Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tunable mmWave Band-Pass Filter Based on Ferroelectric Hafnium Zirconium Oxide Varactors

This paper presents a tunable 60 GHz band-pass filter, based on a coplanar waveguide (CPW) transmission line, periodically loaded with ferroelectric Hafnium Zirconium Oxide (HZO) variable metal-ferroelectric-metal (MIM) capacitors (varactors), developed for back-end-of-line (BEoL) integration. Derived from the nonlinear capacitance of hafnium zirconium oxide and implementing the method-of-moments simulation, it was shown, that with changing the bias voltage between 0.95 and -3 V, the filter’s center frequency can be tuned between 60.5 and 69,7 GHz, respectively. Hereby, a minimum insertion loss of -3.3 dB is realized. The chip area of the filter is only 0.062 mm2, making it the smallest among tunable V-band filters.
SYMPTOM ASSESSMENT AND MANAGEMENT IN PATIENTS WITH HEART FAILURE

OF DISSERTATION SYMPTOM ASSESSMENT AND MANAGEMENT IN PATIENTS WITH HEART FAILURE Patients with heart failure (HF) must monitor and recognize escalating symptoms to manage worsening HF in a timely manner. However, routine symptom monitoring is not commonly performed by this population. Providing a symptom diary along with an education and counseling session may help HF patients promote symptom monitoring and interpretation. The accumulated information about changes in daily symptoms will allow patients to easily compare current symptom status to the past without depending on memory and can rapidly capture worsening HF. To date, few studies have tested the effect of a daily symptom diary. The purpose of this dissertation was to develop and test a symptom diary intervention to improve outcomes in HF patients. Prior to testing the intervention, preliminary work included: (1) determining the impact of symptom clusters on cardiac event-free survival; (2) evaluating the quality of existing symptom measures designed for HF patients; (3) evaluating the effect of physical symptom items that were often included in a depressive symptom instrument on cardiac event-free survival; and (4) evaluating the association between symptom monitoring and self-care management. Based on this information, a randomized, controlled pilot study was conducted to test the effect of a symptom diary with an education and counseling intervention on prognosis, healthrelated quality of life (HRQOL), and self-care maintenance at 3 months follow-up. A total of 44 hospitalized patients with HF were randomly assigned to either usual care or intervention providing a daily symptom diary with education and counseling. There were trends toward fewer HF events and improved self-care maintenance in the intervention group compared to the usual care group. However, there was no difference in HRQOL between the two groups. The results of this dissertation suggest the importance of assessing symptom clusters and further studies to improve the quality of existing HF symptom measures. Results from this dissertation also provided the evidence of the advantages of regular symptom monitoring to facilitate early identification of worsening HF and initiation of timely responses. However, further studies are needed to provide additional evidence of the positive impact of a use of daily symptom diary in patients with HF.
Big Data in Power Systems: An Introduction to Julia Linear Models using Tensor Flow

This paper proposes a set of tools to deal with high dimensional data that is often encountered in power systems. The tool set seems promising to network utilities, energy companies, data enthusiasts and others who are involved with the future planning, development, maintenance and operation of the power systems. The tool facilitates modelling and prediction based on Linear Model topology as well as variable screening based on user-discretion. Though the same tool set can be applied in a wide range of applications, here, as an example, the most significant variables that contribute to the energy consumption of a customer is obtained from among a pool of variables. Here, we put use to the immense data available in the power system paradigm, which, at the moment is not utilised to its full potential. The novelty of the paper is in using Julia and TensorFlow framework together in this dimensionality reduction. The training set input to the TensorFlow algorithm is utilised to establish a Linear Model and is later optimised to reduce the error through Gradient Descent optimisation. The final model is used to predict the energy usage, R-Squared values after every iteration is observed to give a flavor of variable screening process.
Active Distribution Network Energy Management with Cooperative Scheduling of Multi-microgrids

This paper focuses on the energy management of active distribution network with microgrids for the full use of the coordination and complementarity amongst microgrids. Firstly, combining the hierarchical control theory with the distribution network architecture of multiple microgrids, and taking the optimal coordination of the distribution network as the target, this paper proposes a two-step dynamic hierarchical scheduling strategy. Microgrid scheduling modes are categorized into three types, namely coordinated scheduling, separate grid-connected scheduling and islanding operation, and different modes correspond to different microgrid scheduling strategies. In addition, the dispatching mode could switch reasonably with the mode information sent to the energy management center of the distribution network. Finally, taking the IEEE33 system with four microgrids as an example, the effect of the number of microgrids participating in coordinated dispatching and switching of microgrid dispatching modes on load characteristics and the operation economy are investigated in detail.
Dual Active-Feedback Frequency Compensation for Output-Capacitorless LDO With Transient and Stability Enhancement in 65-nm CMOS

An output-capacitorless low-dropout regulator (OCL-LDO) using a dual-active feedback frequency compensation (DAFFC) scheme with both transient and stability enhancement has been presented in this paper. The DAFFC scheme consists of two parallel active feedback paths, which creates two pole-zero pairs to effectively enhance the stability and transient response for the proposed OCL-LDO. Compared to the conventional single-path active-feedback frequency compensation method, the proposed DAFFC technique has provided one more design freedom with one more active feedback loop deployed and has been proved to be capable of obtaining better compensation effects with the same capacitor budget. Besides, the induced extra ac currents by the two active feedback loops have also enhanced the transient response of the proposed OCL-LDO. To substantiate the proposed DAFFC, a telescopic cascode output stage for error amplifier, and two on-chip compensation capacitors (5 and 1 pF, respectively) are needed. The proposed OCL-LDO has been implemented in 65-nm CMOS technology and the active chip area is 0.0105 mm2. The output voltage is 0.8 V, and the minimum input voltage is 0.95 V at 100-mA loading current. The proposed OCL-LDO can work stably in a load range of 0 to 100 mA with 14-μA quiescent current.
Measuring Tone Disclosure as a Complementary Approach: The Case of the Banking Sector at Bahrain Bourse

The aim of this study is to propose four behavioral measures of corporate tone disclosure based on emotion analysis of corporate annual reports using the banking sector listed on the Bahrain Bourse from 2007–2017. Our final data set consists of 88 bank-year observations. The suggested tone disclosure measures are (trust & disgust), (joy & sadness), (anticipation & surprise) and (fear & anger). The proposed measures quantify the integrity; enthusiastic; precognitive and anxiety behaviors of top management. The study found that the integrity behavior of top management is associated with its precognitive behavior while the relationship between the enthusiastic and anxiety behaviors do exist. The current study argues that tone disclosure in corporate annual reports can be a valuable tool to stakeholders because it can reflect the behavior of top management, which may affect actions towards corporate financial reporting. The study suggests using the proposed tone measures as a complementary or diagnostic approach rather than an alternative to the different approaches used by analysts, auditors and other users of financial reports.
SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

Data mining is one of the most important applications of machine learning. In machine learning algorithms, the fusion kernel principle component analysis (KPCA) and support vector machine (SVM) algorithm is used in complex data classification. To solve the problem that the fusion KPCA and SVM algorithm does not have promising classification performance, the SVM based on a feature selection algorithm for differential space fusion (DSF-FS) is proposed. First, the original data is processed to obtain differential space data by principle component analysis (PCA), and the KPCA algorithm is performed respectively on the original data and differential space data to get the differential space fusion features. Second, the ReliefF algorithm is used to get the weight of features, and the optimal feature combination is selected by a preliminary classification evaluation metric. Third, the SVM algorithm is used to classify the dimensionality reduction data. Finally, some experimental results on the five UCI datasets show that the proposed DSF-FS algorithm can not only improve the classification accuracy, but it can also reduce the computational complexity of the classification process. Moreover, the DSF-FS algorithm can be successfully applied in diabetic fundus image classification, and the encouraging results further demonstrate its strong feasibility and applicability.
Noncontact Current Source Reconstruction Based on Independent Component Analysis-A Simulation Study

This paper implemented a current reconstruction system based on independent component analysis and magnetic field sensors. In this paper both the normal approximation and interfered approximation of magnetic flux density generated by current carrying conductors and sensed by four magnetic sensors are presented and discussed. The output voltage of the sensor array with and without interference was calculated based on the comprehensive independent component analysis algorithm. The relative reconstruction error based on the algorithm for array output voltage are within acceptable levels for protective devices proving the feasibility of the algorithm for source reconstruction.
Soft Computing Techniques for Noise Filtration in the Image Recognition Processes

In this paper, methods for noise filtration in the image recognition processes are considered. The following noise filtration methods were analyzed: arithmetic averaging filter, geometric averaging filter, median filtering, adaptive median filtration, Gaussian filtration and filtration using soft computing techniques, in particular the fuzzy color preserving Gaussian noise reduction method (FCG filter). Besides, the different types of noise that may occur on a digital image are discussed. All methods were evaluated using metrics like mean squared error (MSE), peak signal-to-noise ratio (PSNR) and structure similarity of images (SSIM). It has been found that all of the above methods can well filter out only a certain type of noise. In this paper, a combination of adaptive median filtering and FGG filter is proposed for removal of combined pulse and Gaussian noises
PLC Commanded Intelligent Logistics Model Based on Discrete Event Systems

The main issue of this paper is to design a controller for Discrete Event Systems (DES), based on the Supervisory Control Theory (SCT), applied in transport or logistic areas. This theory is used mostly for command and control of many cybernetic, manufacturing, communication, military or transportation as well as many others. The controller designed for the DES applications is currently named as a supervisor. The SCT based applications involve many dedicated languages and automation concepts taken from the classic control domain. By using SCT, any logistic chain can be modeled by using automation sequences. The automation itself is a true event sequence generator. The desired and controlled behavior of that logistic system is clearly defined by its specifications. By using SCT elements, any supervisor can be particularly synthesized, avoiding the transportation to reach some undesired states. After defining all operations, the resulted supervisor can be implemented in practice to perform real control tasks. Programmable Logic Controllers (PLCs) are widely applied for control tasks in today industry, and, by consequent, the supervisor for an industrial logistic chain could be implemented by means of PLCs. The language which is mostly used for programming PLCs is based on the Ladder Logic Diagram (LLD). Any supervisor described in an automation sequence form could be easily converted to an LLD code. As an example, in this paper, a small manufacturing system that consists of two machines and a buffer is considered. All pieces must be vehiculated between the machines on a simple logistic sequence, depending on their state. The implementation of the supervisor is also provided.
Bringing Geosciences to K-12 Classrooms: A Teacher Training Program Developed by the Sterling Hill Mining Museum

Science, Technology, Engineering and Math (STEM) outreach can be provided to many different stakeholders in the education ecosystem. This includes providing STEM training and experiences to teachers that can make a positive impact in the classroom. STEM covers a broad range of technologies and sciences, and one area that can use further support is the geological sciences. This paper discusses a program to aid in bringing the geosciences into the classroom through a teacher training program conducted at the Sterling Hill Mining Museum. A review of geoscience based outreach programs is presented along with a description of the Sterling Hill Mining Museum teacher training program. Key learning objectives are outlined to aid other teachers interested in bringing these materials, skills and knowledge into the classroom. Survey findings are also presented from the teacher participants that were collected pre, post and approximately six months after the program was conducted. With this data examples provided by participants on how these materials were integrated into the classroom are presented. Overall it is hoped that these findings will bring more geoscience into the classroom.
Decision Support System for Tourist Destination using the Combination of AHP and SAW

Traveling is one of the popular activities today. When traveling, the tourist usually decides to visit the interesting tourism destination. The number of tourist destinations available, often makes the tourist confused to select the destination according to their preferences. This paper aims to demonstrate multi criteria decision making (MCDM) by combining Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). The standard SAW method uses the criteria weight input by user. Users are often confused to give the score that represent their preference for the criteria appropriately. In this study, the AHP was used to get the weight of criteria based on user’ priority, while the SAW was used to rank all the alternatives. The case study used four criteria and ten alternatives. The result shows that combining AHP and SAW can be applied to the case study. The combining method work well by result the criteria weight based on the user preference and then rank all alternatives based on the alternatives score in each criterion and the weight of each criterion. The case study show that value of consistency ratio was 0.043, which means the comparison matrix inputted by user, was consistent.
Wind Power Prediction Based on Principal Component Analysis and Long Short-Term Memory Networks

In recent years, the scale of new energy represented by wind power and photovoltaic has been continuously developed at a high speed. Due to the strong randomness and volatility of wind energy, it poses a severe challenge for the safety, reliability, stability and economy of grid operation. The effective improvement of wind power prediction accuracy is of great significance to power grid operation. Along with the continuous development of artificial intelligence methods, the emerging wind power prediction methods have been greatly improved in terms of prediction accuracy and calculation rate. As a current predictive model for deep learning, Long Short-Term Memory (LSTM) is excellent for predicting long-term sequences. This paper proposes a wind power prediction method based on LSTM. In addition, Principal Component Analysis (PCA) method is used to achieve the dimension reduction of data. The simulation results demonstrate the effectiveness and advantages of the proposed PCA-LSTM method.
High Gain Microstrip-Fed Patch Antenna using Periodic Dielectric Substrate

A microstrip line fed patch antenna using dielectric sheets is proposed for gain enhancement. The proposed antenna consists of 20 periodic dielectric sheets placed perpendicularly above the insert fed patch antenna. By varying the distance between the patch antenna and periodic dielectric sheets and also the periodicity between the sheets, the peak gain of the designed patch-dielectric structure is improved from 5.37 dBi to 10 dBi. At S11 = -10 dB, the simulated impedance bandwidth of the antenna is 3.91% (2.404 – 2.5) GHz. At the operating bandwidth, the gain varies from 9.6 dBi to 10 dBi.
Design and Implementation of Power Efficient Encoder for Transition Reduction in Off-Chip Buses Using Adaptive Bus Encoding Technique

This paper deals with the design and implementation of power efficient encoder for transition reduction in off chip buses using ABE (Adaptive Bus Encoding) technique. In both the embedded and high performance systems, the effect of power consumption is becoming highly prominent, whereas the off-chip data buses remains to be a considerable power consumer. In most cases, the input data streams are not known because of the fact that the data streams are more random and less sequential. With subject to this, the adaptive bus encoding (ABE) mechanism is introduced which is executed without the preliminary knowledge about the required input streams. The proposed methodology of this paper make use of the spatial redundancy for the prevention of data loss while recovering them. This ABE framework reduces the transitions in highly capacitive off-chip buses and also this technique is associated with the power dissipation in the off chip buses for the significance of power efficiency and high speed communication. Thus, it minimises the extra dynamic power consumption due to the encoding circuitry mechanism.
Estimation of the Number of Stochastic EM Sources with Partially Correlated Radiation in Far-Field Using Neural Model

Neural model based on multilayer perceptron (MLP) neural networks, capable to determine the number of partially correlated stochastic radiation EM sources present in a monitoring spatial sector, is proposed in the paper. Stochastic radiation sources are moving in the azimuth plane along a line trajectory. Sources are mutually correlated with the same value of correlation level but this value can change during their movement. Also, sources can enter or leave the observed sector so that their number in the sector varies. Signals coming from the sources in the monitoring sector are sampled by a uniform linear antenna array and based on this sampling a spatial correlation matrix is created. The values of correlation matrix elements are used as an input to the proposed neural model that has to estimate the number of stochastic sources present in the observed sector. The neural model presented in this paper can determine a presence of maximum three stochastic sources in the 30° wide sector while the level of mutual correlation of sources can vary between 0.05 and 0.5.
Optimal Energy Management of Vanadium Redox Flow Batteries Energy Storage System for Frequency Regulation and Peak Shaving in an Islanded Microgrid

This paper proposes a centralized control method of vanadium redox flow battery (VRFB) energy storage system (ESS) that can achieve frequency regulation with cost minimization and peak shaving in a microgrid. A particle swarm optimization-based approach is used to optimize the ESS operation and it determines the optimal power dispatch of VRFB ESS and other distributed generation (DG) units. Simulation tests are conducted on an isolated microgrid system with VRFB ESS, DGs and PVs for frequency regulation and a load demand profile for peak shaving. Various case studies, such as the load step up, load step down and peak shaving, are conducted to show the validity of the proposed method. Simulation results show that the designed algorithm can achieve frequency regulation with reduced operation costs and peak shaving.
Analysis and Design of Half-Bridge LCC Resonant Converter with Buck-Boost Integration Adopting Dual Carrier Modulation

A half-bridge (HB) LCC resonant converter with Buck-Boost integration adopting dual carrier modulation is proposed in this paper. By integrating forestage buck-boost circuit with post-stage HB LCC converter, advantages of low cost and high power density can be easily achieved. Furthermore, the dual carrier modulation technique is adopted in the proposed converter, benefiting from wide zero-voltage-switching (ZVS) operation range, low conduction loss and reduced switching frequency variation. The proposed converter can effectively deal with the wide input voltage and/or wide output power, which is the common situation in applications like renewable energy interface and data center power supply. The working principle and detailed analysis of proposed converter are discussed in this paper. And a 500W prototype with 100-200V input voltage is provided to verify the theoretical analysis. The maximum efficiency of prototype reaches 93.9%.
Application of the Characteristic Basis Function Method Using Geometrically Optimized Function Sets

We present in this work an efficient strategy for the generation of macro-basis functions based on the location of the sources, the observation points or directions and the geometrical features of the scenario. A ray-tracing processing calculates the position of a number of critical points on the surface, which are subsequently used to determine the optimal number of macro-basis functions for each block. A representative test case is included in order to validate the proposed technique.
Electromagnetic Situational Awareness of Cognitive Radios supported by Radio Environment Maps

In this paper we present the concept of the Radio Environment Map (REM) designed to ensure electromagnetic situational awareness of cognitive radio networks. The map construction techniques based on spatial statistics are presented. The results of field tests done for UHF range with different number of sensors are shown. Exemplary maps with selected interpolation techniques are presented and control points where the signal from licensed users is correctly estimated are identified. Finally, the map quality is analyzed and the most promising interpolation techniques are selected.
PhytoChem & BioSub Journal

PhytoChem and BioSub Journal (PCBS Journal) is a peer-reviewed research journal published by Phytochemistry and Organic Synthesis Laboratory. The PCBS Journal publishes innovative research papers, reviews, mini-reviews, short communications and technical notes that contribute significantly to further the scientific knowledge related to the field of Phytochemistry and Bioactives Substances (Medicinal Plants, Ethnopharmacology, Pharmacognosy, Phytochemistry, Natural products, Analytical Chemistry, Organic Synthesis, Medicinal Chemistry, Pharmaceutical Chemistry, Biochemistry, Computational Chemistry, Molecular Drug Design, Pharmaceutical Analysis, Pharmacy Practice, Quality Assurance, Microbiology, Bioactivity and Biotechnology of Pharmaceutical Interest )