Biomimetic natural biomaterials for tissue engineering and regenerative medicine:new biosynthesis methods,recent advances,and emerging applications

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering(TE)and regenerative medicine.In contrast to conventional biomaterials or synthetic materials,biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix(ECM).Additionally,such materials have mechanical adaptability,micro-structure interconnectivity,and inherent bioactivity,making them ideal for the design of living implants for specific applications in TE and regenerative medicine.This paper provides an overview for recent progress of biomimetic natural biomaterials(BNBMs),including advances in their preparation,functionality,potential applications and future challenges.We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM.Moreover,we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications.Finally,we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Facile synthesis of hierarchical NaX zeolite from natural kaolinite for efficient Knoevenagel condensation

Zeolite catalysts have found extensive applications in the synthesis of various fine chemicals.However,the micropores of zeolites impose diffusion limitations on bulky molecules,greatly reducing the catalytic efficiency.Herein,we explore an economic and environmentally friendly method for synthesizing hierarchical NaX zeolite that exhibits improved catalytic performance in the Knoevenagel condensation reaction for producing the useful fine chemical 2-cyano-3-phenylacrylate.The synthesis was achieved via a low-temperature activation of kaolinite and subsequent in-situ transformation strategy without any template or seed.Systematic characterizations reveal that the synthesized NaX zeolite has both intercrystalline and intra-crystalline mesopores,smaller crystal size,and larger external specific surface area compared to commercial NaX zeolite.Detailed mechanism investigations show that the inter-crystalline mesopores are generated by stacking smaller crystals formed from in-situ crystallization of the depolymerized kaolinite,and the intra-crystalline mesopores are inherited from the pores in the depolymerized kaolinite.This synthesis strategy provides an energy-saving and effective way to construct hierarchical zeolites,which may gain wide applications in fine chemical manufacturing.

Genetic and epigenetic targets of natural dietary compounds as anti-Alzheimer's agents

Alzheimer’s disease is a progressive neurodegenerative disorder and the most common cause of dementia that principally affects older adults.Pathogenic factors,such as oxidative stress,an increase in acetylcholinesterase activity,mitochondrial dysfunction,genotoxicity,and neuroinflammation are present in this syndrome,which leads to neurodegeneration.Neurodegenerative pathologies such as Alzheimer’s disease are considered late-onset diseases caused by the complex combination of genetic,epigenetic,and environmental factors.There are two main types of Alzheimer’s disease,known as familial Alzheimer’s disease(onset<65 years)and late-onset or sporadic Alzheimer’s disease(onset≥65 years).Patients with familial Alzheimer’s disease inherit the disease due to rare mutations on the amyloid precursor protein(APP),presenilin 1 and 2(PSEN1 and PSEN2)genes in an autosomaldominantly fashion with closely 100%penetrance.In contrast,a different picture seems to emerge for sporadic Alzheimer’s disease,which exhibits numerous non-Mendelian anomalies suggesting an epigenetic component in its etiology.Importantly,the fundamental pathophysiological mechanisms driving Alzheimer’s disease are interfaced with epigenetic dysregulation.However,the dynamic nature of epigenetics seems to open up new avenues and hope in regenerative neurogenesis to improve brain repair in Alzheimer’s disease or following injury or stroke in humans.In recent years,there has been an increase in interest in using natural products for the treatment of neurodegenerative illnesses such as Alzheimer’s disease.Through epigenetic mechanisms,such as DNA methylation,non-coding RNAs,histone modification,and chromatin conformation regulation,natural compounds appear to exert neuroprotective effects.While we do not purport to cover every in this work,we do attempt to illustrate how various phytochemical compounds regulate the epigenetic effects of a few Alzheimer’s disease-related genes.

Natural Disaster Risk Monitoring for Immovable Cultural Relics Based on Digital Twin

Natural disaster risk monitoring is an important task for disaster prevention and reduction.In the case of immovable cultural relics,however,the feedback mechanism,risk factors,monitoring logic,and monitoring indicators of natural disaster risk monitoring are complex.How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers.Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics,this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin.This framework focuses on risk monitoring,including the physical entities of natural disaster risk for immovable cultural relics,monitoring indicators,and virtual entity construction.A platform for monitoring the natural disaster risk of immovable cultural relics is proposed.Using the Puzhou Ancient City Site as a test bed,the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.

Numerical analysis of downward progressive landslides in long natural slopes with sensitive clay

Landslides occurring in sensitive clay often result in widespread destruction,posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation.Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms.While studies have extensively explored upward progressive landslides in sensitive clays,understanding downward progressive cases remains limited.In this study,we utilised the nodal integration-based particle finite element method(NPFEM)with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes,induced by surcharge loads near the crest.We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strainsoftening characteristics.The simulation results revealed the typical pattern for downward landslides,which typically start with a localised failure in proximity to the surcharge loads,followed by a combination of different types of failure mechanisms,including single flow slides,translational progressive landslides,progressive flow slides,and spread failures.Additionally,inclined shear bands occur within spread failures,often adopting distinctive ploughing patterns characterised by triangular shapes.The sensitive clay thickness at the base,the clay strength gradient,the sensitivity,and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement.Remarkably,some of these effects mirror those observed in upward progressive landslides,underscoring the interconnectedness of these phenomena.This study contributes valuable insights into the complex dynamics of sensitive clay landslides,shedding light on the intricate interplay of factors governing their behaviour and progression.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Natural rubber latex as a potential additive for water-based drilling fluids

The environmental hazards and"carbon footprint"of oil and gas drilling can be significantly reduced by replacing traditional petroleum-based chemical additives with natural materials derived from plants and animals.This paper explored for the first time the interaction mechanism between natural rubber latex(NRL)and bentonite suspensions(BTs)through a series of characterization experiments,as well as the potential applications in water-based drilling fluids(WBDF).The gel viscoelasticity experiments showed that NRL could decrease the consistency coefficient(k)and flow index(n)of BTs,and enhance the shear thinning performance of BTs as pseudo-plastic fluids.In addition,0.5 w/v%NRL not only increased the critical yield stress and strengthened the structural strength between the bentonite particles,but also facilitated the compatibility of pressure loss and flow efficiency.The evaluation of colloidal stability and WBDF performance indicated that NRL particles could promote the hydration and charge stability on the surface of BTs particles,and optimize the particle size distribution and flow resistance of WBDF under the"intercalation-exfoliation-encapsulation"synergistic interaction.Moreover,NRL can improve the rheological properties of WBDF at high temperatures(<150.C),and form a dense blocking layer by bridging and sealing the pores and cracks of the filter cake,which ultimately reduces the permeability of the cake and the filtration loss of WBDF.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making

Reinforcement Learning(RL)has emerged as a promising data-driven solution for wargaming decision-making.However,two domain challenges still exist:(1)dealing with discrete-continuous hybrid wargaming control and(2)accelerating RL deployment with rich offline data.Existing RL methods fail to handle these two issues simultaneously,thereby we propose a novel offline RL method targeting hybrid action space.A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent way.This allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid policy.Critically,a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution actions.Our method demonstrates superior performance and generality across different tasks,particularly in typical realistic wargaming scenarios.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

DNA G-Quadruplexes as Targets for Natural Product Drug Discovery

DNA guanine(G)-quadruplexes(G4s)are unique secondary structures formed by two or more stacked Gtetrads in G-rich DNA sequences.These structures have been found to play a crucial role in highly transcribed genes,especially in cancer-related oncogenes,making them attractive targets for cancer therapeutics.Significantly,targeting oncogene promoter G4 structures has emerged as a promising strategy to address the challenge of undruggable and drug-resistant proteins,such as MYC,BCL2,KRAS,and EGFR.Natural products have long been an important source of drug discovery,particularly in the fields of cancer and infectious diseases.Noteworthy progress has recently been made in the discovery of naturally occurring DNA G4-targeting drugs.Numerous DNA G4s,such as MYC-G4,BCL2-G4,KRAS-G4,PDGFR-b-G4,VEGF-G4,and telomeric-G4,have been identified as potential targets of natural products,including berberine,telomestatin,quindoline,sanguinarine,isaindigotone,and many others.Herein,we summarize and evaluate recent advancements in natural and nature-derived DNA G4 binders,focusing on understanding the structural recognition of DNA G4s by small molecules derived from nature.We also discuss the challenges and opportunities associated with developing drugs that target DNA G4s.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Natural Silicate as a Solid Support for the Calix[4]Thiophosphorus Derivative for Removal Mercury (II), as Picrate from Water

Currently a technique widely used for gold extraction is mercury by amalgamation technique, the tailing produced pollutes water of all kinds, so it is necessary to develop a form of selective mitigation, for which it is necessary to use complexing agents based on calixarene functionalized with mercury sequestering agents. These are immobilized by adding supports based on natural silica to form polymers and make them insoluble in all types of solvents, so that they can be used as an extractor and at the same time regenerate to their original properties for continuous reuse.

Stand dynamics of old-growth hemlock forests in central Bhutan are shaped by natural disturbances

Understanding how past disturbances have influenced the development of forests is critical for deciphering their current structure and composition and forecasting future changes.In this study,dendrochronological methods were applied to uncover the disturbance history of old-growth hemlock-dominated forests in central Bhutan.Analysis of tree-ring samples from two old-growth hemlock stands,located in two different topographic settings,identified the importance of gap-phase dynamics in facilitating recruitment and growth releases and producing complex,multi-aged structure s over time.One site showed evidence of a near stand-replacing disturbance in the late 1700s,while the other showed no evide nce of high-severity disturbance at any time over the last 400 years.At both sites low-to medium-severity disturbances,some of which appear to be associated with cyclones originating in the Bay of Bengal,dominated the disturbance regime.The hemlock stands exhibited a significant positive association between cyclone occurrence and growth release events and between recruitment pulses and growth release events.From 1800 to 1970 there was an increase in recruitment of angiosperm tree species at most sites and a corresponding decline in conifer recruitment.Over the past 50 years there has been little new recruitment;this may be due to light limitation in the understory from shade-tolerant angiosperms and bamboo in the lower strata of these stands.Significant variations in disturbance dynamics and recruitment were observed across the study sites,suggesting that other factors,such as topography and climate,may be influencing long-term stand development patterns.This study highlights the complex interplay between historical disturbance regimes and tree recruitment in shaping the age and size structures of old-growth hemlock forests in central Bhutan.It also provides new insights into the dynamics of these forests that can be used to support effective forest conservation and management in the future.

Energy Design and Optimization of Greenhouse by Natural Convection

This study addresses the pressing need for energy-efficient greenhouse management by focusing on the innovative application of natural ventilation.The primary objective of this study is to evaluate various ventilation strategies to enhance energy efficiency and optimize crop production in agricultural greenhouses.Employing advanced numerical simulation tools,the study conducts a comprehensive assessment of natural ventilation’s effectiveness under real-world conditions.The results underscore the crucial role of the stack effect and strategic window positioning in greenhouse cooling,providing valuable insights for greenhouse designers.Our findings shed light on the significant benefits of optimized ventilation and also offer practical implications for improving greenhouse design,ensuring sustainable and efficient agricultural practices.The study demonstrated energy savings in cooling from November to April,with a maximum saving of 680 kWh in March,indicating the effectiveness of strategically positioning windows to leverage the stack effect.This approach enhances plant growth and reduces the need for costly cooling systems,thereby improving overall energy efficiency and lowering operational expenses.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.