Učinkoviti pristup odabira broja baterijskih modula kod hibridnih električnih vozila

Selection of higher values for Degree of Hybridization (DOH) increases the fuel economy and reduces the emissions in the Hybrid Electric Vehicles (HEVs). Previously presented methodologies for deciding about the number of battery modules (as an important factor influencing the vehicle performance), presents poor vehicle performance for higher DOHs. In this paper, a new technique has been proposed for deciding about the number of battery modules in Hybrid Electric Vehicles (HEVs), by which the high performance of the vehicle is guaranteed for higher DOHs. The proposed methodology is based on satisfying two key designing factors: Maximum charge and discharge capability and satisfaction of the PNGV criteria. The proposed methodology, allows us to choose higher DOHs in HEVs, which leads to lower emissions and higher levels of fuel economy. To evaluate efficiency of proposed methodology, it has been applied on model of a test parallel passenger hybrid car available in the ADvanced VehIcle SimulatOR (ADVISOR) software. The obtained results have been compared with that of formerly presented techniques. Simulation results confirm the effectiveness of proposed methodology.Povećanje stupnja hibridizacije (DOH) ima za posljedicu smanjenje potrošnje goriva te emisije štetnih plinova kod hibridnih električnih vozila (HEV). Postojeći pristupi za odabir broja baterijskih modula (kao važan faktor koji utječe na performanse vozila) kod većeg stupnja DOH-a rezultiraju lošim performansama vozila. U ovom radu, predložen je novi pristup odabira broja baterijskih modula HEV-a, koji garantira visoke performanse HEV-a i za visok stupanj DOH-a. Predloženi pristup temeljen je na zadovoljavanju dva ključna zahtjeva: maksimalna sposobnost punjenja i pražnjenja te PNGV kriterij. Predložen pristup omogućuje odabir visokog stupnja DOH-a u hibridnim električnim vozilima. Pristup je validiran korištenjem modela hibridnog automobila dostupnog u ADvanced VehIcle SimulatoOR programu. Dobiveni rezultati uspoređeni su s rezultatima trenutno korištenih pritupa odabira broja baterijskih modula te je potvrđena učinkovitost predloženog pristupa

Co-word Analysis of World Scientific Productions in the Field of Religion and Health

The purpose of this study is to evaluate the World Science Productions in the field of religion and health using co-word analysis.The research reviewed scientific documents indexed from 1958 to 2019 on Web of Science (WoS). For analyzing the data and drawing the scientific map, Ravar Matrix and Vosviewer tools were used.The data analysis shows that Iran provided only 1.52 percent of global scientific production in this field with 32 scientific documents, and countries such as the US, UK, and Germany are ahead of the others. The co-word analysis shows that the most important topics include spirituality and health, mental health, and medical ethics. In the past 60 years of studies on the three major religions of Islam, Judaism, and Christianity, the relation of spirituality and health has been one of the most significant topics. The findings suggest that the role of spirituality cannot be ignored in promoting the health of communities.https://dorl.net/dor/20.1001.1.20088302.2022.20.3.14.1  

Deep Residual-Dense Lattice Network for Speech Enhancement

Convolutional neural networks (CNNs) with residual links (ResNets) and causal dilated convolutional units have been the network of choice for deep learning approaches to speech enhancement. While residual links improve gradient flow during training, feature diminution of shallow layer outputs can occur due to repetitive summations with deeper layer outputs. One strategy to improve feature re-usage is to fuse both ResNets and densely connected CNNs (DenseNets). DenseNets, however, over-allocate parameters for feature re-usage. Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage. This is managed through the topology of the RDL blocks, which limit the number of outputs used for dense aggregations. Our extensive experimental investigation shows that RDL-Nets are able to achieve a higher speech enhancement performance than CNNs that employ residual and/or dense aggregations. RDL-Nets also use substantially fewer parameters and have a lower computational requirement. Furthermore, we demonstrate that RDL-Nets outperform many state-of-the-art deep learning approaches to speech enhancement.Comment: 8 pages, Accepted by AAAI-202

The efficiency of life skill training on emotional intelligence in chronic addicted women with a history of spousal abuse

BACKGROUND: Low emotional intelligence (EI) could affect individuals' coping strategies and make them vulnerable to violence and addiction. This study aims to study the effect of life skill training to improve EI in chronic addicted women with a history of spousal abuse.METHODS: The study was semi-experimental with a pre-test, post-test design. Conducted between October 2016 and January 2017, this study included women addicted to cannabis with a history of spousal abuse referring to some addiction intervention clinics in Tehran, Iran. 30 individuals were selected based on the inclusion and exclusion criteria and also cut-off point for EI using the convenience sampling method. They were then assigned to two groups randomly (each group n = 15). In six sessions, the experimental group received life skill training and the control group were in the waiting list. Both groups were evaluated in baseline and after the intervention by Ghahari’s domestic violence questionnaire and Bar-on Emotional Quotient Inventory (EQ-i). Data were analyzed using multivariate analysis of covariance (MANCOVA) in SPSS software.RESULTS: The experimental group had improvements in total score and components of EI including interpersonal EQ (F = 312.30, P < 0.050) and intrapersonal EQ (F = 295.04, P < 0.050).CONCLUSION: Life skill training could improve EI in addicted women with a history of spousal abuse

Reactive Power Planning for Loss Minimization Using Simulated Annealing

Abstract: This paper addresses an optimal Reactive Power Planning (RPP) of power system. The Static Var Compensator (SVC) is introduced into power system in order to reactive power support and voltage control. The locations and the outputs of SVCs are determined using our proposed optimal reactive power planning model. The proposed method optimizes several objective functions at the same time within one general objective. The optimized objectives are minimization of total investment in reactive power support, average voltage deviation and minimization of total system loss. These objective functions are one of the most important objectives for every transmission and distribution systems. Simulated Annealing technique (SA) is used to solve the optimization problem. The validity of the proposed method is tested on a typical power system

Androgen receptor (AR)-CAG trinucleotide repeat length and idiopathic male infertility

CAG trinucleotide repeats in androgen receptor (AR) gene encode a polyglutamine tract in AR N-terminal transactivation domain. Studies have been conducted to evaluate the effect of CAG repeat length on male infertility, which have yielded contradictory results. This study aimed to explore the number of AR-CAG repeats in 150 fertile controls and 150 idiopathic infertile men, divided into four azoospermia, oligozoospermia, asthenozoospermia, and teratozoospermia subgroups. In addition, a meta-analysis was conducted based on previous studies to assess the association of the mentioned variation with male infertility in recent years. Polymerase chain reaction (PCR) targeting followed by an electrophoresis on polyacrylamide gel was used for AR-CAG genotype detecting. Moreover, a systematic search was performed in PubMed, Web of Science, Science Direct, and Google Scholar databases to collect eligible studies for meta-analysis purpose. According to the results, a significant association was observed between increased length of AR-CAG polymorphism and male infertility (p< 0.0001). Furthermore, there were similar significant associations in the azoospermia (p= 0.048), asthenozoospermia (p= 0.013) and teratozoospermia (p= 0.002) subgroups. In addition, meta-analysis on forty studies showed a significant association between AR-CAG polymorphism in the overall analysis (SMD= 0.199, 95 % CI= 0.112-0.287, p<0.001) and the Caucasian subgroup (SMD= 0.151, 95 % CI= 0.040-0.263, p= 0.008). Our results elucidated that long stretches of CAG repeat might lead to AR dysfunction, contributing to male infertility especially in the Caucasian population

Universal programmable logic gate and routing method

An universal and programmable logic gate based on G.sup.4-FET technology is disclosed, leading to the design of more efficient logic circuits. A new full adder design based on the G.sup.4-FET is also presented. The G.sup.4-FET can also function as a unique router device offering coplanar crossing of signal paths that are isolated and perpendicular to one another. This has the potential of overcoming major limitations in VLSI design where complex interconnection schemes have become increasingly problematic

Using G4FETs as a Data Router for In-Plane Crossing of Signal Paths

Theoretical analysis and some experiments have demonstrated that siliconon- insulator (SOI) 4-gate transistors the type known as G(exp 4)FETs could be efficiently used for in-plane crossing of signal paths. Much of the effort of designing very-large-scale integrated (VLSI) circuits is focused on area-efficient routing of signals. The main source of difficulty in VLSI signal routing is the requirement to prevent crossing, in the same plane, of wires that are meant to be kept electrically insulated from each other. Consequently, it often becomes necessary to design and build VLSI circuits in multiple layers with vias (connections between conductors in different layers at selected locations). Suitable devices that would prevent, or at least sufficiently suppress, undesired electrical coupling (cross-talk) between wires crossing in the same plane would enable compact, simpler implementation complex interconnection networks with in-plane crossings that, heretofore, have not been possible in VLSI circuitry. The use of G4FETs as in-plane signal-crossing devices or routers, in combination with the use of G(exp 4)FETs as universal programmable logic gates, would create opportunities for reducing complexity in VLSI design

A Model to Measure the Spread Power of Rumors

Nowadays, a significant portion of daily interacted posts in social media are infected by rumors. This study investigates the problem of rumor analysis in different areas from other researches. It tackles the unaddressed problem related to calculating the Spread Power of Rumor (SPR) for the first time and seeks to examine the spread power as the function of multi-contextual features. For this purpose, the theory of Allport and Postman will be adopted. In which it claims that there are two key factors determinant to the spread power of rumors, namely importance and ambiguity. The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach, which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual features are introduced to measure SPR and their impact on classification are investigated, then 42 features in two categories "importance" (28 features) and "ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is verified on two labelled datasets, which are collected from Twitter and Telegram. The results show that (i) the proposed new features are effective and efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach focused only on contextual features while existing techniques are based on Structure and Content features, but RSPMM achieves considerably outstanding results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, besides it can be useful as a new method to verify the trueness of rumors

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP