bluuebunny/crossref_metadata_2025 huggingface dataset:
Interaction between Heavy Water and Single-Strand DNA: A SERS Study
Authors: Jiang Chengshun , Liu Yan , Wang Lianghua , Lu Feng
Published: September 2022

The structure and function of biological macromolecules change due to intermolecular deuterium bond formation or deuterium substitution with environmental D2O. In this study, surface-enhanced Raman spectroscopy (SERS) was used to detect interaction sites between D2O and ssDNA and their action mechanisms. SERS peaks of ssDNA changed with increasing D2O proportions, and the site of action mainly involved A and G bases, whose number strengthened the interaction between sequences and D2O and hence the SERS peak intensities. Fixing the number of A and G bases prevented changes in their positions from significantly altering the map. We also identified the interaction between ssDNA sequences that easily formed a G-quadruplex structure and D2O. The amplitude of the SERS peak intensity change reflected the ssDNA structural stability and number of active sites. These findings are highly significant for exploring genetic exchanges and mutations and could be used to determine the stability and structural changes of biological macromolecules.
Spectroscopic Analysis of an Antimalarial Drug’s (Quinine) Influence on Human Serum Albumin Reduction and Antioxidant Potential
Authors: Rogóż Wojciech , Lemańska Olga , Pożycka Jadwiga , Owczarzy Aleksandra , Kulig Karolina , Muhammetoglu Tammam , Maciążek-Jurczyk Małgorzata
Published: September 2022

Quinine (Qi) is a well-known drug used in malaria therapy; it is also a potential anti-arrhythmic drug used in the treatment of calf cramps, rheumatoid arthritis, colds, and photodermatitis. Moreover, it is used in the food industry for the production of tonics. This study aimed to analyze the interaction between quinine and a transporting protein—human serum albumin (HSA)—as well as the influence of Qi on both protein reduction and antioxidant potential. It was found that Qi (via spectrofluorometric measurements and circular dichroism spectroscopy) binds to HSA with a low affinity and slightly affects the secondary structure of albumin. As demonstrated by the use of ABTS and FRAP assays, HSA has a higher antioxidant and reduction potential than Qi, while their mutual interaction results in a synergistic effect in antioxidant activity and reduction potential.
Spectroscopic Behaviour of Copper(II) Complexes Containing 2-Hydroxyphenones
Authors: Chiyindiko Emmie , Langner Ernst H. G. , Conradie Jeanet
Published: September 2022

Theoretical investigations by density functional theory (DFT) and time-dependent DFT (TD-DFT) methods shed light on how the type of ligand or attached groups influence the electronic structure, absorption spectrum, electron excitation, and intramolecular and interfacial electron transfer of the Cu(II) complexes under study. The findings provide new insight into the designing and screening of high-performance dyes for dye-sensitized solar cells (DSSCs).
Thermal Progress of Unsteady Separated Stagnation Point Flow with Magnetic Field and Heat Generation in Hybrid Ferrofluid
Authors: Khashi’ie Najiyah Safwa , Waini Iskandar , Zainal Nurul Amira , Hamzah Khairum Bin , Kasim Abdul Rahman Mohd , Arifin Norihan Md , Pop Ioan
Published: September 2022

This paper examines the unsteady separated stagnation point (USSP) flow and thermal progress of Fe3O4–CoFe2O4/H2O on a moving plate subject to the heat generation and MHD effects. The model of the flow includes the boundary layer and energy equations. These equations are then simplified with the aid of similarity variables. The numerical results are generated by the bvp4c function and then presented in graphs and tables. The magnetic and acceleration (strength of the stagnation point flow) parameters are the contributing factors in the augmentation of the skin friction and heat transfer coefficients. However, the enhancement of heat generation parameter up to 10% shows a reduction trend in the thermal rate distribution of Fe3O4–CoFe2O4/H2O. This finding reveals the effectiveness of heat absorption as compared to the heat generation in the thermal flow process. From the stability analysis, the first solution is the physical solution. The streamline for the first solution acts as a normal stagnation point flow, whereas the second solution splits into two regions, proving the occurrence of reverse flow.
Maternal and Neonatal Vitamin D Binding Protein Polymorphisms and 25-Hydroxyvitamin D Cutoffs as Determinants of Neonatal Birth Anthropometry
Authors: Karras Spyridon N. , Dursun Erdinç , Alaylıoğlu Merve , Gezen-Ak Duygu , Pilz Stefan , Annweiler Cedric , Al Anouti Fatme
Published: September 2022

Background: Vitamin D-binding protein (VDBP) is a vital regulator of optimal vitamin D homeostasis and bioavailability. Apart from its well-documented role as a key component in vitamin D dynamic transfer and circulation, it has a myriad of immunoregulatory functions related to innate immunity, which becomes particularly critical in states of increased immunological tolerance including pregnancy. In this regard, VDBP dyshomeostasis is considered to contribute to the development of several fetal, maternal, and neonatal adverse outcomes. However, precise physiological pathways, including the contribution of specific VDBP polymorphisms behind such phenomena, are yet to be fully deciphered. Our aim was to assess the combined effect of maternal and neonatal VDBP polymorphism heterogeneity in conjunction with different maternal and neonatal 25(OH)D cutoffs on the neonatal anthropometric profile at birth. Methods: The study included data and samples from a cohort of 66 mother–child pairs at birth. The inclusion criterion was full-term pregnancy (gestational weeks 37–42). Neonatal and maternal 25(OH)D cutoffs were included according to vitamin D status at birth and delivery. Concentrations of 25(OH)D2 and 25(OH)D3 were measured using liquid chromatography–tandem mass spectrometry. Results: The upper arm length of neonates with 25(OH)D ≤ 25 nmol/L was higher in neonate CC carriers for rs2298850. The upper thigh neonatal circumference was also higher in the ones with either 25(OH)D ≤ 50 or ≤75 nmol/L in rs2298850 CG + GG or rs4588 GT + TT carriers. We did not observe any significant effect for maternal VDBP polymorphisms nor for birth maternal 25(OH)D concentrations, on birth neonatal anthropometry. Conclusions: Our findings emphasize a potential role for neonatal VDBP genotypes rs2298850 and rs4588, in conjunction with specific neonatal 25(OH)D cutoffs, in the range of sufficiency on neonatal growth and development.
Childhood Obesity: Position Statement of Polish Society of Pediatrics, Polish Society for Pediatric Obesity, Polish Society of Pediatric Endocrinology and Diabetes, the College of Family Physicians in Poland and Polish Association for Study on Obesity
Authors: Mazur Artur , Zachurzok Agnieszka , Baran Joanna , Dereń Katarzyna , Łuszczki Edyta , Weres Aneta , Wyszyńska Justyna , Dylczyk Justyna , Szczudlik Ewa , Drożdż Dorota , Metelska Paulina , Brzeziński Michał , Kozioł-Kozakowska Agnieszka , Matusik Paweł , Socha Piotr , Olszanecka-Glinianowicz Magdalena , Jackowska Teresa , Walczak Mieczysław , Peregud-Pogorzelski Jarosław , Tomiak Elżbieta , Wójcik Małgorzata
Published: September 2022

Childhood obesity is one of the most important problems of public health. Searching was conducted by using PubMed/MEDLINE, Cochrane Library, Science Direct, MEDLINE, and EBSCO databases, from January 2022 to June 2022, for English language meta-analyses, systematic reviews, randomized clinical trials, and observational studies from all over the world. Five main topics were defined in a consensus join statement of the Polish Society of Pediatrics, Polish Society for Pediatric Obesity, Polish Society of Pediatric Endocrinology and Diabetes and Polish Association for the Study on Obesity: (1) definition, causes, consequences of obesity; (2) treatment of obesity; (3) obesity prevention; (4) the role of primary care in the prevention of obesity; (5) Recommendations for general practitioners, parents, teachers, and regional authorities. The statement outlines the role of diet, physical activity in the prevention and treatment of overweight and obesity, and gives appropriate recommendations for interventions by schools, parents, and primary health care. A multisite approach to weight control in children is recommended, taking into account the age, the severity of obesity, and the presence of obesity-related diseases. Combined interventions consisting of dietary modification, physical activity, behavioral therapy, and education are effective in improving metabolic and anthropometric indices. More actions are needed to strengthen the role of primary care in the effective prevention and treatment of obesity because a comprehensive, multi-component intervention appears to yield the best results.
Recent Developments in Stereoselective Reactions of Sulfonium Ylides
Authors: Mondal Mukulesh , Connolly Sophie , Chen Shi , Mitra Shubhanjan , Kerrigan Nessan J.
Published: September 2022

This review describes advances in the literature since the mid-1990s in the area of reactions of sulfonium ylide chemistry, with particular attention paid to stereoselective examples. Although the chemistry of sulfonium ylides was first popularized and applied in a substantial way in the 1960s, there has been sustained interest in the chemistry of sulfonium ylides since then. Many new ways of exploiting sulfonium ylides in productive stereoselective methodologies have emerged, often taking advantage of advances in organocatalysis and transition metal catalysis, to access stereodefined structurally complex motifs. The development of many different chiral sulfides over the last 20–30 years has also played a role in accelerating their study in a variety of reaction settings. In general, formal cycloaddition reactions ([2 + 1] and [4 + 1]) of sulfonium ylides follow a similar mechanistic pathway: initial addition of the nucleophilic ylide carbanion to an electrophile to form a zwitterionic betaine intermediate, followed by cyclization of the zwitterionic intermediate to afford the desired three-membered cyclic product (e.g., epoxide, cyclopropane, or aziridine), five-membered monocyclic (e.g., oxazolidinone), or fused bicyclic product (e.g., benzofuran, indoline).
Identification of Amblyomma ovale Koch, 1844 (Acari: Ixodidae) in a Bloodhound Dog from Oaxaca, Mexico
Authors: González-Álvarez Vicente Homero , Almazán Consuelo
Published: September 2022

An adult engorged female of the hard tick Amblyomma ovale was found during a routine examination of a bloodhound dog in a private clinic from a small community in the state of Oaxaca, in Southern Mexico. Previously, this tick species had been identified in dogs from four states in the country, where it has been involved as a carrier of Rickettsia spp. To our knowledge, this is the first time that A. ovale has been detected in Oaxaca. Public and animal health authorities should be aware of this and other tick species that may be acting as vectors of tick-borne pathogens (TBP) to both humans and animals.
Binding-Induced Diversity of a Human Telomeric G-Quadruplex Stability Phase Space
Authors: Oblak Domen , Hadži San , Podlipnik Črtomir , Lah Jurij
Published: September 2022

The structural polymorphism of G-quadruplex nucleic acids is an important factor in their recognition by proteins and small-molecule ligands. However, it is not clear why the binding of several ligands alters G-quadruplex topology. We addressed this question by following the (un)folding and binding of the human telomeric fragment 5′-(GGGTTA)3GGGT-3′ (22GT) by calorimetry (DSC, ITC) and spectroscopy (CD). A thermodynamic analysis of the obtained data led to a detailed description of the topological phase space of stability (phase diagram) of 22GT and shows how it changes in the presence of a specific bisquinolinium ligand (360A). Various 1:1 and 2:1 ligand–quadruplex complexes were observed. With increasing temperature, the 1:1 complexes transformed into 2:1 complexes, which is attributed to the preferential binding of the ligand to the folding intermediates. Overall, the dissection of the thermodynamic parameters in combination with molecular modelling clarified the driving forces of the topological quadruplex transformations in a wide range of ligand concentrations and temperatures.
Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
Authors: Liu Yi , Long Haimiao , Chen Ming , Cheng Yun , Zhou Taoyun
Published: September 2022

Transmission capacity and receiver sensitivity of an intensity-modulation direct detection (IM-DD) optical discrete multi-tone (DMT) system can be improved by using the probabilistically shaping (PS) technique. However, different probabilistic distributions will be required owing to the unbalanced signal-to-noise ratio (SNR) among data-carrying subcarriers (SCs) induced by the imperfect frequency response of optical/electrical devices, which can increase the implementation complexity of the PS-DMT transceiver. In this work, different signal pre-processing schemes including pre-equalization, Walsh–Hadamard transform (WHT)-based full data-carrying SCs precoding (FDSP) and fast WHT-based partial data-carrying SCs precoding (PDSP) are investigated for SNR equalization in a short-reach PS-DMT transmission system. After transmission over 50 km single-mode fiber, the experimental results indicated that three pre-processed signals have almost the same generalized mutual information (GMI) performance and receiver sensitivity improvements. The proposed fast WHT-based PDSP scheme may be a good option for the implementation of the PS-DMT transmission systems with a large SC SNR fluctuation regarding computational complexity.
Scotch-Tape and Graphene-Oxide Photomobile Polymer Film
Authors: Castagna Riccardo , Di Donato Andrea , Castaldo Rachele , Avolio Roberto , Francescangeli Oriano , Lucchetta Daniele Eugenio
Published: September 2022

In this work, we report on the fabrication and photonic activation of a novel kind of photomobile polymer (PMP) film based mainly on a double layered asymmetric configuration. The PMP is cheap and extremely easy to make. It is made of PVC/isoprene tape with a layer of graphene-oxide (GO) attached. Under illumination at different intensities, and with coherent and incoherent light sources, the bending of the PMP film changes considerably. In particular, we noticed a more efficient bending effect when the film is directly exposed to high light intensities or to NIR radiation in the case of incoherent light sources. For the exposure times used in our experiments, the process is completely reversible when the light source is switched off. Additionally, if we paint the side of the PVC tape exposed to light black, the film is not able to return to its starting position and the bending results are permanent. This suggests that the presence of the GO-layer is responsible for the restoration of the position of the PMP film.
Improvement of Resistance to Clubroot Disease in the Ogura CMS Restorer Line R2163 of Brassica napus
Authors: Chen Jiao , Li Jiahui , Ma Mengya , Li Bao , Zhou Yuanwei , Pan Yongzhong , Fan Youjun , Yi Bin , Tu Jinxing
Published: September 2022

Oilseed rape (Brassica napus) has significant heterosis and Ogura CMS is a major way to use it. Ogura CMS has the advantages of complete and stable male sterility and easy-to-breed maintainers. Therefore, to breed better restorers has become an important goal for this system. Incidentally, clubroot is a soil-borne disease that is difficult to control by fungicidal chemicals, and it has been the main disease of oilseed rape in recent years in China, severely restricting the development of the oilseed rape industry. At present, the most effective method for controlling clubroot disease is to cultivate resistant varieties. One Ogura CMS restorer line (R2163) has shown much better combining ability, but lacks the clubroot disease resistance. This study was carried out to improve R2163 through marker-assisted backcross breeding (MABB). The resistant locus PbBa8.1 was introduced into the restorer R2163, and we then selected R2163R with clubroot disease resistance. Using the new restorer R2163R as the male parent and the sterile lines 116A and Z11A as the female parent, the improved, new resistant hybrids Kenyouza 741R and Huayouza 706R performed well, providing strong resistance and good agronomic traits. This work advances the utilization of heterosis and breeding for clubroot disease resistance in B. napus.
Carbon Footprint of Manufacturing Processes: Conventional vs. Non-Conventional
Authors: Stavropoulos Panagiotis , Panagiotopoulou Vasiliki Christina
Published: September 2022

The calculation of carbon emissions is important to determine the carbon footprint and environmental impact of manufacturing processes to assess which steps could be further optimized and make processes greener and more sustainable. A previously published holistic approach to carbon footprint calculation was applied in conventional and laser-based material removal processes to determine which is the most carbon intensive one. The carbon footprint and environmental impact were calculated and the results show that conventional drilling was more carbon intensive than laser-based drilling, while the reverse was true for the case of laser-based grooving and conventional milling. This is because the cutting forces in conventional milling are higher than the cutting forces in conventional drilling due to increased mechanical resistance from the material in the first case. In the case of laser-based processes, the energy consumption is linked to material absorptivity which remained the same in this study. Carbon emissions on the process level were the lowest contributing factor, with the largest share being the production and rolling of steel allocated at the system level. The determination of the most carbon intensive steps and processes will allow companies to better design production lines towards carbon neutrality as dictated by the Green Deal.
A Review on the Pharmacological Activities of Salvia Miltiorrhizae Radix Using International Classification of Disease, 10th Revision (ICD-10) Codes
Authors: Kim Jang-Hoon , Song Jin-Woo , Joo Hyejin , Park Jae-Woo , Lee Beom-joon , Cho Jae-Heung , Lee Kyungjin , Lee Hyangsook , Park Jihye , Jeong Ji-Woon , Kim Eui-Joong , Bu Youngmin
Published: September 2022

Salvia Miltiorrhizae Radix (SMR) is a widely-used herbal medicine for the treatment of various blood stasis-related diseases (mainly circulatory system). It has been extensively studied in the field of pharmacology over the last few decades. In addition, several reviews concerning the effect of SMR are available. The purpose of this study was to review the pharmacological activities of SMC based on the 10th revision of the international disease classification (ICD-10). After an analysis of the literatures in the Medline database between January 1988 and August 2018, 691 eligible articles were chosen and 971 results were obtained (395 in vitro, 536 in vivo, and 40 human). The extracted data were categorized into the disease chapters of the ICD-10 and the major chapters were: IX Diseases of the circulatory system, II Neoplasms, XI Diseases of the digestive system, XIX Injury, poisoning and certain other consequences of external causes, IV Endocrine, nutritional, and metabolic diseases, VI Diseases of the nervous system, V Mental and behavioral disorders, etc. The major diseases and the pharmacological results of each chapter of the ICD-10 were described assiduously, along with the statistical details. The current study provided a comprehensive understanding and insight of SMR in terms of pharmacological activities.
Comprehensive Analysis and Targeting of Distillation Integrated into Overall Process Considering Operating Pressure Change
Authors: Duan Wenting , Yang Minbo , Feng Xiao
Published: September 2022

Distillation is important to chemical processes but it is energy intensive, and its optimization is of great significance to energy savings and emissions reduction. Varying the operating pressures of distillation columns could assist heat integration of distillation columns into the overall process, thereby reducing energy consumption. However, influences of varying column pressures on the energy profiles of the overall process have not been systematically analyzed in previous studies. This paper presents an insightful analysis of heat integration of distillation into the overall process considering the change of operating pressure. Firstly, effects of changing the operating pressure of a distillation column on its own utility requirements and the related process streams are studied. Next, such effects are graphically represented and incorporated into the grand composite curve (GCC). The change tendencies of the GCC, pinch temperature, and total utility consumption are analyzed and presented. On this basis, rules to identify the best operating pressure that minimize the overall energy consumption are proposed. A continuous reforming unit in a petrochemical enterprise is quantitatively analyzed to verify the obtained rules. The result indicates that the hot utility of the overall process can be reduced by 758 kW when the column pressure is lowered by 260 kPa.
Thermodynamic and Economic Analysis of a High Temperature Cascade Heat Pump System for Steam Generation
Authors: Lu Zhenneng , Yao Yuan , Liu Guangping , Ma Weibin , Gong Yulie
Published: September 2022

A high temperature cascade heat pump (HTCHP) system using low-grade heat from waste water to generate steam for industrial processes is studied from the thermodynamic and economic view. The effects of intermediate temperature, heat source temperature, heat sink temperature, working fluids, and the pinch point temperature difference at the evaporator, condenser, and intermediate heat exchanger on the thermodynamic performance and cost-effectiveness of the HTCHP system are investigated. The PBP varying with the gas price and electricity price is evaluated as well. The results show that optimal intermediate temperatures exist under different operational conditions, not only for the COP, but also for the PBP. A high heat source temperature and low heat sink temperature are conducive to increasing the COP and shortening the PBP. Working fluid pair R1234Ze(E)-R1233zd(Z) shows a promising application in the HTCHP system for its relatively high COP, short PBP, and low GWP. A lower pinch point temperature difference will cause a higher COP and shorter PBP, but a higher SEC. The energy price has a great effect on the economic viability of the system. When the price ratio is lower than 1.81, the PBP is lower than 4.6 years.
Failure Risk Assessment of Coal Gasifier Based on the Integration of Bayesian Network and Trapezoidal Intuitionistic Fuzzy Number-Based Similarity Aggregation Method (TpIFN-SAM)
Authors: Liu Yunpeng , Wang Shen , Liu Qian , Liu Dongpeng , Yang Yang , Dan Yong , Wu Wei
Published: September 2022

The coal gasifier is the core unit of the coal gasification system. Due to its exposure to high temperatures, high pressures, and aggressive media, it is highly susceptible to serious accidents in the event of failure. Therefore, it is important for the gasifier to perform failure-risk assessment to understand its safety status and provide safety measures. Bayesian networks (BNs) for risk analysis of process systems has received a lot of attention due to its powerful inference capability and its ability to reflect complex relationships between risk factors. However, the acquisition of basic probability data in a Bayesian network is always a great challenge. In this study, an improved Bayesian network integrated with a trapezoidal intuitionistic fuzzy number-based similarity aggregation method (TpIFN-SAM) is proposed for the failure-risk assessment of process systems. This approach used the TpIFN-SAM to collect and aggregate experts’ opinions for obtaining the prior probabilities of the root events in the BN. In the TpIFN-SAM, the intuitionistic fuzzy analytic-hierarchy-process method (IF-AHP) was adopted to assign the expert weights for reducing subjectivity or the bias caused by individual differences. To clarify the suitability of the proposed method, a case study of a coal gasifier was demonstrated, and both prediction and diagnosis analyses of the BN were performed; finally, the weak links of the gasifier were identified.
Antioxidant and Sensorial Properties: Meat Analogues versus Conventional Meat Products
Authors: Abdullah Fouad Ali Abdullah , Dordevic Dani , Kabourkova Eliska , Zemancová Johana , Dordevic Simona
Published: September 2022

Meat-product alternatives have become more popular among consumers, mainly due to concern for animal welfare and demand for more eco-friendly production. This study focused on the comparison between the antioxidant capacities of selected types of meat products and those of their plant-based alternatives. The analogues of the following products were analyzed: minced meat, burger, steak, Hungarian sausages, Frankfurter sausages and salami. Total polyphenol contents and antioxidant capacities and sensory profiles of the products were determined. The highest polyphenol content (1.85 mg Gallic acid/g) and antioxidant capacity values (DPPH: 41.80% inhibition, CUPRAC: 9.21 Trolox mmol/kg, FRAP: 7.51 mmol/g, ABTS: 7.45% inhibition) were observed in the analogue samples of Hungarian sausages due to the oat flour presence in these products. The results indicated that antioxidant properties of meat analogue products (plant sources) were superior compared to conventional meat products (produced from animal sources). The sensorial attributes indicated no significant (p > 0.05) differences in taste (except the Frankfurter sausages). The novelty of the study can be seen in the fact that it confirmed that the sensory properties of meat analogue products can be close to those of traditional meat products.
Multi-Response Optimization during the High-Speed Drilling of Composite Laminate Using the Grey Entropy Fuzzy Method (GEF)
Authors: Babu Jalumedi , Madarapu Anjaiah , Paul Lijo , Ahmed A. N. Khaleel , Davim J. Paulo
Published: September 2022

The machining of glass-fiber-reinforced composites is complex due to their heterogeneous structures. Research has indicated that high-speed machining, at high spindle speeds and feed rates, not only increases productivity but also reduces drilling defects, such as delamination. However, there are several challenges with high-speed machining, such as the heat generated during drilling between the drill tool and chip contact surfaces that can result in hole size errors. Therefore, it is essential to determine the optimum drilling parameters. This paper presents an innovative hybrid optimization approach called Grey Entropy Fuzzy. This approach is a combination of entropy-based weight integrated Grey Relation analysis and fuzzy logic. With the consideration of all responses simultaneously, an optimum machining combination was obtained at a spindle speed of 12,000 rpm and a feed rate of 0.02 mm/rev using a drill bit with a point angle of 118° and a drill diameter of 6 mm.
Optimizing the Gamma Ray-Based Detection System to Measure the Scale Thickness in Three-Phase Flow through Oil and Petrochemical Pipelines in View of Stratified Regime
Authors: Mayet Abdulilah Mohammad , Chen Tzu-Chia , Alizadeh Seyed Mehdi , Al-Qahtani Ali Awadh , Alanazi Abdullah K. , Ghamry Nivin A. , Alhashim Hala H. , Eftekhari-Zadeh Ehsan
Published: September 2022

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source (241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector.
Number of Convolution Layers and Convolution Kernel Determination and Validation for Multilayer Convolutional Neural Network: Case Study in Breast Lesion Screening of Mammographic Images
Authors: Zhang Feng-Zhou , Lin Chia-Hung , Chen Pi-Yun , Pai Neng-Sheng , Su Chun-Min , Pai Ching-Chou , Ho Hui-Wen
Published: September 2022

Mammography is a low-dose X-ray imaging technique that can detect breast tumors, cysts, and calcifications, which can aid in detecting potential breast cancer in the early stage and reduce the mortality rate. This study employed a multilayer convolutional neural network (MCNN) to screen breast lesions with mammographic images. Within the region of interest, a specific bounding box is used to extract feature maps before automatic image segmentation and feature classification are conducted. These include three classes, namely, normal, benign tumor, and malignant tumor. Multiconvolution processes with kernel convolution operations have noise removal and sharpening effects that are better than other image processing methods, which can strengthen the features of the desired object and contour and increase the classifier’s classification accuracy. However, excessive convolution layers and kernel convolution operations will increase the computational complexity, computational time, and training time for training the classifier. Thus, this study aimed to determine a suitable number of convolution layers and kernels to achieve a classifier with high learning performance and classification accuracy, with a case study in the breast lesion screening of mammographic images. The Mammographic Image Analysis Society Digital Mammogram Database (United Kingdom National Breast Screening Program) was used for experimental tests to determine the number of convolution layers and kernels. The optimal classifier’s performance is evaluated using accuracy (%), precision (%), recall (%), and F1 score to test and validate the most suitable MCNN model architecture.
Transforming Loneliness: An Orthodox Christian Answer to an Increasing Loneliness in Disabled Populations
Authors: Marginean Emil M.
Published: September 2022

Social isolation and inactivity have a profound effect on one’s quality of life. In recent times, the COVID-19 pandemic has impacted the social life of many. When it comes to disabled populations, emotional well-being is greatly affected by an increasing trend of social isolation. Research shows that people with disabilities perceive loneliness as unbelonging in childhood and disaffiliation to normative institutions in adulthood. Certainly, the efforts of building community bring richness and quality to life, but there are other solutions to addressing loneliness and solitude. Therefore, finding the true meaning of life is what can bring a positive vision of one’s world. In the Eastern Orthodox Christian ascetical theology, loneliness was transformed into a positive voluntary solitude and has been a central point of daily life, manifested from the ancient Christian sites to modern-day monastic and eremitic life. The present paper proposes a two-folded solution for reframing loneliness into empowerment. It starts with an insight into Frankl’s Man’s Search for Meaning and continues with finding a positive value of loneliness. The study examines different perspectives on loneliness and solitude which can improve the spiritual and emotional well-being of people with disabilities.
Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning
Authors: Fang Yuanhao , Huang Yizhi , Qu Bo , Zhang Xingnan , Zhang Tao , Xia Dazhong
Published: September 2022

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.
Mode Recognition of Orbital Angular Momentum Based on Attention Pyramid Convolutional Neural Network
Authors: Qu Tan , Zhao Zhiming , Zhang Yan , Wu Jiaji , Wu Zhensen
Published: September 2022

In an effort to address the problem of the insufficient accuracy of existing orbital angular momentum (OAM) detection systems for vortex optical communication, an OAM mode detection technology based on an attention pyramid convolution neural network (AP-CNN) is proposed. By introducing fine-grained image classification, the low-level detailed features of the similar light intensity distribution of vortex beam superposition and plane wave interferograms are fully utilized. Using ResNet18 as the backbone of AP-CNN, a dual path structure with an attention pyramid is adopted to detect subtle differences in the light intensity in images. Under different turbulence intensities and transmission distances, the detection accuracy and system bit error rate of basic CNN with three convolution layers and two full connection layers, i.e., ResNet18 and ResNet18, with a specified mapping relationship and AP-CNN, are numerically analyzed. Compared to ResNet18, AP-CNN achieves up to a 7% improvement of accuracy and a 3% reduction of incorrect mode identification in the confusion matrix of superimposed vortex modes. The accuracy of single OAM mode detection based on AP-CNN can be effectively improved by 5.5% compared with ResNet18 at a transmission distance of 2 km in strong atmospheric turbulence. The proposed OAM detection scheme may find important applications in optical communications and remote sensing.
Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss
Authors: Alotaibi Ashwaq , Hussain Muhammad , AboAlSamh Hatim , Abdul Wadood , Bebis George
Published: September 2022

A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder–decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.
Investigation of Focusing Properties on Astigmatic Gaussian Beams in Nonlinear Medium
Authors: Tao Shishi , Xue Jiayun , Guo Jiewei , Zhao Xing , Zhang Zhi , Lin Lie , Liu Weiwei
Published: September 2022

Ultra-short laser filamentation has been intensively studied due to its unique optical properties for applications in the field of remote sensing and detection. Although significant progress has been made, the quality of the laser beam still suffers from various optical aberrations during long-range transmission. Astigmatism is a typical off-axis aberration that is often encountered in the off-axis optical systems. An effective method needs to be proposed to suppress the astigmatism of the beam during filamentation. Herein, we numerically investigated the impact of the nonlinear effects on the focusing properties of the astigmatic Gaussian beams in air and obtained similar results in the experiment. As the single pulse energy increases, the maximum on-axis intensity gradually shifted from the sagittal focus to the tangential focus and the foci moved forward simultaneously. Moreover, the astigmatism could be suppressed effectively with the enhancement of the nonlinear effects, that is, the astigmatic difference and the degree of beam distortion were both reduced. Through this approach, the acoustic intensity of the filament (located at the tangential focal point) increased by a factor of 22.8. Our work paves a solid step toward the practical applications of the astigmatism beam as the nonlinear lidar.
Null Broadening Robust Adaptive Beamforming Algorithm Based on Power Estimation
Authors: Yu Zhenhua , Cui Weijia , Du Yuxi , Ba Bin , Quan Mengjiao
Published: September 2022

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness.
A Long Short-Term Memory-Based Approach for Detecting Turns and Generating Road Intersections from Vehicle Trajectories
Authors: Wan Zijian , Li Lianying , Yu Huafei , Yang Min
Published: September 2022

Owing to the widespread use of GPS-enabled devices, sensing road information from vehicle trajectories is becoming an attractive method for road map construction and update. Although the detection of intersections is critical for generating road networks, it is still a challenging task. Traditional approaches detect intersections by identifying turning points based on the heading changes. As the intersections vary greatly in pattern and size, the appropriate threshold for heading change varies from area to area, which leads to the difficulty of accurate detection. To overcome this shortcoming, we propose a deep learning-based approach to detect turns and generate intersections. First, we convert each trajectory into a feature sequence that stores multiple motion attributes of the vehicle along the trajectory. Next, a supervised method uses these feature sequences and labeled trajectories to train a long short-term memory (LSTM) model that detects turning trajectory segments (TTSs), each of which indicates a turn occurring at an intersection. Finally, the detected TTSs are clustered to obtain the intersection coverages and internal structures. The proposed approach was tested using vehicle trajectories collected in Wuhan, China. The intersection detection precision and recall were 94.0% and 91.9% in a central urban region and 94.1% and 86.7% in a semi-urban region, respectively, which were significantly higher than those of the previously established local G* statistic-based approaches. In addition to the applications for road map development, the newly developed approach may have broad implications for the analysis of spatiotemporal trajectory data.
Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment
Authors: Erişen Serdar
Published: September 2022

The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.
D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning
Authors: Guan Xin , Lv Tiejun , Lin Zhipeng , Huang Pingmu , Zeng Jie
Published: September 2022

Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.
The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks
Authors: Devi Sharmila , Sangwan Anju , Sangwan Anupma , Mohammed Mazin Abed , Kumar Krishna , Nedoma Jan , Martinek Radek , Zmij Petr
Published: September 2022

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.