Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning and Ensemble Techniques for Water Quality Analysis of Korattur Lake

Water quality analysis is a critical component of environmental monitoring and management, as it helps ensure the safety and sustainability of water resources. Traditional methods of water quality analysis can be time-consuming and labour-intensive, and may not always provide accurate or timely results. As such, there is growing interest in applying machine learning (ML) and deep learning (DL) models to water quality analysis, to improve the efficiency and accuracy of this important task [1]. In this research paper, we explore the use of various ML models and ensemble techniques for water quality analysis, with the aim of identifying the most effective and efficient methods for this application. We compare and contrast the performance of different models, also evaluate the impact of different data pre-processing techniques, feature selection methods, and model hyperparameters on model performance. Our results demonstrate the potential of ML models and ensemble techniques for water quality analysis, with many models achieving high levels of accuracy and efficiency in predicting various water quality parameters. The samples were taken from Chennai’s Korattur Lake and examined for the required hydro-chemical characteristics. Various machine learning models such as XG Boost, K-Nearest Neighbors and various ensemble methods like Bagging with Decision Trees as the base estimator, Bagging using SVC(Support Vector Classifier) and Boosting using AdaBoost with Logistic Regression as base estimator for multi-class classification in this study. The models were evaluated using accuracy, precision, and execution time as metrics. The purpose was to compare and analyse the performance of the models. The results revealed that XG Boost and Bagging using Decision Tree models achieved the highest accuracy of approximately 100% and was also the most efficient in terms of execution time compared to the other deep learning models.
Implementing Signatures for Transactional Memory

Transactional Memory (TM) systems must track the read and write sets - items read and written during a transaction - to detect conflicts among concurrent transactions. Several TMs use signatures, which summarize unbounded read/write sets in bounded hardware at a performance cost of false positives (conflicts detected when none exists). This paper examines different organizations to achieve hardware-efficient and accurate TM signatures. First, we find that implementing each signature with a single k-hash- function Bloom filter (True Bloom signature) is inefficient, as it requires multi-ported SRAMs. Instead, we advocate using k single-hash-function Bloom filters in parallel (Parallel Bloom signature), using area-efficient single-ported SRAMs. Our formal analysis shows that both organizations perform equally well in theory and our simulation- based evaluation shows this to hold approximately in practice. We also show that by choosing high-quality hash functions we can achieve signature designs noticeably more accurate than the previously proposed implementations. Finally, we adapt Pagh and Rodler's cuckoo hashing to implement Cuckoo-Bloom signatures. While this representation does not support set intersection, it mitigates false positives for the common case of small read/write sets and performs like a Bloom filter for large sets.
Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments

The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge–cloud deployment. A new edge–cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge–cloud deployment.
Classification of Heart Disease: Comparative Analysis using KNN, Random Forest, Gaussian Naive Bayes, XGBoost, SVM, Decision Tree, and Logistic Regression

The heart is the center of the human body, and when it is not functioning well, the body cannot operate effectively. This research aims to compare some of the classifier models to find the best model for classifying heart disease by its symptoms for early detection, so users will be able to know whether their symptoms belong to a type of heart disease that requires them to undergo a check-up or not to carry out early heart disease detection. The classifier models used in this research are K-NN, Random Forest, Naive Bayes, XGBoost, SVM, Decision Tree, and Logistic Regression algorithms. As for the results, the Logistic Regression model shows the best result with its metrics on average being 92.61%, and the second best result was achieved by Gaussian Naive Bayes with its metrics on average being 90.78%, while the other models results were below 90%. So, this research shows that Logistic Regression is the best model to classify heart disease for early detection because of its higher results than other classifier models.
Cooperative management of an initial training program: case study of a Czech production site of a Japanese globalized manufacturing firm

PurposeThis study examines the case of an initial training, called “Dojo”, invented and implemented at a production site in the Czech Republic. It clarifies the initial training program implementation process and offers a conceptual framework for cooperative management of subsidiary activities at the site and firm.Design/methodology/approachThis study conducts an in-depth analysis of qualitative data from the Czech production site over a five-year period. The theoretical base is the theorization and labeling phase of management innovation (MI), the final phase of which legitimizes a new management practice. Interview data, archival data, pictures and financial data are used for the analysis.FindingsTo legitimize the Dojo in the operational flow controlled by the site and firm, the Czech production site acquires validation of the Dojo from employees and board members of the Japanese and European headquarters, helping the site build trustful relationships with them. Training programs, process standardization and skills standardization of the workers offer benefits to the trainees, production site and firm.Originality/valueThe authors offer theoretical insights into MI at the subsidiary-level, which past studies have not differentiated at the firm-level. The authors also provide details of the implementation and management of initial training for newly hired blue-collar workers at the production site. The findings complement related literature on human resource management and operational management.
Revisiting Child Labour in the Artisanal and Small-Scale Gold Mines of Ghana; Assessing Local Perceptions and Attitudes towards Child Labour and Education in Nangodi, Ghana

Several studies have looked into the issue of child labour especially in the artisanal and small-scale mining (ASM) sector in Ghana. However, a key issue that such studies have failed to cover relates to local perceptions and attitudes towards child labour in ASM. This paper fills that gap in knowledge. Using a case study approach, the paper assesses local perceptions and attitudes towards child labour and education in Nangodi. It goes further to highlight how children had their education affected as a result of working at mining pits. In light of the findings presented, viable recommendations are offered in order to reduce, if not curb the practice of child labour in ASM.
The Effect of Taxes on CEO Performance

In this paper we investigate the effect of higher personal income taxes on CEO and firm performance in publicly traded US firms. In response to higher taxes on compensation, CEOs are less likely to reach performance goals and spend more time working in boards outside of their firm. At the same time, firm performance drops before eventually recovering as investment projects with below average profitability are disregarded and due to adjustments in CEO compensation
Comment Usefulness Classification on Youtube using Artificial Neural Networks

Social media represents a vast and constantly evolving data resource, utilized across various domains, including business. However, discerning valuable data for business purposes demands significant analysis and labor, leading to potential errors. To address this, we propose employing deep learning models for classifying useful data, focusing on a case study of quality comments on YouTube in the Thai language. Specifically, we experiment with four sequence-to-sequence models: Recurrent Neural (RNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). The experimental results reveal that the Bi-LSTM model exhibits the most promising performance, achieving an accuracy of 94%. Furthermore, Bi-LSTM demonstrates remarkable precision of 0.94, recall of 0.95, and an F1-score of 0.95, underscoring its proficiency in the precise classification of quality comments.
Information content of visible and midinfrared radiances for retrieving tropical ice cloud properties

Hyperspectral instruments such as Atmospheric Infrared Sounder (AIRS) have spectrally dense observations effective for ice cloud retrievals. However, due to the large number of channels, only a small subset is typically used. It is crucial that this subset of channels be chosen to contain the maximum possible information about the retrieved variables. This study describes an information content analysis designed to select optimal channels for ice cloud retrievals. To account for variations in ice cloud properties, we perform channel selection over an ensemble of cloud regimes, extracted with a clustering algorithm, from a multiyear database at a tropical Atmospheric Radiation Measurement site. Multiple satellite viewing angles over land and ocean surfaces are considered to simulate the variations in observation scenarios. The results suggest that AIRS channels near wavelengths of 14, 10.4, 4.2, and 3.8 μm contain the most information. With an eye toward developing a joint AIRS‐MODIS (Moderate Resolution Imaging Spectroradiometer) retrieval, the analysis is also applied to combined measurements from both instruments. While application of this method to MODIS yields results consistent with previous channel sensitivity studies, the analysis shows that this combination may yield substantial improvement in cloud retrievals. MODIS provides most information on optical thickness and particle size, aided by a better constraint on cloud vertical placement from AIRS. An alternate scenario where cloud top boundaries are supplied by the active sensors in the A‐train is also explored. The more robust cloud placement afforded by active sensors shifts the optimal channels toward the window region and shortwave infrared, further constraining optical thickness and particle size.
Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor

Obesity is a cause of comorbid diseases such as type 2 diabetes mellitus, dyslipidemia, hypertension which is based on low-level chronic inflammation. The GPR-120 receptor plays a role in insulin sensitization which is related to diabetes mellitus which is a comorbid obesity. Omega-3 fatty acids are believed to possess anti-inflammatory properties, hence potentially serving as a preventive measure against obesity-related comorbidities. The aim of this study is to do a stability analysis of the binding affinity between nine specific chemicals derived from omega-3 and the active site of the human GPR120 receptor using molecular dynamics simulations. Docking analysis was performed using Discovery Studio Visualizer, AutoDock Tools 1.5. 6, and molecular dynamic simulation with AMBER 16. In this study, we used neurotensin 8–13 as a natural ligand to bind with the neurotensin receptor. Based on the neurotensin receptor docking results, the ΔG values for the following compounds are close to the values for neurotensin 8–13 -6.41 kcal/mol; docosahexaenoic acid -8.96 kcal/mol; eicosapentaenoic acid -7.41 kcal/mol; and heneicosapentaenoic acid -6.34 kcal/mol. Neurotensin 8–13 forms hydrogen bonds with TYR146, ARG213, and PHE344 of the neurotensin receptor, whereas docosahexaenoic acid forms hydrogen bonds with TYR146. Meanwhile, the average RMSD fluctuations for each system, namely docosahexaenoic acid, eicosapentaenoic acid, and heneicosapentaenoic acid, were 0.672, 0.437, and 0.650, respectively. The SASA of the neurotensin receptor-ligand complex showed similar fluctuations, with the average values for docosahexaenoic acid, eicosapentaenoic acid, and heneicosapentaenoic acid being 230.40, 229.89, and 230.20 nm2.
Disruptive Effects of Two Curcuminoids (Demethoxycurcumin and Bisdemethoxycurcumin) on the Larval Development of Drosophila melanogaster

Simple Summary Several plant species have diterpene compounds with juvenile hormone (JH) disruptor activity in insects. Demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) are two curcuminoid components of turmeric, interfering with the JH-mediated formation of the JH receptor complex. In vitro, DMC and BDMC also inhibit larval and pupal development in Drosophila melanogaster. The results suggest that both DMC and BDMC are JH disruptors that affect insect growth and development by regulating JH-mediated gene expression. Abstract Juvenile hormones (JHs) play a central role in insect development, reproduction, and various physiological functions. Curcuminoids generally exhibit a wide range of biological activities, such as antioxidant, anti-inflammatory, antibacterial, and insecticidal, and they exhibit insect growth inhibitory effects. However, research on insecticidal properties of curcuminoids has been limited. Moreover, to the best of our knowledge, studies on JHs of insects and curcuminoids are lacking. Therefore, this study aimed to identify the substances that act as JH disruptors (JHDs) from edible plants. Demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC), two curcuminoids from the turmeric plant Curcuma longa L. inhibited the formation of a methoprene-tolerant (Met)–Taiman (Tai) heterodimer complex in Drosophila melanogaster, as shown through in vitro yeast two-hybrid assays. An artificial diet containing 1% (w/v) DMC or BDMC significantly reduced the number of D. melanogaster larvae in a concentration-dependent manner; larval development was disrupted, preventing the progression of larvae to pupal stages, resulting in an absence of adults. Building on the results obtained in this study on curcuminoids, researchers can use our study as a reference to develop eco-friendly pesticides.
SOIL DEGRADATION IN DRYLANDS

The paper attempts to highlight some of the issues of soil degradation. The paper has been divided into two. The first part analysed the issues of soil degradation were it shows that Soil degradation is widely recognized as a global problem associated with desertification in arid, semiarid and dry sub-humid zones, commonly called the ‘drylands’. Yet, land degradation is a contested topic in its determinants, degree, distribution and effects. While the second part analyses the use of GIS and remote sensing in the analysis, determination and quantification of land degradation which shows that is more accurate and economical than the traditional method. The use of GIS and remote sensing in analyzing erosion and salinity has been determined. It also indicated that several models were applied for the analysis especially the use of RUSLE model when analyzing soil erosion. The study concluded that most of the World degraded areas; Africa has fallen within the most highly vulnerable zone.
The Re-examination of the Dangers and Implications of Artificial Intelligence for the Future of Scholarship and Learning

Technology is rapidly developing, and integrating artificial intelligence (AI) into education has become a topic of great interest. While it promises to revolutionise how we learn and acquire knowledge, some significant downsides remain. From reducing human interaction to potentially losing jobs for educators, the impact of AI in education is far-reaching. In this article, we will explore the downsides of artificial intelligence in education and its effect on future generations. The study shows that the dangers inherent in integrating AI into scholarship and learning are multi-faceted. From the potential loss of human judgment and unintended consequences in education delivery to fostering dependency and narrowing research avenues, these risks emphasise the need for an informed and cautious approach. As the academic community embraces the benefits of AI, it must navigate these challenges to ensure that the core values of scholarship and learning remain intact and resilient.
Enhanced ultraviolet photo-detecting by constructing TiO2/KNbO3 heterojunction

The demands of the ultraviolet (UV) radiation monitoring is rising in fields of human health and survival surroundings. Among many types of UV detectors, the photoelectrochemical (PEC) type UV detectors based on solar sensitized cells have gained a lot attention because of their fast response, low cost, simple manufacturing process, self-powered and so on. The heterojunction can accelerate the separation and migration of photogenerated carriers and then improve the photoresponse of UV detectors. In this work, the nanostructure KNbO3 (KNO) films were modified by chemical bath deposition to form a TiO2/KNO heterojunction. Compared with the UV detectors based on pure KNO and TiO2, the responsivity of the developed PEC UV photodetectors based on TiO2/KNO reached 11.99 mA W−1 under UV illumination of 35 mW cm−2 @ 365 nm, which is higher than that of the pure KNO UV detector (1.26 mA W−1) and the TiO2 UV detector (3.96 mA W−1). Besides, the lifetime of photogenerated carrier, response time and recovery time of the detector are all promoted. The results can be attributed to the formation of a TiO2/KNO heterojunction, in which the generated built-in electric field can promote the separation of photogenerated carriers and hinder the recombination of photogenerated electron–hole pairs, thus improving the detection performance of the UV detectors.
REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5 METEOROLOGICAL FIELDS FOR VARIOUS AIR QUALITY MODELING APPLICATIONS

Prognostic meteorological models are often used in a retrospective mode to provide inputs to air quality models. In turn, these air quality models are used for environmental planning. The meteorological inputs govern the advection, diffusion, chemical transformation, and eventual deposition of pollutants within regional air quality models such as the Community Multi-scale Air Quality (CMAQ) modeling system (Byun and Schere, 2006). The air quality models have traditionally been subjected to a rigorous performance assessment, but in many cases the meteorological inputs to these models are accepted as is, even though this component of the modeling arguably contains equal uncertainty. Before initiating the air quality simulations, it is important to identify the biases and errors associated with the meteorological modeling inputs. The goal of the meteorological evaluation should be to move toward an understanding of how the bias and error of the meteorological input data may impact the resultant AQ modeling. Typically, there are two specific objectives. First, determine if the meteorological model output fields represent a reasonable approximation of the actual meteorology that occurred during the modeling period (i.e., the "operational" evaluation). The second goal should be to identify how the existing biases and errors in the meteorological predictions may affect the air quality modeling results (i.e., the "phenomenological" evaluation). Once these two sets of information are generated, it is important to highlight the parts of the analysis expected to most influence the air quality model.
Carbon-Based Nanomaterials Decorated Electrospun Nanofibers in Biosensors: A Review

Nanomaterials have revolutionized scientific research due to their exceptional physical and chemical capabilities. Carbon-based nanomaterials such as graphene and its derivates have excellent electrical, optical, thermal, physical, and chemical properties that have made them indispensable in several industries worldwide, including medicine, electronics, and energy. By incorporating carbon-based nanomaterials as nanofillers in electrospun nanofibers (ESNFs), smoother and highly conductive nanofibers can be achieved that possess a large surface area and porosity. This approach provides a superior alternative to traditional materials in the development of improved biosensors. Carbon-based ESNFs, among the most exciting new-generation materials, have many applications, including filtration, pharmaceuticals, biosensors, and membranes. The electrospinning technique is a highly efficient and cost-effective method for producing desired nanofibers compared to other methods. Various types of natural and synthetic organic polymers have been successfully utilized in solution electrospinning to produce nanofibers directly. To create diagnostics devices, various biomolecules like antibodies, enzymes, aptamers, ligands, and even cells can be bound to the surface of nanofibers. Electrospun nanofibers can serve as an immobilization matrix to create a biofunctional surface. Thus, biosensors with desired features can be produced in this way. This study comprehensively reviews biosensors that integrate nanodiamonds, fullerenes, carbon nanotubes, graphene oxide, and carbon dots into electrospun nanofibers.