Search

Abstract Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

This paper presents a complete investigation into the phenomenon of dual convection flow within a bulging enclosure along with non-Newtonian power-law fluids and employs the Galerkin finite element method for simulation. The main objective is to study the heat and mass transfer and entropy generation within the enclosure. Various factors, such as the Rayleigh number, power law coefficient, Lewis number, and effects of magnetic inclination are evaluated for their influence on flow dynamics and heat distribution. The study also investigates the relationship between these parameters and entropy generation, providing insights into the irreversible processes within the system. A comparative analysis of averaged Nusselt and Sherwood coefficients reveals distinct thermal and fluidic behaviours across varying power-law indices, emphasizing differences between shear thickening, shear thinning, and Newtonian fluid behaviours. The findings provide valuable insights into non-Newtonian power-law fluids in complex flows, enhancing our understanding of flow and heat transfer in bulging enclosures.

Abstract Automatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.

Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical datasets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data augmentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCResNet for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment.

This research work proposed an economic and eco-efficient idea to supplement the ductility and durability of concrete by the simultaneous incorporation of several processed waste materials i.e., ground blast furnace slag (GBFS), recycled coarse aggregate (RCA), and coconut fibre (CF). Two concrete families were produced containing 0% and 100% coarse RCA. GBFS was incorporated as by 25% replacement for cement. CF was used as a fibre reinforcement at 0.25% volume fraction. The results revealed that the recycled-aggregate concrete (RAC) modified with GBFS, CF, and modified plasticizer dosage can attain similar or higher mechanical performance than unmodified natural-aggregate concrete (NAC). At a later age, the strength gain in RAC mixes due to 25% GBFS addition was found to be notably greater than that observed in NAC mixes. At 91 days, RAC containing 0.25% CF and 25% GBFS correspondingly showed 30.5% and 33% more flexural strength and splitting tensile strength, as compared to conventional plain NAC. GBFS inclusion can effectively eliminate the corrosion risk of medium-strength NAC and RAC and control the negative effects of CF incorporation on the durability of concrete. RAC with 0.25% CF and 25% GBFS yielded 1.5% and 16% lower water absorption than plain NAC, at 28 and 91 days, respectively. The modification of SP dosage can substantially enhance the strength and permeability-resistance of CF-reinforced RAC.

Abstract Cancer is one of the leading causes of death worldwide. The increasing prevalence and resistance to chemotherapy is responsible for driving the search of novel molecules to combat this disease. In search of novel compounds with pro-apoptotic potential, pyrazolo-pyridine and pyrazolo-naphthyridine derivatives were investigated against cervical cancer (HeLa) and breast cancer (MCF-7) cells. The anti-proliferative activity was determined through the MTT assay. Potent compounds were then analyzed for their cytotoxic and apoptotic activity through a lactate dehydrogenase assay and fluorescence microscopy after propidium iodide and DAPI staining. Flow cytometry was used to determine cell cycle arrest in treated cells and pro-apoptotic effect was verified through measurement of mitochondrial membrane potential and activation of caspases. Compounds 5j and 5k were found to be most active against HeLa and MCF-7 cells, respectively. G0/G1 cell cycle arrest was observed in treated cancer cells. Morphological features of apoptosis were also confirmed, and an increased oxidative stress indicated the involvement of reactive oxygen species in apoptosis. The compound-DNA interaction studies demonstrated an intercalative mode of binding and the comet assay confirmed the DNA damaging effects. Finally, potent compounds demonstrated a decrease in mitochondrial membrane potential and increased levels of activated caspase-9 and -3/7 confirmed the induction of apoptosis in treated HeLa and MCF-7 cells. The present work concludes that the active compounds 5j and 5k may be used as lead candidates for the development of lead drug molecules against cervical and breast cancer.

Various supervised machine-learning algorithms for wind power forecasting have been developed in recent years to manage wind power fluctuations and effectively correlate to energy consumption; Meanwhile, the performance of the model does suffer from missing values. To address the issue of missing values in wind power forecast, this paper proposes two methods: Clue-based missing at random (CMAR) and patterned k-nearest Neighbor (PkNN). In addition, a hybrid wind energy forecasting system has been created that is built on 1D-Convolutional neural networks, which are used to extract features from raw input, and Long Short-Term Memory, which employs time series data internal representation learning to improve the accuracy of month-wise wind power forecasting. The efficacy of the proposed model on generated datasets is also compared to the classic machine learning model to check the generalization ability. The strength of the Convolutional LSTM has been estimated in terms of different performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), R2, and explained variance score (EVS). The experimental results show that the use of the PkNN algorithm for data imputation; integrated with regression-based Convolutional LSTM is much more efficient in prediction over other deep neural network models.

In an asymmetric information environment, investors diversify their investments to minimize risk and maximize their wealth. Such diversification ranges from one market to another market and from one country to another country. Investors prefer foreign portfolio investment over foreign direct investment because of the economy's turmoil, changes in macroeconomic indicators, and market liberation. This study analyzes the dynamic relationship among stock market volatility, foreign portfolio investment, and macroeconomic indicators (foreign exchange rate, interest rate, and Gross Domestic Product) using the dynamic long-run Auto-regressive Distributed Lag (ARDL) model concerning the Pakistan environment. This study also considers the impact of multiple structural breaks, representing variables' endogenous and exogenous shocks. The secondary data is used from Oct. 01, 2009, to Sept. 30, 2019, with monthly frequency. The results indicate a co-integration between SMV, FPI, FXR, IR, and GDP. In short-run analyses, the error correction term is statistically significant, while in the long run, the SMV, FPI, and FXR are not impacted. As no evidence of volatility has been found between SMV and FPI, unidirectional or bi-directional policies can be devised to further attract the new FPI for strengthening the foreign reserves, the balance of payments, and other macroeconomic variables. Additionally, investors should update their knowledge based on considering the endogenous and exogenous shocks on the SMV.

The effort of copper nanoparticle exploration for antibacterial cleaning agents needs to prevent infection or contamination of microorganisms such as bacteria and viruses due to its potential characteristic as an environmentally benign material. The synthesis of copper nanoparticles using 0.02 M Cu(NO3)2 as a precursor and red dragon fruit extract as a bioreduction agent has been conducted. The extraction process of red dragon fruit was done by the maceration method using methanol as solvent. Copper nanoparticles were prepared through green chemistry with the reduction method. The compound in red dragon fruit extract reduced Cu2+ to Cu. The experimental nanoparticle synthesis method was performed with a combination of red dragon fruit extract and Cu(NO3)2 solution mixed with a volume ratio of bioreductor extract toward Cu2+ precursor variation of 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, and 1:9. The mixtures were stirred using a magnetic stirrer for 30 minutes and then incubated. The samples were characterized using UV-Vis (Ultraviolet-Visible), FTIR (Fourier Transform Infrared), and a digital microscope to observe morphology. The observation continued to assess its antibacterial activity and potency as an active disinfectant. UV-Vis analysis showed that the absorbance value for 30 minutes tended to be stable. Particle size estimation showed the smallest size of 122.12 nm at a volume ratio of 1:9. Functional group analysis using FTIR showed a shift in wavenumber between dragon fruit extract and copper nanoparticle (CuNPs), indicating a functional group interaction. The results of morphological analysis using a digital microscope showed that the particles with fine powder granules were produced with the composition of fruit extract: Cu(NO3)2 of 1:9. Antibacterial activity against Staphylococcus aureus and Escherichia coli indicated that the composition of Cu-nanoparticles of 1:9 had given an inhibition value.

Rapid spectrum usage in wireless networks may reduce energy efficiency, requiring cognitive radio networks to be more efficient than conventional ones. Due to increased data transmission demand, cognitive radio networks arose from a lack of spectrum bandwidth. Spectrum sensing and handoff decision are two cognitive radio network strategies that help avoid interference, channel access, and cohabitation between primary and secondary users. Current research focuses on handoff decision and cooperative spectrum sensing to improve sensing efficiency and system throughput while neglecting energy efficiency and handoff latency. Spectrum mobility and sensing are essential for energy-efficient cognitive radio networks. This study provides a second priority user transmission system using cooperative spectrum sensing to sense available channels. An energy detection technique optimizes the sensing process's energy usage, leading to energy efficiency. A primary user traffic pattern-based threshold approach is presented for spectrum mobility management. A threshold approach is utilized to calculate probabilistic stay-and-wait and QoS handoff values. The transmission channel is selected using a hybrid handoff strategy based on dynamic spectrum aggregation. Moreover, a cooperative spectrum sensing algorithm is described and simulated to identify the optimum channel with the greatest throughput and minimum energy consumption. The proposed approach increases energy efficiency and throughput while maintaining handoff delay and avoids miss-detection and false alarm. This technique improves energy efficiency, sensing performance, throughput, and handoff time. miss-detection.

The main purpose of this study is to investigate the thermal behavior of power law fluid within a plus-shaped cavity under the influence of natural convection, also taking into account the Darcy number and magnetohydrodynamics (MHD). The problem is formulated as a system of partial differential equations considering the power law fluid’s rheological behavior. The left-side walls are maintained at a specific low temperature while the lower and the right-side walls have uniform maximum temperatures. The boundary condition is designed to enhance heat transfer efficiency within the cavity, utilizing advanced thermal insulation methodologies. Finite element method (FEM) simulations are conducted, and a grid independence test is performed to validate the results. The impact of relevant parameters on the variation in momentum and thermal distributions is investigated using streamline and isothermal contour plots. The results indicate that as the Rayleigh number increases, the kinetic energy also increases, whereas the viscosity and circulation zones expand with an increase in the power law index. The Nusselt number exhibits a higher value in the shear-thinning case (n = 0.7) compared to the Newtonian (n = 1) and shear-thickening (n = 1.2) cases. This empirical observation underscores the vital role that fluid rheology plays in molding the overall heat transfer performance within the cavity. The study concludes that there is a distinct correlation between the heat transfer rate and the Rayleigh number (Ra). As Ra increases, there is a significant improvement in the heat transfer rate within the flow domain. Furthermore, the fluid behavior and heat transfer performance within the cavity are significantly influenced by the presence of magnetohydrodynamics (MHD) and the Darcy effect.

IntroductionCervical cancer (CCa) is the fourth most frequent and a common cause of cancer mortality in women, the majority of whom live in low- and middle-income countries. Data on CCa mortality and its determinants have been poorly studied in Nigeria, resulting in a paucity of information that can assist patient management and cancer control policy.AimThe purpose of this study was to assess the mortality rate among CCa patients in Nigeria as well as the major factors influencing CCa mortality.Study designData from the medical records of 343 CCa patients seen at the Lagos University Teaching Hospital and NSIA-LUTH Cancer Center from 2015 to 2021 were used in a retrospective cohort analysis. The hazard ratios (HR) and confidence intervals (CI) associated with the exposure variables and CCa mortality were calculated using Cox proportional hazard regression.ResultsThe CCa mortality rate was 30.5 per 100 women-years after 2.2 years of median follow-up. Clinical factors such as HIV/AIDS (adjusted HR [aHR]: 11.9; 95% CI: 4.6, 30.4), advanced clinical stage (aHR: 2.7; 95% CI: 1.5, 4.7), and anemia at presentation (aHR: 1.8; 95% CI: 1.1, 3.0) were associated with a higher mortality risk, as were non-clinical factors such as age at diagnosis >50 years (aHR: 1.4; 95% CI: 1.0, 1.9) and family history of CCa (aHR: 3.5; 95%CI: 1.1, 11.1)ConclusionCCa has a high mortality rate in Nigeria. Incorporating these clinical and non-clinical factors into CCa management and control policies may improve women’s outcomes.

This study examines the physical, mechanical, microstructural, and attenuation properties of high-density concrete exposed to temperatures ranging from 200°C to 1200°C. For this purpose, heavy-density concrete containing 25%, 50%, 75%, and 100% dolerite aggregates was developed and compared with three ordinary concrete mixes. Pre- and post-heated concrete specimens were evaluated for mass and density loss, compressive strength, rebound hammer, X-ray and gamma-ray attenuation, Half Value Layer (HVL), and Ten Value Layer (TVL) along with microstructural properties determined by scanning electron microscopy and Energy Dispersive X-ray. The results showed that the incorporation of 75% dolerite aggregate during pre- and post-heating yielded high compressive strength whereas low mass and density loss. The same mixture showed significant improvement in gamma ray shielding at all temperatures. The Half Value Layer and Ten Value Layer values showed a reduction in the thickness of concrete as a shield. It is recommended that dolerite heavy-density concrete is a potential radiation shield at high temperatures ranging from 200°C–1200°C in fourth-generation nuclear power plants.

Background: Paediatric cancer patients in Nigeria continue to arrive at specialist centers with advanced-stage disease. The reasons for this are myriad, not least of which are delays in detection, diagnosis, and referral for treatment. While delayed presentation has often been reported from the perspective of delays from caregivers' decisions, institutional deficiencies in the health care system may account for an unmeasured portion of the factors leading to delayed presentation. This project centered around training of health-care professionals at community level to detect potential paediatric cancer signs and refer appropriately. Aim: The aim of the study is to access the immediate impact of training on early detection and referral of possible paediatric cancer cases in the community and primary level health care workers and professionals in the Southwest Nigeria. Materials and Methods: This was a retrospective review of the training impact in three South-Western states in Nigeria. Scores before and after the training were analyzed using the IBM SPSS statistics, version 23 (IBM, Armonk, NY, USA). Results: A total of 732 primary health care workers were trained. In the pre assessment evaluations, 44.8% of participants reported that cancer did not occur in children, 47.2% did not know any referral pathway for a child suspected of cancer. The post training assessment indicated an improvement in participants' understanding of common paediatric cancers types and how to refer a suspected case for specialist diagnosis and attention. Mean scores before and after the training were 3.5/15 and 12.5/15, respectively. Conclusion: Training health-care professionals working at the community level can have an immediate and measurable impact on early detection and referral for paediatric cancers, as seen by the difference in pre training and post training assessment scores. There remains a need for continuous training to ensure early referral and ultimately increase survival indices of children diagnosed with cancer in Nigeria.

Abstract Highly selective and sensitive 2,7-naphthyridine based colorimetric and fluorescence “Turn Off” chemosensors (L1-L4) for detection of Ni2+ in aqueous media are reported. The receptors (L1-L4) showed a distinct color change from yellow to red by addition of Ni2+ with spectral changes in bands at 535–550 nm. The changes are reversible and pH independent. The detection limits for Ni2+ by (L1-L4) are in the range of 0.2–0.5 µM by UV–Visible data and 0.040–0.47 µM by fluorescence data, which is lower than the permissible value of Ni2+ (1.2 µM) in drinking water defined by EPA. The binding stoichiometries of L1-L4 for Ni2+ were found to be 2:1 through Job’s plot and ESI–MS analysis. Moreover the receptors can be used to quantify Ni2+ in real water samples. Formation of test strips by the dip-stick method increases the practical applicability of the Ni2+ test for “in-the-field” measurements. DFT calculations and AIM analyses supported the experimentally determined 2:1 stoichiometries of complexation. TD-DFT calculations were performed which showed slightly decreased FMO energy gaps due to ligand–metal charge transfer (LMCT).

Glass fiber-reinforced polymer (GFRP) reinforcements are useful alternatives to traditional steel bars in concrete structures, particularly in vertical structural elements such as columns, as they are less prone to corrosion, and impart increasing strength and endurance of buildings. There is limited research on the finite element analysis (FEA) of the structural behavior of hollow glass fiber-reinforced polymer reinforced concrete (GFRPRC) columns. The hollow portion can be used for the service duct and for reducing the self-weight of the members. Numerical analysis of the compressive response of circular hollow concrete columns reinforced with GFRP bars and spirals is performed in this study. This article aims to investigate the axial behavior of hollow GFRP concrete columns and compare it with that of solid steel reinforced concrete (RC) columns as well as hollow steel RC columns. The Abaqus software is used to construct finite element models. After calibration of modeling using an experimental test result as a control model, a parametric study is conducted. The columns with the same geometry, loading, and boundary conditions are analyzed in the parametric study. It is resulted that the hollow GFRP concrete columns provide a greater confinement effect than the solid steel RC columns. The average variation in the ultimate axial load-carrying capacities of the experimental results, from that of the FEA values, is noted to be only 3.87%, while the average difference in the corresponding deformations is 7.08%. Moreover, the hollow GFRP concrete columns possess greater axial load and deformation capacities compared with the solid steel RC columns.

The change in the dynamic behaviour of reinforced concrete (RC) structures after the fire events is still an unsolved issue for research communities and regulatory authorities. To fill the gap, this paper presents the experimental results of the shift in dynamic characteristics of a single scaled-down two-storey RC structure after exposure to short and long-duration real fires. The structure having normal strength concrete was constructed and well-instrumented with temperature sensors to obtain a large database of temperature profiles in the compartment and various element sections. Short Duration Fire Test (SDFT) of 20 min with a maximum temperature of 852 °C and Long Duration Fire Test (LDFT) of 100 min with a maximum temperature of 813 °C were created using wooden logs to represent a realistic office fire. The structure was further tested for dynamic characteristics such as natural frequencies and mode shapes by gently hitting it with a mass source that generated an impulse leading to free vibration in the structure. The natural frequency reduction of 14.71% and 49.5% were observed after the structure was exposed to short and long duration fires respectively. The mode shapes after the fires also showed variation indicating damages in the structure due to fires. The study will be useful in understanding the dynamic behaviour of structures in short and long-duration real fires as well as it will serve as a database for numerical modelling of RC structures.

A one-pot three-component protocol was designed for cascade synthesis of novel heterocyclic amino-1,8-naphthyridines. Materials synthesis is confirmed by NMR (1H and 13C) and structures are verified by computational models. The in vitro phytotoxic and urease enzyme inhibition studies with IC50 ranges from 23.76 ± 0.027 µM to 67.46 ± 3.93 indicating significant pharmacological activity of synthesized compounds. Osiris calculations results confirm the non-mutagenicity and non-irritating patterns. Molinspiration calculations values of 3.49–3.94 indicate the permeability of the derivates through membranes. Amino-1,8-naphthyridines are used for the first time in electrochemical sensing of DNA. The mechanism of interaction between DNA and amino-1,8-naphthyridine derivatives is evaluated via a constant current potentiometric method. Different electrochemical parameters such as scan rate, binding time, and effect of concentration of DNA are optimized. The strong interaction of these compounds with the human DNA under physiological conditions justified their potential applications as biosensors after further optimization.

Researchers have conducted numerous investigations on various repair techniques to restore the mechanical properties of fire-damaged concrete. However, most of the techniques are either expensive or require high technical skills. As a continuous work, this study involved an experimental investigation of the effects of various low-cost repair techniques to restore the mechanical properties of fire-damaged concrete. Sixty cylindrical concrete samples were subjected to heat ranging from 400°C to 800°C in a propane gas furnace. The samples were categorized into six groups, including the following: undamaged, fire-damaged, and fire-damaged and repaired using water curing; fire-damaged and repaired using cement-based slurry injection; re-curing fire-damaged and repaired with steel wire mesh along with epoxy resin wrapping; and fire-damaged and repaired using epoxy injection. The samples were tested for non-destructive and destructive tests, including ultrasonic pulse velocity, rebound hammer, and compressive strength. The results showed that the use of cement-based slurry injection along with the water-curing repair technique could regain almost 90% of its ultimate strength and secant stiffness, making it the most effective low-cost repair technique for fire-damaged concrete exposed up to 700°C.

Abstract A number of resources, every year, being spent to tackle early detection of cardiac abnormalities which is one of the leading causes of deaths all over the Globe. The challenges for healthcare systems includes early detection, portability and mobility of patients. This paper presents a categorical review of smartphone-based systems that can detect cardiac abnormalities by the analysis of Electrocardiogram (ECG) and Photoplethysmography (PPG) and the limitation and challenges of these system. The ECG based systems can monitor, record and forward signals for analysis and an alarm can be triggered in case of abnormality, however the limitation of smart phone’s processing capabilities, lack of storage and speed of network are major challenges. The systems based on PPG signals are non-invasive and provides mobility and portability. This study aims to critically review the existing systems, their limitation, challenges and possible improvements to serve as a reference for researchers and developers.

Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient technique for image reconstruction using under-sampled MR data. In most cases, the performance of a particular model’s reconstruction must be improved by using a substantial proportion of the training data. However, gathering tens of thousands of raw patient data for training the model in actual clinical applications is difficult because retaining k-space data is not customary in the clinical process. Therefore, it is imperative to increase the generalizability of a network that was created using a small number of samples as quickly as possible. This research explored two unique applications based on deep learning-based GAN and transfer learning. Seeing as MRI reconstruction procedures go for brain and knee imaging, the proposed method outperforms current techniques in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM). As compared to the results of transfer learning for the brain and knee, using a smaller number of training cases produced superior results, with acceleration factor (AF) 2 (for brain PSNR (39.33); SSIM (0.97), for knee PSNR (35.48); SSIM (0.90)) and AF 4 (for brain PSNR (38.13); SSIM (0.95), for knee PSNR (33.95); SSIM (0.86)). The approach that has been described would make it easier to apply future models for MRI reconstruction without necessitating the acquisition of vast imaging datasets.

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events.

Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph neural networks (GNN) is becoming an important research area. The multilayered structure of the GNN with a number of epochs takes more training time. It is also hard to train the model with saturating nonlinearities. The batch normalization technique is applied to each mini-batch to reduce epochs in training and obtain a steady distribution of activation values. It improves model training and prediction accuracy. This study proposes a deep learning model batch normalization graph convolutional neural network (BNGCNN) for early earthquake detection. It consists of two main components: CNN and GNN. Input to the CNN model is multi-station and three-component waveform data with magnitude ≥3.0 were collected from January 2000 to January 2015 for Southern California. The extracted features of CNN are appended with location information and input to GNN model for earthquake detection. After hyperparameter tuning of the BNGCNN, when testing and evaluating the model on the Southern California dataset, our method shows promising results to the baseline model GNN by obtaining a low error rate to predict the magnitude, depth, and location of an earthquake.

Background: The footprints of zinc dietary nutrient deficiency are resulting in a widespread burden of stunting, wasting and underweight in children under five years, and reproductive development in women over a population of 100 million in Pakistan. The zinc malnutrition could be addressed by consumption of biofortified high zinc wheat flour, a staple food, in complemented with food supplementation, food diversification and food fortification interventions in the country. Methodology: This evidence-based research publication focused on micronutrients malnutrition on human health and capital. Literature was taken from last 20 years papers published in several peers reviewed scientific journals. Papers regarding malnutrition were mainly focused. The primary data was collected from R&D institutions and secondary data from Pakistan National Nutrition Survey 2011. Various sites, viz. www.harvestplus.org, www.ifpri.org, http://www.ars.usda.gov/ba/bhnrc/ndl, http://data.unicef.org: www.fao.org: http://wholibdoc.who.int/publications were also visited. Results: In Pakistan, the recently released biofortified high zinc wheat variety “Zincol-2016” had a higher quantum of 37 mg Zn kg-1 compared to 25.0 mg Zn kg-1 in grain portion in other commercially grown wheat varieties. Upon consuming biofortified wheat flour, zinc concentration increased from 681.3 to 792.3 µg L-1 in human plasma. Furthermore, even an increase in dietary zinc by an amount of 2.0 mg Zn capita-1 day-1 may result in halving the endemic problem of zinc malnutrition, with a little change in food diversity pattern. Conclusion: The consumption of biofortified high zinc wheat variety “Zincol-2016” containing 37 mg Zn kg-1 compared to 25.0 mg Zn kg-1 in conventional wheat varieties could result in alleviating zinc malnutrition to the extent of 50% in the malnourished population. Thereby, it is a potential complementary staple food with other contemporary interventions to address malnutrition in Pakistan.

A series of fifteen N4-benzyl substituted 5-chloroisatin-3-thiosemicarbazones 5a–o were synthesized and screened mainly for their antiurease and antiglycation effects. Lemna aequinocitalis growth and Artemia salina assays were carried out to determine their phytotoxicity and cytotoxicity potential. All the compounds proved to be extremely effective urease inhibitors, demonstrating enzyme inhibition much better than the reference inhibitor, thiourea (IC50 values 1.31 ± 0.06 to 3.24 ± 0.15 vs. 22.3 ± 1.12 μM). On the other hand, eight out of fifteen compounds tested, i.e. 5b, 5c, 5h–k, 5m and 5n were found to be potent glycation inhibitors. Of these, five viz. 5c, 5h–j and 5n proved to be exceedingly efficient, displaying glycation inhibition greater than the reference inhibitor, rutin (IC50 values 114.51 ± 1.08 to 229.94 ± 3.40 vs. 294.5 ± 1.5 μM).

Background: Treatment planning systems (TPSs) have proved to be a useful tool in predetermining how a treatment outcome will be in radiotherapy. The accuracy of any TPS to calculate dose to any arbitrary point within a material is largely dependent on the mathematical algorithm used. Aims: The purpose of this study was to design a local trunk phantom and use the phantom to check the percentage dose accuracy of the Area Integration Algorithm of a Precise PLAN 2.16 TPS if it is in agreement with results obtained from manufacturer's verification by varying the gantry angle and whether it is within ± 5% International Commission on Radiation Units and Measurements (ICRU) minimal limit. Materials and Methods: The study was executed with a locally designed phantom made of Plexiglas with six insert and an ionization chamber port. The phantom was simulated using a HiSpeed NX/i computed tomography scanner and Precise PLAN 2.16 TPS for application of beam setup parameters. The mimicked organs for the inserts were: 25%–75% Glycerol-Water for liver, pure carboxyl methyl cellulose was used for lungs, 30%–70% Glycerol-Water for muscle, 40%–60% Glycerol-Water was used for adipose, pure Sodium hypochlorite was used for bone and pure sodium laureth sulfate (Texapon) for kidney. Results: The maximum percentage (%) deviation with a large field for six inhomogeneous inserts and with bone only homogeneous inserts were 3.4% and 2.9%, respectively. The maximum % deviation with a small field for six inhomogeneous inserts was 3.2%. The % deviation between the solid water phantom and the locally designed phantom was 3.5%. Conclusion: The Area Integration Algorithm has shown an overall accuracy of 4% below 5% ICRU minimal limit. There was no statistically significant difference in field sizes and in inhomogeneity/homogeneity, respectively. Variation exists in % deviation for small field size with parallel opposed field between our verification and the manufacturers.

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

Background: The cost of commercial pelvis phantom is a burden to the quality assurance in radiotherapy of small and/or low-income radiotherapy centers. That an algorithm is accurate with short treatment time is a prized asset in treatment planning. Objectives: The purpose of this study was to develop a hybrid algorithm that has balance between accuracy and treatment time and design a pelvis phantom for evaluating the accuracy of a linear accelerator monitor unit. Materials and Methods: A pelvis phantom was designed using Plaster of Paris, styrofoam and water with six hollows for inserting materials mimicking different biological tissues, and the ionization chamber. Computed tomography images of the phantom were transferred to the CMS XiO treatment planning system with three different algorithms. Monitor units were obtained with clinical linear accelerator with isocentric setup. The phantom was tested using convolution (C), fast superposition (FSS), and superposition (S) algorithms with respect to an established reference dose of 1 Gy from a large water phantom. Data analysis value was done using GraphPad Prism 5.0. Results: FSS algorithm showed better accuracy than C and S with bone, lung, and solid water inhomogeneous insert. C algorithm was better in terms of treatment time than S. There was no statistically significant difference between the mean doses for all the three algorithms against the reference dose. The maximum percentage deviation was ±4%, which was below ±5% International Commission on Radiation Units and Measurement minimal limit. Conclusion: This algorithm can be employed in the calculation of dose in advance techniques such as intensity-modulated radiation therapy and RapidArc by radiotherapy centers with multiple algorithm system because it is easy to implement. The materials used for the construction of the phantom are very affordable and simple for low-budget radiotherapy centers.

Bu çalışmanın amacı, polimer içerikli membranlar (PIMs) ile Cr (VI) giderimi için geliştirilecek yapay sinir ağı (YSA) modelinde optimum YSA mimarisi için en uygun öğrenme algoritmasının belirlenmesidir. Bu amaçla, geliştirilen yapay sinir ağı modelinde Levenberg-Marquardt, Bayesian Regularization, Ölçeklenmiş Konjuge Gradyan olmak üzere 3 faklı öğrenme algoritması uygulanmıştır. Ağ mimarisinin ve kullanılan öğrenme algoritmasının ağın tahmin performansına etkisinin belirlenmesinde Regresyon katsayısı (R2) ve ortalama karesel hata (OKH) teknikleri kullanılmıştır. Sonuç olarak geliştirilen bir YSA modelinde doğru öğrenme algoritması seçiminin ağın tahmin kabiliyeti açısından önemli olduğu sonucuna varılmıştır.

An amendment to this paper has been published and can be accessed via the original article.

Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation.

Decision making implies selection of the best decision from a set of possible options. In some cases, this selection is based on past experience. Past experience is used to analyse the situations and the choice made in these situations. The aim of this work is to analyse the strength of the nonlinear component (S-box) of block cipher for image encryption applications based on Interval Valued Intuitionistic Fuzzy Sets (IVIFS). S-box is the only component in every block cipher which creates confusion in the data. First, we transform the three dimensional matrix corresponding to colour image with the help of nonlinear component and then use the algebraic structure of IVIFS to choose the best substitution box for image encryption based on entropy, contrast, homogeneity, correlation, energy and mean of absolute devotion. The analyses show that the readings of S8 S-box is very good for image data.

Waste foundry sand (WFS) is the by-product of the foundry industry. Utilizing it in the construction industry will protect the environment and its natural resources, and enable sustainable construction. WFS was employed in this research as a fractional substitution of natural sand by 0%, 10%, 20%, 30%, and 40% in concrete. Several tests, including workability, compressive strength (CS), splitting tensile strength (STS), and flexural strength (FS), ultrasonic pulse velocity (USPV), Schmidt rebound hammer number (RHN), and residual compressive strengths (RCS) tests were performed to understand the behavior of concrete before and after exposure to elevated temperatures. Test findings showed that the strength characteristics were increased by including WFS at all the phases. For a substitute rate of 30%, the maximum compressive, splitting tensile, and flexural strength were observed. Replacement with WFS enhanced the 28-day compressive, splitting tensile, and flexural strength by 7.82%, 9.87%, and 10.35%, respectively at a 30% replacement level, and showed continuous improvement until the age of 91 days. The RCS of foundry sand concrete after one month of air cooling at ambient temperature after exposing to 300 °C, 400 °C, 500 °C, 600 °C, 700 °C, and 800 °C was found to be in the range of 67.50% to 71.00%, 57.50% to 61.50%, 49.00% to 51.50%, 38% to 41%, 31% to 35% and 26% to 31.5% of unheated compressive strength values for 0% to 40% replacement of WFS, respectively. The RCS decreases with increasing temperature; however, with increasing WFS, the RCS was enhanced in comparison to the control samples. In addition, the replacement of 30% yielded excellent outcomes. Hence, this study provides a sustainable construction material that will preserve the Earth’s natural resources and provide a best use of WFS.

The present study aims to introduce the notion of bipolar neutrosophic Hamacher aggregation operators and to also provide its application in real life. Then neutrosophic set (NS) can elaborate the incomplete, inconsistent, and indeterminate information, Hamacher aggregation operators, and extended Einstein aggregation operators to the arithmetic and geometric aggregation operators. First, we give the fundamental definition and operations of the neutrosophic set and the bipolar neutrosophic set. Our main focus is on the Hamacher aggregation operators of bipolar neutrosophic, namely, bipolar neutrosophic Hamacher weighted averaging (BNHWA), bipolar neutrosophic Hamacher ordered weighted averaging (BNHOWA), and bipolar neutrosophic Hamacher hybrid averaging (BNHHA) along with their desirable properties. The prime gain of utilizing the suggested methods is that these operators progressively provide total perspective on the issue necessary for the decision makers. These tools provide generalized, increasingly exact, and precise outcomes when compared to the current methods. Finally, as an application, we propose new methods for the multi-criteria group decision-making issues by using the various kinds of bipolar neutrosophic operators with a numerical model. This demonstrates the usefulness and practicality of this proposed approach in real life.

The experience shows that fire-damaged concrete structures both technically and economically can be reinstated after fire due to high fire resistance and high residual strength. The residual strength of fire-damaged concrete structural member depends on the peak temperature reached during fire, fire duration and the distribution of temperature within the structural member. The assessment of the residual strength of post-heated concrete structural members in a professional way is a prime factor to take a decision about the reinstatement or demolition of fire-damaged structure. This paper provides an easy and efficient approach to predict the residual strength of reinforced concrete columns based on the estimated temperature which may have occurred within the concrete cross-section during a fire. A finite element model was developed to evaluate the distribution of temperature within the cross-section of the reinforced concrete columns. Twelve reinforced concrete square columns were heated experimentally up to 500°C at 150°C/hour. A comparison of the experimental temperature values of the tested columns was made with the model results. A good agreement was found between the experimental and the finite model results. Based on the temperature distribution obtained from the finite element model, the residual strength of concrete and reinforcement could be evaluated by using the relationships for concrete, steel and temperature proposed by various researchers.

A series of twenty one N4-aryl substituted 5-trifluoromethoxyisatin-3-thiosemicarbazones 3a-3u was synthesized by the reaction of trifluoromethoxyisatin 1 with different arylthiosemicarbazides 2 in aqueous ethanol (50%), containing a few drops of acetic acid. Their structures were established on the basis of analytical (CHN) and spectral (IR, 1H-NMR, EIMS) data. All the synthesized compounds were evaluated for their toxicity potential by a brine shrimp lethality bioassay. Ten compounds i.e., 3a, 3e, 3i-3l and 3n-3q proved to be active in this assay, displaying promising toxicity (LD50 = 1.11 × 10−5 M − 1.80 × 10−4 M). Amongst these, 3k, 3n and 3o were found to be the most active ones (LD50 = 1.11 × 10−5 M − 1.43 × 10−5 M). Compound 3k showed the highest activity with a LD50 value of 1.11 × 10−5 M and can, therefore, be used as a lead for further studies. Structure-activity relationship (SAR) studies revealed that the presence of strong inductively electron-attracting trifluoromethoxy substituent at position-5 of the isatin moiety played an important role in inducing or enhancing toxic potentiality of some of the synthesized compounds.

In the title compound, C16H13ClN4OS, the isatin ring system is oriented at dihedral angles of 10.60 (7) and 72.60 (3)° with respect to the thiosemicarbazide and 2-chlorobenzyl groups, respectively. The near planarity of the isatin and thiosemicarbazide groups [r.m.s. deviations of 0.0420 and 0.0163 Å, respectively] is reinforced by intramolecular N—H...O and N—H...N hydrogen bonds, which generate S(6) and S(5) rings, respectively. In the crystal, inversion dimers linked by pairs of N—H...O hydrogen bonds generate R22(8) loops. Aromatic π–π stacking interactions between the centroids of heterocyclic five-membered and benzene rings [distance = 3.6866 (11) Å] are also observed.

The title compound, C19H18N4O4S, exists as a zwitterion in the solid state, with nominal proton transfer from a phenol group to the imine N atom. The 2,3-dihydroxybenzaldehyde fragment is oriented at a dihedral angle of 35.51 (11)° to the adajacent aniline group and makes a dihedral angle of 76.99 (6)° with the 4,6-dimethylpyrimidin-2-amine group. Intramolecular O—H...O and N—H...O hydrogen bonds close S(5) and S(6) rings, respectively; the same O atom accepts both bonds. In the crystal, polymeric chains along [001] are formed from molecules joined end-to-end by N—H...O and O—H...N hydrogen bonds; these feature R23(6) loops. The polymeric chains are linked by C—H...O interactions and there are π–π interactions between the pyrimidine rings with a centroid–centroid distance of 3.446 (2) Å.

In the title compound, C15H10FN5O3S, an intramolecular N—H...N hydrogen bond generates an S(5) ring, whereas N—H...O and C—H...S interactions complete S(6) ring motifs. The dihedral angle between the isatin ring system and the fluorobenzene ring is 5.96 (6)° and the complete molecule is close to planar (r.m.s. deviation for all the non-H atoms = 0.112 Å). In the crystal, molecules are linked by N—H...O hydrogen bonds to form C(8) chains along the [100] direction and C—H...O interactions are also observed.

The title compound, C24H14O9·H2O, displays a cup-shaped form. The water molecule is disordered over two set of sites with an occupancy ratio of 0.78:0.22. The molecule of the compound has four stereocenters and corresponds to the SSRR/RRSS diastereoisomer. In the molecule, the maximum dihedral angle between the planar benzene rings is 80.40 (4)°. The H atoms of the hydroxy groups are engaged in hydrogen bonding, forming infinite chains parallel to the a axis. These chains are interlinked through water molecules, resulting in the formation of a two-dimensional network parallel to the (001) plane. Futhermore C—H...O, C—H...π and slipped π–π interactions result in the formation of a three-dimensional network.

In the title compound, C15H11FN4OS, there are three independent molecules, each with a disordered 3-fluorophenyl group [occupancy ratios = 0.547 (17):0.453 (17), 0.645 (5):0.355 (5) and 0.626 (15):0.374 (15)] and displaying dihedral angles of 4.2 (3), 25.2 (6) and 32.4 (5)° between the 2-oxoindoline and fluoro-substituted phenyl rings. Strong intramolecular N—H...N and N—H...O and weak intramolecular C—H...S hydrogen bonds complete S(5) and S(6) ring motifs, while strong intermolecular N—H...O hydrogen bonds interconnect the three independent molecules through R33(12) ring motifs. The three-molecule units are in turn linked into polymeric sheets via C—H...F and C—H...S hydrogen bonds and π–π interactions [centroid–centroid distances in the range 3.520 (2)–3.820 (9) Å].

The crystal structure of the title compound, C16H11F3N4O2S, is stabilized in the form of polymeric chains by N—H...O interactions. In the molecular structure, two S(5) ring motifs are formed by intramolecular N—H...N and N—H...O hydrogen bonding and two S(6) rings are present due to N—H...O and C—H...S interactions. π–π interactions are present with distances of 3.2735 (17), 3.563 (2) and 3.664 (4)/3.688 (3) Å between the centroids of the heterocyclic rings, between the centroids of the heterocyclic ring and trifluoromethoxy-substituted phenyl ring, and between the centroids of the trifluoromethoxy-substituted phenyl rings, respectively. The trifluoromethoxyphenyl group is disordered over two sites with an occupancy ratio of 0.642 (10):0.358 (10).

The name of one of the authors in the paper by Hussain et al. [Acta Cryst. (2010), E66, o1881] is corrected.

In the title compound, C14H16N2O3S, the dihedral angle between the phenyl and 5-methylfuran groups is 54.89 (14)° and the C=N bond assumes a trans conformation. In the crystal, inversion dimers linked by pairs of N—H...O hydrogen bonds generate R22(8) ring motifs. The dimers are interlinked by N—H...N hydrogen bonds, resulting in the formation of infinite chains extending along the b axis. The packing is consolidated by weak C—H...π interactions.

In the title compound, C15H13NO4·H2O, the organic molecule adopts a V-shaped conformation in which the dihedral angle between the five-membered rings is 68.33 (5)°. The cyclohexenone ring adopts an envelope conformation, with one of the methylene C atoms displaced by 0.607 (4) Å from the plane through the other atoms. In the crystal, intermolecular N—H...(O,O) and O—H...O hydrogen bonds link the components into (001) sheeets and C–H...O interactions and aromatic π–π stacking [centroid–centroid separation = 3.6176 (19) Å] help to consolidate the packing.

In the title compound, C15H11IN4OS, intramolecular N—H...N, N—H...O and C—H...S interactions generate one S(5) and two S(6) ring motifs. In the crystal, molecules form centrosymmetric dimers via pairs of N—H...O interactions, generating R22(8) ring motifs. In addition a short intermolecular I...S contact of 3.352 (3) Å is observed.

The title compound, C17H16N4OS, is stabilized in the form of a two-dimensional polymeric network due to intermolecular N—H...S and N—H...O hydrogen bonds. An intramolecular N—H...N hydrogen bond forms an S(5) ring, whereas interactions of the N—H...O and C—H...S types complete S(6) ring motifs. π–π interactions with a centroid–centroid distance of 3.6514 (10) Å are found between the ethyl-substituted benzene ring and the heterocyclic ring of the isatin derivative.

In the title compound, C14H12ClN3O2·H2O, the benzene ring and the pyridine rings are oriented at a dihedral angle of 57.73 (12)° and an intramolecular O—H...N hydrogen bond generates an S(6) ring. In the crystal, the water molecule forms O—H...O and O—H...N hydrogen bonds to the organic molecule, leading to chains containing R44(16) loops. In addition, weak aromatic π–π stacking interactions between the centroids of pyridine rings [at distance of 3.864 (2) and 4.013 (2) Å] and C—H...π interactions occur.