Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximizing Sustainability in Reservoir Operation under Climate Change Using a Novel Adaptive Accelerated Gravitational Search Algorithm

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem.
The Influence of Product Placement on Brand Awareness of Ellips Hair Vitamin on Indonesia's Next Top Model Youtube Channel

This research is conducted based on the phenomenon of Indonesia’s Next Top Model (further abbreviated to INTM) on YouTube which appearing the product of Ellips hair vitamin and the appearance is continuously being talked about by viewers on social media. No wonder, it seems to impact the activity of social media of Ellips itself. The research aims at finding out whether product placement influences brand awareness of Ellips hair vitamin or not. The quantitative method in this research is engaged by using a questionnaire instrument spread to the population which is the subscribers of Indonesia's Next Top Model on YouTube. The questionnaires are shared to a hundred samples gained by systematic random sampling technique. A series of tests are also conducted, namely data analysis test by using classic assumption tests, correlation coefficient test, determination coefficient test, simple linear regression test, and hypothetical test. The result shows that the activity of product placement influences positively and significantly on Ellips brand awareness as well as having a strong and direct relationship. The variable of product placement and brand awareness has a good recapitulation value of percentage which is above 80% of the whole dimension from both variables. By keeping on going on remarkable performance on product placement, it will also increase brand awareness of Ellips. To conclude, the next activities on product placement shall choose which media or program that is more attractive or engaging to the viewers accordingly to the target market of Ellips
e-investigation of Amanita flavipes: Identification and Characterization Based on rDNA-ITS Sequence, Morphology and Anatomy

Swat valley is Himalayan moist temperate forest of Pakistan. It is also called mini Switzerland of Pakistan. This area has diverse flora. A wide diversity of mushrooms is present over here but number of species explored is very small. In the present study, a species of Amanita was collected during field tour and identified on the basis of morpho-anatomical and molecular markers which confirmed its identification as Amanita flavipes. BLAST analysis of this species has been re-investigated from new locality swat and is being reported first time from this area.
An explicit stabilization scheme for Q-compensated reverse time migration

Attenuation compensation in prestack depth migration typically requires non-physical frequency-dependent energy amplification, which may lead to numerical instability. An explicit stabilization approach is developed for seismic Q compensation after deriving the Kspace Greens function of the compensated constant Q wave equation, which has decoupled fractional Laplacians (DFLs). At high wavenumbers, as time increases, the time propagator of K-space Greens function increases exponentially. Therefore, an exponential window function is introduced to stabilize the exponentially divergent time propagator. Unlike the conventional low-pass filtering approach in the frequency or wavenumber domain, the proposed method assumes that the exponent of the chosen window is a power function of the wavenumber magnitude, which only involves explicit stabilization terms in the timespace domain. An explicit stabilization form helps to perform seismic data Q compensation more conveniently. We outline the basic structure of the proposed approach with explicit stabilization and highlight some numerical details using CUDA-based implementations. The strong scaling analysis justifies the good performance of the developed code package in terms of computational efficiency and scalability. Besides, we further analyze the optimal scheme parameter selection and the influence of parameters on filtering performance. The proposed Q-RTM is applied on the Marmousi model and both synthetic and real crosswell examples to verify its feasibility and numerical stability.
Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace clustering (MVKCSC) and random subspace MVKCSC (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multiview learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multifeature information. Moreover, a new method of constructing the Laplacian matrix using kernel CR coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase the diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.
Parametric Approach to Fault Detection in Aircraft Control Surfaces

THE required technological advances identified by the aeronautics sector to achieve the long-term goals of greener aviation are multiple and challenging. New technological options will be needed to produce incrementally more efficient and environmentally friendlier aircraft. The early and robust detection of faults that have an influence on structural loads contribute to the overall optimization of aircraft design, and therefore to weight savings for better overall performance in terms of fuel burn, noise, range, and environmental footprint.
Digital innovations in the Czech Republic: developing the inner circle of the Triggering Change Model

ABSTRACT Purpose To assess how the primary actors within farmers’ microAKIS (self-assembled knowledge networks) changed as a technology evolved, utilising a case study of precision farming. Methodology Semi-structured interviews were conducted with 35 farmers in two Czech Regions. Findings The evolution of digital technologies led to an ongoing, incremental pattern of innovation, where innovating (pioneering) and early-adopting farmers experimented and developed new uses for the technologies. Technology suppliers were identified as the most important information sources, but pioneering farmers often exceeded advisors’ knowledge through farmers’ own experimentation, reflecting highly educated farm staff and the Czech history of on-farm experimentation. Pioneering farmers engaged in reciprocal advisory relationships. Practical Implications There may be limited demand for ‘independent’ advice on digital technological issues from large-scale farms. Smaller farms struggled to access both the technology and the advice because technical advice was dependent on prospective equipment purchase. Theoretical Implications Integration of the microAKIS concept and Triggering Change Model demonstrated how the characteristics of the innovation – particularly mutability – influence the role of advisors. Originality Findings advanced the Triggering Change Model by applying it to digital technological innovations and developing the inner circle of ‘co-innovation’, whereby advances to technologies are evaluated and integrated on an ongoing basis.
Key Genes and Biochemical Networks in Various Brain Regions Affected in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegenerative brain disorders, affecting millions of people around the world. Ageing remains one of the strongest risk factors associated with the disease and the increasing trend of the ageing population globally has significantly increased the pressure on healthcare systems worldwide. The pathogenesis of AD is being extensively investigated, yet several unknown key components remain. Therefore, we aimed to extract new knowledge from existing data. Ten gene expression datasets from different brain regions including the hippocampus, cerebellum, entorhinal, frontal and temporal cortices of 820 AD cases and 626 healthy controls were analyzed using the robust rank aggregation (RRA) method. Our results returned 1713 robust differentially expressed genes (DEGs) between five brain regions of AD cases and healthy controls. Subsequent analysis revealed pathways that were altered in each brain region, of which the GABAergic synapse pathway and the retrograde endocannabinoid signaling pathway were shared between all AD affected brain regions except the cerebellum, which is relatively less sensitive to the effects of AD. Furthermore, we obtained common robust DEGs between these two pathways and predicted three miRNAs as potential candidates targeting these genes; hsa-mir-17-5p, hsa-mir-106a-5p and hsa-mir-373-3p. Three transcription factors (TFs) were also identified as the potential upstream regulators of the robust DEGs; ELK-1, GATA1 and GATA2. Our results provide the foundation for further research investigating the role of these pathways in AD pathogenesis, and potential application of these miRNAs and TFs as therapeutic and diagnostic targets.
Influence of Personal, Environmental, and Community Factors on Cigarette Smoking in Adolescents: A Population-Based Study from Taiwan

Understanding the factors that influence cigarette smoking among adolescents is critical. We identified personal, community, and environmental factors associated with current cigarette smoking among adolescents. This population-based cross-sectional analysis study was conducted using the 2012 Taiwan Global Youth Tobacco Survey and the sociodemographic statistics of the city or county from Taiwan’s Ministry of the Interior. A total of 27,524 participants (age: 12–18-years) was included. The associated factors were identified through logistic regression. A path analysis was performed to examine the pathway from the associated factors to current cigarette smoking. According to this analysis, the following factors were prominently and positively associated with adolescent cigarette smoking: one personal factor (pocket money), five environmental factors (home secondhand smoke (SHS) exposure, smoker friends, outside SHS exposure, school SHS exposure, and smoker parents), and two community factors (free cigarettes from tobacco companies and indigenous population). By contrast, five personal factors (feeling less comfortable smoking at social occasions, feeling indifferent about smoking or not smoking at social occasions, female sex, feeling that quitting is difficult, and feeling that quitting after having smoked is harmful to health) and one environmental factor (school antismoking education) had negative effects. Thus, comprehensive interventions promoting the perception of harm caused by smoking and interrupting access to cigarettes through social networks can reduce cigarette smoking in adolescents.
Validation of a Korean Version of the Body-Appreciation Scale (K-BAS) in Young Women

Despite concerns regarding body image in young Korean women, no measurement tool has yet been developed or is available. This study examined the validity and reliability of the Korean version of the Body-Appreciation Scale (K-BAS) to assess body image positivity among young women. For this purpose, convenience sampling was conducted using social network services. The participants were 245 women (N = 245) aged 20 to 40 years. Validity and reliability were examined using item analysis, factor analysis, and correlation with body mass index, body dissatisfaction, and Cronbach’s alpha. Twelve items were selected for the study. Two factors were extracted through factor analysis, explaining 64.82% of the variance and showing a good model fit in the K-BAS. The K-BAS score was negatively correlated with body mass index (r = −0.33, p < 0.001) and body dissatisfaction (r = −0.41, p < 0.001). Reliability was high, as indicated by a Cronbach’s alpha of 0.91. These results indicate that the K-BAS may serve as an appropriate instrument for measuring body image positivity among young Korean women. It may also be useful for identifying women with abnormal body perceptions.
Predictive Control of Adaptive Micro-Grid Energy Management System Considering Electric Vehicles Integration

This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid. Keywords: Adaptive model predictive control, Energy management system, Electric vehicles, Vehicle to grid technology, Grid reliability, Load shifting, Optimization problem and MATLAB/Simulink.
Household Food Insecurity in DKI Jakarta Province at The Beginning of The Covid-19 Pandemic

Food insecurity is a global issue that’s concern not only in poor and developing countries, but also in developed countries. Its conditions have worsened since the beginning of the Covid-19 pandemic where social restrictions and economic contraction caused many people to lose their jobs, incomes, and increased poverty. DKI Jakarta was one of the most economically affected provinces at the beginning of the Covid-19 pandemic where economic growth in the first quarter of 2020 recorded grow 5.06 percent year on year (the lowest in the last ten years) and slowed down by 0.56 percent overall quarter to quarter, and an increase of poverty 1.11 percent, the highest in Indonesia. This study examines the effect of household characteristics in DKI Jakarta on their food insecurity status at the beginning of the Covid-19 pandemic. The data used is the March 2020 Susenas which was analyzed descriptively and inferentially using firth logistic regression. The results showed that there were 4.47 percent of households in DKI Jakarta had food insecurity status at the beginning of the Covid-19 pandemic. In general, households with food insecurity status are poor, don’t have social security, the head of the household doesn’t work and less than high school education.
The Role of Diet in the Pathogenesis of Erectile Dysfunction

Erectile dysfunction (ED), characterized by a man’s constant or recurrent disability to obtain and/or maintain penile erection sufficient to achieve and complete satisfactory sexual intercourse, is a common ailment. Its incidence and prevalence increases with age. ED is caused by several factors -psychological factors, organic factors, and metabolic factors. ED may also be iatrogenic and related to some drugs, pelvic surgery, or radiotherapy. The main etiology is vascular in nature, with risk factors that are common to other cardiovascular diseases. Several drugs, mechanical and prosthetic devices, and surgical procedures are used to alleviate ED. Lifestyle modulation is increasingly being added as first-line treatments for ED. These include lack of physical activity and cigarette smoking. Obesity and an unhealthy quality of diet are also lifestyles that affect ED. This manuscript narratively reviews the impact of excessive caloric intake, various dietary ingredients, and special diets on ED.
Traumatic Impact Assessment of CPR Load on a Human Ribcage

Chest compression is a parameter of injury criteria assessment for human beings. Additionally, it is used to find the external compression response as a result of vehicle crashes, falls, or sporting impacts. This behavioral feature is described by many deterministic models related to specific experimental tests, hindering distinct scenarios. The present study evaluates the energy absorbed as a function of rib compression. The proposed model was obtained from three different computed tomography (CT) studies. The anthropometric values are interpolated to obtain a parametric curve for a human rib’s average size. The computed results are compared against an STL-DICOM® file used to obtain a virtual reconstruction of one rib. A numerical model of the behavior of the thorax displacement expressed injury in the human rib model’s stiffness. The proposed model is used to determine the correlation of the input payload versus the numerical stiffness value. The outcome is confirmed by numerical analyses applied to a virtual human rib reconstruction.
Integration of Different Graphene Nanostructures with PDMS to Form Wearable Sensors

This paper presents a substantial review of the fabrication and implementation of graphene-PDMS-based composites for wearable sensing applications. Graphene is a pivotal nanomaterial which is increasingly being used to develop multifunctional sensors due to their enhanced electrical, mechanical, and thermal characteristics. It has been able to generate devices with excellent performances in terms of sensitivity and longevity. Among the polymers, polydimethylsiloxane (PDMS) has been one of the most common ones that has been used in biomedical applications. Certain attributes, such as biocompatibility and the hydrophobic nature of PDMS, have led the researchers to conjugate it in graphene sensors as substrates or a polymer matrix. The use of these graphene/PDMS-based sensors for wearable sensing applications has been highlighted here. Different kinds of electrochemical and strain-sensing applications have been carried out to detect the physiological signals and parameters of the human body. These prototypes have been classified based on the physical nature of graphene used to formulate the sensors. Finally, the current challenges and future perspectives of these graphene/PDMS-based wearable sensors are explained in the final part of the paper.
Studies on the Amylolytic Enzyme Associated with the Black Mould Deterioration of Eko, a Nigerian Fermented Food

Aims: To characterize amylase enzymes responsible for the spoilage of Eko, a Nigerian fermented food and to provide the best way to preserve it. Study Design: The experiment was carried out to extract the enzyme involved in the deterioration of Eko, it was partially purified using ion-exchange chromatography. Place and Duration of Study: Department of Microbiology, Obafemi Awolowo University, Ile Ife, between 1994 and 1996. Methodology: Eko was obtained from the new market Ile Ife and cut into slices, the slices were inoculated with spores of Aspergillus niger, on the eleventh day after inoculation, the enzyme was extracted, partially purified using ion-exchange chromatograpghy and characterized. Results: Within eleven days of inoculation, the Eko slices were extensively covered by the fungal spores, the amylase secreted by the Aspergillus niger was harvested and characterized. Its molecular weight was 70,000 Da, the optimum pH and temperature were 6 and 35 0C respectively, enzyme activity increased with increase in starch concentration with optimum activity at 0.9 mg/ml, the apparent Km for starch hydrolysis was 0.4 mg/ml. Cations such as Na+ and K+ stimulated amylase activity but was inhibited by EDTA and HgCl2 Conclusion: The result of this work showed that amylase enzyme was produced by Aspergillus niger in the infected Eko slices also that the enzyme contributed to the degradation process, therefore, keeping Eko in conditions that will hinder the secretion of amylase enzyme or hinder its activity and the subsequent growth of A. niger will prolong the shelf life of Eko.