Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology based Infrastructure for Crisis Situations

Recent terrorist attacks and natural disasters have forced humanity to respond to crisis situations effectively as possible. In these situations especially the first hours rescue workers cannot always rely on existing communication infrastructure. Knowledge about the situation is to be gathered to obtain an aggregate world model of the situation. Decisions can be taken based on this world model. The solution we propose consists of using a Mobile Ad-Hoc Network (MANET), in which the nodes are organized in a topology in order to facilitate the necessary functionalities. Communication between the nodes takes place via a distributed blackboard structure. This architecture supports services developed with the purpose of assisting rescue workers. The agents (humans/sensors) in the network provide data as input to the network. Our approach takes care of processing of this input data to provide users with appropriate information and to obtain a shared world model. As a proof of concept we implemented a prototype of our approach on a number of mobile devices and tested the idea in real life.
A Novel Radial-Axial Flux Switching Permanent Magnet Generator

In this paper a novel Radial-Axial (RA) Flux Switching Permanent Magnet Generator (FSPMG) is introduced. In contrast to other RA flux machines, a major advantage of this generator is its implementation only by the use of conventional Non-Oriented Electrical Steel Sheets (NOESSs), even though the flux linkage of the novel RA-FSPMG has a three-dimensional (3D) path. Besides that, it benefits from the flux concentration strategy and a passive rotor. A concrete foundation of the novel RA-FSPMG is provided, in which, the 3D-Flux path is described, working principles are discussed, and power-sizing equation is derived. No-load (NL) characteristics and the Full Load (FL) performance of an optimized RA-FSPMG are investigated in contrast to an optimized Double Stator Radial (DSR) FSPMG and an optimized Double Stator Axial (DSA) FSPMG with similar volume, number of Permanent Magnets (PMs), and electrical loading. The results show the competitiveness of the novel RA-FSPMG, in particular, in the output power and the required copper mass. Furthermore, the PM demagnetization possibility and the rotor axial pull force of the optimized RA-FSPMG are investigated, in detail. All discussions are supported by the 3D Finite Element Analysis (3D-FEA) and experimental results.
Metastability reduction by aperture transformation

A methodology is proposed, which involves the use of an aperture transformer comprising a new controlled clock circuit. For reducing overall metastability by first transforming unsafe edge arrival times into metastability of the control signals. A large reduction in metastability has been demonstrated by the development of a new bit synchroniser built using the methodology.
The Effect of Additional Information on the Prediction Quality of Wafer Fabrication Operation with a Neural Network and Clustering Approach

Most of the performance assessment of semiconductor manufacturer is based on their self-appraisal or subjective judgments. The needs to measure fabrication (fab) operation performance along with its various dimensions have led to the development of a large number of quantitative performance indicators. An overall scheme to measure the performance of fab operation involving multi-input and multi-effects (output) has not been well established yet. In this study, we approach the performance assessment and prediction by combining clustering approaches with Artificial Neural Networks (ANN) approaches. We use historical data from a Taiwan semiconductor major player which comprise input/ investment data (such as headcount, salary, cost for machines, running the fab, etc.) as well as output of each fab (such as margin, waferoutput rate, stepmove, number of patents). The data comprise several years, during which some of the fabs have been ramped up. In the first phase of our approach, we studied several clustering algorithms (K-Means, X-means, Kernel K-Means, SIB, and EM) on the data. We found several clusterings that were meaningful according to human experts. One of the clustering approaches clearly divided one older fab from newer fabs, and also was able to distinguish fabs in ramping up phase from fabs that are in stable operation phase. Other approaches formed clusters according to the grade of performance (bad, medium-bad, medium-good, good) of the data sets. Second, we use the classification to let a neural network learn the status of a fab, so that for a new fab, the status can be judged by the neural net. In a third step, we let a neural network learn the relationship between the multiple inputs and outputs. As result we found a neural net structure that is able to predict changes in the inputs of a fab on the different output factors. By this we enable the fab management to obtain a prediction, which effect a planned measure (e.g. increase or decrease of headcount) has on the output of the fab, that is the performance figures.
Project Management in Public Research Organisations: Strategic Change in Complex Scenarios

The efficient organisation of public research bodies is important to increase the production of scientific research, which is more and more necessary for competitiveness in modern economies. The purpose of this paper is to analyse the new project-based organisation of the biggest public research body in Italy. The analysis shows that unless project management is properly designed and implemented within Public Research Bodies (PRBs), it tends to become unmanageable over time, creating conflicts, confusion and the proliferation of communication channels, which lead to information log jams. The reality is that the Italian National Research Council (CNR) has never really implemented project management rules; as a matter of fact, because of coordination and area problems as well as the public structure of CNR, it hybridises project management rules with the old organisation since this is the only way to cope with a turbulent and uncertain domestic context.
FDTD simulation of a multistatic radar using ultra‐wideband pulses for indoor environments

A numerical simulation of a multistatic radar using the finite‐difference time‐domain (FDTD) method is performed to identify the presence of intruders inside a residence. The radar operates with ultra‐wideband pulses to obtain high resolution. The radar transmits monocycle pulses with the maximum amplitude of their spectrum at 1‐GHz frequency and a half‐power bandwidth of approximately 1 GHz. One transmitter and three receivers, positioned outside the residence, are used to estimate the target (intruder) location without ambiguity. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 45: 430–434, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20845
Behavioral Healthcare Providers’ Experiences Related to use of Telehealth as a Result of the COVID-19 Pandemic: An Exploratory Study

Background: Due to the COVID-19 pandemic, healthcare providers were forced to shift many services quickly from in-person to virtual, including substance use disorder (SUD) and mental health (MH) treatment services. This led to a sharp increase in use of telehealth services, with health systems seeing patients virtually at hundreds of times the rate as before the onset of the COVID-19 pandemic.By analyzing qualitative data about SUD and MH care providers’ experiences using telehealth, this study aims to elucidate emergent themes related to telehealth use by the front-line behavioral health workforce.Methods: This study uses qualitative data from large-scale web surveys distributed to SUD and MH providers between May and August 2020. At the end of these surveys, the following question was posed in free-response form: “Is there anything else you would like to say about use of telehealth during or after the COVID-19 pandemic? ” The 391 responses to this question were analyzed for emergent themes using a conventional approach to content analysis.Results: Three major themes emerged in the data: COVID-specific experiences with telehealth, general experiences with telehealth, and recommendations to continue telehealth delivery. Convenience, access to new populations, and lack of commute were frequently cited advantages, while perceived ineffectiveness of and limited access to technology were frequently cited disadvantages. Also commonly mentioned was the relaxation of reimbursement regulations. Providers supported continuation of relaxed regulations, increased institutional support, and using a combination of telehealth and in-person care in their practices. Conclusions: This study advanced our knowledge of how the behavioral health workforce experiences telehealth delivery. Further longitudinal research comparing treatment outcomes of those receiving in-person and virtual services will be necessary to undergird organizations’ financial support, and perhaps also legislative support, of virtual SUD and MH services.
The Structural Nonlinear Damage Detection Based on Linear Time Series Algorithm

Two new algorithms for nonlinear damage detection are proposed based on linear model with autoregressive moving average (ARMA) in this paper. Firstly, a novel DSF is defined and the DSFs are identified and classified followed by cluster analysis or Bayesian discrimination. Secondly, the performances of the presented algorithms are evaluated and verified by the experimental data of a three-story building structure. Finally, the illustrated results show the algorithms are efficient tools for nonlinear damage detection. They grant a higher accuracy and improve the reliability of nonlinear damage detection whilst reducing computational costs. It can thus be inferred that the proposed algorithms are applicable for Structural Health Monitoring (SHM) in situ.
A Learning-Based Physical Model of Charge Transport in Room-Temperature Semiconductor Detectors

Room-temperature semiconductor radiation detectors (RTSDs) such as CdTe are becoming popular in computed tomography (CT) imaging. These detectors are often pixelated, requiring cumbersome postinteraction 3-D event reconstruction, which can benefit from detailed material characterization at the micron level. Transport properties and material defects with respect to electrons and holes are to be characterized, which is a labor-intensive process. Current state-of-the-art characterization is done either as a whole or at most pixel-by-pixel over the detector material. In this article, we propose a novel learning-based physical model to infer material properties at the microscopic level for RTSD. Our approach uses a novel physics-inspired learning model based on physical transport of charges with trapping centers for electrons and holes in the detector. The proposed model learns these material properties from known or measured input charges to the detector along with known or measured output signals and distributed charges in the bulk of the RTSD. The actual physical detector is divided into voxels in space and takes into account different material properties (such as drift, trapping, detrapping, and recombination) in each voxel as learnable model parameters. The model is based on a physics-inspired recurrent neural network model instead of traditional convolutional or fully connected networks. The advantage of our approach is the one-to-one relationship between the actual physical parameters of the voxels and learnable weights in the model, far fewer trainable parameters compared to traditional neural network approaches and less training time. The performance of our model has been evaluated on cadmium zinc telluride (CdZnTe), with voxels of three sizes, 25, 50, and $100~ \mu{\mathrm {m}}$ , for single charge input as well as multiple charge inputs at different voxel positions. Our learning-based model provides material properties with higher spatial resolution and performs well in all scenarios and matches the actual physical parameters better than state-of-the-art classical approaches.
Design, fabrication, and analysis of SiGeC heterojunction PMOSFETs

We present the evaluation of the strain-stabilizing capabilities of C in the Si/sub 1-x/Ge/sub x/ system. To demonstrate these effects, we have designed Si/sub 1-x-y/Ge/sub x/C/sub y/ heterojunction PMOSFET devices over a range of Ge concentrations, with thicknesses that would typically result in related or metastable films under normal processing conditions. The dc characteristics of Si/sub 1-x-y/Ge/sub x/C/sub y/, SiCe, and Si PMOSFETs (L=10 /spl mu/m) were evaluated at room temperature and at 77 K. In general, the saturation mobility in Si/sub 1-x-y/Ge/sub x/C/sub y/ devices is higher than that of Si/sub 1-x/Ge/sub x/ and Si devices at low gate bias and room temperature. This enhancement is attributed to the strain stabilization effect of C. With proper optimization of Ge and C concentrations, it is possible to fabricate devices with significant improvements in drive current under normal operating conditions (0-3 V, 300 K). This application of Si/sub 1-x-y/Ge/sub x/C/sub y/ in PMOSFETs demonstrates the potential benefits of using of C in the Column IV heterostructure system.
BENEFITS OF APPLYING QUALITY MANAGEMENT TECHNIQUES TO SUPPORT SUPPLY CHAIN MANAGEMENT

As the customer demands change, businesses grow, technology evolves, and the competition gets more intense companies realize the importance of managing their supply chains. Quality management (QM) is a well-accepted system of management that aims at long term success through continuous improvement, which is supported by various quality techniques. This paper addresses that blending QM and SCM would result in higher benefits than their sole application. Until now, research has mainly studied the critical QM factors and their effects on supply chain processes and supply chain performance. However, there is little evidence that addresses the benefits of applying QM techniques to support SCM activities. This paper tries to shed some light on this issue with the help of three case studies taken from industry and aims to explore how QM techniques support and improve SCM activities throughout the supply chain.
Phase-Variable Control of a Powered Knee-Ankle Prosthesis over Continuously Varying Speeds and Inclines

Most controllers for lower-limb robotic prostheses require individually tuned parameter sets for every combination of speed and incline that the device is designed for. Because ambulation occurs over a continuum of speeds and inclines, this design paradigm requires tuning of a potentially prohibitively large number of parameters. This limitation motivates an alternative control framework that enables walking over a range of speeds and inclines while requiring only a limited number of tunable parameters. In this work, we present the implementation of a continuously varying kinematic controller on a custom powered knee-ankle prosthesis. The controller uses a phase variable derived from the residual thigh angle, along with real-time estimates of ground inclination and walking speed, to compute the appropriate knee and ankle joint angles from a continuous model of able-bodied kinematic data. We modify an existing phase variable architecture to allow for changes in speeds and inclines, quantify the closed-loop accuracy of the speed and incline estimation algorithms for various references, and experimentally validate the controller by observing that it replicates kinematic trends seen in able-bodied gait as speed and incline vary.
A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage

Photographs of bridges can reveal considerable technical information such as the part of the structure that is damaged and the type of damage. Maintenance and inspection engineers can benefit greatly from a technology that can automatically extract and express such information in readable sentences. This is possibly the first study on developing a deep learning model that can generate sentences describing the damage condition of a bridge from images through an image captioning method. Our study shows that by introducing an attention mechanism into the deep learning model, highly accurate descriptive sentences can be generated. In addition, often multiple forms of damage can be observed in the images of bridges; hence, our algorithm is adapted to output multiple sentences to provide a comprehensive interpretation of complex images. In our dataset, the scores of Bilingual Evaluation Understudy (BLEU)‐1 to BLEU‐4 were 0.782, 0.749, 0.711, and 0.693, respectively, and the percentage of correctly output explanatory sentences is 69.3%. All of these results are better than the model without the attention mechanism. The developed method makes it possible to provide user‐friendly, text‐based explanations of bridge damage in images, making it easier for engineers with relatively little experience and even administrative staff without extensive technical expertise to understand images of bridge damage. Future research in this field is expected to lead to the unification of field expertise with artificial intelligence (AI), which will be the foundation of the evolutionary development of bridge inspection AI.
Knowledge sharing behaviour influences

The prime focus of this study is to measure knowledge sharing behaviour of Information Science and Library Management (ISLM) faculties in Bangladesh. Determining factors that may influence knowledge sharing behaviour constitutes an important area of research. A survey questionnaire was developed and used to collect data on faculties’ demographic and academic information, perception, attitude, intention and intrinsic motivation to share knowledge. In order to analyze the influence of faculties’ demographic and individual characteristics on their attitude, intention and intrinsic motivation, Mann-Whitney and Kruskal-Wallis tests were carried out. Results showed that no significant difference was found between knowledge sharing behaviour of LIS educators with different Major Research Questions (MRQs). The researchers found a significant relationship 0.000 (p-value<0.05) between attitude of educators toward knowledge sharing and their intention to share knowledge. It is believed that the findings will assist knowledge managers charged with the design of flexible knowledge sharing system. This is the first time an effort has been made to assess faculties’ perception, attitude, intention and intrinsic motivation to share knowledge of ISLM faculties in Bangladesh. The authors feel that this study may encourage more such research on knowledge sharing behaviour in Bangladesh and further.
Copula in Wildfire Analysis: A Systematic Literature Review

AbstractCopula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik.
Impact of Salinity Stress on Germination and Growth of Pea (Pisum sativum L) Plants

The aim of the present study was to evaluate the effects of salinity stress on germination and growth of pea (Pisum sativum L) plants. A laboratory experiment was conducted to evaluate the effect of salinity stress on germination and growth of pea Pisum sativum L plant. Seeds of pea were sown in Petri dishes and pots and treated with four different levels of salinity (0, 50, 100, and 150mM NaCl) with completely randomized designs in four replications. Results revealed that seeds of pea were able to germinate at low salinity levels (NaCl 50mM NaCl) without a significant decrease in germination and growth traits, at the same time as a severe decrease in those traits were recorded at higher levels of salinity (100 and 150mM NaCl). The results indicated that seed germination and seedling establishment were inhibited due to the decrease of water potential, which results in the decline in water uptake by seeds, and seed germination was prevented by a high level of salinity stress (150mM NaCl). The results pointed out that germination percentage (GP), mean daily germination (MDG), germination speed (GS), and vigor index (SVI) varied under moderate and high salinity levels. All the studied parameters were reduced with increasing the NaCl level. The max and min GP, MDG, GS, and SVI were observed under control conditions (0mM NaCl) and highest salinity level (150mM NaCl) respectively. The same trend was seen in plant growth traits including: plant height, branch number, leaf number, leaf area, and shoot fresh and dry weight. The results provided important reference information for research on the impact of salinity on germination and growth of pea.
Application of wavelet on floating-object image processing

Images, photographed through a submarine periscope, are ambiguous because of submarine periscope light path energy dissipation and bad conditions. With wavelet and Mallat algorithm, we can remove image noise and enhance image edge. The processing efficient is not very good because the most optimum adjusting coefficients are not used in wavelet reconstruction. In this paper, we utilize the Orthogonal Experiment Design to optimize adjusting coefficients in enhancing edge and removing noises. Experiments show that the results are better.
Comparison of self-pulsation in multisection lasers with distributed feedback and intracavity saturable absorbers

The authors report a comparison of the self-pulsing characteristics of two types of semiconductor laser. They show that the self-pulsing frequency of the DFB laser is decoupled from the resonance frequency. They discuss the various characteristics, their significance in relation to optical synchronisation, and use the results to suggest a possible explanation for the mechanism for self-pulsation in the DFB laser.