Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation of Media and Entertainment Industry: Identification of Most Important Factors in OTT Consumption

Purpose: The main goal of this study is to gain insights into the OTT (over the top) landscape within the Media and Entertainment (M&E) industry, with a focus on uncovering the factors that influence consumer engagement. Additionally, the aim is to compile interconnected elements that affect the mindset of OTT consumers. There is fierce competition in the dynamic Indian market of OTT platforms, and dearth of studies focussed on understanding the factors that drive the membership subscriptions, customer’s long-term association and creation of life-time value and involvement in word of mouth. This can help in formulation of right kind of strategies for seeking and retention of customers. Design/Methodology/Approach: A questionnaire-based survey was undertaken to comprehend patterns of consumer engagement. Exploratory factor analysis was employed to investigate the connections between variables, deliberately avoiding a predetermined fixed number of factors. Findings: The study gave us three complex factors which can guide us to design the right marketing strategies. Practical Implications: The primary objective of this study is to pinpoint the combined factors that influence media consumption via OTT platforms. This identification aims to assist stakeholders in tailoring content and marketing strategies in alignment with customer engagement trends.
PHOSPHATE AND NITRATE DISTRIBUTION IN INNER AMBON BAY DURING NORTHWEST AND SOUTHEAST MONSOON

This study has compared the distribution of nutrients phosphate and nitrate concentrations in the inner Ambon Bay during two different seasons: Northwest and Southeast monsoon. The result showed that the nutrient concentrations in the Southeast monsoon were higher than those in the Northwest monsoon, both for phosphate and nitrate. The ranges of phosphate concentrations on the surface and near the bottom of the bay during the Northwest monsoon were 0.0471-0.0549 mg/L and 0.0549–0.1176 mg/L, while the ranges of nitrate concentrations were from undetected to 0.0976 mg/L on the surface, and 0.0956–0.5870 mg/L near the bottom of the bay. Meanwhile, the phosphate and nitrate concentration on the surface during the Southeast monsoon were 0.0495–0.0676 mg/L and 0.0247–0.4019 mg/L, while near the bottom the concentrations were 0.0495–0.1802 mg/L and 0.0247– 0.7944 mg/L. These results indicated that phosphate and nitrate concentrations in the inner Ambon Bay have exceeded the marine water standard quality, which can be categorized as polluted. High concentrations of phosphate and nitrate during Northwest monsoon could be caused by run-off from the mainland area that enters the bay trough the river. On the other hand, the enrichment of nutrients in the inner Ambon Bay during the Southeast monsoon could be dominated by the “nutrient-rich water mass” from Banda Sea that enters the inner Ambon Bay.
A comparison of methods for specifying optimal random effects structures

Using Monte Carlo simulations, this study compared the performance of various approaches to the specification of random effects structures in linear mixed effects models (LMMs), including the minimal approach, the maximal approach, the forward search, the backward search, and the all-possible structures approach. The results showed that if the predictor of interest is at the within-cluster level or involves a cross-level interaction, the maximal approach, the best-path forward search, and the best-path backward search are all desirable methods. If the predictor of interest is at the cluster level, it is not essential to specify random slopes of Level-1 predictors. In addition, it is important to specify random slopes of within-cluster control variables, as they can increase the statistical power for testing the main within-cluster variables, especially when the sample size is small and the variance of the random slope of the control variable is large.
Nutritional Assessments by Bioimpedance Technique in Dialysis Patients

Bioelectrical impedance analysis (BIA) has been extensively applied in nutritional assessments on the general population, and it is recommended in establishing the diagnosis of malnutrition and sarcopenia. The bioimpedance technique has become a promising modality through which to measure the whole-body composition in dialysis patients, where the presence of subclinical volume overload and sarcopenic obesity may be overlooked by assessing body weight alone. In the past two decades, bioimpedance devices have evolved from applying a single frequency to a range of frequencies (bioimpedance spectroscopy, BIS), in which the latter is incorporated with a three-compartment model that allows for the simultaneous measurement of the volume of overhydration, adipose tissue mass (ATM), and lean tissue mass (LTM). However, clinicians should be aware of common potential limitations, such as the adoption of population-specific prediction equations in some BIA devices. Inherent prediction error does exist in the bioimpedance technique, but the extent to which this error becomes clinically significant remains to be determined. Importantly, reduction in LTM has been associated with increased risk of frailty, hospitalization, and mortality in dialysis patients, whereas the prognostic value of ATM remains debatable. Further studies are needed to determine whether modifications of bioimpedance-derived body composition parameters through nutrition intervention can result in clinical benefits.
The age of the Mont-Blanc granite (Western Alps): a heterogeneous isotopic systeme dated by Rb-Sr whole rock determinations on its microgranular enclaves

New Rb-Sr whole rock age data have been obtained for the Mont-Blanc calc-alkaline Hercynian granite. Different facies of the granite have been sampled, as well as locally abundant microgranular enclaves of magmatic origin. The analytical data for the granite and for some of the enclaves are scattered within the Sr evolution diagram, which points to isotopic disequilibrium. Other enclaves form a homogeneous system which gives an age of 316.1 ± 19.5 Ma and an initial 87Sr/86Sr ratio of 0.7058 ± 0.0005. The following evolution is plausible: - individualization of microgranular enclaves due to the dismembering of mafic synplutonic dikes within the granitic magma; - complete isotopic equilibrium between granite and enclaves during a late-magmatic stage; - remobilization ofthe granite and certain enclaves in a post-magmatic stage and during Alpine orogenesis. Certain enclaves are not affected and retain the original characteristics of the system. The 316 Ma age of these "relics" and their initial ratio of 0.7058 are therefore thought to be valid for the granite as well. These results are similar to those obtained for other calc-alkaline Hercynian plutons in Western Europe. They indicate an origin for the granitic magma in the deep crust with involvement of mantle-derived material in unknown proportions, represented by the microgranular enclaves.
Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile

MicroRNAs (miRNAs) can regulate the expression of genes involved in the establishment of the window of implantation (WOI) in the endometrium. Recent studies indicated that cell-free miRNAs in uterine fluid and blood samples could act as alternative and non-invasive sample types for endometrial receptivity analysis. In this study, we attempt to systematically evaluate whether the expression levels of cell-free microRNAs in blood samples could be used as non-invasive biomarkers for assessing endometrial receptivity status. We profiled the miRNA expression levels of 111 blood samples using next-generation sequencing to establish a predictive model for the assessment of endometrial receptivity status. This model was validated with an independent dataset (n = 73). The overall accuracy is 95.9%. Specifically, we achieved accuracies of 95.9%, 95.9%, and 100.0% for the pre-receptive group, the receptive group, and the post-respective group, respectively. Additionally, we identified a set of differentially expressed miRNAs between different endometrial receptivity statuses using the following criteria: p-value < 0.05 and fold change greater than 1.5 or less than −1.5. In conclusion, the expression levels of cell-free miRNAs in blood samples can be utilized in a non-invasive manner to distinguish different endometrial receptivity statuses.
Overexpression of Growth Differentiation Factor 15 in Glioblastoma Stem Cells Promotes Their Radioresistance

Simple Summary Glioblastoma stem cells (GSCs) are characterized by high radioresistance and are responsible for the frequent recurrence of glioblastoma (GBM); therefore, it is imperative to better understand the molecular mechanisms and identify the novel factors involved in these processes. Here, we report that GDF15, a cytokine belonging to the TGF-β superfamily, is induced by irradiation (IR) and the transcription factor, WWTR1/TAZ. It also promotes GSC radioresistance. These data suggest that blocking GDF15 may be a novel approach in radiosensitizing GSCs. Abstract GSCs play an important role in GBM recurrence. Understanding the resistance mechanisms in these cells is therefore crucial for radiation therapy optimization. In this study, using patient-derived GSCs, we demonstrate that GDF15, a cytokine belonging to the TGF-β superfamily, is regulated by irradiation (IR) and the transcription factor WWTR1/TAZ. Blocking WWTR1/TAZ using specific siRNAs significantly reduces GDF15 basal expression and reverses the upregulation of this cytokine induced by IR. Furthermore, we demonstrate that GDF15 plays an important role in GSC radioresistance. Targeting GDF15 expression by siRNA in GSCs expressing high levels of GDF15 sensitizes the cells to IR. In addition, we also found that GDF15 expression is critical for GSC spheroid formation, as GDF15 knockdown significantly reduces the number of GSC neurospheres. This study suggests that GDF15 targeting in combination with radiotherapy may be a feasible approach in patients with GBM.
PENERJEMAHAN PRONOMINA PERSONA BAHASA JEPANG PADA TAKARIR BAHASA INDONESIA ANIME TADA NEVER FALLS IN LOVE

Personal pronouns are one of the characteristics that distinguishes each language from others. Personal pronouns are linguistic elements that refer to self-referential words. Personal pronouns in Japanese are classified into three types: first-person pronouns, second-person pronouns, and third-person pronouns. Personal pronouns are increasingly being studied not only in linguistics but also in applied linguistics, such as translation studies. Subtitles are used in audiovisual media translation. This research deals at how Japanese personal pronouns are translated into Indonesian subtitles of Yoshiko Nakamura's anime Tada Never Falls in Love. This study utilized two methods, namely data collection and data analysis. The translational equivalent method has been used in this study. Based on the findings, it is possible to conclude that personal pronouns in both the source and target languages have the same meaning components. There are, however, differences in the "gender" meaning component, with the source language preferring a specific gender and the target language preferring neutrality. Other findings from this study include the notion that subtitles in the target language have an informality tendency in the pronoun equivalent, resulting in the loss of gender-preferred speech of personal pronouns in the source language after translation. Furthermore, word-level equivalence can be gained although some meaning and nuance cannot be displayed in the target language.
Fractional-order sliding-mode control of a wind turbine for tracking maximum power point

This study examines a nonlinear control technique based on fractional-order sliding mode theory to track the highest power point of a wind energy conversion system. The proposed method achieves chatter-free fractional sliding-mode control by using a continuous control strategy. The main advantages of the proposed fractional-order SMC law are accurate reference tracking, elimination of the chattering problem and minimization of overshoot. To demonstrate the boundedness and convergence characteristics of the closed-loop signals, Lyapunov's stability theory is used. Extensive simulations results are presented to evaluate the robustness and effectiveness of the proposed control under various uncertainty.
Efficient Spectrum Slicing in 5G Networks: An Overlapping Coalition Formation Approach

Mobile Network Operators perceive Spectrum Slicing and Sharing as an efficient and viable option to meet the growing mobile user data demands in the upcoming 5G network deployments. Efficient spectrum sharing requires optimized spectrum partitioning, i.e., slicing of the spectrum for sharing among operators, to improve spectrum utilization, offer services with high quality of experience, and increase their revenues. This paper proposes Spectrum Slicing Coalition Formation (SSCF) protocol, a novel solution to address effective spectrum slicing required to meet the dynamic data demands at every 5G base station. The base stations’ cooperation is formulated as a coalition formation game with overlapping coalitions, where a base station's contribution is not restricted to a single coalition, instead a base station participates intelligently in multiple coalitions. The SSCF protocol enables a base station to dynamically slice and share its allocated spectrum to service its load based spectral requirements, a key requirement for effective radio access network softwarization. The study explores operator base stations’ options to determine with whom, when, and how much to slice and share the spectrum and the payoffs. Extensive simulations using real-world operator base station deployments highlight that SSCF solution brings in operational efficiencies, improved users’ quality of experience, and revenue maximization.
A QTL of eggplant shapes the rhizosphere bacterial community, co-responsible for resistance to bacterial wilt

Abstract Resistant crop cultivars can recruit beneficial rhizobacteria to resist disease. However, whether this recruitment is regulated by quantitative trait loci (QTL) is unclear. The role of QTL in recruiting specific bacteria against bacterial wilt (BW) is an important question of practical significance to disease management. Here, to identify QTL controlling BW resistance, Super-BSA was performed in F2 plants derived from resistant eggplant cultivar R06112 × susceptible cultivar S55193. The QTL was narrowed down through BC1F1-BC3F1 individuals by wilting symptoms and KASP markers. Rhizosphere bacterial composition of R06112, S55193, and resistant individuals EB158 (with the QTL) and susceptible individuals EB327 (without QTL) from BC2F1 generation were assessed by Illumina sequencing-based analysis, and the activation of plant immunity by the bacterial isolates was analyzed. Evidence showed that BW-resistant is controlled by one QTL located at the 270 kb region on chromosome 10, namely EBWR10, and nsLTPs as candidate genes confirmed by RNA-Seq. EBWR10 has a significant effect on rhizobacteria composition and significantly recruits Bacillus. pp. A SynCom of three isolated Bacillus. pp trains significantly reduced the disease incidence, changed activities of CAT, PPO, and PAL and concentration of NO, H2O2, and O2−, activated SA and JA signaling-dependent ISR, and displayed immune activation against Ralstonia solanacearum in eggplant. Our findings demonstrate for the first time that the QTL can recruit beneficial rhizobacteria, which jointly promote the suppression of BW. This method charts a path to develop the QTL in resistant cultivar-driven probiotics to ameliorate plant diseases.
Botulinum Toxin: A Review of Cosmetic Applications and Hyperhidrosis Treatment

Injections of botulinum toxin for the treatment of face wrinkles are the most popular cosmetic operation both internationally as well as in Turkey. The U.S. Food and Drug Administration has approved the treatment of frown lines, crow's feet, and horizontal forehead lines as aesthetic purposes. This procedure produces predictable results, with a few side effects, and is linked to high patient satisfaction. A potent neurotoxin, botulinum toxin prevents acetylcholine from being released at the neuromuscular junction. Compared to static wrinkles, which are evident at rest, dynamic wrinkles, are seen during muscular contraction and produce more striking results. In this evaluation, only uses for aesthetics and hyperhidrosis will be investigated thorougly.
Lipid And Polymer Based Nano-Phytotherapeutics

The development of efficient drug delivery systems is pivotal in modern pharmacotherapy, aiming to enhance biological efficacy while minimizing the adverse effects of pharmaceutical agents. Recent focus has shifted towards lipid as well as polymer-containing nano-phytotherapeutics, amalgamating the benefits of natural and synthetic materials. Lipid-containing nanocarriers, like liposomes and lipid nanoparticles, are particularly suited for encapsulating hydrophobic phytochemicals, thereby augmenting their bioavailability and stability. Incorporating biodegradable polymers like chitosan and polyethylene glycol facilitates controlled release and target-specific delivery. Furthermore, the utilization of plant-derived phytochemicals offers reduced toxicity compared to synthetic drugs. This chapter outlines current research in this domain, emphasizing the synergistic potential of lipid-based nanocarriers and biocompatible polymers for phytochemical delivery. Strategies encompass formulation techniques, surface modifications, and targeted drug release mechanisms. The potential applications of these systems in treating diverse diseases, including cancer, cardiovascular disorders, and infectious diseases, are also discussed. Overall, lipid and polymer-based Nano-phytotherapeutics exhibit promise as adaptable and biocompatible drug delivery platforms, heralding benefits for efficient and targeted phytochemical delivery, potentially revolutionizing modern medicine. Further advancement in this field is anticipated to yield novel therapeutic solutions with enhanced clinical outcomes and reduced side effects.
Cranioplasty With a Customized Polymethylmethacrylate Implant Using a 3D Printer

The occurrence of cranial injuries has increased dramatically, and due to greater awareness among the people concerned with aesthetics needing skull reconstruction, rehabilitation of these defects has also increased in our modern age. Rehabilitation of these deficiencies with prostheses not only works as a protective shell but also improves the patient's neurological state. These cranial deformities necessitate surgical correction or repair, known as cranioplasty. Its goal is not only to rehabilitate the imperfection aesthetically, but also to alleviate psychological issues and improve the patient's social acceptance and performance. New biomedical tools, technologies, and materials are available to surgeons and prosthodontists to improve aesthetics and functions. The purpose is to develop a novel method involving affordable 3D printing technology for creating individualized polymethylmethacrylate (PMMA) implants, aiming to democratize technology in prosthodontics.
A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing

The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.
Hydrologic Modeling of a Drained Pine Plantation on Poorly Drained Soils

Three experimental watersheds in eastern North Carolina have been continuously monitored since 1988 to study long-term hydrology of loblolly pine (Pinus taeda L.) forests on poorly drained soils. This study was conducted to test the forestry version of an agricultural hydrology model DRAINMOD with 10 yr (1988-1997) of data collected at one of these watersheds under conventional (open ditch) drainage. The model, which is based on hourly water balance for the land between parallel drainage ditches, simulates interception, evapotranspiration (ET) as the sum of canopy transpiration and soil evaporation, drainage, and surface runoff. Results showed that model predictions of daily water table elevations and flow rates on an average annual basis were within 0.15 m and 0.61 mm, respectively, compared to the measured data. Relative errors on drainage outflow varied from -18% to 23%, with an average of 0.4%. Errors in measured flow rates during weir submergence, missing rainfall and weather data, and uncertainty in estimates of stomatal conductance contributed to the differences between model predictions and field observations. It was concluded that the model is a reliable tool for assessing hydrologic impacts of silvicultural and water management treatments, as well as climate changes, on these pine stands. FOR. SCI. 47(1):103-114.
Failure Identification Method of Sound Signal of Belt Conveyor Rollers under Strong Noise Environment

Accurately extracting faulty sound signals from belt conveyor rollers within the high-noise environment of coal mine operations presents a formidable challenge. To address this issue, this study introduces an innovative fault diagnosis method that merges the variational modal de-composition (VMD) model with the Swin Transformer deep learning network model. First, the study employed the adaptive VMD method to eliminate intense noise from the original signal of the rollers, while also assessing the reconstruction accuracy of the VMD signal across different modal components. Subsequently, we delved into the impact of the parameter structure of the Swin Transformer network model on the fault diagnosis accuracy. Finally, the accuracy of the method was validated using a sound test dataset from the rollers. The results indicated that optimizing the K-value of the VMD method effectively reduced the noise in the reconstructed signal, and the Swin Transformer excelled in extracting both local and global features. Specifically, on the conveyor roller sound dataset, it was shown that, after the VMD reconstruction of the signal so that the highest Pearson correlation coefficient corresponded to a modal component of 3 and adjusting the parameters of the Swin Transformer coding layer, the combination of the VMD+Swin-S model achieved an accuracy of 99.36%, while the VMD+Swin-T model achieved an accuracy of 98.6%. Meanwhile, the accuracy of the VMD+Swin-S model was higher than that of the VMD + CNN model combination, with 95.4% accuracy, and the VMD+ViT model, with 97.68% accuracy. In the example application experiments, compared with other models the VMD+Swin-S model achieved the highest accuracy rate at all three speeds, with 98.67%, 98.32%, and 97.65%, respectively. Overall, this approach demonstrated high accuracy and robustness, rendering it an optimal choice for diagnosing conveyor belt roller faults within environments characterized by strong noise.