Stress analysis of multi-layer electronic and mechanical systems (MEMS) under fatigue and impact loading conditions

Detailed micro electronic and mechanical systems (MEMS) for a mobile microprocessor complex shape were modeled using Finite Element (FE) processing. Fatigue and impact conditions were performed on the Ball Grid Array (BGA) Integrated Circuit (IC) using Abaqus\CAE finite element analysis software. The main objective of this research is to make sure that BGA products can endure the roughness of the daily usage, where a portable electronic product is habitually coupled with potential damage of functional failure when the device falls.     

An ASN.1 Model and Integration Approach for Structured Text

A language for manipulating ASN.1 print files is defined as an addition to the ASN.1 data definition language as specified in the ISO standards 8824 and 8825. The language has syntax similar in spirit to SQL, and provides analogous operations. Together, ASN.1 and the manipulation language define a model that can represent attribute hierarchies, multivalued attributes, optional attributes, both ordered and unordered attributes and tuples, and unnamed attributes—constructs that are absent in the traditional relational model. In addition, the proposed model allows the semantics of order, homogeneity, optionality, choosability, and uniqueness to be attached to constructs and construct elements. The approach is well suited for modeling data where form is important, such as text and flat file databases having ad hoc formats. It also supports modeling relations. The primary motivation for developing this language is to provide a common model for a database integration approach where some of the databases are in the form of formatted flat file text, while others are relational. Although the approach is general and can be applied in many different areas, scientific database and tool integration was the primary motive for its development.     

Evolving comprehensible neural network trees using genetic algorithms

This paper presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it can be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem. The article is published in the original. Elsayed Mostafa. Received the B.Sc. degree in electrical (Communication) Eng., Cairo University at 1967. Dipl.-Ing, and Dr-Ing. from Stuttgart University at 1977, 1981 respectively. He is a member of ECS and EEES. He is currently a professor of electronic circuits, Faculty of Engineering, University of Helwan. Amr Kamel. Graduated from Computer Department, Faculty of Engineering of Ain Shams University, Egypt in 1999, and studying M.Sc. degree in computer engineering from the Faculty of Engineering of Helwan University. His special fields of interest include neural networks and genetic algorithms. Alaa Hamdy. Was born in Giza in Egypt, on August 17, 1966. He graduated from the Telecommunications and Electronics Department, Faculty of Engineering and Technology of Helwan University, Cairo, Egypt in 1989. He received the M.Sc. degree in computer engineering from the same university in 1996 and the Ph.D. degree from the Faculty of Electrical Engineering, Poznan University of Technology, Poland in 2004. Currently he is working as a lecturer in the Faculty of Engineering of Helwan University. His special fields of interest, include image processing, pattern analysis, and machine vision.     

Optimal Sizing of a DBD Ozone Generator Using Response Surface Modeling

Dielectric barrier discharge (DBD) reactors used as ozone generators are well known today and widely used for water treatment and air disinfection. The purpose of this article is to propose an experimental procedure based on the response surface modeling in order to optimize the geometrical dimensions of the cylindrical shape ozone generator, i.e., the discharge gap and the electrodes length. Because an effective ozone generator is expected to give high ozone concentration with a minimum of power requirements, the applied high voltage was associated with the geometrical parameters to carry out a composite centered faces design. Obtained results indicate that for an efficient ozone generator, length of the electrodes needs to be optimized while the discharge gap should be minimized.     

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P&O) algorithm dispositive.     

Lamin-Related Congenital Muscular Dystrophy Alters Mechanical Signaling and Skeletal Muscle Growth

Laminopathies are a clinically heterogeneous group of disorders caused by mutations in the LMNA gene, which encodes the nuclear envelope proteins lamins A and C. The most frequent diseases associated with LMNA mutations are characterized by skeletal and cardiac involvement, and include autosomal dominant Emery–Dreifuss muscular dystrophy (EDMD), limb-girdle muscular dystrophy type 1B, and LMNA-related congenital muscular dystrophy (LMNA-CMD). Although the exact pathophysiological mechanisms responsible for LMNA-CMD are not yet understood, severe contracture and muscle atrophy suggest that mutations may impair skeletal muscle growth. Using human muscle stem cells (MuSCs) carrying LMNA-CMD mutations, we observe impaired myogenic fusion with disorganized cadherin/β catenin adhesion complexes. We show that skeletal muscle from Lmna-CMD mice is unable to hypertrophy in response to functional overload, due to defective fusion of activated MuSCs, defective protein synthesis and defective remodeling of the neuromuscular junction. Moreover, stretched myotubes and overloaded muscle fibers with LMNA-CMD mutations display aberrant mechanical regulation of the yes-associated protein (YAP). We also observe defects in MuSC activation and YAP signaling in muscle biopsies from LMNA-CMD patients. These phenotypes are not recapitulated in closely related but less severe EDMD models. In conclusion, combining studies in vitro, in vivo, and patient samples, we find that LMNA-CMD mutations interfere with mechanosignaling pathways in skeletal muscle, implicating A-type lamins in the regulation of skeletal muscle growth.     

价格已经提高到了过去几十年从未见过的水平

从海湾合作委员会(GCC)生活用纸制造业的前景来看,回顾2021第四季度,或许中东、北非和远东地区的每位参与者都对供应链困境感到惊讶,其中大部分原材料供应比以往任何时候都要困难得多。这对供需前景造成了严重的负面影响,由于缺乏供应,需求大幅增加。然而,这个消极因素很可能会产生市场的连锁反应。价格也提高到了过去几十年从未见过的水平。纸张供应商之间的价格战以前从未发生过,值得注意的是,尽管销售量减少,但利润率令人惊讶地满意。从海湾合作委员会(GCC)一些首次公开募股(IPO)公司公布的业绩,就能理解与一年前的同期相比业绩明显向好。