Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.
Cocycle superrigidity from higher rank lattices to $ {{\rm{Out}}}{(F_N)} $

<p style='text-indent:20px;'>We prove a rigidity result for cocycles from higher rank lattices to <inline-formula><tex-math id="M2">\begin{document}$ \mathrm{Out}(F_N) $\end{document}</tex-math></inline-formula> and more generally to the outer automorphism group of a torsion-free hyperbolic group. More precisely, let <inline-formula><tex-math id="M3">\begin{document}$ G $\end{document}</tex-math></inline-formula> be either a product of connected higher rank simple algebraic groups over local fields, or a lattice in such a product. Let <inline-formula><tex-math id="M4">\begin{document}$ G \curvearrowright X $\end{document}</tex-math></inline-formula> be an ergodic measure-preserving action on a standard probability space, and let <inline-formula><tex-math id="M5">\begin{document}$ H $\end{document}</tex-math></inline-formula> be a torsion-free hyperbolic group. We prove that every Borel cocycle <inline-formula><tex-math id="M6">\begin{document}$ G\times X\to \mathrm{Out}(H) $\end{document}</tex-math></inline-formula> is cohomologous to a cocycle with values in a finite subgroup of <inline-formula><tex-math id="M7">\begin{document}$ \mathrm{Out}(H) $\end{document}</tex-math></inline-formula>. This provides a dynamical version of theorems of Farb–Kaimanovich–Masur and Bridson–Wade asserting that every homomorphism from <inline-formula><tex-math id="M8">\begin{document}$ G $\end{document}</tex-math></inline-formula> to either the mapping class group of a finite-type surface or the outer automorphism group of a free group, has finite image. </p><p style='text-indent:20px;'>The main new geometric tool is a barycenter map that associates to every triple of points in the boundary of the (relative) free factor graph a finite set of (relative) free splittings. </p>
‘I'm broken but I'm alive’: gender, COVID-19 and higher education in Australia

ABSTRACT This paper reports on a project conducted between July 2020 and March 2021 that was developed within the context of COVID-19 and explored the ways in which Australian universities responded to the pandemic and the gendered effects of these responses. This paper demonstrates that sexist and gender discriminatory practices were amplified by the pandemic and that the gendered division of labour within higher education was made more apparent. We contribute to a growing body of work on gender and the COVID-19 crisis by reporting on a survey conducted in Australia. The survey asked participants to reflect on their experiences qualitatively. It is this qualitative element that has been missing from much of the research in the field thus far. In addition, the diversity of participants means that we bring an intersectional lens to the ongoing effects of the pandemic upon higher education, including exploring the experiences and reflections of academics who identify as culturally and linguistically diverse (CALD), LGBTIQA+, and Aboriginal and Torres Strait Islander experiences and reflections. This paper, therefore, reports on some of the key findings from the survey and focuses upon the gendered dimensions of the pandemic, domestic life and pandemic living-working, and affect, emotion and wellbeing.
Exploration of Mycotoxin Accumulation and Transcriptomes of Different Wheat Cultivars during Fusarium graminearum Infection

Fusarium graminearum is one of the most devastating diseases of wheat worldwide, and can cause Fusarium head blight (FHB). F. graminearum infection and mycotoxin production mainly present in wheat and can be influenced by environmental factors and wheat cultivars. The objectives of this study were to examine the effect of wheat cultivars and interacting conditions of temperature and water activity (aw) on mycotoxin production by two strains of F. graminearum and investigate the response mechanisms of different wheat cultivars to F. graminearum infection. In this regard, six cultivars of wheat spikes under field conditions and three cultivars of post-harvest wheat grains under three different temperature conditions combined with five water activity (aw) conditions were used for F. graminearum infection in our studies. Liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis showed significant differences in the concentration of Fusarium mycotoxins deoxynivalenol (DON) and its derivative deoxynivalenol-3-glucoside (D3G) resulting from wheat cultivars and environmental factors. Transcriptome profiles of wheat infected with F. graminearum revealed the lower expression of disease defense-factor-related genes, such as mitogen-activated protein kinases (MAPK)-encoding genes and hypersensitivity response (HR)-related genes of infected Annong 0711 grains compared with infected Sumai 3 grains. These findings demonstrated the optimal temperature and air humidity resulting in mycotoxin accumulation, which will be beneficial in determining the conditions of the relative level of risk of contamination with FHB and mycotoxins. More importantly, our transcriptome profiling illustrated differences at the molecular level between wheat cultivars with different FHB resistances, which will lay the foundation for further research on mycotoxin biosynthesis of F. graminearum and regulatory mechanisms of wheat to F. graminearum.
High Performance Modified Model Predictive Control of a Voltage Source Inverter

Abstract This paper proposes a modified model predictive control (MMPC) method for a two-level voltage source inverter (VSI) by applying ‘Midpoint Euler's Approximation’ technique to achieve a high performance. Therefore, a new predictive mathematical modeling is derived and presented in this paper to predict the future behavior of the inductive load model, which is compared with the reference signal to determine the cost function of the system. In this MMPC method, all the possible switching vectors of the converter are utilized to obtain the cost functions, and the corresponding switching vector for minimum cost function is selected to actuate the power converter in the next sampling time interval. The proposed MMPC method and the traditional MPC schemes are verified in MATLAB-Simulink. The experimental validation is performed in DS1104 R&D Controller Board to justify and confirm the performance of the system. Simulation and experimental results validate the robustness of the proposed MMPC method, and improve the results compared to traditional MPC method in terms of total harmonic distortion (THD) and current reference tracking mean error.
Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems

The increasing power consumption and related environmental implications currently generated by large data networks have become a major concern over the last decade. Given the drastic traffic increase expected in 5G dense environments, the energy consumption problem becomes even more concerning and challenging. In this context, Software-Defined Networks (SDN), a key technology enabler for 5G systems, can be seen as an attractive solution. In these programmable networks, an energy-aware solution could be easily implemented leveraging the capabilities provided by control and data plane separation. This paper investigates the impact of energy-aware routing on network performance. To that end, we propose a novel energy-aware mechanism that reduces the number of active links in SDN with multiple controllers, considering in-band control traffic. The proposed strategy exploits knowledge of the network topology combined with traffic engineering techniques to reduce the overall power consumption. Therefore, two heuristic algorithms are designed: a static network configuration and a dynamic energy-aware routing. Significant values of switched-off links are reached in the simulations where real topologies and demands data are used. Moreover, the obtained results confirm that crucial network parameters such as control traffic delay, data path latency, link utilization and Ternary Content Addressable Memory (TCAM) occupation are affected by the performance-agnostic energy-aware model.
Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision

Background Freezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment. Objective A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital. Methods In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model. Results We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%. Conclusion A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.
Network Proximity-Based Drug Repurposing Strategy for Early and Late Stages of Primary Biliary Cholangitis

Primary biliary cholangitis (PBC) is a chronic, cholestatic, immune-mediated, and progressive liver disorder. Treatment to preventing the disease from advancing into later and irreversible stages is still an unmet clinical need. Accordingly, we set up a drug repurposing framework to find potential therapeutic agents targeting relevant pathways derived from an expanded pool of genes involved in different stages of PBC. Starting with updated human protein–protein interaction data and genes specifically involved in the early and late stages of PBC, a network medicine approach was used to provide a PBC “proximity” or “involvement” gene ranking using network diffusion algorithms and machine learning models. The top genes in the proximity ranking, when combined with the original PBC-related genes, resulted in a final dataset of the genes most involved in PBC disease. Finally, a drug repurposing strategy was implemented by mining and utilizing dedicated drug–gene interaction and druggable genome information knowledge bases (e.g., the DrugBank repository). We identified several potential drug candidates interacting with PBC pathways after performing an over-representation analysis on our initial 1121-seed gene list and the resulting disease-associated (algorithm-obtained) genes. The mechanism and potential therapeutic applications of such drugs were then thoroughly discussed, with a particular emphasis on different stages of PBC disease. We found that interleukin/EGFR/TNF-alpha inhibitors, branched-chain amino acids, geldanamycin, tauroursodeoxycholic acid, genistein, antioestrogens, curcumin, antineovascularisation agents, enzyme/protease inhibitors, and antirheumatic agents are promising drugs targeting distinct stages of PBC. We developed robust and transparent selection mechanisms for prioritizing already approved medicinal products or investigational products for repurposing based on recognized unmet medical needs in PBC, as well as solid preliminary data to achieve this goal.
Background of the Ergotool

To support the participatory and integral approach to improve ergonomics and efficiency of production lines, a software tool was developed within the PSIM project. It helps in the description and evaluation of current and future assembly processes. The E/S tool (Ergonomics/Sociotechnics) tool has five modules, lour of which deals with ergonomics: physical load. environmental hazards. mental load. and process flow characteristics. Together those four are called also as Ergotool. l'he focus of this chapter is on the background information and the development of the Ergotool. The procedural aspects ofthe tool are outlined in Chapter 10 (E/S tool). The tests of the Ergotool are described in Chapters I I (Volvo). 12 (Fintand Post). l3 (Fiat), 14 (Ford), and Chapter 15 (Yamatake).
Dimensional reduction in Bose-Einstein-condensed alkali-metal vapors

We investigate the effects of dimensional reduction in atomic Bose-Einstein condensates (BECs) induced by a strong harmonic confinement in the cylindric radial direction or in the cylindric axial direction. The former case corresponds to a transition from three dimensions (3D) to 1D in cigar-shaped BECs, while the latter case corresponds to a transition from 3D to 2D in disk-shaped BECs. We analyze the first sound velocity in axially homogeneous cigar-shaped BECs and in radially homogeneous disk-shaped BECs. We consider also the dimensional reduction in a BEC confined by a harmonic potential both in the radial direction and in the axial direction. By using a variational approach, we calculate monopole and quadrupole collective oscillations of the BEC. We find that the frequencies of these collective oscillations are related to the dimensionality and to the repulsive or attractive interatomic interaction.
CAN CLIMATE-SMART AGRICULTURE HELP TO ASSURE FOOD SECURITY THROUGH SHORT SUPPLY CHAINS? A SYSTEMATIC BIBLIOMETRIC AND BIBLIOGRAPHIC LITERATURE REVIEW

Purpose – This paper aims to reveal potential research possibilities for enhancing climate-smart agriculture through short supply chains. Research question – How can short supply chains assure food security and the promotion of climate-smart agriculture? Research methodology – Bibliographic and bibliometric coupling techniques were employed using data from 1990–2022. The raw data was processed using the VOSviewer 1.6. 18 software version. Findings – The results confirm the positive effect of the systemically important relationship between the short supply chain and food security. Research limitations – Climate-smart agriculture is a complex and multifaceted phenomenon. Additional variables may have moderating and mediating effects on the impact of short supply chains on food security. Practical implications – The results establish the importance of having a short supply chain for food security in different aspects of the process from the harvest to the table. Originality and value – This study confirms the rationale for developing shorter food supply chains to assure food security and climate-smart agriculture when possible.
Interpretable Malware Classification based on Functional Analysis

: Malware is the crux of cyber-attacks, especially in the attacks of critical cyber(-physical) infrastructures, such as financial systems, transportation systems, smart grids, etc. Malware classification has caught extensive attention because it can help security personnel to discern the intent and severity of a piece of malware before appropriate actions will be taken to secure a critical cyber infrastructure. Existing machine learning-based malware classification methods have limitations on either their performance or their abilities to interpret the results. In this paper, we propose a novel malware classification model based on functional analysis of malware samples with the interpretability to show the importance of each function to a classification result. Experiment results show that our model outperforms existing state-of-the-art methods in malware family and severity classification and provide meaningful interpretations.
Observed Covariations in Boundary Layer and Cumulus Cloud Layer Processes

In this study we examine variations in boundary-layer processes spanning the shallow to deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, USA. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provides the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection (LFC). In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense, but are weak when compared to the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.
Significant others’ perspectives on experiences of meal-oriented support and diet counselling for adults with intellectual disabilities who live in supported housing

The quality of meal-oriented support for people with intellectual disabilities is important for their health. The aim of the present study was to explore the experiences of meal-oriented support and diet counselling for adults with intellectual disabilities living in supported housing, from the perspective of housing staff and mothers. Five focus group interviews, including nine supporting staff members and nine mothers, were conducted. The interviews were analyzed using systematic text condensation. Five themes appeared; Extensive needs of the individual, Staff skills determine the food intake, Informal caregivers make up for shortage of support, Effective collaboration with a registered dietitian is needed and Responsibility of the organization state that professionalization of staff is needed. Lacking resources, such as time and nutritional knowledge, insufficient considerations of individual needs, and high staff turnover influence the meal-orientated services negatively. This study brings to the fore, staff working practices and the complexity of providing meal-oriented support for people with intellectual disabilities. Staff need skills to perform individually tailored support. This is best accomplished through effective collaboration between housing staff and relatives underpinned by knowledge from a registered dietitian. The working practices must be structured at the organizational level of the services.
A High Efficiency Optical Power Splitter in a Y-Branch Photonic Crystal for DWDM Optical Communication Systems

Abstract In this paper a high efficiency optical power splitter based on two-dimensional photonic crystals is proposed. The photonic crystal cavity is assumed to be constructed by TiO2 nanorods with refractive index n=2.609. To do so, first we implement some techniques to design a 1*2 optical power splitter, which include Y-shape waveguides and various types of defects such as point and line defects. The results of numerical simulations show that at the wavelength range of DWDM systems, the proposed 1*2 power splitter has the maximum efficiency of 99.616 %. A similar method is employed to design a 1*4 splitter with high performance operation at the same wavelength range which relates to the efficiency of 99.21 % that is the highest amount between these type of power splitters. The final size of these high efficiency power splitters is 130μm2 (10μm*13μm) which make them suitable for integrated optical circuits.
An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome

Rett Syndrome (RTT) is a complex neurodevelopmental disorder that has multi-system involvement with co-occurring epilepsy, breathing problems and autonomic dysregulation. Autonomic dysregulation can increase the risk of cardiorespiratory vulnerability in this patient group. Assessment of heart rate variability (HRV) provides an overview of autonomic health in RTT and offers insight into how the sympathetic and parasympathetic components of the nervous system function. However, to our knowledge, no study has evaluated HRV in Rett patients to assess how the dynamics of autonomic function vary with age and changes during the day and/or night. Using non-invasive wearable sensors, we measured HRV in 45 patients with RTT and examined the time and frequency domain sympathetic and parasympathetic indices. Among the HRV indices assessed, heart rate decreases with age and is lower in the night across all ages studied. The sympathetic index (SDNN) and the parasympathetic indices (RMSSD and pNN50) are not seen to change with age. Nevertheless, these indices were all higher during the day when compared to the night. Our findings appear to show that Rett patients are less adaptable to autonomic changes during the night. In the clinical setting, this might be more relevant for patients with severe psychopathology.