Miracle-dz/UniFAR-FTUs huggingface dataset:
Weed-Crop Segmentation in Drone Images with a Novel Encoder-Decoder Framework Enhanced via Attention Modules

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.
Sentiment Classification of Movie Reviews Based on the Ensemble Machine Learning Model

Film reviews play a pivotal role in influencing audience decisions, necessitating accurate classification of sentiments as positive or negative, which holds significant importance for the film industry. To address this, the present study introduces an innovative ensemble learning approach that integrates artificial neural networks, LightGBM, and logistic regression models through a stacking technique. The ensemble model is empirically examined using the IMDB dataset, with a comparative analysis conducted against an individual Artificial Neural Network (ANN) model. The findings demonstrate remarkable enhancements, particularly in terms of accuracy and other relevant metrics, achieved by the ensemble model compared to the individual ANN model, specifically yielding an increase in accuracy from 0.8791 to 0.8904. This substantiates the substantial improvement in accuracy offered by the ensemble model, thereby underscoring the efficacy and potential of ensemble learning for sentiment classification in movie reviews. Moreover, an analysis of the confusion matrix reveals that the ensemble model predominantly improves the classification of reviews labeled as 'positive,' as evidenced by an increase in true positive instances from 4359 to 4451, accompanied by a decrease in false positive instances from 668 to 576. By amalgamating predictions from distinct models, the ensemble model effectively mitigates the limitations inherent to individual models and attains superior performance compared to relying on a single model alone.
On the distance spectrum of certain distance biregular graphs

In this article we present an inﬁnite family of bipartite distance biregular graphs having an arbitrarily large diameter and whose distance matrices have exactly four distinct eigenvalues. This result answers a question posed by F. Atik and P. Panigrahi in On the distance spectrum of distance regular graphs (Linear Algebra and its Applications, 478 (2015), pp. 256 − 273) about the existence of connected graphs with diameter d that are not distance regular, whose distance matrix has less than d + 1 distinct eigenvalues.
Unleashing Genetic Secrets: Unveiling Hidden Potential and Pioneering Frontiers in Medicine and Evolution

This essay explores the impact of genetic research in uncovering hidden potential within our genes and pioneering new frontiers in medicine and evolution. The discussion highlights the transformative nature of genetic discoveries, including personalized medicine and the use of gene editing technologies. It also explores the insights gained from studying human evolution and the interconnectedness of all life forms. Ethical considerations surrounding genetic research are addressed. The abstract provides a concise overview of the key points discussed in the essay.
Preliminary survey of the Taita Falcon Falco fasciinucha in the Drakensberg escarpment region of Mpumalanga and Limpopo Provinces, South Africa

The Taita Falcon Falco fasciinucha is a rare and poorly known species, with less than 40 known nest sites scattered across eastern sub-Saharan Africa. This project was initiated to determine the size and conservation value of a possible population of Taita Falcons in northern South Africa, centred on the Mpumalanga/Limpopo escarpment region. A team of expert observers was assembled in September 2006, and dispatched to known or likely Taita Falcon nest sites to search for breeding birds over a two-week survey period. Two new nest sites were located, in addition to two sites already known in the area. Three pairs of Peregrine Falcon, 12 pairs of Lanner Falcon, 23 pairs of Rock Kestrel, five pairs of Verreaux’s Eagle and five pairs of Jackal Buzzard were also located and mapped. Not all possible or likely Taita Falcon sites were checked in 2006 because of time and logistical constraints. A repeat survey in 2007 is proposed in order to complete the project, and a Pan-African workshop is suggested to discuss research priorities for this threatened species.
Simulation of spatially distributed sources, transport, and transformation of nitrogen from fertilization and septic system in an exurban watershed

Excess export of reactive nitrogen in the form of nitrate (NO 3– ) export from exurban watersheds is a major source of water quality degradation and threatens the health of downstream and coastal waterbodies. Ecosystem restoration and best management practices (BMPs) can be introduced to reduce in-stream NO 3– loads by promoting vegetation uptake and denitrification on uplands. However, accurately evaluating the effectiveness of these practices and setting regulations for nitrogen inputs requires an understanding of how human sources of nitrogen interact with ecohydrological systems. We 20 evaluated how the spatial and temporal distribution of nitrogen sources, and the transport and transformation processes along hydrologic flowpaths control nitrogen cycling, export, and the development of “hot spots” of nitrogen flux in suburban ecosystems. We chose a well-monitored exurban watershed, Baisman Run in Baltimore County, Maryland, USA, to evaluate patterns of in-stream NO 3– concentrations and upland nitrogen-related processes in response to three common activities: irrigation, fertilization, and on-site sanitary wastewater disposal (septic systems). We augmented a distributed ecohydrological 25 model, RHESSys, with estimates of these additional loads to improve prediction and understanding of the factors generating both upland nitrogen cycling and stream NO 3– concentrations. The augmented model predicted streamflow-weighted NO 3– concentrations of 1.37 mg NO 3– -N/L, compared to observed 1.44 mg NO 3– -N/L, while the model predicted concentrations of 0.28 mg NO 3– -N /L without the additional loads from human activities from water year 2013 to 2017. Estimated denitrification rates in grass lawns, a dominant land cover in suburban landscapes, were in the range of measured values. The highest predicted 30 denitrification rates were downslope of lawn and septic locations in a constructed wetland, and at a sediment accumulation zone at the base of a gully receiving street drainage. These locations illustrate the development of hot spots for nitrogen cycling and export in both planned and “accidental” retention features. Appropriate siting of best BMPs and the identification of spontaneously developed nutrient hot spots should be pursued to retain nutrients and improve water quality.
Developer’s Cognitive Effort Maintaining Monoliths vs. Microservices - An Eye-Tracking Study

—The microservices architectural style improves flexibility and performance but might come at the cost of complexity and cognitive effort. Understanding how architectural design decision affects cognitive effort can support software engineers in designing and building more maintainable systems. However, little research exists measuring the impact of architectural styles on developers’ cognitive processes. To the best of our knowledge, no empirical study on navigation and comprehension strategies compares a microservice-based and monolithic variant of a trading system, which is widely used for empirical research on information system evolution. To assess the cognitive processes, we conducted an eye-tracking study (n=42) of typical maintenance tasks. We randomly assigned participants to two groups performing a feature location and a code change task using the trading system’s monolithic or microservices-based implementation. Efficacy is significantly higher in the monolith variant. We investigate the reasons and propose a cognitively-grounded method to analyse comprehension strategies and estimate maintenance effort. We measure differences in comprehension strategies (top-down and bottom-up comprehension) and conclude that 1) there is a significant difference in visual effort and time taken to identify a specific feature, and 2) bottom-up comprehension is more frequently applied in microservices than in the monolith variant. Finally, we discuss our findings and their implications for practitioners and the research community.
Development and Functionality of a Parsimonious Digital Food Frequency Questionnaire for a Clinical Intervention among an Indigenous Population

Nutrition-related chronic diseases are a major problem among Indigenous populations. Appropriate dietary intake assessment tools are needed for nutritional surveillance and intervention; however, tools designed to measure the habitual dietary intake of Indigenous persons are largely lacking. We developed a digital food frequency questionnaire (FFQ) to measure habitual consumption among Australian Aboriginal adults and support personalized nutrition counseling. The primary contributors to energy, select nutrients, and inter-person variation (83 food groups) were identified from nationally representative 24 h recall (24HR) data, and they accounted for >80% of the total intake and inter-person variation of the nutrients of interest. Based on community input, a meal-based FFQ format was adopted, with a main food/beverage list of 81 items and the capacity to report on >300 additional items via the digital platform. The nutrient database was based on the Australian Food and Nutrient Database. Data for the first 60 study participants (70% female; median age: 48 years) were used to assess the FFQ’s utility. The participants’ median [IQR] reported energy intake (10,042 [6968–12,175] kJ/day) was similar to their median [IQR] estimated energy expenditure (10,197 [8636–11,551] kJ/day). Foods/beverages on the main FFQ list accounted for between 66% and 90% of the participants’ reported energy and nutrient intakes; the remainder came from participant-selected extra items. The digital FFQ platform provides a potentially valuable resource for monitoring habitual dietary intake among Aboriginal adults and supporting chronic disease prevention and management interventions.
A neural network model for automated prediction of avalanche danger level

Abstract. Snow avalanches cause danger to human lives and property worldwide in high-altitude mountainous regions. Mathematical models based on past data records can predict the danger level. In this paper, we are proposing a neural network model for predicting avalanches. The model is trained with a quality-controlled sub-dataset of the Swiss Alps. Training accuracy of 79.75 % and validation accuracy of 76.54 % have been achieved. Comparative analysis of neural network and random forest models concerning metrics like precision, recall, and F1 has also been carried out.
The Roles that Institutional Investor Playing in Corporate Governance: An International Literature Review

The increase in institutional investors has changed the shareholding structure of listed companies. Research on the roles that institutional investor playing in corporate governance has attracted attention. Scholars have conducted a great deal of normative research on this and have also provided empirical evidence. In the early days of the study, scholars studied investors as a whole. During this period, there were mainly three different views on the roles that institutional investors playing in corporate governance. With the development of research, scholars gradually realized that institutional investors have different characteristics, and classified research began. There are mainly four types of classification standard. Classified research provided meaningful exploration for institutional investor research, however even with the same classification, completely opposite conclusions often appear. This paper reviews scholars' research on the roles that institutional investors playing in corporate governance, analyzes the reasons for the opposite conclusion, and makes suggestions for future research.
Diet‐induced plasticity of life‐history traits and gene expression in outbred Drosophila melanogaster population

Food is fundamental for the survival of organisms, governing growth, maintenance, and reproduction through the provision of essential macronutrients. However, access to food with optimum macronutrient composition, which will maximize the evolutionary fitness of an organism, is not always guaranteed. This leads to dietary mismatches with potential impacts on organismal performance. To understand the consequences of such dietary mismatches, we examined the effects of isocaloric diets varying in macronutrient composition on eight key organismal traits spanning across the lifespan of a large outbred Drosophila melanogaster population (n ∼ 2500). Our findings reveal that carbohydrate-reduced isocaloric diets correlates to accelerated pre-adult development and boosts reproductive output without impacting pre-adult viability and body size. Conversely, an elevated dietary carbohydrate content correlated to reduced lifespan in flies, evidenced by accelerated functional senescence including compromised locomotor activity and deteriorating gut integrity. Furthermore, transcriptomic analysis indicated a substantial difference in gene regulatory landscapes between flies subject to high carbohydrate vs high protein diet, with elevated protein levels indicating transcriptomes primed for reduced synthesis of fatty acids. Taken together, our study helps advance our understanding of the effect of macronutrient composition on life history traits and their interrelations, offering critical insights into potential adaptive strategies that organisms might adopt against the continual dietary imbalances prevalent in the rapidly evolving environment.
Central Lancashire Online Knowledge (CLoK)

This article reports on a mixed methods study investigating the effectiveness of pushed and non-pushed speaking tasks in a UK university setting with upper-intermediate students. Specifically, the study addressed a) if a pushed speaking task produced more language related episodes (LREs) than a non-pushed speaking task, b) the differences in the types of LREs produced by each task and c) whether a pushed speaking task resulted in more accurate usage of past narrative forms. Results showed that the pushed storytelling task produced significantly more LREs than the non-pushed task and it also identified that the most common LRE type for both pushed and non-pushed learners related to some form of output correction. The pushed group achieved greater accuracy gains from pretest and posttest scores but these gain scores were not found to be statistically significant. The study concludes that creating a push during spoken output activities can increase the occurrence of opportunities for linguistic processing, and subsequently interlanguage development, to occur.
Sensitivity of the first indirect aerosol effect to an increase of cloud droplet spectral dispersion with droplet number concentration

Observations show that an increase in anthropogenic aerosols leads to concurrent increases in the cloud droplet concentration and the relative dispersion of the cloud droplet spectrum, other factors being equal. It has been suggested that the increase in effective radius resulting from increased relative dispersion may substantially negate the indirect aerosol effect, but this is usually not parameterized in global climate models (GCMs). Empirical parameterizations, designed to represent the average of this effect, as well as its lower and upper bounds, are tested in the CSIRO GCM. Compared to a control simulation, in which the relative dispersion of the cloud droplet spectrum is prescribed separately over land and ocean, inclusion of this effect reduces the magnitude of the first indirect aerosol effect by between 12% and 35%.
С BUDGET IN THE AGROECOSYSTEMS OF MAIZE (ZEA MAYS L.) AND RAPESEED (BRASSICA NAPUS L.)

The aim of this research was to calculate and compare the C budget changes for maize (Zea mays L.) and rapeseed (Brassica napus L.), identifying soil seasonal respiratory CO 2 (R a+h ) and assimilated photosynthetic CO 2 at plant different growth stages. The research was carried out for maize (Zea mays L.) and rapeseed (Brassica napus L.), during the vegetation period (2014 June – September) at the PI ASU Training Farm, Kaunas district. The mean soil CO 2 emissions were 1.971±0.12 µmol m -2 s -1 for maize, and 2.199±0.25 µmol m -2 s -1 for rapeseed. The highest measured soil CO 2 emissions 2.432±0.23 µmol m -2 s -1 for rapeseed in June, and 2. 963±0.28 µmol m -2 s -1 for the maize in the second half of July. The mean C budget in maize agro-ecosystems was 15.54 t ha -1 , while it was 10.30 t ha -1 in rapeseed. Keywords: Brassica napus, CO 2 emissions, Zea mays. Article DOI: http://doi.org/10.15544/RD.2015. 036
GEOTHERMAL POTENTIAL OF VALLES CALDERA, NEW MEXICO

Valles caldera is a large, Quaternary silicic volcanic complex that contains a hot, but relatively small, liquiddominated geothermal resource (210 o to 300 o C; 20 MWe proven). The portion of the caldera having geothermal significance is now part of the recently created Valles Caldera National Preserve. Past development problems, small size, an uncertain power market, and new public status make future development of the Valles geothermal resource uncertain. GEOLOGIC AND GEOPHYSICAL SETTING Valles caldera is a 22-km-diameter resurgent cauldron that formed in the approximate center of the Jemez Mountains volcanic field (JMVF) at about 1.2 Ma (Figures 1 and 2)(Smith and Bailey, 1968). The JMVF consists primarily of calc-alkaline basalt, andesite, dacite, and rhyolite erupted from about 13 Ma to 55 ka (Toyoda, et al., 1995; Goff and Gardner, in press). Volumetrically, two-pyroxene andesite domes and lavas are most abundant (about 1,000 km 3 ), but volcanism culminated with formation of the Valles and comparably sized
Central Lancashire Online Knowledge (CLoK)

Objectives. Psychological factors may be important in the assessment and management of ankylosing spondylitis (AS). Our primary objective was to describe associations between disease and psychological status in AS, using AS-specific assessment tools and questionnaires. Our secondary objectives were to identify patient subgroups based on such associations and to determine the stability of the measures over time. Methods. One hundred and ten patients were assessed at 6-monthly intervals up to 4 times using tools to measure disease (BASDAI, BASFI, BASMI), psychological (HADS, HLC-C) and generic health (SF-36) status. Data were stored and analysed in SPSS and Stata. Results. Eighty-nine participants completed all 4 assessments. Throughout the study, BASDAI, BASFI and BASMI scores correlated significantly with anxiety, depression, internality and health status, but not with levels of belief in chance or powerful others. Clinically anxious or depressed subgroups had significantly worse BASDAI and BASFI, but not BASMI, scores. BASMI scores were least closely linked to psychological status. Mean scores for disease, psychological and health status were clinically stable over the 18 month period. Conclusions
Development of an Immunochromatographic Strip for Rapid Detection of H9 Subtype Avian Influenza Viruses

ABSTRACT An immunochromatographic strip was developed for the detection of the H9 subtype of avian influenza viruses (H9AIVs) in poultry, using two monoclonal antibodies (MAb), 4C4 for H9AIV hemagglutinin (HA) and 4D4 for nucleoprotein. The 4C4 MAb was labeled with colloidal gold as the detection reagent, and the 4D4 MAb was blotted on the test line while a goat anti-mouse antibody was used on the control line of the nitrocellulose membrane. In comparison with the HA and HA inhibition (HI) tests, the strip was specific for the detection of H9AIV, with a sensitivity at 0.25 HA units within 10 min. Storage of the strips at room temperature for 6 months or at 4°C for 12 months did not change their sensitivity and specificity. Evaluation of the strip with experimental tracheal and cloacal swab samples collected from H9N2-infected chickens revealed that the strip detected the H9N2 viruses on day 3 postinoculation, earlier than the appearance of clinical symptoms. Application of the strip for the analysis of 157 tracheal or cloacal samples from potentially infected chickens on five poultry farms showed that four farms had chickens that were infected with H9AIV. Further characterization of 10 positive and 30 negative randomly selected samples showed that no single sample was false positive or negative, as determined by the standard virus isolation and HI assays. Therefore, the immunochromatographic strip for the detection of H9AIVs has high specificity, sensitivity, and stability. This finding, together with the advantages of rapid detection and easy operation and without the requirement for special skills and equipment, makes the strip suitable for onsite detection and the differentiation of H9AIVs from other viruses in poultry.
Volume-Based Hydrology Examining the shift in focus from peak flows and pollution treatment to mimicking predevelopment volumes

Every 20 years or so, urban stormwater practitioners seem to stop and take stock of how we are doing. Sixty years ago, we figured that efficient drainage was the way to do things, using separate stormwater systems of pipes. Forty years ago, we figured out that efficient drainage was causing flooding problems and switched to a detention design standard. Twenty years ago, we found that detention ponds were failing for a number of reasons and switched to a more comprehensive master planning approach—that is, those who could afford all that modeling, understand its output, and had the wherewithal to actually construct regional systems for stormwater treatment. In the ensuing 20 years, we have seen the rapid diversification of stormwater design from a simple consideration of flooding and conveyance to channel erosion, stormwater pollution, groundwater recharge, and natural approaches to stormwater design—more or less.