Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling of in vivo LIPUS stimulation of murine intestinal wall

Low Intensity Pulsed Ultrasound could enable a “wireless biopsy” of gut diseases such as ulcerative colitis, by triggering the release of pathology-characterizing molecules from the intestinal wall cells. Prior to the in vivo validation, the prediction of the acoustic propagation through living tissues results fundamental to guide the design of the experimental setting and to be able to reach the target with the desired ultrasound regime. In this work we propose a simulation framework able to precisely predict the ultrasound dose at the target (i.e., mouse colon) for the above-mentioned application.
Volumetric Super-Resolution Ultrasound with a 1D array probe: a simulation study

Super-Resolution (SR) Ultrasound imaging overcomes the wave diffraction limit and can reveal microvasculature by localizing spatially isolated contrast agents. Volumetric SR ultrasound imaging, however, is limited by both the fabrication and computational complexity associated with 2D matrix array probes. The most used diagnostic imaging modality, X-ray radiography, requires much simpler hardware than X-ray CT and can present volumetric information in a single projection image. Inspired by X-ray radiography, this study aims to investigate the feasibility of volumetric image projection super-resolution (VIP-SR) ultrasound using a 1D transducer array probe in simulation. Results show that, without a focusing lens, the capability of capturing volumetric information for a 1D array probe is significantly improved over that of a focused 1D array probe and is comparable to that of a 1.75D array probe with the same aperture size. Here we demonstrated the potential of VIP-SR as a high-resolution microvascular imaging technique with volumetric information and significantly reduced cost.
Quantum Leap in Simulation Technologies for Radio Frequency Surface and Bulk Acoustic Wave Devices Gifted by Hierarchical Cascading Technique

This paper describes how the hierarchical cascading technique (HCT) expanded capabilities of FEM-based numerical simulation in radio frequency surface and bulk acoustic wave (SAW/BAW) devices. The HCT was originally proposed for speed up and size-reduction of two-dimensional FEM simulation of SAW resonators. The authors' group applied 2D and 3D HCT to attack various problems on SAW/BAW excitation and scattering including those believed to be impossible. The traveling wave excitation source proposed by the author's group fits well with HCT and can be adapted efficiently in these analyses. First basics of HCT and its extensions are explained, and then these techniques are applied to various practical problems as demonstration.
Electronic beam tilting of base station antenna: Rotman lens fed

A new electronically controlled down-tilt mechanism for wireless communication base station antenna is presented. Its main feature consists of a microwave switch, a symmetrical bootlace lens which is a beam forming device like Rotman (1963) lens, and linearly arrayed antennas. Each beam port of the symmetrical bootlace lens is designed to give a linear phase front across the radiating elements, yielding a radiation spatially summed in a direction normal to the phase front. Furthermore, a few watts small power amplifiers may be inserted between the array ports of the lens and radiating elements to replace the conventional single high power amplifier and heavy cable runs and fixed directive sector antenna with a cost-effective tower-top phased array antenna. These concepts are checked by experimental results.
Transverse Mode Suppression of Thickness Shear Bulk Acoustic Resonators on Lithium Niobate Using Standard and Broadband Piston Mode Designs

This paper describes complete suppression of transverse modes in thickness shear bulk acoustic resonators (TSBARs) using the lithium niobate plate. Previously, the authors conjected from the experiment that cut-off edges with crystal X-plane serve as ideal reflectors for the piston mode operation, and complete suppression is possible by adding the standard piston mode design to the other edges normal to the X-axis. Following to this conjecture, the traditional piston mode structure was designed using two-dimensional finite element method, and the TSBAR was fabricated following to the design. The experiment showed complete suppression of the transverse mode resonances, and the conjecture was verified.
I2QFD: A blackboard-based multiagent system for supporting concurrent engineering projects

Although Concurrent Engineering has been accredited as a superior approach to product development, its intrinsic complexity in coordinating the multidisciplinary project team is further augmented when team members are remotely distributed. Assuming the teamwork is supported by an Intranet, this paper proposes the framework of a blackboard-based multiagent system, called I 2 QFD, to facilitate the communication and coordination of the design projects. Configured within a hierarchical structure, this system is defined with IDEF0 and high-level Petri nets to depict the operations of Intelligent Agencies and their affiliated agents. Based on temporal logic, the operation of the project team with I 2 QFD is simulated to manifest the dynamics of the design process as well as to generate the project's schedule that ensures the intended concurrency.
Wireless Powered Programable Swimming Robots Driven by Lamb Wave Resonators

Microrobotics have emerged as a fascinating field with great potential for drug delivery, non-invasive microsurgery, and precise materials assembly. Acoustic devices have been exploited as effective strategies for developing underwater microrobotics. This work presents an integrated wirelessly powered microelectromechanical swimming robot driven by Lamb Wave Resonators (LWRs). Improvements have been made to the structure of LWR driver. The asymmetric distribution of reflectors and leakage hole of the LWR leads to the difference of acoustic wave transmission. Unidirectional drive was realized with the asymmetric acoustic streaming effect. Aided by programming the energy distribution of the LWRs, flexible 2-D motions can be realized, demonstrating an artificial acoustic compartment for tiny swimmers.
Density Peak Clustering Algorithm and Optimization Based on Measurements of Unlikeness Properties in Position Sensor Environment

With the increasing popularity of position sensors, the rapid development of the mobile Internet and increasingly high quality of communication facilities based on 5G networks, all walks of life are producing moving object trajectory data at an increasingly rapid rate. The density peak clustering algorithm (<italic>DPC</italic>) is an effective method based on the attributes of local density and relative distance. <italic>DPC</italic> can find a peak density by identifying the clustering center using a decision graph without specifying the quantity of clusters beforehand and can identify clusters with arbitrary shapes. Unfortunately, because the computations of local density and relative distance only rely on the likeness matrix based on distance measurements, <italic>DPC</italic> results are unsatisfactory in the following cases: 1) when the data dimension is high, and the distribution is uneven; 2) no uniform measurement exists for the computation of local density, requiring different degrees to be selected based on different data sets; and 3) the measurement of the truncated distance <inline-formula> <tex-math notation="LaTeX">${d}_{c}$ </tex-math></inline-formula> focuses on global data and disregards local information, allowing variations in <inline-formula> <tex-math notation="LaTeX">${d}_{c}$ </tex-math></inline-formula> to affect the result. This paper proposes an optimized density peak clustering algorithm based on measurements of unlikeness properties (<italic>UDPC</italic>) to solve these problems. <italic>UDPC</italic> uses the block-based unlikeness measurement method to compute the likeness matrix, determines k-nearest neighbor information based on the newly formed likeness matrix, and determines the local density measurement method based on the k-nearest neighbor information. Experiments on classic datasets show that the density peak clustering algorithm based on measurements of unlikeness properties performs better than the <italic>DPC</italic>, <italic>FKNN-DPC</italic> and <italic>DPC-KNN</italic> algorithms. The proposed algorithm unifies the measurement of local density and mitigates the effects of the truncated distance <inline-formula> <tex-math notation="LaTeX">${d}_{c}$ </tex-math></inline-formula> on clustering.
A Brief Survey on Brain Tumor Identification Models Using Deep Neural Networks

A brain tumour occurs when abnormal cells increase in the brain. Non-cancerous (Benign) brain tumours include astrocytoma, glioblastoma, and oligodendroglioma. Cancerous (Malignant) brain tumours include craniopharyngioma and ependymoma. The survival rate of a patient with a high risk of developing a brain tumour is difficult to estimate due to the limited occurrence of brain tumours and their assortment. About 15 out of every 100 persons with brain cancer will have a chance of survival for ten or more years after being diagnosed, according to the UK study. The sort of tumour, how abnormal the cells are, and where it is in the brain all influence how the patient will be treated for brain tumour. Advances in machine learning are utilised to accurately diagnose the brain tumour by analysing Magnetic Resonance Imaging (MRI) pictures. The classification of brain tumours has a significant impact on clinical diagnosis and on providing the most appropriate treatment. The current practise of making a diagnosis and classifying brain tumours still depends on microscopic study of biopsy samples. At now, the process is lengthy, complex, and error-prone. While these drawbacks illustrate how important it is to have a completely automated classification process for many classifications of brain tumours, deep learning offers an even better alternative. This paper presents a brief survey on several models for brain tumour detection using deep learning techniques.
Comparative Assessment of Plane Wave Imaging with 256-element CMUT and Single Crystal Probes

Non-standard scan modes, based on the transmission of plane and diverging waves, are the key to enable high frame rate and volumetric imaging modalities and are promising in the implementation of novel system architectures for ultra-integrated (high channel count) scanners. So far, capacitive micromachined ultrasonic transducer (CMUT) arrays have been benchmarked against standard piezoelectric transducers only in standard modalities. In this work, a 256-element CMUT probe and a state-of-the art single crystal composite piezoelectric probe, sharing the same array geometry and bandwidth, are compared in plane wave (PW) imaging mode. The CMUT probe provided, on average, improved signal power (+9 dB) and contrast (+3 dB) for all PW aperture angles. Also, compared to the piezoelectric probe, the CMUT one offered comparable axial resolution, but slightly worse (10%) lateral resolution.
Polyolefin and olefin production in Iran: Current and future capacities

Due to easy availability of cheaper raw material and increase in new applications, the use of polyolefins in various industries is becoming a major priority. The Middle East region, on account of its vast oil and gas reserves has, in the last decade or so, been developing many new petrochemical complexes with their expansion into colossal polyolefin production capacities. The predictions are that by 2020 the Middle East region will dominate the polyolefin industry as a whole. Furthermore, with proven oil reserves of about 21.7 thousand million tons (4 th world ranking) and natural gas of 34.0 trillion cubic meters (1 st world ranking), Iran’s petrochemical industry is supported by diverse and abundant feedstock reserves. In line with other polyolefin producers’ developments in the Middle East, Iran's National Petrochemical Company (NPC) has undergone massive structural and technological transformations in the last two decades in order to set up ambitious plans for further capacity increase and native technology developments. This article mainly focuses on Iran today’s position and its future plans in the polyolefins industry. Polyolefins J 3:11-22
Titer plate formatted continuous flow thermal reactors for high throughput applications: fabrication and testing

A high throughput, multi-well (96) polymerase chain reaction (PCR) platform, based on a continuous flow (CF) mode of operation, was developed. Each CFPCR device was confined to a footprint of 8 × 8 mm2, matching the footprint of a well on a standard micro-titer plate. While several CFPCR devices have been demonstrated, this is the first example of a high-throughput multi-well continuous flow thermal reactor configuration. Verification of the feasibility of the multi-well CFPCR device was carried out at each stage of development from manufacturing to demonstrating sample amplification. The multi-well CFPCR devices were fabricated by micro-replication in polymers, polycarbonate to accommodate the peak temperatures during thermal cycling in this case, using double-sided hot embossing. One side of the substrate contained the thermal reactors and the opposite side was patterned with structures to enhance thermal isolation of the closely packed constant temperature zones. A 99 bp target from a λ-DNA template was successfully amplified in a prototype multi-well CFPCR device with a total reaction time as low as ∼5 min at a flow velocity of 3 mm s−1 (15.3 s cycle−1) and a relatively low amplification efficiency compared to a bench-top thermal cycler for a 20-cycle device; reducing the flow velocity to 1 mm s−1 (46.2 s cycle−1) gave a seven-fold improvement in amplification efficiency. Amplification efficiencies increased at all flow velocities for 25-cycle devices with the same configuration.
JUDGMENT-BASED CROSS-IMPACT METHOD FOR PREDICTING COST VARIANCE FOR HIGHLY UNCERTAIN PROJECTS

Construction projects are very sensitive to project-specific conditions. Any possible combination of risk factors can significantly affect the success of a project's performance. Risk analysis has been used to predict the impact of various uncertainties that are inherent in construction projects. However, one key problem with present-day risk analysis methods is that they are basically mathematical or probability-oriented approaches which are not easy to adopt in real business because they easily become complex to capture the relations between the complex variables. This paper presents the use of a judgment-based method called Cross Impact Analysis (CIA) to model the cost risks of a construction project in a highly uncertain environment and to predict their impact on cost variance in the early stage of a bidding process. Then, a case study is performed to demonstrate the approach, which allows complex risk analysis to be performed simply by common users in capturing the risk variables' relations, while maintaining an acceptable accuracy limit.
Bacterial Diversity and Predicted Enzymatic Function in a Multipurpose Surface Water System – From Wastewater E uent Discharges to Drinking Water Production

Ananda Tiwari (  ananda.tiwari@thl. ) Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos https://orcid.org/0000-0001-8026-6885 Anna-Maria Hokajärvi Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Jorge Santo Domingo O ce of Research and Development, United States Environmental Protection Agency Michael Elk Development and Research Center of State Council Balamuralikrishna Jayaprakash Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Hodon Ryu O ce of Research and Development, United States Environmental Protection Agency Sallamaari Siponen Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Asko Vepsäläinen Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Ari Kauppinen Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Osmo Puurunen Turku Region Water Ltd. Aki Artimo Turku Region Water Ltd. Noora Perkola Finnish Environment Institute: Suomen ymparistokeskus Timo Huttula Finnish Environment Institute: Suomen ymparistokeskus Ilkka T. Miettinen Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos Tarja Pitkänen Finnish Institute for Health and Welfare: Terveyden ja hyvinvoinnin laitos
Do I care what you think of me? Varying observability in a public goods experiment

In social dilemmas the level of observability by others often differs, either by design or by coincidence, and it seems intuitive that this might affect prosocial behaviour. For example; the potential for negative emotions such as guilt or shame may be greater when behaviour is more easily observed by others. A 3× 1 between–subject design is used where the level of ex-post disclosure/feedback others receive is varied in a lab–based, anonymous, one–shot, two–player public goods game. Incentivised first order beliefs (what a subject believes their partner will contribute) and second order beliefs (what a subject believes their partner expects them to contribute) are directly elicited. A within–subjects analysis, mitigating any consensus effect concerns (people believing that others think and act like them), finds that at least some people are “shame averse”; when able to be observed ex–post, there is a stronger response to their second order belief of what they believe their partner expects. The same analysis also finds robust evidence for guilt aversion, this being possible even when there is no observability by others. When available, receiving information on the past behaviour of others has the effect of reinforcing any shift in behaviour.
Core Loss Model for Switched Reluctance Motor in Electric Vehicles

The paper attempts to evolve a model for determining the core loss of a switched reluctance motor (SRM), widely used as a drive for propulsion in electric vehicles (EV). The core loss forges to be one of the key factors in deciding the performance of the drive motor and requires extensive considerations to minimize it. The SRM offers promising characteristics to be suitable for the propulsion of a vehicle owing to its outstanding merits and use of steel lamination as the core material. The study involves the use of 2D-FEM analysis on a SRM with non-oriented electrical steel possibilities and brings out a strong correlation between the material flux density and permeability. The results relate the variation of motor core loss for different materials and exhibit the effect on core loss. The model manifests an empirical approach to the calculation of core loss besides establishing the strong relationship among the parameters of the magnetic material. IndexTerms Core loss, FEM, non-oriented steels, SRM, time stepped 2D analysis.
A survey on: multiple approaches for knowledge extraction over semantic data

: Web mining and its usage over the different result, analysis over the large input is required. Web mining gets multiple techniques such as semantic, synaptic and other relevant approach. Different resource platform often generate the short text which is unable to understand by the software process and thus data usage cannot be apply over it. There are various ways of understanding such text which leads to noise removal, replication removal and processing large data over segmentation and other process. Semantic knowledge and matching content also help in understanding various given text. Different challenges such as typing New York City as “nyc” is not recognized by the standard system. In this paper our survey is performed over different approach which helps in understanding short text and processing its knowledge extraction over the large data.The paper discuss about the segmentation and other previous technique to solve in understanding short text and problem associate with the previous approach over it. A further analysis need to be done is to determine refined approach over it.
Few-Shot Novel Concept Learning for Semantic Parsing

Humans are capable of learning novel concepts from very few examples; in contrast, state-of-the-art machine learning algorithms typically need thousands of examples to do so. In this paper, we propose an algorithm for learning novel concepts by representing them as programs over existing concepts. This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept. In addition, we perform a theoretical analysis of our approach for the case where the program deﬁning the novel concept over existing one is context-free. We show that given a learned grammar-based parser and a novel production rule, we can augment the parser with the production rule in a way that provably generalizes. We evaluate our approach by learning concepts in the semantic parsing domain extended to the few-shot novel concept learning setting, showing that our approach signiﬁcantly outperforms end-to-end neural semantic parsers.
Supplementary Material: Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation

Considering that scribble-level annotations can be treated as the extension of click-level annotations, we also apply the seminar learning to the scribble-level supervised semantic segmentation. We use the scribble annotation set first proposed in [1] to train our model. As shown in Table 1, we use ‘regularized loss’ method [2] as our baseline and gain 75.18% mIOU under our experimental settings. After applying the seminar learning, we gain a competitive result of 76.17% (0.99% higher). The results validate that our method has the potential to improve the performance of weak supervision at various levels. Notably, since scribble-level labels are able to cross major regions of an object and carry stronger information, it is unnecessary to expand the learning range of the network by building a teacher-student network. However, uncertainties are still inevitable in such a setting as insufficient weak annotations covering the full objects. Therefore, we simplify our seminar learning pipeline for scribble-level supervision, excluding the teacher-student modules but retaining the two-stage training of the student-student module. specifically, the ancillary model is trained with the Lscribble loss, which is defined as
Generator of automated tools for program instrumentation

Instrumentation is one of the methods used in dynamic program analysis for assessing software performance. This paper proposes a technology for constructing software instrumentation tools for different programming languages. The instrumentation functions are described within this approach using several grammar-based DSLs. The obtained instrumentation toolkit is the result of generating a new system based on formal descriptions of the instrumentation process. The tracing functions are embedded into the original program using a TXL utility; a conversion program is also generated for this utility. The developed prototype was tested on 4 large projects written in different programming languages: Java, Python, C++ and Object Pascal. The tests confirmed the efficiency of the approach and the applicability of the developed prototype.