Design of Piezoelectric Tile for Energy Harvesting: Experimental Approach

The generation of electricity by renewable energies is an important need of today's society. Piezoelectric energy harvesting is one of these useful technologies which can generate electricity by applying external force on piezoelectric material. This study illustrates more power generation from piezoelectric tile by changing the situation of piezo discs and connect to proportional electrical circuit. Two different designs of piezoelectric tile are presented by performing experimental analyses. The experimental results showed that placing piezoelectric elements in a bending position leads to higher power generation in comparison with traditional flat positioning, which was approximately 78 times far superior. It is also revealed that by design of an electrical circuit, the tile can be advantageous for lighting in crowded sidewalks with required lighting time. The results of this paper can be beneficial in the design and fabrication of these tiles for different applications

HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution

The rise of large language models (LLMs) had a transformative impact on search, ushering in a new era of search engines that are capable of generating search results in natural language text, imbued with citations for supporting sources. Building generative information-seeking models demands openly accessible datasets, which currently remain lacking. In this paper, we introduce a new dataset, HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset) for building end-to-end generative information-seeking models that are capable of retrieving candidate quotes and generating attributed explanations. Unlike recent efforts that focus on human evaluation of black-box proprietary search engines, we built our dataset atop the English subset of MIRACL, a publicly available information retrieval dataset. HAGRID is constructed based on human and LLM collaboration. We first automatically collect attributed explanations that follow an in-context citation style using an LLM, i.e. GPT-3.5. Next, we ask human annotators to evaluate the LLM explanations based on two criteria: informativeness and attributability. HAGRID serves as a catalyst for the development of information-seeking models with better attribution capabilities.Comment: Data released at https://github.com/project-miracl/hagri

Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization

Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model's (a student) generalization by transferring the knowledge from a larger model (a teacher). Although KD methods achieve state-of-the-art performance in numerous settings, they suffer from several problems limiting their performance. It is shown in the literature that the capacity gap between the teacher and the student networks can make KD ineffective. Additionally, existing KD techniques do not mitigate the noise in the teacher's output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. We propose a new KD method that addresses these problems and facilitates the training compared to previous techniques. Inspired by continuation optimization, we design a training procedure that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. Our method (Continuation-KD) achieves state-of-the-art performance across various compact architectures on NLU (GLUE benchmark) and computer vision tasks (CIFAR-10 and CIFAR-100).Comment: Published at EMNLP 2022 (Findings

Comparison between structural configurations designed by steel shear wall, moment resistant frame and X shape bracing systems

Nowadays, in order to increase construction of tall structures, the importance of choosing optimum systems, with a huge energy absorption capacity against wind and earthquake loads, has been widely considered. Since four decades ago, steel shear walls had been used as a stiff and high performance lateral system. This study is about the effect of concrete filled steel tubes (CFT) columns as vertical boundary elements of steel shear wall on seismic behavior of steel structures. Due to do this, three 10- storey steel structures, with similar plans and lateral load career systems of steel shear wall, coinciding X-bracing, and moderate steel frame were analyzed by means of non-linear, time-history method through SAP2000 software, and the results of roof displacement of them were compared with each other. Also after validating a two-storey, single-span frame sample with steel shear walls and CFT columns, 3 single-storey structures were analyzed by means of hysteresis and pushover, through ABAQUS software. The results of this study showed that a shear wall system presents suitable stiffness, resistance and ductility in comparison with other lateral bearing systems.Peer ReviewedPostprint (published version

Association of White Blood Cell Count With Metabolic Syndrome in Obese Men and Women

Background Despite the widespread obesity epidemic in the world, not all obese people are equally susceptible to the complications of obesity. Inflammatory factors play an important role in the complications of obesity. Objective This study aims to evaluate the association of White Blood Cell (WBC) count with metabolic syndrome in overweight/obese men and women. Methods This cross-sectional study is a part of the Qazvin Metabolic Disease Study (QMDS) conducted in 2010 in Qazvin, Iran. Participants were 622 obese people with a body mass index (BMI) ≥25 kg/m2, recruited from the QMDS. Metabolic syndrome was defined according to the Adult Treatment Panel III criteria. Data were analyzed using Chi-square test, t-test, and logistic regression analysis (to evaluate the relationship between WBC count quartiles and metabolic syndrome). Findings Prevalence of metabolic syndrome was not significantly different between men and women. In men, prevalence of metabolic syndrome and its components were not different between WBC quartiles. In women, 32.2% and 60.5% had metabolic syndrome in the first and fourth quartiles of WBC count, respectively (P<0.001). Moreover, the prevalence of insulin resistance was higher in fourth quartile compared to the first quartile (47.7% vs. 25.6%, P<0.001). After controlling the effects of age and BMI factors, the risk of metabolic syndrome in the fourth quartile of WBC count remained significant in women (OR=2.56, P<0.01). Conclusion Association of WBC count with metabolic syndrome is significant in obese women compared to obese men

Nuclear data for the cyclotron production of 66Ga, 86Y, 76Br, 64Cu and 43Sc in PET imaging

Positron emission tomography (PET) is a powerful diagnostic tool, which provides superior spatial resolution and an opportunity to obtain quantitative information concerning distribution of radioactivity in vivo. Most interesting positron emitters for the purpose of diagnose are 64Cu, 124I, 18F, 86Y, 48V, 52Mn, 140Pr, 72As, 74As, 89Zr, 82Sr, 68Ga, 66Ga, 45Ti, 76Br and 82Rb. Aim of the presented study is to compare the calculated cross sections of several radioisotopes of positron emitters as follows 86Y, 43Sc, 64Cu, 66Ga and 76Br with incident proton energy up to 30 MeV. In this work, excitation function of positron emitters via the 86Sr(p,n)86Y, 43Ca(p,n)43Sc, 66Zn(p,n)66Ga, 64Ni(p,n)64Cu and 76Se(p,n)76Br reactions were calculated by ALICE/ASH 0.1 (GDH model and hybrid model) and TALYS-1.2 (equilibrium and pre-equilibrium) codes and compared to existing data. Requisite for optimal thicknesses of targets were obtained by the stopping and range of ions in matter (SRIM) code for each reaction

A genetic algorithm approach for open-pit mine production scheduling

In an Open-Pit Production Scheduling (OPPS) problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA) is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D) copper orebody model. The orebody is featured as two-dimensional (2D) array of blocks. Likewise, counterpart 2D GA array was used to represent the OPPS problem’s solution space. Thereupon, the fitness function is defined according to the OPPS problem’s objective function to assess the solution domain. Also, new normalization method was used for the handling of block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficiently in solving OPPS problem

Investigating of Brand Equity on Hospital Image 1

Abstract: This study identifies five factors that influence the creation of brand equity through successful customer relationships: trust, customer satisfaction, relationship commitment, brand loyalty and brand awareness. An empirical test of the relationships among these factors suggests that hospitals can be successful in creating image and positive brand equity if they can manage their customer relationships well. The subjects were 318 customers of hospitals in Tehran area. Structural Equation Modeling (SEM) with Lisrel software was used for the data analysis. Results from the research hypothesis testing suggest the following information. First, the study found that trust, customer satisfaction and relationship commitment all had a positive influence on brand loyalty and brand awareness. And brand equity, trust, customer satisfaction and relationship commitment also had a significant positive influence on hospital image. All of hypothesis is supported

Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models

Knowledge Distillation (KD) is a prominent neural model compression technique which heavily relies on teacher network predictions to guide the training of a student model. Considering the ever-growing size of pre-trained language models (PLMs), KD is often adopted in many NLP tasks involving PLMs. However, it is evident that in KD, deploying the teacher network during training adds to the memory and computational requirements of training. In the computer vision literature, the necessity of the teacher network is put under scrutiny by showing that KD is a label regularization technique that can be replaced with lighter teacher-free variants such as the label-smoothing technique. However, to the best of our knowledge, this issue is not investigated in NLP. Therefore, this work concerns studying different label regularization techniques and whether we actually need the teacher labels to fine-tune smaller PLM student networks on downstream tasks. In this regard, we did a comprehensive set of experiments on different PLMs such as BERT, RoBERTa, and GPT with more than 600 distinct trials and ran each configuration five times. This investigation led to a surprising observation that KD and other label regularization techniques do not play any meaningful role over regular fine-tuning when the student model is pre-trained. We further explore this phenomenon in different settings of NLP and computer vision tasks and demonstrate that pre-training itself acts as a kind of regularization, and additional label regularization is unnecessary