Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial Sensor Algorithm to Estimate Walk Distance

The “total distance walked” obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts.
Environment, Environmental Crimes, Environmental Forensic Medicine, Environmental Risk Management and Environmental Criminology

Forensic medicine has always held the human environment, either seen as a source for pathological agents or the background of judicial events, in great consideration. The concept of the environment has evolved through time, expanding itself to include all the physical and virtual sub-spaces in which we exist. We can nowadays talk of technoenvironmental reality; virtual spaces exploded because of the COVID-19 pandemic making us come to terms with the fact that those are the places where we work, where we socialize and, even, where we meet our doctors and can be cured. Artificial Intelligence (AI) has contributed to shaping new virtual realities that have got their own rules yet to be discovered, carved and respected. We already fight a daily battle to save our natural environment: along with the danger of green crimes, comes the need for environmental justice and environmental forensic medicine that will probably develop a forensic branch and an experimental branch, to implement our technical culture leading to definition of the real dimension of the risk itself to improve the role of legal medicine in the Environmental Risk Management. While green criminology addresses widespread green crimes, a virtual environment criminology will also develop, maybe with a contribution of AI in the justice field. For a sustainable life, the environmental revolution must rapidly take place, and there is the need for a new justice, a new forensic medicine and a new criminology too.
Interplay between rule learning and rule switching in a perceptual categorization task

When performing a task in a changing world, sometimes we switch between rules already learned; at other times we must learn rules anew. Often we must do both, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.
Performance Analysis of Spectral Amplitude Coding Based OCDMA System with Gain and Splitter Mismatch

Abstract This paper presents the practical analysis of the optical code-division multiple-access (O-CDMA) systems based on perfect difference codes. The work carried out use SNR criterion to select the optimal value of avalanche photodiodes (APD) gain and shows how the mismatch in the splitters and gains of the APD used in the transmitters and receivers of network can degrade the BER performance of the system. The investigations also reveal that higher APD gains are not suitable for such systems even at higher powers. The system performance, with consideration of shot noise, thermal noise, bulk and surface leakage currents is also investigated.
Numerical study and optimization of photonic crystals

Photonic crystals (PhCs) are engineered nanostructures that enable an extraordinary control over the flow of light. These structures can be fabricated out of common semiconductors, are compatible with existing industrial fabrication technologies, and are expected to play a major role in future devices integrating photonic circuits - e.g. for telecommunications or in future quantum technologies. In this thesis, we explore a wide range of properties of the most common class of PhCs, formed by a lattice of circular holes in a semiconductor slab. To compute the electromagnetic eigenmodes of a given structure, we use fast mode-expansion methods, which are presented in detail here. The first application consists in a detailed analysis of the effects of fabrication disorder on the PhC structures. It is by now well-known that disorder is in many cases the limiting factor in device performance. Here, we shed more light on its effects, by statistically comparing various designs for PhC cavities with a high quality factor, and by analyzing the effect of irregular hole shapes on a PhC waveguide. The second application presented here stems from the fact that PhCs are in fact tremendously flexible, and their features are determined by a large number of controllable parameters. This is on one hand a great advantage, but on the other a great challenge when it comes to finding the optimal device for a given application. To face this challenge, we have developed an automated optimization procedure, using a global optimization algorithm for the exploration of an insightfully selected parameter space. This was applied to various devices of interest, and inevitably resulted in a vast improvement of their qualities. Specifically, we demonstrate various high-Q cavity designs, and a slow-light coupled-cavity waveguide with extraordinary features. We also present several experimental confirmations of the validity of our designs. Finally, we discuss two domains in which PhCs (and our optimization procedure) can be expected to play a major role. The first one is integrating quantum dots with the goal of long-range, photon-assisted dot-dot coupling, with implications for quantum information processing. We develop a semi-classical formalism, and analyze the magnitude and attenuation length of this coupling in large PhC cavities, as well as in a waveguide. The second outlook is in the field of topological photonics. We describe an array of resonators, in which an effective gauge field for photons can be induced through an appropriate time-periodic modulation of the resonant frequencies. This results in a Quantum Hall effect for light, and, in a finite system, one-directional edge states immune to fabrication disorder are predicted. We discuss the possibilities for a practical implementation, for which a PhC slab is among the most promising platforms.
CRL4Cdt2 Ubiquitin Ligase, A Genome Caretaker Controlled by Cdt2 Binding to PCNA and DNA

The ubiquitin ligase CRL4Cdt2 plays a vital role in preserving genomic integrity by regulating essential proteins during S phase and after DNA damage. Deregulation of CRL4Cdt2 during the cell cycle can cause DNA re-replication, which correlates with malignant transformation and tumor growth. CRL4Cdt2 regulates a broad spectrum of cell cycle substrates for ubiquitination and proteolysis, including Cdc10-dependent transcript 1 or Chromatin licensing and DNA replication factor 1 (Cdt1), histone H4K20 mono-methyltransferase (Set8) and cyclin-dependent kinase inhibitor 1 (p21), which regulate DNA replication. However, the mechanism it operates via its substrate receptor, Cdc10-dependent transcript 2 (Cdt2), is not fully understood. This review describes the essential features of the N-terminal and C-terminal parts of Cdt2 that regulate CRL4 ubiquitination activity, including the substrate recognition domain, intrinsically disordered region (IDR), phosphorylation sites, the PCNA-interacting protein-box (PIP) box motif and the DNA binding domain. Drugs targeting these specific domains of Cdt2 could have potential for the treatment of cancer.
Numerical treatment of multi-term time fractional nonlinear KdV equations with weakly singular solutions

ABSTRACT The main aim of this work is to construct an efficient recursive numerical technique for solving multi-term time fractional nonlinear KdV equation. The fractional derivatives are defined in Caputo sense. A modified Laplace decomposition method is introduced to approximate the solution. The Adomian polynomials play an important role to execute such a recursive process. In addition, the mathematical importance and some applications of KdV equation are discussed. The approximate solution obtained by the proposed method can be expressed in the form of an infinite convergent series. The experimental evidences demonstrate the effectiveness of the proposed method.
What are the mathematics houses in Iran and what they have done to popularize statistics?

Here, we introduce Isfahan Mathematics House (IMH) which is a community center that aims to provide a learning environment and opportunities for students and teachers at all levels for experiencing deeper understanding of mathematical concepts and developing creativity through working on real-life problems by team work and cooperation. Also, we will present some of IMH activities, then we will discuss the necessity of popularizing statistics among the public by promoting and enhancing statistics education among the teachers and students. The last part will be devoted to what we have done at mathematics houses for this enhancement, and why we established a statistics house in Isfahan.
Performance analysis on a residential radiant chilled ceiling system and evaluation on indoor thermal environment in summer: an application

The performance of a residential radiant chilled ceiling system combined with an outdoor air handling unit under steady operating conditions in summer was tested, and the stable operating characteristics of this system were analyzed. Indoor thermal environment parameters of room which employed this system were measured and analyzed and the thermal comfort evaluation of occupants was given. The results show that the composite system works well in summer, which can provide low- and high-temperature chilled water for the air supply terminal and the radiant terminal devices. Good thermal comfort can be achieved in room which employed this system, and no condensation risk occurs when it is applied in hot summer and cold winter zone of China. All the analysis provides a technical support for the popularization of the residential radiant chilled ceiling system combined with an outdoor air handling unit in China, and provides a reference for the further optimization of this system.
Surgical management of fracture odontoid type II by anterior screw fixation: an institutional experience

Fractures of the odontoid process (the dens) of the axis account for 10-20% of acute cervical spine fractures. Recent advances in intra-operative imaging including neuron avigation and O-arm can now increase accuracy and lower morbidity of odontoid screw placement. The present study was done in the department of surgery, in the medical college and associated hospital. During study period, total of 22 patients with type II fracture of odontoid were included in the study. All operations were done with the same technique of Cloward anterior approach using cannulated single lag screw. In the majority of patients, the follow up has been for 2 years and in 3 cases the follow up has been for one year. At the time of presentation, the major complains of the patient included in the study was of pain in neck area. The 14 patients were discharged to home on third postoperative day with Philadelphia neck collar, 6 patients with weakness stayed in hospital one week on physiotherapy and 2 patients were in ICU for three days post-operative due to associated lung contusion. Follow up CT after 1.5 and 3 months show well alignment with good fusion with no signs of mal union or pseudo arthrosis and patients were followed up for six months. Anterior screw fixation is a good technique to practice. It achieves good outcome and good fusion in young generation with horizontal fracture line.
Typical Damage Prediction and Reliability Analysis of Superheater Tubes in Power Station Boilers Based on Multisource Data Analysis

The superheater and re-heater piping components in supercritical thermal power units are prone to creep and fatigue failure fracture after extensive use due to the high pressure and temperature environment. Therefore, safety assessment for superheaters and re-heaters in such an environment is critical. However, the actual service operation data is frequently insufficient, resulting in low accuracy of the safety assessment. Based on such problems, in order to address the issues of susceptibility of superheater and re-heater piping components to creep, inaccurate fatigue failure fracture, and creep–fatigue coupling rupture in a safety assessment, their remaining life prediction and reliability, as well as the lack of actual service operation data, multisource heterogeneous data generated from actual service of power plants combined with deep learning technology was used in this paper. As such, three real-time operating conditions’ data (temperature, pressure, and stress amplitude) during equipment operation are predicted by training a deep learning architecture long short-term memory (LSTM) neural network suitable for processing time-series data and a backpropagation through time (BPTT) algorithm is used to optimize the model and compared with the actual physical model. Damage assessment and life prediction of final superheater tubes of power station boilers are carried out. The Weibull distribution model is used to obtain the trend of cumulative failure risk change and assess and predict the safety condition of the overall system of pressurized components of power station boilers.
Three-Level Reduced Switch AC/DC/AC Power Conversion System for High Voltage Electric Vehicles

Two of the main challenges of recent electric vehicles (EVs) are the charging time and high initial cost. To solve the problem associated with long charging time, the car manufacturers are moving from 400 V battery EV (BEV) to 800 V BEV, which enables the utilization of multi-level converters in EV applications. This paper presents a power conversion system consisting of a Vienna rectifier and a two/three level hybrid inverter as a machine-side inverter to drive a permanent-magnet synchronous motor (PMSM). The Vienna rectifier improves the quality of the grid-side current and provides a regulated DC-link voltage. The proposed inverter, known as a 10-switch inverter, offers high output current quality with a lower number of active switches, making it compact and cost-effective. The field-oriented control (FOC), along with the SPWM modulation, is implemented to control the system. A reliable and cost-effective PMSM drive system demands sensorless control; therefore, a sliding mode observer (SMO) is used to estimate the rotor position and velocity. The accuracy of the proposed system was proved through the simulation results from MATLAB/Simulink.
Machine Learning Assisted Energy Optimization in Smart Grid for Smart City Applications

Peer-to-peer electricity transaction is predicted to play a substantial role in research into future power infrastructures as energy consumption in intelligent microgrids increases. However, the on-demand usage of Energy is a major issue for families to obtain the best cost. This article provides a machine learning predictive power trading framework for supporting distributed power resources in real-time, day-to-day monitoring, and generating schedules. Furthermore, the energy optimization algorithm used in machine learning (EOA-ML) is proposed in this article. The machine learning-based platform suggested two modules: fuel trading and intelligent contracts based on machine learning implemented predictive analytical components. The Blockchain module enables peers to track energy use in real-time, manage electricity trading, model rewards, and irreversible transaction records of electricity trading. A predictive analysis component based on previous power usage data is designed to anticipate short-term energy usage in the Intelligent Contracts. This study utilizes data from the provincial Jeju, Korea’s electricity department on true energy utilization. This study seeks to establish optimal electricity flow and crowdsourced, promoting electricity between consumers and prosumers. Power trading relies on day-to-day, practical environmental control and the planning of decentralized power capitals to satisfy the demands of smart grids. Furthermore, it employs data mining technologies to obtain and study time-series research from the past electricity utilization data. Thus, the time series analytics promotes power controllingto better future efficient planning and managingelectricity supplies. It utilized numerous statistical methods to assess the effectiveness of the suggested prediction model, mean square error in different models of machine learning, recurring neural networks. The efficacy of the proposed system regarding the delay, throughput, and resource using hyperleader caliper is shown. Finally, the suggested approach is successfully applied for power crowdsourcing between prosumer and customer to reach service reliability based on trial findings. The actual and predicted cost analysis has been increased (95%). It minimizes the delay rate to (40.3%) by improving the efficiency rate.
Effect of Laser and Temperature Treatment on the Optical Properties of Titanium Dioxide Nanoparticles Prepared Via Pulsed Laser Ablation

Nanomaterials based on titanium dioxide are of considerable interest as promising photocatalysts for water and air purification and hydrogen generation. To increase the efficiency of TiO 2 , it is necessary to extend the spectral range of its absorption to the visible region. In this work, nanocolloids of dark titanium dioxide, initially prepared by pulsed laser ablation (Nd:YAG laser, 1064 nm, 7 ns) in water, are subjected to an additional laser treatment from the same source. Colloidal solutions are dried and TiO 2 powders are annealed. A comparative analysis of the optical properties of the materials prepared with and without extra treatment by the methods of diffuse reflection spectroscopy, fluorescence, and Raman spectroscopy is carried out. The bandgap of the materials is estimated, and the nature of the defect states responsible for intense absorption in the visible region of the spectrum is determined.
Identification of an IL-4-Related Gene Risk Signature for Malignancy, Prognosis and Immune Phenotype Prediction in Glioma

Background: Emerging molecular and genetic biomarkers have been introduced to classify gliomas in the past decades. Here, we introduced a risk signature based on the cellular response to the IL-4 gene set through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Methods: In this study, we provide a bioinformatic profiling of our risk signature for the malignancy, prognosis and immune phenotype of glioma. A cohort of 325 patients with whole genome RNA-seq expression data from the Chinese Glioma Genome Atlas (CGGA) dataset was used as the training set, while another cohort of 667 patients from The Cancer Genome Atlas (TCGA) dataset was used as the validating set. The LASSO model identified a 10-gene signature which was considered as the optimal model. Results: The signature was confirmed to be a good predictor of clinical and molecular features involved in the malignancy of gliomas. We also identified that our risk signature could serve as an independently prognostic biomarker in patients with gliomas (p < 0.0001). Correlation analysis showed that our risk signature was strongly correlated with the Tregs, M0 macrophages and NK cells infiltrated in the microenvironment of glioma, which might be a supplement to the existing incomplete innate immune mechanism of glioma phenotypes. Conclusions: Our IL-4-related gene signature was associated with more aggressive and immunosuppressive phenotypes of gliomas. The risk score could predict prognosis independently in glioma, which might provide a new insight for understanding the IL-4 involved mechanism of gliomas.
300 mm Large Area Wire Grid Polarizers with 50 nm Half-Pitch by ArF Immersion Lithography

The large area wire grid polarizers (LA-WGPs) with 50 nm half-pitch were fabricated using ArF immersion lithography overcoming the limit of the shot field size. To realize the 50 nm line and space patterns on a 300 mm wafer, a zero-distance stitching process that connects the shot fields is suggested. To compensate for mutual interference between the shot fields which is called the local flare effect (LFE), the shot field arrangement is changed with optical proximity correction (OPC). Using a master wafer produced by the suggested method, 300 mm large-area WGPs were fabricated by the nano-imprint process. The WGPs have more than 80% transmittance in the visible light region, and the possibility of performance improvement can be confirmed depending on the number and method of the etch process.
High-voltage electrical equipment with the SF6 (sulfur hexafluoride) gas insulation: analysis of accident rate and service experience

The paper was devoted by the questions of high- voltage electrical equipment with SF6 insulation service, is carried out the analysis of accident rate and its basic reasons are examined. There is given design deficiencies in different types of the current transformers, measure for their elimination, and inspection of SF6 insulation current transformers by thermal-vision control, measuring the acoustic activity of the partial discharges (PD) into the insulation of transformers, the gas pressure in the housing, data of electrical tests, the analysis of the quality of SF6 by different methods. The norms of the quality of SF6 in the high-voltage equipment are accepted in accordance with IEC 60480-2004.