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Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by energy diffusion and partial disconnection in the brain, with its main feature being an insidious onset and subtle clinical symptoms. Electroencephalogram (EEG) as a primary tool for assessing and aiding in the diagnosis of brain diseases has been widely used in AD detection. Accurate diagnosis is crucial for preventing the transition from early cognitive impairment to AD and providing early treatment for AD patients. This study aims to establish a hybrid model based on the Improved Artificial Fish Swarm Algorithm (IAFS) and Genetic Algorithm (GA)-IAFS-GA, to determine the optimal channel combination for AD detection under multiple EEG signals. Geometric features and complexity features of AD EEG signals were extracted using Second Order Difference Plot (SODP) and entropy analysis across the full frequency band. Subsequently, Pearson correlation was used for feature ranking, selecting the six least correlated features for each channel. The Relief algorithm was then used to fuse these selected features, with one fused feature representing one channel. Based on this, a feature selection optimization algorithm, IAFS-GA, combining the improved artificial fish swarm algorithm and genetic algorithm, was proposed. Finally, the feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. The feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. Using a five-fold cross-validation strategy across the entire frequency band, the classification accuracy reached 93.53%, with a sensitivity of 98.74%, specificity of 98.25%, and an AUC area of 97.82%. This framework can quickly select appropriate brain channels to enhance the efficiency of detecting AD and other neurological diseases. Moreover, it is the first time that an improved artificial fish swarm genetic combination algorithm and SODP features has been used for channel selection in EEG, proving to be an effective method for AD detection. It is based on SODP analysis, entropy analysis, and intelligent algorithms, which can assist clinicians in rapidly diagnosing AD, reducing the misdiagnosis rate of false positives, and expanding our understanding of brain function in patients with neurological diseases., Competing Interests: Conflict of interestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential Conflict of interest., (© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

This paper presents the global research landscape and scientific progress on occupant thermal comfort in naturally ventilated buildings (OTC-NVB). Despite the growing interest in the area, comprehensive papers on the current status and future developments on the topic are currently lacking. Hence, the publication trends, bibliometric analysis, and systematic literature review of the published documents on OTC-NVB were examined. The search query "Thermal Comfort" AND "Natural Ventilation" AND "Buildings" was designed and executed to recover related documents on the topic from the Elsevier Scopus database. Results showed that 976 documents (comprising articles, conference papers, reviews, etc.) were published on the topic from 1995 to 2021. Further analysis showed that 97.34% of the publications were published in the English language. Richard J.de Dear (University of Sydney, Australia) is the most prolific researcher on OTC-NVB research, while Energy and Buildings has the highest publications. Bibliometric analysis showed high publications, citations, keywords, and co-authorships among researchers, whereas the most occurrent keywords are ventilation, natural ventilation, thermal comfort, buildings, and air conditioning. Systematic literature review demonstrated that OTC-NVB research has progressed significantly from empirical to computer-based studies involving complex mathematical equations, programs, or software like artificial neural networks (ANN) and computational fluid dynamics (CFD). In general, OTC-NVB research findings indicate that physiological, social, and environmental factors considerably influence OTC in NVBs. Future studies will likely employ artificial intelligence or building performance simulation (BPS) tools to examine relationships between OTC and indoor air/environmental quality, human behavior, novel clothing, or building materials in NVBs., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Growing electroencephalogram (EEG) studies have linked the abnormities of functional brain networks with disorders of consciousness (DOC). However, due to network data's high-dimensional and non-Euclidean properties, it is difficult to exploit the brain connectivity information that can effectively detect the consciousness levels of DOC patients via deep learning. To take maximum advantage of network information in assessing impaired consciousness, we utilized the functional connectivity with convolutional neural network (CNN) and employed three rearrangement schemes to improve the evaluation performance of brain networks. In addition, the gradient-weighted class activation mapping (Grad-CAM) was adopted to visualize the classification contributions of connections among different areas. We demonstrated that the classification performance was significantly enhanced by applying network rearrangement techniques compared to those obtained by the original connectivity matrix (with an accuracy of 75.0%). The highest classification accuracy (87.2%) was achieved by rearranging the alpha network based on the anatomical regions. The inter-region connections (i.e., frontal-parietal and frontal-occipital connectivity) played dominant roles in the classification of patients with different consciousness states. The effectiveness of functional connectivity in revealing individual differences in brain activity was further validated by the correlation between behavioral performance and connections among specific regions. These findings suggest that our proposed assessment model could detect the residual consciousness of patients., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Evidence shows that the dendritic polarization induced by weak electrical field (EF) can affect the neuronal input-output function via modulating dendritic integration of AMPA synapses, indicating that the supralinear dendritic integration of NMDA synapses can also be influenced by dendritic polarization. However, it remains unknown how dendritic polarization affects NMDA-type dendritic integration, and then contributes to neuronal input-output relationship. Here, we used a computational model of pyramidal neuron with inhomogeneous extracellular potentials to characterize the relationship among EF, dendritic integration, and somatic output. Basing on singular perturbation we analyzed the subthreshold dynamics of membrane potentials in response to NMDA synapses, and found that the equilibrium mapping of a fast subsystem can characterize the asymptotic subthreshold input-output (sI/O) relationship for EF-regulated supralinear dendritic integration, allowing us to predict the tendency of EF-regulated dendritic integration by showing the variation of equilibrium mapping under EF stimulation. EF-induced depolarization at distal dendrites receiving synapses plays a crucial role in shifting the steep change of sI/O left by facilitating dendritic NMDA spike generation and in decreasing the plateau of sI/O via reducing driving force. And more effective EF modulation appears at sparsely activated NMDA receptors compared with clustered synaptic inputs. During the action potential (AP) generation, the respective contribution of EF-regulated dendritic integration and EF-induced somatic polarization was identified to show their synergetic or antagonistic effect on AP generation, depending on neuronal excitability. These results provided insight in understanding the modulation effect of EF on neuronal computation, which is important for optimizing noninvasive brain stimulation., Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09922-y., Competing Interests: Conflict of interestThe authors declare they have no financial interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise ( SNR ) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases., Competing Interests: Conflict of interestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The 3D printing of lung-equivalent phantoms using conventional polylactic acid (PLA) filaments requires the use of low in-fill printing densities, which can produce substantial density heterogeneities from the air gaps within the resulting prints. Light-weight foaming PLA filaments produce microscopic air bubbles when heated to 3D printing temperatures. In this study, the expansion of foaming PLA filament was characterised for two 3D printers with different nozzle diameters, in order to optimise the printing flow rates required to achieve a low density print when printed at 100% in-fill printing density, without noticeable internal air gaps. Effective densities as low as 0.28 g cm - 3 were shown to be achievable with only microscopic air gaps. Light-weight foaming PLA filaments are a cost-effective method for achieving homogeneous lung-equivalency in 3D printed phantoms for use in radiotherapy imaging and dosimetry, featuring smaller air gaps than required to achieve low densities with conventional PLA filaments., (© 2023. The Author(s).)

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now., (© 2023. The Author(s).)

Finding patterns among risk factors and chronic illness can suggest similar causes, provide guidance to improve healthy lifestyles, and give clues for possible treatments for outliers. Prior studies have typically isolated data challenges from single-disease datasets. However, the predictive power of multiple diseases is more helpful in establishing a healthy lifestyle than investigating one disease. Most studies typically focus on single-disease datasets; however, to ensure that health advice is generalized and contemporary, the features that predict the likelihood of many diseases can improve health advice effectiveness when considering the patient's point of view. We construct and present a novel knowledge-based qualitative method to remove redundant features from a dataset and redefine the outliers. The results of our trials upon five annual chronic disease health surveys demonstrate that our Knowledge Graph-based feature selection, when applied to many machine learning and deep learning multi-label classifiers, can improve classification performance. Our methodology is compatible with future directions, such as graph neural networks. It provides clinicians with an efficient process to select the most relevant health survey questions and responses regarding single or many human organ systems., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Rice is the most important cereal food crop in the world, and half of the world's population uses rice as a staple food for its energy source. The yield production qualities and quantities are affected by biotic and abiotic factors namely viruses, soil fertility, bacteria, pests, and temperature. Rice plant disease is the most crucial factor behind communal, economic, and agricultural losses in the agricultural field. Farmers detect and identify diseases through the naked eye, which takes more time and resources, leading to crop loss and unhealthy farming. To overcome these issues, this paper presents a novel rice plant disease detection approach named the crossover boosted artificial hummingbird algorithm based AX-RetinaNet (CAHA-AXRNet) approach. This current research paper mainly concentrates on the effectiveness of rice plant disease detection and classification. The hyperparameters of the AX-RetinaNet model are optimized through the CAHA optimization model. In this paper, three types of disease detection datasets namely rice plant dataset, rice leaf dataset, and rice disease dataset are included to classify rice plants as healthy or unhealthy. The most essential performance metrics are precision, F1-score, accuracy, specificity, and recall, employed to validate the effectiveness of disease detection. The proposed CAHA-AXRNet approach demonstrates its effectiveness compared to other existing rice plant disease detection methods and achieved an accuracy rate of 98.1%., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects. Results revealed increased DFA exponents that implies higher long-range temporal correlations (LRTC), especially in the central brain area in alpha and beta bands. On short time scales, declined bursts of oscillations were also observed. All the metrics exhibited lower individual variability in the UWS or MCS group, which may be attributed to the reduced spatial variability of oscillation dynamics. In addition, the temporal dynamics of EEG oscillations showed significant correlations with the behavioral responsiveness of patients. In summary, our findings shows that loss of consciousness is accompanied by alternation of temporal structure in neural oscillations on multiple time scales, and thus may help uncover the mechanism of underlying neuronal correlates of consciousness., Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09852-9., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Carbon neutrality is a research hotspot and achieves increasing interests in recent years. In this paper, utilizing the Web of Science database as the data resource, we conduct a series of analyses for the carbon neutrality-related literature from the past decade based on CiteSpace, including the visualization analysis for the research hotspots and trends, discovery of intellectual structure and influential directions, as well as the cooperation analysis for representative researchers, organizations, and countries. The findings demonstrate that (a) the relationship between carbon emissions and economic growth has achieved increasing academic interests in recent years. (b) There are mainly four knowledge groups at present in this area including renewable energy and carbon emissions; international energy cooperation and investment; energy regulations and policies of different countries; technological innovation and economic growth. (c) Cooperations exist widely within various authors, institutions, as well as countries, with academic clusters developed towards the goals of energy transitions, environmental sustainability, city development, etc., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Lung cancer is a deadly disease showing uncontrolled proliferation of malignant cells in the lungs. If the lung cancer is detected in early stages, it can be cured before critical stage. In recent years, new technologies have gained much attention in the healthcare industry however, the unpredictable appearance of tumors, finding their presence, determining its shape, size and high discrepancy in medical images are the challenging tasks. To overcome this issue a novel Ant lion-based Autoencoders (ALbAE) model is proposed for efficient classification of lung cancer and pneumonia. Initially Computed Tomography (CT) images are pre-processed using median filters to remove noise artifacts and improving the quality of the images. Consequently, the relevant features such as image edges, pixel rates of the images and blood clots are extracted by ant lion-based autoencoder (ALbAE) technique. Finally, in classification stage, the lung CT images are classified into three different categories such as normal lung, cancer affected lung and pneumonia affected lung using Random forest technique. The effectiveness of the implemented design is estimated by different parameters such as precision, recall, Accuracy and F 1-measure. The proposed approach attains 97% accuracy; 98% of recall and F-measure rate is attained through the developed design and the proposed model gains 96% of precision score. Experimental outcomes show that the proposed model performs better than existing SVM, ELM, and MLP in classifying lung cancer and pneumonia., Competing Interests: Conflict of interestsThis paper has no conflict of interest for publishing., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results., Competing Interests: Conflict of interestNot applicable., (© The Author(s) 2023.)

The presence of an audience and its feedback could affect people's performance and experience during an event, especially related to sports such as tennis or boxing. Similarly, in videogames, players' gameplay could be affected if there is an audience and its feedback in response to players' performance in the environment. The inclusion of an audience with non-player characters (NPC) is common in videogames in general. However, there is a limited exploration of the use of an NPC audience in virtual reality (VR) exergames, especially focusing on elderly players. To fill this gap, this work examines the effect of an NPC audience and its associated feedback (with/without) on elderly users of VR exergames. In a user study, we used 120 NPC in a virtual audience. Results showed that the presence of the NPC audience with responsive feedback led to higher performance (with a higher success rate of performing gesture actions, more successful combinations of actions (or combos for short) performed, and more opponent's combos prevented) and better gameplay experience (with higher levels of competence, autonomy, relatedness, immersion, and intuitive controls) of elderly players. Our results can help frame the design and engineering of VR exergames that are targeted at elderly users to help them have an enhanced gameplay experience and improve their health., Competing Interests: Conflict of interestThere are no conflicts of interest to report., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Due to the increase in pollution, the number of deaths caused by lung disease is rising rapidly. It is essential to predict the disease in earlier stages by means of high-level knowledge and acquaintance. Deep learning-based lung cancer prediction plays a vital role in assisting the medical practioners for diagnosing lung cancer in earlier stage. Computer-Aided diagnosis is considered to bring a boost to the field of medicine by tying it to automated systems. In this research paper, several models are experimented by using chest X-ray image or CT scan as an input to detect a particular disease. This research work is carried out to identify the best performing deep learning techniques for lung disease prediction. The performance of the method is evaluated using various performance metrics, such as precision, recall, accuracy and Jaccard index., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain., Competing Interests: Conflict of interestThe authors share no conflict of interests., (© The Author(s) 2022, corrected publication 2022.)

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively., (© The Author(s) 2022.)

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs., Competing Interests: Conflict of interestAll authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

In recent years, various medical diagnosis problems have been addressed from the perspective of multi-attribute decision making. Among them, the three-way decision theory can provide a novel scheme to solve medical diagnosis issues under the framework of multi-attribute decision making via considering transforming relationships between loss functions and decision matrices. In this paper, we primarily explore a three-way decision method with tolerance dominance relations in ordered decision information systems. In existing three-way decision models, all objects can be divided into two states, we utilize decision attributes to obtain the set of two states in ordered decision information systems. Then, in order to improve the accuracy of patient classifications, the paper simultaneously considers the influence of loss and gain functions for each object, and uses loss and gain functions to obtain net profit functions as new measurement functions. Meanwhile, a class of three-way decisions in terms of multi-attribute decision making rules based on a tolerance dominance relation is established. In light of the proposed three-way decision method, we further construct a multi-attribute decision making method by using tolerance dominance relations and the constructed method is applied to a medical diagnosis issue of Lymphography. Finally, a comparison analysis and an experimental evaluation are performed to illustrate the feasibility and effectiveness of the presented methodology., Competing Interests: Conflicts of interestThe authors declared that they have no conflicts of interest to this work., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy., (© The Author(s) 2022.)

The operating theatre is the most crucial and costly department in a hospital due to its expensive resources and high patient admission rate. Efficiently allocating operating theatre resources to patients provides hospital management with better utilization and patient flow. In this paper, we tackle both tactical and operational planning over short-term to medium-term horizons. The main goal is to determine an allocation of blocks of time on each day to surgical specialties while also assigning each patient a day and an operating room for surgery. To create a balance between improving patients welfare and satisfying the expectations of hospital administrators, we propose six novel deterioration rates to evaluate patients total clinical condition deterioration. Each deterioration rate is defined as a function of the clinical priorities of patients, their waiting times, and their due dates. To optimize the objective functions, we present mixed integer programming (MIP) models and two dynamic programming based heuristics. Computational experiments have been conducted on a novel well-designed and carefully chosen benchmark dataset, which simulates realistic-sized instances. The results demonstrate the capability of the MIP models in finding excellent solutions (maximum average gap of 4.71% across all instances and objective functions), though, requiring large run-times. The heuristic algorithms provide a time-efficient alternative, where high quality solutions can be found in under a minute. We also analyse each objective function's ability in generating high quality solutions from different perspectives such as patients waiting times, the number of scheduled patients, and operating rooms utilization rates. We provide managerial insights to the decision makers in cases where their intention is to meet KPIs and/or maintaining trade-offs between patients and administrators expectations, more fair assignments, or ensuring that the most urgent patients are taken care of first., (© The Author(s) 2022.)

Agriculture is a distinct sector of a country's economy. In recent years, new patterns have evolved in the agricultural industry. In conjunction with sensor scaling down and precision agriculture, the field of remote sensor networks, such as the wireless sensor network (WSN), was developed. Its major purpose is to make horticultural operations simpler to identify, assess, and manage. This paper uses the proposed DCNN to predict soil moisture and plan irrigation for precision agriculture farmers to reduce water consumption used for cultivation and increase production yield by comparing water content during various stages of plant growth and integrating IoT applications into agriculture. It also optimizes the water level for future irrigation decisions to maintain crop growth and water stability. The data must be served and stored in the form of a grid view, according to Apriori and GRU (gated recurrent unit). Using numerous sensor and parameter modelling methodologies, this system assists in the prediction of irrigation planning based on irrigation needs. The predicted parameters include soil moisture, temperature, and humidity. This observed experimental data supports smart irrigation in crop production with a high yield and little water use. DCNN has a 98.5% experimental result accuracy rate and the MSE value is predicted in DCNN 99.25% of the time., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis. However, due to the complex morphology of atherosclerosis plaques and the lack of well-annotated data, the segmentation of lumen and wall is very challenging. Different from popular deep learning methods, in this paper, we propose an integration segmentation framework by introducing a lightweight prediction model and improved optimal surface graph cuts (OSG), which adopts a simplified flow line sampling and post-reconstructing method to reduce the cost of graph construction. Moreover, a flexibly adaptive smoothing penalty is presented for maintaining the shape of diseased carotid surface. For the experiments, we have collected an MR image dataset from patients with carotid atherosclerosis and evaluated our method by cross-validation. It can reach 89.68%/80.29% of dice coefficients and 0.2480 mm/0.3396 mm of average surface distances on the lumen/wall segmentation, respectively. The experimental results show that our method can generate precise and reliable segmentation of both lumen and wall of diseased carotid artery with a quite small training cost., (© 2022. International Federation for Medical and Biological Engineering.)

Healthcare relies upon the accurate and safe delivery of patient care. This is only achievable when systems are developed to ensure high quality, robust outcomes, for instance quality management systems. The concept of quality management can take on a different meaning depending on the context in which it is found. To add complication, the amount of education required for quality management will vary depending on one's exposure to the implementation of quality systems. In part to address these issues, the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) Queensland Branch held a quality management webinar for members and non-members across Australia and New Zealand. The purpose of the webinar was to educate and facilitate discussion regarding the application of quality management principles for the ACPSEM profession. In conjunction, a pre- and post-webinar survey was conducted to gain an insight into existing knowledge and attitudes within the professions governed by the ACPSEM and students undertaking related studies. This paper authored by the webinar speakers reintroduces the quality management principles that were discussed in webinar, exemplifies the importance of quality management skills within the ACPSEM professions and presents the results of the surveys, promoting the need for more educational resources on quality management tools., (© 2022. Australasian College of Physical Scientists and Engineers in Medicine.)

Excess molybdenum (Mo) and cadmium (Cd) are harmful to animals, but neurotoxicity caused by Mo and Cd co-exposure in ducks is yet unknown. To assess joint impacts of Mo and Cd on autophagy via calcium homeostasis and unfolded protein response (UPR) in duck brain, 40 healthy 7-day-old ducks (Anas platyrhyncha) were assigned to 4 groups at random and fed diets supplemented with different doses of Mo or/and Cd for 16 weeks, respectively. Brain tissues were excised for experiment. Results exhibited that Mo or/and Cd disturbed calcium homeostasis by decreased ATPase activities and increased calcium (Ca) content, and upregulated calcium homeostasis-related factors Ca 2+ /CAM-dependent kinase IIɑ (CaMKIIɑ), calcineurin (CaN), inositol-1,4,5-trisphosphate receptor (IP 3 R), and calreticulin (CRT) expression levels. Meanwhile, the upregulation of UPR-related factor expression levels indicated that Mo or/and Cd activated UPR. Moreover, Mo or/and Cd triggered autophagy through promoting the number of autophagosomes and LC3II immunofluorescence intensity and altering autophagy key factor expression levels. Correlation analysis showed that there were obvious connections among Ca 2+ homeostasis, endoplasmic reticulum (ER) stress, and autophagy induced by Mo or/and Cd. Thence, it can be speculated that autophagy initiated by Mo or/and Cd may be associated with interfering Ca 2+ homeostasis and triggering UPR., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

During the last two decades, the world has experienced three major outbreaks of Coronaviruses, namely severe acute respiratory syndrome (SARS- CoV), middle east respiratory syndrome (MERS-CoV), and the current ongoing pandemic of severe acute respiratory syndrome 2 (SARS-CoV-2). The SARS-CoV-2 caused the disease known as Coronavirus Disease 2019 (COVID-19). Since its discovery for the first time in Wuhan, China, in December 2019, the disease has spread very fast, and cases have been reported in more than 200 countries/territories. In this study, the idea of Smarandache's pathogenic set is used to discuss the novel COVID-19 spread. We first introduced plithogenic graphs and their subclass, like plithogenic fuzzy graphs. We also established certain binary operations like union, join, Cartesian product, and composition of pathogenic fuzzy graphs, which are helpful when we discuss combining two different graphs. In the end, we investigate the spreading trend of COVID-19 by applying the pathogenic fuzzy graphs. We observe that COVID-19 is much dangerous than (MERS-CoV) and (SARS-CoV). Moreover, as the SARS-CoV and MERS-CoV outbreaks were controlled, there are greater chances to overcome the current pandemic of COVID-19 too. Our model suggests that all the countries should stop all types of traveling/movement across the borders and internally too to control the spread of COVID-19. The proposed model also predicts that in case precautionary measures have not been taken then there is a chance of severe outbreak in future., Competing Interests: Conflict of interestAuthors declare that there is no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.)

The greatest threat to the word in recent days is the spread of COVID 19 virus throughout the world. To tackle this problem government of India has implemented various restrictions to be followed to stop the spread of the COVID 19 virus. But most of the time general public forget their responsibilities and don't follow these restrictions, especially in situations like when their favourite hero's movie releases in the theatre, and in spending time in hotels, malls and in other entertainment places in spite of governments occupancy restrictions in those places. In order to address this problem we propose an IoT based Smart System for monitoring the occupancy in such entertainment spots and screen the public entry if they dint follow the protocols such as if they dint wear mask or if they have body temperature. This proposed system is implemented on a Raspberry Pi 3B+ processor which runs on a Broadcom processor. For monitoring the occupancy and screen the visitors for mask, we use a Passive Infrared Sensors and Pi camera to count the person entering into the premises. And we use a MLX90614 Infrared temperature sensor for screening the public entry with high temperature. The complete system is implemented using python programming and the details will be uploaded to cloud, authorities can monitor this from a remote place so that the spread of COVID 19 can be restricted in pubic entertainment spots., Competing Interests: Conflict of interestThe authors declare that they do not have any conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

3D printing in modern radiotherapy allows users the ability to create custom devices which can be a valuable tool for use in brachytherapy source calibration. Radiotherapy centres may verify their brachytherapy source activity with a calibrated Farmer chamber. For this purpose, a jig was designed, 3D printed and commissioned for in-air source strength calibration. Measurements on four afterloaders with varied equipment and environments were completed. A full uncertainty budget was developed and measurements with the in-air jig were consistently within 3% of the certificate source strength, and within the 4.1% combined uncertainty for comparing a well chamber measurement (1.7%) with the in air jig (3.75%). By creating a jig that is able to be customised to multiple catheter sizes and cylindrical chamber designs, centres can be provided with the option of independently checking their source strength with ease and for little cost., (© 2021. Australasian College of Physical Scientists and Engineers in Medicine.)

Given the existing literature on the subject, there is obviously a need for specific advice on quality assurance (QA) tolerances for departments using or implementing 3D printed bolus for radiotherapy treatments. With a view to providing initial suggested QA tolerances for 3D printed bolus, this study evaluated the dosimetric effects of changes in bolus geometry and density, for a particularly common and challenging clinical situation: specifically, volumetric modulated arc therapy (VMAT) treatment of the nose. Film-based dose verification measurements demonstrated that both the AAA and the AXB algorithms used by the Varian Eclipse treatment planning system (Varian Medical Systems, Palo Alto, USA) were capable of providing sufficiently accurate dose calculations to allow this planning system to be used to evaluate the effects of bolus errors on dose distributions from VMAT treatments of the nose. Thereafter, the AAA and AXB algorithms were used to calculate the dosimetric effects of applying a range of simulated errors to the design of a virtual bolus, to identify QA tolerances that could be used to avoid clinically significant effects from common printing errors. Results were generally consistent, whether the treatment target was superficial and treated with counter-rotating coplanar arcs or more-penetrating and treated with noncoplanar arcs, and whether the dose was calculated using the AAA algorithm or the AXB algorithm. The results of this study suggest the following QA tolerances are advisable, when 3D printed bolus is fabricated for use in photon VMAT treatments of the nose: bolus relative electron density variation within [Formula: see text] (although an action level at [Formula: see text] may be permissible); bolus thickness variation within [Formula: see text] mm (or 0.5 mm variation on opposite sides); and air gap between bolus and skin [Formula: see text] mm. These tolerances should be investigated for validity with respect to other treatment modalities and anatomical sites. This study provides a set of baselines for future comparisons and a useful method for identifying additional or alternative 3D printed bolus QA tolerances., (© 2021. Australasian College of Physical Scientists and Engineers in Medicine.)

Accurate segmentation and delineation of the sub-tumor regions are very challenging tasks due to the nature of the tumor. Traditionally, convolutional neural networks (CNNs) have succeeded in achieving most promising performance for the segmentation of brain tumor; however, handcrafted features remain very important in identification of tumor's boundary regions accurately. The present work proposes a robust deep learning-based model with three different CNN architectures along with pre-defined handcrafted features for brain tumor segmentation, mainly to find out more prominent boundaries of the core and enhanced tumor regions. Generally, automatic CNN architecture does not use the pre-defined handcrafted features because it extracts the features automatically. In this present work, several pre-defined handcrafted features are computed from four MRI modalities (T2, FLAIR, T1c, and T1) with the help of additional handcrafted masks according to user interest and fed to the convolutional features (automatic features) to improve the overall performance of the proposed CNN model for tumor segmentation. Multi-pathway CNN is explored in this present work along with single-pathway CNN, which extracts simultaneously both local and global features to identify the accurate sub-regions of the tumor with the help of handcrafted features. The present work uses a cascaded CNN architecture, where the outcome of a CNN is considered as an additional input information to next subsequent CNNs. To extract the handcrafted features, convolutional operation was applied on the four MRI modalities with the help of several pre-defined masks to produce a predefined set of handcrafted features. The present work also investigates the usefulness of intensity normalization and data augmentation in pre-processing stage in order to handle the difficulties related to the imbalance of tumor labels. The proposed method is experimented on the BraST 2018 datasets and achieved promising results than the existing (currently published) methods with respect to different metrics such as specificity, sensitivity, and dice similarity coefficient (DSC) for complete, core, and enhanced tumor regions. Quantitatively, a notable gain is achieved around the boundaries of the sub-tumor regions using the proposed two-pathway CNN along with the handcrafted features. Graphical Abstract This data is mandatory. Please provide., (© 2021. International Federation for Medical and Biological Engineering.)

Purpose: Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features extracted from intermediate layers may not be appropriate to represent CXR images as they have a few semantic regions. Though the Bag of Visual Words (BoVW)-based features are shown to be more appropriate for different types of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other Pneumonia-related infections., Methods: In this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding the deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from Pneumonia., Results: We evaluate the effectiveness of our proposed BoDVW features in CXR image classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on four publicly available COVID-19 CXR image datasets (D1, D2, D3, and D4) reveal that our features produce stable and prominent classification accuracy (82.00% on D1, 87.86% on D2, 87.92% on D3, and 83.22% on D4), particularly differentiating COVID-19 infection from other Pneumonia., Conclusion: Our method could be a very useful tool for the quick diagnosis of COVID-19 patients on a large scale., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.)

This study investigates and validates the use of the Octavius 4D system for patient specific quality assurance on Halcyon, which is capable of rotating at 4 revolutions per minute (RPM). A commercially available PTW Octavius 4D system was used for this study which had a maximum rotation speed of 3 RPM. Initial validation included testing the accuracy of the inclinometer, percent depth doses (PDD), output factors, and dose profiles for selected static square fields. The same static fields were also subject to a gamma comparison with the TPS. This was followed by an evaluation of twelve clinical treatment plans and seven non-clinical plans with varying gantry rotation speeds. All testing was completed using detector array measurement times of 200 ms and 100 ms. Inclinometer accuracy was within 0.3° of actual gantry angle. Output factors varied less than 0.6%, PDD differences were no greater than 1.4%, and dose profile differences were less than 2.2%. Gamma pass rates for the static fields were 96.7% (2%/2mm) and 99.7% (3%/3mm). A prototype control unit, which had a maximum rotation speed of 4 RPM was also used to test the clinical and non-clinical plans. For the clinical plans, the mean gamma pass rates (2%/2mm) were 86.1% and 88.1% for the commercial unit and prototype unit respectively. Results using a measurement time of 200 ms were superior to those using 100 ms. For Halcyon deliveries greater than 3 RPM, worst case gamma results for the commercial unit were 28.6% compared to 98.5% using the prototype unit. Accurate patient specific quality assurance results can be obtained using the Octavius 4D system with a Halcyon linac, provided that the system measurement time is kept at 200 ms and the rotation speed of Halycon does not exceed 3 RPM. For higher RPM deliveries, an Octavius 4D unit with 4 RPM rotation capability is recommended.

Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of toddler, child, adolescent and adult. We have evaluated state-of-the-art classification and feature selection techniques to determine the best performing classifier and feature set, respectively, for these four ASD datasets. Our experimental results show that multilayer perceptron (MLP) classifier outperforms among all other benchmark classification techniques and achieves 100% accuracy with minimal number of attributes for toddler, child, adolescent and adult datasets. We also identify that 'relief F' feature selection technique works best for all four ASD datasets to rank the most significant attributes., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.)

Background Prescription opioids are a central aspect of pain management and as the prevalence of pain is increasing so is the rate of use of prescription opioids. Increased opioid prescriptions increases the risk of deaths and morbidity. Objective To (a) describe the 22-year trend of prescription opioid dispensing in Queensland, (b) examine the effect of opioid dose, formulation and socioeconomic status on the number of prescriptions dispensed. Design/setting Retrospective analysis of data from the Monitoring of Drugs of Dependence system of the Monitored Medicines Unit of Queensland Health, Australia. Participants Queensland residents (3.3 million) from 18 years old dispensed 18.8 million opioid prescriptions from January 1997 to December 2018. Results Opioid prescriptions dispensed annually increased to over two million in 2018 from about 150,000 prescriptions in 1997. The number of prescriptions for modified-release formulations dispensed annually was three times higher compared to the immediate-release formulations. Oxycodone accounted for over 60% of prescriptions for pharmaceutical opioids since 2013. There was an increase in the number of prescriptions dispensed as socioeconomic status decreased and modified-release opioid formulations positively affects the pattern of dispensing. The highest increase in number of prescriptions dispensed (for all opioids) was observed among the high socioeconomic status (IRR = 1.25, 95% CI 1.25, 1.26). The disparities in the annual number of prescriptions across dose categories are wider in the modified-release than the immediate-release formulations. Conclusion The dispensing of opioids increased significantly in Queensland. There was a positive relationship between the increased dispensing of opioids and locations of lower socioeconomic status.

Background There is an association between the duration of prescription opioids use and an increased risk of serious harm, often unintentional. Objective (1) Describe the trends in duration of prescription opioids dispensing and, (2) determine the risk of long-term use (≥4 months) based on patients' socioeconomic status, daily dose in oral daily morphine milligram equivalent, and opioid formulation. Setting Residents of Queensland (2,827,727), Australia from the age 18 years and who were dispensed pharmaceutical opioids from 1 January 1997 to 31 December 2018. Method Retrospective, longitudinal population-based analysis using data obtained from the Monitoring of Drugs of Dependence system of the Monitored Medicines Unit of Queensland Health. Main outcome measure Contribution of socioeconomic status, and daily dose and opioid formulation (modified-release or immediate-release) to the risk of long-term opioid use. Results There was little difference between the number of patients dispensed opioids for ≥4 months and ≤3 months between 1997 and 2011. Thereafter, the number for those using opioids long-term increased. The highest risk of having opioids dispensed for ≥4 months were for patients in the lowest level of socioeconomic status (adjusted odds ratio 1.36; 95% CI, 1.34, 1.38), compared to people in the highest socioeconomic status areas, followed by the low-socioeconomic status areas, mid-socioeconomic status areas, and high-socioeconomic status areas respectively. The risk of being dispensed prescription opioids for ≥4 months significantly increased as the dose increased: adjusted odds ratio 1.73; 95% CI, 1.71, 1.75, adjusted odds ratio 1.89; 95% CI, 1.87, 1.92, and adjusted odds ratio 3.63; 95% CI, 3.58, 3.69 for the ≥20 to <50 oral daily morphine milligram equivalent, ≥50 to <100 oral daily morphine milligram equivalent and ≥100 oral daily morphine milligram equivalent dose categories, respectively. Conclusion Higher doses and living in a low socioeconomic status areas were associated with increased risk of long-term dispensing of opioid prescriptions.

Different ZnO nanostructures can be grown using low-cost chemical bath deposition. Although this technique is cost-efficient and flexible, the final structures are usually randomly oriented and hardly controllable in terms of homogeneity and surface density. In this work, we use colloidal lithography to pattern (100) silicon substrates to fully control the nanorods' morphology and density. Moreover, a sol-gel prepared ZnO seed layer was employed to compensate for the lattice mismatch between the silicon substrate and ZnO nanorods. The results show a successful growth of vertically aligned ZnO nanorods with controllable diameter and density in the designated openings in the patterned resist mask deposited on the seed layer. Our method can be used to fabricate optimized devices where vertically ordered ZnO nanorods of high crystalline quality are crucial for the device performance.

Purpose: Obstructive sleep apnoea (OSA) is a prevalent sleep disorder with significant health consequences. Sleep fragmentation is a feature of OSA and is often determined by the arousal index (ArI), a metric based on the electroencephalograph (EEG). The ArI has a weak correlation with neurocognitive outcomes in OSA patients. In this study, we examine whether changing from the current minimum EEG arousal duration of 3 s improves the association between sleep fragmentation and neurocognitive outcomes., Methods: In a retrospective study, we selected OSA patients without any other comorbidities that are associated with neurocognitive impairment. The OSA patients were clustered into two groups based on their psychomotor vigilance task (PVT) performance to represent impaired and unimpaired neurocognition., Results: While no differences were found in demographics or usual sleep study statistics, the impaired group had a greater number of EEG arousals greater than 5 s (P = 0.034), 7 s (P = 0.041), and 15 s (P = 0.036) in duration. There were no differences in the number of EEG arousals associated with sleep-disordered breathing events. These differences also corresponded with quality of life outcomes between the two groups. An ArI with a duration of 5 s or greater had the best combination of sensitivity (70.0%) and specificity (66.7%) compared with the usual 3 s duration (sensitivity and specificity of 70.0% and 53.3%, respectively)., Conclusion: A re-examination of the EEG arousal scoring rules, and their duration, may help with allocation of health resources to OSA patients most in need.

Monte Carlo simulations of lung equivalent materials often involve the density being artificially lowered rather than a true lung tissue (or equivalent plastic) and air composition being simulated. This study used atomic composition analysis to test the suitability of this method. Atomic composition analysis was also used to test the suitability of 3D printing PLA or ABS with air to simulate lung tissue. It was found that there was minimal atomic composition difference when using an artificially lowered density, with a 0.8 % difference in Nitrogen the largest observed. Therefore, excluding infill pattern effects, lowering the density of the lung tissue (or plastic) in simulations should be sufficiently accurate to simulate an inhaled lung, without the need to explicitly include the air component. The average electron density of 3D printed PLA and air, and ABS and air were just 0.3 % and 1.3 % different to inhaled lung, confirming their adequacy for MV photon dosimetry. However large average atomic number differences (5.6 % and 20.4 % respectively) mean that they are unlikely to be suitable for kV photon dosimetry.

Purpose: Hypertension is a common finding in patients with obstructive sleep apnea (OSA), but it has remained unclear whether or not the amount of disturbed breathing and characteristics of individual respiratory events differ between hypertensive and normotensive patients with severe OSA., Methods: Full polysomnographic recordings of 323 men and 89 women with severe OSA were analyzed. Differences in the duration of individual respiratory events, total apnea and hypopnea times, and the percentage of disturbed breathing from total sleep time (AHT%) were compared between normotensive and hypertensive patients separately by genders. Furthermore, differences in the respiratory event characteristics were assessed between three AHT% groups (AHT% ≤ 30%, 30% < AHT% ≤ 45%, and AHT% > 45%)., Results: Hypertensive women had lower percentage apnea time (15.2% vs. 18.2%, p = 0.003) and AHT% (33.5% vs. 36.5%, p = 0.021) when compared with normotensive women. However, these differences were not observed between hypertensive and normotensive men. Percentage hypopnea time was higher in hypertensive men (13.5% vs. 11.2%, p = 0.043) but not in women (15.2% vs. 12.2%, p = 0.130) compared with their normotensive counterparts. The variation in AHI explained 60.5% (ρ = 0.778) and 65.0% (ρ = 0.806) of the variation in AHT% in normotensive and hypertensive patients, respectively. However, when AHT% increased, the capability of AHI to explain the variation in AHT% declined., Conclusions: There is a major inter- and intra-gender variation in percentage apnea and hypopnea times between hypertensive and normotensive patients with severe OSA. OSA is an important risk factor for hypertension and thus, early detection and phenotyping of OSA would allow timely treatment of patients with the highest risk of hypertension.

Investment in the share market helps generate more profit than the other financial instruments but has the threat of market risk that might lead to a high loss. This risk factor refrains many potential investors from investing in the share market directly. Instead, they invest in different mutual funds that are being managed by experienced portfolio managers. To avoid the risk factors and increase the gain, they put the accumulated capital in multiple stocks. They need to perform many calculations and predictions to overcome the uncertainties and unpredictability and need to ensure higher gains to the investors of that mutual fund. In this research work initially, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price. Based on the above analysis, we propose a framework to diversify the investment of the capital fund. This method employs buy and hold strategy using both statistical features and basic domain knowledge of the share market. The proposed framework distributes the capital first, by distributing sector-wise, and then for each sector, investing company-wise, as a diversified approach among different stocks for higher return but maintaining lower risks. Experimental results show that the proposed framework performs well and generates a good yield compared to some benchmark and ranked mutual funds in the Indian stock market., (© Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

In the current trends, face recognition has a remarkable attraction towards favorable and inquiry of an image. Several algorithms are utilized for recognizing the facial expressions, but they lack in the issues like inaccurate recognition of facial expression. To overcome these issues, a Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) is proposed for recognizing the facial expressions. The main motive of this work is to recognize human emotions in an effective manner. Initially, the face image is identified using the Viola-Jones algorithm. Subsequently, the facial parts such as right eye, left eye, nose and mouth are extracted from the detected facial image. The edge-based invariant transform feature is utilized to extract the features from the extracted facial parts. From this edge-based invariant features, the dimensions are optimized using Weighted Visibility Graph which produces the graph-based features. Also, the shape appearance-based features from the facial parts are extracted. From these extracted features, facial expressions are recognized and classified using a Self-Organizing Map based Neural Network Classifier. The performance of this GFE-HCA approach is evaluated and compared with the existing techniques, and the superiority of the proposed approach is proved with its increased recognition rate., (© Springer-Verlag GmbH Germany, part of Springer Nature 2020.)