Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical application of the multi-level perspective: tracing the history of ground-source heat pumps systems in Finland

Abstract The emergence and evolution of more sustainable technologies and related industrial fields is a core concern for sustainability transitions scholars. This interest is accentuated as it has become evident that the upscaling of transition-relevant technologies follows different pathways in varying national and geographic contexts. The usual research approach to studying such industry-field dynamics in particular contexts has been to use the technological innovation systems (TIS) framework, focusing on the emergence of functioning TISs. The current calls for life-cycle TIS and the few existing examples of operationalizing the multi-level perspective (MLP) in a more focused way underscore the need to better account for the contextual specifics, contingencies, and later phases in the proliferation of transition technologies. We elaborate on the benefits of using the MLP in long-term analyses of transition technologies by examining the history of ground-source heat pump (GSHP) systems in Finland from the era of the energy crises in the 1970s until the present day. The investigation reveals how the present success of GSHPs has not followed just from simple innovation-system dynamics or niche-regime landscape relations but is also a result of variations and extent of landscape pressure as well as unplanned support from neighboring niche technologies.
(2018). Are wives and daughters disadvantaged in polygynous households? A case study of the Arsi Oromo of Ethiopia. Evolution and Human Behavior , 39 (2), 160-165.

Whether polygyny is harmful for women and their children is a long-standing question in anthropology. Few studies, however, have explored whether the effect of polygyny varies for women of different wife order, and whether there are different outcomesfor theirsons and daughters.Becausemales havehigherreproductive var- iance, especially when they are allowed to take multiple wives, parents may have higher ﬁ tness returns from investing in sons over daughters in polygynous households. Moreover, previous studies have found that ﬁ rst wives and their children are advantaged over monogamous and second order wives (who marry into unions later). Here we test the predictions that children of ﬁ rst wives will have an advantage over children to monoga- mous or second wives, and that sex-biased investment will be strongest among ﬁ rst wives. Using data from the ArsiOromoofEthiopia(n~6200children)wetestwhetherassociationswithmother'swifeorderextendbeyond childhood into adulthood by examining simultaneously child survival, education and age at marriage. We ﬁ nd that polygynous ﬁ rst wives have no child survival disadvantage, ﬁ rst wives' sons bene ﬁ t in terms of longer edu- cationanddaughtershaveanearlierageatmarriagethandaughtersofmonogamouswomen.Secondwiveshave lower child survival than monogamous women, but surviving children experience advantages in later life out- comes, particularly marriage. These ﬁ ndings challenge the view that polygynous women are always doing the ‘ best of a bad job ’ . Rather, our results suggest that via their surviving sons and daughters there may be long- term bene ﬁ ts for some polygynous women.
Functional Responses and Ecosystem Dynamics: Influences of satiation, food-limitation and acclimation. ICES CM 2008/Q:03

Predicting how climate change will affect zooplankton requires an understanding of how their ingestion rates change with prey density. Modelling studies have shown that the formulation of this functional response significantly affects the dynamics of planktonic ecosystem models. Here we present results from our recent paper (Gentleman and Neuheimer, 2008) which explain why different grazing responses can have profoundly differing influences on modelled dynamics, and how common practices may limit models due to misguided characterisation of feeding behaviours. Use of different ingestion functions in a Nutrient-Phytoplankton-Zooplankton (NPZ) model result in oscillating vs. constant densities. Contrary to the conclusions of previous studies, it is shown that these results are not due to zooplankton satiation vs. nonsatiation. Analysis of a predator-prey model is used to derive the necessary condition for ecological stability, which is related to food-limited clearance rates. Sensitivity studies demonstrate that zooplankton clearance rates have a strong influence on the dynamics of more complex models. Moreover, it is shown that acclimation time lags can dramatically alter results from those where zooplankton instantly adapt to changing prey densities due to the corollary effect on clearance rates. These results are discussed in terms of practical advice to modellers who face uncertainty in choosing expressions for the functional response.
On the Use of Hyperparameter Optimization in Big Data Processing Pipelines: A Case Study

The term "Data Analytics" sparks a wide range of multidisciplinary fields since it requires a high analytical expertise in different domains. Data Analytics applications include many successive steps, such as data collection, outlier detection, missing data imputation, feature selection, clustering analysis, classification model selection and result interpretation. The whole procedure can be seen as a chain of steps, organized in a pipeline manner, where the output of the upper layer is the input of the lower layer. Thus, the tasks of every step depend directly on the results of the previous steps. Moreover, there are alternative algorithmic methods for each step in Data Analytics. Opting for one methodology over the other requires high-skilled data scientists with a huge technical background. The key point of such a decision is optimizing the parameters of every layer in the pipeline. In this study, we develop an automated pipeline with different layers. Every layer contains several methods. We investigate the implementation of a suitable hyperparameter optimization algorithm, which allows the pipeline to be autonomous and select wisely the best algorithm for every layer. We discuss the specifics of the proposed prototype and the details of the used frameworks. We evaluate the prototype with an experimental study. The results are pertinent.
Distributed H2/ H∞ filter design for discrete-time switched systems

This paper addresses an infinite horizon distributed H2/ H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed H2 and H∞ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with H2/ H∞ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.
Quasi-Elliptic Waveguide Dual-Band Bandpass Filters

This article presents a novel folded configuration of dual-band filter employing TE $_{11m}$ dual-mode elliptical cavity resonators. The proposed side-coupled configuration leads to a significant reduction of filter footprint compared with previously reported in-line dual-band filter structures. Such configuration also facilitates cross couplings to realize advanced quasi-elliptic filter functions. The structure employs irises for both sequential and cross couplings. The limitations of bandwidth realization and spurious-free window are analyzed in detail, and new coupling structures are proposed to improve the overall in-band and out-of-band responses. All the proposed dual-band designs have low insertion loss and allow for independent control of each frequency and coupling parameter. To demonstrate the concept, two 8-pole C-band elliptical cavity dual-band filters are designed, manufactured, measured, and compared. To the best of the authors’ knowledge, this is the first reported folded dual-band filter configuration based on metallic waveguide cavities.
NOMA-aided Hybrid SWIPT for Full-Duplex Massive MIMO Systems

This paper studies the hybrid simultaneously wireless information and power transfer (SWIPT) protocol for full-duplex (FD) massive multiple-input multiple-output (MIMO) systems. A wireless energy transmission method combining time-switching (TS) and power-splitting (PS) is proposed to enable the sensor to receive more energy from the base station (BS). In the case of relatively concentrated sensor or user distribution, non-orthogonal multiple access (NOMA) is applied during the information transmission phase. At last, we optimize the beamforming matrix to maximize the downlink (DL) sum-rate. Simulation results show that the proposed NOMA-aided hybrid SWIPT protocol outperforms traditional schemes.
Dynamic and Non-Centric Networking Approach Using Virtual Gateway Platforms for Low Power Wide Area Systems

To actively develop and promote Internet of Things related techniques, we propose a dynamic and non-centric networking approach using virtual gateway platforms, and evaluate it based on low-power wide-area networks that adopt the traditional star and mesh topologies. In our approach, for each local device, we first present a device management method in which two working modes are set to indicate whether the device works as a virtual gateway (VGW) or provides relay services. Then an internal parameter is defined to manage the mode by considering the device’s current states, such as battery power remaining, presence or absence of an external power supply, etc. We thereafter propose a mode switching method where two time-varying threshold values are configured and help a local device to switch between the two modes through the use of an internal parameter. Finally, a path determination algorithm is applied to find routes for data transmission with or without devices playing the role of VGWs. Computer simulation results indicate that the proposed networking approach can take advantage of both traditional star and mesh systems, enables dynamic and non-centric changes in network configurations, and finally ensures that in most cases, the transmission performance is equivalent to or better than that of other network setups.
Satellite Network Oriented TCP Slow Start Algorithm

This Satellite network has many disadvantages such as long round-trip-time (RTT), high bit error rate. The issues above pose challenge to existing Slow start algorithm. Slow start algorithm is a key component which directly affects startup performance of TCP. This paper mainly improves startup performance of TCP and proposes a slow start algorithm that adopts logarithmic function to overcome multiple congestion packet loss caused by inaccurate ssthresh (slow start threshold) estimation and improves throughput performance.
A Novel Pencil Drawing Algorithm Based on Non-Symmetry and Anti-Packing Pattern Representation Model

The artistic stylization transformation of images is an important part in the research of Non-Photorealistic Rendering (NPR). This kind of non-realistic stylized research has a very strong realistic romantic temperament and it can express the artistic pursuit of researchers very well. It is a good example of practical application combining computer science and art. In the technology of image style conversion, the simulation of the realistic art style includes mainly cartoon style, oil painting style, ink painting style and pencil drawing style. Previously, some researchers proposed a style conversion method based on the prior knowledge that there might be crosses at the junction of two lines where the tone distribution has a certain rule. However, in this paper, we have focused on the use of an edge extraction algorithm based on Non-symmetry and Anti-packing pattern representation Model (NAM) to enhance the pencil strokes and highlight the details of the image. Then, by combining the line pencil strokes with the tone drawing, we can transform the image into an image with pencil sketch style. Finally, compared with the most important previous methods, the experimental results presented in this paper showed that our proposed method showed more clearly the scene and the content and more details of the image than the previous methods. We also verified our method and calculated Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of original images and color pencil drawings with different algorithms on the 7 nature images and the BSDS500 data set.
Analysis of Data Management in Blockchain-Based Systems: From Architecture to Governance

In a blockchain-based system, data and the consensus-based process of recording and updating them over distributed nodes are central to enabling the trustless multi-party transactions. Thus, properly understanding what and how the data are stored and manipulated ultimately determines the degree of utility, performance, and cost of a blockchain-based application. While blockchains enhance the quality of the data by providing a transparent, immutable, and consistent data store, the technology also brings new challenges from a data management perspective. In this paper, we analyse blockchains from the viewpoint of a developer to highlight important concepts and considerations when incorporating a blockchain into a larger software system as a data store. The work aims to increase the level of understanding of blockchain technology as a data store and to promote a methodical approach in applying it to large software systems. First, we identify the common architectural layers of a typical software system with data stores and conceptualise each layer in blockchain terms. Second, we examine the placement and flow of data in blockchain-based applications. Third, we explore data administration aspects for blockchains, especially as a distributed data store. Fourth, we discuss the analytics of blockchain data and trustable data analytics enabled by blockchain. Lastly, we examine the data governance issues in blockchains in terms of privacy and quality assurance.
Modelling and Flame Segmentation for Real-Time Monitoring of Rotary Kilns

Rotary kilns are important industrial plants for the refinery of materials. Effective monitoring of rotary kilns is therefore crucial to the successful operation in order to optimise the flame and reduce waste. In this industrial application-driven work, we describe such a system recently developed and successfully deployed. Our work aims at efficient real-time performance and accuracy for given operating constraints. Our contributions include novel methods that provide accurate depth calibration and mapping, detect and segment the flame, and estimate the flame bed position accurately in challenging conditions. For preprocessing, we use a multi-frame approach and carefully select an optimal window size. Then, we derive a mathematical model for depth calibration and mapping of the kiln and demonstrate the accuracy of the model. Finally, we propose a comprehensive pipeline for segmenting and detecting flame and provide an in-depth analysis of our method. Our evaluation shows that the proposed system performs well and meet real-time requirements whilst using only low-cost hardware.
SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus

Cervical nuclei contain important diagnostic characteristics useful for identifying abnormality in cervical cells. Therefore, an accurate segmentation of nuclei is the primary step in computer-aided diagnosis. However, cell overlapping, uneven staining, poor contrast, and presence of debris elements make this task challenging. A novel method is presented in this paper to detect and segment nuclei from overlapping cervical smear images. The proposed framework segments nuclei by merging superpixels generated by statistical region merging (SRM) algorithm using pairwise regional contrasts and gradient boundaries. To overcome the limitation of finding the optimal parameter value, which controls the coarseness of the segmentation, a new approach for SRM superpixel generation was introduced. Quantitative and qualitative assessment of the proposed framework is carried out using Overlapping Cervical Cytology Image Segmentation Challenge — ISBI 2014 dataset of 945 cervical images. In comparison with the state-of-the-art methods, the proposed methodology achieved superior segmentation performance in terms of Dice similarity coefficient 0.956 and pixel-based recall 0.962. Other evaluation measures such as pixel-based precision 0.930, object-based precision 0.987, and recall 0.944, also compare favorably with some recently published studies. The experimental results demonstrate that the proposed framework can precisely segment nuclei from overlapping cervical cell images, while keeping high level of precision and recall. Therefore, the developed framework may assist cytologists in computerized cervical cell analysis and help with early diagnosis of cervical cancer.
Design of Anomaly Detection and Visualization Tool for IoT Blockchain

This paper proposes and implements a method to verify the blockchain healthiness and to detect a malicious node using data collected from the IoT blockchain such as the transaction generation interval, the blockchains generation rate, and the statistics of IoT data stored in the blockchain. Furthermore, this paper proposes, designs, and implements a visualization tool to efficiently monitor the blockchain healthiness and IoT data in a consistent view via visualization of real-time blockchain update events, blockchain network statistics, and finally the IoT sensing data stored in blockchain as a transaction.
A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement Learning

This paper proposes a novel Quality of Service (QoS) grid routing protocol in Wireless Multimedia Sensor Networks (WMSN) based on reinforcement learning to guarantee Quality of Service in WMSN based on the sensing layer of the Internet of Vehicles (IoV). The sensing layer of IoV acquires abundant information to handle complex road traffic problems. Moreover, WMSN is rich in perceptual data. This suggests a need for complex acquisition, processing, storage, transfer of text and video data. These issues are elevated due, impart, increased requirements for QoS in WMSN. However, WMSN is heterogeneous, and its network topology is changing dynamically. Therefore, ensuring high QoS in a complex environment has become a challenge. This research suggests that least delay can be accomplished by calculating the distance among the nodes through grid identification number (GID) to acquire the nearest path from the source to the sink. Additionally, optimal grid coordinators with the highest reliability can be elected by making all the nodes in the grid for reinforcement learning to acquire their performance knowledge of reliability and delay. This enables high QoS performance in terms of reliability and end-to-end delay. The results indicate that the QoS of QoS-awared grid routing (QAGR) protocol is higher compared with the traditional grid-based clustering routing protocol.
Flexible Single Band-notched Ultra-wideband MIMO Antenna

A single band-notched ultra-wideband MIMO antenna with ultra-thin liquid crystal polymer as substrate is designed. The antenna consists of two radiation units. The two antenna radiation units have the same structure and adopt a feed mode of microstrip feed. The MIMO antenna unit is a U-shaped ultra-wideband monopole antenna. A band-notched for WiMAX is added to the working frequency by engraved a U-shaped groove on the antenna unit. In the middle of the two antenna units, a T-shaped floor branch is introduced as an isolation structure for improving the isolation of the MIMO antenna. The simulated results show that the antenna covers 2.08-10.61GHz, and the isolation is lower than −20dB in most of the frequency bands. UWB wireless communication requirements can be met in actual engineering.
The Diagnosis of Alzheimer's Disease Based on Enhanced Residual Neutral Network

Alzheimer's disease (AD) is a neurodegenerativesickness. Recently, the end-to-end process of using neural net-works to classify patterns has gradually replaced the tediousprocess of manually extracting features. The residual neural net-work (ResNet) can effectively avoid gradient explosion, gradientdisappearance and network degradation. For AD, this paperattempts to apply ResNet to the diagnosis. Although the ResNetworks better compared with the machine learning methods. How-ever, the performance of Mild Cognitive Impairment (MCI) andNormal Control (NC) recognition is poor due to the insignificantdifference in magnetic resonance imaging (MRI) brain imagefeatures. In this paper, the selective kernel network (SKNet) andchannel shuffle are introduced in the Resnet network, and anenhanced Resnet (EResNet) is proposed to accurately diagnosethe AD symptoms. The method solves the influence of the multiplescales of input information on the network through SKNet, andevenly disturbs the channel feature through the channel shuffle, so that the network can fully utilize the feature information ofthe input image to improve the network identification capability. To evaluate the performance of the EResNet, MRI images of thebrain were used to diagnosis of AD. The classification accuracyin AD / MCI and MCI / NC control groups reached 96.47% and90.70%. The experimental results show that the proposed modelpresents a good performance on AD classification.
Tourism and Heritage Routes Including Visitants Profile Adaptation and Context Awareness

The project TheRoute (Tourism and Heritage Routes including Ambient Intelligence with Visitants' Profile Adaptation and Context Awareness) aim is to conduct studies, research and experimentation around the challenge of automatic generation of routes for visitors. The suggested routes fit the profile of visitors and groups of visitors, including aspects like emotion/mood/personality, and aware of the context (e.g. weather, security). The focus region is north of Portugal, developed around a location (urban or rural) or path (e.g. Portuguese Santiago Route or Douro) or around a theme (e.g. musician, writer). Points of Interest (POI) will be modeled. Also, the system is able to use different platforms for the tourist (desktop, notebook, tablet, Smartphone). TheRoute has been developed considering the notion of Ambient Intelligence (AmI) and Context Awareness. It consists of a context-aware touristic recommendation system which can suggest a series of locations to a user based on their personality, preferences, and location. This paper focuses on the proposed route generation processes, including examples of the overall resulting behavior. We discuss two proposed algorithms (an iterative branch-and-bound based process & sequential addition of the nearest feasible sub-route) for route generation, considering contextual model designing based on user's psychology, and point of interest and availability. The route generation tests that were performed included some of the most influential Porto points of interest.