Miracle-dz/UniFAR-FTUs huggingface dataset:
“The darkest time in our history”: An analysis of news media constructions of liquor theft in Canada’s settler colonial context

In September 2018, there was a surge of news stories about liquor store theft in Winnipeg, Canada that resulted in public and political calls for action, and ultimately led to the introduction of a range of new security and surveillance measures at government owned liquor stores. This brief news cycle provided opportunities for various social actors, politicians, and authorities to make claims about the nature of crime and society more broadly. This article analyzes recent news media coverage of liquor store theft in Winnipeg, Canada and the social construction of an ostensibly new crime trend in the city: “brazen” liquor store thefts. We employ a qualitative content analysis of news articles about liquor store theft published in local Winnipeg news media between 2018 and 2020 (n = 147). Drawing on the social constructionist paradigm, and Fishman’s conceptualization of “crime waves,” we argue that the framing of liquor theft via news media reflects longstanding cultural tropes and myths about crime, as well as hinting at but never fully confronting, deeply engrained colonial and racialized stereotypes. This paper contributes to our understanding of the ways putative social problems are made intelligible in the media. We demonstrate how “crime waves” are shaped by and shape dominant tropes about crime, safety, and citizenship. We argue that something as mundane as liquor theft reveals much about the historical, colonial and social roots of crime in local and national contexts.
Some Aspects of Engine Noise

Although a great deal has been said and written in the past eight years or so about engine noise, previous discussion has been very largely concerned with the physical aspects of jet noise and with the possibility of suppressing it by means of special propelling nozzles and ground mufflers. There is however another, and in general a better, way of dealing with jet noise, namely by using engines of low specific thrust, i.e. of low exhaust velocity and one purpose of the paper is to examine the possibilities of such engines. This leads to a consideration of blade noise from fans and turbines for as jet noise is reduced this tends to become the dominant factor. But first of all it is necessary to re-examine the nature of the noise problem.
Predicting Egyptian Customers’ Intention to Use Financial Technology Services in Banking Sector in the era of COVID-19: Testing a Mediating Role of Theory of Planned Behaviour (TPB) model

: Purpose – Financial Technology (Fin-tech) has attracted a wide range of attention and is rapidly proliferating. In addition, it is often seen today as the new marriage of financial services and information technology. So that This paper examines the determinants of the indivi dual’s intention to use Fin -tech services. The study highlights the importance of the Theory of Planned Behaviour (TPB) to predict whether individuals will accept the use of Fin-tech. Finally, the purpose of this paper is to examine the relationships among dimensions of Financial Technology service quality (Reliability, service security, Ease of Use) towards the overall behavioral intentions to use Fin - tech services in the Egyptian banking sector through the COVID-19 pandemic situation and explain the indirect effects between service quality and behavioral intentions to use Fin - tech services using TPB variables as the mediating variables. Design/methodology/approach
Plant Leaf Diseases Fine-grained Categorization using Convolutional Neural Networks

The difference of leaf disease images within the class is large but the difference between the class is small, so it is very important to represent the features of the local area of the target. Moreover, the complex network occupies a large amount of computer memory and wastes a large amount of computing resources, which is difficult to meet the needs of low-cost terminals. This paper proposes a fine-grained disease categorization method based on attention network to solve the problem. In “Classification Model”, attention mechanism is used to increase identification ability. “Reconstruction-Generation Model” were added during training and the “Classification Model” have to pay more attention to differentiate areas to find differences instead of paying more attention to global features. And adversarial loss was applied to distinguish the generated image from the original image to suppress the noise introduced by the “Discrimination Model”. Due to the feature that “Reconstruction-Generation Model” and “Discrimination Model” are only used in training and do not participate in the operation of inference phase, which can not increase the complexity of the model. Compared with the traditional classification network, the method of generalization ability enhancement further enhances the identification accuracy. And the method needs less memory but can achieve low performance terminal real-time identification of peach and tomato leaf diseases. And it can be applied in other crop disease identification fields with the similar application scenarios.
Unveiling Neuromarketing and Its Research Methodology

Neuromarketing is the union of cognitive psychology, which studies mental processes, neurology and neurophysiology, which study the functioning and responses of the brain and body physiology to external stimuli, and marketing, which studies valuable exchanges, to explain marketing effects on customers’ and consumers’ behaviours and on buying and decision processes. It includes a set of research techniques that, by observing and evaluating how the brain and other body parts respond, avoids possible biases and provides truthful and objective information on consumer subconscious. The term “consumer neuroscience” covers academic approaches using techniques such as fMRI, Eye Tracking, or EED. The objectives of this entry are to show what neuromarketing is and what added value it brings to the study of consumer behaviour and purchase decision processes. The conclusions show a favourable future and positive attitudes towards neuromarketing.
Intelligent Selection Algorithm of Optimal Logistics Distribution Path Based on Supply Chain Technology

How to realize the intelligence of logistics distribution is a hot research topic at present. How to reasonably allocate vehicles, optimize driving routes and travel time, deliver goods to customers on time at the lowest cost, and realize efficient and low-cost operation of the logistics distribution system has always been a problem in academia and industry for many years. Logistics enterprises face problems such as low efficiency of logistics operation, lack of scientific rationality of logistics resource planning, and lack of overall optimization of logistics management operation mode. These are severe tests that steel companies must accept. Under the background of logistics supply chain, the integrated service platform of logistics supply chain has become an urgent research topic. This study takes a steel enterprise as the main research background. On this basis, the two core modules of warehousing and distribution in the logistics business of iron and steel enterprises are qualitatively analyzed, the concept of business process reengineering is proposed, and the logistics supply chain of iron and steel enterprises is established. The concept of comprehensive service platform is realized through RFID technology. In addition, this study conducts a comprehensive analysis and research on the logistics distribution path optimization and vehicle scheduling problem, designs and implements a logistics vehicle scheduling management system, and then adopts the multiobjective method to solve the logistics distribution path planning problem, SMEs. Genetic algorithm and a simulation decision-making subsystem suitable for this problem are designed, which can better solve the problem of route optimization and vehicle scheduling in small-scale distribution.
Predicting footfal-induced vibration. Part I

The current paper reviews different methods that are presently available for the prediction of footfall-induced vibration in structures. Five such methods are considered in detail and it will be shown that these vary considerably in formulation, applicability and predicted response. Where possible, the theoretical basis, complexity, robustness, rigour, reliability and limitations of each are considered. This paper is presented in two parts. Part I deals with bridges and floors having vertical natural frequencies below 10 Hz, part 2 deals with floors whose first vertical natural frequency is over 10 Hz.
Google News and Machine Gatekeepers: Algorithmic Personalisation and News Diversity in Online News Search

Abstract Through a mixed methods research design, we address normative aspects of news recommendation engines by examining whether search personalisation and news diversity are evident on Google News in the UK. First, in a quasi-experimental design, we asked a diverse set of participants (N = 78) to search Google News using four search terms and report the first five articles recommended for each term. We found little evidence of news personalisation, which challenges the claim that news search algorithms contribute to weakened viewpoint diversity. We also found a high degree of homogeneity in news search results, with legacy media brands dominating. Second, we conducted a manual content analysis of the articles recommended by Google News for our search terms (N = 192), focusing on favourability towards each term. We found that while there was little relationship between the favourability slant of the articles and political leanings of participants, there were two exceptions: self-identified right-wing participants were more likely to see unfavourable stories about 1) immigration, and 2) a left-wing politician. This reopens the question of news search engines’ contributions to polarisation and viewpoint diversity for certain news consumers.
Online Time and Frequency Domain Identification of a Resonating Mechanical System in Electric Drives

Niko Nevaranta Online Time and Frequency Domain Identification of a Resonating Mechanical System in Electric Drives Lappeenranta 2016, 67 pages Acta Universitatis Lappeenrantaensis 713 Diss. Lappeenranta University of Technology ISBN 978-952-265-995-8, ISBN 978-952-265-996-5 (PDF) ISSN-L 1456-4491, ISSN 1456-4491 In modern machinery, the dynamic performance of electric drives is often limited by the mechanical characteristics of the system such as flexibilities. With the growing demand for high-performance machinery, there is an increasing need for techniques to estimate mathematical models in real time that describe these mechanical systems and the possible changes in order to obtain a high-performance control. At the same time, requirements for high reliability are continuously increasing, which significantly motivates to improve system identification methods for the diagnostics and condition monitoring of mechanical parts in electric drives. A proper real-time system identification method is of great importance in order to obtain an analytical model that sufficiently represents the most important characteristics of the identified system. Even though many identification methods have been proposed in the system identification literature, there is a strong motivation to develop computationally efficient algorithms for online frequency response estimation. Especially, online nonparametric identification could provide several opportunities for fault diagnostics and robust controller design. In this doctoral dissertation, the online system identification of a resonating mechanical system in an electrical drive is studied. The discussion covers closed-loop identification approaches, which are based on both time and frequency domain observations. The time domain identification approach employs a closed-loop output error-based identification routine. In addition, two different types of frequency domain identification approaches are proposed that are based on a time-frequency representation of signals by applying sliding-DFT and Kalman filters. It is shown that the proposed online frequency domain methods provide a good alternative to the conventional time domain online identification solutions. Theoretical approaches are tested with experimental mechanical test setups that can be regarded as resonating two-mass systems. The experimental results confirm the feasibility of the identification methods by verifying the obtained models according to a given validation criterion, thereby showing that the system dynamics can be identified with an accuracy that makes it possible to apply the proposed approaches for online frequency response analysis.
Sense of Coherence Predicts Physical Activity Maintenance and Health-Related Quality of Life: A 3-Year Longitudinal Study on Cardiovascular Patients

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. A physically active lifestyle can improve the health-related quality of life (HRQoL) of people with CVD. Nevertheless, adherence to a physically active lifestyle is poor. This study examined the longitudinal (pre-event, 6-, 12-, 24-, and 36-month follow-ups) physical activity profiles in 275 patients (mean age = 57.1 years; SD = 7.87; 84% men) after the first acute coronary event. Moreover, it investigated the associations among physical activity, sense of coherence (SOC), and HRQoL. Physical activity profiles were identified through latent class growth analysis, and linear regressions were then performed to explore the association between physical activity, SOC, and HRQoL. After the cardiovascular event, 62% of patients reached adequate physical activity levels and maintained them over time (virtuous profile). The remaining 38% could not implement (23%) or maintain (15%) a healthy behavior. A strong SOC at baseline (standardized β = 0.19, p = 0.002) predicted the probability of belonging to the virtuous profile. Moreover, a strong SOC at baseline (standardized β = 0.27, p < 0.001), together with the probability of belonging to the virtuous profile (standardized β = 0.16, p = 0.031), predicted a better HRQoL at the final follow-up. Findings showed a strong relationship between SOC, the ability to adopt a physically active lifestyle stably over time, and HRQoL in patients with CVD. They suggest the importance of tailoring physical activity interventions by promoting resilience resources such as SOC to improve patients’ quality of life after an acute coronary event.
Service performance and ISO 9000: Exploring the Academic Library Users Satisfaction (ALUS) model as proxy for university quality level

This study aims to examine a first-order CFA model designed to test the multi-facet of academic library user satisfaction theoretical construct. Particularly, the study aims to test the hypothesis that ALUS (Academic Library Users Satisfaction) is a multidimensional construct consisted of five factors - quality of staff services, quality of electronic services, suitability of library services, availability of library physical, and library service accessibility.This study involved 3605 samples.The data accessed across multiple groups of library user's background.The reliability tests displayed the internal consistencies of the five factors were satisfactorily (a=0.94). To evaluate the extent to which the hypothesized model fitted or in other word,adequately described the sample data, the results showed that the GFI was 0.91, AGFI was 0.89, and the PGFI was 0.75. This result indicated that the model was highly goodness fit and all dimensional constructs supported the proposed theoretical model.
Analysis of Three Actors: Roles of Government, Private Sector, and University toward Startup Growth in Yogyakarta

Information, communication, and technology advancements in 21 st century encourage startups to innovate and develop their business further. Because it’s an ICT based business which is supported by the existence of internet, this kind of business starts to become borderless. As a result, the distributions of the products start to become unlimited. The conveniences offered by these advancements invite more ICT based product developers. The positive impact as a result of that such as more new jobs, easier way to access the markets, and also cheaper production cost with guaranteed profits. In addition to that, this of course also becomes an added value to the country because it increases Network Readiness Index and nation’s income from taxes. This research aims to analyze startup’s growth, in addition to its relation with related actors such as government, private sectors, and universities. Analysis on every actor will emphasize on their contribution to startup developments in Daerah Istimewa Yogyakarta directly or indirectly. In conclusion, there will be an illustration about how big the contributions and how close the actors are between startups and the three actors.
Use of Intravenous Paracetamol Preoperatively Favors Lower Risk of Delirium and Functional Recovery in Elderly Patients with Hip Fracture: A Propensity Score-Matched Analysis

We aimed to investigate whether the use of intravenous paracetamol (IVP) preoperatively in intertrochanteric fracture (IF) patients aged 65 years or over receiving intramedullary fixation had significantly benefits on the pain score at discharge, delirium incidence, length of hospital stay (LOS), functional outcomes, and mortality. A retrospective analysis of all surgically treated patients presenting with IF was conducted at a single Level I trauma center in China between Jan. 2016 and Jan. 2020. The data concerning patients' demographics, injury-related data, surgery-related data, operation-related data, in-hospital data, and postoperative outcomes were extracted. To minimize potential confounding and selection bias, the propensity score matching (PSM) method was performed via the caliper matching method by using a 1 : 1 ratio. After PSM, McNemar's chi-square tests were used to examine the association of using IVP with outcome analyses. The Spearman correlations of IVP using, pain scores, and the factors which may influence them were also computed. After screening 2963 consecutive patients, 2166 were included finally, including 1576 in the non-IVP group and 590 in the IVP group. After PSM, 531 remained in each group. The pain scores at discharge were significantly between the two groups before and after matching (all p < 0.001). The differences of delirium rate and functional outcomes became significant after propensity score-based matching (p=0.001 and 0.033, respectively), although they were not significant before matching. No significant difference was observed in other operation-related data, LOS, and crude mortality rates at 30-day, 90-day, and 12-month before and after PSM. In conclusion, this study highlights the need for preoperative IVP use to optimize pain control, postoperative functional recovery, and minimize pain-related comorbidities such as delirium in elderly patients with hip fracture.
On the Joint Exploitation of Satellite DInSAR Measurements and DBSCAN-Based Techniques for Preliminary Identification and Ranking of Critical Constructions in a Built Environment

The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.
DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
An Entry Handbook for the Conceptual Design of Mars Missions

The purpose of this investigation is to investigate trends in Mars entry, descent and landing conceptual mission design and propose a method of presenting this information as a handbook for conceptual design. The premise of the project is that Mars entry, descent and landing can be parameterized with five variables: (1) entry mass, (2) entry velocity, (3) entry flight path angle, (4) vehicle aeroshell diameter, and (5) vertical lift-to-drag ratio. For combinations of these input parameters, the following trajectory information will be determined: peak deceleration, peak heat rate, heat load, and the altitude at which Mach 2 is reached (for parachute deployment).