Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Partitioning for VVC Intra-Picture Encoding with a CNN Minimizing the Rate-Distortion-Time Cost

This paper presents a CNN to reduce the encoding time of a VVC-based intra-picture encoder. For encoding a 32 x 32 block, the CNN estimates two partitioning parameters that restrict the allowed coding block width and height. To estimate them such that the encoder skips testing inefficient partitioning modes, we train the CNN as follows: First, we generate training data by encoding sequences without the CNN. While encoding, we test all combinations of the two parameters for each 32 x 32 block and store the resulting Lagrangian rate-distortion-time (RDT) cost. We use the recorded cost to derive the loss function when training the CNN. Consequently, the CNN is trained such that it minimizes the Lagrangian RDT cost. Our CNN reduces the encoding time by 50% with a bit rate increase of 0.9%, which outperforms existing CNN-based approaches. Our generic training approach could also be applied for other encoder parameters.
Invariant Incoherent MIMO Reception Over Doubly Selective Channels

Nowadays, there is a need for wireless communications to operate over a variety of scenarios, including high mobility multiple-input multiple-output (MIMO) applications, that bring along a challenging problem. In channels where Doppler spread and delay spread are high, complexity of coherent detectors and pilot overhead are both raised. This work proposes a non-coherent reception technique, easily scalable to any number of antennas for the MIMO case, using simple coding and decoding structures which take advantage of channel diversity addition in three domains: time, frequency and space, by using virtual trajectories along with space time block-coding. Coarse analytics on the system performance in terms of the bit error rate are computed using Chernoff Boundaries as a function of the channel diversity and the order of the differential modulation, validating the efficiency of the proposed receiver.
Poly(ethylene glycol)-based shape-memory polymers

ABSTRACT Poly(ethylene glycol) (PEG) blends photo-curable and thermal activated shape-memory polymers (SMPs), with different activation temperature (Tswitch), have been synthesized and characterized. PEG blends with different molecular weights were chain-end functionalized with isocyanate ethyl methacrylate and photo-cured with UV lamp. Degree of cross-linking of the blend network, determined by gel content measurement, resulted as higher than 95%. The thermal and thermomechanical properties of these SMPs PEG blends were characterized by differential scanning calorimetry and dynamic mechanical analysis. The shape-memory properties of the networks were quantified using thermomechanical three-point bending experiments and showed strain fixity rates higher than 99% and a minimum strain recovery ratio of 82%.
An Efficient Congestion Control Model utilizing IoT wireless sensors in Information-Centric Networks

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN. The proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme. The evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks. These results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors.
Role of serum miR-21 and miR-92a in colorectal cancer diagnosis as novel molecular biomarkers

max 250 words, single paragraph): The abstract should be complete and understandable without citation, references, table or figure. Use structured abstract: Background, Aim, Materials & Methods, Results and Conclusion. The context and the rationale of the study are presented succinctly to support the objectives. The experimental methods and main results are summarized but should not be overburdened by numerical values or probability values. The abstract ends with a short and clear conclusion.
AP selection in Cell-Free Massive MIMO system using Machine Learning Algorithm

Current cellular networks work by creating autonomous cells that fail to serve many users due to uneven coverage area. With the world becoming more reliant on the wireless communication system, a cellular network with multiple wireless access points, high spectral and energy efficiency is a must. These requirements are fulfilled by a cell-free Massive multiple-input multiple-output (MIMO) network. Apart from being ideal for the current wireless communication's requirements, it can also resolve a lot of currently faced interference issues. With this work, we aim to reduce the complexity so this system becomes more practical to deploy in the real world and reduce one of the limitations of a cell free massive MIMO system. We propose a new access point selection algorithm called CAPS (Cluster based AP selection) for the cell-free Massive MIMO which is aimed at reducing the computations workload and pilot contamination by introducing machine learning algorithm for clustering which in our work is the K-means++ clustering algorithm. The simulations and calculations show that using the proposed algorithm have performed better compared to prevailing approaches.
Sugar Intake Disrupts some Reproductive Functions in Female Wistar Rats

The increasing rate of infertility has raised a lot of concern. Studies show that lifestyle and dietary factors affect reproductive functions. Sugar, a universal sweetener in the diet has been linked with metabolic syndrome and disruption of male reproductive functions. The objective of the current study is the assessment of the effects of sugar on some reproductive functions of female Wistar rats. Twenty (20) adult female Wistar rats (180-200 g) randomly divided into four (n=5), received 10 mL/Kg distilled water (group 1 - control), 6.25, 20, and 64 mg/Kg Sugar (groups 1, 2, 3) daily by oral gavage for three (3) weeks, respectively. Parameters evaluated were fasting blood glucose, estrous cycle, histology and relative weights of reproductive organs, follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol hormone levels. Sugar significantly decreased (p < 0.05) fasting blood glucose level and relative weights of ovary and uterus compared to control. It also significantly reduced the frequency of estrus and diestrus phases but caused significant increases (p < 0.05) in the metestrus phase of the estrous cycle and FSH level in comparison with the control. There were anomalies in the histology of the ovary and uterus. Therefore sugar disrupted the estrous cycle, altered the weights and cytoarchitecture of the ovary and uterus. Sugar consumption has harmful effects on some reproductive functions of female Wistar rats.
Learning to Localize Objects Using Limited Annotation, With Applications to Thoracic Diseases

Motivation: The localization of objects in images is a longstanding objective within the field of image processing. Most current techniques are based on machine learning approaches, which typically require careful annotation of training samples in the form of expensive bounding box labels. The need for such large-scale annotation has only been exacerbated by the widespread adoption of deep learning techniques within the image processing community: deep learning is notoriously data-hungry. Method: In this work, we attack this problem directly by providing a new method for learning to localize objects with limited annotation: most training images can simply be annotated with their whole image labels (and no bounding box), with only a small fraction marked with bounding boxes. The training is driven by a novel loss function, which is a continuous relaxation of a well-defined discrete formulation of weakly supervised learning. Care is taken to ensure that the loss is numerically well-posed. Additionally, we propose a neural network architecture which accounts for both patch dependence, through the use of Conditional Random Field layers, and shift-invariance, through the inclusion of anti-aliasing filters. Results: We demonstrate our method on the task of localizing thoracic diseases in chest X-ray images, achieving state-of-the-art performance on the ChestX-ray14 dataset. We further show that with a modicum of additional effort our technique can be extended from object localization to object detection, attaining high quality results on the Kaggle RSNA Pneumonia Detection Challenge. Conclusion: The technique presented in this paper has the potential to enable high accuracy localization in regimes in which annotated data is either scarce or expensive to acquire. Future work will focus on applying the ideas presented in this paper to the realm of semantic segmentation.
Similar Event Detection and Event Topic Mining in Social Network Platform

Social media is widely used to share information globally and it also aids to gain attention from the world. When socially sensitive incidents like rape, human rights march, corruption, political controversy, chemical attacks occur, they gain immense attention from people all over the world, causing microblogging platforms like Twitter to get flooded with tweets related to such events. When an event evolves many other events of a similar nature have happened in and around the same time frame. These are similar events because they are linked to the nature of the main event. Discussions in state-of-the-art papers are restricted to event detection, user interest discovery and detecting influential spreaders but overlook similar event detection and topic evolution. This leads to difficulty in tracing the nature of evolving events. Hence, similar event detection is critical in determining the nature of events. Similar events have fulcrums points, i.e.,topics around which the discussion is focused, as the event evolves which must be considered in topic evolution. We have proposed Event Detection model which detects similar events that are similar with regards to their temporal nature resulting from main events. The model also considers event topics, user supremacy index to calculate sub event detection factor ($\alpha$). A self-tuning clustering algorithm is proposed to combine tweets, forming clusters which are composed of key posts with similar context and hence, similar topics. The sub event detection algorithm reveals events that overlap in both time and context to evaluate the effects of these similar events on deliberate human actions. The topic evolution algorithm puts into perspective the change in topics for an event's lifetime. The experimental results on a real Twitter data set demonstrate the effectiveness and precision of our proposed model for similar sub event detection during the evolution of similar events.
A Cumulative Increasing Kemelized Nearest-Neighbor Bagging Method for Early Course-Level Study Performance Prediction

Early course-level study performance prediction is a significant educational data mining task to forecast the success of each current student in a course using the historical data of the students in the previous same course. This task can be resolved by different machine learning approaches in various educational contexts. However, how easily and effectively a solution is deployed in practice is restricted by many factors. Two main factors that have not yet been discussed simultaneously are incremental mining and interpretability when the task is prolonged course after course. Therefore, in this paper, we propose a novel cumulative increasing kernelized nearest-neighbor bagging method for early course-level study performance prediction. Our method is a lazy learning one with an inherent incremental mining mechanism, defined as an ensemble method. Although it works in a feature space to handle a non-linearly separated data space, interpretability is enabled with instance-based learning and a confidence score of each prediction is further provided for practical applications. Experimental results on several public datasets confirm the effectiveness of our method as compared to other traditional prediction methods and well-known ensemble ones. Its better early predictions can help both students and lecturers make appropriate course changes for students’ ultimate success.
Sensitivity assessment of nanoscale double gate MOSFET based biosensor using numerical simulation

In this paper, a semi-quantum analytical model is developed for current transport simulation in a dielectrically modulated double gate (DG) MOSFET for biosensing applications. The model incorporates a self-consistent solution of two-dimensional Poisson’s equation, terminal MOSFET capacitances and the number of conductance channels or modes to compute the drain current under ballistic limits of operation. The electrical characteristics of the sensor are investigated with the high-k gate dielectric layer for detecting different biomolecules. The findings from this study show that high-k dielectric in the MOSFET not only improved sensitivity but also shown significant variation of drain current when exposed to different types of biomolecules
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI

Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). However, hardware failure, such as stuck-at-fault defects, is one of the main concerns that impedes the ReRAM devices to be a feasible solution for real implementations. The existing solutions to address this issue usually require an optimization to be conducted for each individual device, which is impractical for mass-produced products (e.g., IoT devices). In this paper, we rethink the value of weight pruning in ReRAM-based DNN design from the perspective of model fault tolerance. And a differential mapping scheme is proposed to improve the fault tolerance under a high stuck-on fault rate. Our method can tolerate almost an order of magnitude higher failure rate than the traditional two-column method in representative DNN tasks. More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme. The improvement is universal and does not require the optimization process for each individual device.
Multi-Band Linear Frequency Modulation in External Cavity FP-LD Subjected to Multi-Input Injection

In this letter, we experimentally demonstrate multi-band linear frequency modulation (LFM) signal generation subjected to multi-input optical injection in an external cavity-based Fabry-Pérot laser diode (ECFP-LD). The ECFP-LD is a specially designed FP-LD with a self-injected single longitudinal mode tuned up to 10 nm. The non-linear dynamics in period-one oscillation in ECFP-LD subjected to multi-input optical injection shows unique characteristics for multi-band linear frequency modulation. In the proposed scheme, four external input beams are injected to ECFP-LD, among which one is power modulated with the saw-tooth like waveform, and others are with the constant power. The varying power beam red-shifts the self-injected mode of ECFP-LD and mode-hopping on attaining a particular power level. Hence, multi-band LFM signals are generated by beating self-injected and multi-input injected beams with a large time-bandwidth product (TBWP). The total bandwidths of 12GHz (LFM1: 33–36 GHz, LFM2: 29–32 GHz, LFM3: 25–28 GHz, LFM4: 22–25 GHz) for four-band linear frequency modulation signals with a TBWP of 12000 is measured. The generated multi-band LFM signal provides flexibility on central frequency tunability and dynamic chirp rate for a multifunctional radar system.
Servant Leadership Styles: A Theoretical Approach

The main objective of the article is to examine the leadership style through a number of analyses, evaluations and researches from available sources, in order to discover suggested facts related to leadership activities through the prisms of servant leadership theory. So far, many leadership theories have been developed, proposing different leadership styles. One of the most discussed leadership styles is the servant leadership, which implies that a person must be human first, leader second. Every leader must whole heartedly serves an organization to which he/she belongs. This is both an honor and a great responsibility for the leader. The main methods used in the article are analysis, synthesis, evaluation based on literature review. The article aims to point out the outstanding strengths of the servant leadership style that a leader needs. The article focuses on specifying the strengths in servant leadership that leaders need in the current context of ever changing world, and hence, proposes guidelines for leaders to learn and practice so that they can better serve their public organizations. Doi: 10.28991/esj-2021-01273 Full Text: PDF
Effect of Different Levels of Molina (Lagenariasiceraria)on the Healthy Status of Rats with Anemia

The potential health benefits of molinaas functional food supplement to improve iron absorption as well as to prevent and treat anemia associated with deficiency in iron intake were evaluated in rats. Three experimental groups were fed diet supplemented with different levels of Molina for 4 weeks versus the control rat group fed basal diet free in iron . At the end of the experiment, rat groups fed levels of molinasupplemented diets were characterized by significant dose-related increases in the level of serum Fe (69.11 ± 4.55 to 84.3 ± 2.12 μg/dl). In addition, there were variable increases in the measured levels of hemoglobin (11.11 ± 1.1 to 14.25 ± 1.1 g/L), hematocrit (39.15 ± 0.15 to 42.15 ± 1.27 %) and ferritin (49.55 ± 2.25 to 65.12 ± 0.15 μg/dl) in levels of tested plantfed groups in a dose-dependent fashion compared with the control group. These data suggested that 10% of molina could provide with iron absorption and bioavailability of iron when incorporated in daily diets and therefore, could be considered as a very effective food supplement to prevent and treat anemia.
Parametric study of Sandwich Plates with Viscoelastic, Auxetic Viscoelastic and Orthotropic Viscoelastic Core Using a New Higher Order Global-Local Theory

In this paper, sandwich plates with flexible core and composite surfaces as well as viscoelastic and auxetic core are investigated under dynamic loading. A new higher order global-local theory is used for simulation of the dynamic behavior of sandwich plates. The presented theory is able to consider the thickness change of the sandwich plates as well as transverse stresses accurately. It is obvious that considering these parameters are so crucial in study behavior of thick sandwich plates or sandwich plates with soft core. Furthermore, in terms of solving equations, an iterative incremental method based on the formulation of transient nonlinear finite element as well as a real time algorithm was employed to simulate viscoelastic behavior accurately. The results indicate a significant increase in the stiffness of the sandwich plate due to the auxetic properties of the core materials, leading consequently to the reduction of the vibration amplitude and stresses level. Some of the innovations belonging to this paper are: 1) presenting a global-local higher-order theory while considering the changes in the thickness of the sandwich plates; 2) calculating transverse stresses using the three-dimensional elasticity method as well as modifying the results obtained from displacement and inertial effects based on this method; 3) simulating sandwich plate with viscoelastic and auxetic cores; 4) taking orthotropic properties for the viscoelastic core into account.
INFLUENCE OF BUSINESS COMPETENCY, CUSTOMER PERSONALITY, BRAND PERSONALITY, CUSTOMER SATISFACTION ON BRAND LOYALTY FOR BRANDED MOBILES

This paper aims to determine the influence of business competency, customer personality, brand personality, and customer satisfaction on brand loyalty for branded mobiles in Tamil Nadu, India. Today, mobile phones are inseparable, and it is a second soul for modern youth. For this study, 859 respondents were identified as samples, and data were collected through a combination of judgment and convenience sampling method. The result indicates that customer perceives that the mobile handset decides the customer's social character, self and personification or uniqueness. Similarly, individual customer satisfaction is highly based on the mobile phone's design, package, value for money, and functionality. Customers are loyal to a brand for its brand name, quality, and the retailers' services. Hence, brand loyalty depends on the cumulative effects of business competency, customer personality, brand personality, and satisfaction.
Framing of hepatitis in Nigerian newspapers and public attitude towards the disease

The world is plagued with Hepatitis as one of the deadliest diseases it has ever witnessed. The situation in Nigeria is worrisome considering that the way the media presents issues affects understanding and attitude towards the disease. This study sought to analyse the patterns of reportage of hepatitis in Nigeria’s mainstream newspapers and examine public attitude toward the disease. Four national dailies-Daily Sun, Vanguard, Nation and Daily Trustwere analysed over a period of three years to ascertain the dominant frame, subject matter, and extent of interpretation of the reportage of Hepatitis. Survey was adopted to ascertain public attitude towards the disease. Hinged on the Framing theory and Health Belief Model, findings revealed that the mutation-contagion (severity) frame was the most dominant frame used with a percentage figure of 61%. This implies that most of the stories laid emphasis on how deadly the disease is, hence feeding the audience with information about how bad the disease is. Further findings showed that the stories were interpretative as interpretative stories constituted 52% of the stories analysed. Other stories analysed (43%) portrayed causes and solutions of the disease as themes showing that the newspapers were balanced in their reports. Findings also showed that the public were partially aware of Hepatitis as they had little knowledge of the causes. They also had positive attitude towards the disease, however, this positive attitude can only achieve so little seeing that they had little knowledge of the causes, hence prevention was difficult. It was also observed that the information need of the public was met in the reports, but the public still had little knowledge of the causes of the disease showing a partial correlation between media reports and audience knowledge level. It was therefore recommended among others that more awareness should be created on the causes of the disease and that further studies should be carried out to explore other sources of public information on Hepatitis.