Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-aligned hybrid carrier-based PWM for modular multilevel converters

The self-aligned hybrid carrier-based pulse-width modulation (PWM) for modular multilevel converters (MMC) based on decentralized control is proposed in this paper. It is based on parallel cells in a submodule (SM) using self-aligned hybrid carrier-based PWM, which combines the concept of level-shifted carrier-based PWM (LSC-PWM) and phase-shifted carrier-based PWM (PSC-PWM) methods. By implementing a decentralized control system digitally, hybrid carriers align themselves. Each SM interacts with the other SMs and each parallel cell in an SM communicates with the other cells to generate hybrid carriers. The proposed control strategy makes advantage of the redundancy idea by adding or removing a cell in the case of cell malfunction and system reconfiguration. A current balancing method is incorporated into the decentralized control system to ensure current balance among parallel cells in an SM. The simulation results illustrate the effectiveness of the presented control method in piecewise linear electrical circuit simulation (PLECS) software.
Patient's Pain Recognition by Using Deep Models Based on Transfer Learning

Undetected pain threatens the quality of human life by prevents them from understanding the nature of their pain. Therefore, an urgent need to find an alternative that can recognize pain in patients suffering from difficulty in express their pain for reasons related to age or the ability of explain feeling of pain. The structure of a proposed system depends on the transfer learning technique, where two pre-trained deep convolution neural network (ResNet-50and Xception) models, have been used as a feature extractor. These models receive the images from the database, which was collected especially for this work from 100 subjects of (10–55) ages in different times and natural environments. Extracted features, which formed a features vector, were taken from the Global Average Pooling (GAP) layer. These feature vectors are passed to a supervised classification method such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and build a Deep Neural Network (DNN) classifier from scratch. The proposed model's performance was evaluated by comparing it with related work, which adopted the concept of a pre-trained model and used the database collected in a natural environment in the training and testing process. The proposed model demonstrates outstanding results by the combination of exploiting the Transfer Learning (TL) technique in the Xception model and building the DNN as a classifier. The accuracy of the proposed model was 98.17% which demonstrates promising results.
Factor affecting technical efficiency of the banking sector: Evidence from Ethiopia

Abstract An efficient bank is more robust to shocks, fosters competitiveness, and promotes stability of the financial system. This study estimates Ethiopia’s commercial banks’ level of efficiency and its determinants during the period 2014–2020. Data Envelopment Analysis (DEA), Malmquist DEA, and Tobit regression were employed to analyze the data. The result indicated that the average efficiency score of banks in the constant returns to scale (CRS), variable returns to scale (VRS), and scale efficiency (SE) models were 95.5%, 99.85%, and 96.95% , respectively. Furthermore, in the VRS model, a state bank is more efficient than private banks. During the study period, the Total Factor Productivity (TFP) of Banks improved by 1%. According to the Tobit model, the efficiency of banks grows with an increment in the number of branches, bank size, and credit risk. However, when, liquidity risk and the log of the fixed asset increase, bank efficiency will decrease. The level of capitalization, log of GDP, and inflation, on the other hand, do not influence bank efficiency. Therefore, banks should pay close attention to aspects that influence technical efficiency.
Throughput-Guaranteed Cross-Layer Design for Multi-channel Wireless Networks

Applying multi-channel technology to wireless networks can improve the utilization rate of spectrum resources and increase system capacity. For multi-channel wireless networks with multiple paths between source and destination node, a novel routing selection algorithm is presented in this paper according to the existing scheduling policy LDCSA for single-hop flows. After that, a cross-layer scheme named M-LDCSA across the MAC layer and network layer is formed by jointly considering routing and scheduling. Using Lyapunov drift, the scheme is proved to guarantee a stable throughput capacity region, which is a subregion of the maximum capacity region. That is, M-LDCSA is throughput-guaranteed. Simulation results show that the throughput performance of the proposed M-LDCSA algorithm is better than that of other comparable cross-layer schemes. In addition, the implementation complexity of M-LDCSA does not increase with network size, which gives M-LDCSA the advantage of low-complexity in practical distributed network systems.
A Markov gamma random field for modelling disease mapping data

In this paper we introduce a Markov gamma random field prior for modelling relative risks in disease mapping data. This prior process allows for a different dependence effect with different neighbours. We describe the properties of the prior process and derive posterior distributions. The model is extended to cope with covariates and a data set of respiratory infections of children in Mexico is used as an illustration.
Feasibility Analysis of Hybrid Renewable Energy Systems in a Commercial Building by Enhancing Renewable Resources using HOMER Software

Integrating and expanding renewable energy resources on the customer's end is revolving as a hot trending topic especially in developing countries for sustainable energy trading. Owing to that, India welcomes the active participation of prosumers for self-utilization and to contribute to the local energy markets. The biggest challenge from the customer end is integrating renewable resources having intermittent nature due to adverse environmental factors. To mitigate the challenges of using highly dynamic renewable sources, a detailed investigation of the Hybrid Energy System (HES) is in need. The presented paper creates a proposal for upgrading the existing system – Quantanics Techserv private limited located in the Madurai district of Tamil Nadu, India. The system comprises four different energy resources namely solar PV panels as a renewable source, diesel generation, and battery storage as supporting supply systems along with grid supply from the state electricity board (Tamil Nadu Electricity Board - TNEB). The study mainly accounted to verify the feasibility of updating the existing PV system concerning location data and load traces using the Hybrid Optimization Model for Electric Renewables (HOMER) software. The optimal results are extracted based on economic and environmental aspects. This techno-economic analysis gives comparative results under two distinguished dispatch strategies (Load Following and Cycle charging). The optimal configuration for both existing and upgrading PV system is found with a minimum Net Price Cost (NPC) and Levelized Cost of Energy (COE) respectively.
Sustainable Low-Cost Phosphorus Recovery Using Nanostructured Materials with Reusability Potential

A new low-cost material with a polymeric base formed from sodium silicate was developed. The material presents a nanostructured, highly rich iron surface with a large phosphorus retention capacity and potential reuse as a crop fertilizer. In the present study, we demonstrate that iron is the element that acts as an adsorbent for phosphate, while the polymeric base functions exclusively as a support for iron. The iron is uniformly adsorbed on the surface of the material, forming nanostructures, which ensure that iron works similarly to nanoparticles in solution but avoid other problems, such as particle agglomeration or the difficulty of separating them after the removal process. Materials were characterised by SEM, EDS, N2 sorption, and image processing, and the effect of pH, ionic strength, and temperature was studied. Sorption kinetics were analysed using Boyd’s diffusion model, and adsorption equilibria were studied using several adsorption models. A maximum iron adsorption on the polymeric base of 23.9 ± 0.3 mg Fe∙g−1 was found, while maximum phosphorus adsorption was 366 ± 21 mg P∙g−1 Fe. Thus, phosphorus is recovered from the aqueous medium with an inexpensive material that has the potential to be used directly as a fertilizer.
HIGHER ORDER WHITNEY FORMS

Compatible finite element discretizations of second-order boundary value problems set in the function spaces H(Ω), H(curl, Ω), and H(div, Ω) will naturally rely on discrete differential forms. Their lowest-order representatives are the well-knowns linear Lagrangian finite elements, edge elements, and face elements. However, given the interior smoothness of the solutions of many boundary value problems, approximation by local polynomials of high degree offers superior efficiency (hp-version of FEM). The first part of the presentation will focus on the algebra behind the construction of higher order Whitney forms. The calculus of differential forms will be used to devise a unified description of discrete differential forms of any order and polynomial degree on simplicial meshes in any spatial dimension. A general formula for suitable degrees of freedom is also available. Fundamental properties of nodal interpolation can be established easily. It turns out that higher order spaces, including variants with locally varying polynomial order, emerge from the usual Whitney-forms by local augmentation. The second part of the talk will review the concrete construction of suitable basis functions for higher order Whitney forms. Discrete 1-forms will serve as main example. Recent inventions of p-hierarchical bases with good conditioning properties will be discussed. Time permitting, the third part of the talk will investigate p-uniform stability properties of high order Whitney forms. It will show how tools from differential geometry and discrete topology can be used to obtain a puniform discrete Poincaré-Friedrichs inequality that plays a key role in the convergence theory for the p-version of discrete differential forms.
Attractor tempos for metrical structures

Through new mathematical modelling based on experimentally-substantiated principles of cognitive science, this article provides robust principles for identifying “attractor tempos” which optimise the salience of metrical structures. This sheds lights on core musical phenomena such as the inclination towards parti-cular tempos in given metrical contexts, the use of (non-optimal) tempos as a source of expressive tension in music, and even what it means for music to be “fast” or “slow” in the first place. The study does not purport to set out a comprehensive model of metrical listening, but simply to identify the core principles which are necessarily involved in establishing attractor tempos. The (limited) dependence of those principles on the initial modelling parameters is discussed. Furthermore, there is no prescription of “correct” tempos here, instead the attractors model a set of defaults against which musicians may select tempos for expressive effect. An Online Supplement to this article, providing a subsidiary data table and further mathematical details, may be accessed at http://dx.doi. org/10.1080/17459737.2014. 980343.
WindArms: More Efficient DGF Coverage-Guided Tracing

Directed Graybox Fuzzing is an enhanced fuzzing technique that is widely used in security testing of network communication protocols, desktop software, file systems, etc. It guides the execution flow of the program to the code area of the target site by constructing specific input data, and realizes the test of the specific code area. It is usually used in scenarios such as patch testing, crash reproduction, and static analysis report verification. However, we found that coverage-guided tracing used by the existing Directed Graybox Fuzzing not only has high performance overhead but also has little value, because most test cases cannot discover new execution paths, and are even less likely to reach the target area. It doesn't make much sense to trace the coverage of these test cases. To address this challenge, we propose a new coverage-guided tracing mechanism and implement it prototype, WindArms. It will only trace the coverage of those which can discover new execution paths or reach the target area to reduce the performance overhead of those meaningless coverage-guided tracing. Our evaluation show WindArms not only significantly improves the performance of Directed Graybox Fuzzing, but also can discover security vulnerabilities in real-world software.
The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability

AbstractWhile internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindu.
Air shower detectors in gamma-ray astronomy

Extensive air shower (EAS) arrays directly detect the particles in an EAS that reach the observation altitude. This detection technique effectively makes air shower arrays synoptic telescopes—they are capable of simultaneously and continuously viewing the entire overhead sky. Typical air shower detectors have an effective field-of-view of 2 sr and operate nearly 100% of the time. These two characteristics make them ideal instruments for studying the highest energy gamma rays, extended sources and transient phenomena. Until recently air shower arrays have had insufficient sensitivity to detect gamma-ray sources. Over the past decade, the situation has changed markedly. Milagro, in the US, and the Tibet ASγ array in Tibet, have detected very-high-energy gamma-ray emission from the Crab Nebula and the active galaxy Markarian 421 (both previously known sources). Milagro has discovered TeV diffuse emission from the Milky Way, three unidentified sources of TeV gamma rays, and several candidate sources of TeV gamma rays. Given these successes and the suite of existing and planned instruments in the GeV and TeV regime (AGILE, GLAST, HESS, VERITAS, CTA, AGIS and IceCube) there are strong reasons for pursuing a next generation of EAS detectors. In conjunction with these other instruments the next generation of EAS instruments could answer long-standing problems in astrophysics.
A visual feedback method of motion information on alpine skiing experience in head mounted display

One of the sports training methods is VR training, where users watch a video image to recognize the situation, estimate the timing to move, and move their bodies by the situation. In this paper, we propose a real-time visual feedback method for sports motion information on the VR experience of Alpine skiing. We prepare a carefully designed visual feedback panel that presents the user's center of gravity and head height as sports motion information. An HMD presents the skiing situation on the slopes in VR180 style. The load sensor of our preliminary system is placed under the user's feet, and it acquires the center of gravity position. The tracking function of the HMD estimates head height. In the evaluation experiment, we investigated the appropriate parameters to realize the good visibility of the visual feedback panel during VR training.
Accident Emergency Alert System using Deep Learning

An accident is an unexpected and unwanted event. One of the primary causes of car accidents is speed. Many lives could be saved if rescue services received accident information and responds to it quickly. Human lives became simpler with the emergence of technology and infrastructure. Given the concerning growth in the number of accidents occurring in India, this method ensures that authorities are notified prior to an accident or at the moment of the event. The advancement of technology has also resulted in an increase in traffic dangers and road accidents which result in huge loss of life and property due to inadequate emergency services. The failure to receive timely care is the leading cause of mortality in traffic accidents accounting for half of all fatalities. Since, after an accident every second counts and immediate action is required to avoid the death of a person, intelligent transportation systems have lately evolved as a powerful means of improving the interpretation of transportation systems and promoting travel safety. One of the most effective technologies is accident detection systems so that the rate of death in road accidents can be decreased by treating the victims as soon as possible. This article is based on computer vision which is an emergency alert system built on python which uses object tracking techniques to detect the occurrence of an accident. An efficient automatic accident detection system featuring automated reporting of the accident site to emergency responders is critical for saving valuable human life. This system intends to identify accidents beforehand and communicate the information to emergency responders in order to give rapid aid to the wounded individual. The research work purpose is to determine the severity of an accident and to warn the rescue crew in time.
Evaluation of Trabecular Microstructure of Cancellous Bone Using Quarter-Detector Computed Tomography

Quarter-detector computed tomography (QDCT) is an ultra-high-spatial-resolution imaging technique. This study aimed to verify the validity of trabecular structure evaluation using a QDCT scanner in the diagnosis of osteoporosis. We used a cancellous bone specimen image of the second lumbar vertebrae of an adult male with moderate osteoporosis. To obtain QDCT images, we created a three-dimensional model from micro-CT images of the specimen. Statistical analysis was performed on the relationship between micro-CT and QDCT imaging modalities. The differences between micro-CT and QDCT were assessed based on their significance with respect to the calculated mean measurements using the Mann–Whitney test. Single regression analysis was performed using linear regression, with micro-CT and QDCT as the explanatory and objective variables, respectively, to determine the relationship of the measured values between the two modalities. By applying the necessary correction to the micro-CT measured values, it is possible to perform an analysis equivalent to micro-CT, which offers higher spatial resolution than QDCT. We found evidence that if QDCT can be used, trabecular structure evaluation may contribute to image diagnosis to evaluate practical bone fragility.
Statistical Modelling of Massive MIMO Channel at FR2 Frequency Bands for B5G Networks

The rapid growth of extreme data traffic and over occupied networks are the current challenges in the wireless communication networks. However, next generation networks tackle them by utilizing vast frequency bands. These are FR2/FR1 frequency bands that play an important role in next-generation technologies, namely B5G or 6G. mmWave being one of those technologies, also utilize such frequency bands. Nevertheless, massive antennas and propagation characteristics of the channel model describes its behavior and some crucial aspects that need to be considered when operating on FR2 frequency bands for 5G NR networks. This paper analyzes the behavior of a mmWave massive MIMO channel at FR1 and FR2 frequency bands under for specific atmospheric conditions. The channel behavior was simulated using NYUSIM software and the performance metrics include path loss, received power, and path loss exponent of the channel power delay profile.
Use of Antibiotics in Poisonous Ingestions of Corrosives and Organophosphates: A Retrospective Cohort Study

The use of antibiotics following oral poisoning by corrosives and organophosphates is controversial. We assessed the clinical outcomes of using antibiotics in acute poisonous ingestion involving corrosives or organophosphates by conducting a retrospective cohort study of patients presenting to the emergency department following ingestion of corrosives or organophosphates who received either antibiotics or supportive care. The endpoints included clinical stability, length of stay (LOS), and mortality. Of 95 patients, 40 received antibiotics and 55 received supportive care. The median age was 2.1 and 2.7 years, respectively (p = 0.053). Bacterial growth was shown in only 2 of 28 cultures (both were respiratory), but with hospital-acquired organisms as it was shown ≥4 days post-admission. Clinical stability rates were 60% and 89.1% in the antibiotic and supportive care groups, respectively (p < 0.001). Median LOS was 3 vs. 0 days (p < 0.001), and no mortality was recorded. NG/G-tube placement was the only factor associated with clinical failure (OR, 20.97; 95% CI, 2.36–186.13). Antibiotic use was not associated with higher chances of clinical stability, which may suggest that their use was unnecessary. Clinicians are encouraged to use antibiotics wisely, and only in the presence of a clear indication of an infection. This study provides a basis for future prospective studies to confirm its findings.
Differential Activation of NRF2 Signaling Pathway in Renal-Cell Carcinoma Caki Cell Lines

Renal-cell carcinoma (RCC) is a heterogeneous disease consisting of several subtypes based on specific genomic profiles and histological and clinical characteristics. The subtype with the highest prevalence is clear-cell RCC (ccRCC), next is papillary RCC (pRCC), and then chromophobe RCC (chRCC). The ccRCC cell lines are further subdivided into prognostic expression-based subtypes ccA or ccB. This heterogeneity necessitates the development, availability, and utilization of cell line models with the correct disease phenotypic characteristics for RCC research. In this study, we focused on characterizing proteomic differences between the Caki-1 and Caki-2 cell lines that are commonly used in ccRCC research. Both cells are primarily defined as human ccRCC cell lines. Caki-1 cell lines are metastatic, harboring wild-type VHL, whereas Caki-2 are considered as the primary ccRCC cell lines expressing wild-type von Hippel–Lindau protein (pVHL). Here, we performed a comprehensive comparative proteomic analysis of Caki-1 and Caki-2 cells using tandem mass-tag reagents together with liquid chromatography mass spectrometry (LC/MS) for the identification and quantitation of proteins in the two cell lines. Differential regulation of a subset of the proteins identified was validated using orthogonal methods including western blot, q-PCR, and immunofluorescence assays. Integrative bioinformatic analysis identifies the activation/inhibition of specific molecular pathways, upstream regulators, and causal networks that are uniquely regulated and associated with the two cell lines and RCC subtypes, and potentially the disease stage. Altogether, we have identified multiple molecular pathways, including NRF2 signaling, which is the most significantly activated pathway in Caki-2 versus Caki-1 cells. Some of the differentially regulated molecules and signaling pathways could serve as potential diagnostic and prognostic biomarkers and therapeutic targets amongst ccRCC subtypes.