Miracle-dz/UniFAR-FTUs huggingface dataset:
Doxorubicin-Induced Cardiotoxicity May Be Alleviated by Bone Marrow Mesenchymal Stem Cell-Derived Exosomal lncRNA via Inhibiting Inflammation

Purpose To explore the therapeutic mechanism of bone marrow mesenchymal stem cells derived exosomes (BMSC-Exos) for doxorubicin (DOX)-induced cardiotoxicity (DIC) and identify the long noncoding RNAs’ (lncRNAs’) anti-inflammation function derived by BMSC-Exos. Materials and Methods High-throughput sequencing and transcriptome bioinformatics analysis of lncRNA were performed between DOX group and BEC (bone marrow mesenchymal stem cells derived exosomes coculture) group. Elevated lncRNA (ElncRNA) in the cardiomyocytes of BEC group compared with DOX group were confirmed. Based on the location and co-expression relationship between ElncRNA and its target genes, we predicted two target genes of ElncRNA, named cis_targets and trans_targets. The target genes were analyzed by enrichment analyses. Then, we identified the key cellular biological pathways regulating DIC. Experiments were performed to verify the therapeutic effects of exosomes and the origin of lncRNAs in vitro and in vivo. Results Three hundred and one lncRNAs were differentially expressed between DOX and BEC groups (fold change >1.5 and p < 0.05), of which 169 lncRNAs were elevated in the BEC group compared with the DOX group. GO enrichment analysis of target genes of ElncRNAs showed that they were predominantly involved in inflammation-associated processes. KEGG analysis indicated that their regulatory pathways were mainly involved in oxidative stress-induced inflammation and proliferation of cardiomyocyte. The verification experiments in vitro showed that the oxidative stress and cell deaths were decreased in BEC groups. Moreover, from the top 10 ElncRNAs identified in the sequencing results, MSTRG.98097. 4 and MSTRG.58791. 2 were both decreased in the DOX group and elevated in BEC group. While in verification experiments in vivo, only the expression of MSTRG.58791. 2 is consistent with the result in vitro. Conclusion Our results show that ElncRNA, MSTRG.58791. 2, is possibly secreted by the BMSC-Exos and able to alleviate DIC by suppressing inflammatory response and inflammation-related cell death.
Survey on PMIPv6-based Mobility Management Architectures for Software-Defined Networking

Software-Defined Networking (SDN) has changed the network landscape. Meanwhile, IP-based mobility management still evolves, and SDN affects it dramatically. Integrating Proxy Mobile IPv6 (PMIPv6) – a network-based mobility management protocol – with the SDN paradigm has created several promising approaches. This paper will present an extensive survey on the joint research area of PMIPv6 and SDN mobility management by detailing the available SDN-integrated network-based techniques and architectures that intend to accelerate handover and mitigate service disruption of mobility events in softwareized telecommunication networks. The article also provides an overview of where PMIPv6 can be used and how SDN may help reach those ways.
Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network

Autonomous vehicles need a driving method to be less dependent on localization data to navigate intersections in unstructured environments because these data may not be accurate in such environments. Methods of distinguishing branch roads existing at intersections using vision and applying them to intersection navigation have been studied. Model-based detection methods recognize patterns of the branch roads, but are sensitive to sensor noise and difficult to apply to various complex situations. Therefore, this study proposes a method for detecting the branch roads at the intersection using deep learning. The proposed multi-task deep neural network distinguishes the branch road into a shape of rotated bounding boxes and also recognizes the drivable area to consider obstacles inside the box. Through the output of the network, an occupancy grid map consisting of one branch road at an intersection is obtained, which can be used as an input to the existing motion-planning algorithms that do not consider intersections. Experiments in real environments show that the proposed method detected the branch roads more accurately than the model-based detection method, and the vehicle drove safely at the intersection.
INTEGRATED REPORTING ADOPTION: SUSTAINABLE CORPORATE STRATEGY IN ACHIEVING SDG 2030

The environmental crisis has initiated companies to implement sustainability accounting as a business sustainability strategy. One indicator of this implementation based on POJK No 51/2017 is the availability of integrated reports (financial reports and sustainability reports) that can be accessed by the public. This paper aims to explain the essence of implementing sustainability strategy through adopting integrated reporting schemes in the company. Therefore, the company does not only implement it to the extent of fulfilling the demands of the government, but for the benefit of the company in the long term. The author uses a quantitative and qualitative analysis approach with the variable earnings per share and the growth of the company's stock price in the sector of financial services institutions and public companies. Furthermore, the author also analyses the fundamental weakness of the integrated reporting systematics which makes it difficult to compare between companies. This paper offers a universal measurement standard that can be used by external and internal parties of the company in assessing how far the company is implementing its sustainability strategy through creating integrated reports. This standard will prove the company's ability to maintain its business continuity in the long term and affect its current valuation. However, it cannot be denied that the sustainability strategy launched by the Indonesian government still has several weaknesses. Therefore, this paper also answer the challenges of the role of accountants in Indonesia in implement sustainability accounting, especially in adopting integrated reporting scheme.
A phase-space method for the Bose–Hubbard model: application to mean-field models

We present a phase-space method for the Bose–Hubbard model based on the Q-function representation. In particular, we consider two model Hamiltonians in the mean-field approximation; the first is the standard ‘one-site’ model where quantum tunnelling is approximated entirely using mean-field terms; the second ‘two-site’ model explicitly includes tunnelling between two adjacent sites while treating tunnelling with other neighbouring sites using the mean-field approximation. The ground state is determined by minimizing the classical energy functional subject to quantum mechanical constraints, which take the form of uncertainty relations. For each model Hamiltonian, we compare the ground state results from the Q-function method with the exact numerical solution. The results from the Q-function method, which are easy to compute, give a good qualitative description of the main features of the Bose–Hubbard model including the superfluid to Mott insulator. We find the quantum mechanical constraints dominate the problem and show there are some limitations of the method particularly in the weak lattice regime.
Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model

: With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.
Learning Global Average Attention Pooling (GAAP) on Resnet50 Backbone for Person Re-identification Problem

—Person re-identification has been an extremely chal- lenging task in computer vision which has been seen as a success with deep learning approaches. Despite successful models, there are gaps in the form of unbalanced labels, poor resolution, uncertain bounding box annotations, occlusions, and unlabelled datasets. Previous methods applied deep learning approaches based on feature representation, metric learning, and ranking optimization. In this work, we propose Global Average Attention Pooling (GAAP) on Resnet50 applied on four benchmark Re-ID datasets for classification tasks. We also perform an extensive evaluation on the proposed Attention module with different deep learning pipelines as backbone architecture. The four benchmark person Re-ID datasets used is Market-1501, RAiD, Partial-iLIDS, and RPIfield. We computed cumulative matching characteristics (CMC) and mean Average Precision (mAP) as the performance evaluation parameters of the proposed against the state of the art. The results obtained have shown that the added attention layer has improved the overall recognition precision over the baselines.
Increasing Functional Performance in Older Women

ABSTRACT The purpose of this study was to evaluate differential effects of a 16-week protocol where participants were randomized into a strength-training (ST) group, Activities of Daily Living (ADL) training group, and a control group. Six physical outcome measures were assessed using the Senior Fitness Test, pre and post study, across ninety women in an assisted living community. A series of repeated measures ANOVA revealed significant group × time interactions across the six physical domains measured (p ≤ .01). Post hoc analysis indicated that ST and ADL groups improved significantly more than the control group across each physical domain. The ST group also outperformed the ADL group in the 30 Second Chair Stand and 30 second arm curl. The key finding is that it is important to incorporate both strength and ADL training sessions because both facilitated significant improvements across all six fitness domains above and beyond controls.
The deep multichannel discrete‐time cellular neural network model for classification

High latency and power consumption are two major problems that need to be addressed in convolutional neural networks (CNN). In this paper, the convolutional layer is replaced with a discrete‐time cellular neural network (CellNN) to overcome these problems. Multiple configurations of CellNNs are trained in a framework called TensorFlow to classify objects from the CIFAR‐10 database. Effects of the number of iterations, the number of channels, batch normalization, and activation functions on the classification accuracies are presented. It is shown that TensorFlow is a tool that is capable of training discrete‐time CellNNs. Although the accuracies of the proposed networks on CIFAR‐10 are slightly lesser than the existing CNNs, with reduced parameters and multiply‐accumulates (MACs), power consumption and computation time of our networks will be less than CNNs.
Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews

Background Tracking and visualizing health data using mobile apps can be an effective self-management strategy for mental health conditions. However, little evidence is available to guide the design of mental health–tracking mechanisms. Objective The aim of this study was to analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms for future apps. Methods We systematically reviewed depression self-management apps on Google Play and iOS App stores. English-language reviews of eligible apps published between January 1, 2018, and December 31, 2021, were extracted from the app stores. Reviews that referenced health tracking and data visualization were included in sentiment and qualitative framework analyses. Results The search identified 130 unique apps, 26 (20%) of which were eligible for inclusion. We included 783 reviews in the framework analysis, revealing 3 themes. Impact of app-based mental health tracking described how apps increased reviewers’ self-awareness and ultimately enabled condition self-management. The theme designing impactful mental health–tracking apps described reviewers’ feedback and requests for app features during data reporting, review, and visualization. It also described the desire for customization and contexts that moderated reviewer preference. Finally, implementing impactful mental health–tracking apps described considerations for integrating apps into a larger health ecosystem, as well as the influence of paywalls and technical issues on mental health tracking. Conclusions App-based mental health tracking supports depression self-management when features align with users’ individual needs and goals. Heterogeneous needs and preferences raise the need for flexibility in app design, posing challenges for app developers. Further research should prioritize the features based on their importance and impact on users.
A Review: Nanoparticles NiFe2O4 Synthesis and Its Application as Hyperthermia Agents in Biomedicine

Nickel ferrite (NiFe2O4) is one of the spinel ferrite nanoparticles that is useful for scientific and technological, like biomedicine. This paper discusses the synthesis of NiFe2O4 nanoparticles by various methods. This paper aims to find out the most efficient method for the synthesis of NiFe2O4 nanoparticles which are used as hyperthermia agents in biomedicine. The method used is a literature study of an article with publications from 1956-2018 by discussing several methods of NiFe2O4 syntheses, such as sol-gel, hydrothermal, co-precipitation, and combustion. From these methods, it was found that co-precipitation is the most efficient method among the other methods because it produces a relatively small NiFe2O4 nanoparticle size, which is 28 nm, causing these small particles to tend to be superparamagnetic and is good to be used as hyperthermia agent in biomedicine. This study is expected to be used as a reference to determine the method for synthesizing NiFe2O4 nanoparticles.
Filipinos and Indians as English Language Teachers in the Emirates: A Phenomenology

Non-native English speakers, venturing abroad to work as English teachers, have drastically grown in number. In the United Arab Emirates, a country that regards English as a vital aspect of progress and sustainability and a home to more than 200 nationalities, Filipino and Indian teachers have found their platform to practice their profession, while facing the reality that such a country openly prefers to hire native speakers over non-native ones. Hence, this study was conducted to explore the lived experiences of 12 non-native English speakers (six Filipinos and six Indians) in the context of working as English language teachers in the UAE. Responses, collected through in-depth interviews, were analyzed to shed light on the overarching question. Findings revealed that informants came to the UAE to earn a bigger salary, grow personally and professionally, and enhance their communication skills. Furthermore, issues and challenges experienced by these teachers were clustered into personal matters (homesickness, new environment adjustments, and language barriers) and professional matters (preference for native speakers, curriculum adjustment, classroom environment, and teacher-parent connection). Data also unraveled the coping mechanisms of the informants: recalling motivations, building connections, utilizing various teaching approaches, and practicing cultural sensitivity. Finally, this study reflected the insights of the teachers, categorized in themes of unlimited learning, native vs. non-native speakers’ salary difference, uninterested learners, and the importance of understanding the work contract. Based on the findings, implications on administrative and pedagogical practices were explicated, leading to recommendations for future research on the phenomenon of teacher migration.
Parallelization of the Red-Black Algorithm on Solving the Second-Order PN Transport Equation with the Hybrid Finite Element Method

The Red-Black algorithm has been successfully applied on solving the second-order parity transport equation with the PN approximation in angle and the Hybrid Finite Element Method (HFEM) in space, i.e., the Variational Nodal Method (VNM) [1,2,3,4,5]. Any transport solving techniques, including the Red-Black algorithm, need to be parallelized in order to take the advantage of the development of supercomputers with multiple processors for the advanced modeling and simulation. To our knowledge, an attempt [6] was done to parallelize it, but it was devoted only to the z axis plans in three-dimensional calculations. General parallelization of the Red-Black algorithm with the spatial domain decomposition has not been reported in the literature. In this summary, we present our implementation of the parallelization of the Red-Black algorithm and its efficiency results.
Tourism contribution to the SDGs: applying a well-being lens

Human well-being is an essential dimension of sustainable development. This implies that an understanding of well-being outcomes and associated indicators is necessary to determine the success of the global tourism industry in meeting the SDG 2030 Agenda. It is argued that assessment of tourism’s progress toward achievement of the SDGs is incomplete without a full accounting of the outcomes of tourism development on human well-being. In addition to standard indicators of tourism industry performance, a ‘well-being lens’ is proposed comprising indicators that inform tourism stakeholders about changes in resident well-being, promoting sustainable development, and enabling a more comprehensive assessment of tourism’s progress in fulfilling each SDG. The implications of this original proposal for tourism participation in the 2030 SDG agenda are highlighted and discussed.
Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Opinion mining is a natural language processing based on sentiment classification technique to determine the sentiment of the reviews. The major existing text Convolutional Neural Network (CNN) algorithms are derived based on 3 × 3 size kernels which extract ineffective review text-features and lead to less classification accuracy. Moreover, most of the traditional CNN versions output three classes such as positive, negative, and neutral as their classification results. Hence, a novel algorithm namely ‘RAVO driven Multi-Size Kernel structured Text CNN for classifying ecommerce reviews (MSKTCNN-RAVO)’ is proposed in this work. This proposed approach utilizes five multi-size kernels (3 × 7,5 × 7,1 × 3,1 × 5,1 × 7), multi-dimensional kernels (1D & 2D), and integrates varying size kernels to extract text-features effectively. In addition, the performance of multi-kernel CNN is highly enhanced by RAVO algorithm based on rider optimization. Moreover, the proposed approach is highly effective to process 'review-stop-words removal' that decrease the complexity and time consumption of the opinion mining process. Most existing systems use single pooling operations which reduce feature map processing performance, hence, dual pooling operations (both Max and Average pooling) are employed in this research. Furthermore, it is configured to generate five classification outputs such as bad, fair, neutral, good, and excellent to support better decisionmaking with 95.5% accuracy. This method is evaluated using different quality metrics and five review-databases to measure the performance, and the results reveal that the proposed method outperforms the other existing review classification algorithms. Keywords—Natural language processing; opinion mining; convolutional neural network; text sentiment classification; ecommerce review
Corneal Epithelium Asymmetry in Children With Atopy: The Effect of Hand Dominance

Purpose To evaluate the effect of eye rubbing on the epithelial thickness profile in tomographically normal corneas by AS-OCT and to compare right and left eyes in right-handed children. Methods Thirty right-handed boys (mean age 11.2 years) with ocular allergy and history of eye rubbing were evaluated using Scheimpflug (Pentacam HR, Oculus Wetzlar, Germany) and anterior segment optical coherence tomography. Epithelial thickness (ET) and full corneal thickness (CT) parameters were compared between right and left eyes with a non-parametric Mann–Whitney test. A p-value lower than 0.05 was considered for statistical significance. Results No eyes had topometric nor tomographic criteria for keratoconus. The min-max ET was lower in right eyes (−2.8 µm vs −3.5; p = 0.02). The difference between inferior and superior (I-S) octants was lower in right eyes (1.1 µm vs 1.9 µm; p = 0.03) as a result of inferotemporal thinning. The highest ET difference was registered between nasal and temporal octants and was more pronounced in the right eyes (2 µm vs 3.1 µm; p < 0.001). Conclusion AS-OCT analyses reveal different epithelial thickness patterns between the eyes in young atopic patients, likely eye rubbers. Inferior and temporal epithelial thickness seem to be more affected by thinning in the eye on the side of the dominant hand.
Progress in Traction Battery Recycling Industry for Electric Vehicles

With the strong support of the government, China's new energy automotive industry has developed rapidly, and the quantity of the waste EV batteries have been produced. From its huge quantity, environmental protection and resource regeneration point of view, waste lithium-ion batteries have a high recovery value and potential. This paper summarizes the current policy requirements of the Chinese government on the recycling of EV batteries and the development of related enterprises in the industry, focusing on the progress of physical recovery and chemical recovery of EV battery recycling. The existing problems are how to implement the policy, how to recover low-value materials, explore the power of the industry chain circulation. The future development trend is to study the sustainable development of the recovery model and industry chain loopholes in the industry common problems.