Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Approaches for Knowledge Discovery and Retrieval in Biomedicine

Background: The growth of published neuro-imaging articles and experimental results in neuroscience, brings a demand for dedicated information retrieval tools for this specific biomedical domain. There is a need to expand results pool of the functional imaging coordinate-based databases like SumsDB, BrainMap or Brede, because, despite their limited size, they are arguably richer for neuroimaging than conventional keyword-based retrieval services. Results: In this work we propose a general methodology for linking coordinate-based and keyword-based retrieval systems. The input to the presented pipeline of methods is any paper retrieved by a coordinate-based service. First, an automatic extraction of significant keywords is performed by mapping noun phrases to Unified Medical Language System’s ontological concepts using the Metamap software. Each of the extracted keywords is automatically classified into one or more semantic groups relevant to neuroscience. The semantic groups and, associated with them, extracted keywords, are later used for construction of logical queries executed on the PubMed database. The context of the search is defined by selection of semantic groups of interest. A semantic group representing brain parts is always stipulated in any search to ensure that the retrieved papers are similar to the input article by brain part or location. Conclusions: The discussed methodology may be considered as an extension, in the domain of neuroscience, of the common publication search engines like PubMed or Google Scholar. We propose integration of the methodology with the BredeQuery plugin for enabling searches directly from within the Statistical Parametric
Color-Based Proprioception of Soft Actuators Interacting With Objects

Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments.
Self-cleaning and colour-preserving efficiency of photocatalytic concrete: case study of the Jubilee Church in Rome

ABSTRACT The Jubilee Church in the south-eastern outskirts of Rome is one of the first buildings constructed with super white reinforced concrete with self-cleaning photocatalytic cement. However, 16 years after the opening of the building, the self-cleaning and colour-preserving properties arising from the titania particles (TiO2) within the concrete mix are not meeting the design requirements and the concrete is showing premature evidence of decay. While the form of the decay is affecting the appearance of the building and not its structural soundness, the ageing pattern of the building's components is resulting in a high maintenance cost, one not easily affordable within the ordinary budget supported by a small parish. This study comprises the first comprehensive step in understanding the causes of the accelerated ageing pattern of the concrete, highlighting methods to improve the long-term durability of the concrete and therefore reduce the cost of its maintenance. Moreover, this research offered the opportunity to test the durability and the effectiveness of the TiO2 in the real conditions on an actual building featuring non-standard geometries. The findings highlight how the ageing pattern directly connects with the geometry of the building and inadequate consideration of the local weathering at the design stage.
Review of Agent Based Modelling of Social Attachment in Crisis Situations

Human behaviour during crisis evacuations is social in nature. In particular, social attachment theory posits that proximity of familiar people, places, objects, etc., promotes calm and a feeling of safety, while their absence triggers panic or flight. In closely bonded groups such as families, members seek each other and evacuate as one. This makes attachment bonds necessary in the development of realistic models of mobility during crises. This article presents a review of evacuation behaviour, theories on social attachment, crisis mobility, and agent-based models. It was found that social attachment influences mobility in the different stages of evacuation (pre, during and post). Based on these findings, a multi-agent model of mobility during seismic crises (SOLACE) is being developed, and it is implemented using the belief, desire and intention (BDI) agent architecture.
HB and FB MMC Based Onshore Converter in Series-Connected Offshore Wind Farm

In contrast to the traditional high-voltage direct current (HVdc) based offshore wind farms, series-connected offshore wind farms do not require transformers, voltage source converters (VSCs), and platforms in offshore, thereby significantly decreasing costs. In this study, the onshore converter, which is based on the half-bridge (HB) modular multilevel converter (MMC), and full-bridge (FB) MMC, is proposed to reduce the costs and power loss of the series-connected offshore wind farm. In this type of onshore converter, the dc voltage of the HB-MMC is controlled to be the rated dc voltage, while the dc voltage of FB-MMC can vary from negative rated dc voltage to positive rated dc voltage. With the variable dc voltage of the FB-MMC, the dc-link current of the series-connected offshore wind farm can be controlled at will. Moreover, the dc-link current control strategies are proposed to reduce the power loss of the offshore wind farm. The capacitor voltage balance control, which is suitable for FB-MMC with variable dc voltage, is proposed to ensure the normal operation of the onshore converter. Finally, simulation and experimental results verify the feasibility of the proposed onshore converter and the corresponding control algorithm.
Design and Modeling of an Extensible Soft Robotic Arm

Soft robotic arms are receiving more and more attention for their intrinsic safety and natural compliance. Instead of traditional serialized rotary joints, soft robotic arms often have complex joints with coupled degrees of freedom like bending, rotation, and elongation, enabling them with more freedoms in achieving sophisticated movements. However, currently soft robotic arms are mostly focusing on bending and rotation, whereas elongation is either unavailable due to the inextensible backbone or only has a small range because of the limited contraction ratio of actuators. Furthermore, the coupling of actuators and complex structure makes the modeling and control hard. In this letter, we introduced an extensible soft robotic arm with a large elongation ratio of 400%, and give a novel modeling method from a new perspective of the force balance of the arm. The models were experimentally tested both in loaded and unloaded situations, showing the effectiveness of predicting the bending, rotation, and elongation of the arm.
Acoustic Length Sensor for Soft Extensible Pneumatic Actuators With a Frequency Characteristics Model

In this study, we present a length sensor that can be used for extensible soft pneumatic actuators. Conventional length sensors detect the changes in electrical resistance and capacitance owing to the deformation of the actuator; hence, deterioration and destruction occur when they are used with an actuator that has a large expansion ratio. In addition, their low resolution and linearity makes them unsuitable for use in actuator control. Our proposed sensor comprises only a speaker and a microphone installed at one end of the actuator. We propose a method to deterministically measure the length of a tube by generating a broadband acoustic signal in a tube and measuring the resonance characteristics determined by the shape of the tube. Our experimental results demonstrate that the error in the measurement with our sensor is not more than 4% with a strain up to 200%. Unlike conventional acoustic sensing methods that measure the time of flight by using ultrasound, our proposed method yields accurate results even when the tube is bent. Therefore, the proposed method can be applied to various types of pneumatic actuators.
Perception Analysis On Msme Financing Submission At Sharia Bank

This study aims to determine whether there are differences in perceptions between customers who apply for financing at BRI Syariah in Jember Regency. The variables used in this study include Age, Type of Business, Business Income, Education. Some of these variables are based on four aspects, including aspects of Capital, Financing Procedure aspects, Guarantee aspects, Margin Level aspects. The data analysis method used is explanatory research with a quantitative approach and uses a different test analysis. Hypothesis testing is done by using a different Kruskall-Wallis test. The sample size in this study were as many as 37 respondents by using Purposive sampling. The results of this study indicated that there were differences in perceptions among customers as shown by several variables. First, based on the Age factor in terms of the procedure aspects. The second is based on Business Type factors in terms of financing pro cedures, and finally the Education factor in terms of aspects of financing procedures, guarantees and margin levels. As for the income factor, it shows different results where there is no difference in perception between customers at all based on all aspects studied.
Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation

Employing Deep Learning (DL) technologies to solve Civil Engineering problems is an emerging topic in recent years. However, due to the lack of labeled data, it is difficult to obtain accurate results with DL. One commonly used method to tackle this issue is to use affine transformation to augment the data set, but it can only generate new images that are highly correlated with the original ones. Moreover, unlike normal natural objects, distribution of structural images is much more complex and mixed. To address these challenges, Generative Adversarial Network (GAN) can be one feasible choice. We introduce one specific generative model, namely, Deep Convolutional Generative Adversarial Network (DCGAN) and propose a Leaf‐Bootstrapping (LB) method to improve the performance of this DCGAN. To effectively and quantitatively evaluate the quality of the synthetic images generated by DCGAN to complement human evaluation, Self‐Inception Score (SIS) and Generalization Ability (GA) are proposed. We also propose a pipeline based on Transfer Learning (TL) using synthetic images to help enhance a weak classifier performance under the condition of low‐data regime and limited computational resources. Finally, we conduct computer experiments with the proposed methods for two scenarios (scene level identification and damage state check) and one special synthetic data aggregation case. The results demonstrate the effectiveness and robustness of the proposed methods.
Environmental and economic implications of building envelope design

There is a wealth literature on operational energy consumption of buildings and how building skins contribute to that. Little is known about the life-cycle environmental impacts of building skins and it is not clear if the operational energy savings that are achieved by improvement strategies in building skin (such as more insulation, external shading devices, PV systems) would indeed result in lower environmental impacts from a life-cycle perspective. Even less clear is how economic and life-cycle environmental impacts of buildings would vary by the changes in architectural design parameters. In the present study, we quantify the variations in operational energy, environmental impacts and costs as a result of change in building skin design and construction parameters. We will examine building envelopes in lowrise office buildings from economic and environmental perspectives. For this purpose, 91 different design combinations of a building envelope are considered with different thermal resistance values of wall, wall-to-window ratios, window types, and frame materials. We then use Environmental Life Cycle Assessment (LCA) to study the variations of design combination with respect to global warming, acidification, eutrophication, and smog formation. Simultaneously, Life-Cycle Cost Analysis (LCCA) is applied to examine the cost changes in design combinations. Then, regression analysis is conducted to find the association between design combinations and changes in environmental impacts and cost fluctuations.
6. Balanced Scorecard and Hoshin Kanri: Why
and how they might be used together

Aligning day-to-day operations with the company’s long-term strategy is a challenging managerial task. This paper explores how Hoshin Kanri (HK), from Total Quality Management, can complement the Balanced Scorecard (BSC), from strategic management, to increase the company’s strategic alignment. Previous studies have discussed this combination of management tools theoretically, but they are sparse on empirical evidence. The contribution of this paper is to demonstrate empirically how HK can complement the BSC, so that lower levels of the organization can link their shortterm goals and improvement initiatives to the organization’s strategy. Based on the findings, I recommend managers to learn and adopt the HK as a complement to the BSC. I specify the roles of the two tools and discuss what is required to make the combination work. In the end, I also suggest future research opportunities.
A Hybrid Cockcroft–Walton/Dickson Multiplier for High Voltage Generation

This paper presents a voltage multiplier topology that is a hybrid between a Cockcroft–Walton multiplier and a Dickson charge pump. The Cockcroft–Walton structure exhibits significant output voltage drop under load as the number of multiplier stage increases. This is because all coupling capacitors are connected in series. Dickson charge pump mitigates this issue by connecting all capacitors in parallel. But this solution comes at the expense of large capacitor voltage stress at the last multiplier stage. The proposed hybrid structure arranges some capacitors in parallel and others in series, thereby achieving low output voltage drop and low capacitor voltage stress at the same time. We develop a model that predicts hybrid multiplier's performance and validates it experimentally. We also demonstrate a 60–2.25 kV dc–dc converter based on a 16-stage hybrid voltage multiplier which achieves a voltage gain of 12.8 while keeping the highest capacitor voltage stress to 660 V.
Kinetics of mass transfer during osmotic dehydration of auyama (Caribbean pumpkin) in ternary solutions

The aim of this study was to address the modeling of aspects such as weight reduction (WR), solid gain (SG) and weight-loss (WL) in Caribbean pumpkin samples osmotically dehydrated in ternary solutions. For this purpose, osmotic dehydration was carried out using two ternary solutions: stevia-salt-water (S1) and glucose-salt-water (S2) at different concentrations (20%, 30% and 40% w/w), and processing times of 40 min, 80 min, 120 min, and 160 min. Also, the diffusion coefficient was determined and the analysis of variance with orthogonal polynomials was performed to find the best treatment for dehydration of auyama samples (p≤0.05). The results suggest that the solution constituted by stevia-salt-water, allows greater reduction of weight and gain of solids when a concentration of 40% is used at a time of 78.12min until reaching a humidity percentage of 60.06%. Similarly, the values of the diffusion coefficient for this solution ratify that the increase in concentration generates less resistance to mass transfer.
Clearing Series AC Arc Faults and Avoiding False Alarms Using Only Voltage Waveforms

A series ac arc fault detection method based only on the voltage waveform measured at the power source is implemented. A relay de-energizes the arc fault after receiving an arc fault detected signal. Traditional arc fault detection methods use the current waveform, which may have false alarms with loads, such as electric hand drills (which intentionally generate small electric arcs) and dimmer switches because they show similar energy characteristics to an arc fault. However, in the proposed voltage-based method, a symmetric energy profile is observed, unique to the arc fault, which allows improved detection. This symmetry in the voltage waveform is exploited to derive threshold values from the sample energy that are eventually used to detect the ac arc fault. The approach avoids non-stationary signal analysis, machine learning, and advanced filtering techniques. The experimental results validate that the algorithm can successfully detect an arc fault and avoid common unwanted tripping events. Finally, devices, such as surge protectors will be able to integrate this detection algorithm without the addition of current sensors.
The Effectiveness of Remedial Courses: An Analysis on Freshmen in Industrial Engineering

Under-preparation is one of the main driving forces behind poor academic performance by under-graduate students. Higher education institutions often launch remedial courses as an inclusive solution to help academically deprived freshmen fill their knowledge gaps. However, the substantial resources absorbed by remedial courses raise recurrent concerns about their suitability. This paper exploits 2012-2016 administrative data from a medium-size university in Northern Italy to appraise the effectiveness of the remedial courses undertaken by newly enrolled students in industrial engineering who fail to achieve the university entry test cut-off score. The outcomes of the empirical analysis provide an articulated picture. The two-year drop-out rate of students who successfully complete a remedial course is not statistically different from that of nonremedial students, whereas significant differences exist in the number of earned credits. However, in two years also this difference disappears when the control group restricts to students who could enrol in an undergraduate course without need for remedial education thanks to a performance just above the cut-off score.
A New Anti-Windup Compensator Based on Quantitative Feedback Theory for an Uncertain Linear System with Input Saturation

This paper devotes to the robust stability problem for an uncertain linear time invariant (LTI) feedback system with actuator saturation nonlinearity. Based on a three degree of freedom (DOF) non-interfering control structure, the robust stability is enforced with the describing function (DF) approach for an uncertain LTI system to avoid the limit cycle. A new type of anti-windup (AW) compensator is designed using the quantitative feedback theory (QFT) graphical method, which results in a simple design procedure and low-order AW control system. One of the most significant benefits of the proposed method is free of the non-convexity (intractable) drawback of the linear matrix inequality (LMI)-based approach. The analysis conducted on the benchmark problem clearly reveals that the proposed QFT-based anti-windup design is able to handle both saturation and uncertainty in a very effective manner.
A Signal Conditioning Antiwindup Approach for Digital Stationary Frame Current Regulators

AC current regulators are susceptible to controller windup, i.e., the condition in which the controller dynamic states ramp the controller output up to very large values when the commanded reference cannot be achieved because of inverter saturation. Recovering from saturation can take considerable time once the linear mode has been restored, which degrades the current regulator's transient performance and can also impact on its stability. This paper proposes an improved formulation using the concept of signal conditioning, where windup is avoided by feeding the controller states entirely from the constrained inverter pulsewidth modulation command. Unlike previous approaches, this technique has the benefit of a simplified realization, since the controller transfer functions do not require algebraic reformulation. This makes it particularly suitable for multiple cascaded resonators used in harmonic compensation systems. The technique has been validated using simulation and experimental investigations for a grid-connected inverter.
On some aspects of recovery of sparse signals in high dimensions from nonlinear measurements using compressed sensing

In data processing we often aim for the recovery of signals in very high dimensions from linear measurements. In many applications such as medical imaging, it turns out that the signal of interest is sparse (or allows a sparse representation in a certain dictionary), i.e., most of its entries are zero or at least very small. Some ten years ago, compressed sensing emerged as a novel method for the recovery of sparse signals in high dimensions, where the sparsity assumption is used to heavily reduce the needed number of linear measurements. In the first main part of this thesis we deal with the theory of s-numbers. Our main interest is the so-called Carl’s inequality, which estimates the asymptotic behavior of important instances of s-numbers such as the Gelfand numbers. The main result in this part is given by Carl’s inequality for Gelfand numbers on quasi-Banach spaces. In particular, in the context of compressed sensing Carl’s inequality can be used to estimate the minimal needed number of linear measurements in order to obtain reasonable approximations of a sparse signal. The basic setting of compressed sensing deals with the recovery of sparse signals from linear measurements. However, many applications gain only access to nonlinear measurements. Thus, in the remaining part of this thesis we discuss specific nonlinearities in the measurement process. More precisely, first we will discuss the problem of 1-bit compressed sensing, where we only obtain the signs of the linear measurements. Although getting less information from the measurement process, it is still possible to recover the signal (up to some scalar multiple) from the same amount of linear measurements as in the usual compressed sensing setting. Recently, several algorithms where proposed to solve the 1-bit compressed sensing problem. We will analyze the so-called l1-support vector machines, which are often used in machine learning applications. After discussing the 1-bit compressed sensing problem, we will discuss the approximation of ridge functions, which we can interpret in the following two ways. On the one hand, in the context of compressed sensing we can interpret the approximation of ridge function as a generalization of the 1-bit compressed sensing problem. Instead of the sign, here the measurements get disturbed by some unknown nonlinearity. However, in the theory of ridge functions we usually assume a differentiability condition on the nonlinearity, which is obviously not fulfilled for the sign function. On the other hand, we observe that the approximation of multivariate functions in high dimensions often suffers from the curse of dimensionality. It turns out that even the approximation of arbitrarily often differentiable multivariate functions is intractable in general. Hence, if we still want to approximate multivariate functions