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Abstract Background Despite the many benefits of exclusive breastfeeding to infants and mothers, only 33% of Jamaican infants are exclusively breastfed up to the recommend six months. This study was conducted to identify factors affecting mothers’ feeding choices focusing on barriers to exclusive breastfeeding of infants six weeks to less than six months old. Methods A qualitative study consisting of four focus group discussion sessions was conducted among 22 mothers attending postnatal clinics in western Jamaica from May to August 2016. The transcripts were coded by three independent coders and content analysis conducted to generate themes. Results Four themes were identified namely, perceived advantages of breastfeeding centered mainly on the benefits of breastfeeding for the infant and mother, perceived barriers of breastfeeding highlighting physical pain and fatigue, supplementing culturally acceptable complementary foods and herbal remedies, and cultural norms including perception of how breastfeeding affects a woman’s body, societal sources of breastfeeding information, satiation of infants, and family and other support. Mothers overwhelmingly agreed that breastfeeding was inexpensive, allowed them to bond with their infants and was good for the overall health and intellectual development of the infants. They identified painful nipples, engorged breasts, lack of sleep, physical exhaustion and pressure to return to work as barriers to breastfeeding. Mothers named a number of complementary foods, such as pumpkin, carrots, potato, banana, and chocho (Chayote), that were culturally accepted for feeding infants in Jamaica and discussed herbs that were considered to aid in infants’ nutrition and overall health. Other cultural factors that were noted to influence exclusive breastfeeding were mothers feeling that breastfeeding would help their bodies, especially their bellies, go back to their pre-maternity figure, sources of breastfeeding information in the society including the internet, belief that breast milk alone does not satisfy babies, and family and other support. Conclusion Mothers in this study identified unique challenges to exclusive breastfeeding that if addressed, would help to increase exclusive breastfeeding so that the World Health Organization’s exclusive breastfeeding recommendations can be achieved.

Abstract Background In Alabama only 4% of those eligible have been screened for lung cancer. The ALCASE project focused on navigating eligible individuals to lung cancer screening. Methods Trained local staff enrolled screen eligible individuals from seven rural counties and one urban county. Demographics and knowledge of and barriers to lung cancer screening were collected using questionnaires. Education was provided and individuals were navigated to undergo screening. Descriptive statistics for enrolled and screened participants were computed using SAS 9.4. Debriefing interviews were conducted with the ALCASE staff regarding facilitators/barriers to implementing this project and lessons learned. Using NVivo, themes were identified through a combined deductive and inductive process. Results In total, 447 people were contacted of which 257 were enrolled. Participants were predominantly African American (86.8%), female (56.8%), and 86.4% had health insurance. Study participants acknowledged the need for more education of lung cancer/screening procedures; help navigating clinics for screening services and having healthcare facilities close to home. The top five barriers to getting screened were transportation, financial issues, emotional concerns, healthcare insurance, and COVID‐19. Of the 257, 106 participants (41%) completed a primary care referral and were screened for lung cancer. Debriefing interviews revealed: (i) Overall impressions of implementing ALCASE were positive. (ii) Barriers in implementing ALCASE were identified predominantly at the physician and institutional level. (iii) Facilitators in implementing ALCASE were identified at multiple levels. (iv) Suggestions on improving lung cancer screening leaned toward mitigating barriers at the institutional and structural level. Conclusion Ability to get screened is severely challenged by both personal and structural barriers.

AbstractIntelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges.

This research article discusses the challenges faced by the Network-on-Chip (NoC) architecture due to increased integration density and proposes a novel fault-tolerant multi-application mapping approach called ”FANC.” The approach is based on Machine Learning (ML) and can provide solutions for unseen graphs and topologies without prior training. The proposed technique uses an ML-based model to extract relevant information from the search data and incorporate it into the search process. This results in a more robust model with a higher convergence rate and solution quality. The approach is evaluated using a variety of simulation parameters, such as communication cost, network latency, throughput, and power usage. Static simulations are performed in a Python programming environment, while dynamic simulations are performed with a SystemC-based cycle-accurate NoC simulator and the Orion2.0 Power tool. The results show that FANC reduces communication costs by 266%. It also improves network latency by 9%, throughput by 1%, and power consumption by 7%. The approach also simplifies and minimizes the search area in the design exploration process and can be used as an auxiliary component for other optimization algorithms.

Background: In Alabama only 4% of those eligible have been screened for lung cancer. The ALCASE project focused on navigating eligible individuals to lung cancer screening. Methods: Trained local staff enrolled screen eligible individuals from seven rural counties and one urban county. Demographics and knowledge of and barriers to lung cancer screening were collected using questionnaires. Education was provided and individuals were navigated to undergo screening. Descriptive statistics for enrolled and screened participants were computed using SAS 9.4. Debriefing interviews were conducted with the ALCASE staff regarding facilitators/barriers to implementing this project and lessons learned. Using NVivo, themes were identified through a combined deductive and inductive process. Results: In total, 447 people were contacted of which 257 were enrolled. Participants were predominantly African American (86.8%), female (56.8%), and 86.4% had health insurance. Study participants acknowledged the need for more education of lung cancer/screening procedures; help navigating clinics for screening services and having healthcare facilities close to home. The top five barriers to getting screened were transportation, financial issues, emotional concerns, healthcare insurance, and COVID‐19. Of the 257, 106 participants (41%) completed a primary care referral and were screened for lung cancer. Debriefing interviews revealed: (i) Overall impressions of implementing ALCASE were positive. (ii) Barriers in implementing ALCASE were identified predominantly at the physician and institutional level. (iii) Facilitators in implementing ALCASE were identified at multiple levels. (iv) Suggestions on improving lung cancer screening leaned toward mitigating barriers at the institutional and structural level. Conclusion: Ability to get screened is severely challenged by both personal and structural barriers. [ABSTRACT FROM AUTHOR]

The article discusses the Supreme Court's ruling in 2023 that declared the use of race in college admissions unconstitutional. This decision has sparked a backlash against diversity and inclusion efforts in various states. The article highlights the importance of diversity in the healthcare workforce and suggests alternative strategies, such as holistic review and considering "distance traveled," to promote diversity in medical education despite legal constraints. It also emphasizes the need for structural reforms to achieve racial equity and eliminate disparities in opportunity and rewards. In the field of pulmonary, critical care, and sleep medicine (PCCSM), the article emphasizes the limitations of current approaches to achieving diversity and suggests strategies such as early recruitment, promoting diverse leaders, and addressing implicit bias and structural racism. The authors stress the importance of creating a safe and inclusive learning and working environment. [Extracted from the article]

Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.

The effect of varying the amounts of metals Fe, Cu, K, and Mo was studied on a catalyst supported on activated carbon (AC), which is an item of novelty of this paper. The base-case catalyst contains 16% Fe, 0.9% K, 6% Mo, and 0.8% Cu relative to the AC support. For all of the catalysts used, alcohol production is small. The production of hydrocarbons depends upon the amount of Fe and other promoters used. The amount of Fe was increased from 0% to 32% on the catalyst containing base-case amounts of the other materials. While 0% Fe shows no activity towards Fischer–Tropsch synthesis (FTS), 32% Fe shows a marginal increase in FTS activity when compared with 16% Fe. Furthermore, the amount of K was increased from 0% to 1.8%, with the other metals in their base-case amounts. The selectivity of C1–C4 decreases with the addition of K, while the selectivity of C5+ increases. Analogously, the amount of Mo was increased from 0% to 12%. A small amount of Mo results in an increase in FTS activity but decreases with the addition of more Mo. Cu on the catalyst was increased from 0% to 1.6%, with 0.8% Cu proving optimum for FTS.

Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

Sympatico–vagal balance is essential for regulating cardiac electrophysiology and plays an important role in arrhythmogenic conditions. Various noninvasive methods, including electrocardiography (ECG), have been used for clinical assessment of the sympatico–vagal balance. This study aimed to use a custom-designed wearable device to record ECG and ECG-based cardiac function biomarkers to assess sympatico–vagal balance during tonic pain in healthy controls. Nineteen healthy volunteers were included for the ECG measurements using the custom-designed amplifier based on the Texas Instruments ADS1299. The ECG-based biomarkers of the sympatico–vagal balance, (including heart rate variability, deceleration capacity of the heart rate, and periodic repolarization dynamic), were calculated and compared between resting and pain conditions (tonic pain). The custom-designed device provided technically satisfactory ECG recordings. During exposure to tonic pain, the periodic repolarization dynamics increased significantly (p = 0.02), indicating enhancement of sympathetic nervous activity. This study showed that custom-designed wearable devices can potentially be useful in healthcare as a new telemetry technology. The ECG-based novel biomarkers, including periodic repolarization dynamic and deceleration capacity of heart rate, can be used to identify the cold pressor-induced activation of sympathetic and parasympathetic systems, making it useful for future studies on pain-evoked biomarkers.