Miracle-dz/UniFAR-FTUs huggingface dataset:
The cotton leaf cuticle and absorption of foliar-applied agrochemicals

Inconsistent results are often experienced with foliar-applied agrochemicals in cotton (Gossypium hirsutum L.) production. It has been speculated that this may be associated with environmental conditions, the nature of the chemical applied, the water status of the crop, and the nature of the cuticle. The studies summarized here were designed to study the characteristics of the cotton leaf surface, the effects of water deficit stress, and the absorption of foliar-applied chemicals. The cuticle constituted a continuous waxy covering over the underlying epidermis, interspersed with numerous stomates and glandular trichomes. Superimposed on this was the epicuticular layer of predominantly lipid material. Also noted was the presence of waxy ledges extending partially over the stomatal pore, and the presence of an internal cuticle extending through the stomatal pore and covering some of the substomatal mesophyll cells. Water deficit stress increased the cuticle thickness by 33% and also changed the qualitative composition of cuticular waxes. The leaf wax from water stressed cuticles contained more long chain molecular waxes of greater hydrophobicity. The thickness of the leaf cuticle was shown to increase with leaf age from 20 to 60 days after leaf unfolding. This was negatively associated with a decrease in absorption of foliar-applied 15N-urea. In related studies, the uptake of foliar-applied plant growth regulators as well as defoliants were significantly decreased by increased leaf wax due to water deficit stress. Subsequent studies showed that the uptake of foliar-applied chemicals, e.g., KNO3 could be increased by the use of adjuvants. These studies showed the dynamic nature of the leaf cuticle and indicated that this needs to be taken into consideration for efficient foliar application of chemi-
Scale dependence of the True Score MTMM model

This chapter starts by reviewing the consequences of scale dependence of Structural Equation Models. Next it shows the scale dependence of the True Score MTMM model presented in Chapter 1 and its practical consequences. Finally, it suggests alternative sets of non-linear constraints for the True Score MTMM model which make the model to be scale invariant without loss of parsimony. The scale dependent behaviour of the standard specification and the scale invariant behaviour of the alternative specifications are illustrated on a real data set.
Deep Representation Learning for Improving Speech Emotion Recognition

Speech emotion recognition (SER) is an active area of research with potential applications in healthcare [1, 2], customer centres [3], and designing naturalistic spoken dialog systems [4]. Despite signicant progress in machine learning (ML), the performance of state-of-the-art SER systems is quite low. Data scarcity is one of the major challenges in this eld [5]. Available SER corpora are relatively small in size compared to datasets in computer vision and other speech-related applications such as speaker identication and speech recognition [4]. is also causes poor generalisation in SER systems against cross-corpus and cross-language seings which limits their performance and deployments in real-life [6]. To solve these issues, we focus on the utilisation of deep representation techniques for SER systems. Particularly, we aim to develop deep learning (DL) models that learn beer emotional representation from fewer labelled data samples and can be trained in semi-supervised or weakly supervised seings to enables the eective utilisation of abundantly available unlabelled data to improve performance as well as generalisation of the system. Besides, the generative modelling ability of Generative adversarial networks (GANs) [7] is utilised to generate synthetic data to augment SER systems.
Origins of Institutions and Inequality in Latin America

Colonial institutions are considered to be an important determinant of post-colonial development with many scholars concluding that former British colonies inherited better institutions. This paper provides an empirical analysis of the origins of Latin American institutions and shows that colonial features have only indirect eﬀects on institutions through their eﬀects on economic and political redistribution. It also explains the role of British intervention in the region. After independence, Britain exerted an important economic inﬂuence in Latin America which contributed signiﬁcantly to the consolidation of the economic and political institutions in the region. Another aspect of the region that aﬀect the institutions is the economic dependence on natural resources which has a negative eﬀect on institutions. Peru, Bolivia, Uruguay and Costa Rica are used as examples to explain how colonial inheritance, natural resources, inequality, institutions interact in the Latin American context. (JEL F54, O17, O54, P48)
Safety and Efficacy of Oxycodone in Cancer Patients with

Background: The study aimed at evaluating the safety and tolerability of oxycodone in patients with moderate-to-severe cancer pain at a medical center in southern Taiwan. Materials and Methods: This was a subgroup analysis of a 12-week, uncontrolled, open-label, multicentric study. During the treatment phase, all participants received control-released (CR) oxycodone and/or immediate-released (IR) oxycodone. The primary end point was the number and percentage of patients with adverse events (AEs) and serious adverse events (SAEs). The secondary end points included patient-reported outcomes and titration of oxycodone. Results: A total of 19 patients were enrolled at this medical center. A total of 56 AEs were documented in 12/19 (63.2%) patients, of which, only 4/56 (7.1%) AEs were treatment-related adverse events occurring in 3/19 (15.8%) patients and no treatment-related SAEs were observed. Most AEs were mild and typical for opioids administered to patients with cancer pain. The most AEs involved the gastrointestinal systems (23%), such as nausea, constipation, and vomiting. At the study end, pain intensity of Numeric Rating Scale score had significantly decreased from 6.3 to 1.6 points; the quality of life on the European Quality of Life Visual Analog Scale (EQ-VAS) median score had improved from 50 to 60 points; and proportion of good/excellent quality of analgesia (QoA) had increased from 5.3% to 100%. The interesting findings of EQ5D item analyses that the top two improvements were anxiety/ depression and pain/discomfort, whereas “mobility” and “self-care” became worse, demonstrated that improvement in cancer pain seems to have more improvement on patients’ anxiety/depression. ” The median stable dose was 20 mg/day and the median time to reach stable dose was 1 day. Conclusion: CR and IR oxycodone are tolerable and effective in managing moderate-to-severe cancer pain among patients with colorectal cancers s at this medical center. Neither new safety signals nor significant bowel function disorders were noted. Together with the high acceptability and improvements on anxiety/depression and pain/discomfort on Taiwanese cancer patients, CR and IR oxycodone can be another valuable pain management option used for the daily control of moderate-to-severe cancer pain.
Quantum Tensor Networks for Variational Reinforcement Learning

As a major machine learning paradigm, reinforcement learning has recently been found to have enormous potential in applications across disciplines. However, existing approaches may require exhaustive exploration to converge, e.g., an exponential number of transitions. In this paper, we propose a novel quantum tensor network approach for variational reinforcement learning. First, we derive a variational reinforcement learning framework by reformulating the Bellman equation into the Hamiltonian equation. The task of searching for the optimal policy in the state-action space is equivalent to ﬁnding the ground state (lowest-energy state) of a quantum-mechanical system. Then, we propose a quantum tensor network scheme that approximates the policy with matrix product state (MPS), alleviating the curse of dimensionality for searching in a huge state-action space. On the Gridworld puzzle example, we empirically show that our algorithm has better stability and higher inference accuracy from partial observations, compared with Q-learning.
The impact of IT resources on SMEs innovation performance

This work aims to develop a research framework to examine the impact of information technology resources on the innovation performance of Saudi small-and-medium enterprises (SMEs). SMEs innovation capability influences growth and technological progress (Bruque & Moyano, 2007). However, many developing countries exhibit moderate or even low innovation performance. For instance, Saudi Arabia is ranked 54th by the Global Innovation Index (GII 2011). Innovation systems studies focus on the alignment between the interactions of innovation actors with their constantly changing environment toward better innovation performance (Etzkowitz & Leydesdorff, 2000). The dynamic capabilities of organisations have been highlighted as a crucial characteristic that helps to achieve a sustainable competitive advantage (Teece et al., 1997). The indirect impact IT resources on innovation performance represents an attractive research area (Benitez-Amado et al., 2010). Therefore, we argue that a closer look at Saudi SMEs? information technology resources and their impact on dynamic capability and innovation performance would make a significant contribution to existing knowledge. Areas such as the organisation strategies of developing countries, innovation management, dynamic capabilities and strategic information systems are few examples of areas that might benefit from this work.
Hybrid Approaches based on Simulated Annealing, Tabu Search and Ant Colony Optimization for Solving the k-Minimum Spanning Tree Problem

In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the combination of simulated annealing, tabu search and ant colony optimization algorithms. The performances of the proposed methods are compared to other approaches from the literature using the same well-known library of benchmark instances. Keywords—k-Minimum spanning tree; metaheuristics; simulated annealing; ant colony optimization algorithms; tabu search; approximation algorithms
Enzyme-catalyzed ring-opening polymerization of 1,3-dioxan-2-one to poly(trimethylene carbonate)

It was fopun dthat cyclic carbonate 1,3-dioxan-2-one was readily polymerized in the presence of lipase at a temperature between 60 and 100°C for 24h. Among the lipases tested, porcine pancreatic lipase showed the best results with respect to the monomer conversion and the molecular weight of the polymer
Molecular dynamics simulations of a flexible liquid crystal

Molecular dynamics simulations have been performed for a liquid crystal composed of a Gay—Berne core site with two alkyl chains of different length (C7 and C3) at either end of the molecule. Calculations have been carried out for 512 molecules in the NVT ensemble for simulation times of up to 8.0ns at two distinct densities. The liquid crystal phases of the material have been fully characterized by measurements of orientational order parameters and radial distribution functions in each phase. Results are also presented for conformational distributions and effective torsional potentials of the system. We conclude that models of this nature represent a powerful approach to the study of flexibility in mesogenic systems and open up possibilities for predicting both the phase behaviour and bulk properties of liquid crystals based solely on a prior knowledge of intermolecular interactions.
Characterization of blood-brain barrier disruption in a focal model of neuromyelitis optica

Neuromyelitis optica (NMO) is a demyelinating autoimmune disease of the central nervous system (CNS), with serum anti-aquaporin4 (AQP4) antibodies (Ab) detected in the majority of patients. Binding of NMO-Ab to AQP4 results in complement- and cell-mediated astrocyte depletion. In addition, a severe impairment of the blood-brain barrier (BBB) is observed as evidenced by gadolinium-enhanced lesions on magnetic resonance imaging (MRI). To investigate the structural, molecular and cellular correlates of the BBB breakdown, we employed a focal model of NMO-like lesions in rats which is based on the intracerebral injection of a human recombinant NMO-Ab directed against AQP4, and human complement. Astrocytes and pericytes are considered crucial for maintenance and repair of the BBB. In NMO-like lesions, we demonstrated that a transient breakdown of the BBB coincided with the onset of astrocyte loss. However, the BBB integrity to vascular tracers was rapidly restored, even in the absence of astrocytes. No loss of pericytes from NMO-like lesions was observed indicating that pericytes might contribute to the rapid restoration of the BBB. Tight junctions (TJ) restrict the paracellular diffusion of solutes across the BBB. Therefore, we analyzed the TJ in NMO-like lesions and detected a transient loss of the TJ protein occludin, while the expression of claudin-3 and claudin-5 was not altered. However, no morphological alterations of the TJ were observed on the ultrastructural level, and the integrity of the BBB to vascular tracers was re-established in the absence of occludin. Inflammatory cells infiltrating the CNS were shown to contribute to BBB disruption. In early NMO-like lesions we demonstrated that polymorphonuclear cells (PMN) were the most abundant infiltrating leukocytes and that the number of PMN in the lesions correlated with the extent of vascular tracer extravasation. Depletion of PMN prevented not only the breakdown of the BBB, but interestingly also the loss of astrocytes. Furthermore, we could show that inhibition of the complement component C5a receptor (C5aR), which is important for PMN attraction and activation, reduced the area of astrocyte loss in NMO-like lesions. In addition, proteases released from PMN granules upon activation were implied in BBB disruption. We demonstrated that matrix metalloproteinase 9 (MMP9) decreased the electrical resistance of endothelial monolayers and that inhibition of the neutrophil elastase (NE), another PMN protease, reduced the loss of astrocytes and PMN infiltration in vivo. This study affirms the importance of PMN in the development of NMO-like lesions and is the first to show that infiltrating PMN mediate the breakdown of the BBB. These findings, together with the detection of PMN in early human NMO lesions, identify the PMN mediated breakdown of the BBB as a promising target for future therapeutic approaches.
Hybrid Tools and Techniques for Sentiment Analysis: A Review

— Sentiment analysis and opinion mining is closely coupled with each other. An extensive research work is being carried out in these areas by using different methodologies. Sentiments in a given text are identified by these methodologies as either positive, negative or neutral. Tweets, facebook posts, user comments about certain topics and reviews regarding product, software and movies can be the good source of information. Sentiment Analysis techniques can be used on such data by businesses executives for future planning and forecasting. As the data is obtained from multiple sources and it depends directly on the user which can be from any part of the world so the noisiness in data is a common issue such as mistake in spellings, grammatical errors and improper punctuation. Different approaches are available for sentiment analysis which can automatically sort and categorize the data. These approaches are mainly categorized as Machine Learning based, Lexicon based and Hybrid. A hybrid approach is the combination of machine learning and lexicon based approach for the optimum results, this approach generally yields better results. In this research work different hybrid techniques and tools have been discussed and analyzed from different aspects.
Category Aware Explainable Conversational Recommendation

Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-based neural architecture on the conversation. Then, we translate the user model into item recommendation scores using a Feed Forward Network. User preferences during the conversation in our approach are represented by category vectors which are directly interpretable. The experimental results on the real conversational recommendation dataset ReDial demonstrate comparable performance to the state-of-the-art, while our approach is explainable. We also show the potential power of our framework by involving an oracle setting of category preference prediction.
Safety of Quark/Gluon Jet Classification

The classification of jets as quark- versus gluon-initiated is an important yet challenging task in the analysis of data from high-energy particle collisions and in the search for physics beyond the Standard Model. The recent integration of deep neural networks operating on low-level detector information has resulted in significant improvements in the classification power of quark/gluon jet tagging models. However, the improved power of such models trained on simulated samples has come at the cost of reduced interpretability, raising concerns about their reliability. We elucidate the physics behind quark/gluon jet classification decisions by comparing the performance of networks with and without constraints of infrared and collinear safety, and identify the nature of the unsafe information by revealing the energy and angular dependence of the learned models. This in turn allows us to approximate the performance of the low-level networks (by 99\% or higher) using equivalent sets of interpretable high-level observables, which can be used to probe the fidelity of the simulated samples and define systematic uncertainties.
Inclined Quadrotor Landing using Deep Reinforcement Learning

Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conventional control methods. We propose a deep reinforcement learning approach to design an autonomous landing controller for inclined surfaces. Using the proximal policy optimization (PPO) algorithm with sparse rewards and a tailored curriculum learning approach, an inclined landing policy can be trained in simulation in less than 90 minutes on a standard laptop. The policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully performs real inclined landings in a flying arena. A single policy evaluation takes approximately 2.5 ms, which makes it suitable for a future embedded implementation on the quadrotor.
All‐Electrical Magnon Transport Experiments in Magnetically Ordered Insulators

Angular momentum transport is one of the cornerstones of spintronics. Spin angular momentum is not only transported by mobile charge carriers but also by the quantized excitations of the magnetic lattice in magnetically ordered systems. In this regard, magnetically ordered insulators (MOIs) provide a platform for magnon spin transport experiments without additional contributions from spin currents carried by mobile electrons. In combination with charge‐to‐spin current conversion processes in conductors with finite spin–orbit coupling, it is possible to realize all‐electrical magnon transport schemes in thin‐film heterostructures. Herein, an insight into such experiments and recent breakthroughs achieved is provided. Special attention is given to charge‐current‐based manipulation via an adjacent normal metal of magnon transport in MOIs in terms of spin‐transfer torque. Moreover, the influence of two magnon modes with opposite spin in antiferromagnetic insulators on all‐electrical magnon transport experiments is discussed.
Behavior-Tree-Based Person Search for Symbiotic Autonomous Mobile Robot Tasks

We consider the problem of people search by a mobile social robot in case of a situation that cannot be solved by the robot alone. Examples are physically opening a closed door or operating an elevator. Based on the Behavior Tree framework, we create a modular and easily extendable action sequence with the goal of finding a person to assist the robot. By decomposing the Behavior Tree as a Discrete Time Markov Chain, we obtain an estimate of the probability and rate of success of the options for action, especially where the robot should wait or search for people. In a real-world experiment, the presented method is compared with other common approaches in a total of 588 test runs over the course of one week, starting at two different locations in a university building. We show our method to be superior to other approaches in terms of success rate and duration until a finding person and returning to the start location.
Effect of Butanol and Propylene Glycol in Amorphous MnO2 Nanoparticles

Amorphous MnO2 are synthesized using KMnO4, butanol, and propylene glycol in an aqueous solution by using the precipitation method, under ambient conditions. The structure, molecular vibrations, surface morphological and optical properties of MnO2 powders are studied using XRD, FTIR, SEM and UV-vis spectroscopy. From the XRD patterns, it is clear that both butanol and propylene glycol assisted MnO2 particles are amorphous in nature. While the strong peak is noticed at 1384 cm -1 in the FTIR spectra, which is due to the characteristic peak of MnO2. SEM photographs show spherical shapes of MnO2 particles, and the average size of a particle is about 200 nm for butanol assisted samples and is around 500 nm for propylene glycol assisted samples. Butanol assisted MnO2 particles exhibit higher optical absorption in the visible region than propylene glycol. However, the estimated optical band gap of both the samples is found around 2.33 eV.
Queuing Analysis of Opportunistic Cognitive Radio IoT Network with Imperfect Sensing

In this paper, we analyze a Cognitive Radio-based Internet-of-Things (CR-IoT) network comprising a Primary Network Provider (PNP) and an IoT operator. The PNP uses its licensed spectrum to serve its users. The IoT operator identifies the white-space in the licensed band at regular intervals and opportunistically exploits them to serve the IoT nodes under its coverage. IoT nodes are battery-operated devices that require periodical energy replenishment. We employ the Microwave Power Transfer (MPT) technique for its superior energy transfer efficiency over long-distance. The white-space detection process is not always perfect and the IoT operator may jeopardize the PNP's transmissions due to misdetection. To reduce the possibility of such interferences, some of the spectrum holes must remain unutilized, even when the IoT nodes have data to transmit. The IoT operator needs to decide what percentage of the white-space to keep unutilized and how to judiciously use the rest for data transmission and energy-replenishment to maintain an optimal balance between the average interference inflicted on PNP's users and the Quality-of-Service (QoS) experienced by IoT nodes. Due to the periodic nature of the spectrum-sensing process, Discrete Time Markov Chain (DTMC) method can realistically model this framework. In literature, activities of the PNP and IoT operator are assumed to be mutually exclusive, for ease of analysis. Our model incorporates possible overlaps between these activities, making the analysis more realistic. Using our model, the sustainability region of the CR-IoT network can be obtained. The accuracy of our analysis is demonstrated via extensive simulation.