Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage Capacity of Cognitive Radio in Rayleigh Fading Environments with Imperfect Channel Information

This paper considers a cognitive radio system in which a Secondary User (SU) shares the same radio spectrum band licensed to a Primary User (PU) with the constraint that the interference to the PU caused by the SU is below a prescribed threshold. In addition to the average interference power constraint at the PU, the peak and average transmit power constraints of the SU are also considered, simultaneously, modeling the situation that the PU’s operation is limited by the average interference level and the SU transmit power is limited due to hardware restrictions. Compared with the traditional research, we assume that only partial of this Channel Side Information (CSI) is available to the SU. Based on these assumptionthe SU outage capacity is investigated. In particular, we obtain the optimal power allocation strategy to achieve the SU outage capacity, and further derive a closed-form expression for the minimum outage probability for a given threshold of SU outage capacity in Rayleigh fading channels. Our results are confirmed in numerical simulations. It is shown that the imperfect CSI of SU-PU link has no impact on the SU outage capacity as long as the average interference power constraint is sufficiently large compared to the transmit power constraints. Furthermore, it is shown that the impact of imperfect CSI of SU-PU link on the SU outage capacity is negligible in the low outage probability regime.
The Causal Role of Aggregate Properties

An aggregate property, such as pressure, population, or concentration, is one whose realization is achieved by the joint instantiation of many more or less independent properties. Working for the most part with a biological example, this paper asks: can an aggregate property be causally relevant to a phenomenon if only some aspects of its realization play a direct role in causing that phenomenon, while the rest are, as it were, causal bystanders? I show that it is difficult for any known theory of causal relevance to distinguish aggregate properties that are genuinely causal from those that are not (such as combinations of causally relevant properties with arbitrary, unrelated properties). I then develop a strategy for understanding the relevance of partially causal aggregates, diagnosing it as explanatory rather than strictly causal.
PI CONTROLLER BASED OF MULTI-LEVEL UPQC USING DQ0 TRANSFORMATION TO IMPROVE POWER QUALITY IN DISTRIBUTION SYSTEM

The aim of this paper is to improve power quality in the source side of distribution system using unified power quality conditioner (UPQC) by realization of cascaded multilevel inverter (MLI). The UPQC is an integration of series and shunt active power filters via common dc link which can mitigates various power quality issues like voltage sag, swell, harmonics, interruptions etc. The cascaded MLI offers low THD in the output waveform and flexible circuit layout. The sinusoidal pulse width modulation (SPWM) switching scheme is employed for better operation of MLI. The control mechanism of UPQC is derived from dq0 transformation which is simple in its design and gives better control characteristics. The PI controller can efficiently reduce the steady state error and this can be used in UPQC controller for better response. The regulation of source voltage in the distribution system using multilevel UPQC under nonlinear load condition is analyzed in MATLAB/SIMULINK environment.
Parkinson’s Disease Identification using Glottal flow analysis

: Background: Parkinson's is a neurological disorder not as rare as projected by maestros in this field, most commonly as it is considered a taboo in general societal norms or rebuffed as an old age syndrome. The complexity in detection resulting from the lack of reliable tests at the early stages of PD makes it hard to deal with chronic stages. Methods: This paper focuses on using extracted features from speech signals to detect an abnormality and deducing whether the patient is suffering from PD. Glottal Closure Instants (GCI) detection algorithms such as Speech Event Detection using Residual Excitation and the Mean Based Signal (SEDREAMS) is compared with electroglottographic (EGG) recordings as reference ground. The speech samples are taken from the UCI Machine Learning repository, which contains multiple speakers' recordings. Results: The early onset of PD can be predicted at benign stages of the disorder and help in treatment at later stages of Parkinson's disease. SEDREAMS showed an accuracy of 88.7%. Conclusion: SEDREAMS is used to diagnose Parkinson’s disease early with precise detection of Glottal closing and opening instants.
The Relationship between Natural Environment Relatedness and Emotional Well-Being: A Systematic Review and Proposed Conceptual Framework Experience

The influence of the natural environment on emotional well-being has been highlighted at the peak of the Covid 19 pandemic that saw most countries impose prolonged total lockdowns and movement restrictions on their citizens. While researchers have recently emphasized the need to focus not only on students’ academic achievement but also on their inner needs, which include their emotional well-being, studies have found nature connectedness has a positive effect on emotional well-being and physical health. Many studies on nature and well-being have focused on physiological and psychological experimental research to examine before and after exposure to nature. However, there is still a need for further investigation how emotional connection between nature environments in the aspect of restorativeness and spiritual values improve mental health. This paper systematically reviewed previous studies about the relationship between exposure to the natural environment and well-being focusing on key variables, underpinning theories, methodological processes and samples. As a result of the review, a conceptual framework is proposed for future studies on relationship between exposure to the natural environment and well-being. We hope that the proposed framework will be empirically tested in future studies.
Using Random Forest for Indoor Solar Energy Estimates on Embedded Systems

As IoT systems have gained a lot of interest in the last years, powering these devices have become a major hurdle. Energy harvesting is a solution to this problem but comes with its own difficulties. As the harvested energy is very scarce and big energy storages are costly and have an environmental impact, the energy usage have to be scheduled carefully with power availability in mind. It was suggested, that random forest is suitable for this task. In this thesis, I implement an energy prediction algorithm based on random forest on a microcontroller and evaluate its performance. Further a technique is implemented to improve models on the device itself.
Regulation of B cell receptor signaling and its therapeutic rele- vance in aggressive B-cell lymphomas

The proliferation and survival signals emanating from the B-cell receptor (BCR) constitute a crucial aspect of mature lymphocyte’s life. Dysregulated BCR signaling is considered a potent contributor to tumor survival in different subtypes of B cell non-Hodgkin lymphomas (B-NHLs). In the last decade, emergence of BCR-associated kinases as rational therapeutic targets has led to the development and approval of several small molecule inhibitors targeting either Bruton's tyrosine kinase (BTK), spleen tyrosine kinase (SYK), or phosphatidylinositol 3 kinase (PI3K), offering alternative treatment options to standard chemoimmunotherapy, and making some of these drugs valuable assets in the anti-lymphoma armamentarium. Despite their initial effectiveness, these precision medicine strategies are limited by primary resistance in aggressive B-cell lymphoma like diffuse large B cell lymphoma (DLBCL) and mantle cell lymphoma (MCL), especially in the case of first generation BTK inhibitors. In these patients, BCR-targeting drugs often fail to produce durable responses, and nearly all cases eventually progress with a dismal outcome, due to secondary resistance. This review will discuss our current understanding of the role of antigen-dependent and antigen-independent BCR signaling in DLBCL and MCL and will cover both approved inhibitors and investigational molecules being evaluated in early preclinical studies. We will discuss how the mechanisms of action of these molecules, and their off/on-target effects can influence their effectiveness and lead to toxicity, and how our actual knowledge supports the development of more specific inhibitors and new, rationally based, combination therapies, for the management of MCL and DLBCL patients.
INDIVIDUAL AND CONTEXTUAL FACTORS AFFECTING DPT IMMUNIZATION IN INDONESIA

Nowadays, diphtheria cases always increase from year to year. Until now, no drug has been found to cure diphtheria, but there is the most effective way of prevention through immunization. It is known that diphtheria sufferers who don’t get immunizations increase every year. The purpose of this study is to determine the individual and contextual factors that influence the status of DPT immunization in Indonesia and its trends and to know the diversity between cities. The data used in this study are Susenas KOR and consumption and expenditure (KP) modules. The results of multilevel binary logistic regression analysis indicate that individual factors that influence the status of DPT immunization are residence classification, highest maternal education, ownership of immunization cards, birth order, and household poverty status. While the contextual are the ratio of posyandu to 100,000 population and PDRB. Characteristics of children aged 12-59 who do not get immunizations tend to live in rural areas, have mothers with the highest education in junior high school, don’t have immunization cards, who born late in households with many children, and come from poor households. Besides that, there is a diversity of characteristics between cities, which amounted to 22,19%.
Validation of onco-assist survival prediction tool in stage I, II and III colon cancer among Asian patients

Clinical calculators and predictors are now commonly used in clinical practice to predict most accurate clinical outcome and provide guidance for appropriate therapy. One of the most commonly used calculator is onco-assist. This study was conducted to compare onco-assist prediction of the patients diagnosed with colon cancer Stage I, II and III. Data was retrospectively collected from 88 patients of colon cancer diagnosed over the period of 11 years (2008 to 2018) and registered at Nuclear Institute of medicine and radiotherapy (NIMRA), Hospital, Jamshoro Sindh. These patients received primary surgical therapy without any neo-adjuvant systemic chemotherapy. Survival assessed on onco-assist prediction algorithm using the defined parameters and compared with the actual survival according to the grade of the tumour. The clinical calculator onco-assist incorporated seven variables: gender, age number of lymph nodes examined, number of tumor-involved lymph nodes, T = (1-4), grade (low / high), adjuvant chemo received (yes / no) if yes then only 5FU or 5FU plus Oxaliplatin based. Onco-assist predicted five-year survival rate in well differentiated tumours with and without chemotherapy as 84% and 80% respectively, in moderately differentiated tumour with and without chemotherapy as 78% and 76% respectively. For poorly differentiated tumours the predicted survival rate with and without chemotherapy was 73%. While actual achieved survival was 35%, 52% and 17% for well, moderately and poorly differentiated cancers. This clinical calculator onco-assist includes limited parameters and limited adjuvant therapy options thus the prediction of cancer survival following surgery in stage I –III colon cancer does not appear to accurately predict outcome in Asian population.
A Novel Compact Tri-Band Wilkinson Power Divider Based on Coupled Lines

Abstract The purpose of this article is to propose a novel design method for miniaturized tri-band Wilkinson power dividers with independently controllable center frequencies and a simple structure. Different from the traditional circuit of tri-band power dividers using three-section transmission-line transformers, this proposed structure consists of three-section coupled lines and three isolation resistors. Due to flexible even- and odd-mode parameters and inherent small size features of parallel coupled lines, this compact divider not only provides arbitrary tri-band operation but also increases the degrees of freedom in determining isolation performances. Furthermore, the simplified design equations for electrical parameters are obtained, and several ideal examples are demonstrated for initial theoretical verifications. As a remarkable example, a compact microstrip power divider operating at three industrial, scientific, and medical bands, namely, (0.92, 2.45, and 5.8 GHz), is designed, fabricated, and measured. There are good return loss (lower than −15.9 dB), small insertion loss (better than −3.8 dB), and excellent isolation (larger than 23.18 dB) performances at these desired triple bands in both the calculated and measured results.
Force-based Visualization of Peer-to-peer Nodes Semester

In this thesis, we examine several methods to automatically generate graphs. The graphs are used to display the topology of a peer-to-peer network. Evaluation criteria are stated to compare the results of different drawing algorithms. The chosen algorithm is a variation of the force-based algorithms. It includes sophisticated heuristics to improve convergence behavior. The implemented solution is tested with both generated data and actual data from the running network. The resulting graphs are compared to the stated evaluation criteria.
Hands-on brain slicing activity based on Gagné’s instructional model: A novel pedagogy for mastering neuroanatomical cross sections

*Corresponding author: Dinesh Kumar. V, MBBS, MD; Department of Anatomy, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry – 605006, India Mob: +91-99-94038701; Email: dinesh.88560@gmail.com Please cite this paper as: Rajasekhar SSSN, Kumar VD. Hands-on brain slicing activity based on Gagné’s instructional model: A novel pedagogy for mastering neuroanatomical cross sections. J Adv Med Educ Prof. 2022;10(1):64-65. DOI: 10.30476/ JAMP.2020. 86497.1242. Received: 13 May 2020 Accepted: 20 May 2020 Journal of Advances in Medical Education & Professionalism
The Kinetic Expansion of Solar-wind Electrons: Transport Theory and Predictions for the Very Inner Heliosphere

We propose a transport theory for the kinetic evolution of solar-wind electrons in the heliosphere. We derive a gyro-averaged kinetic transport equation that accounts for the spherical expansion of the solar wind and the geometry of the Parker spiral magnetic field. To solve our three-dimensional kinetic equation, we develop a mathematical approach that combines the Crank–Nicolson scheme in velocity space and a finite-difference Euler scheme in configuration space. We initialize our model with isotropic electron distribution functions and calculate the kinetic expansion at heliocentric distances from 5 to 20 solar radii. In our kinetic model, the electrons evolve mainly through the combination of ballistic particle streaming, the magnetic mirror force, and the electric field. By applying fits to our numerical results, we quantify the parameters of the electron strahl and the core part of the electron velocity distributions. The strahl fit parameters show that the density of the electron strahl is around 7% of the total electron density at a distance of 20 solar radii, the strahl bulk velocity and strahl temperature parallel to the background magnetic field stay approximately constant beyond a distance of 15 solar radii, and β ∥s (i.e., the ratio of the strahl parallel thermal pressure to the magnetic pressure) is approximately constant with heliocentric distance at a value of about 0.02. We compare our results with data measured by the Parker Solar Probe. Furthermore, we provide theoretical evidence that the electron strahl is not scattered by the oblique fast-magnetosonic/whistler instability in the near-Sun environment.
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand

Many past attempts at modeling repeated Cournot games assume that demand is stationary. This does not align with real-world scenarios in which market demands can evolve over a product’s lifetime for a myriad of reasons. In this paper, we model repeated Cournot games with non-stationary demand such that firms/agents face separate instances of non-stationary multi-armed bandit problem. The set of arms/actions that an agent can choose from represents discrete production quantities; here, the action space is ordered. Agents are independent and autonomous, and cannot observe anything from the environment; they can only see their own rewards after taking an action, and only work towards maximizing these rewards. We propose a novel algorithm Adaptive with Weighted Exploration (AWE) -greedy which is remotely based on the well-known -greedy approach. This algorithm detects and quantifies changes in rewards due to varying market demand and varies learning rate and exploration rate in proportion to the degree of changes in demand, thus enabling agents to better identify new optimal actions. For efficient exploration, it also deploys a mechanism for weighing actions that takes advantage of the ordered action space. We use simulations to study the emergence of various equilibria in the market. In addition, we study the scalability of our approach in terms number of total agents in the system and the size of action space. We consider both symmetric and asymmetric firms in our models. We found that using our proposed method, agents are able to swiftly change their course of action according to the changes in demand, and they also engage in collusive behavior in many simulations.
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series

Bayesian Optimization algorithm has become a promising approach for nonlinear global optimization problems and many machine learning applications. Over the past few years, improvements and enhancements have been brought forward and they have shown some promising results in solving the complex dynamic problems, systems of ordinary differential equations where the objective functions are computationally expensive to evaluate. Besides, the straightforward implementation of the Bayesian Optimization algorithm performs well merely for optimization problems with 10-20 dimensions. The study presented in this paper proposes a new high dimensional Bayesian Optimization algorithm combining Recurrent neural networks, which is expected to predict the optimal solution for the global optimization problems with high dimensional or time series decision models. The proposed RNN-BO algorithm can solve the optimal control problems in the lower dimension space and then learn from the historical data using the recurrent neural network to learn the historical optimal solution data and predict the optimal control strategy for any new initial system value setting. In addition, accurately and quickly providing the optimal control strategy is essential to effectively and efficiently control the epidemic spread while minimizing the associated financial costs. Therefore, to verify the effectiveness of the proposed algorithm, computational experiments are carried out on a deterministic SEIR epidemic model and a stochastic SIS optimal control model. Finally, we also discuss the impacts of different numbers of the RNN layers and training epochs on the trade-off between solution quality and related computational efforts.
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function

In this paper, we revisit Stochastic Continuous Submodular Maximization in both offline and online settings, which can benefit wide applications in machine learning and operations research areas. We present a boosting framework covering gradient ascent and online gradient ascent. The fundamental ingredient of our methods is a novel non-oblivious function $F$ derived from a factor-revealing optimization problem, whose any stationary point provides a $(1-e^{-\gamma})$-approximation to the global maximum of the $\gamma$-weakly DR-submodular objective function $f\in C^{1,1}_L(\mathcal{X})$. Under the offline scenario, we propose a boosting gradient ascent method achieving $(1-e^{-\gamma}-\epsilon^{2})$-approximation after $O(1/\epsilon^2)$ iterations, which improves the $(\frac{\gamma^2}{1+\gamma^2})$ approximation ratio of the classical gradient ascent algorithm. In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$. Meanwhile, we verify that this boosting online algorithm achieves a regret of $O(\sqrt{D})$ against a $(1-e^{-\gamma})$-approximation to the best feasible solution in hindsight, where $D$ is the sum of delays of gradient feedback. To the best of our knowledge, this is the first result to obtain $O(\sqrt{T})$ regret against a $(1-e^{-\gamma})$-approximation with $O(1)$ gradient inquiry at each time step, when no delay exists, i.e., $D=T$. Finally, numerical experiments demonstrate the effectiveness of our boosting methods.
Detail-Preserving Transformer for Light Field Image Super-Resolution

Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resolution views. Despite delivering encouraging results, these approaches are all convolution-based, and are naturally weak in global relation modeling of sub-aperture images necessarily to characterize the inherent structure of light fields. In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. In particular, our model regards sub-aperture images of each vertical or horizontal angular view as a sequence, and establishes long-range geometric dependencies within each sequence via a spatial-angular locally-enhanced self-attention layer, which maintains the locality of each sub-aperture image as well. Additionally, to better recover image details, we propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence. The two branches are finally fused to obtain comprehensive feature representations for reconstruction. Evaluations are conducted on a number of light field datasets, including real-world scenes and synthetic data. The proposed method achieves superior performance comparing with other state-of-the-art schemes. Our code is publicly available at: https://github.com/BITszwang/DPT.
(
2+1
)-dimensional black holes in 
f(R,ϕ)
 gravity

Thanasis Karakasis, ∗ Eleftherios Papantonopoulos, † Zi-Yu Tang, 3, ‡ and Bin Wang 5, § Physics Division, National Technical University of Athens, 15780 Zografou Campus, Athens, Greece. School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China Center for Gravitation and Cosmology, College of Physical Science and Technology, Yangzhou University, Yangzhou 225009, China