bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-Step Solvothermal Process for Nanoarchitectonics of Metastable Hexagonal WO3 Nanoplates
Authors: Qiu Zanlin , Jinschek Joerg R. , Gouma Pelagia-Irene
Published: April 2023

Hexagonal tungsten trioxide (h-WO3) has shown great potential for application in electrochromic devices, gas sensors, battery electrodes, and as photo-catalysts. The h-WO3 structure features a unique large network of open hexagonal channels that allow for intercalation. The hydrothermal synthesis of h-WO3 using sodium tungstate dihydrate as a precursor is widely explored, however, the residual alkaline ions are difficult to eliminate during the synthesis. The solvothermal synthesis using tungsten hexachloride as starting materials largely avoids the use of alkaline ions, but the effect of various synthesis parameters is not well-understood yet. To resolve these ambiguities, this study provides a reliable route to obtain h-WO3 via solvothermal synthesis and dehydration annealing. The effects of precursor concentration, water content, synthesis temperature, and synthesis time, as well as dehydration temperature, on the as-synthesized crystal structure and crystal morphology are studied.
Effects of Bacillus cereus on Survival, Fecundity, and Host Adaptability of Pine Wood Nematode
Authors: Yuan Yuchao , Yan Zhengmei , Chen Yangxue , Ye Jianren , Tan Jiajin
Published: April 2023

To clarify the role of bacteria in PWD, three PWNs with different virulence (strongly virulent strain AMA3, normally virulent strain AA3, and weakly virulent strain YW4) were selected as research objects, and three strains of Bacillus cereus (nematode-associated bacteria GD1, Pinus massoniana endophytic bacteria GD2, and P. elliottii endophytic bacteria NJSZ-13) at different concentrations were used to determine their effects on the survival and fecundity of the nematodes. The results showed that strains GD1 and GD2 could significantly improve the survival and fecundity of PWNs at three different concentrations, while NJSZ-13 showed the opposite effects. The inoculation experiments showed that the disease index of P. massoniana under different treatments was as follows: AMA3 < a mixture of AMA3 and GD1 < a mixture of AMA3 and GD2. Similar results were shown in the Larix kaempferi inoculation experiment. Further, using RNA-sequencing analysis, we found that the up-regulated genes in PWN were sHsp 21, Hsp 70, and Hsp 72 after being treated by strains GD1 and GD2. The longevity regulatory pathways, MAPK signaling pathways, glutathione metabolic pathways, and cytochrome P450 metabolic pathways related to these genes are clearly enriched. These results show that the bacteria can improve the host adaptability of PWN, and endophytic bacteria of pine trees may be more effective in improving the host adaptability of nematodes than the associated bacteria of nematodes.
Conservation Status and Effectiveness of the National and International Policies for the Protection and Conservation of Sturgeons in the Danube River and Black Sea Basin
Authors: Strat Daniela , Gheorghe Iuliana
Published: April 2023

Rivers and their watersheds have been subject to various changes, including biodiversity, caused by anthropogenic activities. The Danube River and Black Sea basin overlap the geographic range of six species of anadromous fish belonging to the family Acipenseridae. Nowadays, they are endangered or at risk of extinction in the wild due to fragmentation and destruction of natural habitats, blocking their migration to spawning grounds, water pollution, and poaching. This paper presents a review of the historical and current distribution and evolution of the sturgeon populations of sturgeon species in the Danube River basin. Conservation status and measures for species protection, conservation, and restoration at the national, regional, and global levels are presented.
An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation
Authors: Houssein Essam H. , Mohamed Gaber M. , Abdel Samee Nagwan , Alkanhel Reem , Ibrahim Ibrahim A. , Wazery Yaser M.
Published: April 2023

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.
Endometriosis-Related Ovarian Cancers: Evidence for a Dichotomy in the Histogenesis of the Two Associated Histotypes
Authors: Bergamini Alice , Mangili Giorgia , Ambrosi Alessandro , Taccagni Gianluca , Rabaiotti Emanuela , Bocciolone Luca , Candotti Giorgio , Cioffi Raffaella , Pella Francesca , Sabetta Giulia , Saponaro Costanza , Candiani Massimo
Published: April 2023

Evidence indicates that different pathways of malignant degeneration underlie the development of endometriosis-associated ovarian tumors of endometrioid and clear cell histotypes. The aim of this study was to compare data from patients affected by these two histotypes to investigate the hypothesis of a dichotomy in the histogenesis of these tumors. Clinical data and tumor characteristics of 48 patients who were diagnosed with either pure clear cell ovarian cancer and mixed endometrioid–clear cell ovarian cancer arising from endometriosis (ECC, n = 22) or endometriosis-associated endometrioid ovarian cancer (EAEOC, n = 26) were compared. A previous diagnosis of endometriosis was detected more frequently in the ECC group (32% vs. 4%, p = 0.01). The incidence of bilaterality was significantly higher in the EAOEC group (35% vs. 5%, p = 0.01) as well as a solid/cystic rate at gross pathology (57.7 ± 7.9% vs. 30.9 ± 7.5%, p = 0.02). Patients with ECC had a more advanced disease stage (41% vs. 15%; p = 0.04). A synchronous endometrial carcinoma was detected in 38% of EAEOC patients. A comparison of the International Federation of Gynecology and Obstetrics (FIGO) stage at diagnosis showed a significantly decreasing trend for ECC compared to EAEOC (p = 0.02). These findings support the hypothesis that the origin, clinical behavior and relationship with endometriosis might be different for these histotypes. ECC, unlike EAEOC, seems to develop within an endometriotic cyst, thus representing a window of possibility for ultrasound-based early diagnosis.
Anterior and Posterior Nutcracker Syndrome Combined with May–Thurner Syndrome: First Report of This Unique Case
Authors: Tiralongo Francesco , Galioto Federica , Distefano Giulio , Palmucci Stefano , Basile Antonio , Di Rosa Salvatore
Published: April 2023

Background: Anterior nutcracker syndrome is defined as the compression of the left renal vein (LRV) between the superior mesenteric artery (SMA) and the aorta, whereas posterior nutcracker syndrome refers to the compression of the retroaortic LRV between the aorta and the vertebral column—the presence of the circumaortic left renal vein may predispose to “combined nutcracker syndrome”. May–Thurner syndrome consists of obstruction of the left common iliac vein caused by the crossing right common iliac artery. We report a unique case of combined nutcracker syndrome associated with May–Thurner syndrome. Case presentation: A 39-year-old Caucasian female came to our radiology unit for triple-negative breast cancer computed tomography (CT) staging. She complained of pain in hermid-back and low-back regions and intermittent abdominal pain in the left flank region. Multidetector computed tomography (MDCT) incidentally revealed a circumaortic left renal vein draining to the inferior vena cava, with bulbous dilatation of both the antero-superior and posterior-inferior branches, which was associated with pathological serpiginous dilation of the left ovarian vein with varicose pelvic veins. Axial CT imaging of the pelvis also showed compression of the left common iliac vein by the overlying right common iliac artery consistent with May–Thurner syndrome without signs of venous thrombosis. Conclusion: Contrast-enhanced CT is the best imaging modality for suspected vascular compression syndromes. CT findings showed a combination of anterior and posterior nutcracker syndrome in the left circumaortic renal vein, associated with May–Thurner syndrome, which has not previously been described in the literature.
Anatomical and Simulation Studies Based on Three-Dimensional-Computed Tomography Image Reconstruction of Femoral Offset
Authors: Shimizu Tomohiro , Takahashi Daisuke , Ishizu Hotaka , Yokota Shunichi , Hasebe Yoshihiro , Uetsuki Keita , Iwasaki Norimasa
Published: April 2023

Although the hip joint morphology varies by race, few studies have investigated the associations between two-dimensional (2D) and three-dimensional (3D) morphologies. This study aimed to use computed tomography simulation data and radiographic (2D) data to clarify the 3D length of offset, 3D changes in the hip center of rotation, and femoral offset as well as investigate the anatomical parameters associated with the 3D length and changes. Sixty-six Japanese patients with a normal femoral head shape on the contralateral side were selected. In addition to radiographic femoral, acetabular, and global offsets, 3D femoral and cup offsets were investigated using commercial software. Our findings revealed that the mean 3D femoral and cup offsets were 40.0 mm and 45.5 mm, respectively; both were distributed around the mean values. The difference between the 3D femoral and cup offsets (i.e., 5 mm) was associated with the 2D acetabular offset. The 3D femoral offset was associated with the body length. In conclusion, these findings can be applied to the design of better ethnic-specific stem designs and can help physicians achieve more accurate preoperative diagnoses.
An Update on Reports of Atypical Presentations of Kawasaki Disease and the Recognition of IVIG Non-Responder Children
Authors: Conte Cristiano , Sogni Francesco , Rigante Donato , Esposito Susanna
Published: April 2023

Kawasaki disease (KD) is an acute vasculitis with an intrinsic risk of severe involvement of coronary arteries. The worldwide spread of KD and the importance of early diagnosis for preventing cardiovascular complications have ascertained the need for updating guidelines for prompt disease recognition and treatment efficacy assessment. All KD patients who comply with the definition of classic or atypical disease should be treated with intravenous immunoglobulin (IVIG) soon after diagnosis. The objective of our narrative review was to analyze the medical literature about case reports with atypical KD in relation to diagnosis and potential identification of predictors of non-responsiveness to IVIG. Our analysis has shown that the seminal challenge in KD management is the timeliness of diagnosis, although both extreme variability and transience of clinical manifestations make this goal difficult. A non-negligible percentage of patients, especially in the first 6 months of life, might have atypical manifestations of KD, whose painstaking differential diagnosis may be tricky. Many attempts to develop universal scoring systems and detect children at higher risk of IVIG resistance have been rather unsuccessful. Additionally, KD may show different evolutions according to unraveled demographic, genetic, or epigenetic factors. Further research is needed to elucidate all open questions about KD and clarify the long-term outcome of its potential complications.
Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
Authors: Oliveira Stephanie Wutke , Cardoso-Sousa Leia , Georjutti Renata Pereira , Shimizu Jacqueline Farinha , Silva Suely , Caixeta Douglas Carvalho , Guevara-Vega Marco , Cunha Thúlio Marquez , Carneiro Murillo Guimarães , Goulart Luiz Ricardo , Jardim Ana Carolina Gomes , Sabino-Silva Robinson
Published: April 2023

Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student’s t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm−1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.
Design Procedure of a Low-Cost System for Energy Replenishment in a Quadrotor UAV through a Battery Exchange Mechanism
Authors: Lozano-Hernández Yair , Martínez de la Cruz Ismael , Gutiérrez-Frías Octavio , Lozada-Castillo Norma , Luviano-Juárez Alberto
Published: April 2023

This paper describes the design and construction of an energy replenishment service station for a quadrotor. The prototype includes a small number of actuators, making it a low-cost solution. The system consists of three batteries: two charged and one discharged (within the quadrotor). Once the quadrotor lands, the battery with the highest charge is selected, which is then exchanged for the discharged battery. In order to perform this action, position control is used, in which the desired value depends on the location of the sensor that detects the highest voltage. In addition, the system has a mechanical design that facilitates the coupling of the unmanned aerial vehicle (UAV) with the structure for battery exchange, ensuring that the discharged battery is always in the same position. Furthermore, the design of a mechanism to release and hold the battery placed in the quadrotor is presented, which works by means of voltage and force sensors that identify the instant that the battery is discharged and when the UAV has landed on the exchange platform, thus initiating the exchange process. Likewise, the criteria for selecting the elements used, acquiring and processing signals, and routines for changing batteries are detailed.
Dynamic Soaring Parameters Influence Regularity Analysis on UAV and Soaring Strategy Design
Authors: Wang Wei , An Weigang , Song Bifeng
Published: April 2023

Dynamic soaring helps albatross achieve long-distance migration. From a bionic view, dynamic soaring has great potential to enhance unmanned aerial vehicles (“UAVs”) flight range and endurance. The previous application studies focus on flight strategies to guide UAV soaring. However, the energy harvesting efficiency problem emerges. The lack of clear dynamic soaring influencing factors hinders dynamic soaring UAV design and flight strategy design from a theoretical perspective. Hence this paper aims to analyze the influence law of different UAV mass, initial airspeed, and entering angle. Trajectories and flight data in different factors are obtained through trajectory optimization. The results show that UAV mass has a positive influence on energy harvesting. The initial airspeed and entering angle affect both energy efficiency and trajectory. For UAV design, weight balance needs to be considered rather than a pursuit of the lightest. For flight strategy design, finding an optimal initial state will improve energy efficiency.
Differential Fairness: An Intersectional Framework for Fair AI
Authors: Islam Rashidul , Keya Kamrun Naher , Pan Shimei , Sarwate Anand D. , Foulds James R.
Published: April 2023

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. Our theoretical results show that our criteria meaningfully operationalize AI fairness in terms of real-world harms, making the measurements interpretable in a manner analogous to differential privacy. We provide a simple learning algorithm using deterministic gradient methods, which respects our intersectional fairness criteria. The measurement of fairness becomes statistically challenging in the minibatch setting due to data sparsity, which increases rapidly in the number of protected attributes and in the values per protected attribute. To address this, we further develop a practical learning algorithm using stochastic gradient methods which incorporates stochastic estimation of the intersectional fairness criteria on minibatches to scale up to big data. Case studies on census data, the COMPAS criminal recidivism dataset, the HHP hospitalization data, and a loan application dataset from HMDA demonstrate the utility of our methods.
Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model
Authors: Zhou Linxueying , Liu Shangkun , Zheng Weimin
Published: April 2023

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.
A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks
Authors: Cinaglia Pietro , Cannataro Mario
Published: April 2023

In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of the network (i.e., dynamic networks), in addition to a classic static representation (i.e., static networks). Bioinformatics solutions for network analysis allow knowledge extraction from the features related to a single network of interest or by comparing networks of different species. For instance, we may align a network related to a well known species to a more complex one in order to find a match able to support new hypotheses or studies. Therefore, the network alignment is crucial for transferring the knowledge between species, usually from simplest (e.g., rat) to more complex (e.g., human). Methods: In this paper, we present Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise alignment of dynamic networks that applies the temporal embedding to investigate the topological similarities between the two input dynamic networks. The main idea of DANTE is to consider the evolution of interactions and the changes in network topology. Briefly, the proposed solution builds a similarity matrix by integrating the tensors computed via the embedding process and, subsequently, it aligns the pairs of nodes by performing its own iterative maximization function. Results: The performed experiments have reported promising results in terms of precision and accuracy, as well as good robustness as the number of nodes and time points increases. The proposed solution showed an optimal trade-off between sensitivity and specificity on the alignments produced on several noisy versions of the dynamic yeast network, by improving by ∼18.8% (with a maximum of 20.6%) the Area Under the Receiver Operating Characteristic (ROC) Curve (i.e., AUC or AUROC), compared to two well known methods: DYNAMAGNA++ and DYNAWAVE. From the point of view of quality, DANTE outperformed these by ∼91% as nodes increase and by ∼75% as the number of time points increases. Furthermore, a ∼23.73% improvement in terms of node correctness was reported with our solution on real dynamic networks.
Complexity of Recent Earthquake Swarms in Greece in Terms of Non-Extensive Statistical Physics
Authors: Sardeli Eirini , Michas Georgios , Pavlou Kyriaki , Vallianatos Filippos , Karakonstantis Andreas , Chatzopoulos Georgios
Published: April 2023

Greece exhibits the highest seismic activity in Europe, manifested in intense seismicity with large magnitude events and frequent earthquake swarms. In the present work, we analyzed the spatiotemporal properties of recent earthquake swarms that occurred in the broader area of Greece using the Non-Extensive Statistical Physics (NESP) framework, which appears suitable for studying complex systems. The behavior of complex systems, where multifractality and strong correlations among the elements of the system exist, as in tectonic and volcanic environments, can adequately be described by Tsallis entropy (Sq), introducing the Q-exponential function and the entropic parameter q that expresses the degree of non-additivity of the system. Herein, we focus the analysis on the 2007 Trichonis Lake, the 2016 Western Crete, the 2021–2022 Nisyros, the 2021–2022 Thiva and the 2022 Pagasetic Gulf earthquake swarms. Using the seismicity catalogs for each swarm, we investigate the inter-event time (T) and distance (D) distributions with the Q-exponential function, providing the qT and qD entropic parameters. The results show that qT varies from 1.44 to 1.58, whereas qD ranges from 0.46 to 0.75 for the inter-event time and distance distributions, respectively. Furthermore, we describe the frequency–magnitude distributions with the Gutenberg–Richter scaling relation and the fragment–asperity model of earthquake interactions derived within the NESP framework. The results of the analysis indicate that the statistical properties of earthquake swarms can be successfully reproduced by means of NESP and confirm the complexity and non-additivity of the spatiotemporal evolution of seismicity. Finally, the superstatistics approach, which is closely connected to NESP and is based on a superposition of ordinary local equilibrium statistical mechanics, is further used to discuss the temporal patterns of the earthquake evolution during the swarms.
On Decoder Ties for the Binary Symmetric Channel with Arbitrarily Distributed Input
Authors: Chang Ling-Hua , Chen Po-Ning , Alajaji Fady
Published: April 2023

The error probability of block codes sent under a non-uniform input distribution over the memoryless binary symmetric channel (BSC) and decoded via the maximum a posteriori (MAP) decoding rule is investigated. It is proved that the ratio of the probability of MAP decoder ties to the probability of error grows most linearly in blocklength when no MAP decoding ties occur, thus showing that decoder ties do not affect the code’s error exponent. This result generalizes a similar recent result shown for the case of block codes transmitted over the BSC under a uniform input distribution.
Economic Role of Government Budget Revision in the Presence of COVID-19
Authors: Shahini Ledjon , Grabova Perseta
Published: April 2023

Fiscal policies are one of the most important instruments of government to guide the progress of the country’s economic development. They find significant use in cases where the economy is experiencing a period of recession, such as the current one caused by COVID-19. This study aims to assess the multiplier effects that budget revision has on the economy for the case of Albania, and more specifically by referring to the initial and revised budget scenario for the year 2020 which is characterized by significant changes caused by the presence of COVID-19. Referring to the multipliers from the input–output tables (IOT) the total effect that the state budget brings to the economy for a certain year is derived. From this paper, it appears that the budget restructuring that takes place during the year does not take into account the multiplier effect in the economy, but is mostly done for specific purposes related to certain government functions. In this context, it is very important that various options during budget revision are evaluated, concluding with the option that has the highest returns for the economy.
Dynamics of Macroeconomic Uncertainty on Economic Growth in the Presence of Fiscal Consolidation in South Africa from 1994 to 2022
Authors: Buthelezi Eugene
Published: April 2023

This paper investigates the effects of macroeconomic uncertainty on economic growth in the presence of fiscal consolidation in South Africa. Markov-switching dynamic regression (MSDR) and time-varying parameter vector autoregression (TVP-VAR) were performed using time series data from 1994 to 2022. Less attention has been given directly to the investigation of macroeconomic uncertainty in different regimes of economic growth in South Africa. Three states are found for economic growth, with mean growth rates of negative 6.29% and positive 3.90% and 1.47%, respectively. Macroeconomic uncertainty was found to have a negative impact of 6.72%, 4.38%, and 3.08% in states 1 to 3, respectively. Fiscal consolidation provided an accommodative policy, as it reduced the negative impact of macroeconomic uncertainty by 3.17%, 1.80%, and 0.92% in states 1 to 3, respectively. However, fiscal consolidation does not completely reduce the negative impact of macroeconomic uncertainty. The transition probabilities of economic growth moving and returning to the same states are 29.46%, 34.07%, and 58.02%, in each state, respectively. The time-varying impulse response functions showed that the shock of macroeconomic uncertainty harms economic growth. Nevertheless, the multiplier effect is not large; however, the economy operates below equilibrium and does not restore equilibrium after the effect of macroeconomic uncertainty. This reflects that it takes time for macroeconomic uncertainty to filter out of the South African economy. It is recommended that fiscal consolidation be considered as an accommodative fiscal policy to reduce macroeconomic uncertainty but not as a main policy for economic growth.
Dual-Branch Person Re-Identification Algorithm Based on Multi-Feature Representation
Authors: Cui Xinyu , Liang Yu , Zhang Wei
Published: April 2023

Person re-identification can identify specific pedestrians across cameras and solve the visual limitations of a single fixed camera scene. It achieves trajectory analysis of target pedestrians, facilitating case analysis by public security personnel. Person re-identification has become a challenging problem due to occlusion, blur, posture change, etc. The key to affecting the accuracy is whether sufficient information of pedestrians can be obtained. Most existing deep learning methods only consider a single category of characteristics, resulting in problems of information loss and single feature expression. This paper proposed a dual-branch person re-identification algorithm based on multi-scale and multi-granularity branches to obtain complete pedestrian information, called MSMG-Net. The multi-scale branch extracted features from different layers and fused them through the bidirectional cross-pyramid structure. It made up for the loss of information caused by using only high-level semantic features. The multi-granularity branch combined the convolutional neural network (CNN) and Transformer module based on improved self-attention mechanisms to obtain global information, and it performed horizontal segmentation of feature maps to obtain local information. It combined global and local features and solved the problem of single feature expression. The experimental results show that mAP/Rank-1 of MSMG-Net reached 88.6%/96.3% on the Market-1501 dataset and 80.1%/89.9% on the DukeMTMC-ReID dataset. Compared with many state-of-the-art methods, the performance of the proposed algorithm was improved significantly.
Sum Rate Optimization for Multiple Access in Multi-FD-UAV-Assisted NOMA-Enabled Backscatter Communication Network
Authors: Wang Siqiang , Guo Jing , Yu Hanxiao , Zhang Han , Gong Yuping , Fei Zesong
Published: April 2023

With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently.
Electromagnetic Vibration Characteristics of Inter-Turn Short Circuits in High Frequency Transformer
Authors: Ding Haibo , Zhao Wenliang , Diao Chengwu , Li Min
Published: April 2023

As a common fault of transformer winding, inter-turn short circuits cause severe consequences, such as excessive current and serious deformation of winding. Aiming to solve the problem of inter-turn short circuit at the end-winding and middle-winding of high frequency transformers (HFT), this paper considers the electromagnetic vibration characteristics of inter-turn short circuits (interleaved winding and continuous winding) at different positions, and the HFT is established by the multi-physical field coupling principle. Coupling equations for the inter-turn short circuit, as well as electromagnetic force and sound pressure level, are established to characterize the vibration noise mechanism of inter-turn short circuits. Furthermore, the HFT equivalent model is simulated in 3D finite element method (FEM) to emulate the real transformer operation and investigate the impact of interleaved winding and continuous winding under inter-turn short circuit faults. The short-circuit current and axial flux leakage, as well as the harmonic response of vibration acceleration and sound pressure level distribution, are obtained when inter-turn short circuits occur at different positions. Finally, the results show that the electromagnetic effect of the inter-turn short circuit in end-winding is worse than it is in middle-winding. Advantages in resisting impulse current make interleaved winding superior to continuous winding in terms of vibration and noise.
Self-Constructed Deep Fuzzy Neural Network for Traffic Flow Prediction
Authors: An Jiyao , Zhao Jin , Liu Qingqin , Qian Xinjiao , Chen Jiali
Published: April 2023

Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be considered to enhance persuasiveness, particularly in the era of deep-learning-based traffic cognition. Although some studies have explored interpretable neural networks from the feature and result levels, model-level explanation, which explains the reasoning process of traffic prediction through transparent models, remains underexplored and requires more attention. In this paper, we propose a novel self-constructed deep fuzzy neural network, SCDFNN, for traffic flow prediction with model interpretability. By leveraging recent advances in neuro-symbolic computation for automatic rule learning, SCDFNN learns interpretable human traffic cognitive rules based on deep learning, incorporating two innovations: (1) a new fuzzy neural network hierarchical architecture constructed for spatial-temporal dependences in the traffic feature domain; (2) a modified Wang–Mendel method used to fuse regional differences in traffic data, resulting in adaptive fuzzy-rule weights without sacrificing interpretability. Comprehensive experiments on well-known traffic datasets demonstrate that the proposed approach is comparable to state-of-the-art deep models, and the SCDFNN’s unique hierarchical architecture allows for transparency.
A New Design for the Peer-to-Peer Electricity and Gas Markets Based on Robust Probabilistic Programming
Authors: Sedgh Seyed Amin , Aghamohammadloo Hossein , Khazaei Hassan , Mehdinejad Mehdi , Asadi Somayeh
Published: April 2023

This paper presents a fully-decentralized peer-to-peer (P2P) electricity and gas market for retailers and prosumers with coupled energy units, considering the uncertainties of wholesale electricity market price and prosumers’ demand. The goal is to improve the overall economy of the proposed market while increasing its flexibility. In this market, the retailers are equipped with self-generation and energy storage units and can bilaterally negotiate for electricity and gas transactions with prosumers to maximize their profit. Furthermore, they can sell power to the upstream market in addition to prosumers. The prosumers have access to several retailers to supply their required electricity and gas and can freely provide their energy needs from every retailer, contributing to dynamicity in the proposed market. Given that they have an energy hub consisting of boiler units, combined heat and electricity (CHP) units, and electric pumps, they can switch their energy supply source from electricity to gas and vice versa. A robust possibilistic programming approach is applied to address the uncertainties. A fully-decentralized approach called the alternating direction method of multipliers (ADMM) is utilized to solve the presented decentralized robust problem. The proposed decentralized algorithm finds an optimum solution by establishing a smart balance between the average expected value, optimality robustness, and feasibility robustness. The feasibility and competitiveness of the proposed approach are evaluated through numerical studies on a distribution system with two retailers and three prosumers. The data analysis of the simulation results verifies the effectiveness of the proposed decentralized robust framework as well as the proposed decentralized solution. According to the maximum deviation, the expected optimal value in the robust case, the retailer’s profit has decreased by 12.1 percent, and the prosumers’ cost has increased by 27.4 percent due to the feasibility penalty term.
Advanced Exergoeconomic Assessment of CO2 Emissions, Geo-Fluid and Electricity in Dual Loop Geothermal Power Plant
Authors: Güler Onur Vahip , Gürbüz Emine Yağız , Georgiev Aleksandar G. , Keçebaş Ali
Published: April 2023

Binary geothermal power plants (GPPs) are mostly encountered in geothermal fields with medium and low temperatures. The design and operation of dual binary GPPs can be difficult due to the geothermal fluid properties. This affects their performance and feasibility. Thermoeconomics are essential elements for the design and operation of the GPPs. In this study, advanced exergoeconomic analysis is applied to a true dual binary GPP (as a case study) to further evaluate it from performance and economic perspectives. In analysis, the specific exergy cost (SPECO) method is used. Then, some specific indicators are presented to evaluate the performance and economics of the GPP. Thus, technical and economic solutions have been developed in the design and operation stages through the analysis. The results of the study indicated that the total operating cost of 1218 USD/h could be reduced to 186 USD/h by improving the operating conditions. This corresponds to an 85% decrease. The cost per electricity generated, cost per geothermal energy input, and cost per CO2 emission of the GPP are determined as 0.049 USD/kWh, 5.3 USD/GJ, and 0.13 USD/kg, respectively. As a result, while the savings potential of the GPP is 15%, it can result in a 15% reduction in CO2 emission cost.
Advances in Reduction Technologies of Gas Emissions (CO2, NOx, and SO2) in Combustion-Related Applications
Authors: Sung Yonmo
Published: April 2023

Global energy production and consumption have increased continuously over the past few decades [...]
Predicting Post-Production Biomass Prices
Authors: Górna Aleksandra , Szabelska-Beręsewicz Alicja , Wieruszewski Marek , Starosta-Grala Monika , Stanula Zygmunt , Kożuch Anna , Adamowicz Krzysztof
Published: April 2023

This paper presents the application of prediction in the analysis of market price volatility in Polish conditions of wood processing by-products in the form of biomass. The ARIMA model, which takes into account cyclical, seasonal, irregular fluctuations of historical data on the basis of which the forecast and long-term trends of selected wood products were made, was used in predicting prices. Comparisons were made between the ARIMA prediction method and the multiplicative Winters–Holt model. During the period studied (2017–2022), the changes in the market price of biomass were characterized by a wide spread of values. On average, the price of these products increased from 2017 to the end of 2022 by 125%. The price prediction analysis showed seasonal fluctuations in the case of wood chips. The uncertainty in price prediction is due to changes in supply resulting from the influence of global factors. The Diebold–Mariano test of matching accuracy confirms that the price prediction of the analyzed by-product sorts using the ARIMA and WH models is possible. The conclusion reached by comparing these two methods is that each can be used under certain market conditions of certain assortments. In the case of a stable wood product, the choice of the ARIMA model should be resolved, while in the case of price volatile products, WH will be a better choice. The difference between the predicted and actual price with ARIMA ranged from 2.4% to 11.6% and for WH from 3.7% to 29.8%.
Optimal Power Scheduling and Techno-Economic Analysis of a Residential Microgrid for a Remotely Located Area: A Case Study for the Sahara Desert of Niger
Authors: Tahirou Halidou Issoufou Tahirou , Or Rashid Howlader Harun Or Rashid , Gamil Mahmoud M. , Elkholy M. H. , Senjyu Tomonobu
Published: April 2023

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.
Data-Driven Internal Carbon Pricing Mechanism for Improving Wood Procurement in Integrated Energy and Material Production
Authors: Palander Teijo
Published: April 2023

More than 25% of the total energy consumption in Finland has been produced with wood fuels. Since 2012, the share has been greater than that of oil, coal, or natural gas. Internal carbon pricing is used to manage the risks in wood procurement after wood import from Russia ended. Further, the EU announced plans to sell more carbon emission permits to fund the EU’s exit from Russian energy. To manage these challenges, a data-driven internal carbon pricing mechanism (DDICPM) has been developed for wood procurement optimization. Particularly, local changes are considered via available information about growth-based carbon sinks (GBCS). The results of the new scenario were compared to the basic national scenario that ensures carbon neutrality in forestry. The DDICPM may provide the optimum wood-procurement operations maintaining carbon neutrality in the integrated energy and material industry (IEMI). In this study, the use of DDICPM increased profitability b 16.2, 16.1, and 16.0% between adapted wood procurement areas at the EU’s emission allowance prices of 30, 65, and 98 € t−1 CO2. The experiments’ results also revealed that the DDICPM could consistently and significantly outperform the conventional solution adopted by the company in terms of economic costs. A significant conclusion is that an increase in profitability is possible if the size of wood procurement areas is allowed to vary optimally with respect to transport distance to take advantage of the GBCS as a new application of the renewable carbon sink.
Application of the Hybrid MCDM Method for Energy Modernisation of an Existing Public Building—A Case Study
Authors: Radomski Bartosz , Mróz Tomasz
Published: April 2023

The existing public utility building belonging to the Forest Experimental Station of the Poznań University of Life Sciences, due to high energy consumption and related costs, has qualified for deep energy modernisation or consideration for the construction of a new building. One of the goals is to achieve carbon neutrality and have a positive energy balance. The article uses the hybrid DEMATEL-AHP/ANP-VIKOR method. The methodology used is distinguished by the creation of a set of decision-making criteria and the identification of the relationship between them, which is determined by conducting a survey of a group of experts using the Delphi method, as well as determining the preferences of the decision-maker using a survey of the target group using social research. Two different models of the decision-maker’s preferences have been developed, taking into account the selected decision criteria, and four acceptable technical solutions have been identified. As a result of the calculations performed, a ranking of the solutions has been developed, from the most preferred to the least accepted. Variant 3B has been identified as the best solution with respect to eight evaluation criteria for both of the adopted models of the decision-maker’s preferences. The ranking index Ri coefficient for this variant ranged between 0.733 and 0.901, while for the other variants, it was lower and amounted to between 0.106 and 0.274 for variant 1, 0.166 and 0.290 for variant 2 and 0.403 and 0.437 for variant 3A. The methodology used for the case study has proved to be applicable. The presented methodology can be used to design new buildings (not only residential) with almost zero energy consumption, as well as those with a positive energy balance, and can also be used for deep energy modernisation. In this article, it was applied for the first time to the energy modernisation of an existing public building.