Miracle-dz/UniFAR-FTUs huggingface dataset:
Eco-Tourism and the Learning-Scape: Consumers’ Learning Experience at an Eco-Lodge in Australia

Eco-tourism aims to have less negative impact on communities and the environment, while simultaneously promoting conservational behaviour and attitudes. Thus, eco-tourism is an excellent tool to educate tourists on how to become more responsible. Previous research has focused on learning experiences in eco-activities, while a focus on learning in sustainable accommodation experiences has been neglected even though these experiences play an important role in the overall tourist experience. Therefore, this study aims to get an increased understanding of the learning experience guests encounter while residing in an ecolodge. The analysis and findings are based on a qualitative case study conducted through interviews, complemented by observations at a specific eco-lodge in Australia. The findings from this article show that there is a gap between the learning opportunities at the lodge and guests’ aspirations of the learning experience. From these findings a learning-scape is proposed as a way to enhance the learning experience for consumers by combining three aspects: appealing to emotions and interests, playing on previous knowledge and involvement, and providing more learning opportunities.
Intercomparison of tropospheric and stratospheric mesoscale kinetic energy resolved by the high‐resolution global reanalysis datasets

With the development of advanced data assimilation and computing techniques, many modern global reanalysis datasets aim to resolve the atmospheric mesoscale spectrum. However, large uncertainties remain with respect to the representation of mesoscale motions in these reanalysis datasets, for which a clear understanding is lacking. The aforementioned challenges have served as a strong motivation to reveal and quantify their mesoscale differences. This study presents the first comprehensive global intercomparison of the tropospheric and stratospheric mesoscale kinetic energy and its spectra over two selected periods of summer and winter events among six leading high‐resolution atmospheric reanalysis products: European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), China Meteorological Administration Reanalysis, Modern‐Era Retrospective Analysis for Research and Applications version 2, National Centers for Environmental Prediction's Climate Forecast System version 2 (CFSv2), Japanese 55‐year Reanalysis, and ECMWF Reanalysis‐Interim. A state‐of‐the‐art global operational model is adopted as a supplementary reference. Although all reanalysis datasets can reproduce broad distribution characteristics that are grossly consistent with the 9 km model, there are substantial discrepancies among them in magnitudes. The ability to capture mesoscale signals is closely linked to their resolutions, but it is also impacted by other factors, including, but not limited to, the selected types of energy, seasons, altitudes, latitudes, model diffusions, parametrization schemes, moist condition, assimilation methods, and observation inputs. Moreover, all datasets illustrate conclusive behaviors for the prevalence of the rotational component in the troposphere, whereas only very few products fail to exhibit the dominance of the divergent component in the stratosphere. Overall, stratospheric ERA5 and CFSv2 outperform the other reanalysis datasets, and only these two can reproduce the feature of the canonical kinetic energy spectrum with a distinct shift from a steeper slope (approximately −3) at lower wave numbers to a shallower slope (approximately −5/3) at higher wave numbers. In addition, the relative disparities among datasets increase dramatically with height, and they are more pronounced in the divergent component. It is also found that the correlations among these datasets are much weaker in the Tropics.
Multi-Point RCNN for Predicting Deformation in Deep Excavation Pit Surrounding Soil Mass

Accurate prediction and forecasting of soil mass deformation in deep excavation pits are pivotal for risk monitoring and safety assessment. Nonetheless, the complex underlying dynamics inherent in field sensing measurements pose challenges to the forecasting endeavor. In light of these challenges, the present study leverages recent strides in deep learning and introduces a spatiotemporal learning framework tailored to forecast soil mass deformation marked by resilient temporal interconnections and spatial associations. This study focuses on developing a Multi-Point Recurrent Convolutional Neural Network (RCNN) model for predicting sensor-based temporal patterns. This model integrates data feature fusion to extract spatiotemporal latent features from the dataset, thereby constructing a surrogate model for forecasting soil mass deformation. The proposed methodology is deployed to forecast strain responses in a deep excavation pit using a dataset spanning over five months. A comparative analysis is conducted, contrasting the performance of the proposed approach with that of a conventional temporal-only network. The analysis reveals that the prediction errors generated by the Multi-Point RCNN are predominantly concentrated within the range of 10% for all sensors, with a high-confidence interval (CI) of 96%, compared to the RCNN model (82%) and the LSTM model (79%). The compelling outcomes underscore the efficacy of the Multi-Point RCNN approach as a promising, dependable, and computationally efficient method for accurately predicting soil mass deformation in deep excavation pits, grounded in data-driven principles.
Towards a Modular Pathological Tremor Simulation System Based on the Stewart Platform

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson’s disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients’ wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson’s disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices.
Response of Jakobshavn Isbrae, Greenland, to Holocene climate change

Rapid fluctuations in the velocity of Greenland Ice Sheet (GIS) outlet glaciers over the past decade have made it difficult to extrapolate ice-sheet change into the future. This significant short-term variability highlights the need for geologic records of preinstrumental GIS margin fluctuations in order to better predict future GIS response to climate change. Using 10 Be surface exposure ages and radiocarbon-dated lake sediments, we constructed a detailed chronology of ice-margin fluctuations over the past 10 k.y. for Jakobshavn Isbrae, Greenland9s largest outlet glacier. In addition, we present new estimates of corresponding local temperature changes using a continuous record of insect (Chironomidae) remains preserved in lake sediments. We find that following an early Holocene advance just prior to 8 ka, Jakobshavn Isbrae retreated rapidly at a rate of ∼100 m yr −1 , likely in response to increasing regional and local temperatures. Ice remained behind its present margin for ∼7 k.y. during a warm period in the middle Holocene with sustained temperatures ∼2 °C warmer than today, then the land-based margin advanced at least 2–4 km between A.D. 1500–1640 and A.D. 1850. The ice margin near Jakobshavn thus underwent large and rapid adjustments in response to relatively modest centennial-scale Holocene temperature changes, which may foreshadow GIS response to future warming.
Optimal chest position of auscultation for chronic obstructive pulmonary disease diagnosis using machine learning

Digital Stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the personnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope's diaphragm to objectively diagnose Chronic Obstructive Pulmonary Disease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, Anterior left (Al), Anterior right (Ar), Lateral left (Ll), Lateral right (Lr), Posterior left (Pl), Posterior right (Pr) and Trachea (Tc), were analyzed in this study. COPD+ and COPD- at diagnosis, each chest position was done objectively using Mel-Frequency Cepstral Coefficients (MFCC) features and machine learning models namely Support Vector Machine and Decision Tree. The results indicate that the Posterior right (Pr) chest position offers superior precision, recall, and F1 score, with a recognition accuracy of 99.7% in COPD screening.
Customer Profiling System with Residual Network-Based Face Recognition

Customer profiling is an essential aspect of customer relationship management. Knowing who your customers are, what they need, and how to reach them is crucial in creating an effective marketing strategy. However, it can be challenging for some sellers to identify and track their loyal customers. This is where a customer profiling system can be invaluable. Such a system uses data analysis and deep learning techniques to track customer behaviour and identify preferences. One approach to customer profiling is through face recognition technology. Facial recognition is an effective method for identifying people, and it can be used to track customer attendance and identify regular customers. Therefore, this work presented the development of a customer profiling system using a deep learning technique to detect customer faces in real time. Experimental results showed that the system obtained 90% accuracy in detecting customers' faces. This work conducted a user acceptance test (UAT) to evaluate the system's effectiveness. The results indicated that the system provides many benefits and advantages to customers and sellers, including improved customer loyalty and satisfaction.
Micro Manager: a Python package for adaptive and flexible two-scale coupling

The Micro Manager facilitates coupling between simulation software packages, which solve problems at different physical scales. Broadly speaking, simulation-based analysis is an effective tool to gain insights in real-world scenarios without incurring the high cost of prototyping and testing. Complex simulations are oftentimes broken down into simpler components, which are resolved by tailor-made software. Such complex simulations can be of multiphysics nature, meaning different physics are solved in different parts of the domain. To do such multiphysics simulations, we can couple different software packages together. preCICE (Chourdakis et al., 2022) is an open-source coupling library for partitioned multiphysics simulations. However, sometimes the coupling is not just between different physics but also different physical scales, which is frequently referred to as multiscale coupling. To understand how the scales affect each other, both scales need to be solved simultaneously while being coupled in a bi-directional manner. The Micro Manager, together with preCICE, is capable of handling a multiphysics and multiscale coupling. We refer to the coarse scale as the macro scale, and the fine scale as the micro scale. The name Micro Manager is derived from its core functionality of controlling a set of micro-scale simulations and coupling them to one macro-scale simulation via preCICE.
m6A regulates breast cancer proliferation and migration through stage-dependent changes in Epithelial to Mesenchymal Transition gene expression

While many factors have been implicated in breast cancer progression, effective treatments are still lacking. In recent years, it has become clear that posttranscriptional regulation plays a key role in the aberrant gene expression underlying malignancy and metastasis. For example, the mRNA modification N6-methyladenosine (m6A) is involved in numerous post-transcriptional regulation processes and has been implicated in many cancer types, including breast cancer. Despite intense study, even within a single type of cancer, there is little consensus, and often conflicting results, as to the role of m6A, suggesting other factors must influence the process. The goal of this study was to determine if the effects of m6A manipulation on proliferation and migration differed based on the stage of disease progression. Using the MCF10 model of breast cancer, we reduced m6A levels by targeting METTL3, the main cellular m6A RNA methyltransferase. Knocking down Mettl3 at different stages of breast cancer progression indeed shows unique effects at each stage. The early-stage breast cancer line showed a more proliferative phenotype with the knockdown of Mettl3 while the transformed breast cancer line showed a more migratory phenotype. Interestingly, the metastasized breast cancer cell line showed almost no effect on phenotype with the knockdown of Mettl3. Furthermore, transcriptome wide analysis revealed EMT as the probable pathway influencing the phenotypic changes. The results of this study may begin to address the controversy of m6A’s role in cancer and suggest that m6A may have a dynamic role in cancer that depends on the stage of progression.
Geophysical characterization of the lithology and structure of the Olobanita Well Field, Lower Lake Baringo Basin, Kenya Rift: Implication on Groundwater occurrence

The task of the research project is the use of geophysical technologies for the characteri2ation of the lithology and structure beneath the Olobanita well field, and determine their implications on the groundwater drilling operations in the basin. Development of the aquifer since the 2000’s to date by the government agencies and its development partners through drilling, has resulted in generation over time of data and information on its water resources. Consequendy, borehole collapse problems have been reported that have resulted from the drilling activities. This has been used by my research as a guide to the study of the subsurface of the well field. Geophysical techniques employed includes electrical resistivity (vertical electrical sounding (VES) and electrical resistivity tomography (ERT)), magnetics and geospatial techniques like geographical information systems (GIS) and global positioning system (GPS) for development of maps. The Olobanita Basin with an area of 625km2, a volcano-sedimentary basin is located in the Central Kenya Rift, Nakuru district of Rift Valley Province. It is located between latitudes 0°00’N and 0°15’ S and longitudes 36°00’ and 36°15’ E. For the study, establishments on the subsurface geology of the wellfield were made from; borehole geologic logs, vertical electrical sounding interpreted data, electrical resistivity tomographic interpreted data and magnetic data analysis results and reports by other researchers. The vertical electrical sounding and the 2-Dimension (2D) Resistivity imaging allowed a distinction to be made between very high resistive volcanic sands, the moderately high resistivity freshwater saturated zone and the very low resistive clays in localised areas. The geological interpretation of the surveys correlated very well with the borehole data. Vertical faults have resulted in minor down-throwing of blocks in areas close to the rift wall on the east and some few metres to the west margins of the VES grid area. It is concluded that the well-field lies in a volcano-sedimentary basin, where the results from the analysis shows that there exists volcanic sand and silt formation to a depth ofiabout 40 metres underlain by volcanic ashes and fragments to a depth of about 80 metres with intercalations of hard rock formations mainly; welded tuffs and trachyte. These loose unconsolidated geologic materials are vulnerable to collapsing by any chance of disturbance. To the depth beyond 80 metres to about 300 metres exists loose sediments with rounded fragments; thus, contributes to the borehole blockage and collapsing challanges.
Ice flow velocities over Vostok Subglacial Lake, East Antarctica, determined by 10 years of GNSS observations

Abstract Repeated Global Navigation Satellite Systems (GNSS) observations were carried out at 50 surface markers in the Vostok Subglacial Lake (East Antarctica) region between 2001 and 2011. The horizontal ice flow velocity vectors were derived with accuracies of 1 cm a−1 and 0.5°, representing the first reliable information on ice flow kinematics in the northern part of the lake. Within the lake area, ice flow velocities do not exceed 2 m a−1. The ice flow azimuth is southeast in the southern part of the lake and turns gradually to east-northeast in the northern part. In the northern part, as the ice flow enters the lake at the western shore, the velocity decreases towards the central lake axis, then increases slightly past the central axis. In the southern part, a continued acceleration is observed from the central lake axis across the downstream grounding line. Based on the observed flow velocity vectors and ice thickness data, mean surface accumulation rates are inferred for four surface segments between Ridge B and Vostok Subglacial Lake and show a steady increase towards the north.
RVD-SLAM: A Real-Time Visual SLAM Toward Dynamic Environments Based on Sparsely Semantic Segmentation and Outlier Prior

In visual simultaneous localization and mapping (vSLAM) systems, the rigid assumption of the environment is usually overcome by introducing geometry-based or learning-based methods. However, these methods either rely heavily on depth information or tend to suffer from poor real-time performance. In this article, we present real-time vSLAM (RVD-SLAM), a real-time simultaneous localization and mapping (SLAM) in dynamic environments for red green blue-depth (RGB-D)/stereo/monocular sensors based on sparsely semantic segmentation and outlier prior. RVD-SLAM utilizes feature matching information, which does not rely on depth, to detect outliers and dynamic features. First, an affine consistency constraint is proposed and seamlessly integrated into the matching optimization of the system to efficiently detect outliers. Second, we skillfully combine affine information with the efficiently semantic model, which enables the reuse of previously obtained information, to detect dynamic objects. Finally, to adapt to slightly dynamic environments and reduce the computational cost, the ratio of outliers in the matching set is employed as prior information for sparsely using the semantic method. Moreover, an improved map update method considering dynamic features is designed to ensure the effectiveness of the map used for long-term tracking. The wide experiments on the public TUM and KITTI datasets demonstrate quantitatively that our method improves camera tracking compared to state-of-the-art (SOTA) methods and runs in real-time. To further evaluate RVD-SLAM’s generalization capabilities and performance of dynamic object detection, we also test it in real dynamic scenarios by using handheld RGB-D and stereo cameras.
Anaerobic Digestion of Food Waste Prevents Greenhouse Gas Emissions and Produces Energy

Landfilling organic waste is common in the US. Even “stateoftheart” landfills that have double liners, gas collection systems and leachate collection systems, negatively impact the environment. Leachate from landfills contains pollutants, such as heavy metals, which can impact groundwater and surface waters. Other pollutants are found in leachate like dissolved organic matter, inorganic macrocomponents and xenobiotic organic compounds. At first, all of these pollutants have a strong presence in the leachate, then they gradually decrease in concentration. Ammonia’s concentration is one, however, which remains strong (Kjeldsen et. al). Ammonia can lead to eutrophication, soil acidification and fertilization (Colorado State). This in itself is a concern, especially due to ammonia’s longstanding presence in leachate. Each pollutant in leachate has its own environmental impact with ammonia being one with a severe cause for concern.
EBi2 ER Protocol for Self-Sustainable UAV-Assisted Wireless Networks

This letter proposes the energy borrowing assisted integrated information and energy relaying (EBi2ER) protocol for wireless powered communication. The EBi2ER protocol enables energy borrowing from the power grid to create a self-sustainable network assisted by an unmanned aerial vehicle (UAV) as a relay for energy or information transfer. A penalty constant is used to deter excessive borrowing and ensure that the system operates within its energy limits. The deep deterministic policy gradient (DDPG) algorithm is utilized to maximise the average net bit rate and estimate the channel and energy harvesting conditions in an unknown environment. Extensive simulations demonstrate the effectiveness of the EBi2ER protocol and achieve a significant improvement of up to 49.88% in the net bit rate compared to a greedy benchmark scheme.