Miracle-dz/UniFAR-FTUs huggingface dataset:
Companion Crops for Intercropping with Onion Production in the Dry Season at Fogera District of South Gondar Zone in Ethiopia

Fogera and its surrounding districts and zones, particularly south and north Gondar zones and Bahir zuria are suitable for the cultivation of various kinds of horticultural crops both under rain fed and in the off season using irrigation. Favourable climate and soil, and availability of irrigation water are motivating growers to produce vegetables twice and three times in a year. These areas have rich water potential and immense cultivable land for irrigated horticulture. Converting available water resources in their surroundings to food production in the dry season has now become the tradition of small scale farmers. This expansion of irrigation has led to shifting subsistence production system towards targeting to maximize return per unit area, which further encouraged farmers to emphasize on high value vegetables such as onions.
Research Africa Network (RAN) RAN Working Paper

The Okada & Samreth (2012, E L) and Asongu (2012, EB; 2013, EEL) debate on ‘the effect of forei gn aid on corruption’ has had an important influence in policy and academic circles. This paper provides a unifying framework by using investment and fiscal behavior transmission channels in 53 African countries for the period 1996-2010. Findings unite the two streams of the debate and broadly suggest that while the ‘government’s final consumption expenditure’ channel is consistent with the latter author, the investment and tax effort channels are in line with the former authors. Justifications for the nexuses are provided. Policy implications on how to use foreign aid constraints in managing fiscal behavior as means of reducing (increasing) corruption (corruption-control) are discussed.
A G D I Working Paper

Purpose – This study investigates how ICT adoption enhances the innovativeness of informal firms in West Africa, using the cases of Ghana and Nigeria. Design/methods/approach – The study used the World Bank Enterprise Survey data 2014 for Ghana and Nigeria with binary logistic regression analysis to achieve this. Four different innovations are modelled. They include: first, whether a firm has innovated based on producing a new product or significantly improved product; second, whether a firm has innovated in its methods of production or services; third, whether a firm has innovated in terms of its organisational structure; and fourth, whether a firm has introduced a new and improved marketing method. Findings – The results show that the use of email, cellphone and website has a positive impact on the four types of innovations modelled. However, these effects varied markedly between Ghana and Nigeria. Firms’ spending on R&D, firm giving its employees the chance to develop their ideas and when firm competes with others; all positively impact on the four types of innovations. Thus, the study recommends that policies should be geared towards making firm have more access to ICTs to enable them to be more innovative to serve clients and the economy. Originality/value – This study differs by concentrating on how the adoption of ICTs could help firms to introduce innovations into their companies in two West African countries, namely: Ghana and Nigeria. Thus, it complements literature on informal firms’ innovation efforts in West Africa.
Differentiated thyroid carcinoma: what the nonspecialists needs to know

ABSTRACT Differentiated thyroid carcinoma (DTC) accounts for most cases of thyroid cancer, and the heterogeneity of DTC requires that management decisions be taken by a multidisciplinary team involving endocrinologists, head and neck surgeons, nuclear medicine physicians, pathologists, radiologists, radiation oncologists, and medical oncologists. It is important for nonspecialists to recognize and refer patients with DTC who will benefit from a specialized approach. Recent advances in knowledge and changes in management of DTC call for the need to raise awareness on the part of these nonspecialist physicians, including general endocrinologists and medical oncologists at large. We provide an overview of diagnostic and therapeutic principles in DTC, especially those that bear direct implication on day-to-day management of these patients by generalists. Patients with DTC may be broadly categorized as having localized, locally persistent/recurrent, or metastatic disease. Current recommendations for DTC include a three-tiered system that classifies patients with localized disease into low, intermediate, or high risk of persistent or recurrent disease. Risk stratification should be performed at baseline and repeated on an ongoing basis, depending on clinical evolution. One of the overarching goals in the management of DTC is the need to personalize treatment by tailoring its modality and intensity according to ongoing prognostic stratification, evolving knowledge about the disease, and patient characteristics and preference. In metastatic disease that is refractory to radioactive iodine, thyroid tumors are being reclassified into molecular subtypes that better reflect their biological properties and for which molecular alterations can be targeted with specific agents.
Colyers Island: Polynesia's southernmost adze manufacturing complex

Bluff Harbour, in southern New Zealand (Murihiku), is the southernmost location of Polynesian adze production. Here, an industry developed during the colonisation phase based on the exploitation of numerous sources of argillite - a fine-grained, homogeneous rock with conchoidal fracture properties. Argillite from the Bluff and Riverton sources was a major attractor during the earliest period of human occupation and Bluff argillite adzes were distributed widely throughout the lower South Island. The most extensive sources of Bluff argillite are on Colyers Island, where there is evidence of quarrying and preform production along much of the coast. The sites on Colyers Island were surveyed as the first part of an archaeological investigation of quarrying and adze production in Bluff Harbour. The quarried boulders, outcrops and artefact distributions at these sites indicated that the raw material was exploited by highly organised and skilled craft specialists. The initial stages of adze manufacture were undertaken at the quarry, and then preforms were taken to multipurpose camp sites around the harbour for finishing. Adze blanks on Colyers Island were made from large flakes, tabular blocks and cobbles, using imported hammer stones. This paper documents the significance of Colyers Island as an important adze production complex in New Zealand.
The Development of RBL-STEM Learning Materials to Improve Students' Conjecture Thinking Skills in Solving the Antimagic Rainbow Coloring Problem in Greenhouse using Artificial Neural Networks

Learning materials are instruments or equipment used to carry out learning activities. Learning tools serve as guidance during teaching and learning, whether in the classroom, laboratory, or outside the class. This research aims to determine the validity, practicality, and effectiveness of the developed learning tools. This development research refers to the Thiagarajan model (4D), which includes the stages of defining, designing, developing, and disseminating. The developed teaching materials have met the criteria for validity, practicality, and effectiveness. The validity scores obtained for each tool are 3.6 for RTM, 3.5 for LKM, and 3.6 for THB. Practicality scores were obtained from observations of the implementation of learning, resulting in a score of 3.8 with a percentage of 94%, indicating that the learning was well-implemented. Based on the test results, the researcher found that 19 students scored above 60, which means that 83% of the students in this class have completed the course and met one of the effectiveness criteria.
INTERVERTEBRAL DISC DETECTION IN X-RAY IMAGES USING FASTER R-CNN : A DEEP LEARNING APPROACH

Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for diagnosing ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents and is a precursor for other related diseases. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilize the technology in medical image processing field. In this work, we try to address this problem by fine-tuning a deep network, in particular Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, as shown in Fig. 1, one can achieve satisfactory results. We achieved high average precision of 0.651, evaluated by widely used VOC2007 evaluation metric and the time is reduced to 0.2 second per image, which is significantly faster than traditional sliding window based classification techniques.
Recurrent Neural Networks with Attention for Genre Classification

Automated genre classification of songs through audio input alone has depended on manual feature engineering to achieve good performance [1]. Recently, Recurrent Neural Networks (RNN) have shown success in many sequential tasks including Machine Translation and Speech Recognition, out-performing state-of-the-art systems which use hand-crafted features [2,3,4]. In this direction, we present a novel application of recent advances in RNNs to genre classification on the GTZAN Genre Collection dataset1. More explicitly, we use Long Short Term Memory Networks (LSTM) with (and without) a soft attention mechanism [4] to sequences of audio signals in order to classify songs by genre. The best model is an LSTM without attention, achieving 79% accuracy on the test set.
Alterations in the neural circuits from peripheral afferents to the spinal cord: possible implications for diabetic polyneuropathy in streptozotocin-induced type 1 diabetic rats

Diabetic polyneuropathy (DPN) presents as a wide variety of sensorimotor symptoms and affects approximately 50% of diabetic patients. Changes in the neural circuits may occur in the early stages in diabetes and are implicated in the development of DPN. Therefore, we aimed to detect changes in the expression of isolectin B4 (IB4, the marker for nonpeptidergic unmyelinated fibers and their cell bodies) and calcitonin gene-related peptide (CGRP, the marker for peptidergic fibers and their cell bodies) in the dorsal root ganglion (DRG) and spinal cord of streptozotocin (STZ)-induced type 1 diabetic rats showing alterations in sensory and motor function. We also used cholera toxin B subunit (CTB) to show the morphological changes of the myelinated fibers and motor neurons. STZ-induced diabetic rats exhibited hyperglycemia, decreased body weight gain, mechanical allodynia and impaired locomotor activity. In the DRG and spinal dorsal horn, IB4-labeled structures decreased, but both CGRP immunostaining and CTB labeling increased from day 14 to day 28 in diabetic rats. In spinal ventral horn, CTB labeling decreased in motor neurons in diabetic rats. Treatment with intrathecal injection of insulin at the early stages of DPN could alleviate mechanical allodynia and impaired locomotor activity in diabetic rats. The results suggest that the alterations of the neural circuits between spinal nerve and spinal cord via the DRG and ventral root might be involved in DPN.
Acoustic diversity and ecology of freshwater environments: exploration in temperate environments

An important diversity of animal species produces sounds during commu- nication, orientation, movement, or prey-predator acts. These sounds are not distributed randomly in space and time and are therefore thought to follow assembly rules forming either acoustic populations or acoustic communities. Freshwater environments, and ponds in particular, are considered as primary resources for biological diversity and as such host a potentially significant number of soniferous species. However the acoustic diversity of these natural environments remains totally unexplored. The main aim of this PhD was to explore for the first time the acoustic diversity found in temperate freshwater by studying the patterns and structural processes of a selection of acoustic populations and communities recored in several types of freshwater environments. A review of the literature on sound production by freshwater organisms along with laboratory recordings of target species revealed that a valuable acoustic diversity can be found in temperate freshwater environments. To understand how the acoustic diversity is structured, the acoustic communities of three temperate ponds were acoustically monitored. This study revealed that the three ponds were characterised by rich and distinct acoustic communities with specific spatio-temporal dynamics. To further understand the potential factors structuring freshwater acoustic communities, environmental variables were assessed along with the composition of acoustic communities found in six secondary channels of the Rhone riverine floodplain. Two environmental variables were investigated: the water temperature and the level of lateral connectivity of the secondary channels to the main river. Acoustic communities in the Rhone riverine floodplain were clearly struc- tured by lateral connectivity suggesting a role of this key variable as an assembly rule. Finally to understand the possible processes linking animal acoustics and the natural en- vironment, a population of aquatic insect, Micronecta scholtzi, was acoustically monitored in a Mediterranean pond. The level of M. scholtzi acoustic activity was assessed continuously using a network of twelve synchronised acoustic sensors. The acoustic activity of M. scholtzi showed a regular daily pattern that was modified in amplitude and phase by the playback of an anthropogenic noise. This experiment revealed that the effects of noise pollution may emerge at an aquatic insect population level. This PhD unraveled the existence of a significant amount of unexplored acoustic diversity in freshwater environments and identified links between acoustics and the environment. This research opens interesting perspectives in the use of acoustic to tackle fundamental and applied ecological questions in freshwater environments.
Distributed consensus control for multi‐agent systems using terminal sliding mode and Chebyshev neural networks

This paper investigates the problem of consensus tracking control for second‐order multi‐agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication ﬂow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite‐time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural‐network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering‐free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time‐varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite‐time stability in both the reaching phase and the sliding phase is guaranteed by a Lyapunov‐based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane‐based sliding mode controller. Copyright © 2011 John Wiley & Sons, Ltd.
Coralline red algae from the Silurian of Gotland indicate that the order Corallinales (Corallinophycidae, Rhodophyta) is much older than previously thought

Aguirrea fluegelii gen. et sp. nov. (Corallinales, Corallinophycidae, Rhodophyta) is described from the mid‐Silurian of Gotland Island, Sweden (Högklint Formation, lower Wenlock). The holotype is of dimerous construction and includes a uniporate conceptacle with a sporangium, thus providing evidence that taxa of the Corallinales/Corallinaceae existed at least 300 million years earlier than previously documented. Aguirrea fluegelii cannot be unequivocally placed in any of seven currently recognized lineages/subfamilies/groups of the Corallinaceae as not all diagnostic characters are preserved, and thus is accorded incertae sedis status within the family Corallinaceae and order Corallinales. Extant evolutionary history studies of Corallinophycidae involving molecular clocks now require updating using new calibration points to take account of the much earlier unequivocal mid‐Silurian record of uniporate conceptacle‐bearing taxa of Corallinales/Corallinaceae as well as the parallel record of Graticula, a genus attributed to the Sporolithales.
Artificial prediction markets for online prediction of continuous variables

In this dissertation, we propose an online machine learning technique – named Artificial Continuous Prediction Market (ACPM) – to predict the value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions such as changing the quality of data streams, (ii) integrating the results of several analysis models for each data source when the most suitable model for a given data source is not known a priori, (iii) dynamically weighting the prediction of each analysis model and data source to form the system prediction. We adapt the concept of prediction market, motivated by their success in forecasting accurately the outcome of many events [Nikolova and Sami, 2007]. Our proposed model instantiates a sequence of prediction markets in which artificial agents play the role of market participants. Agents participate in the markets with the objective of increasing their own utility and hence indirectly cause the markets to aggregate their knowledge. Each market is run in a number of rounds in which agents have the opportunity to send their prediction and bet to the market. At the end of each round, the aggregated prediction of the crowd is announced to all agents, which provides a signal to agents about the private information of other agents so they can adjust their beliefs accordingly. Once the true value of the record is known, agents are rewarded according to accuracy of their prediction. Using this information, agents update their models and knowledge, with the aim of improving their performance in future markets. This thesis proposes two trading strategies to be utilised by agents when participating in a market. While the first one is a naive constant strategy, the second one is an adaptive strategy based on Q-Learning technique [Watkins, 1989]. We evaluate the performance of our model in different situations using real-world and synthetic data sets. Our results suggest that ACPM: i) is either better or very close to the best performing agents, ii) is resilient to the addition of agents with low performance, iii) outperforms many well-known machine learning models, iv) is resilient to quality drop-out in the best performing agents, v) adapts to changes in quality of agents predictions.
Bridging the gap between research and operations in the National Weather Service : collaborative activities among the Huntsville Meteorological Community

A major goal of the National Weather Service (NWS) is to provide improved customer service and products to our local communities and customers. The rapid advancements in computer and communication technology have led to an explosion in the amount of weather information available to the public and private sectors. To maintain its standing as a vital agency and avoid losing its customer base, it is incumbent upon the NWS to adapt to the ever-changing times and “think outside the box” for new and creative ways to reach our customers. In an effort to achieve this goal, the new Weather Forecast Office (WFO) in Huntsville, Alabama has embarked upon a pro-active training, outreach and familiarization program geared towards assessing the needs and concerns of the local users.
Stochastic hybrid modeling of DNA replication across a complete genome

DNA replication in eukaryotic cells initiates from hundreds of origins along their genomes, leading to complete duplication of genetic information before cell division. The large number of potential origins, coupled with system uncertainty, dictates the need for new analytical tools to capture spatial and temporal patterns of DNA replication genome-wide. We have developed a stochastic hybrid model that reproduces DNA replication throughout a complete genome. The model can capture different modes of DNA replication and is applicable to various organisms. Using genome-wide data on the location and firing efficiencies of origins in the fission yeast, we show how the DNA replication process evolves during S-phase in the presence of stochastic origin firing. Simulations reveal small regions of the genome that extend S-phase to three times its reported duration. The low levels of late replication predicted by the model are below the detection limit of techniques used to measure S-phase length. Parameter sensitivity analysis shows that increased replication fork speeds genome-wide, or additional origins are not sufficient to reduce S-phase to its reported length. We model the redistribution of a limiting initiation factor during S-phase and show that it could shorten S-phase to the reported duration. Alternatively, S-phase may be extended, and what has traditionally been defined as G2 may be occupied by low levels of DNA synthesis with the onset of mitosis delayed by activation of the G2/M checkpoint.
"Intellect", a Mobile Health Application based on cognitive-behavioral therapy principles, improves Anxiety and Worry: A Randomized Controlled Trial with an Active Control and a 2-Week Follow-Up

Digital self-guided mobile health (mHealth) applications are cost-effective, accessible, and well-suited to improve mental health at scale. This randomized controlled trial (RCT) evaluated the effectiveness of a recently developed mHealth programme based on cognitive-behavioral therapy (CBT) principles in improving worry. We also examined psychological mindedness (PM) as a mediator by which app engagement is thought to improve outcomes. The intervention group completed a 2-week "Anxiety and Worry" programme with daily CBTinformed activities, while the active waitlist-control completed a matched 2-week mHealth programme on procrastination. Participants filled out the Generalized Anxiety Disorder (GAD7), Patient Health Questionnaire (PHQ-9), and Psychological Mindedness Scale (PMS) at baseline, post-intervention and 2-week follow-up. App engagement was measured at postintervention only. Both groups showed significant improvements on anxiety and depression scores from baseline to post-intervention, but no group differences were observed. From postintervention to follow-up, only the intervention group showed further improvements for anxiety levels. Higher engagement with the mHealth app reported lower anxiety at post-intervention, and this relationship was fully mediated by psychological mindedness. This study provides evidence that (a) engaging in a CBT mHealth App can effectively reduce anxiety and worry, and (b) Psychological mindedness is a potential pathway by which engaging with a mHealthapp improves worry. While overall effect sizes were small, at the population level, these can make significant contributions to public mental health.