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Abstract This research tries to establish the emissions diffusion model of high-speed motor vehicles on the Capital Ring Expressway (CRE) around Beijing, China, applying the MOVES and the CALPUFF models. Data required for this study have been collected from different sources. The results show that small buses and large trucks have the highest pollutant emissions on the CRE. The pollutants like CO and HC mainly comes from gasoline vehicles, while NOx, PM10 and PM2.5 comes from diesel vehicles. The average concentrations of the diffusion of CO, NOx, CH4, PM10, and PM2.5 along with the CRE are 6.5 mg/m3, 2.3 mg/m3, 0.27 mg/m3, 0.055 mg/m3 and 0.056 mg/m3 respectively. The results of this study have practical significance and influence on the emission reduction and concentration control strategies under different circumstances, and put forward suggestions on changing transportation modes, strengthening supervision and so on. It has produced a number of recommendations for the policy makers including (1) active promotion of new energy vehicles on expressways, (2) strategic change of the mode of passenger and freight transportation on expressways, (3) strengthen traffic management and supervision of motor vehicles on expressways, and (4) strengthen vegetation protection in rural and ecologically sound areas along the route.

Abstract Background Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. Materials and methods The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. Results Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). Conclusion This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient’s CVD prognosis.

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.

IntroductionGlobal Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.MethodsThis paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.ResultsOn the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.DiscussionThese findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model’s use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

As an eco-friendly land management strategy, agroforestry can combine farming, forestry, and livestock to provide an assortment of ecosystem services (ES). Piper Chaba- based agroforestry system (PCAFS), a prevalent agroforestry system in northern Bangladesh, shows promise for environmental protection and diverse ES, yet lacks sufficient literature-based support. Addressing this discrepancy, the study surveyed 105 Piper chaba farmers from the Chinai union of Rajarhat upazila in the Kurigram district of Bangladesh to assess the PCAFS's present potentials in providing ecosystem services and environmental benefits as well as the potential conservation initiatives that could be adopted to sustain this agroforestry system. Data were collected using mixed-method techniques including direct observation, surveying through semi-structured questionnaires, key informant interviews, and focus group discussions before data triangulation. The study identified twenty common PCAFS combinations, having betel nut-Piper chaba-banana (67.62%), betel nut-Piper chaba-papaya (49.52%), and mango-Piper chaba-paddy (48.57%) as the most prominent. As indicated by the Ecosystem Improvement Index (EII) derived from farmer perceptions, PCAFS significantly enhanced various types of ecosystem services (ES). PCAFS played a vital role in provisioning services by supplying diversified products. It significantly improved air and water quality as well as reduced soil erosion, ranking second and third among the ecosystem services. EII identified the most significant overall improvement as the enhancement of soil fertility, a crucial supporting service delivered by PCAFS. The study revealed that, according to the opinion of the respondents, PCAFS positively influenced various aspects critical to environmental functioning through its contribution to ecosystem services. This agroforestry system excelled in key areas like cost-efficiency, product diversity, and livelihood enhancement. However, it faced significant challenges, notably disease outbreaks and a fragile market system. To address these weaknesses, eight inclusive strategies were pinpointed, with the most critical involving the establishment of a robust market system and the formation of farmers' collectives. The study recommends enhancing PCAFS utilization by investing in more research, development, and oversight to maximize its ecosystem benefits.

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations; HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep Compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper introduces a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia’s air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value

Twitter is a useful tool for effectively tracking and managing crisis-related incidents. However, due to many irrelevant features in textual data, the problem of high dimensionality arises, which eventually increases the computational cost and decreases classification performance. Thus, to handle such a problem, this work presents a Spark-based hybrid binary Bat (BBA) and binary Salp swarm algorithm (BSSA) named SBBASSA for feature selection and classification of crisis-related tweets. In the proposed technique, the hybridization of standard BBA and BSSA algorithms is performed to enhance their exploration capabilities, then the combined algorithm is implemented in parallel using Apache Spark framework to reduce the overall execution time during the feature selection process. A support vector machine (SVM) classifier is applied during the wrapper-based feature subset selection and classification. The performance of the proposed SBBASSA was analyzed on six benchmark crisis tweet datasets, namely Hurricane Sandy, Boston Bombings, Oklahoma Tornado, West Texas Explosion, Alberta Floods, and Queensland Floods, and then compared with standard BSSA, BBA, and binary particle swarm optimization (BPSO). Results showed that SBBASSA performed competently in the feature selection and classification, outperformed other algorithms in crisis tweet classification, and achieved the highest accuracy with the lowest feature set in a reduced execution time.

The Internet of Things (IoT) represents a dynamic infrastructure, leveraging sensing and network communication technology to establish ubiquitous connectivity among people, machines, and objects. Due to its end devices’ limited computing resources and storage space, it is not feasible to merely transpose traditional internet security technologies directly to IoT endpoints. Maintaining security while concurrently ensuring performance is a particularly challenging endeavor. This paper provides a review of key agreements and authentication protocols pivotal to the security of IoT. First, this survey discusses the applications that need authentication and key agreement to strengthen their security and current research on these application fields. Subsequently, this paper engages in an in-depth exploration of the phase involved in the scheme of authentication and key agreement, including an examination of the cryptographic techniques employed within these processes. This survey also thoroughly studies the scheme’s security services, potential attacks, formal analysis and informal analysis to ensure resilience against such threats. This study aims to provide a profound understanding of the recent research on authentication and key agreement in IoT applications. It strives to contribute towards strengthening security systems for IoT applications, ensuring their sustainability in the face of evolving threats.

In contemporary society, the proliferation of online hateful messages has emerged as a pressing concern, inflicting deleterious consequences on both societal fabric and individual well-being. The automatic detection of such malevolent content online using models designed to recognize it, holds promise in mitigating its harmful impact. However, the advent of “Hateful Memes” poses fresh challenges to the detection paradigm, particularly within the realm of deep learning models. These memes, constituting of a textual element associated with an image are individually innocuous but their combination causes a detrimental effect. Consequently, entities responsible for disseminating information via web browsers are compelled to institute mechanisms that regulate and automatically filter out such injurious content. Effectively identifying hateful memes demands algorithms and models endowed with robust vision and language fusion capabilities, capable of reasoning across diverse modalities. This research introduces a novel approach by leveraging the multimodal Contrastive Language-Image Pre-Training (CLIP) model, fine-tuned through the incorporation of prompt engineering. This innovative methodology achieves a commendable accuracy of 87.42%. Comprehensive metrics such as loss, AUROC, and f1 score are also meticulously computed, corroborating the efficacy of the proposed strategy. Our findings suggest that this approach presents an efficient means to regulate the dissemination of hate speech in the form of viral meme content across social networking platforms, thereby contributing to a safer online environment.

The Internet of Things (IoT) is transforming how we live and work, and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail. Considering its wide range of applications, addressing the security challenges arising from IoT devices’ massive collection and transmission of user data is vital. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research needs a comprehensive summary of deep learning-based IDS in IoT. This paper introduces intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

Energy-outlook from past to future specific years has become essential in energy-economy. Malaysia is a member of ASEAN (Association of South-east Asian Nations), and ASEAN is increasing the use of renewable energy by 2050 to reduce carbon dioxide (CO2) emissions. Coal is Malaysia's primary fossil fuel for energy generation, producing large amounts of CO2 and exacerbating greenhouse problems, including air pollution. Moreover, renewable energy capacity has increased significantly worldwide since 2020 during the COVID-19 pandemic. Therefore, this study provides an energy-outlook study from 1990 to 2050 as a rationale for Malaysia's emphasis on sustainable energy in the post-COVID-19 years, which has been felt to be absent in previous studies. Here, past data as well as projections of future coal trade, hydropower growth, electricity consumption, renewable energy consumption, and CO2 emissions are shown. An energy-outlook procedure is explored by reviewing the literature to derive data visualizations and projections. Business-as-usual (BAU) and Alternative-policy Scenarios (APS) methods have been used for projections. The article prioritizes coal since coal is the main fossil fuel for electricity generation in Malaysia. The study observed a significant increase in CO2 emissions with increasing energy demand in Malaysia, which is a concern, and the article emphasized the importance of renewable energy in mitigating CO2 emissions. Finally, Long-short-term memory (LSTM) has been used as an initial experiment on how Artificial Intelligence (AI) can be used in the energy outlook study, which indicates the research scope of AI.