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Abstract The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the Walrus Optimization Algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models.

This paper considers a Markovian retrial queueing system with an optional service, unreliable server, balking and feedback. An arriving customer can avail of immediate service if the server is free. If the potential customer encounters a busy server, it may either join the orbit or balk the system. The customers may retry their request for service from the orbit after a random amount of time. Each customer gets the First Essential Service (FES). After the completion of FES, the customers may seek the Second Optional Service (SOS) or leave the system. In the event of unforeseen circumstances, the server may encounter a breakdown, at which point an immediate repair process will be initiated. After the service completion, the customer may leave the system or re-join the orbit if not satisfied and demand regular service as feedback. In this investigation, the stationary queue size distributions are framed using a recursive approach. Various system performance measures are derived. The effects induced by the system parameters on the performance metrics are numerically and graphically analysed.

Transparent conducting metal oxides (TCOs) are well known for their application in optoelectronic devices. However, the nonlinear optical (NLO) study of TCOs helps to understand the characteristic features which give insight into their practical utilization as well as device application in each optical spectrum. In this work, we studied the structural, electrical, optical, and nonlinear optical properties of Sn-doped CdO. The structural, electrical, optical, and nonlinear optical properties of Sn-doped CdO were studied by using an X-ray diffractometer (XRD), Hall measurement system, UV–vis spectrometer, and Z-scan measurement. The XRD analysis shows that Sn-doped CdO is a polycrystalline material with a preferential peak of a plane (111) shifting slightly toward the (200) plane as Sn doping suddenly increases. Sn-doped CdO proved good electrical properties with conductivity increase from 0.192 × 102 Ω−1 cm−1 to 2.03 × 102 Ω−1 cm−1 and decrease in resistivity from 52.1 × 10−3 Ωcm to 4.92 × 10−3 Ωcm as the molar dopant concentration increase from 0.0001 M to 0.0005 M. The obtained bandgap for undoped and 0.0005 M, 0.0004 M, 0.0003 M, 0.0002 M, 0.0001 M Sn-doped CdO are 2.37, 1.63, 1.70, 1.81, 1.86, and 2.09 respectively. The NLO absorption coefficient (β) for 0.0003 M, 0.0004 M and 0.0005 M are 6.31 × 10−3 m/W, 11.47 × 10−3 m/W and 10.6 × 10−3 m/W respectively.

Plastics are a synthetic or semisynthetic material that is extensively used in our routine life because of its remarkable characteristics such as thermal, mechanical properties, durability, and stability, so they have widespread application. This massive use of plastics has caused various issues to human health and the environment. Nearly 700 species, including endangered species,have been affected by plastics. Enzyme - mediated and microbial degradation of plastic waste is a potential technique in which the process of biodeterioration, thermal decomposition, and integration actually took place and is ultimately transformed into gases and a microbial community, where the process leaves few residual effects. Recent studies have shown that bacteria and bacterial consortia, biofilm formation and fungi may biodegrade many polymers. Depending on the organisms, the molecular weight of the plastics, the kind of polymer, and the climate, waste plastic can be converted into methane, CO2, biomass, water and inorganic chemicals. Plastics induce oxidative stress, enhanced and inflammatory reactions, translocation or absorption, in people, animals and plants. Numerous studies have shown that people are susceptible to metabolic disorders, neurodegeneration, and malignancy.We have also talked about how we are working on using the metabolic processes of bacteria to minimize soil and water pollution by microplastics. More study is required to discover the crucial ecological characteristics and elements that affect plastic degradation in order to predict the fate of plastics in various contexts and to enable the development of solutions for minimising plastic waste.

Natural disasters are utmost incidents inside the earth''''s system that lead to sudden demise or bruise to humans, and destruction of precious materials, involving buildings, conveyance systems, farming land, forest and natural environment. Occurrences of economic losses due to natural disasters have resulted owing to the escalated susceptibility of the society globally and also due to weather-related disasters. Satellite image sensing remains the hypothetical instrument for disaster management as it provides information spanning wide-reaching areas and also at short time period. In this work we plan to develop a method called, Enhanced Frost Filter and Tanimoto Similarity Classification (EFF-TSC) for efficient disaster management using satellite images is proposed. The EFF-TSC method for disaster management is split into three steps. They are pre-processing, segmentation and classification. With the input image collected from satellite image database, first preprocessing is performed to preserve important features at the edges and remove the noisy pixel by means of an Enhanced Frost Filter Preprocessing model. Second, to the pre-processed satellite image, Threshold Pixel Segmentation is applied to partition into multiple segments. Finally, to the partitioned images, Tanimoto Similarity Classification is applied to classify the segmented image into two types, namely disastrous image and non-disastrous image. With this, an efficient disaster management is carried out with better accuracy and minimal time consumption. The application of the study is demonstrated using the Disaster image data set collected from Kaggle during the 2017. The results show the capability of the proposed EFFTSC method for disaster management across time and space from different images with considerable accuracy by also reducing peak signal to noise ratio with considerable time. The findings also suggest that the potential for forensic analysis of disasters using pixel segmentation and classification based on collected images can be utilized to several locations affected by disasters.

Undoped CdO films and three different molarities of cadmium oxide (CdO) films were doped with fluorine on glass slides via a chemical spray pyrolysis method. The samples were analyzed using X-ray diffractometry (XRD), scanning electron microscopy (SEM), UV–VIS-NIR spectroscopy, electrical measurements, and Z-scan measurements. XRD measurement revealed better crystallinity with preferred orientation at [111] plane. Nanospheres of varying sizes were obtained from the surface micrographs. The direct band gap energy decreased as the fluorine concentration increased. The film resistivity recorded a decline with increased carrier mobility as the flourine concentration increased. The nonlinear optical absorption coefficient (β) gotten for 0.0003 M, 0.0004 M, and 0.0005 M at 632.8 nm wavelength are 3.83×10–3 m/W, 5.7 × 10–3 m/W and 5.48×10–3 m/W respectively. The deposited films find potential application in solar cell and optoelectronic devices.

The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

“The golden highways of the technological route serve as the ultimate metaphor for the society of artificial intelligence,” explains the author of this present excursion. Unsupervised predictor is a technique that is used in all upbringings and algometry computations, and it is the foundation of all of them. The accuracy-based artificial intelligence and genetic algorithm-based prediction are becoming more important in business intelligence, with the crossover revealing the highest percentage of interest-based profit in the food industry. Our suggested approach aims to determine the utmost amplification of the food business profit with the help of food sale prediction. Hence, owners of outlet sale, supermarket sale, grocery store, and so on can maintain the stock. Therefore, our study strives to elucidate the relationship between food business intelligence and artificial intelligence in the context of soft computing. The study combines the previous data with the record of the current data and compares the existing development of business management in order to create the topic of the TOR method, which will be the artificial intelligence buzzword in the town-based food business intelligence. This paper uses artificial intelligence, business intelligence, and neural networks to forecast the food sale prediction by evaluating the data of each individual client. Consider the situation in which this technology is applied in every grocery store in the country. Our suggested model has achieved low mean squared error and low variance. Food mart owners will have a far better grasp of their clientele and will be able to categorise them in this situation, enabling them to proceed with future sales forecasting.

In the title compound, C22H17NO3S, the sulfonyl-bound phenyl ring is almost orthogonal to the indole ring system, making a dihedral angle of 84.89 (7)°. The carbonyl-bound phenyl ring forms a dihedral angle of 57.32 (5)° with the indole ring system. The two phenyl rings are inclined at 52.68 (7)°. The S atom has a distorted tetrahedral configuration. In the crystal, weak C—H...O interactions link the molecules, forming a helical chain along the b-axis direction.

In the title compound, C22H16BrNO3S, the phenyl rings make dihedral angles of 84.81 (16) and 61.67 (17)° with the indole ring system (r.m.s. deviation = 0.012 Å), while the phenyl rings are inclined to one another by 69.5 (2)°. The molecular structure is stabilized by weak intramolecular C—H...O hydrogen bonds. The sulfonyl S atom has a distorted tetrahedral configuration. In the crystal, there are no significant intermolecular interactions present.

In the title compound, C17H14BrNO3S, the phenyl ring makes a dihedral angle of 89.78 (16)° with the plane of the indole ring system. The terminal Br atom and the methyl group are disordered over two sets of sites, with site occupancies of 0.860 (2) and 0.140 (2). In the crystal, molecules are linked into a chain along the b-axis direction by weak C—H...O hydrogen bonds. The chains are further linked by C—H...π interactions, forming layers parallel to the bc plane.

In the title compound C24H17BrN2O5S, the phenyl ring makes dihedral angles of 85.4 (2) and 8.8 (2)° with the indole ring system and the nitrobenzene ring, respectively, while the indole ring system and nitrobenzene ring make a dihedral angle of 80.1 (2)°. In the crystal, weak C—H...O interactions link the molecules, forming a two-dimensional network parallel to the bc plane.

In the title compound, C25H20N2O5S, the phenyl ring makes dihedral angles of 89.88 (8) and 13.98 (8)°, respectively, with the indole ring system and the nitrobenzene ring. The dihedral angle between the indole ring system and the nitrobenzene ring is 88.48 (11)°. The molecular structure is stabilized by a weak intramolecular C—H...O interaction. In the crystal, π–π interactions, with centroid–centroid distances of 3.6741 (18) and 3.8873 (17) Å, link the molecules into layers parallel to the ab plane.

In the title compound, C18H16ClNO2S, the indole ring system forms a dihedral angle of 75.07 (8)° with the phenyl ring. The molecular structure is stabilized by a weak intramolecular C—H...O hydrogen bond. In the crystal, molecules are linked by weak C—H...O hydrogen bonds, forming a chain along [10-1]. C—H...π interactions are also observed, leading to a three-dimensional network.

In the title compound, C25H19FN2O5S, the substituted phenyl ring makes a dihedral angle of 12.26 (9)° with the indole ring system. The nitro group is twisted at an angle of 26.92 (8)° out of the plane of the ring to which it is attached. The molecular structure is stabilized by weak C—H...O hydrogen bonds. In the crystal, weak C—H...O, C—H...F and π–π [centroid–centroid distance = 3.6645 (11) Å] interactions link the molecules, forming a three-dimensional network.

In the title compound, C18H16BrNO3S, the dihedral angle between the phenyl ring and the indole ring system is 89.91 (11)°. The molecular structure features weak C—H...O and C—H...Br hydrogen bonds. In the crystal, molecules are linked by weak C—H...O hydrogen bonds, forming chains along the a-axis direction. The chains are further linked by C—H...π interactions, forming a layer parallel to the ab plane.

In the title compound, C25H19ClN2O5S, the phenyl ring forms dihedral angles of 79.62 (12) and 80.02 (13)° with the indole ring system and the benzene ring, respectively. The nitro group is twisted at an angle of 22.39 (11)° with respect to the attached benzene ring. In the crystal, molecules assemble into double layers in the ab plane via C—H...O interactions.

In the title compound, C17H15NO4S, the six-membered ring of the indole unit makes a dihedral angle of 72.40 (5)° with the phenyl ring. The molecular structure features a short C—H...O contact.

In the title compound, C16H13NO3S, the sulfonyl-bound phenyl ring forms a dihedral angle of 84.17 (6)° with the indole ring system. An intramolecular C—H...O hydrogen bond generates an S(6) ring motif. The crystal structure exhibits weak intermolecular C—H...O hydrogen bonds and π–π interactions between the five- and six-membered rings of the indole group [centroid–centroid distance = 3.6871 (9) Å].

In the title compound, C22H18N2O4S, the dihedral angle between the phenylsulfonyl ring and the methylphenyl ring is 67.78 (7)°. In the crystal, molecules are linked by weak intermolecular C—H...O interactions into a zigzag chain along the [101] direction.

In the title compound, C12H14O4, the two acetoxy groups are inclined by 57.92 (5)° and 62.71 (6)° to the benzene ring. An intermolecular C—H...O interaction involving the two acetoxy groups generates a centrosymmetric dimer via an R22(16) ring motif.

In the title compound, C26H23NO4S, the phenyl, tolyl and ester groups make dihedral angles of 82.28 (5), 77.67 (6) and 8.52 (6)°, respectively, with the indole ring system. The S atom of the sulfonyl group is displaced by 0.1968 (4) Å from the indole mean plane. The molecular structure is stabilized by weak intramolecular C—H...O interactions. The crystal structure structure features short intramolecular C—H...O contacts and π–π stacking interactions between the phenyl and tolyl groups [centroid–centroid distance = 3.9448 (11) Å].

Background: Severe acute respiratory virus coronavirus-2 disease (COVID-19) vaccination among antenatal mothers plays a vital role in preventing maternal and neonatal morbidity and mortality. We attempted to find out the COVID-19 vaccine uptake rate and to determine the factors associated with COVID-19 vaccine uptake. Methods: Community-based analytical cross-sectional study was conducted for 6 months amongst antenatal mothers who are residing in the field practice area of primary healthcare (PHC). The participants were selected based on simple random sampling. Face-to-face interviews were conducted in their house using a pre-designed structured questionnaire after obtaining consent. Results: Amongst 246 antenatal mothers, 82.5% were in the 20-29 years of age group. Only 17.9% of the mothers were illiterates, 88.2% of the participants were homemakers. Majority (97%) belonged to the Hindu religion and more than half of them lived in nuclear families. One hundred and fifty (61%) had taken at least one dose of the vaccine. The logistic regression model was statistically significant (P < 0.05) and had explained 14.2% of the variance in acceptance of the COVID-19 vaccine and correctly classified 67.9% of cases. Fear concerning the safety of the vaccine and its side effects was the most commonly reported reason for not taking the vaccine. Conclusion: Targeted interventions to maximise COVID-19 vaccine uptake among pregnant women from ethnic minority communities and those living in deprived areas should be explored. [ABSTRACT FROM AUTHOR]