Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer Preferences Towards Book Formats: How to Address Technological Disruption in the Publishing Industry

Consumer Preferences Towards Book Formats: How to Address Technological Disruption in the Publishing Industry By Felipe Cotrino April 2021 Chair: Naveen Donthu Program: Doctorate in Business Administration The digital era has disrupted the publishing industry in many fronts during the last two decades. The way people consume media content has evolved dramatically, creating alternative platforms to the traditional printed material. Specifically for the book segment, publishing companies have developed new formats in order to capture some of the new digital trends, offering to the public not only the traditional paper-based book, but also an electronic and audio version. These new formats have helped the companies in the industry to partially keep the pace of the market dynamics, but on the other hand have severely increased the cost of production and commercialization for each title, limiting their ability to increase the product offer and consequently to maintain a sustainable and profitable growth on the mid and long term. Additionally, the providers of the publishing industry, specifically, the printing suppliers have been severely affected by these new megatrends and uncertainty on the publishing houses. Historically, the printing industry has been a solid driver of the whole value chain of books production; providers of ink, paper, other supplies as well as printing equipment and technology have faced similar impact, significantly threatening their stability.
2D-to-3D Lifting of Sign Language Body Poses with Recurrent Neural Networks

This paper aims at improving the quality of a dataset that contains multiple sequences of 3D poses extracted from American Sign Language videos. Each pose consists of 147 points with three coordinates each. We propose an algo-rithm able to correct missing points as well as to add some constraints such as the length of the bones. To prove the quality of the algorithm’s outcome, we evaluate the task of lifting 2D to 3D poses with a deep learning model trained on raw data, and another one trained with the preprocessed data.
EHANCING THE IMAGE SUPER-RESOLUTION VIA U-NET ARCHITECTURE FOR IMPROVED VISUAL QUALITY

: Classical method for upscaling an image has been done using Bi-Linear Interpolation, a method which interpolates a pixel in a 2d image. This bi-linear interpolation takes 4 nearest neighbors for the current selected pixel and outputs the results based on the weighted average taken. This traditional bilinear interpolation upscaling method leaves the scaled image blurry due to over-smoothening of neighbouring pixels i.e. averaging the pixels of the neighbors.In order to avoid the loss of fine detail, we are in need of a new technology which up scales an image without losing the quality. We implemented a deep convolutional neural network (U-Net) which is widely used in the area of Image segmentation. It helps us to upscale the image without losing the quality. We used a subset of the ImageNet dataset and Oxford-IIIT PETS dataset for training and testing the model. The prediction results are far better than the classic image upscaling methods with remarkably fine details. Thus, we think our model serves the need of a good alternative to the Classical Image Upscaling method which is used in the area of Image Processing and Image Restoration.
Efficacy of Some Pyrethroid Nanoemulsions against cotton Leafworm Spodoptera littoralis (Boisd.): Toxicity, Biochemical and Molecular Docking Studies

Although pesticides are favourable for pest controlling because more than 30–40% of the food production is lost due to pests, the extensive use of pesticides has harmful effects on the environment and health. Nanotechnology techniques are recently used in developing agrochemical formulations. Nanoemulsions as new approaches have attracted great attention in delivering many poorly soluble in water active compounds. In this study, the most widely used pyrethroid insecticides (cypermethrin, deltamethrin, and lambda-cyhalothrin) were formulated as nanoemulsions by high-energy ultrasonication. The droplet sizes of prepared nanoemulsions have been investigated by Dynamic Light Scattering (DLS) and Transmission Electron Microscope (TEM), characterization result reveals the size of particles in the range 24.42-84.99 nm. Formulated nanoemulsions in comparison with the active ingredient (a.i) and emulsifier concentrate (EC) were evaluated against 4 instar larvae of S. littoralis. The in vivo activity levels of targeted enzymes, acetylcholinesterase (AChE), and adenosine triphosphate (ATPase) were studied in two bioassay methods (leaf dipping and topical application). Our finding showed that cypermethrin nanoemulsion has the highest activity with LC50 = 19.92 mg/l and LD50= 2.11 ng/larva. Furthermore, the developed nano-formulation showed adequate toxicity levels on AChE and ATPase compared to the active ingredient and EC formulations. The protein-ligand docking was also studied, and the docking poses showed that the insecticides had an excellent binding affinity to the active site of the target enzymes. On this basis, these results suggest that oil in water (O/W) nanoemulsions would be beneficial and can be applied for delivery insecticide formulations.
A COMPARITIVE STUDY OF FABRIC DETECTION AND CLASSIFICATION USING EFFICIENT HYBRID LEARNING ALGORITHM

Textile industries are one among important industries that contribute to the GDP of a nation. Century after century there has been advancement in this particular industry. The revenue generated by this sector is decided by the quality of the fabric items. Any fabric that is defect less has a good reception in the market and defective material produces only half of the production cost. Manual defect identification is a tiresome procedure where a individual is assigned to identify the defect in the running fabric. The success rate of such system is only 60%. An automated defect detection technique is the best solution for most of the textile industry as it produces around 96% efficiency in identifying the defect. The proposed algorithm is a new approach to embed with supervised and unsupervised learning classification techniques like support vector machine and K-Means clustering. The technique uses the GLCM texture features for the classification. The output depends on both of the classification results. The accuracy obtained in this embedded technique is 95%.
Population-based tooth loss and risk factors

The importance of dental health to a person's general health and well-being cannot be overstated. One of the most significant oral health markers is the capacity to keep more teeth throughout life. According to the World Health Organization's (WHO) oral health goals for 2020, there should be an increase in the number of people with functioning dentitions (21 or more natural teeth) between the ages of 35 and 44 and 65 and 74 [1].
Remoção de resíduos de fungicidas por meio de chuva simulada e de irrigação em batateira e tomateiro

Estudaram-se os residuos de mancozeb (Mn), mancozeb NT (NT) e hidroxido de cobre (Hc) em batateira e tomateiro em condicoes controladas, para analisar a influencia de diferentes intensidades (0, 30, 40, 60 e 97 mm/h), duracao de chuva simulada (5, 10, 20 e 40 min) e tempo de secagem da calda fungicida (1, 2, 4, 6 e 24 h). Em condicoes de campo, avaliou-se a remocao de residuos da folhagem de batateira pela irrigacao e chuva em dois ensaios. Sob condicoes controladas, pulverizaram-se plantas com 45 a 60 dias apos o plantio com Mn e NT, ambos a 2,4 kg de i.a. /ha, e Hc a 1,0 kg de i.a. /ha; a seguir, elas foram submetidas aos tratamentos em simulador de chuvas. Com o aumento da intensidade de chuva simulada, houve queda exponencial de Mn e NT; a mesma tendencia foi observada para a remocao de residuos de Hc. Em ambas as hospedeiras, houve reducao de residuos com o aumento do tempo de duracao de chuva, inclusive Hc em tomateiro. Nao houve diferenca significativa entre as perdas de Mn e NT tanto da superficie foliar de batata quanto de tomate. Quanto maior o tempo de secagem, menor a remocao de residuos de todos os fungicidas. Os ensaios foram realizados em lavoura de batata e com os mesmos fungicidas. No segundo ensaio, a lâmina de agua aplicada foi 65 mm maior que no primeiro. Duas amostragens foram realizadas (semanas 1 e 2). O modelo linear descreveu a remocao de todos os fungicidas. Nas semanas 1 e 2 do primeiro ensaio, a taxa de remocao de Mn foi maior que a de NT. A intensidade de requeima foi baixa, porem a testemunha no E1 diferiu dos tratamentos em relacao a area abaixo da curva de progresso da doenca (AACPD) e severidade maxima (P= 0,0001). Nao houve diferenca entre os tratamentos quanto a severidade na metade da epidemia (Y50). No segundo ensaio, nao houve diferenca entre os tratamentos quanto a severidade maxima, tampouco na metade da epidemia, diferindo apenas quanto a AACPD entre a testemunha e os demais tratamentos. Apos a aplicacao dos fungicidas, chuvas a partir de 30 mm/h podem remover residuos de fungicidas da superficie foliar. E necessario tempo de secagem minimo de 6h para assegurar menor remocao desses residuos da superficie foliar. %%%%The aim of this study was to evaluate the removal of mancozeb (Mn), mancozeb NT (NT), and copper hydroxide (Hc) residues from potato and tomato leaflets by rain and/or irrigation. Under controlled conditions different intensities of simulated rain (0, 30, 40, 60, and 97 mm / h), duration (5, 10, 20, and 40 min) and time of drying of the sprayed fungicide suspension (1, 2, 4, 6, and 24 h) were assessed. Greenhouse- grown plants were sprayed with Mn and NT at a rate of 2.4 kg of a.i. / ha and 1.0 kg of Hc a.i. / ha; and let to dry before being taken to the rain simulator. Increasing intensities of simulated rain caused exponential drop of Mn and NT; the same trend was observed for removal of Hc. In both hosts, there was a reduction of residues of all fungicides by increasing the rain duration. No significant difference between the removal of NT and Mn in both potato and…
On the Benefits of Randomly Adjusting Anytime Weighted A

Anytime Weighted A*---an anytime heuristic search algorithm that uses a weight to scale the heuristic value of each node in the open list---has proven to be an effective way to manage the trade-off between solution quality and computation time in heuristic search. Finding the best weight, however, is challenging because it depends on not only the characteristics of the domain and the details of the instance at hand, but also the available computation time. We propose a randomized version of this algorithm, called Randomized Weighted A*, that randomly adjusts its weight at runtime and show a counterintuitive phenomenon: RWA* generally performs as well or better than AWA* with the best static weight on a range of benchmark problems. The result is a simple algorithm that is easy to implement and performs consistently well without any offline experimentation or parameter tuning.
Model Organisms for Studying Decision-Making: A Phylogenetically Expanded Perspective

This article explores the use of model organisms in studying the cognitive phenomenon of decision-making. Drawing on the framework of biological control to develop a skeletal conception of decision-making, we show that two core features of decision-making mechanisms can be identified by studying model organisms, such as E. coli, jellyfish, C. elegans, lamprey, and so on. First, decision mechanisms are distributed and heterarchically structured. Second, they depend heavily on chemical information processing, such as that involving neuromodulators. We end by discussing the implications for studying distinctively human decision-making.
Assessment of post-operative pain, swelling and trismus following mandibular third molar extraction after local administration of dexamethasone-a retrospective study

The aim of this study was to assess the post operative pain, swelling and trismus following mandibular third molar extraction after local administration of dexamethasone. This study was conducted among 142 patients who had undergone surgical removal of mandibular third molar reported to a private dental hospital from June 2019 to March 2020 . Data was collected and tabulated in excel. Data was analysed using SPSS version 20 . Results of this study showed that among 142 patients, consisting of 63 females (44.37%) and 79 males (55.63%). There was no statistically significant association between age, gender, tooth number and dexamethasone. Patients experienced less postoperative pain, swelling and trismus after local administration of dexamethasone. We conclude from the present study that the dexamethasone decreases the pain, swelling and trismus following mandibular third molar extraction.
EFFECT OF EMPOWERMENT AND SELF EFFICACY ON NURSES WORK SATISFACTION IN MAKASSAR CITY HOSPITAL AND STELLA MARIS HOSPITAL

Job satisfaction is the attitude that an employee has about the job and the organization in which they carry out the job as an employee's affective reaction to a job that is based on a comparison between actual results and desired results. This study aims to analyze the influence of Empowerment and Self Efficacy on Job Satisfaction of Nurses at Makassar City Hospital and Stella Maris Hospital. This type of research is a quantitative study using an observational study with a cross sectional study design. Sampling using stratified random sampling so that the sample in this study were nurses in inpatient installations, amounting to 186 respondents. The results showed that there was a relationship between empowerment and self-efficacy with job satisfaction of nurses and the variables that had the most influence on job satisfaction of hospital nurses in Makassar City Hospital and Stella Maris Hospital were self-efficacy variables. Therefore, it is hoped that hospital management for nurse work satisfaction by creating a safe and comfortable work environment, preparation of SOPs for actions that are easy to understand and easy to implement in accordance with the competence of nurses, assigning tasks to employees is expected that the leadership can assign tasks to employees in accordance with the ability of employees, provide training and provide good infrastructure so that employees can carry out their duties to the best of their abilities.
ACCEPTANCE SAMPLING FOR NETWORK INTRUSION DETECTION WITH A DECISION TREE CLASSIFIER

: A Network Intrusion Detection System (NIDS) detects anomalous network flows by inspecting the incoming packets for malicious patterns and notifies to the network administrator, if any. The Acceptance Sampling for Network Intrusion Detection (ASNID)method inspects a random sample of packets in a network flow to detect whether it is anomalous or not. But ASNID used sequential search for pattern matching. The NIDS with decision tree conducts nonlinear search for pattern matching but they inspect all packets of network flows which is computationally prohibitive. This study proposes Network Intrusion Detection with Decision Tree adopting acceptance sampling (ASNIDDT) method. It is proved experimentally that the Geometric Mean Accuracy Index (GMAI), the performance metric increases asthe sample of percentage of network flows increases and the computation time is further decreased.
Emerging infectious diseases: the Fifth stage

Epidemlologlc transition theory The demographic record of anatomically modern human beings suggests that, for the vast majority of our 130 OOO-year existence, birth rates and death rates have remained at very high levels somewhere between 30 to 50/1 OOO (Omran, 1971). Although birth rates have probably been quite stable for most of human history, death rates have been volatile a volatility caused by the ever-present deadly force of locally-concentrated epidemic infectious and parasitic diseases (IPDs) in our early ancestors, but punctuated by the episodic influence of pandemic plagues and infections that wiped out relatively large segments of the population (McNeil, 1977). The historical record also suggests that persistently high levels of "extrinsic" mortality (dominated by IPD deaths, but also including accidents, homicide, suicide, and predation) are the primary reason that survival to sexual maturity has been denied to the majority of human beings ever born. It has only been within the last few hundred years that social and economic changes have permitted people in the developed world to avoid the causes of death that have killed the vast majority of our ancestors. Sadly, the same cannot be said for developing nations. In these countries, where the majority of the human population resides, IPDs are still a major influence on the day-to-day lives of most people. The transition from high to low vital rates has been referred to as the demographic transition a phenomenon that has been documented and discussed extensively in the scientific literature (Davis, 1963; McNamara, 1982; Teitelbaum, 1975). The mortality component of this transition has been further elaborated within what is called epidemiologic transition theory (Omran, 1971). According to Omran, the transition from high to low death rates has been characterized by a secular transformation of vital rates along a three-stage process population changes that have led to rapid increases in life expectancy at birth, a shift in the age distribution of death from the young to the old, and an accompanying shift in the underlying causes of death. The First stage of the epidemiologic transition Omran's Age of Pestilmce and Famine refers to a
Interactive comment on “Physical processes of cooling and megadrought in 4.2 ka BP event: results from TraCE-21ka simulations” by Mi Yan et al

manuscript. The Ning et al. paper uses the same model simulation (TraCE-21ka) to analyze the 8.2 ka and 4.2 ka events. Both papers present analysis using the same techniques (anomaly maps, principal component analysis). The Yan et al. paper (this review) provides a more in-depth analysis of the 4.2 ka event, but it is unclear why two papers are necessary. Perhaps even more important, the two papers come to conflicting conclusions about the cause of the 4.2 ka event. In Ning et al., it is stated “We speculate that long term changes in insolation related to precessional forcing led to cooling, which passed a threshold around 4500 years B.P., leading to a reduction in the AMOC and associated teleconnections across the globe. Based on widespread paleoclimatic evidence for the onset of neoglaciation (Solomina et al., 2015), it seems clear that there was a fundamental shift in climate around this time. ” Whereas, Yan et al. argue that stochastic variability internal to the climate system caused the 4.2 ka event independent of any external climate forcing.
A COMPARATIVE ANALYSIS OF ECDSA V/S RSA ALGORITHM A COMPARATIVE ANALYSIS OF ECDSA V/S RSA ALGORITHM

-Elliptic curve Cryptography with its various protocols implemented in terms of accuracy and fast observation of results for better security solution. ECC applied on two finite fields: prime field and binary field. Because it is public key cryptography so, it also focus on generation of elliptic curve and shows why finite fields are introduced. But for accurate observation we do analysis on category of cryptographic primitives used to solve given security problem. RSA & ECDSA both have basic criteria of production of keys and method of encryption and decryption in basic application as per security and other properties which are authentication, non-repudiation, privacy, integrity.
Deep Auto-Encoding and Biohashing for Secure Finger Vein Recognition

Biometric recognition systems relying on finger vein have gained a lot of attention in recent years. Besides security, the privacy of finger vein recognition systems is always a crucial concern. To address the privacy concerns, several biometric template protection (BTP) schemes are introduced in the literature. However, despite providing privacy, BTP algorithms often affect the recognition performance. In this paper, we propose a deep-learning-based approach for secure finger vein recognition. We use a convolutional auto-encoder neural network with a multi-term loss function. In addition to the auto-encoder loss function, we deploy triplet loss for the embedding features. Next, we apply Biohashing to our deep features to generate protected templates. The experimental results indicate that the proposed method achieves superior performance to previous finger vein recognition methods protected with Biohashing. Besides, our proposed method has less execution time and requires less memory.1
Solitons in Cellular Neural Networks

The two-dimensional autonomous cellular neural networks (CNNs) having one layer or two layers of memristor coupling can exhibit many interesting nonlinear waves and bifurcation phenomena. In this paper, we study the nonlinear waves (solitons) in the one-dimensional CNN diﬀerence equations . From our computer simulations, we found that the CNN diﬀerence equations can exhibit many interesting behaviors. The most remarkable thing is that the ﬁrst-order linear CNN diﬀerence equation can exhibit a train of solitary waves, if the initial condition is given by the unit step function . Furthermore, the second-order linear CNN diﬀerence equation can exhibit soliton-like behavior, if the initial condition is given by a pulse wave . That is, the solitary waves pass through one another and emerge from the collision. Furthermore, the solution exhibits the area-preserving behavior , and it returns exactly to its initial state (the recurrence of the initial state ). In the case of the nonlinear CNN diﬀerence equation, we observed the following interesting behaviors. In the Korteweg-de Vries CNN diﬀerence equation, the three-dimensional plot of the interaction of the solitary waves looks like a chicken cockscomb . In the Toda lattice CNN diﬀerence equation, a train of solitary waves with a negative amplitude interact with a train of solitary waves with a positive amplitude, and they emerge from the collisions. Furthermore, after a certain period of time, the solution breaks down. In the Sine-Gordon CNN diﬀerence equation, the solution moves at constant speed, and it emerges from the collision. Furthermore, the solution returns the state which is roughly similar to the initial state. In the memristor CNN diﬀerence equations, the three-dimensional plots of solitary waves exhibit more complicated (chaotic or distorted) behavior.