Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling

ABSTRACT This study evaluates the performance of the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil and Water Assessment Tool (SWAT), feedforward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and multilinear regression (MLR) for modelling the rainfall–runoff–sediment process in Katar catchment, Ethiopia. Afterward, neural network ensemble (NE), weighted average ensemble (WE) and simple average ensemble (SE) techniques were developed to improve the performance of single models. The performance of the models was evaluated using Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ANFIS model outperformed the other single models for rainfall–runoff–sediment modelling. Moreover, the integration of artificial intelligence and physically-based models resulted in improved performance, with the NE technique demonstrating better accuracy by improving individual models by 5.8–27.6% for rainfall–runoff and 3.59–37.9% for suspended sediment load modelling in the validation phase.
Office‐based facial plastics procedures: Neuromodulators

Abstract Botulinum toxin is a potent neuromodulator commonly used for cosmetic applications in the clinic. In this article, we reviewed the various formulations of botulinum toxin type A commercially available in the United States, as well as clinical pearls for preprocedural planning, common in‐office injections, and management of complications.
Maximizing job performance through HRD mechanisms: An empirical investigation of the mediating role of multidimensional capital using PLS-SEM analysis

The primary objective of this study was to investigate the effect of HRD mechanisms on human capital (HC), social capital (SC), and psychological capital (PsyC), and their ultimate impact on job performance (JP). The study collected 201 fully completed questionnaires from nurses, pharmacists, dentists, physicians, and psychologists out of 330 healthcare professionals. The survey questionnaire was used to assess the impact of HRD mechanisms on the three capitals, which are human, psychological, and social, and their effect on job performance. The Smart PLS-SEM software was employed to test the research hypotheses and fit the conceptual model of the research. The results revealed that the dimensions of HRD mechanisms have a significant and positive impact on job performance, and human capital, social capital, and psychological capital have an influential mediating role. The findings can assist healthcare settings in enhancing job performance by focusing on the indicators highlighted in the study. This paper provides empirical evidence supporting the theory of HRD mechanism and its impact on psychological, human, and social capital, ultimately leading to enhanced job performance in the healthcare industry. The study presents a comprehensive comparison lens to examine the relative contribution of HRD mechanisms and the three capitals to healthcare professionals' job performance. This comparison helps to define the roles of each of the capitals in healthcare sectors and advances the development of theories concerning the significant relationship between HRD mechanisms and the three capitals leading to job performance.
Crustal Structure Beneath Mexico City From Joint Inversion of Receiver Functions and Dispersion Curves

Over the years, it has been clear to seismologists that the crustal structure beneath Mexico City is highly complex. This fact may influence the seismic waves' behavior when traveling through it. Consequently, many studies have been done to understand this crustal structure. This article presents a high‐resolution S‐wave velocity model obtained from joint inversion of radial receiver functions and group‐velocity dispersion curves beneath Mexico City (VMRFDC). This model includes azimuthal variations observed with receiver functions, which may be interpreted as crustal discontinuities. Receiver functions were retrieved from velocity and acceleration records, and dispersion curves were collected from a previous tomographic study. We performed a geostatistical analysis that revealed a horizontal spatial correlation of the crustal structure for up to 7.5 km. This distance was obtained from the S‐wave velocity omnidirectional semivariogram range per depth. Using ordinary kriging, we utilized this spatial correlation to estimate S‐wave velocities per depth and introduced two novel techniques: a resolution test for kriging and a geostatistical‐dependent smoothing technique. Finally, we compare our results with previous works, making our model consistent with geological and geophysical features reported in the literature.
Inflammatory Markers or ‘Composite Index’ in COVID-19 as ‘Point Of Care Test’: Which One is the Best Choice to Tackle Dragon!

COVID-19 is first corona virus related global pandemic after SARS and MERS and due to rapid evolution of pandemic fast track developments in diagnostics, monitoring and treatment options and vaccine developments has occurred to prevent its significant impact on mortality and morbidity. COVID-19 infection involves a complex interplay of the immunological and inflammatory responses. Inflammatory parameters are closely linked to the COVID-19 severity and mortality. Inflammatory parameters could be used to predict the transition from mild to severe/critical infection in patients of COVID-19. Numerous Inflammatory markers were analysed and used as point of care test to predict severity of illness, monitoring of illness, treatment planning and predicting final outcomes such as CRP, LDH, IL-6, D-dimer and ferritin. Robust data of these inflammatory markers is available and proved crucial in predicting pathophysiological trends such as cytokine storm, coagulation abnormalities, oxygenation status and response to therapy. Isolated assessment of inflammatory markers in absence of clinical and radiological parameters were observed as ‘double edged weapon’ due to overestimation and its impact on health burden and underprediction resulting into progression of illness and resultant poor outcome and increased health care cost. Composite index is combination clinical, radiological and laboratory inflammatory marker assessment. Combination of any two abnormalities were observed crucial role in early suspicion, diagnosis, monitoring, and recognition of complications, management and disposition of patients. Composite index rather than single biomarkers may provide more reliable information. Availability and cost issues cannot be ignored. It would be impossible for clinicians to consolidate and critically analyse the enormous data that is continuously added to the COVID-19 literature to extract practically useful information for the benefit of patients. Still, as of now Composite index should be considered as ‘point of care test’ to honour successful treatment outcome and prevent mortality and morbidity due to this ‘Dragon Pandemic’.
Structured H∞‐control of infinite‐dimensional systems

We develop a novel frequency‐based H∞‐control method for a large class of infinite‐dimensional linear time‐invariant systems in transfer function form. A major benefit of our approach is that reduction or identification techniques are not needed, which avoids typical distortions. Our method allows to exploit both state‐space or transfer function models and input/output frequency response data when only such are available. We aim for the design of practically useful H∞‐controllers of any convenient structure and size. We use a nonsmooth trust‐region bundle method to compute arbitrarily structured locally optimal H∞‐controllers for a frequency‐sampled approximation of the underlying infinite‐dimensional H∞‐problem in such a way that (i) exponential stability in closed loop is guaranteed and that (ii) the optimal H∞‐value of the approximation differs from the true infinite‐dimensional value only by a prior user‐specified tolerance. We demonstrate the versatility and practicality of our method on a variety of infinite‐dimensional H∞‐synthesis problems, including distributed and boundary control of partial differential equations, control of dead‐time and delay systems, and using a rich testing set.
Non-fragile controller design of uncertain saturated polynomial fuzzy systems subjected to persistent bounded disturbance

This paper investigates the problem of designing a non-fragile polynomial fuzzy controller for a class of polynomial fuzzy models subjected to persistent bounded disturbances and system uncertainty. The proposed controller is considered to satisfy the input saturation constraint. Furthermore, the resilient controller is affected by linear fractional uncertainties. The overall structure of the controller is obtained by the polynomial parallel distributed compensation concept. The sufficient conditions guarantee an L1 performance of the perturbed system and exponential stability of the non-perturbed system is achieved in terms of sum-of-squares decomposition conditions. Subsequently, the sum-of-squares-based conditions lead to minimization of the L1 performance such that the above-mentioned hard constraints on the control input and robustness of the closed-loop system against the system and controller uncertainties will be guaranteed. The controller gain matrices will be achieved by numerically solving the sum-of-squares-based conditions with the third-party MATLAB toolbox ‘SOSTOOLS’. Finally, extensive studies and hardware-in-the-loop simulations on a ball-and-beam system are presented, which illustrate the proposed controller can accurately and smoothly track the set point frequency. Furthermore, the proposed control approach is more robust over prior-art controllers for all the simulation scenarios.
Comments on “Frequency Diverse Array Antenna Using Time-Modulated Optimized Frequency Offset to Obtain Time-Invariant Spatial Fine Focusing Beampattern”

In the recent papers [1-4] including above paper time modulated frequency diverse arrays (FDAs) have been presented to obtain time invariant spatial patterns. The presented FDAs in [1-3] have the feature of time-invariant spatial focusing which means they have a constant maximum in a time duration, T, at a point with desired range, r, and angle, {\theta}. In [4] the pattern is time invariant, yet, not focused. However, in this communication it is indicated that the patterns are obtained using incorrect definition of time in some equations. The equation system of [3] is explained here that can be extended to [1, 2, 4], explicitly.
Non-Neuronal Acetylcholinesterase Activity Shows Limited Utility for Early Detection of Sepsis

(1) Background: Sepsis is a severe systemic inflammatory condition characterized by rapid clinical deterioration and organ dysfunction. The cholinergic system has been implicated in modulating the inflammatory response. Acetylcholinesterase (AChE), an enzyme primarily responsible for the hydrolysis of acetylcholine, has been proposed as a potential early indicator of sepsis onset. However, the exact role of non-neuronal AChE activity in sepsis and its correlation with disease severity and patient outcomes remain unclear. This study aimed to investigate the involvement of AChE activity in sepsis and evaluate its association with disease severity and clinical outcomes. (2) Methods: A prospective study included 43 septic patients. AChE activity was measured at sepsis detection, as well as 7 and 28 days later. Inflammatory biomarkers, disease severity scores, and patient outcomes were evaluated. (3) Results: AChE activity remained stable for 7 days and decreased at 28 days. However, there was no correlation between initial AChE activity and inflammatory biomarkers, disease severity scores, ICU stay, or hospital stay. (4) Conclusions: Non-neuronal AChE activity may not reliably indicate early sepsis or predict disease severity.
Numerical simulation of unsteady mixed convection of nanofluid in a lid-driven square cavity

The behavior of unsteady mixed convection flow of $Cu-$water based nanofluids is investigated numerically inside a square lid-driven partially heated flow below. Dual Reciprocity Boundary Element Method is used to solve stream function-vorticity form of the governing equations of the problem. The need of time integration scheme is eliminated by transforming the vorticity transport and energy equations to modified Helmholtz equations. This procedure also diminishes the stability problems. The numerical results are given for several values of Reynolds number $(Re)$, Rayleigh number $(Ra)$, heat source location $(D)$, heat source length $(B)$ and solid volume fraction $(\phi)$. The steady-state results are in good agreement with the results available in the literature.
An analytical-numerical approach in the calculation of photovoltaic module parameters operating under partial shaded conditions

The aim of this work is to determine the parameters and characteristics of a PV module in case of partial shading conditions. Both numerical and analytical methods are used in order to attain this objective. The iterative method namely Newton-Raphson is used to evaluate the characteristics of the PV module under partial shading conditions at its peak point. For various shading conditions, the lost power by the module is evaluated in order to show the influence of the partial shading conditions. The combined analytic method with the iterative method used allowed fairly accurate results on the behaviour of the shaded PV modules with less difficulties compared to the analytical method alone.
Extracellular Vesicles and Cancer Multidrug Resistance: Undesirable Intercellular Messengers?

Cancer multidrug resistance (MDR) is one of the main mechanisms contributing to therapy failure and mortality. Overexpression of drug transporters of the ABC family (ATP-binding cassette) is a major cause of MDR. Extracellular vesicles (EVs) are nanoparticles released by most cells of the organism involved in cell–cell communication. Their cargo mainly comprises, proteins, nucleic acids, and lipids, which are transferred from a donor cell to a target cell and lead to phenotypical changes. In this article, we review the scientific evidence addressing the regulation of ABC transporters by EV-mediated cell–cell communication. MDR transfer from drug-resistant to drug-sensitive cells has been identified in several tumor entities. This was attributed, in some cases, to the direct shuttle of transporter molecules or its coding mRNA between cells. Also, EV-mediated transport of regulatory proteins (e.g., transcription factors) and noncoding RNAs have been indicated to induce MDR. Conversely, the transfer of a drug-sensitive phenotype via EVs has also been reported. Additionally, interactions between non-tumor cells and the tumor cells with an impact on MDR are presented. Finally, we highlight uninvestigated aspects and possible approaches to exploiting this knowledge toward the identification of druggable processes and molecules and, ultimately, the development of novel therapeutic strategies.
Empirical Processes in Survey Sampling with (Conditional) Poisson Designs

It is the main purpose of this paper to study the asymptotics of certain variants of the empirical process in the context of survey data. Precisely, Functional Central Limit Theorems are established under usual conditions when the sample is drawn from a Poisson or a rejective sampling design. The framework we develop encompasses sampling designs with non‐uniform first order inclusion probabilities, which can be chosen so as to optimize estimation accuracy. Applications to Hadamard differentiable functionals are considered.
Multipartite Quantum Coherence and Distribution under the Unruh Effect

Quantum coherence of the tripartite W state and Greenberger–Horne–Zeilinger (GHZ) state under the Unruh effect are explored based on the model of a two‐level detector qubit coupled to a massless scalar field. The results reveal that Unruh thermal noise really destroys tripartite quantum resources. It is worth mentioning that the quantum coherence of the GHZ state reaches zero in the infinite acceleration limit, but that of the W‐state always remains nonzero. Coherence of the GHZ state displays a sudden death as the coupling parameter grows, while coherence freezing can be witnessed for the W state. It can be concluded that the W state is more robust than the GHZ state against Unruh radiation. Moreover, the related investigation can be expanded to N‐qubit quantum systems and the corresponding analytical solution is obtained. It indicates that the larger number W‐type entangled qubit can be as a better quantum resource for quantum information tasks under the Unruh effect.
From sicker to sure: the contact-induced lexical layering within the Medieval English adjectives of certainty

The major Old English adjective of certainty was (ge)wiss, which in early Middle English came to be replaced with sicker derived from very weakly attested Old English sicor, a word of ultimate Romance origin (from Latin sēcūrus). The relative paucity of occurrences of both adjectives in the Dictionary of Old English corpus is attributed to their use in mostly spoken language. The rapid increase in the usage of sicker in the thirteenth century is a mystery with possible, yet difficult to prove, Norse and/or Anglo-Norman influence. The fourteenth century marks the appearance of sure and certain borrowed from Anglo-Norman first by bilingual speakers and writers, and the quick diffusion of the new lexemes to all dialects and genres. This article looks at the adoption of the different senses of these polysemous adjectives into Middle English in the context of subjectification, which appears to affect not only semantic developments within one language but also the process of borrowing. When sure and certain were used epistemically, they tended to occur in the predicative position, usually following the copula. It took several centuries of lexical layering (coexistence of synonyms) before sicker was lost from Standard English in the sixteenth century.
Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning

Simple Summary The timely detection and accurate classification of esophageal cancer are critical for providing optimal treatment. However, assessing and categorizing pathological conditions related to the esophagus face limitations as they rely on reference document photo-documentation, and the accuracy heavily relies on the endoscopist’s expertise. In recent times, computer-aided endoscopic image classification has achieved remarkable success in this domain. For this study, a dataset of 1002 endoscopic images, comprising 650 white-light images and 352 narrow-band images, was collected for training. The esophageal neoplasms were categorized into three groups: squamous cell carcinoma, high-grade dysplasia, and normal cases. To enhance the prediction results, a hybrid model was proposed, yielding an impressive accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%. The introduction of AI-based diagnostic platforms is expected to effectively support medical professionals in formulating well-informed treatment regimens. Abstract Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician’s expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learning, tailored to optimize performance with limited data. Additionally, we propose a hybrid model integrating EfficientNet and Vision Transformer networks to enhance prediction accuracy. Conducting rigorous evaluations on a carefully curated dataset comprising 1002 endoscopic images (comprising 650 white-light images and 352 narrow-band images), our model achieved exceptional outcomes. Our combined model achieved an accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%, surpassing state-of-the-art models and individual components, substantiating its potential for precise medical image classification. The AI-based medical image prediction platform presents several advantageous characteristics, encompassing superior prediction accuracy, a compact model size, and adaptability to low-data scenarios. This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis.
Modeling of growth of the macroalga Ulva sp. in a controlled photobioreactor based on nitrogen accumulation dynamics

ABSTRACT Macroalgal biomass production models that capture nutrient dynamics, temperature, light and salinity are important for the design and operation of large-scale farms. The goal of this study is to understand how the nitrogen fertilizing regime, relating to dose (µM N week−1), amplitude (µM N) and duration (hours) of fertilization, affects the dynamics of nitrogen content and biomass production of Ulva sp. We hypothesize that the nitrogen fertilizing regime controls the Ulva Nitrogen Use Efficiency (NUE), defined here as the fraction of fertilizer nitrogen that is utilized and allocated to yield N, and, accordingly, also nitrogen assimilation in the biomass and the growth rate. We test this hypothesis by measuring internal nitrogen and biomass weight and by calculating NUE under various fertilization regimes in controlled photobioreactors. Based on these experimental data, we developed a biomass productivity model that predicts nitrogen and biomass dynamics temporally over three weeks of cultivation. This study highlights efficient fertilizing regimes and enables the development of a comprehensive understanding of the dynamic relationship between external N, internal N and biomass production of Ulva sp. under varying external N levels, which is important for real-world agricultural applications. This study provides a better understanding of the external N-internal N-biomass triangle leading to an improved dynamic cultivation model, enabling better control of nutrient application and biomass production in macroalgal farming for a sustainable marine bioeconomy.