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Background: Mild cognitive impairment (MCI) is becoming an emerging problem for developing countries where there is an increase in expected age. There is no specific curative therapeutic treatment available for these patients. Objective: The objective of this study was to evaluate short and long-term changes in the electroencephalogram (EEG) parameters and cognition of MCI patients with aerobic exercises. Methods: A randomized controlled trial was conducted on 40 patients which were randomly divided into two groups, "aerobic exercise treatment group (n=21)" and "no-aerobic control group (n=19)". Short-term effects of exercise were measured after single session of exercise and long-term effects were measured after an 18 sessions (6 weeks) treatment. The outcomes which were measured were, electroenphelogram paramaters (slowness and complexity of the EEG) and cognitive functions (using mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), and trail making test (TMT) A and B). Results: After one session of aerobic exercise there were significant improvements in slowness (delta waves; 0.678+0.035 vs 0.791+0.033; p=0.015) and complexity (0.601+0.051 vs 0.470+0.042; p=0.027) of the EEG in aerobic exercise treated group as compared to no-aerobic exercise group. After six weeks there were significant improvements in slowness (delta waves; 0.581+0.036 vs 0.815+0.025; p=0.005) and complexity (0.751+0.045 vs 0.533+0.046; p=0.001) of the EEG in the aerobic group as compared to no-aerobic group. Moreover, significant improvements were observed in the MMSE (p=0.032), MoCA (p=0.036), TMT-A (p=0.005) and TMT-B (p=0.007) in aerobic exercise group as compared to no-aerobic group. A c c e p t e d M a n u s c r i p t 3 Conclusion: Aerobic exercise showed improvement in cognition after short and long-term treatment in MCI subjects and can be used as potential therapeutic candidate.

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The intuitionistic hesitant fuzzy set (IHFS) is an enriched version of hesitant fuzzy sets (HFSs) that can cover both fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). By assigning membership and non-membership grades as subsets of [0, 1], the IHFS can model and handle situations more proficiently. Another related theory is the theory of set pair analysis (SPA), which considers both certainties and uncertainties as a cohesive system and represents them from three aspects: identity, discrepancy, and contrary. In this article, we explore the suitability of combining the IHFS and SPA theories in multi-attribute decision making (MADM) and present the hybrid model named intuitionistic hesitant fuzzy connection number set (IHCS). To facilitate the design of a novel MADM algorithm, we first develop several averaging and geometric aggregation operators on IHCS. Finally, we highlight the benefits of our proposed work, including a comparative examination of the recommended models with a few current models to demonstrate the practicality of an ideal decision in practice. Additionally, we provide a graphical interpretation of the devised attempt to exhibit the consistency and efficiency of our approach.

In today’s fast-paced and dynamic business environment, investment decision making is becoming increasingly complex due to the inherent uncertainty and ambiguity of the financial data. Traditional decision-making models that rely on crisp and precise data are no longer sufficient to address these challenges. Fuzzy logic-based models that can handle uncertain and imprecise data have become popular in recent years. However, they still face limitations when dealing with complex, multi-criteria decision-making problems. To overcome these limitations, in this paper, we propose a novel three-way group decision model that incorporates decision-theoretic rough sets and intuitionistic hesitant fuzzy sets to provide a more robust and accurate decision-making approach for selecting an investment policy. The decision-theoretic rough set theory is used to reduce the information redundancy and inconsistency in the group decision-making process. The intuitionistic hesitant fuzzy sets allow the decision makers to express their degrees of hesitancy in making a decision, which is not possible in traditional fuzzy sets. To combine the group opinions, we introduce novel aggregation operators under intuitionistic hesitant fuzzy sets (IHFSs), including the IHF Aczel-Alsina average IHFAAA operator, the IHF Aczel-Alsina weighted average IHFAAWAϣ operator, the IHF Aczel-Alsina ordered weighted average IHFAAOWAϣ operator, and the IHF Aczel-Alsina hybrid average IHFAAHAϣ operator. These operators have desirable properties such as idempotency, boundedness, and monotonicity, which are essential for a reliable decision-making process. A mathematical model is presented as a case study to evaluate the effectiveness of the proposed model in selecting an investment policy. The results show that the proposed model is effective and provides more accurate investment policy recommendations compared to existing methods. This research can help investors and financial analysts in making better decisions and achieving their investment goals.