Miracle-dz/UniFAR-FTUs huggingface dataset:
Greenhouse gas emissions from alternative futures of deforestation and agricultural management in the southern Amazon

The Brazilian Amazon is one of the most rapidly developing agricultural areas in the world and represents a potentially large future source of greenhouse gases from land clearing and subsequent agricultural management. In an integrated approach, we estimate the greenhouse gas dynamics of natural ecosystems and agricultural ecosystems after clearing in the context of a future climate. We examine scenarios of deforestation and postclearing land use to estimate the future (2006–2050) impacts on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions from the agricultural frontier state of Mato Grosso, using a process-based biogeochemistry model, the Terrestrial Ecosystems Model (TEM). We estimate a net emission of greenhouse gases from Mato Grosso, ranging from 2.8 to 15.9 Pg CO2-equivalents (CO2-e) from 2006 to 2050. Deforestation is the largest source of greenhouse gas emissions over this period, but land uses following clearing account for a substantial portion (24–49%) of the net greenhouse gas budget. Due to land-cover and land-use change, there is a small foregone carbon sequestration of 0.2–0.4 Pg CO2-e by natural forests and cerrado between 2006 and 2050. Both deforestation and future land-use management play important roles in the net greenhouse gas emissions of this frontier, suggesting that both should be considered in emissions policies. We find that avoided deforestation remains the best strategy for minimizing future greenhouse gas emissions from Mato Grosso.
Increased levels of circulating interleukin 6, interleukin 8, C-reactive protein, and risk of lung cancer.

BACKGROUND Previous studies that were based primarily on small numbers of patients suggested that certain circulating proinflammatory cytokines may be associated with lung cancer; however, large independent studies are lacking. METHODS Associations between serum interleukin 6 (IL-6) and interleukin 8 (IL-8) levels and lung cancer were analyzed among 270 case patients and 296 control subjects participating in the National Cancer Institute-Maryland (NCI-MD) case-control study. Results were validated in 532 case patients and 595 control subjects in a nested case-control study within the prospective Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Association with C-reactive protein (CRP), a systemic inflammation biomarker, was also analyzed. Associations between biomarkers and lung cancer were estimated using logistic regression models adjusted for smoking, stage, histology, age, and sex. The 10-year standardized absolute risks of lung cancer were estimated using a weighted Cox regression model. RESULTS Serum IL-6 and IL-8 levels in the highest quartile were associated with lung cancer in the NCI-MD study (IL-6, odds ratio [OR] = 3.29, 95% confidence interval [CI] = 1.88 to 5.77; IL-8, OR = 2.06, 95% CI = 1.19 to 3.57) and with lung cancer risk in the PLCO study (IL-6, OR = 1.48, 95% CI = 1.04 to 2.10; IL-8, OR = 1.57, 95% CI = 1.10 to 2.24), compared with the lowest quartile. In the PLCO study, increased IL-6 levels were only associated with lung cancer diagnosed within 2 years of blood collection, whereas increased IL-8 levels were associated with lung cancer diagnosed more than 2 years after blood collection (OR = 1.57, 95% CI = 1.15 to 2.13). The 10-year standardized absolute risks of lung cancer in the PLCO study were highest among current smokers with high IL-8 and CRP levels (absolute risk = 8.01%, 95% CI = 5.77% to 11.05%). CONCLUSIONS Although increased levels of both serum IL-6 and IL-8 are associated with lung cancer, only IL-8 levels are associated with lung cancer risk several years before diagnosis. Combination of IL-8 and CRP are more robust biomarkers than either marker alone in predicting subsequent lung cancer.
Models of Cognitive Ability and Emotion Can Better Inform Contemporary Emotional Intelligence Frameworks

Emotional intelligence (EI) stands at the nexus between intelligence and emotion disciplines, and we outline how EI research might be better integrated within both theoretical frameworks. From the former discipline, empirical research focused upon whether EI is an intelligence and what type of intelligence it constitutes. It is clear that ability-based tests of EI form a group factor of cognitive abilities that may be integrated into the Cattell–Horn–Carroll framework; less clear is the lower order factor structure of EI. From the latter discipline, research linking EI with theoretical frameworks from emotion research remain relatively sparse. Emotion regulation and appraisal theory may be key to explain how EI may reflect different processes. We propose a research agenda to advance the EI study.
Stability and persistence of food webs with omnivory: Is there a general pattern?

The relationship between omnivory and stability has been the subject of a longstanding debate in ecology. Early theory predicted that omnivory would decrease the probability of food webs being stable. While early empirical data appeared to support the prediction that omnivory should be rare, detailed study of food webs later revealed that omnivory is ubiquitous across ecosystems and taxa. Recent years have seen renewed interest in the omnivory-stability debate, and advances in mechanistic non-equilibrium models demonstrated that omnivory can both increase and decrease stability. Current efforts have therefore focused on identifying biological mechanisms that promote the persistence of food webs with omnivory. We synthesize recent evidence that omnivory often stabilizes food webs when it occurs as life-history omnivory, when prey experience reduced predation rates due to refuges or adaptive antipredator defences, and when omnivores interfere with each other or feed adaptively. Empirical research has lagged behind theory and there remains a shortage of studies directly measuring the stability of diverse natural communities that vary in the number and strength of omnivorous interactions. Early microcosm experiments indicated a narrow range of conditions for the persistence of simple omnivorous modules, while studies of omnivory embedded within larger natural networks have demonstrated its stabilizing effects. These new findings alter our view of food web dynamics and show that rather than looking for a simple and general omnivory-stability relationship, we should focus on identifying conditions under which omnivory is a stabilizing feature of more complex natural systems.
Genomic Editing of Non-Coding RNA Genes with CRISPR/Cas9 Ushers in a Potential Novel Approach to Study and Treat Schizophrenia

Schizophrenia is a genetically related mental illness, in which the majority of genetic alterations occur in the non-coding regions of the human genome. In the past decade, a growing number of regulatory non-coding RNAs (ncRNAs) including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been identified to be strongly associated with schizophrenia. However, the studies of these ncRNAs in the pathophysiology of schizophrenia and the reverting of their genetic defects in restoration of the normal phenotype have been hampered by insufficient technology to manipulate these ncRNA genes effectively as well as a lack of appropriate animal models. Most recently, a revolutionary gene editing technology known as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated nuclease 9 (Cas9; CRISPR/Cas9) has been developed that enable researchers to overcome these challenges. In this review article, we mainly focus on the schizophrenia-related ncRNAs and the use of CRISPR/Cas9-mediated editing on the non-coding regions of the genomic DNA in proving causal relationship between the genetic defects and the pathophysiology of schizophrenia. We subsequently discuss the potential of translating this advanced technology into a clinical therapy for schizophrenia, although the CRISPR/Cas9 technology is currently still in its infancy and immature to put into use in the treatment of diseases. Furthermore, we suggest strategies to accelerate the pace from the bench to the bedside. This review describes the application of the powerful and feasible CRISPR/Cas9 technology to manipulate schizophrenia-associated ncRNA genes. This technology could help researchers tackle this complex health problem and perhaps other genetically related mental disorders due to the overlapping genetic alterations of schizophrenia with other mental illnesses.
Noise PSD Estimation Using Blind Source Separation in a Diffuse Noise Field

This paper considers the problem of two-channel speech enhancement with a single target speaker in a diffuse noise field and proposes a novel algorithm for estimating the power spectral density (PSD) of the background noise. First, a reference noise signal is computed as the output of a blocking matrix based on blind source separation (BSS). Then the ratio between the target noise PSD and the reference noise PSD is calculated by exploiting the separation matrix obtained from BSS and the spatial coherence of the noise field which is assumed to be known. The proposed algorithm can also operate during speech active periods, which is advantageous especially in fast-changing non-stationary noise fields. Experimental results show that the proposed algorithm outperforms existing methods, both in terms of noise PSD estimation and noise suppression.
Based on Mobile Internet Application Security Sensitive CS-SVM Technology Research

The security issue can't be ignored in the applications of Mobile Internet. the present mechanism is very difficult to secure the complex and sensitive data information in the applications of Mobile Internet. Using the method of support vector machine to build the multi-classifier, with the consideration of the cost of classification, different sensitivity is given to different types of sensitive data by classification. Then, adopting appropriate protective measures based on the sensitivity levels to protect the data information in the application of Mobile Internet.
Collective destabilising phenomena in socio-economic systems

This thesis reports on two different research topics belonging to the same project. The first research avenue, which is thoroughly explained in chapter 3, is a modelling approach to the dynamics of trust in a networked society. The second, whose description can be found in chapter 4, is an experimental approach to study human decisions when people trade an asset with a positive average growth per period in a controlled laboratory environment. One of the common links between these two topics is collective action, which is a key player in a number of phenomena, for example in the dynamics of panic, bankruptcies and, consequently, systemic risk. Therefore, the author hopes that this work will contribute to the study of collective action phenomena, especially in the field of quantitative finance, in which it is more likely that the specific findings from the above mentioned trust model and trading experiment can be used in their present form.
Mobile robot localization based on consecutive range sensor scanning and optical flow measurements

Estimating the configuration is one of the key issues for successful mobile robot navigation. Dead-reckoning is simple and fast but it can lead to inaccurate or unreliable estimates. Range sensors are widely used in known environment for localization, but it may not always work due to sensor range limit. Optical flow sensor such as the optical mouse gives accurate relative movement but it requires strict vertical placement. This paper introduces new way of estimating robot configuration by combining the consecutive range sensor data and optical flow information in known environment. Feature extraction from range sensor information and estimation of the relative movement using optical flow sensors are investigated. The proposed method is implemented and tested with a mobile robot
GA-tuned fuzzy logic control of knee-FES-ergometer for knee swinging exercise

Knee-FES-ergometer for knee swinging exercise is introduced as a hybrid exercise for restoration of function of the knee for stroke patients through the application of functional electrical stimulation (FES). The aim of the new knee-FES-ergometer is to provide high intensity knee swinging exercise. It is able to reduce required electrical stimulation and will able to elongate the exercise duration while avoiding early muscle fatigue. Fuzzy logic control (FLC) is used to control the knee trajectory for the purpose of smooth knee swinging exercise. However, conventional FLC rely on human experiences and trial and error for parameter identifications. In this work, a genetic algorithm (GA) is used to tune the FLC to maintain a smooth swinging exercise. The performance of the proposed GA tuned FLC is compared with a manually tuned FLC. Results shows that the GA tuned FLC offers encouragingly better performance.
Vision-based pedestrian detection: will ants help?

This work presents the vision-based system for detecting pedestrians in road environments implemented on the ARGO prototype vehicle developed by the University of Parma. The system is aimed at the localization of pedestrians in various poses, positions and clothing, and is not limited to moving people. Initially, attentive vision techniques relying on the search for specific characteristics of pedestrians such as vertical symmetry and strong presence of edges, allow to select interesting regions likely to contain pedestrians. Then, such candidates areas are validated verifying the actual presence of pedestrians by means of an shape detection technique based on the application of autonomous agents.
Logic synthesis for programmable gate arrays

The problem of combinational logic synthesis is addressed for two interesting and popular classes of programmable gate array architecture: table-lookup and multiplexer-based. The constraints imposed by some of these architectures require new algorithms for minimization of the number of basic blocks of the target architecture, taking into account the wiring resources. The presented algorithms are general and can be used for both of the above-mentioned architectures. <<ETX>>
Identity crisis: a theoretical analysis of ‘team identification’ research

ABSTRACT Research question Existing team identification research uses various definitions, conceptualisations, and theoretical frameworks. In this paper, we provide a theoretical analysis of previous research using the two dominant theoretical approaches: identity theory and the social identity approach. Our primary purpose is to provide a theoretical framework for the on-going study of ‘team’ identification in sport management research. Findings Scholars have used identity theory (role) and the social identity approach (group) in their quest to understand team identification, however, limited attention has been paid to the differences between the two frameworks. We focus on two aspects of role and group identification that epitomise divergence in terms of analytical focus and explanations for behaviour: the basis for identification and salience. Implications The manuscript concludes with three recommendations for future research. First, with the aim of making future research more specific, we recommend the use of fan (spectator) identification in studies using identity theory and team identification (organisation/brand) in studies exploring the influence of group identity. Second, we outline definitions for role (fan) and group (team) identification in sport research. Finally, we reflect on the measurement of team identification.
Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?

The paper proposes a new measure of exogenous oil supply shocks. The timing, the magnitude, and the sign of this measure may differ greatly from current state-of-the-art estimates. It is shown that only a small fraction of the observed oil price increases during oil crisis periods can be attributed to exogenous oil production disruptions. Exogenous oil supply shocks cause a sharp drop of U.S. real GDP growth after five quarters rather than an immediate and sustained reduction in economic growth and a spike in CPI inflation after three quarters. Overall, exogenous oil supply shocks made remarkably little difference for the evolution of the U.S. economy since the 1970s, although they did matter for some historical episodes.
A Genetic Algorithm for the Resolution of Superimposed Motor Unit Action Potentials

This paper presents a novel method, which aims at resolving difficult superimpositions of motor unit action potentials (MUAPs) obtained from single-channel intramuscular electromyographic recordings. Resolution is achieved by means of a genetic algorithm (GA) combined with a gradient descent method. This dual optimization scheme has been tested by means of simulations of isolated superimpositions involving two to six MUAPs, along with simulated extended signals of 10-s duration where the density reached 300 MUAPs/s. Of the hundreds of isolated superimpositions tested, more than 90% of the MUAPs were positively identified. With extended signals, identification rates of better than 85% were obtained. The GA alone accounted for up to an 8% improvement over the decomposition conducted using only template matching.
Trusting spam reporters: A reporter-based reputation system for email filtering

Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trust-maintenance component for users, based on their spam-reporting behavior. The challenge that we consider is that of maintaining a reliable system, not vulnerable to malicious users, that will provide early spam-campaign detection to reduce the costs incurred by users and systems. We report on the utility of a reputation system for spam filtering that makes use of the feedback of trustworthy users. We evaluate our proposed framework, using actual complaint feedback from a large population of users, and validate its spam-filtering performance on a collection of real email traffic over several weeks. To test the broader implication of the system, we create a model of the behavior of malicious reporters, and we simulate the system under various assumptions using a synthetic dataset.
Low complexity dense motion estimation using phase correlation

We propose a low-complexity dense motion estimation scheme particularly attractive for real-time video applications. Our scheme is based on overlapped block-based motion estimation using phase correlation at critical pixel locations. These form an irregularly sampled grid capturing salient motion features of a scene. The dense vector field is obtained by applying normalized convolution on the irregular grid. Our experiments show that our scheme provides reliable sub-pixel accuracy motion vectors corresponding to actual scene motion, outperforms differential and phase-based methods and yields comparable performance to more complex and time consuming robust motion estimation techniques.
News Admins are users too

In this paper we discuss some of the tasks administrators of Usenet news servers are dealing with. Although significant progress has been made in regard to server technology (both hardware and software) most news server administration tools still are unixoid, text−based tools. Traffic visualizations are commonly used but graphical interfaces as they might be envisioned by HCI people hardly exist. We speculate about the "mental image" news administrators might have and if a high− level language / graphical programming environment like LabView were useful to the realm of Usenet servers.
The Effect of Humidity on Partial Discharge Measurement

This paper is a study of the effect of humidity on partial discharge measurement in rotating machine. As we know, partial discharge measurement is effected by several factors and the humidity is one of the most important factors. To study the effect of humidity on partial discharge test, slot defect stator bar models were manufactured for data acquisition at 40%, 60% and 70% relative humidity respectively. The data were then analyzed by visual inspection to see the difference of three dimension distribution of Hn( (phi,q) and two dimension distribution of Hqmax(phi), Hqn(phi), Hn (phi), H(q) and H(p). Statistical values were also calculated in order to compare the trend of those parameters at each level of relative humidity. The test result shows that visual inspection in the same stator bar model at each level of relative humidity has different patterns. In addition, the statistical parameters change upon the variation of relative humidity. Therefore, partial discharge measurement without consideration on humidity can lead to misinterpretation
A novel gamma correction approach using optimally clipped sub-equalization for dark image enhancement

In this paper, an efficient statistical approach employing a highly adaptive gamma correction based on adaptively clipped and locally equalized histogram using mean-median statistical pair, is presented for the enhancement of low contrast dark images without losing their intrinsic features. For this purpose, linearly stretched intensity range segmentation, first based on median and mean distribution sub-histograms are derived for local equalization after optimal clipping. Later on, non-linear transformational mapping has been imposed by suitable gamma-correction using the required gamma value-set, which itself is derived by cumulative distribution of the intensity values in adaptively equalized histogram. The proposed methodology clearly outperforms the other state-of-the-art methods in terms of complexity as well as quantitative and qualitative performance; and hence, can be appreciably used for a wide and dynamic range of image-database belonging to various domains ranging from biomedical images to remotely sensed satellite images.
nalysis of scalability for AODV routing protocol in wireless ensor networks inod

This paper presents preliminary work to address scalability concern over AODV protocol in wireless sensor network. Firstly, we discussed the scalability design issues with related work in context of wireless sensor networks (WSN). Following, we designed and illustrated wireless sensor network model. Finally, significance of scalability on the behaviour of application, MAC, transport and physical layer performance
Genetic algorithm-piecewise support vector machine model for short term wind power prediction

Short term wind power prediction is one of the effective ways to cope with the operational problems caused by the variability of the wind energy resource when a large penetration of wind power is integrated into electric power systems. In this paper, a model combining a Genetic Algorithm with a Piecewise Support Vector Machine (GA-PSVM) is developed that improves the precision of short term wind power prediction systems based on power curves of the wind turbine generator systems. A Genetic Algorithm (GA) is used to search automatically for the parameters of a Piecewise Support Vector Machine (PSVM) model. The resulting GA-PSVM model can be used to predict wind power generation from one to six hours ahead. Operational data from a wind farm in North China are used to evaluate the proposed model. The results show that the mean relative errors (MRE) of GA-PSVM model are 2.03% lower than that of the standard SVM model applied to the same data set.