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This paper aims to introduce the novel concept of the bipolar picture fuzzy set (BPFS) as a hybrid structure of bipolar fuzzy set (BFS) and picture fuzzy set (PFS). BPFS is a new kind of fuzzy sets to deal with bipolarity (both positive and negative aspects) to each membership degree (belonging-ness), neutral membership (not decided), and non-membership degree (refusal). In this article, some basic properties of bipolar picture fuzzy sets (BPFSs) and their fundamental operations are introduced. The score function, accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy numbers (BPFNs). Additionally, the concept of new distance measures of BPFSs is presented to discuss geometrical properties of BPFSs. In the context of BPFSs, certain aggregation operators (AOs) named as \"bipolar picture fuzzy weighted geometric (BPFWG) operator, bipolar picture fuzzy ordered weighted geometric (BPFOWG) operator and bipolar picture fuzzy hybrid geometric (BPFHG) operator\" are defined for information aggregation of BPFNs. Based on the proposed AOs, a new multicriteria decision-making (MCDM) approach is proposed to address uncertain real-life situations. Finally, a practical application of proposed methodology is also illustrated to discuss its feasibility and applicability.

A q-rung orthopair fuzzy set (q-ROFS), an extension of the Pythagorean fuzzy set (PFS) and intuitionistic fuzzy set (IFS), is very helpful in representing vague information that occurs in real-world circumstances. The intention of this article is to introduce several aggregation operators in the framework of q-rung orthopair fuzzy numbers (q-ROFNs). The key feature of q-ROFNs is to deal with the situation when the sum of the qth powers of membership and non-membership grades of each alternative in the universe is less than one. The Einstein operators with their operational laws have excellent flexibility. Due to the flexible nature of these Einstein operational laws, we introduce the q-rung orthopair fuzzy Einstein weighted averaging (q-ROFEWA) operator, q-rung orthopair fuzzy Einstein ordered weighted averaging (q-ROFEOWA) operator, q-rung orthopair fuzzy Einstein weighted geometric (q-ROFEWG) operator, and q-rung orthopair fuzzy Einstein ordered weighted geometric (q-ROFEOWG) operator. We discuss certain properties of these operators, inclusive of their ability that the aggregated value of a set of q-ROFNs is a unique q-ROFN. By utilizing the proposed Einstein operators, this article describes a robust multi-criteria decision making (MCDM) technique for solving real-world problems. Finally, a numerical example related to integrated energy modeling and sustainable energy planning is presented to justify the validity and feasibility of the proposed technique.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Malaria is caused by Plasmodium parasite. It is transmitted by female Anopheles bite. Thick and thin blood smears of the patient are manually examined by an expert pathologist with the help of a microscope to diagnose the disease. Such expert pathologists may not be available in many parts of the world due to poor health facilities. Moreover, manual inspection requires full concentration of the pathologist and it is a tedious and time consuming way to detect the malaria. Therefore, development of automated systems is momentous for a quick and reliable detection of malaria. It can reduce the false negative rate and it can help in detecting the disease at early stages where it can be cured effectively. In this paper, we present a computer aided design to automatically detect malarial parasite from microscopic blood images. The proposed method uses bilateral filtering to remove the noise and enhance the image quality. Adaptive thresholding and morphological image processing algorithms are used to detect the malaria parasites inside individual cell. To measure the efficiency of the proposed algorithm, we have tested our method on a NIH Malaria dataset and also compared the results with existing similar methods. Our method achieved the detection accuracy of more than 91% outperforming the competing methods. The results show that the proposed algorithm is reliable and can be of great assistance to the pathologists and hematologists for accurate malaria parasite detection.

Extrusion-based additive manufacturing (EAM) is a relatively new process developed for the production of complex metallic and ceramic parts needed in smaller quantities. The debinding and sintering step of EAM is adopted from a well-known powder injection molding process. However, the 3D printing step needs special consideration to make EAM competent in the era of rapid manufacturing. This study is intended to investigate the effect of common printing parameters on the microstructure and mechanical properties of sintered stainless steel 316L (SS316L) parts manufactured through the EAM process. Part orientation (Ori), extrusion velocity ( V e ), and layer height ( h ) were changed in experimental runs by following a full factorial design. Extrusion pressure as an indicator of melt stability and a grey relational grade as a combined response of sintered properties were analyzed against varying printing parameters. Physical characteristics measured during debinding and sintering show near isotopic shrinkage and the process is stable. Metallographic characterization in terms of porosity and grain size indicated minor differences when V e and h were altered. Sintered parts showed improved properties when printed with vertical part orientation and h = 0.5 mm, whereas V e which contributes significantly to the build-up rate was found to be responsible for melt stability. V e at 12.5 mm/s exhibited melt stability and higher sintered properties.

 Abstract Introduction : Varicocele, characterized by the enlargement of scrotal veins, is a common contributor to male infertility, but its genetic underpinnings remain largely unknown. Aim : The goal of this study is to identify potential biomarkers associated with varicocele in order to better understand its molecular mechanisms. Materials and methods : Using the three primary databases, NCBI, DisGeNET, and OpenTarget, we analyzed gene variants and found 79 pertinent genes associated with varicocele. Protein-protein interaction analysis was performed using STRING and visualized with Cytoscape. Molecular Complex Detection (MCODE) and CytoHubba tools helped identify significant protein clusters. Results : The gene ontology analysis shows that there are 79 proteins involved in the inflammatory process, the regulation of gene expression, and cellular components that play a role in oxidative stress and angiogenesis. Our results revealed three key biomarkers: Interleukin-1 beta (IL1B), B-cell lymphoma 2 (BCL2), and matrix metalloproteinase-9 (MMP-9). These proteins are involved in critical processes, such as inflammation, oxidative stress, angiogenesis, and vascular damage, that are central to the pathophysiology of varicocele. Conclusion : The identification of IL1B, BCL2, and MMP-9 offers new insights into varicocele’s molecular mechanisms and suggests potential targets for diagnostic and therapeutic strategies, advancing personalized treatment approaches for fertility restoration.

The production of rapid tools for plastic molding, sheet metal forming, and blanking has always been a critical and important goal for applied research, and a very large number of alternative methods have been proposed over the decades for their production. Among these methods, the use of extrusion-based additive manufacturing (EAM), such as fused filament fabrication (FFF) or similar technologies, has not been frequently considered and needs to be explored extensively. EAM is generally considered a low-cost, low-quality, low-performance class of AM and not suited to produce real functional parts, but only for aesthetical prototypes. However, the capabilities of EAM technologies have greatly evolved and now it is possible to extrude a wide range of materials such as polymeric materials including both the low strength polymeric materials (such as nylon or PLA) and the high strength polymeric materials (such as PEI and PEEK), metals (such as tool steel), and even ceramics (such as zirconia). Starting from an extensive literature review, the purpose of the present paper is to further demonstrate the potential applicability and versatility of EAM as a rapid tool manufacturing technology for different applications in shearing, bending, deep drawing, and injection molding.

This study developed and evaluated plant-based mayonnaise formulations in which egg yolk was replaced with aquafaba (15–25%) and stabilized with gum tragacanth (0.3–1.0%). Formulations were prepared using canola oil and stored at 4 °C for 28 days. Aquafaba extract was characterized for total phenolic content (TPC) and total flavonoid content (TFC), while mayonnaise samples were assessed for physicochemical composition, creaming index, antioxidant activity, viscosity, texture, sensory properties, and microbiological stability. Total phenolic content (TPC) rose from 17.52 mg GAE/g at 10 µg to 135.34 mg GAE/g at 100 µg (p < 0.05), while total flavonoid content (TFC) increased from 76.95 to 192.42 mg TE/g over the same concentration range. These increases demonstrate the high antioxidant potential of aquafaba extract. The 25% aquafaba + 1% gum tragacanth formulation (T3) showed the highest protein content, viscosity, firmness, and antioxidant capacity, with improved storage stability compared to the control. FTIR analysis identified functional groups such as phenols, esters, and carboxylic acids, suggesting contributions to antioxidant activity and emulsion stability. Sensory evaluation indicated strong acceptance for T3. These results demonstrate that aquafaba combined with gum tragacanth can effectively replace egg yolk while maintaining desirable quality attributes.

The linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM method based on proposed novel aggregation operators (AOs) under linear Diophantine fuzzy set (LDFS). A q-Rung orthopair fuzzy set (q-ROFS), Pythagorean fuzzy set (PFS), and intuitionistic fuzzy set (IFS) are rudimentary concepts in computational intelligence, which have diverse applications in modeling uncertainty and MCDM. Unfortunately, these theories have their own limitations related to the membership and nonmembership grades. The linear Diophantine fuzzy set (LDFS) is a new approach towards uncertainty which has the ability to relax the strict constraints of IFS, PFS, and q–ROFS by considering reference/control parameters. LDFS provides an appropriate way to the decision experts (DEs) in order to deal with vague and uncertain information in a comprehensive way. Under these environments, we introduce several AOs named as linear Diophantine fuzzy Einstein weighted averaging (LDFEWA) operator, linear Diophantine fuzzy Einstein ordered weighted averaging (LDFEOWA) operator, linear Diophantine fuzzy Einstein weighted geometric (LDFEWG) operator, and linear Diophantine fuzzy Einstein ordered weighted geometric (LDFEOWG) operator. We investigate certain characteristics and operational laws with some illustrations. Ultimately, an innovative approach for MCDM under the linear Diophantine fuzzy information is examined by implementing suggested aggregation operators. A useful example related to a country’s national health administration (NHA) to create a fully developed postacute care (PAC) model network for the health recovery of patients suffering from cerebrovascular diseases (CVDs) is exhibited to specify the practicability and efficacy of the intended approach.

Propranolol hydrochloride is a beta-blocker used for the management and treatment of hypertension, angina, coronary artery disease, heart failure, fibrillation, tremors, migraine etc. The objective of the present study was to design Propranolol Hydrochloride floating tablets by direct compression method and to explore the role of a new gum as a matrix former. A 2 2 full factorial design was selected for the present study. Prunus domestica gum and HPMC (K4M) were used as independent variables, swelling index and drug dissolution at 12 hours as dependent variables. Formulations were subjected to pre- and post-compression tests that showed good micromeritics and buoyancy characteristics (Carr’s index 11.76%–14.00%, Hausner’s ratio 1.13°–1.16°, angle of repose 22.67°–25.21°, floating lag time 56–76 seconds, total floating time 18–25 hours and swelling index 59.87%–139.66%). The cumulative drug release in 0.1 N HCl at 12 hours was 72%–90% (p<0.05). Weibull model was found to be the best fit model (R 2 >0.99) among all other studied models. Multiple regression showed a significant effect of Prunus domestica gum and HPMC K4M on the swelling index and dissolution profiles of propranolol HCl (p<0.05). On the basis of better in-vitro performance and cost-effectiveness, formulation F4 was the best formulation. It is evident from the results that Prunus domestica gum possesses excellent drug release retardant potential for the floating drug delivery system and this new gum should be further explored alone or with other natural and synthetic polymers in future studies.