Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Influence of Social Environments on Wellbeing in Urban Communities: A Literature Review and Key Indicators for Future Research

In the rapidly urbanizing context, community well-being is vital for societal progress. Malaysia's growing urban landscape highlights the interplay between the social environment and urban community well-being. Amidst cultural diversity, where ethnicities converge, understanding the multifaceted social dynamics becomes pivotal. As traditions merge and languages harmonize, the social environment fosters unity and empathy. This study explores nuanced dimensions and their impact on urban well-being. Through an extensive scholarly review, it uncovers connections between sociodemographic, residential attributes, neighborhood dynamics, relationship support, community engagement, safety perceptions, identity, play, design, interaction variety, shared spaces, and outdoor opportunities. These facets shape urban life, influencing interactions and attachment. Malaysia's cultural tapestry emphasizes the social environment's role in harmonizing diverse communities, enhancing well-being, and resilience. Findings inform urban planning, policy-making, and community development, propelling inclusive and harmonious urban communities. Ultimately, this research underscores the social environment's potential as a catalyst for unity, contentment, and well-being in Malaysia's urban areas.
Sentiment Polarity Classification for Khmer

Sentiment polarity classification is a natural language processing (NLP) technique that determines whether a given piece of text has a positive, negative, or neutral sentiment. This article investigates the application of a well-known shallow neural network classifier, FastText, to the Khmer language, which is a low-resource language. To our knowledge, there isn’t a freely available polarity corpus for Khmer. Thus, one of the primary objectives of this research is to develop a human-annotated benchmark corpus tailored for sentiment analysis in this language. Our findings indicate that the FastText model achieves a precision and recall of 0.740 using our modest-sized Khmer polarity corpus, which consists of 9,000 training sentences and 1,000 testing sentences. Among traditional machine learning methods, the support vector machine (SVM) outperforms the rest.
AI Based Touchless Attendance System

Attendance system has always been an important part of an organization as it is used to track the attendance of employees and students daily. The process of taking attendance has evolved from manual methods to utilizing modern technologies such as next-generation biometric attendance systems and machine learning. Traditional attendance systems had various loopholes and therefore it could be easily misused. Organizations have had to deal with common frailties such as buddy punching and human errors, which consume a significant amount of time. And after covid, the world has moved to contactless form of work which led to organizations deploying more advanced contactless biometric systems such as face recognition, palm recognition, mask detection and thermal scanning. In this work, we have implemented face recognition mobile application using the flutter framework in which the employee can mark his attendance by scanning his face, and the app will mark his attendance if he/she is present in the organization. This will help in an easy contactless attendance system and won't require indulgence of any human as attendance will be directly stored in the cloud. An AI-based touchless attendance system built on the Android OS is conceived and implemented in this research paper. Comparing the proposed system to alternative traditional attendance systems, which is faster, less expensive, and more accessible technology for tracking student attendance online and automatically producing attendance reports.
A Capacitive Ice-Sensor Based on Graphene Nano-Platelets Strips

This paper investigates the possibility of realizing ice sensors based on the electrical response of thin strips made from pressed graphene nano-platelets. The novelty of this work resides in the use of the same graphene strips that can act as heating elements via the Joule effect, thus opening the route for a combined device able to both detect and remove ice. A planar capacitive sensor is designed and fabricated, in which the graphene strip acts as one of the armatures. The sensing principle is based on the high sensitivity of the planar capacitor to the change in electrical permittivity in the presence of ice, as shown in the experimental case study discussed here, can also be interpreted by means of a simple circuit and electromagnetic model. The properties of the sensor are analyzed, and the frequency range for its use as an ice detector has been established.
A Survey on Brain Tumor Prediction with Various Machine Learning Approaches

Brain tumor prediction plays a crucial role in the accurate diagnosis and timely treatment of this life-threatening medical condition. Traditional methods relying on manual analysis of medical images are subjective and time-consuming. Machine learning, particularly deep learning, has shown promise in improving brain tumor prediction. However, individual approaches may have limitations. This literature survey explores the emerging field of hybrid machine learning for brain tumor prediction. By combining multiple techniques, hybrid models aim to enhance prediction accuracy, robustness, and interpretability. The survey reviews recent research papers, analyzes techniques used in hybrid models, and assesses their performance against traditional approaches. The findings highlight the potential of hybrid machine learning in advancing brain tumor prediction for improved patient outcomes.
Flywheel Romanian Deadlift: Intra- and Inter-Day Kinetic and Kinematic Reliability of Four Inertial Loads Using Cluster Sets

The primary aim of this study was to investigate the intra- and inter-day reliability of flywheel cluster set training in concentric power (CON), eccentric power (ECC), and ECC overload during the Romanian deadlift exercise (RDL). A secondary aim was to assess the acute effect of internal and external attentional focus instructions on mean power when performing the flywheel RDL. Fourteen collegiate male field sport athletes (age, 23.3 ± 3.7 years; mass, 80.8 ± 9.9 kg; height, 1.79 ± 0.06 m) were randomized into internal (n = 7) or external (n = 7) attentional focus groups and attended four testing sessions, with a between-session separation of 7 days. Sessions consisted of four cluster sets of fifteen repetitions “excluding momentum repetitions” (4 × (5 + 5 + 5)) using a specific inertial load (0.025, 0.050, 0.075, and 0.100 kg·m−2) for a given set in a randomized ascending or descending order. Cluster sets were separated by a 45 s intra-set rest period. Both instructional focus groups attained familiarization, although the time taken to achieve familiarization (outcome stability) differed between groups. The external instructional group attained familiarization post-session 2 (Cohen’s d (ES), ES = 0.11–0.65) with little volatility between performance measures (CV% = 4.61–9.59). Additionally, the internal group reported inconsistencies among all inertial loads, reporting large differences in MP in the 0.100 kg·m−2 inertial load from day 2 to day 3 (ES = 1.22) and both 0.050 kg·m2 (p = 0.010) and 0.075 kg·m−2 (p = 0.016) between day 3 and day 4. The flywheel RDL cluster set approach is a reliable training modality for maintaining mean power output during cluster set repetitions.
The role of space agencies in remotely sensed essential biodiversity variables

The Group on Earth Observations Biodiversity Observation Network (GEO BON) is developing the Essential Biodiversity Variables (EBVs) as the key variables needed, on a regular and global basis, to understand and monitor changes in the Earth's biodiversity. A subset of these EBVs can be derived from space‐based remote sensing, within this paper referred to as remotely sensed EBVs (RS‐EBVs). Given the global, periodic and standardized character of satellite remote sensing measures, RS‐EBVs may be seen as easier to generate than non‐remotely sensed EBVs, which have to be assembled from disparate and local sources of information. Particularly because they are global and periodic, RS‐EBVs are of special relevance for monitoring the state of and changes to biodiversity, notably the structure and function of ecosystems. If well developed, RS‐EBVs can provide key information for global biodiversity assessments as well as for national governments to meet their obligations under the Convention on Biological Diversity (CBD), in particular to formulate and implement appropriate management responses to biodiversity losses. However, the relevance and usage of globally produced RS‐EBVs in wide‐scale ecological modelling, such as in species distribution and abundance studies or in ecosystem integrity analyses, are still to be demonstrated, in particular when it comes to deriving biodiversity indicators for policy making and implementation. The biodiversity community at large, from those conducting scientific ecological studies to those involved in the development of remote sensing applications for biodiversity monitoring, can gain value from RS‐EBVs, but doing so requires close cooperation with space agencies. Effective interaction is only likely to result if the biodiversity community understands how space agencies determine their observation and product requirements. To develop these requirements, space agencies need to precisely specify the physical measurements for their spaceflight instruments, as well as the algorithmic approaches, to generate RS‐EBV products from these measurements. Here, we address the biodiversity community to discuss the role space agencies should play in the development of EBVs arising from satellite remote sensing. Importantly, we explain the necessity for translating the observational needs of the biodiversity community into specific satellite remote sensing measurement and algorithm requirements. By summarizing the prerequisite conditions that are required for obtaining a collective and strong engagement of space agencies in the co‐development of RS‐EBVs, we aim to facilitate collaborative efforts between the biodiversity community and the space agencies, which can ultimately contribute to a global and comprehensive biodiversity knowledge system.
GreedW: A Flexible and Efficient Decentralized Framework for Distributed Machine Learning

With the ever-increasing demand for computing power in deep learning, distributed training techniques have proven to be effective in meeting these demands. However, current existing state-of-the-art distributed training frameworks, such as Parameter Server (PS), Ring-All-Reduce, and their varieties, still face significant challenges. In particular, the existence of communication bottlenecks can severely limit the efficiency and scalability of distributed training frameworks, making it difficult to fully and effectively exert the computing power of large-scale clusters, especially in the presence of dynamic and ever-changing network environments. To address these issues and further maximize the utilization of the computing power of clusters, in this paper we propose an efficient and dynamic distributed training framework, named GreedW. GreedW can greatly improve the training efficiency of workers by dynamically constructing an adaptive customized communication network and adaptively scheduling the workload. Specifically, GreedW employs a greedy strategy to dynamically construct the communication network tree in each iteration for gradient transmission with minimum communication cost and applies a heterogeneity-aware workload allocation scheme to adaptively balance the heavy traffic across heterogeneous workers in the cluster taking into account the available computing capabilities of each node, which effectively alleviates the network bottleneck. It is worth noting that GreedW is enabled to dynamically adjust the assigned job on each worker node based on their completion time during each round of model aggregation to ensure that each worker node completes its assignments around the same time, thus mitigating the intractable straggler issue and minimizing their idle waiting time. Comprehensive experimental evaluations on three different-scaled training models (i.e., Mnist-2NN, Mnist-CNN, and TextCNN) for image recognition and natural language processing tasks demonstrate that GreedW outperforms the existing state-of-the-art frameworks in terms of training efficiency, system flexibility, and robustness.
A Case Study on Analyzing the Similarity Between Two Similar Stocks: Building the CrC-LSTM Model

Nowadays, there is a huge innovation change in financial sector that requires complex digital technologies. For stock market investors, a good algorithm and model can output more accurate stock price for prediction of the future. This allows them to have a better portfolio management. Since the stock market price is a time-series data, most researchers tend to use algorithms which relates to the Long Short-Term Memory (LSTM) from Recurrent Neural Network. The forget gate in LSTM allows us to determine what kind of previous information can be stored and transferred. However, the original model only relies on its own past data. Thus, we start to implement a Cross-Correlated-LSTM that has a fourth gate named as the Link Gate. The aim of this link gate is to extract useful information from other similar time-series data. In this paper, we present the logic behind the CrC-LSTM via a case study by analyzing the similarity of the two candlestick graphs between the Apple Inc. and the Meta Platform Inc. combined with a dry run of the CrC-LSTM and obtained some results. The analysis and the prototype results have shown the potential of the CrC-LSTM in predicting the stock prices.
Estimation of Forest Stock Volume Combining Airborne LiDAR Sampling Approaches with Multi-Sensor Imagery

Timely and reliable estimation of forest stock volume is essential for sustainable forest management and conservation. Light detection and ranging (LiDAR) data can provide an effective depiction of the three-dimensional structure information of forests, but its large-scale application is hampered by spatial continuity. This study aims to construct a LiDAR sampling framework, combined with multi-sensor imagery, to estimate the regional forest stock volume of natural secondary forests in Northeast China. Two sampling approaches were compared, including systematic sampling and classification-based sampling. First, the forest stock volume was mapped using a combination of field measurement data and full-coverage LiDAR data. Then, the forest stock volume obtained in the first step of estimation was used as a reference value, and optical images and topographic features were combined for secondary modeling to compare the effectiveness and accuracy of different sampling methods, including 12 systematic sampling and classification-based sampling methods. Our results show that the root mean square error (RMSE) of the 12 systematic sampling approaches ranged from 55.81 to 57.42 m3/ha, and the BIAS ranged from 21.55 to 24.89 m3/ha. The classification-based LiDAR sampling approach outperformed systematic sampling, with an RMSE of 55.56 (<55.81 m3/ha) and a BIAS of 20.68 (<21.55 m3/ha). This study compares different LiDAR sampling approaches and explores an effective LiDAR sample collection scheme for estimating forest stock, while balancing cost and accuracy. The classification-based LiDAR sampling approach described in this study is easy to apply and portable and can provide a reference for future LiDAR sample collection.
Evaluation of the DigiBete App, a Self-Management App for Type 1 Diabetes: Experiences of Young People, Families, and Healthcare Professionals

Type 1 diabetes (T1DM) is a public health issue for children, young people, and families (CYPF) and requires innovative interventions. The DigiBete app is a self-management and educational app to help CYPF and healthcare professionals (HCPs) manage T1DM, featuring educational advice and resources such as guidance, quizzes, and educational and instructional videos on how to manage T1DM. To assess the impact and implementation of the app, the service-level evaluation deployed a mixed-methods design. App data were captured via the DigiBete platform and an online survey with a non-probability sample of HCPs (N = 178) and CYPF (N = 1165) = 1343. Overall, 55.7% (n = 512/919) of app users were female, and 4855 videos were viewed across the participating areas, with an average of 1213 videos per site (range 776–1679) and 4.4 videos per app user. The most popular videos were how to give a glucagon injection and “My Sick Day Rules”, which showed what to do when CYPF were unwell due to T1DM. Interviews (n = 63) were undertaken with 38 CYPF and 25 HCPs. The findings indicate that CYPF and HCPs found the app an essential tool in the management of T1DM. CYPF and HCPs felt the app provided a valuable educational resource in a central location that was invaluable in an emergency or unknown situation. The app was a trusted and bona-fide source of information that could be accessed at any time. HCPs validated DigiBete in helping CYPF to manage their T1DM. At the same time, the app saved HCPs’ service time and money and helped CYPF take back some of the control in managing their diabetes.
Efficacy of embedding IT in human resources (HR) practices in education management

The ways that managers at various firms manage their human resources are significantly impacted by technology. This study investigated the efficacy of embedding IT in Human Resources (HR) practices in Education Management. More attention was also put on the effect of IT tools used in HR processes on education management, the relationship between IT embedded HR practices and education management, the effect of benefits of using IT in HR Processes on education management. The study used a cross-sectional survey design and data was collected from 320 HR professionals from the education sector of Greece and 10 officials from the ministry of education of Greece using a survey questionnaire and interview guide respectively. The study revealed that IT tools used in HR processes and IT embedded HR practices have a positive effect on education management. It was also confirmed that benefits of using IT in HR Processes positively influence the level of education management. The activities of the human resources department have changed as a result of technical developments like as software, devices, and the Internet of Things. In a larger sense, technology has changed how businesses conduct their operations throughout the world. The operation of human resource practices has seen a significant increase in communication within Greece's educational sector. It is important for different education institutions to focus on training HR managers on using different IT tools so as to enhance the quality of education management.
Spatial Interpolation of Daily Precipitation in a High Mountainous Watershed Based on Gauge Observations and a Regional Climate Model Simulation

AbstractPrecipitation is a primary climate forcing factor in catchment hydrology, and its spatial distribution is essential for understanding the spatial variability of ecohydrological processes in a catchment. In mountainous areas, meteorological stations are generally too sparse to represent the spatial distribution of precipitation. This study develops a spatial interpolation method that combines meteorological observations and regional climate model (RCM) outputs. The method considers the precipitation–elevation relationship in the mountain region and the topographic effects, especially the mountain blocking effect. Furthermore, using this method, this study produced a 3-km-resolution precipitation dataset from 1960 to 2014 in the middle and upper reaches of the Heihe River basin located on the northern slope of the Qilian Mountains in the northeastern Tibetan Plateau. Cross validation based on the station observations showed that this method is reasonable. The rationality of the interpolated precipit.
Predict Student Performance with Machine Learning method: A - Review

In educational institutions, a significant amount of effort and important resources are allocated to influence, predict, and understand students' decisions when choosing their educational paths. Leveraging student data and achievements with the assistance of machine learning to predict students' academic success can make a substantial contribution to all parties involved in devising appropriate strategies to support students. This review assesses the performance of machine learning algorithms in classifying and predicting student academic success. Four algorithms are considered for predicting student academic success: K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), and Decision Tree. Additionally, this review utilizes data that categorizes students' academic success into two or three categories, such as pass/fail or fail/pass/excellent.
IGF1 and Insulin Receptor Single Nucleotide Variants Associated with Response in HER2-Negative Breast Cancer Patients Treated with Neoadjuvant Chemotherapy with or without a Fasting Mimicking Diet (BOOG 2013-04 DIRECT Trial)

Simple Summary Insulin and insulin-like growth factor 1 (IGF1) are metabolic hormones, which are often upregulated to stimulate proliferation in breast cancer. A fasting mimicking diet (FMD) targets insulin signaling pathway downregulation to hamper tumor growth. Genes encoding for the insulin receptors on the cell’s surface contain genetic variation between patients, which can affect insulin receptor function and cellular response. Therefore, a group of 113 patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy with or without a fasting mimicking diet were investigated. We found that two IGF1 receptor variants were associated with worse pathological response compared to the reference alleles, out of the 17 interrogated common variants. Additionally, two IGF1 receptor variants could interact negatively within the FMD group regarding radiological response. These results emphasize that genetic variation harbors predictive clinical relevance to optimize and personalize cancer therapy. Abstract Aim: We aimed to investigate associations between IGF1R and INSR single nucleotide variants (SNVs) and clinical response in patients with breast cancer treated with neoadjuvant chemotherapy with or without a fasting mimicking diet (FMD) from the DIRECT trial (NCT02126449), since insulin-like growth factor 1 (IGF1) and the insulin pathway are heavily involved in tumor growth and progression. Methods: Germline DNA from 113 patients was tested for 17 systematically selected candidate SNVs in IGF1R and INSR with pathological and radiological response. Results: IGF1R variants A > G (rs3743259) and G > A (rs3743258) are associated with worse pathological response compared to reference alleles p = 0.002, OR = 0.42 (95%CI: 0.24; 0.73); p = 0.0016; OR = 0.40 (95%CI: 0.23; 0.70). INSR T > C (rs1051690) may be associated with worse radiological response p = 0.02, OR = 2.92 (95%CI: 1.16; 7.36), although not significant after Bonferroni correction. Exploratory interaction analysis suggests that IGF1R SNVs rs2684787 and rs2654980 interact negatively with the FMD group regarding radiological response p = 0.036, OR = 5.13 (95%CI: 1.12; 23.63); p = 0.024, OR = 5.71 (95%CI: 1.26; 25.85). Conclusions: The IGF1R variants rs3743259 and rs3743258 are negatively associated with pathological response in this cohort, suggesting potential relevance as a predictive biomarker. Further research is needed to validate these findings and elucidate the underlying mechanisms and interaction with FMD.