A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia

Disease surveillance aims to collect data at different times or locations, to assist public health authorities to respond appropriately. Surveillance of the simian malaria parasite, Plasmodium knowlesi, is sparse in some endemic areas and the spatial extent of transmission is uncertain. Zoonotic transmission of Plasmodium knowlesi has been demonstrated throughout Southeast Asia and represents a major hurdle to regional malaria elimination efforts. Given an arbitrary spatial prediction of relative disease risk, we develop a flexible framework for surveillance site selection, drawing on principles from multi-criteria decision-making. To demonstrate the utility of our framework, we apply it to the case study of Plasmodium knowlesi malaria surveillance site selection in western Indonesia. We demonstrate how statistical predictions of relative disease risk can be quantitatively incorporated into public health decision-making, with specific application to active human surveillance of zoonotic malaria. This approach can be used in other contexts to extend the utility of modelling outputs.

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) was improved by combining numerical simulations and experiments. A finite element model of the MPSA was developed; its maximum and mean relative absolute errors of electromagnetic force were 4.3% and 2.3%, respectively, under typical work conditions. Seven design parameters including the cone angle, cone length, depth of the inner hole of the coil skeleton, cone width of the armature, inner cone diameter, and initial position of the moving-iron core were selected for developing the model, and the coefficient of the variation in electromagnetic force, nominal acceleration, 95% of the maximum stable output electromagnetic force, and corresponding response time were used as the performance indicators. The constraint relation between each performance indicator and the influence of each design parameter on the performance indicators were revealed using the uniform Latin hypercube experiment design, correlation analysis, and the main effect analysis method. A multi-objective optimization mathematical model of the MPSA was developed by combining traditional surrogate and machine learning models. The Pareto solution set was obtained using the nondominated sorting genetic algorithm II (NSGA-II), and three decision schemes with different attitudes were determined using the Hurwicz multi-criteria decision-making method. The results showed that a strong contradiction exists among the 95% of the maximum stable output electromagnetic force and its corresponding response time and the coefficient of the variation in electromagnetic force. The cone angle considerably influenced the performance indicators. Compared with the initial design, the coefficient of the variation in electromagnetic force was reduced by 54.08% for the positive decision, the corresponding response time was shortened by 15.65% for the critical decision, and the corresponding acceleration was enhanced by 10.32% for the passive decision. Thus, the overall performance of the long-stroke MPSA effectively improved.

Proposal of a Decision-Making Model for the Provisional Restoration Alternatives in Single-Tooth Implant Treatment

Background The decision-making of the most appropriate provisional restoration option in single-tooth implant practice is complex under multi-criteria conditions. The aim of our study is to conduct a case study on the determination of the appropriate provisional treatment option to be used in a single-tooth dental implant interim period after placement with the help of an entropy-based additive ratio assessment. Methodology Eight important criteria for fulfilling this purpose have been extracted from the literature search: "esthetic potential," "patient comfort," "treatment time," "laboratory cost," "occlusal clearance," "ease of removal," "durability," and "ease of modification." Provisional treatment alternatives are "removable partial denture," "vacuum-formed appliances," "bonded extracted tooth or denture," "metal or fiber-reinforced resin-bonded fixed partial denture," "wire-retained resin-bonded fixed partial denture," "acrylic resin provisional fixed partial denture," and "implant-supported fixed provisional restoration." It has been examined which of these alternatives is most appropriate in terms of both reported specifications and artificially generated dominance scenarios. The scenarios employed are S0 (criteria are equal-weighted), S1 (the criterion is tri-fold dominant), and S2 (the criterion is two-fold dominant). Results "Patient comfort" was the most important criterion (wj = 0.19). The remaining criteria were ranked as "modifications," "treatment time," "durability," "esthetic potential," "laboratory cost," "occlusal clearance," and "ease of removal." The "implant-supported fixed provisional restoration" treatment option had the maximum degree of utility in the S0 (Ki = 0.782) and S2 (Ki = 0.80) categories. If "treatment time" or "occlusal clearance" is the dominant variable, "vacuum-formed appliances" had the highest degree of utility (Ki = 0.69) in S1. Conclusions According to the rankings and scenarios created utilizing entropy-based additive ratio assessment methods, the "implant-supported fixed provisional restoration" is the appropriate provisional option for a single-tooth implant treatment. If "treatment time" or "occlusal clearance" is an absolute criterion, the "vacuum-formed provisional appliance" will replace the appropriate option.

Polymeric Materials Selection for Flexible Pulsating Heat Pipe Manufacturing Using a Comparative Hybrid MCDM Approach

The right choice of polymeric materials plays a vital role in the successful design and manufacture of flexible fluidic systems, as well as heat transfer devices such as pulsating heat pipes. The decision to choose an acceptable polymeric material entails a variety of evaluation criteria because there are numerous competing materials available today, each with its own properties, applications, benefits, and drawbacks. In this study, a comparative hybrid multi-criteria decision-making (MCDM) model is proposed for evaluating suitable polymeric materials for the fabrication of flexible pulsating heat pipes. The decision model consists of fourteen evaluation criteria and twelve alternative materials. For this purpose, three different hybrid MCDM methods were applied to solve the material selection problems (i.e., AHP-GRA, AHP-CoCoSo, and AHP-VIKOR). According to the results obtained, PTFE, PE, and PP showed promising properties. In addition, Spearman's rank correlation analysis was performed, and the hybrid methods used produced consistent rankings with each other. By applying MCDM methods, it was concluded that PTFE is the most suitable material to be preferred for manufacturing flexible pulsating heat pipes. In addition to this result, PE and PP are among the best alternatives that can be recommended after PTFE. The study supports the use of MCDM techniques to rank material choices and enhance the selection procedure. The research will greatly assist industrial managers and academics involved in the selection process of polymeric materials.

Toward One Health: a spatial indicator system to model the facilitation of the spread of zoonotic diseases

Recurrent outbreaks of zoonotic infectious diseases highlight the importance of considering the interconnections between human, animal, and environmental health in disease prevention and control. This has given rise to the concept of One Health, which recognizes the interconnectedness of between human and animal health within their ecosystems. As a contribution to the One Health approach, this study aims to develop an indicator system to model the facilitation of the spread of zoonotic diseases. Initially, a literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify relevant indicators related to One Health. The selected indicators focused on demographics, socioeconomic aspects, interactions between animal and human populations and water bodies, as well as environmental conditions related to air quality and climate. These indicators were characterized using values obtained from the literature or calculated through distance analysis, geoprocessing tasks, and other methods. Subsequently, Multi-Criteria Decision-Making (MCDM) techniques, specifically the Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, were utilized to combine the indicators and create a composite metric for assessing the spread of zoonotic diseases. The final indicators selected were then tested against recorded zoonoses in the Valencian Community (Spain) for 2021, and a strong positive correlation was identified. Therefore, the proposed indicator system can be valuable in guiding the development of planning strategies that align with the One Health principles. Based on the results achieved, such strategies may prioritize the preservation of natural landscape features to mitigate habitat encroachment, protect land and water resources, and attenuate extreme atmospheric conditions.

Influence of Selected Environmental Factors on the Business Type of Dentist's Practice in Germany: A Multi-Criteria Decision-Making Process With an Analytical Hierarchy Process

In the interests of satisfying the dental services demands of German citizens area-wide, constant, and thoughtful planning of supply and demand is essential. With an anonymous online survey of 375 dentists a pairwise comparison of 9 factors extracted as relevant from the existing scientific literature were analyzed with an analytic hierarchy process (AHP) and ranked considering the various business types. In general, 5 local environmental factors have a dominant impact on founders' decision in German dentistry. In order: environment for the family, quality of life in private environment, real income, location of the practice, infrastructure. Real income is in first place (p = 0.287) for dentists who want to start a new single practice. For preferring a new community practice, it is on third place (p = 0.177) and for dentists who favor a takeover a single practice (p = 0.130) or joining a community practice (p = 0.096) or employment (p = 0.111) it is fourth place. For this purpose, the location of the practice is of greater priority than the real income for dentists who prefer not to start a new practice. The AHP method is a way to picture a priority list out of all relevant factors for setting up of a dental practice.

Location Factors Impact the Career Choice of German Dental Practitioners - An Empirical Analytical Approach to Multi-Criteria Decision-Making

Objectives

In light of the increasing number of employed dentists and the decreasing rate of self-employed dentists, the factors that impact the decision to set up a dental office in Germany were investigated. Central to this approach is the provision of comprehensive dental care.

Methods

Using a pairwise comparison technique, the analytic hierarchy process (AHP), location factors identified as relevant in a systematic literature review and then prioritized by the professionals were weighted and ranked.

Results

According to this, five factors generally dominate the decision to open a dental office. These are, in descending order: environment for the family, quality of life in the private environment, real income, perception of location, and good infrastructure. The strongest impact on the rank order of the influencing factors is the socio-demographic characteristic of gender. For female dentists, the family environment is in the first place (p  =  .3196/C.R.  =  0.1502). For male colleagues, this influence ranks third (p  =  .1550/C.R.  =  0.1468) and real income receives the first place (p  =  .244/C.R.  =  0.1468). For female dentists, the influence of income ranks fifth (p  =  .076/C.R.  =  0.1502). Female and male dentists who grew up in rural areas were less likely to prefer employment (13.6%) than subjects of urban origin (40.2%).

Conclusion

The method of AHP is a way to map a priority list of all relevant factors. It can successfully show variations related to specific personal attributes. Obviously, there are factors that are of greater importance for the decision-making process to set up a dental office.

MULTIMOORA Method-Based Schweizer–Sklar Operations for CO2 Geological Storage Site Selection Under Pythagorean Fuzzy Environment

The site selection of CO₂ geological storage facilities is essential for the development of safe and efficient carbon capture, utilization, and storage (CCUS) projects. Normally, CO₂ geological storage site selection can be regarded as a complex multi-criteria decision-making (MCDM) problem. The aim of this paper is to present an integrated decision-making method for solving the site selection problem for CO₂ geological storage. To achieve this goal, this method is based on multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method and prioritized aggregation operators in Pythagorean fuzzy environment. The academic contributions of this study include: first, some Pythagorean fuzzy Schweizer–Sklar prioritized aggregation (PFSSPA) operators are proposed, which take into account the priority levels of criteria and the risk preferences of decision makers. The excellent properties of these operators are given. Then this study extends the classical MULTIMOORA method based on the developed aggregation operators (named PFSSPA-MULTIMOORA), and the calculation process of this method is described in detail. Subsequently, on the basis of the constructed criteria system, the PFSSPA-MULTIMOORA method is applied to rank the alternatives. Finally, we successfully utilized the PFSSPA-MULTIMOORA method to solve the site selection problem of CO₂ geological storage in China. A comparative analysis of existing methods verifies the effectiveness and robustness of the proposed method. This work can provide advanced decision support for researchers and practitioners.

Assessing the sustainability of smart healthcare applications using a multi-perspective fuzzy comprehensive evaluation approach

A smart healthcare application can be judged as sustainable if it was already widely used before and will also be prevalent in the future. In contrast, if a smart healthcare application developed during the COVID-19 pandemic is not used after it, then it is not sustainable. Assessing the sustainability of smart healthcare applications is a critical task for their users and suppliers. However, it is also a challenging task due to the availability of data, users' subjective beliefs, and different perspectives. In response to this problem, this study proposes a multi-perspective fuzzy comprehensive evaluation approach to evaluate the sustainability of a smart healthcare application from qualitative, multi-criteria decision-making and time-series perspectives. The proposed methodology has been used to evaluate the sustainability of eight smart healthcare applications. The experimental results showed that the sustainability of a smart healthcare application evaluated from different perspectives may be different. Nevertheless, another technique can be used to confirm the evaluation result generated using one technique. In other words, these views compensate for each other.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review

The rational allocation of spatial resources is an important factor to ensure the sustainable development of rural areas, and effective pre-emptive spatial evaluation is the prerequisite for identifying the predicament of rural resource allocation. Multi-criteria decision-making analysis has advantages in solving multi-attribute and multi-objective decision-making problems, and has been used in sustainability evaluation research in various disciplines in recent years. Previous studies have proved the value of spatial evaluation using multi-criteria decision analysis in guiding rural incremental development and inventory updates, but systematic reviews of the previous literature from a multidisciplinary perspective and studies of the implementation steps of the evaluation framework are lacking. In the current paper, the research is reviewed from the two levels of quantitative statistics and research content, and through vertical and horizontal comparisons based on three common operating procedures: standard formulation, weight distribution, and ranking and verification. Through the results, the application status and characteristics of the MCDA method in related research are determined, and five research foci in the future are proposed.

Adaptive pressure-driven multi-criteria spatial decision-making for a targeted placement of green and grey runoff control infrastructures

Traditional runoff control measures ignore the spatial imbalance of regional pressures, thereby failing to achieve a site-specific placement for green and grey infrastructure simultaneously. A multi-criterion decision-making framework for runoff control infrastructure spatial planning was therefore developed in this study. The pressure-state-response framework was applied to creatively match the pressure induced adjustment demands with the infrastructure effectiveness. The pressures were quantified from the perspective of environment, economy, and ecology on a grid scale. States were considered as the relative priority of regional pressure adjustment demand in multiple perspectives. Responses were presented as state-targeted green and grey infrastructure placement. Multi-perspective effectiveness of different green and grey infrastructure was simultaneously evaluated at an effective scale of controlling 1 m³/s runoff for comparison. Methods such as data mining, hydrological model simulation, and remote sensing inversion were combined to quantify the regional pressures. The capital investment and ecological impact of infrastructures were quantified from a life cycle perspective. A case study was carried out in Wuhan, China. The study area was clustered by gridded pressure into three regions. In region Ⅰ, ecological and environmental pressure were of higher weight. In region Ⅱ, the environmental pressure was dominant. In region Ⅲ, the ecological pressure took precedence over the environmental and economic constraints. The area ratios of the region Ⅰ, Ⅱ, and Ⅲ were 43%, 36%, and 21% respectively. The result indicated a synergy and spatial heterogeneity of multi-perspective pressures, and further demonstrating that expert experience tends to fail to weigh the multi-function of green and grey infrastructures for coping with the pressures. Results also stated that green infrastructures were more acceptable in areas that aspire to achieve simultaneous runoff control and ecological improvement. The decision-making framework developed in this study can maximize the overall performance by providing targeted infrastructure placement solutions.

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs' heterogeneity to cause trust problems and manipulate the behavior to delude devices' reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

A new spherical aggregation function with the concept of spherical fuzzy difference for spherical fuzzy EDAS and its application to industrial robot selection

In this article, a new fully fuzzy approach is developed for the evaluation based on distance from average solution (EDAS) for multi-criteria decision-making (MCDM) using spherical fuzzy sets (SFSs). The proposed approach avoids the current limitations and drawbacks of distance-based methods in general and the EDAS method in particular using spherical fuzzy information namely, early defuzzification, the flaws of distance measures, and the undefined spherical fuzzy subtraction and division operations. First, the approach employs the score function only in the final step for ranking. Second, the concept of the spherical fuzzy difference is introduced to make up for the subtraction operation which is the backbone of EDAS and as a substitute for distance measures. The spherical fuzzy difference is utilized to indicate any increase or decrease in the membership degree, the non-membership degree, and the hesitancy degree in the performance of an alternative for a criterion than that of its peer in the average solution. Then, the weighted spherical differences are calculated. The total weighted spherical differences from the average solution of each alternative for the assessment criteria are aggregated in the appraisal score. Due to a flaw in the extant aggregation operators, their results might be misleading. Therefore, an aggregation function is introduced that guarantees a balanced and fair aggregation. The appraisal scores are defuzzified, and the alternative with the highest appraisal score is the best. Two practical examples in MCDM are solved and a comparative study is presented to demonstrate and validate the algorithm.

Internal Modifications to Optimize Pollution and Emissions of Internal Combustion Engines through Multiple-Criteria Decision-Making and Artificial Neural Networks

The present work proposes several modifications to optimize both emissions and consumption in a commercial marine diesel engine. A numerical model was carried out to characterize the emissions and consumption of the engine under several performance parameters. Particularly, five internal modifications were analyzed: water addition; exhaust gas recirculation; and modification of the intake valve closing, overlap timing, and cooling water temperature. It was found that the result on the emissions and consumption presents conflicting criteria, and thus, a multiple-criteria decision-making model was carried out to characterize the most appropriate parameters. In order to analyze a high number of possibilities in a reasonable time, an artificial neural network was developed.

Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component

Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul

As the number of end-of-life vehicles (ELVs) increases rapidly, their management has become one of the most important environmental topics worldwide. This study is conducted to evaluate various alternatives for location selection of an authorized dismantling center (ADC) for ELVs using a multi-criteria decision-making (MCDM) approach. An intuitionistic fuzzy MCDM-based combinative distance-based assessment (CODAS) approach is proposed to aid waste managers and solve their problem. The intuitionistic fuzzy weighted averaging operator is utilized to aggregate individual opinions of decision-makers into a group opinion. The intuitionistic fuzzy Euclidean and Hamming distances are used to calculate the assessment score of alternatives. A real-life case study of Istanbul is provided to illustrate how this novel intuitionistic fuzzy MCDM-based CODAS approach can be used for alternative selection in real-world applications. The comparison with the available state-of-the-art intuitionistic fuzzy set-based MCDM approaches approves the validity and consistency of the proposed intuitionistic fuzzy CODAS approach. The intuitionistic fuzzy CODAS, WASPAS, and TOPSIS approaches generate exactly the same ordering of alternatives for the new ADC in Istanbul. The results show that the intuitionistic fuzzy CODAS approach indicates valid results and is an effective decision-making technique for vagueness and uncertainty nature of linguistic assessments.

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

With the rapid outbreak of COVID-19, most people are facing antivirus mask shortages. Therefore, it is necessary to reasonably select antivirus masks and optimize the use of them for everyone. However, the uncertainty of the effects of COVID-19 and limits of human cognition add to the difficulty for decision makers to perfectly realize the purpose. To maximize the utility of the antivirus mask, we proposed a decision support algorithm based on the novel concept of the spherical normal fuzzy (SpNoF) set. In it, firstly, we analyzed the new score and accuracy function, improved operational rules, and their properties. Then, in line with these operations, we developed the SpNoF Bonferroni mean operator and the weighted Bonferroni mean operator, some properties of which are also examined. Furthermore, we established a multi-criteria decision-making method, based on the proposed operators, with SpNoF information. Finally, a numerical example on antivirus mask selection over the COVID-19 pandemic was given to verify the practicability of the proposed method, which the sensitive and comparative analysis was based on and was conducted to demonstrate the availability and superiority of our method.

[Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers]

OBJECTIVE

To evaluate rectal toxicity of radiotherapy for prostate cancer using a novel predictive model based on multi-modality and multi-classifier fusion.

METHODS

We retrospectively collected the clinical data from 44 prostate cancer patients receiving external beam radiation (EBRT), including the treatment data, clinical parameters, planning CT data and the treatment plans. The clinical parameter features and dosimetric features were extracted as two different modality features, and a subset of features was selected to train the 5 base classifiers (SVM, Decision Tree, K-nearest-neighbor, Random forests and XGBoost). To establish the multi-modality and multi-classifier fusion model, a multi-criteria decision-making based weight assignment algorithm was used to assign weights for each base classifier under the same modality. A repeat 5-fold cross-validation and the 4 indexes including the area under ROC curve (AUC), accuracy, sensitivity and specificity were used to evaluate the proposed model. In addition, the proposed model was compared quantitatively with different feature selection methods, different weight allocation algorithms, the model based on single mode single classifier, and two integrated models using other fusion methods.

RESULTS

Repeated (5 times) 5-fold cross validation of the proposed model showed an accuracy of 0.78 for distinguishing toxicity from non-toxicity with an AUC of 0.83, a specificity of 0.79 and a sensitivity of 0.76.

CONCLUSIONS

Compared with the models based on a single mode or a single classifier and other fusion models, the proposed model can more accurately predict rectal toxicity of radiotherapy for prostate cancer.

Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment

Copper-based alloy (C93200) composites reinforced with a different weight percentage of marble dust particles (1.5, 3, 4.5, and 6 wt.%) were developed by stir casting method under vacuum environment. By using this type of reinforcement, it was possible to detect a suitable material for bearing applications. The manufactured material was characterized for its mechanical properties using a micro-hardness tester. A universal INSTRON-5967 machine was used to detect the yield and tensile strength. Further the hardness features were measured using a Walter Uhl model machine, whereby the wear characteristics were simulated under the pin-on-disc tribometer under different working conditions in ambient temperature (23 °C). Next, the preference selection index (PSI) technique that considers multi-criteria decision-making was proposed to validate which material was the best candidate. For the selection of material criteria, some specific material intrinsic properties—such as, density, void fraction, hardness resistance along with tensile, compressive, and flexural strength—were proposed and the surface characteristics linked to friction coefficients along wear properties. It was found that the novel composite material containing 4.5 wt.% of marble dust provided the best combination of properties and is a suitable candidate material for bearing applications.

Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages 'fda', 'kappalab' and 'relaimpo' were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews.