Biomaterials and tissue engineering in traumatic brain injury:novel perspectives on promoting neural regeneration

Traumatic brain injury is a serious medical condition that can be attributed to falls, motor vehicle accidents, sports injuries and acts of violence, causing a series of neural injuries and neuropsychiatric symptoms. However, limited accessibility to the injury sites, complicated histological and anatomical structure, intricate cellular and extracellular milieu, lack of regenerative capacity in the native cells, vast variety of damage routes, and the insufficient time available for treatment have restricted the widespread application of several therapeutic methods in cases of central nervous system injury. Tissue engineering and regenerative medicine have emerged as innovative approaches in the field of nerve regeneration. By combining biomaterials, stem cells, and growth factors, these approaches have provided a platform for developing effective treatments for neural injuries, which can offer the potential to restore neural function, improve patient outcomes, and reduce the need for drugs and invasive surgical procedures. Biomaterials have shown advantages in promoting neural development, inhibiting glial scar formation, and providing a suitable biomimetic neural microenvironment, which makes their application promising in the field of neural regeneration. For instance, bioactive scaffolds loaded with stem cells can provide a biocompatible and biodegradable milieu. Furthermore, stem cells-derived exosomes combine the advantages of stem cells, avoid the risk of immune rejection, cooperate with biomaterials to enhance their biological functions, and exert stable functions, thereby inducing angiogenesis and neural regeneration in patients with traumatic brain injury and promoting the recovery of brain function. Unfortunately, biomaterials have shown positive effects in the laboratory, but when similar materials are used in clinical studies of human central nervous system regeneration, their efficacy is unsatisfactory. Here, we review the characteristics and properties of various bioactive materials, followed by the introduction of applications based on biochemistry and cell molecules, and discuss the emerging role of biomaterials in promoting neural regeneration. Further, we summarize the adaptive biomaterials infused with exosomes produced from stem cells and stem cells themselves for the treatment of traumatic brain injury. Finally, we present the main limitations of biomaterials for the treatment of traumatic brain injury and offer insights into their future potential.

Synthesis of Reviews on Auscultation, Approaches, and Methods for Engineering Structures

Topometric auscultation is used to monitor the durability of structures, measure deformations linked to the structure of a structure or to the movement of the ground over a part of the globe, set up warning systems, etc. It first appeared as a visual method and rapidly evolved through the various techniques used. Some of these techniques using topography are used in several fields (civil engineering, geodesy, topography, mechanics, nuclear engineering, hydraulics, physics, etc.). These topometric techniques have undergone major changes as a result of technological advances, growing needs in the monitoring of movements or deformations, increased requirements and new challenges. The methodology adopted depends on the measuring instrument used, the parameters to be estimated and access to the area to be measured. There are two types of methods: destructive and non-destructive. In addition to the visual method, they can also be classified as mechanical, physico-chemical, dynamometric, electrophysical and geometric. The estimated parameter varies according to the methodology adopted. It can be defined by coordinates, distances, potential, electrical resistance, etc.

Constructing a biofunctionalized 3D-printed gelatin/sodium alginate/chitosan tri-polymer complex scaffold with improvised biological andmechanical properties for bone-tissue engineering

Sodium alginate(SA)/chitosan(CH)polyelectrolyte scaffold is a suitable substrate for tissue-engineering application.The present study deals with further improvement in the tensile strength and biological properties of this type of scaffold to make it a potential template for bone-tissue regeneration.We experimented with adding 0%–15%(volume fraction)gelatin(GE),a protein-based biopolymer known to promote cell adhesion,proliferation,and differentiation.The resulting tri-polymer complex was used as bioink to fabricate SA/CH/GEmatrices by three-dimensional(3D)printing.Morphological studies using scanning electron microscopy revealed the microfibrous porous architecture of all the structures,which had a pore size range of 383–419μm.X-ray diffraction and Fourier-transform infrared spectroscopy analyses revealed the amorphous nature of the scaffold and the strong electrostatic interactions among the functional groups of the polymers,thereby forming polyelectrolyte complexes which were found to improve mechanical properties and structural stability.The scaffolds exhibited a desirable degradation rate,controlled swelling,and hydrophilic characteristics which are favorable for bone-tissue engineering.The tensile strength improved from(386±15)to(693±15)kPa due to the increased stiffness of SA/CH scaffolds upon addition of gelatin.The enhanced protein adsorption and in vitro bioactivity(forming an apatite layer)confirmed the ability of the SA/CH/GE scaffold to offer higher cellular adhesion and a bone-like environment to cells during the process of tissue regeneration.In vitro biological evaluation including the MTT assay,confocal microscopy analysis,and alizarin red S assay showed a significant increase in cell attachment,cell viability,and cell proliferation,which further improved biomineralization over the scaffold surface.In addition,SA/CH containing 15%gelatin designated as SA/CH/GE15 showed superior performance to the other fabricated 3D structures,demonstrating its potential for use in bone-tissue engineering.

In vitro investigations on the effects of graphene and graphene oxide on polycaprolactone bone tissue engineering scaffolds

Polycaprolactone(PCL)scaffolds that are produced through additive manufacturing are one of the most researched bone tissue engineering structures in the field.Due to the intrinsic limitations of PCL,carbon nanomaterials are often investigated to reinforce the PCL scaffolds.Despite several studies that have been conducted on carbon nanomaterials,such as graphene(G)and graphene oxide(GO),certain challenges remain in terms of the precise design of the biological and nonbiological properties of the scaffolds.This paper addresses this limitation by investigating both the nonbiological(element composition,surface,degradation,and thermal and mechanical properties)and biological characteristics of carbon nanomaterial-reinforced PCL scaffolds for bone tissue engineering applications.Results showed that the incorporation of G and GO increased surface properties(reduced modulus and wettability),material crystallinity,crystallization temperature,and degradation rate.However,the variations in compressive modulus,strength,surface hardness,and cell metabolic activity strongly depended on the type of reinforcement.Finally,a series of phenomenological models were developed based on experimental results to describe the variations of scaffold’s weight,fiber diameter,porosity,and mechanical properties as functions of degradation time and carbon nanomaterial concentrations.The results presented in this paper enable the design of three-dimensional(3D)bone scaffolds with tuned properties by adjusting the type and concentration of different functional fillers.

Metabolic engineering and genome editing strategies for enhanced lipid production in microalgae

Depleting global petroleum reserves and skyrocketing prices coupled with succinct supply have been a grave concern,which needs alternative sources to conventional fuels.Oleaginous microalgae have been explored for enhanced lipid production,leading towards biodiesel production.These microalgae have short life cycles,require less labor,and space,and are easy to scale up.Triacylglycerol,the primary source of lipids needed to produce biodiesel,is accumulated by most microalgae.The article focuses on different types of oleaginous microalgae,which can be used as a feedstock to produce biodiesel.Lipid biosynthesis in microalgae occurs through fatty acid synthesis and TAG synthesis approaches.In-depth discussions are held regarding other efficient methods for enhancing fatty acid and TAG synthesis,regulating TAG biosynthesis bypass methods,blocking competing pathways,multigene approach,and genome editing.The most potential targets for gene transformation are hypothesized to be a malic enzyme and diacylglycerol acyltransferase while lowering phosphoenolpyruvate carboxylase activity is reported to be advantageous for lipid synthesis.

Engineering electrolyte additives for stable zinc-based aqueous batteries:Insights and prospects

Zn-based aqueous batteries(ZABs) are gaining widespread popularity due to their low cost and high safety profile. However, the application of ZABs faces significant challenges, such as dendrite growth and parasitic reactions of metallic Zn anodes. Therefore, achieving high-energy–density ZABs necessitates addressing the fundamental thermodynamics and kinetics of Zn anodes. Various strategies are available to mitigate these challenges, with electrolyte additive engineering emerging as one of the most efficient and promising approaches. Despite considerable research in this field, a comprehensive understanding of the intrinsic mechanisms behind the high performance of electrolyte additives remains limited. This review aims to provide a detailed introduction to functional electrolyte additives and thoroughly explore their underlying mechanisms. Additionally, it discusses potential directions and perspectives in additive engineering for ZABs, offering insights into future development and guidelines for achieving high-performance ZABs.

Precursor engineering enables high-performance all-inorganic CsPbIBr_(2) perovskite solar cells with a record efficiency approaching 13%

All-inorganic CsPbIBr_(2) perovskite has attracted widespread attention in photovoltaic and other optoelectronic devices because of its superior thermal stability.However,the deposition of high-quality solutionprocessed CsPbIBr_(2) perovskite films with large thicknesses remains challenging.Here,we develop a triple-component precursor(TCP) by employing lead bromide,lead iodide,and cesium bromide,to replace the most commonly used double-component precursor(DCP) consisting of lead bromide and cesium iodide.Remarkably,the TCP system significantly increases the solution concentration to 1.3 M,leading to a larger film thickness(~390 nm) and enhanced light absorption.The resultant CsPbIBr_(2) films were evaluated in planar n-i-p structured solar cells,which exhibit a considerably higher optimal photocurrent density of 11.50 mA cm^(-2) in comparison to that of DCP-based devices(10.69 mA cm^(-2)).By adopting an organic surface passivator,the maximum device efficiency using TCP is further boosted to a record efficiency of 12.8% for CsPbIBr_(2) perovskite solar cells.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

A new one-parameter Chris-Jerry distribution,created by mixing exponential and gamma distributions,is discussed in this article in the presence of incomplete lifetime data.We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry(CJ)distribution.When the indicated censored data is present,Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices,including the hazard rate and reliability functions.We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity.Additionally,via the squared-error loss,the Bayes’estimators are obtained using gamma prior.The Bayes estimators cannot be expressed theoretically since the likelihood density is created in a complex manner;nonetheless,Markov-chain Monte Carlo techniques can be used to evaluate them.The effectiveness of the investigated estimations is assessed,and some recommendations are given using Monte Carlo results.Ultimately,an analysis of two engineering applications,such as mechanical equipment and ball bearing data sets,shows the applicability of the proposed approaches that may be used in real-world settings.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Structural engineering of Fe single-atom oxygen reduction catalyst with high site density and improved mass transfer

Fe-N-C catalysts are widely considered as promising non-precious-metal candidates for electrocatalytic oxygen reduction reaction(ORR),Yet despite their high catalytic activity through rational modulation,challenges remain in their low site density and unsatisfactory mass transfer structure.Herein,we present a structural engineering approach employing a soft-template coating strategy to fabricate a hollow and hierarchically porous N-doped carbon framework anchored with atomically dispersed Fe sites(FeNCh) as an efficient ORR catalyst.The combination of hierarchical porosity and high exterior surface area is proven crucial for exposing more active sites,which gives rise to a remarkable ORR performance with a half-wave potential of 0.902 V in 0.1 m KOH and 0.814 V in 0.1 m HClO_(4),significantly outperforming its counterpart with solid structure and dominance of micropores(FeNC-s).The mass transfer property is revealed by in-situ electrochemical impedance spectroscopy(EIS) measurement.The distribution of relaxation time(DRT) analysis is further introduced to deconvolve the kinetic and mass transport processes,which demonstrates an alleviated mass transport resistance for FeNC-h,validating the effectiveness of structural engineering.This work not only provides an effective structural engineering approach but also contributes to the comprehensive mass transfer evaluation on advanced electrocatalyst for energy conversion applications.

Analysis on the development of chemically-improved soil in railway engineering

Purpose-Explore the development trend of chemically-improved soil in railway engineering.Design/methodology/approach–In this paper,the technical standards home and abroad were analyzed.Laboratory test,field test and monitoring were carried out.Findings–The performance design system of the chemically-improved soil should be established.Originality/value–On the basis of the performance design,the test methods and standards for various properties of chemically-improved soil should be established to evaluate the improvement effect and control the engineering quality.

Building Custom Spreadsheet Functions with Python: End-User Software Engineering Approach

End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Model-Based Systems Engineering Approach to Design a Human Settlement to Better Serve Displaced People

The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.

An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

Vacancy engineering mediated hollow structured ZnO/ZnS S-scheme heterojunction for highly efficient photocatalytic H_(2) production

Designing a step-scheme(S-scheme)heterojunction photocatalyst with vacancy engineering is a reliable approach to achieve highly efficient photocatalytic H_(2)production activity.Herein,a hollow ZnO/ZnS S-scheme heterojunction with O and Zn vacancies(VO,Zn-ZnO/ZnS)is rationally constructed via ion-exchange and calcination treatments.In such a photocatalytic system,the hollow structure combined with the introduction of dual vacancies endows the adequate light absorption.Moreover,the O and Zn vacancies serve as the trapping sites for photo-induced electrons and holes,respectively,which are beneficial for promoting the photo-induced carrier separation.Meanwhile,the S-scheme charge transfer mechanism can not only improve the separation and transfer efficiencies of photo-induced carrier but also retain the strong redox capacity.As expected,the optimized VO,Zn-ZnO/ZnS heterojunction exhibits a superior photocatalytic H_(2) production rate of 160.91 mmol g^(-1)h^(-1),approximately 643.6 times and 214.5 times with respect to that obtained on pure ZnO and ZnS,respectively.Simultaneously,the experimental results and density functional theory calculations disclose that the photo-induced carrier transfer pathway follows the S-scheme heterojunction mechanism and the introduction of O and Zn vacancies reduces the surface reaction barrier.This work provides an innovative strategy of vacancy engineering in S-scheme heterojunction for solar-to-fuel energy conversion.

The Expanding Earth, Climate Engineering and Earthquake Processes

This article examines the issue of future directions of climate engineering in the light of the consequences of the Earth’s expansion process. One of the directions of climate engineering should be the study of seismic problems, because the state of the geosphere affects not only the atmosphere, but also the processes taking place in the bowels of the planet. If we accept the hypothesis of an expanding Earth [1], then rapid changes in meteorological conditions on the planet will become clear, and the secrets of earthquake processes will come out of the shadow of existing misconceptions among most geophysicists of the world and scientists will understand the mechanisms of energy formation of seismic processes. But, there are multiple arguments of world geophysicists testifying against the hypothesis of an expanding Earth, and in their opinion, scientists supporting this hypothesis allegedly did not provide mechanisms for the expansion of the planet [2]. In turn, the development of the theory of plate tectonics and the alleged discovery of the processes of formation of subduction zones led to the recognition of the hypothesis of plate tectonics by the world scientific community as the main theory of geophysics and sent science straight into a dead end of false conclusions, from which modern geophysics has not found a way out. And it was enough just to listen to A. Einstein and a march into the jungle of unfounded fantasies could be very easily avoided. Everything is extremely simple, but this makes it obvious and incomprehensible to most geophysicists that energy is matter, and matter is energy. For example, only the total amount of solar energy that our planet absorbs, including the atmosphere, land surface, and mirrors of the seas and oceans, is ~3,850,000 EJ per year [3]. And this is without taking into account the energy supply from space in the form of highly energetic particles. This scientific fact, which cannot be denied, must inevitably lead to the formation of matter and, consequently, to the expansion of the planet, because any high school student knows the physical concept of the equivalence of mass and energy arising from the theory of relativity A. Einstein [4], according to which the energy of a body at rest is equivalent to its mass multiplied by the square of the speed of light in a vacuum: E = mc2. That is, whether we like it or not, but the energy of the Sun and Space, as it has been transformed for billions of years into matter familiar to us: rocks, gases, minerals, fluids, will be transformed, in accordance with the laws of science. Otherwise, all the proponents of the expanding Earth hypothesis will have to declare that Mr. Einstein’s formula E = mc2 does not correspond to reality, and recognize the great scientist as a falsifier. Therefore, no matter what far-fetched arguments in the form of mythical subduction zones geophysicists give, no matter what “exotic laws of local significance” they invent, no matter how cynically they mock the fundamental laws of science—all energy entering the planet is necessarily processed and will be processed into matter with an increase in the volume of the planet. Without any exceptions! Only one biochemical process of photosynthesis continuously occurring in algae in one year brings ~3.6 × 1011 tons of oxygen into the Earth’s atmosphere [5], which significantly exceeds the amount of hydrogen and helium “immigrating” into space. Even if we take a geological epoch of one hundred million years, the evidence of an increase in the volume of the Earth only due to oxygen (3.6 × 1011 × 107 tons) becomes quite convincing. the surface area of the Earth is constantly increasing, then the processes of expansion of the planet increase exponentially, which inevitably leads to an increase in seismic activity and volcanic activity, and the increase in the volume of the planet itself serves as a lever for changing the meteorological conditions of the planet’s existence and one of the sources of seismic energy formation. In this article, we will consider seismic processes in the light of the expanding Earth hypothesis.

Professional Cognitive Education Model for Agricultural Water Conservancy Engineering under the Background of New Agricultural Science

The construction of new agricultural science requires the use of modern scientific and technological means to transform and enhance current agricultural related majors.The agricultural water conservancy engineering major,with its inherent disciplinary advantages,plays an indispensable and important role in the construction of new agricultural science.In recent years,the lack of professional cognitive education has gradually become a significant problem in the training of talents in agricultural water conservancy engineering.Therefore,this paper deeply analyzes the problems and reasons faced by professional cognitive education,and proposes specific educational strategies for several key aspects such as enrollment promotion,freshman enrollment education,construction of teacher team,combination of scientific research and teaching,and strengthening professional cognition through competition activities.It aims to provide reference for improving the quality of professional cognitive education and exploring effective ways.

Enhanced Electrical Properties of Bi_(2−x)Sb_(x)Te_(3) Nanoflake Thin Films Through Interface Engineering

The structure–property relationship at interfaces is difficult to probe for thermoelectric materials with a complex interfacial microstructure.Designing thermoelectric materials with a simple,structurally-uniform interface provides a facile way to understand how these interfaces influence the transport properties.Here,we synthesized Bi_(2−x)Sb_(x)Te_(3)(x=0,0.1,0.2,0.4)nanoflakes using a hydrothermal method,and prepared Bi_(2−x)Sb_(x)Te_(3) thin films with predominantly(0001)interfaces by stacking the nanoflakes through spin coating.The influence of the annealing temperature and Sb content on the(0001)interface structure was systematically investigated at atomic scale using aberration-corrected scanning transmission electron microscopy.Annealing and Sb doping facilitate atom diffusion and migration between adjacent nanoflakes along the(0001)interface.As such it enhances interfacial connectivity and improves the electrical transport properties.Interfac reactions create new interfaces that increase the scattering and the Seebeck coefficient.Due to the simultaneous optimization of electrical conductivity and Seebeck coefficient,the maximum power factor of the Bi_(1.8)Sb_(0.2)Te_(3) nanoflake films reaches 1.72 mW m^(−1)K^(−2),which is 43%higher than that of a pure Bi_(2)Te_(3) thin film.