Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Significance of the Project Scope in the Successful Implementation of Infrastructure Projects – A Case Study of Level Two Health Projects

Projects addressing Healthcare stand out to be a critical pillar for sustainable development. According to the Abuja declarations in the year 2000 during the formulation of Millenium Development Goals, the parties agreed to increase their healthcare budget to a whopping 15%. By the end of the Millennium Development Goals, partly no country had reached such a target. It is therefore imperative for Countries to make sure their healthcare budget can address the prevailing needs of their citizens. To make sure the set goals are achieved, it is thus incumbent upon the government to set achievable targets. The focus of this research was to ascertain how project planning plays a nexus role in supporting the healthcare system at level two. The major variable was to assess how the project scope affects such an endeavor and how the existing government policy intervenes in such achievements a case of Kenya Health Policy established in 2014 and running till 2030. The research will be guided by an exploratory research design since it will expedite both Quantitative and Qualitative data from a sample size of 100 respondents. Quantitative data was analysed using SPSS version 25 while Qualitative data was analyzed using the content analysis method. The study findings postulated that most projects were completed within budget while others were not. Delay of disbursement of Funds by the Government to the health facilities was picked as another cause of failure to meet the goal, politics was also noted to play a role in such endeavors. With such findings, the study thus recommended the establishment of a framework that supports better collaboration between the primary parties in the government and the health facilities. The study also acknowledges budget, cost, and planning attributes are crucial to the successful implementation of level-two health projects. This study focused on adding value to the existing body of knowledge and practice as enshrined in the Kenya Vision 2030 blueprint and Kenya Health Policy 2014-2030 blueprint.
Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.
Exploring the use of 3D GIS as an analytical tool in archaeological excavation practice

Several digital technologies are now available to record and document archaeological excavations. A large number of studies have been published concerning the use of laser scanning, image based 3D modelling and GIS. By integrating different typologies of 3D data from Uppakra, an Iron Age central place in southern Sweden, this thesis focuses on the development and evaluation of how 3D Geographic Information Systems (3D GIS) affect archaeological practice. In specific a Digital Terrain Model (DTM) was created for the site and the surrounding landscape. A UAV (drone) was used to document the excavation area in higher resolution, and image based 3D modelling was used to record the ongoing excavation on a single-context level. These different typologies of data were subsequently imported in a 3D GIS system (ArcScene) in order to conduct various types of spatial analysis (e.g. hillshade and slope analysis) as well as to create 3D drawings of the excavated contexts, using the textured 3D models as a geometrical reference. The ability to virtually revisit previous stages of the excavation and the use of tablet PC’s for documentation and discussion at the trowel’s edge increased reflexivity on the excavation and stimulated on-site interpretation by the excavation team. The model based drawing approach furthermore improved the drawing resolution compared to traditional documentation using a total station, especially for complicated contexts. This approach allowed connecting different typologies of data in the same virtual space, 1) increasing the possibility of researchers and scholars to gain a complete overview of all the information available, as well as 2) exponentially increasing the possibilities to perform new analysis. The ability to interact and navigate with all the data in 3D improved the impact of the data and comes closer to simulating the real world. Though some challenges still have to be faced, such as inaccurate georeferencing and unrealistic colour projection, the method was found to significantly improve the documentation quality by creating a multi-scale 3D documentation platform. By further developing this method, it can help us to improve the standards of archaeological excavation documentation.
Efficient Management of Cache Accesses to Boost GPGPU Memory Subsystem Performance

To support the massive amount of memory accesses that GPGPU applications generate, GPU memory hierarchies are becoming more and more complex, and the Last Level Cache (LLC) size considerably increases each GPU generation. This paper shows that counter-intuitively, enlarging the LLC brings marginal performance gains in most applications. In other words, increasing the LLC size does not scale neither in performance nor energy consumption. We examine how LLC misses are managed in typical GPUs, and we find that in most cases the way LLC misses are managed are precisely the main performance limiter. This paper proposes a novel approach that addresses this shortcoming by leveraging a tiny additional Fetch and Replacement Cache-like structure (FRC) that stores control and coherence information of the incoming blocks until they are fetched from main memory. Then, the fetched blocks are swapped with the victim blocks (i.e., selected to be replaced) in the LLC, and the eviction of such victim blocks is performed from the FRC. This approach improves performance due to three main reasons: i) the lifetime of blocks being replaced is enlarged, ii) the main memory path is unclogged on long bursts of LLC misses, and iii) the average LLC miss latency is reduced. The proposal improves the LLC hit ratio, memory-level parallelism, and reduces the miss latency compared to much larger conventional caches. Moreover, this is achieved with reduced energy consumption and with much less area requirements. Experimental results show that the proposed FRC cache scales in performance with the number of GPU compute units and the LLC size, since, depending on the FRC size, performance improves ranging from 30 to 67 percent for a modern baseline GPU card, and from 32 to 118 percent for a larger GPU. In addition, energy consumption is reduced on average from 49 to 57 percent for the larger GPU. These benefits come with a small area increase (by 7.3 percent) over the LLC baseline.
The radiative processes involving some non-symmetric systems relevant for astrochemistry: data needed for modeling

The photo-association, charge-exchange, and photo-dissociation absorption processes in non-symmetric systems including hydrogen and calcium atoms, ions, molecules, and molecular-ions are investigated in this paper. For the current computation, we have calculated the data for molecules and molecular state characterizations. We obtained the cross-sections and corresponding spectral absorption rate coeﬃcients - as the functions of wavelengths, and temperatures. The collected data has the potential for an impact on vast additional applications, such as modeling of laboratory plasmas and numerous astrophysical objects.
Active Disturbance Rejection Control of Bearingless Permanent Magnet Slice Motor Based on RPROP Neural Network Optimized by Improved Differential Evolution Algorithm

In order to realize the strong antidisturbance capability and the precise control of suspension forces in a bearingless permanent magnet slice motor (BPMSM), an active disturbance rejection control (ADRC) strategy based on the combination of resilient backpropagation neural network (RPROPNN) and improved differential evolution (IDE) algorithm is proposed. Based on the mathematical model of the BPMSM, the first-order ADRC controller of the rotating part and the second-order ADRC controller of the suspension part are designed, respectively, for the BPMSM according to the different orders of the BPMSM system. Then, the elite group bootstrap mechanism and parameter adaptive techniques are introduced to improve the DE algorithm to speed up its convergence and enhance the global search capability, and the initial weights of RPROPNN are optimized using the IDE algorithm to obtain the best network model. According to the feedback information of the BPMSM, the optimization mechanism and self-learning capability of RPROPNN are used to adjust the parameters of ADRC to reduce the dependence of ADRC on parameters. The comparative experimental results indicate that the proposed ADRC optimization design method has stronger robustness when disturbances occur, and the method is feasible and effective.
Google Bard Generated Literature Review: Metaverse

Literature review articles aim to present studies in a field to researchers in a regular, systematic and meaningful way. It is often a very difficult process to reach all the studies in the field to be reviewed or to identify and evaluate the important ones. With this study, the use of Artificial Intelligence robots and moreover generative artificial intelligence in literature review processes is evaluated. Google Bard was used to detect artificial intelligence's ability to generate a literature review article. First, Bard was asked questions to write about some of the Metaverse-related topics in this article. Some of the texts in the study were generated entirely by the answers given to the questions asked by Bard. In addition, 10 articles on Metaverse published in the last three years (2021, 2022 and 2023) were collected by searching Google Scholar with the word "Metaverse". Afterwards, these studies were interpreted by Bard. Bard was told to paraphrase the summary parts of the related studies and the produced texts were shared in the study. All produced texts were checked through ithenticate and the results were evaluated. In addition, the texts were evaluated semantically. Additionally, a comparison with the capabilities of OpenAI ChatGPT is given. The results are promising; However, it was observed that the plagiarism matching rate of the paraphrased texts was higher when compared to the answers given to the questions. This article is an experiment to show that the collection and expression of knowledge can be accelerated with the help of artificial intelligence. It is considered that the relevant tools will be used more and more effectively in academic literature in the future.
Evaluation of a Large Area, 83 μm Pixel Pitch Amorphous Selenium Indirect Flat Panel Detector

Dual-layer detectors provide a low-cost solution to improved material decomposition and lesion differentiation in X-ray imaging, while eliminating motion artifacts from multiple exposures. Most designs utilize two indirect detectors with scintillators designed for low-energy and higher-energy detection and separated by a copper filter to harden the beam for high energy detection. To improve the performance of the bottom detector and lower dose requirements, we have previously proposed an alloyed amorphous selenium photodetector to achieve improved resolution and absorption at green wavelengths, better suited to high-performance scintillators such as CsI:Tl. In this work, we demonstrate a baseline prototype for the bottom layer—a continuous, large area $83~\mu \text{m}$ pixel pitch flat panel indirect detector with well-established amorphous selenium as the photodetector—and verify the architecture’s performance and detector design. We characterize lag, noise-power spectrum, detective quantum efficiency, and modular transfer function of the detector, and show resolution up to 6 lp/mm when operated at an applied bias of 150 V. This provides a starting point for evaluating the alloyed selenium materials, and shows promise for this detector in the future dual-layer design.
Extending IPv6 to support Carbon Aware Networking

In this position paper we first review the concepts of Carbon awareness and of its application to the ICT industry with Carbon aware computing. We focus on the need of providing accurate and granular attribution of carbon footprint and energy consumption to ICT users and applications. By granular we mean an attribution over relatively short time scales (e.g. hours and minutes…). Starting from this requirement we focus on the “networking” component of the ICT applications. The challenges of introducing carbon awareness in networking are discussed and a conceptual model for Carbon Aware Networking is depicted. To address the requirements of accurate and granular attribution, our position is that it should be possible to dynamically collect carbon related information using packets in transit. We focus on IPv6 networks and propose protocol extensions that can support Carbon Aware Networking within a framework called EIP (Extensible In-band Processing). This proposal to support Carbon Aware Networking with EIP is still in the early maturity phase, and we identify the needs for further work at a multi-disciplinary level.
Abundance and composition of invertebrate-feeding leeches in relation to fish status in ponds

Abstract Leeches feeding on invertebrates are an important component of freshwater communities, including those dominated by fish, but the effects of fish on their abundance and distribution are not well explored. The relative densities and assemblage structure of two leech families, Erpobdellidae and Glossiphoniidae, were studied from May to August 2014 in ponds stocked with low biomass densities of young common carp Cyprinus carpio, too small to prey on leeches (low-fish ponds), and in ponds containing high biomass densities of large (one- or two-year-old) carp (high-fish ponds), using two types of activity traps (placed either on the bottom or near the water surface). The most common and numerous species were Erpobdella octoculata, Helobdella stagnalis, and Erpobdella nigricollis. The traps near the water surface captured relatively small numbers of leeches, yet the catches showed that in summer some of the leeches moved to the upper layers of the water column, which was free of macrophytes. No differences in leech species richness or diversity were found with respect to fish status of ponds; however, bottom erpobdellids were more abundant in high-fish ponds. Redundancy analysis identified the fish status of the ponds and total nitrogen concentration in water as the only significant variables explaining the distribution of leeches among ponds. Species distribution was nested, but the nestedness pattern was not affected by the fish status of the pond. While fish are generally believed to adversely affect macroinvertebrate diversity and abundance, our study shows that some predatory leech species can persist in the presence of fish and possibly indirectly benefit from it.
Retinol-Binding Protein 4 and Visfatin Levels in Patients with Periodontitis and Obesity/Overweight: A Systematic Review and Meta-Analysis

Prior studies demonstrated an equivocal conclusion about the association between the level of retinol-binding protein 4 (RBP4)/visfatin and periodontitis patients with obesity. The aim of our study (Prospero ID: CRD42023469058) was to systematically review the available articles linking the biofluid levels of RBP4/visfatin to the comorbidity of periodontitis and obesity. Clinical trials were screened in accordance with specific inclusion criteria from seven databases up to November 2023. A quality assessment was performed with the Newcastle–Ottawa Scale and ROBINS-I tools for observational and interventional trials, respectively. The standard mean difference (SMD) with a 95% confidence interval (CI) related to the RBP4 level was recorded; the other indicators related to the visfatin level were measured via the mean difference (MD) with the corresponding 95% CI, and Fisher’s Z transformation was measured to reveal the association using Review Manager 5.4. The current evidence was based on five observational studies and two interventional studies. All of them were included in the systematic review, and six of them were in the meta-analysis. Statistical analysis indicated that there was no significant difference in the circulating levels of RBP4 in the periodontitis patients with obesity or without, who were labeled as OP or NP, respectively (155 OP-107 NP: SMD = 1.38; 95% CI: −0.18–2.94, p = 0.08), as well as the periodontal healthy patients with a normal weight, who were labelled as NnP (116 OP-79 NnP: SMD = 6.76; 95% CI: −5.34–18.87, p = 0.27). Meanwhile, a significant higher level of serum visfatin was found in the OP patients than that of the NP (86 OP-45 NP: MD = 4.21; 95% CI: 2.65–5.77, p < 0.00001)/NnP (164 OP-88 NnP: MD = 13.02; 95% CI: 7.34–18.70, p < 0.00001) group. In addition, a positive association was observed between the serum RBP4 and body mass index/clinical attachment loss (CAL). And, then, there was a positive association between the serum visfatin and periodontal parameters, including the probing depth, CAL, and plaque index, as well as metabolic parameters, including the total cholesterol, triglycerides, fasting blood glucose, and low-density lipoprotein cholesterol. Here, the circulating RBP4 level was not independently related to the comorbidity of periodontitis and obesity, while serum visfatin was significantly associated with periodontitis and obesity. Notably, the positive association between circulating RBP4/visfatin and the periodontal parameters/metabolic parameters firmly suggested that the higher severity of the obese or periodontal status was associated with an elevated level of serum visfatin or RBP4 in the OP group. With more rigorous longitudinal research, the exact causations between RBP4/visfatin and the patients affected by obesity and periodontitis could be disentangled. RBP4 and visfatin might be novel, enlightening prospective bio-indexes for the targeted treatment of comorbidities.
The Regionalized Health Care System and Access for Mobile Populations in Southern Ontario

This case study of the Ontario regionalized health care system has investigated health care access in the context of an increasingly mobile population (including commuting and seasonal travel). Semi-structured interviews were conducted with staff members of Local Health Integration Networks and a Health Centre within southern Ontario alongside secondary research on federal and provincial health system structures and governance. The results of this research suggest there are mobility and geography system barriers to health care access in this region. The participants characterized obtaining transportation to health services as a current access challenge, and urban development and population growth as future concerns. Commuting and seasonal travel were not considered to present significant barriers outside logistics in arranging appointments. The health care system’s ability to mitigate such barriers may be limited by the structure of the health care system due to the regional-focus of planning and the necessity of inter-organizational
Assessment of spatial variability of soil properties in hot semi- arid northern transition zone of India through remote sensing and geographic information system (GIS)

function of Arc GIS software through spline interpolation method. Soils were non-gravelly to gravelly with the red soils showing high gravel content (2.0 to 60.6%) than black soils (2.6 to 15.3%). The pH of the study area was slightly acidic to alkaline with normal electrical conductivity. The red soils were slightly acidic to alkaline with pH of 5.2 to 8.3 while the black soils were neutral to alkaline with pH of 6.4 to 8.6. The available N, P, K and S varied from low to medium, with majority of samples showing low values. The available Mn status of soils was sufficient (2.8 to 36.1 ppm for black soils and 0.2 to 88.7 ppm for red soils). The available iron was deficient to excess (0.5 to 7.1 ppm and 0.5 to 3.0 ppm for red and black soils, respectively), with majority of the samples deficient to sufficient in iron. Only 16.5 per cent of the study area was found to contain excess Fe. The available Zn and Cu was deficient to sufficient but major portion of the study area showed deficiency of available zinc and copper. The exchangeable Ca was moderate to high (3.2 to 28.6 cmol (p+) kg -1 and 8.6 to 35.8 cmol (p+) kg -1 for red and black soils respectively) but major portion of the study area was sufficient in exchangeable Ca while exchangeable Mg ranged from low to medium (1.9 to 16.5 cmol(p+) kg -1 and 6.0 to 23.2 cmol(p+) kg -1 for red and black soils respectively) . The observed spatial variability in various soil properties that influence soil fertility will help farmers in making crop management decisions that would help to increase crop productivity and improve farmers’ livelihood.
The Tweet and the City: Comparing Twitter Activities in Informational World Cities

This paper informetrically monitors Twitter activities that are related to 31 Informational World Cities. It is a big data analysis of 18 million tweets that have been downloaded via Twitter’s Search API (content-based approach) and Twitter’s Streaming API (location-based approach). The Tweets have been filtered either by search terms (i. e. the city’s name) or geo-locations (coordinates of a city). The analysis was made by mainly using quantitative statistic methods endorsed by several qualitative investigations. It shows that tweet activity related to Informational World Cities varies from city to city. A city’s area or its size of population does not necessarily affect these activities. Factors like the penetration rate of smart phones, number of tourists etc. influences the amount of tweets that are produced in or about a city. Topics are mostly event-driven or related to sports and politics. City names are popular in spam tweets and they are often chained to draw the attention to messages which are not city-related at all (e. g., religious comments). The paper presents an approach for quantitatively analysing tweeting behaviour in Information World Cities to prospectively find distinct indicators of how Twitter activities in Informational World Cities can be classified and how they vary between the different cities.