bluuebunny/crossref_metadata_2025 huggingface dataset:
Nitric Oxide Sensing by a Blue Fluorescent Protein
Authors: Montali Chiara , Abbruzzetti Stefania , Franzen Arne , Casini Giorgia , Bruno Stefano , Delcanale Pietro , Burgstaller Sandra , Ramadani-Muja Jeta , Malli Roland , Gensch Thomas , Viappiani Cristiano
Published: November 2022

S-Nitrosylation of cysteine residues is an important molecular mechanism for dynamic, post-translational regulation of several proteins, providing a ubiquitous redox regulation. Cys residues are present in several fluorescent proteins (FP), including members of the family of Aequorea victoria Green Fluorescent Protein (GFP)-derived FPs, where two highly conserved cysteine residues contribute to a favorable environment for the autocatalytic chromophore formation reaction. The effect of nitric oxide on the fluorescence properties of FPs has not been investigated thus far, despite the tremendous role FPs have played for 25 years as tools in cell biology. We have examined the response to nitric oxide of fluorescence emission by the blue-emitting fluorescent protein mTagBFP2. To our surprise, upon exposure to micromolar concentrations of nitric oxide, we observed a roughly 30% reduction in fluorescence quantum yield and lifetime. Recovery of fluorescence emission is observed after treatment with Na-dithionite. Experiments on related fluorescent proteins from different families show similar nitric oxide sensitivity of their fluorescence. We correlate the effect with S-nitrosylation of Cys residues. Mutation of Cys residues in mTagBFP2 removes its nitric oxide sensitivity. Similarly, fluorescent proteins devoid of Cys residues are insensitive to nitric oxide. We finally show that mTagBFP2 can sense exogenously generated nitric oxide when expressed in a living mammalian cell. We propose mTagBFP2 as the starting point for a new class of genetically encoded nitric oxide sensors based on fluorescence lifetime imaging.
Insulin Requirement for Gestational Diabetes Control Is Related to Higher Vitamin D Levels up to 1 Year Postpartum: A Prospective Cohort Study
Authors: Molina-Vega María , Picón-César María José , Lima-Rubio Fuensanta , Gutiérrez-Repiso Carolina , Linares-Pineda Teresa María , Suárez-Arana María , Fernández-Ramos Ana María , Tinahones Francisco J. , Morcillo Sonsoles
Published: November 2022

Vitamin D deficiency is highly prevalent in pregnant women and has been related to a higher risk of gestational diabetes mellitus (GDM). The aim of this study is to analyze vitamin D status evolution in a population of pregnant women with and without GDM. Two-hundred women were included from January 2019 to February 2022 as follows: Control group -CG-, Lifestyle group -LG- (GDM not requiring insulin), and Insulin group -IG- (GDM requiring insulin). Visits were carried out at baseline, antenatal, postpartum, and 1 year after birth. Vitamin D levels, weight, and insulin resistance were measured at every visit. Data about the season, vitamin D supplementation, Mediterranean diet adherence, and physical activity were included. In the three groups, 134 women were included in the CG, 43 in the LG, and 23 in the IG. Vitamin D levels were similar among the groups at baseline, but they were significantly higher in the LG and IG in comparison with the CG at the antenatal visit and significantly higher in the IG vs. CG and LG at the postpartum and 1 year after birth visits. Vitamin D levels were independently related to vitamin D supplementation and the season at baseline, to the season and belonging to the LG or IG at the antenatal visit, and were only independently associated with belonging to the IG at postpartum and 1 year after birth visits. In conclusion, in our population, women with GDM requiring insulin had higher levels of vitamin D in comparison with those not requiring insulin and healthy controls at postpartum and 1 year after pregnancy. Requiring insulin during pregnancy seems to be a factor that independently determines the levels of vitamin D until 1 year after birth. More studies are required to reproduce these data in other populations and to elucidate the mechanisms underlying these findings.
High-Yield Recovery of Antioxidant Compounds from Bambusa chungii Culms Using Pressurized Hot Water Extraction
Authors: Cao Xianshuang , Zhang Yaoyao , Xun Hang , Wang Jin , Tang Feng
Published: November 2022

A large amount of waste from Bambusa chungii culms is generated from the bamboo pulping industry, causing disposal problems. Nevertheless, bamboo culms are a suitable source of functional ingredients, such as antioxidant compounds. However, because of the high compactness and tightness in their material structure, obtaining phytochemicals from bamboo culms using conventional organic solvent extraction methods can be inefficient. In this research, we developed a pressurized hot water extraction (PHWE) method to recover 19 target phenolic compounds from Bambusa chungii culms. The extracted compounds were determined by ultra-high performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UPLC–QTOF–MS). The antioxidant potential of the extracts was evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and ferric reducing antioxidant power (FRAP) assays. We investigated the effects of temperature, extraction time, and the material-to-liquid ratio on PHWE, and these parameters were optimized with a Box–Behnken design experiment and response surface tool. The optimal extraction condition was found at 170 °C, with a 1:30 g/mL material-to-liquid ratio and a 14 min extraction time. Following these optimal parameters, the total yield of target phenolic compounds (TYPC) reached 3.85 mg/g of raw material, and the half-maximal inhibitory concentrations (IC50) for the DPPH and ABTS tests were 94.7 mg/L and 21.88 mg/L, respectively. The FRAP value was 1.23 μmol FSE/mg of dried extract. A strong correlation between TYPC and the antioxidant activity of the extract was confirmed. The TYPC and antioxidant capacity of the optimal PHWE extract of the Bambusa chungii culms were both considerably higher than those of extracts obtained from conventional solvent extractions. These results indicated that PHWE is an excellent green technique for recovering phenolic compounds from bamboo culms, and the PHWE extracts of Bambusa chungii culms may be a good source of natural antioxidants.
Hydrogen Sulfide Metabolizing Enzymes in the Intestinal Mucosa in Pediatric and Adult Inflammatory Bowel Disease
Authors: Stummer Nathalie , Weghuber Daniel , Feichtinger René G. , Huber Sara , Mayr Johannes A. , Kofler Barbara , Neureiter Daniel , Klieser Eckhard , Hochmann Sarah , Lauth Wanda , Schneider Anna M.
Published: November 2022

Hydrogen sulfide (H2S) is a toxic gas that has important regulatory functions. In the colon, H2S can be produced and detoxified endogenously. Both too little and too much H2S exposure are associated with inflammatory bowel disease (IBD), a chronic intestinal disease mainly classified as Crohn’s disease (CD) and ulcerative colitis (UC). As the pathogenesis of IBD remains elusive, this study’s aim was to investigate potential differences in the expression of H2S-metabolizing enzymes in normal aging and IBD. Intestinal mucosal biopsies of 25 adults and 22 children with IBD along with those of 26 healthy controls were stained immunohistochemically for cystathionine-γ-lyase (CSE), 3-mercapto-sulfurtransferase (3-MST), ethylmalonic encephalopathy 1 protein (ETHE1), sulfide:quinone oxidoreductase (SQOR) and thiosulfate sulfurtransferase (TST). Expression levels were calculated by multiplication of the staining intensity and percentage of positively stained cells. Healthy adults showed an overall trend towards lower expression of H2S-metabolizing enzymes than healthy children. Adults with IBD also tended to have lower expression compared to controls. A similar trend was seen in the enzyme expression of children with IBD compared to controls. These results indicate an age-related decrease in the expression of H2S-metabolizing enzymes and a dysfunctional H2S metabolism in IBD, which was less pronounced in children.
Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
Authors: Diaz-de-Leon Alejandra , Cuan-Urquizo Enrique , Roman-Flores Armando , Gordillo J. L.
Published: November 2022

Bi-stable mechanisms can remain in two positions without a power input or locking system. These mechanisms can achieve large displacements with low energy, requiring power only during switching. However, their design and analysis are challenging due to the non-linear behavior of their flexible members. Researchers commonly use experimental approaches to study and validate bi-stability, which demands efficient and affordable setups. This work presents a versatile and easy-to-fabricate setup to investigate the bi-stable behavior of flexure beams made of styrene plastic. The testing setup is made of laser-cut acrylic connectors and 3D-printed fixtures. The experiments demonstrate that all tested beams are bi-stable and highlight the impact of thickness on their behavior. The critical forces obtained range from 0.0277 N to 5.2724 N between the thinnest and the thickest samples. The distance traveled before snapping increments, with thickness, ranges from 18.78 mm to 32.6 mm. The samples were subjected to a cyclic compressive load and demonstrated a considerable decrement in the critical forces after the first load. Thicker flexural beams present a more significant deformation, causing fractures in some cases after the five loads. Regardless of the thickness, all samples traveled the same total linear displacement of 45 mm. The presented setup demonstrated consistency and rapidness in experimental bi-stability characterization, with the styrene plastic flexure beams proving to be suitable for studying bi-stability.
Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things
Authors: Hasan Ahmad , Khan Muazzam A. , Shabir Balawal , Munir Arslan , Malik Asad Waqar , Anwar Zahid , Ahmad Jawad
Published: November 2022

The internet of things (IoT) is prone to various types of denial of service (DoS) attacks due to their resource-constrained nature. Extensive research efforts have been dedicated to securing these systems, but various vulnerabilities remain. Notably, it is challenging to maintain the confidentiality, integrity, and availability of mobile ad hoc networks due to limited connectivity and dynamic topology. As critical infrastructure including smart grids, industrial control, and intelligent transportation systems is reliant on WSNs and IoT, research efforts that forensically investigate and analyze the cybercrimes in IoT and WSNs are imperative. When a security failure occurs, the causes, vulnerabilities, and facts behind the failure need to be revealed and examined to improve the security of these systems. This research forensically investigates the performance of the ad hoc IoT networks using the ad hoc on-demand distance vector (AODV) routing protocol under the blackhole attack, which is a type of denial of service attack detrimental to IoT networks. This work also examines the traffic patterns in the network and nodes to assess the attack damage and conducts vulnerability analysis of the protocol to carry out digital forensic (DF) investigations. It further reconstructs the networks under different modes and parameters to verify the analysis and provide suggestions to design roubust routing protocols.
Influence of Ambient Temperature on Optical Characteristics and Power Consumption of LED Lamp for Automotive Headlamp
Authors: Chang Chun-Hsin , Teng Tun-Ping , Teng Tun-Chien
Published: November 2022

In this study, the authors propose a method for evaluating the influence of ambient temperature (Ta) on the optical characteristics and power consumption of a LED lamp used for the automotive headlamp, which helps the designer to figure out the acceptable range of the ambient temperature for the LED lamps to work well in the practical case. The LED lamp was fixed on the test holder and placed in a fixed position in the air circulation temperature control oven to measure the illuminance, spectrum in three different directions, and power consumption under various Ta. The experimental results indicate that Ta has little effect on the correlated color temperature (CCT), chromaticity coordinates, and angular distribution of the emitting light. In contrast, Ta has a significant effect on the lamp’s resistance, thus affecting illuminance and power consumption. When the Ta increased from 30 °C to 60 °C, the illuminance of the low beam and high beam decreased on average by 16.6% and 21.7%, respectively, while the power consumption of the low beam and high beam decreased on average by 21.4% and 22.2%, respectively. The drop in the luminous flux output indicates the probability of originally qualified automotive LED headlamps becoming unqualified in high Ta.
Experimental and Numerical Study on UHPC–RC Decks within Hogging Moment Region
Authors: Long Guanxu , Zhou Ruigen , Ma Haiying , Xin Gongfeng , Emadi Seyyedbehrad , Shi Xuefei
Published: November 2022

Steel–concrete composite continuous bridges can take full advantage of concrete and steel, but in regions with hogging moments, cracking of the concrete deck is a big issue affecting the durability of bridges. In order to solve cracking problems within the hogging moment region, this study proposes a composite deck method using ultra-high-performance concrete (UHPC) and regular concrete (RC). In this way, the layers of UHPC and RC are composited to take advantage of the high tensile strength of UHPC materials to improve the anti-crack performance of the concrete deck within the hogging moment region. Four different specimens were designed to account for different layer thickness of UHPC. Bending experiments of the UHPC–RC composite deck were undertaken and a corresponding finite element model was established to study the behavior of the UHPC–RC composite deck. The regularity of crack development in different UHPC layer thicknesses was revealed, and the load-displacement results were compared to investigate the ultimate capacity of a steel–concrete composite bridge structure using a UHPC–RC composite deck. Finally, with consideration of material cost, a reasonable UHPC layer thickness suitable for the composite deck was obtained to provide a reference for the design of a UHPC–RC composite deck.
Multi-Scale Toolbox: An Automated ArcGIS Tool for Evaluating Pupil–Teacher Ratios in U.S. Public School Districts
Authors: Wu Xiu , Zhang Jinting , Zhang Yaoxuan , Zhang Daojun
Published: November 2022

Due to the teacher shortage in the U.S., an automatic toolbox with secondary development based on the ArcPy package was created to explore the spatial imbalance of the pupil–teacher ratio. It consists of four tools (or toolsets) for multi-scale spatial visualization, a sensitivity analysis with a heat map, the ordinary least squares regression with spatial autocorrelation, and the random forest tree regression. This study demonstrated the application of the toolset in the evaluation of educational resource spatial misallocation. Firstly, multi-scale analysis results showed that, the loss of teachers was mainly distributed in Oregon, Nevada, Arizona, and California from the state level, while it focused on such counties as Terrebonne Parish, Concordia Parish, and Bienville Parish in Louisiana in the county level. Secondly, it was found through heatmap sensitivity analysis that pupil–teacher ratios were highly related to low levels of student support services staff, free lunch programs, and low levels of local education agency (LEA) administrators. Then, the OLS tool was used to automatically calculate the spatial weighted matrix, the Moran I, R2, and AICC indices, AdjR2, F-Stat, F-Prob, and the Wald statistic, which showed whether the model was significant or not. This was followed by random forest tree regression modeling, which found that the LEA administrative support staff and the totally free lunch number highly impacted pupil–teacher ratios. Besides, the designed tool provided ribbons for the Common Core of Data (CCD) to link to other data sources.
Software for Monitoring the In-Service Efficiency of Hydraulic Pumps
Authors: Anton Alin-Adrian , Cococeanu Adrian , Muntean Sebastian
Published: November 2022

The present paper introduces the creation of an algorithm and the software used to determine the energetic performance and monitor the efficiency of hydraulic pumps working in various industrial applications, such as water supply systems, water treatment processes, and irrigation systems, particularly in the cases where there is no permanent monitoring. Our field investigations and the surveyed literature show that the only parameter that is neither monitored nor computed is the efficiency of the pumps. The software implementation allows for determining the in-service efficiency of the pumps and comparing it to the value associated with the best efficiency point (BEP). The solution is user-friendly and can be easily installed on any computer or smartphone. The software has been applied and tested in the Hydraulic Machines Laboratory at the “Politehnica” University Timişoara and at the AQUATIM S.A. regional water supply company. The software module monitors the operating regimes of the pumps and supports the deployment of predictive maintenance and servicing.
Changes in Soil Biological Properties after Sewage Sludge and Pesticide Application in Wheat Cultivation
Authors: Wydro Urszula , Jankowska Magdalena , Wołejko Elżbieta , Kondzior Paweł , Łozowicka Bożena , Kaczyński Piotr , Rodziewicz Joanna , Janczukowicz Wojciech , Pietryczuk Anna , Cudowski Adam , Jabłońska-Trypuć Agata
Published: November 2022

One of the solutions implemented in order to improve the quality of soils exposed to pesticides is the application of sewage sludge, which is a by-product of wastewater treatment. As an organic substrate, it provides soil with important nutrients, such as nitrogen and phosphorus, and enriches it with organic matter, thanks to which it can be a valuable fertilizer. The aim of the presented research was to evaluate the influence of granulated sewage sludge (GSS) on the biological properties of soil treated with herbicides (MCPA and dicamba) and fungicides (thiophanate-methyl and azoxystrobin). The following aspects were investigated: the activity of selected soil enzymes, the genetic biodiversity of bacteria and fungi, and the abundance of the bacterial gene responsible for ammonia oxidation. A field experiment was conducted, in which granulated sewage sludge (GSS) was applied to soil at a single dose of 3 t/ha. Wheat (Triticum aestivum L.) was sown on the prepared plots. The herbicides (H) and fungicides (F) as well as their mixture (F + H) were applied to the plants in the appropriate growth phases in the doses recommended by the producer. The control was soil without sewage sludge (C). The samples taken were tested for: dehydrogenases, catalases and urease activities, genetic biodiversity structure of bacteria and fungi by TRFLP assay, and the abundance of the bacterial amoA gene by qPCR. On the basis of the obtained results, it was found that the application of pesticides to soil fertilized with sewage sludge influenced the enzymatic activity of soil, and their activity differed depending on the tested enzyme. The activity of URE and DHA on the plots with GSS was higher by approx. 20% and 30%, respectively, as compared to the plots without GSS application. Moreover, both the genetic biodiversity of microorganisms and the abundance of amoA gene differed depending on the variant of the experiment. The GSS treatment of soil significantly influenced the growth of the studied gene as compared to C, and its abundance was 9.15 log10 gene copies/g DW of soil. Due to the content of nutrients in sewage sludge, it can be a valuable fertilizer in agricultural crops treated with pesticides.
Use of Data Mining to Predict the Influx of Patients to Primary Healthcare Centres and Construction of an Expert System
Authors: Cubillas Juan J. , Ramos María I. , Feito Francisco R.
Published: November 2022

In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the management of healthcare resources in primary care centres. The main objective of this work is to develop a model capable of predicting the number of patients who will demand health care in a primary care centre on a daily basis. This model is integrated into a decision support system that is accessible and easy to use by the manager through a web application. In this case, data from a primary care centre in the city of Jaén, Spain, were used. The model was estimated using spatial-temporal training data, the daily health demand data in that centre for five years, and a series of meteorological data. Different regression algorithms have been employed. The workflow requires selecting the parameters that influence the health demand prediction and discarding those that distort the model. The main contribution of this research is the daily prediction of the number of patients attending the health centre with absolute errors better than 3%, which is crucial for decision-making on the sizing of health resources in a primary care health centre.
Deep Learning for Intelligent Human–Computer Interaction
Authors: Lv Zhihan , Poiesi Fabio , Dong Qi , Lloret Jaime , Song Houbing
Published: November 2022

In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human–Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children’s speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.
Development and Investigation of a Synthetic Inertia Algorithm
Authors: Cicėnas Paulius , Radziukynas Virginijus
Published: November 2022

In this article, we present a synthetic inertia (SI) algorithm that allows for the simulation of the inertia response of a traditional generator to an electrical power system. To obtain the algorithm, detailed dynamic calculations were performed using a large real-system dynamic model in Siemens PSS/E modeling packages (PSS/E). Output error (OE), autoregressive moving average model with exogenous inputs (ARMAX), and Box–Jenkins (BJ) models of parametric identification were used to obtain the SI algorithm. The dynamic calculation results such as active power output, frequency variation in the presence of the active power deficit, surplus, and short circuit in the power system were used to compare the algorithm accuracy with comparable generator results. For this purpose, the power system stabilizer (PSS) and the turbine governor were not evaluated to obtain the most accurate possible active power change due to the characteristics of the generator. The errors were evaluated by using the models to determine the error estimates for the correlation coefficient (Ryŷ), root mean square deviation (RMSE), and coefficient of determination (R2). Based on the obtained results, we established that the OE mathematical model should be used, as it is more efficient compared to the ARMAX and BJ models.
A Behavioral-Based Fingerprint Liveness and Willingness Detection System
Authors: Almehmadi Abdulaziz
Published: November 2022

Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers are created, to gaining access to the decision-maker level, where the decision is made based on low matching criteria. Even though fingerprint sensor-level countermeasures have developed advanced metrics to detect any attempt to dupe the system, attackers still manage to outwit a fingerprint verification system. In this paper, we present the Micro-behavioral Fingerprint Analysis System (MFAS), a system that records the micro-behavior of the user’s fingertips over time as they are placing their fingerprint on the sensor. The system captures the stream of ridges as they are formed while placed on a sensor to combat the attacks that deceive the sensor. An experiment on 24 people was conducted, wherein the fingerprints and the behavior of the fingertip as it is placed were collected. Subsequently, a gummy finger was created to try to fool the system. Further, a legitimate user was chosen to participate in an experiment that mimicked an attempt to use their fingertip unwillingly to detect coerced fingerprint placement. After applying the micro-behavior, the system reported 100% true positives and 0% false-negatives when providing legitimate vs. gummy-based fingerprints to authenticate a malicious user. The system also reported a 100% accuracy in differentiating between a voluntary and a coerced fingerprint placement. The results improve the fingerprint robustness against attacks on a fingerprint sensor by factoring in micro-behavior, thus helping to overcome fake and coerced fingerprint attacks.
Communication-Efficient Secure Federated Statistical Tests from Multiparty Homomorphic Encryption
Authors: Annamalai Meenatchi Sundaram Muthu Selva , Jin Chao , Aung Khin Mi Mi
Published: November 2022

The power and robustness of statistical tests are strongly tied to the amount of data available for testing. However, much of the collected data today is siloed amongst various data owners due to privacy concerns, thus limiting the utility of the collected data. While frameworks for secure multiparty computation enable functions to be securely evaluated on federated datasets, they depend on protocols over secret shared data, which result in high communication costs even in the semi-honest setting.In this paper, we present methods for securely evaluating statistical tests, specifically the Welch’s t-test and the χ2-test, in the semi-honest setting using multiparty homomorphic encryption (MHE). We tested and evaluated our methods against real world datasets and found that our method for computing the Welch’s t-test and χ2-test statistics required 100× less communication than equivalent protocols implemented using secure multiparty computation (SMPC), resulting in up to 10× improvement in runtime. Lastly, we designed and implemented a novel protocol to perform a table lookup from a secret shared index and use it to build a hybrid protocol that switches between MHE and SMPC representations in order to calculate the p-value of the statistics efficiently. This hybrid protocol is 1.5× faster than equivalent protocols implemented using SMPC alone.
A Low-Temperature and Low-Pressure Distillation Plant for Dairy Wastewater
Authors: Altieri Giuseppe , De Luca Vincenzo , Genovese Francesco , Matera Attilio , Scarano Luciano , Di Renzo Giovanni Carlo
Published: November 2022

The paper investigates an alternative treatment plant for the typical wastewater effluent of a cheese-making industry, mainly composed of acid or sweet whey mixed with washing-water. Two variable parameters have been considered during the tests: four treatment temperatures (39, 46, 53 and 60 °C) and three solid content values (30%, 50% and 70%) of the concentrated product. The minimum and maximum values of the removal efficiency (pollutant amount into concentrate related to raw whey) range from 94.6% to 97.7% for conductivity, from 98.3% to 99.5% for BOD5, from 98.7% to 99.6% for COD and from 98.2% to 99.3% for Total Nitrogen. The plant capacity ranges from about 2 L/h (at 39 °C) to 6 L/h (at 60 °C) of processed whey. On the basis of the experimental findings, the proposed purification technology has demonstrated its suitability both to purify the effluent wastewater and to recover high-quality products (e.g., whey protein concentrate, lactose), thanks to its low thermal damage on the treated product and to its relatively low energy consumption from 0.4 kWh/L (at 60 °C) to 1.0 kWh/L (at 39 °C) of processed whey, with a Coefficient of Performance from approximately 0.6 up to 1.5.
Dynamic Monitoring of the Standard Penetration of PHC Tubular Piles and Analysis of the Construction Effect Based on Monocular Visual Digital Photography
Authors: Zhang Guojian , Liu Zhiyong , Xiong Wei , Zhang Sifeng , Liu Shengzhen , Wang Zhiwei , Wang Yushuai
Published: November 2022

The penetration of large-diameter tubular piles and their relevant construction effect have significant influences on the bearing capacity of piles and nearby buildings (structures). This study focuses on developing an indoor test model device for the dynamic field monitoring of the construction effect of penetrated large-diameter tubular piles based on monocular visual digital photography. The results show that the relationship between the penetration of the tubular piles and time function (blow counts) changes from a logarithmic function to a linear function when piles penetrate from the loose layer to the sandy layer, and then to the silty soil layer. The penetration rates differ significantly under different formation conditions. There are obvious plugging and squeezing effects as tubular piles penetrate different strata. The plugging effect radiates outward in a rectangular shape. The influence sphere of the squeezing effect is divided into the shear failure zone, radial squeezing zone, and hemispherical expansion zone. According to the measurement data, the squeezing effect increases first and then weakens during the construction of tubular piles. This makes the adjacent pile deviate from the initial position by 17.4 mm, making the next pile deviate from the initial position by 6.4 mm, to the maximum extent. This further verifies the superiority of pile-jumping construction. The research conclusions can provide reasonable suggestions and a reference basis to improve the penetration parameters of tubular piles and optimise their construction techniques.
Biotechnological Conversions of Mizithra Second Cheese Whey by Wild-Type Non-Conventional Yeast Strains: Production of Yeast Cell Biomass, Single-Cell Oil and Polysaccharides
Authors: Vasilakis Gabriel , Karayannis Dimitris , Massouras Theofilos , Politis Ioannis , Papanikolaou Seraphim
Published: November 2022

The cultivation of oleaginous yeasts on various agro-industrial residues and the subsequent production of microbial lipids (single-cell oils), which can be used as starting materials for the synthesis of both “second generation” biodiesel and various types of oleochemicals, is considered as one of the most important approaches of the Industrial Biotechnology, in terms of circular economy and green and sustainable development. In this study, seven wild-type non-conventional yeast strains were evaluated for their growth on a semi-defined medium with cheese whey lactose as a sole carbon source. Five of these strains were further batch-cultivated into the complex substrate that contained second cheese whey, which is the main by-product of Mizithra whey cheese manufacture, after centrifugation and filtration without any extra mineral salts, carbon, or nitrogen source addition. All these five strains grown in second cheese whey produced mainly yeast biomass and to lesser extent microbial lipids and other interesting metabolites, such as polysaccharides. The strain Cryptococcus curvatus ATCC 20509 produced the highest total dry weight (TDW) amount (22.4 g/L), that contained 3.4 g/L of intra-cellular lipids, followed by C. curvatus NRRL Y-1511, which produced 20.6 g/L of TDW and 3.2 g/L lipids. A novel, non-previously systematically studied strain, namely Papiliotrema laurentii NRRL Y-2536, produced significant quantities of TDW (22.0 g/L) and, interestingly, secreted quantities of exopolysaccharides. Fed-batch shake-flask cultivation of C. curvatus ATCC 20509 in pretreated second cheese whey, pulse-supplemented with condensed cheese whey-derived lactose, led to the significant TDW quantity of 38.1 g/L that contained c. 57% w/w of total lipids (lipids at a concentration 21.7 g/L were produced). Cellular lipids of all microorganisms, mainly stored as triacylglycerols, contained in variable quantities the fatty acids Δ9C18:1, C16:0, Δ9,12C18:2 and C18:0, constituting perfect candidates for the synthesis of “second generation” biodiesel.
Accumulation of Heavy Metals in Bottom Sediment and Their Migration in the Water Ecosystem of Kapshagay Reservoir in Kazakhstan
Authors: Ismukhanova Laura , Choduraev Temirbek , Opp Christian , Madibekov Azamat
Published: November 2022

The bottom sediment of reservoirs has many functions. Among them, matter sorption is a very important one, and results in many side-effects on the reservoir sediment forming the water–bottom sediment system. As a result, bottom sediment can also be an indicator of anthropogenic water pollution. There is only very little knowledge of this situation in the study area. The main objective was the analysis of heavy metal accumulation in bottom sediment, as well as their ability to migrate throughout the water–bottom sediment system and their spatial distribution in the Kapshagay Reservoir in Kazakhstan. Heavy metal concentrations, in the both water samples and the bottom sediment, were determined using the atomic absorption spectrophotometric method. Surfer software was used to visualize the processes of migration and accumulation. Another objective was the development of model maps of the spatial distribution of metals in the reservoir water area, which indicated significant anthropogenic loads. It is obvious that both the transboundary inflow of the Ili River and the inflow from small rivers in the territory of Kazakhstan are the reasons for the anthropogenic water and sediment load. The results of the spectrometric analysis verify the water pollution in the reservoir, revealing increased concentrations of zinc reaching up to 10.8 µg/L and lead up to 32.7 µg/L, transported by the transboundary runoff of the Ili River and by the small rivers on the left bank into the Kapshagay Reservoir. Sediment concentrations close to the central part and dam zone of the reservoir reached the following values: zinc up to 37.0 mg/kg and lead up to 8.8 mg/kg. The results of this study indicate a significant anthropogenic load of the ecological conditions of the Kapshagay Reservoir. This is discussed and compared with other relevant studies.
The Impact of Vairimorpha (Nosema) ceranae Natural Infection on Honey Bee (Apis mellifera) and Bee Bread Microbiota
Authors: Georgi Irini , Asoutis Didaras Nikos , Nikolaidis Marios , Dimitriou Tilemachos G. , Charistos Leonidas , Hatjina Fani , Amoutzias Grigoris D. , Mossialos Dimitris
Published: November 2022

Honey bees face new challenges, ranging from climate crisis to emerging pathogens such as Vairimorpha (Nosema) ceranae that synergistically cause a syndrome designated as colony collapse disorder (CCD). This study employed a metataxonomic approach in order to investigate if V. ceranae affects gut microbiota (bacteria and fungi) of adult A. mellifera honey bees as well as microbiota of bee bread (BB) stored in colonies demonstrating severe V. ceranae infection (spore counts >2,500,000 per bee) as compared with colonies exhibiting very low spore counts (<40,000 per bee). Alpha-diversity analysis revealed an overall decrease in microbial diversity reflected by number of observed unique operating taxonomic units (OTUs) regarding both bacteria and fungi in honey bee and ΒΒ samples. Further analysis demonstrated that Podosphaera spp. were absent in BB samples collected from colonies with high spore counts, while relative abundance of Blumeria spp. was significantly decreased. Interestingly, relative abundance of Rosenbergiella spp. was increased in BB samples collected from colonies with high spore counts. The reason for these findings remains elusive. Although further research is warranted, overall reduced microbial diversity and relative abundance of certain microbial groups may serve as biomarkers of colony collapse.
Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network
Authors: Liu Chien-Liang , Tseng Chun-Jan , Hsaio Wen-Hoar , Wu Sheng-Hao , Lu Shu-Rong
Published: November 2022

Predicting the wafer material removal rate (MRR) is an important step in semiconductor manufacturing for total quality control. This work proposes a deep learning model called a fusion network to predict the MRR, in which we consider separating features into shallow and deep features and use the characteristics of deep learning to perform a fusion of these two kinds of features. In the proposed model, the deep features go through a sequence of nonlinear transformations and the goal is to learn the complex interactions among the features to obtain the deep feature embeddings. Additionally, the proposed method is flexible and can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Once the learning of deep features is completed, the proposed model uses the shallow features and the learned deep feature embeddings to obtain new features for the subsequent layers. This work performs experiments on a dataset from the 2016 Prognostics and Health Management Data Challenge. The experimental results show that the proposed model outperforms the competition winner and three ensemble learning methods. The proposed method is a single model, whereas the comparison methods are ensemble models. Besides the experimental results, we conduct extensive experiments to analyze the proposed method.
The Relationship between Atherosclerosis and Gut Microbiome in Patients with Obstructive Sleep Apnoea
Authors: Szabo Helga , Piroska Marton , Hernyes Anita , Zoldi Luca , Juhasz Janos , Ligeti Balazs , Makra Nora , Szabo Dora , Bikov Andras , Kunos Laszlo , Tarnoki Adam Domonkos , Tarnoki David Laszlo
Published: November 2022

Background: Obstructive sleep apnoea (OSA) and gut dysbiosis are known risk factors for atherosclerosis. However, only very few studies have been focused on the relationship between OSA, atherosclerosis, and the intestinal microbiome, all in animal models. Methods: Twenty-two patients with OSA, 16 with and 6 without carotid atherosclerosis were involved in the study. After a diagnostic sleep examination, the intima media thickness (IMT) was measured and plaques were found using carotid ultrasound. Blood was also drawn for metabolic profile, and a stool sample was provided for 16S ribosomal RNA microbiome investigation. Results: An increased maximal common carotid artery (CCA) IMT was significantly associated with decreased phylum-level diversity. The level of Peptostreptococcaceae was significantly lower in atherosclerotic subjects. Some other candidate microbes appeared in the two groups at the genus level as well: Bilophila, Romboutsia, Slackia, and Veillonella in the non-atherosclerotic group; and Escherichia-Shigella, Prevotella, and Ruminococcaceae in the atherosclerotic group. Conclusions: This is the first pilot research to analyze the association between the gut microbiome and atherosclerosis in adult patients with OSA with and without carotid atherosclerosis. Dysbiosis and individual bacteria may contribute to the development of carotid atherosclerosis in patients with OSA. Further investigations are necessary to reveal a more precise background in a larger sample.
Research on the Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network
Authors: Chen Honglin , Li Chunting , Chaomurilige Chaomurilige
Published: November 2022

Visual tracking is one of the key research fields in computer vision. Based on the combination of correlation filter tracking (CFT) model and deep convolutional neural networks (DCNNs), deep correlation filter tracking (DCFT) has recently become a critical issue in visual tracking because of CFT’s rapidity and DCNN’s better feature representation. However, DCNNs are often complex in structure, which most possibly results in the conflict between the rapidity and accuracy of DCFT. To reduce such conflict, this paper proposes a model mainly including: (1) Based on the pre-pruning network obtained by feature channel importance, an optimal global tracking pruning rate (GTPR) is determined in terms of the contribution of filter channels to tracking response. (2) Based on (GTPR), an alternative convolutional kernel is defined to replace non-important channel kernels, which leads to the further pruning of the feature network. (3) An online updating pruned feature network with a structural similarity index is employed to adapt the model to tracking scene changes. (4) The proposed model was performed on OTB2013; experimental results demonstrate the model can effectively enhance speed with a 45% increment while guaranteeing tracking accuracy, and improve tracking accuracy with a 4% increment when tracking scene changes take place.
RCM: A Remote Cache Management Framework for Spark
Authors: Song Yixin , Yu Junyang , Li Bohan , Li Han , He Xin , Wang Jinjiang , Zhai Rui
Published: November 2022

With the rapid growth of Internet data, the performance of big data processing platforms is attracting more and more attention. In Spark, cache data are replaced by the Least Recently Used (LRU) Algorithm. LRU cannot identify the cost of cache data, which leads to replacing some important cache data. In addition, the placement of cache data is random, which lacks a measure to find efficient cache servers. Focusing on the above problems, a remote cache management framework (RCM) for the Spark platform was proposed, including a cache weight generation module (CWG), cache replacement module (CREP), and cache placement module (CPL). CWG establishes initial weights from three main factors: the response time of the query database, the number of queries, and the data size. Then, CWG reduces the old data weight through a time loss function. CREP promises that the sum of cache data weights is maximized by a greedy strategy. CPL allocates the best cache server for data based on the Kuhn-Munkres matching algorithm to improve cooperation efficiency. To verify the effectiveness of RCM, RCM is implemented on Redis and deployed on eight computing nodes and four cache servers. Three groups of benchmark jobs, PageRank, K-means and WordCount, is tested. The result of experiments confirmed that compared with MCM, SACM and DMAOM, the execution time of RCM is reduced by 42.1% at most.
An Analytical Solution to Two-Region Flow Induced by Constant-Head Pumping in an Unconfined Aquifer
Authors: Zong Yijie , Xiao Liang
Published: November 2022

This paper proposes an analytical solution to the problem of two-region flow induced by constant-head pumping tests in an unconfined aquifer based on the Dupuit assumption. The two-region flow includes a finite non-Darcian pattern near the pumping well and a semi-finite Darcian pattern in the rest region. By Izbash’s equation, the solution to the two-region flow is derived using the Boltzmann method. The reliability of the proposed solution is investigated through comparisons with the numerical solution by COMSOL Multiphysics and the hydraulic head−time data from the pumping test in Wisconsin. The influences of the finite non-Darcian flow pattern on the hydraulic head and pumping rate are also discussed. The results demonstrate that a greater degree of non-Darcian turbulence can effectively increase the pumping flow rate, although such influence can be reduced over time. A method for determining the range of the non-Darcian region has been developed and validated by the specific discharge and Reynolds number.
A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
Authors: Hernandez-Almazan Jorge-Arturo , Chalmeta Ricardo , Roque-Hernández Ramón Ventura , Machucho-Cadena Rubén
Published: November 2022

A Collaborative Networked Organization (CNO) is a set of entities that operate in heterogeneous contexts and aim to collaborate to take advantage of a business opportunity or solve a problem. Big data allows CNOs to be more competitive by improving their strategy, management and business processes. To support the development of big data ecosystems in CNOs, several frameworks have been reported in the literature. However, these frameworks limit their application to a specific CNO manifestation and cannot conduct intelligent processing of big data to support decision making at the CNO. This paper makes two main contributions: (1) the proposal of a metaframework to analyze existing and future frameworks for the development of big data ecosystems in CNOs and (2) to show the Collaborative Networked Organizations–big data (CNO-BD) framework, which includes guidelines, tools, techniques, conceptual solutions and good practices for the building of a big data ecosystem in different kinds of Collaborative Networked Organizations, overcoming the weaknesses of previous issues. The CNO-BD framework consists of seven dimensions: levels, approaches, data fusion, interoperability, data sources, big data assurance and programmable modules. The framework was validated through expert assessment and a case study.