bluuebunny/crossref_metadata_2025 huggingface dataset:
“I’ll Be There”: Informal and Formal Support Systems and Mothers’ Psychological Distress during NICU Hospitalization
Authors: Kestler-Peleg Miri , Stenger Varda , Lavenda Osnat , Bendett Haya , Alhalel-Lederman Shanee , Maayan-Metzger Ayala , Strauss Tzipora
Published: December 2022

Mothers of infants hospitalized in the Neonatal Intensive Care Unit (NICU) are at a high risk for psychological distress, which is of concern to health and social professionals due to the negative implications for mothers and infants. A model for explaining maternal psychological distress, consisting of intolerance to uncertainty and support from informal (spouse, family, and friends) and formal (medical staff) systems was examined. Data was collected from one of the largest NICUs in Israel; 129 mothers of 215 preterm infants completed self-report questionnaires regarding their background variables, intolerance to uncertainty, perceived informal support and perceived medical staff support. The NICU’s medical staff provided indicators for the infants of participating mothers. A hierarchical multiple regression analysis was conducted. The examined model explained 29.2% of the variance in maternal psychological distress. Intolerance of uncertainty positively predicted psychological distress. Informal support, and in particular, spousal support negatively predicted psychological distress above and beyond intolerance of uncertainty. Medical staff support negatively predicted psychological distress above and beyond intolerance to uncertainty and informal support. Our findings suggest that maternal psychological distress is reduced through a family-centered care approach in NICUs. Medical professionals and social services should develop further solutions for addressing preterm mothers’ need for certainty and support.
Implications of Cellular Immaturity in Necrosis and Microvascularization in Glioblastomas IDH-Wild-Type
Authors: Orasanu Cristian Ionut , Aschie Mariana , Deacu Mariana , Bosoteanu Madalina , Vamesu Sorin , Enciu Manuela , Bălţătescu Gabriela Izabela , Cozaru Georgeta Camelia , Mitroi Anca Florentina , Voda Raluca Ioana
Published: December 2022

Necrosis and increased microvascular density in glioblastoma IDH-wild-type are the consequence of both hypoxia and cellular immaturity. Our study aimed to identify the main clinical-imaging and morphogenetic risk factors associated with tumor necrosis and microvascular in the prognosis of patient survival. We performed a retrospective study (10 years) in which we identified 39 cases. We used IDH1, Ki-67 and Nestin immunomarkers, as well as CDKN2A by FISH. The data were analyzed using SPSS Statistics. The clinical characterization identified only age over 50 years as a risk factor (HR = 3.127). The presence of the tumor residue, as well as the absence of any therapeutic element from the trimodal treatment, were predictive factors of mortality (HR = 1.024, respectively HR = 7.460). Cellular immaturity quantified by Nestin was associated with reduced overall survival (p = 0.007). Increased microvascular density was associated with an increased proliferative index (p = 0.009) as well as alterations of the CDKN2A gene (p < 0.001). CDKN2A deletions and cellular immaturity were associated with an increased percentage of necrosis (p < 0.001, respectively, p = 0.017). The main risk factors involved in the unfavorable prognosis are moderate and increased Nestin immunointensity, as well as the association of increased microvascular density with age over 50 years. Necrosis was not a risk factor.
New Scaling Laws for Pinning Force Density in Superconductors
Authors: Talantsev Evgueni F.
Published: December 2022

Since the report by Fietz and Webb (Phys. Rev.1968, 178, 657–667), who considered the pinning force density, Fp→=Jc→×B→ (where Jc is the critical current density and B is applied magnetic flux density), in isotropic superconductors as a unique function of reduced magnetic field, BBc2 (where Bc2 is the upper critical field), Fp→ has been scaled based on the BBc2 ratio, for which there is a widely used Kramer–Dew–Hughes scaling law of Fp→B=Fp,maxBBc2p1−BBc2q, where Fp,max, Bc2, p, and q are free-fitting parameters. To describe Fp→B in high-temperature superconductors, the Kramer–Dew–Hughes scaling law has been modified by (a) an assumption of the angular dependence of all parameters and (b) by the replacement of the upper critical field, Bc2, by the irreversibility field, Birr. Here, we note that Fp→ is also a function of critical current density, and thus, the Fp→Jc scaling law should exist. In an attempt to reveal this law, we considered the full Fp→B,Jc function and reported that there are three distinctive characteristic ranges of BBc2,JcJcsf (where Jcsf is the self-field critical current density) on which Fp→B,Jc can be splatted. Several new scaling laws for Fp→Jc were proposed and applied to MgB2, NdFeAs(O,F), REBCO, (La,Y)H10, and YH6. The proposed scaling laws describe the in-field performance of superconductors at low and moderate magnetic fields, and thus, the primary niche for these laws is superconducting wires and tapes for cables, fault current limiters, and transformers.
Porcelain Aorta in a Young Person Living with HIV Who Presented with Angina
Authors: Bajdechi Mircea , Scafa-Udriste Alexandru , Ploscaru Vlad , Calmac Lucian , Bajeu Teodor , Gurghean Adriana , Rugina Sorin
Published: December 2022

People living with human immunodeficiency virus have an increased cardiovascular risk due to higher prevalence of traditional risk factors, such as smoking, dyslipidemia, hypertension, diabetes, or obesity, and particular risk factors, such as inflammation, endothelial dysfunction, and antiretroviral therapy. Thus, people living with human immunodeficiency virus can develop accelerated atherosclerosis. The incidence of coronary artery disease in these patients may be twice as high compared with that of HIV-negative individuals with similar characteristics. “Porcelain aorta” is a term used to describe extensive circumferential calcification of the thoracic aorta. The pathophysiology of porcelain aorta is not fully understood. We present a case of a young man who was a smoker and living with HIV since childhood, without other traditional cardiovascular risk factors, who presented to the emergency room with a positive stress test for myocardial ischemia. Transthoracic echocardiography revealed normal regional and global myocardial wall motion, ascending aorta ectasia, and moderate aortic regurgitation. Coronary angiography showed a critical calcified proximal left anterior descending artery stenosis and an important calcification of the thoracic aorta. Therefore, the most important challenge was the management of coronary syndrome in a young person living with HIV, with associated porcelain aorta and aortic regurgitation.
Third-Generation High-Vision Ultrathin Endoscopy Using Texture and Color Enhancement Imaging and Narrow-Band Imaging to Evaluate Barrett’s Esophagus
Authors: Sugimoto Mitsushige , Kawai Yusuke , Akimoto Yoshika , Hamada Mariko , Iwata Eri , Murata Masaki , Mizuno Hitomi , Niikura Ryota , Nagata Naoyoshi , Fukuzawa Masakatsu , Itoi Takao , Kawai Takashi
Published: December 2022

It remains unclear whether texture- and color-enhancement imaging (TXI) and narrow-band imaging (NBI) provide an advantage over white-light imaging (WLI) in Barrett’s esophagus. We compared endoscopic findings and color differences between WLI and image-enhanced endoscopy (IEE) using a third-generation ultrathin endoscope. We retrospectively enrolled 40 patients who evaluated Barrett’s esophagus using WLI, TXI, and NBI. Color differences determined using the International Commission on Illumination 1976 (L∗, a∗, b∗) color space among Barrett’s epithelium, esophageal, and gastric mucosa were compared among the endoscopic findings. As the secondary outcome, we assessed the subjective visibility score among three kinds of endoscopic findings. The prevalence of Barrett’s esophagus and gastroesophageal reflux disease (GERD) in WLI was 82.5% and 47.5%, respectively, and similar among WLI, TXI, and NBI. Color differences between Barrett’s epithelium and esophageal or gastric mucosa on NBI were significantly greater than on WLI (all p < 0.05). However, the color difference between Barrett’s epithelium and esophageal mucosa was significantly greater on NBI than TXI (p < 0.001), and the visibility score of Barrett’s epithelium detection was significantly greater on TXI than NBI (p = 0.022), and WLI (p = 0.016). High-vision, third-generation ultrathin endoscopy using NBI and TXI is useful for evaluating Barrett’s epithelium and GERD compared with WLI alone.
Prediction of Masked Uncontrolled Hypertension Detected by Ambulatory Blood Pressure Monitoring
Authors: Coccina Francesca , Borrelli Paola , Pierdomenico Anna M. , Pizzicannella Jacopo , Guagnano Maria T. , Cuccurullo Chiara , Di Nicola Marta , Renda Giulia , Trubiani Oriana , Cipollone Francesco , Pierdomenico Sante D.
Published: December 2022

The aim of this study was to provide prediction models for masked uncontrolled hypertension (MUCH) detected by ambulatory blood pressure (BP) monitoring in an Italian population. We studied 738 treated hypertensive patients with normal clinic BPs classified as having controlled hypertension (CH) or MUCH if their daytime BP was < or ≥135/85 mmHg regardless of nighttime BP, respectively, or CH or MUCH if their 24-h BP was < or ≥130/80 mmHg regardless of daytime or nighttime BP, respectively. We detected 215 (29%) and 275 (37%) patients with MUCH using daytime and 24-h BP thresholds, respectively. Multivariate logistic regression analysis showed that males, those with a smoking habit, left ventricular hypertrophy (LVH), and a clinic systolic BP between 130–139 mmHg and/or clinic diastolic BP between 85–89 mmHg were associated with MUCH. The area under the receiver operating characteristic curve showed good accuracy at 0.78 (95% CI 0.75–0.81, p < 0.0001) and 0.77 (95% CI 0.73–0.80, p < 0.0001) for MUCH defined by daytime and 24 h BP, respectively. Internal validation suggested a good predictive performance of the models. Males, those with a smoking habit, LVH, and high-normal clinic BP are indicators of MUCH and models including these factors provide good diagnostic accuracy in identifying this ambulatory BP phenotype.
The Accuracy of Lateral Cephalogram for Measuring Alveolar Bone Thickness and Root Diameter on Mandibular Incisors
Authors: Limsakul Thippawan , Chanmanee Pannapat , Charoemratrote Chairat
Published: December 2022

Background: The objective of the study was to ascertain whether the alveolar bone and root of the mandibular central incisor measured from cephalograms can represent the same measurements of both mandibular central and lateral incisors from CBCT. Methods: A total of 38 sets of CBCT images and cephalograms before treatment were selected for this study. Thicknesses included alveolar bone, cortical bone, and cancellous bone at the labial and lingual sides. Root diameter and total root-bone thickness were also evaluated. The measurements were performed at 3, 6, and 9 mm from the cemento-enamel junction. Heights included labial bone height and lingual bone height. All measurements were performed on cephalograms and CBCT images of the mandibular central incisor (L1CT) and mandibular lateral incisor (L2CT). The data were statistically analyzed using one-way ANOVA and Bonferroni tests (p < 0.01) to compare the cephalograms, L1CT, and L2CT. Results: The cephalograms presented thicker alveolar bone and cortical bone (labial: 0.16–0.31 mm, lingual: 0.14–0.29 mm; p < 0.001) as well as higher alveolar crest (labial: 0.46–0.48 mm, lingual: 0.38–0.39 mm; p < 0.001) than the CBCT images on both the labial and lingual sides, whereas lingual cancellous thicknesses were not significantly different (p = 0.257). The cephalograms presented greater total root-bone thicknesses than L1CT (0.19–0.30 mm; p < 0.001), whereas the cephalograms traced thinner roots than L1CT (0.18–0.23 mm; p < 0.001) and L2CT (0.39–0.59 mm; p < 0.001). Conclusion: Lateral cephalograms cannot represent both mandibular central and lateral incisor dentoalveolar thicknesses, heights, and root diameters the same as CBCT. However, the differences were less than 0.5 mm.
Anastomotic Leakage after Oesophagectomy: Upper Endoscopy or Computed Tomography First? Time Is of the Essence
Authors: El-Sourani Nader , Alfarawan Fadl , Miftode Sorin
Published: December 2022

Introduction: Anastomotic leakage (AL) following oesophageal surgery is the most feared complication. Therefore, it is of utmost importance to diagnose it in a timely and safe manner. The diagnostic algorithm, however, differs across institutions world-wide, with no clear consensus or guidelines. The aim of this study was to analyse whether computed tomography (CT) or upper endoscopy (UE) should be performed first. Material and Methods: Records of 185 patients undergoing oesophageal surgery for underlying malignancy were analysed. All patients that developed an AL were further analysed. Results of CT and UE were compared to calculate sensitivity. Results: Overall, 33 out of 185 patients were diagnosed with an AL after oesophagectomy. All patients received a CT and a UE. The CT identified 23 out of 33 patients correctly. Sensitivity was 69.7% for CT, compared to 100% for UE. Conclusion: If patients are clinically suspicious regarding development of an AL after oesophagectomy, UE should be performed prior to CT as it has a sensitivity of 100%. In addition, treatment by means of endoluminal vacuum therapy (EVT) or self-expanding-metal stents (SEMS) can be initiated promptly.
A Hybrid Quantum Image-Matching Algorithm
Authors: Shu Guoqiang , Shan Zheng , Di Shiqin , Ding Xiaodong , Feng Congcong
Published: December 2022

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Entanglement and Fisher Information for Atoms–Field System in the Presence of Negative Binomial States
Authors: Berrada Kamal , Abdel-Khalek Sayed , Algarni Mariam , Eleuch Hichem
Published: December 2022

We developed a quantum scheme of two atoms (TAs) and field initially in a negative binomial state (NBS). We displayed and discussed the physical implications of the obtained results in terms of the physical parameters of the model. By considering that the TAs were initially prepared in a maximally entangled state, and that the single-mode field was in the NBS, the dynamics of quantum phenomena such TAs–field entanglement, TAs entanglement, and parameter estimation were examined. We found that the quantum quantifiers exhibited randomly quasi-periodic and periodic oscillations that depended on the success probability, photon number transition, and the intensity-dependent coupling effect. Furthermore, we analyzed the connection between the dynamical behavior of the quantifiers. This system can be compared with some other ones that are being discussed in the literature, in order to realize the quantum entanglement, and to control the precision of the parameter estimation.
Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans
Authors: Cristín Javier , Méndez Vicenç , Campos Daniel
Published: December 2022

While approaches based on physical grounds (such as the drift-diffusion model—DDM) have been exhaustively used in psychology and neuroscience to describe perceptual decision making in humans, similar approaches to complex situations, such as sequential (tree-like) decisions, are still scarce. For such scenarios that involve a reflective prospection of future options, we offer a plausible mechanism based on the idea that subjects can carry out an internal computation of the uncertainty about the different options available, which is computed through the corresponding Shannon entropy. When the amount of information gathered through sensory evidence is enough to reach a given threshold in the entropy, this will trigger the decision. Experimental evidence in favor of this entropy-based mechanism was provided by exploring human performance during navigation through a maze on a computer screen monitored with the help of eye trackers. In particular, our analysis allows us to prove that (i) prospection is effectively used by humans during such navigation tasks, and an indirect quantification of the level of prospection used is attainable; in addition, (ii) the distribution of decision times during the task exhibits power-law tails, a feature that our entropy-based mechanism is able to explain, unlike traditional (DDM-like) frameworks.
Digital Pandemic Stress in Higher Education in Venezuela
Authors: Antón-Sancho Álvaro , Vergara Diego , Medina Elsy , Sánchez-Calvo María
Published: December 2022

The COVID-19 pandemic had a great impact on the process of integrating digital technologies in higher education and caused digital stress among professors, mainly in countries with a lower level of digitalization. In this work, quantitative research was carried out on the stress of professors in Venezuela due to the digitalization of their teaching activities caused by the pandemic, and gender gaps were identified in this regard. This digital stress was compared with that of professors in other countries with a low level of digitalization. For this purpose, a questionnaire designed by the authors was used. The questionnaire was distributed to a sample of 129 Venezuelan professors and 132 professors from countries with low digitalization levels. As a result, it was found that Venezuelan professors have lower digital competence and lower digital stress than their colleagues in weakly digitized countries, and that digital stress decreases as digital competence increases. Moreover, among Venezuelan professors, there was a strong gender gap in digital stress, which was higher among females in all subject areas, except for Health Sciences. This gender gap is specific to Venezuela since it differs from that in countries with low digital levels. According to the results, we urgently recommend investing resources in the digital training of faculty members, especially in regards to the integration of female professors.
CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume
Authors: Chen Jun , Duan Hui , Song Yuanxin , Tang Ming , Cai Zemin
Published: December 2022

Motion estimation for complex fluid flows via their image sequences is a challenging issue in computer vision. It plays a significant role in scientific research and engineering applications related to meteorology, oceanography, and fluid mechanics. In this paper, we introduce a novel convolutional neural network (CNN)-based motion estimator for complex fluid flows using multiscale cost volume. It uses correlation coefficients as the matching costs, which can improve the accuracy of motion estimation by enhancing the discrimination of the feature matching and overcoming the feature distortions caused by the changes of fluid shapes and illuminations. Specifically, it first generates sparse seeds by a feature extraction network. A correlation pyramid is then constructed for all pairs of sparse seeds, and the predicted matches are iteratively updated through a recurrent neural network, which lookups a multi-scale cost volume from a correlation pyramid via a multi-scale search scheme. Then it uses the searched multi-scale cost volume, the current matches, and the context features as the input features to correlate the predicted matches. Since the multi-scale cost volume contains motion information for both large and small displacements, it can recover small-scale motion structures. However, the predicted matches are sparse, so the final flow field is computed by performing a CNN-based interpolation for these sparse matches. The experimental results show that our method significantly outperforms the current motion estimators in capturing different motion patterns in complex fluid flows, especially in recovering some small-scale vortices. It also achieves state-of-the-art evaluation results on the public fluid datasets and successfully captures the storms in Jupiter’s White Ovals from the remote sensing images.
Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks
Authors: Abdulsattar Nejood Faisal , Mohammed Dheyaa Abdulameer , Alkhayyat Ahmed , Hamed Shemaha Z. , Hariz Hussein Muhi , Abosinnee Ali S. , Abbas Ali Hashim , Hassan Mustafa Hamid , Jubair Mohammed Ahmed , Abbas Fatima Hashim , Algarni Abeer D. , Soliman Naglaa F. , El-Shafai Walid
Published: December 2022

In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider’s CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET.
Torque-Pulse-Based Initial Rotor Polarity Detection for IPMSM with Low Saturation Effect
Authors: Yang Weibin , Wang Yuanlin , Guo Hao , Sun Xinxin , Dajaku Gurakuq , Riaz Saleem , Zaman Haider , Gerling Dieter
Published: December 2022

Referring to the sensorless control of interior permanent magnet synchronous motor (IPMSM), the initial rotor polarity is normally estimated based on the motor saturation effect. However, for certain special IPMSMs, the saturation effect is weak even at the rated point, making the saturation-based rotor polarity detection methods invalid. Therefore, this paper proposes a novel rotor polarity detection method based on torque-pulse injection. Two current pulses are imposed on the positive and negative directions of the q-axis estimated by the high-frequency injection (HFI) method, respectively; the signs of speed peaks indicate the rotor polarity. To derive the rotor polarity from the tiny speed signal, a new speed estimation method is presented. To improve the dynamic performance of speed estimation, a special current filter is adopted in the HFI method. To choose the proper width and amplitude of the current pulse under different load and inertia conditions, an automatic program is designed. The proposed method has the advantages of high accuracy, short identification time, tiny movement, and automatic operation, making it applicable to various load and inertia conditions. The effectiveness of the proposed method is validated by experiments on an IPMSM with low saturation effect.
A BIST Scheme for Dynamic Comparators
Authors: Tang Xiao-Bin , Tachibana Masayoshi
Published: December 2022

This paper proposes a built-in self-test (BIST) scheme for detecting catastrophic faults in dynamic comparators. In this scheme, a feedback loop is designed using the characteristics of the comparator; monitoring the voltage in the feedback loop can determine the presence of a circuit fault. The proposed BIST scheme and the circuit under testing are realized at the transistor level. The proposed BIST scheme was simulated using HSPICE. The simulated fault coverage is approximately 87.8% with 90 test circuits. To further verify the effectiveness of the proposed BIST scheme, six faults were injected into the real circuit. The test results were consistent with the simulation results.
Metric-Based Key Frame Extraction for Gait Recognition
Authors: Wei Tuanjie , Li Rui , Zhao Huimin , Chen Rongjun , Zhan Jin , Li Huakang , Wan Jiwei
Published: December 2022

Gait recognition is one of the most promising biometric technologies that can identify individuals at a long distance. From observation, we find that there are differences in the length of the gait cycle and the quality of each frame in the sequence. In this paper, we propose a novel gait recognition framework to analyze human gait. On the one hand, we designed the Multi-scale Temporal Aggregation (MTA) module that models temporal and aggregate contextual information with different scales, on the other hand, we introduce the Metric-based Frame Attention Mechanism (MFAM) to re-weight each frame by the importance score, which calculates using the distance between frame-level features and sequence-level features. We evaluate our model on two of the most popular public datasets, CASIA-B and OU-MVLP. For normal walking, the rank-1 accuracies on the two datasets are 97.6% and 90.1%, respectively. In complex scenarios, the proposed method achieves accuracies of 94.8% and 84.9% on CASIA-B under bag-carrying and coat-wearing walking conditions. The results show that our method achieves the top level among state-of-the-art methods.
Load Power Oriented Large-Signal Stability Analysis of Dual-Stage Cascaded dc Systems Based on Lyapunov-Type Mixed Potential Theory
Authors: Chen Zhe , Chen Xi , Zheng Feng , Ma Hui , Zhu Binxin
Published: December 2022

Dual-stage cascaded dc systems are some of the most widely applied power interfaces in dc distributed power systems. However, in some practical situations, these systems might be unstable, especially if they incorporate tightly regulated load converters that operate as constant power loads (CPLs), whose power fluctuations could exert a cascading impact on the operation of the systems. Existing studies tend to describe the instability phenomena using bifurcation diagram analysis and the loci of eigenvalue analysis. However, it is usually difficult to derive the explicit expressions of the stability criterion. This paper addresses the large-signal stability issue of the dual-stage cascaded dc systems from a standpoint of load power and obtains the explicit form large-signal stability boundary in terms of load power by using Lyapunov-type mixed potential theory. Moreover, the prototype dual-stage cascaded dc system, in which the control strategies for the feeder converter and the load converter are different, is used as an example in this study. According to the results, the system remains stable when the load power is in [5.8, 23.2] W. When load power is less than 5.8 W or increased to [23.2, 32.8] W, the system is in a period-2 subharmonic oscillation state. Moreover, when the load power exceeds 32.8 W, the system falls into a chaotic state. The deduced boundary is highly consistent with the analysis results of both a bifurcation diagram and Jacobian matrix based analysis. Finally, both circuit-level simulation and experimental results validate the effectiveness of the load power stability boundary.
Energy Balance of Hydrogen Production in the Cathodic Regime of Plasma-Driven Solution Electrolysis of Na2CO3 Aqueous Solution with Argon Carrier Gas
Authors: Bespalko Sergii , Mizeraczyk Jerzy
Published: December 2022

In this paper, the results of an experimental study on hydrogen production at a tungsten discharge electrode with negative polarity in the DC electrolysis of a typical 10 wt% Na2CO3 aqueous solution in three operational regimes (the Faradaic, transition, and plasma-driven solution electrolysis (PDSE)) are presented for the first time. To focus the study on hydrogen production, a flowing inert gas (argon) was used to transport the gas mixture produced at the discharge electrode and prevent any other potential chemical reactions. The results showed that the highest hydrogen production rate of 0.147 g(H2)/h was achieved in the cathodic PDSE regime at the applied DC voltage of 198 V. However, the energy yield of hydrogen production of 0.405 g(H2)/kWh obtained at the applied voltage range of 141–170 V in the PDSE regime was lower than that obtained in the Faradaic regime (0.867 g(H2)/kWh) at 28 V. The energy balance of hydrogen production in the cathodic PDSE regime for the typical aqueous solution of Na2CO3 carried out for the first time showed that a significant share (˃98%) of the electrical energy consumed is spent on heating and evaporation of the electrolytic solution. This explains why the energy yield of hydrogen production is low in the PDSE regime. Because most of the energy is consumed for heat generation in the cathodic PDSE regime, organic liquid hydrogen carriers, such as alcohols, which have a lower boiling temperature, heat of evaporation, and standard Gibbs free energy, should be considered better aqueous electrolytic solutions in terms of the energy yield of hydrogen production in the PDSE regime.
Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters
Authors: Śmiałkowski Tomasz , Czyżewski Andrzej
Published: December 2022

Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model.
A Multi-Criteria Approach for Optimizing the Placement of Electric Vehicle Charging Stations in Highways
Authors: Skaloumpakas Panagiotis , Spiliotis Evangelos , Sarmas Elissaios , Lekidis Alexios , Stravodimos George , Sarigiannis Dimitris , Makarouni Ioanna , Marinakis Vangelis , Psarras John
Published: December 2022

The electric vehicle (EV) industry has made significant progress but, in many markets, there are still barriers holding back its advancement. A key issue is the anxiety caused to the drivers by the limited range of current EV models and the inadequate access to charging stations in long-distance trips, as is the case on highways. We propose an intuitive multi-criteria approach that optimally places EV charging stations on highways that (partially) lack such points. The approach, which is applied in an iterative fashion to dynamically evaluate the alternatives, considers a set of practical criteria related to the traffic intensity and the relative location of the charging stations with interchanges, major cities, and existing stations, thus supporting decisions in a pragmatic way. The optimal locations are determined by taking into consideration constraints about the EV driving range and installation preferences to improve the operation of the highway while ensuring reasonable cost of investment. The proposed approach is showcased in the Egnatia Motorway, the longest highway in Greece that runs a total of 670 km but currently involves a single EV charging point. Our findings illustrate the utility of the proposed approach and highlight its merits as a decision-support tool.
System Profit Improvement of a Thermal–Wind–CAES Hybrid System Considering Imbalance Cost in the Electricity Market
Authors: Chakraborty Mitul Ranjan , Dawn Subhojit , Saha Pradip Kumar , Basu Jayanta Bhusan , Ustun Taha Selim
Published: December 2022

Studying a renewable energy integrated power system’s features is essential, especially for deregulated systems. The unpredictability of renewable sources is the main barrier to integrating renewable energy-producing units with the current electrical grid. Due to its unpredictable nature, integrating wind power into an existing power system requires significant consideration. In a deregulated electricity market, this paper examines the implications of wind farm (WF) integration with CAES on electric losses, voltage profile, generation costs, and system economics. Comparative research was done to determine the impact of wind farm integration on regulated and deregulated environments. Four randomly chosen locations in India were chosen for this investigation, together with real-time information on each location’s real wind speed (RWS) and predicted wind speed (PWS). Surplus charge rates and deficit charge rates were created to assess the imbalance cost arising from the discrepancy between predicted and real wind speeds to calculate the system economics. When the effect of imbalance cost is considered, the daily system profit shows a variation of about 1.9% for the locations under study. Customers are always seeking electricity that is dependable, affordable, and efficient due to the reorganization of the power system. As a result, the system security limit could be exceeded or the system might function dangerously. The final section of this paper presents an economic risk analysis using heuristic algorithms such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO). It also discusses how the CAES is used to correct the deviation of WF integration in the real-time electricity market. Economic risk analysis tools include value-at-risk (VaR) and conditional value-at-risk (CVaR). The entire piece of work was validated using a modified IEEE 30-bus test system. This works shows that with a three-fold increase in wind generation, the risk coefficient values improves by 1%.
Models and Indices of Sustainability Assessment in the Energy Context
Authors: Ziemba Paweł , Becker Aneta , Becker Jarosław
Published: December 2022

Today, the energy sector is characterized by a high degree of unsustainability in terms of sources and supply systems, infrastructure, and policies, including climate policy. Therefore, it is necessary to strengthen the functions of planning and to implement new energy strategies, which should lead to the sector’s sustainability in the environmental, social, and economic dimensions. In this context, the aim of the article is to develop a model for the sustainability assessment process and to use it as a benchmarking framework for sustainability assessment indices used in energy problems. The study included 14 indices to assess various aspects of sustainable development. The indices were tested in terms of their sustainability dimensions, data sources, data normalization methods, index aggregation methods, and other elements of the sustainability assessment process. As a result of the research, it was found that none of the analyzed indices meet all the requirements for indices that are to be used for the assessment of sustainability. Therefore, the use of these indices in research problems related to energy sustainability requires a conscious analysis of their features and adaptation to specific research problems related to energy sustainability.
Carbon Footprint Assessment of Hydrogen and Steel
Authors: Suer Julian , Traverso Marzia , Jäger Nils
Published: December 2022

Hydrogen has the potential to decarbonize a variety of energy-intensive sectors, including steel production. Using the life cycle assessment (LCA) methodology, the state of the art is given for current hydrogen production with a focus on the hydrogen carbon footprint. Beside the state of the art, the outlook on different European scenarios up to the year 2040 is presented. A case study of the transformation of steel production from coal-based towards hydrogen- and electricity-based metallurgy is presented. Direct reduction plants with integrated electric arc furnaces enable steel production, which is almost exclusively based on hydrogen and electricity or rather on electricity alone, if hydrogen stems from electrolysis. Thus, an integrated steel site has a demand of 4.9 kWh of electric energy per kilogram of steel. The carbon footprint of steel considering a European sustainable development scenario concerning the electricity mix is 0.75 kg CO2eq/kg steel in 2040. From a novel perspective, a break-even analysis is given comparing the use of natural gas and hydrogen using different electricity mixes. The results concerning hydrogen production presented in this paper can also be transferred to application fields other than steel.
Dynamic Process Modeling of Topside Systems for Evaluating Power Consumption and Possibilities of Using Wind Power
Authors: Eyni Leila , Stanko Milan , Schümann Heiner , Qureshi Ali
Published: December 2022

Norwegian offshore wind farms may be able to supply power to offshore oil and gas platforms in the near future thanks to the expeditious development of offshore wind technology. This would result in a reduction in CO2 emissions from oil and gas offshore installations, which are currently powered predominantly by gas turbines. The challenge with using wind power is that offshore oil and gas installations require a fairly constant and stable source of power, whereas wind power typically exhibits significant fluctuations over time. The purpose of this study is to perform a technical feasibility evaluation of using wind power to supply an offshore oil and gas installation on the basis of dynamic process simulations. Throughout the study, only the topside processing system is considered, since it is the most energy-intensive part of an oil and gas facility. An offshore field on the Norwegian Continental Shelf is used as a case study. The results indicate that, when the processing system operates in steady-state conditions, it cannot be powered solely by wind energy, and another power source is required to compensate for low wind power generation intervals. An alternative would be to store wind energy during periods of high generation (e.g., by producing hydrogen or ammonia) and use it during periods of low generation. Utilizing energy storage methods, wind energy can be continuously used for longer periods of time and provide a suitable constant power source for the studied case. Higher constant power can also be provided by increasing the efficiency of energy recovery and storage processes. Alternatively, these two technologies may be integrated with gas turbines if the required storage cannot be provided or higher power is required. It was estimated that the integration of wind energy could result in noticeable reductions in CO2 emissions for the case study. Additionally, according to the results, the production, storage, and reuse of hydrogen and ammonia on-site may be viable options for supplying power.
Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot
Authors: Chen Xingyu , Chen Yinan , Duan Wenke , Akinyemi Toluwanimi , Yi Guanlin , Jiang Jie , Du Wenjing , Omisore Olatunji
Published: December 2022

Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.
Ganoderma lucidum Ethanol Extraction Promotes Dextran Sulphate Sodium Induced Colitis Recovery and Modulation in Microbiota
Authors: Li Miaoyu , Yu Leilei , Zhai Qixiao , Liu Bingshu , Zhao Jianxin , Chen Wei , Tian Fengwei
Published: December 2022

Popular edible mushrooms Ganoderma lucidum and Gloeostereum incarnatum can improve physical health as a prebiotic and positively alter intestinal microbiota. Our research investigated the prebiotic effects of Ganoderma lucidum and Gloeostereum incarnatum on colon inflammation through G. lucidum water extraction polysaccharides (GLP), G. incarnatum water extraction polysaccharides (GIP), G. lucidum ethanol extraction (GLE), and G. incarnatum ethanol extraction (GIE) administered in mice after 7 days of dextran sulphate sodium (DSS) administration. Among the extracts, GLE showed reduced mortality rates, prevention of weight loss, mitigated colon length shortening, and decreased disease activity indices and histological scores. COX-2, MPO, and iNOS activities and the inflammatory cytokines’ expressions were determined to demonstrate the inhibition inflammation by GLE. Meanwhile, GLE upregulated the levels of MUC2, ZO-1, claudin-3, and occluding to protect the intestinal barrier. Furthermore, GLE modulated the composition of gut microbiota disturbed by DSS, as it decreased the abundance of Bacteroides, Staphylococcus, and Escherichia_Shigella, and increased Turicibacter and Bifidobacterium. Through cell experiment, GLE had a positive influence on adherens junction, tight junction, and TRAF6/MyD88/NF-κB signaling pathways. In conclusion, GLE supplementation promotes DSS-induced colitis recovery by regulating inflammatory cytokines, preserving the intestinal mucosal barrier, positively modulating microbiota changes, and positively influences immune response in TRAF6/MyD88/NF-κB signaling pathways.
Effect of Spanish-Style Table Olive Processing on Fatty Acid Profile: A Compositional Data Analysis (CoDA) Approach
Authors: Garrido-Fernández Antonio , Cortés-Delgado Amparo , López-López Antonio
Published: December 2022

This manuscript considers that the composition of Manzanilla and Hojiblanca fats are compositional data (CoDa). Thus, the work applies CoDa analysis (CoDA) to investigate the effect of processing and packaging on the fatty acid profiles of these cultivars. To this aim, the values of the fat components in percentages were successively subjected to exploratory CoDA tools and, later, transformed into ilr (isometric log-ratio) coordinates in the Euclidean space, where they were subjected to the standard multivariate techniques. The results from the first approach (bar plots of geometric means, tetrahedral plots, compositional biplots, and balance dendrograms) showed that the effect of processing was limited while most of the variability among the fatty acid (FA) profiles was due to cultivars. The application of the standard multivariate methods (i.e., Canonical variates, Linear Discriminant Analysis (LDA), ANOVA/MANOVA with bootstrapping and n = 1000, and nested General Linear Model (GLM)) to the ilr coordinates transformed data, following Ward’s clustering or descending order of variances criteria, showed similar effects to the exploratory analysis but also showed that Hojiblanca was more sensitive to fat modifications than Manzanilla. On the contrary, associating GLM changes in ilr with fatty acids was not straightforward because of the complex deduction of some coordinates. Therefore, according to the CoDA, table olive fatty acid profiles are scarcely affected by Spanish-style processing compared with the differences between cultivars. This work has demonstrated that CoDA could be successfully applied to study the fatty acid profiles of olive fat and olive oils and may represent a model for the statistical analysis of other fats, with the advantage of applying appropriate statistical techniques and preventing misinterpretations.
Changes in Key Aroma Compounds and Esterase Activity of Monascus-Fermented Cheese across a 30-Day Ripening Period
Authors: Zeng Hong , Wang Yadong , Han Haoying , Cao Yanping , Wang Bei
Published: December 2022

Monascus-fermented cheese (MC) is a new type of mold-ripened cheese that combines a traditional Chinese fermentation fungus, Monascus purpureus M1, with Western cheese fermentation techniques. In this study, the compositions of the volatile aroma compounds in MC were analyzed during a 30-day ripening period using SPME-Arrow and GC-O-MS. The activity of esterase in MC, which is a key enzyme catalyzing esterification reaction, was determined and compared with the control group (CC). Next, sensory analysis was conducted via quantitative descriptive analysis followed by Pearson correlation analysis between esterase activity and the key flavor compounds. A total of 76 compounds were detected. Thirty-three of these compounds could be smelled at the sniffing port and were identified as the key aroma compounds. The esterase activity in MC was found to be 1.24~1.33 times that of the CC. Moreover, the key odor features of ripened MC were alcohol and fruity flavors, considerably deviating from the sour and cheesy features found for the ripened CC. Furthermore, correlation analysis showed that esterase activity was strongly correlated (|r|> 0.75, p < 0.05) with various acids such as pentanoic and nonanoic acids and several aromatic esters, namely, octanoic acid ethyl ester and decanoic acid ethyl ester, revealing the key role that esterases play in developing the typical aroma of ripened MC.