Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quantitative Sustainability Assessment for Mine Closure and Repurposing Alternatives in Colorado, USA

Responsible mine closure and repurposing are key to contributing to sustainable development by ensuring successful environmental rehabilitation and reducing socioeconomic risks. However, mine closure has primarily focused on remediation and rehabilitation of mined lands with limited consideration of stakeholder perspectives and the broader social, economic, and cultural impacts of closure. In this paper, we use stakeholder input to evaluate and compare three different repurposing alternatives for the tailings dam area of a mine in the state of Colorado, USA, which is expected to close in the next twenty years. By using multi-attribute utility theory (MAUT), we determine which alternative better reflects stakeholder preferences and results in the most economically, environmentally, and socially sustainable outcome. Our results show that although stakeholder groups have different ideas about what constitutes sustainable development in the context of mine closure and repurposing, it is possible to identify to what extent different scenarios can address these perspectives. We argue that integrating stakeholder views into mine closure design and repurposing can lead to more responsible and sustainable mine closure that is unique to a particular setting and stakeholder needs, and we provide a methodology that mining companies may use to understand stakeholder priorities and preferences.
A machine learning model to predict cyber attack

--- All users place a high value on information security. Over the last few decades, cyber security has been one of the most fascinating and essential topics in cyber re-search. The goal of this work is to propose a machine learning method for the problem of cyber security threats. As a result, a multi-model machine learning approach was developed to predict the top ten traits that can be used to detect cyber threats. This work finds the importance of each feature in from the dataset using machine learning. this model trained with random forest model and exclude the features which are least important to the model.
Free word association analysis of Germans' attitudes toward insects

In this paper, the results of two free word association tests were analyzed to gain insight into Germans' attitudes toward insects. We used a novel approach in the form of an association network with the help of the software “Gephi” that was originally developed for social network analysis. The influence of attitude on the willingness to donate (WTD) and actual donation to an insect conservation project was investigated as well. Data collection was conducted via an online questionnaire (n = 515; Mage = 49.36, SD = 16.73; female = 50.1%). For the first test, participants listed three associations for the prompt “insect. ” The associations were assigned to the three components of attitude: affective, cognitive, and behavioral. For the second test, participants named insects they pictured when thinking about “insects. ” The results were taxonomically classified. The WTD was assessed with a Likert‐type scale and an actual donation could be made at the end of the online questionnaire. “Bee” was the most frequently named association, followed by “useful,” “nature,” “pollination,” and “pesky. ” “Pesky” was most often named with “useful,” indicating that being aware of insects' usefulness is not enough to supersede negative associations. In the second test, only 6% of the associations were on the species level, which suggests little taxonomic knowledge about or interest in insects. Linear regression revealed that positive affective associations had a positive influence on the WTD and negative affective associations had a negative influence on the WTD, both with a small effect size. We advise educating people not only about the usefulness of insects but also fostering positive, personal encounters to increase positive affective associations and decrease negative ones.
Rhizosphere Effects along an Altitudinal Gradient of the Changbai Mountain, China

Rhizosphere effects (REs) play important roles in regulating carbon (C) and nutrient cycling in terrestrial ecosystems. However, little is known about the REs of mature trees in the field, especially at the ecosystem scale. This study aimed to explore the variation and patterns of REs in natural ecosystems. Here, combining soil monoliths with an adhering soil (shaking fine roots) method was adopted to sample paired rhizosphere soil and bulk soil along an altitudinal gradient. Based on the relative REs and the percentage of rhizosphere soil mass, the REs on soil C and net nitrogen mineralization rates (Cmin and net Nmin) at the ecosystem scale were estimated. Our results showed that the REs on soil processes, soil microbial biomass C and extracellular enzyme activities (β-glucosidase and N-acetyl-glucosaminidase activities), and soil chemical properties (total C, total N, inorganic N, extractable P, K, Ca, Mg, Fe, and Mn) were significantly positive across altitudinal sites, while soil pH was significantly negative. Although the relative REs on investigated variables varied significantly among altitudes, the relative REs did not show a clear trend with the increased altitudes. Across altitudes, the mean magnitude of ecosystem-level REs on Cmin and net Nmin were 19% (ranging from 4% to 48%) and 16% (ranging from 3% to 34%), respectively. Furthermore, the magnitude of ecosystem-level rhizosphere effects increased linearly with the increased altitudes. The altitudinal patterns of ecosystem-level RE mainly depend on the percentage of rhizosphere soil mass. In conclusion, our results provided a set of new evidence for the REs, and highlighted the need to incorporate REs into land C and N models.
COVID-19’s Effects on Homicide Investigation

COVID-19 affected every aspect of our society, including homicide incidence, investigation, and prosecution. This case study examines the impact of COVID-19 on homicide investigation in one large metropolitan police department. Two homicide investigators were interviewed using a semi-structured interview guide. A thematic analytic approach was used to identify two overarching themes: fear and changes to investigative procedure. Detectives feared getting COVID-19 and possibly transmitting it to their loved ones, particularly in the early day of the pandemic when little was known about transmission. Homicide investigators faced several changes to how they conducted interviews, met with families, and sought warrants. While this case study only offers a glimpse into the challenges faced by law enforcement departments due to COVID-19, it is clear that homicide investigations were greatly affected and may continue to be so. More research is needed to unpack the relationship between COVID-19 and policies that were implemented in response to it.
Anti-inflammatory effect of simvastatin by impeding TNF-α and interleukin-1ß pathways: antiangiogenic activity of simvastatin and simvastatin-loaded silver nanoparticles

Abstract Purpose The present study was carried out to evaluate anti-inflammatory and antiangiogenic attributes of simvastatin and its nanofilms containing silver nanoparticles. Methods Silver nanoparticles and simvastatin-loaded nanocomposite (SNSN) films were formulated by using polymeric solution (pectin + sericin) through casting solution method. Different in vitro and in vivo anti-inflammatory assays were performed. In addition, chick chorioallantoic membrane assay (CAM) was also employed for angiogenesis activity. Results FTIR spectra of the film depicted the presence of intact simvastatin. Differential scanning calorimetry exhibited no endothermic expression in F9 film thermogram. The simvastatin release from all films exhibited a burst effect. Cotton-pellet induced granuloma model study showed that high dose of simvastatin and indomethacin produced comparable (p < 0.05) anti-inflammatory effect. Noteworthy, RT-PCR showed dose-dependent, anti-oedematous effect of simvastatin through downregulation of serum TNF-α and interleukin-1ß levels. While results of CAM assay exhibited remarkable anti-angiogenic potential of SNSN films showing dissolved blood vessels network macroscopically. Conclusion To reiterate, simvastatin and its SNSN films can add significant contribution to the field of biomedicines due to their promising anti-inflammatory and antiangiogenic properties, however, clinical studies are required to validate their commercial use.
Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though it provides convenient, low-cost, and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems for both users and operators. The primary issue is the uneven distribution of bikes caused by ever-changing usage and (available) supply. This imbalance necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluate the performance of our design with the dataset from one of the world’s largest public bike-sharing systems located in Hangzhou, China, which owns more than 2800 stations. The evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for checkin prediction. The preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe that this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for rapid deployment and adoption of bike-sharing systems across the globe.
DEVELOPMENT OF THE DOUBLE-CONTOUR PROTECTION CONCEPT IN SOCIO-CYBERPHYSICAL SYSTEMS

The rapid development of mobile Internet technologies LTE (Long-Term Evolution) not only predetermined the further development of cyber-physical systems, which are based on the synthesis of technologies of classical computer systems and LTE technologies, as well as integration with Internet-of-Things technologies. As a result, the emergence of sociocyberphysical systems predetermines further development based on this integration. The creation of mesh- and sensor networks also allows the development of smart technologies and systems based on their conglomeration. The development and creation of a quantum computer, on the one hand, will make it possible to make a technical breakthrough in computing resources, use artificial intelligence, and on the other hand, it can lead to “chaos” in ensuring the security of modern technologies and systems. So, based on the algorithms of Shor and Grover quantum cryptography, symmetric cryptosystems based on traditional cryptography algorithms, as well as asymmetric cryptosystems, including systems based on elliptic curve cryptography, can be broken. The paper proposes to use a new approach to building security systems based on the concept of internal and external security contours. At the same time, security contours of continuous business processes are considered. This approach provides an objective assessment of the current state of security of the socio-cyber system as a whole.
An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance

Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great interest to some cities in the world. Generally, the existing traffic surveillance systems are made up of costly equipment with complicated operational procedures and have difficulties with congestion, occlusion, and lighting night/day and day/night transitions. In this paper, we propose an embedded system for traffic surveillance that can be utilized under these challenging conditions. This system analyses traffic and particularly focuses on the problem of detecting and categorizing traffic objects in several traffic scenarios. Moreover, it contains a robust detector produced by an original specialization framework. The proposed specialization framework utilizes a generic deep detector so as to improve the detection accuracy in a specific traffic scenario. The experiments demonstrate that the proposed specialization framework presents encouraging results for multi-traffic object detection and outperforms the state-of-the-art specialization frameworks on several public traffic datasets.
A porta de entrada para o diagnóstico da tuberculose no sistema de saúde de Ribeirão Preto/SP

O primeiro contato do doente de tuberculose (TB) com o sistema de saude se da na porta de entrada, e e fundamental para o acesso ao diagnostico. Objetivou-se identificar e analisar a porta de entrada no sistema de saude de Ribeirao Preto para o diagnostico da TB. Baseou-se em um instrumento do Primary Care Assessment Tool, adaptado para a TB no Brasil. Realizou-se entrevista estruturada com 100 doentes de TB diagnosticados entre Junho de 2006 e Julho de 2007. Destes, 61% chegaram ao local de diagnostico por encaminhamento e apenas 29% se apresentaram espontaneamente; 66% procuraram por servicos de atencao primaria, 34% por servicos de nivel secundario e terciario. Ademais, 89% foram diagnosticados em servicos publicos e destes, 44% foram diagnosticados nos pronto-atendimentos. Alem disso, 88% foram diagnosticados fora de sua area de abrangencia. Apesar dos doentes terem procurado atendimento na atencao primaria e mais proximo de suas residencias, o diagnostico se deu na atencao secundaria e terciaria.
A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics

In the past decade, urban last-mile logistics (ULML) has attracted increasing attention with the growth of e-commerce. Under this background, express cabinet has been gradually advocated to improve the efficiency of ULML. This paper focuses on the multi-objective green express cabinet assignment problem (MGECAP) in ULML, where the objectives to be minimised are the total cost and the energy consumption. MGECAP is concerned with optimising the purchase and assignment decision of express cabinets, which is different from conventional assignment problems. To solve MGECAP, firstly, the integer programming model and the corresponding surrogate model are established. Secondly, problem-dependent heuristics, including the solution representation, genetic operators, and repair strategy of infeasible solutions, are proposed. Thirdly, a probability guided multi-objective evolutionary algorithm based on decomposition (PG-MOEA/D) is proposed, which can balance the limited computation resource among sub-problems during the iterative process. Meanwhile, a feedback strategy is put forward to alternatively generate new solutions when the probability condition is not satisfied. Finally, numerical results and a real-life case study demonstrate the effectiveness and the practical values of the PG-MOEA/D.
Root-Associated Microbiota Response to Ecological Factors: Role of Soil Acidity in Enhancing Citrus Tolerance to Huanglongbing

The citrus orchards in southern China are widely threatened by low soil pH and Huanglongbing (HLB) prevalence. Notably, the lime application has been used to optimize soil pH, which is propitious to maintain root health and enhance HLB tolerance of citrus; however, little is known about the interactive effects of soil acidity on the soil properties and root-associated (rhizoplane and endosphere) microbial community of HLB-infected citrus orchard. In this study, the differences in microbial community structures and functions between the acidified and amended soils in the Gannan citrus orchard were investigated, which may represent the response of the host-associated microbiome in diseased roots and rhizoplane to dynamic soil acidity. Our findings demonstrated that the severity of soil acidification and aluminum toxicity was mitigated after soil improvement, accompanied by the increase in root activity and the decrease of HLB pathogen concentration in citrus roots. Additionally, the Illumina sequencing-based community analysis showed that the application of soil amendment enriched functional categories involved in host-microbe interactions and nitrogen and sulfur metabolisms in the HLB-infected citrus rhizoplane; and it also strongly altered root endophytic microbial community diversity and structure, which represented by the enrichment of beneficial microorganisms in diseased roots. These changes in rhizoplane-enriched functional properties and microbial composition may subsequently benefit the plant's health and tolerance to HLB disease. Overall, this study advances our understanding of the important role of root-associated microbiota changes and ecological factors, such as soil acidity, in delaying and alleviating HLB disease.
First Report of a Complete Genome Sequence of a Variant African Swine Fever Virus in the Mekong Delta, Vietnam

The objective of this study is to report the complete-genome sequence of a field African swine fever (ASF) virus (ASFV), namely ASF/VN/CanTho-OM/2021, which caused a fatal outbreak in domestic pigs in the Mekong Delta. Complete-genome sequencing detected an 18 bp nucleotide deletion in the EP402R gene (encoding for serotype-specific proteins CD2v) of ASF/VN/CanTho-OM/2021, which was determined to belong to genotype 2 and serotype 8. This mutation pattern was confirmed as unique in GenBank; thus, ASF/VN/CanTho-OM/2021 can be considered a novel variant, with a potential change of sero-characteristics within genotype 2. An additional unique mutation of 78 bp nucleotide insertion was also observed in the B475L gene. Additionally, four copies of tandem repeat sequences were found in the intergenic region (IGR) located between I73R and I329L, previously assigned as the IGR III variant. This study is the first to report the complete genome of ASFV in the Mekong Delta, and it highlights the necessity of strengthening molecular surveillance to provide further knowledge on the evolution and incursion of ASFV in the Mekong Delta and Vietnam.
Enterprise modelling for interoperable and knowledge-based enterprises

The paper first takes a look at the state of the art on the overlapping areas of: Enterprise Modelling, Enterprise Integration/Interoperability, Organisational Learning and Knowledge Management. We identify requirements, characterisation and recent contributions for the interoperable and knowledge-based enterprises in the area of production of goods or services. It is then advocated that Enterprise Modelling and Knowledge Representation should combine their efforts to develop reference and reusable core manufacturing enterprise ontologies to support Enterprise Integration and Interoperability on one hand and open the door to Organisational Learning and management of active knowledge for the ‘smart’ enterprises of tomorrow on the other hand. This is illustrated by case studies from projects carried out in industry.
Genome-Wide Characterization and Expression Analysis of GeBP Family Genes in Soybean

The glabrous-enhancer-binding protein (GeBP) family is a family of plant-specific transcription factors, whose members share a central DNA-binding domain. Previous studies have already proven that GeBP genes are involved in the control of cell expansion but not cell proliferation in Arabidopsis. However, there has not yet been a versatile analysis of the GeBP genes’ function in soybean (Glycine max L.). Here, we identified and named 9 GmGeBP genes in the soybean genome. These genes were distributed on 7 of the 20 chromosomes and the intron numbers ranged from zero to one. According to the phylogenetic tree, 52 GeBP genes obtained from four plant species were clustered into major four groups. Through the RNA-seq analysis of the nine GmGeBP genes, 8 of 9 GmGeBP genes were be found to expressed differentially across the 14 tissues. Additionally, among nine GmGeBP genes, only GeBP4 were highly expressed in abnormal trichome soybeans, which was predicted to be involved in trichome development. This genome-wide analysis of GmGeBP genes helps to provide an overview of the evolution and functions of two kinds of soybean plants. These results will help to clarify the potential functions and characteristics of GmGeBP genes in the soybean life cycle.
Estimating the Individual Treatment Effect on Survival Time Based on Prior Knowledge and Counterfactual Prediction

The estimation of the Individual Treatment Effect (ITE) on survival time is an important research topic in clinics-based causal inference. Various representation learning methods have been proposed to deal with its three key problems, i.e., reducing selection bias, handling censored survival data, and avoiding balancing non-confounders. However, none of them consider all three problems in a single method. In this study, by combining the Counterfactual Survival Analysis (CSA) model and Dragonnet from the literature, we first propose a CSA–Dragonnet to deal with the three problems simultaneously. Moreover, we found that conclusions from traditional Randomized Controlled Trials (RCTs) or Retrospective Cohort Studies (RCSs) can offer valuable bound information to the counterfactual learning of ITE, which has never been used by existing ITE estimation methods. Hence, we further propose a CSA–Dragonnet with Embedded Prior Knowledge (CDNEPK) by formulating a unified expression of the prior knowledge given by RCTs or RCSs, inserting counterfactual prediction nets into CSA–Dragonnet and defining loss items based on the bounds for the ITE extracted from prior knowledge. Semi-synthetic data experiments showed that CDNEPK has superior performance. Real-world experiments indicated that CDNEPK can offer meaningful treatment advice.
The Dragonfly Spectral Line Mapper: design and first light

The Dragonfly Spectral Line Mapper (DSLM) is the latest evolution of the Dragonfly Telephoto Array, which turns it into the world’s most powerful wide-field spectral line imager. The DSLM will be the equivalent of a 1.6m aperture f/0.26 refractor with a built-in Integral Field Spectrometer, covering a five square degree field of view. The new telescope is designed to carry out ultra-narrow bandpass imaging of the low surface brightness universe with exquisite control over systematic errors, including real-time calibration of atmospheric variations in airglow. The key to Dragonfly’s transformation is the “Filter-Tilter”, a mechanical assembly which holds ultra-narrow bandpass interference filters in front of each lens in the array and tilts them to smoothly shift their central wavelength. Here we describe our development process based on rapid prototyping, iterative design, and mass production. This process has resulted in numerous improvements to the design of the DSLM from the initial pathfinder instrument, including changes to narrower bandpass filters and the addition of a suite of calibration filters for continuum light subtraction and sky line monitoring. Improvements have also been made to the electronics and hardware of the array, which improve tilting accuracy, rigidity and light baffling. Here we present laboratory and on-sky measurements from the deployment of the first bank of lenses in May 2022, and a progress report on the completion of the full array in early 2023.
Design of a High Gain, Broadband Microstrip Monopole Antenna Using Parasitic Reflector

This paper proposes a novel design for a high gain broadband monopole antenna. The design contains a tuning fork-shaped patch with vertical inserts. Three slits are connected to the semi-circular type arc. A parasitic reflector below the ground plane is used to increase the gain of the antenna. The proposed antenna provides a peak gain of about 8.1 dBi and a very broad band of about 11 GHz. This design may be used to operate for a wide range of applications such as WiMAX (3.5/5.5 GHz), WLAN (5.2/5.8 GHz), C-Band (4-8 GHz), Ultra-Wide-Band (3.1-10.6 GHz), X-Band (8-12.5 GHz), partially Ku-Band (12.5-14 GHz), and other wireless networks.