Factors motivating smoking cessation: A cross-sectional study in a lower-middle-income country

Introduction: Only one-quarter of smokers in Pakistan attempt to quit smoking, and less than 3% are successful. In the absence of any literature from the country, this study aimed to explore factors motivating and strategies employed in successful smoking cessation attempts in Pakistan, a lower-middle-income country.Methods: A survey was carried out in Karachi, Pakistan, amongst adult (≥ 18 years) former smokers (individuals who had smoked ≥100 cigarettes in their lifetime but who had successfully quit smoking for \u3e 1 month at the time of survey). Multivariable logistic regression, with number of quit attempts (single vs. multiple) as the dependent variable, was performed while adjusting for age, sex, monthly family income, years smoked, cigarettes/day before quitting, and having suffered from a smoking-related health problem.Results: Out of 330 former smokers, 50.3% quit successfully on their first attempt with 62.1% quitting cold turkey . Only 10.9% used a cessation aid (most commonly nicotine replacement therapy: 8.2%). Motivations for quitting included self-health (74.5%), promptings by one\u27s family (43%), and family\u27s health (14.8%). Other social pressures included peer-pressure to quit smoking (31.2%) and social avoidance by non-smokers (22.7%). Successful smoking cessation on one\u27s first attempt was associated with being married (OR: 4.47 [95% CI: 2.32-8.61]), employing an abrupt cessation mode of quitting (4.12 [2.48-6.84]), and telling oneself that one has the willpower to quit (1.68 [1.04-2.71]).Conclusion: In Pakistan, smoking cessation is motivated by concern for self-health and family\u27s health, family\u27s support, and social pressures. Our results lay a comprehensive foundation for the development of smoking-cessation interventions tailored to the population of the country.Implications: Little is known about the patterns and strategies employed by smokers who are attempting to quit smoking, especially in lower-middle-income countries like Pakistan. Likewise, there are very few smoking cessation programs designed to assist in quitting. Our study will allow for a better understanding of the culture-specific motivating factors and strategies that most contributed to successful quit attempts. Based on these results, evidence based smoking cessation interventions can be developed tailored to the socioeconomic demographic of our country and region, including smoking cessation clinics and public outreach and media campaigns highlighting key elements of successful smoking cessation

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications

Short-term global horizontal irradiance forecasting using weather classified categorical boosting

Accurate short-term solar irradiance (SI) forecasting is crucial for renewable energy integration to ensure unit commitment and economic load dispatch. However, hourly SI prediction is very challenging due to atmospheric conditions and weather fluctuations. This study proposes a hybrid approach using weather classification and boosting algorithms for short-term global horizontal irradiance (GHI) forecasting. In data pre-processing steps, we employ random forest for feature selection and K-means clustering for weather classification. The weather-based clustered data is used for the model training using categorical boosting (CatBoost). The proposed weather-classified categorical boosting (WC-CB) scheme is compared with benchmarks in literature like adaptive boosting (AdaBoost), bi-directional long short-term memory (BiLSTM) and gated recurrent unit (GRU) using datasets from two distinct geographical locations obtained from the National Solar Radiation Database (NSRDB). The results show that the proposed WC-CB hybrid approach has lower forecast errors compared to conventional CatBoost modelling. The error reduction of 16% and 39% in root mean square error and 6% and 9% in mean absolute error is recorded for the two datasets, respectively. These findings demonstrate the importance of weather classification in improving forecasting accuracy with potential implications for broader renewable energy applications

Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE)

FedraTrees: a novel computation-communication efficient federated learning framework investigated in smart grids

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources

Blockchain-assisted UAV communication systems: a comprehensive survey

Unmanned aerial vehicles (UAVs) have recently established their capacity to provide cost-effective and credible solutions for various real-world scenarios. UAVs provide an immense variety of services due to their autonomy, mobility, adaptability, and communications interoperability. Despite the expansive use of UAVs to support ground communications, data exchanges in those networks are susceptible to security threats because most communication is through radio or Wi-Fi signals, which are easy to hack. While several techniques exist to protect against cyberattacks. Recently emerging technology blockchain could be one of promising ways to enhance data security and user privacy in peer-to-peer UAV networks. Borrowing the superiorities of blockchain, multiple entities can communicate securely, decentralized, and equitably. This article comprehensively overviews privacy and security integration in blockchain-assisted UAV communication. For this goal, we present a set of fundamental analyses and critical requirements that can help build privacy and security models for blockchain and help manage and support decentralized data storage systems. The UAV communication system's security requirements and objectives, including availability, authentication, authorization, confidentiality, integrity, privacy, and non-repudiation, are thoroughly examined to provide a deeper insight. We wrap up with a discussion of open research challenges, the constraints of current UAV standards, and potential future research directions

Perspective Chapter: Beyond Delicious – The Hidden Functional Benefits of Cheese

Cheese; a diverse and healthy milkproduct with a long history that stretches back thousands of years. It is available worldwide in varying forms and is valued for its delicious taste and superior nutritional content. Classification of cheese is dependent on texture or moisture content, method of coagulation or coagulating agent, maturation or ripening, type of milk and manufacturing techniques. Cheese is comprised of macronutrients, micronutrients and functional nutrients; major macronutrients in cheese are proteins and fats, major micronutrients in cheese include vitamins and minerals and functional nutrients in cheese include cheese bioactive peptides, polyphenols, probiotic, prebiotic, conjugated linoleic acid, sphingolipids, phytanic acid, lactoferrin, γ aminobutyric acid and organic acids. Other than its great taste and flavor cheese is responsible for providing many health benefits i.e. gut protecting activity, antioxidative activity, anticariogenic activity, antihypertensive, antihyperglycemic, cardioprotective and osteoprotective activity to the body. This chapter will focus on the classification, nutritional composition and health benefits of cheese

A hybrid approach for forecasting occupancy of building’s multiple space types

The occupancy datasets are useful for planning important buildings’ related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants’ count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. The models’ performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively

5G-enabled contactless multi-user presence and activity detection for independent assisted living

Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being