Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation into the Surface Integrity of Micro-Machined High-Speed Steel and Tungsten Carbide Cutting Tools

The performance and lifespan of cutting tools are significantly influenced by their surface quality. The present report highlights recent advances in enhancing the surface characteristics of tungsten carbide and high-speed steel cutting tools using a novel micro-machining technique for polishing and edge-honing. Notably, the main aim was to reduce the surface roughness while maintaining the hardness of the materials at an optimal level. By conducting a thorough analysis of surfaces obtained using different techniques, it was found that the micro-machining method effectively decreased the surface roughness of the cutting tools the most effectively out of the techniques investigated. Significantly, the surface roughness was reduced from an initial measurement of 400 nm to an impressive value of 60 nm. No significant change in hardness was observed, which guarantees the maintenance of the mechanical properties of the cutting tools. This analysis enhances the comprehension of surface enhancement methodologies for cutting tools through the presentation of these findings. The observed decrease in surface roughness, along with the consistent hardness, exhibits potential for improving tool performance. These enhancements possess the capacity to optimise manufacturing processes, increase tool reliability, and minimise waste generation.
A Novel Two-Dimensional Convolutional Neural Network-Based an Hour-Ahead Wind Speed Prediction Method

With increasing penetration of wind power, accurate prediction of wind speed is essential for planning and operation of power grids. In this paper, a novel two-dimensional (2D) convolutional neural network (CNN)-based wind speed forecasting technique is proposed for an hour-ahead wind speed prediction. The wind speed at a specific time can be predicted in less than a few milliseconds using the proposed approach and meteorological data from a few hours earlier. The input feature selection, data preprocessing, and model evaluation of the proposed approach are presented; the efficiency of 2D CNN is compared to that of one-dimensional (1D) CNN, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). A three-year historical wind speed dataset from 2020 to 2022 collected at Saskatoon International Airport in Saskatoon, Saskatchewan, Canada, is used in this study. It is found that 2D CNN shows superior performance in addressing regression and prediction challenges. Experimental results verify that the proposed 2D CNN-based forecasting techniques can provide accurate wind speed prediction. Using deep learning for wind speed prediction can reduce costs while boost energy output and contribute to sustainable and green energy development in Saskatchewan and beyond.
Robust and Intelligent Control for PV System of Two Converters:DC-DC Converter/ Single-Phase Modular Multilevel Converter to Operate Grid Connected

This paper proposed a Modular Multilevel Converter (MMC) incorporating a Sliding Mode Controller (SMC) technique to control the output current of MMC and the Fuzzy Logic (FL) controller maximum power point tracking (MPPT) is used to extract maximum power from the PV array. In photovoltaic systems: the DC-DC Boost converter is usually employed in the adaptation stage between the PV panel and the DC-AC converters on the other hand, it is used for boosting the array voltage and achieving maximum power point tracking for the PV array to the DC-link and a modular multilevel converter that serves as an interface. An MMCs main control objective is to synchronize the grid voltage and current while controlling the DC link voltage. Designing a sliding mode controller of output current for an injected pure sinusoidal current is the main goal of this paper. In previous literature output current strategy was based on a proportional resonant controller (PR); but currently, became advised Robust and Intelligent Controller. For example, in this work, in order to provide a high dynamic performance of MMC and power quality injected into the grid, we build a Sliding Mode Controller-based technique. Then, by using MATLAB/Simulink, the effectiveness and viability of the suggested method are demonstrated.
FACILE FABRICATION OF ANILINE SENSOR BY USING COMMERCIAL CALCIUM SILICATE HYDRATE NANOSHEETS AS SENSING MATERIAL

Various aniline sensing materials have excellent properties based on quartz crystal microbalance (QCM) transducer, but their synthesis processes are complex. Herein, commercial calcium silicate hydrate nanosheets (CSH-S) was utilized for facile fabrication of aniline sensor based on QCM platform. This sensor was capable of detecting 0.1 ppm aniline, and the response time (14 s) and recovery time (16 s) were short. Besides, the devices could restore sensitivity after 30 days, demonstrating excellent stability. In addition, variable temperature experiment combined with simulation calculation methods were carried out to study the adsorption of aniline on CSH-S surfaces, then the sensing mechanism was revealed. This work opens up a rapid and convenient method for detecting gaseous aniline.
Preliminary evaluation of a sound system employing a cancelling auralizer for producing virtual rooms in a modern recording studio

We investigate the operation of a virtual acoustic system (VAT) in the Immersive Media Laboratory (IMLAB) at McGill University, and the benefits offered by a real-time cancelling auralizer currently under development at CCRMA of Stanford University and the University of Limerick. The feedback-cancelling system reduces the likelihood of feedback and howling in live real-time auralizations of acoustic spaces through in-room microphone capture and loudspeaker reproduction. Amplified microphone signals are sent to a feedback cancellation network that convolves them with a variety of captured or synthesized multichannel impulse responses, and reproduces them via loudspeakers. One of the research purposes of VAT is to investigate the degree of similarity that can be achieved between the acoustics of a real space and its recreated virtual version. We present a preliminary analysis focused on measuring the main acoustical parameters according to ISO 3382-1 created under different virtual acoustical conditions. Ongoing research examines the interactions of professional musicians with the current state of the system, which will be continuously investigated in the near future. The work is carried out in collaboration with the authors of the feedback-cancelling technology.
Phase Identification in Power Distribution Systems via Feature Engineering

Phase identification is the problem of determining the phase connection of loads in a power distribution system. In modern times, utility operators will generally accomplish this using smart meter data that requires some form of feature engineering to achieve practical phase identification using data-driven methods. Feature engineering is essential for voltage magnitude data containing noise, seasonality, and trend. We present crucial components of a feature engineering pipeline to perform linear denoising with Singular Value Decomposition, filtering of the denoised data to remove the seasonality and trend, and fuse multiple meter channels. We use the results of the feature engineering to perform phase label correction, a subproblem of phase identification. To evaluate techniques, the authors generate a synthetic dataset from the meshed IEEE 342-Node test feeder circuit with the 2021 Electric Reliability Council of Texas load profiles. Our results show that denoising is quite effective for improving phase identification accuracy in the presence of measurement noise. We present new insight into filtering voltage measurement data to improve accuracy and eliminate the need to determine salient frequencies. We also present the application of a data channel fusion technique that is novel to the phase identification literature. This technique enhances phase identification in cases where both wye and delta-connected loads are present.
Imbalanced learning of remotely sensed data for bioenergy source identification in a forest in the Wallacea region of Indonesia

ABSTRACT Remote sensing technologies have been increasingly crucial to support policy-makers in achieving their ecological strategies. The data provided by such technology can estimate the bioenergy source production rate and monitor deforestation. This work participates in the cause by contributing an aerial dataset and developing an intelligent tree-detection system usable for counting trees with the bioenergy potential. Low-altitude flying units have been vastly used for such a purpose due to their ability to capture high-quality data from distant locations. Despite these potentials, collected images that compose a dataset are often characterized by imbalanced distribution among classes. The class disproportion can affect the overall model performance, as it severely deprives key features of under-represented classes. This study proposes data-level approaches that adopt and extend prior sampling algorithms for object detection problems. The devised techniques try to reduce the number of redundant outputs obtained from sampling methods and reduce the iteration required to achieve the target imbalance ratio by employing a systematic flow. In such a process, the class distribution of an original dataset is used as a guideline for selecting candidates for subsequent processes. Our results show that the modified dataset can reduce the length of a training process shown by fewer iterations required to achieve the final metrics of its original dataset version and lower training losses in each iteration. Additionally, the modified dataset can improve the F-score ( ) and precision metric of object detection algorithm by up to 6%.
Facilitating spatial thinking in world geography using Web-based GIS

Abstract Advocates for geographic information system (GIS) education contend that learning about GIS promotes students’ spatial thinking. Empirical studies are still needed to elucidate the potential of GIS as an instructional tool to support spatial thinking in other geography courses. Using a non-equivalent control group research design, this study examines the effect of using Web-based GIS in a world geography course on the development of students’ spatial thinking abilities. The findings indicate that Web-based GIS activities implemented in this study significantly enhanced students’ spatial thinking skills.
Pattern and Frequency of Nosocomial Infections in the Pediatric Intensive Care Unit at East Jeddah General Hospital, Saudi Arabia

Background Because of the use of invasive devices and procedures in critically sick patients, patients in the pediatric intensive care unit (PICU) are particularly vulnerable to nosocomial infections. Although a significant illness may necessitate admission to the PICU, infections can also emerge after admission. Nosocomial infection is a major public health issue related to increased morbidity, death, and healthcare costs. This study aimed to determine the pattern, frequency, and outcomes of nosocomial infections among children who were admitted to the PICU. Methodology This retrospective, cross-sectional study was conducted in the pediatric population aged from one month to 14 years old who acquired infections after 48 hours of admission to the PICU at East Jeddah General Hospital, Saudi Arabia from 2021 to 2022. The data were collected from medical and laboratory records. Results A total of 51 patients developed 145 nosocomial infections. Central line-associated bloodstream infections (CLABSIs) were the most commonly reported type of nosocomial infections (28.3%). The majority of the isolated organisms (58.7%) were gram-negative, followed by fungal infections (35.1%) and gram-positive organisms (6.2%). The death rate for patients with nosocomial infections was 29.4%. Increased death rates among individuals with CLABSIs and gram-negative isolates were observed to be significantly correlated (p = 0.001). Conclusions Our findings suggest that regular surveillance systems were necessary to assess the relationship between these well-known risk variables with PICU, implying that preventing these infections through particular treatments could be cost-effective and contribute to the safety of healthcare systems.
Natural Polymer-Based Hydrogels: From Polymer to Biomedical Applications

Hydrogels prepared from natural polymer have attracted extensive attention in biomedical fields such as drug delivery, wound healing, and regenerative medicine due to their good biocompatibility, degradability, and flexibility. This review outlines the commonly used natural polymer in hydrogel preparation, including cellulose, chitosan, collagen/gelatin, alginate, hyaluronic acid, starch, guar gum, agarose, and dextran. The polymeric structure and process/synthesis of natural polymers are illustrated, and natural polymer-based hydrogels including the hydrogel formation and properties are elaborated. Subsequently, the biomedical applications of hydrogels based on natural polymer in drug delivery, tissue regeneration, wound healing, and other biomedical fields are summarized. Finally, the future perspectives of natural polymers and hydrogels based on them are discussed. For natural polymers, novel technologies such as enzymatic and biological methods have been developed to improve their structural properties, and the development of new natural-based polymers or natural polymer derivatives with high performance is still very important and challenging. For natural polymer-based hydrogels, novel hydrogel materials, like double-network hydrogel, multifunctional composite hydrogels, and hydrogel microrobots have been designed to meet the advanced requirements in biomedical applications, and new strategies such as dual-cross-linking, microfluidic chip, micropatterning, and 3D/4D bioprinting have been explored to fabricate advanced hydrogel materials with designed properties for biomedical applications. Overall, natural polymeric hydrogels have attracted increasing interest in biomedical applications, and the development of novel natural polymer-based materials and new strategies/methods for hydrogel fabrication are highly desirable and still challenging.
Modelling Reoccurrence of Events in an Event-Based Graph Database for Asset Management

Asset maintenance is a crucial stage in the asset life cycle to ensure that assets can operate with maximum efficiency. Managing historical information on asset maintenance is critical to predict asset condition and performance. Furthermore, any asset may undergo a maintenance event multiple times, necessitating a proper method to model the reoccurrence of an event. Event-based databases centred on graph database is a suitable approach to model assets with their maintenance history, as they are built based on property graphs, allowing flexible storing of multiple attributes. This paper proposed three temporal graph data models to address the problem of storing and modelling multiple interval timestamps for multiple events to support the reoccurrence of maintenance events in an asset management scenario. All data models are described by different relationships and are built on multigraphs grounded on graph theory to support many-to-many (m:n) relationships. Evaluation is carried out based on synthetic datasets to evaluate both approaches based on two objectives, to evaluate the query performance of both approaches, and to assess the ability to query the interval timestamp. We found that the second data model is efficient in querying small amounts of data; however, the third data model is more efficient when querying large amounts of data, a characteristic of big data. The time complexity of the query on both the second and third data models based on differing amounts of data are quadratic times O(n2), corresponding to the nested iteration in the query algorithm. Operations based on graph traversals on our second and third data models enable the query of interval timestamps of the events. The proposed data models provide the foundation for modelling multiple related entities for any event-based data management based on graph databases and are not limited to asset management scenarios.
Traffic Scheduling in Non-Stationary Multipath Non-Terrestrial Networks: A Reinforcement Learning Approach

In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.
Reinforcement Learning Based NSGA-II for Energy-Delay Trade-Off in IAB mmWave Het-Nets

This paper proposes efficient ways for constructing the energy-delay Pareto front in cache-enabled integrated access and backhauling (IAB) heterogeneous wireless network. More specifically, an improved non-dominated sorting genetic algorithm II (NSGA-II) is proposed which is coupled with the operator parameter control ability based on reinforcement learning, to solve the energy-delay trade-off in the multi-objective optimization problem. To estimate the effectiveness of the proposed scheme, key performance indicators that cover the convergence and distribution of the Pareto front solution set are conducted and analyzed. A wide set of numerical investigations show that the proposed algorithm can provide a more evenly distributed result than the state of the art techniques with a 15% gain compared to the nominal case which is the weighted-sum method. Furthermore, and maybe more importantly, the undesirable large gaps between solutions in the Pareto front which are caused by the weighting coefficient choices are avoided. By enabling the operator parameter control ability, the exploration and exploitation process of the proposed algorithm can be balanced, which prevents the frequently faced problems of early convergence and being trapped at a local optimum in the genetic algorithm. The proposed technique can have significant implications in improving the avoidable choices regarding the network operation, and compared with the traditional NSGA-II, the proposed algorithm can provide a near-optimal solution set with 20% more diversity.
Advancing Fault-Tolerant Learning-Oriented Control for Unmanned Aerial Systems

The rapid advancement of automatic control technology has sparked significant interest among researchers in creating more reliable and simplified models of unmanned aerial vehicles (UAVs). This interest is motivated by the need to enhance the performance and resilience of these systems in challenging conditions, such as wind gusts and adverse weather. This paper presents novel strategies for enhancing the resilience of unmanned aerial systems (UAS) with fault-tolerant control (FTC) by learning-oriented control and a constructive fault estimation with Proportional-Integral (PI) observer. The learning-control is deep-deterministic policy gradient (DDPG) which is trained in only one state but used beyond its environment for other states to control. The faults are designed in three divergent conditions and the augmented PI observer is responsible in capturing them. The success of estimating the faults is used for this FTC to compensate the faulty system with learning-oriented control as the advancement of the FTC. The proposed approach has the potential to enhance the performance and resilience of UAVs, thus contributing to the development of more robust and reliable systems.
BERT-BiLSTM Architecture to Modelling Depression Recognition for Indonesian Text from English Social Media

Depression is a common mental health disorder. It can greatly affect our daily lives. Depressed people are generally prone to negative emotions. Hence, recognising a sign of depression is an important task. Several techniques are proposed to model automatic depression recognition from several modalities. However, there is a limited number of datasets and research done in a local language (i.e. Indonesian). Expressing thoughts and feelings are unique based on their backgrounds (e.g. race, religion and culture). Hence, fine-tuning the model to a local language or culture is also important. This research aims to build a model using deep learning to recognise depression signs from the text in the local language (i.e. Indonesian). Seven models are proposed in this research to model depression recognition from social media. The result illustrates that combining Bidirectional Long short-term memory with Bidirectional Encoder Representations from Transformers architecture can improve the performance of the model.
Hybrid Beam Focusing for MIMO OAM Communications

For millimeter or Tera-Hertz communications, channel capacity is often found limited by its channel diversity due to the sparse or line-of-sight transmission conditions in these frequency bands. This diversity problem exacerbates particularly in future non-terrestrial communications. Wireless communication that exploits the diversity of orbital angular momentum (OAM) in electro-magnetic waves is considered one of the potential technologies to push the limit despite the skepticism for its long-rang transmissions in free space. Inspired by the recent advances on hybrid beamforming, we propose a novel OAM beam focusing method to explore the feasibility of multiple input multiple output (MIMO) OAM wireless transmissions. With the proposed optimal beam focusing and power allocation scheme and the use of a concentric uniform circular array (CUCA) of diameter 1.2 meters (m), we show that signals of up to three OAM modes can be delivered to a receive uniform circular array of diameter 20 cm at a distance of 500 m, which presents the potential of MIMO OAM for future 6th generation communication systems.