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Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

This paper provides insights from initiatives to include transgender people in sanitation programming in South Asia. Three case studies of recent actions to make sanitation inclusive for transgender people (in India and Nepal) are presented, accompanied by reflections and recommendations to guide future practice. Practitioners are recommended to: engage with transgender people as partners at all stages of an initiative; recognize that the language of gender identity is not fixed, varying across cultures and between generations; and acknowledge that transgender people are not a single homogeneous group but rather have diverse identities, histories, and priorities. The case studies aim to raise awareness of the diversity of transgender identities, exploring the needs and aspirations of transgender women, transgender men, and third gender people in South Asia.

Background Little is known about parental coronavirus disease 2019 (COVID-19) vaccine hesitancy in children with neurodevelopmental disorders (NDD). This survey estimated the prevalence and predictive factors of vaccine hesitancy among parents of children with NDD. Methods A nationally representative cross-sectional survey was conducted from October 10 to 31, 2021. A structured vaccine hesitancy questionnaire was used to collect data from parents aged ≥ 18 years with children with NDD. In addition, individual face-to-face interviews were conducted at randomly selected places throughout Bangladesh. Multiple logistic regression analysis was conducted to identify the predictors of vaccine hesitancy. Results A total of 396 parents participated in the study. Of these, 169 (42.7%) parents were hesitant to vaccinate their children. Higher odds of vaccine hesitancy were found among parents who lived in the northern zone (AOR = 17.15, 95% CI = 5.86–50.09; p  < 0.001), those who thought vaccines would not be safe and effective for Bangladeshi children (AOR = 3.22, 95% CI = 1.68–15.19; p  < 0.001), those who were either not vaccinated or did not receive the COVID-19 vaccine themselves (AOR = 12.14, 95% CI = 8.48–17.36; p  < 0.001), those who said that they or their family members had not tested positive for COVID-19 (AOR = 2.13, 95% CI = 1.07–4.25), and those who did not lose a family member to COVID-19 (AOR = 2.12, 95% CI = 1.03–4.61; p  = 0.040). Furthermore, parents who were not likely to believe that their children or a family member could be infected with COVID-19 the following year (AOR = 4.99, 95% CI = 1.81–13.77; p  < 0.001) and who were not concerned at all about their children or a family member being infected the following year (AOR = 2.34, 95% CI = 1.65–8.37; p  = 0.043) had significantly higher odds of COVID-19 vaccine hesitancy. Conclusions Given the high prevalence of vaccine hesitancy, policymakers, public health practitioners, and pediatricians can implement and support strategies to ensure that children with NDD and their caregivers and family members receive the COVID-19 vaccine to fight pandemic induced hazards.

This study proposes novel operational laws that extend the Frank t-norm and t-conorm to develop a new class of aggregation operators (AOs), namely the quasirung orthopair fuzzy Frank weighted average, weighted geometric, ordered weighted average, and ordered weighted geometric operators. These operators are specifically designed to manage uncertain and imprecise information within multi-attribute group decision-making (MGADM) environments. The proposed operators exhibit desirable mathematical properties such as flexibility, robustness, and compatibility, making them highly suitable for complex fuzzy decision contexts. Flexibility is notably enhanced through the independent tuning of the parameters , , and , allowing for more refined control over membership (MD), non-membership (NMD), and interaction behaviors. An entropy-based approach is employed to objectively determine unknown attribute weights, minimizing subjective bias. A real-world case study on the selection of an optimal investment location demonstrates the practical applicability of the proposed method. The results show an improvement in decision-making accuracy by approximately 7.5% compared to traditional approaches. Sensitivity analysis confirms the stability and reliability of the proposed operators under varying conditions. Comparative results further highlight the method’s superiority in terms of accuracy, interpretability, and adaptability to input variations. The paper concludes by outlining special cases and acknowledging certain limitations, offering directions for future research.

Purpose To test the hypothesis that the use of a harmonic scalpel increases operative time but results in less estimated blood loss, postoperative pain, drainage volume, and duration of surgery, as well as fewer complications, such as flap necrosis, seroma, and surgical site infection (SSI), than electrocautery. Methods This parallel-group, single-institution blinded randomized controlled trial was conducted at the department of surgery of our institute between April 2010 and July 2011. Women undergoing modified radical mastectomy were randomly allocated to either harmonic dissection ( n  = 76) or electrocautery ( n  = 76). Results Both the groups were comparable for baseline variables with age of 50.5 ± 12.2 and 48.5 ± 14.5 years in the harmonic and electrocautery groups, respectively. Harmonic dissection yielded better outcomes compared to electrocautery with lower estimated blood loss (100 ± 62 vs. 182 ± 92, p  < 0.001), less drain volume (631 ± 275 ml vs. 1035 ± 413 ml, p  < 0.001), fewer drain days (12 ± 3 vs. 17 ± 4, p  < 0.001), less seroma formation (21.3 vs. 33.3 %, p  = 0.071), and less postoperative pain [median (interquartile range) 2 (2–2) vs. 3 (3–4), p  < 0.001], whereas mean operative time (191 ± 44 vs. 187 ± 36 min, p  = 0.49) and SSI (0 vs. 4 %, p  = 0.122) did not differ. On multivariable Cox regression analysis, harmonic dissection was associated with lower risk of significant postoperative pain [adjusted relative risk 0.028 (95 % confidence interval (CI) 0.004–0.2)] and overall complications [adjusted relative risk 0.47, (95 % CI 0.26–0.86)]. On multiple linear regression, duration of drains in the harmonic dissection group was 4.5 days less than electrocautery ( r 2  = 0.28, β  = 11.8, p  < 0.001). Conclusions The harmonic scalpel significantly reduces postoperative discomfort and morbidity to the patient without increasing operating time. We thus recommend preferential use of harmonic dissection in modified radical mastectomy. (ClinicalTrials.gov NCT01587248).

The study of similarity and distance measures plays a key role in understanding the relationships between fuzzy sets and their extensions, especially when applied to decision-making problems. While there has been notable progress in developing similarity measures for various types of generalized fuzzy sets, including fractional fuzzy sets, there is still a lack of well-developed measures suited to the structure of fractional fuzzy sets. This limitation reduces the effectiveness of fuzzy models in complex decision-making tasks where uncertainty needs to be handled more carefully and flexibly. To overcome this issue, we introduce new similarity measures that use three independent fractional exponents , , and corresponding to the membership, neutral, and non-membership degrees. This approach offers greater flexibility and a more detailed way of capturing the relationships between fuzzy values. We also apply these similarity measures within a decision-making model designed to assess alternatives in uncertain environments. The proposed method is tested through a multi-criteria decision-making case study. The results highlight that regulatory and policy barriers ( ​) are the most influential factor, with a final score of , showing the method’s usefulness in real-world settings. Compared to other approaches, our framework adapts better to changes in uncertainty, responds more accurately to variations in input values, and offers clearer, more interpretable results.

This paper presents a new methodology for solving multiple-attribute decision-making problems (MADMs) using a complex Pythagorean normal interval-valued fuzzy set (CPNIVFS), which is an extended concept of a complex Pythagorean fuzzy set. Four types of different aggregating operations (AOs), including CPNIVF weighted averaging (CPNIVFWA), CPNIVF weighted geometric (CPNIVFWG), generalized CPNIVFWA (CGPNIVFWA), and generalized CPNIVFWG (CGPNIVFWG), are discussed. The scoring function, accuracy function, and operational laws of the CPNIVFS are defined. Algebraic structures, such as associative, distributive, idempotent, bounded, commutativity, and monotonicity properties, are also shown to be satisfied by complex Pythagorean normal interval-valued fuzzy numbers. Furthermore, an algorithm is proposed to solve the MADM problems based on the defined AOs. The proposed approach is then used for a medical diagnosis problem about brain tumors because computer science and machine tool technology are among the most important components of brain tumor research. The five types of brain tumors diagnosed in these patients are gliomas, meningiomas, metastases, embryonal tumors, and ependymomas. Several types of treatments are available, which are often combined as part of an overall treatment plan. Brain tumors can be treated in various ways, including surgery, radiation therapy, chemotherapy, immunotherapy, and clinical trials. Based on the comparisons and options gathered, the most suitable treatment can be chosen. In this regard, it is evident that the value of the integer ⅁ plays a significant role in determining the model. The candidate models under consideration can be validated by comparing them with the previously proposed ones. The proposed technique is compared with the existing method to demonstrate its superiority and validity, and the results conclude that the former is more reliable and effective than the latter. Finally, the criteria are evaluated by expert assessments to determine the most appropriate options.

One of the most important innovations brought by digitization is the cryptocurrency, also called virtual or digital currency, which has been discussed in recent years and in particular is a new platform for investors. Different types of cryptocurrencies such as Bitcoin, Ethereum, Binance Coin, and Tether do not depend on a central authority. Decision making is complicated by categorization and transmission of uncertainty, as well as verification of digital currency. The weighted average and weighted geometric aggregation operators are used in this article to define a multi-attribute decision-making approach. This work investigates the uniqueness of q-rung orthopair fuzzy hypersoft sets (q-ROFHSS), which respond to instabilities, uncertainty, ambiguity, and imprecise information. This research also covers some fundamental topics of q-ROFHSS. The model offered here is the best option for learning about electronic currency. This study validates the complexity of decision-making problems with different attributes and subattributes to obtain an optimal choice. We conclude that Bitcoin has a diverse set of applications and that crypto assets are well positioned to become an important asset class in decision making.

Green Supply Chain Management (GSCM) is essential to ensure environmental compliance and commercial growth in the current climate. Businesses constantly look for fresh concepts and techniques for ensuring environmental sustainability. To keep up with the new trends in environmental concerns related to company management and procedures, Green Supplier Selection (GSS) criteria are added to the traditional supplier selection processes. This study aims to identify general and environmental supplier selection criteria to provide a framework that can assist decision-makers in choosing and prioritizing appropriate green supplier selection. The development and implementation of decision support systems aimed to solve these difficulties at a rapid rate. In order to manage inaccurate data and simulate decision-making problems. Fuzzy sets introduced by Zadeh, are a useful technique to handle the imperfectness and uncertainty in different problems. Although fuzzy sets can handle incomplete information in different real worlds problems, but its cannot handle all type of uncertainty such as incomplete and indeterminate data. Therefore different extensions of fuzzy sets such as intuitionistic fuzzy, pythagorean fuzzy and q-rung orthopair fuzzy sets introduced to address the problems of uncertainty by considering the membership and non-membership grade. However, these concepts have some shortcomings in the handling uncertainty with sub-attributes. To overcome this difficulties Khan et al. developed the structure of q-rung orthopair fuzzy hypersoft sets by combining q-rung orthopair fuzzy sets with hypersoft sets. A remarkable and beneficial research work is done in the field of q-rung orthopair fuzzy hypersoft sets, and then we think about the application. In this paper, we use the structure of q-rung orthopair fuzzy hypersoft in multi-criteria supplier selection problems. For this, we present aggregation operator to solve multi-criteria decision-making (MCDM) problems with q-rung orthopair fuzzy hypersoft (q-ROFH) information, known as ordered weighted geometric aggregation operator. Since the uncertainty and vagueness is an unavoidable feature of multi-criteria decision-making problems, the proposed structure can be a useful tool for decision making in an uncertain environment. Further, the expert opinions were investigated using the multi-criteria decision-making (MCDM) technique, which helped identify interrelationship and causal preference of green supplier evaluation aspects that used aggregation operators. Finally, a numerical example of the proposed method for the task of Green Supplier Selection is presented.