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Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

This paper aims to propose a numerical methodology to reduce the number of computations required to optimally design the rotors of synchronous reluctance machines (SynRMs) with multiple barriers.

Two objectives, average torque and torque ripple, have been simulated for thousands of SynRM models using 2D finite element analysis. Different rotor topologies (i.e. number of flux barriers) were statistically analyzed to find their respective design correlation for high average torque solutions. From this information, optimal geometrical constraints were then found to restrict the design space of multiple-barrier rotors.

Statistical analysis of two considered SynRM case studies demonstrated a design similarity between the different number of flux barriers. Upon setting the optimal geometrical constraints, it was observed that the design space of multiple-barrier rotors reduced by more than 56 per cent for both models.

Using the proposed methodology, optimal geometrical constraints of a multiple-barrier SynRM rotor can be found to restrict its corresponding design space. This approach can handle the curse of dimensionality when the number of geometric parameters increases. Also, it can potentially reduce the number of initial samples required prior to a multi-objective optimization.

The agriculture sector is crucial for all economies, especially the developing ones. However, agricultural production is influenced by government intervention, which outshines the significant role of good governance indicators in agricultural productivity. In addition to this, the major climate changes also posed various challenges and led to water shortages and yield losses. Thus affecting agricultural production. In this paper, we address the issue by determining the association between state governance and agricultural productivity in N-11 countries.

Panel data have been collected from 2000 to 2021 through the Governance Indicator, World Development Indicator and World Bank databases. For data analysis, the researcher has utilized the autoregressive distributed lag (ARDL) estimations.

Through ARDL estimations, it is suggested that corruption (CC), employment in agriculture (EAG), political stability and violence absence (PS), rule of law (RL), regulatory equality (RQ) and water quality (WQ) significantly impact agricultural productivity (AGP) in the long run. In the short run, the impact of RL on AGP has been significant.

This study follows the method of data collection from secondary sources, which hinders the effectiveness of this study as, on the basis of the respective data, the potential of the researcher to get specific answers to research questions has been affected. Also, this study examines the context of N-11 countries from 2000 to 2021, which exerts a geographical limitation. While exploring the association between state governance and agricultural productivity, this study neglects the internal aspects of implementing state policies in firms.

On practical grounds, the significant association demonstrated by this study encourages agricultural firms to keenly consider state policies to gain sustainable agricultural development. Moreover, this study encourages agricultural firms to efficiently follow governance policies for efficient productivity. The outcomes of the study have shown that agricultural employment and governance infrastructure can efficiently enhance agricultural productivity. Besides, as per the results, water quality also positively impacts agricultural productivity; thus, relevant steps can be taken by the agricultural sector to improve the quality of water.

This article examines the misinformation on the COVID-19 pandemic in social media and electronic media, as well as whether the existing legal administration and laws in Bangladesh, Singapore, and Vietnam are adequate to combat the infodemic. People who believe misinformation and fake news about Coronavirus, prevention, and treatment may put their lives in danger. False information about Coronavirus has spread throughout the world, not just in South and Southeast Asian countries, causing widespread concern in the global healthcare community. We employed a qualitative approach as well as the case study analysis method. Case studies were conducted using news reports and news channels. We examined the legal provisions of the People's Republic of Bangladesh's Constitution, as well as factual analyses of Singapore and Vietnam. We discovered the impact of misinformation dissemination through social and electronic media, which is prevalent not only among rural Bangladeshis but also in almost all classes in Singapore and Vietnam, and how such influence can be detrimental to the interests of Bangladesh, Vietnam, and Singapore.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.

To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.

The findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.

This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

Risk factors for population relocation as a result of severe catastrophes are increasing on a global scale. The frequency of catastrophic weather events is rising, infrastructure is getting older, the population is expanding, and urbanization is increasing. This study explores the influencing factors of livelihood, vulnerability, and livelihood resilience of climate-induced displaced people in developing countries, particularly in South Asia. A mixed-method approach comprising a systematic review and a narrative review has been applied in this study. A systematic review guided by PRISMA has been used to identify the relevant documents and the extracted information has been described through a narrative review approach. This study reveals that climate-induced displaced people are generally vulnerable to maintaining their livelihood, but there are a few exceptional cases where displaced people could diversify their livelihood strategies. The major influencing factors of their livelihoods are riverbank erosion, loss of assets and properties, food insecurity, seasonal hunger, low access to finance, and low job opportunity. This study argues that climate-displaced people have a long struggle to enhance their livelihood resilience, but it is a challenging task for them, particularly at the household level. The major influencing indicators under adaptive, absorptive, and transformative capacities of livelihood resilience are income and food access, agricultural and non-agricultural assets, sensitivity, climate variability and hazards, basic services, social safety nets, and institutional participation. Appropriate governance in the structural and non-structural transformation of livelihood capitals can enhance the livelihood resilience of climate-induced displaced households. In the case of Bangladesh, the coastal and Riverine Island communities are the key victims of climate-induced hazards, so they migrated frequently to reduce their vulnerability and enhance livelihood resilience. The study recommends ensuring transparency and accountability, proper coordination among stakeholders for promoting the resettlement, disaster-resilient housing and infrastructure, and Khas land (government-owned land) to the displaced people can enhance their livelihood resilience.

While the usage of mobile financial services (MFSs) is increasing rapidly in developing countries, research on users' attitudes and behavioral intention to adopt MFS is limited. Thus, this study aims to investigate customers' attitudes and intentions to adopt MFS from a Bangladeshi perspective.

A mixed research design was employed to conduct this study. Data of 196 respondents were analyzed using partial least squares (PLS) path modeling. For the quantitative part, data collection was conducted using non-probability sampling through a structured survey questionnaire. A focus group discussion with ten MFS users from divergent backgrounds was conducted to validate the quantitative findings.

This paper integrated both the technology acceptance model (TAM) and innovation resistance theory (IRT) to validate the results. The authors found that perceived usefulness (PU), perceived ease of use (PEOU) and perceived trust (PT) positively contribute to customers' attitudes toward MFS adoption. Besides, barriers to acceptance had unfavorable effects on users' attitudes and usage intentions. Furthermore, a focus group discussion revealed valuable insights on the constructs used in this study.

The study results have implications for both MFS providers and researchers. The outputs and recommendations presented in this paper will encourage the MFS practitioners to stimulate users' attitudes and behavioral intentions by ensuring useful, easy to use, credible and risk-free mobile payment platforms.

This is one of the very few studies in Bangladesh that have taken a contemporary and emerging research topic, providing theoretical, methodological and practical contributions regarding the determinants and consequences of attitude toward using MFSs.

This paper aims to assess the impact of the training program on fish traders in building awareness about formalin abuse in food items.

Primary data was collected from the fish traders in Bangladesh by using simple random sampling technique. Binary logistic regression was used to assess the opinions of the fish traders about training in building awareness of formalin abuse.

This study showed that the training of the fish traders played a positive role in improving their awareness level of formalin abuse.

Enhancement of both training and educational support could be an effective strategy in preventing formalin abuse in food.

This study helps in assessing the opinions of the fish traders about the effectiveness of the training programs in building awareness of formalin abuse.

The incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.

Data of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.

The ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.

The study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.