14129 research outputs found
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A comprehensive survey on software-defined networking for smart communities
No full textThe need to provide services closer to the end-user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo-distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo-separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software-defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal
Investigating the Role of Acetyl-CoA Synthetase Short Chain Family Member 2 (ACSS2) in Inflammatory Bowel Disease: Expression in Clinical Samples and Insights from In Vitro Models
No full textInflammatory bowel disease (IBD), encompassing Ulcerative Colitis (UC) and Crohn’s Disease (CD), is characterized by chronic gastrointestinal inflammation driven by genetic, environmental, immune, and microbial factors. Acetyl-CoA Synthetase Short Chain Family Member 2 (ACSS2), an enzyme converting acetate to acetyl-CoA, is known to regulate gene transcription and cellular processes through its nuclear localization under metabolic stress. However, its role in IBD remains unclear. In this study, immunohistochemistry of colon samples from UC patients revealed significantly elevated expression and nuclear localization of ACSS2 in inflamed tissues compared to non-inflamed regions, suggesting its involvement in IBD pathogenesis. Using Caco-2 and HT-29 intestinal epithelial cell lines stimulated with Tumor Necrosis Factor-α (TNF-α) or Dextran Sodium Sulfate (DSS) as in vitro models of inflammation, we observed upregulation of ACSS2 protein expression. ACSS2 inhibition significantly reduced the expression of pro-inflammatory cytokines such as Interleukin 8 (IL-8), IL-1β, and TNF-α, and attenuated activation of NF-κB and MAPK signaling pathways, as evidenced by decreased phosphorylation of p65 and ERK/MEK. These findings highlight a pro-inflammatory role for ACSS2 in IBD and identify it as a potential therapeutic target, warranting further validation in advanced models, including patient-derived organoids and in vivo systems
AI avatars and co-creation in the metaverse
No full textPurpose
While the fields of artificial intelligence (AI) and avatars are growing at a very fast pace, studies are still scarce. This study aims to fill the gap in the literature relating to the implications of highly realistic avatars as well as the consequences of AI-led co-creation on hospitality services in the Metaverse.
Design/methodology/approach
The authors adopted an exploratory qualitative methodology to study the role of AI avatars in the Metaverse within the hospitality context. The study involved interviews with both elite figures and consumers as primary data and also incorporated secondary data sourced from comments on a YouTube video related to avatars.
Findings
Based on data triangulation, the extracted themes dealt with four key areas: (1) avatars’ relational encounters in hospitality, (2) avatars’ realism, (3) self-representation and self-perception skewness and (4) AI co-creation. The findings show that while avatars’ realism would increase the authenticity of virtual social connections, engagement and monetization, the issue of a self-misrepresentation will diminish the effect of virtual encounters. That avatars will be AI led and will digitally cocreate reviews and recommendations further accentuates the findings.
Originality/value
This research advances the field by addressing the literature gap on AI-led avatar realism and co-creation in hospitality services within the Metaverse. It explores the nuanced ways that highly realistic avatars can enhance engagement and self-representation while simultaneously posing challenges related to authenticity and trust. The study provides a foundation for further exploration of AI’s transformative potential in virtual hospitality contexts.Publishe
A Smart Scholarship Management System: All-in-one Solution
Full text linkindividualThis paper proposes a smart Scholarship Management System (SMS) specifically designed for organizations providing scholarships for students in Lebanon. The SMS system aims to enhance efficiency and introduce new concepts to digitalized scholarship management. It will utilize web programming to provide accessibility and usability across devices. By integrating sentiment analysis, calculating the top 20 students, budget tracking, detailed student and donors’ profiles, applying dashboard, generating reports, smart donor matching, receipt scanning, and role-based access, the expected outcome is a robust platform that automates the administrative processes and eases the student’s experience in applying for scholarships while maintaining transparency and accountability. By implementing the advanced features of this system, it will fill significant gaps where such systems and tools are absent in Lebanon, and combined, absent worldwide. Overall, enhancing access to education for underserved communities by enabling more students to achieve their educational and career goals is crucial in this system’s broader context. This paper will discuss the methodology and results and will highlight the significance of the project
Artificially Intelligent 3D-Printed Soft Gripper for Ripeness and Stiffness Identification
No full textWith the shortages experienced in the labor market, coupled with the growth of the agricultural industry, the need for autonomous and intelligent harvesting solutions has been steadily rising. Naturally, the field of robotics has ingrained itself into the agricultural sector by presenting the needed solutions. Soft robotics has recently begun to play a significant role, as its compliant, flexible structure, which is capable of delicately interacting with the environment, allows it to handle delicate objects such as ripe fruits and vegetables with ease. This work focuses on an artificially intelligent 3D printed soft robotic gripper with embedded pneumatic sensing chambers capable of categorizing tomatoes during harvesting. The ripeness identification process involves two stages: a data collection stage and a classification stage. In the first stage, a closed-loop pressure/force control is used to squeeze the tomato with the gripper, and the resulting pressure versus displacement data is recorded and fed to the custom-designed neural network (NN) in the second stage. The developed NN follows a layered structure based on a 1D convolutional neural network (CNN) architecture. The final model achieves a five-fold cross-validation accuracy of 85.87%, with real-time deployment an accuracy of 80.55%. This two-stage approach mimics human behavior of assessing the ripeness of fruit, which involves gently applying pressure to the fruit to identify its stiffness through touch and then handling the produce accordingly. This proposed gripper and the developed NN present a reliable and nondestructive solution for produce handling, both in the harvesting and quality control stages
How AI-Generated Virtual Try-on Tools are Distorting Self-perception, Body Image, and Shaping the Consumer Journey in the Fashion Industry
No full textAs the fashion industry embraces Generative Artificial Intelligence, Virtual Try-on Tools (VTO) have emerged, redefining the consumer shopping journey by offering immersive and personalized experiences. While VTO tools are positioned as pioneering inclusivity and accuracy for the fashion industry, their potential psychological impact on consumers is yet unexplored. This study investigates the impact of the integration of these tools on consumers’ psychological well-being, body image, perception of brands, and the consumer journey. To examine this, an exploratory approach was adopted using in-depth interviews with both consumers and experts in the field. The findings indicate that VTO tools in their current stage, despite their benefits, contribute to distorted self-perceptions, affect consumers’ well-being, and lowers brand trust if the experience lacks inclusivity and accuracy. Findings also indicate that consumers’ interactions with these tools is mainly concentrated in the consideration phase of the consumer journey. As these tools continue to evolve, this paper highlights the need for more inclusive and accurate tools in shaping the future of the fashion industry
Performance Optimization of Monofacial and Bifacial Photovoltaic Systems
No full textThis thesis explores the performance optimization of monofacial and bifacial photovoltaic systems through a combination of irradiance modeling, machine learning, and geometric analysis. A data-driven approach was developed to determine the optimal tilt angles of PV panels using six years of high-resolution irradiance data for 184 land-based locations. Optimal tilt angles were computed for three adjustment strategies: yearly, seasonal, and monthly. The irradiance on tilted surfaces was estimated using the isotropic sky model, enabling efficient simulation of front- and rear-side exposure across a wide range of albedo values. The resulting tilt angles served as ground truth for training thirteen machine learning models using location coordinates and albedo as input features. Model accuracy was validated at 15 independent U.S. cities. Top-performing models for monofacial and bifacial systems, respectively, yielded angular prediction absolute errors below 1.7° and negligible irradiation discrepancies. The thesis also investigates the influence of ground albedo and tilt angle on bifacial module performance. By comparing monofacial and bifacial systems under identical conditions, the results confirm that rear-side irradiance, and hence bifacial gain, increases substantially with enhanced ground reflectivity. Moreover, a finite-element view-factor method combined with five-minute solar geometry and dynamic shadow projection was used to assess the impact of self-shading on annual GTI. The results showed that in highly reflective ground conditions, off-optimum tilt settings can lead to total GTI losses of up to 25.8% and rear-face losses exceeding 54.3%. This detailed geometric analysis highlights the importance of including shading effects when estimating bifacial energy yield and selecting tilt angles
A novel network-based SIS framework for improved GA performance
No full textGenetic algorithms have long been used to solve complex optimization problems
by mimicking natural selection processes. However, they often suffer from premature
convergence, reduced diversity, as well as imbalanced exploration and exploitation.
To address these challenges, this work introduces SIS-NGA which integrates
the Susceptible-Infected-Susceptible (SIS) epidemic model and Genetic Algorithms
within a scale-free network topology, to guide the search for optimal solutions. The
SIS model is typically used to capture how infectious diseases spread and evolve
within populations. In this model, individuals in a population are represented as
interconnected nodes in a network. They can transition between two states, namely
susceptible and infected. In analogy, we adapt this formulation to improve the performance
of genetic algorithms. We represent the set of possible solutions to a complex
optimization problem as interconnected nodes in a scale-free network. We assign fit
solutions as infected, with a certain probability. Then, infected nodes can spread their genetic traits to neighboring susceptible nodes through basic genetic algorithm
operations within the SIS framework and based on defined probabilities. The proposed
approach maintains diversity and delays convergence by promoting promising
and optimal solutions. We evaluated SIS-NGA using several benchmark functions,
and our results and statistical analyses confirm consistent improvements in solution
quality and robustness
Modulation of Chronic Stress Outcomes by Dietary Sweeteners: A Comparative Study of Sucrose and Aspartame in Mice
No full textBackground: Dietary sweeteners may modulate physiological and behavioral responses to stress. It has been suggested that sucrose consumption attenuates stress-induced cortisol secretion, whereas aspartame has been reported to increase activity of glucocorticoids and anxiety-like behavior in rodents
Aim: The present study aimed to assess the differential effects of sucrose and aspartame on the physiological and behavioral response in chronic stress in mice.
Method: Female C57BL/6J mice (n > 275, aged 8–10 weeks) were given plain water, 12.3% sucrose, or 0.03% aspartame for two weeks, then half of each group underwent a 3-week chronic variable stress (CVS) protocol using mild unpredictable stressors. Body fat and serum corticosterone were measured at multiple time points. Behavioral tests for anxiety, sociability, and cognition were conducted in the final week.
Results: Consumption of sucrose led to increased adiposity compared to aspartame or water, consistent with its caloric properties. Chronic Variable Stress was associated with reduced adiposity and behavioral changes indicative of increased anxiety and social withdrawal. Sucrose intake appeared to attenuate some of these stress-related behaviors, particularly in the social domain. In contrast, aspartame did not appear to mitigate behavioral effects of stress.
Conclusion: Sucrose and aspartame showed divergent effects on stress physiology and behavior. Sucrose may decrease some behavioral consequences of chronic stress, particularly in social contexts, whereas aspartame does not appear to confer similar benefits
Natural resource dependence and war nexus: new insights
No full textThis paper examines the channels through which resource dependence affects the probability of war. We consider all types of war within a worldwide panel dataset of all countries and territories, spanning the period 1960-2022. We systematically estimate the probability of war using a set of economic and geographic characteristics, including democracy, demography, legal origin, militarism, and sectoral composition. Using a panel probit model, we find that natural resources rents, military expenditures, and the presence of a common law tradition significantly increase the probability of war occurrence. However, we notice that this probability is decreasing in the level of economic development and democratization, and with the size of the services sector and trade openness. We also find that the effect of resource dependence varies by level of economic development, continent, landlocked status, legal origin, colonial history, and war type with civil wars being the salient type. Overall, we provide novel and updated evidence on an important dimension of the vexing question of the so-called ‘resource curse’.Publishe