Miracle-dz/UniFAR-FTUs huggingface dataset:
Revolutionizing Agriculture: Machine and Deep Learning Solutions for Enhanced Crop Quality and Weed Control

Agricultural systems are being revolutionized due to emerging technologies that aim to make improvements in the traditional agriculture system. The major goal is not just to enhance agricultural output per hectare but also to enhance crop quality while protecting the natural environment. Weeds pose a significant threat to crops as they consume nutrients, water, and light, thereby reducing crop productivity. Spraying the entire field uniformly to control weeds not only incurs high costs but also has adverse environmental effects. To address the limitations of conventional weed control methods, in this research, Machine Learning (ML) and Deep Learning (DL) based techniques are proposed to identify and categorize weeds in crops. For ML-based techniques, several statistical and texture-based features are extracted, including central image and Hu moments, mean absolute deviation, Shannon entropy, gray level co-occurrence matrix (GLCM) and local binary patterns (LBP), contrast, energy, homogeneity, dissimilarity, correlation, and summarized local binary pattern histogram. YOLOv8m is employed to identify weeds and for weed classification, features extracted from two standard benchmark datasets, CottonWeedID15 and Earlycrop-weed are fed to Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN) while employing Synthetic Minority Oversampling Technique (SMOTE) to balance the classes. In addition to ML-based techniques, Deep learners such as VGG16, VGG19, Xception, DenseNet121, DenseNet169, DenseNet201, and ConvNeXtBase are trained on raw data with balanced classes for automated feature extraction and classification. Among the ML-based techniques, SVM with a polynomial kernel achieves 99.5% accuracy on the early crop weed dataset, and Artificial neural network attains 89% accuracy on the Cottonweedid15 dataset. Meanwhile, the combined employment of ConvNeXt and Random Forest results in the highest accuracy among DLs, specifically 98% on the early crop weed dataset and 90% on the Cottonweedid15 dataset. The high accuracy achieved underscores the practical viability of these methods, offering a sustainable and cost-effective solution for modern agriculture.
On the magnitude and frequency of sediment transport in rivers

ON THE MAGNITUDE AND FREQUENCY OF SEDIMENT TRANSPORT IN RIVERS What flow or range of flows is most responsible for transporting sediment and maintaining sediment continuity in a river over human time scales? This question has inspired scores of studies analyzing the magnitude and frequency of sediment transport (MFA) in rivers and has been a part of the ongoing debate regarding process vs. formbased approaches to stable channel design. MFA in rivers is of general scientific and management interest as it influences channel form, water quality, aquatic habitat, and channel restoration design considerations. The research presented in this dissertation asks the following overarching question: What influences how much and how often sediment is transported in a river? In this dissertation, I consider relationships between the drivers of sediment transport at a point in a river (flow regime, sediment size, and channel form) and metrics describing sediment yield, which integrate the relationship between flow regime and transport over time. To study this question, I use theoretical and empirical approaches in a spectrum of stream types from fine bed streams dominated by suspended load transport to gravel and cobble streams dominated by bed load transport. I find that, for example, the
1 Introduction: Nascent Tourism in Victoria, Australia – Insights Into the Evolution of Its Tourism Landscape by Ian D. Clark

This work is concerned with the emergence of tourism in colonial Victoria, Australia, and is part of ongoing research into understanding Victoria’s ‘tourism era of discovery’ (Towner, 1996: 140). It is concerned with the processes of ‘opening up’ new attractions and its focus is the discovery state of the development of tourism or what Young (1983) has termed ‘pretourism’. Victoria’s tourism era of discovery, here defined as ‘nascent tourism’ or ‘pretourism’, is a period that has generally been neglected in tourism histories in Australia, notwithstanding the recent works of Bonyhady (2000), Horne (2005), and Inglis (2007). Nascent tourism, defined as the embryonic or emergent phase in which natural attractions are coming into being as the subject of tourist visitation, will be contextualized in the study of eight tourism sites that will be the primary focus of this work. Travellers’ accounts and other contemporary sources will be used to provide us with insights into Victoria’s nascent tourism – through them; we should be able to see the various places that were emerging as tourist sites in the colonial space. The sources are interrogated as journals or narratives that offer a biography of the journey in ways similar to Carter’s (1988) and Ryan’s (1996) interrogations of the journals of Australian explorers. These accounts enable observations of tourism and travel phenomena to be contrasted and allow geographical and temporal controls to be applied. Accounts from the 1830s and 1840s, for example, capture the nascent state of hospitality and travel as it was centred around squatting stations; the 1850s and 1860s show the evolution of an accommodation industry away from Melbourne and the improvement of transport infrastructure contrasted with the chaos caused by the gold rushes and the emergence of fledgling townships such as Ararat and Ballarat.
FATE OF SILTS AND CLAY FROM RIVER AND ITS CONTRIBUTION TO TRANSPARENCY

Phytoplankton plays a key role as primary producer, forming the base of marine food webs. Knowledge in relation to permeability of light in water is important for the understanding of phytoplankton growth in the euphotic zone. In this study, we conducted laboratory experiments in relation to light attenuation using inorganic particle (silica particle) and field investigations in Osaka Bay. There was a positive correlation between the concentrations of the silica particle and integral values of the absorbance at photosynthetic active radiation (PAR: wavelength 400-700 nm) in the laboratory experiments. The highest integral value of the absorbance at PAR was observed for the particle size of 1.0 μm. In Osaka Bay, high contribution of the inorganic particle to light attenuation was observed compared to the organic particles. Multiple linear regression analysis using the particle size and the amount of total suspended solids (TSS: consisting of three component fractions; organic/inorganic tripton and phytoplanktons) showed that the particle size was an essential factor controlling the light attenuation in the coastal sea.
Multicriteria assessment of water dynamics reveals subcatchment variability in a seemingly homogeneous tropical cloud forest catchment

To improve the current knowledge of the rainfall–runoff phenomena of tropical montane catchments, we explored the usefulness of several hydrological indicators on a nested cloud forest catchment (76.9 km2). The used metrics belong to 5 categories: baseflow mean transit time, physicochemical properties of stream water, land cover, topographic, and hydrometric parameters. We applied diverse statistical techniques for data analysis and to contrast findings. Multiple regression analysis showed that mean transit times of base flow could be efficiently predicted by sodium concentrations (higher during baseflows) and temperatures of stream water, indicating a major influence of geomorphology rather than topographic or land cover characteristics. Principal component analysis revealed that no specific subset of catchment indicators could be identified as prevailing descriptors for all catchments. The agglomerative hierarchical clustering analysis provided concomitant results, implying larger levels of dissimilarity between smaller subcatchments than between larger ones. Overall, results point out an intricate interdependence of diverse processes at surface and subsurface level indicating a high level of heterogeneity. Disregarding heterogeneity of nested or paired catchments could lead to incomplete or misleading conclusions, especially in tropical mountain regions where pronounced spatial and temporal gradients are present.
Walnut By-Products and Elderberry Extracts—Sustainable Alternatives for Human and Plant Health

A current alternative for sustainable development through green chemistry is the replacement of synthetic compounds with natural ones through the superior capitalization of natural resources, with numerous applications in different fields. The benefits of walnuts (Juglans regia L.) and elderberries (Sambucus nigra L.) have been known since ancient times, due to the presence of phytochemicals such as flavonoids, polyphenols, carotenoids, alkaloids, nitrogen-containing compounds, tannins, steroids, anthocyanins, etc. These active compounds have multiple biological activities for human health, including benefits that are antibacterial, antioxidant, anti-inflammatory, antidiabetic, hepatoprotective, antihypertensive, neuroprotective, etc. Like other medicinal plants, the walnut and the elderberry possess important phytosanitary properties (antibacterial, antifungal, and insecticidal) and their extracts can also be used as environmentally safe biopesticides, with the result that they constitute a viable and cheap alternative to environmentally harmful synthetic products. During recent years, walnut by-products and elderberries have attracted the attention of researchers, and investigations have focused on the species’ valuable constituents and active properties. Comparing the information from the literature regarding the phytochemical profile and biological activities, it is highlighted that, apart from the predominant specific compounds, the walnut and the elderberry have common bioactive compounds, which come from six classes (phenols and derivatives, flavonoids, hydroxycinnamic acids, tannins, triterpenoids, and phytosteroids), and act on the same microorganisms. From this perspective, the aim of this review is to provide an overview of the bioactive compounds present in the different constitutive parts of walnut by-products and elderberries, which present a specific or common activity related to human health and the protection of agricultural crops in the context of sustainable development.
Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines

Synchronization of different transit lines is an important activity to increase the level of service in transportation systems. In particular, for passengers, transferring from one line to another, there may be low-frequency periods and transfer zones where walking is needed, or passengers are exposed to adverse weather conditions and uncomfortable infrastructure. In this study, we define the Bus Lines Synchronization Problem that determines the frequency for each line (regarding the even headway), the timetable (including holding times for buses at transfer stops), and passenger-route assignments to minimize the sum of passenger and operational costs. We propose a nonlinear mixed integer formulation with time-indexed variables which allow representing the route choice for passengers and different types of costs. We implement an iterative heuristic algorithm based on fixing variables and solving a simplified formulation with a commercial solver. We implement our proposed heuristic on the transit network in Santiago, Chile. Numerical results indicate that our approach is capable of reducing operating costs and increasing the level of service for large scenarios.
Innovative teaching strategies: A principal component analysis

The paper investigated the degree of innovative teaching practices by secondary school teachers in the Vhembe District, South Africa, using a principal component analysis approach. Innovative teaching strategies refer to creative and efficient actions and performances that teachers use in the selection of materials, techniques, and learner assessments to encourage and develop learner creativity (Yu et al., 2021). Innovative teachers are those whose teaching methods differ from the conventional patterns of knowledge-transfer teaching and teacher-centred teaching. They solve educational problems in novel ways using new technologies and theoretical frameworks while guiding learners in deep learning and creativity (Tan et al., 2022). The objective of the study was to examine the innovative teaching strategies implemented by teachers in rural secondary schools to improve learner performance. A descriptive case study was employed in the paper, which was based on a quantitative research design. Using probability sampling approaches, 338 secondary school teachers were sampled. Data was collected through questionnaires and was analysed using descriptive statistical methods. The variables were reduced from 30 to 9 clusters using principal component analysis with varimax rotation. Clusters were termed stakeholders’ engagement, school and classroom practices, learner support, collaboration, professionalism, technology, relationships, diversity, and expertise.
Impact of Organisational Identification and Moral Identity Centrality on Organisational Engagement of Millennial MBA Students in Sri Lanka: A Social Identity Perspective

Organisational engagement has only recently gained traction in research literature despite engagement being considered as a multidimensional construct encompassing engagement with the work, job or the organisation. Drawing from social identity theory, this research studies the impact of organisational identification and moral identity centrality on organisational engagement of Millennials. The survey was conducted involving 285 Millennial Master of Business Administration (MBA) students in two of the leading universities in Sri Lanka using a structured questionnaire consisting of measures adopted from literature. The data was analysed using structural equation modelling (SEM) technique. The results suggest that organisational identification as well as moral identity centrality have a positive impact on the organisational engagement of Millennials. The results provide valuable insights into the impact of organisational identification and moral identity centrality on organisational engagement and address some of the gaps in understanding social identity as the context of work behaviours.
Efficient Human Violence Recognition for Surveillance in Real Time

Human violence recognition is an area of great interest in the scientific community due to its broad spectrum of applications, especially in video surveillance systems, because detecting violence in real time can prevent criminal acts and save lives. The majority of existing proposals and studies focus on result precision, neglecting efficiency and practical implementations. Thus, in this work, we propose a model that is effective and efficient in recognizing human violence in real time. The proposed model consists of three modules: the Spatial Motion Extractor (SME) module, which extracts regions of interest from a frame; the Short Temporal Extractor (STE) module, which extracts temporal characteristics of rapid movements; and the Global Temporal Extractor (GTE) module, which is responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated for its efficiency, effectiveness, and ability to operate in real time. The results obtained on the Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to various alternatives. In addition, the VioPeru dataset was created, which contains violent and non-violent videos captured by real video surveillance cameras in Peru, to validate the real-time applicability of the model. When tested on this dataset, the effectiveness of our model was superior to the best existing models.
Can conference participation lead to changes in clinical and research practice in stroke care? A survey of stroke conference attendees

Background. Conferences are a widespread method of research dissemination; however, their impact on practice is not well understood empirically. We aimed to investigate how conference participation influences clinical and research practice in the stroke field and to explore which presentation formats are most impactful. Methods. Clinicians, researchers and doctoral students who attended a multidisciplinary stroke conference were invited to participate. Surveys were administered at the conference and 3 months later. Both surveys contained questions about how respondents remember and apply information learnt at conferences in their clinical and/or research practice. Results. Baseline survey responses from 120 conference participants were analysed (80.8% female, 69.0% clinicians, 60.8% aged 31–50 years), and 53 participants completed the follow-up survey. Of the 87 clinician respondents, 73 (83.9%) reported that their clinical practice had changed as a result of attending conferences. Workshops incorporating skill demonstrations were rated most useful for changing clinical practice, whereas oral presentations detailing methodology were more influential on research practice. Attending conferences was rated more impactful on clinical practice than reading journal articles or textbooks. Conclusion. Conference presentations can be a useful method for disseminating research findings to influence clinical practice and should be considered by researchers to maximise the translation and impact of their work.
A Study on Deep Learning Methods to Identify the Infected Regions from Papaya Fruit Images

Fruits account for a significant share of world production across many countries. Many countries desire to maximize fruit production, however, the quality and quantity of the fruits are affected by several diseases that occur during pre- harvesting and post-harvesting of the fruits. To increase the production of fruit with high quality, several types of research have been developed for early detection of fruit diseases with Deep Learning (DL), Machine Learning (ML), and Transfer Learning (TL) techniques from fruit images. However, there exist several limitations along with the advantages of those techniques. A comprehensive review of the existing ML and DL approaches for the classification of fruit disease at the early stage is presented in this study. This research primarily focuses on discussing various fruit diseases and presents an effective way to considerably detect the diseases from papaya fruit images. Various ML and DL models to detect diseases in plants and fruits are discussed in this research. A deep survey has been conducted on conventional methodologies, followed by their purpose, advantages, and limitations. Based on the findings, a suitable framework for future studies to identify disease-infected regions in images of fruits and plants is suggested.
Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Albinism is characterized by a variable degree of hypopigmentation affecting the skin and the hair, and causing ophthalmologic abnormalities. Its oculocutaneous, ocular and syndromic forms follow an autosomal or X-linked recessive mode of inheritance, and 22 disease-causing genes are implicated in their development. Our aim was to clarify the genetic background of a Hungarian albinism cohort. Using a 22-gene albinism panel, the genetic background of 11 of the 17 Hungarian patients was elucidated. In patients with unidentified genetic backgrounds (n = 6), whole exome sequencing was performed. Our investigations revealed a novel, previously unreported rare variant (N687S) of the two-pore channel two gene (TPCN2). The N687S variant of the encoded TPC2 protein is carried by a 15-year-old Hungarian male albinism patient and his clinically unaffected mother. Our segregational analysis and in vitro functional experiments suggest that the detected novel rare TPCN2 variant alone is not a disease-causing variant in albinism. Deep genetic analyses of the family revealed that the patient also carries a phenotype-modifying R305W variant of the OCA2 protein, and he is the only family member harboring this genotype. Our results raise the possibility that this digenic combination might contribute to the observed differences between the patient and the mother, and found the genetic background of the disease in his case.
Surface Wave Inversion Using a Multi-Information Fusion Neural Network

In noninvasive near-surface investigations, with the emergence of massive seismic datasets, surface wave inversion using deep learning (DL) can efficiently attain the shear-wave velocity (Vs) model. Existing researches on DL inversion, however, cannot handle the inversion nonuniqueness effectively. The geological constraint is only reflected in their training dataset. The input of their neural networks only contains dispersion curves (DCs), and thus, the density and compressional-wave velocity (Vp) need self-learning. To decrease the nonuniqueness, we propose a multiinformation fusion neural network (MFNN) in which we add the Vp, density, and sensitivity as parts of the input. To verify the effectiveness of the MFNN, we used a synthetic test and fieldwork, which both contain data from six regions to conduct multiregion simultaneous inversions. We compared the inversion results of the MFNN with the results of a convolutional neural network (CNN) and the ground truth. In the two experiments, although the calculated DCs based on the inverted Vs model from all the methods match well with the observed ones, only the inverted Vs from the MFNN successfully reflect the actual Vs structure and locate the high-velocity layer and low-velocity layer accurately. The constraints on the Vp, density, and sensitivity thus effectively reduce the nonuniqueness of inversions.
PieRank: Embedded Large-Scale Sparse Matrix Processing

I introduce PieRank, a library aimed at embedded large-scale sparse matrix processing. It enjoys significant advantages over previous state-of-the-art for handling big, sparse data sets in scalability, speed, and usability. My approach is also more general, allowing a wider variety of applications beyond graphs. Inspired by Raspberry Pi(e) for its low cost, I name the library PieRank with the goal of dramatically reducing the hardware requirements for cutting-edge sparse matrix processing. My experiments show PieRank outperforms GraphChi by a wide margin, making it possible for a single embedded device to meet or exceed the scalability and performance of server-class computers on big matrices such as AGATHA 2015, a deep-learning network with 5. 8B parameters as the largest instance in the popular SuiteSparse Matrix Collection. Available as an open-source project on GitHub under the permissive MIT license, PieRank compiles and runs on Linux, Mac OS, and Raspberry Pi OS.