Miracle-dz/UniFAR-FTUs huggingface dataset:
Monoculture vs. mixed-species plantation impact on the soil quality of an ecologically sensitive area

Over the past four decades the Western Ghats of India, one of the eight hottest hotspots of biological diversity in the world, witnessed the transformation of its prime forests into other land-use types, mainly monoculture plantations. The present study evaluated the impact of conversion of natural forests to mixed-species (teak) and monoculture (rubber) plantations on the soil quality of a Typic Plinthohumults soil series in the Southern Western Ghats region of Kerala, India. The baseline physicochemical and biological parameters of the different sites were analyzed using standard methods. To comprehend the impact of plantations on the overall soil quality, the soil quality index of the different land-uses was quantified using the forest as the reference land-use. Significant variations in different soil physical, chemical, and biological properties of plantation and forest soils were observed in the present study. The overall soil quality index was found to follow the order: forest (1.0) > teak plantations (0.9) > rubber plantations (0.6), thus signifying the negative impact monoculture rubber plantations had on the soil quality of the study area. The results emphasize the need for the development of better land management practices and mixed-species plantation systems such as the teak plantations in the present study which did not deteriorate the soil quality.
Certificate Verification System Based on Blockchain Technology

One of the most important documents is certificates for graduates from universities and other educational institutions. A certificate is a certificate of qualification for a graduate that is used for a job or other matters relating. Advancement of IT and low cost and high quality office supplies on the marketplace have contributed to the development of essential documents such as certificates, identification cards and passports. However, it is costly and time consuming to check certificates using traditional methods. The aim of this paper is to introduce a theoretical model, which can give the possible solution for the issue and verification of academic certificates using blockchain based. There are many functions such as hash, public and private key cryptography, digital signatures, peer-to-peer networks and work evidence in blockchain technology. The models formulate a system divided into two principal processes using different elements, namely the issuance of a digital certificate of academic signature and the academic certificate verification. The paper proposed to use blockchain technology to validate university certificates. This meets all the criteria for a modern verification program for academic certificates. In addition, the new approach for confirming academic certificates validity fills up the gaps and challenges.
FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human-Robot Interaction

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.
The Effect of Weather Conditions and Some Demographic Data on the Confirmed COVID-19 Cases: Analysis for 12 Statistical Regions of Turkey

COVID-19 has been affecting the world since the beginning of 2020. Although current evidence indicates that the virus spreads through contaminated objects or close contact with infected individuals, there is limited research on under what conditions the virus spreads faster. This study is conducted to reveal the relationship between COVID-19 cases and meteorological conditions and also some demographic characteristics. For this goal, real-feel air temperature, humidity, and wind speed; analyzed as meteorological parameters. The number of individuals over the age of 65, the education level of the individuals, and the population density are also considered as demographical parameters. The analyses are conducted based on Spearman’s correlation coefficients. The results proved that the positive correlation was calculated for real-feel air temperature, wind speed, population density, and the number of individuals over 65, whereas negatively correlated with humidity.
Online Appendix of “Starving and Deceiving? How Disasters Reshape Politicians’ Incentives to Lie”

This section elaborates our method to calculate GDP manipulation. In short, we aim to recover the true GDP data from indicators that are closely related with economic growth (thus with GDP), and are less manipulable. In particular, we follow the estimation methods of Pinkovskiy and Sala-i-Martin (2016) and Clark et al. (2020). We start by collecting data of satellite light and air pollution – frequently used proxies for economic activities that are less manipulable by local officials. We include the sources of all data in appendix Table A1. We proceed to aggregate the data to county-level in annual bases, as the input variables of our GDP prediction. The input variables also include year and county dummies.
iDiary

This article describes iDiary, a system that takes as input GPS data streams generated by users’ phones and turns them into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., “Where did I buy books? ”) and receive textual answers based on their GPS signals. iDiary uses novel algorithms for semantic compression and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. We encode these problems as follows. The k-segment mean is a k-piecewise linear function that minimizes the regression distance to the signal. The (k,m)-segment mean has an additional constraint that the projection of the k segments on Rd consists of only m ≤ k segments. A coreset for this problem is a smart compression of the input signal that allows computation of a (1+ϵ)-approximation to its k-segment or (k,m)-segment mean in O(nlogn) time for arbitrary constants ϵ, k, and m. We use coresets to obtain a parallel algorithm that scans the signal in one pass, using space and update time per point that is polynomial in log n. Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g., LSA or transportation mode recognition). We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state of the art. This is the first GPS system that enables text-searchable activities from GPS data.
Burden of Depression Among Survivors of Ischemic Stroke of Southern Punjab

Background; Depression is one of the most frequent neuropsychiatric disturbances after ischemic stroke. The frequency of depression in stroke patients has varied widely in different populations. Post stroke depression is an important factor limiting recovery and rehabilitation in acute stroke patients. This study was done to ascertain frequency of depression among patients with ischemic stroke in our local population. Objective; To assess the frequency of depression in patients with ischemic stroke in Pakistan. Material and Methods; Consecutive 150 patients fulfilling inclusion criteria was enrolled and this descriptive study was conducted at department of Psychiatry and Neurology OPD, Nishtar Hospital, Multan using non – probability convenient sampling technique. All patients were assessed by single psychiatrist on PHQ-9 scale, for Depression. Results; Of these 150 study cases, 94 (62.7 %) were male patients while 56 (37.3 %) were female patients. Mean age of our study cases was 54.69 ± 7.18 years (with minimum age of our study cases was 44 years while maximum age was 71 years). Of these 150 study cases, 71 (47.3 %) belonged to rural areas and 79 (52.7 %) belonged to urban areas. Monthly family income up to Rs. 50000 was noted in 89 (59.3%) while more than Rs. 50000 was noted in 61 (40.7%). Of these 150 study cases, 83 (55.3%) were illiterate and 67 (44.7%) were literate. Of these 150 study cases, site of lesion in basal ganglia was 61 (40.7%), subcortical in 56 (37.3%) and cortical in 33 (22.0%) and Depression was noted in 77 (51.3%). Conclusion; Very high frequency of depression was observed in our study among patients having ischemic stroke. Depression was significantly associated with gender, occupation, educational level, prolonged duration of illness and site of lesion. Clinicians treating stroke patients should anticipate depressive symptoms and diagnose them to treat them in early stages. Keywords; Ischemic stroke, depression, frequency. DOI: 10.7176/JMPB/69-01 Publication date:March 31 202
ALP

There has been an increasing demand for interactive video transmission over the Internet for applications such as video conferencing, video calls, and telepresence applications. These applications are increasingly moving towards providing High Definition (HD) video quality to users. A key challenge in these applications is to preserve the quality of video when it is transported over best-effort networks that do not guarantee lossless transport of video packets. In such conditions, it is important to protect the transmitted video by using intelligent and adaptive protection schemes. Applications such as HD video conferencing require live interaction among participants, which limits the overall delay the system can tolerate. Therefore, the protection scheme should add little or no extra delay to video transport. We propose a novel Adaptive Loss Protection (ALP) scheme for interactive HD video applications such as video conferencing and video chats. This scheme adds negligible delay to the transmission process and is shown to achieve better quality than other schemes in lossy networks. The proposed ALP scheme adaptively applies four different protection modes to cope with the dynamic network conditions, which results in high video quality in all network conditions. Our ALP scheme consists of four protection modes; each of these modes utilizes a protection method. Two of the modes rely on the state-of-the-art protection methods, and we propose a new Integrated Loss Protection (ILP) method for the other two modes. In the ILP method we integrate three factors for distributing the protection among packets. These three factors are error propagation, region of interest and header information. In order to decide when to switch between the protection modes, a new metric is proposed based on the effectiveness of each mode in performing protection, rather than just considering network statistics such as packet loss rate. Results show that by using this metric not only the overall quality will be improved but also the variance of quality will decrease. One of the main advantages of the proposed ALP scheme is that it does not increase the bit rate overhead in poor network conditions. Our results show a significant gain in video quality, up to 3dB PSNR improvement is achieved using our scheme, compared to protecting all packets equally with the same amount of overhead.
BANGLA ASPECT-BASED SENTIMENT ANALYSIS BY SUPERVISED LEARNING BASED ON ASPECT TERM EXTRACTION

Sentiment Analysis is the process of retrieving human sentiments from the text. Aspect-based sentiment analysis goes one step ahead than sentiment analysis by automatically assigning sentiments to specific aspect terms. It is a text analysis technique that extracts and separates each aspect term and identifies the sentiment polarity associated with each aspect term. Bangla is the seventh most native language in the world. Almost 230 million people are spoken in Bangla. So, sentiment analysis in the Bangla language is considered as crucial and well-timed research topics. Recently, aspect-based sentiment analysis is progressing because of identifying fine-grained opinion polarity associated with specific aspect terms. But due to lack of proper resources like annotated dataset, corpora, etc. As-pect-based sentiment analysis is a complicated task. In this thesis, we have used two publicly available datasets named cricket and restaurant. To perform aspect category extraction known as one of ASBS's task, we have conducted our experiments based on two recent studies from 2018 and 2020. In those studies, researchers used some conventional supervised learning algorithms as 2018 [SVM, RF, KNN] and 2020 [SVM, RF, KNN, NB, LR]. In our work, after pre-processing the dataset, we applied a new technique named PSPWA (Priority Sentence Part Weight Assignment) on the dataset. After that, we used a few conventional supervised learning algorithms (SVM, KNN, RF, LR, and NB) to demonstrate results. Whereas our dataset is imbalanced, we considered F1-score as a performance measure factor. Then we compared our results with the previous research works on the same dataset. In the cricket dataset, SVM, KNN, LR, NB performed better than two existing works during the experiment and resulting F1 score of 46%, 31%, 43%, 32%. In the restaurant dataset, SVM, LR, NB performed better than two existing works during the experiment and resulting in an F1 score of 49%, 44%, 34%. For both cricket and restaurant dataset, SVM achieved the best F1 score between all algorithms and scored 46% and 49% respectively.
Light warps for enhanced surface depiction: Supplementary results

Toler-Franklin et al. [2007] have introduced RGBN images to create different styles (such as toon shading, line drawing, curvature shading, and exaggerated shading) for shape depiction. An RGBN image is the combination of a classical color image used as texture, the corresponding normal field, and an occlusion map that can be used as silhouette weights. Such a representation can thus be directly used with our approach (see Figure 3).
II Sense of Place and Sonic Topologies: Towards a Telemimetic Sublime in the Data Landscape

Global warming implicates the increasing atmospheric level of carbon as a primary agent. Nonetheless, the total worldwide carbon budget, which takes into account all known petrochemical usage on an annual basis, shows that terrestrial systems must be absorbing more carbon than we realize. According to the carbon budget mathematical models, carbon concentrations should be increasing faster than they actually are. The hypothesis is that the carbon flux patterns of selected microsystems worldwide may reveal conditions under which more carbon is been absorbed than is being released. On and near Konza Prairie, since 1997, diurnal and annual data are collected as "eddy correlation" or "eddy covariant" flux measurements. From two of the sites, a located on the Rannels Ranch next to the Konza field station, wireless net carries the live data online for collection and analysis. Jay Ham, PhD, agronomist and climatologist, conducts research into carbon flux dynamics relative to models of climate change, at Kansas State University. He is the scientific partner for the present project, Slipstreamkonza. Slipstreamkonza addresses aesthetics of digital data expression of land as a breathing ecosystem. The time based data stream of carbon flux is interpreted as rhythmic, virtual expression of sound and image in net based and spatial installation.
Functions of CXC chemokines as biomarkers and potential therapeutic targets in the hepatocellular carcinoma microenvironment

Background Several studies have indicated that CXC chemokines influence the prognosis and therapy in patients with hepatocellular carcinoma (HCC). However, there are limited studies on the roles of CXC chemokines in HCC based on data acquired from various databases. This study aimed to conduct an in-depth and comprehensive bioinformatic analysis of the expression and functions of CXC chemokines in HCC. Methods Data was obtained from various databases including ONCOMINE, UALCAN, STRING, GeneMinia, DAVID, Kaplan-Meier plotter, TIMER, GSCALite and NetworkAnalyst for the analysis of the expression and functions of the CXC chemokines in HCC. Results Analysis of the differential expression levels of CXC chemokines between HCC and adjacent normal tissues revealed that the mRNA expression levels of CXCL1/2/5/6/7/12/14 were significantly lower in HCC tissues than those in adjacent normal tissues, whereas the mRNA expression levels of CXCL9/16/17 were significantly higher in HCC tissues. Analysis of the relationship between CXC chemokines and overall survival revealed that high mRNA expression levels of CXCL1/3/5/6/8 were associated with poor overall survival, whereas high mRNA expression levels of CXCL2/4/7/9/10/12 were associated with better overall survival. The functions of CXC chemokines and related genes were associated with cytokine-cytokine receptor interactions and chemokine signaling pathway. Analysis of the association between CXC chemokines and activity of cancer pathways indicated that the DNA damage response and hormone androgen receptor (AR) signaling pathways were inhibited, whereas apoptosis, epithelial-mesenchymal transition (EMT) and Ras/mitogen-activated protein kinase (MAPK) signaling pathways were activated. The expression of CXC chemokines was positively correlated with the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells). Conclusions This study has demonstrated that CXC chemokines can influence survival of patients with HCC by recruiting different types of immune cells into the tumor microenvironment.
IODetector

The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper-layer mobile applications. In this article, we present IODetector: a lightweight sensing service that runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector primarily leverages lightweight sensing resources, such as light sensors, magnetism sensors, and cell tower signals. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 34 traces that include 133 different places during a 6-week period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
Endophytic fungal diversity of endemic carnivorous plant Nepenthes khasiana in Meghalaya, India

The present investigation deals with the isolation of endophytic fungi from leaf, stem, root and pitcher cup tissue segments of the endemic carnivorous plant Nepenthes khaisana collected from its natural habitat for a period of one year at monthly intervals. Out of 576 tissue segments inoculated, a total of 39 fungal endophytes along with mycelia sterilia were isolated from the host plant. To assess the diversity of fungal endophytes, the colonization frequency (%CF) was first recorded using past software and MS excel. The fungal isolates were mainly composed of the phylum Ascomycota, followed by Zygomycota and Oomycota. The highest percentage colonization frequency on an average of three replicates were recorded in pitcher cup tissues followed by root, stem and least was recorded in leaf of the host plant. Among the isolates, Globisporangium irregulare (83.33%) showed high % CF in leaf, Juxtiphoma eupyrena (83.33%) reported to have maximum % CF in the stem, Talaromyces ruber (66.66%) was recorded high % CF in root and mycelia sterilia (white) were showed the highest % CF in the segments of leaf. The diversity index analyses of Shannon-Weiner, Simpson*s index, species richness and species evenness of diversity showed that leaf of N. Khasiana has the highest diversity than the other parts of the plant. So, with the help of the present finding, we conclude that the distribution of fungal endophytes and their % colonization frequencies vary within different tissues of the host plant and thus, this confirms tissue specificity nature of endophytic fungi.
Sample autocovariance operators of long-range dependent Hilbert space-valued linear processes

This article considers linear processes in a separable Hilbert space under long-range dependence. A central limit theorem for the sample autocovariance operators at different time lags in the space of Hilbert-Schmidt operators is investigated. The limiting process follows either a Hilbert space-valued Gaussian process or a non-Gaussian process, represented in terms of stochastic integrals.
Sentiment Analysis based Error Detection for Large-Scale Systems

Today’s large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers’ system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f-score of 91% and 96% respectively, while state of the art ML/deep learning model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.