Problem identification and revitalization strategies for the recovery and reconstruction of traditional villages in the Ms 6.8 Luding Earthquake

Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster reconstruction.This paper summarizes four concepts based on the research on post-disaster reconstruction both domestically and internationally,as well as the recovery and reconstruction of cultural heritage.Through a field survey of traditional villages in the Ms 6.8 Luding earthquake-stricken area,it is found that there are problems such as insufficient awareness of heritage value,misalignment of scientific reconstruction technology,and insufficient protection of reconstruction elements during the reconstruction process.Traditional villages face the risk of declining or even loss of heritage value.In order to effectively protect traditional villages and inherit the carrier of regional culture,four targeted reconstruction response strategies are proposed,i.e.,to"establish special planning for traditional village preservation","emphasize recovery of the authenticity of village heritage","ensure elements for village heritage recovery"and"promote the activation and utilization of village heritage",based on the problems discovered during the survey and the four concepts summarized in the research on post-disaster reconstruction of traditional villages.The research results hope to provide useful reference for ancient cultural areas affected by earthquakes on how to protect cultural heritage during the post-disaster reconstruction process.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

Cryogenic and conventional milling of AZ91 magnesium alloy

Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc.This study focuses on the effectiveness of liquid nitrogen(LN_(2))assisted cryogenic machining on the surface integrity(SI)characteristics of AZ91 magnesium alloy.Face milling using uncoated carbide inserts have been performed under liquid nitrogen(LN_(2))assisted cryogenic condition and compared with conventional(dry)milling.Experiments are performed using machining parameters in terms of cutting speeds of 325,475,625 m/min,feed rates of 0.05,0.1,0.15 mm/teeth and depth of cuts of 0.5,1,1.5 mm respectively.Most significant surface integrity characteristics such as surface roughness,microhardness,microstructure,and residual stresses have been investigated.Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise(S/N)ratio plots.Additionally,the multi criteria decision making(MCDM)techniques such as additive ratio assessment method(ARAS)and complex proportional assessment(COPRAS)have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions.Use of LN_(2)during machining,resulted in reduction in machining temperature by upto 29%with a temperature drop from 251.2℃under dry condition to 178.5℃in cryogenic condition.Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent.Cryogenic machining considerably minimized the roughness by upto 28%and maximised the microhardness by upto 23%,when compared to dry machining.Cutting speed has caused significant impact on surface roughness(95.33%-dry,92.92%-cryogenic)and surface microhardness(80.33%-dry,82.15%-cryogenic).Due to the reduction in machining temperature,cryogenic condition resulted in compressive residual stresses(maximumσ║=-113 MPa)on the alloy surface.Results indicate no harm to alloy microstructure in both conditions,with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area,when compared to before machined(base material)surface.The MCDM approach namely ARAS and COPRAS resulted in identical results,with the optimal condition being cutting speed of 625 m/min,a feed rate of 0.05 mm/teeth,and a depth of cut of 0.5 mm for both dry and cryogenic environments.

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

An integrated spatial planning of the mountainous landscapes for ski sports in a case area at the eastern Türkiye

Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in the highlands.Establishing a ski resort area supports direct and indirect employment in a region,and it stops immigration from mountainous regions to other places.This research aimed to assess the potential ski areas using a multi criteria evaluation technique in the Van region which is located in the eastern part of Türkiye.In this context,snow cover duration,sun effect,slope,slope length,elevation,population density,distance from main roads and lake visibility were used as input factors in the decision making process.Each factor was standardized using a fuzzy technique based on existing well-known ski centers in Türkiye.The weight of inputs was defined by applying a survey to the professional skiers.The most important factors were detected as transportation opportunities and snow covers whereas,the least important factors were elevation and population density.Additionally,lake visibility was very important to make a difference from other existing facilities in the region.Therefore,it was included as constraints and lake visible areas were extracted at the final stage of the research.Potential ski areas were mapped in three levels as professional,intermediate and beginner skiers.One of the suitable areas was selected as a sample projection and for the 3D simulation of the ski investment area.Potential costs and benefits were discussed.It was found that a ski tourism area investment can be amortized in 3 years in the region.

Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

Mahjong,a complex game with hidden information and sparse rewards,poses significant challenges.Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities.The authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐making.By utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile decision.This design reduces de-cision complexity considerably.Additionally,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands.Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs.The action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its opponents.Further-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Shared decision making in rural general practices:a qualitative exploration of older rural South Australians'perceived involvement in clinical consultations with doctors

Background:Shared decision-making(SDM)implementation is a priority for Australian health systems,including general practices but it remains complex for specific groups like older rural Australians.We initiated a qualitative study with older rural Australians to explore barriers to and facilitators of SDM in local general practices.Methods:We conducted a patient-oriented research,partnering with older rural Australians,families,and health service providers in research design.Participants who visited general practices were purposively sampled from five small rural towns in South Australia.A semi-structured interview guide was used for interviews and reflexive thematic coding was conducted.Results:Telephone interviews were held with 27 participants.Four themes were identified around older rural adults’involvement in SDM:(1)Understanding of"patient involvement";(2)Positive and negative outcomes;(3)Barriers to SDM;and(4)Facilitators to SDM.Understanding of patient involvement in SDM considerably varied among participants,with some reporting their involvement was contingent on the“opportunity to ask questions”and the“treatment choices”offered to them.Alongside the opportunity for involvement,barriers such as avoidance of cultural care and a lack of continuity of care are new findings.Challenges encountered in SDM implementation also included resource constraints and time limitations in general practices.Rural knowledge of general practitioners and technology integration in consultations were viewed as potential enablers..Conclusion:Adequate resources and well-defined guidelines about the process should accompany the implementation of SDM in rural general practices of South Australia.Innovative strategies by general practitioners promoting health literacy and culturally-tailored communication approaches could increase older rural Australians'involvement in general.

Applying an Ordinal Priority Approach Based Neutrosophic Fuzzy Axiomatic Design Approach to Develop Sustainable Geothermal Energy Source

Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Performance of the walking trail making test in older adults with white matter hyperintensities

BACKGROUND Several studies have reported that the walking trail making test(WTMT)completion time is significantly higher in patients with developmental coordination disorders and mild cognitive impairments.We hypothesized that WTMT performance would be altered in older adults with white matter hyperintensities(WMH).AIM To explore the performance in the WTMT in older people with WMH.METHODS In this single-center,observational study,25 elderly WMH patients admitted to our hospital from June 2019 to June 2020 served as the WMH group and 20 participants matched for age,gender,and educational level who were undergoing physical examination in our hospital during the same period served as the control group.The participants completed the WTMT-A and WTMT-B to obtain their gait parameters,including WTMT-A completion time,WTMT-B completion time,speed,step length,cadence,and stance phase percent.White matter lesions were scored according to the Fazekas scale.Multiple neuropsychological assessments were carried out to assess cognitive function.The relationships between WTMT performance and cognition and motion in elderly patients with WMH were analyzed by partial Pearson correlation analysis.RESULTS Patients with WMH performed significantly worse on the choice reaction test(CRT)(0.51±0.09 s vs 0.44±0.06 s,P=0.007),verbal fluency test(VFT,14.2±2.75 vs 16.65±3.54,P=0.012),and digit symbol substitution test(16.00±2.75 vs 18.40±3.27,P=0.010)than participants in the control group.The WMH group also required significantly more time to complete the WTMT-A(93.00±10.76 s vs 70.55±11.28 s,P<0.001)and WTMT-B(109.72±12.26 s vs 82.85±7.90 s,P<0.001).WTMT-A completion time was positively correlated with CRT time(r=0.460,P=0.001),while WTMT-B completion time was negatively correlated with VFT(r=-0.391,P=0.008).On the WTMT-A,only speed was found to statistically differ between the WMH and control groups(0.803±0.096 vs 0.975±0.050 m/s,P<0.001),whereas on the WTMT-B,the WMH group exhibited a significantly lower speed(0.778±0.111 vs 0.970±0.053 m/s,P<0.001)and cadence(82.600±4.140 vs 85.500±5.020 steps/m,P=0.039),as well as a higher stance phase percentage(65.061±1.813%vs 63.513±2.465%,P=0.019)relative to controls.CONCLUSION Older adults with WMH showed obviously poorer WTMT performance.WTMT could be a potential indicator for cognitive and motor deficits in patients with WMH.

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.

Type 2 Diabetes—Hard to Select a Healthy Choice

Introduction: The use of foods containing high levels of sugar is increasing all the time. This is a risk factor for increased incidence of type 2 diabetes. There are few studies that have investigated the availability of low-sugar müsli products in grocery stores. Purpose: The study aims to identify which types of müsli contain high respectively low levels of sugar, and which brands are involved. Methods: The material consists of both qualitative interviews and observations from five grocery stores: City Gross, Hemköp, Ica Maxi, Stora Coop and Willy’s in Helsingborg, Sweden. The qualitative interviews had a semi-structured character and were recorded. The interviews took approx. 20 minutes and a textual analysis was conducted of the results. Data from observation was analyzed based on brand, nutritional composition and flavors, and also, where low sugar products were placed on store shelves. Results: The grocery stores provided together brands from AXA, Coop, Finax, Frebaco, Garant, ICA, Risenta, Saltå Kvarn och Urtekram, in total 24 müsli products. Of these products, 19 were high in sugar. The observation reveals that müsli products with high sugar content (17 - 29 g per 100 g müsli) are more prominently displayed than those with low sugar content. From the interviews with the store managers, it became clear that it would be valuable to highlight healthy müsli products on the shelves. However, central bureaucracy puts obstacles to such measures. Discussion: The study emphasizes the need for increased visibility of low-sugar products and proposes solutions such as negotiating with responsible person at the head office in Stockholm. Several reviews have shown that if the grocery store raises the prices of unhealthy food, the consumer is willing to purchase healthier müsli and other products. Conclusion: This study shows the need for grocery stores to upgrade healthy müsli products along with advertising to be able to influence customer’s shopping habits. Also, further research is needed how type 2 diabetes is affected by high intakes of food products with high sugar content.

Preparation and Characterization of Vitamin-D Incorporated Marshmallow

This project looks at a novel way to enhance the sensory experience of vitamin D ingestion by incorporating it into marshmallows. This investigation used a human panel taste test with twelve individuals, an index of swelling, and a stability evaluation. Samples of vitamin D infused marshmallows were prepared and given to participants in the human panel taste test, which evaluated mouthfeel and flavor. By analyzing dissolving behavior, the swelling index test revealed unexpected erosion. In addition, a temperature threshold for storage conditions was found through a temperature sensitivity test. All of these techniques assessed the feasibility and palatability of vitamin D supplementation with marshmallow flavor, offering insights into both the possible advantages and difficulties. The marshmallow infusion technique effectively covered up the disagreeable taste of vitamin D pills, leading to reviews that were overwhelmingly favorable (“Moderate Sweet”) and that indicated a pleasant mouthfeel. During the swelling index test, it showed erosion behavior, suggesting a certain kind of dissolution that is advantageous for nutritional absorption. Furthermore, the study discovered a temperature sensitivity threshold, highlighting how crucial proper storage conditions are.

英语新教材的阅读启蒙第一课--三年级上册Unit 1 Making friends Part B Start to read教学实录

一、教材分析,本课内容选自《义务教育教科书英语(PEP)》三年级上册Unit 1 Making friends。本单元以问题“How do we make friends?”为线索,围绕Part A中的子问题“How do we greet friends?”和Part B中的子问题“How can we be a good friend?”展开。本课为该单元的第6课时,是一节读写启蒙课。教学内容主要为Part B Start to read,包含一张与交友有关的海报。

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

英语新教材“对话教学”实践--三年级上册Unit 1 Making friends Part A Let’s talk教学实录

一、教材分析,本课教学内容选自《义务教育教科书英语(PEP)》三年级上册Unit 1 Making friends。本单元以“How do we make friends?”为线索,围绕Part A中的子问题“How do we greet friends?”和Part B中的子问题“How can we be a good friend?”展开。本课是一节对话课,是本单元的第1课时,教学内容主要是Mike和Wu Binbin互相认识时的对话。