Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Evolution Trend of Carbon Emissions under Exogenous Shocks: Evidences from Russia

Abstract: At present, carbon intensity in the economy has become a realistic problem faced by many countries. Decarbonization and green development have gradually become one of the main trends in the world, and major countries around the world have put forward carbon neutrality targets. Russia is one of the largest greenhouse gas emitters in the world. Therefore, under the current international situation of The Russia-Ukraine war and the exogenous impact on Russia's economy and finance, it is of great significance to study Russia's carbon emissions for Russia's economic development, environmental protection and global green development. In recent years, Russia's carbon intensity has remained high, which may be caused by several factors, such as the decline in actual investment level, single industrial structure, excessive dependence on oil and gas industry, external shocks to the Russian economy in 2014 and other macroeconomic factors. The purpose of this study is to identify trends in carbon intensity during the period of exogenous shocks to the Russian economy and financial sector from 2014 to 2018, and to explain the causes. First, the synthetic control method is used to examine the changes in Russia's carbon intensity since 2014, and the results show that since 2014, Russia's carbon intensity has increased significantly; Secondly, using the mediation effect analysis model to test the impact mechanism, it is found that since 2014, the Russian industrial structure has not been actively improved, but instead increased its dependence on the resource industry, thereby increasing carbon emissions. Combined with the tail effect analysis, Russia has faced significant economic pressure, and its carbon intensity is unlikely to return to the state before 2014 in the short term.
Elevation and cation exchange capacity determine diversity of ferns in a low-montane tropical rainforest in Ecuador

Abstract Ferns (Polypodiophyta) are an abundant floral element of the tropics with high sensitivity to environmental conditions and good indicators of overall biodiversity. Here, we set out to identify which geochemical factors determine fern diversity in a low-montane tropical rainforest in Eastern Ecuador. We conducted a field survey of high-spatial resolution completing a comprehensive fern inventory across two elevational ranges, combined with biochemical characterisation of the underlying soils. While α-diversity was negatively correlated with cation exchange capacity (CEC) and with elevation, β-diversity increased with elevation and with geographic distance, as predicted. Our results confirm that ferns have a high sensitivity to both elevational and environmental gradients, where the latter in this study was derived from enhanced aluminium, iron and calcium contents in some of the studied soils. Further monitoring of fern communities could therefore help to better understand and predict how environmental change may impact biodiversity, with a particular focus on threats potentially arising from toxic elements being released in tropical soils through modified soil CEC.
IMPLICIT MONTE CARLO RADIATION TRANSPORT IN MULTI- PHYSICS SIMULATIONS

This paper addresses the use of the Implicit Monte Carlo radiation transport technique in multiphysics simulations. We discuss how the features of this radiation transport algorithm contribute to and impact simulations involving hydrodynamics, laser ray tracing, and thermonuclear burn. We also discuss modifications of the algorithm needed for massively parallel computations, and address the issue of load balance.
A fiber grating dual-parameter sensor of ditrigon cantilever beam for measurement of strain and temperature

To solve the problem of cross sensitization when the strain and the temperature are measured at the same time, we propose a structure of a ditrigon cantilever beam of uniform strength, realize a sensor structure in which the strain and the temperature can measured at the same time by a single fiber grating. The beam is bended and elongate the grating sticked on the surface of the beam, the reflectance spectrum from the grating is splitted into a double-peak spectrum. The strain can be measured by the space of the double peak. The variation of temperature is converted into the whole displacement of the reflectance spectrum.
Combining GitHub, Chat, and Peer Evaluation Data to Assess Individual Contributions to Team Software Development Projects

Assessing team software development projects is notoriously difficult and typically based on subjective metrics. To help make assessments more rigorous, we conducted an empirical study to explore relationships between subjective metrics based on peer and instructor assessments, and objective metrics based on GitHub and chat data. We studied 23 undergraduate software teams (n = 117 students) from two undergraduate computing courses at two North American research universities. We collected data on teams’ (a) commits and issues from their GitHub code repositories, (b) chat messages from their Slack and Microsoft Teams channels, (c) peer evaluation ratings from the CATME peer evaluation system, and (d) individual assignment grades from the courses. We derived metrics from (a) and (b) to measure both individual team members’ contributions to the team, and the equality of team members’ contributions. We then performed Pearson analyses to identify correlations among the metrics, peer evaluation ratings, and individual grades. We found significant positive correlations between team members’ GitHub contributions, chat contributions, and peer evaluation ratings. In addition, the equality of teams’ GitHub contributions was positively correlated with teams’ average peer evaluation ratings and negatively correlated with the variance in those ratings. However, no such positive correlations were detected between the equality of teams’ chat contributions and their peer evaluation ratings. Our study extends previous research results by providing evidence that (a) team members’ chat contributions, like their GitHub contributions, are positively correlated with their peer evaluation ratings; (b) team members’ chat contributions are positively correlated with their GitHub contributions; and (c) the equality of team’ GitHub contributions is positively correlated with their peer evaluation ratings. These results lend further support to the idea that combining objective and subjective metrics can make the assessment of team software projects more comprehensive and rigorous.
Keeping Up with the Emotets: Tracking a Multi-infrastructure Botnet

Throughout its eight-year history, Emotet has caused substantial damage. This threat reappeared at the beginning of 2022 following a take-down by law enforcement in November 2021. Emotet is arguably one of the most notorious advanced persistent threats, causing substantial damage during its earlier phases and continuing to pose a danger to organizations everywhere. In this article, we present a longitudinal study of several waves of Emotet-based attacks that we observed in VMware’s customer telemetry. By analyzing Emotet’s software development life cycle, we were able to dissect how it quickly changes its command and control (C2) infrastructure, obfuscates its configuration, adapts and tests its evasive execution chains, deploys different attack vectors at different stages, laterally propagates, and continues to evolve using numerous tactics and techniques.
Performance Trade-offs in Transactional Systems

During the last decade a number of systems supporting different forms of distributed transactions have been proposed, each implementing a different performance trade-off in the design space. In this paper we collect the performance features that have been identified by previous works and offer a systematic analysis of known results regarding the impossibility of achieving certain combinations of desirable properties along these dimensions. We also compare previous transactional systems in the light of this set of desirable performance aspects. Finally we discuss how certain combinations of features may be leverage to guide new transactional system designs.
A Novel Clustering Algorithm for Monitoring Paddy Growth Through Satellite Image Processing.

In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encountered when monitoring rice crops through satellite images acquired by remote sensing. This study developed a deep learning method-assisted clustering algorithm (DLCA) which helps to improve the paddy growth identification process and enables the transparency of agricultural activity. Convolution neural network (CNN) has been utilized to identify crop growth which helps to understand drip irrigation and water scarcity for a particular crop. The experimental research shows that the proposed model is improved in identifying the paddy growth, soil availability, high cost of fertilizers, and soil degradation in monitoring paddy crop production through the satellite image process. Overall, the findings of the experiments have been carried out, and the proposed DLCA to achieve a lower error rate of 0.03 and high accuracy of 98.52%, MCC attains 98.43%, and F1-score 99.02% compared to other popular methods.
It Takes Two: Using Co-creation to Facilitate Child-Robot Co-regulation

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot—a need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable, the robot takes control, undermining children’s ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light animations for the robot to use during their conversation. A crucial additional feature is that children are able to coordinate their involvement of the co-creation process. Results from a user study (n = 59 school children, 7–11 years old) showed that the co-creation activity successfully facilitated co-regulation by improving children’s agency. It also positively affected the acceptance of the robot. We furthermore identified five distinct profiles detailing the different needs and motivations children have for the level of involvement they chose during the co-creation process.
Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.
Time-course of antipredator behavioral changes induced by the helminth Pomphorhynchus laevis in its intermediate host Gammarus pulex: the switch in manipulation according to parasite developmental stage differs between behaviors

Many trophically transmitted parasites with complex life cycles manipulate their intermediate host’s antipredatory defenses in ways facilitating their transmission to final host by predation. Some parasites also protect the intermediate host from predation when noninfective during its ontogeny. The acanthocephalan Pomphorynchus laevis, a fish intestinal helminth, infecting freshwater gammarid amphipods as intermediate hosts, is using such a strategy of protection-then-exposure to predation by the definitive host. However, the whole time-course of this sequence of behavioral switch is not yet known, and only one antipredator behavior has been studied to date. Here we show that the protective part of this manipulation begins quite late during the parasite ontogeny, suggesting that the advantages overpass the costs induced by this protective manipulation only at this stage. We confirmed that the refuge use behavior is showing a switch in the few days following the stage mature for the definitive host (switching from overuse to underuse). However, such a switch was not observed for the gammarids activity rate, a behavior also known to make the host less conspicuous to predators when weak. While we predicted a low activity during early development stages, then a switch to high activity, we observed general decrease in activity during the parasite ontogeny. The possible causes for this discrepancy between behaviors are discussed. All these behavioral changes were observed mostly when animals were tested in water scented by potential predators (here brown trout), suggesting condition-dependent manipulation.
Multiscale Model for Ion Transport in Cellular Media and Applications in Smooth Muscle Cells

Ion transport in biological tissues is crucial in the study of many biological and pathological problems. Some multi-cellular structures, like the smooth muscles on vessel walls, can be treated as periodic bi-domain structures consisting of the intracellular space (ICS) and extracellular space (ECS) with semipermeable membranes in between. In this work, we first use a multi-scale asymptotic method to derive a macroscopic homogenized bidomain model from the microscopic electro-neutral (EN) model with different diffusion coefficients and nonlinear interface conditions. Then, the obtained homogenized model is applied to study ion transportation and micro-circulation in multi-celluar tissues under the impact of agonists, an internal calcium source, and extracellular potassium. Our model serves as a useful bridge between existing ordinary differential equation models and partial differential models that take into consideration spatial variation. On the one hand, numerical results show that ECS variables are almost invariant in the first two scenarios and confirm the validity of existing single-domain models, which treat variables in the ECS as constants. On the other hand, only the bidomain model is applicable to consider the effect of local extracellular potassium. Finally, the membrane potential of syncytia formed by connected cells is found to play an important role in the propagation of oscillation from the stimulus region to the non-stimulus region. Author summary Smooth muscle cells (SMCs) play a vital role in neurovascular coupling, which is the mechanism by which changes in neural activity are linked to alterations in blood flow. Dysfunctional SMCs can have significant implications for human health. The activation of SMCs is primarily regulated by the intracellular concentration of calcium ions (Ca2+). A multi-scale model for ion transport in multicellular tissue with varying connectivity has been proposed to investigate SMC activation under different stimuli. The simulation results confirm the critical role of gap junctions in wave propagation and vasoconstriction in the vessel wall. The blockage of gap junctions prevents the spread of the wave. Furthermore, the propagation of membrane potential is the primary cause of wave propagation.
AbNatiV: VQ-VAE-based assessment of antibody and nanobody nativeness for engineering, selection, and computational design

Monoclonal antibodies have emerged as a key class of therapeutics, and nanobodies are rapidly increasing in popularity following the approval of the first nanobody drug in 2019, yet the therapeutic development of these biologics remains a challenge. Computational design is a promising technology to accelerate antibody discovery and optimisation and to address some critical limitations. However, despite the availability of established in vitro directed evolution technologies that are relatively fast and cheap to deploy, the gold standard for generating therapeutic antibodies remains discovery from animal immunization or patients. Immune-system derived antibodies tend to have favourable properties in vivo, including long half-life, low cross reactivity with self-antigens, and low toxicity, which raises the question of whether computational design will ever be able to generate such antibodies. Here, we present AbNatiV, a deep-learning tool for assessing the nativeness of antibodies and nanobodies, i.e., their likelihood of belonging to the distribution of immune-system derived human antibodies or camelid nanobodies. AbNatiV is trained on curated datasets of human antibodies or camelid nanobodies and can accurately predict the nativeness of a given sequence. We show that AbNatiV can be used to design antibodies and nanobodies
The potential of Senicapoc, a KCNN4 inhibitor, for the prevention and treatment of breast cancer

Background Genome-wide association studies have identified a breast cancer risk locus at 19q13.31. The candidate causal variants at this locus are located in the first exon of KCNN4. KCNN4, which regulates membrane potential and Ca2+ signaling, is a good candidate for drug repositioning because its inhibitor, Senicapoc, has been shown to be well tolerated in Phase-II and -III clinical trials for asthma and sickle cell anemia. Methods We evaluated public mRNA expression data to determine whether the allele at 19q13.31 associated with increased breast cancer risk was associated with KCNN4 expression. We also used immunohistochemistry to evaluate the relationship between KCNN4 protein expression and breast cancer survival. We then used Senicapoc in two murine mammary tumor models to determine if it would delay tumor development. We also treated mice bearing 4T1 mammary tumors with Senicapoc, by subcutaneous injection and by oral gavage. Finally we used gene editing to make deletions within Kcnn4 in 4T1 to determine whether Senicapoc had off-target effects on tumor growth. Results Analysis of the Genotype-Tissue Expression Project showed that the allele at 19q13.31 associated with increased breast cancer risk is associated with increased KCNN4 expression, suggesting that inhibiting KCNN4 might reduce breast cancer risk. Using immunohistochemistry in a large breast cancer cohort, we found that membrane and cytoplasmic expression is a marker of poor prognosis in triple negative breast cancer. We then tested the efficacy of Senicapoc to prevent and treat breast cancer. This showed that it delays the development of mammary tumors in two murine models, and slows growth of a syngeneic (4T1) model of triple negative breast cancer. Senicapoc monotherapy showed similar efficacy to anthracycline/taxane-based chemotherapy in these studies, with a stronger effect when they were combined. Conclusions These results provide a rationale for clinical testing of Senicapoc for treating, and even preventing, breast cancer.
Amphiphilic Particle-Stabilized Nanoliter Droplet Reactors with a Multimodal Portable Reader for Distributive Biomarker Quantification

Compartmentalization, leveraging microfluidics, enables highly sensitive assays; but the requirement for significant infrastructure for their design, build, and operation limits access. Newer multi-material particle-based technologies thermodynamically stabilize monodisperse droplets as individual reaction compartments with simple liquid handling steps, precluding the need for expensive microfluidic equipment. Here, we further improve the accessibility of this lab on a particle technology to resource-limited settings by combining this assay system with a portable multi-modal reader, thus enabling nanoliter droplet assays in an accessible platform. We show the utility of this platform in measuring N-terminal propeptide B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, in complex medium and patient samples. We report a limit of detection of ∼0.05 ng/ml and a linear response between 0.2 – 2 ng/ml in spiked plasma samples. We also show that, owing to the plurality of measurements per sample, “swarm” sensing acquires better statistical quantitation with a portable reader. Monte Carlo simulations show the increasing capability of this platform to differentiate between negative and positive samples, i.e. below or above the clinical cut-off for acute heart failure (∼0.1ng/ml), as a function of the number of particles measured. Our platform measurements correlate with gold standard ELISA measurement in cardiac patient samples, and achieve lower variation in measurement across samples compared to the standard well plate-based ELISA. Thus, we show the capabilities of a cost-effective droplet-reader system in accurately measuring biomarkers in nanoliter droplets for diseases that disproportionally affect underserved communities in resource-limited settings.
Collecting-Gathering Biophysics of the Blackworm L. variegatus

Many organisms exhibit collecting and gathering behaviors as a foraging and survival method. Certain benthic macroinvertebrates are classified as collector-gatherers due to their collection of particulate matter as a food source, such as the aquatic oligochaete Lumbriculus variegatus (California blackworms). Blackworms demonstrate the ability to ingest organic and inorganic materials, including microplastics, but previous work has only qualitatively described their possible collecting behaviors for such materials. The mechanism through which blackworms consolidate discrete particles into a larger clumps remains unexplored quantitatively. By analyzing a group of blackworms in a large arena with an aqueous algae solution, we discover that their relative collecting efficiency is proportional to population size. Examining individual blackworms under a microscope reveals that both algae and microplastics physically adhere to the worm’s body due to external mucus secretions, which cause the materials to clump around the worm. We observe that this clumping reduces the worm’s exploration of its environment, potentially due to thigmotaxis. To validate the observed biophysical mechanisms, we create an active polymer model of a worm moving in a field of particulate debris with a short-range attractive force on its body to simulate its adhesive nature. We find that the attractive force increases gathering efficiency. This study offers insights into the mechanisms of collecting-gathering behavior, informing the design of robotic systems, as well as advancing our understanding the ecological impacts of microplastics on benthic invertebrates.