bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulation of 4-HNE via SMARCA4 Is Associated with Worse Clinical Outcomes in Hepatocellular Carcinoma
Authors: Watabe Shiori , Aruga Yukari , Kato Ryoko , Kawade Genji , Kubo Yuki , Tatsuzawa Anna , Onishi Iichiroh , Kinowaki Yuko , Ishibashi Sachiko , Ikeda Masumi , Fukawa Yuki , Akahoshi Keiichi , Tanabe Minoru , Kurata Morito , Ohashi Kenichi , Kitagawa Masanobu , Yamamoto Kouhei
Published: August 2023

Accumulation of 4-hydroxynonenal (4-HNE), a marker of lipid peroxidation, has various favorable and unfavorable effects on cancer cells; however, the clinicopathological significance of its accumulation in hepatocellular carcinoma (HCC) and its metabolic pathway remain unknown. This study analyzed 4-HNE accumulation and its clinicopathological significance in HCC. Of the 221 cases, 160 showed relatively low accumulation of 4-HNE in HCC tissues, which was an independent prognostic predictor. No correlation was found between 4-HNE accumulation and the expression of the antioxidant enzymes glutathione peroxidase 4, ferroptosis suppressor protein 1, and guanosine triphosphate cyclohydrolase 1. Therefore, we hypothesized that 4-HNE metabolism is up-regulated in HCC. A database search was focused on the transcriptional regulation of aldo-keto reductases, alcohol dehydrogenases, and glutathione-S-transferases, which are the metabolic enzymes of 4-HNE, and seven candidate transcription factor genes were selected. Among the candidate genes, the knockdown of SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4 (SMARCA4) increased 4-HNE accumulation. Immunohistochemical analysis revealed an inverse correlation between 4-HNE accumulation and SMARCA4 expression. These results suggest that SMARCA4 regulates 4-HNE metabolism in HCC. Therefore, targeting SMARCA4 provides a basis for a new therapeutic strategy for HCC via 4-HNE accumulation and increased cytotoxicity.
Corticospinal Adaptation to Short-Term Horizontal Balance Perturbation Training
Authors: Hu Nijia , Piirainen Jarmo M. , Kidgell Dawson J. , Walker Simon , Avela Janne
Published: August 2023

Sensorimotor training and strength training can improve balance control. Currently, little is known about how repeated balance perturbation training affects balance performance and its neural mechanisms. This study investigated corticospinal adaptation assessed by transcranial magnetic stimulation (TMS) and Hoffman-reflex (H-reflex) measurements during balance perturbation induced by perturbation training. Fourteen subjects completed three perturbation sessions (PS1, PS2, and PS3). The perturbation system operated at 0.25 m/s, accelerating at 2.5 m/s2 over a 0.3 m displacement in anterior and posterior directions. Subjects were trained by over 200 perturbations in PS2. In PS1 and PS3, TMS and electrical stimulation elicited motor evoked potentials (MEP) and H-reflexes in the right leg soleus muscle, at standing rest and two time points (40 ms and 140 ms) after perturbation. Body sway was assessed using the displacement and velocity of the center of pressure (COP), which showed a decrease in PS3. No significant changes were observed in MEP or H-reflex between sessions. Nevertheless, Δ MEP at 40 ms demonstrated a positive correlation with Δ COP, while Δ H-reflex at 40 ms demonstrated a negative correlation with Δ COP. Balance perturbation training led to less body sway and a potential increase in spinal-level involvement, indicating that movement automaticity may be suggested after perturbation training.
Imaging Markers in Genetic Forms of Parkinson’s Disease
Authors: Droby Amgad , Thaler Avner , Mirelman Anat
Published: August 2023

Parkinson’s disease (PD) is a complex neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, and resting tremor. While the majority of PD cases are sporadic, approximately 15–20% of cases have a genetic component. Advances in neuroimaging techniques have provided valuable insights into the pathophysiology of PD, including the different genetic forms of the disease. This literature review aims to summarize the current state of knowledge regarding neuroimaging findings in genetic PD, focusing on the most prevalent known genetic forms: mutations in the GBA1, LRRK2, and Parkin genes. In this review, we will highlight the contributions of various neuroimaging modalities, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI), in elucidating the underlying pathophysiological mechanisms and potentially identifying candidate biomarkers for genetic forms of PD.
AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images
Authors: Talwar Vivek , Singh Pragya , Mukhia Nirza , Shetty Anupama , Birur Praveen , Desai Karishma M. , Sunkavalli Chinnababu , Varma Konala S. , Sethuraman Ramanathan , Jawahar C. V. , Vinod P. K.
Published: August 2023

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.
A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population
Authors: Akdeniz Bayram Cevdet , Mattingsdal Morten , Dominguez-Valentin Mev , Frei Oleksandr , Shadrin Alexey , Puustusmaa Mikk , Saar Regina , Sõber Siim , Møller Pål , Andreassen Ole A. , Padrik Peeter , Hovig Eivind
Published: August 2023

Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.
Development of a Patient-Derived 3D Immuno-Oncology Platform to Potentiate Immunotherapy Responses in Ascites-Derived Circulating Tumor Cells
Authors: Gerton Thomas J. , Green Allen , Campisi Marco , Chen Minyue , Gjeci Iliana , Mahadevan Navin , Lee Catherine A. A. , Mishra Ranjan , Vo Ha V. , Haratani Koji , Li Ze-Hua , Hasselblatt Kathleen T. , Testino Bryanna , Connor Trevor , Lian Christine G. , Elias Kevin M. , Lizotte Patrick , Ivanova Elena V. , Barbie David A. , Dinulescu Daniela M.
Published: August 2023

High-grade serous ovarian cancer (HGSOC) is responsible for the majority of gynecology cancer-related deaths. Patients in remission often relapse with more aggressive forms of disease within 2 years post-treatment. Alternative immuno-oncology (IO) strategies, such as immune checkpoint blockade (ICB) targeting the PD-(L)1 signaling axis, have proven inefficient so far. Our aim is to utilize epigenetic modulators to maximize the benefit of personalized IO combinations in ex vivo 3D patient-derived platforms and in vivo syngeneic models. Using patient-derived tumor ascites, we optimized an ex vivo 3D screening platform (PDOTS), which employs autologous immune cells and circulating ascites-derived tumor cells, to rapidly test personalized IO combinations. Most importantly, patient responses to platinum chemotherapy and poly-ADP ribose polymerase inhibitors in 3D platforms recapitulate clinical responses. Furthermore, similar to clinical trial results, responses to ICB in PDOTS tend to be low and positively correlated with the frequency of CD3+ immune cells and EPCAM+/PD-L1+ tumor cells. Thus, the greatest response observed with anti-PD-1/anti-PD-L1 immunotherapy alone is seen in patient-derived HGSOC ascites, which present with high levels of systemic CD3+ and PD-L1+ expression in immune and tumor cells, respectively. In addition, priming with epigenetic adjuvants greatly potentiates ICB in ex vivo 3D testing platforms and in vivo tumor models. We further find that epigenetic priming induces increased tumor secretion of several key cytokines known to augment T and NK cell activation and cytotoxicity, including IL-6, IP-10 (CXCL10), KC (CXCL1), and RANTES (CCL5). Moreover, epigenetic priming alone and in combination with ICB immunotherapy in patient-derived PDOTS induces rapid upregulation of CD69, a reliable early activation of immune markers in both CD4+ and CD8+ T cells. Consequently, this functional precision medicine approach could rapidly identify personalized therapeutic combinations able to potentiate ICB, which is a great advantage, especially given the current clinical difficulty of testing a high number of potential combinations in patients.
BRCA1 and NORE1A Form a Her2/Ras Regulated Tumor Suppressor Complex Modulating Senescence
Authors: Nelson Nicholas , Jigo Raphael , Clark Geoffrey J.
Published: August 2023

BRCA1 is a tumor suppressor with a complex mode of action. Hereditary mutations in BRCA1 predispose carriers to breast cancer, and spontaneous breast cancers often exhibit defects in BRCA1 expression. However, haploinsufficiency or suppression of BRCA1 expression leads to defects in DNA repair, which can induce DNA damage responses, leading to senescence. Activating mutation or overexpression of the Her2 oncoprotein are also frequent drivers of breast cancer. Yet, over-activation of Her2, working through the RAS oncoprotein, can also induce senescence. It is thought that additional defects in the p53 and Rb tumor suppressor machinery must occur in such tumors to allow an escape from senescence, thus permitting tumor development. Although BRCA1 mutant breast cancers are usually Her2 negative, a significant percentage of Her2 positive tumors also lose their expression of BRCA1. Such Her2+/BRCA1− tumors might be expected to have a particularly high senescence barrier to overcome. An important RAS senescence effector is the protein NORE1A, which can modulate both p53 and Rb. It is an essential senescence effector of the RAS oncoprotein, and it is often downregulated in breast tumors by promotor methylation. Here we show that NORE1A forms a Her2/RAS regulated, endogenous complex with BRCA1 at sites of replication fork arrest. Suppression of NORE1A blocks senescence induction caused by BRCA1 inactivation and Her2 activation. Thus, NORE1A forms a tumor suppressor complex with BRCA1. Its frequent epigenetic inactivation may facilitate the transformation of Her2+/BRCA1− mediated breast cancer by suppressing senescence.
The Effects of COVID-19 Virtual Learning on Body Fat and Insulin Resistance in Adolescents with Overweight or Obesity
Authors: Stager Lindsay M. , Morgan Casie H. , Watson Caroline S. , Morriss Skylar , Gower Barbara A. , Fobian Aaron D.
Published: August 2023

(1) Background: COVID-19 virtual learning reduced structural supports for adolescent physical activity and diet, threatening metabolic health, especially in teens with overweight or obesity (OWOB). (2) Methods: Adolescents (N = 14) with OWOB completed fasting blood draws (measuring insulin resistance, IR) and Dual Energy X-Ray Absorptiometry (DXA, measuring total body fat percent, TBF%) pre-COVID-19 and during COVID-19. Changes in TBF% and IR were calculated (1) pre-COVID-19 and (2) from pre-COVID-19 to during COVID-19. Age and body mass index (BMI) percentile-matched data assessed normative changes across similar, non-COVID-19 time periods. Paired t-tests compared TBF% change pre- to during COVID-19 with (1) TBF% change pre-COVID19 and (2) TBF% normative change. Two ANCOVAs compared IR change pre- to during COVID-19 with (1) IR change pre-COVID-19 controlling for BMI z-score and difference in time between assessments and (2) normative change in IR controlling for sex/race. (3) Results: The TBF% change pre-COVID-19 and the normative change were similar. The TBF% increased more (~six percentage points) during COVID-19 compared to normative change (p < 0.01). During COVID-19, IR increased more (~2.5 units) than change pre-COVID-19 (p = 0.03) and increased more (~3.5 units) than normative change (p = 0.01). (4) Conclusions: TBF% and IR increased exponentially during COVID-19 in teens with OWOB compared to pre-COVID-19 and normative changes.
Exploring the Potent Anticancer, Antimicrobial, and Anti-Inflammatory Effects of Capparis Spinosa Oil Nanoemulgel
Authors: Eid Ahmad M. , Hawash Mohammed , Abualhasan Murad , Naser Sabreen , Dwaikat Mjd , Mansour Madleen
Published: August 2023

Natural remedies derived from plants have a long history of usage in the treatment of a wide variety of severe diseases. This study aims to develop a Capparis spinosa (C. spinosa) oil nanoemulgel and evaluate its antimicrobial, anticancer, and anti-inflammatory effects. C. spinosa oil was developed into a nanoemulsion using a self-nanoemulsifying method with Span 80 and Tween 80 as emulsifying agents. Carbopol hydrogel was mixed with the nanoemulsion to form nanoemulgel. After this, we tested the particle size, polydispersity index (PDI), rheology, antimicrobial, cytotoxic, and anti-inflammatory activities. The nanoemulsion formulation that has a PDI of 0.159 and a particle size of 119.87 nm is considered to be the optimum formulation. The C. spinosa oil nanoemulgel gave results similar to its nanoemulsion, where it had a PDI lower than 0.2, droplet size below 200 nm, and zeta potential less than −35. Also, it had a pseudoplastic rheological behavior. The C. spinosa oil nanoemulgel showed a significant effect on Methicillin-Resistant Staphylococcus Aureus (MRSA) and Klebsiella pneumoniae (K. pneumonia) (ATCC 13883) with zone inhibition diameters of 33 ± 1.9 mm and 30 ± 1.4 mm, respectively, as well as significant activities on the MCF-7, HepG2, and HeLa cancer cell lines with IC50 values of 194.98, 91.2, and 251.18 µg/mL, respectively, which were better than those of the original oil. Regarding its anti-inflammatory effect, C. spinosa oil had a positive impact on both COX-1 and COX-2 but was more selective for COX-1. Consequently, simple nanotechnology techniques provide a promising step forward in the development of pharmacological dosage forms.
The Effect of Fast Kr Ion Irradiation on the Optical Absorption, Luminescence, and Raman Spectra of BaFBr Crystals
Authors: Akilbekov Abdirash , Kenbayev Daurzhan , Dauletbekova Alma , Polisadova Elena , Yakovlev Victor , Karipbayev Zhakyp , Shalaev Alexey , Elsts Edgars , Popov Anatoli I.
Published: August 2023

In this work, using photoluminescence (PL), optical absorption (OA), Raman spectroscopy (RS), and atomic force microscopy (AFM), the radiation damage of BaFBr crystals irradiated with 147 MeV 84Kr ions to fluences (1010–1014) cm2 was investigated. The manifestations of the oxygen impurity contained in the studied crystals on the effects associated with ion irradiation are also considered. In unirradiated crystals, the PL spectra exhibited bands related to the oxygen impurity. Moreover, it was found that quenching and a shift of the PL maximum occur, which is due to the fact that, with increasing dose, aggregation of defects occurs. Electronic and hole aggregate color centers appear mainly in the bromide sublattice. A detailed study of the Raman spectra and comparison with the corresponding data for KBr single crystals made it possible to reveal the corresponding manifestations of the Raman modes of complex Br3−-type hole centers.
Formation of Fluorapatite in the Equilibrium System CaO–P2O5–HF–H2O at 298 K in a Nitrogen Atmosphere
Authors: Chaikina Marina , Bulina Natalia , Prosanov Igor , Ishchenko Arcady
Published: August 2023

The process of biomineralization of apatite in nature has been studied by scientists from various fields of science for more than a century. Unlike the volcanogenic, hydrothermal, and other types of igneous apatites, the genesis of which is entirely clear, the formation of phosphate ores of marine sedimentary origin is still debatable. Since phosphate concentrations in water bodies are too low for the spontaneous precipitation of solid phosphates, the study of different ways for their concentration is of particular interest. In this work, phase equilibria in the system CaO–P2O5–HF–H2O at 298 K, involving fluorapatite formation, have been studied. Fluorapatite is known to be the most common phosphate mineral and the main source of phosphorus on Earth, playing an important role in the mineralization process of dental tissues in vertebrates. The equilibrium in the system defined above was studied at a low mass fraction of the liquid phase components, i.e., in conditions close to natural. It has been shown that the compounds of variable composition with the fluorapatite structure containing HPO42− ions were formed in the acid region of this system. These compounds are formed at pH ≤ 7.0 and have invariant points with monetite, CaHPO4, and fluorite, CaF2. Stoichiometric fluorapatite was formed at the lowest concentrations of the liquid phase components in a neutral and weakly alkaline medium and had an invariant point with Ca(OH)2. The composition of the resulting equilibrium solid phases was found to be dependent on the Ca/P ratio of the initial components and pH of the equilibrium liquid phase. Fluorite CaF2 was present in each sample obtained in this study.
Ecological Factors and Anthropogenic Disturbance May Restructure the Skin Microbiota of Maoershan Hynobiids (Hynobius maoershanensis)
Authors: Chen Huiqun , Huang Yingying , Pang Guangyan , Cui Zhenzhen , Wu Zhengjun , Huang Huayuan
Published: August 2023

Studies on the skin microbiota of amphibians in different disturbed habitats can clarify the relationship between the skin microbiota composition and environmental factors and have practical implications for the conservation of endangered species. In this study, 16S rRNA high-throughput sequencing was used to profile the skin microbiota of Maoershan hynobiids (Hynobius maoershanensis). Our results illustrate that the alpha diversity of the skin microbiota significantly differed among individuals in higher anthropogenic disturbance-degree (HADD) habitats and lower anthropogenic disturbance-degree (LADD) habitats. The diversity of the skin microbiota in forelimb bud-stage tadpoles from HADD habitats was higher than that in their counterparts from LADD habitats. The richness of the skin microbiota in hindlimb bud-stage tadpoles was greater in HADD habitats than in LADD habitats. However, the alpha diversity of the adult skin microbiota did not differ significantly between the two habitats. Furthermore, stepwise regression analysis indicated that the skin microbiota diversity and relative abundance of dominant bacteria decreased with increasing air temperature, water temperature, and pH; conversely, skin microbiota richness increased with increasing humidity. In addition, the relative abundance of dominant bacteria was influenced by anthropogenic disturbance. We conclude that the skin microbiota of Maoershan hynobiids is affected by ecological factors and anthropogenic disturbance, highlighting the importance of the skin microbiota in response to habitat alteration.
Severe Acute Bronchial Asthma with Sepsis: Determining the Status of Biomarkers in the Diagnosis of the Disease
Authors: Sinha Susmita , Kumar Santosh , Narwaria Mahendra , Singh Arya , Haque Mainul
Published: August 2023

Bronchial asthma is a widely prevalent illness that substantially impacts an individual’s health standard worldwide and has a significant financial impact on society. Global guidelines for managing asthma do not recommend the routine use of antimicrobial agents because most episodes of the condition are linked to viral respiratory tract infections (RTI), and bacterial infection appears to have an insignificant impact. However, antibiotics are recommended when there is a high-grade fever, a consolidation on the chest radiograph, and purulent sputum that contains polymorphs rather than eosinophils. Managing acute bronchial asthma with sepsis, specifically the choice of whether or not to initiate antimicrobial treatment, remains difficult since there are currently no practical clinical or radiological markers that allow for a simple distinction between viral and bacterial infections. Researchers found that serum procalcitonin (PCT) values can efficiently and safely minimize antibiotic usage in individuals with severe acute asthma. Again, the clinical manifestations of acute asthma and bacterial RTI are similar, as are frequently used test values, like C-reactive protein (CRP) and white blood cell (WBC) count, making it harder for doctors to differentiate between viral and bacterial infections in asthma patients. The role and scope of each biomarker have not been precisely defined yet, although they have all been established to aid healthcare professionals in their diagnostics and treatment strategies.
A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding
Authors: Liao Tao , Sun Haojie , Zhang Shunxiang
Published: August 2023

The entity–relationship joint extraction model plays a significant role in entity relationship extraction. The existing entity–relationship joint extraction model cannot effectively identify entity–relationship triples in overlapping relationships. This paper proposes a new joint entity–relationship extraction model based on the span and a cascaded dual decoding. The model includes a Bidirectional Encoder Representations from Transformers (BERT) encoding layer, a relational decoding layer, and an entity decoding layer. The model first converts the text input into the BERT pretrained language model into word vectors. Then, it divides the word vectors based on the span to form a span sequence and decodes the relationship between the span sequence to obtain the relationship type in the span sequence. Finally, the entity decoding layer fuses the span sequences and the relationship type obtained by relation decoding and uses a bi-directional long short-term memory (Bi-LSTM) neural network to obtain the head entity and tail entity in the span sequence. Using the combination of span division and cascaded double decoding, the overlapping relations existing in the text can be effectively identified. Experiments show that compared with other baseline models, the F1 value of the model is effectively improved on the NYT dataset and WebNLG dataset.
Bounded Confidence and Cohesion-Moderated Pressure: A General Model for the Large-Scale Dynamics of Ordered Opinion
Authors: Ding Fangyikuang , Li Yang , Ding Kejian
Published: August 2023

Due to the development of social media, the mechanisms underlying consensus and chaos in opinion dynamics have become open questions and have been extensively researched in disciplines such as sociology, statistical physics, and nonlinear mathematics. In this regard, our paper establishes a general model of opinion evolution based on micro-mechanisms such as bounded confidence, out-group pressure, and in-group cohesion. Several core conclusions are derived through theorems and simulation results in the model: (1) assimilation and high reachability in social networks lead to global consensus; (2) assimilation and low reachability result in local consensus; (3) exclusion and high reachability cause chaos; and (4) a strong “cocoon room effect” can sustain the existence of local consensus. These conclusions collectively form the “ideal synchronization theory”, which also includes findings related to convergence rates, consensus bifurcation, and other exploratory conclusions. Additionally, to address questions about consensus and chaos, we develop a series of mathematical and statistical methods, including the “energy decrease method”, the “cross-d search method”, and the statistical test method for the dynamical models, contributing to a broader understanding of stochastic dynamics.
Debt Maturity and Institutions: Does Creditor Protection Matter?
Authors: Tayem Ghada
Published: August 2023

This study aims to investigate the relationship between creditor protection and the debt maturity structure of corporations in the Gulf Cooperation Council (GCC) countries. The GCC countries enjoy large GDPs, growing capital markets, especially the Islamic bonds (Sukuk) market, and negligible tax environments. Nonetheless, the GCC countries’ financial systems are still dominated by banks, and their private investments are held by concentrated investors. The study utilizes firm-level financial data and country-level institutional data obtained from the World Bank Governance Indicators and Doing Business databases and applies the two-stage least square estimator to test its hypotheses. The findings indicate that stronger regulatory effectiveness is associated with long debt maturities, while better creditor protection is associated with short debt maturities. The latter finding suggests that managers and owners have incentives to utilize short-term debt in economies characterized by stronger liquidation and insolvency rules to avoid the loss of control in the case of a firm default. This finding has policy implications in terms of the importance of considering the dual influence of institutional reforms on the supply of and demand for long-term capital.
Exploring Technological Challenges and Growth in Faculty Transition to Remote Teaching during the COVID-19 Pandemic: A Mixed-Methods Study
Authors: DeVaul Driscoll , Burrell Angela , Lyles Kendria , Reulet Britney , Cole Kristy , Reulet Celia Lea A. , Dear Carley , Gordy Xiaoshan Z.
Published: August 2023

The COVID-19 pandemic forced institutions of higher education into an emergency remote teaching practice. In this study, the researchers utilized a convergent triangulation mixed-methods study design to investigate the technological challenges and growth that faculty experienced in seven schools at the University of Mississippi Medical Center as a result of the transition to emergency remote teaching during the COVID-19 pandemic. Quantitative data were collected through an anonymous online survey that asked faculty to rate their competency in each of the 14 technology-related tasks twice—once for the pre-pandemic period and once for the current period. Qualitative data were collected through semi-structured interviews conducted virtually or in person. One hundred faculty participated in the quantitative survey. A significant difference between faculty self-rated competency levels in the pre-pandemic period and the current period was observed, suggesting technology knowledge growth from the forced transition. Qualitative interviews of 19 faculty revealed five overarching themes: inconsistency in instructional support, pandemic-induced technological growth, power of togetherness, maintaining continuity through flexibility, and resilience against adversity. This study provided faculty with the experience to perform effective self-reflection and gain insights into their pedagogical practices. Future research should focus on professional development that can help faculty remain up-to-date on technology utilization and establish a contingency plan to better prepare for the unknown.
MIMO Dielectric Resonator Antennas for 5G Applications: A Review
Authors: Ahmad Hamza , Jamaluddin Mohd Haizal , Seman Fauziahanim Che , Rahman Muhibur
Published: August 2023

This article presents a thorough literature review of published designs of multiple-input multiple-output (MIMO) dielectric resonator antennas (DRAs) specifically designed for 5G applications. The performance of these designs is discussed in detail, considering various parameters such as gain, isolation, size, bandwidth, profile, and radiation characteristics. The primary objective of this work is to appreciate the significant progress made in this vital area of research. This article also aims to identify any existing gaps in the literature and provide potential directions for future work.
Part I—Advancements in Power Converter Technologies: A Focus on SiC-MOSFET-Based Voltage Source Converters
Authors: Gregor Raúl , Toledo Sergio , Maqueda Edgar , Pacher Julio
Published: August 2023

Power converter technologies have become vital in various applications due to their efficient management of electrical energy. With the growing prominence of renewable energy sources such as solar and wind, the high penetration of power electronic converters has been justified. However, ensuring power quality has emerged as a significant challenge for grid-connected power converters. The divergence from the ideal sinusoidal waveform in terms of magnitude and frequency impacts both grid-side currents and voltages. Several studies have proposed solutions to address power quality issues at the load side. The advancement of power converters has been fueled by the development of high-performance microprocessors and the emergence of high-speed switching devices, such as SiC-MOSFETs. This paper focuses on the design of voltage source converters, particularly those based on SiC-MOSFET semiconductor devices. The article presents the design of H-Bridge cells, discusses two-level voltage source converters based on cascade H-Bridge cells in a parallel configuration with experimental fault analysis, addresses the seven-level voltage source converter topology, and explores the design and experimental results of the matrix converter. The findings underscore the importance of considering the entire converter design for improved performance at high switching frequencies. The article concludes by summarizing the main outcomes and implications of this research.
Planning of an LVAC Distribution System with Centralized PV and Decentralized PV Integration for a Rural Village
Authors: Eam Dara , Vai Vannak , Chhlonh Chhith , Eng Samphors
Published: August 2023

Energy demand is continuously increasing, leading to yearly expansions in low-voltage (LV) distribution systems integrated with PVs to deliver electricity to users with techno-economic considerations. This study proposes and compares different topology planning strategies with and without PVs in a rural area of Cambodia over 30 years of planning. Firstly, the optimal radial topology from a distribution transformer to end-users is provided using the shortest path algorithm. Secondly, two different phase balancing concepts (i.e., pole balancing and load balancing) with different phase connection methods (i.e., power losses and energy losses) are proposed and compared to find the optimal topology. Then, the integration of centralized (CePV) and decentralized PV (DePV) into the optimal topology is investigated for three different scenarios, which are zero-injection (MV and LV levels), no sell-back price, and a sell-back price. Next, the minimum sell-back price from CePV and DePV integration is determined. To optimize phase balancing, including the location and size of PV, an optimization technique using a water cycle algorithm (WCA) is applied. Finally, an economic analysis of each scenario based on the highest net present cost (NPC), including capital expenditure (CAPEX) and operational expenditure (OPEX) over the planning period, is evaluated. In addition, technical indicators, such as autonomous time and energy, and environmental indicator, which is quantified by CO2 emissions, are taken into account. Simulation results validate the effectiveness of the proposed method.
Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems
Authors: Yiming Zhang , Fang Xie , Hordiichuk-Bublivska Olena , Beshley Halyna , Beshley Mykola
Published: August 2023

The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.
Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu
Authors: Singh Nitin Kumar , Fukushima Takuya , Nagahara Masaaki
Published: August 2023

This paper aims to develop a machine-learning model based on a gradient-boosting algorithm to predict the energy-saving awareness of households using a questionnaire survey and 11-month energy data collected from more than 200 smart houses in Kitakyushu, Japan. We utilize the LightGBM (light gradient boosting machine) classifier to perform feature selection for the prediction. By using this approach, we demonstrate that the key features are the standard deviations of electricity purchased between 8 a.m. and 9 a.m. and electricity consumed between 7 p.m. and 9 p.m. Next, by using k-means clustering we split the households based on the obtained features into three groups. Finally, by using statistical hypothesis testing, we prove that these three groups have statistically distinct levels of energy-saving awareness. This model enables us to detect eco-friendly households from their energy data, which may support energy policymaking.
Fuel and Energy Complex of Kazakhstan: Geological and Economic Assessment of Enterprises in the Context of Digital Transformation
Authors: Issatayeva Farida M. , Aubakirova Gulnara M. , Maussymbayeva Aliya D. , Togaibayeva Lyussiya I. , Biryukov Valery V. , Vechkinzova Elena
Published: August 2023

The relevance of the study is dictated by the growing role of the fuel and energy complex of developing countries in the decarbonization of the economy. The article discusses the digital transformation of mining enterprises in Kazakhstan, taking into account the transition to CRIRSCO international standards and growing competition in the global mineral market. The purpose of the study is to assess the current level of digitalization of the mining industry in Kazakhstan and to deepen the methodological apparatus of the geological and economic assessment of the enterprise based on the factual base of deposits. The role of the transformation of the mining sector in achieving the sustainability of the poorly diversified economy of Kazakhstan is shown. The importance of digitalization of the industry to complete the transition to CRIRSCO international standards and improve the assessment of the digital provision of enterprises in order to optimize their financial and economic policies is argued. It has been established that, at present, most of the enterprises in the investment-attractive mining sector have a low potential for the transition to a new technological paradigm. A methodological approach to the geological and economic assessment of these enterprises has been developed. To maintain high standards of management transparency through the digitalization of key business processes, along with well-known practices of economic analysis, the IDEF1 methodology was used. In order to expand the software ecosystem, the formats of electronic geological and economic databases are integrated into the digital infrastructure of the enterprise. It is substantiated that the introduction of high technologies in the mining industry requires institutional changes and coordinated interaction between the state, business, and universities as equal partners.
Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future
Authors: Basmadjian Robert , Shaafieyoun Amirhossein
Published: August 2023

Renewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based forecasting models for renewables. To this end, we considered a consistent dataset spanning the last four years. Assuming four different performance metrics for each of the best ARIMA-based models of our previous works, we derived a new optimal model for each month of the year, as well as for the two different methodologies suggested in those works. We then evaluated the performance of those models, by comparing the two methodologies: in doing so, we proposed a hybrid methodology that took the best models out of those two methodologies. We show that our proposed hybrid methodology has improved yearly accuracy of about 89.5% averaged over 12 months of the year. Also, we illustrate in detail for the four years under study and each month of the year the observed percentage of renewables and its corresponding accuracy compared to the generated forecasts. Finally, we give the implementation details of our open-source REN4KAST software platform, which provides several services related to renewables in Germany.
Insights into the Reaction Kinetics of Hydrazine-Based Fuels: A Comprehensive Review of Theoretical and Experimental Methods
Authors: Wu Jin , Bruce Frederick Nii Ofei , Bai Xin , Ren Xuan , Li Yang
Published: August 2023

While researchers have extensively studied the initial decomposition mechanism of Monomethylhydrazine (MMH, CH3NHNH2) in the MMH/dinitrogen tetroxide (NTO) system, the investigation of Unsymmetrical Dimethylhydrazine (UDMH, (CH3)2NNH2) has been limited due to its high toxicity, corrosiveness, and deterioration rate. Hence, the effects of UDMH’s deterioration products on combustion performance and gas-phase combustion reaction mechanisms remain unclear. This comprehensive review examines the existing research on the reaction kinetics of the three widely used hydrazine-based self-ignition propellants: Hydrazine (HZ, N2H4): MMH: and UDMH, emphasizing the necessity for further investigation into the reaction kinetics and mechanisms of UDMH. It also discusses the implications of these findings for developing safer and more efficient rocket propulsion systems. Additionally, this review underscores the importance of utilizing computational chemistry theory to analyze hydrazine-based fuels’ combustion and decomposition properties, constructing detailed pyrolysis and combustion reaction mechanisms to optimize rocket engine fuel performance and environmental concerns.
Pests and Pathogens of Urban Trees
Authors: Zemek Rostislav , Pastirčáková Katarína
Published: August 2023

Trees play a vital role in enhancing public open spaces, such as city parks, gardens, inter-block spaces of greenery, urban forests, and recreational areas, providing green spaces and leisure opportunities for both residents and visitors [...]
Assessing the Versatility of Bioextraction to Preserve Waterlogged Wood
Authors: Monachon Mathilde , Pelé-Meziani Charlène , Ganesan Sathiyanarayanan , de Weck Sabine , Moll-Dau Friederike , Schramm Janet , Schmidt-Ott Katharina , Joseph Edith
Published: August 2023

An innovative bio method was investigated to extract harmful iron and sulfur species from waterlogged wood samples. The method was compared with a chemical treatment. Both approaches were applied on lacustrine and marine samples, from different wood genera, to evaluate the versatility of the proposed bio method. Non-invasive and non-destructive methods were carried out to investigate both bio-based and chemical treatments. The result was that some wood genera were more affected by the bio approach, with a clear distinction between lacustrine beech and pine against oak and lime wood species. The chemical approach showed potential harm for the wooden structure, with acidic pH values and an increase of maximum water content, both implying degradation of the wood structure. In terms of extraction, no iron or sulfur products were detected by Raman spectroscopy on biologically treated samples, in agreement with extraction rates calculated. It was also suggested that iron bonded to wood was extracted with the chemical approach, and calcium content affected by both approaches.
Development of an Advanced-Generation Multi-Objective Breeding Population for the 4th Cycle of Chinese Fir (Cunninghamia lanceolata (Lamb.) Hook.)
Authors: Zhao Benwen , Bian Liming , Feng Qihang , Wu Jinzhang , Zhang Xuefeng , Zheng Renhua , Zheng Xueyan , Yang Zhiyuan , Chen Zhiqiang , Wu Harry X. , Shi Jisen
Published: August 2023

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is an important timber species native to southern China. While the single, unstructured breeding strategy was employed in the past three cycles of breeding, it is no longer adequate for managing a more advanced breeding population. In this study, we utilized restriction-site-associated DNA-sequencing (RAD-seq) to estimate the genetic diversity of breeding populations and phenotypic values or breeding values to estimate the genetic gain of hundred-grain weight, diameter at breast height, and wood basic density. To achieve a balance between genetic gain and genetic diversity, we combined the multiple populations and core-main populations methods to construct the fourth cycle breeding population. Finally, the fourth cycle breeding population was made up of a core population of 50 individuals with an inbreeding coefficient of ~0, and an additional main population of 183 individuals, with an effective population size of 108. Crossings made within and/or between different trait-targeted subpopulations could facilitate bidirectional gene flow between the core and main populations, depending on the breeding objectives. This structured breeding population of Chinese fir could aim for both short- and long-term genetic gains and has the potential to support the preservation of germplasm resources for future climate change.
Exploring the Fermentation-Driven Functionalities of Lactobacillaceae-Originated Probiotics in Preventive Measures of Alzheimer’s Disease: A Review
Authors: Tripathi Anjali , Pandey Vinay Kumar , Tiwari Vivek , Mishra Rashi , Dash Kshirod Kumar , Harsányi Endre , Kovács Béla , Shaikh Ayaz Mukarram
Published: August 2023

Alzheimer’s disease (AD) is an ascending, neurodegenerative disorder that attacks the brain’s nerve cells, i.e., neurons, resulting in loss of memory, language skills, and thinking and behavioural changes. It is one of the most common causes of dementia, a group of disorders that is marked by the decline of cognitive functioning. Probiotics are living microorganisms that are beneficial for human well-being. They help in balancing the extent of bacteria in the gut and support the defensive immune system of the body. Studies have found that probiotics can help with a variety of conditions, including mental health. Probiotics are beneficial bacteria that can help to maintain and strengthen a healthy gut microbiome. The gut microbiome is important for healthy brain function, as it is linked to the production of neurotransmitters and hormones that regulate mood and behaviour. This review article includes detailed review on the origination of probiotics and its significance in the treatment of AD.
Special Issue on Security and Privacy in Blockchains and the IoT Volume II
Authors: Stach Christoph , Gritti Clémentine
Published: August 2023

In this day and age, data are indispensable commodities and have become an integral part of our daily lives [...]
Computational Study for Fiber Bragg Gratings with Dispersive Reflectivity Using Fractional Derivative
Authors: Tariq Hira , Akram Ghazala , Sadaf Maasoomah , Iftikhar Maria , Guran Liliana
Published: August 2023

In this paper, the new representations of optical wave solutions to fiber Bragg gratings with cubic–quartic dispersive reflectivity having the Kerr law of nonlinear refractive index structure are retrieved with high accuracy. The residual power series technique is used to derive power series solutions to this model. The fractional derivative is taken in Caputo’s sense. The residual power series technique (RPST) provides the approximate solutions in truncated series form for specified initial conditions. By using three test applications, the efficiency and validity of the employed technique are demonstrated. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The analysis of the obtained results reveals that the RPST is a significant addition to exploring the dynamics of sustainable and smooth optical wave propagation across long distances through optical fibers.