Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of Land Subsidence in the Po River Delta (Northern Italy) Using Geodetic Networks

The Po River Delta (PRD, Northern Italy) has been historically affected by land subsidence due to natural processes and human activities, with strong impacts on the stability of the natural ecosystems and significant socio-economic consequences. This paper is aimed to highlight the spatial and temporal evolution of the land subsidence in the PRD area analyzing the geodetic observations acquired in the last decade. The analysis performed using a moving window approach on Continuous Global Navigation Satellite System (CGNSS) time-series indicates that the velocities, in the order of 6 mm/year, are not affected by significant changes in the analyzed period. Furthermore, the use of non-permanent sites belonging to a new GNSS network (measured in 2016 and 2018) integrated with InSAR data (from 2014 to 2017) allowed us to improve the spatial coverage of data points in the PRD area. The results suggest that the land subsidence velocities in the easternmost part of the area of interest are characterized by values greater than the ones located in the western sectors. In particular, the sites located on the sandy beach ridge in the western sector of the study area are characterized by values greater than −5 mm/year, while rates of about −10 mm/year or lower have been observed at the eastern sites located in the Po river mouths. The morphological analysis indicates that the land subsidence observed in the PRD area is mainly due to the compaction of the shallow layers characterized by organic-rich clay and fresh-water peat.
A Review of Recent Vapor Intrusion Modeling Work

This paper reviews vapor intrusion modeling studies published in the last decade (2010–2020). Compared to research carried out in the late 1990s and the early 2000s that was mainly focused on basic vapor transport phenomena and attenuation in the subsurface, the topics addressed in recent years focused on more complex scenarios, including the blocking effect of building footprint and surface pavements, soil and source heterogeneity, the role of capillary fringe, weather conditions such as rain, indoor–outdoor pressure differences, and temperature, building features, screening distances, and building pressure cycling. A brief description of these models and the main findings are reported in this paper. Generally, recent modeling works give more care about the influence of natural factors, which are relatively easy to be quantified and included in the model. Much less attention was given to factors involving human activities, such as preferential pathways, indoor environment structure, and background sources. The latter, however, may play a key role in determining the exposure to people of concern at sites contaminated by volatile contaminants. Thus, future modeling studies should be oriented to address these issues.
In Vivo Micrografting to Eliminate Passiflora Latent Virus from Infected Passion Fruit Plants

An in vivo micrografting method was developed to obtain Passiflora latent virus (PLV)-free passion fruit from infected plants. The scion length required to eliminate PLV was ≤ 2 mm. The method required no aseptic handling and the procedure was relatively simple, and resulted in more than 10 micrografts in one hour. Moreover, no special equipment or procedures were necessary, allowing it to be conducted at an individual farm level. Rapid growth of the scion after grafting was also observed due to the use of fully established seedlings as rootstock. Leaf samples for analysis of PLV infection could therefore be obtained about two months after grafting with fruit harvest possible about four months later. In vivo micrografting was conducted with a scion length of 0.5–1 mm from September to November to determine the optimal air temperature conditions. The graft success rate increased from 18% to 58% with a decrease in the average air temperature from September (28.6°C) to November (23.3°C), although there was no significant difference in PLV-free rates between months (73% to 80%). Accordingly, in vivo micrografting of passion fruit is not recommended under high air temperature conditions. The effect of scion shoot storage conditions was also examined, revealing that in vivo micrografting using shoots stored for one day could be performed without difficulty, whereas the rate of graft success and PLV-free rate were close to those obtained using shoots selected less than 30 min before. These findings suggest that with this method, virus-free plants can be obtained using PLV-infected shoots selected in the field one day earlier. In conclusion, our in vivo micrografting technique is useful to eliminate PLV from infected plants; it is simple and leads to rapid growth of the scion after grafting.
Ultrastructure of Anterior Lens Capsule and Epithelium in High-Myopic Cataracts

BACKGROUND: The mechanism of high-myopic cataract (HMC) is not fully understood yet. The ultrastructure of anterior lens capsule (aLC) and lens epithelial cells (LECs) may provide some clues to this issue.METHODS: Capsulorhexis samples from 10 eyes with HMC and 10 eyes with age-related cataract (ARC) were analyzed for morphologic characteristics using transmission electron microscopy (TEM). RESULTS: Several common pathologic disorders of LECs, including pleomorphic cellular structure, extensive intracellular vacuoles, swollen organelles, degeneration and necrotic lesions, could be observed in both HMC group and ARC group, to a similar extent. Specimens of HMC group disclosed remarkable extracellular spaces, interdigitation of LECs as well as protuberances of aLC towards LECs. CONCLUSIONS: The ultrastructure of aLC and LECs in HMC demonstrated some specific pathologic disorders in comparison with ARC, which might play a role in the formation of cataract in high myopes.
Ontology based semantic interoperability approach in the Internet of Things for healthcare domain

Abstract Recent years have seen enormous growth in the number of devices connected to the Internet. With such rapid development in the Internet of Things (IoT) domain, data generated by these devices is ever increasing. Data is considered as one of the most crucial entities in the Internet of Things, and this data may be homogeneous as well as heterogeneous in nature. Analysis of homogeneous information is a simple process as this will not involve further processing, but analysis of heterogeneous data is a huge task. The heterogeneous nature of data in IoT based healthcare system, an important application, leads to semantic interoperability, which is the ability of systems to exchange data with definite, shared meaning. Semantic Interoperability expedites data interchange unambiguously irrespective of format and source of data. Therefore, a knowledge-based model is required to integrate heterogeneous data-generating devices to expand the applications of IoT. In this paper, an ontology-based model is proposed and implemented to collect the patient’s diverse data using different tools over gateways. Then the generated/collected data is transmitted to the cloud for processing in terms of semantics and structure. This results in a holistic mechanism in the form of an ontology used for the prediction and diagnostics of heart diseases in patients located at distant places by experts based at another location.
Changes in Undergraduate and Graduate Student Perspectives of Online versus Face-to-Face Education

Over 6 years ago at an AACSB Jesuit, Catholic University with a strong focus on teaching, business student perspectives regarding online versus face-to-face courses indicated that students prefer face-to-face classes. However, other researchers have noted changes in student perspectives over time. Other research has noted differences between undergraduate and graduate students with respect to their perceptions. In order to explore these changes and differences, this research explores whether these two populations have changed their perspectives over the past 6 years on individual and program perceptual factors. This research has implications for instructors and administrators. 148 The BRC Academy Journal of Business Vol. 11, No. 1
Characterization and Dewatering of Activated Sludge from the Pulp and Paper Industry

Floc properties and dewatering of activated sludge from the pulp and paper industry were studied prior to and after an oxidative conditioning using Fenton9s reagent Sludge samples were taken from four treatment plants, which differed with respect to organic loadings and wastewater source. Poor dewaterability was typical of heavily loaded sludges and a positive correlation between filtrability and the concentration of exopolysaccharides was observed. Oxidative conditioning improved the dewaterability of all the sludges tested, but especially that of heavily loaded sludges. Lab-scale piston press results were also comparable with those of controls conditioned with polyelectrolytes. As a result of the conditioning, the surface charge density of the flocs decreased and the contact angles of filter cakes were increased, which enhanced flocculation and settleability. It is possible that hydroxyl groups are oxidized during the oxidative treatment to carboxyl groups, which are more hydrophobic at low pH. This hypothesis was partly supported by FTIR-analysis, but obviously other mechanisms were also involved.
Antibacterial and Antiparasitic Activity of Manganese(I) Tricarbonyl Complexes with Ketoconazole, Miconazole, and Clotrimazole Ligands

Five manganese(I) tricarbonyl complexes of the general formula [Mn(CO)3(bpyR,R)(azole)]PF6 with R = H, COOCH3, and azole = ketoconazole (ktz), miconazole (mcz), and clotrimazole (ctz) were synthesized and fully charaterized, including X-ray structure analysis for the ctz compound. The antibacterial activity on a panel of eight Gram-positive and Gram-negative bacterial strains was determined. While there was no effect on the latter microorganisms, the ctz complex showed submicromolar activity on Staphylococcus aureus and S. epidermidis with MIC values of 0.625 μM. Antiparasitic activity was investigated on Leishmania major and Trypanosoma brucei. Coordination of the organic azole drugs to the Mn(CO)3 moiety led to complexes with low micromolar IC50 values, but their potential for antileishmanial therapy is low due to comparable toxicity on mammalian cell lines 293T and J774.1. In contrast, the antitrypanosomal activity is much more promising, and the most potent compound incorparting the ktz ligand has an .
Experiences And Considerations On Performance Test Of Mosquitto-Based Broker

MQTT is a communication protocol for exchanging messages between clients via MQTT broker and is used to build many IoT systems. Currently, various tools have been developed to help measure the performance of application systems using MQTT. However, because they are all versatile tools, a dedicated test system that meets the purpose of the application system needs to be built on their behalf. But building a test system is not that simple. In this paper we discuss a case of building a test system for measuring U-Mosquitto’s performance, which has modified the open source Mosquitto broker to handle urgent messages with priority, and some issues to be considered in the process. In particular, they include selection of the server computer, the structure of the test program, the use of appropriate threads for the client, the appropriate number of clients, the appropriate workload per client, clock synchronization between clients, protocol analyzer issues, and other issues. Our experiences and considerations on these issues are expected to be a good guide for building a test system to measure the performance of MQTT application systems.
Low-dimensional multi-scale Fisher discriminant dictionary learning for intelligent gear-fault diagnosis

Fisher discriminant dictionary learning (FDDL) is an effective classification method that has achieved excellent results in image processing. However, there are still some shortcomings when it is directly applied to intelligent gear-fault diagnosis, such as low classification accuracy, a time-consuming dictionary learning process and unsatisfactory anti-interference capabilities, especially for vibration signals with heavy noise. To solve these problems, this paper proposes a low-dimensional multi-scale FDDL (LM-FDDL) model, which extracts the fault-sensitive and multi-scale information from wavelet packet transform coefficients, and constructs a low-dimensional sample set via L-kurtosis for dictionary learning. This improves the anti-interference capability of the model and greatly reduces the computational cost. The dictionary learned from the low-dimensional sample set has excellent discriminant performance and interpretability, which are verified by the visualization of reconstructed signals. Experimental results for a spur-gear fault dataset and a bevel-gear fault dataset demonstrate the superiority of LM-FDDL over other related methods in terms of fault classification accuracy and computational cost. An added-noise comparison experiment verifies that the dimension -reducing strategy of LM-FDDL has strong robustness to noise and outliers, and thus ensures that the main fault features can be extracted from vibration signals with heavy noise interference.
Effect of Graphene on Ice Polymorph

Recently, ice with the stacking disorder structure, consisting of random sequences of cubic ice (Ic) and hexagonal ice (Ih) layers, is reported to be more stable than pure Ih/Ic. While, due to a much lower free energy barrier of heterogeneous nucleation, in practice, the freezing process of water is usually controlled by heterogeneous nucleation which is triggered by an external medium. Herein, molecular dynamic simulations were carried out to explore the polymorph dependence of ice on the lattice structure of substrates. It turns out that, during the nucleation stage, the polymorph of ice nuclei can be severely altered by the graphene substrate, on which the Ih was found to occupy an absolute majority in new-formed ice. This can be attributed to the structure similarity between graphene and basal face of Ih. Besides the nucleation stage, our results suggest that the substrate can not affect the polymorph of ice which is far from the graphene surface. The polymorph selectivity of graphene to Ih will diminish with the growth of ice layer.
Exploring Factors that Influence the Use and Acceptanceof Virtual Learning Environment onTeaching and Learning Accounting

Virtual Learning Environment (VLE) is rapidly becoming an important educational model for teaching and learning purposes. This study aims to explore the factors that influence the use and acceptance of VLE among Malaysian accounting teachers in teaching the accounting subject. This study adapts the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA) to improve the understanding of factors that influence the usage of VLE. The study is descriptive in nature and was conducted using a quantitative approach.Researchrespondentswho participated in this studyconsist of 156accounting teachers from urban and ruralMalaysiangovernment schools. Data were analyzed using the descriptive analysis to measure mean, percentages, frequency and standard deviations. Descriptive statistical analysis indicated that accounting teachers have positive perceptions and highly motivated towards using VLE tools in teaching and learning accounting.However, the frequency of using Frog VLE among teachers is still at a low level. Furthermore, the result shows thatcomplete infrastructure facilities and teachers’ interests have a significant effect on the use and acceptance of Frog VLE. It is concluded that teachers are able to conduct flexible teaching and learning using Frog VLE application if resources are available, appropriate technology and continuous supports are providedby all parties responsible for the integration of ICT in teaching and learning accounting. More importantly, teachers need to be given ongoing training and motivation to promote and improve their skills to use the latest technology toencourage effective educational processes.
Siamese Recurrent Neural Network with a Self-Attention Mechanism for Bioactivity Prediction

Activity prediction plays an essential role in drug discovery by directing search of drug candidates in the relevant chemical space. Despite being applied successfully to image recognition and semantic similarity, the Siamese neural network has rarely been explored in drug discovery where modelling faces challenges such as insufficient data and class imbalance. Here, we present a Siamese recurrent neural network model (SiameseCHEM) based on bidirectional long short-term memory architecture with a self-attention mechanism, which can automatically learn discriminative features from the SMILES representations of small molecules. Subsequently, it is used to categorize bioactivity of small molecules via N-shot learning. Trained on random SMILES strings, it proves robust across five different datasets for the task of binary or categorical classification of bioactivity. Benchmarking against two baseline machine learning models which use the chemistry-rich ECFP fingerprints as the input, the deep learning model outperforms on three datasets and achieves comparable performance on the other two. The failure of both baseline methods on SMILES strings highlights that the deep learning model may learn task-specific chemistry features encoded in SMILES strings.
Historical and Future Projected Warming of Antarctic Shelf Bottom Water in CMIP6 Models

Understanding warming on the Antarctic shelf is critical for projecting changes in Antarctic ice shelves and ice sheets. Here we assess Antarctic Shelf Bottom Water (ASBW) temperature mean‐state and trends in CMIP6 models. While CMIP6 models do not resolve ice shelves, future shelf water warming will impact ice shelf vulnerability. The CMIP6 multi‐model mean zonal temperature structure and mean‐state ASBW spatial pattern resemble observations, although there is considerable spread across the models and a multi‐model mean warm bias. The multi‐model mean projects an average ASBW warming of 0.36°C (interdecile range 0.07°C–0.60°C) under SSP245 and 0.62°C (interdecile range 0.16°C–0.95°C) under SSP585 by 2100, emphasizing the influence future emissions have on shelf water warming around Antarctica. Changes in the transport of Circumpolar Deep Water onto the shelf associated with changes in the Southern Annular Mode, as well as Circumpolar Deep Water warming, are predicted to conspire to warm ASBW in the future.
Visual working memory load plays limited, to no role in encoding distractor objects during visual search

ABSTRACT Previous research raised the counterintuitive hypothesis that searching for multiple potential targets leads to increased incidental encoding of distractors. Are these previously reported findings due to increased visual working memory (VWM) engagement, or less precise target templates? In four experiments, we examined the effect of VWM load during visual search on incidental encoding of distractors. Consecutive target repetitions indirectly reduce template-related VWM demands but failed to reduce recognition for distractors relative to conditions where the targets were novel. Distractors that were subsequently recognized attracted longer cumulative dwell time, regardless of search condition. When placed in a dual-task situation where search was performed while holding a working memory load, recognition for distractors was marginally improved relative to a search task without additional VWM demands. We ruled out the possibility that the dual-task was not sufficiently difficult to trigger the scrutiny of distractors required for significant encoding benefits by showing a decrement to encoding when search time was limited. This suggests that widening the attentional set is the crucial factor in improved incidental encoding given that observers can assign differential status to various contents of VWM. Thus, utilizing VWM resources in general appears insufficient to meaningfully improve incidental memory.
Differentiation of tumoral and non‐tumoral breast lesions using back reflection diffuse optical tomography: A pilot clinical study

In breast cancer diagnosis, tumoral and non‐tumoral breast lesions differentiation is a challenge. We investigate the feasibility of continuous‐wave back reflection diffuse optical tomography (rDOT) in differentiation tumoral and non‐tumoral breast lesions to solve this problem. Modified Beer–Lambert Law and Rytov solution of diffusion approximation of the radiative energy transfer equation were used together in a continuous wave rDOT system for the near‐infrared imaging the breast with a lesion. The rDOT data were acquired from 15 lesions in 12 patients with a palpable lump after standard clinical examination with mammography, magnetic resonance imaging (MRI), and ultrasonography (US). Also, the measurements symmetrically from the other breast's counterpart were used as a functional imaging base. Histopathological examination revealed the tumor distribution as five invasive ductal carcinomas, one lobular carcinoma, one sclerosing adenosis, and one papilloma. The remaining seven lesions (one hamartoma, one postoperative scar tissue, two cysts, two islands of fibroglandular tissue, and one mastitis) were classified as non‐tumoral breast lesions. They were diagnosed based on clinical, US, mammography, or MRI findings. Tumoral lesions presented a contrast in rDOT tomographic images, whereas non‐tumoral lesions such as hamartoma and cysts did not show any difference except mastitis. In mastitis cases, rDOT showed higher contrast similar to tumoral lesions. After antibiotic treatment, the mastitis area had no high contrast when compared with the healthy breast tissue. We have demonstrated that the rDOT system can differentiate between actual breast tumors from non‐tumoral breast lesions such as cyst, the island of fibroglandular tissue, or hamartoma.
Effect of Psycho-Socio-Spiritual Strategies on Hope Level of Patients with Cancer: A Narrative Review

Context: Cancer diagnosis and its therapeutic methods can lead to considerable psychological problems and decrease the levels of hope in patients. Due to the considerably important multidimensional effects of hope on the life of patients with cancer, this study aimed to assess the effect of psycho-socio-spiritual strategies on the hope level of patients with cancer. Evidence Acquisition: In this narrative review, electronic databases were searched including Google Scholar, Scientific Information Database, Magiran, PubMed, ScienceDirect, Web of Science (ISI), Ovid, and Scopus. Those articles published between 1978 and 2020 were retrieved and assessed through the abstract and full-text appraisal. Finally, 74 articles were included in this study. Results: Psycho-socio-spiritual strategies for affecting the hope level of patients with cancer were classified as follows: (1) psychological interventions (psychotherapy, counseling, coping skills training, stress management training, crisis management, individual or group supporting therapy, hope therapy, and psychoeducational interventions); (2) social interventions (social and family support intervention); and (3) spiritual interventions (logotherapy, religion therapy, and praying). Conclusions: Due to the chronic nature of cancer disease, in addition to the important role of pharmacological treatment such as chemotherapy and radiotherapy, considering non-pharmacological approaches such as hope-fostering interventions for these patients is essential and can lead to a better quality of life.