Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the impact of customer satisfaction, trust, and quality of services on the acceptance of delivery services companies and related applications in Omani context: A Predictive model assessment using PLSpredict

Abstract This paper investigates the impact of the COVID-19 pandemic on the adoption and evolution of delivery services in Oman. While the pandemic has presented new opportunities for delivery companies and related apps, there is a lack of research specifically focused on the Omani context. To address this gap, our study develops an integrated model based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), incorporating additional variables to examine the role of customer satisfaction, trust, and service quality in the adoption of delivery services. Using a quantitative approach, we conducted a study involving systematic random sampling to collect responses from individuals with experience in using online purchase and delivery services. The model was evaluated using in-sample and out-of-sample prediction techniques, as well as measurement and structural analysis employing PLS-SEM. Our proposed model demonstrated strong predictive power and validity. The findings emphasize the significant contributions of service quality and trust to customer satisfaction. Furthermore, social influence emerged as a crucial factor influencing the intention to accept and adopt delivery services after experiencing satisfaction. The inclusion of PLSpredict analysis enhanced the reliability of our conducted analyses. This study provides original insights into the lessons learned from the global impact of COVID-19 and represents the first of its kind conducted in the Gulf Cooperation Council countries. Our research encourages further investigation of these topics in other GCC countries based on our findings. The study concludes by acknowledging its limitations, discussing theoretical and managerial contributions, and suggesting potential avenues for future research.
Prognosis and therapeutic benefits prediction based on NK cell marker genes through single-cell RNA-seq with integrated bulk RNA-seq analysis for hepatocellular carcinoma

Tumor-infiltrating immune cells greatly participate in regulating tumorigenesis and metastasis of hepatocellular carcinoma (HCC). Natural killer cell, as an important role of innate immunity, plays an indispensable role in antitumor immunity and regulate tumor development. In this study, we firstly identified 251 NK cell marker genes of HCC based on single-cell RNA sequencing data. Subsequently, an NK cell marker genes-related prognostic signature (NKPS) was developed in the cancer genome atlas (TCGA) cohort for risk stratification and prognosis prediction. The predictive value of the NKPS in prognosis was well validated in different clinical subgroups and three external datasets (ICGC-LIHC cohort, GSE14520 cohort and Guilin cohort). Moreover, multivariate analysis revealed the independent prognostic value of NKPS for OS in HCC. Further functional analysis indicated the NKPS was associated with basic cellular processes, that may contribute to the development and progression of HCC. Thereafter, immune characteristics as well as the therapeutic benefits in NKPS risk score-defined subgroups were analyzed. Patients with low-risk score exhibited immune-active status, manifested as higher immune scores, more infiltration of CD8+ T cells and macrophage M1, and higher T-cell receptor (TCR) richness and diversity. Remarkably, the NKPS was negatively correlated with immunotherapy response-related signatures. In addition, the low-risk group exhibited significantly improved therapeutic benefits, either from immunotherapy or traditional chemotherapy and target therapy. Overall, the NKPS showed an excellent predictive value for prognosis and therapeutic responses for HCC, which might also provide novel insights into better HCC management strategies.
The Use of Targeted Cytokines as Cancer Therapeutics in Glioblastoma

Simple Summary Despite multi-modal treatment consisting of surgery, chemotherapy, and radiation, glioblastoma inevitably recurs due to its diffuse infiltrative nature. Anti-tumor immune responses, supported by pro-inflammatory cytokines, that can seek out remote cancer vestiges will likely become part of the therapeutic armamentarium but will require thoughtful selection, combinatorial vetting, and innovative delivery strategies. Abstract Cytokines play an important role in regulating the immune response. Although there is great interest in exploiting cytokines for cancer immunotherapy, their clinical potential is limited by their pleiotropic properties and instability. A variety of cancer cell-intrinsic and extrinsic characteristics pose a barrier to effective treatments including cytokines. Recent studies using gene and cell therapy offer new opportunities for targeting cytokines or their receptors, demonstrating that they are actionable targets. Current efforts such as virotherapy, systemic cytokine therapy, and cellular and gene therapy have provided novel strategies that incorporate cytokines as potential therapeutic strategies for glioblastoma. Ongoing research on characterizing the tumor microenvironment will be informative for prioritization and combinatorial strategies of cytokines for future clinical trials. Unique therapeutic opportunities exist at the convergence of cytokines that play a dual role in tumorigenesis and immune modulation. Here, we discuss the underlying strategies in pre- and clinical trials aiming to enhance treatment outcomes in glioblastoma patients.
Food Choices after Cognitive Load: An Affective Computing Approach

Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.
The relationship between social inclusion and life satisfaction among Turkish older people

ABSTRACT The study aimed to identify the relationship between social inclusion and life satisfaction among Turkish older people. The data were gathered from 350 older adults selected through convenience sampling method in Turkey using Demographic Information Form, Social Inclusion Scale (SIS) and Satisfaction with Life (SWLS). Regression analysis was performed to determine the variables affecting life satisfaction. The regression analysis results revealed that as social inclusion and the duration of marriage increased, so did life satisfaction in older people. Such variables as age, gender, education level, number of children, living arrangement, amount of income, social media use, and health perception were not found to be significant in terms of life satisfaction of the older people in the presence of social inclusion and the duration of marriage.
Analysis of The Characteristics of emotional Experience in MOOC Learning

With the rapid development of massive open online courses (MOOCs), researchers have begun to pay attention to the experience of teachers and students in the MOOC classroom. Select the middle school mathematics curriculum standard and textbook research course in the MOOC platform of Chinese universities and collect 66 selfreported data of instantaneous experience and long-term experience of 10 learners on the course learning within two weeks. Qualitative and quantitative analyses were carried out in 8 sub-dimensions, including device usage preferences and problems, and platform tool application. The purpose of the research is to investigate the learning experience of MOOC learning platform more comprehensively and deeply. The research results show that mobile learning has become the main way of MOOC learning. The appearance, education and economy of the tools displayed on the platform directly affect the learning experience of learners. Demonstration tools are highly dependent, but the frequency of application, types and functions of tools are limited, and there is a lack of awareness and application of tools that promote advanced learning, deep learning, and reflective learning; the overall emotional experience of learners is in a positive emotional state and shows a distinct group characteristic. The learning experience of MOOC is directly related to the appearance, education and economy of the display tools in the platform; Learners have diverse experience of platform communication and cooperation tools, and are highly dependent on learning content display tools in the platform; Learners' emotional experience is both positive and negative, but it is dominated by positive emotions and shows distinct group characteristics.
IMPROVEMENT OF RURAL TOURISM IN THE AREA OF BOSNIA AND HERZEGOVINA USING MULTI-CRITERIA ANALYSIS

The aim of the study was to evaluate rural households from five different regions of Bosnia and Herzegovina based on predetermined economic, ecological, and sociological criteria, using assessments from five expert professionals in the field. The study employed the fuzzy multi-criteria decision-making method called TOPSIS to ensure research accuracy. The results indicated that the rural household “Radoja” received the highest rating, which could serve as a solid foundation for future rural tourism development in Bosnia and Herzegovina. Based on the obtained results, it is possible to conduct future research in other regions of the country, providing appropriate guidelines for the development of rural tourism in Bosnia and Herzegovina.
Static Eccentricity Fault Location Diagnosis in Resolvers Using Siamese-Based Few-Shot Learning

Static eccentricity in the resolver, with higher probability and severity occurrence, gradually injects instability into the drive system; thus, its exact location diagnosis is mandatory. Whereas, most existing investigations rely on frequency analysis of signal voltages for resolver fault diagnosis, artificial intelligence (AI) is at the center of attention for electric motors fault diagnosis. Yet, sufficient data collection brings some challenges for AI. Therefore, intrigued by the idea of few-shot learning to overcome the data scarcity challenge, a convolutional neural Siamese network is employed with limited data to diagnose the static eccentricity location under each stator tooth via raw signal voltages, which is ordinarily employed for rotor angle extraction. Accordingly, a Siamese network learns to yield the probability of signal pairs being similar by distance unit. A laboratory-built time-stepping finite-element analysis (TS-FEA) and a resolver test-bench dataset under different training sizes, noise existence, and new unseen locations challenges are employed to assess the employed Siamese network. Comparing the employed Siamese network with one dimensional-convolutional neural network (1D-CNN) shows its proficiency and superiority.
Ecosystem Implementations in Smart City through Blockchain Technology

Smart cities are structured on smart devices to govern urban management. It encapsulates smart governance, smart mobility, smart infrastructure, smart homes, smart streets and many more. However, the essence of such smartness lies in the internet of things (IoT) sensors calibrated through blockchain technology to ensure the security, auditability and traceability of activities within and around the cities. By doing so, governance matters and urban management can be handled efficiently. However, as a novel field, few studies have focused on this arena. This research aims to assess the role of Blockchain in smart city ecosystems through previous literature and to develop novel inferences by validating the theoretical framework through quantitative analysis. It would assist policymakers and governments to optimize the digital infrastructure fully. The considered arena of assessment included Tokenization and attention was also given to decentralized governance (De-governance) in blockchain towards smart city ecosystem. The research inferences would facilitate the literature advancement in the domain of smart cities and blockchain integration. The research would also be resounding towards the role of blockchain technologies in smart governance.
Buffering Instantly Decodable Network Coding for a Heterogenous Cellular Network With Dual Interfaces

Compared with the original instantly decodable network coding (IDNC), buffering IDNC (B-IDNC) can exploit non-instantly decodable packets (NIDPs) containing two unreceiving packets for subsequent network decoding. In this article, we employ B-IDNC in a fully connected D2D networks (FC-D2D) or a partially connected D2D networks (PC-D2D) aided HetNet, where the devices may simultaneously receive two IDNC packets, one from the cellular interface and the other from the device-to-device (D2D) interface. We establish the dual interface B-IDNC graph which can indicate all feasible coding opportunities, conflict-free transmissions, and instant decoding opportunities by exploiting buffered NIDPs. By employing the dual interface B-IDNC graph, a dual interface B-IDNC decoding scheme is developed, in which the IDNC packet which is instantly decodable is preferentially employed to perform network decoding, so as to increase the instant decoding opportunities of the other IDNC packet and the buffered NIDPs. According to the dual interface B-IDNC graph and the dual interface B-IDNC decoding scheme, a heuristic approach named as dual interface maximum decoding clique based maximum weight vertex (D-MDC-MWV) search is proposed to search the transmitting devices and the IDNC packets sent via the dual interfaces. Simulation results verify the effectiveness of the proposed schemes over the existing network coding schemes.
Performance Evaluation for Mean Sea Level Prediction using Multivariate Adaptive Regression Spline and Artificial Neural Network

Mean sea level (MSL) has been used as a vertical datum for geodetic levelling and mapping in most countries all over the world. This is because the MSL approximates the geoid and serves as a realist reference surface that could be determined mostly through tide measurements over a period of time. However, sea levels have been rising over the years due to global warming and its associated climate change which continuous to melt ice sheets around the Polar Regions. This phenomenon is likely to affect the reliability of MSL, thus it is important to determine the local MSL at regular time periods. This study assessed the performance of Artificial Neural Network (ANN) and Multivariate Adaptive Regression Spline (MARS) models in predicting the MSL. Tide gauge records from the Takoradi Harbour of Ghana were used in the study. Monthly maximum, minimum and mean tidal values were derived from the secondary data and used for both model formulation and model testing. A comparative analysis of both models showed that the ANN model performed better than the MARS model. A Root Mean Square Error (RMSE) of 0.0359 m was obtained for the ANN model, whereas 0.0555 m was obtained for the MARS model. Mean Absolute Percentage Error (MAPE) of 3.1414% was obtained for the ANN model and whereas the MARS model yielded 5.6349%. A Mean Absolute Error (MAE) for the ANN model was 0.0284 m as against 0.0446 m for the MARS model. Correlation coefficient values of 0.9720 and 0.8874 were obtained for the ANN model and the MARS model respectively. An optimum ANN structure was found to be ANN 2-11-1. Based on the outcome of this study, it is recommended that ANN model should be adopted for forecasting local mean sea level for the study area. Keywords: Mean Sea Level, Artificial Neural Network, Multivariate Adaptive Regression Spline
The differences of students learning outcome with numbered head together (NHT) and think pair share (TPS) in atomic structure

Abstract: This study determine the differences between student learning outcomes using model of the NHT and TPS type of cooperative teaching on the subject of atomic structure. The population in this study were all grade X at SMA Negeri 1 Percut Sei Tuan. There were 9 classes. The samples were 2 classes with 68 students. The first class was used cooperative teaching model of NHT while the second class was used cooperative teaching model of TPS. The result of the study shows that the application of these two types give influence to the result of student learning outcome in chemistry. The average point of post test for first class is (73.82 + 11.20) and the second class is (77.41 + 10.77). The result is t count <t tab (-1.34<1.998) with α=0.05, this value conclude that there are no significant differences between student learning outcome by using cooperative teaching model with NHT type and TPS on the subject of atomic structure. Keywords: atomic structure; learning outcome; NHT; TPS
Properties of hydroxyapatite from tilapia (Oreochromis niloticus) bones: An approach towards its potential use as a dye adsorbent

The properties of hydroxyapatite (HAp) obtained from tilapia (Oreochromis niloticus) bones and its potential use as a dye adsorbent using methyl orange (MO) as a model dye were evaluated. HAp was produced by the calcination of dried tilapia bones at 900 °C for 4 h. Then, HAp was characterized using thermogravimetric (TGA) and differential thermogravimetric (dTG) analyses, Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM) and energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), x-ray diffraction (XRD), among others. The HAp obtained was used for MO dye adsorption in a batch process. The yield of HAp was 51.6%. FTIR showed characteristic bands of functional groups of HAp [OH- and (PO4)3-]. FESEM images showed HAp with different morphologies and a porous surface. The EDS analysis indicated the presence of calcium and phosphorus with an atomic ratio of Ca/P of 1.60, TEM images revealed the formation of agglomerates and an average particle size of 655.1 nm. HAp and β-tricalcium phosphate (β-TCP) phases were identified through XRD. The HAp point of zero charge (pHpzc) was 9.7 indicating a possible adsorption of anionic dyes at pH< pHpzc and cationic dyes at pH> pHpzc. The HAp was able to successfully adsorb 82.6% of MO dye from aqueous solution. These findings demonstrated that the HAp obtained from tilapia (O. niloticus) bones possesses suitable properties to be used as a potential material to remove dyes.
INTRODUCTION OF EXOTIC BREEDS IN EXTENSIVE LIVESTOCK FARMING SYSTEM OF BURKINA FASO: ASSESSMENT AND PROSPECTS.

A study on the situation of exotic cattle breeds introduced in Burkina Faso was conducted in the province of Soum (Djibo) located in the north of the country, a Sahel area of West Africa. The aim of the study was to i) assess the adaptation of exotic breeds with high productive potential in a difficult climatic context and ii) evaluate their productivity in comparison with the native breeds. A participatory survey associated with field visits were conducted to assess
Diagonal reduction of matrices over finite stable range rings

Definition 1 ([1]). A ring R is called an elementary divisor ring, if for an arbitrary matrix A of order n×m over, there exist invertible matrices P ∈ GLn(R) and Q ∈ GLm(R) such that 1) PAQ = D is diagonal matrix, D = (di); 2) Rdi+1R ⊆ Rdi ∩ diR for each i. Definition 2 ([2]). If for any a, b ∈ R there exists an invertible matrix P ∈ GL2(R) such that (a, b)P = (d, 0) for some d ∈ R then R is a right Hermite ring. If for any a, b ∈ R there exists an invertible matrix P ∈ GL2(R) such that P ( a b ) = ( d 0 ) for some element d ∈ R, then R is a left Hermite ring. If a ring is both left and right Hermite, then it is called an Hermite ring.
An Improved Satellite‐Based Evapotranspiration Procedure for China

Evapotranspiration (ET) is a critical process that regulates the transfer of heat and water between land and the atmosphere. While satellite‐based ET algorithms can provide area‐wide daily ET estimates, few are independent of local meteorological measurements. The Variant of the Moderate Resolution Imaging Spectroradiometer Standard Evapotranspiration Algorithm (VISEA) can potentially run without ground‐based observations. However, the surface energy budgets used in VISEA generally overpredict daily net radiation and related ET. To improve the accuracy of net radiation and ET, we incorporated Brutsaert's atmosphere emissivity model and corrected the routine to calculate the downward long‐wave radiation on cloudy days (which we called VISEA2023 model). The VISEA2023 model predicted net radiation and ET that agreed well with measurements at seven ChinaFlux sites, with an R2 of 0.8 and an Root Mean Square Error (RMSE) of 30 W m−2 for net radiation, and an R2 of 0.6 and an RMSE of 1 mm day−1 for ET, respectively. Additionally, the VISEA2023 model is more robust in comparison to the Variable Infiltration Capacity hydrologic model output in 10 major river basins and 80 sub‐river basins, with an R2 of 0.91 (0.78) and an RMSE of 85 (127.4) mm year−1, than MOD16, which has an R2 of 0.81 (0.82) and an RMSE of 134.8 (157.7) mm year−1, and Advanced Very High Resolution Radiometer ET, which has an R2 of 0.92 (0.62) and an RMSE of 90.1 (136.1) mm year−1. The improved VISEA2023 model can offer near‐real‐time ET on a larger scale, improving our understanding of water cycles and their relation to climate change.
Myeloid-Derived Suppressor Cells (MDSCs) in Ovarian Cancer—Looking Back and Forward

Myeloid-derived suppressor cells (MDSCs) play a significant role in the immune system and have been extensively studied in cancer. MDSCs are a heterogeneous population of myeloid cells that accumulate in the tumor microenvironment. Consequently, the high abundance of these cells often leads to immunosuppression, tumor growth, treatment failure, and poor prognosis. Ovarian cancer ranks fifth in cancer deaths among women, accounting for more deaths than any other cancer of the female genital tract. Currently, there is a lack of effective clinical strategies for the treatment of ovarian cancer. Although several studies underline the negative role of human MDSCs in ovarian cancer, this topic is still understudied. The works on MDSCs are summarized here, along with an explanation of why focusing on these cells would be a promising approach for treating ovarian cancer patients.