Miracle-dz/UniFAR-FTUs huggingface dataset:
Fiber post-processing and atomic spectroscopy for the development of atomic-vapour photonic microcell

This thesis reported on atomic spectroscopy for the development of alkaline atomic vapor photonic microcell (PMC). The work is motivated by reproducing the outstanding laboratory environment based performances achieved in the fields of frequency standard and coherent optics in highly compact and stand-alone devices that can be accessible to a wider scientific community or to a commercial market. In our case these future devices will be based a Hollow-core photonic crystal fiber (HC-PCF) filled with a gas phase material to form a PMC, and outstands with an ultra-long long interaction length and micrometric modal area. However, the micrometric scale of the fiber core harboring the atoms raises several technical and scientific challenges. Among the technical challenges, we list the development of efficient process for atom loading inside long hollow fiber with small core diameter, the suppression or mitigation of physio-chemical reactivity of the atoms (i.e. Rubidium) with the fiber core inner-surface silica etc. In parallel, the large surface-to-volume ratio of the fiber-core raises questions like the coherence relaxation dynamics and the nature and effect of the atom-surface interaction. The thesis reports on fiber-core inner surface coating with different materials to mitigate the physio-chemical reactions of the confined atoms with the surface, on tapering large core inhibited coupling Kagome HC-PCF, and splicing technique that ensures low splice loss and no atomic reactivity during the splicing process. In parallel, the thesis reports on a set of spectroscopy experiments to assess the relaxation dynamics of atoms inside HC-PCF and to report on novel sub-Doppler transparencies.
Sustainable Business Practices by Nigerian Organizations

Sustainability is the lifeline of any organization and it begins with its people. This paper investigates the sustainable business practices of Nigerian organizations. The pillars of sustainability, economic, social and environmental, have been linked to improve business performance when entrenched into long term strategies of a business. To address the challenges of power supply, multiple taxation, pollution and waste management faced in the business environment, it is imperative to develop solutions that will not compromise future needs yet meeting the needs of the present. Recycling, recovery and reuse, safe work initiatives, continuous learning are strategies businesses can adopt to reduce environmental waste, social and economic issues. For this purpose, this chapter examines sustainability practices of businesses from selected industries; construction, manufacturing, banking and hospitality industries were selected based on availability of sustainability reports of industry leaders. This research is expected to help business managers and policy makers understand sustainable business practices and its implications on business performance.
Challenging the Reductionism of “Evidence-Based” Youth Justice

The generation of empirical evidence to explain offending by children and young people has been a central driver of criminological and sociological research for more than two centuries. Across the international field of youth justice, empirical research evidence has become an integral means of complementing and extending the knowledge and understanding of offending offered by the official enquiries and data collection of professional stakeholders and an essential tool for informing ‘evidence-based’ policy, practice and ‘effective intervention’. However, it will be argued that the hegemonic empirical evidence-base created by youth justice research over the past two decades has been generated through methodological reductionism - the oversimplification of complexity, the restriction of conceptual lens and the relative exclusion of competing explanatory paradigms and empirical methodologies, which in turn, has reduced the scope and validity of the policy and practice recommendations derived from it.
Feature concatenation for scene classification of VHR images based on canonical correlation analysis.

The recent advancements of technology in remote sensing enable us to get very high-resolution images (VHR). To do scene classification in these images turned significant and become a challenging problem due to the lack of availability of adequate labelled data. We get over fitting problems by training a limited amount of labelled data. Considering the features obtained by deep learning convolutional nets as inputs we address this issue. Here we utilize the existing VGG16, Alex Net frameworks as a feature extractor to extricate informative features from the authentic VHR Images. Succeeding, discuss feature concatenation and classification framework based on Canonical Correlation Analysis (CCA). This strategy uses the correspondence of two feature vectors of discriminant information and eradicates redundant information within the features. This permits a more effective approach than conventional extraction techniques exclusively. The experimental results demonstrate that the feature concatenation strategy based on the CCA technique produces good informative features and accomplishes a higher accuracy with much dimension reduction than exclusively using the raw deep features. We use a 0.3 sub-meter resolution UC MERCED data set to explore our approach.
Evaluation of Global Gravitational Models Based on DGPS/leveling Data over Baghdad University (IRAQ)

GPS becomes one of the most technical favorite geodesy methods. A major dilemma in GPS survey lies in oval-based elevations, while orthometric heights are commonly used in engineering practice. Converting GPS heights into orthometric heights by applying a precise geode model is therefore necessary. This paper aims to model the local geoid in the using study region (h-H). Compared with many global gravitational models (GGMs) and three of them were selected, namely EGM96, EGM84, and EGM2008. The evaluations of the accuracy of these models and then choose the most accurate and closer to the local geode In the field of work, which is mounted on the campus of the University of Baghdad in the capital of Iraq. Techniques require the use of GGM to represent global differences or long wavelengths of The gravity field of the Earth; the gravitational field of the Earth takes into account much of the direct information about the distributions of mass density in the Earth. It then provides the basis for exploring oil and gas in this respect, and the detection of geodesics forms the basis for exploring minerals. The EGM08 is the closer GGMs to the observed local geodetic dataset, in terms of the difference of geoid undulation that have a maximum of) 0.4011m), a minimum of) 0.3996m), an average of [-0.111256m], and a standard deviation of[ ±0.152859m].
Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration

Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subjective to systematic errors, calibration is necessary for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the potential predictability of ETo may not be fully explored by this approach, which ignores the non-linear interactions of input variables in constructing ETo forecasts. We hypothesize that reducing errors in individual inputs as a precursor to ETo forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies, including (i) calibration directly applied to raw ETo forecasts constructed with raw forecasts of input variables, and (ii) bias-correcting input variables first, and then calibrating the ETo forecasts constructed with bias-corrected input variables. We calibrate ETo forecasts based on weather forecasts of the Australian Community Climate and Earth System Simulator G2 version (ACCESS-G2). Calibrated ETo forecasts with bias-corrected input variables (strategy ii) demonstrate lower bias, higher correlation coefficient, and higher skills than the calibration based on raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could enhance ETo forecast calibration and produce more skillful ETo forecasts. This calibration strategy is expected to enhance future NWP-based ETo forecasting.
Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Internet of Vehicles (IoV) is a complex system that consists of resource types such as vehicles, humans, and sensors. Although the Internet of Vehicles is complex, it improvises communication among vehicles on the roads. Quality of service (QoS) enabled the cooperative driving system (CDS) based on 5G technology, enabling vehicles to communicate and cooperate to improve road traffic efficiency. Due to the high vehicle density and limited resources (bandwidth) of current network infrastructure, sometimes a better channel that meets the requirements of cooperative driving is not available that causes network congestion, which directly influences the overall QoS of the CDS. To overcome this, we proposed a 5G network-based architecture for CDS that incorporates a D2D technology-based resource allocation scheme. The proposed network architecture and cooperative behavior-based scheme helps in improving QoS for CDS. We implemented our proposed scheme by incorporating the density-based scattered clustering algorithm with noise for vehicular clustering. The proposed scheme’s performance shows significant improvement in terms of throughput compared with existing D2D approaches.
BEHAVIOURAL ECONOMICS AND PUBLIC POLICIES: SOME INTRODUCTORY ISSUES

The paper aims to inform the domestic public about the basic concepts of behavioural economics, its historical development and intellectual basis, the application of its findings in policymaking, and its effectiveness in current practical application. In the first part of the paper, the author presents the basic concepts of behavioural economics: the nudge, the choice architecture, the libertarian paternalism, cognitive biases, and others. The second part of the paper provides a concise overview of the critiques directed at behavioural economics and libertarian paternalism (Posner's critique, Mitchell's critique), and a review of the empirical validity of certain behavioural economics phenomena. The third part presents the use of behavioural findings to inform the design of public policy in different domains (the behavioural public policy), along with numerous examples, mostly from the practice of the Behavioural Insights Team in the United Kingdom.
MigratoryBirds as a Potential Reservoirs of Cryptococcus Neoformans

Migratory birds can become long-distance vectors for a wide range of microorganisms. The objective of the present study was to investigate the presence of Cryptococcus neoformans in the cloacae, crop and nasal cavity of migratory birds in order to assess their role as potential reservoirs and/or mechanical vectors of human and animal cryptococcosis. A total of 700 samples (cloacae, crops and nasal secretions) of 300 wild migratory birds (with the permission of the local Department of Environment) were collected and inoculated on Niger seed agar (NSA), incubated for two weeks at 30 oC and daily observed for the presence of brown yeast colonies, which is presumptive for C. neoformans. The species identification was confirmed using conventional and molecular methods. Out of 700 samples, 4 samples (0.6%) from cloacae of 2 Anas crecca (2 cases), Anas platyrhychos (1 case) and Fulica atra (1 case) were positive for C. neoformans. To the best of our knowledge, due to low isolation rate of C. neoformans from cloacae, crops and nasal secretions, transmission from these samples could be as a minimal risk factor for human and animal cryptococcosis, unlike the dry excreta of wild pigeons.
Certain Investigations on Bank Customer and Relationship Improvement using Machine Learning

Visual information investigation includes utilizing present-day P.C. illustrations and show instruments to investigate information. The utilization of such visual research techniques has gotten progressively across the board all through the sociologies. This proposed framework centres around client information in business banks and expects to utilize visual portrayals and connection strategies to give another vision of client relationship the board or customer relationship management (CRM). Features of the discoveries incorporate that consumer loyalty has a remarkable positive impact on client dedication in the financial assistance industry, particularly with the salary improving, the connection between them turns out to be increasingly robust. At last, recommendations are advanced on the most proficient method to enhance consumer loyalty and increment client dedication. They are continually vigilant for utilizing them to improve their business advantages, for example, right item advancement to right sort of clients, typical usage of self-administration channels, diminished visits to branches for money exchanges, etc. Select Bank's analysis of bank customer awareness about who to bank financing, credit to give something constraints. In addition, it can make the guarantor to show signs of improvement in their potential and existing customers to understand. As customers analyze is significantly essential for such activities, analysis of a client to create a clear framework for banks. The central aspect of this study is to use the used dataset (labelled) and to create a new label as the target for classification, which reduces the clustering execution time and gets the best accuracy results. The data set ('default of credit card clients) is obtained from the archive of UCI (University of California, Irvine) M.L. Repository.
Influence of light quality on Chlorella growth, photosynthetic pigments and high-valued products accumulation in coastal saline-alkali leachate

Using saline-alkali leachate to cultivate microalgae is an effective way to realize the utilization of wastewater and alleviate the shortage of water resources. Light source is usually used as an optimized parameter to further improve the cultivation efficiency of microalgae. In this work, the influence of light qualities on the growth and high-valued substances accumulation of Chlorella sp. HQ in coastal saline-alkali leachate were investigated. The specific growth rate of Chlorella in coastal saline-alkali leachate was 0.27–0.60 d−1. At the end of cultivation, the algal density under blue light reached 8.71 ± 0.15 × 107 cells·mL−1, which was significantly higher than the other light groups. The lipid content in the biomass was 29.31–62.95%, and the highest lipid content and TAGs content were obtained under red light and blue-white mixed light, respectively. Percentages of total chlorophylls (0.81–1.70%) and carotenoids (0.08–0.25%) were obtained in the final biomass of the coastal saline-alkali leachate. In addition, the contents of photosynthetic pigments and three high-valued products under mixed light were higher than those of monochromatic light, and the protein, total sugar and starch content under blue-red mixed light was 1.52–3.76 times, 1.54–3.68 times and 1.06–3.35 times of monochromatic blue light and red light, respectively.
Analysis of Students’ Perceptions of Online Learning Media During the Covid-19 Pandemic (Study of E-learning Media: Zoom, Google Meet, Google Classroom, and LMS)

Learning in the pandemic period requires students to be able to use a variety of online learning media. Zoom, Google meet, Google classroom, and LMS platforms are the most frequently used media. Students' behavior to use learning media is influenced by perceptions of the learning media. This study aimed to describe the ease and usefulness perceptions that students feel about the various available learning media. This research is designed as quantitative research with a survey approach. Data analysis was carried out using descriptive statistics. The results show that students' perceived ease of learning media, namely Zoom, Google meet, Google classroom, and LMS, is perceived as relatively easy. Zoom is an application/platform that is considered the easiest of the other media, while LMS is considered more complicated than the other three media. Meanwhile, this study's results also show that the perspective of the usefulness of the learning media is considered to have good benefits. Zoom is the application/platform that is considered the most useful from other media, while LMS is considered less useful than the other three media.
A multi-speed fiscal Europe? Fiscal rules and fiscal performance in the EU former communist countries

ABSTRACT This paper shows that, contrary to their favourable effect in the EU non-FCC (Former Communist Countries), fiscal rules do not significantly affect fiscal performance in the group of EU FCC. This finding, which may echo differences between FCC and other EU inherited from the Cold War period, is robust when considering various estimation methods, dividing fiscal rules along various dimensions, and using several observed and computed measures of fiscal performance. However, when going beyond the simple presence of fiscal rules, we find that an improvement of the strength of fiscal rules significantly affects fiscal performance in EU FCC, with a magnitude higher than that in EU non-FCC. Our findings are particularly important from the perspective of the future Eurozone and EU enlargements, which involve former communist countries, and go along with the adoption of various types of fiscal rules.
Geophysical Monitoring Shows that Spatial Heterogeneity in Thermohydrological Dynamics Reshapes a Transitional Permafrost System

Climate change is causing rapid changes of Arctic ecosystems. Yet, data needed to unravel complex subsurface processes are very rare. Using geophysical and in situ sensing, this study closes an observational gap associated with thermohydrological dynamics in discontinuous permafrost systems. It highlights the impact of vegetation and snow thickness distribution on subsurface thermohydrological properties and processes. Large snow accumulation near tall shrubs insulates the ground and allows for rapid and downward heat flow. Thinner snow pack above graminoid results in surficial freezing and prevents water from infiltrating into the subsurface. Analyzing short‐term disturbances, we found that lateral flow could be a driving factor in talik formation. Interannual measurements show that deep permafrost temperatures increased by about 0.2°C over 2 years. The results, which suggest that snow‐vegetation‐subsurface processes are tightly coupled, will be useful for improving predictions of Arctic feedback to climate change, including how subsurface thermohydrology influences CO2 and CH4 fluxes.
Effect of Stimulus Rates in Chirp and Click Evoked Auditory Brainstem Response in Adults with Normal Hearing Sensitivity

Introduction The effects of increasing stimulus repetition rate on the ABR using click stimuli have been investigated in normal and hearing impaired subjects with neurologic abnormality but there is limited study on the effect of stimulus repetition rate on ABR using chirp stimuli. The present study aims to compare the chirp evoked auditory brainstem responses with reference to changes in latency of peaks, interaural latency differences and interwave latency intervals as a function of rate and compare those responses with the click evoked auditory brainstem responses, in normal hearing subjects. Materials and Methods Total 30 normally hearing adults were considered for this study. All participants were screened for normal hearing sensitivity upto 8 kHz in pure tone audiometry for middle ear pathology and central auditory processing disorder. Four parameters of ABR were considered to assess in this study including absolute latency, interwave latency intervals, latency-rate function and interaural latency. ABR was done based on the protocol of this study. Results Results revealed that there was a significant difference in the absolute latency and interwave intervals when the stimulus repetition rate was increased. Conclusion The latencies of wave III and V increases and waveform morphology changed as the stimulus repetition rate increased above 20/sec. The absolute latency of wave III and V was found to be shorter than clicks and can be used especially in newborn hearing evaluation assuming in shorter time window.
Multi-Criteria Decision Support System for Lung Cancer Prediction

Lung cancer is one of the most common deadly malignant tumours, with the most rapid morbidity and death worldwide. Cancer risk prediction is a challenging and complex task in the field of healthcare. Many studies have been carried out by researchers to analyse and establish lung cancer symptoms and factors. However, further improvements are vital and required to be conducted in order to overcome the persistent challenges. In this study, a multi-criteria decision support system for lung cancer risk prediction based on a web-based survey data has been presented and realised. The proposed framework aims to incorporate the powerful of analytical hierarchy process (AHP) with artificial neural network for constituting lung cancer prediction model. The multiple criteria decision-making strategy (AHP) assigns a weight to each individual cancer symptom feature from survey data. The weighted features are then used to train multi-layer perceptron artificial neural network (ANN) to build a disease prediction model. Experimental analysis and evaluation performed on 276 subjects revealed promising prediction performance of developed lung cancer prediction framework in terms of various classification metrics.