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The gut-brain axis, which is mediated via enteric and central neurohormonal signaling, is known to regulate a broad set of physiological functions from feeding to emotional behavior. Various pharmaceuticals and surgical interventions, such as motility agents and bariatric surgery, are used to modulate this axis. Such approaches, however, are associated with off-target effects or post-procedure recovery time and expose patients to substantial risks. Electrical stimulation has also been used to attempt to modulate the gut-brain axis with greater spatial and temporal resolution. Electrical stimulation of the gastrointestinal (GI) tract, however, has generally required invasive intervention for electrode placement on serosal tissue. Stimulating mucosal tissue remains challenging because of the presence of gastric and intestinal fluid, which can influence the effectiveness of local luminal stimulation. Here, we report the development of a bioinspired ingestible fluid-wicking capsule for active stimulation and hormone modulation (FLASH) capable of rapidly wicking fluid and locally stimulating mucosal tissue, resulting in systemic modulation of an orexigenic GI hormone. Drawing inspiration from Moloch horridus, the “thorny devil” lizard with water-wicking skin, we developed a capsule surface capable of displacing fluid. We characterized the stimulation parameters for modulation of various GI hormones in a porcine model and applied these parameters to an ingestible capsule system. FLASH can be orally administered to modulate GI hormones and is safely excreted with no adverse effects in porcine models. We anticipate that this device could be used to treat metabolic, GI, and neuropsychiatric disorders noninvasively with minimal off-target effects.

Background and objectiveA culture-specific web-based food frequency questionnaire (FFQ) to assess dietary intake in the United Arab Emirates (UAE) adult population was developed using data from the 2009–2010 national nutrition survey. The objective of this study was to assess the relative validity of the newly developed FFQ for use in the adult Emirati population (AE-FFQ), which contained a list of 139 food lines.MethodsA convenient sample of 60 (36 females and 24 males) adult Emiratis completed 3 non-consecutive 24HRs over a period of one month, followed by the AE-FFQ, which assessed the intake over the previous month. Relative validity was evaluated by comparing nutrient and food group intakes from the AE-FFQ with the average three 24HRs using Wilcoxon signed-rank tests, Spearman’s correlation coefficients (CC), Bland-Altman analysis, and cross-classification.ResultsThe AE-FFQ overestimated energy and most nutrients and food groups. Bland–Altman analysis showed significant proportional bias between the 2 methods. Deattenuated energy-adjusted Spearman correlation coefficients were poor to good ranging from 0.06 (iron) to 0.62 (fiber) for nutrients, 0.39 median value, and from –0.01 (cruciferous vegetables) to 0.64 (eggs) for food groups, 0.41 median value. A fairly acceptable agreement was obtained, with correct classification into the same or adjacent quartile ranging from 34% (vitamin B12) to 78% (pyridoxine), median 69% for nutrients and from 55% (diet soft drinks) to 87% (soft drinks), median 67% for food groups.ConclusionsThe AE-FFQ is an acceptable tool for ranking UAE adults (aged 18 to 50) according to their dietary intake to investigate the role of Emirati dietary patterns on health and disease. Caution is needed for assessing absolute intake, however, given the bias observed in assessing group-level agreement.

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy-aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing’s job scheduling problem to maximize users’ QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state-of-the-art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.

Analysis of consumer preferences and willingness-to-pay (WTP) for sustainable foods produced using new agri-food technologies is required to enhance the uptake of innovations that accelerate the transition towards sustainable food systems. Consumers’ willingness to buy new food products, with no or limited consumption experience, mainly depends on their food choice motivational orientations (promotion- vs prevention-orientation). The objective of this study was to elicit consumers’ WTP for foods that are produced with microbial applications during the plant production phase with the aim to reduce the use of synthetic chemicals in crop farming, as well as to understand the associations of food choice motives, personal and socio-demographic factors with the WTP. We used contingent valuation to elicit consumers’ WTP for three food products (wheat bread, consumer potatoes and tomato sauce) through online surveys. Data were collected from 291 consumers, primarily from Italy, Germany and the Netherlands. Descriptive statistics, latent variable modelling and logistic regression were used to analysis data. Results show that more than two-third of the respondents are willing to pay premiums of at least 0.11 euro per kg of food products for reductions in synthetic chemical use by at least 50% due to microbial applications. The amount of WTP increases with the level of reductions in synthetic chemical use. The majority of the respondents are promotion-oriented consumers in relation to their food involvement, and are more likely to pay premiums for the sustainably produced food products. Environmentally concerned consumers are also more likely to pay premiums, whereas health concerned consumers are not. This study contributes to understanding of consumers’ attitude and perceived health risks towards foods obtained using microbial applications, and the heterogeneity of their preferences. Results provide insights for identifying potential buyers of foods produced using microbial applications, and to set prices according to the levels of consumers’ WTP.

One of the significant challenges in the Internet of Things (IoT) is the provisioning of guaranteed security and privacy, considering the fact that IoT devices are resource-limited. Oftentimes, in IoT applications, remote users need to obtain real-time data, with guaranteed security and privacy, from resource-limited network nodes through the public Internet. For this purpose, the users need to establish a secure link with the network nodes. Though the IPv6 over low-power wireless personal area networks (6LoWPAN) adaptation layer standard offers IPv6 compatibility for resource-limited wireless networks, the fundamental 6LoWPAN structure ignores security and privacy characteristics. Thus, there is a pressing need to design a resource-efficient authenticated key exchange (AKE) scheme for ensuring secure communication in 6LoWPAN-based resource-limited networks. This paper proposes a resource-efficient secure remote user authentication scheme for 6LoWPAN-based IoT networks, called SRUA-IoT. SRUA-IoT achieves the authentication of remote users and enables the users and network entities to establish private session keys between themselves for indecipherable communication. To this end, SRUA-IoT uses a secure hash algorithm, exclusive-OR operation, and symmetric encryption primitive. We prove through informal security analysis that SRUA-IoT is secured against a variety of malicious attacks. We also prove the security strength of SRUA-IoT through formal security analysis conducted by employing the random oracle model. Additionally, we prove through Scyther-based validation that SRUA-IoT is resilient against various attacks. Likewise, we demonstrate that SRUA-IoT reduces the computational cost of the nodes and communication overheads of the network.

Background Since the beginning of the COVID-19 outbreak, many pharmaceutical companies have been racing to develop a safe and effective COVID-19 vaccine. Simultaneously, rumors and misinformation about COVID-19 are still widely spreading. Therefore, this study aimed to investigate the prevalence of COVID-19 misinformation among the Yemeni population and its association with vaccine acceptance and perceptions. Methods A cross-sectional online survey was conducted in four major cities in Yemen. The constructed questionnaire consisted of four main sections (sociodemographic data, misinformation, perceptions (perceived susceptibility, severity, and worry), and vaccination acceptance evaluation). Subject recruitment and data collection were conducted online utilizing social websites and using the snowball sampling technique. Descriptive and inferential analyses were performed using SPSS version 27. Results The total number of respondents was 484. Over 60% of them were males and had a university education. More than half had less than 100$ monthly income and were khat chewers, while only 18% were smokers. Misinformation prevalence ranged from 8.9% to 38.9%, depending on the statement being asked. Men, university education, higher income, employment, and living in urban areas were associated with a lower misinformation level (p <0.05). Statistically significant association (p <0.05) between university education, living in urban areas, and being employed with perceived susceptibility were observed. The acceptance rate was 61.2% for free vaccines, but it decreased to 43% if they had to purchase it. Females, respondents with lower monthly income, and those who believed that pharmaceutical companies made the virus for financial gains were more likely to reject the vaccination (p <0.05). Conclusion The study revealed that the acceptance rate to take a vaccine was suboptimal and significantly affected by gender, misinformation, cost, and income. Furthermore, being female, non-university educated, low-income, and living in rural areas were associated with higher susceptibility to misinformation about COVID-19. These findings show a clear link between misinformation susceptibility and willingness to vaccinate. Focused awareness campaigns to decrease misinformation and emphasize the vaccination’s safety and efficacy might be fundamental before initiating any mass vaccination in Yemen.