Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.However,the entire code transformation process has encountered a time-consuming problem.Therefore,the objective of this study is to speed up the code transformation process signi􀀀cantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash(VSH)algorithm and replacing LCS with a piecewise longest common subsequence(PLCS)algorithm to faster the veri􀀀cation process in the test phase.Besides the code transformation model GPT-2,this study has also introduced MicrosoMASS and Facebook BART for a comparative analysis of their performance.Meanwhile,the explainable AI technique using local interpretable model-agnostic explanations(LIME)can also interpret the decision-making ofAImodels.The experimental results show that VSH can reduce the number of quali􀀀ed programs by 22.11%,and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result,the proposed approaches can signi􀀀cantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.

Comparison of Vs and SPT Soil Liquefaction Assessments of NCEER: Including Hypothesis Testing

Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (Vs) or SPT-N profiles (SPT: standard penetration test). This study used the Vs and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the Vs method—was not rejected on a 5% level of significance.

Performance-Oriented Layout Synthesis for Quantum Computing

Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits(qubits)can only interact effectively with their nearest neighbors.This constraint severely impacts the design and efficiency of quantum algorithms,as arranging qubits optimally can significantly reduce circuit depth and improve computational performance.To tackle the layout synthesis challenge,we propose an algorithm based on integer linear programming(ILP).ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint.The algorithm aims to generate layouts that maximize qubit connectivity within the given physical constraints of the quantum device.For experimental validation,we outline a clear and feasible setup using real quantum devices.This includes specifying the type and configuration of the quantum hardware used,such as the number of qubits,connectivity constraints,and any technological limitations.The proposed algorithm is implemented on these devices to demonstrate its effectiveness in producing depth-optimal quantum circuit layouts.By integrating these elements,our research aims to provide practical solutions to enhance the efficiency and scalability of quantum computing systems,paving the way for advancements in quantum algorithm design and implementation.

Impact of disease severity on gastric residual volume in critical patients

AIM: To investigate whether illness severity has an impact on gastric residual volume (GRV) in medical critically ill patients. METHODS: Medical intensive care unit (ICU) patients requiring nasogastric feeding were enrolled. Sequential Organ Failure Assessment (SOFA) score was assessed immediately preceding the start of the study. Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ scores were recorded on the first, fourth, seventh, and fourteenth day of the study period. GRV was measured every 4 h during enteral feeding. The relationship be-tween mean daily GRV and SOFA scores and the correlation between mean daily GRV and mean APACHE Ⅱ score of all patients were evaluated and compared. RESULTS: Of the 61 patients, 43 patients were survivors and 18 patients were non-survivors. The mean daily GRV increased as SOFA scores increased (P < 0.001, analysis of variance). Mean APACHE Ⅱ scores of all patients correlated with mean daily GRV (P = 0.011, Pearson correlation) during the study period. Patients with decreasing GRV in the first 2 d had better survival than patients without decreasing GRV (P = 0.017, log rank test). CONCLUSION: GRV is higher in more severely ill medical ICU patients. Patients with decreasing GRV had lower ICU mortality than patients without decreasing GRV.

Application of Hidden Markov Models in Speech Command Recognition

In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature values.Subsequently,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command recognition.The recorded speech length was three Chinese characters,which were used to test the method.Five phrases pronounced mixing various human voices were recorded and used to test the models.The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.

How the Information and Communications Technology Threaten Our Privacy： The Case of Email and P2P Propagation Models

With the rapid growth of information and communication technology （ICT）, the violation of information privacy has increased in recent years. The privacy concerns now re-emerge right because people perceives a threat from new ICT that are equipped with enhanced capabilities for surveillance, storage, retrieval, and diffusion of personal information. With the trend in the prevalence and the easy use of ICT, it is of necessary to pay much attention to the issue how the ICT can threaten the privacy of individuals on the Internet. While the Email and P2P （Peer-to-Peer） tools are the most popular ICT, this paper aims at understanding their respectively dissemination patterns in spreading of personal private information. To this purpose, this paper using dynamic model technique to simulate the pattern of sensitive or personal private information propagating situation. In this study, an Email propagation model and a Susceptible-lnfected-Removed （SIR） model are proposed to simulate the propagation patterns of Email and P2P network respectively. Knowing their dissemination patterns would be helpful for system designers, ICT manager, corporate IT personnel, educators, policy makers, and legislators to incorporate consciousness of social and ethical information issues into the protection of information privacy.

Effect of Zn Substitution on the Magnetic and Magnetocapacitance Properties of Nanosized Multiferroic GaFeO₃Ceramics

This article aims to investigate the possibility to turn the multiferroic orders and magnetocapacitance effect close to/above room temperature in nanosized GaFeO3 ceramics by a sol-gel preparation method and substitution with non-magnetic Zn atoms. Therefore, in this work, we have synthesized a series of nanocrystalline Ga1-xZnxFeO3(GZFO, x = 0, 0.01, 0.05 and 0.1) ceramic samples and study the effect of Zn substitution on their structural, magnetic, and electric properties. All the GZFO samples have an orthorhombic structure with Pc21n space group and the value of lattice parameters increase systematically with increasing Zn concentration. Interestingly, it shows that magnetic and electric properties are strongly dependent on the Zn substitution concentration. Based on the results of temperature-dependent magnetizations, M(T), it is observed that with increasing Zn-content up to 0.10, the ferrimagnetic transition temperature (TC) increases from 306 to 320 K. It is also found that the nanocrystalline Zn-doped GaFeO3 (GFO) samples exhibit the characteristics of ferroelectricity at room temperature. Furthermore, the?magnetization, ferroelectric polarization and magnetocapacitance of Zn-doped GFO nanosized ceramics are enhanced compared to those of the pristine sample of GFO ferrite. These results open wide perspectives for the applications of room temperature multiferroic devices.

FUZZY.C-MEANS IN FINDING SUBTYPES OF CANCERS IN CANCER DATABASE

Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement error.Most of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of thestructure.Hence,this paper tries to find effective Fuzzy C-Means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective Fuzzy C-Means clustering techriques by incorporating the kermel induceddist ance function,Renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial context.The efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IRIS dataset,Wine dat aset,Checkerboard dataset,Time Series dataset and Yeast dataset.Finlly,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and noncancerous.The clusteringaccuracy has been validat ed through error matrix and silhouette method.

The Improvement of Phytomediation on the Treatment Effectiveness of Heavy Metals with Energy Sunflower Plants with Calcium Peroxide and Phytohormones

Phytomediation is an environmentally friendly green rehabilitation technology that is often incorporated with an application to improve calcium peroxide and phytohormones required for the growth of agricultural plants with the expectation to improve the effectiveness of plant rehabilitation. This study mainly consists of two parts: 1) water culture experiment and 2) pot culture experiment. In the water culture experiment, we attempt to understand the influence of the addition of calcium peroxide, phytohormones (IAA and GA3) and a chelating agent on the growth of sunflower plants. From the growth, we are then able to know the effectiveness of the addition of phytohormones. However, in the pot culture experiment, when hormones and the chelating agent EDTA are introduced to different plant groups at the same time, if the nutrition in the water required by plants is not available, the addition of the hormone cannot negate the toxicity caused by EDTA. In terms of calcium peroxide, due to quick release of oxygen in water, this study fails to apply calcium peroxide to the water culture experiment. When the pot culture experiment is used to examine the influence of hormones at different concentration levels on the growth of sunflowers, GA3 10-8 M is reported to have the optimal effectiveness, followed by IAA 10-8 M;IAA 10-12 M has the lowest effectiveness. According to an accumulation analysis of heavy metals at different levels, GA3 concentrates in leaves to transport nutrition in soil to leaves. This results in an excellent TF value of 2.329 G of GA3 than 1.845 of the control group indicating that the addition of the hormone and chelating agent to GA3 increases the TF value and the chelating agent is beneficial to the sunflower plant. If we examine phytoattenuation ability, the one-month experiment was divided into three stages for ten days each. The concentration level of heavy metals in the soil at each stage dropped continuously while that of the control group decreased from 31.63 mg/kg to 23.96 mg/kg, GA3 from 32.09 mg/kg to 23.04 mg/kg and EDTA from 30.65 mg/kg to 25.93 mg/kg indicating the quickest growth period of the sunflowers from the formation of the bud to blossom. During the stage, the quick upward transportation of nutrition results in quick accumulation of heavy metals;the accumulated speed of heavy metals is found higher than that of directly planted plants. This study shows an improvement in the effectiveness of the addition of hormones on plant extraction and when rehabilitation is incorporated with sunflowers with the beginning bud formation, better treatment effectiveness can be reached.

Isolation of Acidic Mucilage from the Outer Seed Coat of Shaddock (Citrus grandis Osbeck) and Evaluation of Its Functional Properties

After water imbibition, the outer layer seed coat of shaddock (Citrus grandis Osbeck) produces transparent gel-like mucilage (MSS), but its characteristics have never been studied before. This study aimed to assess the physico-chemical and functional properties of MSS. Extractions of MSS with deionized water at room temperature yielded about 3.5% based on the dry weight of seed. The major components were neutral sugars and uronic acids in the amounts of 33.5% and 49.6%, respectively. The acidic nature of MSS was confirmed by ruthenium red staining. Its water holding capacity and viscosity were 44.53 g•g-1 DW and 1660 cP at 10 g/L, respectively. MSS showed a weak quenching activity against DPPH radical, and moderate ferrous ion-chelating and superoxide anion radical scavenging activities, with IC50 value of 1.5 g/L and 1.1 g/L, respectively. A methyl thiazolyl tetrazolium (MTT) assay demonstrated that MSS significantly stimulated the viability of mouse skin fibroblasts (NIH/3T3) at 5 - 300 mg/L. These results impart the potential usefulness of the MSS to food, cosmetics and other applications.

Trends of AI Laws Research Organization in Taiwan

In respond to the legal issue that artificial intelligence(AI)brings,countries all over the world established AI laws research center in succession.AI Laws Research Center of National University of Kaohsiung College of Law and International AI and Law Research Foundation were established in 2018 and 2019,respectively,and have both played the role in providing research information and assist in solving AI legal issues.

Laws and Ethics Policy of Self-driving Cars in Taiwan

Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does not want to fall behind either.As on-road testing and technological development for self-driving cars continue to develop in different countries,the controversial issues of safety,ethics,liability,and the invasion of privacy continue to emerge.In order to resolve these issues,the government of Taiwan seeks to provide a good environment for AI(artificial intelligence)innovation and applications.This article summarizes and highlights relevant content and key points of Unmanned Vehicles Technology Innovative Experimentation Act,which was legislated in Taiwan in 2018.In addition,it points out the fundamental ethics regulation of AI,which has influenced Taiwan legal policy.

Developments of Cr-Si and Ni-Cr Single-Layer Thin-Film Resistors and a Bi-Layer Thin-Film Resistor with Adjustable Temperature Coefficient of Resistor

At first, Cr-Si (28 wt% Cr, 72 wt% Si) and Ni-Cr (80 wt% Ni, 20 wt% Cr) thin-film materials were deposited by using sputtering method at the same parameters, and their physical and electrical properties were investigated. The resistances of Cr-Si and Ni-Cr thin-film resistors decreased with the increase of deposition time (thickness) and their resistivity had no apparent variations as the deposition time increased. The temperature coefficient of resistance (TCR) of single-layer Cr-Si thin-film resistors was negative and the TCR value of single-layer Ni-Cr thin-film resistors was positive. For that, we used Cr-Si thin films as upper (or lower) layer and Ni-Cr thin films as lower (upper) layer to investigate a bi-layer thin-film structure. The deposition time of Ni-Cr thin films was fixed at 10 min and the deposition time of Cr-Si thin films was changed from 10 min to 60 min. We had found that as Cr-Si thin films were used as upper or lower layers they had similar deposition rates. We had also found that the thickness and stack method of Cr-Si thin films had large effects on the resistance and TCR values of the bi-layer thin-film resistors.

Application of Different HSI Color Models to Detect Fire-Damaged Mortar

To obtain a better understanding of the effect of vehicle fires on rigid pavement,a nondestructive test method utilizing an ordinary digital camera to capture images of mortar at five elevated temperatures was undertaken.These images were then analyzed by“image color-intensity analyzer”software.In image analysis,the RGB color model was the basic system used to represent the color information of images.HSI is a derived-color model that is transformed from an RGB model by formulae.In order to understand more about surface color changes and temperatures after a vehicle fire,various transformation formulae used in different research areas were applied in this study.They were then evaluated to obtain the optimum HSI model for further studies of fire-damaged mortar through the use of image analysis.

Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput.This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems.First,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data searching.Next,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job execution.As a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively.

Earthquake Magnitude Probability and Gamma Distribution

The earthquake magnitude probability distribution is one of the underlying input data for certain earthquake analyses, such as probabilistic seismic hazard analysis. Nowadays, the method proposed by McGuire and Arabasz (1990) is commonly used for obtaining the (simulated) earthquake magnitude probability distributions. However, based on the observed earthquake data in 5 regions (Taiwan, Japan, California, Turkey, and Greece), the model did not fit the observation well. Instead, all of the case studies show that using the newly proposed gamma distribution can improve the simulation significantly compared to the conventional method.

Code Transform Model Producing High-Performance Program

This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer(GPT-2)reasonably,improving the program execution performance significantly.Besides,a theoretical estimation in statistics has given the minimum number of generated programs as required,which guarantees to find the best one within them.The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code.In addition to GPT-2,this study develops the variational Simhash algorithm to check the code similarity between sample program and newly generated program,and conceives the piecewise longest common subsequence algorithm to examine the execution’s conformity from the two programs mentioned above.The code similarity check deducts the redundant generated programs,and the output conformity check finds the best-performing generative program.In addition to texts,the proposed approach can also prove the other media,including images,sounds,and movies.As a result,the newly generated program outperforms the sample program significantly because the number of code lines reduces 27.21%,and the program execution time shortens 24.62%.

Security-State Adjustable Gateway with Threat-Based Configuration

This paper proposes a configurable secure gateway architecture which allows the system administrators to dynamically configure the security mechanisms upon deployment or during the run-time. Rather than allowing the system administrators to turn on or off individual security mechanisms, the proposed architecture allows the administrators to configure the gateway based on the security threats to be overcome. The current common architecture leads to tremendous administration overhead and increases the chance of misconfiguration vulnerability. We propose a novel software architecture to aid the product designers to avoid the misconfiguration vulnerability and the end-users to ease the administration overhead. The software architecture makes use of the threats to the gateways and the occurrence relation between the threats to configure the security software components on the gateways. With the software architecture, the end-users can focus on determining the desired security features rather than the software configuration. Moreover, the architecture allows the product designers or security service to incrementally revise the software configuration when new threats appear.