt, indicating an opening of the active site upon C-terminus truncation and giving rationale into the evolutionary exclusion of the C-terminal extension from similar thermophilic and mesophilic ALDH proteins. Additionally, the C-terminus is shown to play a role in controlling substrate specificity of native ALDH, particularly in excluding catalysis of certain large and certain aromatic ortho-substituted aldehydes, as well as modulating the protein's pH tolerance by increasing surface charge. Dynamic light scattering and size-exclusion HPLC methods are used to show the role of the C-terminus in ALDHTt oligomeric stability at the cost of catalytic efficiency. Studying the aggregation rate of ALDHTt with and without a C-terminal extension leads to the conclusion that ALDHTt follows a monomolecular reaction aggregation mechanism.

Effect of humic acid on visible light photocatalytic inactivation of bacteriophage f2 with electrospinning Cu-TiO2 nanofibers: insight into the mechanisms.

Photocatalytic disinfection is a promising technology with low cost and high efficiency. However, most of the current studies on photocatalytic disinfection ignore the widespread presence of natural organic matter (NOM) in water bodies, so the incomplete conclusions obtained may not be applicable. Herein, this paper systematically studied the influence of humic acid (HA), one of the most important components of NOM, on the photocatalytic inactivation of bacteriophage f2 with electrospinning Cu-TiO2 nanofibers. We found that with the addition of HA, the light transmittance of the solution at 550 nm decreased from 94 to 60%, and the band gap of the photocatalyst was increased from 2.96 to 3.05 eV. Compared with reacting without HA, the degradation amount of RNA of f2 decreased by 88.7% after HA was added, and the RNA concentration increased from 1.95 to 4.38 ng·μL-1 after the reaction. Hence, we propose mechanisms of the effect of HA on photocatalytic disinfection: photo-shielding, passivation of photocatalysts, quenching of free radicals, and virus protection. Photo-shielding and photocatalyst passivation lead to the decrease of photocatalyst activity, and the reactive oxygen species (ROSs) (·OH, ·O2-, 1O2, H2O2) are further trapped by HA. The HA in water also can protect the shape of phage f2 and reduce the leakage of protein and the destruction of ribonucleic acid (RNA). This work provides an insight into the mechanisms for the influence of HA in photocatalytic disinfection process and a theoretical basis for its practical application.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Exploring the Intestinal Microbial Community of Lantang Pigs through Metagenome-Assembled Genomes and Carbohydrate Degradation Genes

High-fiber, low-cost agricultural byproducts offer a sustainable alternative for mitigating the competition for crops between humans and livestock. Pigs predominantly utilize dietary fibers through the process of microbial fermentation within the gut. This study explored the gut microbiota and the capacity for carbohydrate degradation in 30 individual Lantang pigs, a breed indigenous to China. Through metagenomic analysis, a total of 671 metagenome-assembled genomes (MAGs) were assembled and assigned into 14 bacterial and 1 archaeal phylum, including 97 species from uncultured microbes. The phylum with the highest abundance were identified as Bacillota_A, Bacteroidota, and Bacillota. Remarkably, the investigation revealed nearly 10,000 genes implicated in the degradation of carbohydrates, with a pronounced prevalence within five principal bacterial genera: Prevotella, Cryptobacteroides, Gemmiger, Vescimonas, and Faecousia. Additionally, 87 distinct types of carbohydrate-degrading enzymes were exclusively identified within the gut microbiota of the Lantang pig. These insights not only enhance our understanding of the microbial diversity specific to native Chinese pig breeds but also augment the body of research regarding porcine fiber degradation capabilities. The implications of this study are twofold: it provides strategic directions for optimizing feed efficiency and reducing breeding costs, and it furnishes an expanded gene pool for the microbial synthesis of industrial enzymes in the future.

MnS-BaS Heterostructures as Effective Catalysts for Oxygen Reduction Reaction.

The inadequate electrical conductivity of metal sulfides, along with their tendency to agglomerate, has hindered their use in energy storage and catalysis. The construction of a heterojunction can ameliorate these deficiencies to some extent. In this paper, MnS-BaS heterojunction catalysts were prepared by a hydrothermal method, which is a simple and inexpensive process. The MnS-BaS heterojunction catalysts exhibited superior performance owing to the strong synergistic interaction between MnS and BaS. Density functional theory (DFT) calculations reveal strong interactions at the heterojunction interface and significant electron transfer between MnS and BaS, which further modulates the electronic structure of Mn. The elevation of the center of the d-band enhances the adsorption of oxygen and oxygen-containing intermediates on the catalyst, thus promoting the oxygen reduction reaction (ORR). The practical application of MnS-BaS catalysts was tested by assembling zinc-air batteries. This study provides a rational strategy for designing transition metal catalysts that are efficient and low cost.