Miracle-dz/UniFAR-FTUs huggingface dataset:
A Potential Game Theoretic Approach to Computation Offloading Strategy Optimization in End-Edge-Cloud Computing

Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud computing (EECC) can enhance the utilization of resources and improve quality of experience (QoE). However, the performance of EECC is significantly affected by its architecture. In this article, we classify EECC into two computing architectures types according to the visibility and accessibility of the cloud to UEs, i.e., hierarchical end-edge-cloud computing (Hi-EECC) and horizontal end-edge-cloud computing (Ho-EECC). In Hi-EECC, UEs can offload their tasks only to ESs. When the resources of ESs are exhausted, the ESs request the cloud to provide resources to UEs. In Ho-EECC, UEs can offload their tasks directly to ESs and the cloud. In this article, we construct a potential game for the EECC environment, in which each UE selfishly minimizes its payoff, study the computation offloading strategy optimization problems, and develop two potential game-based algorithms in Hi-EECC and Ho-EECC. Extensive experiments with real-world data are conducted to demonstrate the performance of the proposed algorithms. Moreover, the scalability and applicability of the two computing architectures are comprehensively analyzed. The conclusions of our work can provide useful suggestions for choosing specific computing architectures under different application environments to improve the performance of EECC and QoE.
HIF-Overexpression and Pro-Inflammatory Priming in Human Mesenchymal Stromal Cells Improves the Healing Properties of Extracellular Vesicles in Experimental Crohn’s Disease

Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) have therapeutic potential in the treatment of several immune disorders, including ulcerative colitis, owing to their regenerative and immunosuppressive properties. We recently showed that MSCs engineered to overexpress hypoxia-inducible factor 1-alpha and telomerase (MSC-T-HIF) and conditioned with pro-inflammatory stimuli release EVs (EVMSC-T-HIFC) with potent immunomodulatory activity. We tested the efficacy of EVMSC-T-HIFC to repolarize M1 macrophages (Mφ1) to M2-like macrophages (Mφ2-like) by analyzing surface markers and cytokines and performing functional assays in co-culture, including efferocytosis and T-cell proliferation. We also studied the capacity of EVMSC-T-HIFC to dampen the inflammatory response of activated endothelium and modulate fibrosis. Finally, we tested the therapeutic capacity of EVMSC-T-HIFC in an acute colitis model. EVMSC-T-HIFc induced the repolarization of monocytes from Mφ1 to an Mφ2-like phenotype, which was accompanied by reduced inflammatory cytokine release. EVMSC-T-HIFc-treated Mφ1 had similar effects of immunosuppression on activated peripheral blood mononuclear cells (PBMC) as Mφ2, and reduced the adhesion of PBMCs to activated endothelium. EVMSC-T-HIFc also prevented myofibroblast differentiation of TGF-β-treated fibroblasts. Finally, administration of EVMSC-T-HIFc promoted healing in a TNBS-induced mouse colitis model in terms of preserving colon length and intestinal mucosa architecture and altering the ratio of Mφ1/ Mφ2 infiltration. In conclusion, EVMSC-T-HIFC have effective anti-inflammatory properties, making them potential therapeutic agents in cell free-based therapies for the treatment of Crohn’s disease and likely other immune-mediated inflammatory diseases.
Impact of Network Factors on Supply Chain Performance: A Case of Textile Sector of Pakistan

This study elaborates the importance of network perspective in driving performance outcomes especially in the context of agriculture (Textile) supply chains. The impact of network factors (i.e. actors, resources, and activities) on overall supply chain performance have been explored. By deploying survey, a two-stage cluster sampling was used to attain study objectives. The Textile firms from Punjab and Sindh were selected for data collection. Through a structured questionnaire, 482 responses were generated and analyzed using PLS-SEM. The findings of the study confirmed that Network Actors (Textile Firms), the activities they perform, and the resources they have, generate a significant and positive impact on supply chain performance. The study recommends the need for understanding the role of integrative initiatives between the studied variables, i.e. network factors. Further, it is argued that integrated Network Factors can generate a significant impact on Supply Chain Performance.
A Model-Based Predictive Controller of the Level of Steel in the Mold with Disturbances Using a Repetitive Structure

Keeping the level of steel in the mold of the continuous casting process constant is fundamental for the quality of the steel produced and, consequently, its commercial value. It is challenging, considering the several disturbances that cause undesired variations in the mold level. The aim of this paper is to apply a repetitive structure composed of two controllers, a generalized predictive controller (GPC) and a repetitive GPC (R-GPC) with constraints to mitigate the bulging and clogging/unclogging disturbances and the casting speed variation in the mold level of the process. The R-GPC controller has the same characteristics as the GPC, such as performance, robustness to disturbances, and insertion of constraints, and its advantage is the elimination of periodic disturbances. The repetitive structure will be implemented with a robustness filter and tuned by a genetic algorithm (GA). The controller tests are performed by simulations of a nonlinear mathematical model of the mold level, validated using real data from the steel industry. The proposed controller reduces the bulging disturbance amplitude by 98.5% and at 25% of the frequency of reversions in the valve. Consequently, the proposed controller allows an increase in the valve life span, a reduction in maintenance costs, and quality improvement in the steel slab.
Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

Various social events, such as holidays, important sporting events, and major celebrations, may result in sudden large-scale passenger flows in certain sections and stations of urban rail transit systems. The sudden inbound passenger flows caused by these events can easily lead to continuous congestion of the subway network, which has a profound impact on the safety, reliability, and stability of a subway system. Because of the large magnitude of swipe data and the high dimensionality of time series, it is difficult to identify the emergence of such large passenger flows. Additionally, the recognition accuracy of the existing identification methods cannot meet the operational monitoring requirements. To address the above-mentioned issues, this paper proposes an optimized symbolic aggregate approximation (SAX) algorithm to identify historical sudden passenger flows caused by large-scale events around subways. Specifically, pre-set cluster types and dynamic time warping (DTW) are proposed to enhance the matching rate. Compared with the K-means method, the proposed method exhibits an average increase of 30% in mining accuracy, and the calculation time is shortened to one-sixteenth of the original value.
Biological Control of Leaf Blight Disease Caused by Pestalotiopsis maculans and Growth Promotion of Quercus acutissima Carruth Container Seedlings Using Bacillus velezensis CE 100

Leaf blight disease caused by Pestalotiopsis maculans lead to deleterious losses in the quality of forest container seedlings. The use of plant growth-promoting bacteria provides a promising strategy to simultaneously control diseases and enhance forest seedling production. This study investigated the biocontrol of leaf blight disease and growth promotion potential of Bacillus velezensis CE 100 in Quercus acutissima Carruth seedlings. B. velezensis CE 100 produced cell wall degrading enzymes, such as chitinase, β-l,3-glucanase, and protease, which caused cell wall lysis and hyphae deformation of P. maculans, leading to mycelial growth inhibition by 54.94%. Inoculation of B. velezensis CE 100 suppressed P. maculans infection and increased seedling survival rate by 1.6-fold and 1.3-fold compared to chemical fertilizer and control, respectively. In addition, B. velezensis CE 100 produced indole-3-acetic acid, which improved root development and nutrient uptake compared to chemical fertilizer and control. Especially, inoculation with B. velezensis CE 100 increased the total nitrogen content of Q. acutissima seedlings, improved the chlorophyll index in the leaves, and increased seedling biomass by 1.3-fold and 2.2-fold compared to chemical fertilizer and control, respectively. Thus, B. velezensis CE 100 could be applied in the eco-friendly production of high-quality forest seedlings.
Learning Deep Direct-Path Relative Transfer Function for Binaural Sound Source Localization

Direct-path relative transfer function (DP-RTF) refers to the ratio between the direct-path acoustic transfer functions of two microphone channels. Though DP-RTF fully encodes the sound spatial cues and serves as a reliable localization feature, it is often erroneously estimated in the presence of noise and reverberation. This paper proposes to learn DP-RTF with deep neural networks for robust binaural sound source localization. A DP-RTF learning network is designed to regress the binaural sensor signals to a real-valued representation of DP-RTF. It consists of a branched convolutional neural network module to separately extract the inter-channel magnitude and phase patterns, and a convolutional recurrent neural network module for joint feature learning. To better explore the speech spectra to aid the DP-RTF estimation, a monaural speech enhancement network is used to recover the direct-path spectrograms from the noisy ones. The enhanced spectrograms are stacked onto the noisy spectrograms to act as the input of the DP-RTF learning network. We train one unique DP-RTF learning network using many different binaural arrays to enable the generalization of DP-RTF learning across arrays. This way avoids time-consuming training data collection and network retraining for a new array, which is very useful in practical application. Experimental results on both simulated and real-world data show the effectiveness of the proposed method for direction of arrival (DOA) estimation in the noisy and reverberant environment, and a good generalization ability to unseen binaural arrays.
Bias-Constrained H₂ Optimal Finite Impulse Response Filtering for Object Tracking Under Disturbances and Data Errors

The <italic>H</italic><sub>2</sub> finite impulse response (FIR) filtering approach allows for optimal object tracking under harsh industrial conditions. In this brief, we propose a bias-constrained <italic>H</italic><sub>2</sub> optimal unbiased FIR (<italic>H</italic><sub>2</sub>-OUFIR) filter for linear discrete time-invariant systems under bounded disturbances, data errors, and initial errors. The <italic>H</italic><sub>2</sub>-OUFIR filter is derived using the backward Euler method by minimizing the squared Frobenius norm of the weighted transfer function. A bias-constrained suboptimal <italic>H</italic><sub>2</sub> FIR filtering algorithm using a linear matrix inequality (LMI) is also designed. Based on experimental examples of global positioning system (GPS)-based vehicle tracking and video human tracking, it is shown that the batch <italic>H</italic><sub>2</sub>-OUFIR filter operating on short horizons with full error matrices is able to outperform the Kalman, optimal finite impulse response (OFIR), and unbiased finite impulse response (UFIR) filters.
Joint CD and DGD estimation enabled by FrFT based time-frequency reconstruction

We developed a joint CD and DGD estimation algorithm using Fractional Fourier Transform (FrFT) based time-frequency reconstruction and multi-task deep neural network (MT- DNN). The estimation error for CD and DGD is 217ps/nm and 0.46ps, respectively. © 2021 The Author(s)
Control of Three-Phase Grid Integrated Multiple Solar Photovoltaic Arrays-BES Based MG

In this article, a three-phase grid integrated multiple solar photovoltaic (PV) arrays-battery energy storage (BES) with a bidirectional converter-based microgrid (MG) system is presented. At dc links of main voltage source converter (VSC) and ancillary VSC, two solar PV arrays are utilized to obtain peak power in cost-effective and proficient single stage configuration through distinct incremental conductance maximum power point (MPP) tracking technique. The individual nonlinear loads are linked in parallel at terminals of VSCs, at the point of common coupling (PCC) and, which facilitate the MG expansion to transfer active power to the grid, and to enhance the reliability of MG in off-grid mode. The main VSC operates through the current control, in the grid interactive mode and during fault in the grid, its shifts transient free to the voltage control in an off-grid mode. At PCC, the voltage control, maintains the frequency and voltage, therefore, the ancillary VSC works with current control. A buck–boost converter is interlinks by BES across the main VSC, which regulates the load levelling and maintains voltage for MPPT at its dc link. In order to improve the dynamic performance of system, a feed-forward constituent of PV array power is utilized in the current control algorithm of VSCs, which also enhances the power quality issues. The MG's performance is found satisfactory for scenarios like, load variation, solar insolation change, load unbalance and during inaccessibility of solar power and the grid. The MG is modeled using MATLAB/ SIMULINK and validated in RT-LAB platform of a real time controller OPAL-RT (OP4510).
Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.
URBAN VGI NETWORKS AS A PATH FOR COLLECTIVE INTELLIGENCE

Abstract. The massive use of contemporary information and communication technology as digital platforms and social networks are ruling social dynamics from urban spaces urban spaces in a remarkable manner. The digital layer of cities became a bidirectional and omnipresent path, creating relational and interactional structures able to exchange data and media. This networked city may be analyzed and debated as a complex open system that demands research concerning communicational plurality and development of urban space representation, considering the increasing of informational and communicational density. Digital traces from social networks developed in PortoAlegre.cc, a collaborative web map registering issues and use of urban space, were used as data input for this research. Social network analysis was used as method for network structures evaluation. The results reveal short paths among network elements, shaped by structures following Small World model. The analysis showed efficient networks for data exchange increasing informational and communicational density. This work contribution is focused on presenting alternative approaches and perspectives for this multidisciplinary area, enhancing the debate about urban space by rethinking its representation and collective knowledge.
Control of meadowsweet Filipendula ulmaria through a change of management from grazing to mowing at an English floodplain meadow.

Floodplain meadow grassland is a diverse habitat which has become increasingly rare throughout Europe. Meadowsweet Filipendula ulmaria, is a characteristic species of its plant community, however it can become overly dominant in the sward resulting in detrimental effects on the community as a whole. High abundance can reduce both the diversity of the sward, by shading out low growing species, and the quality of the hay crop. An eight-year management trial (2011-2019) was undertaken at Fancott Woods and Meadows SSSI by the BCN Wildlife Trust and the Floodplain Meadows Partnership, to test whether it was possible to reduce the dominance of meadowsweet through an annual hay cut with aftermath grazing instead of grazing management only, across three blocks of experimental treatments. A secondary aim of the trial was to investigate whether a change in management could also increase the plant-species diversity. Three plots were cut in June and then aftermath grazed, compared to three control plots that were only grazed from July onwards. The study found that cover of meadowsweet decreased from 55% to 6% under the annual cut and aftermath grazing treatment compared to a decrease from 58% to 35% in the control (grazed only) areas. This was accompanied by an increase in plant-species diversity in the cut areas compared to control plots. The study concluded that annual cutting can be used to control coarse and dominant meadowsweet effectively, and that cutting followed by aftermath grazing in a floodplain meadow delivers greater botanical diversity compared to grazing alone.
Globalization Backlash in Developing Countries: Broadening the Research Agenda

This special issue explores why the globalization backlash is roiling rich industrialized countries. But why is the backlash less salient in developing ones? In this piece, we challenge scholars to consider why the backlash has not diffused widely to the developing world. We argue support for globalization depends on citizens’ expectations of future economic mobility. This is high in the early phases of globalization which encapsulates many developing economies. Since information about globalization’s effects is limited, observed mobility of some sustains optimism that the new economic order will allow everyone to prosper. Over time, unrealized expectations of mobility for less-skilled workers puncture this optimism. Such workers in rich countries are long past the honeymoon phase of globalization and confronting realities of stagnant incomes and job precarity. Barring visionary policies unlikely to emerge from today’s polarized politics, their discontent will soon be shared by their developing country counterparts, dooming future globalization.
Data-Driven Control Algorithm for Snake Manipulator

In some environments where manual work cannot be carried out, snake manipulators are instead used to improve the level of automatic work and ensure personal safety. However, the structure of the snake manipulator is diverse, which renders it difficult to establish an environmental model of the control system. It is difficult to obtain an ideal control effect by using the traditional manipulator control method. In view of this, this paper proposes a data-driven snake manipulator control algorithm. After collecting data, the algorithm uses the strong learning and decision-making ability of the deep deterministic strategy gradient to learn these system data. A data-driven controller based on the deep deterministic policy gradient was trained in order to solve the manipulator system control problem when the control system environment model is uncertain or even unknown. The data of simulation experiments show that the control algorithm has good stability and accuracy in the case of model uncertainty.
Wideband Monostatic Co-Horizontally Polarized Simultaneous Transmit and Receive Antenna Subsystem With Integrated Beamforming Network

A wideband monostatic simultaneous transmit and receive (STAR) antenna subsystem with high isolation and Co-horizontally polarized (Co-HP) omnidirectional radiation is presented. The antenna subsystem consists of a STAR antenna and a beamforming network (BFN). The STAR antenna consists of a planar circular array having four printed dipole elements surrounded by two groups of parasitic strips. The parasitic strips are used to improve the impedance bandwidth (IMBW) and reduce the gain variation in transmit (TX) and receive (RX) modes. To achieve high isolation, the four ports of the STAR antenna are excited with two orthogonal phase modes for TX and RX using the BFN. The BFN is a six-port network consisting of two 180° hybrid couplers, one 90° hybrid coupler, and a Wilkinson power divider. The 180° hybrid coupler is elaborately designed with low imbalances since it is most key to achieve high TX/RX isolation. Measured results show that the proposed STAR subsystem achieves a TX/RX overlapping IMBW of 4.71–6.2 GHz, and a 40 dB TX/RX isolation bandwidth of 4.81–5.88 GHz, while maintaining horizontally polarized omnidirectional radiation patterns for TX and RX over the entire operating frequency band.
The Discourse of Qanun Aceh Number 11 Year 2018 Concerning Sharia Financial Institutions in the Economic Approach

This research purposes to discover about the economic point of view of the law in the Policy of Qanun Number 11 year 2018 concerning Sharia Financial Institutions in Aceh Province. This research is a prescriptive doctrinal law research. The approach used is a conceptual approach which is the researchers based on the view of experts (expert) in building arguments. In this case, the view of experts that used is the idea of Richard A. Posner as the basic to build an economic approach argumentative to the law. The data analysis technique used is qualitative. The results showed that based on the theory from Posner, the policy of the formulate of the Qanun Sharia Financial Institutions was parallel to the theory of wealth maximization (Wealth Maximization Theory) with the indicator of Article 5 which stated that the objectives of the Qanun include to; encourage increase Aceh Original Revenue; increase the access of funding and business for the community; help the increase of economic empowerment and social productivity; and help the increase of social income and welfare. According to the theory of Cost and Benefit Analysis (CBA) the policy of converting from conventional banks to Sharia banks provide benefits to Sharia banks. Meanwhile according to the theory of Behavioral of Law and Economy analyzed using three approaches namely; a positive approach, it was concluded that the implementation of the Qanun will bring positive results for banking performance improvement and public awareness in carrying out economic activities based on sharia principles will make the level of default risk decreases; the prescription approach for the implementation of the Qanun is to avoid economic activities that are incompatible with Sharia principles; the normative approach confirms the enactment of the Qanun to help increase income and welfare of the society. KeywordsQanun, Aceh, economic approach.