American University of Sharjah

Nonlinear optical characteristics of chemically prepared silver selenide (Ag2Se, 3.7 nm) and lead selenide (PbSe, 6.3 nm) colloidal quantum dots (QDs) are investigated using the 1030 nm femtosecond pulses. Ag2Se QDs demonstrated two-photon absorption followed by saturable absorption. PbSe QDs initially showed the saturable absorption at small intensities of 1030 nm probe pulses, followed by reverse saturable absorption at stronger excitation. The difference in the positive nonlinear optical absorption mechanisms in these two QD colloids is attributed to their different bandgaps. The studied QD colloids exhibited the Kerr-induced positive nonlinear refraction. Transient absorption studies of the QDs showed that photoexcitation creates a hot exciton that forms band-edge exciton within 1.2 ps after excitation. Most of the electron-hole pairs further recombine through band-to-band recombination with a time constant of 1490 ps. Furthermore, the results from the transient absorption studies indicate the contribution of trap-assisted recombination occurs on a time scale exceeding that of the experimental time window.

The purpose of this study is to characterize the dynamic response of fluid flow in microchannels, which can show significant delay times before reaching steady flow conditions. Two main sources of these delays are numerically and experimentally investigated, the hydraulic compliance which originates from the flexibility of the system components (microchannel, tubing, syringe, etc.), and the compressibility of the liquid dead volume in the setup, also known as the “bottleneck effect”. A fluid‐structure interaction model is presented for the compliance of rectangular PDMS microchannels that is used to form a numerically based relation for the compliance as a function of the pressure and geometry. This relation is successfully able to predict the dynamics of the flow inside PDMS microchannels in stop‐flow experiments. The time delays associated with the bottleneck effect is also shown when using different syringe volumes, microchannel resistances, and liquid types. In these tests, the bottleneck effect has a much larger effect compared to the compliance of the PDMS microchannels. This is true even when using softer PDMS by increasing the monomer‐to‐curing agent mixing ratio. The characterization that is presented here allows for a simple analysis of microfluidic networks using the hydraulic‐circuit approach.

Considering the significant investment in solar generation systems to improve the efficiency, numerous advanced and computation-based maximum power point tracking (MPPT) algorithms have been developed to achieve optimal convergence time, rapid steady-state and dynamic response, and enhanced tracking efficiency. This paper proposes a novel robust self-adaptive hill-climbing search (SA-HCS) algorithm based on optimal circular search zone for solar MPPT strategy. Initially, an open-circuit voltage estimation (OC-VE) strategy is proposed, followed by the implementation of the SA-HCS algorithm for a hybrid tracking approach. The proposed MPPT strategy uses large, variable, or adjustable step sizes, confirming efficient tracking by covering up to 90% of the search area bidirectionally. This is achieved by deploying an optimized circular curve synthesis, enabling rapid response. In the remaining search region, a small voltage step width is used to reduce steady-state oscillations near the maximum power point (MPP). The SA-HCS algorithm utilizes a universal circle equation to determine the appropriate perturbation step size based on the location of the operating power and OCV points, eliminating the need for prior-tuned perturbation step widths and calculations. The SA-HCS algorithm’s tracking performance is verified using MATLAB/Simulink, considering various solar irradiation conditions and real environmental data from the Benban Solar Park in Upper Egypt. As well, a comprehensive comparison with other counterpart HCS algorithms is investigated. Therefore, the proposed SA-HCS algorithm exhibits a minimal steady-state oscillations (1.2%), rapid settling time (12 ms), and excellent overall system efficiency (99.8%).

This study introduces an innovative scheduling tool for dynamic identical parallel machine environments, leveraging a non-preemptive overlapping load adjustment methodology. Central to this tool is a dual-phase proactive-reactive dynamic scheduling mechanism. Initially, in the proactive phase, the tool utilizes an earliest loading strategy to map out a range of viable scheduling alternatives, capitalizing on job slack times without cementing any schedules prematurely. This is followed by a reactive phase, wherein the tool dynamically adjusts to real-time disturbances via a user-controlled module, thereby crafting adaptable schedules while ensuring continuous feasibility. Enhanced by real-time graphical displays, the tool offers end-users a holistic view of the scheduling process and the implications of unexpected disruptions, aiding in informed decision-making. Extensive computational experiments encompassing 600 instances of varying sizes - small, medium, and large - underscore the tool’s efficiency. These experiments reveal the generation of a wide spectrum of scheduling alternatives, ranging on average from 1676 for small-sized instances to as many as 298,210 for large-sized ones. Notably, the average maximum completion time (Cmax) closely mirrors the results obtained from the Cplex optimizer, with an average deviation of merely 1.07%, 0.48%, and 12.23% for small, medium, and large instances, respectively. Moreover, despite the creation of up to 300,000 potential scheduling solutions for larger instances, the tool consistently yields a low average number of tardy jobs; 1.28, 1.87, and 2.07 for the respective problem sizes, which clearly highlights its effectiveness. The outcomes conclusively demonstrate the tool’s flexibility and efficiency, offering end-users a multitude of superior scheduling options in the presence of unforeseen disruptions that characterize today’s operating environment.

This paper proposes a novel approach for the optimal allocation of a depot for electric buses (EBs) charging in a specific transit service, considering the impact on the power system. The main objective is to minimize the total cost, achieved by minimizing the cost of the new cables connecting the depot station to the distribution system and the upgrade cost of existing lines to meet the additional loads. The outcomes are the optimal location of the depot, the optimal electric node bus in the distribution system to supply it, and the required system upgrades. The optimization problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and solved using the General Algebraic Modeling System (GAMS). The methodology is tested on the H16 EB service line in Barcelona, Spain, and a typical electrical distribution system. Three case studies are presented in this paper. In the first case, the impact of a single service on the distribution network is analyzed, and in the second case, three H16 EB services are assumed to serve the network. The third case handles a multi‐route, multi‐terminal bus service to allocate and supply a depot capable of accommodating all the routes. This case will also include sensitivity analysis to test the robustness and reliability of the model. Results show that for a single H16 EB service, no line upgrades were needed, and the total cost per phase was $120,000. For three H16 EB services, three lines required upgrades, and the total cost per phase increased to$718,000. In the third case, the sensitivity analysis revealed that higher demand factors lead to increased costs due to more update requirements and voltage deviations. The results demonstrate that the minimum distance between the depot and node is not always the optimal or feasible solution that would prevent the depot load installation at a weak spot and meet the power flow constraints.

A major open question in the theory of Toeplitz operator on the Bergman space of the unit disk of the complex plane is the complete characterization of the set of all Toeplitz operators that commute with a given operator. Researchers showed that when a sum S={T}_{{e}^{im\theta }f}+{T}_{{e}^{il\theta }g} , where f and g are radial functions, commutes with a sum T={T}_{{e}^{ip\theta }{r}^{\left(2M+1)p}}+{T}_{{e}^{is\theta }{r}^{\left(2N+1)s}} , then S must be of the form S=cT , where c is a constant. In this article, we will replace {r}^{\left(2M+1)p} and {r}^{\left(2N+1)s} with {r}^{n} and {r}^{d} , where n and d are in {\mathbb{N}} , and we will show that the same result holds.

In this paper we study the problem of multi-source, P2P-assisted, multimedia content delivery of 8K content. Such media constitute a big challenge for contemporary VoD services, as their media rate can surpass the clients’ upload/download speeds. Our proposed methods tackle this shortcoming while also allowing a reduction in the server resource consumption. Two methods are presented and evaluated, that include global and local multi-installment schemes, that are shown to surpass server-only or non-DLT-based content distribution by a fair margin, in all the significant metrics. Our methods can either offer a significant improvement in access time or a boost in the number of clients served, while at the same time reducing the consumed server resources. The performance of the proposed methods is evaluated with a rigorous simulation study based on the current state of the Internet on two geographic locations that represent best-case and average-case scenarios. Our findings suggest that the methodology used to assign the content parts to individual sources, has a big influence of the consumption of server resources, the system’s scalability and service quality. One of the proposed methods (LMI) achieves a maximum of 70.5% improvement on the average client access time, while saving up to 23% of server resources. Based on the system settings the reduced access-time/faster access to content can be balanced against an increase in the served clients by up to 25%.

The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. Yet, direct comparisons of their predictive accuracy in modeling ultrasound-triggered drug release from liposomes remain limited. Existing studies predominantly focus on drug release under static conditions or with limited external stimuli rather than the dynamic, nonlinear responses observed under ultrasound exposure. Objective This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm²). Methods Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. Results RF consistently outperformed SVM, achieving R² scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data. Conclusion RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs.

This research paper conducts a systematic review of the integration of blockchain technology with Enterprise Resource Planning (ERP) systems, a burgeoning field at the intersection of advanced technology and business management. The primary objective is to bridge the knowledge gap by synthesizing existing literature, providing insights into the current state, challenges, and potential of blockchain in enhancing ERP systems. The study follows a structured methodology involving four key stages: Literature Retrieval, Literature Screening, Biblio-metric Analysis, and Content Analysis. A comprehensive literature search in the Scopus database resulted in the identification and analysis of 250 scholarly articles, narrowed down to 66 relevant publications. The findings highlight a significant trend towards decentralized ERP solutions, driven by blockchain’s promise of enhanced security, transparency, and efficiency. Key areas impacted by blockchain integration include supply chain management, accounting, security, traceability, purchasing, and process automation within ERP systems. The bibliometric analysis reveals a growing academic interest in this domain, with a focus on scalability, data security, and interoperability. However, the integration faces challenges such as technical complexity, cultural resistance, and regulatory uncertainties. The study concludes that while blockchain presents transformative opportunities for ERP systems, realizing this potential requires overcoming substantial technical and organizational hurdles. Future research should concentrate on developing scalable, interoperable blockchain solutions, tailored to specific industry needs and compliant with regulatory standards, thereby facilitating widespread adoption and maximizing the benefits of blockchain in ERP systems.

Leader–member exchange (LMX), a well-researched leadership theory that focuses on the dyadic relationships between leaders and subordinates, is associated with positive subordinates’ outcomes. However, the contexts outside the LMX dyadic relationship might influence those favorable outcomes. In this study, we investigate the cross-level moderating effect of leader’s feelings of violation, as a contextual boundary, on LMX outcomes. Based on social exchange theory, crossover model, and the psychological contract literature, we discuss how the relationship between a subordinate’s perceived LMX and favorable subordinate attitudes and behaviors, such as performance, task-focused citizenship behaviors, and organizational commitment, is reduced when the leader experiences feelings of violation toward the organization. Using a three-wave time-lagged multilevel design with a sample of 226 subordinates and 39 leaders, we find that leader’s feelings of violation mitigate the positive association of perceived LMX on citizenship behavior and commitment but have no effect on performance. Research and practical implications are discussed.

This study involves longitudinal neuro-electrophysiological analysis using motor-evoked potentials (MEP) and the Basso, Beattie, and Bresnahan behavioral examinations (BBB) to evaluate moderate mid-thoracic contusive spinal cord injury (SCI) in a rat model. Objectives/Background: The objective of the study is to characterize the onset and progression of contusive SCI over an eight-week period using a clinically applicable tool in an in vivo model. The background highlights the importance of a reliable and reproducible injury model and assessment tools for SCI. Methods: The methods section describes the experimental setup, including randomly assigned rats in three groups: Sham, Control, and Injury (undergoing a moderate contusive SCI using the NYU-Impactor). MEP monitoring and BBB examinations are conducted at baseline and weekly for eight weeks post-injury. Results: The results indicate that the relative MEP power spectral decreased to 11% and 22% in the left and right hindlimbs, respectively, during the first week post-SCI. In the second week, a slight spontaneous recovery was observed, reaching 17% in the left and 31% in the right hindlimbs. Over the following four weeks post-SCI, continuing deterioration of MEP signal power was observed with no detectable recovery. Conclusions: SCI attenuates hindlimb MEP power spectral and reduces locomotion, though the changes in MEP and locomotion exhibit distinct temporal patterns. The MEP monitoring provides valuable insights into the functional integrity of motor pathways following SCI and offer a sensitive and reliable assessment. By implementing MEP monitoring, researchers can track the progression of SCI and evaluate the efficacy of therapeutic interventions quantitatively.

We address a system of equations modeling an incompressible fluid interacting with an elastic body. We prove the local existence when the initial velocity belongs to the space $H^{1.5+\epsilon }$ and the initial structure velocity is in $H^{1+\epsilon }$, where $\epsilon \in (0, 1/20)$.

Coral reef ecosystems support high fish biodiversity through ecological interactions with structural complexity across multiple spatial scales including coral colony architecture and the surrounding seascape structure. In an era where the complexity of coral reef ecosystems is being diminished, understanding the importance of structural characteristics beyond single focal patches has the potential to better inform actions for protecting, restoring or creating habitat for reef‐associated species. A seascape ecology approach was applied to explore the associations between multiple scales of seascape structure and fish assemblage response variables within a small (49.6 km ² ) offshore no‐take MPA, Sir Bu Nair Island Protected Area, in Sharjah, United Arab Emirates. Fish–seascape associations were modelled with single regression trees. Both in situ and remote sensing–derived variables produced the best models with highest contributions from coral cover, amount of hard‐bottom habitat type and structural complexity of the seafloor terrain. Fish species richness was significantly higher where coral cover exceeded 35%. The hard‐bottom areas with coral supported diverse assemblages dominated by carnivorous and omnivorous fishes. The Sir Bu Nair Island Protected Area provides a critical refuge for threatened and regionally overexploited species including those with low resilience to fishing. The ecological success of this protected area is key to safeguarding regional marine biodiversity and recovering fish populations to enhance food security.

Finger tapping is one of the most reliable and widely utilized tasks for evoking activity in the motor cortex area of the brain, both for the brain-computer interface (BCI) and for evaluating the progress of certain brain diseases. Keeping in view the importance of dominance of the right hand, the goal of this study is to understand the response of each finger tapping alongside proposing a suitable finger tapping task both for BCI and medical imaging. With this in mind, we recruited twenty-four healthy subjects. Functional near-infrared spectroscopy (fNIRS) was used for brain imaging while the subjects performed a series of finger-tapping tasks utilizing each of the five fingers individually. From average hemodynamic results, the middle finger tapping task showed a maximum amount of activation in the motor cortex, whereas the index finger tapping task had the minimum activation compared to the other four fingers. The little finger and ring finger tapping tasks gave the most significant and widespread activation, respectively when compared through brain activation maps. The activation was clustered on a single region for the thumb-tapping task, whereas a wider area showed a very strong activation for the little finger and ring finger tapping tasks. Conclusively, this study is a step towards standardizing finger tapping and its related motor area activations, demonstrating that little finger tapping can best suit the purpose of a finger tapping task for BCI and medical imaging applications.

This article presents a novel direct filtering approach for loosely coupled global positioning system (GPS) and inertial navigation system (INS) integration. The proposed model is established based on utilizing the full nonlinear INS state equations in a direct configuration while including vehicle orientation through a unit-quaternion representation. A novel augmented quaternion unscented Kalman filter (AQUKF) is developed and proposed to address the direct nonlinear estimation of vehicle states for outdoor vehicle localization while preserving the non-Euclidean geometry of unit-quaternions. The proposed filter is experimentally validated under full GPS coverage as well as prolonged GPS outages. Results obtained in this article show that the proposed filter outperforms other existing solutions in various experimental testing scenarios.

The construction industry's increasing complexity and dynamic project environments engender advanced risk management strategies. AI-based risk management tools, reliant on complex mathematical models, often impose specialised coding requirements, leading to challenges in accessibility and implementation. In this vein, Generative Artificial Intelligence (GenAI) emerges as a potentially transformative solution, leveraging adaptive algorithms capable of real-time data analysis to enhance predictive accuracy and decision-making efficacy within Construction Risk Management (CRM). However, integrating GenAI into CRM introduces significant challenges, including concerns around data security, privacy, regulatory compliance, and a skills gap. Our research seeks to address these issues by presenting a systematic bibliometric analysis that explores evolving trends, key research contributions, and critical methodological approaches related to GenAI in CRM. Thus far, our investigation has analysed 23 selected research articles from an initial corpus of 212 papers, spanning the period from 2014 to 2024. Early insights delineate a marked escalation in research activity from 2020 onwards, a surge likely engendered by 2 recent advancements in AI technologies and their applicability to construction management. We categorise GenAI's potential benefits into technical, operational, technological, and integration-related advantages, encompassing improvements in risk identification, predictive capabilities, scheduling, and cybersecurity. Simultaneously, we identify significant risks, particularly related to data governance, social acceptance, and the operational impacts of AI-driven decisions. These preliminary findings underscore the imperative for systematic governance frameworks and proactive stakeholder engagement to optimise GenAI's benefits whilst mitigating its latent risks.

In Türkiye, women’s NGOs have gained significant influence in gender politics, especially since the country’s turn towards neoliberalism. A survey conducted among 735 members of women’s NGOs revealed that, contrary to expectations, many members hold gender inequitable attitudes, highlighting a lack of gender consciousness within these organizations. Key findings indicate that support for gender equality is higher among participants in Ankara than in Istanbul, and that factors such as education, political ideology, and socio-economic status significantly shape these attitudes. The persistence of patriarchal beliefs within these organizations suggests the need for a deeper analysis of the socio-political and structural barriers that hinder gender equality. This study provides critical insights into the intersection of civil society, gender attitudes, and advocacy in Türkiye.