Miracle-dz/UniFAR-FTUs huggingface dataset:
Forage yield and some quality attributes of millet (Pennisetum americannum L.) hybrid under various regimes of nitrogen fertilization and harvesting dates

-1 and harvesting times i.e. 45, 60 and 75 days after sowing (DAS) on forage production and quality of millet hybrid (FH-50) was conducted at Agronomic Research Farm, University of Agriculture, Faisalabad, Pakistan during 2009. The treatments were laid out in randomized complete block design (RCBD) with factorial arrangements in four replications. Both the forage yield and quality were significantly differed among the nitrogen rates and harvesting times. The nitrogen application has positive impact on the agronomic and forage quality components and also remained helpful to off set the negative impact of maturity. Plant received low rates of nitrogen were deficient in crude protein (CP), crude fibre (CF), ash and fat contents than plant received higher nitrogen rates. The protein, fat and ash contents were decreased with the advancement in crop growth period and this decreased was associated with increase in CF concentration. Moreover, late harvesting resulted a great loss in leaves which reduces the forage quality. The harvesting at 60 DAS produce fresh and dry matter yield with 8.72% CP, 29.72% CF, 11.21% ash, 1.37% fat and 48.99% nitrogen free extract (NFE) contents. One day delay in harvesting resulted to a loss of 0.072%, 0.081%, 0.008% CP, ash and fat, respectively in 45 days old plants. The nitrogen application at the rate of 125 kg ha -1 and harvesting at 60 DAS seems to be the best compromise between forage yield and quality for millet hybrid FH-50.
Signal and noise neural models of pHEMTs

Low-noise pHEMT transistors, that have excellent performances at microwave frequencies, can be described by their scattering and noise parameters. In this paper, a pHEMT neural model, based on multilayer perceptron neural networks is proposed. The obtained neural models can predict transistor's signal and noise performances very efficiently and accurately for a broad range of bias conditions in the operating frequency range.
Residue Level Inverse Kinematics of Peptide Chains in the Presence of Observation Inaccuracies and Bond Length Changes

The process of calculating the dihedral angles of a peptide chain from atom coordinates in the chain is called residue level inverse kinematics. The uncertainties and experimental observation inaccuracies in the atoms’ coordinates handicap this otherwise simple and straightforward process. In this paper, we present and analyze three new efficient methodologies to find all the dihedral angles of a peptide chain for a given conformation. Comparison of these results with the dihedral angle values reported in the protein data bank (PDB) indicates significant improvements. While these improvements benefit most modeling methods in protein analysis, it is in particular, very significant in homology modeling where the dihedral angles are the generalized coordinates (structural variables). The first method presented here fits a best plane through five atoms of each peptide unit. The angle between the successive planes is defined as the dihedral angle. The second method is based on the zero-position analysis method. Successive links in this method rotate by the dihedral angles so as to minimize the structural error between respective atoms in the model conformation with given atoms’ coordinates. Dihedral angle final values correspond to the minimum structural error configuration. In this method, singular value decomposition technique is used to best fit the atoms in the two conformations. The third method is a variant of the second method. In this instead of rotating all the links successively only three links are matched each time to extract the dihedral angle of the middle link. By doing so, the error accumulation on the successive links is reduced. This paper focuses on the Euclidean norm as the measure of merit (structural error) to compare different methods with the PDB. This Euclidean norm is further, minimized by optimizing the geometrical features of the peptide plane.
The hyperpolarization‐activated current regulates synchronization of gap–junction‐coupled dopaminergic neurons in the midbrain—a combined approach between computational modeling and electrophysiological recording

To examine the functional role of hyperpolarization‐activated and cyclic nucleotide‐gated (HCN) current observed in mesencephalic dopaminergic neurons, we constructed a conductance‐based model that can mimic the electrical properties obtained in electrophysiological recordings of rat brain slices. In the model, blocking the HCN current resulted in a reduction of spontaneous firing rate and a change in the properties of autonomous pacemaking. In addition, reduced one‐dimensional phase equations and their coupled oscillators were analyzed. The analysis indicated that HCN channels can regulate the extent of synchronization of coupled dopaminergic neurons through gap–junction connections. Thus, the HCN current can effectively shape the autonomous and cooperative firing of dopaminergic neurons in the midbrain. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Development and Control of a Small BLDC Motor for Entertainment Robots

Recently, high performance BLDC motor drives are widely used for variable speed drive systems of the industrial applications. In case of the control of robot arms and tracking applications with lower stiffness, the optimal BLDC motor with small size and its robust control scheme are needed. In this paper, a small BLDC motor is developed and its control methodology is also presented. Fuzzy logic controllers are generally considered applicable to plants that are mathematically poorly understood and where the experienced human operators are available. However, the fuzzy control has not been regarded as a rigorous science due to the lack of the guarantee of the global stability and acceptable performance. To overcome this drawback, since Takagi-Sugeno(TS) fuzzy model[1] which can express a highly nonlinear functional relation in spite of a small number of fuzzy implication rules was proposed, there have been significant research on the stability analysis and systematic design of fuzzy controllers[2-4]. In their research, the nonlinear plant is represented by a TS fuzzy model and the control design is carried out based on the fuzzy model via the so-called Parallel Distributed Compensation(PDC) scheme. In order to deal with the uncertainties of nonlinear systems, in the fuzzy control system literature, a considerable amount of adaptive schemes have been suggested[5-6]. An adaptive fuzzy system is a fuzzy logic system equipped with the an adaptive law. The major advantage of adaptive fuzzy controller over the conventional adaptive fuzzy controller is that the adaptive fuzzy controller is capable of incorporating linguistic fuzzy information from human operators. However most of them have consider only SISO plants and not dealt with adaptive control problem for TS fuzzy model has been given in a few cases[7]. In this paper, to control the motor system, we present alternative direct Adaptive Fuzzy Controller (AFC) for MIMO plants with poorly understood dynamics or plants subjected to parameter uncertainties. We utilize TS fuzzy model for the BLDC motor and PDC as the basis of our control scheme which is different structure from those used in the above studies based on TS fuzzy model. The adaptation law for adjusting the parameters in feedback and feedforward gain of PDC controller is designed so that the plant output tracks the reference model output. The developed control law and adaptive law guarantee the boundness of all signals in the closed loop system. In addition, the plant states track the states of the reference model asymptotically with time for any bounded reference input signal. The proposed adaptive fuzzy control scheme is applied to the velocity control of the developed small BLDC motor to verify the validity and effectiveness of the control schemes
Importance of workplace relationships and attitudes toward organizational change in engineering asset-management organizations

Effective asset management is an important factor that can affect public well-being by reducing accidents and break-downs in public utilities. To be effective, asset managers, engineers, and technicians must be open to the continuous change involved in adopting best practice in the industry. The paper examined the impact of workplace relationships (perceived organizational support and leader-member exchange) on attitudes toward organizational change, affective commitment, and psychological well-being for Australian asset managers, engineers, and technicians working in physical asset management. Social exchange theory provided the theoretical framework, and a self-report survey was administered to examine 255 asset managers, asset management engineers, and asset management maintenance employees. The major findings from a structural equation model indicate that perceived organizational support was positively correlated with an employee’s attitude toward organizational change, and supervisor-subordinate relationships and perceived organizational support were positively correlated with an employee’s emotional attachment to the organization. Perceived organizational support, attitude toward organizational change, and affective commitment were positively correlated with employee psychological well-being. The implication of the findings is that Australian engineering asset management employees’ well-being and attitude toward organizational change is influenced more by their relationship with the organization, compared with the relationship with their supervisor. This research provides support for management seeking to enhance employee attitudes toward organizational change and employee outcomes in physical asset management organizations.
Uncertainty Propagation Analysis for Turbine Cooling Blade Multidisciplinary Design

This paper presents an uncertainty propagation analysis method using the Convex Model Theory (CM) for turbine cooling blade multidisciplinary design. Convex Model is used to describe the design variables’ uncertainty, and it’s propagation equation is derived. Monte-Carlo(MC) method and Convex Model Theory (CM) are all employed to calculate target variable’s variation. Result shows that CM method is more time-saving than MC method. First order second moment method is employed to calculate the reliability of target variable’s variation to evaluate the difference of the results of the two methods. As the actual probability density function of design variables is unknown, so reliability analysis considers different probability density functions. Response surface method is used to construct the approximation of the multidisciplinary analysis. Reliability analysis result shows that CM method keeps the same accuracy with MC method.
The Vanishing Half

European pejorative, saraqa, ‘to steal’. Darke traces efforts by European scholars and architects to deny or obscure the dense web of connections to Islam and the Arab world, focusing on eighteenthand nineteenth-century ‘revivalists’ who claimed the arches and spires of Gothic style as products not of Islamic technical innovation but of northern European genius, inspired by the region’s forests and skilfully executed by local craftsmen. Idealised medieval pasts buttressed nascent national identities and provided a romantic refuge from emergent industrial society. Longer histories of European exceptionalism and our twenty-first-century moment of heightened Islamophobia are not Darke’s only prompts. The book is also haunted by the Syrian war. Image captions indicate historic structures that have been damaged by aerial bombing. The text laments illegal excavations in conflict zones, the looting of artefacts, the iconoclasm of ISIS. A London-born writer and journalist, Darke has spent decades living and travelling in the Middle East. Her connection to Syria is described powerfully in an earlier book,MyHouse in Damascus, where she reflects on buying a seventeenth-century home in the old city of Damascus, lovingly restored with local craftsmen but abandoned at the outbreak of war. Stealing from the Saracens relies heavily on secondary sources, and there are regrettably few glimpses of Darke’s personal travels and encounters. When they do appear – a brief mention, for instance, of dodging Israeli border guards to visit sites in the West Bank – they provide welcome grounding to an attimes dizzying architectural tour. The book is caught between a number of impulses. It has the urgency of a polemic, but the detail and density of a textbook. It makes some foundational claims about the history of architecture, but even Darke admits that few of her arguments will appear new to specialist readers. It calls for a break from regional silos of expertise, and yet aside from some fleeting mentions of South Asia, references to ‘Islamic architecture’ beyond the Middle East (and particularly the Eastern Mediterranean) are rare. The banal reflections on urban design in the book’s final pages belie the real value of Darke’s work: her calls to resist the historical silencing effected by borders, to embrace heterogeneity in our understanding of heritage, and to locate creative possibility in the mobility and migrations of people.
Older Adults’ Views and Attitudes on Physical Activity; Reasons to Participate and Abstain

ABSTRACT Purpose: Despite numerous established benefits of exercise, many older adults do not get enough. The purpose of this study was to identify facilitators and barriers to physical activity/exercise; gain greater understanding of older adults’ physical activity self-efficacy; and increase understanding of how to reduce sedentary behavior.Design: Cross-sectional descriptiveMethods: Quantitative with convenience sample of 66 older adultsFindings: Nearly one third of participants reported engaging in regular physical activity. They tended to have rather strong physical activity self-efficacy and limited barriers to being active.Conclusions: The sample had limited involvement in physical activity or exercise, despite feeling confident in their ability to be active. There could be additional barriers or motivating factors that are preventing them from being more active.Clinical Evidence: Honest and directed conversation about older adults’ understanding and attitudes towards exercise, paired with an individualized plan tailored to their interests and lifestyle could help increase physical activity/exercise.
Towards a Throughput and Energy Efficient Association Strategy for Wi-Fi/LiFi Heterogeneous Networks

In very dense or ultra-dense scenarios wherein Wi-Fi alone may not be enough to accommodate the needs of all stations, LiFi (Light Fidelity) access points can help alleviate the strain on the Wi-Fi by offloading some Wi-Fi traffic to LiFi. We study the issue of associating stations in a Wi-Fi/LiFi heterogeneous network composed of a Wi-Fi access point and multiples LiFi access points. We propose a conceptually simple and easy to implement solution to search and find an efficient mapping for the associations between stations and access points using analytical performance models for the individual throughput of each station and for the overall network energy consumption. Using two realistic deployments of heterogeneous networks for offices, we have evaluated the effectiveness of our solution at discovering better trade-offs than baseline strategies. Our numerical results show that significant gains can be obtained in terms of the throughput of the stations as well as overall energy consumption.
Improving the Realism of the Physical Layer of NS-3 by Considering the PAPR Problem of the IEEE 802.11p Transmitter

NS-3 is a network simulator used for the research and development of Vehicular Ad-hoc Networks (VANETs). It allows the IEEE 802.11p standard simulation, which is part of IEEE Wireless Access in the Vehicular Environment (WAVE). One of the limitations of the 802.11p standard in NS-3 is using a simplified theoretical model of the Packet Error Rate (PER). This standard uses for the Physical Layer (PHY) the Orthogonal Frequency Division Multiplexing (OFDM) technique, which suffers from high Peak to Average Power Ratio (PAPR). This high PAPR of the signal causes degradation of the PER when it passes through a non-linear amplifier. In this work, we study the effect of the high PAPR over the PER using a MATLAB implementation of the 802.11p PHY. We also analyze the effect of using the Orthogonal Pilot Sequences (OPS) technique for PAPR reduction. The results show that the high PAPR affects the PER of high data rates, which use 16-QAM and 64-QAM modulation. Finally, these results are extended to NS-3 simulations as a new model of the IEEE 802.11p PHY with high realism since it considers the effect of the non-linear HPA. The NS-3 results show a similar trend compared to the MATLAB simulations.
A Novel Harvesting-Aware RL-based Opportunistic Routing Protocol for Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) have the potential to empower smart ocean applications. However, the widespread use of UWSN applications has been limited due to the many daunting challenges incurred for underwater wireless acoustic communication. Moreover, underwater wireless communication is energy-hungry, which confines UWSN deployment to small-scale due to the risks and costs of missions for at sea replacement of the nodes' battery. The energy harvesting capability of underwater sensor nodes is an important characteristic that has been overlooked in the literature. In this paper, we study the data routing process in UWSNs with energy harvesting capabilities. We proposed a novel opportunistic routing protocol, named RELOR, that is the first in the literature to consider the energy harvesting capability of underwater sensor nodes during routing decisions. RELOR implements a learning framework for the best selection of the forwarder nodes based on the observed environment conditions. We conduct extensive simulations to compare the performance of the proposed protocol to the state-of-the-art solution. Obtained results show that RELOR outperforms the related work in terms of packet delivery ratio, end-to-end latency, and nodes' energy consumption.
Addressing partial observability in reinforcement learning for energy management

Automatic control of energy systems is affected by the uncertainties of multiple factors, including weather, prices and human activities. The literature relies on Markov-based control, taking only into account the current state. This impacts control performance, as previous states give additional context for decision making. We present two ways to learn non-Markovian policies, based on recurrent neural networks and variational inference. We evaluate the methods on a simulated data centre HVAC control task. The results show that the off-policy stochastic latent actor-critic algorithm can maintain the temperature in the predefined range within three months of training without prior knowledge while reducing energy consumption compared to Markovian policies by more than 5%.
A Support Vector Machine Implementation for Traffic Assignment Problem

Simulating urban mobility scenarios is a useful tool for researchers in multiple fields like Urban Planning, Traffic Optimization, CO$^2$ Emissions Analysis, Performance Evaluation of Protocols for Connected Vehicles, among others. SUMO handles microscopic traffic simulations and allows communication to Python language through an API which is also shared by VEINS. This communication channel lets researchers interact with the simulation on-live, facilitating the implementation of state-of-the-art algorithms from Machine Learning (ML) and Artificial Intelligence (AI). On the other hand, OMNeT++ is a framework to manage and analyze communication protocols of mobile networks. We experimentally evaluated the training of a Support Vector Machine (SVM) in the SUMO-VEINS-OMNeT++ framework. Our experiments show the best classification model for a particular traffic light assignment scenario.
Sharing economy meets energy markets: group purchasing of energy plans in retail energy markets

Competitive retail energy markets with selectable energy providers are increasingly common, allowing users to conveniently switch their energy plans from one energy provider to another. Meanwhile, group purchasing is a popular paradigm of sharing economy, enabling users to collectively gain better discounts through bulk buying activities. Thus, it is beneficial to apply the idea of group purchasing to competitive retail energy markets, such that users form a bargaining group to collectively switch among energy providers for discounted energy plans. In this paper, we explore the decision-making algorithms for enabling group purchasing of energy plans. In particular, we study an online decision-making scenario, where users form a coalition for group purchasing in energy markets without the knowledge of future energy plans and energy demands.
Channel selection of subcarrier multiplexed millimetre-wave-band radio-on-fibre signals with optical heterodyne detection

A channel selection scheme of subcarrier multiplexed (SCM) millimetre-wave-band radio-on-fibre (ROF) signals with optical heterodyne detection, of which the channel selection can be performed by tuning the mode interval of dual-mode local light, is proposed. The channel selection of two 60 GHz-band SCM ROF signals after 25 km-long standard singlemode fibre transmission is experimentally demonstrated without any degradation due to the laser phase noise and fibre dispersion effects.
QUILT: quantify, infer and label the thermal efficiency of heating and cooling residential homes

The European Parliament have set a target of at least a 32.5% improvement in energy efficiency by 2030. In the EU residential sector, heating and cooling account for half of the energy usage alone, hence there is a need to make homes more thermally efficient. However, there are currently a lack of tools to evaluate the thermal efficiency of homes at scale as previous methods have been intrusive, costly, or inaccurate. More recent approaches have shown that the thermal efficiency of homes can be inferred with non-intrusive methods using only smart-meter and weather data. However, to deploy such algorithms at scale, steps must be taken to quantify in which scenarios it works, and to account for the uncertainty of the inferred thermal efficiency. To address this, we propose QUILT, a non-intrusive, data-driven framework to quantify the thermal efficiency of a home, grounded in physical models of heat flow, which works with heating and cooling systems. A Bayesian model is used to account for the uncertainty associated with data sources and parameters in the thermal model. QUILT is evaluated on real data to demonstrate its feasibility in real world scenarios, and also on simulated data used by the U.S. Department of Energy to evaluate the performance in scenarios not currently captured in real-world datasets. Our experiments quantify the improvements compared to the current state-of-the-art approach by reducing the mean absolute percentage error from 21.6% to 15.6% when inferring the heating power loss coefficient (HPLC), which defines how efficiently a building maintains a heated state given the external temperature. Furthermore, the term cooling power loss coefficient (CPLC) is introduced which is the equivalent to HPLC when the building is in a cooled state, and the feasibility of inferring this metric with QUILT is shown. The analysis also highlights the importance of identifying when the heating is on, and how the HPLC or CPLC can be inferred within a week if the occupants are on holiday given the right weather conditions.