Articles

Background:</b> Smoking cessation interventions are critical for underserved populations, particularly among low-income individuals who may benefit from tailored support. However, the effectiveness of different intervention formats remains unclear, particularly as virtual and hybrid models gain popularity. <b>Aims:</b> This study compares the effectiveness of three smoking cessation intervention arms in a quasi-experimental design: Self-help group (Arm 1), In-person group (Arm 2), and Virtual/hybrid group (Arm 3). The primary outcome was the rate of successful quit across these different intervention modalities. <b>Methods:</b> The study utilized a community-based intervention approach, controlling for potential confounders. The communities were randomized, and this process was blinded. The effectiveness of the In-person group and the Virtual/hybrid group was compared to the Self-help group. The odds ratio (OR) for successful quit rates was calculated for each group, with corresponding 95% confidence intervals (CIs). <b>Results:</b> Participants included 50.4% of women, 82.8% were Black Americans, 11.6% Whites, and 3.4% other races. In-person group (Arm 2) showed a higher rate of successful quit compared to the Self-help group (OR = 2.67, 95% CI = 1.05, 6.79). Virtual/hybrid group (Arm 3) was not associated with a significantly higher quit rate compared to the Self-help group (OR = 1.48, 95% CI = 0.57, 3.83). <b>Conclusion:</b> The In-person group, which utilizes the CEASE curriculum and incorporates peer motivation, proved to be significantly more effective than both the Self-help and Virtual/hybrid groups. The findings suggest that low-income, underserved smokers may not be fully prepared to benefit from virtual interventions, or the current curriculum may need adaptation to better serve their needs in a virtual format.

Smallholder farmers face challenges when they lack information on their agricultural activities. To address this, we suggest a web-based system that can be used by farmers to help them in decision making considering the fact that all necessary information is provided by the system. Farmers can input crop type they want to grow and area. This data will help to recommend them the best crops that are suitable to be grown in that area and the necessary growing practices that can be done to produce high yield and have maximum profits, considering the average rainfall of that year. A persistent issue we face in Zimbabwe is the lack of access to reliable agricultural data. In the agricultural sector, one major uncertainty for farmers is the outlook of their future harvest. Once their produce is ready for sale, the presence of other potential buyers compels traders to offer prices that align closely with those in the formal market. However, without timely information, traders can take advantage of the situation by purchasing crops at unfairly low rates. Having data that tracks prices across various markets in near real-time would enable farmers to have a precise and complete understanding of their selling choices to maximize their profits.

Nowadays, recommendation systems are existing everywhere in the internet world, online people are presented with the required needs not just for actual physical products, but also for several other things such as songs, places, books, friends, movies, and many more requirements. Most of the systems are developed with the basic collaborative and hybrid filtering, where the people or users are recommended items that the choices are based on the right preferences of other people by applying the machine intelligence strategies. In this research, the importance of network modeling is analyzed in solving real-world problems.

This paper is a continuation of the researches which were carried out by [1,2,3]. Accordingly, this manuscript proposes analytical analysis of a novel triple skin Concrete Filled Steel Tube (CFST) under the effect of sudden impact. Moreover, this is done by extending the double skinned CFST design and installing a third inner CFST inside the second inner tube to achieve the proposed triple skinned CFST design. Furthermore, the propositions consist of two parts. Where the first proposition is a novel triple skin CFST design under the effect of sudden impact, with first sandwich layer filled with Ultra High-Performance Fiber Reinforced Concrete (UHPFRC) and second sandwich layer filled with Normal Strength Concrete (NSC). While the second proposition is a novel triple skin CFST under the effect of sudden impact, with first sandwich layer filled with UHPFRC, second sandwich layer filled with NSC and third skin internal tube filled with NSC. It is strongly believed by the author of this manuscript that (1) the first proposition of novel triple skin CFST will increase the impact resistivity of the structural member by 25 to 32% and (2) it is predicted that the second proposition of novel triple skin CFST will boost the efficiency of the structural member under the even of sudden impact by 28 to 36%.

The major automated plants have produced large volumes of high-quality products at low cost by introducing various technologies, including robotics and artificial intelligence. The code of many defects on the surface of products is embedded with economic loss and sometimes functionality loss because products are rarely found with defects. Therefore, most items’ production is done based on prediction and has an invisible fluctuation in production. The detection process for hidden defect images requires a lot of costs and needs to be supported for better progress and quality enhancement. Paint shop defects should be analyzed from color changes to detect defects effectively by preventing the variability of product demand over time. It is not easy to take visible light images without noise because the paint surfaces are glossy. A few parts of illumination and shadows remain in images, even in larger size and high-resolution images. The various painted surfaces are also needed to reflect both color and texture information in computer vision models to classify defects precisely. Several automated detection systems have been applied to paint shop inspections using lasers, infrared, x-ray, electrical, magnetic, and acoustic sensors. The chance of paint shop defects can be low, unnecessarily low, compared to clouds in the sky, but those chances impact defect functionalities. Thus, they are called as “lessons learned.” Lately, artificial intelligence has been introduced to the field of factory automation, and many defect detection feeds have found footsteps in machine learning and deep learning. Recent attempts at deep learning-based defect detection are proposing simple techniques using specific neural network architectures with big data. However, big data is still in its early stages, and significant challenges exist in normalizing and annotating such data. To get cost-efficient and timely solutions tailored to automotive paint shops, it might be a better consideration to combine deep learning solutions with traditional computer vision and more elaborate machine learning methods.

Key business objectives for digital infrastructure cloud adoption are often framed in terms of reducing cost, improving fault tolerance and resilience, simplifying scale, and enabling innovation. Given the critical nature of the financial sector, however, where timeliness and price can significantly determine an outcome, cloud migration in delivery environments demands greater throughput on the critical path and, in many enterprise-scale settings, forgoes hybrid complexity and multi-cloud risks. Nevertheless, slack in system designs does exist; financial institutions enable market functionality—trading, clearing/best execution—despite potentially being able to meet such sets with lower service levels than other verticals. A cloud multi-account structure for sensitive data, for example, naturally limits exposure when combined with observed risk. Fulfilling predictions of elasticity during periods of high demand usually requires support from a dedicated environment (or environments) located nearer to the operations. Components can consequently be allocated on a per-account basis or maintained as shared sink systems to which the dedicated streams write. The automation code can similarly be targeted for dedicated accounts, avoiding the resource constraints that beset such operations during industry events like emergency triage/contact desking.