Miracle-dz/UniFAR-FTUs huggingface dataset:
Grass functional trait responses to experimental warming and fire in Afromontane grasslands

Climate change is one of the main challenges facing humanity in the coming century. To understand the impact of climate change on biodiversity and ecosystem functioning, we urgently require a better understanding of plant responses to climate change. To address this knowledge gap we established a full-factorial warming experiment using open-top chambers (OTCs) inside a long-term fire-manipulation experiment in Afromontane fire-climax grasslands. Fire is an essential ecosystem driver in these grasslands, but has rarely been included in experimental climate change research. To assess growth responses to elevated temperatures and fire frequency, we measured four functional traits: vegetative height, leaf area (LA), specific leaf area (SLA), and leaf dry matter content (LDMC). Grasses responded to fire exclusion with increased height, and lower LA, SLA, and LDMC. Grasses responded to warming with lower height and LA, and higher LDMC, suggesting that plant growth was negatively affected by warming. This response was mostly attributed to intra-specific trait variability, highlighting an important role for trait plasticity in community-level processes to mediate the response of montane grassland communities to elevated temperatures and associated drought effects. These results are a first step towards establishing a more mechanistic basis for understanding future climatic changes in Afromontane grasslands.
Determination of morphological changes using gamma irradiation technology on capsicum specie varieties

Abstract The recent increase in the demand of hot and chili pepper at local, national, and international market has drawn the interest of breeders’ crop improvement researchers globally. Thus, there is a need to enhance its breeding and development to improve the production of hot and chilies pepper in the quest for food quality and security. This study focused on the germination ability and evaluation of chlorophyll mutations in M2 generation of gamma irradiates on pepper cultivars. The materials used in this experiment were obtained from the M1 generation of gamma-irradiated seeds of pepper cultivars (Capsicum frutescens, Capsicum chinense, and Capsicum annuum (hot pepper, bonnet pepper, and bell pepper, respectively)). Seeds of the pepper varieties were subjected to doses of gamma ray (i.e., 0, 100, 150, 200, 250, 300, 350, and 400 Gy) after which they were planted, raised, and harvested in the M2 generation. The parameters evaluated were germination percentage, survival percentage, epicotyl height, and chlorophyll mutations. However, the highest chlorophyll mutation frequency was observed in C. frutescens. This showed that it was more sensitive to gamma irradiation relative to C. chinense and C. annuum. virescenes were the highest chlorophyll mutant observed in all the three cultivars, while the least was Xanthan. The effective dose ranges between 50 and 250 Gy for germination and survival rate for the three cultivars of pepper give higher desirable mutations in the pepper cultivars. While 400 Gy recorded most efficient for chlorophyll mutation.
Review of Economics

Extreme weather events increasingly threaten the economic situation of households and enterprises around the world. Insurance against extreme weather events is among the climate change adaptation instruments that are currently discussed by the policy community. This overview paper provides a synopsis of the state of research on insurance against extreme weather events, outlining advantages and limitations inherent in three main types of insurance: indemnity-based insurance, index-based insurance, and insurance-linked securities. The paper discusses issues related to insurance uptake, distributional effects, misleading incentives and potentially negative side effects, as well as the role of the state.
Flux‐Free Fusion of Silicate Rock Preceding Acid Digestion for ICP‐MS Bulk Analysis

We present a new method for the decomposition of silicate rocks by flux‐free fusion in preparation for whole‐rock trace element determination (Sc, Rb, Sr, Y, Zr, Nb, Cs, Ba, rare earth elements and Hf) that is especially applicable to zircon‐bearing felsic rocks. The method was verified by analyses of RMs of mafic (JB‐1a, JB‐2, JGb‐1) and felsic rocks (JG‐3, JR‐3, JSd‐1, GSP‐2, G‐2). Pellets of powdered sample (up to 500 mg) without flux were weighed and placed in a clean platinum crucible. The samples were then fused in a Siliconit® tube furnace and quenched to room temperature. The optimum condition for the fusion of granitic rock was determined to be heating for 2–3 min at 1600 °C. The fused glass in the platinum crucible after heating was decomposed using HF and HClO4 in a Teflon® beaker. Decomposed and diluted sample solutions were analysed using a quadrupole inductively coupled plasma‐mass spectrometer. Replicate analyses (n = 4 or 5) of the RMs revealed that analytical uncertainties were generally < 3% for all elements except Zr and Hf (∼ 6%) in JG‐3. These higher uncertainties may be attributed to sample heterogeneity. Our analytical results for the RMs agreed well with recommended concentrations and recently published concentrations, indicating complete decomposition of our rock samples during fusion.
Comparative Studies Of Three Formulated Artificial Litchi Flavored Drinks Under Storage

Abstract The aim to the present study was to formulate of artificial litchi flavored drinks, to assess quality parameters like pH, acidity, TSS and sensory characteristics of three formulated artificial litchi flavored drinks were investigated under 28 days of storage at ambient (30±2°C) temperature. The pH values for all samples varied from 3.53 to 3.65 under the entire storage duration. The acidity found in three different samples ranged from 0.155 to 0.161% under storage condition. The TSS possess among the samples between 15.43 to 16.10°Brix. The sensory profile of three different formulation was evaluated in terms of color, flavor, taste and overall acceptability. The sensory parameters were followed the decreasing trend after end of the storage except sample 3. Although, initially sample 3 found in lowest sensory acceptance but retained the more stable score under the entire storage period which is significantly different (P≤0.05) from other samples. Considering pH, acidity, TSS values under entire storage condition the formulation of sample 3 had shown better results although with few exceptions there was no significant difference (P≤0.05) among the samples. However, end of the study we recommended that, the formulation of sample 3 had better among other sample 1 and sample 2 in terms of quality and cost.
Understanding the Impact of COVID–19 on Global Financial Network Using Graph Based Algorithm: Minimum Spanning Tree Approach

Abstract In this paper effects of COVID–19 pandemic on stock market network are analyzed by an application of operational research with a mathematical approach. For this purpose two minimum spanning trees for each time period namely before and during COVID–19 pandemic are constructed. Dynamic time warping algorithm is used to measure the similarity between each time series of the investigated stock markets. Then, clusters of investigated stock markets are constructed. Numerical values of the topology evaluation for each cluster and time period is computed.
Preliminary Field Evaluation of Soil Compaction in Rain Gardens

AbstractRain gardens are implemented to mitigate runoff volume and associated pollutants to restore the health of a watershed. Rain gardens reduce runoff volume through infiltration and improve water quality as pollutants are filtered through soil media. Infiltration rates, however, can be reduced as a result of soil compaction. Knowledge about the factors that control compaction in rain gardens is currently limited, and studies are lacking on the ability of rain gardens to infiltrate runoff over a long-term period. Twenty-six rain gardens in New Jersey were evaluated during the summers of 2010 and 2011. Depth to soil compaction was measured in the field, and soil texture, site information, and oxidation-reduction potential data were collected to compare different ages of rain gardens with various soil types. There was no significant difference in soil compaction between surveys based upon the age of the rain gardens, but soil texture was found to have an influence on compaction levels. This knowledge wil.
Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network

Gleason grading of histopathology slides has been the “gold standard” for diagnosis, treatment and prognosis of prostate cancer. For the heterogenous Gleason score 7, patients with Gleason score 3+4 and 4+3 show a significant statistical difference in cancer recurrence and survival outcomes. Considering patients with Gleason score 7 reach up to 40% among all prostate cancers diagnosed, the question of choosing appropriate treatment and management strategy for these people is of utmost importance. In this paper, we present a Graph Neural Network (GNN) based weakly supervised framework for the classification of Gleason score 7. First, we construct the slides as graphs to capture both local relations among patches and global topological information of the whole slides. Then GNN based models are trained for the classification of heterogeneous Gleason score 7. According to the results, our approach obtains the best performance among existing works, with an accuracy of 79.5% on TCGA dataset. The experimental results thus demonstrate the significance of our proposed method in performing the Gleason grading task.
Thermophysical Properties of Cotton, Canola, Sunflower and Soybean Oils as a Function of Temperature

Vegetable oils are used in the industry of processed food, including deep-fat frying. This work determined data on the thermophysical properties of cotton, canola, sunflower, corn, and soybean oils. Thermal conductivity, heat capacity, density, and viscosity were measured within the temperature range of 299.15–433.15 K. The data showed that the temperature influenced the thermophysical properties of the oils studied. The developed correlations could be used to predict these properties within the range of temperatures studied.
Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning

A combination of histological and immunohistochemical tissue features can offer better breast cancer diagnosis as compared to histological tissue features alone. However, manual identification of histological and immunohistochemical tissue features for cancerous and healthy tissue requires an enormous human effort which delays the breast cancer diagnosis. In this paper, breast cancer detection using the fusion of histological (H&E) and immunohistochemical (PR, ER, Her2 and Ki-67) imaging data based on deep convolutional neural networks (DCNN) was performed. DCNNs, including the VGG network, the residual network and the inception network were comparatively studied. The three DCNNs were trained using two transfer learning strategies. In transfer learning strategy 1, a pre-trained DCNN was used to extract features from the images of five stain types. In transfer learning strategy 2, the images of the five stain types were used as inputs to a pre-trained multi-input DCNN, and the last layer of the multi-input DCNN was optimized. The results showed that data fusion of H&E and IHC imaging data could increase the mean sensitivity at least by 2% depending on the DCNN model and the transfer learning strategy. Specifically, the pre-trained inception and residual networks with transfer learning strategy 1 achieved the best breast cancer detection.
Heterogeneous Electron-Transfer Kinetics for Flavin Adenine Dinucleotide and Ferrocene through Alkanethiol Mixed Monolayers on Gold Electrodes

The heterogeneous electron-transfer properties of flavin adenine dinucleotide (FAD) were studied at self-assembled monolayer (SAM)-modified gold electrodes and compared to ferrocene-modified SAMs as a reference system. A modified form of FAD, N6-(2-aminoethyl)-FAD, was covalently immobilized onto a mixed self-assembled monolayer (SAMs) formed by two series of alkanethiols (HS−(CH2)n−COOH with a diluent HS−(CH2)n−CH3 where n = 5, 7, 10, or 15 and HS−(CH2)n−COOH with a diluent HS−(CH2)n−CH2OH where n = 5, 7, 10, or 11). The electron-transfer (ET) rates followed an exponential decay with the increasing thickness of the SAM (ket = A exp(−βn); n = number of bonds) with an attenuation factor (β) of 1.0 per bond for FAD with both diluents. Despite the similarity in β the apparent rate constant for electron transfer was 2 orders of magnitude greater with the alcohol-terminated diluent relative to the methyl-terminated SAM. In comparison the ferrocenyl SAMs also had β values of 1.0−1.2 per bond, depending on the c.
Manufacturing Control in Job Shop Environments with Reinforcement Learning

: Computing solutions to job shop problems is a particularly challenging task due to the computational hardness of the underlying optimization problem as well as the often dynamic nature of given environments. To address such scheduling problems in a more ﬂexible way, such that changing circumstances can be accommodated, we propose a reinforcement learning approach to solve job shop problems. As part of our approach, we propose a new reward shaping and devise a novel action space, from which a reinforcement learning agent can sample actions, which is independent of the job shop problem size. A number of experiments demonstrate that our approach outperforms commonly used scheduling heuristics with regard to the quality of the generated solutions. We further show that, once trained, the time required to compute solutions using our methodology increases less sharply as the problem size grows than exact solution methods making it especially suitable for online manufacturing control tasks.
Improvements to Increase the Efficiency of the AlphaZero Algorithm: A Case Study in the Game 'Connect 4'

: AlphaZero is a recent approach to self-teaching gameplay without the need for human expertise. It suffers from the massive computation and hardware requirements, which are responsible for the reduced applicability of the approach. This paper focuses on possible improvements with the goal to reduce the required computation resources. We propose and investigate three modiﬁcations: We model the self-learning phase as an evolutionary process, study the game process as a tree and use network-internal features as auxiliary targets. Then behaviour and performance of these modiﬁcations are evaluated in the game Connect 4 as a test scenario.
Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings

Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered by some analogy test sets for English. Briefly, this paper aims to study how well pretrained Portuguese word embeddings capture such relations. For this purpose, we created a new test, dubbed TALES, with an exclusive focus on Portuguese lexical-semantic relations, acquired from lexical resources. With TALES, we analyse the performance of methods previously used for solving analogies, on different models of Portuguese word embeddings. Accuracies were clearly below the state of the art in analogies of other kinds, which shows that TALES is a challenging test, mainly due to the nature of lexical-semantic relations, i.e., there are many instances sharing the same argument, thus allowing for several correct answers, sometimes too many to be all included in the dataset. We further inspect the results of the best performing combination of method and model to find that some acceptable answers had been considered incorrect. This was mainly due to the lack of coverage by the source lexical resources and suggests that word embeddings may be a useful source of information for enriching those resources, something we also discuss.
Characterization of Software Design and Collaborative Modeling in Open Source Projects

Software design is fundamental to developing high-quality, sustainable, maintainable software. Design languages, such as UML, have become the defacto standard in software design, but their infiltration in the mainstream practices remains vague. Recent studies suggest significant and increasing uptake in mainstream and open source spheres. Mining repositories and the software modeling artifacts often underpin the findings of these studies and focus on counting the instances of modeling artifacts as an indicator for adoption. This study aims to characterize this uptake in greater depth by focusing on analyzing the instances of models in open source projects. The goal is to uncover the profiles of developers who tend to create modeling artifacts, and those developers who maintain them throughout the project life cycle and to uncover the timelines of model creation and manipulation in reference to project evolution. This study sheds light on the nature of modelbased collaboration and interactions and characterizes the role of model-based artifacts well beyond mining their presence in open source repositories. The study finds that, despite the nominal increase in the presence of model-based artifacts, these artifacts are rarely maintained and are typically created by a small and unique set of practitioners. Models are often created early in the project life cycle and do not play any significant role in the collaborative development activities of the subject projects. Life span of these model files is relatively shorter than the code file life span. Unexpectedly, models tend to be more frequently updated and maintained when the project has a relatively fewer number of models.
Thermal diffusivity of oxide perovskite compounds at elevated temperature

The phonon component of thermal diffusivity (D) for eleven compounds (synthetic SrTiO3, SrTiO3:Fe3+, BaTiO3, KTaO3, KNbO3, NdGaO3, YAlO3, YAlO3:Tm, LaAlO3, La0.29Sr0.66Al0.65Ta0.35O3, and natural Ca1.01Mn0.001Fe0.007Ti0.99O3) with various perovskite structures was measured from ambient temperature (T) up to ∼2000 K using contact-free, laser-flash analysis, from which effects of ballistic radiative transfer were removed. Structural transitions (e.g., orthorhombic to tetragonal) below 800 K were manifest as sharp steps in 1/D. Above 800 K, structural transitions occur over intervals of ∼150 K. Similarly broad peaks accompany changes from colorless to black, attributable to partial reduction in Ti, Nb, or Ta from contact with graphite coatings. Otherwise, D decreases with increasing T and, if substitutional disorder exists, approaches a constant (Dsat) near 1600 K. Our data are best described as D−1 following a low order polynomial in T. Ordered, cubic perovskites occupy a single trend for D(T)−1, defining t.
Let's Do the Time Warp Again: Human Action Assistance for Reinforcement Learning Agents

Reinforcement learning (RL) agents may take a long time to learn a policy for a complex task. One way to help the agent to convergence on a policy faster is by offering it some form of assistance from a teacher who already has some expertise on the same task. The teacher can be either a human or another computer agent, and they can provide assistance by controlling the reward, action selection, or state definition that the agent views. However, some forms of assistance might come more naturally from a human teacher than a computer teacher and vice versa. For instance, a challenge for human teachers in providing action selection is that because computers and human operate at different speed increments, it is difficult to translate what constitutes an action selection for a particular state in a human’s perception to that of the computer agent. In this paper, we introduce a system called Time Warp that allows a human teacher to provide action selection assistance to the agent during critical moments of the training for the RL agent. We find that Time Warp is able to help the agent develop a better policy in less time than an RL agent with no assistance and rivals the performance of computer teaching agents. Time Warp also is able to reach the results with only ten minutes of human training
Micro-YOLO: Exploring Efficient Methods to Compress CNN based Object Detection Model

Deep learning models have made significant breakthroughs in the performance of object detection. However, in the traditional models, such as Faster R-CNN and YOLO, the size of these networks make it too difficult to be deployed on embedded mobile devices due to limited computation resources and tight power budgets. Hence, we propose a new light-weight CNN based object detection model, Micro-YOLO based on YOLOv3-Tiny, which achieves a signification reduction in the number of parameters and computation cost while maintaining the detection performance. We propose to replace convolutional layers in the YOLOv3-tiny network with the Depth-wise Separable convolution (DSConv) and the mobile inverted bottleneck convolution with squeeze and excitation block (MBConv), and design a progressive channel-level pruning algorithm to minimize the number of parameters and maximize the detection performance. Hence, the proposed MicroYOLO network reduces the number of parameters by 3.46× and multiply-accumulate operation (MAC) by 2.55× while slightly decreases the mAP evaluated on the COCO dataset by 0.7%, compared to the original YOLOv3-tiny network.
Screw-dislocation motion in b.c.c. transition metals model calculation using a tight-binding-type electronic theory

Abstract Screw-dislocation motion under various types of shear stress has been investigated using a tight-binding-type electronic theory. The repulsive core–core interaction energies are simulated by a Born–Mayer potential, as well as a simple power law potential. Calculated Peierls stresses τp/μ for α-Fe (W) are 0·054 (0·036), 0·058 (0·032) and 0·080 (0·062) for shear on {110} planes, on {112} planes in the twinning direction, and on {112} planes in the anti-twinning direction respectively, where μ is the shear modulus. It is found that the motion of screw dislocations is strongly dependent upon the filling of the d-band and the orientation of the applied stress. These results qualitatively explain the anisotropic plastic behaviour of b.c.c. transition metals and the differences in the orientation dependence of the observed yield (Peierls) stress between the transition metals.
Cyclopentadienyl technetium (99mTc) tricarbonyl piperidine conjugates: biodistribution and imaging studies

Synthesis of organometallic complexes of 99mTc using the precursor ligands N-methylpiperidino-4[(bispentahaptocyclopentadienyl)iron] carboxylate and N-(isopropyl)-piperidino-4[(bispentahaptocyclopentadienyl)iron]carboxylate is described. The labelling method involved reaction of the ligands with 99mTcO in the presence of Mn(CO)5Br in dimethyl formamide at 150°C for 1 h in an oil bath. The purification of the complexes was carried out by preparative TLC using ethery/n-butyl methyl amine (95:5) solvent system. The purified complexes were characterized by HPLC using acetonitrile:water (80:20) solvent system in a PRP-1 column in which both the complexes were eluted as single peaks. Biodistribution studies carried out in rats showed 2.4±0.14 and 1.1±0.42% of the injected activity in the brain tissue 5 min p.i. for cytectrene I and II, respectively. The brain to blood activity ratio was >15:1 for both the complexes at 5 min p.i. Scintigraphic studies in rabbits showed significant uptake of the activity in the brain with fast clearance from blood. The complexes warrant further investigation as agents for brain imaging. Copyright © 2001 John Wiley & Sons, Ltd.