Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of miniaturized dual-mode microstrip bandpass filter based on a novel fractal resonator

In this letter, a fractal structure used for the miniaturized design of microstrip bandpass filter (BPF) is presented. A new resonator called Greek cross fractal resonator (GCFR) in this paper was constructed by combining the proposed fractal structure with a traditional dual-mode meandered loop resonator. Three BPFs based on the new resonator with different orders iteration were analyzed. In addition, the mode-splitting characteristic and impact of the perturbation element on the performance of the BPFs were also investigated. The simulated results show that the new fractal resonator has a certain potential in the miniaturization design and the BPFs possess acceptable frequency characteristics. Therefore, the proposed fractal structure can be used as a candidate for miniaturization technology.
The reaction rate and residual value in southern New South Wales of 12 commercial liming materials from around Australia

ABSTRACT Context. Farmers and consultants are faced with selecting from amongst competing liming materials. Aim. We sought to establish guidelines for the efficient use of competing liming materials. Method. The effectiveness of 12 commercial liming materials from around Australia in increasing soil pH, their rate of movement below the depth of incorporation and their residual value to grain yield were measured in our field study. Key results. Chemical composition (equivalent CaCO3 content) was a major determinant of effectiveness over the longer term. There appeared to be no value to grain yield in the provision of additional Mg above what was already in the soil at this site. Finer commercial products were more effective in the short term and no less effective in the longer term, indicating that fineness also remains a major determinant of effectiveness. The effect of the source of the liming product was a minor determinant of effectiveness. As previously found, there was a tardiness in the reaction rate of dolomites and a slight advantage in the reaction rate of softer limestones but as with fineness, the data for different sources tended to converge over 6–7 years. Conclusion. All liming materials provided a benefit to grain yield over the 7 years and that benefit was proportional to the material’s chemical purity and fineness. Implication. Growers and advisers can select the best value for cost amongst the commercially available products at a given geographical location. The quantitative assessment of chemical composition and particle size remains the best means of assessment over both the short and longer terms.
An Improved Transfer Learning Model for Cyanobacterial Bloom Concentration Prediction

The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a challenging issue. Machine learning techniques can improve the prediction accuracy, but a large amount of historical monitoring data is needed to train these models. For some waters with an inconvenient geographical location or frequent sensor failures, there are not enough historical data to train the model. To deal with this problem, a fused model based on a transfer learning method is proposed in this paper. In this study, the data of water environment with a large amount of historical monitoring data are taken as the source domain in order to learn the knowledge of cyanobacterial bloom growth characteristics and train the prediction model. The data of the water environment with a small amount of historical monitoring data are taken as the target domain in order to load the model trained in the source domain. Then, the training set of the target domain is used to participate in the inter-layer fine-tuning training of the model to obtain the transfer learning model. At last, the transfer learning model is fused with a convolutional neural network to obtain the prediction model. Various experiments are conducted for a 2 h prediction on the test set of the target domain. The results show that the proposed model can significantly improve the prediction accuracy of cyanobacterial blooms for the water environment with a low data volume.
3D printing of elastomer layers for dielectric elastomer transducers

In this contribution, 3D printing of elastomer layers is investigated. 3D printing gains emerging interest due to its versatility in geometry as well as the ability of producing thin layers and therefore is a promising fabrication method for dielectric elastomer transducers (DETs). Direct Ink Writing (DIW) is a specific type of 3D printing based on the extrusion of fluidic elastomer with middle viscosity, which is the used printing technology in this work. The printing requirements for DETs are defined and by varying the process parameters, printing tests are conducted. Finally, the layers are analyzed and the results are discussed. The main results are printing of a single layer elastomer with 40 µm thickness and three elastomer layers printed on top of each other.
Antiviral Polymers: A Review

Polymers, due to their high molecular weight, tunable architecture, functionality, and buffering effect for endosomal escape, possess unique properties as a carrier or prophylactic agent in preventing pandemic outbreak of new viruses. Polymers are used as a carrier to reduce the minimum required dose, bioavailability, and therapeutic effectiveness of antiviral agents. Polymers are also used as multifunctional nanomaterials to, directly or indirectly, inhibit viral infections. Multifunctional polymers can interact directly with envelope glycoproteins on the viral surface to block fusion and entry of the virus in the host cell. Polymers can indirectly mobilize the immune system by activating macrophages and natural killer cells against the invading virus. This review covers natural and synthetic polymers that possess antiviral activity, their mechanism of action, and the effect of material properties like chemical composition, molecular weight, functional groups, and charge density on antiviral activity. Natural polymers like carrageenan, chitosan, fucoidan, and phosphorothioate oligonucleotides, and synthetic polymers like dendrimers and sialylated polymers are reviewed. This review discusses the steps in the viral replication cycle from binding to cell surface receptors to viral-cell fusion, replication, assembly, and release of the virus from the host cell that antiviral polymers interfere with to block viral infections.
portalcasting: Supporting automated forecasting of rodent populations

The portalcasting package provides a model development, deployment, and evaluation system for forecasting how ecological systems change through time, with a focus on a widely used long-term study of mammal population and community dynamics, the Portal Project (Brown, 1998; Brown & Davidson, 1977; Ernest, Yenni, Allington, Bledsoe, Christensen, Diaz, Goheen, et al., 2018). The software is designed to encourage the active engagement of the broad ecological modeling community in understanding the dynamics of this well-sampled empirical system and forecasting future changes. The initial infrastructure for continuous analysis and forecasting at the site provided a functional system for running existing models repeatedly (White et al., 2018). However, it did not facilitate the development and evaluation of new models. The portalcasting package combines existing R packages for modeling (e.g., JAGS), forecasting (e.g., forecast) and data management (e.g., portalr) into a continuous pipeline that produces new forecasts and allows for new model development.
Microstructure modification of cement pastes using nanosilica

The paper describes modifications of cement paste mixtures using nanosilica to affect their physical and chemical properties. Cement pastes from Portland cement CEM I 42.5R with three different substitution ratios of nanosilica 1.5, 3 and 4.5 wt. %/cem. were examined. Micromechanical response and microstructure were characterized using nanoindentation, statistical deconvolution and scanning electron microscopy. About 400 locations were examined on each sample over 40,000 μm2 area. The results indicate an increase of C-S-H phases by 11 vol.% in samples with 4.5 wt. % nanosilica replacement compared to pure Portland cement paste. The increase in C-S-H phases volume was accompanied by the decrease in CH phases by approximately 11 vol.%.
Effect of Intrinsic Tannins on the Fermentation Quality and Associated with the Bacterial and Fungal Community of Sainfoin Silage

Sainfoin (Onobrychis viciifolia) is rich in condensed tannins (CT). CT function includes inhibiting bacterial and fungi activity during the ensiling process. We used polyethylene glycol (PEG) to deactivate tannin activity to find out the effects of CT. The results show that the addition of PEG increased dry-matter loss (8.32% vs. 14.15%, on a dry-matter basis) after 60 d of ensiling, and also increased lactic acid (10.90% vs. 15.90%, on a dry-matter basis) and acetic-acid content (7.32% vs. 13.85%, on a dry-matter basis) after 30 d of ensiling. The PEG-treated group increased its Pediococcus relative abundance (0.37–3.38% vs. 7.82–23.5%,) during the ensiling process, increased its Gibellulopsis relative abundance after 3 d of ensiling (5.96% vs. 19.52%), increased its Vishniacozyma relative abundance after 3 d and 7 d of ensiling (2.36% vs. 17.02%, 3.65% vs. 17.17%), and increased its Aspergillus relative abundance after 7 d, 14 d and 60 d of ensiling (0.28% vs. 1.32%, 0.49% vs. 2.84% and 1.74% vs. 7.56%). However, the PEG-treated group decreased its Alternaria relative abundance during entire ensiling process (14.00–25.21% vs. 3.33–7.49%). These results suggest that condensed tannins inhibit lactic-acid bacteria fermentation though reducing Pediococcus activity, and inhibiting fungi activity depending on different strains.
An intelligent diagnosis method of rolling bearing based on multi-scale residual shrinkage convolutional neural network

The vibration signals of rolling bearings are affected by changing operating conditions and environmental noise, so they are characterized by multi-scale complexity. Deep residual shrinkage network can achieve bearing fault diagnosis in strong noise environment, but ignore the multi-scale complexity feature. To address this problem, we propose a multi-scale residual shrinkage convolutional neural network for fault diagnosis of rolling bearing. In this method, a multi-scale residual shrinkage layer based on multi-scale learning and a residual shrinkage block is constructed. By stacking multiple multi-scale residual shrinkage layers, the features of vibration signals are automatically learned from the input data. In addition, to establish the connection of different vibration signals and to reduce the number of parameters in the network, we design a separable convolution block using residual connections and separable convolution. By verifying the effectiveness of the proposed method in Case Western Reserve University and Mechanical Failure Prevention Technology datasets, the results show that the proposed method not only has good noise resistance in strong noise environments, but also has high diagnostic accuracy and good generalization performance in different load condition domains. The proposed method is compared with three other deep learning methods under the same experimental conditions, and the results show that it is superior in rolling bearing fault diagnosis.
Improving Air Quality Standards in Europe: Comparative Analysis of Regional Differences, with a Focus on Northern Italy

The study reports a consistent comparison of emission inventories, concentration trends, and PM source apportionment in different European regions and, mostly, a thorough investigation of meteorological parameters influencing atmospheric pollutants’ dispersion. The study focuses on the reasons why Northern Italy still has difficulties complying with EU air quality standards for PM10 and NO2, despite strong emission reductions. The study demonstrates that, in the colder seasons, wind speed, PBL height, and atmospheric pressure in the Po basin are three to five times less efficient at diluting and dispersing pollutants than those occurring in regions north of the Alps. Since air quality standards aim at countering health impacts, it is advisable to consider atmospheric particulate toxicity in addition to PM10/PM2.5 mass concentration as a limit value. A discussion is reported about PM toxicity factors depending on source-specific aerosols and PM composition. We obtained PM toxicity factors that can vary by 10 times (according to carbonaceous content) across Europe, suggesting that, even at the same mass concentration, the effects of PM10/PM2.5 on human health are significantly variable. Modern PM source apportionment and reliable toxicity and epidemiological analyses represent the correct tools to build a new consistent health metric for ambient PM.
Study of Antibodies to Influenza Neuraminidase N2

Humoral immunity to influenza neuraminidase (NA) was evaluated among different groups of people including patients with acute influenza infection and healthy people in different age groups using an enzyme linked lectin assay (ELLA). The amino acid composition of NA of seasonal influenza viruses A/Victoria/361/2011(H3N2) and A/Hong Kong/4801/2014(H3N2) differed by 2%, while cross-reacting neuraminidase-inhibiting (NI) antibodies to them in the same serum samples were detected in 10% of cases. Middle-aged patients born from 1977 to 2000 had a high level of hemagglutination-inhibiting (HI) antibodies to A/Hong Kong/4801/2014(H3N2), but almost no NI antibodies, which may indicate that in the case of a change in the hemagglutinin (HA) subtype, this age group will be susceptible to influenza A/H3N2 viruses. Therefore, it could mean there is a need for priority vaccination of this age group with a vaccine against the appropriate strain. It was shown that after intranasal administration of live influenza vaccine (LAIV) for the 2017–2018 season, serum antibody response was not lower compared to that during natural infection. In older people, antibodies to archival A/H2N2 viruses were detected more often than to modern A/H3N2. Since the conversion of antibodies to HA and NA often did not coincide, antibodies to NA can serve as an additional criterion for assessing the immunogenicity of influenza vaccines.
Time-series imaging method for rotating machinery fault diagnosis using unsupervised sparse dictionary learning

Time-series signal collected from rotating machinery is subjected to different environmental and operational conditions. The vibration signal is sensitively affected by external noises and load conditions. To solve these problems, this paper presents a diagnostic method for rotating machinery using the proposed robust time-series imaging method. The overall procedure includes the following three key steps: (1) transformation of a one-dimensional current signal to a twodimensional image in time-domain, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The original RP method provides a binary image that makes it insensitive to detecting faulty signal. The proposed RP method develops from sparse dictionary learning that provides the dominant fault feature representations in a robust way. The proposed RP method can detect the weak difference between normal and fault signal, while enhancing robustness to external noise. The dataset acquired from KAIST rotor testbed is used to examine the proposed method’s capability to monitor the condition of rotating machinery. The results show that the proposed method outperforms vibration signalbased condition monitoring methods.
Finite Element Based Response Surface Methodology to Optimize Segmental Tunnel Lining

The main objective of this paper is to optimize the geometrical and engineering characteristics of concrete segments of tunnel lining using Finite Element (FE) based Response Surface Methodology (RSM). Input data for RSM statistical analysis were obtained using FEM. In RSM analysis, thickness (t) and elasticity modulus of concrete segments (E), tunnel height (H), horizontal to vertical stress ratio (K) and position of key segment in tunnel lining ring (θ) were considered as input independent variables. Maximum values of Mises and Tresca stresses and tunnel ring displacement (UMAX) were set as responses. Analysis of variance (ANOVA) was carried out to investigate the influence of each input variable on the responses. Second-order polynomial equations in terms of influencing input variables were obtained for each response. It was found that elasticity modulus and key segment position variables were not included in yield stresses and ring displacement equations, and only tunnel height and stress ratio variables were included in ring displacement equation. Finally optimization analysis of tunnel lining ring was performed. Due to absence of elasticity modulus and key segment position variables in equations, their values were kept to average level and other variables were floated in related ranges. Response parameters were set to minimum. It was concluded that to obtain optimum values for responses, ring thickness and tunnel height must be near to their maximum and minimum values, respectively and ground state must be similar to hydrostatic conditions.
Sodium alendronate: proposal and reliability of indicators

The present study proposes and evaluates the test-retest reliability of indicators of the correct use of sodium alendronate in elderly patients. This is a test-retest reliability study for use of sodium alendronate. Six questions to evaluate the correct use of this medicine were elaborated after analysis of information in the literature. Data collection was performed through questionnaires in face-to-face in-home interviews by previously trained interviewers. The participants were initially interviewed (test) when they agreed to participate in the study, and secondly (retest), after a period of 7 to 14 days from the first interview. The reliability of the questions was evaluated by means of the agreement percentage and the Kappa coefficient. Fifty-seven pairs (test-retest) were obtained. The mean age was 69.3 (SD = 6.9) years, the majority (92.5%) completed elementary education, and declared themselves white (50.9%). All the questions presented high concordance ranging from 79.0% to 98.3%. The Kappa values ranged from 0.1 (low) to 0.83 (very good). The agreement percentage and the Kappa values suggest adequate reliability of the proposed questions. We suggest that they can be used as a simple and quick way to evaluate the quality of sodium alendronate use among the elderly.
Outcomes of Primary Mucosal Head and Neck Squamous Cell Carcinoma in Solid Organ Transplant Recipients

Introduction Patients who undergo solid organ transplants have a higher risk of developing malignancies and subsequent recurrences. Clinical outcomes in transplant recipients with primary mucosal head and neck squamous cell carcinoma (HNSCC) are not well described in the published literature. Therefore, we retrospectively studied the outcomes in this group of patients. Methods This Institutional Review Board (IRB)-approved analysis included patients who had previously undergone solid organ transplants and subsequently were diagnosed with primary mucosal HNSCC between 2006 and 2021. Our institutional database of solid organ transplant recipients was cross-referenced with our head and neck cancer database to identify the patients included in this cohort. In addition, Kaplan-Meier analyses were performed to calculate overall and disease-free survival. Results Of 1,221 patients, 20 met the inclusion criteria. The median time from organ transplant to HNSCC diagnosis was 5.9 years (range: 0.5-18.5 years). A total of 11 (55.0%) and 9 (45.0%) patients presented with localized and locally advanced disease, respectively. Two-year overall and disease-free survivals were 59.1% and 73.5%, respectively. After initial treatment, six (30.0%) patients experienced a recurrence. All patients who developed a recurrence died within the follow-up period. The median time of death after recurrence for all six patients was 11.5 months (range: 2-22 months). Conclusion This series highlights a high mortality rate following recurrence among patients with primary mucosal HNSCC and a solid organ transplant history. A better understanding of how solid organ transplant history adversely impacts the course of HNSCC could help properly guide treatment, follow-up, and survivorship decisions.
Revising Technology Adoption Factors for IoT-Based Smart Campuses: A Systematic Review

Smart education and the sustainable development of smart campuses have drawn significant research attention. This is enabled by intelligent devices that are widely attracting massive applicability in personal and big business contexts and can increase efficiency and convenience. This paper aims to present a solution to address the lack of a proper adoption model for smart campus initiatives. The evaluation and synthesis of the literature were conducted by following the systematic literature review (SLR) procedure. The study’s findings revealed the taxonomy and IoT technologies leading to the wide adoption of IoT-based smart campuses. The technology adoption models and their corresponding variables help the authors identify and classify a suitable adoption framework for smart campuses. The limitations and challenges of adoption theories as they pertain to smart campuses are discussed. Finally, the study adapts perceived scalability, perceived replicability, perceived reliability, perceived privacy and security, perceived trust, the cost of deployment, usefulness, enjoyment, and technicality as adoption factors of sustainable smart campuses. This study offers practical and theoretical implications regarding the adoption and propagation of emerging smart campuses.
Analysis of Educational Building’s Ventilation Suitability to Prevent the Spread of Coronavirus (SARS-CoV-2)

In a larger educational building in Slovenia, we examined the efficiency of ventilation systems by analysing the operation of the heating, ventilation, and air conditioning (HVAC) system in several classrooms. Using the Federation of European Heating, Ventilation and Air Conditioning Associations (REHVA) COVID-19 ventilation calculator, the probability of infection due to the spread of coronavirus through aerosol particles and the reproduction number were calculated based on the classroom occupancy, ventilation rates, and other parameters (i.e., classroom characteristics, preventive measures). Firstly, different levels of ventilation capacity (50 % and 80 %) were applied. Considering the distance between persons 1.5 m and wearing the masks of all participants, the probability of infection during lectures was always lower than 1 %. Secondly, the maximum number of students that can attend lectures is about 30 %, as calculated according to the legal requirements, recommendations, and given conditions.