Lebanese American University

The GPS‐derived velocity field (1988–2005) for the zone of interaction of the Arabian, African (Nubian, Somalian), and Eurasian plates indicates counterclockwise rotation of a broad area of the Earth's surface including the Arabian plate, adjacent parts of the Zagros and central Iran, Turkey, and the Aegean/Peloponnesus relative to Eurasia at rates in the range of 20–30 mm/yr. This relatively rapid motion occurs within the framework of the slow‐moving (∼5 mm/yr relative motions) Eurasian, Nubian, and Somalian plates. The circulatory pattern of motion increases in rate toward the Hellenic trench system. We develop an elastic block model to constrain present‐day plate motions (relative Euler vectors), regional deformation within the interplate zone, and slip rates for major faults. Substantial areas of continental lithosphere within the region of plate interaction show coherent motion with internal deformations below ∼1–2 mm/yr, including central and eastern Anatolia (Turkey), the southwestern Aegean/Peloponnesus, the Lesser Caucasus, and Central Iran. Geodetic slip rates for major block‐bounding structures are mostly comparable to geologic rates estimated for the most recent geological period (∼3–5 Myr). We find that the convergence of Arabia with Eurasia is accommodated in large part by lateral transport within the interior part of the collision zone and lithospheric shortening along the Caucasus and Zagros mountain belts around the periphery of the collision zone. In addition, we find that the principal boundary between the westerly moving Anatolian plate and Arabia (East Anatolian fault) is presently characterized by pure left‐lateral strike slip with no fault‐normal convergence. This implies that “extrusion” is not presently inducing westward motion of Anatolia. On the basis of the observed kinematics, we hypothesize that deformation in the Africa‐Arabia‐Eurasia collision zone is driven in large part by rollback of the subducting African lithosphere beneath the Hellenic and Cyprus trenches aided by slab pull on the southeastern side of the subducting Arabian plate along the Makran subduction zone. We further suggest that the separation of Arabia from Africa is a response to plate motions induced by active subduction.

Nanotechnology, contrary to its name, has massively revolutionized industries around the world. This paper predominantly deals with data regarding the applications of nanotechnology in the modernization of several industries. A comprehensive research strategy is adopted to incorporate the latest data driven from major science platforms. Resultantly, a broad-spectrum overview is presented which comprises the diverse applications of nanotechnology in modern industries. This study reveals that nanotechnology is not limited to research labs or small-scale manufacturing units of nanomedicine, but instead has taken a major share in different industries. Companies around the world are now trying to make their innovations more efficient in terms of structuring, working, and designing outlook and productivity by taking advantage of nanotechnology. From small-scale manufacturing and processing units such as those in agriculture, food, and medicine industries to larger-scale production units such as those operating in industries of automobiles, civil engineering, and environmental management, nanotechnology has manifested the modernization of almost every industrial domain on a global scale. With pronounced cooperation among researchers, industrialists, scientists, technologists, environmentalists, and educationists, the more sustainable development of nano-based industries can be predicted in the future.

Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.

Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.

Exercise induces beneficial responses in the brain, which is accompanied by an increase in BDNF, a trophic factor associated with cognitive improvement and the alleviation of depression and anxiety. However, the exact mechanisms whereby physical exercise produces an induction in brain Bdnf gene expression are not well understood. While pharmacological doses of HDAC inhibitors exert positive effects on Bdnf gene transcription, the inhibitors represent small molecules that do not occur in vivo. Here, we report that an endogenous molecule released after exercise is capable of inducing key promoters of the Mus musculus Bdnf gene. The metabolite β-hydroxybutyrate, which increases after prolonged exercise, induces the activities of Bdnf promoters, particularly promoter I, which is activity-dependent. We have discovered that the action of β-hydroxybutyrate is specifically upon HDAC2 and HDAC3, which act upon selective Bdnf promoters. Moreover, the effects upon hippocampal Bdnf expression were observed after direct ventricular application of β-hydroxybutyrate. Electrophysiological measurements indicate that β-hydroxybutyrate causes an increase in neurotransmitter release, which is dependent upon the TrkB receptor. These results reveal an endogenous mechanism to explain how physical exercise leads to the induction of BDNF.

Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users’ digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future.

Nanoparticles typically have dimensions of less than 100 nm. Scientists around the world have recently become interested in nanotechnology because of its potential applications in a wide range of fields, including catalysis, gas sensing, renewable energy, electronics, medicine, diagnostics, medication delivery, cosmetics, the construction industry, and the food industry. The sizes and forms of nanoparticles (NPs) are the primary determinants of their properties. Nanoparticles’ unique characteristics may be explored for use in electronics (transistors, LEDs, reusable catalysts), energy (oil recovery), medicine (imaging, tumor detection, drug administration), and more. For the aforementioned applications, the synthesis of nanoparticles with an appropriate size, structure, monodispersity, and morphology is essential. New procedures have been developed in nanotechnology that are safe for the environment and can be used to reliably create nanoparticles and nanomaterials. This research aims to illustrate top-down and bottom-up strategies for nanomaterial production, and numerous characterization methodologies, nanoparticle features, and sector-specific applications of nanotechnology.

Abstract Despite knowing the potential effect of social reporting on firms' continuity, there is limited research into the influence of the composition of boards of directors on CSR disclosure. This paper adds to the emerging CSR literature empirical evidence by examining how board composition relates to a firm's social and environmental disclosure as well as the implementation of social policies. Using a sample of FTSE 350 firms for the period 2007–2012, the results show that higher board independence facilitates the conveying of firms' good citizenship image through enhancing societal conscience. The results also show that female participation on boards is favorably affecting CSR engagement and reporting as well as the establishment of ethical policies. Hence, the research suggests that boards with higher female participation and independence boost the legitimacy of CSR reporting. Board gender diversity and independence facilitates directing part of the firm's scarce resources toward value maximizing social projects and subsequent reporting on these. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

Heavy metal pollution has become one of the most serious environmental problems nowadays. The removal of heavy metals from the environment is of special concern due to their persistence. Batch experiments were conducted to test the ability of activated carbon for the removal of lead, cadmium, nickel, chromium and zinc from water. The Langmuir and Freundlich adsorption isotherms were used to verify the adsorption performance. Nickel showed the highest removal percentages by activated carbon at all concentrations and the removal percentages decreased as the concentration of heavy metal increased. The obtained correlation coefficient (R2) for different adsorbents suggested poor fitting of the experimental data to Langmuir isotherm for Cd, Pb, Ni, and Zn, while R2 obtained using Freundlich model for different adsorbents indicated that it fitted the experimental data well. Silica/activated carbon (2:3) composite was more efficient in the removal of nickel ions than activated carbon and silica nanoparticles. SEM pictures were taken for the three particles under test.

Using 2605 Chinese A-share listed companies in the mining, manufacturing, and energy production and supply sectors from 2009 to 2020, we examine the relationship between climate policy uncertainty (CPU) and firm-level total factor productivity (TFP). The main findings are as follows: First, CPU significantly reduces firm-level TFP, with a greater impact on low-productivity firms than on high-productivity firms; second, the negative effect of CPU on firm-level TFP is most pronounced for non-state-owned, labor-intensive, and capital-intensive companies; third, CPU hinders research and development investment and reduces the amount of free cash flow. These results indicate that CPU exerts negative impacts on firm-level TFP mainly via its effects on the capital status of the companies. Our findings remain valid after a series of robustness tests and controlling for endogeneity. The government should introduce forward-looking climate policies to reduce the negative impact of policy uncertainty.

Trait emotional intelligence (“trait EI”) concerns our perceptions of our emotional abilities, that is, how good we believe we are in terms of understanding, regulating, and expressing emotions in order to adapt to our environment and maintain well-being. In this article, we present succinct summaries of selected findings from research on (a) the location of trait EI in personality factor space, (b) the biological underpinnings of the construct, (c) indicative applications in the areas of clinical, health, social, educational, organizational, and developmental psychology, and (d) trait EI training. Findings to date suggest that individual differences in trait EI are a consistent predictor of human behavior across the life span.

Energy Storage Technology is one of the major components of renewable energy integration and decarbonization of world energy systems. It significantly benefits addressing ancillary power services, power quality stability, and power supply reliability. However, the recent years of the COVID-19 pandemic have given rise to the energy crisis in various industrial and technology sectors. An integrated survey of energy storage technology development, its classification, performance, and safe management is made to resolve these challenges. The development of energy storage technology has been classified into electromechanical, mechanical, electromagnetic, thermodynamics, chemical, and hybrid methods. The current study identifies potential technologies, operational framework, comparison analysis, and practical characteristics. This proposed study also provides useful and practical information to readers, engineers, and practitioners on the global economic effects, global environmental effects, organization resilience, key challenges, and projections of energy storage technologies. An optimal scheduling model is also proposed. Policies for sustainable adaptation are then described. An extensive list of publications to date in the open literature is canvassed to portray various developments in this area.

Exercise promotes learning and memory formation. These effects depend on increases in hippocampal BDNF, a growth factor associated with cognitive improvement and the alleviation of depression symptoms. Identifying molecules that are produced during exercise and that mediate hippocampal <i>Bdnf</i> expression will allow us to harness the therapeutic potential of exercise. Here, we report that an endogenous molecule produced during exercise in male mice induces the <i>Mus musculus Bdnf</i> gene and promotes learning and memory formation. The metabolite lactate, which is released during exercise by the muscles, crosses the blood–brain barrier and induces <i>Bdnf</i> expression and TRKB signaling in the hippocampus. Indeed, we find that lactate-dependent increases in BDNF are associated with improved spatial learning and memory retention. The action of lactate is dependent on the activation of the Sirtuin1 deacetylase. SIRT1 increases the levels of the transcriptional coactivator PGC1a and the secreted molecule FNDC5, known to mediate <i>Bdnf</i> expression. These results reveal an endogenous mechanism to explain how physical exercise leads to the induction of BDNF, and identify lactate as a potential endogenous molecule that may have therapeutic value for CNS diseases in which BDNF signaling is disrupted. <b>SIGNIFICANCE STATEMENT</b> It is established that exercise promotes learning and memory formation and alleviates the symptoms of depression. These effects are mediated through inducing <i>Bdnf</i> expression and signaling in the hippocampus. Understanding how exercise induces <i>Bdnf</i> and identifying the molecules that mediate this induction will allow us to design therapeutic strategies that can mimic the effects of exercise on the brain, especially for patients with CNS disorders characterized by a decrease in <i>Bdnf</i> expression and who cannot exercise because of their conditions. We identify lactate as an endogenous metabolite that is produced during exercise, crosses the blood–brain barrier and promotes hippocampal dependent learning and memory in a BDNF-dependent manner. Our work identifies lactate as a component of the “exercise pill.”

Knowing the beneficial aspects of nanomedicine, scientists are trying to harness the applications of nanotechnology in diagnosis, treatment, and prevention of diseases. There are also potential uses in designing medical tools and processes for the new generation of medical scientists. The main objective for conducting this research review is to gather the widespread aspects of nanomedicine under one heading and to highlight standard research practices in the medical field. Comprehensive research has been conducted to incorporate the latest data related to nanotechnology in medicine and therapeutics derived from acknowledged scientific platforms. Nanotechnology is used to conduct sensitive medical procedures. Nanotechnology is showing successful and beneficial uses in the fields of diagnostics, disease treatment, regenerative medicine, gene therapy, dentistry, oncology, aesthetics industry, drug delivery, and therapeutics. A thorough association of and cooperation between physicians, clinicians, researchers, and technologies will bring forward a future where there is a more calculated, outlined, and technically programed field of nanomedicine. Advances are being made to overcome challenges associated with the application of nanotechnology in the medical field due to the pathophysiological basis of diseases. This review highlights the multipronged aspects of nanomedicine and how nanotechnology is proving beneficial for the health industry. There is a need to minimize the health, environmental, and ethical concerns linked to nanotechnology.

The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called Federated Learning. The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents.

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.

Purpose This paper aims to investigate the impact of board composition on environmental, social and governance (ESG) reporting in the Gulf countries. Despite the vast literature on the significance of ESG disclosure on firms’ performance, trust and reputation, there are relatively few studies on the influence of board structure on ESG disclosure in the Gulf Cooperation Council (GCC) countries. Gulf countries are witnessing a fast growing capital markets and having serious efforts to attract foreign investments to divert their economies from the oil and gas reliance. This could be facilitated by illustrating firms’ good citizenship and communicating the fulfillment of their social obligation. Design/methodology/approach The study examines publically listed companies between 2008 and 2017. Thomson Reuter’s database is used to collect the ESG disclosure scores and governance information. The authors apply multiple panel data regressions and sensitivity testing to ensure the robustness of the results. Findings Examining publically listed companies for a 10-year period shows that higher board independence and female board participation facilitate the transmission of a firm’s positive image by improving social responsibility. Independent boards of directors and participation among women serve as catalysts to strike an effective balance between firms’ financial targets and social responsibilities. In contrast, boards chaired by chief executive officers are less supportive in executing a social agenda and consequently reporting their ESG activities. Practical implications The results suggest that firms that appoint a sustainability and/or governance committee tend to engage in more impactful social and environmental activities and communicate their societal engagements more effectively. Social implications The paper recommends that policymakers, executives and shareholders in the GCC countries support board participation among women, independent directors and formation of sustainability committees to facilitate engaging in effectual social activities. Originality/value Empirical evidence regarding the relationship between board composition and ESG disclosure in the Gulf countries is limited. Prior literature mainly provides results on developed countries in which the governance system is mature and well structured. This study provides useful evidence regarding the Gulf countries that lack privatization and where corporate boards tend to be dominated by families and governments.

ABSTRACTThe launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the use of AI-generated text to cheat on assignments is a new type of cheating that poses unique challenges. This study used the Latent Dirichlet Allocation (LDA) method for topic modeling and the Valence Aware Dictionary for Sentiment Reasoning (VADER) method for sentiment analysis. After data preprocessing, 3870 tweets were still available out of the originally 10,000 tweets that were extracted for the study. The VADER sentiment analysis results revealed that 2013 tweets were categorized as “positive,” with the remaining 804 and 1053 tweets categorized as “negative” and “neutral.” The analysis's findings indicate that the majority of people have favorable things to say about ChatGPT. As a result, educational institutions can mitigate the disruptive effects of this technology and promote academic integrity by developing clear policies and guidelines and designing assessments that include limited AI-generated text.KEYWORDS: ChatGPTAIuniversitycheatingplagiarism Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsIbrahim AdesholaIbrahim Adeshola is an experienced multidisciplinary researcher who has had work published in prestigious journals. He has an in-depth knowledge of a variety of research fields, including management information systems, educational technology, marketing, cloud computing, organizational management, business and environmental sustainability, technological innovation, renewable energy, organizational culture, and business development.Adeola Praise AdepojuAdeola Praise Adepoju is currently a Ph.D. scholar in business administration at Cyprus International University. Her research interests include consumer behavior, business strategy, organizational behavior, and student wellbeing.

Indoor positioning systems (IPSs) locate objects in closed structures such as office buildings, hospitals, stores, factories, and warehouses, where Global Positioning System devices generally do not work. Most available systems apply wireless concepts, optical tracking, and/or ultrasound. This paper presents a standalone IPS using radio frequency identification (RFID) technology. The concept is based on an object carrying an RFID reader module, which reads low-cost passive tags installed next to the object path. A positioning system using a Kalman filter is proposed. The inputs of the proposed algorithm are the measurements of the backscattered signal power propagated from nearby RFID tags and a tag-path position database. The proposed algorithm first estimates the location of the reader, neglecting tag-reader angle-path loss. Based on the location estimate, an iterative procedure is implemented, targeting the estimation of the tag-reader angle-path loss, where the latter is iteratively compensated from the received signal strength information measurement. Experimental results are presented, illustrating the high performance of the proposed positioning system.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.