Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.
Meso-mechanical simulation of fracture grouting in soil

Based on the theory of particle flow,the domain of flow is defined by using the Fish language implemented in PFC,and the formulas for flow and pressure are put forward respectively.Furthermore,the contact-bond model is introduced due to the complicated properties of low permeability soil to establish PFC model,which can reflect Fluid-solid coupling between particle flow and flow domain.Based on the above study,the process of fracture grouting in soil is simulated from the micro-viewpoint under coupling environment.In addition,the emergence and development of crack and grouting pressure in soil is analyzed under different grouting pressures and soil properties.The research results show that pressure plays a key role in consolidating the soil by fracture grouting,but in fact it should be kept in a reasonable value in order to form grouting slurry net entirely.Under certain grouting pressure,it indicates that the macroscopic response of grouting in soil is related to microscopic parameters of soil,such as particle size ratio,friction,and bond properties.The conclusion of numerical simulation agrees well with the field tests and grouting theory.The meaning of this research lies in the fact that PFC can make the microcosmic research of fracture grouting in soil possible and lead to a further understanding of grouting mechanism in soil.The research will be helpful to the application of PFC to the theoretical studies on grouting in the future.
Biogeochemical functioning of the Baltic Sea

Abstract. Location, specific topography, and hydrographic setting together with climate change and strong anthropogenic pressure are the main factors shaping the biogeochemical functioning and thus also the ecological status of the Baltic Sea. The recent decades have brought significant changes in the Baltic Sea. First, the rising nutrient loads from land in the second half of the 20th century led to eutrophication and spreading of hypoxic and anoxic areas, for which permanent stratification of the water column and limited ventilation of deep-water layers made favourable conditions. Since the 1980s the nutrient loads to the Baltic Sea have been continuously decreasing. This, however, has so far not resulted in significant improvements in oxygen availability in the deep regions, which has revealed a slow response time of the system to the reduction of the land-derived nutrient loads. Responsible for that is the low burial efficiency of phosphorus at anoxic conditions and its remobilization from sediments when conditions change from oxic to anoxic. This results in a stoichiometric excess of phosphorus available for organic-matter production, which promotes the growth of N2-fixing cyanobacteria and in turn supports eutrophication. This assessment reviews the available and published knowledge on the biogeochemical functioning of the Baltic Sea. In its content, the paper covers the aspects related to changes in carbon, nitrogen, and phosphorus (C, N, and P) external loads, their transformations in the coastal zone, changes in organic-matter production (eutrophication) and remineralization (oxygen availability), and the role of sediments in burial and turnover of C, N, and P. In addition to that, this paper focuses also on changes in the marine CO2 system, the structure and functioning of the microbial community, and the role of contaminants for biogeochemical processes. This comprehensive assessment allowed also for identifying knowledge gaps and future research needs in the field of marine biogeochemistry in the Baltic Sea.
Study the Antitumor Activity of Copper (II) Complex of 4-Azomalononitrile Antipyrine on Mice Induced With Earlich Ascites Carcinoma Cells

Copper (II) complex of 4-azomalononitril antipyrine has been isolated and characterized basedÂ on IR spectra, 1H NMR, elemental analyses, molar conductance, electronic spectra and magneticÂ moment. A diminished amount of antioxidant enzyme coupled with superoxide production inappears to be general characteristic of the tumor cells. This character can be used in cancerÂ treatment. The present study attempted to investigate the effect of copper (II) complex of 4-Â azomalononitril antipyrine which has SOD like activity on tumor development using EhrlichÂ ascites carcinoma (EAC) implemented in Swiss albino female mice. The results showed thatÂ copper complex with ligand has anti cancer activity in all studied parameters with variable ratios.
Lighting Energy Saving of Tunnel Sidewall Interior Materials

We used lighting design software DIAlux to simulate the real environment of tunnel lighting,and used its unique function of two-way reflection distribution function and light tracking algorithm to calculate the direct reflected light and indirect reflected light reflected by sidewall.Then,based on the calculation result,we analyzed the influence regularity of the reflection coefficient of the tunnel sidewall interior materials and different combinations of tunnel lighting on lighting quality of pavement in tunnel.The research result shows that(1) from security perspective,the tunnel sidewall that using interior decoration material with high reflection coefficient can increase illumination,luminance and uniformity inside tunnel to increase the tunnel lighting quality and make travel more comfortable;(2) from energy saving perspective,considering the energy saving effect of using interior decoration material with high reflection coefficient at the beginning of design can properly reduce the power of the tunnel lamps,which has realistic significance to tunnel energy saving.
Visual inspection via anomaly detection by automated uncertainty propagation

The visual quality inspection of test objects having a complex geometry is a challenging task for automated artificial vision systems. Even for systems where the illumination and image acquisition setups are specifically tailored with respect to the properties of the test object, captured images often show unwanted signal components, e.g., surface reflections, which complicate the detection of present material defects. One way to mitigate this problem is to have an expert define image regions by hand which are excluded from the automated defect detection. Besides being a time-consuming procedure, this also results in the system being blind at the respective regions. Another approach is based on acquiring image value statistics (e.g., mean value and standard deviation) for every pixel of an image series captured from a set of defect-free test objects. This information can then be exploited during the inspection process by comparing image values with respect to the previously calculated statistics. Pixels whose image values lie outside the distribution for the defect-free case might indicate a material defect. Unfortunately, the calculated statistics are invalidated as soon as further preprocessing steps like smoothing or edge detection are applied. The statistics would have to be recalculated by applying the respective preprocessing steps to the images of the defect-free test objects. To resolve this drawback, this contribution presents a novel approach capable of adequately updating the calculated statistics with respect to the chain of required image processing steps. This is achieved by interpreting the statistics as uncertainties and by propagating them through the single processing steps via Gaussian uncertainty propagation. The required gradients are obtained via automated differentiation of the image processing steps. The effectiveness of the proposed approach is demonstrated by means of empirical experiments.
Smart-Data-Driven System for Alzheimer Disease Detection through Electroencephalographic Signals

Background: Alzheimer’s Disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression. Methods: The main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel. Results: The maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI), 81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM vs. ADA) and 56.8% (All vs. All). Conclusions: The proposed method outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.
Effect of MAO-B Inhibitors on Neurometabolic Profile of Patients Affected by Parkinson Disease: A Proton Magnetic Resonance Spectroscopy Study

Parkinson’s Disease (PD) is the most common neurodegenerative movement disorder whose treatment is symptomatic. No suitable methods for assessing the effects of dopaminergic drugs on disease progression in clinical trials have yet been provided. The aim of this longitudinal study is to evaluate the influence of rasagiline and selegiline on neurometabolic profile in de novo PD patients by using Proton Magnetic Resonance Spectroscopy (1H-MRS). We enrolled de novo PD patients who were divided into two groups of 20 patients each, according to the dopaminergic treatment prescribed at the baseline visit (rasagiline or selegiline). At the baseline visit and after 12 months, all patients underwent neurological evaluation as well as 1H-MRS. Forty healthy controls (HC) underwent 1H-MRS at baseline and after 12 months. PD patients, compared to HC, showed significantly lower concentrations of NAA in the motor cortex, while the Cho levels showed a decreasing trend. After 12 months of therapy, the 1H-MRS study revealed that rasagiline and selegiline in a similar way were able to restore the NAA levels to values similar to those of HC. In addition, this neurometabolic change showed a correlation with UPDRS-III scores. This is the first longitudinal study that provides preliminary evidence that 1H-MRS may be a suitable method to evaluate objectively the influence of MAO-B inhibitors on the neurometabolic profile of PD patients. These results could open a new scenario on the hypothesis of a drug-induced slowing effect of PD progression.
Evaluation of Remote Data Compression Methods

: The present era is one of Big Data, digitalization, Internet of Things and Internet of Everything, which imply the daily creation of an enormous amount of useful content with a very high number of producers and consumers for the online information. The ascending trend for Internet data, has made clear the necessity of defining and engineering innovative solutions for coping with redundant transfers, which led to performing smart data transfers for obtaining an increased throughput, data availability and resource utilization and implicitly to a cost reduction and to avoiding bottlenecks and denial of service issues. Internet data employed by an Internet user must be consistent, so distributed systems are gaining research interest with regard to concurrency control, atomic transfers, data replication and synchronization, compression and decompression, correction or other potential problems. Two different versions of a file have a high similarity and as synchronization is concerned, the delta between the second version and the initial version of the file applied to its initial version will provide a better transfer throughput, thus an efficient data deduplication technique is necessary and worth analyzing in order to minimize the cost of synchronization. This paper focuses on optimizing the bandwidth utilization for remote data synchronization, and proposes a prototype based on three classic open-source data compression methods. The experiments carried out show how these compression utilities along with the transfer of data perform the synchronization of large data sets between two remote sites and how the use of compression helps to reduce the data size on storage devices along with decreasing the network bandwidth significantly. The novelty of this paper lies in the fact that it combines two different compression algorithms in order to provide better compression rates.
Three narrative patterns of the city image visually presented on Instagram under the influence of self-presentation

The emerging trend of online self-presentation that cause the selective release of photos and thus create biased city images circulating in social networks deserves greater research attention. As a response to this, the paper investigates the image of Wangfujing Beijing on Instagram, which cements its reputation as a symbol of traditional food culture but limits its potential to be the embodiment of cosmopolitan urban living. After arguing that the exotic images of the food market can better help Instagrammers play an urban adventurer before followers, the paper analyzes over 5500 sets of Instagram data, and then examines three narrative patterns used to describe the place, corresponding to the context description, the connection establishment, and the visual emphasis of the dominant theme. The visual storytelling techniques used in them will assist in understanding the image-based communication between social network users and thereby increase the possibility of managing the online city image.
An Insight into the Molecular Characteristics and Associated Pathology of Chicken Astroviruses

The chicken astrovirus (CAstV) is a ubiquitous enteric RNA virus that has been associated mainly with conditions, such as the runting-stunting syndrome, severe kidney disease, visceral gout, and white chick syndrome, in broiler-type chickens worldwide. Sequence analysis of the capsid genes’ amino acids of the strains involved in these conditions reveals a genetic relationship and diversity between and within the CAstV genogroups and subgroups based on phylogenetic analysis, genetic distance (p-dist), and pathogenicity. While the two genogroups (A and B) are demarcated phylogenetically, their pairwise amino acid sequence identity is 39% to 42% at a p-dist of 0.59 to 0.62. Group-A consists of three subgroups (Ai, Aii, and Aiii) with an inter- and intra-subgroup amino acid identity of 78% to 82% and 92% to 100%, respectively, and a p-dist of 0.18 to 0.22. On the other hand, the six subgroups (Bi, Bii, Biii, Biv, Bv, and Bvi) in Group-B, with a p-dist of 0.07 to 0.18, have an inter- and intra-subgroup amino acid identity of 82% to 93% and 93% to 100%, respectively. However, these groupings have little to no effect on determining the type of CAstV-associated pathology in chickens.
Deciphering the Microbiota and Volatile Profiles of Algerian Smen, a Traditional Fermented Butter

In Algeria, Smen is a fermented butter produced in households using empirical methods. Smen fermentation is driven by autochthonous microorganisms; it improves butter shelf-life and yields highly fragrant products used as ingredients in traditional dishes as well as in traditional medicine. The present study is aimed at investigating microbial diversity and dynamics during Algerian Smen fermentation using both culture-dependent and culture-independent approaches, as well as by monitoring volatile organic compound production. To reach this goal, fifteen Smen samples (final products) produced in households from different regions in Algeria were collected and analyzed. In addition, microbial and volatile compound dynamics at the different stages of Smen manufacturing were investigated for one Smen preparation. The results showed that Smen is a microbiologically safe product, as all hygiene and safety criteria were respected. The dominant microorganisms identified by both techniques were LAB and yeasts. Lactococcus spp. and Streptococcus thermophilus were the main bacterial species involved in spontaneous raw milk fermentation preceding butter-making, while lactobacilli and enterococci were the only bacteria found to be viable during Smen maturation. Regarding fungal diversity, yeast species were only recovered from two mature Smen samples by culturing, while different species (e.g., Geotrichum candidum, Moniliella sp.) were identified in all samples by the culture-independent approach. Using microbial analysis of a single batch, many of these were found viable during manufacturing. Concerning the volatile profiles, they were highly diverse and characterized by a high prevalence of short chain fatty acids, methylketones, and esters. Correlation analysis between microbial diversity and volatile profiles showed that several yeast (Moniliella sp., K. marxianus) and LAB (e.g., Lactococcus spp., S. thermophilus) species were strongly correlated with one or more volatile organic compound families, including several ethyl esters and methyl ketones that can be linked to pleasant, sweetly floral, fruity, buttery, and creamy odors. This study clearly identified key microorganisms involved in Smen fermentation and maturation that could be used in the future for better fermentation control and improvement of quality attributes.
A Novel Data-Driven Mechanical Fault Diagnosis Method for Induction Motors Using Stator Current Signals

Most of the mechanical fault diagnosis methods of induction motors (IMs) are based on vibration signals. However, vibration sensors are expensive and require direct contact with IMs. This article proposes a data-driven mechanical fault diagnosis method for IMs using stator current signals, which is more convenient and low cost, since no additional sensors are required. Aiming at the weak representation of mechanical faults in current signals, an intelligent noise elimination method based on noise reconstruction model is proposed to improve the signal-to-noise ratio. Through the automatic feature extraction and classification of the residual current envelope spectrum, high diagnosis accuracy can be obtained even if some differences have existed among samples. The effectiveness of the proposed method is verified by high accuracy diagnosis results on two experimental platforms. The results show that the average diagnostic accuracy of the proposed algorithm for one bearing fault and two eccentricity faults can reach 96%. Even if the fault type and the fault degree are distinguished at the same time, an accuracy of 90% can be achieved for six kinds of bearing faults.
Experimental analysis of flow instability detection in a centrifugal compressor using variational mode decomposition

In centrifugal compressors, the identification of flow instability signals from experiments is a difficult problem owing to the nonlinear and non-stationary characteristics. Otherwise, the complicated asymmetric structure of the volute brings a huge challenge to the evolution and circumferential nonuniformity characteristics of the flow instabilities. This paper presents experimental and numerical investigations on internal flow field to understand the flow instability characteristics in a centrifugal compressor. Considering nonlinear and non-stationary signals, a method based on Fourier-transform and variational mode decomposition was introduced to analyse the flow instability characteristics. The Fourier spectrum results show that at 0.21kg/s of 80krpm, the pressure signal has a noticeable high-frequency fluctuation, which indicates that the compressor enters the flow instability state. The variational mode decomposition results show that before a surge, the compressor experiences different flow instability stages: the RI stage, the coexistence stage of RI and stall, and the stall stage. Moreover, obvious circumferential nonuniformity characteristics of flow instabilities were observed during the throttling process. RI first occurred at the 180° circumferential position and then the stall first appeared in the circumferential range of 60° to 240°. The simulation results that it is because that the asymmetric volute causes the adverse pressure gradient inside the impeller passage and a high-pressure region (120°–240°) at the upstream of the impeller inlet. Under this combined action of the two, the effect region of tip leakage vortex expands the upstream of the impeller inlet. Meanwhile, the tip leakage vortex core migrates to a lower span of blades. This study demonstrates the ability to analyse nonlinear and non-stationary signals from a centrifugal compressor via variational mode decomposition, and provides a useful guidance for the identification of flow instability signals.
Development of a Subunit Vaccine against Duck Hepatitis A Virus Serotype 3

In this study, we sought to develop a subunit vaccine against the increasingly prevalent Duck hepatitis A virus serotype 3 (DHAV-3). The VP1 protein of DHAV-3 and a truncated version containing the C-terminal region of VP1, termed VP1-C, were expressed recombinantly in Escherichia coli as vaccine antigens. For enhanced immune response, a truncated version of flagellin, nFliC, was included as vaccine adjuvant. Ducklings were vaccinated once for immune response analysis and challenge test. Results showed that VP1-C elicited a higher level of virus-specific antibody response and neutralization titer than VP1. The addition of nFliC further enhanced the antibody response. In terms of cellular immune response, the VP1-C + nFliC vaccine elicited the highest level of T cell proliferation among the vaccine formulations tested. Examination of the cytokine expression profile showed that peripheral blood mononuclear cells from the VP1-C + nFliC vaccine group expressed the highest levels of pro-inflammatory (IL-6) and TH-1 type (IL-12 and IFN-γ) cytokines. Finally, in a DHAV-3 challenge test, the VP1-C + nFliC vaccine provided a 75% protection rate (n = 8), in contrast to 25% for the VP1 vaccine. In conclusion, E. coli-expressed VP1-C has been shown to be a promising antigen when combined with nFliC and may be further developed as a single-dose subunit vaccine against DHAV-3.