bluuebunny/crossref_metadata_2025 huggingface dataset:
Microfluidic Model to Evaluate Astrocyte Activation in Penumbral Region following Ischemic Stroke
Authors: Denecke Kathryn M. , McBain Catherine A. , Hermes Brock G. , Teertam Sireesh Kumar , Farooqui Mehtab , Virumbrales-Muñoz María , Panackal Jennifer , Beebe David J. , Famakin Bolanle , Ayuso Jose M.
Published: August 2022

Stroke is one of the main causes of death in the US and post-stroke treatment options remain limited. Ischemic stroke is caused by a blood clot that compromises blood supply to the brain, rapidly leading to tissue death at the core of the infarcted area surrounded by a hypoxic and nutrient-starved region known as the penumbra. Recent evidence suggests that astrocytes in the penumbral region play a dual role in stroke response, promoting further neural and tissue damage or improving tissue repair depending on the microenvironment. Thus, astrocyte response in the hypoxic penumbra could promote tissue repair after stroke, salvaging neurons in the affected area and contributing to cognitive recovery. However, the complex microenvironment of ischemic stroke, characterized by gradients of hypoxia and nutrients, poses a unique challenge for traditional in vitro models, which in turn hinders the development of novel therapies. To address this challenge, we have developed a novel, polystyrene-based microfluidic device to model the necrotic and penumbral region induced by an ischemic stroke. We demonstrated that when subjected to hypoxia, and nutrient starvation, astrocytes within the penumbral region generated in the microdevice exhibited long-lasting, significantly altered signaling capacity including calcium signaling impairment.
Drug-Induced Hepatitis in Children: The Experience of a Single Center in Romania
Authors: Dijmărescu Irina , Guță Oana Maria , Brezeanu Livia Elena , Dijmărescu Adrian Dumitru , Becheanu Cristina Adriana , Păcurar Daniela
Published: August 2022

Drug-induced liver injury (DILI) is uncommon but potentially lethal. Over 6 years, 2533 children with acute liver disease were identified in our center, 48 of which suffered from toxic hepatitis, and 40 exhibited DILI (22 paracetamol-related, 14 albendazole-related). The most affected children were in the 13–17-year-old age group. The mean time between drug ingestion and disease diagnosis was 25.4 h for paracetamol-related DILI and 21.6 days for the albendazole-related group. Clinical features were mostly gastrointestinal, jaundice being reported in 30% of the cases. Regarding the type of liver injury, for 70% of the patients it was hepatocellular (mostly paracetamol toxicity), for 11% cholestatic, and for 19% mixed (albendazole-related). The mean initial ALT value was 1020 U/L for all DILIs. Coagulopathy was only identified for the acetaminophen-related group. The median number of hospitalization days was 6.9 for DILI related to acetaminophen ingestion, compared with 7 for the idiosyncratic pattern. When applying the DILI severity index, 81% of the patients were categorized as having a mild hepatic ailment, while 19% had a moderate–severe or severe disease. No deaths were reported in the study group. The diagnosis of DILI involves the exclusion of other causes of liver injury; therefore, it is considered one of the most challenging diagnoses in hepatology.
Long-Term Evaluation of Gastroesophageal Reflux in Neonates with and without Preventive Anti-reflux Surgery at the Time of Congenital Diaphragmatic Hernia Repair
Authors: von Schrottenberg Christoph , Deeg Susanne , Weiss Christel , Adam Rüdiger , Wessel Lucas M. , Boettcher Michael , Zahn Katrin B.
Published: August 2022

One potential comorbidity after congenital diaphragmatic hernia (CDH) is gastroesophageal reflux (GER), which can have a substantial effect on patients’ quality of life, thriving, and complications later in life. Efforts have been made to reduce gastroesophageal reflux with a preventive anti-reflux procedure at the time of CDH repair. In this follow-up study of neonates participating in a primary RCT study on preventive anti-reflux surgery, symptoms of GER were assessed longitudinally. Long-term data with a median follow-up time of ten years was available in 66 patients. Thirty-one neonates received an initial fundoplication. Secondary anti-reflux surgery was necessary in 18% and only in patients with large defects. It was required significantly more often in patients with intrathoracic herniation of liver (p = 0.015) and stomach (p = 0.019) and patch repair (p = 0.03). Liver herniation was the only independent risk factor identified in multivariate regression analysis. Primary fundopexy and hemifundoplication did not reveal a protective effect regarding the occurrence of GER symptoms, the need for secondary antireflux surgery or the gain of body weight regardless of defect size neither in the short nor in the long term. Symptoms of GER must be assessed carefully especially in children with large defects, as these are prone to require secondary anti-reflux surgery in the long-term. Routine evaluation of GER including endoscopy and impedance measurement should be recommended especially for high-risk patients.
Associations between Sedentary Time and Sedentary Patterns and Cardiorespiratory Fitness in Chinese Children and Adolescents
Authors: Li Ming , Yin Xiaojian , Li Yuqiang , Sun Yi , Zhang Ting , Zhang Feng , Liu Yuan , Guo Yaru , Sun Pengwei
Published: August 2022

The increase in sedentary behavior in children and adolescents has become a worldwide public health problem. This study aimed to explore the associations between sedentary time (ST) and sedentary patterns (SP) and the cardiorespiratory fitness (CRF) of Chinese children and adolescents. The CRF of 535 participants was determined using a 20-m shuttle run test. ST and SP were measured with accelerometers. Questionnaires were used to investigate the different types of ST. Multiple linear regression models were used to test the associations between ST and SP and CRF. In this study, only some ST and SP indicators were found to be significantly associated with CRF in girls. With each additional 10 min of screen time or passive traffic time, VO2max decreases by 0.06 mL/kg/min (B = −0.006, 95% CI: −0.010~−0.001) and 0.31 mL/kg/min (B = −0.031, 95% CI: −0.061~−0.002), respectively, with MVPA control. With each additional 10 min of breaks in ST or duration of breaks in ST, VO2max increases by 0.41 mL/kg/min (B = 0.041, 95% CI: 0.007~0.076) and 0.21 mL/kg/min (B = 0.021, 95% CI: 0.007~0.035), respectively, with control total ST. Breaks in ST (B = 0.075, 95% CI: 0.027~0.123) and the duration of breaks in ST (B = 0.021, 95% CI: 0.012~0.146) were positively correlated with CRF when controlling for LPA, but these associations were not significant when controlling for MVPA (B = 0.003, 95% CI: −0.042~0.048; B = 0.001, 95% CI: −0.024~0.025). The total ST of children and adolescents was found to not be correlated with CRF, but when ST was divided into different types, the screen time and passive traffic time of girls were negatively correlated with CRF. More breaks in ST and the duration of breaks in ST were positively associated with higher CRF in girls. MVPA performed during breaks in ST may be the key factor affecting CRF. Schools and public health departments should take all feasible means to actively intervene with CRF in children and adolescents.
The Management of Asymptomatic Congenital Pulmonary Airway Malformation: Results of a European Delphi Survey
Authors: Kersten Casper M. , Hermelijn Sergei M. , Mullassery Dhanya , Muthialu Nagarajan , Cobanoglu Nazan , Gartner Silvia , Bagolan Pietro , Mesas Burgos Carmen , Sgrò Alberto , Heyman Stijn , Till Holger , Suominen Janne , Schurink Maarten , Desender Liesbeth , Losty Paul , Steyaert Henri , Terheggen-Lagro Suzanne , Metzelder Martin , Bonnard Arnaud , Sfeir Rony , Singh Michael , Yardley Iain , Rikkers-Mutsaerts Noor R. V. M. , van der Ent Cornelis K. , Qvist Niels , Cox Des W. , Peters Robert , Bannier Michiel A. G. E. , Wessel Lucas , Proesmans Marijke , Stanton Michael , Hannon Edward , Zampoli Marco , Morini Francesco , Tiddens Harm A. W. M. , Wijnen René M. H. , Schnater Johannes M.
Published: August 2022

Consensus on the optimal management of asymptomatic congenital pulmonary airway malformation (CPAM) is lacking, and comparison between studies remains difficult due to a large variety in outcome measures. We aimed to define a core outcome set (COS) for pediatric patients with an asymptomatic CPAM. An online, three-round Delphi survey was conducted in two stakeholder groups of specialized caregivers (surgeons and non-surgeons) in various European centers. Proposed outcome parameters were scored according to level of importance, and the final COS was established through consensus. A total of 55 participants (33 surgeons, 22 non-surgeons) from 28 centers in 13 European countries completed the three rounds and rated 43 outcome parameters. The final COS comprises seven outcome parameters: respiratory insufficiency, surgical complications, mass effect/mediastinal shift (at three time-points) and multifocal disease (at two time-points). The seven outcome parameters included in the final COS reflect the diversity in priorities among this large group of European participants. However, we recommend the incorporation of these outcome parameters in the design of future studies, as they describe measurable and validated outcomes as well as the accepted age at measurement.
Identifying Western North American Tree Populations Vulnerable to Drought under Observed and Projected Climate Change
Authors: Levesque Kathryn , Hamann Andreas
Published: August 2022

Global climate change has affected forest health and productivity. A highly visible, direct climate impact is dieback caused by drought periods in moisture-limited forest ecosystems. Here, we have used a climate moisture index (CMI), which has been developed in order to map forest–grassland transitions, to investigate the shifts of the zero-CMI isopleths, in order to infer drought vulnerabilities. Our main objective was to identify populations of the 24 most common western North American forest tree species that are most exposed to drought conditions by using a western North American forest inventory database with 55,700 plot locations. We have found that climate change projections primarily increase the water deficits for tree populations that are already in vulnerable positions. In order to test the realism of this vulnerability assessment, we have compared the observed population dieback with changes in index values between the 1961–1990 reference period and a recent 1991–2020 average. The drought impacts that were predicted by negative CMI values largely conformed to the observed dieback in Pinus edulis, Populus tremuloides, and Pinus ponderosa. However, there was one notable counter-example. The observed dieback in the Canadian populations of Populus tremuloides were not associated with directional trends in the drought index values but were instead caused by a rare extreme drought event that was not apparently linked to directional climate change. Nevertheless, a macro-climatic drought index approach appeared to be generally suitable to identify and forecast the drought threats to the tree populations.
Psychological Aspects of Sensitive Skin: A Vicious Cycle
Authors: Farage Miranda A.
Published: August 2022

Sensitive Skin Syndrome (SSS) has been the subject of intense research in the past several years. Recent reviews confirm that about 40% of the population report moderate or very sensitive skin, and an additional 30% report slightly sensitive skin. Although certain phenotypes are more susceptible, anyone can suffer from SSS and this condition can manifest in all anatomic sites. A wide variety of environmental and lifestyle factors can trigger SSS symptoms of itching, stinging, burning, pain, and tingling. In order to avoid such triggers, the SSS individuals often alter their behaviors and habits such as restricting their daily activities, and modifying the use of everyday products that non-sensitive individuals take for granted. In addition, there is an association between SSS and some common psychological problems. Sensitive skin symptoms such as itching, stinging, burning and pain can result in sleep disorders, fatigue, stress and anxiety. Conversely, lack of sleep and stress from external sources can make the SSS sufferer more prone to the symptoms. This becomes a vicious cycle that impacts consumers’ quality of life and well-being. We are beginning to understand the importance of the underlying causes that can impact skin conditions. However, in order to better understand the SSS individual, we need to also be aware of the psychological factors that can trigger and/or worsen this skin condition, as well as the psychological stresses the condition places on the individual.
Anti-Oxidant and Anti-Aging Activities of Callus Culture from Three Rice Varieties
Authors: Vichit Wannisa , Saewan Nisakorn
Published: August 2022

The aims of this study were to induce calli from the seeds of three rice varieties (Hommali 105, Munpu, and Niawdum) and investigate their anti-aging potential. First, rice seeds were cultured on a Murashige and Skoog medium (MS medium) supplemented with 2 mg/L of 2,4-Dichlorophenoxyacetic acid (2,4-D), 1 mg/L of 1-Naphthalene acetic acid (NAA), and 1 mg/L of 6-Benzylaminopurine (BAP). After three weeks, the calli were extracted with ethanol. Then, their phenolic contents were determined by spectrophotometer and the amino acids were identified by ultra-performance liquid chromatography (UPLC). Their cytotoxicity, anti-oxidant (potassium ferricyanide reducing power assay (PFRAP), DPPH radical scavenging assay (DPPH), lipid peroxidation inhibition (LPO), and superoxide dismutase activity (SOD)), and anti-aging (keratinocyte proliferation, anti-collagenase, anti-inflammation, and anti-tyrosinase) activities were also investigated. Munpu callus (385%) was obtained with a higher yield than Hommali (322%) and Niawdum (297%) calli. The results revealed that the phenolic and amino acid contents were enhanced in the calli. Moreover, the calli were rich in glutamic acid, alanine, and gamma aminobutyric acid (GABA). The callus extracts showed no cytotoxic effects at a concentration of equal to or lower than 0.25 mg/mL. The highest anti-oxidant activities (PFRAP (0.81 mg AAE/mL), DPPH (68.22%), LPO (52.21%), and SOD (67.16%)) was found in Munpu callus extract. This extract also had the highest keratinocyte proliferation (43.32%), anti-collagenase (53.83%), anti-inflammation (85.40%), and anti-tyrosinase (64.77%) activities. The experimental results suggest that the amounts of bioactive compounds and anti-aging activities of rice seeds can be enhanced by the induction of callus formation.
Effect of Cobalt Substitution on the Structural and Magnetic Properties of Bismuth Ferrite Powders
Authors: Chinchay-Espino Hector , Montes-Albino Gina , Morales-Cruz Carlex , Dobbertin-Sanchez Segundo , Rojas-Flores Segundo
Published: August 2022

BiFeO3 (BFO) is a multiferroic material with excellent ferroelectric properties but with poor magnetic behavior. Therefore, we focused principally on the enhancement of the magnetic order of BFO. These multiferroic properties make BFO an excellent candidate for magnetoelectric devices at room temperature. Pure and Co-BiFeO3 powders were successfully synthesized via the sol–gel method at 700 °C. The effect of Co substitution on the corresponding structural and magnetic properties of BFO was studied. X-ray diffraction and Fourier Transform Infrared Spectroscopy measurements confirmed the rhombohedral perovskite structure in all samples. A secondary phase of CoFe2O4 (CFO) was detected for 9, 10, and 15% of Co doping. The scanning electron microscopy images of the Co-BFO particles showed a reduction in the particle size compared to the pure BFO powders. Vibrating sample magnetometry measurements evidenced the ferromagnetic hysteresis loop for the Co-BFO powders with values of saturation magnetization of 4.1 emu/g and a coercivity of 1083 Oe for 15% of Co doping. In this work, we report impurity free samples with notable magnetic properties at the same time, which is a difficult challenge in bismuth ferrite synthesis. This is the first step for later applications in future technology.
Strain-Induced Self-Rolling of Electrochemically Deposited Co(OH)2 Films into Organic–Inorganic Microscrolls
Authors: Schwan Jonas , Rosenfeldt Sabine , Schenk Anna S.
Published: August 2022

Strain-induced self-folding is a ubiquitous phenomenon in biology, but is rarely seen in brittle geological or synthetic inorganic materials. We here apply this concept for the preparation of three-dimensional free-standing microscrolls of cobalt hydroxide. Electrodeposition in the presence of structure-directing water-soluble polyelectrolytes interfering with solid precipitation is used to generate thin polymer/inorganic hybrid films, which undergo self-rolling upon drying. Mechanistically, we propose that heterogeneities with respect to the nanostructural motifs along the surface normal direction lead to substantial internal strain. A non-uniform response to the release of water then results in a bending motion of the two-dimensional Co(OH)2 layer accompanied by dewetting from the substrate. Pseudomorphic conversion into Co3O4 affords the possibility to generate hierarchically structured solids with inherent catalytic activity. Hence, we present an electrochemically controllable precipitation system, in which the biological concepts of organic matrix-directed mineralization and strain-induced self-rolling are combined and translated into a functional material.
A Case for Offering HPV Self-Sampling to Well-Screened Women. Comment on Lesack et al. Willingness to Self-Collect a Sample for HPV-Based Cervical Cancer Screening in a Well-Screened Cohort: HPV FOCAL Survey Results. Curr. Oncol. 2022, 29, 3860–3869
Authors: Kraut Roni , Manca Donna , Lofters Aisha , Babenko Oksana
Published: August 2022

Lesack et al. recently published a cross-sectional study that focused on human papillomavirus (HPV) self-sampling in the screened population, a population not conventionally thought of for HPV self-sampling. They found 52% of well-screened, highly educated women who participated in the Human Papillomavirus For Cervical Cancer (HPV FOCAL) screening trial in British Columbia, Canada, would be willing to self-collect an HPV sample. We published a similar study in 2021 on well-screened, highly educated women affiliated with a family medicine clinic in Edmonton, Alberta, Canada, and found that 60% of these women preferred to have the option of HPV self-sampling. Our findings reinforce Lesack et al.’s results and together provide evidence for offering HPV self-sampling as an option for the well-screened population.
Next Wave of Targets in the Treatment of Advanced Renal Cell Carcinoma
Authors: Cardenas Luisa M. , Deluce Jasna E. , Khan Shahrukh , Gulam Omar , Maleki Vareki Saman , Fernandes Ricardo , Lalani Aly-Khan A.
Published: August 2022

While surgical resection has remained the mainstay of treatment in early-stage renal cell carcinoma (RCC), therapeutic options in the advanced setting have remarkably expanded over the last 20 years. Tyrosine kinase inhibitors targeting the vascular endothelial growth factor receptor (VEGF-TKIs) and anti-programmed cell death 1 (PD-1)/anti-programmed death-ligand 1 (PD-L1)-based immune checkpoint inhibitors (ICIs) have become globally accepted options in the upfront metastatic setting, with different ICI-based combination strategies improving overall survival compared to single-agent Sunitinib. Although some patients benefit from long-term responses, most eventually develop disease progression. Ongoing efforts to better understand the biology of RCC and the different mechanisms of acquired resistance have led to the identification of promising therapeutic targets. Belzutifan, a novel agent targeting the angiogenic pathway involving hypoxia-inducible factors (HIFs), has already been approved for the treatment of early-stage tumors associated with VHL disease and represents a very promising therapy in advanced RCC. Other putative targets include epigenetic regulation enzymes, as well as several metabolites such as adenosine, glutaminase and tryptophan, which are critical players in cancer cell metabolism and in the tumor microenvironment. Different methods of immune regulation are also being investigated, including CAR-T cell therapy and modulation of the gut microbiome, in addition to novel agents targeting the interleukin-2 (IL-2) pathway. This review aims to highlight the emergent novel therapies for RCC and their respective completed and ongoing clinical trials.
Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives
Authors: Xie Yuting , Zaccagna Fulvio , Rundo Leonardo , Testa Claudia , Agati Raffaele , Lodi Raffaele , Manners David Neil , Tonon Caterina
Published: August 2022

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.
Strategic Significance of Low Viral Load of Human Papillomavirus in Uterine Cervical Cytology Specimens
Authors: Park Nora Jee-Young , Park Claire Su-Yeon , Jeong Ji Yun , Kim Moonsik , Yoo Su Hyun , Chong Gun Oh , Hong Dae Gy , Park Ji Young
Published: August 2022

Infection with high-risk (HR) Human Papillomavirus (HPV) is associated with the development of precancerous lesions or invasive carcinoma of the uterine cervix. Thus, the high viral load (VL) of HR-HPV DNA currently serves as a representative quantitative marker for cervical cancer. However, the clinical significance of low HPV DNA VL remains undetermined. This study aimed to evaluate the clinical association between the low HPV DNA VL and cytology/histologic diagnosis of cervical samples. We searched the electronic medical databases for the resultant analyses of HPV genotyping among patients who underwent treatment for any cervical lesion or who had undergone gynecological examinations with any positive HPV results according to the national cancer screening service between 2015 and 2016. HPV testing with genotyping and semi-quantitative VL measurement was conducted using an AnyplexTM II H28 Detection assay (H28 assay, Seegene, Seoul, Republic of Korea). The H28 assay is a multiplex semi-quantitative real-time PCR test using the tagging of oligonucleotide cleavage and extension (TOCE) technology. The VL was semi-quantified as high (3+; positive signal before 31 PCR cycles), intermediate (2+; positive between 31 and 39 PCR cycles), or low (1+; positive after 40 PCR cycles). Out of 5940 HPV VL analyses, 356 assays (5.99%) were reported as low VL (1+) of HPV DNA. Matched cytology diagnoses were mostly negative findings (n = 347, 97.5%), except for seven cases of atypical squamous cells of undetermined significance (1.9%) and two cases of atypical glandular cells (0.6%). During the follow-up periods, abnormal cytologic diagnoses were identified, including one case of high-grade squamous intraepithelial lesion (HSIL) and two low-grade squamous intraepithelial lesions (LSILs). The matched, confirmative histologic diagnosis of HSIL cytology was compatible with chronic inflammation, wherein the two LSILs had regular check-ups. None revealed clinically concerned outcomes associated with HPV-related squamous lesions. The cytology was most likely negative for malignancy when the VL of HPV DNA was low (1+). Additional strategic monitoring and management may thus be unnecessary.
Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
Authors: Yu Wenbin , Chen Fei , Xu Zeyu , Zhang Yifan , Liu Alex X. , Zhang Chengjun
Published: August 2022

In this paper, we propose a novel multi-user access in wireless optical communication based on the quantum detection of the coherent state. In this case, the coherent states are used as the signal carrier and a technique of quantum detection is applied to distinguish between signals from different users. To accomplish this task, two main quantum measurement methods are introduced; one is minimum error discrimination (MED), and the other is unambiguous state discrimination (USD). The theoretical derivation implies that the two methods can both distinguish between the signals from different users efficiently when the average photon number is large enough. Typically, the numerical result shows that in the two-user case, the channel capacity will approach the theoretical maximum limit when the average photon number is greater than 2.5 for MED and 5 for USD in the absence of noise. The MED gains more channel capacity than the USD at the same average photon number. However, the USD wins the error-correction scene with its free-error capability. Furthermore, the detection error probability and channel capacity for the USD with the thermal noise are examined. The result shows that increasing the signal average photon number can continue the USD’s advantage of error-free detection even if in the presence of thermal noise. In addition, compared with non-orthogonal multiple access (NOMA), the bit error rate (BER) against signal-to-noise rate (SNR) performance of USD has been improved.
Progressively Discriminative Transfer Network for Cross-Corpus Speech Emotion Recognition
Authors: Lu Cheng , Tang Chuangao , Zhang Jiacheng , Zong Yuan
Published: August 2022

Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space. In this case, excessively narrowing the domain distance will impair the emotion discrimination of speech features since it is difficult to maintain the completeness of the emotion space only by an emotion classifier. To overcome this issue, we propose a progressively discriminative transfer network (PDTN) for cross-corpus SER in this paper, which can enhance the emotion discrimination ability of speech features while eliminating the mismatch between the source and target corpora. In detail, we design two special losses in the feature layers of PDTN, i.e., emotion discriminant loss Ld and distribution alignment loss La. By incorporating prior knowledge of speech emotion into feature learning (i.e., high and low valence speech emotion features have their respective cluster centers), we integrate a valence-aware center loss Lv and an emotion-aware center loss Lc as the Ld to guarantee the discriminative learning of speech emotions except an emotion classifier. Furthermore, a multi-layer distribution alignment loss La is adopted to more precisely eliminate the discrepancy of feature distributions between the source and target domains. Finally, through the optimization of PDTN by combining three losses, i.e., cross-entropy loss Le, Ld, and La, we can gradually eliminate the domain mismatch between the source and target corpora while maintaining the emotion discrimination of speech features. Extensive experimental results of six cross-corpus tasks on three datasets, i.e., Emo-DB, eNTERFACE, and CASIA, reveal that our proposed PDTN outperforms the state-of-the-art methods.
Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
Authors: Kim Brian , Sagduyu Yalin , Davaslioglu Kemal , Erpek Tugba , Ulukus Sennur
Published: August 2022

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.
The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets
Authors: De Collibus Francesco Maria , Piškorec Matija , Partida Alberto , Tessone Claudio J.
Published: August 2022

In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse.
Predicting the Performance Deterioration of a Three-Shaft Industrial Gas Turbine
Authors: Salilew Waleligne Molla , Abdul Karim Zainal Ambri , Lemma Tamiru Alemu , Fentaye Amare Desalegn , Kyprianidis Konstantinos G.
Published: August 2022

The gas turbine was one of the most important technological developments of the early 20th century, and it has had a significant impact on our lives. Although some researchers have worked on predicting the performance of three-shaft gas turbines, the effects of the deteriorated components on other primary components and of the physical faults on the component measurement parameters when considering the variable inlet guide valve scheduling and secondary air system for three-shaft gas turbine engines have remained unexplored. In this paper, design point and off-design performance models for a three-shaft gas turbine were developed and validated using the GasTurb 13 commercial software. Since the input data were limited, some engineering judgment and optimization processes were applied. Later, the developed models were validated using the engine manufacturer’s data. Right after the validation, using the component health parameters, the physical faults were implanted into the non-linear steady-state model to investigate the performance of the gas turbine during deterioration conditions. The effects of common faults, namely fouling and erosion in primary components of the case study engine, were simulated during full-load operation. The fault simulation results demonstrated that as the severity of the fault increases, the component performance parameters and measurement parameters deviated linearly from the clean state. Furthermore, the sensitivity of the measurement parameters to the fault location and type were discussed, and as a result they can be used to determine the location and kind of fault during the development of a diagnosis model.
Multi-Image Encryption Algorithm Based on Cascaded Modulation Chaotic System and Block-Scrambling-Diffusion
Authors: Wang Ting , Ge Bin , Xia Chenxing , Dai Gaole
Published: August 2022

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.
Financial Network Analysis on the Performance of Companies Using Integrated Entropy–DEMATEL–TOPSIS Model
Authors: Liew Kah Fai , Lam Weng Siew , Lam Weng Hoe
Published: August 2022

In this paper, we propose a multi-criteria decision making (MCDM) model by integrating the entropy–DEMATEL with TOPSIS model to analyze the causal relationship of financial ratios towards the financial performance of the companies. The proposed model is illustrated using the financial data of the companies of Dow Jones Industrial Average (DJIA). The financial network analysis using entropy–DEMATEL shows that the financial ratios such as debt to equity ratio (DER) and return on equity (ROE) are classified into the cause criteria group, whereas current ratio (CR), earnings per share (EPS), return on asset (ROA) and debt to assets ratio (DAR) are categorized into the effect criteria group. The top three most influential financial ratios are ROE, CR and DER. The significance of this paper is to determine the causal relationship of financial network towards the financial performance of the companies with the proposed entropy–DEMATEL–TOPSIS model. The ranking identification of the companies in this study is beneficial to the investors to select the companies with good performance in portfolio investment. The proposed model has been applied and validated in the portfolio investment using a mean-variance model based on the selection of companies with good performance. The results show that the proposed model is able to generate higher mean return than the benchmark DJIA index at minimum risk. However, short sale is not allowed for the applicability of the proposed model in portfolio investment.
Expanding Integrated Competency-Focused Health Worker Curricula for Maternal Infant and Young Child Nutrition
Authors: Becker Genevieve Ellen , Cashin Jennifer , Nguyen Tuan T. , Zambrano Paul
Published: August 2022

Maternal, infant, and young child nutrition (MIYCN) is important for health, survival, productivity, and development. Knowledgeable, skilled health workers are vital to provide quality care, improve health outcomes, and meet global nutrition targets. When nutrition is not adequately addressed in pre-service curricula, health workers may not be equipped with the knowledge, skills and confidence required to deliver nutrition services and accurate information free of commercial influence. Curriculum review, revision, and competency structuring provides an opportunity to benchmark course topics and content to global standards, to increase focus on learner outcomes, and to facilitate mutual recognition of qualifications across countries and regions. This paper discusses a multistage process to map an existing curriculum, analyse expected competencies, and recognize broader factors when developing a competency-focused curriculum in pre-service education that includes MIYCN. Examples of tools are provided which can be used for review and discussion of curricula and competency at local and national level and to integrate skills such as communication and counselling.
CONNA: Configurable Matrix Multiplication Engine for Neural Network Acceleration
Authors: Park Sang-Soo , Chung Ki-Seok
Published: August 2022

Convolutional neural networks (CNNs) have demonstrated promising results in various applications such as computer vision, speech recognition, and natural language processing. One of the key computations in many CNN applications is matrix multiplication, which accounts for a significant portion of computation. Therefore, hardware accelerators to effectively speed up the computation of matrix multiplication have been proposed, and several studies have attempted to design hardware accelerators to perform better matrix multiplications in terms of both speed and power consumption. Typically, accelerators with either a two-dimensional (2D) systolic array structure or a single instruction multiple data (SIMD) architecture are effective only when the input matrix has shapes that are close to or similar to a square. However, several CNN applications require multiplications of non-squared matrices with various shapes and dimensions, and such irregular shapes lead to poor utilization efficiency of the processing elements (PEs). This study proposes a configurable engine for neural network acceleration, called CONNA, whose computation engine can conduct matrix multiplications with highly utilized computing units, regardless of the access patterns, shapes, and dimensions of the input matrices by changing the shape of matrix multiplication conducted in the physical array. To verify the functionality of the CONNA accelerator, we implemented CONNA as an SoC platform that integrates a RISC-V MCU with CONNA on Xilinx VC707 FPGA. SqueezeNet on CONNA achieved an inference performance of 100 frames per second (FPS) with 2.36 mm2 and 83.55 mW in a 65 nm process, improving efficiency by up to 34.1 times better than existing accelerators in terms of FPS, silicon area, and power consumption.
Estimation of Demographic Traits of the Deputies through Parliamentary Debates Using Machine Learning
Authors: Polat Huseyin , Korpe Mesut
Published: August 2022

One of the most impressive applications of the combined use of natural language processing (NLP), classical machine learning, and deep learning (DL) approaches is the estimation of demographic traits from the text. Author Profiling (AP) is the analysis of a text to identify the demographics or characteristics of its author. So far, most researchers in this field have focused on using social media data in the English language. This article aims to expand the predictive potential of demographic traits by focusing on a more diverse dataset and language. Knowing the background of deputies is essential for citizens, political scientists and policymakers. In this study, we present the application of NLP and machine learning (ML) approaches to Turkish parliamentary debates to estimate the demographic traits of the deputies. Seven traits were determined: gender, age, education, occupation, election region, party, and party status. As a first step, a corpus was compiled from Turkish parliamentary debates between 2012 and 2020. Document representations (feature extraction) were performed using various NLP techniques. Then, we created sub-datasets containing the extracted features from the corpus. These sub-datasets were used by different ML classification algorithms. The best classification accuracy rates were more than 31%, 27%, 35%, 41%, 29%, 59%, and 32% according to the majority baseline for gender, age, education, occupation, election region, party, and party status, respectively. The experimental results show that the demographics of deputies can be estimated effectively using NLP, classical ML, and DL approaches.
Block Diagonal Least Squares Regression for Subspace Clustering
Authors: Fan Lili , Lu Guifu , Liu Tao , Wang Yong
Published: August 2022

Least squares regression (LSR) is an effective method that has been widely used for subspace clustering. Under the conditions of independent subspaces and noise-free data, coefficient matrices can satisfy enforced block diagonal (EBD) structures and achieve good clustering results. More importantly, LSR produces closed solutions that are easier to solve. However, solutions with block diagonal properties that have been solved using LSR are sensitive to noise or corruption as they are fragile and easily destroyed. Moreover, when using actual datasets, these structures cannot always guarantee satisfactory clustering results. Considering that block diagonal representation has excellent clustering performance, the idea of block diagonal constraints has been introduced into LSR and a new subspace clustering method, which is named block diagonal least squares regression (BDLSR), has been proposed. By using a block diagonal regularizer, BDLSR can effectively reinforce the fragile block diagonal structures of the obtained matrices and improve the clustering performance. Our experiments using several real datasets illustrated that BDLSR produced a higher clustering performance compared to other algorithms.
Performance Evaluation of Reconfigurable Intelligent Surface against Distributed Antenna System at the Cell Edge
Authors: Kamaruddin Nuraqila Alini , Mahmud Azwan , Alias Mohamad Yusoff Bin , Abd Aziz Azlan , Yaakob Syamsuri
Published: August 2022

In small spaces in which typical cell towers cannot be constructed, distributed antenna systems (DASs) are the preferable approach for increasing network coverage, as they are a superior solution for congested, high-volume areas. However, due to their high cost and complexity in backhaul routing, reconfigurable intelligent surfaces (RISs) are a promising solution to overcome the major drawbacks of DAS systems while improving network coverage. Thus, this work investigated a correlation of execution in an RIS-aided system cell framework and DAS-aided system cell framework where a simple and precise structure for the performance measurement of area spectral efficiency (ASE) and energy efficiency (EE) under realistic channel presumptions was introduced. The analysis started with the downlink ergodic capacity with regards to the RIS framework and DAS framework under a generalized Nakagami-m fading channel with the presence of path-loss attenuation and interference from co-channel base stations (BSs), and was simplified further by utilizing a moment-generating function (MGF)-based approach. From the computed expression, the effects of traffic activity, EE and ASE were derived and analyzed for both systems in the presence of co-channel interference. The results were then verified by comparing them with Monte Carlo simulations, and the findings show that the two outcomes generally match. Based on these, it is demonstrated that the ASE performance of the RIS-assisted system in various traffic activity conditions outperforms the DAS-aided system; however, in high signal-to-noise (SNR) regions with full traffic activity, the ASEs are highest for both systems; by only 0.005 bits/s/Hz/km2.
Singularity-Free Fixed-Time Adaptive Control with Dynamic Surface for Strict-Feedback Nonlinear Systems with Input Hysteresis
Authors: Feng Xuxiang , Chen Jun , Niu Tongyao
Published: August 2022

An adaptive fixed-time dynamic surface tracking control scheme is developed in this paper for a class of strict-feedback nonlinear systems, where the control input is subject to hysteresis dynamics. To deal with the input hysteresis, a compensation filter is introduced, reducing the difficulty of design and analysis. Based on the universal approximation theory, the radial basis function neural networks are employed to approximate the unknown functions in the nonlinear dynamics. On this basis, fixed-time adaptive laws are constructed to approximate the unknown parameters. The dynamic surface technique is utilized to handle the complexity explosion problem, where fixed-time performance is ensured. Moreover, the designed controller can avoid singularities and achieve fixed-time convergence of error signals. Simulation results verify the efficacy of the method developed, where a comparison between the scheme developed with existing results is provided.