Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation into the financial challenges affecting the success of entrepreneurs in South Africa

The object of this study is the financial difficulties that impacted the success of business owners in Hatfield, Gauteng province, South Africa. Compared to a global failure rate of 50 %, five out of every seven entrepreneurs in Hatfield, South Africa fail during the first year of operation. This study aimed at looking into the relationship between entrepreneur failure and financial literacy. The methodology employed in this study is interpretive philosophy-based qualitative research. An ethnographic research technique was also used to analyze the current economic condition of business owners in Hatfield. The population was sampled using non-probability purposive sampling techniques. Semi-structured interviews were used to gather the study's leading source of data. The primary research results are thematically examined while also considering the secondary sources. Eliminating financial and liquidity constraints was listed as a goal of financial literacy. Findings garnered revealed that financial, liquidity and credit restrictions are the primary causes of business failures in Hatfield. The lack of financial resources for new businesses in Hatfield, as revealed, prompted several liquidity issues in Hatfield, and further lowers the growth rate of Hatfield businesses. It was discovered that Hatfield's entrepreneurs usually experienced premature failure due to inadequate financial education and training. Impliedly, Hatfield business owners possessed poor cash management, defaulting on loan payments due to lack of financial education. Conversely, only few entrepreneurs and business owners in Hatfield, South Africa possess financial literacy competence with the necessary skills needed to better analyze their financial statements appropriately and increase profitability of their business. The practical implication of this finding is that, most entrepreneurs have high possibility to experience premature business liquidity when they have low or no financial literacy.
Distributed Repairing Multiple Erasures in Reed-Solomon Codes

The application of Reed-Solomon (RS) codes in distributed storage systems is widespread. A lot of literature have proposed repair schemes for RS codes with one or multiple failed symbols, which can reduce communication cost during repair (i.e. repair bandwidth). ˆA However, for repair schemes of RS codes with multiple failed symbols, ˆA the existing distributed repair schemes can only repair $ e=2,3 $ erasures. In this paper, by intelligently dividing the set of evaluation points corresponding to failed symbols, a distributed repair scheme for RS codes with $ e \ geq 4 $ erasures are proposed.
Discussion Paper Series Communication of monetary policy in unconventional times

Monetary policy communication is particularly important during unconventional times, because high uncertainty about the economy, the introduction of new policy tools and possible limits to the central bank’s toolkit could hamper the predictability of policy actions. We study how monetary policy communication should and has worked under such circumstances. Our main results relate to announcements of asset purchase programmes and the use of forward guidance. We show that announcements of asset purchase programmes have lowered market uncertainty, particularly when accompanied by a contextual release of implementation details such as the envisaged size of the programme. We also show that forward guidance reduces uncertainty more effectively when it is state ‐ contingent or when it provides guidance about a long horizon than when it is open ‐ ended or covers only a short horizon, and that the credibility of forward guidance is strengthened if the central bank also has embarked on an asset purchase programme. JEL codes: E43, E52, E58
NLPADADE: Leveraging Natural Language Processing for Automated Detection of Adverse Drug Effects

Pharmacovigilance is a systematic and scientifically rigorous discipline that assumes responsibility for the safety of pharmaceuticals, with its primary objective being the mitigation of risks while optimizing the benefits associated with medication usage. This mission-critical undertaking plays an indispensable role in preserving public health. At its core, pharmacovigilance entails the methodical collection and proficient management of data pertaining to medication safety. Additionally, these activities encompass the vigilant scrutiny of data to detect emerging "signals" indicative of new or evolving safety concerns. The expert evaluation of this data facilitates well-informed decision-making regarding matters of drug safety. Furthermore, proactive risk management strategies are deployed to effectively mitigate potential associated risks. In the pursuit of proactive health protection, regulatory actions are swiftly executed. Concurrently, the World Health Organization (WHO) underscores the global importance of establishing a robust pharmacovigilance framework. It advocates for the establishment of a comprehensive pharmacovigilance system, defined as encompassing "the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other problem related to drugs or any other healthcare product. " In this dynamic landscape, a pivotal question arises: "How can the automated identification and extraction of references to diseases, medications, and adverse effects from clinical notes and biomedical literature be achieved? " Central to this discourse are adverse drug effects (ADEs), which present a formidable public health challenge, manifesting as a significant source of patient morbidity and mortality. To expedite the utilization of real-world data (RWD) for the enhancement of pharmacovigilance practices, our focus has gravitated towards the development of a high-performance natural language processing (NLP) model. This model aims to facilitate the rapid detection of potential ADEs linked to medications. Our innovative system, designed for the extraction of diseases and ADEs, leverages the synergy of an open-source NLP component system. The pinnacle of our achievement is the model obtained, which boasts a remarkable Score of 0.97 at step 1800. With a Precision of 1.00, a Recall of 0.9412, and an F-Score of 0.9697, our NLP model showcases its efficiency in extracting and identifying pertinent information from textual data. These results underscore the effectiveness of our approach in recognizing diseases and adverse effects related to medications, setting a new benchmark for pharmacovigilance practices.
with Parkinson Disease and Freezing of Gait Regional Gray Matter Atrophy in Patients

BACKGROUND AND PURPOSE: FOG is a troublesome symptom of PD. Despite growing evidence suggesting that FOG in PD may be associated with cognitive dysfunction, the relationship between regional brain atrophy and FOG has been poorly investigated. MATERIALS AND METHODS: Optimized VBM was applied to 3T brain MR images of 24 patients with PD and 12 HC. Patients were classified as either FOG (cid:1) or FOG (cid:2) ( n (cid:3) 12) based on their responses to a validated FOG Questionnaire and clinical observation. All patients with PD also underwent a detailed neuropsychological evaluation. RESULTS: The VBM analysis in patients with FOG (cid:2) showed a reduced GM volume in the left cuneus, precuneus, lingual gyrus, and posterior cingulate cortex compared with both patients with FOG (cid:1) and HC. We did not detect any significant change of GM volume when comparing HC versus all patients with PD (FOG (cid:1) and FOG (cid:2) ). FOG clinical severity was significantly correlated with GM loss in posterior cortical regions. Finally, patients with FOG (cid:2) scored lower on tests of frontal lobe function. CONCLUSIONS: Our findings provide the first evidence that the development of FOG in patients with PD is associated with posterior GM atrophy, which may play a role in the complex pathophysiology of this disabling symptom. ABBREVIATIONS: 18 F
Revised Structure by Computational Methods for a Coumarin Isolated from Zanthoxylum Rhoifolium (Rutaceae)

On the basis of theoretical methods using DFT/B3LYP/6-31G* geometries and HF/6-311+G** GIAO NMR predictions, the 13C NMR spectroscopic data of a coumarin from Zanthoxylum rhoifolium was reassigned. The structure was revised to be 4-methoxy-3-(3-methylbut-2-enyl)-2H-chromen-2-one (4) and not 3-methoxy-4-(3-methylbut-2-enyl)-2H-chromen-2-one (2), as assumed previously.
Opportunistic Spectrum Sharing and White Space Access: The Practical Reality

Details the paradigms of opportunistic spectrum sharing and white space access as effective means to satisfy increasing demand for high-speed wireless communication and for novel wireless communication applications This book addresses opportunistic spectrum sharing and white space access, being particularly mindful of practical considerations and solutions. In Part I, spectrum sharing implementation issues are considered in terms of hardware platforms and software architectures for realization of flexible and spectrally agile transceivers. Part II addresses practical mechanisms supporting spectrum sharing, including spectrum sensing for opportunistic spectrum access, machine learning and decision making capabilities, aggregation of spectrum opportunities, and spectrally-agile radio waveforms.
A Coordination Protocol for Target Tracking in Wireless Sensor Networks

Usually, traditional multi-agent systems (MAS) run on powerful nodes such as desktops and the like. Over the past few years, agent concepts and technologies have been introduced as an option to increase the network lifetime and to provide application reprogramming functionalities in wireless sensor networks (WSN). One important problem is however how agents impact those computing systems, which are typically highly resource-constrained. In this paper we propose a tiny multi-agent model for WSN using target tracking as the motivational application. The goal is to use this model and application to assess potential benefits of using agent concepts in wireless sensor networks.
Calibration of road surface temperature forecasts

At FMI there is a long tradition to forecast physical road conditions. The first attempts date back to 1970’s and nowadays an operation road weather model is used to provide guidance for forecasters and customers. The present operational road weather model is a 1-dimensional energy balance model, which calculates vertical heat transfer in the ground and at the road-atmosphere interface, taking into account the special conditions prevailing at the road surface and below it [5]. The meteorological forcing variables are ambient temperature, relative humidity, wind speed, short-wave radiation, long-wave radiation (mostly from clouds) and precipitation. The forcing data is from operational HIRLAM model or from ECMWF model. A calibration test with Kalman filter has been made using local road surface temperature observations and corresponding forecasts for several road weather stations in Finland. The focus of the work is to find optimal ranges of measurement noise and system noise for a practical application. A simple adaptive estimation algorithm for system noise is presented. The results have been promising so far and show strong support for operative application.
Engineering Applications of Artificial Intelligence

Railway track accidents continue to occur despite manual inspections, which are often inaccurate and can lead to catastrophic events. While artificial intelligence has been applied in the railway sector, few studies have focused on defect detection using object detection tools. Additionally, there is a lack of studies that compare different models using the same dataset. This paper proposes new data-driven techniques that identify railway track faults using three object detection models: YOLOv5, Faster RCNN, and EfficientDet. These models are compared by testing a dataset of 31 images that contain three different railway track elements (clip, rail, and fishplate), both faulty and non-faulty. Six classes were differentiated in the training of the models: one faulty and one non-faulty for each of the three classes. Image pre-processing steps included data augmentation techniques and image resizing. Results show good precision (equivalent to 1) in detecting non-defective elements, but recall values for defective elements vary among models, with Faster RCNN performing the best (0.93), followed by EfficientDet (0.81), and YOLOv5 (0.68). The full paper discusses the strengths and weaknesses of these proposed techniques for railway fault detection.
Generalization and Completeness of Evolutionary Computation

The need of a structured framework for evolutionary computation has been acknowledged. In order to achieve this we designed a set of operational semantics and defined a “general form” of evolutionary computation. Our second approach towards a generalization was to study the relationship between different algorithms and the problems they solve from a performance standpoint. Lastly, we tried to analyze the convergence and complexity of evolutionary algorithms. This led to a set of computability results, the main one being that evolutionary computation is Turing-complete.
Optical and Near-infrared Observations of the Distant but Bright “New Year’s Burst”

High-redshift gamma-ray bursts ( GRBs ) are useful to probe the early Universe, but only a few candidates have been detected so far. Here, we report the optical and near-infrared observations of the afterglow of a relatively high-redshift event GRB 220101A, which was triggered on New Year ’ s Day of 2022
What Causes the Arabian Gulf Signiﬁcant Summer Sea Surface Temperature Warming Trend?

: The present study investigated the signiﬁcant sea surface temperature (SST) warming trend during the summer season over the Arabian Gulf (AG) and its links with the large-scale atmospheric driver, namely, the Atlantic multidecadal oscillation (AMO), from 1900 to 2021. The link between the AMO and the AGs oceanic circulations has received little scientiﬁc attention. It has been found that there is a signiﬁcant spatial positive trend, with a maximum of up to 0.6 ◦ C per decade over the far northern end, while the time series trend shows a signiﬁcant shift after 1995, with an average value of about 0.36 ◦ C per decade. The spatial trend in the AG is eight times and four times higher than the global value from 1980 to 2005 using HadISST and OISST, respectively. The AMOs signiﬁcant role in the AGs SST signiﬁcant warming trend has been conﬁrmed by the spatial and temporal correlation coefﬁcient, which is above 0.50 and 0.48, respectively, with statistical signiﬁcance at the 99% level. The underlying mechanisms that explained the AMO-related AGs SST decadal variability can be explained as follows: when the AMO is in a positive phase, the surface northwesterly wind weakens, leading to (1) less advection of the relatively cold air masses from Southern Europe to the AG and surrounding areas, (2) less evaporation, and thus less surface cooling (3); thus, this enhances the water masses stratiﬁcation and decreases the upwelling process, and vice versa occurs for the negative phase. For the air temperature, the positive AMO phase coincides with the occurrence of warm air masses covering all of the Arabian Peninsula, North Africa, and Southern Europe. These processes prove that the AMO is a possible candidate for the AGs SST decadal variability, hence enabling a better evaluation of future climate scenarios for this important region. Our results provide initial insights into the AMO-driven spatiotemporal variability in the SST over the AG and prove that the relation is nonstationary over time. Further analyses are required to explore whether the impacts of the AMO are extended to other oceanic variables such as evaporation rate, heat transport, etc.
ON INFORMATICS

— Stock investing is known worldwide as a passive income available for everyone. To increase the profit possibly gained, many researchers and investors brainstorm to gain a strategy with the most profit. Machine learning and deep learning are two of these approaches to predicting the stock's movement and deciding the strategy to gain as much as possible. To reach this goal, the researcher experiments with Random Forest (RF) and Long Short-Term Memory (LSTM) by trying them individually and merging them into an ensembled model. The researcher used RF to classify the results from LSTM models obtained throughout the Hyperparameter Optimization (HPO) process. This idea is implemented to lessen the time needed to train and optimize each LSTM model inside the ensembled model. Another anticipation done in this research to overcome the time needed to train the model is classifying the return for longer periods. The dataset used in this model is 45 stocks listed in LQ45 as of August 2021 This research results in showing that LSTM gives better results than RF model especially when using Bayesian Optimization as the HPO method, and that the ensembled model can return better precision in predicting stocks in comparison to the LSTM model itself. Future improvement can focus on the model structure, additional model types as the ensemble model estimator, improvement on the model efficiency, and datasets research to be used in predicting the stock movement prediction.
Necessary and Sufﬁcient Conditions for Satisfying Linear Temporal Logic Constraints using Control Barrier Certiﬁcates

—Temporal logic speciﬁcations have been used to express complex tasks for control systems. Discretization-free approaches, which do not require discretizing the state and input spaces of the system, have been proposed for control synthesis under temporal logic speciﬁcations. Among these approaches, control barrier certiﬁcate (CBC)-based solutions have attracted increasing attention. The existing CBC-based approaches, however, have no guarantee on always ﬁnding control laws to satisfy the speciﬁcation, and hence are sound but not complete. In this paper, we derive the necessary and sufﬁcient conditions for a control law to satisfy a temporal logic speciﬁcation over ﬁnite traces using CBCs. By leveraging the equivalence between satisfying the speciﬁcation and violating the negated speciﬁcation, we ﬁrst negate the speciﬁcation and construct the deterministic ﬁnite automaton (DFA) as a representation. We then decompose the DFA into a set of safety problems, where each decomposed problem corresponds to a transition in the DFA. We derive the necessary and sufﬁcient conditions for a control law to solve each safety problem via CBC-based approach. We further develop the necessary and sufﬁcient conditions to verify whether the control laws associated with different safety problems are composable or not. The composability captures whether a sequence of transitions in the DFA can be realized by the system or not. If the set of composable control laws does not render an accepting run on the DFA, then the system can satisfy the speciﬁcation. We illustrate the proposed approach using a numerical case study on a multi-agent system.
Nucleolins from different model organisms have conserved sequences reflecting the conservation of key cellular functions through evolution

Sequences available in public protein databases belonging to nucleolin or nucleolin-like proteins have been aligned using public domain software, in order to obtain relevant data regarding the degree of their conservation, which could be a reflection of the degree of conservation of the functions currently attributed to this protein. Nucleolin is known to be a nucleolar multifunctional protein, involved in different steps of pre-rRNA transcription and processing. Three domains are constantly present in all nucleolins, namely a series of acidic/serin (Ac/Ser) sequences, a number of RNA recognition motifs (RRM) and a region rich in glycin and arginin (GAR). The number of motifs present in each one of the three domains is variable. Furthermore, we have characterized in all nucleolins the presence of a bipartite consensus nuclear localization sequence (NLS). The only cases in which this sequence with a definite structure was not totally evident were in the yeast S. pombe (a possible monopartite structure) and in the protozoan T. thermophyla, in which it appears to be absent. Finally, we have constructed the phylogenetic tree of the 15 species investigated, taking exclusively the data regarding this protein. Interestingly, the tree obtained closely resembles the organization of these taxonomic groups throughout evolution, as it is presently known. We conclude that nucleolin is a highly conserved protein, whose gene was already present in an ancestor eukaryotic species, at an early stage of the evolutionary process, from which it has evolved very slowly. This is a reflection of the fundamental functions carried out by this protein, which were already fixed in the ancestor species.
Quantifying Charcoal Degradation and Negative Priming of Soil Organic Matter with a 14C-Dead Tracer

Abstract Converting biomass to charcoal produces physical and chemical changes greatly increasing environmental recalcitrance, leading to great interest in the potential of this carbon form as a long-term sequestration strategy for climate change mitigation. Uncertainty remains, however, over the timescale of charcoal’s environmental stability, with estimates varying from decadal to millennial scales. Uncertainty also remains over charcoal’s effect on other aspects of carbon biogeochemical cycling and allied nutrient cycles such as nitrogen. Radiocarbon is a powerful tool to investigate charcoal mineralization due to its sensitivity; here we report the results of a study using 14C-dead charcoal (pMC=0.137±0.002) in organic-rich soil (pMC=99.76±0.46), assessing charcoal degradation over 55 days of incubation. Using this method, we discriminated between decomposition of indigenous soil organic matter (SOM) and charcoal by microorganisms. SOM was the major source of carbon respired from the soil, but there was also a contribution from charcoal carbon mineralization. This contribution was 2.1 and 1.1% on days 27 and 55, respectively. We also observed a negative priming effect due to charcoal additions to soil, where SOM mineralization was repressed by up to 14.1%, presumably arising from physico-chemical interactions between soil and charcoal.
Assessing the tourism potential of an Australian industrial icon

In 2015, the remote mining community of Broken Hill became the first Australian city to be inscribed on the National Heritage List. The City Council’s strategic plans reflect an expectation that the inscription will lead to an increase in tourism. A better understanding of the core dimensions of a successful and sustainable cultural heritage tourism product would enhance the effectiveness of this planning process. There are, however, few instruments designed to assess the tourism potential of complex and extensive industrial heritage landscapes like Broken Hill. Building on previous work by McKercher and Ho (2006), this paper identifies five core value dimensions for such an instrument – cultural, physical, product, experience and sustainability. The instrument is then tested on Broken Hill and three comparable industrial World Heritage sites. While Broken Hill’s cultural, physical and sustainability dimensions rate strongly, the product and experience dimensions rate poorly effectively precluding the city from functioning as a viable attraction in its current state. The city’s remoteness and isolation from other attractions, the complexity and deterioration of mining infrastructure, and the lack of a major mining-related tourist attraction exacerbate the weaknesses.