A case study on the source apportionment of indoor air quality at a kindergarten in eastern Peninsular Malaysia using cluster analysis

BACKGROUND

Indoor air quality is an important concern for kindergartener because young children are more vulnerable to the effects of poor air quality. Poor indoor air quality can cause respiratory problems and other health issues, which can negatively affect a child's ability to learn and grow. Aim of this study is to determine the trend and status of indoor air pollutants in study areas by using descriptive statistics and cluster analysis.

MATERIALS AND METHODS

Air temperature (T), relative humidity (RH), air movement (AM), carbon dioxide (CO2), formaldehyde (HCHO), and particulate matter (PM) are the monitored parameters. Monitoring was carried out in the kindergarten for three consecutive days starting from 8.00am to 12.00pm.

RESULTS

Indoor carbon dioxide readings were higher at 0800 when parents drove to kindergarten to drop off their children without turning off the engine. In addition to this, the PM10 reading at 1000 was high but still within the standard range according to ICOP-IAQ 2010.

CONCLUSION

The findings highlight the importance of indoor air quality improvement measures for kindergarten buildings which can be used to improve indoor air quality in kindergarten environments.

Viability of unmanned aerial vehicles in identifying potential breeding sites for mosquito

INTRODUCTION

Surveillance of mosquito breeding sites is essential because it provides the information needed to assess risks and thus respond to dengue outbreaks. This article aims to review existing research on the viability of using unmanned aerial vehicles (drones) to identify potential breeding sites for Aedes mosquitoes and highlight the issues related to their implementation.

MATERIALS AND METHODS

The authors conducted a literature search in four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) and completed it in December 2022. Articles that do not directly address the application of drones for surveillance and control of mosquito breeding sites were excluded.

RESULTS

The initial search using the keywords yielded 623 documents. After screening abstracts and reviewing the full text, only 17 articles met the inclusion criteria. Most of the studies were in the proof-of-concept stage. Many studies have also incorporated drone technologies and machine learning techniques into surveillance efforts. The authors have highlighted seven key issues related to the operational aspects of using drones. Those are hardware, software, law and regulation, operating time, expertise, geography, and community involvement.

CONCLUSION

With rapid developments in drone technologies and machine learning techniques, the viability of drones as surveillance tools can be enhanced, thus effectively responding to global public health concerns.

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

Complex Fermatean fuzzy extended TOPSIS method and its applications in decision making

The fuzzy set has its own limitations due to the membership function only. The fuzzy set does not describe the negative aspects of an object. The Fermatean fuzzy set covers the negative aspects of an object. The complex Fermatean fuzzy set is the most effective tool for handling ambiguous and uncertain information. The aim of this research work is to develop new techniques for complex decision-making based on complex Fermatean fuzzy numbers. First, we construct different aggregation operators for complex Fermatean fuzzy numbers, using Einstein t-norms. We define a series of aggregation operators named complex Fermatean fuzzy Einstein weighted average aggregation (CFFEWAA), complex Fermatean fuzzy Einstein ordered weighted average aggregation (CFFEOWAA), and complex Fermatean fuzzy Einstein hybrid average aggregation (CFFEHAA). The fundamental properties of the proposed aggregation operators are discussed here. The proposed aggregation operators are applied to the decision-making technique with the help of the score functions. We also construct different algorithms based on different aggregation operators. The extended TOPSIS method is described for the decision-making problem. We apply the proposed extended TOPSIS method to MAGDM problem "selection of an English language instructor". We also compare the proposed models with the existing models.

A new three way decision making technique for supplier selection in logistics service value Cocreation under intuitionistic double hierarchy linguistic term set

In today's business world, choosing a logistics supplier is a critical factor for companies to improve operational efficiency and reduce business costs. With the development of market economy, it is very difficult for companies to choose a suitable logistics provider according to specific rules. Therefore, this study proposes a new three-way decision making (TWD) technique for supplier selection in logistics service value creation. For this, we first develop a new concept called intuitionistic double hierarchy linguistic term set (IDHLTSs) that can describe uncertainty and ambiguity in a more flexible way. Some Hamacher aggregation operators for collecting IDHLTSs information and its basic aspect are proposed. The unknown weight vector for decision experts and criteria is determined by using entropy measures. In addition, the conditional probability is determined using TOPSIS which makes the decision making process more rational. And the decision result is conducted according to minimum loss principle. Finally, an example of 3 PL supplier selection in the logistics service value co-creation environment and comparison is given to validate and demonstrate the effectiveness of the developed method.

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in daily life. We need some future and optimal classifier selection by using different techniques for depression data extraction using EEG. Intelligent decision support is a decision-making process that is automated based on some input information. The primary goal of this proposed work is to create an artificial intelligence-based fuzzy decision support system (AI-FDSS). Based on the given criteria, the AI-FDSS is considered for classifier selection for EEG under depression information. The proposed intelligent decision technique examines classifier alternatives such as Gaussian mixture models (GMM), k-nearest neighbor algorithm (k-NN), Decision tree (DT), Nave Bayes classification (NBC), and Probabilistic neural network (PNN). For analyzing optimal classifiers selection for EEG in depression patients, the proposed technique is criterion-based. First, we develop a general algorithm for intelligent decision systems based on non-linear Diophantine fuzzy numbers to examine the classifier selection technique using various criteria. We use classifier methods to obtain data from depression patients in normal and abnormal situations based on the given criteria. The proposed technique is criterion-based for analyzing optimal classifier selection for EEG in patients suffering from depression. The proposed model for analyzing classifier selection in EEG is compared to existing models.

Implications of the COVID-19 pandemic on the shanghai, New York, and Pakistan stock exchanges

This research aims to determine the impact of COVID-19 on the stock markets of Pakistan (Islamabad), China (Shanghai), and the United States of America (New York). These three stock markets were chosen to demonstrate the variation in the degree of influence based on varied times in which the respective nations were impacted by COVID-19. COVID-19, a pandemic virus, was still present in China in December 2020. The one-year timeline helps us understand the pattern of the effect on different stock markets that show onward to guide us to indicate that in this situation, the lack of economic movement (due to the lockdown) had a more negative effect on stock prices than the increase in the number of new confirmed cases of the COVID-19 virus. This study was carried out to assess the influence of COVID-19 on the financial sectors, including the stock market. The effects were assessed by employing the Autoregressive Distributed Lag Model (ARDL) to demonstrate correlations between three stock markets (Pakistan, Shanghai, and New York) and COVID-19 instances. The study's major goal is to demonstrate the differences in the three countries' levels of influence. We got empirical results and discovered that the confirmed cases had a detrimental influence on three stock exchanges. However, all three countries saw an increase in the number of recovery cases. The number of deaths was minor for Pakistan and China but had a detrimental impact on the New York Stock Exchange.

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

This study proposed wavelet-based approaches to characterise random vibration road excitations for durability prediction of coil springs. Conventional strain-life approaches require long computational time, while the accuracy of the vibration fatigue methods is unsatisfactory. It is therefore a necessity to establish an accurate fatigue life prediction model based on vibrational features. Wavelet-based methods were applied to determine the low-frequency energy and multifractality of road excitations. Strain-life models were applied for fatigue life evaluation from strain histories. ANFIS modelling was subsequently adopted to associate the vibration features with the fatigue life of coil springs. Results showed that the proposed wavelet-based methods were effective to determine the signal energy and multifractality of vibration signals. The established vibration-based models showed good fatigue life conservativity with a data survivability of more than 90%. The highest Pearson coefficient of 0.955 associated with the lowest RMSE of 0.660 was obtained by the Morrow-based model. It is suggested that the low-frequency energy and multifractality of the vibration signals can be used as fatigue-related features in life predictions of coil springs under random loading. Finally, the proposed model is an acceptable fatigue life prediction method based on vibration features, and it can reduce the dependency on strain data measurement.

A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19

The control of spreading of COVID-19 in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a spherical intelligent fuzzy decision model for control and diagnosis of COVID-19. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of COVID19. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the spherical fuzzy information. In the proposed work, we will develop a newly and generalized technique for COVID19 based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under spherical fuzzy environment. Finally, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results.

Multicriteria group decision making for COVID-19 testing facility based on picture cubic fuzzy aggregation information

The information aggregation of cubic fuzzy numbers and picture fuzzy numbers have played an important role in decision making. This paper introduces a novel approach to address the problem of testing facility of COVID-19 under picture cubic fuzzy environment. As the picture cubic fuzzy set is a generalized fuzzy structure to handle more uncertainty and ambiguity in decision making problems We discuss its various properties. Based on geometric aggregation operators and Hamacher operations, we introduce some Hamacher geometric aggregation operators under picture cubic fuzzy information. Namely, picture cubic fuzzy Hamacher weighted geometric aggregation operator, picture cubic fuzzy Hamacher hybrid geometric operator, picture cubic fuzzy Hamacher order weighted geometric aggregation operator. Discuss some properties of the defined operators. To verify the importance of the proposed operators, develop multicriteria group decision making (MCGDM) algorithm under picture cubic fuzzy environment and apply this strategy for the selection of an authentic laboratory for COVID-19 test. Further to validate the supremacy of our proposed operators, we present a comparative analysis with pre-existing aggregation operators. Results show that the proposed technique is more effective and suitable for MCGDM problems.

A novel approach on spherical fuzzy rough set based-EDA𝒮 method for group decision support system

In daily life, the decision making problem is a complicated work related to uncertainties and vagueness. To overcome this vagueness and uncertainties, many fuzzy sets and theories have been presented by different scholars and researchers. EDA𝒮 (Evaluation based on distance from average solution) method plays a major role in decision-making problems. Especially, when multi-attribute group decision-making (MAGDM) problems have more conflicting attribute. In this paper, a new approach known as Spherical fuzzy rough-EDA𝒮 (SFR-EDA𝒮) method is used to handle these uncertainties in the MAGDM problem. The aggregation operators have the ability to combine different sources of information, which plays an essential role in decision making (DM) problem. Keeping in view the increasing complexity of the DM problem, it will be useful to combine the aggregation operators with the fuzzy sets in solving DM problem. Therefore, an aggregation operator known as SFR-EDA𝒮 method is utilized. For this propounded some new averaging and geometric aggregation is investigated. Moreover, the essential and desirable properties with some particular cases are deliberated and discussed detail. To evaluate the emergency program, a MAGDM approach is used based on the new introduced operators. Later on, the viability and applicability the proposed method is certified by a detailed analysis with the other existing approaches.

Mathematical calculation of COVID-19 disease in Pakistan by emergency response modeling based on complex Pythagorean fuzzy information

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results.

Review on the extraction of bioactive compounds and characterization of fruit industry by-products

The by-products produced from fruit processing industries could be a potential hazard to environmental pollution. However, these by-products contain several biologically active molecules (essential fatty acid, phenolic compounds, flavonoids, coloring pigments, pectin, proteins, dietary fibers, and vitamins), which can be utilized for various applications in the food, pharmaceutical, cosmetic and textile industries. Nevertheless, during extraction, these bioactive compounds' recovery must be maximized using proper extraction technologies, keeping both economy and environment under consideration. In addition, the characteristics of the extract obtained from those by-products depend mainly on the parameters considered during the extraction process. In this review, an overview of different technologies used to extract bioactive compounds from fruit industry by-products such as seeds and peels has been briefly discussed, along with their mechanisms, process, advantages, disadvantages, and process parameters. In addition, the characteristics of the extracted bioactive compounds have also been briefly discussed in this review.

A new approach to vague soft toplogical structures concerning soft points

This paper concerns the study of the notion of vague soft β-open set and vague soft separation axioms in vague soft topological spaces. By using such notions and that of the vague soft pints, we study the separation axioms βi (with i = 0, 1, 2, 3, 4) in vague soft topological spaces. We give some peculiar examples about them and we prove some relationships between them. The relationship of βi (with i = , 1, 2, 3, 4) spaces with the closer of vague soft β-open set by means of soft points, vague soft countable spaces and their relationship with βi (with i = , 1, 2) spaces by means of soft points are addressed. In continuation, vague soft topological, vague soft inverse topological spaces properties, Bolzano Weirstrass Property(BVP) and its topological characteristics, compact spaces and sequentially compact spaces and their relationship with separation axioms by means soft points are addressed in vague soft topological spaces.

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

Entropy based extended TOPOSIS method for MCDM problem with fuzzy credibility numbers

<abstract><p>Due to the vagueness and uncertainty of human cognition/judgments as related to complicated decision-making problems, existing fuzzy decision-making approaches merely signal fuzzy assessment values and lack degrees/levels of credibility for the fuzzy assessment values in alternatives over attributes. As a result, the fuzzy evaluative value's credibility degree highlights its significance and importance in the fuzzy decision-making problem. To improve the degrees/levels of credibility of fuzzy evaluation values, the fuzzy assessment values should be tightly linked to their credibility measures, which would result in more abundant and reliable assessment information. The major goal of this research was to describe new procedures for credible fuzzy numbers based on the Dombi t-norm and Dombi t-conorm. Dombi operations can benefit from the operational parameter's best tractability. These operations are more generalized for credibility fuzzy numbers. Furthermore, using the basic operational laws of Dombi t-norm and Dombi t-conorm, we develop a series of fuzzy credibility Dombi aggregation operators, like the fuzzy credibility Dombi geometric aggregation operator, fuzzy credibility Dombi ordered geometric aggregation operator and fuzzy credibility Dombi hybrid geometric aggregation operator. To handle this sort of decision-making problem, an extended TOPSIS (technique for order of preference by similarity to ideal solution) is proposed. Finally, we present an example, along with a discussion of the comparative results to check the accuracy and validation of the proposed methods, to confirm that their results are credible and feasible.</p></abstract>

Decision support system based on fuzzy credibility Dombi aggregation operators and modified TOPSIS method

<abstract><p>The operational law plays an important role in the aggregation operator for group decision system. The aggregation information has high influence in aggregating group decision information. Therefore, the main objective of the proposed work is to develop some operational laws as aggregation operator for fuzzy credibility numbers based on Dombi norms. Dombi operations can benefit from the best operational parameter flexibility. To the best of our knowledge, Dombi operations have so far not been used in for fuzzy credibility numbers (FCNs). Using these Dombi t-norm and t-conorm to define some different fuzzy credibility aggregation operators. i.e., fuzzy credibility Dombi weighted averaging (FCDWA) operator, fuzzy credibility Dombi ordered weighted averaging (FCDOWA) operator, fuzzy credibility Dombi hybrid weighted averaging (FCDHWA) operator. Next, we used TOPSIS method procedure for multi-attribute grouped decision-making (MAGDM). Finally, we provided an example, as well as a discussion of the comparative result analysis, to ensure that their findings are credible and practical.</p></abstract>

A Review of Surgically Treated Distal Radius Fractures in a University Hospital

Introduction:

Distal radius fracture (DRF) is the most common orthopaedic injury with a reported incidence of 17.5%. It is commonly seen in young males and elderly females. Over the last two decades, there is an increasing tendency to treat DRF surgically by open reduction and internal fixation (ORIF) with plate and screws owing to improved device design, better fixation and operative technique. The purpose of this study was to evaluate the demographic characteristics, type and method of fixation, and outcome in all surgically treated DRF cases from 2014 to 2018 in a university hospital.

Materials and methods:

A retrospective review of all surgically treated DRF cases with one year follow-up in a tertiary hospital in Malaysia was done. Patients who left the follow-up clinic before one-year post-surgery or before fracture union were excluded. A total of 82 patients with 88 DRF were finally included into the study and outcome in terms of union time and need of multiple surgeries were analysed along with the predictors.

Results:

In this study, mean age of the patient was 46.2 years. Motor vehicle accident was the commonest cause of the fracture and AO Type C fracture was the commonest fracture type. Seventeen (19.3%) out of 88 fractures were compound fracture. Open reduction and internal fixation with volar plate was the most common surgical technique done in this series (93.2%). Three (3.5%) out of 88 fractures required multiple surgeries and eighty-three (94.3%) DRF cases were united before nine months of the surgery in this study. There was statistically significant association between clinical type of the fracture and the union time (p-value <0.05).

Conclusion:

There was a 1.7:1 male-female ratio with AO-C fracture being the most common type of fracture. The most common method of fixation was ORIF with volar locked plate. Patients with closed fractures have a higher rate of union compared to open fractures at nine months.

Epidemiology and Factors Affecting Functional Outcome of Distal Radial Fracture in an Urban Tertiary Medical Centre in Malaysia

Introduction

Distal radial fracture is a commonly encountered fracture. This study aims to study the epidemiology of distal radial fracture and factors affecting the patients' functional outcome one to two years after the injury.

Materials and methods

This is a retrospective cohort study. The records of patients, fulfilling the radiographical diagnosis of distal radial fracture, and aged 18 and above, who presented to our Emergency Department from 1st January 2018 to 31st December 2018 were retrieved. According to AO classification, we grouped our patients into A (extra-articular), B (partial articular) and C (complete articular). Patients with congenital abnormalities were excluded. Epidemiological data and relevant medical history were obtained and tabulated. A Malaysian language translation of Disability of the Arm, Shoulder and Hand (DASH) questionnaire was used to assess the functional outcome.

Results

Out of 168 patients' data retrieved, only 110 patients' data were found complete for purposes of this study. The mean DASH score was 13.7 ± 7.87 approximately one to two years post-injury regardless of treatment method. Increasing age was associated with higher DASH score with r=0.407(p<0.001). Several variables had significantly better functional outcome: male gender (p=0.01), Type A fracture configuration (p=0.007) and non-operational treatment (p=0.03). There was no significant difference between treatment modalities in Type A fracture (p=0.094), but Type B (p=0.043) and Type C (p=0.007) had better outcome without surgery. There was no significant difference between different ethnic groups, open or closed fracture and mechanism of injury.

Conclusion

Better functional outcome after sustaining distal radial fracture was associated with young age, male gender, type A fracture and treated non-operatively. Interestingly, more complex fracture pattern had better functionality were observed without surgery.

Evaluation of the product quality of the online shopping platform using t-spherical fuzzy preference relations

As a generalization of Pythagorean fuzzy sets and picture fuzzy sets, spherical fuzzy sets provide decision makers more flexible space in expressing their opinions. Preference relations have received widespread acceptance as an efficient tool in representing decision makers’ preference over alternatives in the decision-making process. In this paper, some new preference relations are investigated based on the spherical fuzzy sets. Firstly, the deficiency of the existing operating laws is elaborated in detail and three cases are described to identify the accuracy of the proposed operating laws in the context of t-spherical fuzzy environment. Also, a novel score function is proposed to obtain the consistent value in ranking of the alternatives. The backbone of this research, t-spherical fuzzy preference relation, consistent t-spherical fuzzy preference relations, incomplete t-spherical fuzzy preference relations, consistent incomplete t-spherical fuzzy preference relations, and acceptable incomplete t-spherical fuzzy preference relations are established. Additionally, some ranking and selection algorithms are established using the proposed novel score function and preference relations to tackle the uncertainty in real-life decision-making problems. Finally, evaluation of the product quality of the online shopping platform problem is demonstrated to show the applicability and reliability of proposed technique.

Adolescent girls’ recommendations for designing Human Papillomavirus Vaccination Program in Pakistan

 

In Pakistan, cervical cancer is the third most frequent cancer among women. Most cases are caused by sexually-acquired infection with human papillomaviruses (HPV). Vaccination of adolescent girls against HPV significantly reduces the incidence of cervical cancer. HPV vaccination is available in Pakistan, but plans to develop a HPV vaccination program are currently at a very early stage. We conducted a formative study to explore adolescent girls' perspectives on HPV and cervical cancer, and collected their recommendations for implementing an HPV vaccination program. We conducted qualitative focus group discussions (FGDs), with unmarried adolescent girls aged 16-19 years, residing in Karachi. We conducted four FGDs with 12 participants each. The topic guide assessed i) girls' knowledge of cervical cancer and HPV vaccination, ii) vaccination decision-making dynamics within families, and iii) factors girls would consider essential for the successful implementation of HPV vaccination program. Overall, participants displayed a positive attitude towards the HPV vaccine. However, many basic concepts related to female reproductive health were unfamiliar to them. Female relatives were indicated as girls' preferred point of contact for discussions on HPV and cervical cancer, but fathers were portrayed as the definitive decision-making authority on vaccination. Girls gave critical input for the development of a HPV vaccination program. Eligible girls should be recruited through individual household visits and the vaccine should be administered at community-based camps, which would ensure both good accessibility and a large outreach. A solid foundation of trust between girls' families, program managers, and other stakeholders emerged as a key asset for the program's success. Adolescents demonstrated great capacity and shared recommendations for a future program's acceptability. Integrating their perspectives will be essential to design an effective program in local context.

Key messages

Adolescent girls’ perspectives should be incorporated to develop context-specific recommendations for the implementation of a HPV vaccination program. Community-based camps and household visits by adolescent girls to recruit the target population could potentially increase program penetration among communities.

Emergency decision support modeling under generalized spherical fuzzy Einstein aggregation information

Dominant emergency action should be adopted in the case of an emergency situation. Emergency is interpreted as limited time and information, harmfulness and uncertainty, and decision-makers are often critically bound by uncertainty and risk. This framework implements an emergency decision-making approach to address the emergency situation of COVID-19 in a spherical fuzzy environment. As the spherical fuzzy set (SFS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). Keeping in view the features of the SFSs, the purpose of this paper is to present some robust generalized operating laws in accordance with the Einstein norms. In addition, list of propose aggregation operators using Einstein operational laws under spherical fuzzy environment are developed. Furthermore, we design the algorithm based on the proposed aggregation operators to tackle the uncertainty in emergency decision making problems. Finally, numerical case study of COVID-19 as an emergency decision making is presented to demonstrate the applicability and validity of the proposed technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique.

Efficient network selection using multi fuzzy criteria for confidential data transmission in wireless body sensor networks

In the last decade, due to wireless technology’s enhancement, the people’s interest is highly increased in Wireless Body Sensor Networks (WBSNs). WBSNs consist of many tiny biosensor nodes that are continuously monitoring diverse physiological signals such as BP (systolic and diastolic), ECG, EMG, SpO2, and activity recognition and transmit these sensed patients’ sensitive information to the central node, which is straight communicate with the controller. To disseminate this sensitive patient information from the controller to remote Medical Server (MS) needs to be prolonged high-speed wireless technology, i.e., LTE, UMTS, WiMAX, WiFi, and satellite communication. It is a challenging task for the controller to choose the optimal network to disseminate various patient vital signs, i.e., emergency data, normal data, and delay-sensitive data. According to the nature of various biosensor nodes in WBSNs, monitor patient vital signs and provide complete intelligent treatment when any abnormality occurs in the human body, i.e., accurate insulin injection when patient sugar level increased. In this paper, first, we select the optimal network from accessible networks using four different fuzzy attribute-based decision-making techniques (Triangular Cubic Hesistent Fuzzy Weighted Averaging Operator, Neutrosophic Linguistic TOPSIS method, Trangualar Cubic Hesistent Fuzzy Hamacher Weighted Averaging Operator and Cubic Grey Relational Analysis) depending upon the quality of service requirement for various application of WBSNs to prolong the human life, enhanced the society’s medical treatment and indorse living qualities of people. Similarly, leakage and misuse of patient data can be a security threat to human life. Thus, confidential data transmission is of great importance. For this purpose, in our proposed scheme, we used HECC for secure key exchange and an AES algorithm to secure patient vital signs to protect patient information from illegal usage. Furthermore, MAC protocol is used for mutual authentication among sensor nodes and Base Stations (BS). Mathematical results show that our scheme is efficient for optimal network selection in such circumstances where conflict arises among diverse QoS requirements for different applications of WBSNs.

Complex intuitionistic fuzzy Maclaurin symmetric mean operators and its application to emergency program selection

Due to the indeterminacy and uncertainty of the decision-makers (DM) in the complex decision making problems of daily life, evaluation and aggregation of the information usually becomes a complicated task. In literature many theories and fuzzy sets (FS) are presented for the evaluation of these decision tasks, but most of these theories and fuzzy sets have failed to explain the uncertainty and vagueness in the decision making issues. Therefore, we use complex intuitionistic fuzzy set (CIFS) instead of fuzzy set and intuitionistic fuzzy set (IFS). A new type of aggregation operation is also developed by the use of complex intuitionistic fuzzy numbers (CIFNs), their accuracy and the score functions are also discussed in detail. Moreover, we utilized the Maclaurin symmetric mean (MSM) operator, which have the ability to capture the relationship among multi-input arguments, as a result, CIF Maclarurin symmetric mean (CIFMSM) operator and CIF dual Maclaurin symmetric mean (CIFDMSM) operator are presented and their characteristics are discussed in detail. On the basis of these operators, a MAGDM method is presented for the solution of group decision making problems. Finally, the validation of the propounded approach is proved by evaluating a numerical example, and by the comparison with the previously researched results.

Problems in Malaysian children with large angle infantile esotropia: Children and parents' perspectives

INTRODUCTION

There is limited information regarding the problems faced by children with large angle infantile esotropia (LAIE). The aim of this study was to explore the problems that children with LAIE encounter from both their perspectives and those of their parents.

METHODS

This study included children who had LAIE (with angle of 40 prism dioptres or greater), aged 5 and 17 years who had attended the Ophthalmology Clinic, Hospital Universiti Sains Malaysia from March to September 2016. The children and their parents or guardians were interviewed face-to-face using a validated semi-structured interview guide. Interviews were tape-recorded and transcribed verbatim. Content analysis was performed using the NVivo 12 software.

RESULTS

A total of 30 children and 30 parents were interviewed. The most common problems identified by the children were social interactions (73.3%, 22 children), visual functions (60.0%, 18 children), emotions (60.0%, 18 children), physical issues (40.0%, 12 children) and difficulties regarding treatment options (26.7%, eight children). The parents reported that their children were more affected in terms of visual functions (100.0%, 30 parents), social interactions (56.7%, 17 parents), emotions (43.3%, 13 parents), physical issues (20.0%, six parents), and difficulties regarding treatment options (16.7%, five parents).

CONCLUSION

The major problems that the children with LAIE identified were social interactions, while the parents observed that problems with visual functions was the most common issue encountered by their children. This suggests that the children affected have different perspectives from their parents and require support.

Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information and their application in decision support system

Probabilistic hesitant fuzzy Set (PHFs) is the most powerful and comprehensive idea to support more complexity than developed fuzzy set (FS) frameworks. In this paper a novel and improved TOPSIS-based method for multi-criteria group decision making (MCGDM) is explained through the probabilistic hesitant fuzzy environment, in which the weights of both experts and criteria are completely unknown. Firstly, we discuss the concept of PHFs, score functions and the basic operating laws of PHFs. In fact, to compute the unknown weight information, the generalized distance measure for PHFs was defined based on the Probabilistic hesitant fuzzy entropy measure. Second, MCGDM will be presented with the PHF information-based decision-making process.

Correlated charge noise and relaxation errors in superconducting qubits

The central challenge in building a quantum computer is error correction. Unlike classical bits, which are susceptible to only one type of error, quantum bits (qubits) are susceptible to two types of error, corresponding to flips of the qubit state about the X and Z directions. Although the Heisenberg uncertainty principle precludes simultaneous monitoring of X- and Z-flips on a single qubit, it is possible to encode quantum information in large arrays of entangled qubits that enable accurate monitoring of all errors in the system, provided that the error rate is low¹. Another crucial requirement is that errors cannot be correlated. Here we characterize a superconducting multiqubit circuit and find that charge noise in the chip is highly correlated on a length scale over 600 micrometres; moreover, discrete charge jumps are accompanied by a strong transient reduction of qubit energy relaxation time across the millimetre-scale chip. The resulting correlated errors are explained in terms of the charging event and phonon-mediated quasiparticle generation associated with absorption of γ-rays and cosmic-ray muons in the qubit substrate. Robust quantum error correction will require the development of mitigation strategies to protect multiqubit arrays from correlated errors due to particle impacts.

An approach for supplier selection problem based on picture cubic fuzzy aggregation operators

This article is an advanced approach to picture fuzzy set through the application of cubic set theory. For instance, we establish the idea of the picture cubic fuzzy sets (PCFSs) theory and define several operations for PCFS. Also, presented some weighted aggregation operators under picture cubic fuzzy information, so called picture cubic fuzzy weighted averaging (PCFWA) operator, picture cubic fuzzy order weighted averaging (PCFOWA) operator, picture cubic fuzzy weighted geometric (PCFWG) operator, and picture cubic fuzzy order weighted geometric (PCFOWG) operator. Further, we study their fundamental properties and showed the relationship among these aggregation operators. In order to determine the feasibility and practicality of the mentioned new technique, we developed multi-attribute group decision -making algorithm with picture cubic fuzzy environment. Further, the developed method applied to supply chain management and for implementation, consider numerical application of supply chain management. Compared the developed approach with other preexisting aggregation operators, and we concluded that the defined technique is better, reliable and effective.

Coordinating a Postgraduate Orthopaedic Exam During the COVID-19 Pandemic

The Coronavirus (COVID-19) pandemic and institution of the Movement Control Order (MCO) had resulted in the cancellation of a major orthopaedic exam in April 2020. The exam is known as the Malaysian Orthopaedic Specialist Committee (OSC) Part I Examinations. It is similar to the British Royal Colleges of Surgeons Membership (MRCS) exams and held twice annually in April and October. There are up to 200 candidates involved. With implementation of new guidelines and standard operating procedures (SOP), the OSC Part I exam was successfully held by Universiti Kebangsaan Malaysia (UKM) from 5th-9th October 2020. Here we highlight the challenges we faced whilst coordinating a major exam at a national level during the COVID-19 pandemic with recommendations for future exams.

Three-way decisions with decision-theoretic rough sets based on covering-based q-rung orthopair fuzzy rough set model

Based on decision theory rough sets (DTRSs), three-way decisions (TWDs) provide a risk decision method for solving multi-attribute decision making (MADM) problems. The loss function matrix of DTRS is the basis of this method. In order to better solve the uncertainty and ambiguity of the decision problem, we introduce the q-rung orthopair fuzzy numbers (q-ROFNs) into the loss function. Firstly, we introduce concepts of q-rung orthopair fuzzy β-covering (q-ROF β-covering) and q-rung orthopair fuzzy β-neighborhood (q-ROF β-neighborhood). We combine covering-based q-rung orthopair fuzzy rough set (Cq-ROFRS) with the loss function matrix of DTRS in the q-rung orthopair fuzzy environment. Secondly, we propose a new model of q-ROF β-covering DTRSs (q-ROFCDTRSs) and elaborate its relevant properties. Then, by using membership and non-membership degrees of q-ROFNs, five methods for solving expected losses based on q-ROFNs are given and corresponding TWDs are also derived. On this basis, we present an algorithm based on q-ROFCDTRSs for MADM. Then, the feasibility of these five methods in solving the MADM problems is verified by an example. Finally, the sensitivity of each parameter and the stability and effectiveness of these five methods are compared and analyzed.

EDAS method for decision support modeling under the Pythagorean probabilistic hesitant fuzzy aggregation information

The significance of emergency decision-making (EmDM) has been experienced recently due to the continuous occurrence of various emergency situations that have caused significant social and monetary misfortunes. EmDM assumes a manageable role when it is important to moderate property and live misfortunes and to reduce the negative effects on the social and natural turn of events. Genuine world EmDM issues are usually described as complex, time-consuming, lack of data, and the effect of mental practices that make it a challenging task for decision-makers. This article shows the need to manage the various types of vulnerabilities and to monitor practices to resolve these concerns. In clinical analysis, how to select an ideal drug from certain drugs with efficacy values for coronavirus disease has become a common problem these days. To address this issue, we are establishing a multi-attribute decision-making approach (MADMap) based on the EDAS method under Pythagorean probabilistic hesitant fuzzy information. In addition, an algorithm is developed to address the uncertainty in the selection of drugs in EmDM issues with regards to clinical analysis. The actual contextual analysis of the selection of the appropriate drug to treat coronavirus ailment is utilized to show the practicality of our proposed technique. Finally, with the help of a comparative analysis of the TOPSIS technique, we demonstrate the efficiency and applicability of the established methodology.

A new approach to q-linear Diophantine fuzzy emergency decision support system for COVID19

The emergency situation of COVID-19 is a very important problem for emergency decision support systems. Control of the spread of COVID-19 in emergency situations across the world is a challenge and therefore the aim of this study is to propose a q-linear Diophantine fuzzy decision-making model for the control and diagnose COVID19. Basically, the paper includes three main parts for the achievement of appropriate and accurate measures to address the situation of emergency decision-making. First, we propose a novel generalization of Pythagorean fuzzy set, q-rung orthopair fuzzy set and linear Diophantine fuzzy set, called q-linear Diophantine fuzzy set (q-LDFS) and also discussed their important properties. In addition, aggregation operators play an effective role in aggregating uncertainty in decision-making problems. Therefore, algebraic norms based on certain operating laws for q-LDFSs are established. In the second part of the paper, we propose series of averaging and geometric aggregation operators based on defined operating laws under q-LDFS. The final part of the paper consists of two ranking algorithms based on proposed aggregation operators to address the emergency situation of COVID-19 under q-linear Diophantine fuzzy information. In addition, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.