bluuebunny/crossref_metadata_2025 huggingface dataset:
Downregulation of Glutathione-Mediated Detoxification Capacity by Binge Drinking Aggravates Acetaminophen-Induced Liver Injury through IRE1α ER Stress Signaling
Authors: Kim Sou Hyun , Choi Hun Ji , Seo Hyeji , Kwon Doyoung , Yun Jaesuk , Jung Young-Suk
Published: December 2021

Overdose of acetaminophen (APAP) can cause severe liver injury. Although alcohol is considered a risk factor for APAP toxicity, the mechanism underlying the interaction between alcohol and APAP remains unclear. Binge alcohol (5 g/kg every 12 h, 3 doses) reduced the concentration of cysteine and glutathione (GSH) and decreased expression of cystathionine β-synthase (CβS), cystathionine γ-lyase (CγL), and glutamate cysteine ligase catalytic subunit (GCLC) in the livers of male C57BL/6 mice. Furthermore, the levels of GSH S-transferase (GST) and GSH peroxidase (GPx) were decreased. To evaluate the effect of binge drinking on APAP-induced liver injury, 300 mg APAP was administered following alcohol binges. APAP in the binge group significantly amplified the serum ALT more than two fold and enhanced the pro-apoptotic proteins with a severe centrilobular necrosis compared to APAP alone. APAP treatment after alcohol binges caused lower levels of hepatic cysteine and GSH than APAP alone over 24 h, indicating that alcohol binges reduced GSH regenerating potential. Exposure to APAP after binge treatment significantly increased oxidative stress (lipid peroxidation) and endoplasmic reticulum (ER) stress (Grp78 and ATF6) markers at 6 h after treatment. Notably, the IRE1α/ASK1/MKK4/JNK pathway was activated, whereas CHOP expression was reduced by APAP administration in mice with pre-exposed alcohol binges compared with APAP alone. Thus, pretreatment with binge alcohol decreases GSH-mediated antioxidant capacity and contributes to augmentation of liver injury caused by subsequent APAP administration through differential ER stress signaling pathway.
Clinical Diagnosis and Treatment of Leigh Syndrome Based on SURF1: Genotype and Phenotype
Authors: Lee Inn-Chi , Chiang Kuo-Liang
Published: December 2021

SURF1 encodes the assembly factor for maintaining the antioxidant of cytochrome c oxidase (COX) stability in the human electron respiratory chain. Mutations in SURF1 can cause Leigh syndrome (LS), a subacute neurodegenerative encephalopathy, characterized by early onset (infancy), grave prognosis, and predominant symptoms presenting in the basal ganglia, thalamus, brainstem, cerebellum, and peripheral nerves. To date, more than sixty different SURF1 mutations have been found to cause SURF1-associated LS; however, the relationship between genotype and phenotype is still unclear. Most SURF1-associated LS courses present as typical LS and cause early mortality (before the age of ten years). However, 10% of the cases present with atypical courses with milder symptoms and increased life expectancy. One reason for this inconsistency may be due to specific duplications or mutations close to the C-terminus of the SURF1 protein appearing to cause less protein decay. Furthermore, the treatment for SURF1-associated LS is unsatisfactory. A ketogenic diet is most often prescribed and has proven to be effective. Supplementing with coenzyme Q and other cofactors is also a common treatment option; however, the results are inconsistent. Importantly, anti-epileptic drugs such as valproate—which cause mitochondrial dysfunction—should be avoided in patients with SURF1-associated LS presenting with seizures.
New Perspectives in the Development of the Artificial Sport Trainer
Authors: Fister Iztok , Salcedo-Sanz Sancho , Iglesias Andrés , Fister Dušan , Gálvez Akemi , Fister Iztok
Published: December 2021

The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines.
Analysis of Sea Storm Events in the Mediterranean Sea: The Case Study of 28 December 2020 Sea Storm in the Gulf of Naples, Italy
Authors: Fortelli Alberto , Fedele Alessandro , De Natale Giuseppe , Matano Fabio , Sacchi Marco , Troise Claudia , Somma Renato
Published: December 2021

The coastline of the Gulf of Naples, Italy, is characterized by a series of infrastructures of strategic importance, including touristic and commercial ports between Pozzuoli to Sorrento, main roads, railways, and urban areas. Furthermore, the Gulf of Naples hosts an intense traffic of touristic and commercial maritime routes. The risk associated with extreme marine events is hence very significant over this marine and coastal area. On 28 December 2020, the Gulf of Naples was hit by an extreme sea storm, with severe consequences. This study focuses on the waterfront area of Via Partenope, where the waves overrun the roadway, causing massive damage on coastal seawall, road edges, and touristic structures (primarily restaurants). Based on the analysis of the meteorological evolution of the sea storm and its effects on the waterfront, we suggest that reflective processes induced on the sea waves by the tuff cliffs at the base of Castel dell’Ovo had an impact in enhancing the local-scale waves magnitude. This caused in turn severe flooding of the roadway and produced widespread damage along the coast. The analysis of the event of 28 December 2020, also suggests the need of an effective mitigation policy in the management of coastal issues induced by extreme sea storm events. Wind-based analysis and prediction of the sea wave conditions are currently discussed in the literature; however, critical information on wave height is often missing or not sufficient for reliable forecasting. In order to improve our ability to forecast the effects of sea storm events on the coastline, it is necessary to analyze all the components of the coastal wave system, including wave diffraction and reflection phenomena and the tidal change. Our results suggest in fact that only an integrated approach to the analysis of all the physical and anthropic components of coastal system may provide a correct base of information for the stakeholders to address coastal zone planning and protection.
Analysis of Clinical Profiles, Deformities, and Plantar Pressure Patterns in Diabetic Foot Syndrome
Authors: Giacomozzi Claudia , Lullini Giada , Leardini Alberto , Caravaggi Paolo , Ortolani Maurizio , Marchesini Giulio , Baccolini Luca , Berti Lisa
Published: December 2021

Diabetic foot syndrome refers to heterogeneous clinical and biomechanical profiles, which render predictive models unsatisfactory. A valuable contribution may derive from identification and descriptive analysis of well-defined subgroups of patients. Clinics, biology, function, gait analysis, and plantar pressure variables were assessed in 78 patients with diabetes. In 15 of them, the 3D architecture of the foot bones was characterized by using weight-bearing CT. Patients were grouped by diabetes type (T1, T2), presence (DN) or absence (DNN) of neuropathy, and obesity. Glycated hemoglobin (HbA1c) and plantar lesions were monitored during a 48-month follow-up. Statistical analysis showed significant differences between the groups for at least one clinical (combined neuropathy score, disease duration, HbA1c), biological (age, BMI), functional (joint mobility, foot alignment), or biomechanical (regional peak pressure, pressure-time integral, cadence, velocity) variable. Twelve patients ulcerated during follow-up (22 lesions in total), distributed in all groups but not in the DNN T2 non-obese group. These showed biomechanical alterations, not always occurring at the site of lesion, and HbA1c and neuropathy scores higher than the expected range. Three of them, who also had weight-bearing CT analysis, showed >40% of architecture parameters outside the 95%CI. Appropriate grouping and profiling of patients based on multi-instrumental clinical and biomechanical analysis may help improve prediction modelling and management of diabetic foot syndrome.
Modelling of the Motion and Interaction of a Droplet of an Inkjet Printing Process with Physically Treated Polymers Substrates
Authors: Tofan Tim , Jasevičius Raimondas
Published: December 2021

This study examines the effect of energetic surface treatment on the adhesion strength of high-density polyethylene (HDPE), polypropylene (PP) and polyethylene terephthalate (PET) substrates. The purpose of this work is to determine the surface wettability of polymers suitable for food contact. These plastics have been treated with various pre-treatment methods to improve surface tension and good adhesion for inkjet printing and avoid any visual changes. It is important to determine the adhesion of the ink to the polymer surface to improve post-consumer recycling. Digital inks have been tested on various treated plastics to analyse coating properties and adhesion forces in accordance with DIN ISO 2409 standards. The impact of the inkjet droplet on the treated and non-treated surface was also investigated using the COMSOL computer simulation software.
Surrogate-Based Optimization of a Centrifugal Pump with Volute Casing for an Automotive Engine Cooling System
Authors: De Donno Remo , Fracassi Alessia , Ghidoni Antonio , Morelli Alessandro , Noventa Gianmaria
Published: December 2021

This paper investigates the capability of a surrogate-based optimization technique for the advanced design of centrifugal pumps. The centrifugal pump considered in this work is designed for the automotive cooling system and consists of an impeller, a vaneless diffuser and a volute. A fully three-dimensional geometry parametrization based on Bézier surfaces is presented. The optimization procedure includes the following software packages: Scilab for the geometric parametrization, Ansys-CFX for the CFD simulations and DAKOTA for the optimization management. The initial geometry is defined by a 0D code that provides a preliminary design of the pump, given the operating conditions, i.e., the volumetric flow rate, the head and the rotating speed. In this work an operative point typical of high performance gasoline cars is considered.
K-EPIC: Entity-Perceived Context Representation in Korean Relation Extraction
Authors: Hur Yuna , Son Suhyune , Shim Midan , Lim Jungwoo , Lim Heuiseok
Published: December 2021

Relation Extraction (RE) aims to predict the correct relation between two entities from the given sentence. To obtain the proper relation in Relation Extraction (RE), it is significant to comprehend the precise meaning of the two entities as well as the context of the sentence. In contrast to the RE research in English, Korean-based RE studies focusing on the entities and preserving Korean linguistic properties rarely exist. Therefore, we propose K-EPIC (Entity-Perceived Context representation in Korean) to ensure enhanced capability for understanding the meaning of entities along with considering linguistic characteristics in Korean. We present the experimental results on the BERT-Ko-RE and KLUE-RE datasets with four different types of K-EPIC methods, utilizing entity position tokens. To compare the ability of understanding entities and context of Korean pre-trained language models, we analyze HanBERT, KLUE-BERT, KoBERT, KorBERT, KoELECTRA, and multilingual-BERT (mBERT). The experimental results demonstrate that the F1 score increases significantly with our K-EPIC and that the performance of the language models trained with the Korean corpus outperforms the baseline.
Damage Pattern Recognition and Crack Propagation Prediction for Crumb Rubber Concrete Based on Acoustic Emission Techniques
Authors: Sun Jianjie , Chen Xi , Fu Zhengwu , Lacidogna Giuseppe
Published: December 2021

In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment of the specimens was a four-point bending load. Six clustering methods including k-means, fuzzy c-means (FCM), self-organizing mapping (SOM), Gaussian mixture model (GMM), hierarchical model, and density peak clustering method were analyzed; the results illustrated that the density peak clustering has the best performance. Next, the optimal clustering algorithm was used to cluster AE signals so as to study the evolution behavior of different damage modes, and the ultimate load of crumb rubber concrete was predicted by an artificial neural network. The results indicated that the combination of AE techniques and appropriate clustering methods such as the density peak clustering method and the artificial neural network could be used as a practical tool for structural health monitoring of crumb rubber concrete.
Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition
Authors: Cui Hongjiang , Guan Ying , Deng Wu
Published: December 2021

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.
Population Bias on Tornado Reports in Europe
Authors: Pîrloagă Răzvan , Ene Dragoş , Antonescu Bogdan
Published: December 2021

Tornadoes are associated with damages, injuries, and even fatalities in Europe. Knowing the spatial distribution of tornadoes is essential for developing disaster risk reduction strategies. Unfortunately, there is a population bias on tornado reporting in Europe. To account for this bias, a Bayesian modeling approach was used based on tornado observations and population density for relatively small regions of Europe. The results indicated that the number of tornadoes could be 53% higher that are currently reported. The largest adjustments produced by the model are for Northern Europe and parts of the Mediterranean regions.
A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan
Authors: Din Shahab Ud , Muhammad Khan , Khan Muhammad Fawad Akbar , Bashir Shahid , Sajid Muhammad , Khan Asif
Published: December 2021

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.
Is the Treatment of the Tear Trough Deformity with Hyaluronic Acid Injections a Safe Procedure? A Systematic Review
Authors: D’Amato Salvatore , Fragola Romolo , Bove Pierfrancesco , Lo Giudice Giorgio , Gennaro Paolo , Vitagliano Rita , Staglianò Samuel
Published: December 2021

Among the various therapeutic options for the treatment of tear trough deformities, the use of hyaluronic acid-based fillers has constantly been increasing. The aim of this research is to conduct a systematic review of the published literature related to the use of hyaluronic acid-based dermal fillers for the treatment of tear trough deformities and possible related complications. A search of the published literature was conducted following the PRISMA guidelines, including PubMed, Cochrane Library, and Ovid databases. Text words and Medical Search Headings (MeSH terms) were used to identify nine articles included in our analysis. The most used filler was Restylane (Galderma). The injection technique was performed through the use of a cannula or, more frequently, with a needle, through the execution of boluses or retrograde release. The injection plane was predominantly the supra-periosteal layer. The most observed side effects were mild and included redness, edema, contour irregularities, bruising, and blue-gray dyschromia. The degree of patient satisfaction was high, with an optimal aesthetic result that was maintained for 6 to 12 months. Although the duration of treatment of tear trough deformities with HA fillers is not comparable to surgical treatment, this is a minimally invasive, safe procedure, quick to perform, and with a high degree of patient satisfaction.
Verification of the Radio Wave Absorption Effect in the Millimeter Wave Band of SWCNTs and Conventional Carbon-Based Materials
Authors: Chiba Seiki , Waki Mikio
Published: December 2021

Using a sample coated with three types of carbon-based paints, namely single-wall carbon nanotube (SWCNTs), carbon black, and graphite, the amount of radio wave absorption for each was measured. SWCNTs proved to have the superior radio wave absorption effect in the millimeter band. Considering the change in the amount of radio wave absorption depending on the coating amount, three different coating thicknesses were prepared for each test material. The measurement frequency was set to two frequency bands of 28 GHz and 75 GHz, and the measurement method was carried out based on Japanese Industrial Standard (JIS) R1679 “Radio wave absorption characteristic measurement method in the millimeter wave band of the radio wave absorber.” As for the amount of radio wave absorption in the 28 GHz band, a maximum amount of radio wave absorption of about 6 dB was obtained when 35 m of CNT spray paint was applied. It was confirmed that the carbon black paint came to about 60% that of the SWCNT, and the graphite paint did not obtain much radio wave absorption even when the coating thickness was changed. Furthermore, even in the 75 GHz band, the radio wave absorption was about 7 dB when 16 μm of CNT spray paint was applied, showing the maximum value. Within these experimental results, the CNT spray paint has a higher amount of radio wave absorption in the millimeter wave band than paints using general carbon materials. Its effectiveness could be confirmed even with a very thin coating thickness of 35 μm or less. It was also confirmed that even with the same paint, the radio wave absorption effect changes depending on the difference in coating thickness and the condition of the coated surface.
Experimental Investigation on Thermal Comfort of COVID-19 Nucleic Acid Sampling Staff in Hot and Humid Environment: A Pilot Study of University Students
Authors: Zhao Yingying , Liu Jiying , Kim Moon Keun , Zhou Shiyu , Du Yanqiu
Published: December 2021

The current situation of Coronavirus Disease 2019 (COVID-19) prevention and control coupled with the need to work in high-temperature harsh environments makes it necessary to ensure the health and efficiency of medical staff. An experimental outdoor work tent was set up and university students were used to study the thermal comfort of personnel wearing protective clothing in hot and humid environments. The experiment was carried out simultaneously through subjective and objective field tests and physiological tests of personnel. The wet bulb globe temperature (WBGT) index was investigated to divide the outdoor thermal environment into four working conditions: 21–23 °C, 23–25 °C, 25–27 °C and 27–29 °C. Under the different thermal environment intensities, the variations of physiological parameters of test personnel were monitored. The results showed that when WBGT was increased to 27–29 °C, 100% of the participants expected the external temperature to become cooler and the humidity to decrease after one hour. When the temperature was close to 30 °C and the relative humidity was close to 60%, it was necessary to take cooling measures to reduce the thermal stress of the participants. Moreover, relationships between subjective feelings and physiological parameters of the nucleic acid sampling personnel were obtained. Results also found that the forehead, chest and back were the highest skin temperature parts, so it is most effective to give priority to improving the thermal comfort of these three locations. As an early attempt to conduct the real outdoor experimental study on the thermal comfort of COVID-19 nucleic acid sampling staff, this study provided a theoretical basis for follow-up research to develop cooling strategies for protective clothing in hot and humid outdoor environments.
Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
Authors: Xie Yuting , Chi Xiaowei , Li Haiyuan , Wang Fuwen , Yan Lutao , Zhang Bin , Zhang Qinjian
Published: December 2021

Coal gangue is a kind of industrial waste in the coal mine preparation process. Compared to conventional manual or machine-based separation technology, vision-based methods and robotic grasping are superior in cost and maintenance. However, the existing methods may have a poor recognition accuracy problem in diverse environments since coals and gangues’ apparent features can be unreliable. This paper analyzes the current methods and proposes a vision-based coal and gangue recognition model LTC-Net for separation systems. The preprocessed full-scale images are divided into n × n local texture images since coals and gangues differ more on a smaller scale, enabling the model to overcome the influence of characteristics that tend to change with the environment. A VGG16-based model is trained to classify the local texture images through a voting classifier. Prediction is given by a threshold. Experiments based on multi-environment datasets show higher accuracy and stability of our method compared to existing methods. The effect of n and t is also discussed.
Design and Experimental Validation of Power Electric Vehicle Emulator for Testing Electric Vehicle Supply Equipment (EVSE) with Vehicle-to-Grid (V2G) Capability
Authors: García-Martínez Eduardo , Muñoz-Cruzado-Alba Jesús , Sanz-Osorio José F. , Perié Juan Manuel
Published: December 2021

Nowadays, the global decarbonization and electrification of the world’s energy demands have led to the quick adoption of Electric Vehicle (EV) technology. Therefore, there is an urgent need to provide a wide network of fast Vehicle-to-Grid (V2G) charging stations to support the forecast demand and to enable enough autonomy of such devices. Accordingly, V2G charging stations must be prepared to work properly with every manufacturer and to provide reliable designs and validation processes. In this way, the development of power electric vehicle emulators with V2G capability is critical to enable such development. The paper presents a complete design of a power electric vehicle emulator, as well as an experimental testbench to validate the behaviour of the proposal.
Model Test on Mechanical and Deformation Property of a Geomembrane Surface Barrier for a High Rockfill Dam
Authors: Wu Haimin , Feng Luming , Teng Zhaoming , Shu Yiming
Published: December 2021

The cushion of a geomembrane surface barrier of a high rockfill dam built on deep overburden is prone to crack and fail because of excessive flexural deformation. This study proposes a geomembrane surface barrier for a high rockfill dam on deep overburden. The proposed geomembrane surface barrier uses polyurethane bonded aggregates as the cushion material. The loading and deformation performance of the barrier system under uniform water pressure was investigated using a self-developed structure model test device. The mechanical and deformation property of each layer of the barrier, and the interaction mode between adjacent layers, were obtained through external videos and internal sensor monitoring. The results demonstrated that the polyurethane bonded aggregate cushion exhibited good adaptability to flexural deformation during the entire loading process and maintained good contact and coordinate deformation with the upper protective and the lower transition layers. The geomembrane surface barrier created using polyurethane bonded aggregates as the cushion material can adapt to the flexural deformation of a high rockfill dam surface on deep overburden.
How to Harvest Haylage Bales in Sustainable Agriculture
Authors: Borowski Sylwester , Kaszkowiak Jerzy , Dulcet Edmund
Published: December 2021

Storing silage in round bales (balage) is a commonly used method for preserving forage for use as stock fodder that has a higher nutritional content than hay. Baling at the optimum density is important for ensuring ideal fermentation conditions. In the manuscript, we present the research methodology and the results of the experiment. We did experiments over the density of haylage bales. We investigated the effect of the moisture content in the harvested material, the length of the cut material and the pressing pressure in the round baler. We used the Barenbrug BG-5 forage mix at different moisture content levels (69, 63, 56, 49, and 42%), that was either unchopped or chopped by the round baler’s cutter bars (312 and 183 mm length) and baled at three different pressing pressures (0.9, 1.4, and 1.8 MPa). The results showed that forage density in the bales reached the highest value at a moisture content of 56% and a pressing pressure of 1.8 MPa, with the forage chopped by the cutter bars.
Supply Chain Management and Big Data Concept Effects on Economic Sustainability of Building Design and Project Planning
Authors: Mandičák Tomáš , Mésároš Peter , Kanáliková Andrea , Špak Matej
Published: December 2021

Building design and project planning face significant challenges in the 21st century. On the one hand, it is the requirement of building users. On the other hand, new possibilities and technologies contribute to the overall efficiency of construction projects. The progress of information and communication technologies is one of the most extensive opportunities that can significantly streamline designing sustainable buildings. The big data concept is increasingly being implemented in all areas of industry. The design of buildings and the entire civil engineering industry is where these processes are becoming automated with the help of information technology. Supply chain management based on information systems represents a great potential in the design of buildings and planning of construction projects in materials procurement. These facts lead to the assumption that the concept of big data and supply chain management systems can effectively influence the sustainable design of buildings. These technologies are expected to improve economic sustainability by reducing costs and shortening materials’ delivery time and construction time. The research is carried out on a sample of construction projects. The level of use of the concept of big data and SCM systems and their impact on economic sustainability in the form of parameters such as construction project costs, delivery time of materials and building construction were examined. Data collection took place through a questionnaire survey. Within the used methods, this research worked data-free but also scaled to the Likert scale. Data processing was performed using Pearson dependence and the use of a correlation coefficient. The main goal of the research was to analyze the impact of using the concept of big data and supply chain management on the cost of the building, the time of delivery of materials and the construction of the building. It was found that the use of SCM and BD has an impact on the monitored parameters, and the correlation rate is high.
Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease
Authors: Chou Fu-I , Huang Tian-Hsiang , Yang Po-Yuan , Lin Chin-Hsuan , Lin Tzu-Chao , Ho Wen-Hsien , Chou Jyh-Horng
Published: December 2021

This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.
Comparing End-to-End Machine Learning Methods for Spectra Classification
Authors: Sun Yue , Brockhauser Sandor , Hegedűs Péter
Published: December 2021

In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved status of the sample), which helps guide the experiment. The two major spectral changes what we aim to capture are either the change in intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. This study aims to develop deep learning (DL) classification frameworks for one-dimensional (1D) spectral time series. In this work, we deal with the spectra classification problem from two different perspectives, one is a general two-dimensional (2D) space segmentation problem, and the other is a common 1D time series classification problem. We focused on the two proposed classification models under these two settings, the namely the end-to-end binned Fully Connected Neural Network (FCNN) with the automatically capturing weighting factors model and the convolutional SCT attention model. Under the setting of 1D time series classification, several other end-to-end structures based on FCNN, Convolutional Neural Network (CNN), ResNets, Long Short-Term Memory (LSTM), and Transformer were explored. Finally, we evaluated and compared the performance of these classification models based on the High Energy Density (HED) spectra dataset from multiple perspectives, and further performed the feature importance analysis to explore their interpretability. The results show that all the applied models can achieve 100% classification confidence, but the models applied under the 1D time series classification setting are superior. Among them, Transformer-based methods consume the least training time (0.449 s). Our proposed convolutional Spatial-Channel-Temporal (SCT) attention model uses 1.269 s, but its self-attention mechanism performed across spatial, channel, and temporal dimensions can suppress indistinguishable features better than others, and selectively focus on obvious features with high separability.
Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
Authors: Shiao Yaojung , Hoang Thang , Chang Po-Yao
Published: December 2021

Exercise is good for health, quality of life, and maintenance of human muscles. Dumbbells are popular indoor exercise equipment with several benefits such as low cost, high flexibility in space and time, easy operation, and suitability for people of all ages. Facilitated by advances in the Internet of Things, smart dumbbells that provide automatic counting and motion monitoring functions have been developed. To perform these tasks, the key process is identification of exercise mode. This study proposes a method to identify essential muscle groups’ (biceps, triceps, and deltoids) exercise modes of a dumbbell using an inertial measurement unit to provide three-axis angular velocities and accelerations. The motion angles were estimated from the axial acceleration and angular velocity. Phase diagrams and time plots of the axial angle, angular velocity, and acceleration were used to extract significant features of each exercise. Machine Learning and weighting functions were developed to combine these features into an identification index value for accurate identification and classification of the exercise modes. An algorithm was developed to verify the exercise mode identification. The results show that the proposed method and weighting function can successfully identify the six exercise modes. The identification algorithm was 99.5% accurate. The exercise mode identification of the dumbbell is confirmed.
Prevalence of Anisakis Larvae in Different Fish Species in Southern Albania: Five-Year Monitoring (2016–2020)
Authors: Ozuni Enkeleda , Vodica Ani , Castrica Marta , Brecchia Gabriele , Curone Giulio , Agradi Stella , Miraglia Dino , Menchetti Laura , Balzaretti Claudia M. , Andoni Egon
Published: December 2021

Anisakidae are nematodes that commonly parasitize in the coelomic cavity and viscera of several fish species. They can be found in flesh, which is why they have an important economic and public health impact. The aim of the current work was to assess the presence and prevalence of Anisakis larvae in fish species caught in the coastal area of the Karaburun Peninsula in Vlora Bay (Albania). A total of 856 of wild teleosts and 219 specimens of farmed fish were collected over a 5-year period (from 2016 to 2020). The results showed that out of a total of 1075 analyzed samples, 361 (33.58%) were parasitized with L3 larvae. In particular, only Solea vulgaris returned negative results, while Sparus aurata, Dicentrarchus labrax, and Sardinella aurita showed the lowest prevalence (4.55%, 9.17%, and 10.53%, respectively) and mean abundance (0.84, 1.19, and 0.92, respectively). Conversely, Scomber japonicus and Scomber scombrus showed the highest prevalence (74.07% and 68.00%, respectively) and mean abundance (188.24 and 249.82, respectively). The data suggest that the coastal area of the Karaburun Peninsula (southern Albania) may be a high-risk area for zoonotic diseases, and the consumption of raw or undercooked fish caught in the Vlora district could result in the acquisition of human anisakiasis. For these reasons, it is necessary to improve the surveillance plan.
Recommendations for Using QPN Formalism for Preparation of Incoming Request Stream Generator in Modeled System
Authors: Rak Tomasz , Rzonca Dariusz
Published: December 2021

Simulation models are elements of science that use software tools to solve complex mathematical problems. They are beneficial in areas such as performance engineering and communications systems. Nevertheless, to achieve more accurate results, researchers should use more detailed models. Having an analysis of the system operations in the early modeling phases could help one make better decisions relating to the solution. In this paper, we introduce the use of the QPME tool, based on queueing Petri nets, to model the system stream generator. This formalism was not considered during the first tool development. As a result of the analysis, an alternative design model is proposed. By comparing the behavior of the proposed generator against the one already developed, a better adjustment of the stream to the customer’s needs was obtained. The study results show that appropriately adjusting queueing Petri net models can help produce better streams of data (tokens).
The Relationship between Clouds Containing Multiple Layers 7.5–30 m Thick and Surface Weather Conditions
Authors: McCullough Emily M. , Wing Robin , Drummond James R.
Published: December 2021

Previous studies have identified finely laminated, or layered, features within Arctic clouds. This study focuses on quasi-horizontal layers that are 7.5 to 30 m thick, within clouds from 0 to 5 km altitude. No pre-selection for any particular cloud types was made prior to the identification of laminations. We capitalize on the 4-year measurement record available from Eureka, Nunavut (79.6∘ N, 85.6∘ W), using the Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) Rayleigh–Mie–Raman Lidar (CRL; 1 min, 7.5 m resolution). Laminated features are identified on 18% of all days, from 2016–2019. Their presence is conclusively excluded on 12% of days. March, April, and May have a higher measurement cadence and show laminations on 41% of days. Individual months show laminations on up to 50% of days. Our results suggest that laminations are not rare phenomena at Eureka. To determine laminations’ likely contribution to Arctic weather and climate, local weather reports were obtained from the nearby Environment and Climate Change Canada (ECCC) weather station. Days with laminated clouds are strongly correlated with precipitating snow (r = 0.63), while days with non-laminated clouds (r = −0.40) and clear sky days (r = −0.43) are moderately anti-correlated with snow precipitation.
Reactivity of a Carene-Derived Hydroxynitrate in Mixed Organic/Aqueous Matrices: Applying Synthetic Chemistry to Product Identification and Mechanistic Implications
Authors: McAlister Addison B. , Vesto James I. , Huang Aaron , Wright Kathryn A. , McLaughlin Sta. Maria Emily J. , Bailey Gabriela M. , Kretekos Nicole P. , Baldwin Petra R. , Carrasquillo Anthony J. , LaLonde Rebecca Lyn
Published: December 2021

β-hydroxynitrates (HN) are a major class of products formed during OH and NO3 initiated oxidation of terpenes. Their production contributes significantly to secondary organic aerosol (SOA) formation and NOx sequestration. However, studying the condensed phase reactions of this important class of molecules has been hindered by the lack of commercially available authentic standards. The goal of this work was to examine the influence of water concentration and solvent identity on product yields of a tertiary HN derived from 3-carene prepared in house. To assess the role of water on conversion chemistry, bulk-phase reactions were conducted in DMSO-d6, a non-nucleophilic solvent, with a gradient of water concentrations, and analyzed with 1H NMR. Product identifications were made by comparison with authentic standards prepared in house. Four major products were identified, including an unexpected diol produced from carbocation rearrangement, diol diastereomers, and trans-3-carene oxide, with varying yields as a function of water concentration. Product yields were also measured in two protic, nucleophilic solvents, MeOD-d4 and EtOD-d6. Finally, reactions with added chloride formed alkyl chloride products in yields approaching 30%. These results are among the first to highlight the complexities of nucleophilic reactions of hydroxynitrates in bulk, mixed aqueous/organic media and to identify new, unexpected products.
Construction of a Simple Domeless Net Radiometer for Demonstrating Energy Balance Concepts in a Laboratory Activity
Authors: Da Ros Carvalho Henrique , McInnes Kevin J. , Heilman James L.
Published: December 2021

Even though energy balance concepts are fundamental to solutions of problems in a number of disciplines in the agricultural and life sciences, they are seldom demonstrated in a laboratory activity. Here, we introduce a simple domeless net radiometer to demonstrate how the surface temperature of an object aboveground is regulated by the properties of the surfaces and environmental conditions. The device is based on the early designs of all-wave net radiometers and is composed of a foam disc with its opposing surfaces coated with either white or black paint. Temperatures of the disc’s surfaces are monitored using thermocouple temperature sensors. Using a combination of solar irradiance, albedo of the ground surface, air temperature, and wind speed measurements, the temperatures of the disc’s surfaces can be calculated by means of an energy balance model. We found good agreement between calculated and measured temperatures. In addition to demonstrate important physical concepts under natural outdoor conditions, we believe that the proposed laboratory activity will benefit students by allowing them to gain some experience and practical skills in working with environmental sensors, programming data acquisition systems, and analyzing data. Stimulating students’ creativity as well as developing their analytical and problem-solving skills is another goal of the proposed activity.
Circular Dichroism in the Photoionization of Unpolarized Atoms by Two Crossing Photon Beams
Authors: Meremianin Alexei V. , Manakov Nikolai L.
Published: December 2021

The polarization dependence of the photoionization probability was analyzed in the case when a randomly oriented atom is irradiated by two crossing polarized monochromatic photon beams with the same frequency. It was found that the angular distributions of photoelectrons exhibit the effect of circular dichroism (CD), which consists of the dependence of the photoionization probability on the sign of the circular polarization degree of each beam. We demonstrate that the CD effect exists only for coherent crossing photon beams. It was shown that CD effects are strongly dependent on the phase difference between the electric field vectors of the photon beams and have a quite large magnitude. The possibilities of the experimental observation of CD effects are discussed.
A Generalized Bochner Technique and Its Application to the Study of Conformal Mappings
Authors: Rovenski Vladimir , Stepanov Sergey , Tsyganok Irina
Published: December 2021

This article is devoted to geometrical aspects of conformal mappings of complete Riemannian and Kählerian manifolds and uses the Bochner technique, one of the oldest and most important techniques in modern differential geometry. A feature of this article is that the results presented here are easily obtained using a generalized version of the Bochner technique due to theorems on the connection between the geometry of a complete Riemannian manifold and the global behavior of its subharmonic, superharmonic, and convex functions.
The Effect of Diabetes Mellitus on IGF Axis and Stem Cell Mediated Regeneration of the Periodontium
Authors: Hussein Nancy M. S. , Meade Josie L. , Pandit Hemant , Jones Elena , El-Gendy Reem
Published: December 2021

Periodontitis and diabetes mellitus (DM) are two of the most common and challenging health problems worldwide and they affect each other mutually and adversely. Current periodontal therapies have unpredictable outcome in diabetic patients. Periodontal tissue engineering is a challenging but promising approach that aims at restoring periodontal tissues using one or all of the following: stem cells, signalling molecules and scaffolds. Mesenchymal stem cells (MSCs) and insulin-like growth factor (IGF) represent ideal examples of stem cells and signalling molecules. This review outlines the most recent updates in characterizing MSCs isolated from diabetics to fully understand why diabetics are more prone to periodontitis that theoretically reflect the impaired regenerative capabilities of their native stem cells. This characterisation is of utmost importance to enhance autologous stem cells based tissue regeneration in diabetic patients using both MSCs and members of IGF axis.
Diagnostic Impact of Radiological Findings and Extracellular Vesicles: Are We Close to Radiovesicolomics?
Authors: Serafini Francesco Lorenzo , Lanuti Paola , Delli Pizzi Andrea , Procaccini Luca , Villani Michela , Taraschi Alessio Lino , Pascucci Luca , Mincuzzi Erica , Izzi Jacopo , Chiacchiaretta Piero , Buca Davide , Catitti Giulia , Bologna Giuseppina , Simeone Pasquale , Pieragostino Damiana , Caulo Massimo
Published: December 2021

Currently, several pathologies have corresponding and specific diagnostic and therapeutic branches of interest focused on early and correct detection, as well as the best therapeutic approach. Radiology never ceases to develop newer technologies in order to give patients a clear, safe, early, and precise diagnosis; furthermore, in the last few years diagnostic imaging panoramas have been extended to the field of artificial intelligence (AI) and machine learning. On the other hand, clinical and laboratory tests, like flow cytometry and the techniques found in the “omics” sciences, aim to detect microscopic elements, like extracellular vesicles, with the highest specificity and sensibility for disease detection. If these scientific branches started to cooperate, playing a conjugated role in pathology diagnosis, what could be the results? Our review seeks to give a quick overview of recent state of the art research which investigates correlations between extracellular vesicles and the known radiological features useful for diagnosis.