Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Product Diversity, Price and Location on Consumer Satisfaction

The primary objective of this study is to ascertain the inherent importance of various factors in influencing consumer satisfaction, with the ultimate goal of identifying the most influential factor among them. The data collection process encompassed a comprehensive array of methodologies, including meticulous observation, extensive literature review, thought-provoking questionnaires, meticulous documentation analysis, and insightful interviews. A comprehensive study was conducted, encompassing a sample size of 40 individuals who were selected through the implementation of accidental sampling methodology. The data underwent rigorous analysis employing quantitative methodologies, encompassing assessments of validity and reliability, alongside the application of multiple linear regression analysis. The findings of the study reveal that various factors, namely product diversity, price, and location, exert a noteworthy influence on consumer satisfaction when examined in isolation. The statistical analysis, specifically the t-test, revealed that the coefficients pertaining to all three factors exhibited a noteworthy level of significance (p < 0.05). Moreover, the statistical analysis conducted through the F-test has demonstrated that the combined influence of the three factors under investigation exerts a noteworthy impact on consumer satisfaction, as indicated by the obtained p-value of less than 0.05. The results of the determination test have revealed that the combined influence of the variables under consideration accounts for a substantial portion, specifically 74.2%, of the observed variability in consumer satisfaction.
Effect of doping on the structural properties of ZnO nanowires synthesised by ultrasonic-assisted immersion technique

Various procedures for producing high-quality zinc oxide (ZnO) nanowires (ZnO NWs) have been developed. Nevertheless, most of it rely on harsh circumstances such as high temperature, high pressure, costly materials, and complicated procedures. As a result, this study introduces an alternative ultrasonic-assisted immersion technique due to its many advantages such as low cost, ease of handling, and low energy consumption, as well as studying the effect of different precursors on the morphological, structural, and optical properties of the ZnO NWs, thus supporting and consolidating previous discoveries and providing a clearer understanding of the mechanism of ZnO formation. The most promising desirable features have been demonstrated for chromium doped ZnO NWs. Field emission scanning electron microscopy (FESEM) was used to examine the surface morphology of the samples, and x-ray diffraction (XRD) and UV-visible (UV-Vis) were utilised to investigate the structural and optical characteristics of the ZnO NWs. It was discovered that inserting Cr as a dopant for ZnO enhanced ZnO NWs by preventing quick electron-hole recombination, revealing it as the best dopant. This id due to reduced band gap (3.231 eV), relax strain (−0.2383%) and stress (0.560 GPa), and near zero porosities
Predicting Student Dropout with Minimal Information

Student dropout is a problem for both students and universities. However, in the crises that Lebanon is going through, it is becoming a serious financial problem for Lebanese private universities. To try to minimize it, it must be predicted in order to implement the appropriate actions. In this paper, a method to build the appropriate prediction system is presented. First, it generates a data source of predictor variables from student dataset collected from a faculty of economic sciences in Beirut between 2010 and 2020. Then, it will build a prediction model using data classification techniques based on identified predictor variables and validate it. Using open-source software and free cloud environments, a prediction program was developed. It consolidates, corrects, and normalizes the student's data. Then, it applies simple linear regression to show the correlation between the different variables and the student dropout, which allows us to select the factors that are highly correlated. From this point on, the program tries to predict the student dropout using different classification algorithms by machine learning on student dataset who left their courses either in success or in failure. Lastly, it measures the accuracy of the results and determines the best algorithm. In this study, the Artificial Neural Networks - Multilayer Perceptron showed an accuracy of 98.1% using only five variables. Finally, we evoke new avenues to further research and improve the model.
Modeling Analysis and Study of Unbalanced Funds Impact on Power Spot Market with High Share of Clean Energy

Under the background of "double carbon" and large-scale grid connection of clean energy, the uncertainty of clean energy output and the market reforms to deal with this uncertainty will affect the interests of market participants through settlement and unbalanced funds. Focusing on the background of the power spot market for clean energy, this paper first analyzes the unbalanced funds items specific to Chinese power spot market, then conducts a qualitative analysis of the impact of high proportions of clean energy on unbalanced funds. Finally, from the perspective of quantitative analysis, three prediction models for the impact of unbalanced funds are proposed to provide technical means for prediction and evaluation of the changes in the interests of power market participants under the background of China's energy transition in the future.
Governing growth in organic farming: The evolving capacities of organic groups in the United Kingdom and Denmark

The question of the ‘policy capacity’ of interest groups is increasingly gaining prominence as a key variable in governing and transformative capacities. This raises the issue of whether group policy capacities can be developed. While group scholars have long talked of group capacity, this has largely amounted to compiling a ‘shopping list’ of possible capacities general to all groups. There has not been much attention to variations in capacity among groups, or with the development of capacity by a single group over time. This paper takes a tentative step towards filling this gap. In pursuing this general line of inquiry we argue that (i) initial ‘selection’ of group type shapes scope of capacity development, (ii) groups seek to adapt capacity to changing policy contexts, and (iii) adaptive efforts are shaped by the ‘legacy’ of the originating type – change is bounded unless the group engages in ‘radical’ organisational changes (e.g. redefinition of entire purpose). This general argument is fleshed out by comparing and contrasting the evolution of the key organic interest groups in both the UK and Denmark.
On a Novel Model for ECG Signals and its Statistical Properties

An accurate mathematical ECG model helps comprehend the heart’s workings, which in turn helps identify various heart-related abnormalities. A typical ECG waveform consists of recurrent QRS complexes at almost regular intervals. In the most general scenario, the amplitude, the period after which it repeats, and the shape of the QRS complex may change from pulse to pulse. This paper proposes a mathematical model for the ECG signals capable of capturing these features. Since statistical analysis of the the general model is more complex, we make certain assumptions to make the problem analytically tractable. With these assumptions at our disposal, we provide closed-form expressions for time-varying covariance/spectra of the process generated by the proposed model. In order to demonstrate the approximation capability of the model, that is, the ability to model ECG signals, we synthesize ECG signals for different health states by first extracting the QRS complex of that health state and using the mean time period also extracted from the realization of the same health state. We find that the degree of non-stationarity of the real-world ECG and the one generated by the model match closely. In reality, the signal for each state appears to be a close match. Based on our experimental findings, we observe that the measure of the degree of non-stationarity interestingly falls in different ranges, depending upon the type of heart-related abnormality. This observation makes our conjecture that this measure can serve as a biomarker for various related abnormalities.
Penaeid Shrimp Chromosome Studies Entering the Post-Genomic Era

Chromosome studies provide the foundation for comprehending inheritance, variation, systematics, and evolution. Penaeid shrimps are a group of crustaceans with great economic importance. Basic cytogenetic information obtained from these shrimps can be used to study their genome structure, chromosome relationships, chromosome variation, polyploidy manipulation, and breeding. The study of shrimp chromosomes experienced significant growth in the 1990s and has been closely linked to the progress of genome research since the application of next-generation sequencing technology. To date, the genome sequences of five penaeid shrimp species have been published. The availability of these genomes has ushered the study of shrimp chromosomes into the post-genomic era. Currently, research on shrimp cytogenetics not only involves chromosome counting and karyotyping, but also extends to investigating submicroscopic changes; exploring genome structure and regulation during various cell divisions; and contributing to the understanding of mechanisms related to growth, sexual control, stress resistance, and genome evolution. In this article, we provide an overview of the progress made in chromosome research on penaeid shrimp. We emphasize the mutual promotion between studies on chromosome structure and genome research and highlight the impact of chromosome-level assembly on studies of genome structure and function. Additionally, we summarize the emerging trends in post-genomic-era shrimp chromosome research.
Marine disposal of monosulfide from a eutrophic estuary system and the impact on water chemistry

The sediments of the Peel Harvey Estuary system (PHES) contain accumulations of monosulfidic black ooze (MBO) related to hypereutrophication and discharge from active acid sulfate soils. MBOs may release acidity and contaminants (nutrients, trace metals) when disturbed. We investigated release of acidity during resuspension of MBOs in a laboratory study and during a dredging event in the estuary. Selected samples from the laboratory study were analysed using XANES. In the laboratory study, decreases in pH (approx. 1 pH unit) of MBO suspensions coincided with the re-establishment of oxidizing conditions. Sediment analyses illustrated near-complete oxidation of acid-volatile sulfide (AVS, a proxy for monosulfide). The XANES data supported the chemical AVS analysis, showing the disappearance of peaks corresponding to mackinawite and greigite and appearance of the sulfate peak, consistent with AVS oxidation products. Pyrite and organic S peaks showing minimal changes throughout the resuspension. The data for estuary water quality from dredge monitoring showed consistent transient decreases in Eh at the point of sediment disposal, but pH remained well-buffered. The decreases in Eh were associated with increases in concentrations of ammonium and orthophosphate, with high variability. Laboratory experiments demonstrate the high reactivity of MBOs from the PHES following disturbance, but high variability and buffering in the field may limit the observation of these effects.
A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method. Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets. Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.
Modern Doppler Navigation Systems

This paper, which was presented at an Ordinary Meeting of the Institute in London on 21 November 1979, with S. Ratcliffe in the Chair, traces the evolution of airborne doppler navigation systems over the past two decades and discusses the treatment of errors introduced when flying over water. Both authors are with Decca Radar Ltd. The object of this paper is to describe briefly the major developments which have taken place in doppler navigation systems during the past 15 years or so, to indicate the current state of the art and to examine in some detail the behaviour of doppler systems when flying over water. The year 1960 is taken as a starting point since by that time commercial doppler systems were established. A typical general purpose system of that generation (Fig. 1) consisted of the following units: 1. An antenna, probably mechanically stabilized. 2. A transmitter/receiver using either pulse or FMCW modulation. 3. A tracking system. 4. An analog computer. 5. A display of ground-speed and drift-angle. 6. A display of present position.
Levels of Some Blood Clotting Factors in COVID-19 Patients: Comparative Study Before and After Pfizer- BioNTech Vaccinations

One of the health issues that a coronavirus can induce is blood clotting. Coronavirus can be prevented in a number of ways. Vaccination is one of the critical methods for preventing illness or lessening its impact. This study seeks to estimate a few blood coagulation variables. 147 samples were collected from the Baghdad Governorate in the autumn of 2021. The samples were split into three groups: COVID-19 patients, healthy individuals before and after receiving the (Pfizer-BioNTech) vaccine, and healthy individuals only. Prothrombin Time (PT), Partial Thromboplastin Time (PTT), Protein C (PTN-C), Protein S (PTN-S), and International Normalized Ratio (INR) for 49 samples were measured and computed for each group. The results have shown that the PTN-C and PTN-S concentrations were significantly decreased in the COVID-19 patients compared to unvaccinated healthy individuals. While INR of COVID-19 patients showed a highly significant increase when compared with unvaccinated healthy individuals. At the same time, there was no significant difference for each PT and PTT between these studied groups. The parameters exhibit identical findings when COVID-19 patients are compared to those who have had vaccinations, with the exception of PT, which reveals a highly significant rise in COVID-19 patients when compared to those who have received vaccinations. Conclusion: COVID-19 causes blood clots and may be recognized by a decrease in PTN-C and PTN-S content.
Enhancing resolution of nonstationary seismic data by molecular-Gabor transform

ABSTRACTWe propose an adaptive spectrum-broadening method (ASBM) to improve the resolution of nonstationary seismic data. This method assumes that a seismic trace can be split into segments, each of which can be considered as approximately stationary. We construct a set of specific windows, called molecular-Gabor (MG) windows, by solving an optimization problem, such that the seismic trace in each of the MG windows is stationary. A time-frequency (t-f) transform, called MG transform, can be obtained from a MG frame constructed using the MG windows. For a seismic trace, we first transform it into the t-f domain, then spectrum-broadening and/or amplitude compensation are performed in each of the MG windows. Subsequently, a high-resolution version of the nonstationary seismic trace will be obtained after the inverse MG transform. Applications of this method to synthetic and field data show that the ASBM works well for a general earth Q-model that varies with traveltime. It can restore the attenuated energy a.
Optimización de portafolios con capital en riesgo acotado / Optimal portfolios under bounded Capital at Risk.

En anos recientes se ha introducido en el mercado el Capital en Riesgo o CaR, por sus siglas en ingles, como medida de riesgo en reemplazo de la varianza en los problemas de seleccion de portafolios optimos. En este trabajo se hizo un estudio, usando la metodologia clasica de control estocastico, acerca de las consecuencias de introducir la medida de CaR en un modelo de mercado Black-Scholes simple, en un modelo de mercado de Black-Scholes con saltos y en un modelo de mercado de Difusion Inverso Generalizado. Se confrontaron los resultados teoricos con datos reales tomados de la Bolsa de Valores de Colombia, para el caso de Ecopetrol e Isa. / Abstract. In recent years Capital at Risk has been brought into the market as a way to minimizing risks in the replacement of the variance in optimal portfolio selection problems. A study was conducted for this work, by utilizing the classical stochastic control methodology on the consequences of using the Capital at Risk measure in a Black-Scholes simple market model,into a Black-Scholes market with jumps, and into a Generalized Inverse Di? usion market. Theoretical results were compared to real data taken from bolsa de Valores de Colombia, for the cases of Ecopetrol and Isa.
Marine Bioactives and Their Application in the Food Industry: A Review

The structurally diverse bioactive compounds found in marine organisms represent valuable resources for the food and pharmaceutical industries. The marine ecosystem encompasses over half of the world’s biota, providing an extensive range of bioactive compounds that can be extracted from various marine life forms, including marine microorganisms (such as bacteria, cyanobacteria, and actinobacteria), algae (both macroalgae and microalgae), invertebrates (including sponges, mollusks, echinoderms, and crustaceans), and, most importantly, fish. Many of these organisms thrive in extreme marine environments, leading to the production of complex molecules with unique biological functions. Consequently, marine biomolecules, such as lipids (especially polyunsaturated fatty acids), proteins/peptides, polysaccharides, carotenoids, phenolics, and saponins, exhibit a wide range of biological properties and can serve as valuable components in nutraceuticals and functional foods. Nevertheless, most of these biomolecules are susceptible to oxidation and degradation; encapsulation-based technologies tend to preserve them and increase their bioavailability and functions. These biological compounds demonstrate diverse activities, including antioxidant, anticancer, antithrombotic, anticoagulant, anti-inflammatory, antiproliferative, antidiabetic, antimicrobial, and cardioprotective effects, making them promising candidates for applications in the food industry. Despite their numerous health benefits, marine bioactive compounds have remained underutilized, not only in the food industry but also in the pharmaceutical and nutraceutical sectors. Therefore, this review aims to provide an overview of the various sources of marine bioactive compounds and their potential contributions to the food industry.
The effects of female education on child education: a prospective analysis

This study estimates the e ﬀ ects of women ’ s education on their o ﬀ spring using quasi-experimental evidence from six educational reforms that increased the length of compulsory schooling in several European countries. The empirical analysis uses data from the Survey of Health, Ageing and Retirement in Europe and instrumental variable estimation to estimate the e ﬀ ects of female education on fertility and on children ’ s education. This study provides the ﬁ rst analysis using quasi-experimental variation in education to estimate prospective models of intergenerational e ﬀ ects. These models start with a birth cohort and link information on their fertility and on their children ’ s outcomes. These models account for the e ﬀ ect of female education on the probability that women have children when estimating the e ﬀ ect of female education. The direct e ﬀ ect of female education on children ’ s educational attainment, i.e. the e ﬀ ect conditional on the birth of a child, is positive. In addition, higher female education increases fertility. Therefore, the probability that a woman has a child with a high educational attainment is increased when considering the e ﬀ ect of female education on fertility. Studies that estimate retrospective models of intergenerational e ﬀ ects using reforms in the length of compulsory schooling may underestimate the total e ﬀ ect of female on child education.
Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience

Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely available dashcams and other sensing devices. However, it is still a non-trivial task to utilize crowdsourced data, such as dashcam images and video, to efficiently create or update high-quality point clouds using technologies like Structure from Motion (SfM). This study assesses and compares different image matching options available in open-source SfM software, analyzing their applicability and limitations for mapping urban scenes in different practical scenarios. Furthermore, the study analyzes the impact of various camera setups (i.e., the number of cameras and their placement) and weather conditions on the quality of the generated 3D point clouds in terms of completeness and accuracy. Based on these analyses, our study provides guidelines for creating more accurate point clouds.