Alternative fuel vehicles (AFVs) offer opportunities to lower fuel costs as well as to reduce greenhouse gas emissions, and, therefore, they are a feasible option for customers in the market. Due to technological advancements, decisions about suitable alternative fuel vehicles are a challenging problem for fleet operators. This paper aims to introduce a multi-attribute decision-analysis framework to rank and select the “alternative fuel vehicles (AFVs)” for a private home healthcare service provider in Chandigarh, India. The selection of AFVs can be treated as a decision-making problem, because of the presence of various qualitative and quantitative attributes. Thus, the current work introduces an integrated decision-making framework based on intuitionistic fuzzy-“method based on the removal effects of criteria (MEREC)”, “ranking sum (RS)”, and the “double normalization-based multi-aggregation (DNMA)” framework for assessing the AFVs. The combination of MEREC and RS is applied to assess the objective and subjective weighting values of various parameters for AFV assessment. The DNMA approach is utilized to prioritize the different AFVs over various significant parameters. According to the outcomes, the most significant parameters for AFV assessment are social benefits, fueling/charging infrastructure, and financial incentives, respectively. In this context, globally existing AFVs for the sustainable transportation sector are identified, and then prioritized against fifteen different criteria relevant to the environmental, economic, technological, social, and political aspects of sustainability. It is distinguished that electric vehicles (G₂), hybrid electric vehicles (G₁), and hydrogen vehicles (G₃) achieve higher overall performance compared to the other technologies available in India. The assessment outcomes prove that electric vehicles can serve as a valuable alternative for decreasing carbon emissions and negative effects on the environment. This technology contributes to transportation sector development and job creation in less developed areas of the country. Moreover, a comparison with existing studies and a sensitivity investigation are conferred to reveal the robustness and stability of the developed framework.

Optimal renewable energy source (RES) selection needs a strategic decision for reducing environmental pollutions, use of conventional resources, and improving economic development. In the process of RESs evaluation, several aspects like environmental, economic, social, and technical requirements play an important role. In addition, diverse factors affect the appropriate RES selection problem which adheres to uncertain and imprecise data. Thus, this selection process can be considered as a complex uncertain multi-criteria decision making (MCDM) problem. This study aims to introduce a novel integrated methodology based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) methods within single-valued neutrosophic sets (SVNSs) context, wherein the decision-makers and criteria weights are completely unknown. In the proposed approach, the criteria weights are determined by the SWARA method, and the most suitable RES alternative is determined by an improved CoCoSo method under the SVN context. Further, an illustrative case study of RES selection is considered to demonstrate the thorough execution process of the proposed method. Moreover, a comparison with existing methods is discussed to analyze the validity of the obtained result. This study performs sensitivity analysis with a various set of criteria weights to reveal the robustness of the developed approach. The strength of the proposed method is its practical applicability and ability to provide solutions under uncertain, imperfect, indeterminate, and inconsistent information.

Antimicrobial resistance (AMR) is a global health challenge and antimicrobial use (AMU) in the livestock sector has been considered as one of the contributing factors towards the development of AMR in bacteria. This study summarizes the results of a point prevalence survey conducted to monitor farm-level AMU in commercial broiler chicken farms in Punjab and Khyber Pakhtunkhwa (KPK) provinces of Pakistan. A cross-sectional study was conducted to quantify AMU and to check seasonal variations of AMU in 12 commercial broiler chicken farms (six from each province) during the summer and winter seasons of the year 2020–2021. AMU was recorded using three AMU metrics: kg, mg per population correction unit (mg/PCU), and mg/kg of final flock weight. A total of 22 antimicrobial drugs (348.59 kg) were used for therapeutic or prophylactic purposes in surveyed broiler chicken farms. The total combined AMU for all the broiler chicken farms was 462.57 mg/PCU. The use of most of the antimicrobials increased during winter flocks compared to summer. The top three antimicrobial drugs used during the summer were neomycin (111.39 mg/PCU), doxycycline (91.91 mg/PCU), and tilmicosin (77.22 mg/PCU), whereas doxycycline (196.81 mg/PCU), neomycin (136.74 mg/PCU), and amoxicillin (115.04 mg/PCU) during the winter. Overall, 60% of the antibiotics used in broiler chicken were critically important antimicrobial classes (CIA) for human medicine as characterized by the World Health Organization. Our findings showed high AMU in broiler chicken production and a call for urgent actions to regulate CIA use in food animals in Pakistan. This baseline survey is critical for the design and implementation of a subsequent national level AMU surveys that can include additional farming types, animals’ species, and geographical locations over a longer period of time.

Alzheimer’s Disease (AD) and Dementia represent critical healthcare challenges worldwide, underscoring the need for reliable and early diagnostic tools. This study presents an innovative classification framework that combines the strengths of fuzzy logic and Recurrent Neural Networks (RNNs) to enhance the detection of AD and dementia. Leveraging Electroencephalogram (EEG) signals, a non-invasive and informative modality reflecting cognitive function, we propose a Fuzzy RNN model that integrates the sequential learning capabilities of RNNs with the interpretability and uncertainty handling of fuzzy logic. The model is trained on a comprehensive dataset comprising EEG recordings from individuals with Alzheimer’s Disease, various types of dementia, and healthy controls. Evaluation metrics, including sensitivity (96.49%–97.19%), specificity (96.72%–97.85%), and accuracy (97.82%–98.82%), highlight the model’s robustness in differentiating between diagnostic categories. Comparative analyses against traditional classifiers demonstrate the Fuzzy RNN’s superior ability to capture intricate temporal patterns in EEG signals, establishing its potential as a reliable tool for early and accurate dementia diagnosis.