Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction Technique for the Shallow-Buried Subway Tunnel with Large-Section by Long-Distance Horizontal Ground Freezing Method and Undermining Method

Based on the artificially frozen soil test,finite element numerical simulation and large-scale physical model test,the construction technology of long-distance horizontal ground freezing method and undermining method adopted for the construction of shallow-buried subway tunnel with large-section was studied.The physical and mechanical indexes of the frozen soil were obtained after the artificially frozen soil test on the in-situ undisturbed soil sample.The finite element numerical analysis results indicate that the strength and the stability of the frozen wall are improved along with the increase of the frozen wall thickness.The results of the large-scale physical model test show that both the frost heaving and the thawing settlement increase obviously with the thickness of the frozen wall.It is predicted that when the thickness of the frozen wall is 4.5 m,the surface frost heaving and the thawing settlement both have exceeded the allowable values in the practical engineering.The design and optimization of the freezing construction scheme were conducted according to the above research results.During the construction process,the connection mode was improved for the long-distance horizontal freezing pipe,and the new technology of drilling with simultaneous casing was adopted to ensure the completion quality of the freezing holes.The surface frost heaving and thawing settlement were effectively controlled by several technical measures,such as applying pressure relief vents,increasing brine flow,tracing and grouting the thawing settlement,which ensured the successful completion of the engineering.
Influence of Absorption Cross-Sections on Retrieving the Ozone Vertical Distribution at the Siberian Lidar Station

The purpose of this paper is to study how the application of different sets of absorption cross-sections influence the ozone vertical distribution (OVD) in the upper layers of the troposphere—stratosphere in the altitude range ~(5–45) km, using a differential absorption lidar (DIAL), operating at the sensing wavelengths 299/341 nm and 308/353 nm. We analyzed the results of lidar measurements of OVD obtained in 2021 using meteorological data from the IASI/MetOp satellite at the Siberian Lidar Station (SLS). The retrieval was performed using the data of four groups concerning the absorption cross-sections: Gorshelev et al., Malicet et al., SCIAMACHY, and GOME. To estimate how the absorption cross-sections influence the OVD retrieval from lidar measurements, we calculated the average deviations between the profiles retrieved using different sets both in a particular case on 2 January 2021 and throughout 2021. Our study showed that, out of the four absorption cross-section sets, the data of Gorshelev et al. should be used for long-term lidar monitoring of the ozone. These data show a more discrete dependence of the absorption cross-sections on the temperature values, which is more urgent for tropospheric and stratospheric ozone measurements.
Analog Data Assimilation for the Selection of Suitable General Circulation Models

Abstract. Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the performance of model selection based on model evidence computed using data-driven data assimilation, where dynamical models are emulated using machine learning methods. In this work, the methodology is tested with the three-variable Lorenz' model and with an intermediate complexity atmospheric general circulation model (a.k.a. the SPEEDY model). Numerical experiments show that the data-driven implementation of the model selection algorithm performs as well as the one that uses the dynamical model. The technique is able of selecting the best model among a set of possible models and also to characterize the spatio-temporal variability of the model sensitivity. Moreover, the technique is sensitive to differences in the model dynamics which are not reflected in the moments of the climatological probability distribution of the state variables. This suggests the implementation of this technique using available long-term observations and model simulations.
Slope Aspects and Elevation Influenced Herbaceous Diversity and Soil Characteristics in Tropical Forests of Indian Desert

Aim: Understanding the interactive effects of aspects and elevation on soil properties and vegetation diversity in hill forests of the desert environment is essential for devising strategies to restore such degraded hills. Place and Duration: Observations were recorded from September to October months of both 2017 and 2018 in a hill forest area in Thar Desert of Rajasthan, India. Methodology: Three-hundred-twenty plots of 1 m2 (clustered at sixty-four positions based on eight slope aspects and eight elevations) were studied for herbaceous diversity and soil properties by sorting vegetation to species level and soil sampling in each plot. Community population (P), height, soil water content (SWC), pH and organic carbon (SOC) were measured and species-richness (R), Shannon-Weiner diversity (Hꞌ), dominance (D) and evenness (J') were calculated. Results: Out of 174 species recorded from 34 families and 122 genera, 163 species showed IVI <5. Highest number of species (48) were from family Poaceae. Soil pH, SWC, SOC, P and height were greater in 2017, whereas R, Hꞌ and Jꞌ were greater in 2018. Soil pH, vegetation height and D were lowest in northeast and highest (1.04-1.54-fold) in west to southeast. SOC, SWC, R and J' were 1.16-2.35-fold greater in northeast than south aspect. P, height and H′ showed a reverse trend with 1.15-1.53-fold variation. SOC, height, R and H' increased by 1.30-2.35-fold with an increase in elevation from <230m to >600m, whereas D and pH showed a decreasing trend. The highest values of SWC, P and Jꞌ were in 800-900m, 700-800m and 600-700m respectively. Though varied with aspects, <230m area was dominated by xeric species, middle by Aristida adscensionis and higher ones by Apluda mutica/Heteropogon contortus. Conclusion: Altitude had stronger impact on all variables except SWC, which was influenced strongly by aspects making southern slopes drier than the northern slopes and influenced species structure and composition. Such areas require effective conservation, but aspect and elevation should be given due importance in devising restoration strategies for efficient management of biodiversity and mitigating climate change.
Backcasting Technical and Policy Targets for Constructing Low-Carbon Power Systems

Achieving the low-carbon transition of power systems requires the coordination of renewable energy generation and other emerging electricity technologies. Their technical indicators are changing rapidly since these technologies are in the initial stages of development. Determining the technical and policy targets for achieving the low-carbon transition is critical for regulators and power system planners. This paper defines this problem as a backcasting problem, whose concept is derived from environmental science research. We introduce it into the power system planning field and innovatively model it as a bi-level optimization problem. According to the characteristics of the backcasting problem, an improved barrier method is proposed to solve it efficiently. The effectiveness of the proposed model and algorithm are verified through case studies on a modified Garver’s 6-bus system and a realistic Northwest China power system, and they are compared with state-of-the-art solution techniques. The results of the backcasting problem provide a new perspective on the economic impacts of the low-carbon transition and are helpful in energy policy formulation for power systems.
Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.
Social Acceptability of Quarantine in Public Health Emergencies: A Systematic Review

Background: Quarantine and isolation is an effective method of controlling outbreaks of emerging and re-emerging infectious diseases. However, the effectiveness of these interventions depends on a high compliance rate, which is often compromised by multiple reasons to break quarantine or refuse isolation. In this systematic review, we highlight public attitudes and reactions towards quarantine including factors that may hinder quarantine measures during public health epidemics, public preferences for using quarantine during epidemics or infectious disease outbreaks and key considerations for the use of quarantine in public health epidemics.Methods: We searched five databases for publications on quarantine and isolation, and screened for empirical studies on social acceptability.Results: We found 17 articles that met the inclusion criteria. A review of the articles showed some factors could impede compliance with quarantine and isolation. These include the feelings of guilt and social distress, concern about loss of income, and self and social stigma and/or discrimination. On the other hand, compliance with quarantine and isolation was positively associated with perceptions of being a civic duty or fears of infecting loved ones. The articles concluded that, quarantine compliance can be enhanced through assurance of income and promoting safe interaction with loved ones during isolation/quarantine. Conclusions: This review provides public health experts, emergency planners, and policy makers with key considerations to improve public compliance with isolation and quarantine measures during public health emergencies.
Continuous two-phase in vitro co-culture model of the enthesis

The enthesis is the interfacial tissue between ligament or tendon, and bone, which connects tissues of distinctly different mechanical properties. Although ligament and enthesis injury is commonplace, the development and healing mechanisms of these tissues are yet unclear. Current models for investigating these mechanisms are primarily in vivo animal models as in vitro models have been limited. In this study, an in vitro enthesis model was developed using a modified gel aspiration-ejection (GAE) method. Continuous two-phase aligned dense collagen (ADC) hydrogels with an overlapping interface were fabricated within 2 hours. The mechanical properties of acellular two-phase ADC confirmed the continuous nature of this model, as the mechanical properties showed no significant difference compared to single-phase ADC and maintained comparable structural and mechanical characteristics of immature ligaments and unmineralized bone. Human anterior cruciate ligament (ACL) fibroblasts and human spine vertebral osteoblasts were isolated from donor tissues and were seeded to form the enthesis model. These were cultured for 14 days, at which the viability and proliferation was observed to be 85 ± 7.5% and 230 ± 52%, respectively. Histological and immunofluorescence analyses at day 14 revealed extensive cell-driven matrix remodelling, and the seeded fibroblasts and osteoblasts maintained their phenotype within their compartments of the layered co-culture model. These results demonstrate the rapid fabrication of a two-phase co-culture system that can be utilized as an in vitro model to further understand the degenerative and regenerative mechanisms within the enthesis.
PROFILING COLLOCATION USE IN ENGLISH TEXTBOOKS FOR VIETNAMESE STUDENTS

The study investigates (1) collocational profiles of an English textbook series for students from elementary to high schools in Vietnam, (2) the relevance of collocations targeted to high frequency collocation lists suggested in the literature, and (3) the recycling of the targeted collocations. An English corpus of 312,770 word tokens was built from the textbook series from which 13,292 collocations of verb-noun and 11,079 collocations of adjective-noun patterns were identified. The study found that frequencies of occurrences of collocation tokens and types increase from one grade level to another. Collocations targeted in the textbooks only cover 10.5% of the collocations recommended in an academic collocation list, and 31% were identified not high-frequency collocations. 76% of collocations are not recycled or recycled not to the point where learning is likely to occur. Implications for learning and teaching collocations and materials designing are discussed.
Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN

In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease and poor nutrition of plants. Neural networks and other AI techniques need an initial set of measurement campaigns along with extensive datasets as a training set to baseline and evolve different applications. This paper presents LoRaWAN-based greenhouse monitoring datasets over a period of nine months. The dataset has both the network and sensing information from multiple sensor nodes for tomato crops in two different greenhouse environments. The goal is to provide the research community with a dataset to evaluate performance of LoRaWAN inside a greenhouse and develop more efficient PA monitoring techniques. In this paper, we carried out an exploratory data analysis to infer crop growth by analyzing just the LoRaWAN signals and without inclusion of any extra hardware. This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse. We developed this proof of concept of joint communication and sensing by using generated dataset from the “Proefcentrum Hoogstraten” greenhouse in Belgium. Results for the proposed method yield a root mean square error of 10% in detecting the average plant height inside a greenhouse. In future, we can use this concept of landscape sensing for different supplementary use-cases and to develop optimized methods.
Daily and Seasonal Patterns in the Singing Activity of Birds in East China

The vocal behaviour of birds can change daily and seasonally. Understanding the daily and seasonal acoustic activity patterns of birds helps to understand their behavioural ecology and is crucial for designing effective monitoring protocols. We studied daily and seasonal acoustic activity patterns for a complete annual cycle of three bird species: Alström's Warbler Phylloscopus soror, Brownish-flanked Bush Warbler Horornis fortipes and Streak-breasted Scimitar Babbler Pomatorhinus ruficolli at three different sites in Yaoluoping National Nature Reserve, Anhui Province, East China. All three species displayed a peak in singing activity in the morning; however, they differed in their acoustic activity during the daytime. Alström's Warbler was vocally active for five months (April–August), with higher singing activity earlier in the season. Brownish-flanked Bush Warbler was vocally active for six months (March–August) and showed a significant variation in singing activity over time. Streak-breasted Scimitar Babbler was vocally active for nine months (March–November) and displayed a slight variation in singing activity among months at different sites. The acoustic activity of the Alström's Warbler and Brownish-flanked Bush Warbler was negatively correlated with precipitation, whereas in Streak-breasted Scimitar Babbler there was no relationship with precipitation. Overall, we found large temporal variation in singing activity within a day and between seasons. To better understand the function of the daily and seasonal pattern of singing, future studies should investigate the functioning of different song types used during daytime and morning.
Phosphoproteomic Analysis of Breast Cancer-Derived Small Extracellular Vesicles Reveals Disease-Specific Phosphorylated Enzymes

Small membrane-derived extracellular vesicles have been proposed as participating in several cancer diseases, including breast cancer (BC). We performed a phosphoproteomic analysis of breast cancer-derived small extracellular vesicles (sEVs) to provide insight into the molecular and cellular regulatory mechanisms important for breast cancer tumor progression and metastasis. We examined three cell line models for breast cancer: MCF10A (non-malignant), MCF7 (estrogen and progesterone receptor-positive, metastatic), and MDA-MB-231 (triple-negative, highly metastatic). To obtain a comprehensive overview of the sEV phosphoproteome derived from each cell line, effective phosphopeptide enrichment techniques IMAC and TiO2, followed by LC-MS/MS, were performed. The phosphoproteome was profiled to a depth of 2003 phosphopeptides, of which 207, 854, and 1335 were identified in MCF10A, MCF7, and MDA-MB-231 cell lines, respectively. Furthermore, 2450 phosphorylation sites were mapped to 855 distinct proteins, covering a wide range of functions. The identified proteins are associated with several diseases, mostly related to cancer. Among the phosphoproteins, we validated four enzymes associated with cancer and present only in sEVs isolated from MCF7 and MDA-MB-231 cell lines: ATP citrate lyase (ACLY), phosphofructokinase-M (PFKM), sirtuin-1 (SIRT1), and sirtuin-6 (SIRT6). With the exception of PFKM, the specific activity of these enzymes was significantly higher in MDA-MB-231 when compared with MCF10A-derived sEVs. This study demonstrates that sEVs contain functional metabolic enzymes that could be further explored for their potential use in early BC diagnostic and therapeutic applications.
DIAGNOSIS OF POTATO VIRUSES IN KAZAKHSTAN: MOLECULAR CHARACTERISATION OF ISOLATES

Virus infection is one of the reasons for the low potato yield in Kazakhstan. The combination of modern methods of germplasm improvement and effective virus diagnosis are the most successful measures to overcome potato viruses. Using ELISA and multiplex RT-PCR methods, potato leaf samples from the Almaty and Kostanay regions of Kazakhstan were analysed for the presence of the carlaviruses potato virus M and potato virus S, the potyvirus potato virus Y, the potexvirus potato virus X, and the polerovirus potato leaf roll virus (PVM, PVS, PVY, PVX, and PLRV, respectively). The potato virus spectrum has changed over the last twenty years, and presently carlavirusеs represent the most widespread genus. Of the leaf samples tested, 84.3% and 46.6% were infected by PVM and PVS, respectively, and co-infection by both viruses occurred. Multiplex RT-PCR assays were developed to detect the five potato viruses, and for two strains of PVS. Based on sequence analyses of the central region of the capsid protein, fourteen Kazakh PVS isolates were identified, of which eleven were classified as the ‘ordinary’ strain, and three as ‘Andean’ strains. Our findings confirmed the existence of divergent PVS strains with different origins present in Kazakhstan.
Can warning labels mitigate effects of advertising message claims in celebrity-endorsed Instagram-based electronic cigarette advertisements? Influence on social media users’ E-cigarette attitudes and behavioral intentions

ABSTRACT Electronic cigarette brands are increasingly using social media to advertise their products. This study examined effects of advertising message claim type (reduced risk [‘Healthier than regular cigarettes’], cessation [‘Quit smoking using e-cigarettes’] versus no message claim) and health warning labels (presence versus absence) in celebrity-endorsed Instagram e-cigarette brand advertisements. A 3 × 2 between-subjects experiment was conducted through an online questionnaire, with participants (N = 275) randomly assigned to 1 of 6 experimental conditions. Presence of a health warning label exerted significant main effects on attitude towards the ad, intention to use e-cigarettes, and brand attitude, and interacted with message claim type to affect these dependent measures. Health consciousness, perceived information value, and celebrity identification also significantly moderated between presence of a health warning label and attitude towards the ad, intention to use e-cigarettes, and brand attitude. Presence of health warning labels in social media-based e-cigarette ads may therefore mitigate potential effects of positive advertising claims in these ads. Implications for regulatory agencies and future research are discussed.
A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking.To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.